diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index 03849995735d..ce4f8a1a5b8d 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -29,8 +29,8 @@ RUN apt-get install -y curl wget gnupg python3 python-is-python3 python3-pip git build-essential tmux vim RUN python -m pip install \ - pip==23.3.1 \ - setuptools==68.2.2 \ + pip==24.0.0 \ + setuptools==69.5.1 \ poetry==1.7.1 USER $USERNAME diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS index 0f95b023ecaa..5270bf89ae33 100644 --- a/.github/CODEOWNERS +++ b/.github/CODEOWNERS @@ -15,9 +15,15 @@ README.md @jafermarq @tanertopal @danieljanes # Flower Examples /examples @jafermarq @tanertopal @danieljanes +# Flower Templates +/src/py/flwr/cli/new/templates @jafermarq @tanertopal @danieljanes + # Changelog /doc/source/ref-changelog.md @jafermarq @tanertopal @danieljanes +# Translations +/doc/locales @charlesbvll @tanertopal @danieljanes + # GitHub Actions and Workflows /.github/workflows @Robert-Steiner @tanertopal @danieljanes /.github/actions @Robert-Steiner @tanertopal @danieljanes diff --git a/.github/actions/bootstrap/action.yml b/.github/actions/bootstrap/action.yml index 8527f1f82307..bee90beffa7d 100644 --- a/.github/actions/bootstrap/action.yml +++ b/.github/actions/bootstrap/action.yml @@ -6,17 +6,17 @@ inputs: default: 3.8 pip-version: description: "Version of pip to be installed using pip" - default: 23.3.1 + default: 24.0.0 setuptools-version: description: "Version of setuptools to be installed using pip" - default: 68.2.2 + default: 69.5.1 poetry-version: description: "Version of poetry to be installed using pip" default: 1.7.1 poetry-skip: description: "Option to skip the poetry installation" required: false - default: 'false' + default: "false" outputs: python-version: description: "Version range or exact version of Python or PyPy" diff --git a/.github/workflows/_docker-build.yml b/.github/workflows/_docker-build.yml index 043b6139a79e..b8ddd355eb8e 100644 --- a/.github/workflows/_docker-build.yml +++ b/.github/workflows/_docker-build.yml @@ -60,8 +60,24 @@ jobs: hash = hashlib.sha256('''${{ inputs.namespace-repository }} ${{ inputs.file-dir }} ${{ inputs.build-args }}'''.encode()) + # Adds two spaces to the line breaks to ensure proper indentation + # when passing the multi-line string to the wretry.action. + # Without it, the multi-line string is passed like this: + # + # build-args: | + # ARG1= + # ARG2= + # ARG3= + # + # This causes the Docker action to interpret ARG2 and ARG3 as keys instead + # of values ​​of the multi-line string. + build_args = '''${{ inputs.build-args }}'''.replace("\n", "\n ") + with open(os.environ['GITHUB_OUTPUT'], 'a') as fh: print(f"id={hash.hexdigest()}", file=fh) + print("build-args< $cacheKey" + gh actions-cache delete "$cacheKey" --confirm + fi + done + + echo "Done" + env: + GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} diff --git a/.github/workflows/datasets.yml b/.github/workflows/datasets.yml index 47e9f2aed926..80cece262754 100644 --- a/.github/workflows/datasets.yml +++ b/.github/workflows/datasets.yml @@ -16,6 +16,9 @@ concurrency: group: ${{ github.workflow }}-${{ github.ref == 'refs/heads/main' && github.run_id || github.event.pull_request.number || github.ref }} cancel-in-progress: true +env: + FLWR_TELEMETRY_ENABLED: 0 + defaults: run: working-directory: datasets diff --git a/.github/workflows/docker-base.yml b/.github/workflows/docker-base.yml deleted file mode 100644 index f779043d9932..000000000000 --- a/.github/workflows/docker-base.yml +++ /dev/null @@ -1,60 +0,0 @@ -name: Build docker base image - -on: - workflow_dispatch: - inputs: - flwr-version: - description: "Version of Flower." - required: true - type: string - -permissions: - contents: read - -jobs: - parameters: - name: Collect build parameters - runs-on: ubuntu-22.04 - timeout-minutes: 10 - outputs: - pip-version: ${{ steps.versions.outputs.pip-version }} - setuptools-version: ${{ steps.versions.outputs.setuptools-version }} - - steps: - - uses: actions/checkout@b4ffde65f46336ab88eb53be808477a3936bae11 # v4.1.1 - - - uses: ./.github/actions/bootstrap - id: bootstrap - - - id: versions - run: | - echo "pip-version=${{ steps.bootstrap.outputs.pip-version }}" >> "$GITHUB_OUTPUT" - echo "setuptools-version=${{ steps.bootstrap.outputs.setuptools-version }}" >> "$GITHUB_OUTPUT" - - build-base-images: - name: Build base images - uses: ./.github/workflows/_docker-build.yml - needs: parameters - strategy: - fail-fast: false - matrix: - python-version: ["3.8", "3.9", "3.10", "3.11"] - distro: - - name: ubuntu - version: "22.04" - - name: alpine - version: "3.19" - with: - namespace-repository: flwr/base - file-dir: src/docker/base/${{ matrix.distro.name }} - build-args: | - PYTHON_VERSION=${{ matrix.python-version }} - PIP_VERSION=${{ needs.parameters.outputs.pip-version }} - SETUPTOOLS_VERSION=${{ needs.parameters.outputs.setuptools-version }} - DISTRO=${{ matrix.distro.name }} - DISTRO_VERSION=${{ matrix.distro.version }} - FLWR_VERSION=${{ github.event.inputs.flwr-version }} - tags: ${{ github.event.inputs.flwr-version }}-py${{ matrix.python-version }}-${{ matrix.distro.name }}${{ matrix.distro.version }} - secrets: - dockerhub-user: ${{ secrets.DOCKERHUB_USERNAME }} - dockerhub-token: ${{ secrets.DOCKERHUB_TOKEN }} diff --git a/.github/workflows/docker-images.yml b/.github/workflows/docker-images.yml new file mode 100644 index 000000000000..e341ae62e3f7 --- /dev/null +++ b/.github/workflows/docker-images.yml @@ -0,0 +1,75 @@ +name: Build docker images + +on: + workflow_dispatch: + inputs: + flwr-version: + description: "Version of Flower." + required: true + type: string + +permissions: + contents: read + +jobs: + parameters: + name: Collect build parameters + runs-on: ubuntu-22.04 + timeout-minutes: 10 + outputs: + pip-version: ${{ steps.versions.outputs.pip-version }} + setuptools-version: ${{ steps.versions.outputs.setuptools-version }} + matrix: ${{ steps.matrix.outputs.matrix }} + steps: + - uses: actions/checkout@b4ffde65f46336ab88eb53be808477a3936bae11 # v4.1.1 + + - uses: ./.github/actions/bootstrap + id: bootstrap + + - id: versions + run: | + echo "pip-version=${{ steps.bootstrap.outputs.pip-version }}" >> "$GITHUB_OUTPUT" + echo "setuptools-version=${{ steps.bootstrap.outputs.setuptools-version }}" >> "$GITHUB_OUTPUT" + + - id: matrix + run: | + python dev/build-docker-image-matrix.py --flwr-version ${{ github.event.inputs.flwr-version }} > matrix.json + echo "matrix=$(cat matrix.json)" >> $GITHUB_OUTPUT + + build-base-images: + name: Build base images + uses: ./.github/workflows/_docker-build.yml + needs: parameters + strategy: + fail-fast: false + matrix: ${{ fromJson(needs.parameters.outputs.matrix).base }} + with: + namespace-repository: ${{ matrix.images.namespace_repository }} + file-dir: ${{ matrix.images.file_dir }} + build-args: | + PYTHON_VERSION=${{ matrix.images.python_version }} + PIP_VERSION=${{ needs.parameters.outputs.pip-version }} + SETUPTOOLS_VERSION=${{ needs.parameters.outputs.setuptools-version }} + DISTRO=${{ matrix.images.distro.name }} + DISTRO_VERSION=${{ matrix.images.distro.version }} + FLWR_VERSION=${{ matrix.images.flwr_version }} + tags: ${{ matrix.images.tag }} + secrets: + dockerhub-user: ${{ secrets.DOCKERHUB_USERNAME }} + dockerhub-token: ${{ secrets.DOCKERHUB_TOKEN }} + + build-binary-images: + name: Build binary images + uses: ./.github/workflows/_docker-build.yml + needs: [parameters, build-base-images] + strategy: + fail-fast: false + matrix: ${{ fromJson(needs.parameters.outputs.matrix).binary }} + with: + namespace-repository: ${{ matrix.images.namespace_repository }} + file-dir: ${{ matrix.images.file_dir }} + build-args: BASE_IMAGE=${{ matrix.images.base_image }} + tags: ${{ matrix.images.tags }} + secrets: + dockerhub-user: ${{ secrets.DOCKERHUB_USERNAME }} + dockerhub-token: ${{ secrets.DOCKERHUB_TOKEN }} diff --git a/.github/workflows/docker-serverapp.yml b/.github/workflows/docker-serverapp.yml deleted file mode 100644 index 531ef079c641..000000000000 --- a/.github/workflows/docker-serverapp.yml +++ /dev/null @@ -1,52 +0,0 @@ -name: Build docker ServerApp image - -on: - workflow_dispatch: - inputs: - flwr-version: - description: "Version of Flower." - required: true - type: string - -permissions: - contents: read - -jobs: - build-serverapp-images: - name: Build images - uses: ./.github/workflows/_docker-build.yml - # run only on default branch when using it with workflow_dispatch - if: github.ref_name == github.event.repository.default_branch - strategy: - fail-fast: false - matrix: - image: [ - { - py-version: "3.8", - tags: "${{ github.event.inputs.flwr-version }}-py3.8-ubuntu22.04" - }, - { - py-version: "3.9", - tags: "${{ github.event.inputs.flwr-version }}-py3.9-ubuntu22.04" - }, - { - py-version: "3.10", - tags: "${{ github.event.inputs.flwr-version }}-py3.10-ubuntu22.04" - }, - { - py-version: "3.11", - # those are two tags -py3.11-py3.11-ubuntu22.04 and separated by a \n - tags: "${{ github.event.inputs.flwr-version }}-py3.11-ubuntu22.04\n${{ github.event.inputs.flwr-version }}" - }, - ] - with: - namespace-repository: flwr/serverapp - file-dir: src/docker/serverapp - build-args: | - FLWR_VERSION=${{ github.event.inputs.flwr-version }} - PYTHON_VERSION=${{ matrix.image.py-version }} - UBUNTU_VERSION=ubuntu22.04 - tags: ${{ matrix.image.tags }} - secrets: - dockerhub-user: ${{ secrets.DOCKERHUB_USERNAME }} - dockerhub-token: ${{ secrets.DOCKERHUB_TOKEN }} diff --git a/.github/workflows/docker-superlink.yml b/.github/workflows/docker-superlink.yml deleted file mode 100644 index b6a6b4114ba4..000000000000 --- a/.github/workflows/docker-superlink.yml +++ /dev/null @@ -1,45 +0,0 @@ -name: Build docker SuperLink image - -on: - workflow_dispatch: - inputs: - flwr-version: - description: "Version of Flower." - required: true - type: string - -permissions: - contents: read - -jobs: - build-superlink-images: - name: Build images - uses: ./.github/workflows/_docker-build.yml - # run only on default branch when using it with workflow_dispatch - if: github.ref_name == github.event.repository.default_branch - with: - namespace-repository: flwr/superlink - file-dir: src/docker/superlink - build-args: | - FLWR_VERSION=${{ github.event.inputs.flwr-version }} - PYTHON_VERSION=3.11 - UBUNTU_VERSION=ubuntu22.04 - tags: | - ${{ github.event.inputs.flwr-version }}-py3.11-ubuntu22.04 - ${{ github.event.inputs.flwr-version }} - secrets: - dockerhub-user: ${{ secrets.DOCKERHUB_USERNAME }} - dockerhub-token: ${{ secrets.DOCKERHUB_TOKEN }} - - summary: - name: Summary - runs-on: ubuntu-22.04 - needs: build-superlink-images - timeout-minutes: 10 - steps: - - run: | - echo "### Images" >> $GITHUB_STEP_SUMMARY - echo "" >> $GITHUB_STEP_SUMMARY - for IMAGE in $(echo ${{ toJson(needs.build-superlink-images.outputs.metadata) }} | jq -r '.tags[]' ); do - echo "- $IMAGE" >> $GITHUB_STEP_SUMMARY - done diff --git a/.github/workflows/docker-supernode.yml b/.github/workflows/docker-supernode.yml deleted file mode 100644 index 83b53764b794..000000000000 --- a/.github/workflows/docker-supernode.yml +++ /dev/null @@ -1,52 +0,0 @@ -name: Build docker SuperNode image - -on: - workflow_dispatch: - inputs: - flwr-version: - description: "Version of Flower." - required: true - type: string - -permissions: - contents: read - -jobs: - build-supernode-images: - name: Build images - uses: ./.github/workflows/_docker-build.yml - # run only on default branch when using it with workflow_dispatch - if: github.ref_name == github.event.repository.default_branch - strategy: - fail-fast: false - matrix: - image: [ - { - py-version: "3.8", - tags: "${{ github.event.inputs.flwr-version }}-py3.8-ubuntu22.04" - }, - { - py-version: "3.9", - tags: "${{ github.event.inputs.flwr-version }}-py3.9-ubuntu22.04" - }, - { - py-version: "3.10", - tags: "${{ github.event.inputs.flwr-version }}-py3.10-ubuntu22.04" - }, - { - py-version: "3.11", - # those are two tags -py3.11-py3.11-ubuntu22.04 and separated by a \n - tags: "${{ github.event.inputs.flwr-version }}-py3.11-ubuntu22.04\n${{ github.event.inputs.flwr-version }}" - }, - ] - with: - namespace-repository: flwr/supernode - file-dir: src/docker/supernode - build-args: | - FLWR_VERSION=${{ github.event.inputs.flwr-version }} - PYTHON_VERSION=${{ matrix.image.py-version }} - UBUNTU_VERSION=ubuntu22.04 - tags: ${{ matrix.image.tags }} - secrets: - dockerhub-user: ${{ secrets.DOCKERHUB_USERNAME }} - dockerhub-token: ${{ secrets.DOCKERHUB_TOKEN }} diff --git a/.github/workflows/e2e.yml b/.github/workflows/e2e.yml index 57802e598546..d66362de9d32 100644 --- a/.github/workflows/e2e.yml +++ b/.github/workflows/e2e.yml @@ -118,12 +118,24 @@ jobs: steps: - uses: actions/checkout@v4 - - name: Bootstrap - uses: ./.github/actions/bootstrap + - name: Set up Python + uses: actions/setup-python@v5 with: python-version: 3.8 + - name: Install build tools + run: | + python -m pip install -U pip==23.3.1 + shell: bash + # Using approach described here for Python location caching: + # https://blog.allenai.org/python-caching-in-github-actions-e9452698e98d + - name: Cache Python location + id: cache-restore-python + uses: actions/cache/restore@v4 + with: + path: ${{ env.pythonLocation }} + key: pythonloc-${{ runner.os }}-${{ matrix.directory }}-${{ env.pythonLocation }}-${{ hashFiles(format('./e2e/{0}/pyproject.toml', matrix.directory)) }} - name: Install dependencies - run: python -m poetry install + run: python -m pip install --upgrade . - name: Install Flower wheel from artifact store if: ${{ github.repository == 'adap/flower' && !github.event.pull_request.head.repo.fork && github.actor != 'dependabot[bot]' }} run: | @@ -137,6 +149,9 @@ jobs: - name: Run virtual client test if: ${{ matrix.directory != 'bare-client-auth' }} run: python simulation.py + - name: Run simulation engine test + if: ${{ matrix.directory == 'pytorch' || matrix.directory == 'tensorflow'}} + run: python simulation_next.py - name: Run driver test if: ${{ matrix.directory != 'bare-client-auth' }} run: ./../test_driver.sh "${{ matrix.directory }}" @@ -149,6 +164,13 @@ jobs: - name: Run driver test with client authentication if: ${{ matrix.directory == 'bare-client-auth' }} run: ./../test_driver.sh bare client-auth + - name: Cache save Python location + id: cache-save-python + uses: actions/cache/save@v4 + if: ${{ github.ref_name == 'main' && !steps.cache-restore-python.outputs.cache-hit }} + with: + path: ${{ env.pythonLocation }} + key: pythonloc-${{ runner.os }}-${{ matrix.directory }}-${{ env.pythonLocation }}-${{ hashFiles(format('./e2e/{0}/pyproject.toml', matrix.directory)) }} strategies: runs-on: ubuntu-22.04 diff --git a/.github/workflows/framework.yml b/.github/workflows/framework.yml index 784f04750c5e..feb08229be06 100644 --- a/.github/workflows/framework.yml +++ b/.github/workflows/framework.yml @@ -31,6 +31,8 @@ jobs: steps: - uses: actions/checkout@v4 + with: + fetch-depth: 0 - name: Bootstrap uses: ./.github/actions/bootstrap with: diff --git a/.github/workflows/release-nightly.yml b/.github/workflows/release-nightly.yml index f9668131b5a6..2b72190bede5 100644 --- a/.github/workflows/release-nightly.yml +++ b/.github/workflows/release-nightly.yml @@ -3,7 +3,6 @@ name: Release nightly on: schedule: - cron: "0 23 * * *" - - cron: "30 23 * * *" env: FLWR_TELEMETRY_ENABLED: 0 @@ -17,46 +16,65 @@ jobs: name: ${{ steps.release.outputs.name }} version: ${{ steps.release.outputs.version }} skip: ${{ steps.release.outputs.skip }} + pip-version: ${{ steps.release.outputs.pip-version }} + setuptools-version: ${{ steps.release.outputs.setuptools-version }} steps: - uses: actions/checkout@v4 - name: Bootstrap + id: bootstrap uses: ./.github/actions/bootstrap - name: Release nightly - if: github.event.schedule == '0 23 * * *' + id: release env: PYPI_TOKEN: ${{ secrets.PYPI_TOKEN }} - run: ./dev/publish-nightly.sh - - name: Read nightly version and name - if: github.event.schedule == '30 23 * * *' - id: release run: | - RESULT=$(./dev/publish-nightly.sh --skip-publish) + RESULT=$(./dev/publish-nightly.sh) if [ "$RESULT" == "There were no commits in the last 24 hours." ]; then echo "skip=true" >> $GITHUB_OUTPUT fi echo "name=$(poetry version | awk {'print $1'})" >> $GITHUB_OUTPUT echo "version=$(poetry version -s)" >> $GITHUB_OUTPUT + echo "pip-version=${{ steps.bootstrap.outputs.pip-version }}" >> "$GITHUB_OUTPUT" + echo "setuptools-version=${{ steps.bootstrap.outputs.setuptools-version }}" >> "$GITHUB_OUTPUT" - build-docker-images: - name: Build nightly images - if: github.repository == 'adap/flower' && needs.release-nightly.outputs.skip != 'true' && github.event.schedule == '30 23 * * *' + build-docker-base-images: + name: Build nightly base images + if: github.repository == 'adap/flower' && needs.release-nightly.outputs.skip != 'true' uses: ./.github/workflows/_docker-build.yml needs: release-nightly + with: + namespace-repository: flwr/base + file-dir: src/docker/base/ubuntu + build-args: | + PIP_VERSION=${{ needs.release-nightly.outputs.pip-version }} + SETUPTOOLS_VERSION=${{ needs.release-nightly.outputs.setuptools-version }} + FLWR_VERSION=${{ needs.release-nightly.outputs.version }} + FLWR_PACKAGE=${{ needs.release-nightly.outputs.name }} + tags: | + ${{ needs.release-nightly.outputs.version }} + nightly + secrets: + dockerhub-user: ${{ secrets.DOCKERHUB_USERNAME }} + dockerhub-token: ${{ secrets.DOCKERHUB_TOKEN }} + + build-docker-binary-images: + name: Build nightly binary images + if: github.repository == 'adap/flower' && needs.release-nightly.outputs.skip != 'true' + uses: ./.github/workflows/_docker-build.yml + needs: [release-nightly, build-docker-base-images] strategy: fail-fast: false matrix: images: [ - { repository: "flwr/superlink", file-dir: "src/docker/superlink" }, - { repository: "flwr/supernode", file-dir: "src/docker/supernode" }, - { repository: "flwr/serverapp", file-dir: "src/docker/serverapp" } + { repository: "flwr/superlink", file_dir: "src/docker/superlink" }, + { repository: "flwr/supernode", file_dir: "src/docker/supernode" }, + { repository: "flwr/serverapp", file_dir: "src/docker/serverapp" } ] with: namespace-repository: ${{ matrix.images.repository }} - file-dir: ${{ matrix.images.file-dir }} - build-args: | - FLWR_VERSION=${{ needs.release-nightly.outputs.version }} - FLWR_PACKAGE=${{ needs.release-nightly.outputs.name }} + file-dir: ${{ matrix.images.file_dir }} + build-args: BASE_IMAGE=${{ needs.release-nightly.outputs.version }} tags: | ${{ needs.release-nightly.outputs.version }} nightly diff --git a/README.md b/README.md index 909cf890dccf..5f61d616775b 100644 --- a/README.md +++ b/README.md @@ -150,8 +150,10 @@ Other [examples](https://github.com/adap/flower/tree/main/examples): - Single-Machine Simulation of Federated Learning Systems ([PyTorch](https://github.com/adap/flower/tree/main/examples/simulation-pytorch)) ([Tensorflow](https://github.com/adap/flower/tree/main/examples/simulation-tensorflow)) - [Comprehensive Flower+XGBoost](https://github.com/adap/flower/tree/main/examples/xgboost-comprehensive) - [Flower through Docker Compose and with Grafana dashboard](https://github.com/adap/flower/tree/main/examples/flower-via-docker-compose) -- [Flower with KaplanMeierFitter from the lifelines library](https://github.com/adap/flower/tree/main/examples/federated-kaplna-meier-fitter) +- [Flower with KaplanMeierFitter from the lifelines library](https://github.com/adap/flower/tree/main/examples/federated-kaplan-meier-fitter) - [Sample Level Privacy with Opacus](https://github.com/adap/flower/tree/main/examples/opacus) +- [Sample Level Privacy with TensorFlow-Privacy](https://github.com/adap/flower/tree/main/examples/tensorflow-privacy) +- [Flower with a Tabular Dataset](https://github.com/adap/flower/tree/main/examples/fl-tabular) ## Community diff --git a/baselines/doc/source/conf.py b/baselines/doc/source/conf.py index a9525c44ab7b..8184bd223ea7 100644 --- a/baselines/doc/source/conf.py +++ b/baselines/doc/source/conf.py @@ -37,7 +37,7 @@ author = "The Flower Authors" # The full version, including alpha/beta/rc tags -release = "1.8.0" +release = "1.9.0" # -- General configuration --------------------------------------------------- diff --git a/benchmarks/flowertune-llm/README.md b/benchmarks/flowertune-llm/README.md new file mode 100644 index 000000000000..0cb69e7ff9c7 --- /dev/null +++ b/benchmarks/flowertune-llm/README.md @@ -0,0 +1,61 @@ +![](_static/flower_llm.jpg) + +# FlowerTune LLM Leaderboard + +This repository guides you through the process of federated LLM instruction tuning with a +pre-trained [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.3) model across 4 domains --- general NLP, finance, medical and code. + +Please follow the instructions to run and evaluate the federated LLMs. + +## Create a new project + +As the first step, please register a Flower account on [Flower website](https://flower.ai/login). +Assuming `flwr` package is already installed on your system (check [here](https://flower.ai/docs/framework/how-to-install-flower.html) for `flwr` installation). +We provide a single-line command to create a new project directory based on your selected challenge: + +```shell +flwr new --framework=flwrtune --username=your_flower_account +``` + +Then you will see a prompt to ask your project name and the choice of LLM challenges from the set of general NLP, finance, medical and code. +Type your project name and select your preferred challenge, +and then a new project directory will be generated automatically. + +### Structure + +After running `flwr new`, you will see a new directory generated with the following structure: + +```bash + + ├── README.md # <- Instructions + ├── pyproject.toml # <- Environment dependencies + └── + ├── app.py # <- Flower ClientApp/ServerApp build + ├── client.py # <- Flower client constructor + ├── server.py # <- Sever-related functions + ├── models.py # <- Model build + ├── dataset.py # <- Dataset and tokenizer build + ├── conf/config.yaml # <- User configuration + └── conf/static_config.yaml # <- Static configuration +``` + +This can serve as the starting point for you to build up your own federated LLM fine-tuning methods. +Please note that any modification to the content of `conf/static_config.yaml` is strictly prohibited for those who wish to participate in the [LLM Leaderboard](https://flower.ai/benchmarks/llm-leaderboard). +Otherwise, the submission will not be considered. + +## Run FlowerTune LLM challenges + +With a new project directory created, running a baseline challenge can be done by: + +1. Navigate inside the directory that you just created. + + +2. Follow the `Environments setup` section of `README.md` in the project directory to install project dependencies. + + +3. Run the challenge as indicated in the `Running the challenge` section in the `README.md`. + +## Evaluate pre-trained LLMs + +After the LLM fine-tuning finished, evaluate the performance of your pre-trained LLMs +following the `README.md` in `evaluation` directory. diff --git a/benchmarks/flowertune-llm/_static/flower_llm.jpg b/benchmarks/flowertune-llm/_static/flower_llm.jpg new file mode 100644 index 000000000000..96081d9c2ad1 Binary files /dev/null and b/benchmarks/flowertune-llm/_static/flower_llm.jpg differ diff --git a/datasets/README.md b/datasets/README.md index cf5caac3e1cd..1d8014d57ea3 100644 --- a/datasets/README.md +++ b/datasets/README.md @@ -7,6 +7,21 @@ [![Slack](https://img.shields.io/badge/Chat-Slack-red)](https://flower.ai/join-slack) Flower Datasets (`flwr-datasets`) is a library to quickly and easily create datasets for federated learning, federated evaluation, and federated analytics. It was created by the `Flower Labs` team that also created Flower: A Friendly Federated Learning Framework. + + +> [!TIP] +> For complete documentation that includes API docs, how-to guides and tutorials please visit the [Flower Datasets Documentation](https://flower.ai/docs/datasets/) and for full FL example see the [Flower Examples page](https://github.com/adap/flower/tree/main/examples). + +## Installation + +For a complete installation guide visit the [Flower Datasets Documenation](https://flower.ai/docs/datasets/) + +```bash +pip install flwr-datasets[vision] +``` + +## Overview + Flower Datasets library supports: * **downloading datasets** - choose the dataset from Hugging Face's `datasets`, * **partitioning datasets** - customize the partitioning scheme, @@ -21,43 +36,28 @@ Thanks to using Hugging Face's `datasets` used under the hood, Flower Datasets i * Jax, * Arrow. -Create **custom partitioning schemes** or choose from the **implemented partitioning schemes**: +Create **custom partitioning schemes** or choose from the **implemented [partitioning schemes](https://flower.ai/docs/datasets/ref-api/flwr_datasets.partitioner.html#module-flwr_datasets.partitioner)**: + * Partitioner (the abstract base class) `Partitioner` * IID partitioning `IidPartitioner(num_partitions)` -* Natural ID partitioner `NaturalIdPartitioner` +* Dirichlet partitioning `DirichletPartitioner(num_partitions, partition_by, alpha)` +* InnerDirichlet partitioning `InnerDirichletPartitioner(partition_sizes, partition_by, alpha)` +* Natural ID partitioner `NaturalIdPartitioner(partition_by)` * Size partitioner (the abstract base class for the partitioners dictating the division based the number of samples) `SizePartitioner` -* Linear partitioner `LinearPartitioner` -* Square partitioner `SquarePartitioner` -* Exponential partitioner `ExponentialPartitioner` -* more to come in future releases. - -# Installation - -## With pip +* Linear partitioner `LinearPartitioner(num_partitions)` +* Square partitioner `SquarePartitioner(num_partitions)` +* Exponential partitioner `ExponentialPartitioner(num_partitions)` +* more to come in the future releases (contributions are welcome). +

+ Comparison of partitioning schemes. +
+ Comparison of Partitioning Schemes on CIFAR10 +

-Flower Datasets can be installed from PyPi - -```bash -pip install flwr-datasets -``` - -Install with an extension: - -* for image datasets: - -```bash -pip install flwr-datasets[vision] -``` - -* for audio datasets: - -```bash -pip install flwr-datasets[audio] -``` +PS: This plot was generated using a library function (see [flwr_datasets.visualization](https://flower.ai/docs/datasets/ref-api/flwr_datasets.visualization.html) package for more). -If you plan to change the type of the dataset to run the code with your ML framework, make sure to have it installed too. -# Usage +## Usage Flower Datasets exposes the `FederatedDataset` abstraction to represent the dataset needed for federated learning/evaluation/analytics. It has two powerful methods that let you handle the dataset preprocessing: `load_partition(partition_id, split)` and `load_split(split)`. @@ -67,16 +67,16 @@ Here's a basic quickstart example of how to partition the MNIST dataset: from flwr_datasets import FederatedDataset # The train split of the MNIST dataset will be partitioned into 100 partitions -mnist_fds = FederatedDataset("mnist", partitioners={"train": 100} +fds = FederatedDataset("mnist", partitioners={"train": 100}) -mnist_partition_0 = mnist_fds.load_partition(0, "train") +partition = fds.load_partition(0) -centralized_data = mnist_fds.load_split("test") +centralized_data = fds.load_split("test") ``` For more details, please refer to the specific how-to guides or tutorial. They showcase customization and more advanced features. -# Future release +## Future release Here are a few of the things that we will work on in future releases: @@ -85,6 +85,6 @@ Here are a few of the things that we will work on in future releases: * ✅ More out-of-the-box `Partitioner`s. * ✅ Passing `Partitioner`s via `FederatedDataset`'s `partitioners` argument. * ✅ Customization of the dataset splitting before the partitioning. -* Simplification of the dataset transformation to the popular frameworks/types. +* ✅ Simplification of the dataset transformation to the popular frameworks/types. * Creation of the synthetic data, * Support for Vertical FL. diff --git a/datasets/dev/format.sh b/datasets/dev/format.sh index 8292e0b3ed79..c6977982dd6c 100755 --- a/datasets/dev/format.sh +++ b/datasets/dev/format.sh @@ -3,9 +3,16 @@ set -e cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/../ # Python -echo "Formatting started" +echo "Formatting started: Python" python -m isort flwr_datasets/ python -m black -q flwr_datasets/ python -m docformatter -i -r flwr_datasets/ python -m ruff check --fix flwr_datasets/ -echo "Formatting done" +echo "Formatting done: Python" + +# Notebooks +echo "Formatting started: Notebooks" +python -m black --ipynb -q doc/source/*.ipynb +KEYS="metadata.celltoolbar metadata.language_info metadata.toc metadata.notify_time metadata.varInspector metadata.accelerator metadata.vscode cell.metadata.id cell.metadata.heading_collapsed cell.metadata.hidden cell.metadata.code_folding cell.metadata.tags cell.metadata.init_cell cell.metadata.vscode cell.metadata.pycharm" +python -m nbstripout --keep-output doc/source/*.ipynb --extra-keys "$KEYS" +echo "Formatting done: Notebooks" diff --git a/datasets/doc/source/_static/readme/comparison_of_partitioning_schemes.png b/datasets/doc/source/_static/readme/comparison_of_partitioning_schemes.png new file mode 100644 index 000000000000..ed2e323ef649 Binary files /dev/null and b/datasets/doc/source/_static/readme/comparison_of_partitioning_schemes.png differ diff --git a/datasets/doc/source/conf.py b/datasets/doc/source/conf.py index 755147bc9e1d..11285c375f96 100644 --- a/datasets/doc/source/conf.py +++ b/datasets/doc/source/conf.py @@ -38,7 +38,7 @@ author = "The Flower Authors" # The full version, including alpha/beta/rc tags -release = "0.1.0" +release = "0.2.0" # -- General configuration --------------------------------------------------- diff --git a/datasets/doc/source/how-to-install-flwr-datasets.rst b/datasets/doc/source/how-to-install-flwr-datasets.rst index d2fd7923a817..c690ffb1b09c 100644 --- a/datasets/doc/source/how-to-install-flwr-datasets.rst +++ b/datasets/doc/source/how-to-install-flwr-datasets.rst @@ -42,5 +42,5 @@ If everything worked, it should print the version of Flower Datasets to the comm .. code-block:: none - 0.0.1 + 0.2.0 diff --git a/datasets/doc/source/how-to-use-with-pytorch.rst b/datasets/doc/source/how-to-use-with-pytorch.rst index 4228ead2a281..4e00194e6155 100644 --- a/datasets/doc/source/how-to-use-with-pytorch.rst +++ b/datasets/doc/source/how-to-use-with-pytorch.rst @@ -67,13 +67,20 @@ If you want to divide the dataset, you can use (at any point before passing the partition_train = partition_train_test["train"] partition_test = partition_train_test["test"] +If you want to keep the order of samples intact and need a division into 2 or more subsets, you can use:: + + from flwr_datasets.utils import divide_dataset + train, valid, test = divide_dataset(partition, [0.6, 0.2, 0.2]) + Or you can simply calculate the indices yourself:: partition_len = len(partition) # Split `partition` 80:20 num_train_examples = int(0.8 * partition_len) - partition_train = partition.select(range(num_train_examples)) ) # use first 80% - partition_test = partition.select(range(num_train_examples, partition_len)) ) # use last 20% + # use first 80% + partition_train = partition.select(range(num_train_examples)) ) + # use last 20% + partition_test = partition.select(range(num_train_examples, partition_len)) ) And during the training loop, you need to apply one change. With a typical dataloader, you get a list returned for each iteration:: diff --git a/datasets/doc/source/how-to-visualize-label-distribution.ipynb b/datasets/doc/source/how-to-visualize-label-distribution.ipynb new file mode 100644 index 000000000000..26db72047cff --- /dev/null +++ b/datasets/doc/source/how-to-visualize-label-distribution.ipynb @@ -0,0 +1,1122 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "fb7e89caa9e6d772", + "metadata": {}, + "source": [ + "# Visualize Label Distribution" + ] + }, + { + "cell_type": "markdown", + "id": "67c54a8d7c872547", + "metadata": {}, + "source": [ + "If you partition datasets to simulate heterogeneity through label skew and/or size skew, you can now effortlessly visualize the partitioned dataset using `flwr-datasets`.\n", + "\n", + "In this how-to guide, you'll learn how to visualize and compare partitioned datasets when applying different methods or parameters.\n", + "\n", + "All the described visualization functions are compatible with all ``Partitioner`` you can find in\n", + "[flwr_datasets.partitioner](https://flower.ai/docs/datasets/ref-api/flwr_datasets.partitioner.html#module-flwr_datasets.partitioner)\n" + ] + }, + { + "cell_type": "markdown", + "id": "7220467f2c6ba432", + "metadata": {}, + "source": [ + "## Common Setup" + ] + }, + { + "cell_type": "markdown", + "id": "4e2ad2f0a0f7174d", + "metadata": {}, + "source": [ + "Install Flower Datasets library:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c46514b679f394ce", + "metadata": {}, + "outputs": [], + "source": [ + "! pip install -q \"flwr-datasets[vision]\"" + ] + }, + { + "cell_type": "markdown", + "id": "d7ffd5b6836a5ee0", + "metadata": {}, + "source": [ + "## Plot Label Distribution" + ] + }, + { + "cell_type": "markdown", + "id": "38fbbdfe6b930916", + "metadata": {}, + "source": [ + "### Bar plot" + ] + }, + { + "cell_type": "markdown", + "id": "a5778edf97a7ee04", + "metadata": {}, + "source": [ + "Let's visualize the result of `DirichletPartitioner`.\n", + "We will create a `FederatedDataset` and assign `DirichletPartitioner` to the `train` split:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "42397afaaf50529e", + "metadata": {}, + "outputs": [], + "source": [ + "from flwr_datasets import FederatedDataset\n", + "from flwr_datasets.partitioner import DirichletPartitioner\n", + "from flwr_datasets.visualization import plot_label_distributions\n", + "\n", + "\n", + "fds = FederatedDataset(\n", + " dataset=\"cifar10\",\n", + " partitioners={\n", + " \"train\": DirichletPartitioner(\n", + " num_partitions=10,\n", + " partition_by=\"label\",\n", + " alpha=0.3,\n", + " seed=42,\n", + " min_partition_size=0,\n", + " ),\n", + " },\n", + ")\n", + "\n", + "partitioner = fds.partitioners[\"train\"]" + ] + }, + { + "cell_type": "markdown", + "id": "c4d5855ee8a605d3", + "metadata": {}, + "source": [ + "Once we have the partitioner with the dataset assigned, we are ready to pass it to the plotting function:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f75b48256ed68897", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax, df = plot_label_distributions(\n", + " partitioner,\n", + " label_name=\"label\",\n", + " plot_type=\"bar\",\n", + " size_unit=\"absolute\",\n", + " partition_id_axis=\"x\",\n", + " legend=True,\n", + " verbose_labels=True,\n", + " title=\"Per Partition Labels Distribution\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "be05badab744d9f7", + "metadata": {}, + "source": [ + "You can configure many details directly using the function parameters. The ones that can interest you the most are:\n", + "\n", + "* `size_unit` to have the sizes normalized such that they sum up to 1 and express the fraction of the data in each partition,\n", + "* `legend` and `verbose_labels` in case the dataset has more descriptive names and not numbers,\n", + "* `cmap` to change the values of the bars (for an overview of the available colors, have a look at [link](https://matplotlib.org/stable/users/explain/colors/colormaps.html); check out `cmap=\"tab20b\"`) \n", + "\n", + " And for even greater control, you can specify `plot_kwargs` and `legend_kwargs` as `Dict`, which will be further passed to the `plot` and `legend` functions." + ] + }, + { + "cell_type": "markdown", + "id": "3dbf6dc4ede79f05", + "metadata": {}, + "source": [ + "You can also inspect the exact numbers that were used to create this plot. Three objects are returned (see reference [here](https://flower.ai/docs/datasets/ref-api/flwr_datasets.visualization.plot_label_distributions.html#flwr_datasets.visualization.plot_label_distributions)). Let's inspect the returned DataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f6edd14d8b260e9e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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airplaneautomobilebirdcatdeerdogfroghorseshiptruck
Partition ID
08177941462212343225456384149
11416697530340903868
2041124543511158421
37621591100511201662198213512175
424371421924004251151477
5677917025255247727445900
6422244863809290380506
7122281159721741038172716825154
825629342751848151122401417
91136107350357126711223
\n", + "
" + ], + "text/plain": [ + " airplane automobile bird cat deer dog frog horse ship \\\n", + "Partition ID \n", + "0 817 794 1462 2123 432 25 456 384 14 \n", + "1 1416 6 97 5 3 0 3409 0 3 \n", + "2 0 4 11 2 454 3 511 15 84 \n", + "3 762 159 1100 51 120 166 2 1982 1351 \n", + "4 2 43 714 2 19 2400 425 1 151 \n", + "5 67 79 170 25 2552 477 27 44 590 \n", + "6 422 2 4 486 380 92 90 380 50 \n", + "7 122 2811 597 2174 1038 1727 1 682 515 \n", + "8 256 29 342 75 1 84 8 1511 2240 \n", + "9 1136 1073 503 57 1 26 71 1 2 \n", + "\n", + " truck \n", + "Partition ID \n", + "0 9 \n", + "1 868 \n", + "2 21 \n", + "3 2175 \n", + "4 477 \n", + "5 0 \n", + "6 6 \n", + "7 4 \n", + "8 1417 \n", + "9 23 " + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "markdown", + "id": "2902213a", + "metadata": {}, + "source": [ + "Each row represents a unique partition ID, and the columns represent unique labels (either in the verbose version if `verbose_labels=True` or typically `int` values otherwise, representing the partition IDs).\n", + "That you can index the DataFrame `df[partition_id, label_id]` to get the number of samples in `partition_id` for the specified `label_id.`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8ffe4039", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "714" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.loc[4, \"bird\"]" + ] + }, + { + "cell_type": "markdown", + "id": "2e6c17af529a668f", + "metadata": {}, + "source": [ + "Let's see a plot with `size_unit=\"percent\"`, which is another excellent way to understand the partitions. In this mode, the number of datapoints for each class in a given partition are normalized, so they sum up to 100." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a241894a47f3cc9f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax, df = plot_label_distributions(\n", + " partitioner,\n", + " label_name=\"label\",\n", + " plot_type=\"bar\",\n", + " size_unit=\"percent\",\n", + " partition_id_axis=\"x\",\n", + " legend=True,\n", + " verbose_labels=True,\n", + " cmap=\"tab20b\",\n", + " title=\"Per Partition Labels Distribution\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "e7d1fe2e1c0f14c", + "metadata": {}, + "source": [ + "### Heatmap" + ] + }, + { + "cell_type": "markdown", + "id": "ad6a2e53de3cc084", + "metadata": {}, + "source": [ + "You might want to visualize the results of partitioning as a heatmap, which can be especially useful for binary labels. Here's how:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "14a4b4e574866120", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax, df = plot_label_distributions(\n", + " partitioner,\n", + " label_name=\"label\",\n", + " plot_type=\"heatmap\",\n", + " size_unit=\"absolute\",\n", + " partition_id_axis=\"x\",\n", + " legend=True,\n", + " verbose_labels=True,\n", + " title=\"Per Partition Labels Distribution\",\n", + " plot_kwargs={\"annot\": True},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "5167593e67fa3dbb", + "metadata": {}, + "source": [ + "Note: we used the `plot_kwargs={\"annot\": True}` to add the number directly to the plot." + ] + }, + { + "cell_type": "markdown", + "id": "e2e41273551ac32a", + "metadata": {}, + "source": [ + "If you are a `pandas` fan, then you might be interested that a similar heatmap can be created with the DataFrame object for visualization in jupyter notebook:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fcc90b52bfd650cf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 airplaneautomobilebirdcatdeerdogfroghorseshiptruck
Partition ID          
08177941462212343225456384149
11416697530340903868
2041124543511158421
37621591100511201662198213512175
424371421924004251151477
5677917025255247727445900
6422244863809290380506
7122281159721741038172716825154
825629342751848151122401417
91136107350357126711223
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.style.background_gradient(axis=None, cmap=\"Greens\", vmin=0)" + ] + }, + { + "cell_type": "markdown", + "id": "37d85e1b40d54918", + "metadata": {}, + "source": [ + "## Plot Comparison of Label Distributions" + ] + }, + { + "cell_type": "markdown", + "id": "4f49259a3de7dd17", + "metadata": {}, + "source": [ + "Now, once you know how to visualize a single partitioned dataset, you'll learn how to compare a few of them on a single plot.\n", + "\n", + "Let's compare:\n", + "\n", + "- IidPartitioner,\n", + "- DirichletPartitioner,\n", + "- ShardPartitioner\n", + "still using the `cifar10` dataset.\n", + "\n", + "We need to create a list of partitioners. Each partitioner needs to have a dataset assigned to it (it does not have to be the same dataset so you can also compare the same partitioning on different datasets)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9e84a9192a266f3e", + "metadata": {}, + "outputs": [], + "source": [ + "from flwr_datasets import FederatedDataset\n", + "from flwr_datasets.partitioner import (\n", + " IidPartitioner,\n", + " DirichletPartitioner,\n", + " ShardPartitioner,\n", + ")\n", + "\n", + "partitioner_list = []\n", + "title_list = [\"IidPartitioner\", \"DirichletPartitioner\", \"ShardPartitioner\"]\n", + "\n", + "## IidPartitioner\n", + "fds = FederatedDataset(\n", + " dataset=\"cifar10\",\n", + " partitioners={\n", + " \"train\": IidPartitioner(num_partitions=10),\n", + " },\n", + ")\n", + "partitioner_list.append(fds.partitioners[\"train\"])\n", + "\n", + "## DirichletPartitioner\n", + "fds = FederatedDataset(\n", + " dataset=\"cifar10\",\n", + " partitioners={\n", + " \"train\": DirichletPartitioner(\n", + " num_partitions=10,\n", + " partition_by=\"label\",\n", + " alpha=1.0,\n", + " min_partition_size=0,\n", + " ),\n", + " },\n", + ")\n", + "partitioner_list.append(fds.partitioners[\"train\"])\n", + "\n", + "## ShardPartitioner\n", + "fds = FederatedDataset(\n", + " dataset=\"cifar10\",\n", + " partitioners={\n", + " \"train\": ShardPartitioner(\n", + " num_partitions=10, partition_by=\"label\", num_shards_per_partition=2\n", + " )\n", + " },\n", + ")\n", + "partitioner_list.append(fds.partitioners[\"train\"])" + ] + }, + { + "cell_type": "markdown", + "id": "d18bae80", + "metadata": {}, + "source": [ + "Now let's visualize them side by side" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f2ee2864", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from flwr_datasets.visualization import plot_comparison_label_distribution\n", + "\n", + "fig, axes, df_list = plot_comparison_label_distribution(\n", + " partitioner_list=partitioner_list,\n", + " label_name=\"label\",\n", + " subtitle=\"Comparison of Partitioning Schemes on CIFAR10\",\n", + " titles=title_list,\n", + " legend=True,\n", + " verbose_labels=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "862899eb04695380", + "metadata": {}, + "source": [ + "## Bonus: Natural Id Dataset" + ] + }, + { + "cell_type": "markdown", + "id": "4f3f8aaf", + "metadata": {}, + "source": [ + "Nothing stops you from using the `NaturalIdPartitioner` to visualize a dataset with the `id` in it and does not need the artificial partitioning but has the pre-existing partitions. For that dataset, we use `NaturalIdPartitioner`. Let's look at the `speech-commands` dataset that has `speaker_id`, and there are quite a few speakers; therefore, we will show only the first 20 partitions. And since we have quite a few different labels, let's specify `legend_kwargs={\"ncols\": 2}` to display them in two columns (we will also shift the legend slightly to the right)." + ] + }, + { + "cell_type": "markdown", + "id": "f016d21a", + "metadata": {}, + "source": [ + "You'll be using the [Google SpeechCommands](https://huggingface.co/datasets/google/speech_commands) dataset, which is a speech-based dataset. For this, you'll need to install the `\"audio\"` extension for Flower Datasets. It can be easily done like this:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fd5ca8f4", + "metadata": {}, + "outputs": [], + "source": [ + "! pip install -q \"flwr-datasets[audio]\"" + ] + }, + { + "cell_type": "markdown", + "id": "90ea3642", + "metadata": {}, + "source": [ + "With everything ready, let's visualize the partitions for a naturally partitioned dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4fe70116", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from flwr_datasets import FederatedDataset\n", + "from flwr_datasets.partitioner import NaturalIdPartitioner\n", + "from flwr_datasets.visualization import plot_label_distributions\n", + "\n", + "\n", + "fds = FederatedDataset(\n", + " dataset=\"google/speech_commands\",\n", + " subset=\"v0.01\",\n", + " partitioners={\n", + " \"train\": NaturalIdPartitioner(\n", + " partition_by=\"speaker_id\",\n", + " ),\n", + " },\n", + ")\n", + "\n", + "partitioner = fds.partitioners[\"train\"]\n", + "\n", + "fix, ax, df = plot_label_distributions(\n", + " partitioner=partitioner,\n", + " label_name=\"label\",\n", + " max_num_partitions=20,\n", + " plot_type=\"bar\",\n", + " size_unit=\"percent\",\n", + " partition_id_axis=\"x\",\n", + " legend=True,\n", + " title=\"Per Partition Labels Distribution\",\n", + " verbose_labels=True,\n", + " legend_kwargs={\"ncols\": 2, \"bbox_to_anchor\": (1.25, 0.5)},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "4442c99c", + "metadata": {}, + "source": [ + "## More resources\n", + "\n", + "If you are looking for more resorces, feel free to check:\n", + "\n", + "* `flwr-dataset` documentation\n", + " * [plot_label_distributions](https://flower.ai/docs/datasets/ref-api/flwr_datasets.visualization.plot_label_distributions.html#flwr_datasets.visualization.plot_label_distributions)\n", + " * [plot_comparison_label_distribution](https://flower.ai/docs/datasets/ref-api/flwr_datasets.visualization.plot_comparison_label_distribution.html#flwr_datasets.visualization.plot_comparison_label_distribution)\n", + "* if you want to do any custom modification of the returned plots\n", + " * [matplotlib](https://matplotlib.org/)\n", + " * [seaborn](https://seaborn.pydata.org/)\n", + " * or plot directly using pandas object [pd.DataFrame.plot](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.html)" + ] + }, + { + "cell_type": "markdown", + "id": "52655972", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "flwr", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/datasets/doc/source/index.rst b/datasets/doc/source/index.rst index 2144c527f8cd..bdcea7650bbc 100644 --- a/datasets/doc/source/index.rst +++ b/datasets/doc/source/index.rst @@ -7,6 +7,15 @@ learning/analytics/evaluation. It is created by the ``Flower Labs`` team that al Flower Datasets Framework ------------------------- +Install +~~~~~~~ + +.. code-block:: bash + + python -m pip install "flwr-datasets[vision]" + +Check out all the details on how to install Flower Datasets in :doc:`how-to-install-flwr-datasets`. + Tutorials ~~~~~~~~~ @@ -32,6 +41,7 @@ Problem-oriented how-to guides show step-by-step how to achieve a specific goal. how-to-use-with-tensorflow how-to-use-with-numpy how-to-use-with-local-data + how-to-visualize-label-distribution how-to-disable-enable-progress-bar References @@ -47,15 +57,26 @@ Information-oriented API reference and other reference material. flwr_datasets +.. toctree:: + :maxdepth: 1 + :caption: Reference docs + ref-telemetry Main features ------------- Flower Datasets library supports: -- **downloading datasets** - choose the dataset from Hugging Face's ``dataset`` -- **partitioning datasets** - customize the partitioning scheme +- **downloading datasets** - choose the dataset from Hugging Face's ``dataset`` (`link `_) +- **partitioning datasets** - choose one of the implemented partitioning scheme or create your own. - **creating centralized datasets** - leave parts of the dataset unpartitioned (e.g. for centralized evaluation) +- **visualization of the partitioned datasets** - visualize the label distribution of the partitioned dataset (and compare the results on different parameters of the same partitioning schemes, different datasets, different partitioning schemes, or any mix of them) + + +.. image:: ./_static/readme/comparison_of_partitioning_schemes.png + :align: center + :alt: Comparison of Partitioning Schemes on CIFAR10 + Thanks to using Hugging Face's ``datasets`` used under the hood, Flower Datasets integrates with the following popular formats/frameworks: @@ -67,28 +88,19 @@ Thanks to using Hugging Face's ``datasets`` used under the hood, Flower Datasets - Jax - Arrow -Install -------- - -The simplest install is - -.. code-block:: bash - - python -m pip install flwr-datasets - -If you plan to use the image datasets - -.. code-block:: bash - - python -m pip install flwr-datasets[vision] - -If you plan to use the audio datasets - -.. code-block:: bash +Here are a few of the ``Partitioner`` s that are available: (for a full list see `link `_ ) - python -m pip install flwr-datasets[audio] +* Partitioner (the abstract base class) ``Partitioner`` +* IID partitioning ``IidPartitioner(num_partitions)`` +* Dirichlet partitioning ``DirichletPartitioner(num_partitions, partition_by, alpha)`` +* InnerDirichlet partitioning ``InnerDirichletPartitioner(partition_sizes, partition_by, alpha)`` +* Natural ID partitioner ``NaturalIdPartitioner(partition_by)`` +* Size partitioner (the abstract base class for the partitioners dictating the division based the number of samples) ``SizePartitioner`` +* Linear partitioner ``LinearPartitioner(num_partitions)`` +* Square partitioner ``SquarePartitioner(num_partitions)`` +* Exponential partitioner ``ExponentialPartitioner(num_partitions)`` +* more to come in the future releases (contributions are welcome). -Check out the full details on the download in :doc:`how-to-install-flwr-datasets`. How To Use the library ---------------------- diff --git a/datasets/doc/source/ref-telemetry.md b/datasets/doc/source/ref-telemetry.md new file mode 100644 index 000000000000..a4fc9b6b0061 --- /dev/null +++ b/datasets/doc/source/ref-telemetry.md @@ -0,0 +1,66 @@ +# Telemetry + +The Flower Datasets open-source project collects **anonymous** usage metrics to make well-informed decisions to improve Flower Datasets. Doing this enables the Flower team to understand how Flower Datasets is used and what challenges users might face. + +**Flower is a friendly framework for collaborative AI and data science.** Staying true to this statement, Flower makes it easy to disable telemetry for users that do not want to share anonymous usage metrics. + +## Principles + +We follow strong principles guarding anonymous usage metrics collection: + +- **Optional:** You will always be able to disable telemetry; read on to learn “[How to opt-out](#how-to-opt-out)”. +- **Anonymous:** The reported usage metrics are anonymous and do not contain any personally identifiable information (PII). See “[Collected metrics](#collected-metrics)” to understand what metrics are being reported. +- **Transparent:** You can easily inspect what anonymous metrics are being reported; see the section “[How to inspect what is being reported](#how-to-inspect-what-is-being-reported)” +- **Open for feedback:** You can always reach out to us if you have feedback; see the section “[How to contact us](#how-to-contact-us)” for details. + +## How to opt-out + +When Flower Datasets starts, it will check for an environment variable called `FLWR_TELEMETRY_ENABLED`. Telemetry can easily be disabled by setting `FLWR_TELEMETRY_ENABLED=0`. Assuming you are using Flower Datasets in a Flower server or client, simply do so by prepending your command as in: + +```bash +FLWR_TELEMETRY_ENABLED=0 python server.py # or client.py +``` + +Alternatively, you can export `FLWR_TELEMETRY_ENABLED=0` in, for example, `.bashrc` (or whatever configuration file applies to your environment) to disable Flower Datasets telemetry permanently. + +## Collected metrics + +Flower telemetry collects the following metrics: + +**Flower version.** Understand which versions of Flower Datasets are currently being used. This helps us to decide whether we should invest effort into releasing a patch version for an older version of Flower Datasets or instead use the bandwidth to build new features. + +**Operating system.** Enables us to answer questions such as: *Should we create more guides for Linux, macOS, or Windows?* + +**Python version.** Knowing the Python version helps us, for example, to decide whether we should invest effort into supporting old versions of Python or stop supporting them and start taking advantage of new Python features. + +**Hardware properties.** Understanding the hardware environment that Flower Datasets is being used in helps to decide whether we should, for example, put more effort into supporting low-resource environments. + +**Dataset and Partitioners names.** Knowing what datasets and Partitioners are used enables us to provide more detailed code examples and tutorials and better prioritize work on development and support for them. + +**Cluster.** Flower telemetry assigns a random in-memory cluster ID each time a Flower workload starts. This allows us to understand which device types not only start Flower workloads but also successfully complete them. + +**Source.** Flower telemetry tries to store a random source ID in `~/.flwr/source` the first time a telemetry event is generated. The source ID is important to identify whether an issue is recurring or whether an issue is triggered by multiple clusters running concurrently (which often happens in simulation). For example, if a device runs multiple workloads at the same time, and this results in an issue, then, in order to reproduce the issue, multiple workloads must be started at the same time. + +You may delete the source ID at any time. If you wish for all events logged under a specific source ID to be deleted, you can send a deletion request mentioning the source ID to `telemetry@flower.ai`. All events related to that source ID will then be permanently deleted. + +We will not collect any personally identifiable information. If you think any of the metrics collected could be misused in any way, please [get in touch with us](#how-to-contact-us). We will update this page to reflect any changes to the metrics collected and publish changes in the changelog. + +If you think other metrics would be helpful for us to better guide our decisions, please let us know! We will carefully review them; if we are confident that they do not compromise user privacy, we may add them. + +## How to inspect what is being reported + +We wanted to make it very easy for you to inspect what anonymous usage metrics are reported. You can view all the reported telemetry information by setting the environment variable `FLWR_TELEMETRY_LOGGING=1`. Logging is disabled by default. You may use logging independently from `FLWR_TELEMETRY_ENABLED` so that you can inspect the telemetry feature without sending any metrics. + +```bash +FLWR_TELEMETRY_LOGGING=1 python server.py # or client.py +``` + +The inspect Flower telemetry without sending any anonymous usage metrics, use both environment variables: + +```bash +FLWR_TELEMETRY_ENABLED=0 FLWR_TELEMETRY_LOGGING=1 python server.py # or client.py +``` + +## How to contact us + +We want to hear from you. If you have any feedback or ideas on how to improve the way we handle anonymous usage metrics, reach out to us via [Slack](https://flower.ai/join-slack/) (channel `#telemetry`) or email (`telemetry@flower.ai`). diff --git a/datasets/flwr_datasets/__init__.py b/datasets/flwr_datasets/__init__.py index 0b9a6685427b..bd68fa43c606 100644 --- a/datasets/flwr_datasets/__init__.py +++ b/datasets/flwr_datasets/__init__.py @@ -15,16 +15,20 @@ """Flower Datasets main package.""" -from flwr_datasets import partitioner, resplitter +from flwr_datasets import partitioner, preprocessor from flwr_datasets import utils as utils +from flwr_datasets import visualization from flwr_datasets.common.version import package_version as _package_version from flwr_datasets.federated_dataset import FederatedDataset __all__ = [ "FederatedDataset", + "metrics", "partitioner", - "resplitter", + "preprocessor", "utils", + "visualization", ] + __version__ = _package_version diff --git a/datasets/flwr_datasets/common/__init__.py b/datasets/flwr_datasets/common/__init__.py index b4f12f8641b3..efb4eaf55b70 100644 --- a/datasets/flwr_datasets/common/__init__.py +++ b/datasets/flwr_datasets/common/__init__.py @@ -13,3 +13,12 @@ # limitations under the License. # ============================================================================== """Common components in Flower Datasets.""" + + +from .telemetry import EventType as EventType +from .telemetry import event as event + +__all__ = [ + "EventType", + "event", +] diff --git a/datasets/flwr_datasets/common/telemetry.py b/datasets/flwr_datasets/common/telemetry.py new file mode 100644 index 000000000000..ca484fdda73f --- /dev/null +++ b/datasets/flwr_datasets/common/telemetry.py @@ -0,0 +1,224 @@ +# Copyright 2023 Flower Labs GmbH. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Flower telemetry.""" + + +import datetime +import json +import logging +import os +import platform +import urllib.request +import uuid +from concurrent.futures import Future, ThreadPoolExecutor +from enum import Enum, auto +from pathlib import Path +from typing import Any, Dict, List, Optional, Union, cast + +from flwr_datasets.common.version import package_name, package_version + +FLWR_TELEMETRY_ENABLED = os.getenv("FLWR_TELEMETRY_ENABLED", "1") +FLWR_TELEMETRY_LOGGING = os.getenv("FLWR_TELEMETRY_LOGGING", "0") + +TELEMETRY_EVENTS_URL = "https://telemetry.flower.ai/api/v1/event" + +LOGGER_NAME = "flwr-datasets-telemetry" +LOGGER_LEVEL = logging.DEBUG + + +def _configure_logger(log_level: int) -> None: + console_handler = logging.StreamHandler() + console_handler.setLevel(log_level) + console_handler.setFormatter( + logging.Formatter( + "%(levelname)s %(name)s %(asctime)s | %(filename)s:%(lineno)d | %(message)s" + ) + ) + + logger = logging.getLogger(LOGGER_NAME) + logger.setLevel(log_level) + logger.addHandler(console_handler) + + +_configure_logger(LOGGER_LEVEL) + + +def log(msg: Union[str, Exception]) -> None: + """Log message using logger at DEBUG level.""" + logging.getLogger(LOGGER_NAME).log(LOGGER_LEVEL, msg) + + +def _get_home() -> Path: + return Path().home() + + +def _get_source_id() -> str: + """Get existing or new source ID.""" + source_id = "unavailable" + # Check if .flwr in home exists + try: + home = _get_home() + except RuntimeError: + # If the home directory can’t be resolved, RuntimeError is raised. + return source_id + + flwr_dir = home.joinpath(".flwr") + # Create .flwr directory if it does not exist yet. + try: + flwr_dir.mkdir(parents=True, exist_ok=True) + except PermissionError: + return source_id + + source_file = flwr_dir.joinpath("source") + + # If no source_file exists create one and write it + if not source_file.exists(): + try: + source_file.touch(exist_ok=True) + source_file.write_text(str(uuid.uuid4()), encoding="utf-8") + except PermissionError: + return source_id + + source_id = source_file.read_text(encoding="utf-8").strip() + + try: + uuid.UUID(source_id) + except ValueError: + source_id = "invalid" + + return source_id + + +# Using str as first base type to make it JSON serializable as +# otherwise the following exception will be thrown when serializing +# the event dict: +# TypeError: Object of type EventType is not JSON serializable +class EventType(str, Enum): + """Types of telemetry events.""" + + # This method combined with auto() will set the property value to + # the property name e.g. + # `START_CLIENT = auto()` becomes `START_CLIENT = "START_CLIENT"` + # The type signature is not compatible with mypy, pylint and flake8 + # so each of those needs to be disabled for this line. + # pylint: disable-next=no-self-argument,arguments-differ,line-too-long + def _generate_next_value_(name: str, start: int, count: int, last_values: List[Any]) -> Any: # type: ignore # noqa: E501 + return name + + PING = auto() + + LOAD_PARTITION_CALLED = auto() + LOAD_SPLIT_CALLED = auto() + PLOT_LABEL_DISTRIBUTION_CALLED = auto() + PLOT_COMPARISON_LABEL_DISTRIBUTION_CALLED = auto() + + +# Use the ThreadPoolExecutor with max_workers=1 to have a queue +# and also ensure that telemetry calls are not blocking. +state: Dict[str, Union[Optional[str], Optional[ThreadPoolExecutor]]] = { + # Will be assigned ThreadPoolExecutor(max_workers=1) + # in event() the first time it's required + "executor": None, + "source": None, + "cluster": None, +} + + +# In Python 3.7 pylint will throw an error stating that +# "Value 'Future' is unsubscriptable". +# This pylint disable line can be remove when dropping support +# for Python 3.7 +# pylint: disable-next=unsubscriptable-object +def event( + event_type: EventType, + event_details: Optional[Dict[str, Any]] = None, +) -> Future: # type: ignore + """Submit create_event to ThreadPoolExecutor to avoid blocking.""" + if state["executor"] is None: + state["executor"] = ThreadPoolExecutor(max_workers=1) + + executor: ThreadPoolExecutor = cast(ThreadPoolExecutor, state["executor"]) + + result = executor.submit(create_event, event_type, event_details) + return result + + +def create_event(event_type: EventType, event_details: Optional[Dict[str, Any]]) -> str: + """Create telemetry event.""" + if state["source"] is None: + state["source"] = _get_source_id() + + if state["cluster"] is None: + state["cluster"] = str(uuid.uuid4()) + + if event_details is None: + event_details = {} + + date = datetime.datetime.now(tz=datetime.timezone.utc).isoformat() + context = { + "source": state["source"], + "cluster": state["cluster"], + "date": date, + "package": { + "package_name": package_name, + "package_version": package_version, + }, + "hw": { + "cpu_count": os.cpu_count(), + }, + "platform": { + "system": platform.system(), + "release": platform.release(), + "platform": platform.platform(), + "python_implementation": platform.python_implementation(), + "python_version": platform.python_version(), + "machine": platform.machine(), + "architecture": platform.architecture(), + "version": platform.uname().version, + }, + } + payload = { + "event_type": event_type, + "event_details": event_details, + "context": context, + } + payload_json = json.dumps(payload) + if FLWR_TELEMETRY_LOGGING == "1": + log(" - ".join([date, "POST", payload_json])) + + # If telemetry is not disabled with setting FLWR_TELEMETRY_ENABLED=0 + # create a request and send it to the telemetry backend + if FLWR_TELEMETRY_ENABLED == "1": + request = urllib.request.Request( + url=TELEMETRY_EVENTS_URL, + data=payload_json.encode("utf-8"), + headers={ + "User-Agent": f"{package_name}/{package_version}", + "Content-Type": "application/json", + }, + method="POST", + ) + try: + with urllib.request.urlopen(request, timeout=60) as response: + result = response.read() + + response_json: str = result.decode("utf-8") + + return response_json + except urllib.error.URLError as ex: + if FLWR_TELEMETRY_LOGGING == "1": + log(ex) + + return "disabled" diff --git a/datasets/flwr_datasets/common/telemetry_test.py b/datasets/flwr_datasets/common/telemetry_test.py new file mode 100644 index 000000000000..f46b7b9a2ddf --- /dev/null +++ b/datasets/flwr_datasets/common/telemetry_test.py @@ -0,0 +1,115 @@ +# Copyright 2023 Flower Labs GmbH. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Telemetry tests.""" + + +import time +import unittest +from typing import Callable +from unittest import mock + +from flwr_datasets.common.telemetry import EventType, _get_source_id, event + + +class TelemetryTest(unittest.TestCase): + """Tests for the telemetry module.""" + + @mock.patch("flwr_datasets.common.telemetry.FLWR_TELEMETRY_ENABLED", "1") + def test_event(self) -> None: + """Test if sending works against the actual API.""" + # Prepare + expected = '{\n "status": "created"\n}' + + # Execute + future = event(EventType.PING) + actual = future.result() + + # Assert + self.assertEqual(actual, expected) + + @mock.patch("flwr_datasets.common.telemetry.FLWR_TELEMETRY_ENABLED", "1") + def test_not_blocking(self) -> None: + """Test if the code is blocking. + + If the code does not block duration_actual should be less than + 0.001s. + """ + # Prepare + # Use 0.1ms as any blocking networked call would take longer. + duration_max = 0.001 + start = time.time() + + # Execute + event(EventType.PING) + duration_actual = time.time() - start + + # Assert + self.assertLess(duration_actual, duration_max) + + @mock.patch("flwr_datasets.common.telemetry.FLWR_TELEMETRY_ENABLED", "0") + def test_telemetry_disabled(self) -> None: + """Test opt-out.""" + # Prepare + expected = "disabled" + + # Execute + future = event(EventType.PING) + actual = future.result() + + # Assert + self.assertEqual(actual, expected) + + def test_get_source_id(self) -> None: + """Test if _get_source_id returns an ID successfully. + + This test might fail if the UNIX user invoking the test has no home directory. + """ + # Prepare + # nothing to prepare + + # Execute + source_id = _get_source_id() + + # Assert + # source_id should be len 36 as it's a uuid4 in the current + # implementation + self.assertIsNotNone(source_id) + self.assertEqual(len(source_id), 36) + + def test_get_source_id_no_home(self) -> None: + """Test if _get_source_id returns unavailable without a home dir.""" + + # Prepare + def new_callable() -> Callable[[], None]: + def _new_failing_get_home() -> None: + raise RuntimeError + + return _new_failing_get_home + + except_value = "unavailable" + + # Execute + with mock.patch( + "flwr_datasets.common.telemetry._get_home", + new_callable=new_callable, + ): + source_id = _get_source_id() + + # Assert + self.assertEqual(source_id, except_value) + + +if __name__ == "__main__": + unittest.main() diff --git a/datasets/flwr_datasets/federated_dataset.py b/datasets/flwr_datasets/federated_dataset.py index 6c41eaa3562f..accfb783f368 100644 --- a/datasets/flwr_datasets/federated_dataset.py +++ b/datasets/flwr_datasets/federated_dataset.py @@ -19,12 +19,13 @@ import datasets from datasets import Dataset, DatasetDict +from flwr_datasets.common import EventType, event from flwr_datasets.partitioner import Partitioner -from flwr_datasets.resplitter import Resplitter +from flwr_datasets.preprocessor import Preprocessor from flwr_datasets.utils import ( _check_if_dataset_tested, + _instantiate_merger_if_needed, _instantiate_partitioners, - _instantiate_resplitter_if_needed, ) @@ -35,8 +36,9 @@ class FederatedDataset: Download, partition data among clients (edge devices), or load full dataset. - Partitions are created using IidPartitioner. Support for different partitioners - specification and types will come in future releases. + Partitions are created per-split-basis using Partitioners from + `flwr_datasets.partitioner` specified in `partitioners` (see `partitioners` + parameter for more information). Parameters ---------- @@ -45,32 +47,54 @@ class FederatedDataset: subset : str Secondary information regarding the dataset, most often subset or version (that is passed to the name in datasets.load_dataset). - resplitter : Optional[Union[Resplitter, Dict[str, Tuple[str, ...]]]] - `Callable` that transforms `DatasetDict` splits, or configuration dict for - `MergeResplitter`. + preprocessor : Optional[Union[Preprocessor, Dict[str, Tuple[str, ...]]]] + `Callable` that transforms `DatasetDict` by resplitting, removing + features, creating new features, performing any other preprocessing operation, + or configuration dict for `Merger`. Applied after shuffling. If None, + no operation is applied. partitioners : Dict[str, Union[Partitioner, int]] A dictionary mapping the Dataset split (a `str`) to a `Partitioner` or an `int` (representing the number of IID partitions that this split should be partitioned into). One or multiple `Partitioner` objects can be specified in that manner, but at most, one per split. shuffle : bool - Whether to randomize the order of samples. Applied prior to resplitting, - speratelly to each of the present splits in the dataset. It uses the `seed` - argument. Defaults to True. + Whether to randomize the order of samples. Applied prior to preprocessing + operations, speratelly to each of the present splits in the dataset. It uses + the `seed` argument. Defaults to True. seed : Optional[int] Seed used for dataset shuffling. It has no effect if `shuffle` is False. The - seed cannot be set in the later stages. If `None`, then fresh, unpredictable entropy - will be pulled from the OS. Defaults to 42. + seed cannot be set in the later stages. If `None`, then fresh, unpredictable + entropy will be pulled from the OS. Defaults to 42. Examples -------- Use MNIST dataset for Federated Learning with 100 clients (edge devices): - >>> mnist_fds = FederatedDataset(dataset="mnist", partitioners={"train": 100}) + >>> from flwr_datasets import FederatedDataset + >>> + >>> fds = FederatedDataset(dataset="mnist", partitioners={"train": 100}) >>> # Load partition for client with ID 10. - >>> partition = mnist_fds.load_partition(10, "train") + >>> partition = fds.load_partition(10) >>> # Use test split for centralized evaluation. - >>> centralized = mnist_fds.load_split("test") + >>> centralized = fds.load_split("test") + + Use CIFAR10 dataset for Federated Laerning with 100 clients: + >>> from flwr_datasets import FederatedDataset + >>> from flwr_datasets.partitioner import DirichletPartitioner + >>> + >>> partitioner = DirichletPartitioner(num_partitions=10, partition_by="label", + >>> alpha=0.5, min_partition_size=10) + >>> fds = FederatedDataset(dataset="cifar10", partitioners={"train": partitioner}) + >>> partition = fds.load_partition(partition_id=0) + + Visualize the partitioned datasets + >>> from flwr_datasets.visualization import plot_label_distributions + >>> + >>> _ = plot_label_distributions( + >>> partitioner=fds.partitioners["train"], + >>> label_name="label", + >>> legend=True, + >>> ) """ # pylint: disable=too-many-instance-attributes @@ -79,7 +103,7 @@ def __init__( *, dataset: str, subset: Optional[str] = None, - resplitter: Optional[Union[Resplitter, Dict[str, Tuple[str, ...]]]] = None, + preprocessor: Optional[Union[Preprocessor, Dict[str, Tuple[str, ...]]]] = None, partitioners: Dict[str, Union[Partitioner, int]], shuffle: bool = True, seed: Optional[int] = 42, @@ -87,8 +111,8 @@ def __init__( _check_if_dataset_tested(dataset) self._dataset_name: str = dataset self._subset: Optional[str] = subset - self._resplitter: Optional[Resplitter] = _instantiate_resplitter_if_needed( - resplitter + self._preprocessor: Optional[Preprocessor] = _instantiate_merger_if_needed( + preprocessor ) self._partitioners: Dict[str, Partitioner] = _instantiate_partitioners( partitioners @@ -100,6 +124,9 @@ def __init__( self._dataset: Optional[DatasetDict] = None # Indicate if the dataset is prepared for `load_partition` or `load_split` self._dataset_prepared: bool = False + self._event = { + "load_partition": {split: False for split in self._partitioners}, + } def load_partition( self, @@ -139,7 +166,20 @@ def load_partition( self._check_if_split_possible_to_federate(split) partitioner: Partitioner = self._partitioners[split] self._assign_dataset_to_partitioner(split) - return partitioner.load_partition(partition_id) + partition = partitioner.load_partition(partition_id) + if not self._event["load_partition"][split]: + event( + EventType.LOAD_PARTITION_CALLED, + { + "federated_dataset_id": id(self), + "dataset_name": self._dataset_name, + "split": split, + "partitioner": partitioner.__class__.__name__, + "num_partitions": partitioner.num_partitions, + }, + ) + self._event["load_partition"][split] = True + return partition def load_split(self, split: str) -> Dataset: """Load the full split of the dataset. @@ -162,7 +202,20 @@ def load_split(self, split: str) -> Dataset: if self._dataset is None: raise ValueError("Dataset is not loaded yet.") self._check_if_split_present(split) - return self._dataset[split] + dataset_split = self._dataset[split] + + if not self._event["load_split"][split]: + event( + EventType.LOAD_SPLIT_CALLED, + { + "federated_dataset_id": id(self), + "dataset_name": self._dataset_name, + "split": split, + }, + ) + self._event["load_split"][split] = True + + return dataset_split @property def partitioners(self) -> Dict[str, Partitioner]: @@ -242,8 +295,10 @@ def _prepare_dataset(self) -> None: # Note it shuffles all the splits. The self._dataset is DatasetDict # so e.g. {"train": train_data, "test": test_data}. All splits get shuffled. self._dataset = self._dataset.shuffle(seed=self._seed) - if self._resplitter: - self._dataset = self._resplitter(self._dataset) + if self._preprocessor: + self._dataset = self._preprocessor(self._dataset) + available_splits = list(self._dataset.keys()) + self._event["load_split"] = {split: False for split in available_splits} self._dataset_prepared = True def _check_if_no_split_keyword_possible(self) -> None: diff --git a/datasets/flwr_datasets/federated_dataset_test.py b/datasets/flwr_datasets/federated_dataset_test.py index 5d5179122e3b..f65aa6346f3a 100644 --- a/datasets/flwr_datasets/federated_dataset_test.py +++ b/datasets/flwr_datasets/federated_dataset_test.py @@ -170,20 +170,20 @@ def test_resplit_dataset_into_one(self) -> None: fds = FederatedDataset( dataset=self.dataset_name, partitioners={"train": 100}, - resplitter={"full": ("train", self.test_split)}, + preprocessor={"full": ("train", self.test_split)}, ) full = fds.load_split("full") self.assertEqual(dataset_length, len(full)) # pylint: disable=protected-access def test_resplit_dataset_to_change_names(self) -> None: - """Test resplitter to change the names of the partitions.""" + """Test preprocessor to change the names of the partitions.""" if self.test_split is None: return fds = FederatedDataset( dataset=self.dataset_name, partitioners={"new_train": 100}, - resplitter={ + preprocessor={ "new_train": ("train",), "new_" + self.test_split: (self.test_split,), }, @@ -195,7 +195,7 @@ def test_resplit_dataset_to_change_names(self) -> None: ) def test_resplit_dataset_by_callable(self) -> None: - """Test resplitter to change the names of the partitions.""" + """Test preprocessor to change the names of the partitions.""" if self.test_split is None: return @@ -209,7 +209,7 @@ def resplit(dataset: DatasetDict) -> DatasetDict: ) fds = FederatedDataset( - dataset=self.dataset_name, partitioners={"train": 100}, resplitter=resplit + dataset=self.dataset_name, partitioners={"train": 100}, preprocessor=resplit ) full = fds.load_split("full") dataset = datasets.load_dataset(self.dataset_name) @@ -298,7 +298,7 @@ def resplit(dataset: DatasetDict) -> DatasetDict: fds = FederatedDataset( dataset="does-not-matter", partitioners={"train": 10}, - resplitter=resplit, + preprocessor=resplit, shuffle=True, ) train = fds.load_split("train") @@ -411,7 +411,7 @@ def test_cannot_use_the_old_split_names(self) -> None: fds = FederatedDataset( dataset="mnist", partitioners={"train": 100}, - resplitter={"full": ("train", "test")}, + preprocessor={"full": ("train", "test")}, ) with self.assertRaises(ValueError): fds.load_partition(0, "train") diff --git a/datasets/flwr_datasets/metrics/__init__.py b/datasets/flwr_datasets/metrics/__init__.py new file mode 100644 index 000000000000..e82cb3088822 --- /dev/null +++ b/datasets/flwr_datasets/metrics/__init__.py @@ -0,0 +1,23 @@ +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Metrics package.""" + + +from flwr_datasets.metrics.utils import compute_counts, compute_frequencies + +__all__ = [ + "compute_counts", + "compute_frequencies", +] diff --git a/datasets/flwr_datasets/metrics/utils.py b/datasets/flwr_datasets/metrics/utils.py new file mode 100644 index 000000000000..8f78b2fd4c32 --- /dev/null +++ b/datasets/flwr_datasets/metrics/utils.py @@ -0,0 +1,263 @@ +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Utils for metrics computation.""" + + +import warnings +from typing import List, Optional, Union + +import pandas as pd + +from flwr_datasets.partitioner import Partitioner + + +def compute_counts( + partitioner: Partitioner, + column_name: str, + verbose_names: bool = False, + max_num_partitions: Optional[int] = None, +) -> pd.DataFrame: + """Compute the counts of unique values in a given column in the partitions. + + Take into account all possible labels in dataset when computing count for each + partition (assign 0 as the size when there are no values for a label in the + partition). + + Parameters + ---------- + partitioner : Partitioner + Partitioner with an assigned dataset. + column_name : str + Column name identifying label based on which the count will be calculated. + verbose_names : bool + Whether to use verbose versions of the values in the column specified by + `column_name`. The verbose values are possible to extract if the column is a + feature of type `ClassLabel`. + max_num_partitions : Optional[int] + The maximum number of partitions that will be used. If greater than the + total number of partitions in a partitioner, it won't have an effect. If left + as None, then all partitions will be used. + + Returns + ------- + dataframe: pd.DataFrame + DataFrame where the row index represent the partition id and the column index + represent the unique values found in column specified by `column_name` + (e.g. represeting the labels). The value of the dataframe.loc[i, j] represents + the count of the label j, in the partition of index i. + + Examples + -------- + Generate DataFrame with label counts resulting from DirichletPartitioner on cifar10 + + >>> from flwr_datasets import FederatedDataset + >>> from flwr_datasets.partitioner import DirichletPartitioner + >>> from flwr_datasets.metrics import compute_counts + >>> + >>> fds = FederatedDataset( + >>> dataset="cifar10", + >>> partitioners={ + >>> "train": DirichletPartitioner( + >>> num_partitions=20, + >>> partition_by="label", + >>> alpha=0.3, + >>> min_partition_size=0, + >>> ), + >>> }, + >>> ) + >>> partitioner = fds.partitioners["train"] + >>> counts_dataframe = compute_counts( + >>> partitioner=partitioner, + >>> column_name="label" + >>> ) + """ + if column_name not in partitioner.dataset.column_names: + raise ValueError( + f"The specified 'column_name': '{column_name}' is not present in the " + f"dataset. The dataset contains columns {partitioner.dataset.column_names}." + ) + + if max_num_partitions is None: + max_num_partitions = partitioner.num_partitions + else: + max_num_partitions = min(max_num_partitions, partitioner.num_partitions) + assert isinstance(max_num_partitions, int) + partition = partitioner.load_partition(0) + + try: + # Unique labels are needed to represent the correct count of each class + # (some of the classes can have zero samples that's why this + # adjustment is needed) + unique_labels = partition.features[column_name].str2int( + partition.features[column_name].names + ) + except AttributeError: # If the column_name is not formally a Label + unique_labels = partitioner.dataset.unique(column_name) + + partition_id_to_label_absolute_size = {} + for partition_id in range(max_num_partitions): + partition = partitioner.load_partition(partition_id) + partition_id_to_label_absolute_size[partition_id] = _compute_counts( + partition[column_name], unique_labels + ) + + dataframe = pd.DataFrame.from_dict( + partition_id_to_label_absolute_size, orient="index" + ) + dataframe.index.name = "Partition ID" + + if verbose_names: + # Adjust the column name values of the dataframe + current_labels = dataframe.columns + try: + legend_names = partitioner.dataset.features[column_name].int2str( + [int(v) for v in current_labels] + ) + dataframe.columns = legend_names + except AttributeError: + warnings.warn( + "The verbose names can not be established. " + "The column specified by 'column_name' needs to be of type " + "'ClassLabel' to create a verbose names. " + "The available names will used.", + stacklevel=1, + ) + return dataframe + + +def compute_frequencies( + partitioner: Partitioner, + column_name: str, + verbose_names: bool = False, + max_num_partitions: Optional[int] = None, +) -> pd.DataFrame: + """Compute the frequencies of unique values in a given column in the partitions. + + The frequencies sum up to 1 for a given partition id. This function takes into + account all possible labels in the dataset when computing the count for each + partition (assign 0 as the size when there are no values for a label in the + partition). + + Parameters + ---------- + partitioner : Partitioner + Partitioner with an assigned dataset. + column_name : str + Column name identifying label based on which the count will be calculated. + verbose_names : bool + Whether to use verbose versions of the values in the column specified by + `column_name`. The verbose value are possible to extract if the column is a + feature of type `ClassLabel`. + max_num_partitions : Optional[int] + The maximum number of partitions that will be used. If greater than the + total number of partitions in a partitioner, it won't have an effect. If left + as None, then all partitions will be used. + + Returns + ------- + dataframe: pd.DataFrame + DataFrame where the row index represent the partition id and the column index + represent the unique values found in column specified by `column_name` + (e.g. represeting the labels). The value of the dataframe.loc[i, j] represnt + the ratio of the label j to the total number of sample of in partition i. + + Examples + -------- + Generate DataFrame with label counts resulting from DirichletPartitioner on cifar10 + + >>> from flwr_datasets import FederatedDataset + >>> from flwr_datasets.partitioner import DirichletPartitioner + >>> from flwr_datasets.metrics import compute_frequencies + >>> + >>> fds = FederatedDataset( + >>> dataset="cifar10", + >>> partitioners={ + >>> "train": DirichletPartitioner( + >>> num_partitions=20, + >>> partition_by="label", + >>> alpha=0.3, + >>> min_partition_size=0, + >>> ), + >>> }, + >>> ) + >>> partitioner = fds.partitioners["train"] + >>> counts_dataframe = compute_frequencies( + >>> partitioner=partitioner, + >>> column_name="label" + >>> ) + """ + dataframe = compute_counts( + partitioner, column_name, verbose_names, max_num_partitions + ) + dataframe = dataframe.div(dataframe.sum(axis=1), axis=0) + return dataframe + + +def _compute_counts( + labels: Union[List[int], List[str]], unique_labels: Union[List[int], List[str]] +) -> pd.Series: + """Compute the count of labels when taking into account all possible labels. + + Also known as absolute frequency. + + Parameters + ---------- + labels: Union[List[int], List[str]] + The labels from the datasets. + unique_labels: Union[List[int], List[str]] + The reference all unique label. Needed to avoid missing any label, instead + having the value equal to zero for them. + + Returns + ------- + label_counts: pd.Series + The pd.Series with label as indices and counts as values. + """ + if len(unique_labels) != len(set(unique_labels)): + raise ValueError("unique_labels must contain unique elements only.") + labels_series = pd.Series(labels) + label_counts = labels_series.value_counts() + label_counts_with_zeros = pd.Series(index=unique_labels, data=0) + label_counts_with_zeros = label_counts_with_zeros.add( + label_counts, fill_value=0 + ).astype(int) + return label_counts_with_zeros + + +def _compute_frequencies( + labels: Union[List[int], List[str]], unique_labels: Union[List[int], List[str]] +) -> pd.Series: + """Compute the distribution of labels when taking into account all possible labels. + + Also known as relative frequency. + + Parameters + ---------- + labels: Union[List[int], List[str]] + The labels from the datasets. + unique_labels: Union[List[int], List[str]] + The reference all unique label. Needed to avoid missing any label, instead + having the value equal to zero for them. + + Returns + ------- + The pd.Series with label as indices and probabilities as values. + """ + counts = _compute_counts(labels, unique_labels) + if len(labels) == 0: + frequencies = counts.astype(float) + return frequencies + frequencies = counts.divide(len(labels)) + return frequencies diff --git a/datasets/flwr_datasets/metrics/utils_test.py b/datasets/flwr_datasets/metrics/utils_test.py new file mode 100644 index 000000000000..9e0f3acdf805 --- /dev/null +++ b/datasets/flwr_datasets/metrics/utils_test.py @@ -0,0 +1,201 @@ +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for metrics utils.""" +# pylint: disable=no-self-use + + +import unittest + +import pandas as pd +from parameterized import parameterized, parameterized_class + +import datasets +from datasets import ClassLabel +from flwr_datasets.metrics.utils import ( + _compute_counts, + _compute_frequencies, + compute_counts, + compute_frequencies, +) +from flwr_datasets.partitioner import IidPartitioner + + +@parameterized_class( + ("dataset", "result"), + [ + ( + datasets.Dataset.from_dict({"feature": list(range(10)), "label": [0] * 10}), + pd.DataFrame([[5], [5]], index=pd.Index([0, 1], name="Partition ID")), + ), + ( + datasets.Dataset.from_dict( + {"feature": list(range(10)), "label": [0] * 5 + [1] * 5} + ), + pd.DataFrame([[5, 0], [0, 5]], index=pd.Index([0, 1], name="Partition ID")), + ), + ( + datasets.Dataset.from_dict( + {"feature": list(range(10)), "label": [0, 0, 0, 1, 1] + [1, 1, 1, 1, 2]} + ), + pd.DataFrame( + [[3, 2, 0], [0, 4, 1]], index=pd.Index([0, 1], name="Partition ID") + ), + ), + ], +) +class TestPublicMetricsUtils(unittest.TestCase): + """Test metrics utils.""" + + dataset: datasets.Dataset + result: pd.DataFrame + + def test_compute_counts(self) -> None: + """Test if the counts are computed correctly.""" + iid_partitioner = IidPartitioner(num_partitions=2) + iid_partitioner.dataset = self.dataset + count = compute_counts(iid_partitioner, column_name="label") + pd.testing.assert_frame_equal(count, self.result) + + def test_compute_frequencies(self) -> None: + """Test if the frequencies are computed correctly.""" + iid_partitioner = IidPartitioner(num_partitions=2) + iid_partitioner.dataset = self.dataset + frequencies = compute_frequencies(iid_partitioner, column_name="label") + result = self.result.div(self.result.sum(axis=1), axis=0) + pd.testing.assert_frame_equal(frequencies, result) + + def test_compute_counts_with_verbose_label(self) -> None: + """Test if the counts are computed correctly.""" + iid_partitioner = IidPartitioner(num_partitions=2) + dataset = self.dataset + new_col_names = [ + str(col_id) for col_id in range(len(self.dataset.unique("label"))) + ] + dataset = dataset.cast_column( + "label", + ClassLabel( + num_classes=len(self.dataset.unique("label")), names=new_col_names + ), + ) + iid_partitioner.dataset = dataset + result = self.result.copy() + result.columns = new_col_names + count = compute_counts(iid_partitioner, column_name="label", verbose_names=True) + pd.testing.assert_frame_equal(count, result) + + def test_compute_frequencies_with_verbose_label(self) -> None: + """Test if the frequencies are computed correctly.""" + iid_partitioner = IidPartitioner(num_partitions=2) + dataset = self.dataset + new_col_names = [ + str(col_id) for col_id in range(len(self.dataset.unique("label"))) + ] + dataset = dataset.cast_column( + "label", + ClassLabel( + num_classes=len(self.dataset.unique("label")), names=new_col_names + ), + ) + iid_partitioner.dataset = dataset + result = self.result.copy() + result.columns = new_col_names + result = result.div(result.sum(axis=1), axis=0) + frequencies = compute_frequencies( + iid_partitioner, column_name="label", verbose_names=True + ) + pd.testing.assert_frame_equal(frequencies, result) + + def test_compute_count_with_smaller_max_partitions(self) -> None: + """Test is compute_count works when the max_partitions None: + """Test is compute_count works when the max_partitions>total partitions.""" + iid_partitioner = IidPartitioner(num_partitions=2) + iid_partitioner.dataset = self.dataset + count = compute_counts( + iid_partitioner, column_name="label", max_num_partitions=3 + ) + pd.testing.assert_frame_equal(count, self.result) + + +class TestPrivateMetricsUtils(unittest.TestCase): + """Test metrics utils.""" + + @parameterized.expand( # type: ignore + [ + ([1, 2, 2, 3], [1, 2, 3, 4], pd.Series([1, 2, 1, 0], index=[1, 2, 3, 4])), + ([], [1, 2, 3], pd.Series([0, 0, 0], index=[1, 2, 3])), + ([1, 1, 2], [1, 2, 3, 4], pd.Series([2, 1, 0, 0], index=[1, 2, 3, 4])), + ] + ) + def test__compute_counts(self, labels, unique_labels, expected) -> None: + """Test if the counts are computed correctly.""" + result = _compute_counts(labels, unique_labels) + pd.testing.assert_series_equal(result, expected) + + @parameterized.expand( # type: ignore + [ + ( + [1, 1, 2, 2, 2, 3], + [1, 2, 3, 4], + pd.Series([0.3333, 0.5, 0.1667, 0.0], index=[1, 2, 3, 4]), + ), + ([], [1, 2, 3], pd.Series([0.0, 0.0, 0.0], index=[1, 2, 3])), + ( + ["a", "b", "b", "c"], + ["a", "b", "c", "d"], + pd.Series([0.25, 0.50, 0.25, 0.0], index=["a", "b", "c", "d"]), + ), + ] + ) + def test_compute_distribution(self, labels, unique_labels, expected) -> None: + """Test if the distributions are computed correctly.""" + result = _compute_frequencies(labels, unique_labels) + pd.testing.assert_series_equal(result, expected, atol=0.001) + + @parameterized.expand( # type: ignore + [ + (["a", "b", "b", "c"], ["a", "b", "c"]), + ([1, 2, 2, 3, 3, 3, 4], [1, 2, 3, 4]), + ] + ) + def test_distribution_sum_to_one(self, labels, unique_labels) -> None: + """Test if distributions sum up to one.""" + result = _compute_frequencies(labels, unique_labels) + self.assertAlmostEqual(result.sum(), 1.0) + + def test_compute_counts_non_unique_labels(self) -> None: + """Test if not having the unique labels raises ValueError.""" + labels = [1, 2, 3] + unique_labels = [1, 2, 2, 3] + with self.assertRaises(ValueError): + _compute_counts(labels, unique_labels) + + def test_compute_distribution_non_unique_labels(self) -> None: + """Test if not having the unique labels raises ValueError.""" + labels = [1, 1, 2, 3] + unique_labels = [1, 1, 2, 3] + with self.assertRaises(ValueError): + _compute_frequencies(labels, unique_labels) + + +if __name__ == "__main__": + unittest.main() diff --git a/datasets/flwr_datasets/partitioner/__init__.py b/datasets/flwr_datasets/partitioner/__init__.py index 0d1edbfcb04a..1fc00ed90323 100644 --- a/datasets/flwr_datasets/partitioner/__init__.py +++ b/datasets/flwr_datasets/partitioner/__init__.py @@ -27,14 +27,14 @@ from .square_partitioner import SquarePartitioner __all__ = [ + "DirichletPartitioner", + "ExponentialPartitioner", "IidPartitioner", - "Partitioner", + "InnerDirichletPartitioner", + "LinearPartitioner", "NaturalIdPartitioner", - "DirichletPartitioner", + "Partitioner", + "ShardPartitioner", "SizePartitioner", - "LinearPartitioner", - "InnerDirichletPartitioner", "SquarePartitioner", - "ShardPartitioner", - "ExponentialPartitioner", ] diff --git a/datasets/flwr_datasets/resplitter/__init__.py b/datasets/flwr_datasets/preprocessor/__init__.py similarity index 76% rename from datasets/flwr_datasets/resplitter/__init__.py rename to datasets/flwr_datasets/preprocessor/__init__.py index bf39786e0593..67b2aaadc3d2 100644 --- a/datasets/flwr_datasets/resplitter/__init__.py +++ b/datasets/flwr_datasets/preprocessor/__init__.py @@ -12,15 +12,15 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Resplitter package.""" +"""Preprocessor package.""" -from .divide_resplitter import DivideResplitter -from .merge_resplitter import MergeResplitter -from .resplitter import Resplitter +from .divider import Divider +from .merger import Merger +from .preprocessor import Preprocessor __all__ = [ - "DivideResplitter", - "MergeResplitter", - "Resplitter", + "Divider", + "Merger", + "Preprocessor", ] diff --git a/datasets/flwr_datasets/resplitter/divide_resplitter.py b/datasets/flwr_datasets/preprocessor/divider.py similarity index 98% rename from datasets/flwr_datasets/resplitter/divide_resplitter.py rename to datasets/flwr_datasets/preprocessor/divider.py index 56150b51af85..9d7570de4cea 100644 --- a/datasets/flwr_datasets/resplitter/divide_resplitter.py +++ b/datasets/flwr_datasets/preprocessor/divider.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""DivideResplitter class for Flower Datasets.""" +"""Divider class for Flower Datasets.""" import collections @@ -25,7 +25,7 @@ # flake8: noqa: E501 # pylint: disable=line-too-long -class DivideResplitter: +class Divider: """Dive existing split(s) of the dataset and assign them custom names. Create new `DatasetDict` with new split names with corresponding percentages of data @@ -66,14 +66,14 @@ class DivideResplitter: >>> # Assuming there is a dataset_dict of type `DatasetDict` >>> # dataset_dict is {"train": train-data, "test": test-data} - >>> resplitter = DivideResplitter( + >>> divider = Divider( >>> divide_config={ >>> "train": 0.8, >>> "valid": 0.2, >>> } >>> divide_split="train", >>> ) - >>> new_dataset_dict = resplitter(dataset_dict) + >>> new_dataset_dict = divider(dataset_dict) >>> # new_dataset_dict is >>> # {"train": 80% of train, "valid": 20% of train, "test": test-data} @@ -83,7 +83,7 @@ class DivideResplitter: >>> # Assuming there is a dataset_dict of type `DatasetDict` >>> # dataset_dict is {"train": train-data, "test": test-data} - >>> resplitter = DivideResplitter( + >>> divider = Divider( >>> divide_config={ >>> "train": { >>> "train": 0.8, @@ -92,7 +92,7 @@ class DivideResplitter: >>> "test": {"test-a": 0.4, "test-b": 0.6 } >>> } >>> ) - >>> new_dataset_dict = resplitter(dataset_dict) + >>> new_dataset_dict = divider(dataset_dict) >>> # new_dataset_dict is >>> # {"train": 80% of train, "valid": 20% of train, >>> # "test-a": 40% of test, "test-b": 60% of test} diff --git a/datasets/flwr_datasets/resplitter/divide_resplitter_test.py b/datasets/flwr_datasets/preprocessor/divider_test.py similarity index 79% rename from datasets/flwr_datasets/resplitter/divide_resplitter_test.py rename to datasets/flwr_datasets/preprocessor/divider_test.py index 143297fcc1a7..ed282fbc18be 100644 --- a/datasets/flwr_datasets/resplitter/divide_resplitter_test.py +++ b/datasets/flwr_datasets/preprocessor/divider_test.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""DivideResplitter tests.""" +"""Divider tests.""" import unittest from typing import Dict, Union @@ -20,7 +20,7 @@ from parameterized import parameterized_class from datasets import Dataset, DatasetDict -from flwr_datasets.resplitter import DivideResplitter +from flwr_datasets.preprocessor import Divider @parameterized_class( @@ -80,8 +80,8 @@ ), ], ) -class TestDivideResplitter(unittest.TestCase): - """DivideResplitter tests.""" +class TestDivider(unittest.TestCase): + """Divider tests.""" divide_config: Union[ Dict[str, float], @@ -105,27 +105,27 @@ def setUp(self) -> None: def test_resplitting_correct_new_split_names(self) -> None: """Test if resplitting produces requested new splits.""" - resplitter = DivideResplitter( + divider = Divider( self.divide_config, self.divide_split, self.drop_remaining_splits ) - resplit_dataset = resplitter(self.dataset_dict) + resplit_dataset = divider(self.dataset_dict) new_keys = set(resplit_dataset.keys()) self.assertEqual(set(self.split_name_to_size.keys()), new_keys) def test_resplitting_correct_new_split_sizes(self) -> None: """Test if resplitting produces correct sizes of splits.""" - resplitter = DivideResplitter( + divider = Divider( self.divide_config, self.divide_split, self.drop_remaining_splits ) - resplit_dataset = resplitter(self.dataset_dict) + resplit_dataset = divider(self.dataset_dict) split_to_size = { split_name: len(split) for split_name, split in resplit_dataset.items() } self.assertEqual(self.split_name_to_size, split_to_size) -class TestDivideResplitterIncorrectUseCases(unittest.TestCase): - """Resplitter tests.""" +class TestDividerIncorrectUseCases(unittest.TestCase): + """Divider tests.""" def setUp(self) -> None: """Set up the dataset with 3 splits for tests.""" @@ -144,21 +144,17 @@ def test_doubling_names_in_config(self) -> None: drop_remaining_splits = False with self.assertRaises(ValueError): - resplitter = DivideResplitter( - divide_config, divide_split, drop_remaining_splits - ) - _ = resplitter(self.dataset_dict) + divider = Divider(divide_config, divide_split, drop_remaining_splits) + _ = divider(self.dataset_dict) def test_duplicate_names_in_config_and_dataset_split_names_multisplit(self) -> None: """Test if resplitting raises when the name collides with the old name.""" divide_config = {"train": {"valid": 0.5}} divide_split = None drop_remaining_splits = False - resplitter = DivideResplitter( - divide_config, divide_split, drop_remaining_splits - ) + divider = Divider(divide_config, divide_split, drop_remaining_splits) with self.assertRaises(ValueError): - _ = resplitter(self.dataset_dict) + _ = divider(self.dataset_dict) def test_duplicate_names_in_config_and_dataset_split_names_single_split( self, @@ -167,77 +163,63 @@ def test_duplicate_names_in_config_and_dataset_split_names_single_split( divide_config = {"valid": 0.5} divide_split = "train" drop_remaining_splits = False - resplitter = DivideResplitter( - divide_config, divide_split, drop_remaining_splits - ) + divider = Divider(divide_config, divide_split, drop_remaining_splits) with self.assertRaises(ValueError): - _ = resplitter(self.dataset_dict) + _ = divider(self.dataset_dict) def test_fraction_sum_up_to_more_than_one_multisplit(self) -> None: """Test if resplitting raises when fractions sum up to > 1.0 .""" divide_config = {"train": {"train_1": 0.5, "train_2": 0.7}} divide_split = None drop_remaining_splits = False - resplitter = DivideResplitter( - divide_config, divide_split, drop_remaining_splits - ) + divider = Divider(divide_config, divide_split, drop_remaining_splits) with self.assertRaises(ValueError): - _ = resplitter(self.dataset_dict) + _ = divider(self.dataset_dict) def test_fraction_sum_up_to_more_than_one_single_split(self) -> None: """Test if resplitting raises when fractions sum up to > 1.0 .""" divide_config = {"train_1": 0.5, "train_2": 0.7} divide_split = "train" drop_remaining_splits = False - resplitter = DivideResplitter( - divide_config, divide_split, drop_remaining_splits - ) + divider = Divider(divide_config, divide_split, drop_remaining_splits) with self.assertRaises(ValueError): - _ = resplitter(self.dataset_dict) + _ = divider(self.dataset_dict) def test_sample_sizes_sum_up_to_more_than_dataset_size_single_split(self) -> None: """Test if resplitting raises when samples size sum up to > len(datset) .""" divide_config = {"train": {"train_1": 20, "train_2": 25}} divide_split = None drop_remaining_splits = False - resplitter = DivideResplitter( - divide_config, divide_split, drop_remaining_splits - ) + divider = Divider(divide_config, divide_split, drop_remaining_splits) with self.assertRaises(ValueError): - _ = resplitter(self.dataset_dict) + _ = divider(self.dataset_dict) def test_sample_sizes_sum_up_to_more_than_dataset_size_multisplit(self) -> None: """Test if resplitting raises when samples size sum up to > len(datset) .""" divide_config = {"train_1": 20, "train_2": 25} divide_split = "train" drop_remaining_splits = False - resplitter = DivideResplitter( - divide_config, divide_split, drop_remaining_splits - ) + divider = Divider(divide_config, divide_split, drop_remaining_splits) with self.assertRaises(ValueError): - _ = resplitter(self.dataset_dict) + _ = divider(self.dataset_dict) def test_too_small_size_values_create_empty_dataset_single_split(self) -> None: """Test if resplitting raises when fraction creates empty dataset.""" divide_config = {"train": {"train_1": 0.2, "train_2": 0.0001}} divide_split = None drop_remaining_splits = False - resplitter = DivideResplitter( - divide_config, divide_split, drop_remaining_splits - ) + divider = Divider(divide_config, divide_split, drop_remaining_splits) with self.assertRaises(ValueError): - _ = resplitter(self.dataset_dict) + _ = divider(self.dataset_dict) def test_too_small_size_values_create_empty_dataset_multisplit(self) -> None: """Test if resplitting raises when fraction creates empty dataset.""" divide_config = {"train_1": 0.2, "train_2": 0.0001} divide_split = "train" drop_remaining_splits = False - resplitter = DivideResplitter( - divide_config, divide_split, drop_remaining_splits - ) + divider = Divider(divide_config, divide_split, drop_remaining_splits) with self.assertRaises(ValueError): - _ = resplitter(self.dataset_dict) + _ = divider(self.dataset_dict) if __name__ == "__main__": diff --git a/datasets/flwr_datasets/resplitter/merge_resplitter.py b/datasets/flwr_datasets/preprocessor/merger.py similarity index 96% rename from datasets/flwr_datasets/resplitter/merge_resplitter.py rename to datasets/flwr_datasets/preprocessor/merger.py index 6bb8f23e60dc..2b76dbbafe4b 100644 --- a/datasets/flwr_datasets/resplitter/merge_resplitter.py +++ b/datasets/flwr_datasets/preprocessor/merger.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""MergeResplitter class for Flower Datasets.""" +"""Merger class for Flower Datasets.""" import collections @@ -24,7 +24,7 @@ from datasets import Dataset, DatasetDict -class MergeResplitter: +class Merger: """Merge existing splits of the dataset and assign them custom names. Create new `DatasetDict` with new split names corresponding to the merged existing @@ -43,13 +43,13 @@ class MergeResplitter: >>> # Assuming there is a dataset_dict of type `DatasetDict` >>> # dataset_dict is {"train": train-data, "valid": valid-data, "test": test-data} - >>> merge_resplitter = MergeResplitter( + >>> merger = Merger( >>> merge_config={ >>> "new_train": ("train", "valid"), >>> "test": ("test", ) >>> } >>> ) - >>> new_dataset_dict = merge_resplitter(dataset_dict) + >>> new_dataset_dict = merger(dataset_dict) >>> # new_dataset_dict is >>> # {"new_train": concatenation of train-data and valid-data, "test": test-data} """ diff --git a/datasets/flwr_datasets/resplitter/merge_resplitter_test.py b/datasets/flwr_datasets/preprocessor/merger_test.py similarity index 81% rename from datasets/flwr_datasets/resplitter/merge_resplitter_test.py rename to datasets/flwr_datasets/preprocessor/merger_test.py index ebbdfb4022b0..d5c69387e53d 100644 --- a/datasets/flwr_datasets/resplitter/merge_resplitter_test.py +++ b/datasets/flwr_datasets/preprocessor/merger_test.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Resplitter tests.""" +"""Preprocessor tests.""" import unittest @@ -21,11 +21,11 @@ import pytest from datasets import Dataset, DatasetDict -from flwr_datasets.resplitter.merge_resplitter import MergeResplitter +from flwr_datasets.preprocessor.merger import Merger -class TestResplitter(unittest.TestCase): - """Resplitter tests.""" +class TestMerger(unittest.TestCase): + """Preprocessor tests.""" def setUp(self) -> None: """Set up the dataset with 3 splits for tests.""" @@ -40,29 +40,29 @@ def setUp(self) -> None: def test_resplitting_train_size(self) -> None: """Test if resplitting for just renaming keeps the lengths correct.""" strategy: Dict[str, Tuple[str, ...]] = {"new_train": ("train",)} - resplitter = MergeResplitter(strategy) - new_dataset = resplitter(self.dataset_dict) + merger = Merger(strategy) + new_dataset = merger(self.dataset_dict) self.assertEqual(len(new_dataset["new_train"]), 3) def test_resplitting_valid_size(self) -> None: """Test if resplitting for just renaming keeps the lengths correct.""" strategy: Dict[str, Tuple[str, ...]] = {"new_valid": ("valid",)} - resplitter = MergeResplitter(strategy) - new_dataset = resplitter(self.dataset_dict) + merger = Merger(strategy) + new_dataset = merger(self.dataset_dict) self.assertEqual(len(new_dataset["new_valid"]), 2) def test_resplitting_test_size(self) -> None: """Test if resplitting for just renaming keeps the lengths correct.""" strategy: Dict[str, Tuple[str, ...]] = {"new_test": ("test",)} - resplitter = MergeResplitter(strategy) - new_dataset = resplitter(self.dataset_dict) + merger = Merger(strategy) + new_dataset = merger(self.dataset_dict) self.assertEqual(len(new_dataset["new_test"]), 1) def test_resplitting_train_the_same(self) -> None: """Test if resplitting for just renaming keeps the dataset the same.""" strategy: Dict[str, Tuple[str, ...]] = {"new_train": ("train",)} - resplitter = MergeResplitter(strategy) - new_dataset = resplitter(self.dataset_dict) + merger = Merger(strategy) + new_dataset = merger(self.dataset_dict) self.assertTrue( datasets_are_equal(self.dataset_dict["train"], new_dataset["new_train"]) ) @@ -72,8 +72,8 @@ def test_combined_train_valid_size(self) -> None: strategy: Dict[str, Tuple[str, ...]] = { "train_valid_combined": ("train", "valid") } - resplitter = MergeResplitter(strategy) - new_dataset = resplitter(self.dataset_dict) + merger = Merger(strategy) + new_dataset = merger(self.dataset_dict) self.assertEqual(len(new_dataset["train_valid_combined"]), 5) def test_resplitting_test_with_combined_strategy_size(self) -> None: @@ -82,8 +82,8 @@ def test_resplitting_test_with_combined_strategy_size(self) -> None: "train_valid_combined": ("train", "valid"), "test": ("test",), } - resplitter = MergeResplitter(strategy) - new_dataset = resplitter(self.dataset_dict) + merger = Merger(strategy) + new_dataset = merger(self.dataset_dict) self.assertEqual(len(new_dataset["test"]), 1) def test_invalid_resplit_strategy_exception_message(self) -> None: @@ -92,20 +92,20 @@ def test_invalid_resplit_strategy_exception_message(self) -> None: "new_train": ("invalid_split",), "new_test": ("test",), } - resplitter = MergeResplitter(strategy) + merger = Merger(strategy) with self.assertRaisesRegex( ValueError, "The given dataset key 'invalid_split' is not present" ): - resplitter(self.dataset_dict) + merger(self.dataset_dict) def test_nonexistent_split_in_strategy(self) -> None: """Test if the exception is raised when the nonexistent split name is given.""" strategy: Dict[str, Tuple[str, ...]] = {"new_split": ("nonexistent_split",)} - resplitter = MergeResplitter(strategy) + merger = Merger(strategy) with self.assertRaisesRegex( ValueError, "The given dataset key 'nonexistent_split' is not present" ): - resplitter(self.dataset_dict) + merger(self.dataset_dict) def test_duplicate_merge_split_name(self) -> None: # pylint: disable=R0201 """Test that the new split names are not the same.""" @@ -114,17 +114,17 @@ def test_duplicate_merge_split_name(self) -> None: # pylint: disable=R0201 "test": ("train",), } with pytest.warns(UserWarning): - _ = MergeResplitter(strategy) + _ = Merger(strategy) def test_empty_dataset_dict(self) -> None: """Test that the error is raised when the empty DatasetDict is given.""" empty_dataset = DatasetDict({}) strategy: Dict[str, Tuple[str, ...]] = {"new_train": ("train",)} - resplitter = MergeResplitter(strategy) + merger = Merger(strategy) with self.assertRaisesRegex( ValueError, "The given dataset key 'train' is not present" ): - resplitter(empty_dataset) + merger(empty_dataset) def datasets_are_equal(ds1: Dataset, ds2: Dataset) -> bool: diff --git a/datasets/flwr_datasets/resplitter/resplitter.py b/datasets/flwr_datasets/preprocessor/preprocessor.py similarity index 91% rename from datasets/flwr_datasets/resplitter/resplitter.py rename to datasets/flwr_datasets/preprocessor/preprocessor.py index 206e2e85730c..c137b98eeeee 100644 --- a/datasets/flwr_datasets/resplitter/resplitter.py +++ b/datasets/flwr_datasets/preprocessor/preprocessor.py @@ -12,11 +12,11 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Resplitter.""" +"""Preprocessor.""" from typing import Callable from datasets import DatasetDict -Resplitter = Callable[[DatasetDict], DatasetDict] +Preprocessor = Callable[[DatasetDict], DatasetDict] diff --git a/datasets/flwr_datasets/utils.py b/datasets/flwr_datasets/utils.py index c6f6900a99cd..0ecb96ac9456 100644 --- a/datasets/flwr_datasets/utils.py +++ b/datasets/flwr_datasets/utils.py @@ -20,8 +20,8 @@ from datasets import Dataset, DatasetDict, concatenate_datasets from flwr_datasets.partitioner import IidPartitioner, Partitioner -from flwr_datasets.resplitter import Resplitter -from flwr_datasets.resplitter.merge_resplitter import MergeResplitter +from flwr_datasets.preprocessor import Preprocessor +from flwr_datasets.preprocessor.merger import Merger tested_datasets = [ "mnist", @@ -75,13 +75,13 @@ def _instantiate_partitioners( return instantiated_partitioners -def _instantiate_resplitter_if_needed( - resplitter: Optional[Union[Resplitter, Dict[str, Tuple[str, ...]]]] -) -> Optional[Resplitter]: - """Instantiate `MergeResplitter` if resplitter is merge_config.""" - if resplitter and isinstance(resplitter, Dict): - resplitter = MergeResplitter(merge_config=resplitter) - return cast(Optional[Resplitter], resplitter) +def _instantiate_merger_if_needed( + merger: Optional[Union[Preprocessor, Dict[str, Tuple[str, ...]]]] +) -> Optional[Preprocessor]: + """Instantiate `Merger` if preprocessor is merge_config.""" + if merger and isinstance(merger, Dict): + merger = Merger(merge_config=merger) + return cast(Optional[Preprocessor], merger) def _check_if_dataset_tested(dataset: str) -> None: diff --git a/datasets/flwr_datasets/visualization/__init__.py b/datasets/flwr_datasets/visualization/__init__.py new file mode 100644 index 000000000000..b55e406c71db --- /dev/null +++ b/datasets/flwr_datasets/visualization/__init__.py @@ -0,0 +1,24 @@ +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Visualization package.""" + + +from .comparison_label_distribution import plot_comparison_label_distribution +from .label_distribution import plot_label_distributions + +__all__ = [ + "plot_comparison_label_distribution", + "plot_label_distributions", +] diff --git a/datasets/flwr_datasets/visualization/bar_plot.py b/datasets/flwr_datasets/visualization/bar_plot.py new file mode 100644 index 000000000000..339ff0967906 --- /dev/null +++ b/datasets/flwr_datasets/visualization/bar_plot.py @@ -0,0 +1,142 @@ +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Label distribution bar plotting.""" + + +from typing import Any, Dict, Optional, Tuple, Union + +import numpy as np +import pandas as pd +from matplotlib import colors as mcolors +from matplotlib import pyplot as plt +from matplotlib.axes import Axes + + +# pylint: disable=too-many-arguments,too-many-locals,too-many-branches +def _plot_bar( + dataframe: pd.DataFrame, + axis: Optional[Axes], + figsize: Optional[Tuple[float, float]], + title: str, + colormap: Optional[Union[str, mcolors.Colormap]], + partition_id_axis: str, + size_unit: str, + legend: bool, + legend_title: Optional[str], + plot_kwargs: Optional[Dict[str, Any]], + legend_kwargs: Optional[Dict[str, Any]], +) -> Axes: + if axis is None: + if figsize is None: + figsize = _initialize_figsize( + partition_id_axis=partition_id_axis, num_partitions=dataframe.shape[0] + ) + _, axis = plt.subplots(figsize=figsize) + + # Handle plot_kwargs + if plot_kwargs is None: + plot_kwargs = {} + + kind = "bar" if partition_id_axis == "x" else "barh" + if "kind" not in plot_kwargs: + plot_kwargs["kind"] = kind + + # Handle non-optional parameters + plot_kwargs["title"] = title + + # Handle optional parameters + if colormap is not None: + plot_kwargs["colormap"] = colormap + elif "colormap" not in plot_kwargs: + plot_kwargs["colormap"] = "RdYlGn" + + if "xlabel" not in plot_kwargs and "ylabel" not in plot_kwargs: + xlabel, ylabel = _initialize_xy_labels( + size_unit=size_unit, partition_id_axis=partition_id_axis + ) + plot_kwargs["xlabel"] = xlabel + plot_kwargs["ylabel"] = ylabel + + # Make the x ticks readable (they appear 90 degrees rotated by default) + if "rot" not in plot_kwargs: + plot_kwargs["rot"] = 0 + + # Handle hard-coded parameters + # Legend is handled separately (via axes.legend call not in the plot()) + if "legend" not in plot_kwargs: + plot_kwargs["legend"] = False + + # Make the bar plot stacked + if "stacked" not in plot_kwargs: + plot_kwargs["stacked"] = True + + axis = dataframe.plot( + ax=axis, + **plot_kwargs, + ) + + if legend: + if legend_kwargs is None: + legend_kwargs = {} + + if legend_title is not None: + legend_kwargs["title"] = legend_title + elif "title" not in legend_kwargs: + legend_kwargs["title"] = "Labels" + + if "loc" not in legend_kwargs: + legend_kwargs["loc"] = "outside center right" + + if "bbox_to_anchor" not in legend_kwargs: + max_len_label_str = max([len(str(column)) for column in dataframe.columns]) + shift = min(0.05 + max_len_label_str / 100, 0.15) + legend_kwargs["bbox_to_anchor"] = (1.0 + shift, 0.5) + + handles, legend_labels = axis.get_legend_handles_labels() + _ = axis.figure.legend( + handles=handles[::-1], labels=legend_labels[::-1], **legend_kwargs + ) + + # Heuristic to make the partition id on xticks non-overlapping + if partition_id_axis == "x": + xticklabels = axis.get_xticklabels() + if len(xticklabels) > 20: + # Make every other xtick label not visible + for i, label in enumerate(xticklabels): + if i % 2 == 1: + label.set_visible(False) + return axis + + +def _initialize_figsize( + partition_id_axis: str, + num_partitions: int, +) -> Tuple[float, float]: + figsize = (0.0, 0.0) + if partition_id_axis == "x": + figsize = (6.4, 4.8) + elif partition_id_axis == "y": + figsize = (6.4, np.sqrt(num_partitions)) + return figsize + + +def _initialize_xy_labels(size_unit: str, partition_id_axis: str) -> Tuple[str, str]: + xlabel = "Partition ID" + ylabel = "Count" if size_unit == "absolute" else "Percent %" + + if partition_id_axis == "y": + xlabel, ylabel = ylabel, xlabel + + return xlabel, ylabel diff --git a/datasets/flwr_datasets/visualization/comparison_label_distribution.py b/datasets/flwr_datasets/visualization/comparison_label_distribution.py new file mode 100644 index 000000000000..554f6d78d59a --- /dev/null +++ b/datasets/flwr_datasets/visualization/comparison_label_distribution.py @@ -0,0 +1,245 @@ +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Comparison of label distribution plotting.""" + + +from typing import Any, Dict, List, Optional, Tuple, Union + +import matplotlib.colors as mcolors +import matplotlib.pyplot as plt +import pandas as pd +from matplotlib.axes import Axes +from matplotlib.figure import Figure + +from flwr_datasets.common import EventType, event +from flwr_datasets.partitioner import Partitioner +from flwr_datasets.visualization.constants import PLOT_TYPES +from flwr_datasets.visualization.label_distribution import plot_label_distributions + + +# pylint: disable=too-many-arguments,too-many-locals +def plot_comparison_label_distribution( + partitioner_list: List[Partitioner], + label_name: Union[str, List[str]], + plot_type: str = "bar", + size_unit: str = "percent", + max_num_partitions: Optional[Union[int]] = 30, + partition_id_axis: str = "y", + figsize: Optional[Tuple[float, float]] = None, + subtitle: str = "Comparison of Per Partition Label Distribution", + titles: Optional[List[str]] = None, + cmap: Optional[Union[str, mcolors.Colormap]] = None, + legend: bool = False, + legend_title: Optional[str] = None, + verbose_labels: bool = True, + plot_kwargs_list: Optional[List[Optional[Dict[str, Any]]]] = None, + legend_kwargs: Optional[Dict[str, Any]] = None, +) -> Tuple[Figure, List[Axes], List[pd.DataFrame]]: + """Compare the label distribution across multiple partitioners. + + Parameters + ---------- + partitioner_list : List[Partitioner] + List of partitioners to be compared. + label_name : Union[str, List[str]] + Column name or list of column names identifying labels for each partitioner. + plot_type : str + Type of plot, either "bar" or "heatmap". + size_unit : str + "absolute" for raw counts, or "percent" to normalize values to 100%. + max_num_partitions : Optional[int] + Maximum number of partitions to include in the plot. If None, all partitions + are included. + partition_id_axis : str + Axis on which the partition IDs will be marked, either "x" or "y". + figsize : Optional[Tuple[float, float]] + Size of the figure. If None, a default size is calculated. + subtitle : str + Subtitle for the figure. Defaults to "Comparison of Per Partition Label + Distribution" + titles : Optional[List[str]] + Titles for each subplot. If None, no titles are set. + cmap : Optional[Union[str, mcolors.Colormap]] + Colormap for determining the colorspace of the plot. + legend : bool + Whether to include a legend. If True, it will be included right-hand side after + all the plots. + legend_title : Optional[str] + Title for the legend. If None, the defaults will be takes based on the type of + plot. + verbose_labels : bool + Whether to use verbose versions of the labels. + plot_kwargs_list: Optional[List[Optional[Dict[str, Any]]]] + List of plot_kwargs. Any key value pair that can be passed to a plot function + that are not supported directly. In case of the parameter doubling + (e.g. specifying cmap here too) the chosen value will be taken from the + explicit arguments (e.g. cmap specified as an argument to this function not + the value in this dictionary). + legend_kwargs: Optional[Dict[str, Any]] + Any key value pair that can be passed to a figure.legend in case of bar plot or + cbar_kws in case of heatmap that are not supported directly. In case of + parameter doubling (e.g. specifying legend_title here too) the + chosen value will be taken from the explicit arguments (e.g. legend_title + specified as an argument to this function not the value in this dictionary). + + Returns + ------- + fig : Figure + The figure object containing the plots. + axes_list : List[Axes] + List of Axes objects for the plots. + dataframe_list : List[pd.DataFrame] + List of DataFrames used for each plot. + + Examples + -------- + Compare the difference of using different alpha (concentration) parameters in + DirichletPartitioner. + + >>> from flwr_datasets import FederatedDataset + >>> from flwr_datasets.partitioner import DirichletPartitioner + >>> from flwr_datasets.visualization import plot_comparison_label_distribution + >>> + >>> partitioner_list = [] + >>> alpha_list = [10_000.0, 100.0, 1.0, 0.1, 0.01, 0.00001] + >>> for alpha in alpha_list: + >>> fds = FederatedDataset( + >>> dataset="cifar10", + >>> partitioners={ + >>> "train": DirichletPartitioner( + >>> num_partitions=20, + >>> partition_by="label", + >>> alpha=alpha, + >>> min_partition_size=0, + >>> ), + >>> }, + >>> ) + >>> partitioner_list.append(fds.partitioners["train"]) + >>> fig, axes, dataframe_list = plot_comparison_label_distribution( + >>> partitioner_list=partitioner_list, + >>> label_name="label", + >>> titles=[f"Concentration = {alpha}" for alpha in alpha_list], + >>> ) + """ + event( + EventType.PLOT_COMPARISON_LABEL_DISTRIBUTION_CALLED, + { + "num_compare": len(partitioner_list), + "plot_type": plot_type, + }, + ) + num_partitioners = len(partitioner_list) + if isinstance(label_name, str): + label_name = [label_name] * num_partitioners + elif isinstance(label_name, List): + pass + else: + raise TypeError( + f"Label name has to be of type List[str] or str but given " + f"{type(label_name)}" + ) + figsize = _initialize_comparison_figsize(figsize, num_partitioners) + fig, axes = plt.subplots(1, num_partitioners, layout="constrained", figsize=figsize) + + if titles is None: + titles = ["" for _ in range(num_partitioners)] + + if plot_kwargs_list is None: + plot_kwargs_list = [None] * num_partitioners + + dataframe_list = [] + for idx, (partitioner, single_label_name, plot_kwargs) in enumerate( + zip(partitioner_list, label_name, plot_kwargs_list) + ): + if idx == (num_partitioners - 1): + *_, dataframe = plot_label_distributions( + partitioner=partitioner, + label_name=single_label_name, + plot_type=plot_type, + size_unit=size_unit, + partition_id_axis=partition_id_axis, + axis=axes[idx], + max_num_partitions=max_num_partitions, + cmap=cmap, + legend=legend, + legend_title=legend_title, + verbose_labels=verbose_labels, + plot_kwargs=plot_kwargs, + legend_kwargs=legend_kwargs, + ) + dataframe_list.append(dataframe) + else: + *_, dataframe = plot_label_distributions( + partitioner=partitioner, + label_name=single_label_name, + plot_type=plot_type, + size_unit=size_unit, + partition_id_axis=partition_id_axis, + axis=axes[idx], + max_num_partitions=max_num_partitions, + cmap=cmap, + legend=False, + plot_kwargs=plot_kwargs, + ) + dataframe_list.append(dataframe) + + # Do not use the xlabel and ylabel on each subplot plot + # (instead use global = per figure xlabel and ylabel) + for idx, axis in enumerate(axes): + axis.set_xlabel("") + axis.set_ylabel("") + axis.set_title(titles[idx]) + for axis in axes[1:]: + axis.set_yticks([]) + + # Set up figure xlabel and ylabel + xlabel, ylabel = _initialize_comparison_xy_labels(plot_type, partition_id_axis) + fig.supxlabel(xlabel) + fig.supylabel(ylabel) + fig.suptitle(subtitle) + + fig.tight_layout() + return fig, axes, dataframe_list + + +def _initialize_comparison_figsize( + figsize: Optional[Tuple[float, float]], num_partitioners: int +) -> Tuple[float, float]: + if figsize is not None: + return figsize + x_value = 4 + (num_partitioners - 1) * 2 + y_value = 4.8 + figsize = (x_value, y_value) + return figsize + + +def _initialize_comparison_xy_labels( + plot_type: str, partition_id_axis: str +) -> Tuple[str, str]: + if plot_type == "bar": + xlabel = "Partition ID" + ylabel = "Class distribution" + elif plot_type == "heatmap": + xlabel = "Partition ID" + ylabel = "Label" + else: + raise ValueError( + f"Invalid plot_type: {plot_type}. Must be one of {PLOT_TYPES}." + ) + + if partition_id_axis == "y": + xlabel, ylabel = ylabel, xlabel + + return xlabel, ylabel diff --git a/datasets/flwr_datasets/visualization/constants.py b/datasets/flwr_datasets/visualization/constants.py new file mode 100644 index 000000000000..b3c9cd2a7400 --- /dev/null +++ b/datasets/flwr_datasets/visualization/constants.py @@ -0,0 +1,19 @@ +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Constants for plot types and size units.""" + +PLOT_TYPES = ("bar", "heatmap") +SIZE_UNITS = ("absolute", "percent") +AXIS_TYPES = ("x", "y") diff --git a/datasets/flwr_datasets/visualization/heatmap_plot.py b/datasets/flwr_datasets/visualization/heatmap_plot.py new file mode 100644 index 000000000000..3c87de7693ae --- /dev/null +++ b/datasets/flwr_datasets/visualization/heatmap_plot.py @@ -0,0 +1,104 @@ +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Label distribution heatmap plotting.""" + + +from typing import Any, Dict, Optional, Tuple, Union + +import numpy as np +import pandas as pd +import seaborn as sns +from matplotlib import colors as mcolors +from matplotlib import pyplot as plt +from matplotlib.axes import Axes + + +# pylint: disable=too-many-arguments,too-many-locals +def _plot_heatmap( + dataframe: pd.DataFrame, + axis: Optional[Axes], + figsize: Optional[Tuple[float, float]], + title: str, + colormap: Optional[Union[str, mcolors.Colormap]], + partition_id_axis: str, + size_unit: str, + legend: bool, + legend_title: Optional[str], + plot_kwargs: Optional[Dict[str, Any]], + legend_kwargs: Optional[Dict[str, Any]], +) -> Axes: + if axis is None: + if figsize is None: + figsize = _initialize_figsize( + partition_id_axis=partition_id_axis, + num_partitions=dataframe.shape[0], + num_labels=dataframe.shape[1], + ) + _, axis = plt.subplots(figsize=figsize) + + # Handle plot_kwargs + if plot_kwargs is None: + plot_kwargs = {} + + # Handle optional parameters + if colormap is not None: + plot_kwargs["cmap"] = colormap + elif "cmap" not in plot_kwargs: + plot_kwargs["cmap"] = sns.light_palette("seagreen", as_cmap=True) + + if "fmt" not in plot_kwargs: + plot_kwargs["fmt"] = ",d" if size_unit == "absolute" else "0.2f" + + if legend_kwargs is None: + legend_kwargs = {} + if legend: + plot_kwargs["cbar"] = True + + if legend_title is not None: + legend_kwargs["label"] = legend_title + else: + legend_kwargs["label"] = _initialize_cbar_title(size_unit) + else: + plot_kwargs["cbar"] = False + + if partition_id_axis == "x": + dataframe = dataframe.T + + sns.heatmap( + dataframe, + ax=axis, + **plot_kwargs, + cbar_kws=legend_kwargs, + ) + axis.set_title(title) + return axis + + +def _initialize_figsize( + partition_id_axis: str, + num_partitions: int, + num_labels: int, +) -> Tuple[float, float]: + figsize = (0.0, 0.0) + if partition_id_axis == "x": + figsize = (3 * np.sqrt(num_partitions), np.sqrt(num_labels)) + elif partition_id_axis == "y": + figsize = (3 * np.sqrt(num_labels), np.sqrt(num_partitions)) + + return figsize + + +def _initialize_cbar_title(size_unit: str) -> Optional[str]: + return "Count" if size_unit == "absolute" else "Percent %" diff --git a/datasets/flwr_datasets/visualization/label_distribution.py b/datasets/flwr_datasets/visualization/label_distribution.py new file mode 100644 index 000000000000..0c47bd204a17 --- /dev/null +++ b/datasets/flwr_datasets/visualization/label_distribution.py @@ -0,0 +1,249 @@ +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Label distribution plotting.""" + + +from typing import Any, Dict, Optional, Tuple, Union + +import matplotlib.colors as mcolors +import pandas as pd +from matplotlib.axes import Axes +from matplotlib.figure import Figure + +from flwr_datasets.common import EventType, event +from flwr_datasets.metrics.utils import compute_counts, compute_frequencies +from flwr_datasets.partitioner import Partitioner +from flwr_datasets.visualization.bar_plot import _plot_bar +from flwr_datasets.visualization.heatmap_plot import _plot_heatmap +from flwr_datasets.visualization.utils import _validate_parameters + +# pylint: disable=too-many-arguments,too-many-locals + + +def plot_label_distributions( + partitioner: Partitioner, + label_name: str, + plot_type: str = "bar", + size_unit: str = "absolute", + max_num_partitions: Optional[int] = None, + partition_id_axis: str = "x", + axis: Optional[Axes] = None, + figsize: Optional[Tuple[float, float]] = None, + title: str = "Per Partition Label Distribution", + cmap: Optional[Union[str, mcolors.Colormap]] = None, + legend: bool = False, + legend_title: Optional[str] = None, + verbose_labels: bool = True, + plot_kwargs: Optional[Dict[str, Any]] = None, + legend_kwargs: Optional[Dict[str, Any]] = None, +) -> Tuple[Figure, Axes, pd.DataFrame]: + """Plot the label distribution of the partitions. + + Parameters + ---------- + partitioner : Partitioner + Partitioner with an assigned dataset. + label_name : str + Column name identifying label based on which the plot will be created. + plot_type : str + Type of plot, either "bar" or "heatmap". + size_unit : str + "absolute" or "percent". "absolute" - (number of samples). "percent" - + normalizes each value, so they sum up to 100%. + max_num_partitions : Optional[int] + The number of partitions that will be used. If left None, then all partitions + will be used. + partition_id_axis : str + "x" or "y". The axis on which the partition_id will be marked. + axis : Optional[Axes] + Matplotlib Axes object to plot on. + figsize : Optional[Tuple[float, float]] + Size of the figure. + title : str + Title of the plot. + cmap : Optional[Union[str, mcolors.Colormap]] + Colormap for determining the colorspace of the plot. + legend : bool + Include the legend. + legend_title : Optional[str] + Title for the legend. If None, the defaults will be takes based on the type of + plot. + verbose_labels : bool + Whether to use verbose versions of the labels. These values are used as columns + of the returned dataframe and as labels on the legend in a bar plot and columns/ + rows ticks in a heatmap plot. + plot_kwargs: Optional[Dict[str, Any]] + Any key value pair that can be passed to a plot function that are not supported + directly. In case of the parameter doubling (e.g. specifying cmap here too) the + chosen value will be taken from the explicit arguments (e.g. cmap specified as + an argument to this function not the value in this dictionary). + legend_kwargs: Optional[Dict[str, Any]] + Any key value pair that can be passed to a figure.legend in case of bar plot or + cbar_kws in case of heatmap that are not supported directly. In case of the + parameter doubling (e.g. specifying legend_title here too) the + chosen value will be taken from the explicit arguments (e.g. legend_title + specified as an argument to this function not the value in this dictionary). + + Returns + ------- + fig : Figure + The figure object. + axis : Axes + The Axes object with the plot. + dataframe : pd.DataFrame + The DataFrame where each row represents the partition id and each column + represents the class. + + Examples + -------- + Visualize the label distribution resulting from DirichletPartitioner. + + >>> from flwr_datasets import FederatedDataset + >>> from flwr_datasets.partitioner import DirichletPartitioner + >>> from flwr_datasets.visualization import plot_label_distributions + >>> + >>> fds = FederatedDataset( + >>> dataset="cifar10", + >>> partitioners={ + >>> "train": DirichletPartitioner( + >>> num_partitions=20, + >>> partition_by="label", + >>> alpha=0.3, + >>> min_partition_size=0, + >>> ), + >>> }, + >>> ) + >>> partitioner = fds.partitioners["train"] + >>> figure, axis, dataframe = plot_label_distributions( + >>> partitioner=partitioner, + >>> label_name="label", + >>> legend=True, + >>> verbose_labels=True, + >>> ) + + Alternatively you can visualize each partition in terms of fraction of the data + available on that partition instead of the absolute count + + >>> from flwr_datasets import FederatedDataset + >>> from flwr_datasets.partitioner import DirichletPartitioner + >>> from flwr_datasets.visualization import plot_label_distributions + >>> + >>> fds = FederatedDataset( + >>> dataset="cifar10", + >>> partitioners={ + >>> "train": DirichletPartitioner( + >>> num_partitions=20, + >>> partition_by="label", + >>> alpha=0.3, + >>> min_partition_size=0, + >>> ), + >>> }, + >>> ) + >>> partitioner = fds.partitioners["train"] + >>> figure, axis, dataframe = plot_label_distributions( + >>> partitioner=partitioner, + >>> label_name="label", + >>> size_unit="percent", + >>> legend=True, + >>> verbose_labels=True, + >>> ) + >>> + + You can also visualize the data as a heatmap by changing the `plot_type` from + default "bar" to "heatmap" + + >>> from flwr_datasets import FederatedDataset + >>> from flwr_datasets.partitioner import DirichletPartitioner + >>> from flwr_datasets.visualization import plot_label_distributions + >>> + >>> fds = FederatedDataset( + >>> dataset="cifar10", + >>> partitioners={ + >>> "train": DirichletPartitioner( + >>> num_partitions=20, + >>> partition_by="label", + >>> alpha=0.3, + >>> min_partition_size=0, + >>> ), + >>> }, + >>> ) + >>> partitioner = fds.partitioners["train"] + >>> figure, axis, dataframe = plot_label_distributions( + >>> partitioner=partitioner, + >>> label_name="label", + >>> size_unit="percent", + >>> plot_type="heatmap", + >>> legend=True, + >>> plot_kwargs={"annot": True}, + >>> ) + + You can also visualize the returned DataFrame in Jupyter Notebook + >>> dataframe.style.background_gradient(axis=None) + """ + event( + EventType.PLOT_LABEL_DISTRIBUTION_CALLED, + { + "plot_type": plot_type, + }, + ) + _validate_parameters(plot_type, size_unit, partition_id_axis) + + dataframe = pd.DataFrame() + if size_unit == "absolute": + dataframe = compute_counts( + partitioner=partitioner, + column_name=label_name, + verbose_names=verbose_labels, + max_num_partitions=max_num_partitions, + ) + elif size_unit == "percent": + dataframe = compute_frequencies( + partitioner=partitioner, + column_name=label_name, + verbose_names=verbose_labels, + max_num_partitions=max_num_partitions, + ) + dataframe = dataframe * 100.0 + + if plot_type == "bar": + axis = _plot_bar( + dataframe, + axis, + figsize, + title, + cmap, + partition_id_axis, + size_unit, + legend, + legend_title, + plot_kwargs, + legend_kwargs, + ) + elif plot_type == "heatmap": + axis = _plot_heatmap( + dataframe, + axis, + figsize, + title, + cmap, + partition_id_axis, + size_unit, + legend, + legend_title, + plot_kwargs, + legend_kwargs, + ) + assert axis is not None + return axis.figure, axis, dataframe diff --git a/datasets/flwr_datasets/visualization/utils.py b/datasets/flwr_datasets/visualization/utils.py new file mode 100644 index 000000000000..c2f1846de20e --- /dev/null +++ b/datasets/flwr_datasets/visualization/utils.py @@ -0,0 +1,36 @@ +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Plotting utils.""" + + +from flwr_datasets.visualization.constants import AXIS_TYPES, PLOT_TYPES, SIZE_UNITS + + +def _validate_parameters( + plot_type: str, size_unit: str, partition_id_axis: str +) -> None: + if plot_type not in PLOT_TYPES: + raise ValueError( + f"Invalid plot_type: {plot_type}. Must be one of {PLOT_TYPES}." + ) + if size_unit not in SIZE_UNITS: + raise ValueError( + f"Invalid size_unit: {size_unit}. Must be one of {SIZE_UNITS}." + ) + if partition_id_axis not in AXIS_TYPES: + raise ValueError( + f"Invalid partition_id_axis: {partition_id_axis}. " + f"Must be one of {AXIS_TYPES}." + ) diff --git a/datasets/pyproject.toml b/datasets/pyproject.toml index 36c0aef5ec2c..017374181f59 100644 --- a/datasets/pyproject.toml +++ b/datasets/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api" [tool.poetry] name = "flwr-datasets" -version = "0.1.0" +version = "0.2.0" description = "Flower Datasets" license = "Apache-2.0" authors = ["The Flower Authors "] @@ -54,11 +54,13 @@ exclude = [ [tool.poetry.dependencies] python = "^3.8" numpy = "^1.21.0" -datasets = "^2.14.6" +datasets = ">=2.14.6 <2.20.0" pillow = { version = ">=6.2.1", optional = true } soundfile = { version = ">=0.12.1", optional = true } librosa = { version = ">=0.10.0.post2", optional = true } tqdm ="^4.66.1" +matplotlib = "^3.7.5" +seaborn = "^0.13.0" [tool.poetry.dev-dependencies] isort = "==5.13.2" diff --git a/dev/bootstrap.sh b/dev/bootstrap.sh index b4bb82d73b51..154fe0f1cbaf 100755 --- a/dev/bootstrap.sh +++ b/dev/bootstrap.sh @@ -1,6 +1,6 @@ #!/bin/bash set -e -cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/../ +cd "$(cd "$(dirname "${BASH_SOURCE[0]}")" >/dev/null 2>&1 && pwd)"/../ # Setup environment variables for development ./dev/setup-envs.sh @@ -9,8 +9,8 @@ cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/../ ./dev/rm-caches.sh # Upgrade/install spcific versions of `pip`, `setuptools`, and `poetry` -python -m pip install -U pip==23.3.1 -python -m pip install -U setuptools==68.2.2 +python -m pip install -U pip==24.0.0 +python -m pip install -U setuptools==69.5.1 python -m pip install -U poetry==1.7.1 # Use `poetry` to install project dependencies diff --git a/dev/build-docker-image-matrix.py b/dev/build-docker-image-matrix.py new file mode 100644 index 000000000000..51d7fd0083d1 --- /dev/null +++ b/dev/build-docker-image-matrix.py @@ -0,0 +1,182 @@ +""" +Usage: python dev/build-docker-image-matrix.py --flwr-version +""" + +import argparse +from dataclasses import asdict, dataclass +from enum import Enum +import json +from typing import Any, Callable, Dict, List, Optional + + +class DistroName(str, Enum): + ALPINE = "alpine" + UBUNTU = "ubuntu" + + +@dataclass +class Distro: + name: "DistroName" + version: str + + +LATEST_SUPPORTED_PYTHON_VERSION = "3.11" +SUPPORTED_PYTHON_VERSIONS = [ + "3.8", + "3.9", + "3.10", + LATEST_SUPPORTED_PYTHON_VERSION, +] + +DOCKERFILE_ROOT = "src/docker" + + +@dataclass +class BaseImage: + distro: Distro + python_version: str + namespace_repository: str + file_dir: str + tag: str + flwr_version: str + + +def new_base_image( + flwr_version: str, python_version: str, distro: Distro +) -> Dict[str, Any]: + return BaseImage( + distro, + python_version, + "flwr/base", + f"{DOCKERFILE_ROOT}/base/{distro.name.value}", + f"{flwr_version}-py{python_version}-{distro.name.value}{distro.version}", + flwr_version, + ) + + +def generate_base_images( + flwr_version: str, python_versions: List[str], distros: List[Dict[str, str]] +) -> List[Dict[str, Any]]: + return [ + new_base_image(flwr_version, python_version, distro) + for distro in distros + for python_version in python_versions + ] + + +@dataclass +class BinaryImage: + namespace_repository: str + file_dir: str + base_image: str + tags: List[str] + + +def new_binary_image( + name: str, + base_image: BaseImage, + tags_fn: Optional[Callable], +) -> Dict[str, Any]: + tags = [] + if tags_fn is not None: + tags += tags_fn(base_image) or [] + + return BinaryImage( + f"flwr/{name}", + f"{DOCKERFILE_ROOT}/{name}", + base_image.tag, + "\n".join(tags), + ) + + +def generate_binary_images( + name: str, + base_images: List[BaseImage], + tags_fn: Optional[Callable] = None, + filter: Optional[Callable] = None, +) -> List[Dict[str, Any]]: + filter = filter or (lambda _: True) + + return [ + new_binary_image(name, image, tags_fn) for image in base_images if filter(image) + ] + + +def tag_latest_alpine_with_flwr_version(image: BaseImage) -> List[str]: + if ( + image.distro.name == DistroName.ALPINE + and image.python_version == LATEST_SUPPORTED_PYTHON_VERSION + ): + return [image.tag, image.flwr_version] + else: + return [image.tag] + + +def tag_latest_ubuntu_with_flwr_version(image: BaseImage) -> List[str]: + if ( + image.distro.name == DistroName.UBUNTU + and image.python_version == LATEST_SUPPORTED_PYTHON_VERSION + ): + return [image.tag, image.flwr_version] + else: + return [image.tag] + + +if __name__ == "__main__": + arg_parser = argparse.ArgumentParser( + description="Generate Github Docker workflow matrix" + ) + arg_parser.add_argument("--flwr-version", type=str, required=True) + args = arg_parser.parse_args() + + flwr_version = args.flwr_version + + # ubuntu base images for each supported python version + ubuntu_base_images = generate_base_images( + flwr_version, + SUPPORTED_PYTHON_VERSIONS, + [Distro(DistroName.UBUNTU, "22.04")], + ) + # alpine base images for the latest supported python version + alpine_base_images = generate_base_images( + flwr_version, + [LATEST_SUPPORTED_PYTHON_VERSION], + [Distro(DistroName.ALPINE, "3.19")], + ) + + base_images = ubuntu_base_images + alpine_base_images + + binary_images = ( + # ubuntu and alpine images for the latest supported python version + generate_binary_images( + "superlink", + base_images, + tag_latest_alpine_with_flwr_version, + lambda image: image.python_version == LATEST_SUPPORTED_PYTHON_VERSION, + ) + # ubuntu images for each supported python version + + generate_binary_images( + "supernode", + base_images, + tag_latest_ubuntu_with_flwr_version, + lambda image: image.distro.name == DistroName.UBUNTU, + ) + # ubuntu images for each supported python version + + generate_binary_images( + "serverapp", + base_images, + tag_latest_ubuntu_with_flwr_version, + lambda image: image.distro.name == DistroName.UBUNTU, + ) + ) + + print( + json.dumps( + { + "base": {"images": list(map(lambda image: asdict(image), base_images))}, + "binary": { + "images": list(map(lambda image: asdict(image), binary_images)) + }, + } + ) + ) diff --git a/dev/changelog_config.toml b/dev/changelog_config.toml index 892050cdb084..c5ff1bcdd1c1 100644 --- a/dev/changelog_config.toml +++ b/dev/changelog_config.toml @@ -7,7 +7,7 @@ project = ["framework", "baselines", "datasets", "examples"] scope = "skip" -pattern_template = "^({types})\\(({projects})(?::({scope}))?\\) ([A-Z][^\\.\\n]*(?:\\.(?=[^\\.\\n]))*[^\\.\\n]*)$" +pattern_template = "^({types})\\(({projects})(?::({scope}))?\\) ([A-Z][^\\n]*[^\\.\\n])$" allowed_verbs=[ "Abandon", @@ -44,6 +44,7 @@ allowed_verbs=[ "Align", "Allow", "Alter", + "Amend", "Analyse", "Analyze", "Anchor", diff --git a/dev/check_pr_title.py b/dev/check_pr_title.py index e2679b3e7226..33b7a4664e9f 100644 --- a/dev/check_pr_title.py +++ b/dev/check_pr_title.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""This module is used to check a given PR title format.""" +"""Used to check a given PR title format.""" import pathlib import re @@ -46,10 +46,13 @@ valid = True error = "it doesn't have the correct format" - if not match: + # This check is there to ignore dependabot PRs from title checks + if pr_title.startswith("chore"): + sys.exit(0) + elif not match: valid = False else: - if not match.group(4).split()[0] in allowed_verbs: + if match.group(4).split()[0] not in allowed_verbs: valid = False error = "the doesn't start with a verb in the imperative mood" elif match.group(2) == "*" and match.group(3) is None: @@ -58,16 +61,16 @@ if not valid: print( - f"PR title `{pr_title}` is invalid, {error}.\n\n" - "A PR title should be of the form:\n\n\t() " - f"\n\nOr, if the PR shouldn't appear in the changelog:\n\n\t" - f"(:skip) \n\nwith in [{types}],\n" - f" in [{'|'.join(config['project']) + '|*'}] (where '*' is used " - "when modifying multiple projects and should be used in " + f"PR title `{pr_title}` is invalid, {error}.\n\nA PR title should " + "be of the form:\n\n\t() \n\n" + f"Or, if the PR shouldn't appear in the changelog:\n\n\t" + f"(:skip) \n\nwith in [{types}],\n" + f" in [{'|'.join(config['project']) + '|*'}] " + "(where '*' is used when modifying multiple projects and should be used in " "conjunction with the ':skip' flag),\nand starting with " - "a capitalized verb in the imperative mood and without any punctuation at the end.\n\n" - "A valid example is:\n\n\t`feat(framework) Add flwr build CLI command`\n\n" - "Or, if the PR shouldn't appear in the changelog:\n\n\t" - "`feat(framework:skip) Add new option to build CLI`\n" + "a capitalized verb in the imperative mood and without any punctuation " + "at the end.\n\nA valid example is:\n\n\t`feat(framework) " + "Add flwr build CLI command`\n\nOr, if the PR shouldn't appear in " + "the changelog:\n\n\t`feat(framework:skip) Add new option to build CLI`\n" ) sys.exit(1) diff --git a/dev/format.sh b/dev/format.sh index 6b9cdaf5f44c..71edf9c6065a 100755 --- a/dev/format.sh +++ b/dev/format.sh @@ -3,6 +3,8 @@ set -e cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/../ # Python +python -m flwr_tool.check_copyright src/py/flwr +python -m flwr_tool.init_py_fix src/py/flwr python -m isort --skip src/py/flwr/proto src/py python -m black -q --exclude src/py/flwr/proto src/py python -m docformatter -i -r src/py/flwr -e src/py/flwr/proto @@ -16,6 +18,11 @@ find src/proto/flwr/proto -name *.proto | grep "\.proto" | xargs clang-format -i python -m black -q examples python -m docformatter -i -r examples +# E2E +python -m isort e2e +python -m black -q e2e +python -m docformatter -i -r e2e + # Notebooks python -m black --ipynb -q doc/source/*.ipynb KEYS="metadata.celltoolbar metadata.language_info metadata.toc metadata.notify_time metadata.varInspector metadata.accelerator metadata.vscode cell.metadata.id cell.metadata.heading_collapsed cell.metadata.hidden cell.metadata.code_folding cell.metadata.tags cell.metadata.init_cell cell.metadata.vscode cell.metadata.pycharm" diff --git a/dev/publish-nightly.sh b/dev/publish-nightly.sh index a42af1f17cfc..0c03cdda9f49 100755 --- a/dev/publish-nightly.sh +++ b/dev/publish-nightly.sh @@ -24,16 +24,12 @@ cd "$(cd "$(dirname "${BASH_SOURCE[0]}")" >/dev/null 2>&1 && pwd)"/../ # The version name in the pyproject.toml will be appended with "-dev" and the current date. # The result will be a release on PyPi of the package "flwr-nightly" of version e.g. # "0.1.1.dev20200716" as seen at https://pypi.org/project/flwr-nightly/ -# If the script is called with the flag `--skip-publish`, the name and version are changed -# in the pyproject.toml but the package won't be published. if [[ $(git log --since="24 hours ago" --pretty=oneline) ]]; then sed -i -E "s/^name = \"(.+)\"/name = \"\1-nightly\"/" pyproject.toml sed -i -E "s/^version = \"(.+)\"/version = \"\1.dev$(date '+%Y%m%d')\"/" pyproject.toml - if [ "$1" != "--skip-publish" ]; then - python -m poetry build - python -m poetry publish -u __token__ -p $PYPI_TOKEN - fi + python -m poetry build + python -m poetry publish -u __token__ -p $PYPI_TOKEN else echo "There were no commits in the last 24 hours." fi diff --git a/dev/test.sh b/dev/test.sh index 1ed1b96edea1..8cbe88c9298b 100755 --- a/dev/test.sh +++ b/dev/test.sh @@ -11,11 +11,11 @@ clang-format --Werror --dry-run src/proto/flwr/proto/* echo "- clang-format: done" echo "- isort: start" -python -m isort --check-only --skip src/py/flwr/proto src/py/flwr +python -m isort --check-only --skip src/py/flwr/proto src/py/flwr e2e echo "- isort: done" echo "- black: start" -python -m black --exclude "src\/py\/flwr\/proto" --check src/py/flwr examples +python -m black --exclude "src\/py\/flwr\/proto" --check src/py/flwr examples e2e echo "- black: done" echo "- init_py_check: start" @@ -23,7 +23,7 @@ python -m flwr_tool.init_py_check src/py/flwr src/py/flwr_tool echo "- init_py_check: done" echo "- docformatter: start" -python -m docformatter -c -r src/py/flwr -e src/py/flwr/proto +python -m docformatter -c -r src/py/flwr e2e -e src/py/flwr/proto echo "- docformatter: done" echo "- ruff: start" @@ -58,6 +58,10 @@ echo "- All Markdown checks passed" echo "- Start license checks" +echo "- copyright: start" +python -m flwr_tool.check_copyright src/py/flwr +echo "- copyright: done" + echo "- licensecheck: start" python -m licensecheck -u poetry --fail-licenses gpl --zero echo "- licensecheck: done" diff --git a/doc/locales/fr/LC_MESSAGES/framework-docs.po b/doc/locales/fr/LC_MESSAGES/framework-docs.po index 80222e5409d2..6624d91f9e64 100644 --- a/doc/locales/fr/LC_MESSAGES/framework-docs.po +++ b/doc/locales/fr/LC_MESSAGES/framework-docs.po @@ -3,7 +3,7 @@ msgid "" msgstr "" "Project-Id-Version: Flower Docs\n" "Report-Msgid-Bugs-To: EMAIL@ADDRESS\n" -"POT-Creation-Date: 2024-03-15 14:23+0000\n" +"POT-Creation-Date: 2024-06-17 16:09+0200\n" "PO-Revision-Date: 2023-09-05 17:54+0000\n" "Last-Translator: Charles Beauville \n" "Language: fr\n" @@ -13,7 +13,7 @@ msgstr "" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" -"Generated-By: Babel 2.14.0\n" +"Generated-By: Babel 2.15.0\n" #: ../../source/contributor-explanation-architecture.rst:2 msgid "Flower Architecture" @@ -60,229 +60,249 @@ msgstr "" #: ../../source/contributor-how-to-build-docker-images.rst:4 msgid "" "Flower provides pre-made docker images on `Docker Hub " -"`_ that include all necessary " -"dependencies for running the server. You can also build your own custom " -"docker images from scratch with a different version of Python or Ubuntu " -"if that is what you need. In this guide, we will explain what images " -"exist and how to build them locally." +"`_ that include all necessary dependencies" +" for running the SuperLink, SuperNode or ServerApp. You can also build " +"your own custom docker images from scratch with a different version of " +"Python or Linux distribution (Ubuntu/Alpine) if that is what you need. In" +" this guide, we will explain what images exist and how to build them " +"locally." msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:9 +#: ../../source/contributor-how-to-build-docker-images.rst:10 msgid "" "Before we can start, we need to meet a few prerequisites in our local " "development environment." msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:11 +#: ../../source/contributor-how-to-build-docker-images.rst:12 #, fuzzy msgid "Clone the flower repository." msgstr "**Fourche le dépôt de Flower**" -#: ../../source/contributor-how-to-build-docker-images.rst:17 +#: ../../source/contributor-how-to-build-docker-images.rst:18 +#: ../../source/how-to-run-flower-using-docker.rst:165 msgid "Verify the Docker daemon is running." msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:19 +#: ../../source/contributor-how-to-build-docker-images.rst:20 +#: ../../source/how-to-run-flower-using-docker.rst:167 msgid "" "Please follow the first section on :doc:`Run Flower using Docker ` which covers this step in more detail." msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:23 -msgid "" -"Currently, Flower provides two images, a base image and a server image. " -"There will also be a client image soon. The base image, as the name " -"suggests, contains basic dependencies that both the server and the client" -" need. This includes system dependencies, Python and Python tools. The " -"server image is based on the base image, but it additionally installs the" -" Flower server using ``pip``." -msgstr "" - -#: ../../source/contributor-how-to-build-docker-images.rst:28 +#: ../../source/contributor-how-to-build-docker-images.rst:25 msgid "" "The build instructions that assemble the images are located in the " "respective Dockerfiles. You can find them in the subdirectories of " "``src/docker``." msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:31 +#: ../../source/contributor-how-to-build-docker-images.rst:28 msgid "" -"Both, base and server image are configured via build arguments. Through " -"build arguments, we can make our build more flexible. For example, in the" -" base image, we can specify the version of Python to install using the " -"``PYTHON_VERSION`` build argument. Some of the build arguments have " +"Flower Docker images are configured via build arguments. Through build " +"arguments, we can make the creation of images more flexible. For example," +" in the base image, we can specify the version of Python to install using" +" the ``PYTHON_VERSION`` build argument. Some of the build arguments have " "default values, others must be specified when building the image. All " "available build arguments for each image are listed in one of the tables " "below." msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:38 +#: ../../source/contributor-how-to-build-docker-images.rst:35 #, fuzzy msgid "Building the base image" msgstr "Chargement des données" -#: ../../source/contributor-how-to-build-docker-images.rst:44 -#: ../../source/contributor-how-to-build-docker-images.rst:86 +#: ../../source/contributor-how-to-build-docker-images.rst:41 +#: ../../source/contributor-how-to-build-docker-images.rst:98 #, fuzzy msgid "Build argument" msgstr "Amélioration de la documentation" -#: ../../source/contributor-how-to-build-docker-images.rst:45 -#: ../../source/contributor-how-to-build-docker-images.rst:87 +#: ../../source/contributor-how-to-build-docker-images.rst:42 +#: ../../source/contributor-how-to-build-docker-images.rst:99 #, fuzzy msgid "Description" msgstr "Dépréciations" -#: ../../source/contributor-how-to-build-docker-images.rst:46 -#: ../../source/contributor-how-to-build-docker-images.rst:88 +#: ../../source/contributor-how-to-build-docker-images.rst:43 +#: ../../source/contributor-how-to-build-docker-images.rst:100 #, fuzzy msgid "Required" msgstr "Changements nécessaires" -#: ../../source/contributor-how-to-build-docker-images.rst:47 -#: ../../source/contributor-how-to-build-docker-images.rst:89 +#: ../../source/contributor-how-to-build-docker-images.rst:44 +#: ../../source/contributor-how-to-build-docker-images.rst:101 #, fuzzy msgid "Example" msgstr "Exemples de PyTorch" +#: ../../source/contributor-how-to-build-docker-images.rst:45 +msgid "``DISTRO``" +msgstr "" + +#: ../../source/contributor-how-to-build-docker-images.rst:46 +#, fuzzy +msgid "The Linux distribution to use as the base image." +msgstr "Chargement des données" + +#: ../../source/contributor-how-to-build-docker-images.rst:47 +#: ../../source/contributor-how-to-build-docker-images.rst:51 +#: ../../source/contributor-how-to-build-docker-images.rst:55 +#: ../../source/contributor-how-to-build-docker-images.rst:71 +#: ../../source/contributor-how-to-build-docker-images.rst:104 +#, fuzzy +msgid "No" +msgstr "Aucun" + #: ../../source/contributor-how-to-build-docker-images.rst:48 +msgid "``ubuntu``" +msgstr "" + +#: ../../source/contributor-how-to-build-docker-images.rst:49 #, fuzzy -msgid "``PYTHON_VERSION``" +msgid "``DISTRO_VERSION``" msgstr "Version Python" -#: ../../source/contributor-how-to-build-docker-images.rst:49 -msgid "Version of ``python`` to be installed." +#: ../../source/contributor-how-to-build-docker-images.rst:50 +msgid "Version of the Linux distribution." msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:50 -#: ../../source/contributor-how-to-build-docker-images.rst:54 -#: ../../source/contributor-how-to-build-docker-images.rst:58 -#: ../../source/contributor-how-to-build-docker-images.rst:100 +#: ../../source/contributor-how-to-build-docker-images.rst:52 #, fuzzy -msgid "Yes" -msgstr "Types" +msgid "``22.04``" +msgstr "1.0.0rc1" -#: ../../source/contributor-how-to-build-docker-images.rst:51 +#: ../../source/contributor-how-to-build-docker-images.rst:53 #, fuzzy -msgid "``3.11``" -msgstr "1.0.0rc1" +msgid "``PYTHON_VERSION``" +msgstr "Version Python" -#: ../../source/contributor-how-to-build-docker-images.rst:52 +#: ../../source/contributor-how-to-build-docker-images.rst:54 +msgid "Version of ``python`` to be installed." +msgstr "" + +#: ../../source/contributor-how-to-build-docker-images.rst:56 +msgid "``3.11`` or ``3.11.1``" +msgstr "" + +#: ../../source/contributor-how-to-build-docker-images.rst:57 msgid "``PIP_VERSION``" msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:53 +#: ../../source/contributor-how-to-build-docker-images.rst:58 msgid "Version of ``pip`` to be installed." msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:55 +#: ../../source/contributor-how-to-build-docker-images.rst:59 +#: ../../source/contributor-how-to-build-docker-images.rst:63 +#: ../../source/contributor-how-to-build-docker-images.rst:67 +#: ../../source/contributor-how-to-build-docker-images.rst:108 +#, fuzzy +msgid "Yes" +msgstr "Types" + +#: ../../source/contributor-how-to-build-docker-images.rst:60 #, fuzzy msgid "``23.0.1``" msgstr "1.0.0rc1" -#: ../../source/contributor-how-to-build-docker-images.rst:56 +#: ../../source/contributor-how-to-build-docker-images.rst:61 msgid "``SETUPTOOLS_VERSION``" msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:57 +#: ../../source/contributor-how-to-build-docker-images.rst:62 msgid "Version of ``setuptools`` to be installed." msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:59 +#: ../../source/contributor-how-to-build-docker-images.rst:64 #, fuzzy msgid "``69.0.2``" msgstr "``1.0.0b0``" -#: ../../source/contributor-how-to-build-docker-images.rst:60 -msgid "``UBUNTU_VERSION``" +#: ../../source/contributor-how-to-build-docker-images.rst:65 +msgid "``FLWR_VERSION``" msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:61 -msgid "Version of the official Ubuntu Docker image." +#: ../../source/contributor-how-to-build-docker-images.rst:66 +msgid "Version of Flower to be installed." msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:62 -msgid "Defaults to ``22.04``." +#: ../../source/contributor-how-to-build-docker-images.rst:68 +#, fuzzy +msgid "``1.8.0``" +msgstr "``1.0.0b0``" + +#: ../../source/contributor-how-to-build-docker-images.rst:69 +msgid "``FLWR_PACKAGE``" msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:65 +#: ../../source/contributor-how-to-build-docker-images.rst:70 +msgid "The Flower package to be installed." +msgstr "" + +#: ../../source/contributor-how-to-build-docker-images.rst:72 +msgid "``flwr`` or ``flwr-nightly``" +msgstr "" + +#: ../../source/contributor-how-to-build-docker-images.rst:75 msgid "" -"The following example creates a base image with Python 3.11.0, pip 23.0.1" -" and setuptools 69.0.2:" +"The following example creates a base Ubuntu/Alpine image with Python " +"3.11.0, pip 23.0.1, setuptools 69.0.2 and Flower 1.8.0:" msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:76 +#: ../../source/contributor-how-to-build-docker-images.rst:88 msgid "" "The name of image is ``flwr_base`` and the tag ``0.1.0``. Remember that " "the build arguments as well as the name and tag can be adapted to your " "needs. These values serve as examples only." msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:80 +#: ../../source/contributor-how-to-build-docker-images.rst:92 #, fuzzy -msgid "Building the server image" +msgid "Building the SuperLink/SuperNode or ServerApp image" msgstr "Démarrer le serveur" -#: ../../source/contributor-how-to-build-docker-images.rst:90 +#: ../../source/contributor-how-to-build-docker-images.rst:102 msgid "``BASE_REPOSITORY``" msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:91 +#: ../../source/contributor-how-to-build-docker-images.rst:103 msgid "The repository name of the base image." msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:92 -msgid "Defaults to ``flwr/server``." -msgstr "" - -#: ../../source/contributor-how-to-build-docker-images.rst:94 -msgid "``BASE_IMAGE_TAG``" -msgstr "" - -#: ../../source/contributor-how-to-build-docker-images.rst:95 -msgid "The image tag of the base image." -msgstr "" - -#: ../../source/contributor-how-to-build-docker-images.rst:96 -msgid "Defaults to ``py3.11-ubuntu22.04``." -msgstr "" - -#: ../../source/contributor-how-to-build-docker-images.rst:98 -msgid "``FLWR_VERSION``" +#: ../../source/contributor-how-to-build-docker-images.rst:105 +msgid "``flwr/base``" msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:99 -msgid "Version of Flower to be installed." +#: ../../source/contributor-how-to-build-docker-images.rst:106 +msgid "``BASE_IMAGE``" msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:101 +#: ../../source/contributor-how-to-build-docker-images.rst:107 #, fuzzy -msgid "``1.7.0``" -msgstr "``1.0.0b0``" +msgid "The Tag of the Flower base image." +msgstr "Chargement des données" -#: ../../source/contributor-how-to-build-docker-images.rst:103 -msgid "" -"The following example creates a server image with the official Flower " -"base image py3.11-ubuntu22.04 and Flower 1.7.0:" +#: ../../source/contributor-how-to-build-docker-images.rst:109 +msgid "``1.8.0-py3.10-ubuntu22.04``" msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:114 +#: ../../source/contributor-how-to-build-docker-images.rst:111 msgid "" -"The name of image is ``flwr_server`` and the tag ``0.1.0``. Remember that" -" the build arguments as well as the name and tag can be adapted to your " -"needs. These values serve as examples only." +"The following example creates a SuperLink/SuperNode or ServerApp image " +"with the official Flower base image:" msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:117 +#: ../../source/contributor-how-to-build-docker-images.rst:122 msgid "" "If you want to use your own base image instead of the official Flower " -"base image, all you need to do is set the ``BASE_REPOSITORY`` and " -"``BASE_IMAGE_TAG`` build arguments. The value of ``BASE_REPOSITORY`` must" -" match the name of your image and the value of ``BASE_IMAGE_TAG`` must " -"match the tag of your image." +"base image, all you need to do is set the ``BASE_REPOSITORY`` build " +"argument." msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:131 +#: ../../source/contributor-how-to-build-docker-images.rst:133 msgid "After creating the image, we can test whether the image is working:" msgstr "" @@ -394,146 +414,6 @@ msgid "" " on our `GitHub repo `_." msgstr "" -#: ../../source/contributor-how-to-create-new-messages.rst:2 -msgid "Creating New Messages" -msgstr "Création de nouveaux messages" - -#: ../../source/contributor-how-to-create-new-messages.rst:4 -msgid "" -"This is a simple guide for creating a new type of message between the " -"server and clients in Flower." -msgstr "" -"Voici un guide simple pour créer un nouveau type de message entre le " -"serveur et les clients dans Flower." - -#: ../../source/contributor-how-to-create-new-messages.rst:6 -msgid "" -"Let's suppose we have the following example functions in " -":code:`server.py` and :code:`numpy_client.py`..." -msgstr "" -"Supposons que nous ayons les fonctions suivantes dans :code:`server.py` " -"et :code:`numpy_client.py`..." - -#: ../../source/contributor-how-to-create-new-messages.rst:8 -msgid "Server's side:" -msgstr "Côté serveur :" - -#: ../../source/contributor-how-to-create-new-messages.rst:17 -msgid "Client's side:" -msgstr "Côté client :" - -#: ../../source/contributor-how-to-create-new-messages.rst:26 -msgid "" -"Let's now see what we need to implement in order to get this simple " -"function between the server and client to work!" -msgstr "" -"Voyons maintenant ce que nous devons mettre en œuvre pour que cette " -"simple fonction entre le serveur et le client fonctionne !" - -#: ../../source/contributor-how-to-create-new-messages.rst:30 -msgid "Message Types for Protocol Buffers" -msgstr "Types de messages pour les tampons de protocole" - -#: ../../source/contributor-how-to-create-new-messages.rst:32 -#, fuzzy -msgid "" -"The first thing we need to do is to define a message type for the RPC " -"system in :code:`transport.proto`. Note that we have to do it for both " -"the request and response messages. For more details on the syntax of " -"proto3, please see the `official documentation `_." -msgstr "" -"La première chose à faire est de définir un type de message pour le " -"système RPC dans :code:`transport.proto`. Notez que nous devons le faire " -"à la fois pour les messages de demande et de réponse. Pour plus de " -"détails sur la syntaxe de proto3, veuillez consulter la `documentation " -"officielle `_." - -#: ../../source/contributor-how-to-create-new-messages.rst:35 -msgid "Within the :code:`ServerMessage` block:" -msgstr "Dans le bloc :code:`ServerMessage` :" - -#: ../../source/contributor-how-to-create-new-messages.rst:52 -msgid "Within the ClientMessage block:" -msgstr "Dans le bloc ClientMessage :" - -#: ../../source/contributor-how-to-create-new-messages.rst:70 -msgid "" -"Make sure to also add a field of the newly created message type in " -":code:`oneof msg`." -msgstr "" -"Veille à ajouter également un champ du type de message nouvellement créé " -"dans :code:`oneof msg`." - -#: ../../source/contributor-how-to-create-new-messages.rst:72 -msgid "Once that is done, we will compile the file with:" -msgstr "Une fois que c'est fait, nous compilerons le fichier avec :" - -#: ../../source/contributor-how-to-create-new-messages.rst:78 -msgid "If it compiles successfully, you should see the following message:" -msgstr "S'il se compile avec succès, tu devrais voir le message suivant :" - -#: ../../source/contributor-how-to-create-new-messages.rst:87 -msgid "Serialization and Deserialization Functions" -msgstr "Fonctions de sérialisation et de désérialisation" - -#: ../../source/contributor-how-to-create-new-messages.rst:89 -msgid "" -"Our next step is to add functions to serialize and deserialize Python " -"datatypes to or from our defined RPC message types. You should add these " -"functions in :code:`serde.py`." -msgstr "" -"La prochaine étape consiste à ajouter des fonctions pour sérialiser et " -"désérialiser les types de données Python vers ou à partir des types de " -"messages RPC définis. Tu dois ajouter ces fonctions dans " -":code:`serde.py`." - -#: ../../source/contributor-how-to-create-new-messages.rst:91 -msgid "The four functions:" -msgstr "Les quatre fonctions :" - -#: ../../source/contributor-how-to-create-new-messages.rst:112 -msgid "Sending the Message from the Server" -msgstr "Envoi du message à partir du serveur" - -#: ../../source/contributor-how-to-create-new-messages.rst:114 -msgid "" -"Now write the request function in your Client Proxy class (e.g., " -":code:`grpc_client_proxy.py`) using the serde functions you just created:" -msgstr "" -"Écris maintenant la fonction de demande dans ta classe Client Proxy (par " -"exemple, :code:`grpc_client_proxy.py`) en utilisant les fonctions serde " -"que tu viens de créer :" - -#: ../../source/contributor-how-to-create-new-messages.rst:128 -msgid "Receiving the Message by the Client" -msgstr "Réception du message par le client" - -#: ../../source/contributor-how-to-create-new-messages.rst:130 -msgid "" -"Last step! Modify the code in :code:`message_handler.py` to check the " -"field of your message and call the :code:`example_response` function. " -"Remember to use the serde functions!" -msgstr "" -"Dernière étape ! Modifie le code dans :code:`message_handler.py` pour " -"vérifier le champ de ton message et appeler la fonction " -":code:`example_response`. N'oublie pas d'utiliser les fonctions serde !" - -#: ../../source/contributor-how-to-create-new-messages.rst:132 -msgid "Within the handle function:" -msgstr "Dans le cadre de la fonction de poignée :" - -#: ../../source/contributor-how-to-create-new-messages.rst:139 -msgid "And add a new function:" -msgstr "Et ajoute une nouvelle fonction :" - -#: ../../source/contributor-how-to-create-new-messages.rst:149 -msgid "Hopefully, when you run your program you will get the intended result!" -msgstr "" -"Avec un peu de chance, lorsque tu exécuteras ton programme, tu obtiendras" -" le résultat escompté !" - #: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:2 msgid "Develop in VSCode Dev Containers" msgstr "Utiliser les conteneurs VS Code Remote" @@ -829,17 +709,19 @@ msgid "Open Jupyter Notebooks on Google Colab" msgstr "Ouvre les carnets Jupyter sur Google Colab" #: ../../source/contributor-how-to-install-development-versions.rst:51 +#, fuzzy msgid "" -"Open the notebook ``doc/source/tutorial-get-started-with-flower-" +"Open the notebook ``doc/source/tutorial-series-get-started-with-flower-" "pytorch.ipynb``:" msgstr "" "Ouvrir le notebook ``doc/source/tutorial/Flower-1-Intro-to-FL-" "PyTorch.ipynb`` :" #: ../../source/contributor-how-to-install-development-versions.rst:53 +#, fuzzy msgid "" "https://colab.research.google.com/github/adap/flower/blob/main/doc/source" -"/tutorial-get-started-with-flower-pytorch.ipynb" +"/tutorial-series-get-started-with-flower-pytorch.ipynb" msgstr "" "https://colab.research.google.com/github/adap/flower/blob/main/doc/source" "/tutorial-get-started-with-flower-pytorch.ipynb" @@ -854,9 +736,10 @@ msgstr "" " :" #: ../../source/contributor-how-to-install-development-versions.rst:57 +#, fuzzy msgid "" "https://colab.research.google.com/github/adap/flower/blob/branch-" -"name/doc/source/tutorial-get-started-with-flower-pytorch.ipynb" +"name/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb" msgstr "" "https://colab.research.google.com/github/adap/flower/blob/branch-" "name/doc/source/tutorial-get-started-with-flower-pytorch.ipynb" @@ -938,29 +821,79 @@ msgstr "" msgid "Check the draft release on GitHub, and if everything is good, publish it." msgstr "" +#: ../../source/contributor-how-to-release-flower.rst:15 +#, fuzzy +msgid "Trigger the CI for building the Docker images." +msgstr "Démarrer le serveur" + #: ../../source/contributor-how-to-release-flower.rst:17 +msgid "" +"To trigger the workflow, a collaborator must create a " +"``workflow_dispatch`` event in the GitHub CI. This can be done either " +"through the UI or via the GitHub CLI. The event requires only one input, " +"the Flower version, to be released." +msgstr "" + +#: ../../source/contributor-how-to-release-flower.rst:21 +#, fuzzy +msgid "**Via the UI**" +msgstr "**Review the PR**" + +#: ../../source/contributor-how-to-release-flower.rst:23 +msgid "" +"Go to the ``Build docker images`` workflow `page " +"`_." +msgstr "" + +#: ../../source/contributor-how-to-release-flower.rst:24 +msgid "" +"Click on the ``Run workflow`` button and type the new version of Flower " +"in the ``Version of Flower`` input field." +msgstr "" + +#: ../../source/contributor-how-to-release-flower.rst:25 +msgid "Click on the **green** ``Run workflow`` button." +msgstr "" + +#: ../../source/contributor-how-to-release-flower.rst:29 +msgid "**Via the GitHub CI**" +msgstr "" + +#: ../../source/contributor-how-to-release-flower.rst:31 +msgid "" +"Make sure you are logged in via ``gh auth login`` and that the current " +"working directory is the root of the Flower repository." +msgstr "" + +#: ../../source/contributor-how-to-release-flower.rst:32 +msgid "" +"Trigger the workflow via ``gh workflow run docker-images.yml -f flwr-" +"version=``." +msgstr "" + +#: ../../source/contributor-how-to-release-flower.rst:35 msgid "After the release" msgstr "Après la publication" -#: ../../source/contributor-how-to-release-flower.rst:19 +#: ../../source/contributor-how-to-release-flower.rst:37 msgid "Create a pull request which contains the following changes:" msgstr "Crée une demande de pull qui contient les modifications suivantes :" -#: ../../source/contributor-how-to-release-flower.rst:21 +#: ../../source/contributor-how-to-release-flower.rst:39 msgid "Increase the minor version in ``pyproject.toml`` by one." msgstr "Augmente la version mineure de ``pyproject.toml`` d'une unité." -#: ../../source/contributor-how-to-release-flower.rst:22 +#: ../../source/contributor-how-to-release-flower.rst:40 msgid "Update all files which contain the current version number if necessary." msgstr "" "Mets à jour tous les fichiers qui contiennent le numéro de version actuel" " si nécessaire." -#: ../../source/contributor-how-to-release-flower.rst:23 +#: ../../source/contributor-how-to-release-flower.rst:41 msgid "Add a new ``Unreleased`` section in ``changelog.md``." msgstr "Ajoute une nouvelle section ``Unreleased`` dans ``changelog.md``." -#: ../../source/contributor-how-to-release-flower.rst:25 +#: ../../source/contributor-how-to-release-flower.rst:43 msgid "" "Merge the pull request on the same day (i.e., before a new nightly " "release gets published to PyPI)." @@ -968,15 +901,15 @@ msgstr "" "Fusionne la pull request le jour même (c'est-à-dire avant qu'une nouvelle" " version nightly ne soit publiée sur PyPI)." -#: ../../source/contributor-how-to-release-flower.rst:28 +#: ../../source/contributor-how-to-release-flower.rst:46 msgid "Publishing a pre-release" msgstr "Publier une pré-version" -#: ../../source/contributor-how-to-release-flower.rst:31 +#: ../../source/contributor-how-to-release-flower.rst:49 msgid "Pre-release naming" msgstr "Nom de la pré-version" -#: ../../source/contributor-how-to-release-flower.rst:33 +#: ../../source/contributor-how-to-release-flower.rst:51 msgid "" "PyPI supports pre-releases (alpha, beta, release candidate). Pre-releases" " MUST use one of the following naming patterns:" @@ -985,39 +918,39 @@ msgstr "" "Les préversions DOIVENT utiliser l'un des modèles de dénomination " "suivants :" -#: ../../source/contributor-how-to-release-flower.rst:35 +#: ../../source/contributor-how-to-release-flower.rst:53 msgid "Alpha: ``MAJOR.MINOR.PATCHaN``" msgstr "Alpha : ``MAJOR.MINOR.PATCHaN``" -#: ../../source/contributor-how-to-release-flower.rst:36 +#: ../../source/contributor-how-to-release-flower.rst:54 msgid "Beta: ``MAJOR.MINOR.PATCHbN``" msgstr "Bêta : ``MAJOR.MINOR.PATCHbN``" -#: ../../source/contributor-how-to-release-flower.rst:37 +#: ../../source/contributor-how-to-release-flower.rst:55 msgid "Release candidate (RC): ``MAJOR.MINOR.PATCHrcN``" msgstr "Candidat à la publication (RC) : ``MAJOR.MINOR.PATCHrcN``" -#: ../../source/contributor-how-to-release-flower.rst:39 +#: ../../source/contributor-how-to-release-flower.rst:57 msgid "Examples include:" msgstr "Voici quelques exemples :" -#: ../../source/contributor-how-to-release-flower.rst:41 +#: ../../source/contributor-how-to-release-flower.rst:59 msgid "``1.0.0a0``" msgstr "``1.0.0a0``" -#: ../../source/contributor-how-to-release-flower.rst:42 +#: ../../source/contributor-how-to-release-flower.rst:60 msgid "``1.0.0b0``" msgstr "``1.0.0b0``" -#: ../../source/contributor-how-to-release-flower.rst:43 +#: ../../source/contributor-how-to-release-flower.rst:61 msgid "``1.0.0rc0``" msgstr "``1.0.0rc0``" -#: ../../source/contributor-how-to-release-flower.rst:44 +#: ../../source/contributor-how-to-release-flower.rst:62 msgid "``1.0.0rc1``" msgstr "1.0.0rc1" -#: ../../source/contributor-how-to-release-flower.rst:46 +#: ../../source/contributor-how-to-release-flower.rst:64 msgid "" "This is in line with PEP-440 and the recommendations from the Python " "Packaging Authority (PyPA):" @@ -1025,11 +958,11 @@ msgstr "" "Ceci est conforme au PEP-440 et aux recommandations de l'Autorité de " "l'emballage Python (PyPA) :" -#: ../../source/contributor-how-to-release-flower.rst:49 +#: ../../source/contributor-how-to-release-flower.rst:67 msgid "`PEP-440 `_" msgstr "`PEP-440 `_" -#: ../../source/contributor-how-to-release-flower.rst:50 +#: ../../source/contributor-how-to-release-flower.rst:68 msgid "" "`PyPA Choosing a versioning scheme " "`_" -#: ../../source/contributor-how-to-release-flower.rst:52 +#: ../../source/contributor-how-to-release-flower.rst:70 msgid "" "Note that the approach defined by PyPA is not compatible with SemVer " "2.0.0 spec, for details consult the `Semantic Versioning Specification " @@ -1051,17 +984,17 @@ msgstr "" "Versioning Specification `_ (en particulier le point 11 sur la préséance)." -#: ../../source/contributor-how-to-release-flower.rst:55 +#: ../../source/contributor-how-to-release-flower.rst:73 msgid "Pre-release classification" msgstr "Classification avant publication" -#: ../../source/contributor-how-to-release-flower.rst:57 +#: ../../source/contributor-how-to-release-flower.rst:75 msgid "Should the next pre-release be called alpha, beta, or release candidate?" msgstr "" "La prochaine préversion doit-elle être appelée alpha, bêta ou release " "candidate ?" -#: ../../source/contributor-how-to-release-flower.rst:59 +#: ../../source/contributor-how-to-release-flower.rst:77 msgid "" "RC: feature complete, no known issues (apart from issues that are " "classified as \"won't fix\" for the next stable release) - if no issues " @@ -1072,11 +1005,11 @@ msgstr "" "version stable) - si aucun problème n'apparaît, cette version deviendra " "la prochaine version stable" -#: ../../source/contributor-how-to-release-flower.rst:60 +#: ../../source/contributor-how-to-release-flower.rst:78 msgid "Beta: feature complete, allowed to have known issues" msgstr "Bêta : fonctionnalité complète, autorisée à avoir des problèmes connus" -#: ../../source/contributor-how-to-release-flower.rst:61 +#: ../../source/contributor-how-to-release-flower.rst:79 msgid "Alpha: not feature complete, allowed to have known issues" msgstr "" "Alpha : les fonctionnalités ne sont pas complètes, les problèmes connus " @@ -1115,10 +1048,19 @@ msgstr "" "nous recommandons `Python 3.8 `_." #: ../../source/contributor-how-to-set-up-a-virtual-env.rst:14 -msgid "Virutualenv with Pyenv/Virtualenv" +msgid "" +"Due to a known incompatibility with `ray " +"`_, we currently recommend utilizing at " +"most `Python 3.11 `_ for running Flower " +"simulations." +msgstr "" + +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:19 +#, fuzzy +msgid "Virtualenv with Pyenv/Virtualenv" msgstr "Virutualenv avec Pyenv/Virtualenv" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:16 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:21 msgid "" "One of the recommended virtual environment is `pyenv " "`_/`virtualenv `_ pour plus de " "détails." -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:18 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:23 #, fuzzy msgid "" "Once Pyenv is set up, you can use it to install `Python Version 3.10 " @@ -1140,19 +1082,19 @@ msgstr "" "Une fois Pyenv mis en place, tu peux l'utiliser pour installer `Python " "Version 3.7 `_ ou supérieure :" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:24 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:29 msgid "Create the virtualenv with:" msgstr "Crée le virtualenv avec :" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:31 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:36 msgid "Activate the virtualenv by running the following command:" msgstr "Active la virtualenv en exécutant la commande suivante :" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:39 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:44 msgid "Virtualenv with Poetry" msgstr "Virtualenv et la poésie" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:41 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:46 msgid "" "The Flower examples are based on `Poetry `_ to manage dependencies. After installing Poetry you " @@ -1162,7 +1104,7 @@ msgstr "" "poetry.org/docs/>`_ pour gérer les dépendances. Après l'installation de " "Poetry, il te suffit de créer un environnement virtuel avec :" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:47 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:52 msgid "" "If you open a new terminal you can activate the previously created " "virtual environment with the following command:" @@ -1170,11 +1112,11 @@ msgstr "" "Si tu ouvres un nouveau terminal, tu peux activer l'environnement virtuel" " précédemment créé avec la commande suivante :" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:55 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:60 msgid "Virtualenv with Anaconda" msgstr "Virtualenv avec Anaconda" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:57 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:62 msgid "" "If you prefer to use Anaconda for your virtual environment then install " "and setup the `conda `_. Après l'avoir configuré, tu peux créer un " "environnement virtuel avec :" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:63 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:68 msgid "and activate the virtual environment with:" msgstr "et active l'environnement virtuel avec :" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:71 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:76 msgid "And then?" msgstr "Et ensuite ?" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:73 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:78 msgid "" "As soon as you created your virtual environment you clone one of the " "`Flower examples `_." @@ -1222,7 +1164,7 @@ msgstr "" "reStructuredText (fichiers `.rst`) et Markdown (fichiers `.md`)." #: ../../source/contributor-how-to-write-documentation.rst:10 -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:142 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:169 #, fuzzy msgid "" "Note that, in order to build the documentation locally (with ``poetry run" @@ -1804,7 +1746,7 @@ msgstr "" msgid "Creating and merging a pull request (PR)" msgstr "Créer et fusionner une pull request (PR)" -#: ../../source/contributor-tutorial-contribute-on-github.rst:205 +#: ../../source/contributor-tutorial-contribute-on-github.rst:206 msgid "**Create the PR**" msgstr "**Créer le PR**" @@ -1848,6 +1790,13 @@ msgstr "" #: ../../source/contributor-tutorial-contribute-on-github.rst:193 msgid "" +"The title should be changed to adhere to the :ref:`pr_title_format` " +"guidelines, otherwise it won't be possible to merge the PR. So in this " +"case, a correct title might be ``docs(framework:skip) Fix typos``." +msgstr "" + +#: ../../source/contributor-tutorial-contribute-on-github.rst:196 +msgid "" "The input box in the middle is there for you to describe what your PR " "does and to link it to existing issues. We have placed comments (that " "won't be rendered once the PR is opened) to guide you through the " @@ -1858,16 +1807,11 @@ msgstr "" "commentaires (qui ne seront pas rendus une fois le PR ouvert) pour te " "guider tout au long du processus." -#: ../../source/contributor-tutorial-contribute-on-github.rst:196 -msgid "" -"It is important to follow the instructions described in comments. For " -"instance, in order to not break how our changelog system works, you " -"should read the information above the ``Changelog entry`` section " -"carefully. You can also checkout some examples and details in the " -":ref:`changelogentry` appendix." +#: ../../source/contributor-tutorial-contribute-on-github.rst:199 +msgid "It is important to follow the instructions described in comments." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:200 +#: ../../source/contributor-tutorial-contribute-on-github.rst:201 msgid "" "At the bottom you will find the button to open the PR. This will notify " "reviewers that a new PR has been opened and that they should look over it" @@ -1877,7 +1821,7 @@ msgstr "" "qui informera les réviseurs qu'un nouveau PR a été ouvert et qu'ils " "doivent le consulter pour le fusionner ou demander des modifications." -#: ../../source/contributor-tutorial-contribute-on-github.rst:203 +#: ../../source/contributor-tutorial-contribute-on-github.rst:204 msgid "" "If your PR is not yet ready for review, and you don't want to notify " "anyone, you have the option to create a draft pull request:" @@ -1886,11 +1830,11 @@ msgstr "" " personne, tu as la possibilité de créer un brouillon de demande de " "traction :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:208 +#: ../../source/contributor-tutorial-contribute-on-github.rst:209 msgid "**Making new changes**" msgstr "**Faire de nouveaux changements**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:208 +#: ../../source/contributor-tutorial-contribute-on-github.rst:209 msgid "" "Once the PR has been opened (as draft or not), you can still push new " "commits to it the same way we did before, by making changes to the branch" @@ -1900,11 +1844,11 @@ msgstr "" "toujours y pousser de nouveaux commits de la même manière qu'auparavant, " "en apportant des modifications à la branche associée au PR." -#: ../../source/contributor-tutorial-contribute-on-github.rst:230 +#: ../../source/contributor-tutorial-contribute-on-github.rst:231 msgid "**Review the PR**" msgstr "**Review the PR**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:211 +#: ../../source/contributor-tutorial-contribute-on-github.rst:212 msgid "" "Once the PR has been opened or once the draft PR has been marked as " "ready, a review from code owners will be automatically requested:" @@ -1913,7 +1857,7 @@ msgstr "" " étant prêt, une révision des propriétaires de code sera automatiquement " "demandée :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:215 +#: ../../source/contributor-tutorial-contribute-on-github.rst:216 msgid "" "Code owners will then look into the code, ask questions, request changes " "or validate the PR." @@ -1921,11 +1865,11 @@ msgstr "" "Les propriétaires du code vont alors se pencher sur le code, poser des " "questions, demander des modifications ou valider le RP." -#: ../../source/contributor-tutorial-contribute-on-github.rst:217 +#: ../../source/contributor-tutorial-contribute-on-github.rst:218 msgid "Merging will be blocked if there are ongoing requested changes." msgstr "La fusion sera bloquée s'il y a des changements demandés en cours." -#: ../../source/contributor-tutorial-contribute-on-github.rst:221 +#: ../../source/contributor-tutorial-contribute-on-github.rst:222 msgid "" "To resolve them, just push the necessary changes to the branch associated" " with the PR:" @@ -1933,11 +1877,11 @@ msgstr "" "Pour les résoudre, il suffit de pousser les changements nécessaires vers " "la branche associée au PR :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:225 +#: ../../source/contributor-tutorial-contribute-on-github.rst:226 msgid "And resolve the conversation:" msgstr "Et résous la conversation :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:229 +#: ../../source/contributor-tutorial-contribute-on-github.rst:230 msgid "" "Once all the conversations have been resolved, you can re-request a " "review." @@ -1945,11 +1889,11 @@ msgstr "" "Une fois que toutes les conversations ont été résolues, tu peux " "redemander un examen." -#: ../../source/contributor-tutorial-contribute-on-github.rst:250 +#: ../../source/contributor-tutorial-contribute-on-github.rst:251 msgid "**Once the PR is merged**" msgstr "**Une fois que le PR est fusionné**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:233 +#: ../../source/contributor-tutorial-contribute-on-github.rst:234 msgid "" "If all the automatic tests have passed and reviewers have no more changes" " to request, they can approve the PR and merge it." @@ -1958,7 +1902,7 @@ msgstr "" " de modifications à demander, ils peuvent approuver le PR et le " "fusionner." -#: ../../source/contributor-tutorial-contribute-on-github.rst:237 +#: ../../source/contributor-tutorial-contribute-on-github.rst:238 msgid "" "Once it is merged, you can delete the branch on GitHub (a button should " "appear to do so) and also delete it locally by doing:" @@ -1967,19 +1911,19 @@ msgstr "" "(un bouton devrait apparaître pour le faire) et aussi la supprimer " "localement en faisant :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:244 +#: ../../source/contributor-tutorial-contribute-on-github.rst:245 msgid "Then you should update your forked repository by doing:" msgstr "Ensuite, tu dois mettre à jour ton dépôt forké en faisant :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:253 +#: ../../source/contributor-tutorial-contribute-on-github.rst:254 msgid "Example of first contribution" msgstr "Exemple de première contribution" -#: ../../source/contributor-tutorial-contribute-on-github.rst:256 +#: ../../source/contributor-tutorial-contribute-on-github.rst:257 msgid "Problem" msgstr "Problème" -#: ../../source/contributor-tutorial-contribute-on-github.rst:258 +#: ../../source/contributor-tutorial-contribute-on-github.rst:259 #, fuzzy msgid "" "For our documentation, we've started to use the `Diàtaxis framework " @@ -1988,7 +1932,7 @@ msgstr "" "Pour notre documentation, nous avons commencé à utiliser le cadre " "`Diàtaxis `_." -#: ../../source/contributor-tutorial-contribute-on-github.rst:260 +#: ../../source/contributor-tutorial-contribute-on-github.rst:261 #, fuzzy msgid "" "Our \"How to\" guides should have titles that continue the sentence \"How" @@ -1998,7 +1942,7 @@ msgstr "" "la phrase \"Comment faire pour...\", par exemple, \"Comment passer à " "Flower 1.0\"." -#: ../../source/contributor-tutorial-contribute-on-github.rst:262 +#: ../../source/contributor-tutorial-contribute-on-github.rst:263 msgid "" "Most of our guides do not follow this new format yet, and changing their " "title is (unfortunately) more involved than one might think." @@ -2007,7 +1951,7 @@ msgstr "" "changer leur titre est (malheureusement) plus compliqué qu'on ne le " "pense." -#: ../../source/contributor-tutorial-contribute-on-github.rst:264 +#: ../../source/contributor-tutorial-contribute-on-github.rst:265 #, fuzzy msgid "" "This issue is about changing the title of a doc from present continuous " @@ -2016,7 +1960,7 @@ msgstr "" "Cette question porte sur le changement du titre d'un document du présent " "continu au présent simple." -#: ../../source/contributor-tutorial-contribute-on-github.rst:266 +#: ../../source/contributor-tutorial-contribute-on-github.rst:267 #, fuzzy msgid "" "Let's take the example of \"Saving Progress\" which we changed to \"Save " @@ -2026,21 +1970,21 @@ msgstr "" "remplacé par \"Sauvegarder la progression\". Est-ce que cela passe notre " "contrôle ?" -#: ../../source/contributor-tutorial-contribute-on-github.rst:268 +#: ../../source/contributor-tutorial-contribute-on-github.rst:269 #, fuzzy msgid "Before: \"How to saving progress\" ❌" msgstr "Avant : \"Comment sauvegarder les progrès\" ❌" -#: ../../source/contributor-tutorial-contribute-on-github.rst:270 +#: ../../source/contributor-tutorial-contribute-on-github.rst:271 #, fuzzy msgid "After: \"How to save progress\" ✅" msgstr "Après : \"Comment sauvegarder la progression\" ✅" -#: ../../source/contributor-tutorial-contribute-on-github.rst:273 +#: ../../source/contributor-tutorial-contribute-on-github.rst:274 msgid "Solution" msgstr "Solution" -#: ../../source/contributor-tutorial-contribute-on-github.rst:275 +#: ../../source/contributor-tutorial-contribute-on-github.rst:276 #, fuzzy msgid "" "This is a tiny change, but it'll allow us to test your end-to-end setup. " @@ -2050,12 +1994,12 @@ msgstr "" "configuration de bout en bout. Après avoir cloné et configuré le repo " "Flower, voici ce que tu dois faire :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:277 +#: ../../source/contributor-tutorial-contribute-on-github.rst:278 #, fuzzy msgid "Find the source file in ``doc/source``" msgstr "Trouve le fichier source dans `doc/source`" -#: ../../source/contributor-tutorial-contribute-on-github.rst:278 +#: ../../source/contributor-tutorial-contribute-on-github.rst:279 #, fuzzy msgid "" "Make the change in the ``.rst`` file (beware, the dashes under the title " @@ -2064,7 +2008,7 @@ msgstr "" "Effectue la modification dans le fichier `.rst` (attention, les tirets " "sous le titre doivent être de la même longueur que le titre lui-même)" -#: ../../source/contributor-tutorial-contribute-on-github.rst:279 +#: ../../source/contributor-tutorial-contribute-on-github.rst:280 #, fuzzy msgid "" "Build the docs and `check the result `_" -#: ../../source/contributor-tutorial-contribute-on-github.rst:282 +#: ../../source/contributor-tutorial-contribute-on-github.rst:283 msgid "Rename file" msgstr "Renommer le fichier" -#: ../../source/contributor-tutorial-contribute-on-github.rst:284 +#: ../../source/contributor-tutorial-contribute-on-github.rst:285 msgid "" "You might have noticed that the file name still reflects the old wording." " If we just change the file, then we break all existing links to it - it " @@ -2090,22 +2034,22 @@ msgstr "" "important** d'éviter cela, car briser des liens peut nuire à notre " "classement dans les moteurs de recherche." -#: ../../source/contributor-tutorial-contribute-on-github.rst:287 +#: ../../source/contributor-tutorial-contribute-on-github.rst:288 #, fuzzy msgid "Here's how to change the file name:" msgstr "Voici comment changer le nom du fichier :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:289 +#: ../../source/contributor-tutorial-contribute-on-github.rst:290 #, fuzzy msgid "Change the file name to ``save-progress.rst``" msgstr "Change le nom du fichier en `save-progress.rst`" -#: ../../source/contributor-tutorial-contribute-on-github.rst:290 +#: ../../source/contributor-tutorial-contribute-on-github.rst:291 #, fuzzy msgid "Add a redirect rule to ``doc/source/conf.py``" msgstr "Ajouter une règle de redirection à `doc/source/conf.py`" -#: ../../source/contributor-tutorial-contribute-on-github.rst:292 +#: ../../source/contributor-tutorial-contribute-on-github.rst:293 #, fuzzy msgid "" "This will cause a redirect from ``saving-progress.html`` to ``save-" @@ -2114,11 +2058,11 @@ msgstr "" "Cela entraînera une redirection de `saving-progress.html` vers `save-" "progress.html`, les anciens liens continueront à fonctionner." -#: ../../source/contributor-tutorial-contribute-on-github.rst:295 +#: ../../source/contributor-tutorial-contribute-on-github.rst:296 msgid "Apply changes in the index file" msgstr "Applique les changements dans le fichier d'index" -#: ../../source/contributor-tutorial-contribute-on-github.rst:297 +#: ../../source/contributor-tutorial-contribute-on-github.rst:298 #, fuzzy msgid "" "For the lateral navigation bar to work properly, it is very important to " @@ -2129,16 +2073,16 @@ msgstr "" "très important de mettre également à jour le fichier `index.rst`. C'est " "là que nous définissons toute l'arborescence de la barre de navigation." -#: ../../source/contributor-tutorial-contribute-on-github.rst:300 +#: ../../source/contributor-tutorial-contribute-on-github.rst:301 #, fuzzy msgid "Find and modify the file name in ``index.rst``" msgstr "Trouve et modifie le nom du fichier dans `index.rst`" -#: ../../source/contributor-tutorial-contribute-on-github.rst:303 +#: ../../source/contributor-tutorial-contribute-on-github.rst:304 msgid "Open PR" msgstr "Open PR" -#: ../../source/contributor-tutorial-contribute-on-github.rst:305 +#: ../../source/contributor-tutorial-contribute-on-github.rst:306 #, fuzzy msgid "" "Commit the changes (commit messages are always imperative: \"Do " @@ -2147,282 +2091,128 @@ msgstr "" "Valide les modifications (les messages de validation sont toujours " "impératifs : \"Fais quelque chose\", dans ce cas \"Modifie...\")" -#: ../../source/contributor-tutorial-contribute-on-github.rst:306 +#: ../../source/contributor-tutorial-contribute-on-github.rst:307 msgid "Push the changes to your fork" msgstr "Transmets les changements à ta fourchette" -#: ../../source/contributor-tutorial-contribute-on-github.rst:307 -msgid "Open a PR (as shown above)" -msgstr "Ouvre un RP (comme indiqué ci-dessus)" - #: ../../source/contributor-tutorial-contribute-on-github.rst:308 +msgid "" +"Open a PR (as shown above) with title ``docs(framework) Update how-to " +"guide title``" +msgstr "" + +#: ../../source/contributor-tutorial-contribute-on-github.rst:309 msgid "Wait for it to be approved!" msgstr "Attends qu'elle soit approuvée !" -#: ../../source/contributor-tutorial-contribute-on-github.rst:309 +#: ../../source/contributor-tutorial-contribute-on-github.rst:310 msgid "Congrats! 🥳 You're now officially a Flower contributor!" msgstr "" "Félicitations 🥳 Tu es désormais officiellement une contributrice de " "Flower !" -#: ../../source/contributor-tutorial-contribute-on-github.rst:313 -msgid "How to write a good PR title" -msgstr "Comment écrire un bon titre de PR" +#: ../../source/contributor-tutorial-contribute-on-github.rst:314 +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:548 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:946 +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:727 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:713 +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:367 +msgid "Next steps" +msgstr "Prochaines étapes" -#: ../../source/contributor-tutorial-contribute-on-github.rst:315 +#: ../../source/contributor-tutorial-contribute-on-github.rst:316 msgid "" -"A well-crafted PR title helps team members quickly understand the purpose" -" and scope of the changes being proposed. Here's a guide to help you " -"write a good GitHub PR title:" +"Once you have made your first PR, and want to contribute more, be sure to" +" check out the following :" msgstr "" -"Un titre de PR bien choisi permet aux autres développeurs de rapidement " -"comprendre l'intérêt et le scope des changements proposés. Voici un guide" -" pour vous aider à écrire des bons titres de PR :" +"Une fois que tu auras fait ton premier RP, et que tu voudras contribuer " +"davantage, ne manque pas de consulter les sites suivants :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:317 +#: ../../source/contributor-tutorial-contribute-on-github.rst:318 +#, fuzzy msgid "" -"1. Be Clear and Concise: Provide a clear summary of the changes in a " -"concise manner. 1. Use Actionable Verbs: Start with verbs like \"Add,\" " -"\"Update,\" or \"Fix\" to indicate the purpose. 1. Include Relevant " -"Information: Mention the affected feature or module for context. 1. Keep " -"it Short: Avoid lengthy titles for easy readability. 1. Use Proper " -"Capitalization and Punctuation: Follow grammar rules for clarity." -msgstr "" -"1. Soyez clair et concis : Donnez un résumé clair des changements de " -"manière concise. 1. Utilisez des verbes actionnables : Commencez par des " -"verbes comme \"Add\", \"Update\", ou \"Fix\" pour indiquer le but. 1. " -"Inclure des renseignements pertinents : Mentionner la caractéristique ou " -"le module concerné pour le contexte. 1. Gardez le court : Évitez les " -"longs titres pour une lisibilité facile. 1. Utiliser une bonne " -"capitalisation et une ponctuation : Suivre les règles de grammaire pour " -"la clarté." - -#: ../../source/contributor-tutorial-contribute-on-github.rst:323 -msgid "" -"Let's start with a few examples for titles that should be avoided because" -" they do not provide meaningful information:" -msgstr "" -"Commençons par quelques exemples de titres qui devraient être évités " -"parce qu'ils ne fournissent pas d'information significative :" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:325 -msgid "Implement Algorithm" -msgstr "Implement Algorithm" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:326 -msgid "Database" -msgstr "Database" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:327 -msgid "Add my_new_file.py to codebase" -msgstr "Add my_new_file.py to codebase" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:328 -msgid "Improve code in module" -msgstr "Improve code in module" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:329 -msgid "Change SomeModule" -msgstr "Change SomeModule" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:331 -msgid "" -"Here are a few positive examples which provide helpful information " -"without repeating how they do it, as that is already visible in the " -"\"Files changed\" section of the PR:" -msgstr "" -"Voici quelques bons exemples qui fournissent de l'information utile sans " -"répéter comment ils le font, comme cela est déjà visible dans la section " -"\"Files changed\" de la PR :" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:333 -msgid "Update docs banner to mention Flower Summit 2023" -msgstr "Update docs banner to mention Flower Summit 2023" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:334 -msgid "Remove unnecessary XGBoost dependency" -msgstr "Remove unnecessary XGBoost dependency" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:335 -msgid "Remove redundant attributes in strategies subclassing FedAvg" -msgstr "Remove redundant attributes in strategies subclassing FedAvg" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:336 -#, fuzzy -msgid "Add CI job to deploy the staging system when the ``main`` branch changes" -msgstr "Add CI job to deploy the staging system when the `main` branch changes" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:337 -msgid "" -"Add new amazing library which will be used to improve the simulation " -"engine" -msgstr "" -"Add new amazing library which will be used to improve the simulation " -"engine" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:341 -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:548 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:946 -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:727 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:713 -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:367 -msgid "Next steps" -msgstr "Prochaines étapes" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:343 -msgid "" -"Once you have made your first PR, and want to contribute more, be sure to" -" check out the following :" -msgstr "" -"Une fois que tu auras fait ton premier RP, et que tu voudras contribuer " -"davantage, ne manque pas de consulter les sites suivants :" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:345 -#, fuzzy -msgid "" -":doc:`Good first contributions `, where you should particularly look into the " -":code:`baselines` contributions." +":doc:`Good first contributions `, where you should particularly look into the " +":code:`baselines` contributions." msgstr "" "`Bonnes premières contributions `_, où vous devriez " "particulièrement regarder les contributions :code:`baselines`." -#: ../../source/contributor-tutorial-contribute-on-github.rst:349 +#: ../../source/contributor-tutorial-contribute-on-github.rst:322 #: ../../source/fed/0000-20200102-fed-template.md:60 msgid "Appendix" msgstr "Annexe" -#: ../../source/contributor-tutorial-contribute-on-github.rst:354 -#, fuzzy -msgid "Changelog entry" -msgstr "Changelog" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:356 -msgid "" -"When opening a new PR, inside its description, there should be a " -"``Changelog entry`` header." -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:358 -msgid "" -"Above this header you should see the following comment that explains how " -"to write your changelog entry:" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:360 -msgid "" -"Inside the following 'Changelog entry' section, you should put the " -"description of your changes that will be added to the changelog alongside" -" your PR title." +#: ../../source/contributor-tutorial-contribute-on-github.rst:327 +msgid "PR title format" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:363 -msgid "" -"If the section is completely empty (without any token) or non-existent, " -"the changelog will just contain the title of the PR for the changelog " -"entry, without any description." +#: ../../source/contributor-tutorial-contribute-on-github.rst:329 +msgid "We enforce the following PR title format:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:366 +#: ../../source/contributor-tutorial-contribute-on-github.rst:335 msgid "" -"If the section contains some text other than tokens, it will use it to " -"add a description to the change." +"(or ``(:skip) `` to ignore the PR in the " +"changelog)" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:368 +#: ../../source/contributor-tutorial-contribute-on-github.rst:337 msgid "" -"If the section contains one of the following tokens it will ignore any " -"other text and put the PR under the corresponding section of the " -"changelog:" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:370 -msgid " is for classifying a PR as a general improvement." -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:372 -msgid " is to not add the PR to the changelog" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:374 -msgid " is to add a general baselines change to the PR" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:376 -msgid " is to add a general examples change to the PR" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:378 -msgid " is to add a general sdk change to the PR" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:380 -msgid " is to add a general simulations change to the PR" +"Where ```` needs to be in ``{ci, fix, feat, docs, refactor, " +"break}``, ```` should be in ``{framework, baselines, datasets, " +"examples, or '*' when modifying multiple projects which requires the " +"':skip' flag to be used}``, and ```` starts with a capitalised " +"verb in the imperative mood." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:382 -msgid "Note that only one token should be used." -msgstr "" +#: ../../source/contributor-tutorial-contribute-on-github.rst:341 +#, fuzzy +msgid "Valid examples:" +msgstr "Exemples de PyTorch" -#: ../../source/contributor-tutorial-contribute-on-github.rst:384 -msgid "" -"Its content must have a specific format. We will break down what each " -"possibility does:" +#: ../../source/contributor-tutorial-contribute-on-github.rst:343 +msgid "``feat(framework) Add flwr build CLI command``" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:386 -msgid "" -"If the ``### Changelog entry`` section contains nothing or doesn't exist," -" the following text will be added to the changelog::" +#: ../../source/contributor-tutorial-contribute-on-github.rst:344 +msgid "``refactor(examples:skip) Improve quickstart-pytorch logging``" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:390 -msgid "" -"If the ``### Changelog entry`` section contains a description (and no " -"token), the following text will be added to the changelog::" +#: ../../source/contributor-tutorial-contribute-on-github.rst:345 +msgid "``ci(*:skip) Enforce PR title format``" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:396 -msgid "" -"If the ``### Changelog entry`` section contains ````, nothing will " -"change in the changelog." -msgstr "" +#: ../../source/contributor-tutorial-contribute-on-github.rst:347 +#, fuzzy +msgid "Invalid examples:" +msgstr "Exemples de PyTorch" -#: ../../source/contributor-tutorial-contribute-on-github.rst:398 -msgid "" -"If the ``### Changelog entry`` section contains ````, the " -"following text will be added to the changelog::" +#: ../../source/contributor-tutorial-contribute-on-github.rst:349 +msgid "``feat(framework): Add flwr build CLI command`` (extra ``:``)" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:402 +#: ../../source/contributor-tutorial-contribute-on-github.rst:350 msgid "" -"If the ``### Changelog entry`` section contains ````, the " -"following text will be added to the changelog::" +"``feat(*) Add flwr build CLI command`` (missing ``skip`` flag along with " +"``*``)" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:406 -msgid "" -"If the ``### Changelog entry`` section contains ````, the " -"following text will be added to the changelog::" +#: ../../source/contributor-tutorial-contribute-on-github.rst:351 +msgid "``feat(skip) Add flwr build CLI command`` (missing ````)" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:410 -msgid "" -"If the ``### Changelog entry`` section contains ````, the following " -"text will be added to the changelog::" +#: ../../source/contributor-tutorial-contribute-on-github.rst:352 +msgid "``feat(framework) add flwr build CLI command`` (non capitalised verb)" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:414 -msgid "" -"If the ``### Changelog entry`` section contains ````, the " -"following text will be added to the changelog::" +#: ../../source/contributor-tutorial-contribute-on-github.rst:353 +msgid "``feat(framework) Add flwr build CLI command.`` (dot at the end)" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:418 -msgid "" -"Note that only one token must be provided, otherwise, only the first " -"action (in the order listed above), will be performed." +#: ../../source/contributor-tutorial-contribute-on-github.rst:354 +msgid "``Add flwr build CLI command.`` (missing ``()``)" msgstr "" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:2 @@ -2430,6 +2220,7 @@ msgid "Get started as a contributor" msgstr "Devenez un·e contributeur·ice" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:5 +#: ../../source/how-to-run-flower-using-docker.rst:153 msgid "Prerequisites" msgstr "Prérequis" @@ -2589,10 +2380,55 @@ msgid "Run Linters and Tests" msgstr "Vérifier le format et tester le code" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:106 +msgid "Add a pre-commit hook" +msgstr "" + +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:108 +msgid "" +"Developers may integrate a pre-commit hook into their workflow utilizing " +"the `pre-commit `_ library. The pre-" +"commit hook is configured to execute two primary operations: " +"``./dev/format.sh`` and ``./dev/test.sh`` scripts." +msgstr "" + +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:110 +msgid "There are multiple ways developers can use this:" +msgstr "" + +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:112 +msgid "Install the pre-commit hook to your local git directory by simply running:" +msgstr "" + +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:118 +msgid "" +"Each ``git commit`` will trigger the execution of formatting and " +"linting/test scripts." +msgstr "" + +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:119 +msgid "" +"If in a hurry, bypass the hook using ``--no-verify`` with the ``git " +"commit`` command. ::" +msgstr "" + +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:124 +msgid "" +"For developers who prefer not to install the hook permanently, it is " +"possible to execute a one-time check prior to committing changes by using" +" the following command:" +msgstr "" + +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:130 +msgid "" +"This executes the formatting and linting checks/tests on all the files " +"without modifying the default behavior of ``git commit``." +msgstr "" + +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:133 msgid "Run Github Actions (CI) locally" msgstr "Exécuter les GitHub Actions (CI) localement" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:108 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:135 #, fuzzy msgid "" "Developers could run the full set of Github Actions workflows under their" @@ -2606,7 +2442,7 @@ msgstr "" "fois installé, exécuter la commande suivante dans le dossier principale " "de Flower :" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:115 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:142 msgid "" "The Flower default workflow would run by setting up the required Docker " "machines underneath." @@ -2614,12 +2450,12 @@ msgstr "" "Le workflow par défaut de Flower sera exécuté en configurant les machines" " Docker requises en arrière plan." -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:120 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:147 #, fuzzy msgid "Build Release" msgstr "Inédit" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:122 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:149 msgid "" "Flower uses Poetry to build releases. The necessary command is wrapped in" " a simple script::" @@ -2627,7 +2463,7 @@ msgstr "" "Flower utilise Poetry pour construire les nouvelles versions. La commande" " nécessaire est comprise dans un script simple ::" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:127 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:154 msgid "" "The resulting :code:`.whl` and :code:`.tar.gz` releases will be stored in" " the :code:`/dist` subdirectory." @@ -2635,19 +2471,19 @@ msgstr "" "Les versions résultantes :code:`.whl` et :code:`.tar.gz` seront stockées " "dans le sous-répertoire:code:`/dist`." -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:132 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:159 #, fuzzy msgid "Build Documentation" msgstr "Amélioration de la documentation" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:134 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:161 msgid "" "Flower's documentation uses `Sphinx `_. " "There's no convenience script to re-build the documentation yet, but it's" " pretty easy::" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:140 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:167 msgid "This will generate HTML documentation in ``doc/build/html``." msgstr "" @@ -2788,7 +2624,6 @@ msgstr "" #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:94 #: ../../source/example-jax-from-centralized-to-federated.rst:277 -#: ../../source/example-mxnet-walk-through.rst:356 #: ../../source/example-pytorch-from-centralized-to-federated.rst:310 #: ../../source/tutorial-quickstart-jax.rst:283 msgid "Next Steps" @@ -3002,7 +2837,6 @@ msgstr "" "opération pour plusieurs tours." #: ../../source/example-jax-from-centralized-to-federated.rst:123 -#: ../../source/example-mxnet-walk-through.rst:204 #: ../../source/example-pytorch-from-centralized-to-federated.rst:181 #: ../../source/tutorial-quickstart-jax.rst:129 msgid "" @@ -3018,7 +2852,6 @@ msgstr "" "d'apprentissage fédéré." #: ../../source/example-jax-from-centralized-to-federated.rst:133 -#: ../../source/example-mxnet-walk-through.rst:214 #: ../../source/example-pytorch-from-centralized-to-federated.rst:191 #: ../../source/tutorial-quickstart-jax.rst:139 msgid "We can already start the *server*:" @@ -3065,13 +2898,11 @@ msgstr "" "pour tester le modèle :" #: ../../source/example-jax-from-centralized-to-federated.rst:161 -#: ../../source/example-mxnet-walk-through.rst:242 #: ../../source/tutorial-quickstart-jax.rst:167 msgid ":code:`set_parameters (optional)`" msgstr ":code:`set_parameters (optional)`" #: ../../source/example-jax-from-centralized-to-federated.rst:160 -#: ../../source/example-mxnet-walk-through.rst:241 #: ../../source/example-pytorch-from-centralized-to-federated.rst:219 #: ../../source/tutorial-quickstart-jax.rst:166 msgid "" @@ -3085,7 +2916,6 @@ msgid "transform parameters to NumPy :code:`ndarray`'s" msgstr "transforme les paramètres en NumPy :code:`ndarray`'s" #: ../../source/example-jax-from-centralized-to-federated.rst:162 -#: ../../source/example-mxnet-walk-through.rst:243 #: ../../source/example-pytorch-from-centralized-to-federated.rst:220 #: ../../source/tutorial-quickstart-jax.rst:168 msgid "" @@ -3096,17 +2926,14 @@ msgstr "" ":code:`ndarray`'s (pensez à la liste des couches du réseau neuronal)" #: ../../source/example-jax-from-centralized-to-federated.rst:163 -#: ../../source/example-mxnet-walk-through.rst:244 #: ../../source/example-pytorch-from-centralized-to-federated.rst:221 #: ../../source/tutorial-quickstart-jax.rst:169 -#: ../../source/tutorial-quickstart-mxnet.rst:171 #: ../../source/tutorial-quickstart-pytorch.rst:155 -#: ../../source/tutorial-quickstart-scikitlearn.rst:108 +#: ../../source/tutorial-quickstart-scikitlearn.rst:118 msgid ":code:`get_parameters`" msgstr ":code:`get_parameters`" #: ../../source/example-jax-from-centralized-to-federated.rst:164 -#: ../../source/example-mxnet-walk-through.rst:245 #: ../../source/example-pytorch-from-centralized-to-federated.rst:222 #: ../../source/tutorial-quickstart-jax.rst:170 msgid "" @@ -3118,19 +2945,15 @@ msgstr "" ":code:`flwr.client.NumPyClient` attend)" #: ../../source/example-jax-from-centralized-to-federated.rst:167 -#: ../../source/example-mxnet-walk-through.rst:248 #: ../../source/example-pytorch-from-centralized-to-federated.rst:225 #: ../../source/tutorial-quickstart-jax.rst:173 -#: ../../source/tutorial-quickstart-mxnet.rst:177 #: ../../source/tutorial-quickstart-pytorch.rst:161 -#: ../../source/tutorial-quickstart-scikitlearn.rst:115 +#: ../../source/tutorial-quickstart-scikitlearn.rst:125 msgid ":code:`fit`" msgstr ":code:`fit`" #: ../../source/example-jax-from-centralized-to-federated.rst:166 #: ../../source/example-jax-from-centralized-to-federated.rst:170 -#: ../../source/example-mxnet-walk-through.rst:247 -#: ../../source/example-mxnet-walk-through.rst:251 #: ../../source/example-pytorch-from-centralized-to-federated.rst:224 #: ../../source/example-pytorch-from-centralized-to-federated.rst:228 #: ../../source/tutorial-quickstart-jax.rst:172 @@ -3143,7 +2966,6 @@ msgstr "" " serveur" #: ../../source/example-jax-from-centralized-to-federated.rst:167 -#: ../../source/example-mxnet-walk-through.rst:248 #: ../../source/example-pytorch-from-centralized-to-federated.rst:225 #: ../../source/tutorial-quickstart-jax.rst:173 msgid "train the model on the local training set" @@ -3157,17 +2979,14 @@ msgstr "" "serveur" #: ../../source/example-jax-from-centralized-to-federated.rst:172 -#: ../../source/example-mxnet-walk-through.rst:253 #: ../../source/example-pytorch-from-centralized-to-federated.rst:230 #: ../../source/tutorial-quickstart-jax.rst:178 -#: ../../source/tutorial-quickstart-mxnet.rst:180 #: ../../source/tutorial-quickstart-pytorch.rst:164 -#: ../../source/tutorial-quickstart-scikitlearn.rst:118 +#: ../../source/tutorial-quickstart-scikitlearn.rst:128 msgid ":code:`evaluate`" msgstr ":code:`évaluer`" #: ../../source/example-jax-from-centralized-to-federated.rst:171 -#: ../../source/example-mxnet-walk-through.rst:252 #: ../../source/example-pytorch-from-centralized-to-federated.rst:229 #: ../../source/tutorial-quickstart-jax.rst:177 msgid "evaluate the updated model on the local test set" @@ -3215,7 +3034,6 @@ msgid "Having defined the federation process, we can run it." msgstr "Après avoir défini le processus de fédération, nous pouvons l'exécuter." #: ../../source/example-jax-from-centralized-to-federated.rst:268 -#: ../../source/example-mxnet-walk-through.rst:347 #: ../../source/example-pytorch-from-centralized-to-federated.rst:301 #: ../../source/tutorial-quickstart-jax.rst:274 msgid "And that's it. You can now open two additional terminal windows and run" @@ -3260,311 +3078,6 @@ msgstr "" " modèle plus sophistiqué ou un ensemble de données différent ? Pourquoi " "ne pas ajouter d'autres clients ?" -#: ../../source/example-mxnet-walk-through.rst:2 -msgid "Example: MXNet - Run MXNet Federated" -msgstr "Exemple : MXNet - Exécuter MXNet Federated" - -#: ../../source/example-mxnet-walk-through.rst:4 -#, fuzzy -msgid "" -"This tutorial will show you how to use Flower to build a federated " -"version of an existing MXNet workload. We are using MXNet to train a " -"Sequential model on the MNIST dataset. We will structure the example " -"similar to our `PyTorch - From Centralized To Federated " -"`_ walkthrough. MXNet and PyTorch are very " -"similar and a very good comparison between MXNet and PyTorch is given " -"`here `_. First, we build a centralized " -"training approach based on the `Handwritten Digit Recognition " -"`_" -" tutorial. Then, we build upon the centralized training code to run the " -"training in a federated fashion." -msgstr "" -"Ce tutoriel te montrera comment utiliser Flower pour construire une " -"version fédérée d'une charge de travail MXNet existante. Nous utilisons " -"MXNet pour former un modèle séquentiel sur l'ensemble de données MNIST. " -"Nous structurerons l'exemple de la même manière que notre présentation " -"`PyTorch - De la centralisation à la fédération " -"`_. MXNet et PyTorch sont très similaires et une" -" très bonne comparaison entre MXNet et PyTorch est donnée ici " -"`_. Tout d'abord, nous " -"construisons une approche de formation centralisée basée sur le tutoriel " -"`Handandwritten Digit Recognition " -"`_." -" Ensuite, nous nous basons sur le code de formation centralisé pour " -"exécuter la formation de manière fédérée." - -#: ../../source/example-mxnet-walk-through.rst:10 -msgid "" -"Before we start setting up our MXNet example, we install the " -":code:`mxnet` and :code:`flwr` packages:" -msgstr "" -"Avant de commencer à configurer notre exemple MXNet, nous installons les " -"paquets :code:`mxnet` et :code:`flwr` :" - -#: ../../source/example-mxnet-walk-through.rst:19 -msgid "MNIST Training with MXNet" -msgstr "Formation MNIST avec MXNet" - -#: ../../source/example-mxnet-walk-through.rst:21 -msgid "" -"We begin with a brief description of the centralized training code based " -"on a :code:`Sequential` model. If you want a more in-depth explanation of" -" what's going on then have a look at the official `MXNet tutorial " -"`_." -msgstr "" -"Nous commençons par une brève description du code d'entraînement " -"centralisé basé sur un modèle :code:`Sequential`. Si tu veux une " -"explication plus approfondie de ce qui se passe, jette un coup d'œil au " -"tutoriel officiel `MXNet " -"`_." - -#: ../../source/example-mxnet-walk-through.rst:24 -msgid "" -"Let's create a new file called:code:`mxnet_mnist.py` with all the " -"components required for a traditional (centralized) MNIST training. " -"First, the MXNet package :code:`mxnet` needs to be imported. You can see " -"that we do not yet import the :code:`flwr` package for federated " -"learning. This will be done later." -msgstr "" -"Créons un nouveau fichier appelé :code:`mxnet_mnist.py` avec tous les " -"composants requis pour un apprentissage MNIST traditionnel (centralisé). " -"Tout d'abord, le package MXNet :code:`mxnet` doit être importé. Tu peux " -"voir que nous n'avons pas encore importé le package :code:`flwr` pour " -"l'apprentissage fédéré. Cela sera fait plus tard." - -#: ../../source/example-mxnet-walk-through.rst:42 -msgid "The :code:`load_data()` function loads the MNIST training and test sets." -msgstr "" -"La fonction :code:`load_data()` charge les ensembles d'entraînement et de" -" test MNIST." - -#: ../../source/example-mxnet-walk-through.rst:57 -msgid "" -"As already mentioned, we will use the MNIST dataset for this machine " -"learning workload. The model architecture (a very simple " -":code:`Sequential` model) is defined in :code:`model()`." -msgstr "" -"Comme nous l'avons déjà mentionné, nous utiliserons l'ensemble de données" -" MNIST pour cette charge de travail d'apprentissage automatique. " -"L'architecture du modèle (un modèle :code:`Séquentiel` très simple) est " -"définie dans :code:`model()`." - -#: ../../source/example-mxnet-walk-through.rst:70 -msgid "" -"We now need to define the training (function :code:`train()`) which loops" -" over the training set and measures the loss for each batch of training " -"examples." -msgstr "" -"Nous devons maintenant définir la formation (fonction :code:`train()`) " -"qui passe en boucle sur l'ensemble de la formation et mesure la perte " -"pour chaque lot d'exemples de formation." - -#: ../../source/example-mxnet-walk-through.rst:123 -msgid "" -"The evaluation of the model is defined in function :code:`test()`. The " -"function loops over all test samples and measures the loss and accuracy " -"of the model based on the test dataset." -msgstr "" -"L'évaluation du modèle est définie dans la fonction :code:`test()`. Cette" -" fonction passe en boucle sur tous les échantillons de test et mesure la " -"perte et la précision du modèle en fonction de l'ensemble des données de " -"test." - -#: ../../source/example-mxnet-walk-through.rst:158 -msgid "" -"Having defined the data loading, model architecture, training, and " -"evaluation we can put everything together and train our model on MNIST. " -"Note that the GPU/CPU device for the training and testing is defined " -"within the :code:`ctx` (context)." -msgstr "" -"Après avoir défini le chargement des données, l'architecture du modèle, " -"l'entraînement et l'évaluation, nous pouvons tout assembler et entraîner " -"notre modèle sur MNIST. Note que le dispositif GPU/CPU pour " -"l'entraînement et le test est défini dans le :code:`ctx` (contexte)." - -#: ../../source/example-mxnet-walk-through.rst:184 -msgid "You can now run your (centralized) MXNet machine learning workload:" -msgstr "" -"Tu peux maintenant exécuter ta charge de travail (centralisée) " -"d'apprentissage automatique MXNet :" - -#: ../../source/example-mxnet-walk-through.rst:190 -msgid "" -"So far this should all look fairly familiar if you've used MXNet (or even" -" PyTorch) before. Let's take the next step and use what we've built to " -"create a simple federated learning system consisting of one server and " -"two clients." -msgstr "" -"Jusqu'à présent, tout cela devrait te sembler assez familier si tu as " -"déjà utilisé MXNet (ou même PyTorch). Passons à l'étape suivante et " -"utilisons ce que nous avons construit pour créer un simple système " -"d'apprentissage fédéré composé d'un serveur et de deux clients." - -#: ../../source/example-mxnet-walk-through.rst:194 -msgid "MXNet meets Flower" -msgstr "MXNet rencontre Flower" - -#: ../../source/example-mxnet-walk-through.rst:196 -msgid "" -"So far, it was not easily possible to use MXNet workloads for federated " -"learning because federated learning is not supported in MXNet. Since " -"Flower is fully agnostic towards the underlying machine learning " -"framework, it can be used to federated arbitrary machine learning " -"workloads. This section will show you how Flower can be used to federate " -"our centralized MXNet workload." -msgstr "" -"Jusqu'à présent, il n'était pas facile d'utiliser les charges de travail " -"MXNet pour l'apprentissage fédéré car l'apprentissage fédéré n'est pas " -"pris en charge dans MXNet. Comme Flower est totalement agnostique " -"vis-à-vis du cadre d'apprentissage automatique sous-jacent, il peut être " -"utilisé pour fédérer des charges de travail d'apprentissage automatique " -"arbitraires. Cette section te montrera comment Flower peut être utilisé " -"pour fédérer notre charge de travail MXNet centralisée." - -#: ../../source/example-mxnet-walk-through.rst:198 -msgid "" -"The concept to federate an existing workload is always the same and easy " -"to understand. We have to start a *server* and then use the code in " -":code:`mxnet_mnist.py` for the *clients* that are connected to the " -"*server*. The *server* sends model parameters to the clients. The " -"*clients* run the training and update the parameters. The updated " -"parameters are sent back to the *server* which averages all received " -"parameter updates. This describes one round of the federated learning " -"process and we repeat this for multiple rounds." -msgstr "" -"Le concept pour fédérer une charge de travail existante est toujours le " -"même et facile à comprendre. Nous devons démarrer un *serveur* et ensuite" -" utiliser le code dans :code:`mxnet_mnist.py` pour les *clients* qui sont" -" connectés au *serveur*. Le *serveur* envoie les paramètres du modèle aux" -" clients. Les *clients* exécutent la formation et mettent à jour les " -"paramètres. Les paramètres mis à jour sont renvoyés au *serveur* qui fait" -" la moyenne de toutes les mises à jour de paramètres reçues. Ceci décrit " -"un tour du processus d'apprentissage fédéré et nous répétons cette " -"opération pour plusieurs tours." - -#: ../../source/example-mxnet-walk-through.rst:220 -msgid "" -"Finally, we will define our *client* logic in :code:`client.py` and build" -" upon the previously defined MXNet training in :code:`mxnet_mnist.py`. " -"Our *client* needs to import :code:`flwr`, but also :code:`mxnet` to " -"update the parameters on our MXNet model:" -msgstr "" -"Enfin, nous allons définir la logique de notre *client* dans " -":code:`client.py` et nous appuyer sur l'entraînement MXNet défini " -"précédemment dans :code:`mxnet_mnist.py`. Notre *client* doit importer " -":code:`flwr`, mais aussi :code:`mxnet` pour mettre à jour les paramètres " -"de notre modèle MXNet :" - -#: ../../source/example-mxnet-walk-through.rst:235 -msgid "" -"Implementing a Flower *client* basically means implementing a subclass of" -" either :code:`flwr.client.Client` or :code:`flwr.client.NumPyClient`. " -"Our implementation will be based on :code:`flwr.client.NumPyClient` and " -"we'll call it :code:`MNISTClient`. :code:`NumPyClient` is slightly easier" -" to implement than :code:`Client` if you use a framework with good NumPy " -"interoperability (like PyTorch or MXNet) because it avoids some of the " -"boilerplate that would otherwise be necessary. :code:`MNISTClient` needs " -"to implement four methods, two methods for getting/setting model " -"parameters, one method for training the model, and one method for testing" -" the model:" -msgstr "" -"Implementing a Flower *client* basically means implementing a subclass of" -" either :code:`flwr.client.Client` or :code:`flwr.client.NumPyClient`. " -"Our implementation will be based on :code:`flwr.client.NumPyClient` and " -"we'll call it :code:`MNISTClient`. :code:`NumPyClient` is slightly easier" -" to implement than :code:`Client` if you use a framework with good NumPy " -"interoperability (like PyTorch or MXNet) because it avoids some of the " -"boilerplate that would otherwise be necessary. :code:`MNISTClient` needs " -"to implement four methods, two methods for getting/setting model " -"parameters, one method for training the model, and one method for testing" -" the model:" - -#: ../../source/example-mxnet-walk-through.rst:242 -msgid "transform MXNet :code:`NDArray`'s to NumPy :code:`ndarray`'s" -msgstr "transforme les :code:`NDArray` du MXNet en :code:`ndarray` de NumPy" - -#: ../../source/example-mxnet-walk-through.rst:249 -#: ../../source/example-pytorch-from-centralized-to-federated.rst:226 -msgid "get the updated local model weights and return them to the server" -msgstr "récupère les poids du modèle local mis à jour et les renvoie au serveur" - -#: ../../source/example-mxnet-walk-through.rst:253 -#: ../../source/example-pytorch-from-centralized-to-federated.rst:230 -msgid "return the local loss and accuracy to the server" -msgstr "renvoie la perte locale et la précision au serveur" - -#: ../../source/example-mxnet-walk-through.rst:255 -msgid "" -"The challenging part is to transform the MXNet parameters from " -":code:`NDArray` to :code:`NumPy Arrays` to make it readable for Flower." -msgstr "" -"La partie la plus difficile est de transformer les paramètres MXNet de " -":code:`NDArray` en :code:`NumPy Arrays` pour les rendre lisibles pour " -"Flower." - -#: ../../source/example-mxnet-walk-through.rst:257 -msgid "" -"The two :code:`NumPyClient` methods :code:`fit` and :code:`evaluate` make" -" use of the functions :code:`train()` and :code:`test()` previously " -"defined in :code:`mxnet_mnist.py`. So what we really do here is we tell " -"Flower through our :code:`NumPyClient` subclass which of our already " -"defined functions to call for training and evaluation. We included type " -"annotations to give you a better understanding of the data types that get" -" passed around." -msgstr "" -"Les deux méthodes :code:`NumPyClient` :code:`fit` et :code:`evaluate` " -"utilisent les fonctions :code:`train()` et :code:`test()` définies " -"précédemment dans :code:`mxnet_mnist.py`. Ce que nous faisons vraiment " -"ici, c'est que nous indiquons à Flower, par le biais de notre sous-classe" -" :code:`NumPyClient`, laquelle de nos fonctions déjà définies doit être " -"appelée pour l'entraînement et l'évaluation. Nous avons inclus des " -"annotations de type pour te donner une meilleure compréhension des types " -"de données qui sont transmis." - -#: ../../source/example-mxnet-walk-through.rst:319 -msgid "" -"Having defined data loading, model architecture, training, and evaluation" -" we can put everything together and train our :code:`Sequential` model on" -" MNIST." -msgstr "" -"Après avoir défini le chargement des données, l'architecture du modèle, " -"la formation et l'évaluation, nous pouvons tout rassembler et former " -"notre modèle :code:`Sequential` sur MNIST." - -#: ../../source/example-mxnet-walk-through.rst:353 -msgid "" -"in each window (make sure that the server is still running before you do " -"so) and see your MXNet project run federated learning across two clients." -" Congratulations!" -msgstr "" -"dans chaque fenêtre (assure-toi que le serveur est toujours en cours " -"d'exécution avant de le faire) et tu verras ton projet MXNet exécuter " -"l'apprentissage fédéré sur deux clients. Félicitations !" - -#: ../../source/example-mxnet-walk-through.rst:358 -#, fuzzy -msgid "" -"The full source code for this example: `MXNet: From Centralized To " -"Federated (Code) `_. Our example is of course " -"somewhat over-simplified because both clients load the exact same " -"dataset, which isn't realistic. You're now prepared to explore this topic" -" further. How about using a CNN or using a different dataset? How about " -"adding more clients?" -msgstr "" -"Le code source complet de cet exemple : `MXNet : From Centralized To " -"Federated (Code) `_. Notre exemple est bien sûr un " -"peu trop simplifié parce que les deux clients chargent exactement le même" -" ensemble de données, ce qui n'est pas réaliste. Tu es maintenant prêt à " -"explorer ce sujet plus en profondeur. Pourquoi ne pas utiliser un CNN ou " -"un ensemble de données différent ? Pourquoi ne pas ajouter d'autres " -"clients ?" - #: ../../source/example-pytorch-from-centralized-to-federated.rst:2 msgid "Example: PyTorch - From Centralized To Federated" msgstr "Exemple : PyTorch - De la centralisation à la fédération" @@ -3770,13 +3283,21 @@ msgstr "" msgid ":code:`set_parameters`" msgstr ":code:`set_parameters`" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:232 -msgid "" -"The two :code:`NumPyClient` methods :code:`fit` and :code:`evaluate` make" -" use of the functions :code:`train()` and :code:`test()` previously " -"defined in :code:`cifar.py`. So what we really do here is we tell Flower " -"through our :code:`NumPyClient` subclass which of our already defined " -"functions to call for training and evaluation. We included type " +#: ../../source/example-pytorch-from-centralized-to-federated.rst:226 +msgid "get the updated local model weights and return them to the server" +msgstr "récupère les poids du modèle local mis à jour et les renvoie au serveur" + +#: ../../source/example-pytorch-from-centralized-to-federated.rst:230 +msgid "return the local loss and accuracy to the server" +msgstr "renvoie la perte locale et la précision au serveur" + +#: ../../source/example-pytorch-from-centralized-to-federated.rst:232 +msgid "" +"The two :code:`NumPyClient` methods :code:`fit` and :code:`evaluate` make" +" use of the functions :code:`train()` and :code:`test()` previously " +"defined in :code:`cifar.py`. So what we really do here is we tell Flower " +"through our :code:`NumPyClient` subclass which of our already defined " +"functions to call for training and evaluation. We included type " "annotations to give you a better understanding of the data types that get" " passed around." msgstr "" @@ -4923,6 +4444,166 @@ msgstr "" "Le serveur peut alors utiliser une stratégie personnalisée pour agréger " "les mesures fournies dans ces dictionnaires :" +#: ../../source/how-to-authenticate-supernodes.rst:2 +msgid "Authenticate SuperNodes" +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:4 +msgid "" +"Flower has built-in support for authenticated SuperNodes that you can use" +" to verify the identities of each SuperNode connecting to a SuperLink. " +"Flower node authentication works similar to how GitHub SSH authentication" +" works:" +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:7 +msgid "SuperLink (server) stores a list of known (client) node public keys" +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:8 +msgid "" +"Using ECDH, both SuperNode and SuperLink independently derive a shared " +"secret" +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:9 +msgid "" +"Shared secret is used to compute the HMAC value of the message sent from " +"SuperNode to SuperLink as a token" +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:10 +msgid "SuperLink verifies the token" +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:12 +#, fuzzy +msgid "" +"We recommend you to check out the complete `code example " +"`_ demonstrating federated learning with Flower in an " +"authenticated setting." +msgstr "" +"Réfère-toi à l'exemple de code complet " +"`_ " +"pour en savoir plus." + +#: ../../source/how-to-authenticate-supernodes.rst:15 +msgid "" +"This guide covers a preview feature that might change in future versions " +"of Flower." +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:18 +msgid "" +"For increased security, node authentication can only be used when " +"encrypted connections (SSL/TLS) are enabled." +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:21 +msgid "Enable node authentication in :code:`SuperLink`" +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:23 +msgid "" +"To enable node authentication, first you need to configure SSL/TLS " +"connections to secure the SuperLink<>SuperNode communication. You can " +"find the complete guide `here `_. After configuring secure connections, you" +" can enable client authentication in a long-running Flower " +":code:`SuperLink`. Use the following terminal command to start a Flower " +":code:`SuperNode` that has both secure connections and node " +"authentication enabled:" +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:38 +msgid "Let's break down the authentication flags:" +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:40 +msgid "" +"The first flag :code:`--auth-list-public-keys` expects a path to a CSV " +"file storing all known node public keys. You need to store all known node" +" public keys that are allowed to participate in a federation in one CSV " +"file (:code:`.csv`)." +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:42 +msgid "" +"A valid CSV file storing known node public keys should list the keys in " +"OpenSSH format, separated by commas and without any comments. For an " +"example, refer to our code sample, which contains a CSV file with two " +"known node public keys." +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:44 +msgid "" +"The second and third flags :code:`--auth-superlink-private-key` and :code" +":`--auth-superlink-public-key` expect paths to the server's private and " +"public keys. For development purposes, you can generate a private and " +"public key pair using :code:`ssh-keygen -t ecdsa -b 384`." +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:47 +msgid "" +"In Flower 1.9, there is no support for dynamically removing, editing, or " +"adding known node public keys to the SuperLink. To change the set of " +"known nodes, you need to shut the server down, edit the CSV file, and " +"start the server again. Support for dynamically changing the set of known" +" nodes is on the roadmap to be released in Flower 1.10 (ETA: June)." +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:53 +msgid "Enable node authentication in :code:`SuperNode`" +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:55 +msgid "" +"Similar to the long-running Flower server (:code:`SuperLink`), you can " +"easily enable node authentication in the long-running Flower client " +"(:code:`SuperNode`). Use the following terminal command to start an " +"authenticated :code:`SuperNode`:" +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:66 +msgid "" +"The :code:`--auth-supernode-private-key` flag expects a path to the " +"node's private key file and the :code:`--auth-supernode-public-key` flag " +"expects a path to the node's public key file. For development purposes, " +"you can generate a private and public key pair using :code:`ssh-keygen -t" +" ecdsa -b 384`." +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:70 +msgid "Security notice" +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:72 +msgid "" +"The system's security relies on the credentials of the SuperLink and each" +" SuperNode. Therefore, it is imperative to safeguard and safely store the" +" credentials to avoid security risks such as Public Key Infrastructure " +"(PKI) impersonation attacks. The node authentication mechanism also " +"involves human interaction, so please ensure that all of the " +"communication is done in a secure manner, using trusted communication " +"methods." +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:77 +#: ../../source/how-to-enable-ssl-connections.rst:68 +#: ../../source/how-to-use-built-in-mods.rst:85 +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:287 +msgid "Conclusion" +msgstr "Conclusion" + +#: ../../source/how-to-authenticate-supernodes.rst:79 +msgid "" +"You should now have learned how to start a long-running Flower server " +"(:code:`SuperLink`) and client (:code:`SuperNode`) with node " +"authentication enabled. You should also know the significance of the " +"private key and store it safely to minimize security risks." +msgstr "" + #: ../../source/how-to-configure-clients.rst:2 #, fuzzy msgid "Configure clients" @@ -5197,9 +4878,11 @@ msgid "Enable SSL connections" msgstr "Collecte centralisée des données" #: ../../source/how-to-enable-ssl-connections.rst:4 +#, fuzzy msgid "" -"This guide describes how to a SSL-enabled secure Flower server can be " -"started and how a Flower client can establish a secure connections to it." +"This guide describes how to a SSL-enabled secure Flower server " +"(:code:`SuperLink`) can be started and how a Flower client " +"(:code:`SuperNode`) can establish a secure connections to it." msgstr "" "Ce guide décrit comment démarrer un serveur Flower sécurisé par SSL et " "comment un client Flower peut établir une connexion sécurisée avec lui." @@ -5216,11 +4899,12 @@ msgstr "" "tensorflow>`_." #: ../../source/how-to-enable-ssl-connections.rst:10 +#, fuzzy msgid "" -"The code example comes with a README.md file which will explain how to " -"start it. Although it is already SSL-enabled, it might be less " -"descriptive on how. Stick to this guide for a deeper introduction to the " -"topic." +"The code example comes with a :code:`README.md` file which explains how " +"to start it. Although it is already SSL-enabled, it might be less " +"descriptive on how it does so. Stick to this guide for a deeper " +"introduction to the topic." msgstr "" "L'exemple de code est accompagné d'un fichier README.md qui t'expliquera " "comment le démarrer. Bien qu'il soit déjà activé par SSL, il peut être " @@ -5238,7 +4922,7 @@ msgid "" "server and client. For the purpose of this guide we are going to generate" " self-signed certificates. As this can become quite complex we are going " "to ask you to run the script in :code:`examples/advanced-" -"tensorflow/certificates/generate.sh`" +"tensorflow/certificates/generate.sh` with the following command sequence:" msgstr "" "L'utilisation de connexions compatibles avec le protocole SSL nécessite " "que des certificats soient transmis au serveur et au client. Pour les " @@ -5247,11 +4931,7 @@ msgstr "" "d'exécuter le script dans :code:`examples/advanced-" "tensorflow/certificates/generate.sh`" -#: ../../source/how-to-enable-ssl-connections.rst:23 -msgid "with the following command sequence:" -msgstr "avec la séquence de commandes suivante :" - -#: ../../source/how-to-enable-ssl-connections.rst:30 +#: ../../source/how-to-enable-ssl-connections.rst:29 #, fuzzy msgid "" "This will generate the certificates in :code:`examples/advanced-" @@ -5260,98 +4940,84 @@ msgstr "" "Cela générera les certificats dans :code:`examples/advanced-" "tensorflow/.cache/certificates`." -#: ../../source/how-to-enable-ssl-connections.rst:32 +#: ../../source/how-to-enable-ssl-connections.rst:31 +#, fuzzy msgid "" -"The approach how the SSL certificates are generated in this example can " -"serve as an inspiration and starting point but should not be taken as " -"complete for production environments. Please refer to other sources " -"regarding the issue of correctly generating certificates for production " -"environments." +"The approach for generating SSL certificates in the context of this " +"example can serve as an inspiration and starting point, but it should not" +" be used as a reference for production environments. Please refer to " +"other sources regarding the issue of correctly generating certificates " +"for production environments. For non-critical prototyping or research " +"projects, it might be sufficient to use the self-signed certificates " +"generated using the scripts mentioned in this guide." msgstr "" "L'approche de la génération des certificats SSL dans cet exemple peut " "servir d'inspiration et de point de départ, mais ne doit pas être " "considérée comme complète pour les environnements de production." -#: ../../source/how-to-enable-ssl-connections.rst:36 -msgid "" -"In case you are a researcher you might be just fine using the self-signed" -" certificates generated using the scripts which are part of this guide." -msgstr "" -"Si tu es un chercheur, tu peux très bien utiliser les certificats auto-" -"signés générés à l'aide des scripts qui font partie de ce guide." +#: ../../source/how-to-enable-ssl-connections.rst:39 +#, fuzzy +msgid "Server (SuperLink)" +msgstr "flower-superlink" #: ../../source/how-to-enable-ssl-connections.rst:41 -#: ../../source/ref-api/flwr.server.Server.rst:2 -msgid "Server" -msgstr "Serveur" - -#: ../../source/how-to-enable-ssl-connections.rst:43 +#, fuzzy msgid "" -"We are now going to show how to write a sever which uses the previously " -"generated scripts." +"Use the following terminal command to start a sever (SuperLink) that uses" +" the previously generated certificates:" msgstr "" -"Nous allons maintenant montrer comment écrire un serveur qui utilise les " -"scripts générés précédemment." +"Nous allons maintenant montrer comment écrire un client qui utilise les " +"scripts générés précédemment :" -#: ../../source/how-to-enable-ssl-connections.rst:61 +#: ../../source/how-to-enable-ssl-connections.rst:50 msgid "" "When providing certificates, the server expects a tuple of three " -"certificates. :code:`Path` can be used to easily read the contents of " -"those files into byte strings, which is the data type " -":code:`start_server` expects." +"certificates paths: CA certificate, server certificate and server private" +" key." msgstr "" -"Lorsqu'il fournit des certificats, le serveur attend un tuple de trois " -"certificats. :code:`Path` peut être utilisé pour lire facilement le " -"contenu de ces fichiers en chaînes d'octets, ce qui est le type de " -"données attendu par :code:`start_server`." -#: ../../source/how-to-enable-ssl-connections.rst:65 -#: ../../source/how-to-upgrade-to-flower-1.0.rst:37 -#: ../../source/ref-api/flwr.client.Client.rst:2 -msgid "Client" -msgstr "Client" +#: ../../source/how-to-enable-ssl-connections.rst:54 +#, fuzzy +msgid "Client (SuperNode)" +msgstr "Codes d'état du client." -#: ../../source/how-to-enable-ssl-connections.rst:67 +#: ../../source/how-to-enable-ssl-connections.rst:56 +#, fuzzy msgid "" -"We are now going to show how to write a client which uses the previously " -"generated scripts:" +"Use the following terminal command to start a client (SuperNode) that " +"uses the previously generated certificates:" msgstr "" "Nous allons maintenant montrer comment écrire un client qui utilise les " "scripts générés précédemment :" -#: ../../source/how-to-enable-ssl-connections.rst:84 +#: ../../source/how-to-enable-ssl-connections.rst:64 +#, fuzzy msgid "" -"When setting :code:`root_certificates`, the client expects the PEM-" -"encoded root certificates as a byte string. We are again using " -":code:`Path` to simplify reading those as byte strings." +"When setting :code:`root_certificates`, the client expects a file path to" +" PEM-encoded root certificates." msgstr "" "En définissant :code:`root_certificates`, le client s'attend à recevoir " "les certificats racine codés en PEM sous forme de chaîne d'octets. Nous " "utilisons à nouveau :code:`Path` pour simplifier la lecture de ces " "certificats sous forme de chaînes d'octets." -#: ../../source/how-to-enable-ssl-connections.rst:89 -#: ../../source/how-to-use-built-in-mods.rst:85 -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:287 -msgid "Conclusion" -msgstr "Conclusion" - -#: ../../source/how-to-enable-ssl-connections.rst:91 +#: ../../source/how-to-enable-ssl-connections.rst:70 +#, fuzzy msgid "" "You should now have learned how to generate self-signed certificates " -"using the given script, start a SSL-enabled server, and have a client " +"using the given script, start an SSL-enabled server and have a client " "establish a secure connection to it." msgstr "" "Tu devrais maintenant avoir appris à générer des certificats auto-signés " "à l'aide du script donné, à démarrer un serveur compatible SSL et à " "demander à un client d'établir une connexion sécurisée avec lui." -#: ../../source/how-to-enable-ssl-connections.rst:96 +#: ../../source/how-to-enable-ssl-connections.rst:75 #, fuzzy msgid "Additional resources" msgstr "Ressources supplémentaires" -#: ../../source/how-to-enable-ssl-connections.rst:98 +#: ../../source/how-to-enable-ssl-connections.rst:77 msgid "" "These additional sources might be relevant if you would like to dive " "deeper into the topic of certificates:" @@ -5359,11 +5025,11 @@ msgstr "" "Ces sources supplémentaires peuvent être pertinentes si tu souhaites " "approfondir le sujet des certificats :" -#: ../../source/how-to-enable-ssl-connections.rst:100 +#: ../../source/how-to-enable-ssl-connections.rst:79 msgid "`Let's Encrypt `_" msgstr "`Let's Encrypt `_" -#: ../../source/how-to-enable-ssl-connections.rst:101 +#: ../../source/how-to-enable-ssl-connections.rst:80 msgid "`certbot `_" msgstr "`certbot `_" @@ -5760,6 +5426,7 @@ msgid "Install stable release" msgstr "Installe la version stable" #: ../../source/how-to-install-flower.rst:15 +#: ../../source/how-to-upgrade-to-flower-next.rst:46 msgid "Using pip" msgstr "" @@ -6204,14 +5871,15 @@ msgstr "" msgid "" "The simplest way to get started with Flower is by using the pre-made " "Docker images, which you can find on `Docker Hub " -"`_." +"`__. Supported architectures include " +"``amd64`` and ``arm64v8``." msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:7 +#: ../../source/how-to-run-flower-using-docker.rst:8 msgid "Before you start, make sure that the Docker daemon is running:" msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:14 +#: ../../source/how-to-run-flower-using-docker.rst:15 msgid "" "If you do not see the version of Docker but instead get an error saying " "that the command was not found, you will need to install Docker first. " @@ -6219,7 +5887,7 @@ msgid "" "docker/>`_." msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:20 +#: ../../source/how-to-run-flower-using-docker.rst:21 msgid "" "On Linux, Docker commands require ``sudo`` privilege. If you want to " "avoid using ``sudo``, you can follow the `Post-installation steps " @@ -6227,295 +5895,673 @@ msgid "" "official Docker website." msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:25 +#: ../../source/how-to-run-flower-using-docker.rst:27 +msgid "" +"To ensure optimal performance and compatibility, the SuperLink, SuperNode" +" and ServerApp image must have the same version when running together. " +"This guarantees seamless integration and avoids potential conflicts or " +"issues that may arise from using different versions." +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:32 #, fuzzy -msgid "Flower server" -msgstr "Serveur de Flower" +msgid "Flower SuperLink" +msgstr "flower-superlink" -#: ../../source/how-to-run-flower-using-docker.rst:28 +#: ../../source/how-to-run-flower-using-docker.rst:35 #, fuzzy msgid "Quickstart" msgstr "Démarrage rapide de JAX" -#: ../../source/how-to-run-flower-using-docker.rst:30 +#: ../../source/how-to-run-flower-using-docker.rst:37 msgid "If you're looking to try out Flower, you can use the following command:" msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:37 +#: ../../source/how-to-run-flower-using-docker.rst:43 msgid "" -"The command will pull the Docker image with the tag " -"``1.7.0-py3.11-ubuntu22.04`` from Docker Hub. The tag contains the " -"information which Flower, Python and Ubuntu is used. In this case, it " -"uses Flower 1.7.0, Python 3.11 and Ubuntu 22.04. The ``--rm`` flag tells " -"Docker to remove the container after it exits." +"The command pulls the Docker image with the tag ``1.8.0`` from Docker " +"Hub. The tag specifies the Flower version. In this case, Flower 1.8.0. " +"The ``--rm`` flag tells Docker to remove the container after it exits." msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:44 +#: ../../source/how-to-run-flower-using-docker.rst:49 msgid "" -"By default, the Flower server keeps state in-memory. When using the " +"By default, the Flower SuperLink keeps state in-memory. When using the " "Docker flag ``--rm``, the state is not persisted between container " "starts. We will show below how to save the state in a file on your host " "system." msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:48 +#: ../../source/how-to-run-flower-using-docker.rst:53 msgid "" "The ``-p :`` flag tells Docker to map the ports " "``9091``/``9092`` of the host to ``9091``/``9092`` of the container, " "allowing you to access the Driver API on ``http://localhost:9091`` and " "the Fleet API on ``http://localhost:9092``. Lastly, any flag that comes " -"after the tag is passed to the Flower server. Here, we are passing the " -"flag ``--insecure``." +"after the tag is passed to the Flower SuperLink. Here, we are passing the" +" flag ``--insecure``." msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:55 +#: ../../source/how-to-run-flower-using-docker.rst:60 +#: ../../source/how-to-run-flower-using-docker.rst:259 +#: ../../source/how-to-run-flower-using-docker.rst:376 msgid "" "The ``--insecure`` flag enables insecure communication (using HTTP, not " "HTTPS) and should only be used for testing purposes. We strongly " "recommend enabling `SSL `_ when " +"flower-using-docker.html#enabling-ssl-for-secure-connections>`__ when " "deploying to a production environment." msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:60 +#: ../../source/how-to-run-flower-using-docker.rst:65 msgid "" -"You can use ``--help`` to view all available flags that the server " +"You can use ``--help`` to view all available flags that the SuperLink " "supports:" msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:67 +#: ../../source/how-to-run-flower-using-docker.rst:72 msgid "Mounting a volume to store the state on the host system" msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:69 +#: ../../source/how-to-run-flower-using-docker.rst:74 +msgid "" +"If you want to persist the state of the SuperLink on your host system, " +"all you need to do is specify a directory where you want to save the file" +" on your host system and a name for the database file. By default, the " +"SuperLink container runs with a non-root user called ``app`` with the " +"user ID ``49999``. It is recommended to create new directory and change " +"the user ID of the directory to ``49999`` to ensure the mounted directory" +" has the proper permissions. If you later want to delete the directory, " +"you can change the user ID back to the current user ID by running ``sudo " +"chown -R $USER:$(id -gn) state``." +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:82 msgid "" -"If you want to persist the state of the server on your host system, all " -"you need to do is specify a path where you want to save the file on your " -"host system and a name for the database file. In the example below, we " -"tell Docker via the flag ``-v`` to mount the user's home directory " -"(``~/`` on your host) into the ``/app/`` directory of the container. " +"In the example below, we create a new directory, change the user ID and " +"tell Docker via the flag ``--volume`` to mount the local ``state`` " +"directory into the ``/app/state`` directory of the container. " "Furthermore, we use the flag ``--database`` to specify the name of the " "database file." msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:82 +#: ../../source/how-to-run-flower-using-docker.rst:95 msgid "" -"As soon as the server starts, the file ``state.db`` is created in the " -"user's home directory on your host system. If the file already exists, " -"the server tries to restore the state from the file. To start the server " -"with an empty database, simply remove the ``state.db`` file." +"As soon as the SuperLink starts, the file ``state.db`` is created in the " +"``state`` directory on your host system. If the file already exists, the " +"SuperLink tries to restore the state from the file. To start the " +"SuperLink with an empty database, simply remove the ``state.db`` file." msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:87 +#: ../../source/how-to-run-flower-using-docker.rst:100 +#: ../../source/how-to-run-flower-using-docker.rst:281 +#: ../../source/how-to-run-flower-using-docker.rst:397 #, fuzzy msgid "Enabling SSL for secure connections" msgstr "Collecte centralisée des données" -#: ../../source/how-to-run-flower-using-docker.rst:89 +#: ../../source/how-to-run-flower-using-docker.rst:102 msgid "" -"To enable SSL, you will need a CA certificate, a server certificate and a" -" server private key." +"To enable SSL, you will need a PEM-encoded root certificate, a PEM-" +"encoded private key and a PEM-encoded certificate chain." msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:92 +#: ../../source/how-to-run-flower-using-docker.rst:106 msgid "" "For testing purposes, you can generate your own self-signed certificates." " The `Enable SSL connections `_ page contains a section that " -"will guide you through the process." +"enable-ssl-connections.html#certificates>`__ page contains a section that" +" will guide you through the process." msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:96 +#: ../../source/how-to-run-flower-using-docker.rst:110 msgid "" "Assuming all files we need are in the local ``certificates`` directory, " -"we can use the flag ``-v`` to mount the local directory into the " -"``/app/`` directory of the container. This allows the server to access " -"the files within the container. Finally, we pass the names of the " -"certificates to the server with the ``--certificates`` flag." +"we can use the flag ``--volume`` to mount the local directory into the " +"``/app/certificates/`` directory of the container. This allows the " +"SuperLink to access the files within the container. The ``ro`` stands for" +" ``read-only``. Docker volumes default to ``read-write``; that option " +"tells Docker to make the volume ``read-only`` instead. Finally, we pass " +"the names of the certificates and key file to the SuperLink with the " +"``--ssl-ca-certfile``, ``--ssl-certfile`` and ``--ssl-keyfile`` flag." msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:108 -msgid "Using a different Flower or Python version" -msgstr "" - -#: ../../source/how-to-run-flower-using-docker.rst:110 +#: ../../source/how-to-run-flower-using-docker.rst:128 msgid "" -"If you want to use a different version of Flower or Python, you can do so" -" by changing the tag. All versions we provide are available on `Docker " -"Hub `_." +"Because Flower containers, by default, run with a non-root user ``app``, " +"the mounted files and directories must have the proper permissions for " +"the user ID ``49999``. For example, to change the user ID of all files in" +" the ``certificates/`` directory, you can run ``sudo chown -R 49999:49999" +" certificates/*``." msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:114 -msgid "Pinning a Docker image to a specific version" -msgstr "" +#: ../../source/how-to-run-flower-using-docker.rst:134 +#, fuzzy +msgid "Flower SuperNode" +msgstr "Serveur de Flower" -#: ../../source/how-to-run-flower-using-docker.rst:116 +#: ../../source/how-to-run-flower-using-docker.rst:136 msgid "" -"It may happen that we update the images behind the tags. Such updates " -"usually include security updates of system dependencies that should not " -"change the functionality of Flower. However, if you want to ensure that " -"you always use the same image, you can specify the hash of the image " -"instead of the tag." +"The SuperNode Docker image comes with a pre-installed version of Flower " +"and serves as a base for building your own SuperNode image." msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:121 +#: ../../source/how-to-run-flower-using-docker.rst:141 msgid "" -"The following command returns the current image hash referenced by the " -"``server:1.7.0-py3.11-ubuntu22.04`` tag:" +"The SuperNode Docker image currently works only with the 1.9.0-nightly " +"release. A stable version will be available when Flower 1.9.0 (stable) " +"gets released (ETA: May). A SuperNode nightly image must be paired with " +"the corresponding SuperLink and ServerApp nightly images released on the " +"same day. To ensure the versions are in sync, using the concrete tag, " +"e.g., ``1.9.0.dev20240501`` instead of ``nightly`` is recommended." msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:128 -msgid "Next, we can pin the hash when running a new server container:" +#: ../../source/how-to-run-flower-using-docker.rst:147 +msgid "" +"We will use the ``quickstart-pytorch`` example, which you can find in the" +" Flower repository, to illustrate how you can dockerize your ClientApp." msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:137 -#, fuzzy -msgid "Setting environment variables" -msgstr "Mise en place de l'environnement de codage" - -#: ../../source/how-to-run-flower-using-docker.rst:139 +#: ../../source/how-to-run-flower-using-docker.rst:155 msgid "" -"To set a variable inside a Docker container, you can use the ``-e " -"=`` flag." +"Before we can start, we need to meet a few prerequisites in our local " +"development environment. You can skip the first part if you want to run " +"your ClientApp instead of the ``quickstart-pytorch`` example." msgstr "" -#: ../../source/how-to-run-simulations.rst:2 +#: ../../source/how-to-run-flower-using-docker.rst:159 #, fuzzy -msgid "Run simulations" -msgstr "Simulation de moniteur" +msgid "Clone the Flower repository." +msgstr "**Fourche le dépôt de Flower**" -#: ../../source/how-to-run-simulations.rst:8 -msgid "" -"Simulating Federated Learning workloads is useful for a multitude of use-" -"cases: you might want to run your workload on a large cohort of clients " -"but without having to source, configure and mange a large number of " -"physical devices; you might want to run your FL workloads as fast as " -"possible on the compute systems you have access to without having to go " -"through a complex setup process; you might want to validate your " -"algorithm on different scenarios at varying levels of data and system " -"heterogeneity, client availability, privacy budgets, etc. These are among" -" some of the use-cases where simulating FL workloads makes sense. Flower " -"can accommodate these scenarios by means of its `VirtualClientEngine " -"`_ or " -"VCE." +#: ../../source/how-to-run-flower-using-docker.rst:173 +msgid "Creating a SuperNode Dockerfile" msgstr "" -#: ../../source/how-to-run-simulations.rst:10 +#: ../../source/how-to-run-flower-using-docker.rst:175 +#: ../../source/how-to-run-flower-using-docker.rst:311 +msgid "Let's assume the following project layout:" +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:184 msgid "" -"The :code:`VirtualClientEngine` schedules, launches and manages `virtual`" -" clients. These clients are identical to `non-virtual` clients (i.e. the " -"ones you launch via the command `flwr.client.start_client `_) in the sense that they can be configure by " -"creating a class inheriting, for example, from `flwr.client.NumPyClient " -"`_ and therefore behave in an " -"identical way. In addition to that, clients managed by the " -":code:`VirtualClientEngine` are:" +"First, we need to create a ``requirements.txt`` file in the directory " +"where the ``ClientApp`` code is located. In the file, we list all the " +"dependencies that the ClientApp requires." msgstr "" -#: ../../source/how-to-run-simulations.rst:12 +#: ../../source/how-to-run-flower-using-docker.rst:196 msgid "" -"resource-aware: this means that each client gets assigned a portion of " -"the compute and memory on your system. You as a user can control this at " -"the beginning of the simulation and allows you to control the degree of " -"parallelism of your Flower FL simulation. The fewer the resources per " -"client, the more clients can run concurrently on the same hardware." +"Note that `flwr `__ is already installed " +"in the ``flwr/supernode`` base image, so you only need to include other " +"package dependencies in your ``requirements.txt``, such as ``torch``, " +"``tensorflow``, etc." msgstr "" -#: ../../source/how-to-run-simulations.rst:13 +#: ../../source/how-to-run-flower-using-docker.rst:200 msgid "" -"self-managed: this means that you as a user do not need to launch clients" -" manually, instead this gets delegated to :code:`VirtualClientEngine`'s " -"internals." +"Next, we create a Dockerfile. If you use the ``quickstart-pytorch`` " +"example, create a new file called ``Dockerfile.supernode`` in ``examples" +"/quickstart-pytorch``." msgstr "" -#: ../../source/how-to-run-simulations.rst:14 +#: ../../source/how-to-run-flower-using-docker.rst:203 msgid "" -"ephemeral: this means that a client is only materialized when it is " -"required in the FL process (e.g. to do `fit() `_). The object is destroyed afterwards," -" releasing the resources it was assigned and allowing in this way other " -"clients to participate." +"The ``Dockerfile.supernode`` contains the instructions that assemble the " +"SuperNode image." msgstr "" -#: ../../source/how-to-run-simulations.rst:16 +#: ../../source/how-to-run-flower-using-docker.rst:217 msgid "" -"The :code:`VirtualClientEngine` implements `virtual` clients using `Ray " -"`_, an open-source framework for scalable Python " -"workloads. In particular, Flower's :code:`VirtualClientEngine` makes use " -"of `Actors `_ to " -"spawn `virtual` clients and run their workload." +"In the first two lines, we instruct Docker to use the SuperNode image " +"tagged ``nightly`` as a base image and set our working directory to " +"``/app``. The following instructions will now be executed in the ``/app``" +" directory. Next, we install the ClientApp dependencies by copying the " +"``requirements.txt`` file into the image and run ``pip install``. In the " +"last two lines, we copy the ``client.py`` module into the image and set " +"the entry point to ``flower-client-app`` with the argument " +"``client:app``. The argument is the object reference of the ClientApp " +"(``:``) that will be run inside the ClientApp." msgstr "" -#: ../../source/how-to-run-simulations.rst:20 -msgid "Launch your Flower simulation" +#: ../../source/how-to-run-flower-using-docker.rst:226 +#, fuzzy +msgid "Building the SuperNode Docker image" +msgstr "Démarrer le serveur" + +#: ../../source/how-to-run-flower-using-docker.rst:228 +msgid "" +"Next, we build the SuperNode Docker image by running the following " +"command in the directory where Dockerfile and ClientApp code are located." msgstr "" -#: ../../source/how-to-run-simulations.rst:22 +#: ../../source/how-to-run-flower-using-docker.rst:235 msgid "" -"Running Flower simulations still require you to define your client class," -" a strategy, and utility functions to download and load (and potentially " -"partition) your dataset. With that out of the way, launching your " -"simulation is done with `start_simulation `_ and a minimal example looks" -" as follows:" +"We gave the image the name ``flwr_supernode``, and the tag ``0.0.1``. " +"Remember that the here chosen values only serve as an example. You can " +"change them to your needs." msgstr "" -#: ../../source/how-to-run-simulations.rst:44 +#: ../../source/how-to-run-flower-using-docker.rst:240 #, fuzzy -msgid "VirtualClientEngine resources" -msgstr "Moteur de client virtuel" +msgid "Running the SuperNode Docker image" +msgstr "Démarrer le serveur" -#: ../../source/how-to-run-simulations.rst:45 -msgid "" -"By default the VCE has access to all system resources (i.e. all CPUs, all" -" GPUs, etc) since that is also the default behavior when starting Ray. " -"However, in some settings you might want to limit how many of your system" -" resources are used for simulation. You can do this via the " -":code:`ray_init_args` input argument to :code:`start_simulation` which " -"the VCE internally passes to Ray's :code:`ray.init` command. For a " -"complete list of settings you can configure check the `ray.init " -"`_" -" documentation. Do not set :code:`ray_init_args` if you want the VCE to " -"use all your system's CPUs and GPUs." +#: ../../source/how-to-run-flower-using-docker.rst:242 +msgid "Now that we have built the SuperNode image, we can finally run it." msgstr "" -#: ../../source/how-to-run-simulations.rst:62 -msgid "Assigning client resources" +#: ../../source/how-to-run-flower-using-docker.rst:250 +#: ../../source/how-to-run-flower-using-docker.rst:367 +msgid "Let's break down each part of this command:" msgstr "" -#: ../../source/how-to-run-simulations.rst:63 -msgid "" -"By default the :code:`VirtualClientEngine` assigns a single CPU core (and" -" nothing else) to each virtual client. This means that if your system has" -" 10 cores, that many virtual clients can be concurrently running." +#: ../../source/how-to-run-flower-using-docker.rst:252 +#: ../../source/how-to-run-flower-using-docker.rst:369 +msgid "``docker run``: This is the command to run a new Docker container." msgstr "" -#: ../../source/how-to-run-simulations.rst:65 +#: ../../source/how-to-run-flower-using-docker.rst:253 +#: ../../source/how-to-run-flower-using-docker.rst:370 msgid "" -"More often than not, you would probably like to adjust the resources your" -" clients get assigned based on the complexity (i.e. compute and memory " -"footprint) of your FL workload. You can do so when starting your " -"simulation by setting the argument `client_resources` to " -"`start_simulation `_." -" Two keys are internally used by Ray to schedule and spawn workloads (in " -"our case Flower clients):" +"``--rm``: This option specifies that the container should be " +"automatically removed when it stops." msgstr "" -#: ../../source/how-to-run-simulations.rst:67 -msgid ":code:`num_cpus` indicates the number of CPU cores a client would get." +#: ../../source/how-to-run-flower-using-docker.rst:254 +msgid "``flwr_supernode:0.0.1``: The name the tag of the Docker image to use." msgstr "" -#: ../../source/how-to-run-simulations.rst:68 +#: ../../source/how-to-run-flower-using-docker.rst:255 +#: ../../source/how-to-run-flower-using-docker.rst:372 +msgid "``--insecure``: This option enables insecure communication." +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst msgid "" -":code:`num_gpus` indicates the **ratio** of GPU memory a client gets " -"assigned." +"``--superlink 192.168.1.100:9092``: This option specifies the address of " +"the SuperLinks Fleet" msgstr "" -#: ../../source/how-to-run-simulations.rst:70 -msgid "Let's see a few examples:" +#: ../../source/how-to-run-flower-using-docker.rst +msgid "API to connect to. Remember to update it with your SuperLink IP." msgstr "" -#: ../../source/how-to-run-simulations.rst:89 +#: ../../source/how-to-run-flower-using-docker.rst:269 +msgid "" +"To test running Flower locally, you can create a `bridge network " +"`__, use the ``--network`` argument and pass the " +"name of the Docker network to run your SuperNodes." +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:273 +msgid "" +"Any argument that comes after the tag is passed to the Flower SuperNode " +"binary. To see all available flags that the SuperNode supports, run:" +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:283 +msgid "" +"To enable SSL, we will need to mount a PEM-encoded root certificate into " +"your SuperNode container." +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:285 +msgid "" +"Assuming the certificate already exists locally, we can use the flag " +"``--volume`` to mount the local certificate into the container's " +"``/app/`` directory. This allows the SuperNode to access the certificate " +"within the container. Use the ``--root-certificates`` flag when starting " +"the container." +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:297 +#, fuzzy +msgid "Flower ServerApp" +msgstr "Serveur de Flower" + +#: ../../source/how-to-run-flower-using-docker.rst:299 +msgid "" +"The procedure for building and running a ServerApp image is almost " +"identical to the SuperNode image." +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:301 +msgid "" +"Similar to the SuperNode image, the ServerApp Docker image comes with a " +"pre-installed version of Flower and serves as a base for building your " +"own ServerApp image." +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:304 +msgid "" +"We will use the same ``quickstart-pytorch`` example as we do in the " +"Flower SuperNode section. If you have not already done so, please follow " +"the `SuperNode Prerequisites`_ before proceeding." +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:309 +msgid "Creating a ServerApp Dockerfile" +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:320 +msgid "" +"First, we need to create a Dockerfile in the directory where the " +"``ServerApp`` code is located. If you use the ``quickstart-pytorch`` " +"example, create a new file called ``Dockerfile.serverapp`` in ``examples" +"/quickstart-pytorch``." +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:324 +msgid "" +"The ``Dockerfile.serverapp`` contains the instructions that assemble the " +"ServerApp image." +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:335 +msgid "" +"In the first two lines, we instruct Docker to use the ServerApp image " +"tagged ``1.8.0`` as a base image and set our working directory to " +"``/app``. The following instructions will now be executed in the ``/app``" +" directory. In the last two lines, we copy the ``server.py`` module into " +"the image and set the entry point to ``flower-server-app`` with the " +"argument ``server:app``. The argument is the object reference of the " +"ServerApp (``:``) that will be run inside the " +"ServerApp container." +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:343 +#, fuzzy +msgid "Building the ServerApp Docker image" +msgstr "Démarrer le serveur" + +#: ../../source/how-to-run-flower-using-docker.rst:345 +msgid "" +"Next, we build the ServerApp Docker image by running the following " +"command in the directory where Dockerfile and ServerApp code are located." +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:352 +msgid "" +"We gave the image the name ``flwr_serverapp``, and the tag ``0.0.1``. " +"Remember that the here chosen values only serve as an example. You can " +"change them to your needs." +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:357 +#, fuzzy +msgid "Running the ServerApp Docker image" +msgstr "Démarrer le serveur" + +#: ../../source/how-to-run-flower-using-docker.rst:359 +msgid "Now that we have built the ServerApp image, we can finally run it." +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:371 +msgid "``flwr_serverapp:0.0.1``: The name the tag of the Docker image to use." +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst +msgid "" +"``--superlink 192.168.1.100:9091``: This option specifies the address of " +"the SuperLinks Driver" +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:385 +msgid "" +"To test running Flower locally, you can create a `bridge network " +"`__, use the ``--network`` argument and pass the " +"name of the Docker network to run your ServerApps." +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:389 +msgid "" +"Any argument that comes after the tag is passed to the Flower ServerApp " +"binary. To see all available flags that the ServerApp supports, run:" +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:399 +msgid "" +"To enable SSL, we will need to mount a PEM-encoded root certificate into " +"your ServerApp container." +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:401 +msgid "" +"Assuming the certificate already exists locally, we can use the flag " +"``--volume`` to mount the local certificate into the container's " +"``/app/`` directory. This allows the ServerApp to access the certificate " +"within the container. Use the ``--root-certificates`` flags when starting" +" the container." +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:412 +#, fuzzy +msgid "Advanced Docker options" +msgstr "Options d'installation avancées" + +#: ../../source/how-to-run-flower-using-docker.rst:415 +msgid "Run with root user privileges" +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:417 +msgid "" +"Flower Docker images, by default, run with a non-root user " +"(username/groupname: ``app``, UID/GID: ``49999``). Using root user is not" +" recommended unless it is necessary for specific tasks during the build " +"process. Always make sure to run the container as a non-root user in " +"production to maintain security best practices." +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:422 +msgid "**Run a container with root user privileges**" +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:424 +msgid "" +"Run the Docker image with the ``-u`` flag and specify ``root`` as the " +"username:" +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:430 +msgid "This command will run the Docker container with root user privileges." +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:432 +msgid "**Run the build process with root user privileges**" +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:434 +msgid "" +"If you want to switch to the root user during the build process of the " +"Docker image to install missing system dependencies, you can use the " +"``USER root`` directive within your Dockerfile." +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:454 +msgid "Using a different Flower version" +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:456 +msgid "" +"If you want to use a different version of Flower, for example Flower " +"nightly, you can do so by changing the tag. All available versions are on" +" `Docker Hub `__." +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:460 +msgid "Pinning a Docker image to a specific version" +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:462 +msgid "" +"It may happen that we update the images behind the tags. Such updates " +"usually include security updates of system dependencies that should not " +"change the functionality of Flower. However, if you want to ensure that " +"you always use the same image, you can specify the hash of the image " +"instead of the tag." +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:467 +msgid "" +"The following command returns the current image hash referenced by the " +"``superlink:1.8.0`` tag:" +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:474 +msgid "Next, we can pin the hash when running a new SuperLink container:" +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:483 +#, fuzzy +msgid "Setting environment variables" +msgstr "Mise en place de l'environnement de codage" + +#: ../../source/how-to-run-flower-using-docker.rst:485 +msgid "" +"To set a variable inside a Docker container, you can use the ``-e " +"=`` flag." +msgstr "" + +#: ../../source/how-to-run-simulations.rst:2 +#, fuzzy +msgid "Run simulations" +msgstr "Simulation de moniteur" + +#: ../../source/how-to-run-simulations.rst:8 +msgid "" +"Simulating Federated Learning workloads is useful for a multitude of use-" +"cases: you might want to run your workload on a large cohort of clients " +"but without having to source, configure and mange a large number of " +"physical devices; you might want to run your FL workloads as fast as " +"possible on the compute systems you have access to without having to go " +"through a complex setup process; you might want to validate your " +"algorithm on different scenarios at varying levels of data and system " +"heterogeneity, client availability, privacy budgets, etc. These are among" +" some of the use-cases where simulating FL workloads makes sense. Flower " +"can accommodate these scenarios by means of its `VirtualClientEngine " +"`_ or " +"VCE." +msgstr "" + +#: ../../source/how-to-run-simulations.rst:10 +msgid "" +"The :code:`VirtualClientEngine` schedules, launches and manages `virtual`" +" clients. These clients are identical to `non-virtual` clients (i.e. the " +"ones you launch via the command `flwr.client.start_client `_) in the sense that they can be configure by " +"creating a class inheriting, for example, from `flwr.client.NumPyClient " +"`_ and therefore behave in an " +"identical way. In addition to that, clients managed by the " +":code:`VirtualClientEngine` are:" +msgstr "" + +#: ../../source/how-to-run-simulations.rst:12 +msgid "" +"resource-aware: this means that each client gets assigned a portion of " +"the compute and memory on your system. You as a user can control this at " +"the beginning of the simulation and allows you to control the degree of " +"parallelism of your Flower FL simulation. The fewer the resources per " +"client, the more clients can run concurrently on the same hardware." +msgstr "" + +#: ../../source/how-to-run-simulations.rst:13 +msgid "" +"self-managed: this means that you as a user do not need to launch clients" +" manually, instead this gets delegated to :code:`VirtualClientEngine`'s " +"internals." +msgstr "" + +#: ../../source/how-to-run-simulations.rst:14 +msgid "" +"ephemeral: this means that a client is only materialized when it is " +"required in the FL process (e.g. to do `fit() `_). The object is destroyed afterwards," +" releasing the resources it was assigned and allowing in this way other " +"clients to participate." +msgstr "" + +#: ../../source/how-to-run-simulations.rst:16 +msgid "" +"The :code:`VirtualClientEngine` implements `virtual` clients using `Ray " +"`_, an open-source framework for scalable Python " +"workloads. In particular, Flower's :code:`VirtualClientEngine` makes use " +"of `Actors `_ to " +"spawn `virtual` clients and run their workload." +msgstr "" + +#: ../../source/how-to-run-simulations.rst:20 +msgid "Launch your Flower simulation" +msgstr "" + +#: ../../source/how-to-run-simulations.rst:22 +msgid "" +"Running Flower simulations still require you to define your client class," +" a strategy, and utility functions to download and load (and potentially " +"partition) your dataset. With that out of the way, launching your " +"simulation is done with `start_simulation `_ and a minimal example looks" +" as follows:" +msgstr "" + +#: ../../source/how-to-run-simulations.rst:44 +#, fuzzy +msgid "VirtualClientEngine resources" +msgstr "Moteur de client virtuel" + +#: ../../source/how-to-run-simulations.rst:45 +msgid "" +"By default the VCE has access to all system resources (i.e. all CPUs, all" +" GPUs, etc) since that is also the default behavior when starting Ray. " +"However, in some settings you might want to limit how many of your system" +" resources are used for simulation. You can do this via the " +":code:`ray_init_args` input argument to :code:`start_simulation` which " +"the VCE internally passes to Ray's :code:`ray.init` command. For a " +"complete list of settings you can configure check the `ray.init " +"`_" +" documentation. Do not set :code:`ray_init_args` if you want the VCE to " +"use all your system's CPUs and GPUs." +msgstr "" + +#: ../../source/how-to-run-simulations.rst:62 +msgid "Assigning client resources" +msgstr "" + +#: ../../source/how-to-run-simulations.rst:63 +msgid "" +"By default the :code:`VirtualClientEngine` assigns a single CPU core (and" +" nothing else) to each virtual client. This means that if your system has" +" 10 cores, that many virtual clients can be concurrently running." +msgstr "" + +#: ../../source/how-to-run-simulations.rst:65 +msgid "" +"More often than not, you would probably like to adjust the resources your" +" clients get assigned based on the complexity (i.e. compute and memory " +"footprint) of your FL workload. You can do so when starting your " +"simulation by setting the argument `client_resources` to " +"`start_simulation `_." +" Two keys are internally used by Ray to schedule and spawn workloads (in " +"our case Flower clients):" +msgstr "" + +#: ../../source/how-to-run-simulations.rst:67 +msgid ":code:`num_cpus` indicates the number of CPU cores a client would get." +msgstr "" + +#: ../../source/how-to-run-simulations.rst:68 +msgid "" +":code:`num_gpus` indicates the **ratio** of GPU memory a client gets " +"assigned." +msgstr "" + +#: ../../source/how-to-run-simulations.rst:70 +msgid "Let's see a few examples:" +msgstr "" + +#: ../../source/how-to-run-simulations.rst:89 msgid "" "While the :code:`client_resources` can be used to control the degree of " "concurrency in your FL simulation, this does not stop you from running " @@ -6891,6 +6937,7 @@ msgstr "" "0.x existants." #: ../../source/how-to-upgrade-to-flower-1.0.rst:8 +#: ../../source/how-to-upgrade-to-flower-next.rst:43 msgid "Install update" msgstr "Installer la mise à jour" @@ -6947,6 +6994,7 @@ msgstr "" "l'utilisation de ``start_simulation``)" #: ../../source/how-to-upgrade-to-flower-1.0.rst:24 +#: ../../source/how-to-upgrade-to-flower-next.rst:100 msgid "Required changes" msgstr "Changements nécessaires" @@ -6986,8 +7034,13 @@ msgstr "" "``start_client(server_address=\"127.0.0.1:8080\", " "client=FlowerClient())``" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:39 -msgid "" +#: ../../source/how-to-upgrade-to-flower-1.0.rst:37 +#: ../../source/ref-api/flwr.client.Client.rst:2 +msgid "Client" +msgstr "Client" + +#: ../../source/how-to-upgrade-to-flower-1.0.rst:39 +msgid "" "Subclasses of ``NumPyClient``: change ``def get_parameters(self):``` to " "``def get_parameters(self, config):``" msgstr "" @@ -7206,6 +7259,7 @@ msgstr "" "round_timeout=600.0), ...)``" #: ../../source/how-to-upgrade-to-flower-1.0.rst:89 +#: ../../source/how-to-upgrade-to-flower-next.rst:317 msgid "Further help" msgstr "Aide supplémentaire" @@ -7224,6 +7278,210 @@ msgstr "" "Flower `_ et utilise le canal " "``#questions``." +#: ../../source/how-to-upgrade-to-flower-next.rst:2 +#, fuzzy +msgid "Upgrade to Flower Next" +msgstr "Passe à Flower 1.0" + +#: ../../source/how-to-upgrade-to-flower-next.rst:4 +msgid "" +"Welcome to the migration guide for updating Flower to Flower Next! " +"Whether you're a seasoned user or just getting started, this guide will " +"help you smoothly transition your existing setup to take advantage of the" +" latest features and improvements in Flower Next, starting from version " +"1.8." +msgstr "" + +#: ../../source/how-to-upgrade-to-flower-next.rst:9 +msgid "" +"This guide shows how to reuse pre-``1.8`` Flower code with minimum code " +"changes by using the *compatibility layer* in Flower Next. In another " +"guide, we will show how to run Flower Next end-to-end with pure Flower " +"Next APIs." +msgstr "" + +#: ../../source/how-to-upgrade-to-flower-next.rst:13 +msgid "Let's dive in!" +msgstr "" + +#: ../../source/how-to-upgrade-to-flower-next.rst:48 +#, fuzzy +msgid "" +"Here's how to update an existing installation of Flower to Flower Next " +"with ``pip``:" +msgstr "" +"Voici comment mettre à jour une installation existante vers Flower 1.0 en" +" utilisant soit pip soit Poetry :" + +#: ../../source/how-to-upgrade-to-flower-next.rst:54 +msgid "or if you need Flower Next with simulation:" +msgstr "" + +#: ../../source/how-to-upgrade-to-flower-next.rst:61 +msgid "" +"Ensure you set the following version constraint in your " +"``requirements.txt``" +msgstr "" + +#: ../../source/how-to-upgrade-to-flower-next.rst:71 +msgid "or ``pyproject.toml``:" +msgstr "" + +#: ../../source/how-to-upgrade-to-flower-next.rst:82 +#, fuzzy +msgid "Using Poetry" +msgstr "Utiliser la poésie (recommandé)" + +#: ../../source/how-to-upgrade-to-flower-next.rst:84 +#, fuzzy +msgid "" +"Update the ``flwr`` dependency in ``pyproject.toml`` and then reinstall " +"(don't forget to delete ``poetry.lock`` via ``rm poetry.lock`` before " +"running ``poetry install``)." +msgstr "" +"Poetry : mettez à jour la dépendance ``flwr`` dans ``pyproject.toml`` " +"puis réinstallez (n'oubliez pas de supprimer ``poetry.lock`` via ``rm " +"poetry.lock`` avant d'exécuter ``poetry install``)." + +#: ../../source/how-to-upgrade-to-flower-next.rst:86 +#, fuzzy +msgid "" +"Ensure you set the following version constraint in your " +"``pyproject.toml``:" +msgstr "Augmente la version mineure de ``pyproject.toml`` d'une unité." + +#: ../../source/how-to-upgrade-to-flower-next.rst:102 +msgid "" +"In Flower Next, the *infrastructure* and *application layers* have been " +"decoupled. Instead of starting a client in code via ``start_client()``, " +"you create a |clientapp_link|_ and start it via the command line. Instead" +" of starting a server in code via ``start_server()``, you create a " +"|serverapp_link|_ and start it via the command line. The long-running " +"components of server and client are called SuperLink and SuperNode. The " +"following non-breaking changes that require manual updates and allow you " +"to run your project both in the traditional way and in the Flower Next " +"way:" +msgstr "" + +#: ../../source/how-to-upgrade-to-flower-next.rst:109 +#, fuzzy +msgid "|clientapp_link|_" +msgstr "client" + +#: ../../source/how-to-upgrade-to-flower-next.rst:110 +msgid "" +"Wrap your existing client with |clientapp_link|_ instead of launching it " +"via |startclient_link|_. Here's an example:" +msgstr "" + +#: ../../source/how-to-upgrade-to-flower-next.rst:132 +#, fuzzy +msgid "|serverapp_link|_" +msgstr "serveur" + +#: ../../source/how-to-upgrade-to-flower-next.rst:133 +msgid "" +"Wrap your existing strategy with |serverapp_link|_ instead of starting " +"the server via |startserver_link|_. Here's an example:" +msgstr "" + +#: ../../source/how-to-upgrade-to-flower-next.rst:154 +msgid "Deployment" +msgstr "" + +#: ../../source/how-to-upgrade-to-flower-next.rst:155 +msgid "" +"Run the ``SuperLink`` using |flowernext_superlink_link|_ before running, " +"in sequence, |flowernext_clientapp_link|_ (2x) and " +"|flowernext_serverapp_link|_. There is no need to execute `client.py` and" +" `server.py` as Python scripts." +msgstr "" + +#: ../../source/how-to-upgrade-to-flower-next.rst:158 +msgid "" +"Here's an example to start the server without HTTPS (only for " +"prototyping):" +msgstr "" + +#: ../../source/how-to-upgrade-to-flower-next.rst:174 +msgid "" +"Here's another example to start with HTTPS. Use the ``--ssl-ca-" +"certfile``, ``--ssl-certfile``, and ``--ssl-keyfile`` command line " +"options to pass paths to (CA certificate, server certificate, and server " +"private key)." +msgstr "" + +#: ../../source/how-to-upgrade-to-flower-next.rst:201 +#, fuzzy +msgid "Simulation in CLI" +msgstr "Simulation de moniteur" + +#: ../../source/how-to-upgrade-to-flower-next.rst:202 +msgid "" +"Wrap your existing client and strategy with |clientapp_link|_ and " +"|serverapp_link|_, respectively. There is no need to use |startsim_link|_" +" anymore. Here's an example:" +msgstr "" + +#: ../../source/how-to-upgrade-to-flower-next.rst:232 +msgid "" +"Run |flower_simulation_link|_ in CLI and point to the ``server_app`` / " +"``client_app`` object in the code instead of executing the Python script." +" Here's an example (assuming the ``server_app`` and ``client_app`` " +"objects are in a ``sim.py`` module):" +msgstr "" + +#: ../../source/how-to-upgrade-to-flower-next.rst:249 +msgid "" +"Set default resources for each |clientapp_link|_ using the ``--backend-" +"config`` command line argument instead of setting the " +"``client_resources`` argument in |startsim_link|_. Here's an example:" +msgstr "" + +#: ../../source/how-to-upgrade-to-flower-next.rst:275 +msgid "Simulation in a Notebook" +msgstr "" + +#: ../../source/how-to-upgrade-to-flower-next.rst:276 +msgid "" +"Run |runsim_link|_ in your notebook instead of |startsim_link|_. Here's " +"an example:" +msgstr "" + +#: ../../source/how-to-upgrade-to-flower-next.rst:319 +#, fuzzy +msgid "" +"Some official `Flower code examples `_ " +"are already updated to Flower Next so they can serve as a reference for " +"using the Flower Next API. If there are further questions, `join the " +"Flower Slack `_ and use the channel " +"``#questions``. You can also `participate in Flower Discuss " +"`_ where you can find us answering questions," +" or share and learn from others about migrating to Flower Next." +msgstr "" +"La plupart des `exemples de code Flower officiels " +"`_ sont déjà mis à " +"jour vers Flower 1.0, ils peuvent servir de référence pour l'utilisation " +"de l'API Flower 1.0. Si vous avez d'autres questions, `joins le Slack " +"Flower `_ et utilise le canal " +"``#questions``." + +#: ../../source/how-to-upgrade-to-flower-next.rst:325 +#, fuzzy +msgid "Important" +msgstr "Changements importants :" + +#: ../../source/how-to-upgrade-to-flower-next.rst:328 +msgid "" +"As we continuously enhance Flower Next at a rapid pace, we'll be " +"periodically updating this guide. Please feel free to share any feedback " +"with us!" +msgstr "" + +#: ../../source/how-to-upgrade-to-flower-next.rst:334 +msgid "Happy migrating! 🚀" +msgstr "" + #: ../../source/how-to-use-built-in-mods.rst:2 msgid "Use Built-in Mods" msgstr "" @@ -7643,15 +7901,15 @@ msgstr "Tutoriel" msgid "Quickstart tutorials" msgstr "Quickstart tutorials" -#: ../../source/index.rst:75 ../../source/index.rst:79 +#: ../../source/index.rst:74 ../../source/index.rst:78 msgid "How-to guides" msgstr "Guides" -#: ../../source/index.rst:98 +#: ../../source/index.rst:99 msgid "Legacy example guides" msgstr "" -#: ../../source/index.rst:109 ../../source/index.rst:113 +#: ../../source/index.rst:108 ../../source/index.rst:112 msgid "Explanations" msgstr "Explications" @@ -7659,26 +7917,26 @@ msgstr "Explications" msgid "API reference" msgstr "Référence pour l'API" -#: ../../source/index.rst:138 +#: ../../source/index.rst:137 msgid "Reference docs" msgstr "Référence pour la documentation" -#: ../../source/index.rst:154 +#: ../../source/index.rst:153 #, fuzzy msgid "Contributor tutorials" msgstr "Configuration du contributeur" -#: ../../source/index.rst:161 +#: ../../source/index.rst:160 #, fuzzy msgid "Contributor how-to guides" msgstr "Guide pour les contributeurs" -#: ../../source/index.rst:174 +#: ../../source/index.rst:172 #, fuzzy msgid "Contributor explanations" msgstr "Explications" -#: ../../source/index.rst:180 +#: ../../source/index.rst:178 #, fuzzy msgid "Contributor references" msgstr "Configuration du contributeur" @@ -7748,7 +8006,7 @@ msgstr "" "Une serie de tutoriels de Federated Learning, l'endroit parfait pour " "débuter." -#: ../../source/index.rst:62 +#: ../../source/index.rst:61 #, fuzzy msgid "" "QUICKSTART TUTORIALS: :doc:`PyTorch ` | " @@ -7756,10 +8014,10 @@ msgid "" " ` | :doc:`JAX ` | :doc:`Pandas ` | :doc:`fastai " "` | :doc:`PyTorch Lightning ` | :doc:`MXNet `" -" | :doc:`scikit-learn ` | :doc:`XGBoost " -"` | :doc:`Android ` | :doc:`iOS `" +"quickstart-pytorch-lightning>` | :doc:`scikit-learn ` | :doc:`XGBoost ` | " +":doc:`Android ` | :doc:`iOS `" msgstr "" "QUICKSTART TUTORIALS: :ref:`PyTorch ` | " ":ref:`TensorFlow ` | :ref:`🤗 Transformers " @@ -7770,16 +8028,16 @@ msgstr "" "` | :ref:`Android ` | :ref:`iOS " "`" -#: ../../source/index.rst:64 +#: ../../source/index.rst:63 msgid "We also made video tutorials for PyTorch:" msgstr "" -#: ../../source/index.rst:69 +#: ../../source/index.rst:68 #, fuzzy msgid "And TensorFlow:" msgstr "Exemples de TensorFlow" -#: ../../source/index.rst:77 +#: ../../source/index.rst:76 msgid "" "Problem-oriented how-to guides show step-by-step how to achieve a " "specific goal." @@ -7787,7 +8045,7 @@ msgstr "" "Guides orientés sur la résolutions étapes par étapes de problèmes ou " "objectifs specifiques." -#: ../../source/index.rst:111 +#: ../../source/index.rst:110 msgid "" "Understanding-oriented concept guides explain and discuss key topics and " "underlying ideas behind Flower and collaborative AI." @@ -7795,29 +8053,29 @@ msgstr "" "Guides orientés sur la compréhension et l'explication des sujets et idées" " de fonds sur lesquels sont construits Flower et l'IA collaborative." -#: ../../source/index.rst:121 +#: ../../source/index.rst:120 #, fuzzy msgid "References" msgstr "Référence" -#: ../../source/index.rst:123 +#: ../../source/index.rst:122 msgid "Information-oriented API reference and other reference material." msgstr "Référence de l'API orientée sur l'information pure." -#: ../../source/index.rst:132::1 +#: ../../source/index.rst:131::1 msgid ":py:obj:`flwr `\\" msgstr "" -#: ../../source/index.rst:132::1 flwr:1 of +#: ../../source/index.rst:131::1 flwr:1 of msgid "Flower main package." msgstr "" -#: ../../source/index.rst:149 +#: ../../source/index.rst:148 #, fuzzy msgid "Contributor docs" msgstr "Configuration du contributeur" -#: ../../source/index.rst:151 +#: ../../source/index.rst:150 #, fuzzy msgid "" "The Flower community welcomes contributions. The following docs are " @@ -7833,23 +8091,20 @@ msgid "Flower CLI reference" msgstr "Client de Flower" #: ../../source/ref-api-cli.rst:7 -msgid "flower-superlink" -msgstr "flower-superlink" +#, fuzzy +msgid "flower-simulation" +msgstr "Simulation de moniteur" #: ../../source/ref-api-cli.rst:17 -msgid "flower-driver-api" -msgstr "flower-driver-api" +msgid "flower-superlink" +msgstr "flower-superlink" #: ../../source/ref-api-cli.rst:27 -msgid "flower-fleet-api" -msgstr "flower-fleet-api" - -#: ../../source/ref-api-cli.rst:37 #, fuzzy msgid "flower-client-app" msgstr "Flower ClientApp." -#: ../../source/ref-api-cli.rst:47 +#: ../../source/ref-api-cli.rst:37 #, fuzzy msgid "flower-server-app" msgstr "flower-driver-api" @@ -7859,7 +8114,8 @@ msgstr "flower-driver-api" msgid "flwr" msgstr "Fleur" -#: ../../source/ref-api/flwr.rst:25 ../../source/ref-api/flwr.server.rst:52 +#: ../../source/ref-api/flwr.client.rst:45 ../../source/ref-api/flwr.rst:25 +#: ../../source/ref-api/flwr.server.rst:49 msgid "Modules" msgstr "" @@ -7885,7 +8141,7 @@ msgid ":py:obj:`flwr.server `\\" msgstr "" #: ../../source/ref-api/flwr.rst:35::1 -#: ../../source/ref-api/flwr.server.rst:41::1 flwr.server:1 +#: ../../source/ref-api/flwr.server.rst:38::1 flwr.server:1 #: flwr.server.server.Server:1 of #, fuzzy msgid "Flower server." @@ -7904,6 +8160,7 @@ msgstr "Simulation de moniteur" msgid "client" msgstr "client" +#: ../../source/ref-api/flwr.client.mod.rst:13 #: ../../source/ref-api/flwr.client.rst:13 #: ../../source/ref-api/flwr.common.rst:13 #: ../../source/ref-api/flwr.server.rst:13 @@ -7912,85 +8169,109 @@ msgstr "client" msgid "Functions" msgstr "Les quatre fonctions :" -#: ../../source/ref-api/flwr.client.rst:24::1 +#: ../../source/ref-api/flwr.client.rst:25::1 msgid ":py:obj:`run_client_app `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.client.rst:24::1 -#: flwr.client.app.run_client_app:1 of +#: ../../source/ref-api/flwr.client.rst:25::1 +#: flwr.client.supernode.app.run_client_app:1 of #, fuzzy msgid "Run Flower client app." msgstr "Client de Flower" -#: ../../source/ref-api/flwr.client.rst:24::1 +#: ../../source/ref-api/flwr.client.rst:25::1 +#, fuzzy +msgid ":py:obj:`run_supernode `\\ \\(\\)" +msgstr "serveur.stratégie.Stratégie" + +#: ../../source/ref-api/flwr.client.rst:25::1 +#: flwr.client.supernode.app.run_supernode:1 of +#, fuzzy +msgid "Run Flower SuperNode." +msgstr "Serveur de Flower" + +#: ../../source/ref-api/flwr.client.rst:25::1 msgid "" ":py:obj:`start_client `\\ \\(\\*\\, " "server\\_address\\[\\, client\\_fn\\, ...\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.client.rst:24::1 +#: ../../source/ref-api/flwr.client.rst:25::1 #: flwr.client.app.start_client:1 of msgid "Start a Flower client node which connects to a Flower server." msgstr "" -#: ../../source/ref-api/flwr.client.rst:24::1 +#: ../../source/ref-api/flwr.client.rst:25::1 msgid "" ":py:obj:`start_numpy_client `\\ \\(\\*\\," " server\\_address\\, client\\)" msgstr "" -#: ../../source/ref-api/flwr.client.rst:24::1 +#: ../../source/ref-api/flwr.client.rst:25::1 #: flwr.client.app.start_numpy_client:1 of msgid "Start a Flower NumPyClient which connects to a gRPC server." msgstr "" -#: ../../source/ref-api/flwr.client.rst:26 +#: ../../source/ref-api/flwr.client.mod.rst:30 +#: ../../source/ref-api/flwr.client.rst:27 #: ../../source/ref-api/flwr.common.rst:32 -#: ../../source/ref-api/flwr.server.rst:29 +#: ../../source/ref-api/flwr.server.rst:26 #: ../../source/ref-api/flwr.server.strategy.rst:17 #: ../../source/ref-api/flwr.server.workflow.rst:17 msgid "Classes" msgstr "" -#: ../../source/ref-api/flwr.client.rst:33::1 +#: ../../source/ref-api/flwr.client.rst:34::1 msgid ":py:obj:`Client `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.client.rst:33::1 +#: ../../source/ref-api/flwr.client.rst:34::1 #: flwr.client.client.Client:1 of msgid "Abstract base class for Flower clients." msgstr "" -#: ../../source/ref-api/flwr.client.rst:33::1 +#: ../../source/ref-api/flwr.client.rst:34::1 msgid "" ":py:obj:`ClientApp `\\ \\(\\[client\\_fn\\, " "mods\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.client.rst:33::1 +#: ../../source/ref-api/flwr.client.rst:34::1 #: flwr.client.client_app.ClientApp:1 of #, fuzzy msgid "Flower ClientApp." msgstr "Flower ClientApp." -#: ../../source/ref-api/flwr.client.rst:33::1 +#: ../../source/ref-api/flwr.client.rst:34::1 msgid ":py:obj:`NumPyClient `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.client.rst:33::1 +#: ../../source/ref-api/flwr.client.rst:34::1 #: flwr.client.numpy_client.NumPyClient:1 of msgid "Abstract base class for Flower clients using NumPy." msgstr "" +#: ../../source/ref-api/flwr.client.rst:52::1 +#, fuzzy +msgid ":py:obj:`flwr.client.mod `\\" +msgstr "serveur.stratégie.Stratégie" + +#: ../../source/ref-api/flwr.client.rst:52::1 flwr.client.mod:1 of +#, fuzzy +msgid "Flower Built-in Mods." +msgstr "Client de Flower" + #: flwr.client.client.Client:1 flwr.client.numpy_client.NumPyClient:1 #: flwr.server.client_manager.ClientManager:1 -#: flwr.server.strategy.strategy.Strategy:1 of +#: flwr.server.driver.driver.Driver:1 flwr.server.strategy.strategy.Strategy:1 +#: of msgid "Bases: :py:class:`~abc.ABC`" msgstr "" #: ../../source/ref-api/flwr.client.Client.rst:15 #: ../../source/ref-api/flwr.client.ClientApp.rst:15 #: ../../source/ref-api/flwr.client.NumPyClient.rst:15 +#: ../../source/ref-api/flwr.client.mod.LocalDpMod.rst:15 #: ../../source/ref-api/flwr.common.Array.rst:15 #: ../../source/ref-api/flwr.common.ClientMessage.rst:15 #: ../../source/ref-api/flwr.common.ConfigsRecord.rst:15 @@ -7999,6 +8280,7 @@ msgstr "" #: ../../source/ref-api/flwr.common.Error.rst:15 #: ../../source/ref-api/flwr.common.EvaluateIns.rst:15 #: ../../source/ref-api/flwr.common.EvaluateRes.rst:15 +#: ../../source/ref-api/flwr.common.EventType.rst:15 #: ../../source/ref-api/flwr.common.FitIns.rst:15 #: ../../source/ref-api/flwr.common.FitRes.rst:15 #: ../../source/ref-api/flwr.common.GetParametersIns.rst:15 @@ -8138,7 +8420,7 @@ msgstr "" #: ../../source/ref-api/flwr.common.Error.rst:25 #: ../../source/ref-api/flwr.common.EvaluateIns.rst:25 #: ../../source/ref-api/flwr.common.EvaluateRes.rst:25 -#: ../../source/ref-api/flwr.common.EventType.rst:19 +#: ../../source/ref-api/flwr.common.EventType.rst:165 #: ../../source/ref-api/flwr.common.FitIns.rst:25 #: ../../source/ref-api/flwr.common.FitRes.rst:25 #: ../../source/ref-api/flwr.common.GetParametersIns.rst:25 @@ -8168,6 +8450,7 @@ msgstr "" #: flwr.client.client.Client.evaluate flwr.client.client.Client.fit #: flwr.client.client.Client.get_parameters #: flwr.client.client.Client.get_properties +#: flwr.client.mod.localdp_mod.LocalDpMod #: flwr.client.numpy_client.NumPyClient.evaluate #: flwr.client.numpy_client.NumPyClient.fit #: flwr.client.numpy_client.NumPyClient.get_parameters @@ -8181,7 +8464,6 @@ msgstr "" #: flwr.server.client_manager.SimpleClientManager.register #: flwr.server.client_manager.SimpleClientManager.unregister #: flwr.server.client_manager.SimpleClientManager.wait_for -#: flwr.server.compat.app.start_driver flwr.server.driver.driver.Driver #: flwr.server.driver.driver.Driver.create_message #: flwr.server.driver.driver.Driver.pull_messages #: flwr.server.driver.driver.Driver.push_messages @@ -8234,7 +8516,6 @@ msgstr "" #: flwr.server.client_manager.SimpleClientManager.num_available #: flwr.server.client_manager.SimpleClientManager.register #: flwr.server.client_manager.SimpleClientManager.wait_for -#: flwr.server.compat.app.start_driver #: flwr.server.driver.driver.Driver.create_message #: flwr.server.driver.driver.Driver.pull_messages #: flwr.server.driver.driver.Driver.push_messages @@ -8269,7 +8550,6 @@ msgstr "" #: flwr.server.client_manager.SimpleClientManager.num_available #: flwr.server.client_manager.SimpleClientManager.register #: flwr.server.client_manager.SimpleClientManager.wait_for -#: flwr.server.compat.app.start_driver #: flwr.server.driver.driver.Driver.create_message #: flwr.server.driver.driver.Driver.pull_messages #: flwr.server.driver.driver.Driver.push_messages @@ -8325,10 +8605,11 @@ msgstr "" msgid "ClientApp" msgstr "client" -#: flwr.client.client_app.ClientApp:1 flwr.common.constant.MessageType:1 -#: flwr.common.constant.MessageTypeLegacy:1 flwr.common.context.Context:1 -#: flwr.common.message.Error:1 flwr.common.message.Message:1 -#: flwr.common.message.Metadata:1 flwr.common.record.parametersrecord.Array:1 +#: flwr.client.client_app.ClientApp:1 flwr.client.mod.localdp_mod.LocalDpMod:1 +#: flwr.common.constant.MessageType:1 flwr.common.constant.MessageTypeLegacy:1 +#: flwr.common.context.Context:1 flwr.common.message.Error:1 +#: flwr.common.message.Message:1 flwr.common.message.Metadata:1 +#: flwr.common.record.parametersrecord.Array:1 #: flwr.common.record.recordset.RecordSet:1 flwr.common.typing.ClientMessage:1 #: flwr.common.typing.DisconnectRes:1 flwr.common.typing.EvaluateIns:1 #: flwr.common.typing.EvaluateRes:1 flwr.common.typing.FitIns:1 @@ -8336,9 +8617,9 @@ msgstr "client" #: flwr.common.typing.GetParametersRes:1 flwr.common.typing.GetPropertiesIns:1 #: flwr.common.typing.GetPropertiesRes:1 flwr.common.typing.Parameters:1 #: flwr.common.typing.ReconnectIns:1 flwr.common.typing.ServerMessage:1 -#: flwr.common.typing.Status:1 flwr.server.driver.driver.Driver:1 -#: flwr.server.history.History:1 flwr.server.server.Server:1 -#: flwr.server.server_app.ServerApp:1 flwr.server.server_config.ServerConfig:1 +#: flwr.common.typing.Status:1 flwr.server.history.History:1 +#: flwr.server.server.Server:1 flwr.server.server_app.ServerApp:1 +#: flwr.server.server_config.ServerConfig:1 #: flwr.server.workflow.default_workflows.DefaultWorkflow:1 #: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:1 #: of @@ -8349,9 +8630,9 @@ msgstr "" #: flwr.client.client_app.ClientApp:4 #: flwr.client.client_app.ClientApp.evaluate:4 #: flwr.client.client_app.ClientApp.query:4 -#: flwr.client.client_app.ClientApp.train:4 flwr.server.app.start_server:41 -#: flwr.server.compat.app.start_driver:32 flwr.server.server_app.ServerApp:4 -#: flwr.server.server_app.ServerApp.main:4 +#: flwr.client.client_app.ClientApp.train:4 +#: flwr.client.mod.localdp_mod.LocalDpMod:22 flwr.server.app.start_server:41 +#: flwr.server.server_app.ServerApp:4 flwr.server.server_app.ServerApp.main:4 #: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:29 #: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:22 #: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:21 @@ -8574,10 +8855,248 @@ msgid "" "arbitrary property values back to the server." msgstr "" +#: ../../source/ref-api/flwr.client.mod.rst:2 +msgid "mod" +msgstr "" + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +msgid "" +":py:obj:`adaptiveclipping_mod `\\ " +"\\(msg\\, ctxt\\, call\\_next\\)" +msgstr "" + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#: flwr.client.mod.centraldp_mods.adaptiveclipping_mod:1 of +#, fuzzy +msgid "Client-side adaptive clipping modifier." +msgstr "Logique côté client" + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +msgid "" +":py:obj:`fixedclipping_mod `\\ " +"\\(msg\\, ctxt\\, call\\_next\\)" +msgstr "" + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#: flwr.client.mod.centraldp_mods.fixedclipping_mod:1 of +#, fuzzy +msgid "Client-side fixed clipping modifier." +msgstr "Logique côté client" + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#, fuzzy +msgid ":py:obj:`make_ffn `\\ \\(ffn\\, mods\\)" +msgstr "serveur.stratégie.Stratégie" + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#: flwr.client.mod.utils.make_ffn:1 of +msgid "." +msgstr "" + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +msgid "" +":py:obj:`secagg_mod `\\ \\(msg\\, ctxt\\, " +"call\\_next\\)" +msgstr "" + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#: flwr.client.mod.secure_aggregation.secagg_mod.secagg_mod:1 of +msgid "Handle incoming message and return results, following the SecAgg protocol." +msgstr "" + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +msgid "" +":py:obj:`secaggplus_mod `\\ \\(msg\\, " +"ctxt\\, call\\_next\\)" +msgstr "" + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#: flwr.client.mod.secure_aggregation.secaggplus_mod.secaggplus_mod:1 of +msgid "" +"Handle incoming message and return results, following the SecAgg+ " +"protocol." +msgstr "" + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +msgid "" +":py:obj:`message_size_mod `\\ \\(msg\\," +" ctxt\\, call\\_next\\)" +msgstr "" + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#: flwr.client.mod.comms_mods.message_size_mod:1 of +msgid "Message size mod." +msgstr "" + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +msgid "" +":py:obj:`parameters_size_mod `\\ " +"\\(msg\\, ctxt\\, call\\_next\\)" +msgstr "" + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#: flwr.client.mod.comms_mods.parameters_size_mod:1 of +#, fuzzy +msgid "Parameters size mod." +msgstr "Paramètres du modèle." + +#: ../../source/ref-api/flwr.client.mod.rst:35::1 +msgid "" +":py:obj:`LocalDpMod `\\ \\(clipping\\_norm\\," +" sensitivity\\, ...\\)" +msgstr "" + +#: ../../source/ref-api/flwr.client.mod.rst:35::1 +#: flwr.client.mod.localdp_mod.LocalDpMod:1 of +#, fuzzy +msgid "Modifier for local differential privacy." +msgstr "Confidentialité différentielle" + +#: ../../source/ref-api/flwr.client.mod.LocalDpMod.rst:2 +msgid "LocalDpMod" +msgstr "" + +#: flwr.client.mod.localdp_mod.LocalDpMod:3 of +msgid "" +"This mod clips the client model updates and adds noise to the params " +"before sending them to the server." +msgstr "" + +#: flwr.client.mod.centraldp_mods.adaptiveclipping_mod:12 +#: flwr.client.mod.centraldp_mods.fixedclipping_mod:10 +#: flwr.client.mod.localdp_mod.LocalDpMod:6 of +msgid "It operates on messages of type `MessageType.TRAIN`." +msgstr "" + +#: flwr.client.mod.localdp_mod.LocalDpMod:8 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:15 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:8 +#: of +msgid "The value of the clipping norm." +msgstr "" + +#: flwr.client.mod.localdp_mod.LocalDpMod:10 of +msgid "The sensitivity of the client model." +msgstr "" + +#: flwr.client.mod.localdp_mod.LocalDpMod:12 of +msgid "" +"The privacy budget. Smaller value of epsilon indicates a higher level of " +"privacy protection." +msgstr "" + +#: flwr.client.mod.localdp_mod.LocalDpMod:15 of +msgid "" +"The failure probability. The probability that the privacy mechanism fails" +" to provide the desired level of privacy. A smaller value of delta " +"indicates a stricter privacy guarantee." +msgstr "" + +#: flwr.client.mod.localdp_mod.LocalDpMod:23 of +msgid "Create an instance of the local DP mod and add it to the client-side mods:" +msgstr "" + +#: ../../source/ref-api/flwr.client.mod.adaptiveclipping_mod.rst:2 +msgid "adaptiveclipping\\_mod" +msgstr "" + +#: flwr.client.mod.centraldp_mods.adaptiveclipping_mod:3 of +msgid "" +"This mod needs to be used with the " +"DifferentialPrivacyClientSideAdaptiveClipping server-side strategy " +"wrapper." +msgstr "" + +#: flwr.client.mod.centraldp_mods.adaptiveclipping_mod:6 +#: flwr.client.mod.centraldp_mods.fixedclipping_mod:6 of +msgid "The wrapper sends the clipping_norm value to the client." +msgstr "" + +#: flwr.client.mod.centraldp_mods.adaptiveclipping_mod:8 +#: flwr.client.mod.centraldp_mods.fixedclipping_mod:8 of +msgid "This mod clips the client model updates before sending them to the server." +msgstr "" + +#: flwr.client.mod.centraldp_mods.adaptiveclipping_mod:10 of +msgid "" +"It also sends KEY_NORM_BIT to the server for computing the new clipping " +"value." +msgstr "" + +#: flwr.client.mod.centraldp_mods.adaptiveclipping_mod:15 +#: flwr.client.mod.centraldp_mods.fixedclipping_mod:13 +#: flwr.server.driver.driver.Driver.send_and_receive:18 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:53 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:60 +#: of +#, fuzzy +msgid "Notes" +msgstr "Aucun" + +#: flwr.client.mod.centraldp_mods.adaptiveclipping_mod:16 +#: flwr.client.mod.centraldp_mods.fixedclipping_mod:14 of +msgid "Consider the order of mods when using multiple." +msgstr "" + +#: flwr.client.mod.centraldp_mods.adaptiveclipping_mod:18 of +msgid "Typically, adaptiveclipping_mod should be the last to operate on params." +msgstr "" + +#: ../../source/ref-api/flwr.client.mod.fixedclipping_mod.rst:2 +msgid "fixedclipping\\_mod" +msgstr "" + +#: flwr.client.mod.centraldp_mods.fixedclipping_mod:3 of +msgid "" +"This mod needs to be used with the " +"DifferentialPrivacyClientSideFixedClipping server-side strategy wrapper." +msgstr "" + +#: flwr.client.mod.centraldp_mods.fixedclipping_mod:16 of +msgid "Typically, fixedclipping_mod should be the last to operate on params." +msgstr "" + +#: ../../source/ref-api/flwr.client.mod.make_ffn.rst:2 +msgid "make\\_ffn" +msgstr "" + +#: ../../source/ref-api/flwr.client.mod.message_size_mod.rst:2 +msgid "message\\_size\\_mod" +msgstr "" + +#: flwr.client.mod.comms_mods.message_size_mod:3 of +msgid "This mod logs the size in bytes of the message being transmited." +msgstr "" + +#: ../../source/ref-api/flwr.client.mod.parameters_size_mod.rst:2 +#, fuzzy +msgid "parameters\\_size\\_mod" +msgstr "Paramètres du modèle." + +#: flwr.client.mod.comms_mods.parameters_size_mod:3 of +msgid "" +"This mod logs the number of parameters transmitted in the message as well" +" as their size in bytes." +msgstr "" + +#: ../../source/ref-api/flwr.client.mod.secagg_mod.rst:2 +msgid "secagg\\_mod" +msgstr "" + +#: ../../source/ref-api/flwr.client.mod.secaggplus_mod.rst:2 +#, fuzzy +msgid "secaggplus\\_mod" +msgstr "Flux de travail" + #: ../../source/ref-api/flwr.client.run_client_app.rst:2 msgid "run\\_client\\_app" msgstr "" +#: ../../source/ref-api/flwr.client.run_supernode.rst:2 +#, fuzzy +msgid "run\\_supernode" +msgstr "flower-superlink" + #: ../../source/ref-api/flwr.client.start_client.rst:2 #, fuzzy msgid "start\\_client" @@ -8610,8 +9129,7 @@ msgid "" "increased limit and block larger messages." msgstr "" -#: flwr.client.app.start_client:19 flwr.client.app.start_numpy_client:22 -#: flwr.server.compat.app.start_driver:21 of +#: flwr.client.app.start_client:19 flwr.client.app.start_numpy_client:22 of msgid "" "The PEM-encoded root certificates as a byte string or a path string. If " "provided, a secure connection using the certificates will be established " @@ -9199,12 +9717,11 @@ msgstr "Configurer les clients" #: flwr.common.record.configsrecord.ConfigsRecord:1 of msgid "" "Bases: :py:class:`~flwr.common.record.typeddict.TypedDict`\\ " -"[:py:class:`str`, :py:obj:`~typing.Union`\\ [:py:class:`int`, " -":py:class:`float`, :py:class:`str`, :py:class:`bytes`, :py:class:`bool`, " -":py:class:`~typing.List`\\ [:py:class:`int`], :py:class:`~typing.List`\\ " -"[:py:class:`float`], :py:class:`~typing.List`\\ [:py:class:`str`], " -":py:class:`~typing.List`\\ [:py:class:`bytes`], " -":py:class:`~typing.List`\\ [:py:class:`bool`]]]" +"[:py:class:`str`, :py:class:`int` | :py:class:`float` | :py:class:`str` |" +" :py:class:`bytes` | :py:class:`bool` | :py:class:`~typing.List`\\ " +"[:py:class:`int`] | :py:class:`~typing.List`\\ [:py:class:`float`] | " +":py:class:`~typing.List`\\ [:py:class:`str`] | :py:class:`~typing.List`\\" +" [:py:class:`bytes`] | :py:class:`~typing.List`\\ [:py:class:`bool`]]" msgstr "" #: flwr.common.record.typeddict.TypedDict.clear:1::1 of @@ -9368,19287 +9885,22500 @@ msgstr "" msgid "Bases: :py:class:`str`, :py:class:`~enum.Enum`" msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid ":py:obj:`PING `\\" -msgstr "" - -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid ":py:obj:`START_CLIENT_ENTER `\\" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid "" +":py:obj:`encode `\\ \\(\\[encoding\\, " +"errors\\]\\)" msgstr "" +"Flower 1.0 : ``start_server(..., " +"config=flwr.server.ServerConfig(num_rounds=3, round_timeout=600.0), " +"...)``" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid ":py:obj:`START_CLIENT_LEAVE `\\" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.encode:1 of +msgid "Encode the string using the codec registered for encoding." msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid ":py:obj:`START_SERVER_ENTER `\\" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid "" +":py:obj:`replace `\\ \\(old\\, new\\[\\, " +"count\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid ":py:obj:`START_SERVER_LEAVE `\\" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.replace:1 of +msgid "Return a copy with all occurrences of substring old replaced by new." msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy msgid "" -":py:obj:`RUN_DRIVER_API_ENTER " -"`\\" -msgstr "" +":py:obj:`split `\\ \\(\\[sep\\, " +"maxsplit\\]\\)" +msgstr "serveur.stratégie.Stratégie" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.rsplit:1 flwr.common.EventType.split:1 of msgid "" -":py:obj:`RUN_DRIVER_API_LEAVE " -"`\\" +"Return a list of the substrings in the string, using sep as the separator" +" string." msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" -":py:obj:`RUN_FLEET_API_ENTER " -"`\\" +":py:obj:`rsplit `\\ \\(\\[sep\\, " +"maxsplit\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid "" -":py:obj:`RUN_FLEET_API_LEAVE " -"`\\" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`join `\\ \\(iterable\\, \\/\\)" +msgstr "serveur.stratégie.Stratégie" + +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.join:1 of +msgid "Concatenate any number of strings." msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid "" -":py:obj:`RUN_SUPERLINK_ENTER " -"`\\" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`capitalize `\\ \\(\\)" +msgstr "serveur.stratégie.Stratégie" + +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.capitalize:1 of +msgid "Return a capitalized version of the string." msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid "" -":py:obj:`RUN_SUPERLINK_LEAVE " -"`\\" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`casefold `\\ \\(\\)" +msgstr "serveur.stratégie.Stratégie" + +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.casefold:1 of +msgid "Return a version of the string suitable for caseless comparisons." msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid "" -":py:obj:`START_SIMULATION_ENTER " -"`\\" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`title `\\ \\(\\)" +msgstr "serveur.stratégie.Stratégie" + +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.title:1 of +msgid "Return a version of the string where each word is titlecased." msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" -":py:obj:`START_SIMULATION_LEAVE " -"`\\" +":py:obj:`center `\\ \\(width\\[\\, " +"fillchar\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid ":py:obj:`DRIVER_CONNECT `\\" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.center:1 of +msgid "Return a centered string of length width." msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid ":py:obj:`DRIVER_DISCONNECT `\\" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid "" +":py:obj:`count `\\ \\(sub\\[\\, start\\[\\, " +"end\\]\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid ":py:obj:`START_DRIVER_ENTER `\\" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid "" +"Return the number of non-overlapping occurrences of substring sub in " +"string S[start:end]." msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid ":py:obj:`START_DRIVER_LEAVE `\\" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid "" +":py:obj:`expandtabs `\\ " +"\\(\\[tabsize\\]\\)" +msgstr "" + +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.expandtabs:1 of +msgid "Return a copy where all tab characters are expanded using spaces." msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" -":py:obj:`RUN_CLIENT_APP_ENTER " -"`\\" +":py:obj:`find `\\ \\(sub\\[\\, start\\[\\, " +"end\\]\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" -":py:obj:`RUN_CLIENT_APP_LEAVE " -"`\\" +"Return the lowest index in S where substring sub is found, such that sub " +"is contained within S[start:end]." +msgstr "" + +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`partition `\\ \\(sep\\, \\/\\)" +msgstr "serveur.stratégie.Stratégie" + +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.partition:1 flwr.common.EventType.rpartition:1 of +msgid "Partition the string into three parts using the given separator." msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" -":py:obj:`RUN_SERVER_APP_ENTER " -"`\\" +":py:obj:`index `\\ \\(sub\\[\\, start\\[\\, " +"end\\]\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" -":py:obj:`RUN_SERVER_APP_LEAVE " -"`\\" +":py:obj:`ljust `\\ \\(width\\[\\, " +"fillchar\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.common.FitIns.rst:2 -msgid "FitIns" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.ljust:1 of +msgid "Return a left-justified string of length width." msgstr "" -#: ../../source/ref-api/flwr.common.FitIns.rst:29::1 -msgid ":py:obj:`parameters `\\" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`lower `\\ \\(\\)" +msgstr "serveur.stratégie.Stratégie" + +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.lower:1 of +msgid "Return a copy of the string converted to lowercase." msgstr "" -#: ../../source/ref-api/flwr.common.FitIns.rst:29::1 -msgid ":py:obj:`config `\\" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`lstrip `\\ \\(\\[chars\\]\\)" +msgstr "serveur.stratégie.Stratégie" + +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.lstrip:1 of +msgid "Return a copy of the string with leading whitespace removed." msgstr "" -#: ../../source/ref-api/flwr.common.FitRes.rst:2 -msgid "FitRes" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid "" +":py:obj:`rfind `\\ \\(sub\\[\\, start\\[\\, " +"end\\]\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.common.FitRes.rst:31::1 -msgid ":py:obj:`status `\\" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid "" +"Return the highest index in S where substring sub is found, such that sub" +" is contained within S[start:end]." msgstr "" -#: ../../source/ref-api/flwr.common.FitRes.rst:31::1 -msgid ":py:obj:`parameters `\\" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid "" +":py:obj:`rindex `\\ \\(sub\\[\\, " +"start\\[\\, end\\]\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.common.FitRes.rst:31::1 -msgid ":py:obj:`num_examples `\\" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid "" +":py:obj:`rjust `\\ \\(width\\[\\, " +"fillchar\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.common.FitRes.rst:31::1 -msgid ":py:obj:`metrics `\\" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.rjust:1 of +msgid "Return a right-justified string of length width." msgstr "" -#: ../../source/ref-api/flwr.common.GetParametersIns.rst:2 +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 #, fuzzy -msgid "GetParametersIns" -msgstr ":code:`get_parameters`" +msgid ":py:obj:`rstrip `\\ \\(\\[chars\\]\\)" +msgstr "serveur.stratégie.Stratégie" -#: ../../source/ref-api/flwr.common.GetParametersIns.rst:28::1 -msgid ":py:obj:`config `\\" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.rstrip:1 of +msgid "Return a copy of the string with trailing whitespace removed." msgstr "" -#: ../../source/ref-api/flwr.common.GetParametersRes.rst:2 +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 #, fuzzy -msgid "GetParametersRes" -msgstr ":code:`get_parameters`" +msgid ":py:obj:`rpartition `\\ \\(sep\\, \\/\\)" +msgstr "serveur.stratégie.Stratégie" -#: ../../source/ref-api/flwr.common.GetParametersRes.rst:29::1 -msgid ":py:obj:`status `\\" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid "" +":py:obj:`splitlines `\\ " +"\\(\\[keepends\\]\\)" +msgstr "serveur.stratégie.Stratégie" -#: ../../source/ref-api/flwr.common.GetParametersRes.rst:29::1 -msgid ":py:obj:`parameters `\\" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.splitlines:1 of +msgid "Return a list of the lines in the string, breaking at line boundaries." msgstr "" -#: ../../source/ref-api/flwr.common.GetPropertiesIns.rst:2 -msgid "GetPropertiesIns" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`strip `\\ \\(\\[chars\\]\\)" +msgstr "serveur.stratégie.Stratégie" -#: ../../source/ref-api/flwr.common.GetPropertiesIns.rst:28::1 -msgid ":py:obj:`config `\\" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.strip:1 of +msgid "Return a copy of the string with leading and trailing whitespace removed." msgstr "" -#: ../../source/ref-api/flwr.common.GetPropertiesRes.rst:2 -msgid "GetPropertiesRes" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`swapcase `\\ \\(\\)" +msgstr "serveur.stratégie.Stratégie" -#: ../../source/ref-api/flwr.common.GetPropertiesRes.rst:29::1 -msgid ":py:obj:`status `\\" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.swapcase:1 of +msgid "" +"Convert uppercase characters to lowercase and lowercase characters to " +"uppercase." msgstr "" -#: ../../source/ref-api/flwr.common.GetPropertiesRes.rst:29::1 -msgid ":py:obj:`properties `\\" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`translate `\\ \\(table\\, \\/\\)" +msgstr "serveur.stratégie.Stratégie" + +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.translate:1 of +msgid "Replace each character in the string using the given translation table." msgstr "" -#: ../../source/ref-api/flwr.common.Message.rst:2 +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 #, fuzzy -msgid "Message" -msgstr "Côté serveur" +msgid ":py:obj:`upper `\\ \\(\\)" +msgstr "serveur.stratégie.Stratégie" -#: flwr.common.Message.content:1::1 flwr.common.Message.metadata:1 -#: flwr.common.message.Message:3 of -msgid "A dataclass including information about the message to be executed." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.upper:1 of +msgid "Return a copy of the string converted to uppercase." msgstr "" -#: flwr.common.message.Message:5 of +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" -"Holds records either sent by another entity (e.g. sent by the server-side" -" logic to a client, or vice-versa) or that will be sent to it." +":py:obj:`startswith `\\ \\(prefix\\[\\," +" start\\[\\, end\\]\\]\\)" msgstr "" -#: flwr.common.message.Message:8 of -msgid "" -"A dataclass that captures information about an error that took place when" -" processing another message." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid "Return True if S starts with the specified prefix, False otherwise." msgstr "" -#: ../../source/ref-api/flwr.common.Message.rst:35::1 +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" -":py:obj:`create_error_reply `\\ " -"\\(error\\, ttl\\)" +":py:obj:`endswith `\\ \\(suffix\\[\\, " +"start\\[\\, end\\]\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.common.Message.rst:35::1 -#: flwr.common.message.Message.create_error_reply:1 of -msgid "Construct a reply message indicating an error happened." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid "Return True if S ends with the specified suffix, False otherwise." msgstr "" -#: ../../source/ref-api/flwr.common.Message.rst:35::1 +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" -":py:obj:`create_reply `\\ \\(content\\," -" ttl\\)" +":py:obj:`removeprefix `\\ " +"\\(prefix\\, \\/\\)" msgstr "" -#: ../../source/ref-api/flwr.common.Message.rst:35::1 -#: flwr.common.message.Message.create_reply:1 of -msgid "Create a reply to this message with specified content and TTL." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.removeprefix:1 of +msgid "Return a str with the given prefix string removed if present." msgstr "" -#: ../../source/ref-api/flwr.common.Message.rst:35::1 -msgid ":py:obj:`has_content `\\ \\(\\)" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid "" +":py:obj:`removesuffix `\\ " +"\\(suffix\\, \\/\\)" msgstr "" -#: ../../source/ref-api/flwr.common.Message.rst:35::1 -#: flwr.common.message.Message.has_content:1 of -msgid "Return True if message has content, else False." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.removesuffix:1 of +msgid "Return a str with the given suffix string removed if present." msgstr "" -#: ../../source/ref-api/flwr.common.Message.rst:35::1 -msgid ":py:obj:`has_error `\\ \\(\\)" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`isascii `\\ \\(\\)" +msgstr "serveur.stratégie.Stratégie" -#: ../../source/ref-api/flwr.common.Message.rst:35::1 -#: flwr.common.message.Message.has_error:1 of -msgid "Return True if message has an error, else False." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.isascii:1 of +msgid "Return True if all characters in the string are ASCII, False otherwise." msgstr "" -#: flwr.common.Message.content:1::1 of -msgid ":py:obj:`content `\\" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`islower `\\ \\(\\)" +msgstr "serveur.stratégie.Stratégie" + +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.islower:1 of +msgid "Return True if the string is a lowercase string, False otherwise." msgstr "" -#: flwr.common.Message.content:1 flwr.common.Message.content:1::1 -#: of +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 #, fuzzy -msgid "The content of this message." -msgstr "Évaluer la réponse d'un client." +msgid ":py:obj:`isupper `\\ \\(\\)" +msgstr "serveur.stratégie.Stratégie" -#: flwr.common.Message.content:1::1 of -msgid ":py:obj:`error `\\" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.isupper:1 of +msgid "Return True if the string is an uppercase string, False otherwise." msgstr "" -#: flwr.common.Message.content:1::1 flwr.common.Message.error:1 of -msgid "Error captured by this message." -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`istitle `\\ \\(\\)" +msgstr "serveur.stratégie.Stratégie" -#: flwr.common.Message.content:1::1 of -msgid ":py:obj:`metadata `\\" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.istitle:1 of +msgid "Return True if the string is a title-cased string, False otherwise." msgstr "" -#: flwr.common.message.Message.create_error_reply:3 of -msgid "The error that was encountered." -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`isspace `\\ \\(\\)" +msgstr "serveur.stratégie.Stratégie" -#: flwr.common.Metadata.dst_node_id:1::1 -#: flwr.common.Metadata.ttl:1 flwr.common.message.Message.create_error_reply:5 -#: flwr.common.message.Message.create_reply:9 flwr.common.message.Metadata:16 -#: of -msgid "Time-to-live for this message." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.isspace:1 of +msgid "Return True if the string is a whitespace string, False otherwise." msgstr "" -#: flwr.common.message.Message.create_reply:3 of -msgid "" -"The method generates a new `Message` as a reply to this message. It " -"inherits 'run_id', 'src_node_id', 'dst_node_id', and 'message_type' from " -"this message and sets 'reply_to_message' to the ID of this message." -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`isdecimal `\\ \\(\\)" +msgstr "serveur.stratégie.Stratégie" -#: flwr.common.message.Message.create_reply:7 of -msgid "The content for the reply message." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.isdecimal:1 of +msgid "Return True if the string is a decimal string, False otherwise." msgstr "" -#: flwr.common.message.Message.create_reply:12 of -msgid "A new `Message` instance representing the reply." -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`isdigit `\\ \\(\\)" +msgstr "serveur.stratégie.Stratégie" -#: ../../source/ref-api/flwr.common.MessageType.rst:2 -msgid "MessageType" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.isdigit:1 of +msgid "Return True if the string is a digit string, False otherwise." msgstr "" -#: ../../source/ref-api/flwr.common.MessageType.rst:30::1 -msgid ":py:obj:`EVALUATE `\\" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`isnumeric `\\ \\(\\)" +msgstr "serveur.stratégie.Stratégie" -#: ../../source/ref-api/flwr.common.MessageType.rst:30::1 -msgid ":py:obj:`QUERY `\\" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.isnumeric:1 of +msgid "Return True if the string is a numeric string, False otherwise." msgstr "" -#: ../../source/ref-api/flwr.common.MessageType.rst:30::1 -msgid ":py:obj:`TRAIN `\\" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`isalpha `\\ \\(\\)" +msgstr "serveur.stratégie.Stratégie" -#: ../../source/ref-api/flwr.common.MessageTypeLegacy.rst:2 -msgid "MessageTypeLegacy" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.isalpha:1 of +msgid "Return True if the string is an alphabetic string, False otherwise." msgstr "" -#: ../../source/ref-api/flwr.common.MessageTypeLegacy.rst:29::1 -msgid ":py:obj:`GET_PARAMETERS `\\" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`isalnum `\\ \\(\\)" +msgstr "serveur.stratégie.Stratégie" -#: ../../source/ref-api/flwr.common.MessageTypeLegacy.rst:29::1 -msgid ":py:obj:`GET_PROPERTIES `\\" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.isalnum:1 of +msgid "Return True if the string is an alpha-numeric string, False otherwise." msgstr "" -#: flwr.common.Metadata.dst_node_id:1::1 -#: flwr.common.Metadata.run_id:1 flwr.common.message.Metadata:3 of -msgid "An identifier for the current run." -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`isidentifier `\\ \\(\\)" +msgstr "serveur.stratégie.Stratégie" -#: flwr.common.Metadata.dst_node_id:1::1 -#: flwr.common.Metadata.message_id:1 flwr.common.message.Metadata:5 of -msgid "An identifier for the current message." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.isidentifier:1 of +msgid "Return True if the string is a valid Python identifier, False otherwise." msgstr "" -#: flwr.common.Metadata.dst_node_id:1::1 -#: flwr.common.Metadata.src_node_id:1 flwr.common.message.Metadata:7 of -msgid "An identifier for the node sending this message." -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`isprintable `\\ \\(\\)" +msgstr "serveur.stratégie.Stratégie" -#: flwr.common.Metadata.dst_node_id:1 -#: flwr.common.Metadata.dst_node_id:1::1 -#: flwr.common.message.Metadata:9 of -msgid "An identifier for the node receiving this message." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.isprintable:1 of +msgid "Return True if the string is printable, False otherwise." msgstr "" -#: flwr.common.Metadata.dst_node_id:1::1 -#: flwr.common.Metadata.reply_to_message:1 flwr.common.message.Metadata:11 of -msgid "An identifier for the message this message replies to." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`zfill `\\ \\(width\\, \\/\\)" +msgstr "serveur.stratégie.Stratégie" + +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.zfill:1 of +msgid "" +"Pad a numeric string with zeros on the left, to fill a field of the given" +" width." msgstr "" -#: flwr.common.message.Metadata:13 of +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" -"An identifier for grouping messages. In some settings, this is used as " -"the FL round." +":py:obj:`format `\\ \\(\\*args\\, " +"\\*\\*kwargs\\)" msgstr "" -#: flwr.common.Metadata.dst_node_id:1::1 -#: flwr.common.Metadata.message_type:1 flwr.common.message.Metadata:18 of -msgid "A string that encodes the action to be executed on the receiving end." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid "Return a formatted version of S, using substitutions from args and kwargs." msgstr "" -#: flwr.common.message.Metadata:21 of -msgid "" -"An identifier that can be used when loading a particular data partition " -"for a ClientApp. Making use of this identifier is more relevant when " -"conducting simulations." -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`format_map `\\ \\(mapping\\)" +msgstr "serveur.stratégie.Stratégie" -#: flwr.common.Metadata.dst_node_id:1::1 of -msgid ":py:obj:`dst_node_id `\\" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid "Return a formatted version of S, using substitutions from mapping." msgstr "" -#: flwr.common.Metadata.dst_node_id:1::1 of -msgid ":py:obj:`group_id `\\" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`maketrans `\\" +msgstr "serveur.stratégie.Stratégie" + +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.maketrans:1 of +msgid "Return a translation table usable for str.translate()." msgstr "" -#: flwr.common.Metadata.dst_node_id:1::1 -#: flwr.common.Metadata.group_id:1 of -msgid "An identifier for grouping messages." +#: flwr.common.EventType.capitalize:1::1 of +msgid ":py:obj:`PING `\\" msgstr "" -#: flwr.common.Metadata.dst_node_id:1::1 of -msgid ":py:obj:`message_id `\\" +#: flwr.common.EventType.capitalize:1::1 of +msgid ":py:obj:`START_CLIENT_ENTER `\\" msgstr "" -#: flwr.common.Metadata.dst_node_id:1::1 of -msgid ":py:obj:`message_type `\\" +#: flwr.common.EventType.capitalize:1::1 of +msgid ":py:obj:`START_CLIENT_LEAVE `\\" msgstr "" -#: flwr.common.Metadata.dst_node_id:1::1 of -msgid ":py:obj:`partition_id `\\" +#: flwr.common.EventType.capitalize:1::1 of +msgid ":py:obj:`START_SERVER_ENTER `\\" msgstr "" -#: flwr.common.Metadata.dst_node_id:1::1 -#: flwr.common.Metadata.partition_id:1 of -msgid "An identifier telling which data partition a ClientApp should use." +#: flwr.common.EventType.capitalize:1::1 of +msgid ":py:obj:`START_SERVER_LEAVE `\\" msgstr "" -#: flwr.common.Metadata.dst_node_id:1::1 of -msgid ":py:obj:`reply_to_message `\\" +#: flwr.common.EventType.capitalize:1::1 of +msgid "" +":py:obj:`RUN_DRIVER_API_ENTER " +"`\\" msgstr "" -#: flwr.common.Metadata.dst_node_id:1::1 of -msgid ":py:obj:`run_id `\\" +#: flwr.common.EventType.capitalize:1::1 of +msgid "" +":py:obj:`RUN_DRIVER_API_LEAVE " +"`\\" msgstr "" -#: flwr.common.Metadata.dst_node_id:1::1 of -msgid ":py:obj:`src_node_id `\\" +#: flwr.common.EventType.capitalize:1::1 of +msgid "" +":py:obj:`RUN_FLEET_API_ENTER " +"`\\" msgstr "" -#: flwr.common.Metadata.dst_node_id:1::1 of -msgid ":py:obj:`ttl `\\" +#: flwr.common.EventType.capitalize:1::1 of +msgid "" +":py:obj:`RUN_FLEET_API_LEAVE " +"`\\" msgstr "" -#: ../../source/ref-api/flwr.common.MetricsRecord.rst:2 -msgid "MetricsRecord" +#: flwr.common.EventType.capitalize:1::1 of +msgid "" +":py:obj:`RUN_SUPERLINK_ENTER " +"`\\" msgstr "" -#: flwr.common.record.metricsrecord.MetricsRecord:1 of +#: flwr.common.EventType.capitalize:1::1 of msgid "" -"Bases: :py:class:`~flwr.common.record.typeddict.TypedDict`\\ " -"[:py:class:`str`, :py:obj:`~typing.Union`\\ [:py:class:`int`, " -":py:class:`float`, :py:class:`~typing.List`\\ [:py:class:`int`], " -":py:class:`~typing.List`\\ [:py:class:`float`]]]" +":py:obj:`RUN_SUPERLINK_LEAVE " +"`\\" msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`clear `\\ \\(\\)" +#: flwr.common.EventType.capitalize:1::1 of +msgid "" +":py:obj:`START_SIMULATION_ENTER " +"`\\" msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`count_bytes `\\ \\(\\)" +#: flwr.common.EventType.capitalize:1::1 of +msgid "" +":py:obj:`START_SIMULATION_LEAVE " +"`\\" msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" +#: flwr.common.EventType.capitalize:1::1 of +msgid ":py:obj:`DRIVER_CONNECT `\\" msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`items `\\ \\(\\)" +#: flwr.common.EventType.capitalize:1::1 of +msgid ":py:obj:`DRIVER_DISCONNECT `\\" msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`keys `\\ \\(\\)" +#: flwr.common.EventType.capitalize:1::1 of +msgid ":py:obj:`START_DRIVER_ENTER `\\" msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`pop `\\ \\(k\\[\\,d\\]\\)" +#: flwr.common.EventType.capitalize:1::1 of +msgid ":py:obj:`START_DRIVER_LEAVE `\\" msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +#: flwr.common.EventType.capitalize:1::1 of msgid "" -":py:obj:`update `\\ \\(\\[E\\, " -"\\]\\*\\*F\\)" +":py:obj:`RUN_CLIENT_APP_ENTER " +"`\\" msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`values `\\ \\(\\)" +#: flwr.common.EventType.capitalize:1::1 of +msgid "" +":py:obj:`RUN_CLIENT_APP_LEAVE " +"`\\" msgstr "" -#: ../../source/ref-api/flwr.common.NDArray.rst:2 -msgid "NDArray" +#: flwr.common.EventType.capitalize:1::1 of +msgid "" +":py:obj:`RUN_SERVER_APP_ENTER " +"`\\" msgstr "" -#: ../../source/ref-api/flwr.common.Parameters.rst:29::1 -msgid ":py:obj:`tensors `\\" +#: flwr.common.EventType.capitalize:1::1 of +msgid "" +":py:obj:`RUN_SERVER_APP_LEAVE " +"`\\" msgstr "" -#: ../../source/ref-api/flwr.common.Parameters.rst:29::1 -msgid ":py:obj:`tensor_type `\\" +#: flwr.common.EventType.capitalize:1::1 of +msgid "" +":py:obj:`RUN_SUPERNODE_ENTER " +"`\\" msgstr "" -#: ../../source/ref-api/flwr.common.ParametersRecord.rst:2 +#: flwr.common.EventType.capitalize:1::1 of #, fuzzy -msgid "ParametersRecord" -msgstr "Paramètres du modèle." +msgid "" +":py:obj:`RUN_SUPERNODE_LEAVE " +"`\\" +msgstr "serveur.stratégie.Stratégie" -#: flwr.common.record.parametersrecord.ParametersRecord:1 of +#: flwr.common.EventType.capitalize:1::1 of +#, fuzzy msgid "" -"Bases: :py:class:`~flwr.common.record.typeddict.TypedDict`\\ " -"[:py:class:`str`, :py:class:`~flwr.common.record.parametersrecord.Array`]" -msgstr "" +":py:obj:`RUN_SUPEREXEC_ENTER " +"`\\" +msgstr "serveur.stratégie.Stratégie" -#: flwr.common.record.parametersrecord.ParametersRecord:3 of +#: flwr.common.EventType.capitalize:1::1 of +#, fuzzy msgid "" -"A dataclass storing named Arrays in order. This means that it holds " -"entries as an OrderedDict[str, Array]. ParametersRecord objects can be " -"viewed as an equivalent to PyTorch's state_dict, but holding serialised " -"tensors instead." +":py:obj:`RUN_SUPEREXEC_LEAVE " +"`\\" +msgstr "serveur.stratégie.Stratégie" + +#: flwr.common.EventType.capitalize:3 of +msgid "" +"More specifically, make the first character have upper case and the rest " +"lower case." msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`clear `\\ \\(\\)" +#: flwr.common.EventType.center:3 flwr.common.EventType.ljust:3 +#: flwr.common.EventType.rjust:3 of +msgid "Padding is done using the specified fill character (default is a space)." msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`count_bytes `\\ \\(\\)" +#: flwr.common.EventType.count:1 of +msgid "" +"Return the number of non-overlapping occurrences of substring sub in " +"string S[start:end]. Optional arguments start and end are interpreted as" +" in slice notation." msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" +#: flwr.common.EventType.encode:3 of +msgid "encoding" msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`items `\\ \\(\\)" +#: flwr.common.EventType.encode:4 of +msgid "The encoding in which to encode the string." msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`keys `\\ \\(\\)" +#: flwr.common.EventType.encode:9 of +msgid "errors" msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`pop `\\ \\(k\\[\\,d\\]\\)" +#: flwr.common.EventType.encode:6 of +msgid "" +"The error handling scheme to use for encoding errors. The default is " +"'strict' meaning that encoding errors raise a UnicodeEncodeError. Other " +"possible values are 'ignore', 'replace' and 'xmlcharrefreplace' as well " +"as any other name registered with codecs.register_error that can handle " +"UnicodeEncodeErrors." msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +#: flwr.common.EventType.endswith:1 of msgid "" -":py:obj:`update `\\ \\(\\[E\\, " -"\\]\\*\\*F\\)" +"Return True if S ends with the specified suffix, False otherwise. With " +"optional start, test S beginning at that position. With optional end, " +"stop comparing S at that position. suffix can also be a tuple of strings " +"to try." msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`values `\\ \\(\\)" +#: flwr.common.EventType.expandtabs:3 of +msgid "If tabsize is not given, a tab size of 8 characters is assumed." msgstr "" -#: flwr.common.record.parametersrecord.ParametersRecord.count_bytes:3 of +#: flwr.common.EventType.find:1 flwr.common.EventType.index:1 of msgid "" -"Note that a small amount of Bytes might also be included in this counting" -" that correspond to metadata of the serialized object (e.g. of NumPy " -"array) needed for deseralization." +"Return the lowest index in S where substring sub is found, such that sub " +"is contained within S[start:end]. Optional arguments start and end are " +"interpreted as in slice notation." msgstr "" -#: ../../source/ref-api/flwr.common.ReconnectIns.rst:2 -#, fuzzy -msgid "ReconnectIns" -msgstr "Collecte centralisée des données" +#: flwr.common.EventType.find:5 flwr.common.EventType.rfind:5 of +msgid "Return -1 on failure." +msgstr "" -#: ../../source/ref-api/flwr.common.ReconnectIns.rst:28::1 -msgid ":py:obj:`seconds `\\" +#: flwr.common.EventType.format:1 of +msgid "" +"Return a formatted version of S, using substitutions from args and " +"kwargs. The substitutions are identified by braces ('{' and '}')." msgstr "" -#: ../../source/ref-api/flwr.common.RecordSet.rst:2 -msgid "RecordSet" +#: flwr.common.EventType.format_map:1 of +msgid "" +"Return a formatted version of S, using substitutions from mapping. The " +"substitutions are identified by braces ('{' and '}')." msgstr "" -#: flwr.common.RecordSet.configs_records:1::1 of -msgid ":py:obj:`configs_records `\\" +#: flwr.common.EventType.index:5 flwr.common.EventType.rindex:5 of +msgid "Raises ValueError when the substring is not found." msgstr "" -#: flwr.common.RecordSet.configs_records:1 -#: flwr.common.RecordSet.configs_records:1::1 of -msgid "Dictionary holding ConfigsRecord instances." +#: flwr.common.EventType.isalnum:3 of +msgid "" +"A string is alpha-numeric if all characters in the string are alpha-" +"numeric and there is at least one character in the string." msgstr "" -#: flwr.common.RecordSet.configs_records:1::1 of -msgid ":py:obj:`metrics_records `\\" +#: flwr.common.EventType.isalpha:3 of +msgid "" +"A string is alphabetic if all characters in the string are alphabetic and" +" there is at least one character in the string." msgstr "" -#: flwr.common.RecordSet.configs_records:1::1 -#: flwr.common.RecordSet.metrics_records:1 of -msgid "Dictionary holding MetricsRecord instances." +#: flwr.common.EventType.isascii:3 of +msgid "" +"ASCII characters have code points in the range U+0000-U+007F. Empty " +"string is ASCII too." msgstr "" -#: flwr.common.RecordSet.configs_records:1::1 of -msgid ":py:obj:`parameters_records `\\" +#: flwr.common.EventType.isdecimal:3 of +msgid "" +"A string is a decimal string if all characters in the string are decimal " +"and there is at least one character in the string." msgstr "" -#: flwr.common.RecordSet.configs_records:1::1 -#: flwr.common.RecordSet.parameters_records:1 of -msgid "Dictionary holding ParametersRecord instances." +#: flwr.common.EventType.isdigit:3 of +msgid "" +"A string is a digit string if all characters in the string are digits and" +" there is at least one character in the string." msgstr "" -#: ../../source/ref-api/flwr.common.ServerMessage.rst:2 -#, fuzzy -msgid "ServerMessage" -msgstr "Côté serveur" +#: flwr.common.EventType.isidentifier:3 of +msgid "" +"Call keyword.iskeyword(s) to test whether string s is a reserved " +"identifier, such as \"def\" or \"class\"." +msgstr "" -#: ../../source/ref-api/flwr.common.ServerMessage.rst:31::1 -msgid ":py:obj:`evaluate_ins `\\" +#: flwr.common.EventType.islower:3 of +msgid "" +"A string is lowercase if all cased characters in the string are lowercase" +" and there is at least one cased character in the string." msgstr "" -#: ../../source/ref-api/flwr.common.ServerMessage.rst:31::1 -msgid ":py:obj:`fit_ins `\\" +#: flwr.common.EventType.isnumeric:3 of +msgid "" +"A string is numeric if all characters in the string are numeric and there" +" is at least one character in the string." msgstr "" -#: ../../source/ref-api/flwr.common.ServerMessage.rst:31::1 +#: flwr.common.EventType.isprintable:3 of msgid "" -":py:obj:`get_parameters_ins " -"`\\" +"A string is printable if all of its characters are considered printable " +"in repr() or if it is empty." msgstr "" -#: ../../source/ref-api/flwr.common.ServerMessage.rst:31::1 +#: flwr.common.EventType.isspace:3 of msgid "" -":py:obj:`get_properties_ins " -"`\\" +"A string is whitespace if all characters in the string are whitespace and" +" there is at least one character in the string." msgstr "" -#: ../../source/ref-api/flwr.common.Status.rst:2 -#, fuzzy -msgid "Status" -msgstr "Statut du client." +#: flwr.common.EventType.istitle:3 of +msgid "" +"In a title-cased string, upper- and title-case characters may only follow" +" uncased characters and lowercase characters only cased ones." +msgstr "" -#: ../../source/ref-api/flwr.common.Status.rst:29::1 -msgid ":py:obj:`code `\\" +#: flwr.common.EventType.isupper:3 of +msgid "" +"A string is uppercase if all cased characters in the string are uppercase" +" and there is at least one cased character in the string." msgstr "" -#: ../../source/ref-api/flwr.common.Status.rst:29::1 -msgid ":py:obj:`message `\\" +#: flwr.common.EventType.join:3 of +msgid "" +"The string whose method is called is inserted in between each given " +"string. The result is returned as a new string." msgstr "" -#: ../../source/ref-api/flwr.common.array_from_numpy.rst:2 -msgid "array\\_from\\_numpy" +#: flwr.common.EventType.join:6 of +msgid "Example: '.'.join(['ab', 'pq', 'rs']) -> 'ab.pq.rs'" msgstr "" -#: ../../source/ref-api/flwr.common.bytes_to_ndarray.rst:2 -msgid "bytes\\_to\\_ndarray" +#: flwr.common.EventType.lstrip:3 flwr.common.EventType.rstrip:3 +#: flwr.common.EventType.strip:3 of +msgid "If chars is given and not None, remove characters in chars instead." msgstr "" -#: ../../source/ref-api/flwr.common.configure.rst:2 -#, fuzzy -msgid "configure" -msgstr "Configurer les clients" +#: flwr.common.EventType.maketrans:3 of +msgid "" +"If there is only one argument, it must be a dictionary mapping Unicode " +"ordinals (integers) or characters to Unicode ordinals, strings or None. " +"Character keys will be then converted to ordinals. If there are two " +"arguments, they must be strings of equal length, and in the resulting " +"dictionary, each character in x will be mapped to the character at the " +"same position in y. If there is a third argument, it must be a string, " +"whose characters will be mapped to None in the result." +msgstr "" -#: ../../source/ref-api/flwr.common.event.rst:2 -msgid "event" +#: flwr.common.EventType.partition:3 of +msgid "" +"This will search for the separator in the string. If the separator is " +"found, returns a 3-tuple containing the part before the separator, the " +"separator itself, and the part after it." msgstr "" -#: ../../source/ref-api/flwr.common.log.rst:2 -msgid "log" +#: flwr.common.EventType.partition:7 of +msgid "" +"If the separator is not found, returns a 3-tuple containing the original " +"string and two empty strings." msgstr "" -#: logging.Logger.log:3 of +#: flwr.common.EventType.removeprefix:3 of msgid "" -"To pass exception information, use the keyword argument exc_info with a " -"true value, e.g." +"If the string starts with the prefix string, return string[len(prefix):]." +" Otherwise, return a copy of the original string." msgstr "" -"Pour transmettre des informations sur les exceptions, utilise l'argument " -"mot-clé exc_info avec une valeur vraie, par ex." -#: logging.Logger.log:6 of -#, python-format -msgid "logger.log(level, \"We have a %s\", \"mysterious problem\", exc_info=1)" -msgstr "logger.log(level, \"We have a %s\", \"mysterious problem\", exc_info=1)" +#: flwr.common.EventType.removesuffix:3 of +msgid "" +"If the string ends with the suffix string and that suffix is not empty, " +"return string[:-len(suffix)]. Otherwise, return a copy of the original " +"string." +msgstr "" -#: ../../source/ref-api/flwr.common.ndarray_to_bytes.rst:2 -msgid "ndarray\\_to\\_bytes" +#: flwr.common.EventType.replace:5 of +msgid "count" msgstr "" -#: ../../source/ref-api/flwr.common.ndarrays_to_parameters.rst:2 -msgid "ndarrays\\_to\\_parameters" +#: flwr.common.EventType.replace:4 of +msgid "" +"Maximum number of occurrences to replace. -1 (the default value) means " +"replace all occurrences." msgstr "" -#: ../../source/ref-api/flwr.common.now.rst:2 -msgid "now" +#: flwr.common.EventType.replace:7 of +msgid "" +"If the optional argument count is given, only the first count occurrences" +" are replaced." msgstr "" -#: ../../source/ref-api/flwr.common.parameters_to_ndarrays.rst:2 -msgid "parameters\\_to\\_ndarrays" +#: flwr.common.EventType.rfind:1 flwr.common.EventType.rindex:1 of +msgid "" +"Return the highest index in S where substring sub is found, such that sub" +" is contained within S[start:end]. Optional arguments start and end are " +"interpreted as in slice notation." msgstr "" -#: ../../source/ref-api/flwr.server.rst:2 -msgid "server" -msgstr "serveur" +#: flwr.common.EventType.rpartition:3 of +msgid "" +"This will search for the separator in the string, starting at the end. If" +" the separator is found, returns a 3-tuple containing the part before the" +" separator, the separator itself, and the part after it." +msgstr "" -#: ../../source/ref-api/flwr.server.rst:27::1 -msgid ":py:obj:`run_driver_api `\\ \\(\\)" +#: flwr.common.EventType.rpartition:7 of +msgid "" +"If the separator is not found, returns a 3-tuple containing two empty " +"strings and the original string." msgstr "" -#: ../../source/ref-api/flwr.server.rst:27::1 -#: flwr.server.app.run_driver_api:1 of -#, fuzzy -msgid "Run Flower server (Driver API)." -msgstr "flower-driver-api" +#: flwr.common.EventType.rsplit:7 flwr.common.EventType.split:7 of +msgid "sep" +msgstr "" -#: ../../source/ref-api/flwr.server.rst:27::1 -msgid ":py:obj:`run_fleet_api `\\ \\(\\)" +#: flwr.common.EventType.rsplit:4 flwr.common.EventType.split:4 of +msgid "The separator used to split the string." msgstr "" -#: ../../source/ref-api/flwr.server.rst:27::1 -#: flwr.server.app.run_fleet_api:1 of -#, fuzzy -msgid "Run Flower server (Fleet API)." -msgstr "flower-fleet-api" +#: flwr.common.EventType.rsplit:6 flwr.common.EventType.split:6 of +msgid "" +"When set to None (the default value), will split on any whitespace " +"character (including \\\\n \\\\r \\\\t \\\\f and spaces) and will discard" +" empty strings from the result." +msgstr "" -#: ../../source/ref-api/flwr.server.rst:27::1 -msgid ":py:obj:`run_server_app `\\ \\(\\)" +#: flwr.common.EventType.rsplit:11 flwr.common.EventType.split:11 of +msgid "maxsplit" msgstr "" -#: ../../source/ref-api/flwr.server.rst:27::1 -#: flwr.server.run_serverapp.run_server_app:1 of -#, fuzzy -msgid "Run Flower server app." -msgstr "Serveur de Flower" - -#: ../../source/ref-api/flwr.server.rst:27::1 -msgid ":py:obj:`run_superlink `\\ \\(\\)" +#: flwr.common.EventType.rsplit:10 flwr.common.EventType.split:10 of +msgid "" +"Maximum number of splits (starting from the left). -1 (the default value)" +" means no limit." msgstr "" -#: ../../source/ref-api/flwr.server.rst:27::1 -#: flwr.server.app.run_superlink:1 of -msgid "Run Flower server (Driver API and Fleet API)." +#: flwr.common.EventType.rsplit:13 of +msgid "Splitting starts at the end of the string and works to the front." msgstr "" -#: ../../source/ref-api/flwr.server.rst:27::1 +#: flwr.common.EventType.split:13 of msgid "" -":py:obj:`start_driver `\\ \\(\\*\\[\\, " -"server\\_address\\, server\\, ...\\]\\)" +"Note, str.split() is mainly useful for data that has been intentionally " +"delimited. With natural text that includes punctuation, consider using " +"the regular expression module." msgstr "" -#: ../../source/ref-api/flwr.server.rst:27::1 -#: flwr.server.compat.app.start_driver:1 of -#, fuzzy -msgid "Start a Flower Driver API server." -msgstr "Tout d'abord, démarre un serveur Flower :" - -#: ../../source/ref-api/flwr.server.rst:27::1 +#: flwr.common.EventType.splitlines:3 of msgid "" -":py:obj:`start_server `\\ \\(\\*\\[\\, " -"server\\_address\\, server\\, ...\\]\\)" -msgstr "" - -#: ../../source/ref-api/flwr.server.rst:27::1 -#: flwr.server.app.start_server:1 of -msgid "Start a Flower server using the gRPC transport layer." +"Line breaks are not included in the resulting list unless keepends is " +"given and true." msgstr "" -#: ../../source/ref-api/flwr.server.rst:41::1 -msgid ":py:obj:`ClientManager `\\ \\(\\)" +#: flwr.common.EventType.startswith:1 of +msgid "" +"Return True if S starts with the specified prefix, False otherwise. With " +"optional start, test S beginning at that position. With optional end, " +"stop comparing S at that position. prefix can also be a tuple of strings " +"to try." msgstr "" -#: ../../source/ref-api/flwr.server.rst:41::1 -#: flwr.server.client_manager.ClientManager:1 of -msgid "Abstract base class for managing Flower clients." +#: flwr.common.EventType.title:3 of +msgid "" +"More specifically, words start with uppercased characters and all " +"remaining cased characters have lower case." msgstr "" -#: ../../source/ref-api/flwr.server.rst:41::1 +#: flwr.common.EventType.translate:5 of #, fuzzy +msgid "table" +msgstr "Database" + +#: flwr.common.EventType.translate:4 of msgid "" -":py:obj:`Driver `\\ " -"\\(\\[driver\\_service\\_address\\, ...\\]\\)" +"Translation table, which must be a mapping of Unicode ordinals to Unicode" +" ordinals, strings, or None." msgstr "" -"Flower 1.0 : ``start_server(..., " -"config=flwr.server.ServerConfig(num_rounds=3, round_timeout=600.0), " -"...)``" -#: ../../source/ref-api/flwr.server.rst:41::1 -#: flwr.server.driver.driver.Driver:1 of -msgid "`Driver` class provides an interface to the Driver API." +#: flwr.common.EventType.translate:7 of +msgid "" +"The table must implement lookup/indexing via __getitem__, for instance a " +"dictionary or list. If this operation raises LookupError, the character " +"is left untouched. Characters mapped to None are deleted." msgstr "" -#: ../../source/ref-api/flwr.server.rst:41::1 -msgid ":py:obj:`History `\\ \\(\\)" +#: flwr.common.EventType.zfill:3 of +msgid "The string is never truncated." msgstr "" -#: ../../source/ref-api/flwr.server.rst:41::1 -#: flwr.server.history.History:1 of -msgid "History class for training and/or evaluation metrics collection." +#: ../../source/ref-api/flwr.common.FitIns.rst:2 +msgid "FitIns" msgstr "" -#: ../../source/ref-api/flwr.server.rst:41::1 -msgid "" -":py:obj:`LegacyContext `\\ \\(state\\[\\, " -"config\\, strategy\\, ...\\]\\)" +#: ../../source/ref-api/flwr.common.FitIns.rst:29::1 +msgid ":py:obj:`parameters `\\" msgstr "" -#: ../../source/ref-api/flwr.server.rst:41::1 -#: flwr.server.compat.legacy_context.LegacyContext:1 of -msgid "Legacy Context." +#: ../../source/ref-api/flwr.common.FitIns.rst:29::1 +msgid ":py:obj:`config `\\" msgstr "" -#: ../../source/ref-api/flwr.server.rst:41::1 -msgid "" -":py:obj:`Server `\\ \\(\\*\\, client\\_manager\\[\\, " -"strategy\\]\\)" +#: ../../source/ref-api/flwr.common.FitRes.rst:2 +msgid "FitRes" msgstr "" -#: ../../source/ref-api/flwr.server.rst:41::1 -#, fuzzy -msgid "" -":py:obj:`ServerApp `\\ \\(\\[server\\, config\\, " -"strategy\\, ...\\]\\)" -msgstr "serveur.stratégie.Stratégie" - -#: ../../source/ref-api/flwr.server.rst:41::1 -#: flwr.server.server_app.ServerApp:1 of -#, fuzzy -msgid "Flower ServerApp." -msgstr "Serveur de Flower" - -#: ../../source/ref-api/flwr.server.rst:41::1 -#, fuzzy -msgid "" -":py:obj:`ServerConfig `\\ \\(\\[num\\_rounds\\," -" round\\_timeout\\]\\)" +#: ../../source/ref-api/flwr.common.FitRes.rst:31::1 +msgid ":py:obj:`status `\\" msgstr "" -"Flower 1.0 : ``start_server(..., " -"config=flwr.server.ServerConfig(num_rounds=3, round_timeout=600.0), " -"...)``" -#: ../../source/ref-api/flwr.server.rst:41::1 -#: flwr.server.server_config.ServerConfig:1 of -#, fuzzy -msgid "Flower server config." -msgstr "Serveur de Flower" - -#: ../../source/ref-api/flwr.server.rst:41::1 -msgid ":py:obj:`SimpleClientManager `\\ \\(\\)" +#: ../../source/ref-api/flwr.common.FitRes.rst:31::1 +msgid ":py:obj:`parameters `\\" msgstr "" -#: ../../source/ref-api/flwr.server.rst:41::1 -#: flwr.server.client_manager.SimpleClientManager:1 of -msgid "Provides a pool of available clients." +#: ../../source/ref-api/flwr.common.FitRes.rst:31::1 +msgid ":py:obj:`num_examples `\\" msgstr "" -#: ../../source/ref-api/flwr.server.rst:60::1 -#, fuzzy -msgid ":py:obj:`flwr.server.strategy `\\" -msgstr "serveur.stratégie.Stratégie" - -#: ../../source/ref-api/flwr.server.rst:60::1 -#: flwr.server.strategy:1 of -msgid "Contains the strategy abstraction and different implementations." +#: ../../source/ref-api/flwr.common.FitRes.rst:31::1 +msgid ":py:obj:`metrics `\\" msgstr "" -#: ../../source/ref-api/flwr.server.rst:60::1 +#: ../../source/ref-api/flwr.common.GetParametersIns.rst:2 #, fuzzy -msgid ":py:obj:`flwr.server.workflow `\\" -msgstr "serveur.stratégie.Stratégie" +msgid "GetParametersIns" +msgstr ":code:`get_parameters`" -#: ../../source/ref-api/flwr.server.rst:60::1 -#: flwr.server.workflow:1 of -#, fuzzy -msgid "Workflows." -msgstr "Flux de travail" +#: ../../source/ref-api/flwr.common.GetParametersIns.rst:28::1 +msgid ":py:obj:`config `\\" +msgstr "" -#: ../../source/ref-api/flwr.server.ClientManager.rst:2 +#: ../../source/ref-api/flwr.common.GetParametersRes.rst:2 #, fuzzy -msgid "ClientManager" -msgstr "client" +msgid "GetParametersRes" +msgstr ":code:`get_parameters`" -#: flwr.server.client_manager.ClientManager.all:1::1 of -msgid ":py:obj:`all `\\ \\(\\)" +#: ../../source/ref-api/flwr.common.GetParametersRes.rst:29::1 +msgid ":py:obj:`status `\\" msgstr "" -#: flwr.server.client_manager.ClientManager.all:1 -#: flwr.server.client_manager.ClientManager.all:1::1 -#: flwr.server.client_manager.SimpleClientManager.all:1 -#: flwr.server.client_manager.SimpleClientManager.all:1::1 of -msgid "Return all available clients." +#: ../../source/ref-api/flwr.common.GetParametersRes.rst:29::1 +msgid ":py:obj:`parameters `\\" msgstr "" -#: flwr.server.client_manager.ClientManager.all:1::1 of -msgid ":py:obj:`num_available `\\ \\(\\)" +#: ../../source/ref-api/flwr.common.GetPropertiesIns.rst:2 +msgid "GetPropertiesIns" msgstr "" -#: flwr.server.client_manager.ClientManager.all:1::1 -#: flwr.server.client_manager.ClientManager.num_available:1 -#: flwr.server.client_manager.SimpleClientManager.all:1::1 -#: flwr.server.client_manager.SimpleClientManager.num_available:1 of -msgid "Return the number of available clients." +#: ../../source/ref-api/flwr.common.GetPropertiesIns.rst:28::1 +msgid ":py:obj:`config `\\" msgstr "" -#: flwr.server.client_manager.ClientManager.all:1::1 of -msgid ":py:obj:`register `\\ \\(client\\)" +#: ../../source/ref-api/flwr.common.GetPropertiesRes.rst:2 +msgid "GetPropertiesRes" msgstr "" -#: flwr.server.client_manager.ClientManager.all:1::1 -#: flwr.server.client_manager.ClientManager.register:1 -#: flwr.server.client_manager.SimpleClientManager.all:1::1 -#: flwr.server.client_manager.SimpleClientManager.register:1 of -msgid "Register Flower ClientProxy instance." +#: ../../source/ref-api/flwr.common.GetPropertiesRes.rst:29::1 +msgid ":py:obj:`status `\\" msgstr "" -#: flwr.server.client_manager.ClientManager.all:1::1 of -msgid "" -":py:obj:`sample `\\ " -"\\(num\\_clients\\[\\, min\\_num\\_clients\\, criterion\\]\\)" +#: ../../source/ref-api/flwr.common.GetPropertiesRes.rst:29::1 +msgid ":py:obj:`properties `\\" msgstr "" -#: flwr.server.client_manager.ClientManager.all:1::1 -#: flwr.server.client_manager.ClientManager.sample:1 -#: flwr.server.client_manager.SimpleClientManager.all:1::1 -#: flwr.server.client_manager.SimpleClientManager.sample:1 of -msgid "Sample a number of Flower ClientProxy instances." -msgstr "" +#: ../../source/ref-api/flwr.common.Message.rst:2 +#, fuzzy +msgid "Message" +msgstr "Côté serveur" -#: flwr.server.client_manager.ClientManager.all:1::1 of -msgid ":py:obj:`unregister `\\ \\(client\\)" +#: flwr.common.Message.content:1::1 flwr.common.Message.metadata:1 +#: flwr.common.message.Message:3 of +msgid "A dataclass including information about the message to be executed." msgstr "" -#: flwr.server.client_manager.ClientManager.all:1::1 -#: flwr.server.client_manager.ClientManager.unregister:1 -#: flwr.server.client_manager.SimpleClientManager.all:1::1 -#: flwr.server.client_manager.SimpleClientManager.unregister:1 of -msgid "Unregister Flower ClientProxy instance." +#: flwr.common.message.Message:5 of +msgid "" +"Holds records either sent by another entity (e.g. sent by the server-side" +" logic to a client, or vice-versa) or that will be sent to it." msgstr "" -#: flwr.server.client_manager.ClientManager.all:1::1 of +#: flwr.common.message.Message:8 of msgid "" -":py:obj:`wait_for `\\ " -"\\(num\\_clients\\, timeout\\)" +"A dataclass that captures information about an error that took place when" +" processing another message." msgstr "" -#: flwr.server.client_manager.ClientManager.all:1::1 -#: flwr.server.client_manager.ClientManager.wait_for:1 -#: flwr.server.client_manager.SimpleClientManager.all:1::1 -#: flwr.server.client_manager.SimpleClientManager.wait_for:1 of -msgid "Wait until at least `num_clients` are available." +#: ../../source/ref-api/flwr.common.Message.rst:35::1 +msgid "" +":py:obj:`create_error_reply `\\ " +"\\(error\\[\\, ttl\\]\\)" msgstr "" -#: flwr.server.client_manager.ClientManager.num_available:3 -#: flwr.server.client_manager.SimpleClientManager.num_available:3 of -msgid "**num_available** -- The number of currently available clients." +#: ../../source/ref-api/flwr.common.Message.rst:35::1 +#: flwr.common.message.Message.create_error_reply:1 of +msgid "Construct a reply message indicating an error happened." msgstr "" -#: flwr.server.client_manager.ClientManager.register:6 -#: flwr.server.client_manager.SimpleClientManager.register:6 of +#: ../../source/ref-api/flwr.common.Message.rst:35::1 msgid "" -"**success** -- Indicating if registration was successful. False if " -"ClientProxy is already registered or can not be registered for any " -"reason." +":py:obj:`create_reply `\\ " +"\\(content\\[\\, ttl\\]\\)" msgstr "" -#: flwr.server.client_manager.ClientManager.unregister:3 -#: flwr.server.client_manager.SimpleClientManager.unregister:3 of -msgid "This method is idempotent." +#: ../../source/ref-api/flwr.common.Message.rst:35::1 +#: flwr.common.message.Message.create_reply:1 of +msgid "Create a reply to this message with specified content and TTL." msgstr "" -#: ../../source/ref-api/flwr.server.Driver.rst:2 -#, fuzzy -msgid "Driver" -msgstr "serveur" - -#: flwr.server.driver.driver.Driver:3 of -msgid "" -"The IPv4 or IPv6 address of the Driver API server. Defaults to " -"`\"[::]:9091\"`." +#: ../../source/ref-api/flwr.common.Message.rst:35::1 +msgid ":py:obj:`has_content `\\ \\(\\)" msgstr "" -#: flwr.server.app.start_server:28 flwr.server.driver.driver.Driver:6 of -msgid "" -"Tuple containing root certificate, server certificate, and private key to" -" start a secure SSL-enabled server. The tuple is expected to have three " -"bytes elements in the following order: * CA certificate. * " -"server certificate. * server private key." +#: ../../source/ref-api/flwr.common.Message.rst:35::1 +#: flwr.common.message.Message.has_content:1 of +msgid "Return True if message has content, else False." msgstr "" -#: flwr.server.app.start_server:28 flwr.server.driver.driver.Driver:6 of -msgid "" -"Tuple containing root certificate, server certificate, and private key to" -" start a secure SSL-enabled server. The tuple is expected to have three " -"bytes elements in the following order:" +#: ../../source/ref-api/flwr.common.Message.rst:35::1 +msgid ":py:obj:`has_error `\\ \\(\\)" msgstr "" -#: flwr.server.app.start_server:32 flwr.server.driver.driver.Driver:10 of -#, fuzzy -msgid "CA certificate." -msgstr "Certificats" - -#: flwr.server.app.start_server:33 flwr.server.driver.driver.Driver:11 of -#, fuzzy -msgid "server certificate." -msgstr "Certificats" +#: ../../source/ref-api/flwr.common.Message.rst:35::1 +#: flwr.common.message.Message.has_error:1 of +msgid "Return True if message has an error, else False." +msgstr "" -#: flwr.server.app.start_server:34 flwr.server.driver.driver.Driver:12 of -#, fuzzy -msgid "server private key." -msgstr "stratégie.du.serveur" +#: flwr.common.Message.content:1::1 of +msgid ":py:obj:`content `\\" +msgstr "" -#: flwr.server.driver.driver.Driver.close:1::1 of +#: flwr.common.Message.content:1 flwr.common.Message.content:1::1 +#: of #, fuzzy -msgid ":py:obj:`close `\\ \\(\\)" -msgstr "serveur.stratégie.Stratégie" +msgid "The content of this message." +msgstr "Évaluer la réponse d'un client." -#: flwr.server.driver.driver.Driver.close:1 -#: flwr.server.driver.driver.Driver.close:1::1 of -msgid "Disconnect from the SuperLink if connected." +#: flwr.common.Message.content:1::1 of +msgid ":py:obj:`error `\\" msgstr "" -#: flwr.server.driver.driver.Driver.close:1::1 of -msgid "" -":py:obj:`create_message `\\ " -"\\(content\\, message\\_type\\, ...\\)" +#: flwr.common.Message.content:1::1 flwr.common.Message.error:1 of +msgid "Error captured by this message." msgstr "" -#: flwr.server.driver.driver.Driver.close:1::1 -#: flwr.server.driver.driver.Driver.create_message:1 of -msgid "Create a new message with specified parameters." +#: flwr.common.Message.content:1::1 of +msgid ":py:obj:`metadata `\\" msgstr "" -#: flwr.server.driver.driver.Driver.close:1::1 of -msgid ":py:obj:`get_node_ids `\\ \\(\\)" +#: flwr.common.message.Message.create_error_reply:3 of +msgid "The error that was encountered." msgstr "" -#: flwr.server.driver.driver.Driver.close:1::1 -#: flwr.server.driver.driver.Driver.get_node_ids:1 of -msgid "Get node IDs." +#: flwr.common.message.Message.create_error_reply:5 +#: flwr.common.message.Message.create_reply:9 of +msgid "" +"Time-to-live for this message in seconds. If unset, it will be set based " +"on the remaining time for the received message before it expires. This " +"follows the equation: ttl = msg.meta.ttl - (reply.meta.created_at - " +"msg.meta.created_at)" msgstr "" -#: flwr.server.driver.driver.Driver.close:1::1 of +#: flwr.common.message.Message.create_error_reply:5 +#: flwr.common.message.Message.create_reply:9 of msgid "" -":py:obj:`pull_messages `\\ " -"\\(message\\_ids\\)" +"Time-to-live for this message in seconds. If unset, it will be set based " +"on the remaining time for the received message before it expires. This " +"follows the equation:" msgstr "" -#: flwr.server.driver.driver.Driver.close:1::1 -#: flwr.server.driver.driver.Driver.pull_messages:1 of -msgid "Pull messages based on message IDs." +#: flwr.common.message.Message.create_error_reply:9 +#: flwr.common.message.Message.create_reply:13 of +msgid "ttl = msg.meta.ttl - (reply.meta.created_at - msg.meta.created_at)" msgstr "" -#: flwr.server.driver.driver.Driver.close:1::1 of +#: flwr.common.message.Message.create_reply:3 of msgid "" -":py:obj:`push_messages `\\ " -"\\(messages\\)" -msgstr "" - -#: flwr.server.driver.driver.Driver.close:1::1 -#: flwr.server.driver.driver.Driver.push_messages:1 of -msgid "Push messages to specified node IDs." +"The method generates a new `Message` as a reply to this message. It " +"inherits 'run_id', 'src_node_id', 'dst_node_id', and 'message_type' from " +"this message and sets 'reply_to_message' to the ID of this message." msgstr "" -#: flwr.server.driver.driver.Driver.close:1::1 of -#, fuzzy -msgid "" -":py:obj:`send_and_receive `\\ " -"\\(messages\\, \\*\\[\\, timeout\\]\\)" +#: flwr.common.message.Message.create_reply:7 of +msgid "The content for the reply message." msgstr "" -"Flower 1.0 : ``start_server(..., " -"config=flwr.server.ServerConfig(num_rounds=3, round_timeout=600.0), " -"...)``" -#: flwr.server.driver.driver.Driver.close:1::1 -#: flwr.server.driver.driver.Driver.send_and_receive:1 of -msgid "Push messages to specified node IDs and pull the reply messages." +#: flwr.common.message.Message.create_reply:16 of +msgid "A new `Message` instance representing the reply." msgstr "" -#: flwr.server.driver.driver.Driver.create_message:3 of -msgid "" -"This method constructs a new `Message` with given content and metadata. " -"The `run_id` and `src_node_id` will be set automatically." +#: ../../source/ref-api/flwr.common.MessageType.rst:2 +msgid "MessageType" msgstr "" -#: flwr.server.driver.driver.Driver.create_message:6 of -msgid "" -"The content for the new message. This holds records that are to be sent " -"to the destination node." +#: ../../source/ref-api/flwr.common.MessageType.rst:30::1 +msgid ":py:obj:`EVALUATE `\\" msgstr "" -#: flwr.server.driver.driver.Driver.create_message:9 of -msgid "" -"The type of the message, defining the action to be executed on the " -"receiving end." +#: ../../source/ref-api/flwr.common.MessageType.rst:30::1 +msgid ":py:obj:`QUERY `\\" msgstr "" -#: flwr.server.driver.driver.Driver.create_message:12 of -msgid "The ID of the destination node to which the message is being sent." +#: ../../source/ref-api/flwr.common.MessageType.rst:30::1 +msgid ":py:obj:`TRAIN `\\" msgstr "" -#: flwr.server.driver.driver.Driver.create_message:14 of -msgid "" -"The ID of the group to which this message is associated. In some " -"settings, this is used as the FL round." +#: ../../source/ref-api/flwr.common.MessageTypeLegacy.rst:2 +msgid "MessageTypeLegacy" msgstr "" -#: flwr.server.driver.driver.Driver.create_message:17 of -msgid "" -"Time-to-live for the round trip of this message, i.e., the time from " -"sending this message to receiving a reply. It specifies the duration for " -"which the message and its potential reply are considered valid." +#: ../../source/ref-api/flwr.common.MessageTypeLegacy.rst:29::1 +msgid ":py:obj:`GET_PARAMETERS `\\" msgstr "" -#: flwr.server.driver.driver.Driver.create_message:22 of -msgid "" -"**message** -- A new `Message` instance with the specified content and " -"metadata." +#: ../../source/ref-api/flwr.common.MessageTypeLegacy.rst:29::1 +msgid ":py:obj:`GET_PROPERTIES `\\" msgstr "" -#: flwr.server.driver.driver.Driver.pull_messages:3 of -msgid "" -"This method is used to collect messages from the SuperLink that " -"correspond to a set of given message IDs." +#: flwr.common.Metadata.created_at:1::1 +#: flwr.common.Metadata.run_id:1 flwr.common.message.Metadata:3 of +msgid "An identifier for the current run." msgstr "" -#: flwr.server.driver.driver.Driver.pull_messages:6 of -msgid "An iterable of message IDs for which reply messages are to be retrieved." +#: flwr.common.Metadata.created_at:1::1 +#: flwr.common.Metadata.message_id:1 flwr.common.message.Metadata:5 of +msgid "An identifier for the current message." msgstr "" -#: flwr.server.driver.driver.Driver.pull_messages:9 of -msgid "**messages** -- An iterable of messages received." +#: flwr.common.Metadata.created_at:1::1 +#: flwr.common.Metadata.src_node_id:1 flwr.common.message.Metadata:7 of +msgid "An identifier for the node sending this message." msgstr "" -#: flwr.server.driver.driver.Driver.push_messages:3 of -msgid "" -"This method takes an iterable of messages and sends each message to the " -"node specified in `dst_node_id`." +#: flwr.common.Metadata.created_at:1::1 +#: flwr.common.Metadata.dst_node_id:1 flwr.common.message.Metadata:9 of +msgid "An identifier for the node receiving this message." msgstr "" -#: flwr.server.driver.driver.Driver.push_messages:6 -#: flwr.server.driver.driver.Driver.send_and_receive:7 of -msgid "An iterable of messages to be sent." +#: flwr.common.Metadata.created_at:1::1 +#: flwr.common.Metadata.reply_to_message:1 flwr.common.message.Metadata:11 of +msgid "An identifier for the message this message replies to." msgstr "" -#: flwr.server.driver.driver.Driver.push_messages:9 of +#: flwr.common.message.Metadata:13 of msgid "" -"**message_ids** -- An iterable of IDs for the messages that were sent, " -"which can be used to pull replies." +"An identifier for grouping messages. In some settings, this is used as " +"the FL round." msgstr "" -#: flwr.server.driver.driver.Driver.send_and_receive:3 of -msgid "" -"This method sends a list of messages to their destination node IDs and " -"then waits for the replies. It continues to pull replies until either all" -" replies are received or the specified timeout duration is exceeded." +#: flwr.common.message.Metadata:16 of +msgid "Time-to-live for this message in seconds." msgstr "" -#: flwr.server.driver.driver.Driver.send_and_receive:9 of -msgid "" -"The timeout duration in seconds. If specified, the method will wait for " -"replies for this duration. If `None`, there is no time limit and the " -"method will wait until replies for all messages are received." +#: flwr.common.Metadata.created_at:1::1 +#: flwr.common.Metadata.message_type:1 flwr.common.message.Metadata:18 of +msgid "A string that encodes the action to be executed on the receiving end." msgstr "" -#: flwr.server.driver.driver.Driver.send_and_receive:14 of -msgid "**replies** -- An iterable of reply messages received from the SuperLink." +#: flwr.common.message.Metadata:21 of +msgid "" +"An identifier that can be used when loading a particular data partition " +"for a ClientApp. Making use of this identifier is more relevant when " +"conducting simulations." msgstr "" -#: flwr.server.driver.driver.Driver.send_and_receive:18 -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:53 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:60 -#: of +#: flwr.common.Metadata.created_at:1::1 of #, fuzzy -msgid "Notes" -msgstr "Aucun" +msgid ":py:obj:`created_at `\\" +msgstr "serveur.stratégie.Stratégie" -#: flwr.server.driver.driver.Driver.send_and_receive:19 of -msgid "" -"This method uses `push_messages` to send the messages and `pull_messages`" -" to collect the replies. If `timeout` is set, the method may not return " -"replies for all sent messages. A message remains valid until its TTL, " -"which is not affected by `timeout`." +#: flwr.common.Metadata.created_at:1 +#: flwr.common.Metadata.created_at:1::1 of +msgid "Unix timestamp when the message was created." msgstr "" -#: ../../source/ref-api/flwr.server.History.rst:2 -msgid "History" +#: flwr.common.Metadata.created_at:1::1 of +msgid ":py:obj:`dst_node_id `\\" msgstr "" -#: flwr.server.history.History.add_loss_centralized:1::1 of -msgid "" -":py:obj:`add_loss_centralized " -"`\\ \\(server\\_round\\, " -"loss\\)" +#: flwr.common.Metadata.created_at:1::1 of +msgid ":py:obj:`group_id `\\" msgstr "" -#: flwr.server.history.History.add_loss_centralized:1 -#: flwr.server.history.History.add_loss_centralized:1::1 of -#, fuzzy -msgid "Add one loss entry (from centralized evaluation)." -msgstr "Évaluation centralisée" - -#: flwr.server.history.History.add_loss_centralized:1::1 of -msgid "" -":py:obj:`add_loss_distributed " -"`\\ \\(server\\_round\\, " -"loss\\)" +#: flwr.common.Metadata.created_at:1::1 +#: flwr.common.Metadata.group_id:1 of +msgid "An identifier for grouping messages." msgstr "" -#: flwr.server.history.History.add_loss_centralized:1::1 -#: flwr.server.history.History.add_loss_distributed:1 of -msgid "Add one loss entry (from distributed evaluation)." +#: flwr.common.Metadata.created_at:1::1 of +msgid ":py:obj:`message_id `\\" msgstr "" -#: flwr.server.history.History.add_loss_centralized:1::1 of -msgid "" -":py:obj:`add_metrics_centralized " -"`\\ \\(server\\_round\\, " -"metrics\\)" +#: flwr.common.Metadata.created_at:1::1 of +msgid ":py:obj:`message_type `\\" msgstr "" -#: flwr.server.history.History.add_loss_centralized:1::1 -#: flwr.server.history.History.add_metrics_centralized:1 of -#, fuzzy -msgid "Add metrics entries (from centralized evaluation)." -msgstr "Évaluation centralisée" - -#: flwr.server.history.History.add_loss_centralized:1::1 of -msgid "" -":py:obj:`add_metrics_distributed " -"`\\ \\(server\\_round\\, " -"metrics\\)" +#: flwr.common.Metadata.created_at:1::1 of +msgid ":py:obj:`partition_id `\\" msgstr "" -#: flwr.server.history.History.add_loss_centralized:1::1 -#: flwr.server.history.History.add_metrics_distributed:1 of -msgid "Add metrics entries (from distributed evaluation)." +#: flwr.common.Metadata.created_at:1::1 +#: flwr.common.Metadata.partition_id:1 of +msgid "An identifier telling which data partition a ClientApp should use." msgstr "" -#: flwr.server.history.History.add_loss_centralized:1::1 of -msgid "" -":py:obj:`add_metrics_distributed_fit " -"`\\ \\(server\\_round\\," -" ...\\)" +#: flwr.common.Metadata.created_at:1::1 of +msgid ":py:obj:`reply_to_message `\\" msgstr "" -#: flwr.server.history.History.add_loss_centralized:1::1 -#: flwr.server.history.History.add_metrics_distributed_fit:1 of -msgid "Add metrics entries (from distributed fit)." +#: flwr.common.Metadata.created_at:1::1 of +msgid ":py:obj:`run_id `\\" msgstr "" -#: ../../source/ref-api/flwr.server.LegacyContext.rst:2 -msgid "LegacyContext" +#: flwr.common.Metadata.created_at:1::1 of +msgid ":py:obj:`src_node_id `\\" msgstr "" -#: flwr.server.compat.legacy_context.LegacyContext:1 of -msgid "Bases: :py:class:`~flwr.common.context.Context`" +#: flwr.common.Metadata.created_at:1::1 of +msgid ":py:obj:`ttl `\\" msgstr "" -#: ../../source/ref-api/flwr.server.LegacyContext.rst:32::1 -#, fuzzy -msgid ":py:obj:`config `\\" -msgstr "serveur.stratégie.Stratégie" +#: flwr.common.Metadata.created_at:1::1 flwr.common.Metadata.ttl:1 +#: of +msgid "Time-to-live for this message." +msgstr "" -#: ../../source/ref-api/flwr.server.LegacyContext.rst:32::1 -#, fuzzy -msgid ":py:obj:`strategy `\\" -msgstr "serveur.stratégie.Stratégie" +#: ../../source/ref-api/flwr.common.MetricsRecord.rst:2 +msgid "MetricsRecord" +msgstr "" -#: ../../source/ref-api/flwr.server.LegacyContext.rst:32::1 -msgid ":py:obj:`client_manager `\\" +#: flwr.common.record.metricsrecord.MetricsRecord:1 of +msgid "" +"Bases: :py:class:`~flwr.common.record.typeddict.TypedDict`\\ " +"[:py:class:`str`, :py:class:`int` | :py:class:`float` | " +":py:class:`~typing.List`\\ [:py:class:`int`] | :py:class:`~typing.List`\\" +" [:py:class:`float`]]" msgstr "" -#: ../../source/ref-api/flwr.server.LegacyContext.rst:32::1 -#, fuzzy -msgid ":py:obj:`history `\\" -msgstr "serveur.stratégie.Stratégie" +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid ":py:obj:`clear `\\ \\(\\)" +msgstr "" -#: ../../source/ref-api/flwr.server.LegacyContext.rst:32::1 -#, fuzzy -msgid ":py:obj:`state `\\" -msgstr "serveur.stratégie.Stratégie" +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid ":py:obj:`count_bytes `\\ \\(\\)" +msgstr "" -#: flwr.server.server.Server.client_manager:1::1 of -msgid ":py:obj:`client_manager `\\ \\(\\)" +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" msgstr "" -#: flwr.server.server.Server.client_manager:1 -#: flwr.server.server.Server.client_manager:1::1 of -msgid "Return ClientManager." +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid ":py:obj:`items `\\ \\(\\)" msgstr "" -#: flwr.server.server.Server.client_manager:1::1 of -msgid "" -":py:obj:`disconnect_all_clients " -"`\\ \\(timeout\\)" +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid ":py:obj:`keys `\\ \\(\\)" msgstr "" -#: flwr.server.server.Server.client_manager:1::1 -#: flwr.server.server.Server.disconnect_all_clients:1 of -msgid "Send shutdown signal to all clients." +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid ":py:obj:`pop `\\ \\(k\\[\\,d\\]\\)" msgstr "" -#: flwr.server.server.Server.client_manager:1::1 of +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of msgid "" -":py:obj:`evaluate_round `\\ " -"\\(server\\_round\\, timeout\\)" +":py:obj:`update `\\ \\(\\[E\\, " +"\\]\\*\\*F\\)" msgstr "" -#: flwr.server.server.Server.client_manager:1::1 -#: flwr.server.server.Server.evaluate_round:1 of -msgid "Validate current global model on a number of clients." +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid ":py:obj:`values `\\ \\(\\)" msgstr "" -#: flwr.server.server.Server.client_manager:1::1 of -msgid ":py:obj:`fit `\\ \\(num\\_rounds\\, timeout\\)" +#: ../../source/ref-api/flwr.common.NDArray.rst:2 +msgid "NDArray" msgstr "" -#: flwr.server.server.Server.client_manager:1::1 -#: flwr.server.server.Server.fit:1 of -msgid "Run federated averaging for a number of rounds." +#: ../../source/ref-api/flwr.common.Parameters.rst:29::1 +msgid ":py:obj:`tensors `\\" msgstr "" -#: flwr.server.server.Server.client_manager:1::1 of -msgid "" -":py:obj:`fit_round `\\ \\(server\\_round\\," -" timeout\\)" +#: ../../source/ref-api/flwr.common.Parameters.rst:29::1 +msgid ":py:obj:`tensor_type `\\" msgstr "" -#: flwr.server.server.Server.client_manager:1::1 -#: flwr.server.server.Server.fit_round:1 of -msgid "Perform a single round of federated averaging." +#: ../../source/ref-api/flwr.common.ParametersRecord.rst:2 +#, fuzzy +msgid "ParametersRecord" +msgstr "Paramètres du modèle." + +#: flwr.common.record.parametersrecord.ParametersRecord:1 of +msgid "" +"Bases: :py:class:`~flwr.common.record.typeddict.TypedDict`\\ " +"[:py:class:`str`, :py:class:`~flwr.common.record.parametersrecord.Array`]" msgstr "" -#: flwr.server.server.Server.client_manager:1::1 of +#: flwr.common.record.parametersrecord.ParametersRecord:3 of msgid "" -":py:obj:`set_max_workers `\\ " -"\\(max\\_workers\\)" +"A dataclass storing named Arrays in order. This means that it holds " +"entries as an OrderedDict[str, Array]. ParametersRecord objects can be " +"viewed as an equivalent to PyTorch's state_dict, but holding serialised " +"tensors instead." msgstr "" -#: flwr.server.server.Server.client_manager:1::1 -#: flwr.server.server.Server.set_max_workers:1 of -msgid "Set the max_workers used by ThreadPoolExecutor." +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid ":py:obj:`clear `\\ \\(\\)" msgstr "" -#: flwr.server.server.Server.client_manager:1::1 of -msgid ":py:obj:`set_strategy `\\ \\(strategy\\)" +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid ":py:obj:`count_bytes `\\ \\(\\)" msgstr "" -#: flwr.server.server.Server.client_manager:1::1 -#: flwr.server.server.Server.set_strategy:1 of -#, fuzzy -msgid "Replace server strategy." -msgstr "stratégie.du.serveur" +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" +msgstr "" -#: ../../source/ref-api/flwr.server.ServerApp.rst:2 -#, fuzzy -msgid "ServerApp" -msgstr "serveur" +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid ":py:obj:`items `\\ \\(\\)" +msgstr "" -#: flwr.server.server_app.ServerApp:5 of -#, fuzzy -msgid "Use the `ServerApp` with an existing `Strategy`:" -msgstr "Utilise une stratégie existante" +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid ":py:obj:`keys `\\ \\(\\)" +msgstr "" -#: flwr.server.server_app.ServerApp:15 of -msgid "Use the `ServerApp` with a custom main function:" +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid ":py:obj:`pop `\\ \\(k\\[\\,d\\]\\)" msgstr "" -#: flwr.server.server_app.ServerApp.main:1::1 of -#, fuzzy -msgid ":py:obj:`main `\\ \\(\\)" -msgstr "serveur.stratégie.Stratégie" +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid "" +":py:obj:`update `\\ \\(\\[E\\, " +"\\]\\*\\*F\\)" +msgstr "" -#: flwr.server.server_app.ServerApp.main:1 -#: flwr.server.server_app.ServerApp.main:1::1 of -msgid "Return a decorator that registers the main fn with the server app." +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid ":py:obj:`values `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.server.ServerConfig.rst:2 +#: flwr.common.record.parametersrecord.ParametersRecord.count_bytes:3 of +msgid "" +"Note that a small amount of Bytes might also be included in this counting" +" that correspond to metadata of the serialized object (e.g. of NumPy " +"array) needed for deseralization." +msgstr "" + +#: ../../source/ref-api/flwr.common.ReconnectIns.rst:2 #, fuzzy -msgid "ServerConfig" -msgstr "serveur" +msgid "ReconnectIns" +msgstr "Collecte centralisée des données" -#: flwr.server.server_config.ServerConfig:3 of -msgid "" -"All attributes have default values which allows users to configure just " -"the ones they care about." +#: ../../source/ref-api/flwr.common.ReconnectIns.rst:28::1 +msgid ":py:obj:`seconds `\\" msgstr "" -#: ../../source/ref-api/flwr.server.ServerConfig.rst:29::1 -msgid ":py:obj:`num_rounds `\\" +#: ../../source/ref-api/flwr.common.RecordSet.rst:2 +msgid "RecordSet" msgstr "" -#: ../../source/ref-api/flwr.server.ServerConfig.rst:29::1 -msgid ":py:obj:`round_timeout `\\" +#: flwr.common.RecordSet.configs_records:1::1 of +msgid ":py:obj:`configs_records `\\" msgstr "" -#: ../../source/ref-api/flwr.server.SimpleClientManager.rst:2 -msgid "SimpleClientManager" +#: flwr.common.RecordSet.configs_records:1 +#: flwr.common.RecordSet.configs_records:1::1 of +msgid "Dictionary holding ConfigsRecord instances." msgstr "" -#: flwr.server.client_manager.SimpleClientManager:1 of -msgid "Bases: :py:class:`~flwr.server.client_manager.ClientManager`" +#: flwr.common.RecordSet.configs_records:1::1 of +msgid ":py:obj:`metrics_records `\\" msgstr "" -#: flwr.server.client_manager.SimpleClientManager.all:1::1 of -msgid ":py:obj:`all `\\ \\(\\)" +#: flwr.common.RecordSet.configs_records:1::1 +#: flwr.common.RecordSet.metrics_records:1 of +msgid "Dictionary holding MetricsRecord instances." msgstr "" -#: flwr.server.client_manager.SimpleClientManager.all:1::1 of -msgid "" -":py:obj:`num_available `\\" -" \\(\\)" +#: flwr.common.RecordSet.configs_records:1::1 of +msgid ":py:obj:`parameters_records `\\" msgstr "" -#: flwr.server.client_manager.SimpleClientManager.all:1::1 of -msgid "" -":py:obj:`register `\\ " -"\\(client\\)" +#: flwr.common.RecordSet.configs_records:1::1 +#: flwr.common.RecordSet.parameters_records:1 of +msgid "Dictionary holding ParametersRecord instances." msgstr "" -#: flwr.server.client_manager.SimpleClientManager.all:1::1 of -msgid "" -":py:obj:`sample `\\ " -"\\(num\\_clients\\[\\, min\\_num\\_clients\\, criterion\\]\\)" +#: ../../source/ref-api/flwr.common.ServerMessage.rst:2 +#, fuzzy +msgid "ServerMessage" +msgstr "Côté serveur" + +#: ../../source/ref-api/flwr.common.ServerMessage.rst:31::1 +msgid ":py:obj:`evaluate_ins `\\" msgstr "" -#: flwr.server.client_manager.SimpleClientManager.all:1::1 of -msgid "" -":py:obj:`unregister `\\ " -"\\(client\\)" +#: ../../source/ref-api/flwr.common.ServerMessage.rst:31::1 +msgid ":py:obj:`fit_ins `\\" msgstr "" -#: flwr.server.client_manager.SimpleClientManager.all:1::1 of +#: ../../source/ref-api/flwr.common.ServerMessage.rst:31::1 msgid "" -":py:obj:`wait_for `\\ " -"\\(num\\_clients\\[\\, timeout\\]\\)" +":py:obj:`get_parameters_ins " +"`\\" msgstr "" -#: flwr.server.client_manager.SimpleClientManager.wait_for:3 of +#: ../../source/ref-api/flwr.common.ServerMessage.rst:31::1 msgid "" -"Blocks until the requested number of clients is available or until a " -"timeout is reached. Current timeout default: 1 day." +":py:obj:`get_properties_ins " +"`\\" msgstr "" -#: flwr.server.client_manager.SimpleClientManager.wait_for:6 of -msgid "The number of clients to wait for." -msgstr "" +#: ../../source/ref-api/flwr.common.Status.rst:2 +#, fuzzy +msgid "Status" +msgstr "Statut du client." -#: flwr.server.client_manager.SimpleClientManager.wait_for:8 of -msgid "The time in seconds to wait for, defaults to 86400 (24h)." +#: ../../source/ref-api/flwr.common.Status.rst:29::1 +msgid ":py:obj:`code `\\" msgstr "" -#: flwr.server.client_manager.SimpleClientManager.wait_for:11 of -msgid "**success**" +#: ../../source/ref-api/flwr.common.Status.rst:29::1 +msgid ":py:obj:`message `\\" msgstr "" -#: ../../source/ref-api/flwr.server.run_driver_api.rst:2 -#, fuzzy -msgid "run\\_driver\\_api" -msgstr "flower-driver-api" - -#: ../../source/ref-api/flwr.server.run_fleet_api.rst:2 -msgid "run\\_fleet\\_api" +#: ../../source/ref-api/flwr.common.array_from_numpy.rst:2 +msgid "array\\_from\\_numpy" msgstr "" -#: ../../source/ref-api/flwr.server.run_server_app.rst:2 -msgid "run\\_server\\_app" +#: ../../source/ref-api/flwr.common.bytes_to_ndarray.rst:2 +msgid "bytes\\_to\\_ndarray" msgstr "" -#: ../../source/ref-api/flwr.server.run_superlink.rst:2 -#, fuzzy -msgid "run\\_superlink" -msgstr "flower-superlink" - -#: ../../source/ref-api/flwr.server.start_driver.rst:2 +#: ../../source/ref-api/flwr.common.configure.rst:2 #, fuzzy -msgid "start\\_driver" -msgstr "start_client" +msgid "configure" +msgstr "Configurer les clients" -#: flwr.server.compat.app.start_driver:3 of -msgid "" -"The IPv4 or IPv6 address of the Driver API server. Defaults to " -"`\"[::]:8080\"`." +#: ../../source/ref-api/flwr.common.event.rst:2 +msgid "event" msgstr "" -#: flwr.server.compat.app.start_driver:6 of -msgid "" -"A server implementation, either `flwr.server.Server` or a subclass " -"thereof. If no instance is provided, then `start_driver` will create one." +#: ../../source/ref-api/flwr.common.log.rst:2 +msgid "log" msgstr "" -#: flwr.server.app.start_server:9 flwr.server.compat.app.start_driver:10 -#: flwr.simulation.app.start_simulation:28 of +#: logging.Logger.log:3 of msgid "" -"Currently supported values are `num_rounds` (int, default: 1) and " -"`round_timeout` in seconds (float, default: None)." +"To pass exception information, use the keyword argument exc_info with a " +"true value, e.g." msgstr "" +"Pour transmettre des informations sur les exceptions, utilise l'argument " +"mot-clé exc_info avec une valeur vraie, par ex." -#: flwr.server.app.start_server:12 flwr.server.compat.app.start_driver:13 of -msgid "" -"An implementation of the abstract base class " -"`flwr.server.strategy.Strategy`. If no strategy is provided, then " -"`start_server` will use `flwr.server.strategy.FedAvg`." -msgstr "" +#: logging.Logger.log:6 of +#, python-format +msgid "logger.log(level, \"We have a %s\", \"mysterious problem\", exc_info=1)" +msgstr "logger.log(level, \"We have a %s\", \"mysterious problem\", exc_info=1)" -#: flwr.server.compat.app.start_driver:17 of -msgid "" -"An implementation of the class `flwr.server.ClientManager`. If no " -"implementation is provided, then `start_driver` will use " -"`flwr.server.SimpleClientManager`." +#: ../../source/ref-api/flwr.common.ndarray_to_bytes.rst:2 +msgid "ndarray\\_to\\_bytes" msgstr "" -#: flwr.server.compat.app.start_driver:25 of -msgid "The Driver object to use." +#: ../../source/ref-api/flwr.common.ndarrays_to_parameters.rst:2 +msgid "ndarrays\\_to\\_parameters" msgstr "" -#: flwr.server.app.start_server:37 flwr.server.compat.app.start_driver:28 of -msgid "**hist** -- Object containing training and evaluation metrics." +#: ../../source/ref-api/flwr.common.now.rst:2 +msgid "now" msgstr "" -#: flwr.server.compat.app.start_driver:33 of -msgid "Starting a driver that connects to an insecure server:" +#: ../../source/ref-api/flwr.common.parameters_to_ndarrays.rst:2 +msgid "parameters\\_to\\_ndarrays" msgstr "" -#: flwr.server.compat.app.start_driver:37 of -msgid "Starting a driver that connects to an SSL-enabled server:" +#: ../../source/ref-api/flwr.server.rst:2 +msgid "server" +msgstr "serveur" + +#: ../../source/ref-api/flwr.server.rst:24::1 +msgid ":py:obj:`run_server_app `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.server.start_server.rst:2 +#: ../../source/ref-api/flwr.server.rst:24::1 +#: flwr.server.run_serverapp.run_server_app:1 of #, fuzzy -msgid "start\\_server" -msgstr "serveur.start_server" +msgid "Run Flower server app." +msgstr "Serveur de Flower" -#: flwr.server.app.start_server:3 of -msgid "The IPv4 or IPv6 address of the server. Defaults to `\"[::]:8080\"`." +#: ../../source/ref-api/flwr.server.rst:24::1 +msgid ":py:obj:`run_superlink `\\ \\(\\)" msgstr "" -#: flwr.server.app.start_server:5 of +#: ../../source/ref-api/flwr.server.rst:24::1 +#: flwr.server.app.run_superlink:1 of +#, fuzzy +msgid "Run Flower SuperLink (Driver API and Fleet API)." +msgstr "flower-fleet-api" + +#: ../../source/ref-api/flwr.server.rst:24::1 msgid "" -"A server implementation, either `flwr.server.Server` or a subclass " -"thereof. If no instance is provided, then `start_server` will create one." +":py:obj:`start_server `\\ \\(\\*\\[\\, " +"server\\_address\\, server\\, ...\\]\\)" msgstr "" -#: flwr.server.app.start_server:16 of +#: ../../source/ref-api/flwr.server.rst:24::1 +#: flwr.server.app.start_server:1 of +msgid "Start a Flower server using the gRPC transport layer." +msgstr "" + +#: ../../source/ref-api/flwr.server.rst:38::1 +msgid ":py:obj:`ClientManager `\\ \\(\\)" +msgstr "" + +#: ../../source/ref-api/flwr.server.rst:38::1 +#: flwr.server.client_manager.ClientManager:1 of +msgid "Abstract base class for managing Flower clients." +msgstr "" + +#: ../../source/ref-api/flwr.server.rst:38::1 +#, fuzzy +msgid ":py:obj:`Driver `\\ \\(\\)" +msgstr "serveur.stratégie.Stratégie" + +#: ../../source/ref-api/flwr.server.rst:38::1 +#: flwr.server.driver.driver.Driver:1 of +msgid "Abstract base Driver class for the Driver API." +msgstr "" + +#: ../../source/ref-api/flwr.server.rst:38::1 +msgid ":py:obj:`History `\\ \\(\\)" +msgstr "" + +#: ../../source/ref-api/flwr.server.rst:38::1 +#: flwr.server.history.History:1 of +msgid "History class for training and/or evaluation metrics collection." +msgstr "" + +#: ../../source/ref-api/flwr.server.rst:38::1 msgid "" -"An implementation of the abstract base class `flwr.server.ClientManager`." -" If no implementation is provided, then `start_server` will use " -"`flwr.server.client_manager.SimpleClientManager`." +":py:obj:`LegacyContext `\\ \\(state\\[\\, " +"config\\, strategy\\, ...\\]\\)" msgstr "" -#: flwr.server.app.start_server:21 of +#: ../../source/ref-api/flwr.server.rst:38::1 +#: flwr.server.compat.legacy_context.LegacyContext:1 of +msgid "Legacy Context." +msgstr "" + +#: ../../source/ref-api/flwr.server.rst:38::1 msgid "" -"The maximum length of gRPC messages that can be exchanged with the Flower" -" clients. The default should be sufficient for most models. Users who " -"train very large models might need to increase this value. Note that the " -"Flower clients need to be started with the same value (see " -"`flwr.client.start_client`), otherwise clients will not know about the " -"increased limit and block larger messages." +":py:obj:`Server `\\ \\(\\*\\, client\\_manager\\[\\, " +"strategy\\]\\)" msgstr "" -#: flwr.server.app.start_server:42 of +#: ../../source/ref-api/flwr.server.rst:38::1 #, fuzzy -msgid "Starting an insecure server:" -msgstr "Démarrer le serveur" +msgid "" +":py:obj:`ServerApp `\\ \\(\\[server\\, config\\, " +"strategy\\, ...\\]\\)" +msgstr "serveur.stratégie.Stratégie" -#: flwr.server.app.start_server:46 of +#: ../../source/ref-api/flwr.server.rst:38::1 +#: flwr.server.server_app.ServerApp:1 of #, fuzzy -msgid "Starting an SSL-enabled server:" -msgstr "Démarrer le serveur" +msgid "Flower ServerApp." +msgstr "Serveur de Flower" -#: ../../source/ref-api/flwr.server.strategy.rst:2 +#: ../../source/ref-api/flwr.server.rst:38::1 #, fuzzy -msgid "strategy" -msgstr "stratégie.du.serveur" - -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`Bulyan `\\ \\(\\*\\, " -"fraction\\_fit\\, fraction\\_evaluate\\, ...\\)" +":py:obj:`ServerConfig `\\ \\(\\[num\\_rounds\\," +" round\\_timeout\\]\\)" msgstr "" +"Flower 1.0 : ``start_server(..., " +"config=flwr.server.ServerConfig(num_rounds=3, round_timeout=600.0), " +"...)``" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.bulyan.Bulyan:1 of +#: ../../source/ref-api/flwr.server.rst:38::1 +#: flwr.server.server_config.ServerConfig:1 of #, fuzzy -msgid "Bulyan strategy." -msgstr "Stratégies intégrées" +msgid "Flower server config." +msgstr "Serveur de Flower" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -msgid "" -":py:obj:`DPFedAvgAdaptive `\\ " -"\\(strategy\\, num\\_sampled\\_clients\\)" +#: ../../source/ref-api/flwr.server.rst:38::1 +msgid ":py:obj:`SimpleClientManager `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive:1 of -msgid "Wrapper for configuring a Strategy for DP with Adaptive Clipping." +#: ../../source/ref-api/flwr.server.rst:38::1 +#: flwr.server.client_manager.SimpleClientManager:1 of +msgid "Provides a pool of available clients." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -msgid "" -":py:obj:`DPFedAvgFixed `\\ " -"\\(strategy\\, num\\_sampled\\_clients\\, ...\\)" -msgstr "" +#: ../../source/ref-api/flwr.server.rst:57::1 +#, fuzzy +msgid ":py:obj:`flwr.server.strategy `\\" +msgstr "serveur.stratégie.Stratégie" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed:1 of -msgid "Wrapper for configuring a Strategy for DP with Fixed Clipping." +#: ../../source/ref-api/flwr.server.rst:57::1 +#: flwr.server.strategy:1 of +msgid "Contains the strategy abstraction and different implementations." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -msgid "" -":py:obj:`DifferentialPrivacyClientSideAdaptiveClipping " -"`\\ " -"\\(...\\)" +#: ../../source/ref-api/flwr.server.rst:57::1 +#, fuzzy +msgid ":py:obj:`flwr.server.workflow `\\" +msgstr "serveur.stratégie.Stratégie" + +#: ../../source/ref-api/flwr.server.rst:57::1 +#: flwr.server.workflow:1 of +#, fuzzy +msgid "Workflows." +msgstr "Flux de travail" + +#: ../../source/ref-api/flwr.server.ClientManager.rst:2 +#, fuzzy +msgid "ClientManager" +msgstr "client" + +#: flwr.server.client_manager.ClientManager.all:1::1 of +msgid ":py:obj:`all `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:1 -#: of -msgid "Strategy wrapper for central DP with client-side adaptive clipping." +#: flwr.server.client_manager.ClientManager.all:1 +#: flwr.server.client_manager.ClientManager.all:1::1 +#: flwr.server.client_manager.SimpleClientManager.all:1 +#: flwr.server.client_manager.SimpleClientManager.all:1::1 of +msgid "Return all available clients." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -msgid "" -":py:obj:`DifferentialPrivacyServerSideAdaptiveClipping " -"`\\ " -"\\(...\\)" +#: flwr.server.client_manager.ClientManager.all:1::1 of +msgid ":py:obj:`num_available `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:1 -#: of -msgid "Strategy wrapper for central DP with server-side adaptive clipping." +#: flwr.server.client_manager.ClientManager.all:1::1 +#: flwr.server.client_manager.ClientManager.num_available:1 +#: flwr.server.client_manager.SimpleClientManager.all:1::1 +#: flwr.server.client_manager.SimpleClientManager.num_available:1 of +msgid "Return the number of available clients." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -msgid "" -":py:obj:`DifferentialPrivacyClientSideFixedClipping " -"`\\ " -"\\(...\\)" +#: flwr.server.client_manager.ClientManager.all:1::1 of +msgid ":py:obj:`register `\\ \\(client\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:1 -#: of -msgid "Strategy wrapper for central DP with client-side fixed clipping." +#: flwr.server.client_manager.ClientManager.all:1::1 +#: flwr.server.client_manager.ClientManager.register:1 +#: flwr.server.client_manager.SimpleClientManager.all:1::1 +#: flwr.server.client_manager.SimpleClientManager.register:1 of +msgid "Register Flower ClientProxy instance." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.client_manager.ClientManager.all:1::1 of msgid "" -":py:obj:`DifferentialPrivacyServerSideFixedClipping " -"`\\ " -"\\(...\\)" +":py:obj:`sample `\\ " +"\\(num\\_clients\\[\\, min\\_num\\_clients\\, criterion\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:1 -#: of -msgid "Strategy wrapper for central DP with server-side fixed clipping." +#: flwr.server.client_manager.ClientManager.all:1::1 +#: flwr.server.client_manager.ClientManager.sample:1 +#: flwr.server.client_manager.SimpleClientManager.all:1::1 +#: flwr.server.client_manager.SimpleClientManager.sample:1 of +msgid "Sample a number of Flower ClientProxy instances." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -msgid "" -":py:obj:`FedAdagrad `\\ \\(\\*\\[\\, " -"fraction\\_fit\\, ...\\]\\)" +#: flwr.server.client_manager.ClientManager.all:1::1 of +msgid ":py:obj:`unregister `\\ \\(client\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fedadagrad.FedAdagrad:1 of -#, fuzzy -msgid "FedAdagrad strategy - Adaptive Federated Optimization using Adagrad." +#: flwr.server.client_manager.ClientManager.all:1::1 +#: flwr.server.client_manager.ClientManager.unregister:1 +#: flwr.server.client_manager.SimpleClientManager.all:1::1 +#: flwr.server.client_manager.SimpleClientManager.unregister:1 of +msgid "Unregister Flower ClientProxy instance." msgstr "" -"`FedAdam` et `FedAdam` correspondent à la dernière version de l'article " -"sur l'optimisation fédérée adaptative." -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.client_manager.ClientManager.all:1::1 of msgid "" -":py:obj:`FedAdam `\\ \\(\\*\\[\\, " -"fraction\\_fit\\, ...\\]\\)" +":py:obj:`wait_for `\\ " +"\\(num\\_clients\\, timeout\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fedadam.FedAdam:1 of -msgid "FedAdam - Adaptive Federated Optimization using Adam." +#: flwr.server.client_manager.ClientManager.all:1::1 +#: flwr.server.client_manager.ClientManager.wait_for:1 +#: flwr.server.client_manager.SimpleClientManager.all:1::1 +#: flwr.server.client_manager.SimpleClientManager.wait_for:1 of +msgid "Wait until at least `num_clients` are available." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -msgid "" -":py:obj:`FedAvg `\\ \\(\\*\\[\\, " -"fraction\\_fit\\, fraction\\_evaluate\\, ...\\]\\)" +#: flwr.server.client_manager.ClientManager.num_available:3 +#: flwr.server.client_manager.SimpleClientManager.num_available:3 of +msgid "**num_available** -- The number of currently available clients." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fedavg.FedAvg:1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:1 of -#, fuzzy -msgid "Federated Averaging strategy." -msgstr "Stratégie de moyenne fédérée." - -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.client_manager.ClientManager.register:6 +#: flwr.server.client_manager.SimpleClientManager.register:6 of msgid "" -":py:obj:`FedAvgAndroid `\\ " -"\\(\\*\\[\\, fraction\\_fit\\, ...\\]\\)" +"**success** -- Indicating if registration was successful. False if " +"ClientProxy is already registered or can not be registered for any " +"reason." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -msgid "" -":py:obj:`FedAvgM `\\ \\(\\*\\[\\, " -"fraction\\_fit\\, ...\\]\\)" +#: flwr.server.client_manager.ClientManager.unregister:3 +#: flwr.server.client_manager.SimpleClientManager.unregister:3 of +msgid "This method is idempotent." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fedavgm.FedAvgM:1 of +#: ../../source/ref-api/flwr.server.Driver.rst:2 #, fuzzy -msgid "Federated Averaging with Momentum strategy." -msgstr "Stratégie de moyenne fédérée." +msgid "Driver" +msgstr "serveur" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.driver.driver.Driver.create_message:1::1 of +#, fuzzy msgid "" -":py:obj:`FedMedian `\\ \\(\\*\\[\\, " -"fraction\\_fit\\, ...\\]\\)" +":py:obj:`create_message `\\ " +"\\(content\\, message\\_type\\, ...\\[\\, ttl\\]\\)" msgstr "" +"Flower 1.0 : ``start_server(..., " +"config=flwr.server.ServerConfig(num_rounds=3, round_timeout=600.0), " +"...)``" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fedmedian.FedMedian:1 of -#, fuzzy -msgid "Configurable FedMedian strategy implementation." -msgstr "Configuration de l'évaluation fédérée" +#: flwr.server.driver.driver.Driver.create_message:1 +#: flwr.server.driver.driver.Driver.create_message:1::1 of +msgid "Create a new message with specified parameters." +msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -msgid "" -":py:obj:`FedOpt `\\ \\(\\*\\[\\, " -"fraction\\_fit\\, fraction\\_evaluate\\, ...\\]\\)" +#: flwr.server.driver.driver.Driver.create_message:1::1 of +msgid ":py:obj:`get_node_ids `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fedopt.FedOpt:1 of -#, fuzzy -msgid "Federated Optim strategy." -msgstr "Stratégie de moyenne fédérée." +#: flwr.server.driver.driver.Driver.create_message:1::1 +#: flwr.server.driver.driver.Driver.get_node_ids:1 of +msgid "Get node IDs." +msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.driver.driver.Driver.create_message:1::1 of msgid "" -":py:obj:`FedProx `\\ \\(\\*\\[\\, " -"fraction\\_fit\\, ...\\]\\)" +":py:obj:`pull_messages `\\ " +"\\(message\\_ids\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fedprox.FedProx:1 of -#, fuzzy -msgid "Federated Optimization strategy." -msgstr "Stratégie de moyenne fédérée." +#: flwr.server.driver.driver.Driver.create_message:1::1 +#: flwr.server.driver.driver.Driver.pull_messages:1 of +msgid "Pull messages based on message IDs." +msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.driver.driver.Driver.create_message:1::1 of msgid "" -":py:obj:`FedTrimmedAvg `\\ " -"\\(\\*\\[\\, fraction\\_fit\\, ...\\]\\)" +":py:obj:`push_messages `\\ " +"\\(messages\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:1 of -msgid "Federated Averaging with Trimmed Mean [Dong Yin, et al., 2021]." +#: flwr.server.driver.driver.Driver.create_message:1::1 +#: flwr.server.driver.driver.Driver.push_messages:1 of +msgid "Push messages to specified node IDs." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.driver.driver.Driver.create_message:1::1 of +#, fuzzy msgid "" -":py:obj:`FedXgbBagging `\\ " -"\\(\\[evaluate\\_function\\]\\)" +":py:obj:`send_and_receive `\\ " +"\\(messages\\, \\*\\[\\, timeout\\]\\)" msgstr "" +"Flower 1.0 : ``start_server(..., " +"config=flwr.server.ServerConfig(num_rounds=3, round_timeout=600.0), " +"...)``" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging:1 of -msgid "Configurable FedXgbBagging strategy implementation." +#: flwr.server.driver.driver.Driver.create_message:1::1 +#: flwr.server.driver.driver.Driver.send_and_receive:1 of +msgid "Push messages to specified node IDs and pull the reply messages." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.driver.driver.Driver.create_message:3 of msgid "" -":py:obj:`FedXgbCyclic `\\ " -"\\(\\*\\*kwargs\\)" +"This method constructs a new `Message` with given content and metadata. " +"The `run_id` and `src_node_id` will be set automatically." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic:1 of -msgid "Configurable FedXgbCyclic strategy implementation." +#: flwr.server.driver.driver.Driver.create_message:6 of +msgid "" +"The content for the new message. This holds records that are to be sent " +"to the destination node." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.driver.driver.Driver.create_message:9 of msgid "" -":py:obj:`FedXgbNnAvg `\\ \\(\\*args\\, " -"\\*\\*kwargs\\)" +"The type of the message, defining the action to be executed on the " +"receiving end." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg:1 of -msgid "Configurable FedXgbNnAvg strategy implementation." +#: flwr.server.driver.driver.Driver.create_message:12 of +msgid "The ID of the destination node to which the message is being sent." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.driver.driver.Driver.create_message:14 of msgid "" -":py:obj:`FedYogi `\\ \\(\\*\\[\\, " -"fraction\\_fit\\, ...\\]\\)" +"The ID of the group to which this message is associated. In some " +"settings, this is used as the FL round." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fedyogi.FedYogi:1 of -msgid "FedYogi [Reddi et al., 2020] strategy." +#: flwr.server.driver.driver.Driver.create_message:17 of +msgid "" +"Time-to-live for the round trip of this message, i.e., the time from " +"sending this message to receiving a reply. It specifies in seconds the " +"duration for which the message and its potential reply are considered " +"valid. If unset, the default TTL (i.e., `common.DEFAULT_TTL`) will be " +"used." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.driver.driver.Driver.create_message:23 of msgid "" -":py:obj:`FaultTolerantFedAvg " -"`\\ \\(\\*\\[\\, " -"fraction\\_fit\\, ...\\]\\)" +"**message** -- A new `Message` instance with the specified content and " +"metadata." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg:1 of -msgid "Configurable fault-tolerant FedAvg strategy implementation." +#: flwr.server.driver.driver.Driver.pull_messages:3 of +msgid "" +"This method is used to collect messages from the SuperLink that " +"correspond to a set of given message IDs." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -msgid "" -":py:obj:`Krum `\\ \\(\\*\\[\\, " -"fraction\\_fit\\, fraction\\_evaluate\\, ...\\]\\)" +#: flwr.server.driver.driver.Driver.pull_messages:6 of +msgid "An iterable of message IDs for which reply messages are to be retrieved." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.krum.Krum:1 of -msgid "Krum [Blanchard et al., 2017] strategy." +#: flwr.server.driver.driver.Driver.pull_messages:9 of +msgid "**messages** -- An iterable of messages received." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.driver.driver.Driver.push_messages:3 of msgid "" -":py:obj:`QFedAvg `\\ \\(\\*\\[\\, " -"q\\_param\\, qffl\\_learning\\_rate\\, ...\\]\\)" +"This method takes an iterable of messages and sends each message to the " +"node specified in `dst_node_id`." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.qfedavg.QFedAvg:1 of -msgid "Configurable QFedAvg strategy implementation." +#: flwr.server.driver.driver.Driver.push_messages:6 +#: flwr.server.driver.driver.Driver.send_and_receive:7 of +msgid "An iterable of messages to be sent." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#, fuzzy -msgid ":py:obj:`Strategy `\\ \\(\\)" -msgstr "serveur.stratégie.Stratégie" - -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.strategy.Strategy:1 of -msgid "Abstract base class for server strategy implementations." +#: flwr.server.driver.driver.Driver.push_messages:9 of +msgid "" +"**message_ids** -- An iterable of IDs for the messages that were sent, " +"which can be used to pull replies." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.Bulyan.rst:2 -msgid "Bulyan" +#: flwr.server.driver.driver.Driver.send_and_receive:3 of +msgid "" +"This method sends a list of messages to their destination node IDs and " +"then waits for the replies. It continues to pull replies until either all" +" replies are received or the specified timeout duration is exceeded." msgstr "" -#: flwr.server.strategy.bulyan.Bulyan:1 -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg:1 -#: flwr.server.strategy.fedavgm.FedAvgM:1 -#: flwr.server.strategy.fedmedian.FedMedian:1 -#: flwr.server.strategy.fedopt.FedOpt:1 flwr.server.strategy.fedprox.FedProx:1 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:1 -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging:1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic:1 -#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg:1 -#: flwr.server.strategy.krum.Krum:1 flwr.server.strategy.qfedavg.QFedAvg:1 of -msgid "Bases: :py:class:`~flwr.server.strategy.fedavg.FedAvg`" +#: flwr.server.driver.driver.Driver.send_and_receive:9 of +msgid "" +"The timeout duration in seconds. If specified, the method will wait for " +"replies for this duration. If `None`, there is no time limit and the " +"method will wait until replies for all messages are received." msgstr "" -#: flwr.server.strategy.bulyan.Bulyan:3 of -msgid "Implementation based on https://arxiv.org/abs/1802.07927." +#: flwr.server.driver.driver.Driver.send_and_receive:14 of +msgid "**replies** -- An iterable of reply messages received from the SuperLink." msgstr "" -#: flwr.server.strategy.bulyan.Bulyan:5 -#: flwr.server.strategy.fedadagrad.FedAdagrad:5 -#: flwr.server.strategy.fedadam.FedAdam:5 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:5 -#: flwr.server.strategy.fedavgm.FedAvgM:5 flwr.server.strategy.fedopt.FedOpt:5 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:5 -#: flwr.server.strategy.fedyogi.FedYogi:5 flwr.server.strategy.krum.Krum:5 of -msgid "Fraction of clients used during training. Defaults to 1.0." +#: flwr.server.driver.driver.Driver.send_and_receive:19 of +msgid "" +"This method uses `push_messages` to send the messages and `pull_messages`" +" to collect the replies. If `timeout` is set, the method may not return " +"replies for all sent messages. A message remains valid until its TTL, " +"which is not affected by `timeout`." msgstr "" -#: flwr.server.strategy.bulyan.Bulyan:7 -#: flwr.server.strategy.fedadagrad.FedAdagrad:7 -#: flwr.server.strategy.fedadam.FedAdam:7 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:7 -#: flwr.server.strategy.fedavgm.FedAvgM:7 flwr.server.strategy.fedopt.FedOpt:7 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:7 -#: flwr.server.strategy.fedyogi.FedYogi:7 flwr.server.strategy.krum.Krum:7 of -msgid "Fraction of clients used during validation. Defaults to 1.0." +#: ../../source/ref-api/flwr.server.History.rst:2 +msgid "History" msgstr "" -#: flwr.server.strategy.bulyan.Bulyan:9 -#: flwr.server.strategy.fedadagrad.FedAdagrad:9 -#: flwr.server.strategy.fedadam.FedAdam:9 flwr.server.strategy.fedavg.FedAvg:13 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:9 -#: flwr.server.strategy.fedavgm.FedAvgM:9 flwr.server.strategy.fedopt.FedOpt:9 -#: flwr.server.strategy.fedprox.FedProx:45 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:9 -#: flwr.server.strategy.fedyogi.FedYogi:9 flwr.server.strategy.krum.Krum:9 of -msgid "Minimum number of clients used during training. Defaults to 2." +#: flwr.server.history.History.add_loss_centralized:1::1 of +msgid "" +":py:obj:`add_loss_centralized " +"`\\ \\(server\\_round\\, " +"loss\\)" msgstr "" -#: flwr.server.strategy.bulyan.Bulyan:11 -#: flwr.server.strategy.fedadagrad.FedAdagrad:11 -#: flwr.server.strategy.fedadam.FedAdam:11 -#: flwr.server.strategy.fedavg.FedAvg:15 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:11 -#: flwr.server.strategy.fedavgm.FedAvgM:11 -#: flwr.server.strategy.fedopt.FedOpt:11 -#: flwr.server.strategy.fedprox.FedProx:47 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:11 -#: flwr.server.strategy.fedyogi.FedYogi:11 flwr.server.strategy.krum.Krum:11 of -msgid "Minimum number of clients used during validation. Defaults to 2." +#: flwr.server.history.History.add_loss_centralized:1 +#: flwr.server.history.History.add_loss_centralized:1::1 of +#, fuzzy +msgid "Add one loss entry (from centralized evaluation)." +msgstr "Évaluation centralisée" + +#: flwr.server.history.History.add_loss_centralized:1::1 of +msgid "" +":py:obj:`add_loss_distributed " +"`\\ \\(server\\_round\\, " +"loss\\)" msgstr "" -#: flwr.server.strategy.bulyan.Bulyan:13 -#: flwr.server.strategy.fedadagrad.FedAdagrad:13 -#: flwr.server.strategy.fedadam.FedAdam:13 -#: flwr.server.strategy.fedavg.FedAvg:17 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:13 -#: flwr.server.strategy.fedavgm.FedAvgM:13 -#: flwr.server.strategy.fedopt.FedOpt:13 -#: flwr.server.strategy.fedprox.FedProx:49 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:13 -#: flwr.server.strategy.fedyogi.FedYogi:13 flwr.server.strategy.krum.Krum:13 of -msgid "Minimum number of total clients in the system. Defaults to 2." +#: flwr.server.history.History.add_loss_centralized:1::1 +#: flwr.server.history.History.add_loss_distributed:1 of +msgid "Add one loss entry (from distributed evaluation)." msgstr "" -#: flwr.server.strategy.bulyan.Bulyan:15 flwr.server.strategy.krum.Krum:15 of -msgid "Number of malicious clients in the system. Defaults to 0." +#: flwr.server.history.History.add_loss_centralized:1::1 of +msgid "" +":py:obj:`add_metrics_centralized " +"`\\ \\(server\\_round\\, " +"metrics\\)" msgstr "" -#: flwr.server.strategy.bulyan.Bulyan:17 -#: flwr.server.strategy.fedadagrad.FedAdagrad:15 -#: flwr.server.strategy.fedadam.FedAdam:15 -#: flwr.server.strategy.fedavg.FedAvg:19 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:15 -#: flwr.server.strategy.fedavgm.FedAvgM:15 -#: flwr.server.strategy.fedopt.FedOpt:15 -#: flwr.server.strategy.fedprox.FedProx:51 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:15 -#: flwr.server.strategy.fedyogi.FedYogi:17 -#: flwr.server.strategy.fedyogi.FedYogi:18 -#: flwr.server.strategy.fedyogi.FedYogi:19 flwr.server.strategy.krum.Krum:20 of -msgid "Optional function used for validation. Defaults to None." -msgstr "" +#: flwr.server.history.History.add_loss_centralized:1::1 +#: flwr.server.history.History.add_metrics_centralized:1 of +#, fuzzy +msgid "Add metrics entries (from centralized evaluation)." +msgstr "Évaluation centralisée" -#: flwr.server.strategy.bulyan.Bulyan:19 -#: flwr.server.strategy.fedadagrad.FedAdagrad:17 -#: flwr.server.strategy.fedadam.FedAdam:17 -#: flwr.server.strategy.fedavg.FedAvg:21 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:17 -#: flwr.server.strategy.fedavgm.FedAvgM:17 -#: flwr.server.strategy.fedopt.FedOpt:17 -#: flwr.server.strategy.fedprox.FedProx:53 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:17 -#: flwr.server.strategy.fedyogi.FedYogi:20 flwr.server.strategy.krum.Krum:22 of -msgid "Function used to configure training. Defaults to None." +#: flwr.server.history.History.add_loss_centralized:1::1 of +msgid "" +":py:obj:`add_metrics_distributed " +"`\\ \\(server\\_round\\, " +"metrics\\)" msgstr "" -#: flwr.server.strategy.bulyan.Bulyan:21 -#: flwr.server.strategy.fedadagrad.FedAdagrad:19 -#: flwr.server.strategy.fedadam.FedAdam:19 -#: flwr.server.strategy.fedavg.FedAvg:23 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:19 -#: flwr.server.strategy.fedavgm.FedAvgM:19 -#: flwr.server.strategy.fedopt.FedOpt:19 -#: flwr.server.strategy.fedprox.FedProx:55 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:19 -#: flwr.server.strategy.fedyogi.FedYogi:22 flwr.server.strategy.krum.Krum:24 of -msgid "Function used to configure validation. Defaults to None." +#: flwr.server.history.History.add_loss_centralized:1::1 +#: flwr.server.history.History.add_metrics_distributed:1 of +msgid "Add metrics entries (from distributed evaluation)." msgstr "" -#: flwr.server.strategy.bulyan.Bulyan:23 -#: flwr.server.strategy.fedadagrad.FedAdagrad:25 -#: flwr.server.strategy.fedadam.FedAdam:21 -#: flwr.server.strategy.fedavg.FedAvg:25 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:21 -#: flwr.server.strategy.fedavgm.FedAvgM:21 -#: flwr.server.strategy.fedopt.FedOpt:21 -#: flwr.server.strategy.fedprox.FedProx:57 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:21 -#: flwr.server.strategy.fedyogi.FedYogi:24 flwr.server.strategy.krum.Krum:26 of -msgid "Whether or not accept rounds containing failures. Defaults to True." +#: flwr.server.history.History.add_loss_centralized:1::1 of +msgid "" +":py:obj:`add_metrics_distributed_fit " +"`\\ \\(server\\_round\\," +" ...\\)" msgstr "" -#: flwr.server.strategy.bulyan.Bulyan:25 -#: flwr.server.strategy.fedadagrad.FedAdagrad:27 -#: flwr.server.strategy.fedadam.FedAdam:23 -#: flwr.server.strategy.fedavg.FedAvg:27 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:24 -#: flwr.server.strategy.fedavgm.FedAvgM:23 -#: flwr.server.strategy.fedopt.FedOpt:23 -#: flwr.server.strategy.fedprox.FedProx:59 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:23 -#: flwr.server.strategy.fedyogi.FedYogi:26 flwr.server.strategy.krum.Krum:28 of -#, fuzzy -msgid "Initial global model parameters." -msgstr "Initialise le modèle global" - -#: flwr.server.strategy.bulyan.Bulyan:27 of -msgid "" -"Byzantine resilient aggregation rule that is used as the first step of " -"the Bulyan (e.g., Krum)" +#: flwr.server.history.History.add_loss_centralized:1::1 +#: flwr.server.history.History.add_metrics_distributed_fit:1 of +msgid "Add metrics entries (from distributed fit)." msgstr "" -#: flwr.server.strategy.bulyan.Bulyan:29 of -msgid "arguments to the first_aggregation rule" +#: ../../source/ref-api/flwr.server.LegacyContext.rst:2 +msgid "LegacyContext" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`aggregate_evaluate " -"`\\ \\(server\\_round\\, " -"results\\, ...\\)" +#: flwr.server.compat.legacy_context.LegacyContext:1 of +msgid "Bases: :py:class:`~flwr.common.context.Context`" msgstr "" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1 -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1 -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1 -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of +#: ../../source/ref-api/flwr.server.LegacyContext.rst:32::1 #, fuzzy -msgid "Aggregate evaluation losses using weighted average." -msgstr "Résultats globaux de l'évaluation." - -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`aggregate_fit `\\ " -"\\(server\\_round\\, results\\, failures\\)" -msgstr "" +msgid ":py:obj:`config `\\" +msgstr "serveur.stratégie.Stratégie" -#: flwr.server.strategy.bulyan.Bulyan.aggregate_fit:1 -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: ../../source/ref-api/flwr.server.LegacyContext.rst:32::1 #, fuzzy -msgid "Aggregate fit results using Bulyan." -msgstr "Résultats globaux de l'évaluation." +msgid ":py:obj:`strategy `\\" +msgstr "serveur.stratégie.Stratégie" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`configure_evaluate " -"`\\ \\(server\\_round\\, " -"parameters\\, ...\\)" +#: ../../source/ref-api/flwr.server.LegacyContext.rst:32::1 +msgid ":py:obj:`client_manager `\\" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.configure_evaluate:1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.configure_evaluate:1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.configure_evaluate:1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.configure_evaluate:1 -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.configure_evaluate:1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.configure_evaluate:1 -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.configure_evaluate:1 -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.qfedavg.QFedAvg.configure_evaluate:1 -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 -#: flwr.server.strategy.strategy.Strategy.configure_evaluate:1 of +#: ../../source/ref-api/flwr.server.LegacyContext.rst:32::1 #, fuzzy -msgid "Configure the next round of evaluation." -msgstr "Configuration de l'évaluation côté serveur" +msgid ":py:obj:`history `\\" +msgstr "serveur.stratégie.Stratégie" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`configure_fit `\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +#: ../../source/ref-api/flwr.server.LegacyContext.rst:32::1 +#, fuzzy +msgid ":py:obj:`state `\\" +msgstr "serveur.stratégie.Stratégie" + +#: ../../source/ref-api/flwr.server.Server.rst:2 +msgid "Server" +msgstr "Serveur" + +#: flwr.server.server.Server.client_manager:1::1 of +msgid ":py:obj:`client_manager `\\ \\(\\)" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.configure_fit:1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.configure_fit:1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.configure_fit:1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.configure_fit:1 -#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive.configure_fit:1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.configure_fit:1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.configure_fit:1 -#: flwr.server.strategy.fedprox.FedProx.configure_fit:1 -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.configure_fit:1 -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.qfedavg.QFedAvg.configure_fit:1 -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 -#: flwr.server.strategy.strategy.Strategy.configure_fit:1 of -msgid "Configure the next round of training." +#: flwr.server.server.Server.client_manager:1 +#: flwr.server.server.Server.client_manager:1::1 of +msgid "Return ClientManager." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.server.Server.client_manager:1::1 of msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" +":py:obj:`disconnect_all_clients " +"`\\ \\(timeout\\)" msgstr "" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.evaluate:1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.evaluate:1 -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.evaluate:1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg.evaluate:1 -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of -msgid "Evaluate model parameters using an evaluation function." +#: flwr.server.server.Server.client_manager:1::1 +#: flwr.server.server.Server.disconnect_all_clients:1 of +msgid "Send shutdown signal to all clients." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.server.Server.client_manager:1::1 of msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +":py:obj:`evaluate_round `\\ " +"\\(server\\_round\\, timeout\\)" msgstr "" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.initialize_parameters:1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.initialize_parameters:1 -#: flwr.server.strategy.fedavgm.FedAvgM.initialize_parameters:1 -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of -#, fuzzy -msgid "Initialize global model parameters." -msgstr "Initialise le modèle global" - -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +#: flwr.server.server.Server.client_manager:1::1 +#: flwr.server.server.Server.evaluate_round:1 of +msgid "Validate current global model on a number of clients." msgstr "" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.num_evaluation_clients:1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.num_evaluation_clients:1 -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.qfedavg.QFedAvg.num_evaluation_clients:1 of -msgid "Use a fraction of available clients for evaluation." +#: flwr.server.server.Server.client_manager:1::1 of +msgid ":py:obj:`fit `\\ \\(num\\_rounds\\, timeout\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`num_fit_clients `\\" -" \\(num\\_available\\_clients\\)" +#: flwr.server.server.Server.client_manager:1::1 +#: flwr.server.server.Server.fit:1 of +msgid "Run federated averaging for a number of rounds." msgstr "" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.num_fit_clients:1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.num_fit_clients:1 -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.qfedavg.QFedAvg.num_fit_clients:1 of -msgid "Return the sample size and the required number of available clients." +#: flwr.server.server.Server.client_manager:1::1 of +msgid "" +":py:obj:`fit_round `\\ \\(server\\_round\\," +" timeout\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.DPFedAvgAdaptive.rst:2 -msgid "DPFedAvgAdaptive" -msgstr "DPFedAvgAdaptive" +#: flwr.server.server.Server.client_manager:1::1 +#: flwr.server.server.Server.fit_round:1 of +msgid "Perform a single round of federated averaging." +msgstr "" -#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive:1 of -msgid "Bases: :py:class:`~flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed`" +#: flwr.server.server.Server.client_manager:1::1 of +msgid "" +":py:obj:`set_max_workers `\\ " +"\\(max\\_workers\\)" msgstr "" -#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive:3 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed:3 of -msgid "This class is deprecated and will be removed in a future release." +#: flwr.server.server.Server.client_manager:1::1 +#: flwr.server.server.Server.set_max_workers:1 of +msgid "Set the max_workers used by ThreadPoolExecutor." msgstr "" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" +#: flwr.server.server.Server.client_manager:1::1 of +msgid ":py:obj:`set_strategy `\\ \\(strategy\\)" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of +#: flwr.server.server.Server.client_manager:1::1 +#: flwr.server.server.Server.set_strategy:1 of #, fuzzy -msgid "Aggregate evaluation losses using the given strategy." -msgstr "Résultats globaux de l'évaluation." +msgid "Replace server strategy." +msgstr "stratégie.du.serveur" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`aggregate_fit " -"`\\ " -"\\(server\\_round\\, results\\, failures\\)" -msgstr "" +#: ../../source/ref-api/flwr.server.ServerApp.rst:2 +#, fuzzy +msgid "ServerApp" +msgstr "serveur" -#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive.aggregate_fit:1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of -msgid "Aggregate training results as in DPFedAvgFixed and update clip norms." -msgstr "" +#: flwr.server.server_app.ServerApp:5 of +#, fuzzy +msgid "Use the `ServerApp` with an existing `Strategy`:" +msgstr "Utilise une stratégie existante" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +#: flwr.server.server_app.ServerApp:15 of +msgid "Use the `ServerApp` with a custom main function:" msgstr "" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:1 of -msgid "Configure the next round of evaluation using the specified strategy." -msgstr "" +#: flwr.server.server_app.ServerApp.main:1::1 of +#, fuzzy +msgid ":py:obj:`main `\\ \\(\\)" +msgstr "serveur.stratégie.Stratégie" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`configure_fit " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +#: flwr.server.server_app.ServerApp.main:1 +#: flwr.server.server_app.ServerApp.main:1::1 of +msgid "Return a decorator that registers the main fn with the server app." msgstr "" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.ServerConfig.rst:2 +#, fuzzy +msgid "ServerConfig" +msgstr "serveur" + +#: flwr.server.server_config.ServerConfig:3 of msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" +"All attributes have default values which allows users to configure just " +"the ones they care about." msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.evaluate:1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.evaluate:1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.evaluate:1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.evaluate:1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.evaluate:1 of -msgid "Evaluate model parameters using an evaluation function from the strategy." +#: ../../source/ref-api/flwr.server.ServerConfig.rst:29::1 +msgid ":py:obj:`num_rounds `\\" msgstr "" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +#: ../../source/ref-api/flwr.server.ServerConfig.rst:29::1 +msgid ":py:obj:`round_timeout `\\" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.initialize_parameters:1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.initialize_parameters:1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.initialize_parameters:1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.initialize_parameters:1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.initialize_parameters:1 of -msgid "Initialize global model parameters using given strategy." +#: ../../source/ref-api/flwr.server.SimpleClientManager.rst:2 +msgid "SimpleClientManager" msgstr "" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:3 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:6 -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:3 -#: flwr.server.strategy.strategy.Strategy.aggregate_fit:3 -#: flwr.server.strategy.strategy.Strategy.configure_evaluate:3 -#: flwr.server.strategy.strategy.Strategy.configure_fit:3 -#: flwr.server.strategy.strategy.Strategy.evaluate:6 of -#, fuzzy -msgid "The current round of federated learning." -msgstr "Qu'est-ce que l'apprentissage fédéré ?" - -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:7 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:10 -#: flwr.server.strategy.strategy.Strategy.configure_evaluate:7 -#: flwr.server.strategy.strategy.Strategy.configure_fit:7 -#: flwr.server.strategy.strategy.Strategy.initialize_parameters:3 of -msgid "The client manager which holds all currently connected clients." +#: flwr.server.client_manager.SimpleClientManager:1 of +msgid "Bases: :py:class:`~flwr.server.client_manager.ClientManager`" msgstr "" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:10 -#: flwr.server.strategy.strategy.Strategy.configure_evaluate:10 of -msgid "" -"**evaluate_configuration** -- A list of tuples. Each tuple in the list " -"identifies a `ClientProxy` and the `EvaluateIns` for this particular " -"`ClientProxy`. If a particular `ClientProxy` is not included in this " -"list, it means that this `ClientProxy` will not participate in the next " -"round of federated evaluation." +#: flwr.server.client_manager.SimpleClientManager.all:1::1 of +msgid ":py:obj:`all `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.DPFedAvgFixed.rst:2 -msgid "DPFedAvgFixed" -msgstr "DPFedAvgFixed" - -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed:1 -#: flwr.server.strategy.fedavg.FedAvg:1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:1 of -msgid "Bases: :py:class:`~flwr.server.strategy.strategy.Strategy`" +#: flwr.server.client_manager.SimpleClientManager.all:1::1 of +msgid "" +":py:obj:`num_available `\\" +" \\(\\)" msgstr "" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of +#: flwr.server.client_manager.SimpleClientManager.all:1::1 of msgid "" -":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" +":py:obj:`register `\\ " +"\\(client\\)" msgstr "" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of +#: flwr.server.client_manager.SimpleClientManager.all:1::1 of msgid "" -":py:obj:`aggregate_fit " -"`\\ " -"\\(server\\_round\\, results\\, failures\\)" +":py:obj:`sample `\\ " +"\\(num\\_clients\\[\\, min\\_num\\_clients\\, criterion\\]\\)" msgstr "" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_fit:1 of -msgid "Aggregate training results using unweighted aggregation." +#: flwr.server.client_manager.SimpleClientManager.all:1::1 of +msgid "" +":py:obj:`unregister `\\ " +"\\(client\\)" msgstr "" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of +#: flwr.server.client_manager.SimpleClientManager.all:1::1 of msgid "" -":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`wait_for `\\ " +"\\(num\\_clients\\[\\, timeout\\]\\)" msgstr "" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of +#: flwr.server.client_manager.SimpleClientManager.wait_for:3 of msgid "" -":py:obj:`configure_fit " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +"Blocks until the requested number of clients is available or until a " +"timeout is reached. Current timeout default: 1 day." msgstr "" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:1 of -msgid "" -"Configure the next round of training incorporating Differential Privacy " -"(DP)." +#: flwr.server.client_manager.SimpleClientManager.wait_for:6 of +msgid "The number of clients to wait for." msgstr "" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" +#: flwr.server.client_manager.SimpleClientManager.wait_for:8 of +msgid "The time in seconds to wait for, defaults to 86400 (24h)." msgstr "" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +#: flwr.server.client_manager.SimpleClientManager.wait_for:11 of +msgid "**success**" msgstr "" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:3 of -msgid "" -"Configuration of the next training round includes information related to " -"DP, such as clip norm and noise stddev." -msgstr "" +#: ../../source/ref-api/flwr.server.run_driver_api.rst:2 +#, fuzzy +msgid "run\\_driver\\_api" +msgstr "flower-driver-api" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:13 -#: flwr.server.strategy.strategy.Strategy.configure_fit:10 of -msgid "" -"**fit_configuration** -- A list of tuples. Each tuple in the list " -"identifies a `ClientProxy` and the `FitIns` for this particular " -"`ClientProxy`. If a particular `ClientProxy` is not included in this " -"list, it means that this `ClientProxy` will not participate in the next " -"round of federated learning." +#: ../../source/ref-api/flwr.server.run_fleet_api.rst:2 +msgid "run\\_fleet\\_api" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyClientSideAdaptiveClipping.rst:2 -msgid "DifferentialPrivacyClientSideAdaptiveClipping" +#: ../../source/ref-api/flwr.server.run_server_app.rst:2 +msgid "run\\_server\\_app" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:3 -#: of -msgid "Use `adaptiveclipping_mod` modifier at the client side." -msgstr "" +#: ../../source/ref-api/flwr.server.run_superlink.rst:2 +#, fuzzy +msgid "run\\_superlink" +msgstr "flower-superlink" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:5 -#: of -msgid "" -"In comparison to `DifferentialPrivacyServerSideAdaptiveClipping`, which " -"performs clipping on the server-side, " -"`DifferentialPrivacyClientSideAdaptiveClipping` expects clipping to " -"happen on the client-side, usually by using the built-in " -"`adaptiveclipping_mod`." +#: ../../source/ref-api/flwr.server.start_server.rst:2 +#, fuzzy +msgid "start\\_server" +msgstr "serveur.start_server" + +#: flwr.server.app.start_server:3 of +msgid "The IPv4 or IPv6 address of the server. Defaults to `\"[::]:8080\"`." msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:10 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:3 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:10 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:3 -#: of -msgid "The strategy to which DP functionalities will be added by this wrapper." +#: flwr.server.app.start_server:5 of +msgid "" +"A server implementation, either `flwr.server.Server` or a subclass " +"thereof. If no instance is provided, then `start_server` will create one." msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:12 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:5 -#: of -msgid "The noise multiplier for the Gaussian mechanism for model updates." +#: flwr.server.app.start_server:9 flwr.simulation.app.start_simulation:28 of +msgid "" +"Currently supported values are `num_rounds` (int, default: 1) and " +"`round_timeout` in seconds (float, default: None)." msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:14 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:7 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:17 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:10 -#: of -msgid "The number of clients that are sampled on each round." +#: flwr.server.app.start_server:12 of +msgid "" +"An implementation of the abstract base class " +"`flwr.server.strategy.Strategy`. If no strategy is provided, then " +"`start_server` will use `flwr.server.strategy.FedAvg`." msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:16 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:9 -#: of +#: flwr.server.app.start_server:16 of msgid "" -"The initial value of clipping norm. Defaults to 0.1. Andrew et al. " -"recommends to set to 0.1." +"An implementation of the abstract base class `flwr.server.ClientManager`." +" If no implementation is provided, then `start_server` will use " +"`flwr.server.client_manager.SimpleClientManager`." msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:19 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:12 -#: of -msgid "The desired quantile of updates which should be clipped. Defaults to 0.5." +#: flwr.server.app.start_server:21 of +msgid "" +"The maximum length of gRPC messages that can be exchanged with the Flower" +" clients. The default should be sufficient for most models. Users who " +"train very large models might need to increase this value. Note that the " +"Flower clients need to be started with the same value (see " +"`flwr.client.start_client`), otherwise clients will not know about the " +"increased limit and block larger messages." msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:21 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:14 -#: of +#: flwr.server.app.start_server:28 of msgid "" -"The learning rate for the clipping norm adaptation. Defaults to 0.2. " -"Andrew et al. recommends to set to 0.2." +"Tuple containing root certificate, server certificate, and private key to" +" start a secure SSL-enabled server. The tuple is expected to have three " +"bytes elements in the following order: * CA certificate. * " +"server certificate. * server private key." msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:24 -#: of +#: flwr.server.app.start_server:28 of msgid "" -"The stddev of the noise added to the count of updates currently below the" -" estimate. Andrew et al. recommends to set to `expected_num_records/20`" +"Tuple containing root certificate, server certificate, and private key to" +" start a secure SSL-enabled server. The tuple is expected to have three " +"bytes elements in the following order:" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:30 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:23 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:22 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:15 -#: of +#: flwr.server.app.start_server:32 of #, fuzzy -msgid "Create a strategy:" +msgid "CA certificate." +msgstr "Certificats" + +#: flwr.server.app.start_server:33 of +#, fuzzy +msgid "server certificate." +msgstr "Certificats" + +#: flwr.server.app.start_server:34 of +#, fuzzy +msgid "server private key." msgstr "stratégie.du.serveur" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:34 -#: of -msgid "" -"Wrap the strategy with the " -"`DifferentialPrivacyClientSideAdaptiveClipping` wrapper:" +#: flwr.server.app.start_server:37 of +msgid "**hist** -- Object containing training and evaluation metrics." msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:40 -#: of -msgid "On the client, add the `adaptiveclipping_mod` to the client-side mods:" -msgstr "" +#: flwr.server.app.start_server:42 of +#, fuzzy +msgid "Starting an insecure server:" +msgstr "Démarrer le serveur" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`aggregate_evaluate " -"`\\" -" \\(server\\_round\\, results\\, ...\\)" -msgstr "" +#: flwr.server.app.start_server:46 of +#, fuzzy +msgid "Starting an SSL-enabled server:" +msgstr "Démarrer le serveur" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:2 +#, fuzzy +msgid "strategy" +msgstr "stratégie.du.serveur" + +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`aggregate_fit " -"`\\" -" \\(server\\_round\\, results\\, failures\\)" +":py:obj:`Bulyan `\\ \\(\\*\\, " +"fraction\\_fit\\, fraction\\_evaluate\\, ...\\)" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_fit:1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_fit:1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.bulyan.Bulyan:1 of #, fuzzy -msgid "Aggregate training results and update clip norms." -msgstr "Résultats globaux de l'évaluation." +msgid "Bulyan strategy." +msgstr "Stratégies intégrées" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`configure_evaluate " -"`\\" -" \\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`DPFedAvgAdaptive `\\ " +"\\(strategy\\, num\\_sampled\\_clients\\)" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`configure_fit " -"`\\" -" \\(server\\_round\\, parameters\\, ...\\)" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive:1 of +msgid "Wrapper for configuring a Strategy for DP with Adaptive Clipping." msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`evaluate " -"`\\" -" \\(server\\_round\\, parameters\\)" +":py:obj:`DPFedAvgFixed `\\ " +"\\(strategy\\, num\\_sampled\\_clients\\, ...\\)" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`initialize_parameters " -"`\\" -" \\(client\\_manager\\)" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed:1 of +msgid "Wrapper for configuring a Strategy for DP with Fixed Clipping." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyClientSideFixedClipping.rst:2 -#, fuzzy -msgid "DifferentialPrivacyClientSideFixedClipping" -msgstr "Confidentialité différentielle" - -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:3 -#: of -msgid "Use `fixedclipping_mod` modifier at the client side." +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +msgid "" +":py:obj:`DifferentialPrivacyClientSideAdaptiveClipping " +"`\\ " +"\\(...\\)" msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:5 +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:1 #: of -msgid "" -"In comparison to `DifferentialPrivacyServerSideFixedClipping`, which " -"performs clipping on the server-side, " -"`DifferentialPrivacyClientSideFixedClipping` expects clipping to happen " -"on the client-side, usually by using the built-in `fixedclipping_mod`." +msgid "Strategy wrapper for central DP with client-side adaptive clipping." msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:12 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:5 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -"The noise multiplier for the Gaussian mechanism for model updates. A " -"value of 1.0 or higher is recommended for strong privacy." +":py:obj:`DifferentialPrivacyServerSideAdaptiveClipping " +"`\\ " +"\\(...\\)" msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:15 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:8 +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:1 #: of -msgid "The value of the clipping norm." +msgid "Strategy wrapper for central DP with server-side adaptive clipping." msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:26 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -"Wrap the strategy with the `DifferentialPrivacyClientSideFixedClipping` " -"wrapper:" +":py:obj:`DifferentialPrivacyClientSideFixedClipping " +"`\\ " +"\\(...\\)" msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:32 +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:1 #: of -msgid "On the client, add the `fixedclipping_mod` to the client-side mods:" +msgid "Strategy wrapper for central DP with client-side fixed clipping." msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`aggregate_evaluate " -"`\\" -" \\(server\\_round\\, results\\, ...\\)" +":py:obj:`DifferentialPrivacyServerSideFixedClipping " +"`\\ " +"\\(...\\)" msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:1 #: of +msgid "Strategy wrapper for central DP with server-side fixed clipping." +msgstr "" + +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`aggregate_fit " -"`\\" -" \\(server\\_round\\, results\\, failures\\)" +":py:obj:`FedAdagrad `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, ...\\]\\)" msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_fit:1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fedadagrad.FedAdagrad:1 of #, fuzzy -msgid "Add noise to the aggregated parameters." -msgstr "Puis sérialise le résultat agrégé :" +msgid "FedAdagrad strategy - Adaptive Federated Optimization using Adagrad." +msgstr "" +"`FedAdam` et `FedAdam` correspondent à la dernière version de l'article " +"sur l'optimisation fédérée adaptative." -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`configure_evaluate " -"`\\" -" \\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`FedAdam `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, ...\\]\\)" msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`configure_fit " -"`\\" -" \\(server\\_round\\, parameters\\, ...\\)" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fedadam.FedAdam:1 of +msgid "FedAdam - Adaptive Federated Optimization using Adam." msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`evaluate " -"`\\" -" \\(server\\_round\\, parameters\\)" +":py:obj:`FedAvg `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, fraction\\_evaluate\\, ...\\]\\)" msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fedavg.FedAvg:1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:1 of +#, fuzzy +msgid "Federated Averaging strategy." +msgstr "Stratégie de moyenne fédérée." + +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`initialize_parameters " -"`\\" -" \\(client\\_manager\\)" +":py:obj:`FedAvgAndroid `\\ " +"\\(\\*\\[\\, fraction\\_fit\\, ...\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyServerSideAdaptiveClipping.rst:2 -msgid "DifferentialPrivacyServerSideAdaptiveClipping" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +msgid "" +":py:obj:`FedAvgM `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, ...\\]\\)" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:17 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fedavgm.FedAvgM:1 of +#, fuzzy +msgid "Federated Averaging with Momentum strategy." +msgstr "Stratégie de moyenne fédérée." + +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -"The standard deviation of the noise added to the count of updates below " -"the estimate. Andrew et al. recommends to set to " -"`expected_num_records/20`" +":py:obj:`FedMedian `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, ...\\]\\)" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:27 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fedmedian.FedMedian:1 of +#, fuzzy +msgid "Configurable FedMedian strategy implementation." +msgstr "Configuration de l'évaluation fédérée" + +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -"Wrap the strategy with the DifferentialPrivacyServerSideAdaptiveClipping " -"wrapper" +":py:obj:`FedOpt `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, fraction\\_evaluate\\, ...\\]\\)" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fedopt.FedOpt:1 of +#, fuzzy +msgid "Federated Optim strategy." +msgstr "Stratégie de moyenne fédérée." + +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`aggregate_evaluate " -"`\\" -" \\(server\\_round\\, results\\, ...\\)" +":py:obj:`FedProx `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, ...\\]\\)" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fedprox.FedProx:1 of +#, fuzzy +msgid "Federated Optimization strategy." +msgstr "Stratégie de moyenne fédérée." + +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`aggregate_fit " -"`\\" -" \\(server\\_round\\, results\\, failures\\)" +":py:obj:`FedTrimmedAvg `\\ " +"\\(\\*\\[\\, fraction\\_fit\\, ...\\]\\)" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`configure_evaluate " -"`\\" -" \\(server\\_round\\, parameters\\, ...\\)" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:1 of +msgid "Federated Averaging with Trimmed Mean [Dong Yin, et al., 2021]." msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`configure_fit " -"`\\" -" \\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`FedXgbBagging `\\ " +"\\(\\[evaluate\\_function\\]\\)" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`evaluate " -"`\\" -" \\(server\\_round\\, parameters\\)" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging:1 of +msgid "Configurable FedXgbBagging strategy implementation." msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`initialize_parameters " -"`\\" -" \\(client\\_manager\\)" +":py:obj:`FedXgbCyclic `\\ " +"\\(\\*\\*kwargs\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyServerSideFixedClipping.rst:2 -#, fuzzy -msgid "DifferentialPrivacyServerSideFixedClipping" -msgstr "Confidentialité différentielle" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic:1 of +msgid "Configurable FedXgbCyclic strategy implementation." +msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:19 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -"Wrap the strategy with the DifferentialPrivacyServerSideFixedClipping " -"wrapper" +":py:obj:`FedXgbNnAvg `\\ \\(\\*args\\, " +"\\*\\*kwargs\\)" msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`aggregate_evaluate " -"`\\" -" \\(server\\_round\\, results\\, ...\\)" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg:1 of +msgid "Configurable FedXgbNnAvg strategy implementation." msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`aggregate_fit " -"`\\" -" \\(server\\_round\\, results\\, failures\\)" +":py:obj:`FedYogi `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, ...\\]\\)" msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_fit:1 -#: of -msgid "Compute the updates, clip, and pass them for aggregation." +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fedyogi.FedYogi:1 of +msgid "FedYogi [Reddi et al., 2020] strategy." msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`configure_evaluate " -"`\\" -" \\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`FaultTolerantFedAvg " +"`\\ \\(\\*\\[\\, " +"fraction\\_fit\\, ...\\]\\)" msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`configure_fit " -"`\\" -" \\(server\\_round\\, parameters\\, ...\\)" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg:1 of +msgid "Configurable fault-tolerant FedAvg strategy implementation." msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`evaluate " -"`\\" -" \\(server\\_round\\, parameters\\)" +":py:obj:`Krum `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, fraction\\_evaluate\\, ...\\]\\)" msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.krum.Krum:1 of +msgid "Krum [Blanchard et al., 2017] strategy." +msgstr "" + +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`initialize_parameters " -"`\\" -" \\(client\\_manager\\)" +":py:obj:`QFedAvg `\\ \\(\\*\\[\\, " +"q\\_param\\, qffl\\_learning\\_rate\\, ...\\]\\)" msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_fit:3 -#: of -msgid "Afterward, add noise to the aggregated parameters." +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.qfedavg.QFedAvg:1 of +msgid "Configurable QFedAvg strategy implementation." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.FaultTolerantFedAvg.rst:2 +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 #, fuzzy -msgid "FaultTolerantFedAvg" -msgstr "server.strategy.FaultTolerantFedAvg" +msgid ":py:obj:`Strategy `\\ \\(\\)" +msgstr "serveur.stratégie.Stratégie" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.strategy.Strategy:1 of +msgid "Abstract base class for server strategy implementations." msgstr "" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`aggregate_fit " -"`\\ " -"\\(server\\_round\\, results\\, failures\\)" +#: ../../source/ref-api/flwr.server.strategy.Bulyan.rst:2 +msgid "Bulyan" msgstr "" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_fit:1 -#: flwr.server.strategy.fedadagrad.FedAdagrad.aggregate_fit:1 -#: flwr.server.strategy.fedadam.FedAdam.aggregate_fit:1 -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.aggregate_fit:1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_fit:1 -#: flwr.server.strategy.fedavgm.FedAvgM.aggregate_fit:1 -#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg.aggregate_fit:1 -#: flwr.server.strategy.fedyogi.FedYogi.aggregate_fit:1 -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_fit:1 of -msgid "Aggregate fit results using weighted average." +#: flwr.server.strategy.bulyan.Bulyan:1 +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg:1 +#: flwr.server.strategy.fedavgm.FedAvgM:1 +#: flwr.server.strategy.fedmedian.FedMedian:1 +#: flwr.server.strategy.fedopt.FedOpt:1 flwr.server.strategy.fedprox.FedProx:1 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:1 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging:1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic:1 +#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg:1 +#: flwr.server.strategy.krum.Krum:1 flwr.server.strategy.qfedavg.QFedAvg:1 of +msgid "Bases: :py:class:`~flwr.server.strategy.fedavg.FedAvg`" msgstr "" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +#: flwr.server.strategy.bulyan.Bulyan:3 of +msgid "Implementation based on https://arxiv.org/abs/1802.07927." msgstr "" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`configure_fit " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +#: flwr.server.strategy.bulyan.Bulyan:5 +#: flwr.server.strategy.fedadagrad.FedAdagrad:5 +#: flwr.server.strategy.fedadam.FedAdam:5 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:5 +#: flwr.server.strategy.fedavgm.FedAvgM:5 flwr.server.strategy.fedopt.FedOpt:5 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:5 +#: flwr.server.strategy.fedyogi.FedYogi:5 flwr.server.strategy.krum.Krum:5 of +msgid "Fraction of clients used during training. Defaults to 1.0." msgstr "" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" +#: flwr.server.strategy.bulyan.Bulyan:7 +#: flwr.server.strategy.fedadagrad.FedAdagrad:7 +#: flwr.server.strategy.fedadam.FedAdam:7 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:7 +#: flwr.server.strategy.fedavgm.FedAvgM:7 flwr.server.strategy.fedopt.FedOpt:7 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:7 +#: flwr.server.strategy.fedyogi.FedYogi:7 flwr.server.strategy.krum.Krum:7 of +msgid "Fraction of clients used during validation. Defaults to 1.0." msgstr "" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +#: flwr.server.strategy.bulyan.Bulyan:9 +#: flwr.server.strategy.fedadagrad.FedAdagrad:9 +#: flwr.server.strategy.fedadam.FedAdam:9 flwr.server.strategy.fedavg.FedAvg:13 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:9 +#: flwr.server.strategy.fedavgm.FedAvgM:9 flwr.server.strategy.fedopt.FedOpt:9 +#: flwr.server.strategy.fedprox.FedProx:45 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:9 +#: flwr.server.strategy.fedyogi.FedYogi:9 flwr.server.strategy.krum.Krum:9 of +msgid "Minimum number of clients used during training. Defaults to 2." msgstr "" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +#: flwr.server.strategy.bulyan.Bulyan:11 +#: flwr.server.strategy.fedadagrad.FedAdagrad:11 +#: flwr.server.strategy.fedadam.FedAdam:11 +#: flwr.server.strategy.fedavg.FedAvg:15 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:11 +#: flwr.server.strategy.fedavgm.FedAvgM:11 +#: flwr.server.strategy.fedopt.FedOpt:11 +#: flwr.server.strategy.fedprox.FedProx:47 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:11 +#: flwr.server.strategy.fedyogi.FedYogi:11 flwr.server.strategy.krum.Krum:11 of +msgid "Minimum number of clients used during validation. Defaults to 2." msgstr "" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`num_fit_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +#: flwr.server.strategy.bulyan.Bulyan:13 +#: flwr.server.strategy.fedadagrad.FedAdagrad:13 +#: flwr.server.strategy.fedadam.FedAdam:13 +#: flwr.server.strategy.fedavg.FedAvg:17 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:13 +#: flwr.server.strategy.fedavgm.FedAvgM:13 +#: flwr.server.strategy.fedopt.FedOpt:13 +#: flwr.server.strategy.fedprox.FedProx:49 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:13 +#: flwr.server.strategy.fedyogi.FedYogi:13 flwr.server.strategy.krum.Krum:13 of +msgid "Minimum number of total clients in the system. Defaults to 2." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.FedAdagrad.rst:2 -#: ../../source/ref-changelog.md:839 -msgid "FedAdagrad" -msgstr "FedAdagrad" +#: flwr.server.strategy.bulyan.Bulyan:15 flwr.server.strategy.krum.Krum:15 of +msgid "Number of malicious clients in the system. Defaults to 0." +msgstr "" -#: flwr.server.strategy.fedadagrad.FedAdagrad:1 -#: flwr.server.strategy.fedadam.FedAdam:1 -#: flwr.server.strategy.fedyogi.FedYogi:1 of -msgid "Bases: :py:class:`~flwr.server.strategy.fedopt.FedOpt`" +#: flwr.server.strategy.bulyan.Bulyan:17 +#: flwr.server.strategy.fedadagrad.FedAdagrad:15 +#: flwr.server.strategy.fedadam.FedAdam:15 +#: flwr.server.strategy.fedavg.FedAvg:19 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:15 +#: flwr.server.strategy.fedavgm.FedAvgM:15 +#: flwr.server.strategy.fedopt.FedOpt:15 +#: flwr.server.strategy.fedprox.FedProx:51 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:15 +#: flwr.server.strategy.fedyogi.FedYogi:17 +#: flwr.server.strategy.fedyogi.FedYogi:18 +#: flwr.server.strategy.fedyogi.FedYogi:19 flwr.server.strategy.krum.Krum:20 of +msgid "Optional function used for validation. Defaults to None." msgstr "" -#: flwr.server.strategy.fedadagrad.FedAdagrad:3 -#: flwr.server.strategy.fedadam.FedAdam:3 flwr.server.strategy.fedopt.FedOpt:3 -#: flwr.server.strategy.fedyogi.FedYogi:3 of -#, fuzzy -msgid "Implementation based on https://arxiv.org/abs/2003.00295v5" +#: flwr.server.strategy.bulyan.Bulyan:19 +#: flwr.server.strategy.fedadagrad.FedAdagrad:17 +#: flwr.server.strategy.fedadam.FedAdam:17 +#: flwr.server.strategy.fedavg.FedAvg:21 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:17 +#: flwr.server.strategy.fedavgm.FedAvgM:17 +#: flwr.server.strategy.fedopt.FedOpt:17 +#: flwr.server.strategy.fedprox.FedProx:53 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:17 +#: flwr.server.strategy.fedyogi.FedYogi:20 flwr.server.strategy.krum.Krum:22 of +msgid "Function used to configure training. Defaults to None." msgstr "" -"FedYogi - Stratégie d'apprentissage fédéré utilisant Yogi côté serveur. " -"Mise en oeuvre basée sur https://arxiv.org/abs/2003.00295" -#: flwr.server.strategy.fedadagrad.FedAdagrad:21 -#: flwr.server.strategy.fedadagrad.FedAdagrad:23 -#: flwr.server.strategy.fedadam.FedAdam:25 -#: flwr.server.strategy.fedadam.FedAdam:27 -#: flwr.server.strategy.fedavg.FedAvg:29 flwr.server.strategy.fedavg.FedAvg:31 -#: flwr.server.strategy.fedopt.FedOpt:25 flwr.server.strategy.fedopt.FedOpt:27 -#: flwr.server.strategy.fedprox.FedProx:61 -#: flwr.server.strategy.fedprox.FedProx:63 -#: flwr.server.strategy.fedyogi.FedYogi:28 -#: flwr.server.strategy.fedyogi.FedYogi:30 of -msgid "Metrics aggregation function, optional." +#: flwr.server.strategy.bulyan.Bulyan:21 +#: flwr.server.strategy.fedadagrad.FedAdagrad:19 +#: flwr.server.strategy.fedadam.FedAdam:19 +#: flwr.server.strategy.fedavg.FedAvg:23 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:19 +#: flwr.server.strategy.fedavgm.FedAvgM:19 +#: flwr.server.strategy.fedopt.FedOpt:19 +#: flwr.server.strategy.fedprox.FedProx:55 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:19 +#: flwr.server.strategy.fedyogi.FedYogi:22 flwr.server.strategy.krum.Krum:24 of +msgid "Function used to configure validation. Defaults to None." msgstr "" -#: flwr.server.strategy.fedadagrad.FedAdagrad:29 -#: flwr.server.strategy.fedadam.FedAdam:29 -#: flwr.server.strategy.fedopt.FedOpt:29 of -msgid "Server-side learning rate. Defaults to 1e-1." +#: flwr.server.strategy.bulyan.Bulyan:23 +#: flwr.server.strategy.fedadagrad.FedAdagrad:25 +#: flwr.server.strategy.fedadam.FedAdam:21 +#: flwr.server.strategy.fedavg.FedAvg:25 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:21 +#: flwr.server.strategy.fedavgm.FedAvgM:21 +#: flwr.server.strategy.fedopt.FedOpt:21 +#: flwr.server.strategy.fedprox.FedProx:57 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:21 +#: flwr.server.strategy.fedyogi.FedYogi:24 flwr.server.strategy.krum.Krum:26 of +msgid "Whether or not accept rounds containing failures. Defaults to True." msgstr "" -#: flwr.server.strategy.fedadagrad.FedAdagrad:31 -#: flwr.server.strategy.fedadam.FedAdam:31 -#: flwr.server.strategy.fedopt.FedOpt:31 of -msgid "Client-side learning rate. Defaults to 1e-1." +#: flwr.server.strategy.bulyan.Bulyan:25 +#: flwr.server.strategy.fedadagrad.FedAdagrad:27 +#: flwr.server.strategy.fedadam.FedAdam:23 +#: flwr.server.strategy.fedavg.FedAvg:27 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:24 +#: flwr.server.strategy.fedavgm.FedAvgM:23 +#: flwr.server.strategy.fedopt.FedOpt:23 +#: flwr.server.strategy.fedprox.FedProx:59 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:23 +#: flwr.server.strategy.fedyogi.FedYogi:26 flwr.server.strategy.krum.Krum:28 of +#, fuzzy +msgid "Initial global model parameters." +msgstr "Initialise le modèle global" + +#: flwr.server.strategy.bulyan.Bulyan:27 of +msgid "" +"Byzantine resilient aggregation rule that is used as the first step of " +"the Bulyan (e.g., Krum)" msgstr "" -#: flwr.server.strategy.fedadagrad.FedAdagrad:33 -#: flwr.server.strategy.fedadam.FedAdam:37 -#: flwr.server.strategy.fedopt.FedOpt:37 of -msgid "Controls the algorithm's degree of adaptability. Defaults to 1e-9." +#: flwr.server.strategy.bulyan.Bulyan:29 of +msgid "arguments to the first_aggregation rule" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" ":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" +"`\\ \\(server\\_round\\, " +"results\\, ...\\)" msgstr "" +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1 +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of +#, fuzzy +msgid "Aggregate evaluation losses using weighted average." +msgstr "Résultats globaux de l'évaluation." + #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`aggregate_fit `\\" -" \\(server\\_round\\, results\\, failures\\)" +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" +#: flwr.server.strategy.bulyan.Bulyan.aggregate_fit:1 #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" +#, fuzzy +msgid "Aggregate fit results using Bulyan." +msgstr "Résultats globaux de l'évaluation." + +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +msgid "" ":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +"`\\ \\(server\\_round\\, " +"parameters\\, ...\\)" msgstr "" +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.configure_evaluate:1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.configure_evaluate:1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.configure_evaluate:1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.configure_evaluate:1 +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.configure_evaluate:1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.configure_evaluate:1 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.configure_evaluate:1 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.qfedavg.QFedAvg.configure_evaluate:1 +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 +#: flwr.server.strategy.strategy.Strategy.configure_evaluate:1 of +#, fuzzy +msgid "Configure the next round of evaluation." +msgstr "Configuration de l'évaluation côté serveur" + #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`configure_fit `\\" -" \\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" +msgstr "" + +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.configure_fit:1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.configure_fit:1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.configure_fit:1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.configure_fit:1 +#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive.configure_fit:1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.configure_fit:1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.configure_fit:1 +#: flwr.server.strategy.fedprox.FedProx.configure_fit:1 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.configure_fit:1 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.qfedavg.QFedAvg.configure_fit:1 +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 +#: flwr.server.strategy.strategy.Strategy.configure_fit:1 of +msgid "Configure the next round of training." msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`evaluate `\\ " +":py:obj:`evaluate `\\ " "\\(server\\_round\\, parameters\\)" msgstr "" +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.evaluate:1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.evaluate:1 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.evaluate:1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg.evaluate:1 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of +msgid "Evaluate model parameters using an evaluation function." +msgstr "" + #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" ":py:obj:`initialize_parameters " -"`\\ " +"`\\ " "\\(client\\_manager\\)" msgstr "" +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.initialize_parameters:1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.initialize_parameters:1 +#: flwr.server.strategy.fedavgm.FedAvgM.initialize_parameters:1 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of +#, fuzzy +msgid "Initialize global model parameters." +msgstr "Initialise le modèle global" + #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" ":py:obj:`num_evaluation_clients " -"`\\ " +"`\\ " "\\(num\\_available\\_clients\\)" msgstr "" +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.num_evaluation_clients:1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.num_evaluation_clients:1 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.qfedavg.QFedAvg.num_evaluation_clients:1 of +msgid "Use a fraction of available clients for evaluation." +msgstr "" + #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`num_fit_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`num_fit_clients `\\" +" \\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.FedAdam.rst:2 -#, fuzzy -msgid "FedAdam" -msgstr "FedAdagrad" +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.num_fit_clients:1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.num_fit_clients:1 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.qfedavg.QFedAvg.num_fit_clients:1 of +msgid "Return the sample size and the required number of available clients." +msgstr "" -#: flwr.server.strategy.fedadam.FedAdam:33 -#: flwr.server.strategy.fedyogi.FedYogi:36 of -msgid "Momentum parameter. Defaults to 0.9." +#: ../../source/ref-api/flwr.server.strategy.DPFedAvgAdaptive.rst:2 +msgid "DPFedAvgAdaptive" +msgstr "DPFedAvgAdaptive" + +#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive:1 of +msgid "Bases: :py:class:`~flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed`" msgstr "" -#: flwr.server.strategy.fedadam.FedAdam:35 -#: flwr.server.strategy.fedyogi.FedYogi:38 of -msgid "Second moment parameter. Defaults to 0.99." +#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive:3 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed:3 of +msgid "This class is deprecated and will be removed in a future release." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: of msgid "" ":py:obj:`aggregate_evaluate " -"`\\ \\(server\\_round\\," -" results\\, ...\\)" +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: of +#, fuzzy +msgid "Aggregate evaluation losses using the given strategy." +msgstr "Résultats globaux de l'évaluation." + +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: of msgid "" -":py:obj:`aggregate_fit `\\ " +":py:obj:`aggregate_fit " +"`\\ " "\\(server\\_round\\, results\\, failures\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive.aggregate_fit:1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: of +msgid "Aggregate training results as in DPFedAvgFixed and update clip norms." +msgstr "" + +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: of msgid "" ":py:obj:`configure_evaluate " -"`\\ \\(server\\_round\\," -" parameters\\, ...\\)" +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:1 of +msgid "Configure the next round of evaluation using the specified strategy." +msgstr "" + +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: of msgid "" -":py:obj:`configure_fit `\\ " +":py:obj:`configure_fit " +"`\\ " "\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: of msgid "" -":py:obj:`evaluate `\\ " +":py:obj:`evaluate `\\ " "\\(server\\_round\\, parameters\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.evaluate:1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.evaluate:1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.evaluate:1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.evaluate:1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.evaluate:1 of +msgid "Evaluate model parameters using an evaluation function from the strategy." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: of msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`num_fit_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.initialize_parameters:1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.initialize_parameters:1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.initialize_parameters:1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.initialize_parameters:1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.initialize_parameters:1 of +msgid "Initialize global model parameters using given strategy." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.FedAvg.rst:2 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:3 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:6 +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:3 +#: flwr.server.strategy.strategy.Strategy.aggregate_fit:3 +#: flwr.server.strategy.strategy.Strategy.configure_evaluate:3 +#: flwr.server.strategy.strategy.Strategy.configure_fit:3 +#: flwr.server.strategy.strategy.Strategy.evaluate:6 of #, fuzzy -msgid "FedAvg" -msgstr "DP-FedAvg" +msgid "The current round of federated learning." +msgstr "Qu'est-ce que l'apprentissage fédéré ?" -#: flwr.server.strategy.fedavg.FedAvg:3 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:3 of -msgid "Implementation based on https://arxiv.org/abs/1602.05629" +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:7 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:10 +#: flwr.server.strategy.strategy.Strategy.configure_evaluate:7 +#: flwr.server.strategy.strategy.Strategy.configure_fit:7 +#: flwr.server.strategy.strategy.Strategy.initialize_parameters:3 of +msgid "The client manager which holds all currently connected clients." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg:5 flwr.server.strategy.fedprox.FedProx:37 -#: of +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:10 +#: flwr.server.strategy.strategy.Strategy.configure_evaluate:10 of msgid "" -"Fraction of clients used during training. In case `min_fit_clients` is " -"larger than `fraction_fit * available_clients`, `min_fit_clients` will " -"still be sampled. Defaults to 1.0." +"**evaluate_configuration** -- A list of tuples. Each tuple in the list " +"identifies a `ClientProxy` and the `EvaluateIns` for this particular " +"`ClientProxy`. If a particular `ClientProxy` is not included in this " +"list, it means that this `ClientProxy` will not participate in the next " +"round of federated evaluation." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg:9 flwr.server.strategy.fedprox.FedProx:41 -#: of -msgid "" -"Fraction of clients used during validation. In case " -"`min_evaluate_clients` is larger than `fraction_evaluate * " -"available_clients`, `min_evaluate_clients` will still be sampled. " -"Defaults to 1.0." -msgstr "" +#: ../../source/ref-api/flwr.server.strategy.DPFedAvgFixed.rst:2 +msgid "DPFedAvgFixed" +msgstr "DPFedAvgFixed" -#: flwr.server.strategy.fedavg.FedAvg:33 of -msgid "Enable (True) or disable (False) in-place aggregation of model updates." +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed:1 +#: flwr.server.strategy.fedavg.FedAvg:1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:1 of +msgid "Bases: :py:class:`~flwr.server.strategy.strategy.Strategy`" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: of msgid "" ":py:obj:`aggregate_evaluate " -"`\\ \\(server\\_round\\, " -"results\\, ...\\)" +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: of msgid "" -":py:obj:`aggregate_fit `\\ " +":py:obj:`aggregate_fit " +"`\\ " "\\(server\\_round\\, results\\, failures\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_fit:1 of +msgid "Aggregate training results using unweighted aggregation." +msgstr "" + +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: of msgid "" ":py:obj:`configure_evaluate " -"`\\ \\(server\\_round\\, " -"parameters\\, ...\\)" +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: of msgid "" -":py:obj:`configure_fit `\\ " +":py:obj:`configure_fit " +"`\\ " "\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:1 of msgid "" -":py:obj:`evaluate `\\ " +"Configure the next round of training incorporating Differential Privacy " +"(DP)." +msgstr "" + +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: of +msgid "" +":py:obj:`evaluate `\\ " "\\(server\\_round\\, parameters\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: of msgid "" ":py:obj:`initialize_parameters " -"`\\ " +"`\\ " "\\(client\\_manager\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:3 of msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +"Configuration of the next training round includes information related to " +"DP, such as clip norm and noise stddev." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:13 +#: flwr.server.strategy.strategy.Strategy.configure_fit:10 of msgid "" -":py:obj:`num_fit_clients `\\" -" \\(num\\_available\\_clients\\)" +"**fit_configuration** -- A list of tuples. Each tuple in the list " +"identifies a `ClientProxy` and the `FitIns` for this particular " +"`ClientProxy`. If a particular `ClientProxy` is not included in this " +"list, it means that this `ClientProxy` will not participate in the next " +"round of federated learning." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.FedAvgAndroid.rst:2 -#, fuzzy -msgid "FedAvgAndroid" -msgstr "DPFedAvgAdaptive" +#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyClientSideAdaptiveClipping.rst:2 +msgid "DifferentialPrivacyClientSideAdaptiveClipping" +msgstr "" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:3 #: of -msgid "" -":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" +msgid "Use `adaptiveclipping_mod` modifier at the client side." msgstr "" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:5 #: of msgid "" -":py:obj:`aggregate_fit " -"`\\ " -"\\(server\\_round\\, results\\, failures\\)" +"In comparison to `DifferentialPrivacyServerSideAdaptiveClipping`, which " +"performs clipping on the server-side, " +"`DifferentialPrivacyClientSideAdaptiveClipping` expects clipping to " +"happen on the client-side, usually by using the built-in " +"`adaptiveclipping_mod`." msgstr "" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:10 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:3 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:10 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:3 #: of -msgid "" -":py:obj:`bytes_to_ndarray " -"`\\ \\(tensor\\)" +msgid "The strategy to which DP functionalities will be added by this wrapper." msgstr "" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.bytes_to_ndarray:1 of -#, fuzzy -msgid "Deserialize NumPy array from bytes." -msgstr "Désérialise le tableau numérique NumPy à partir d'octets." +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:12 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:5 +#: of +msgid "The noise multiplier for the Gaussian mechanism for model updates." +msgstr "" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:14 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:7 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:17 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:10 #: of -msgid "" -":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +msgid "The number of clients that are sampled on each round." msgstr "" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:16 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:9 #: of msgid "" -":py:obj:`configure_fit " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +"The initial value of clipping norm. Defaults to 0.1. Andrew et al. " +"recommends to set to 0.1." msgstr "" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:19 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:12 #: of -msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" +msgid "The desired quantile of updates which should be clipped. Defaults to 0.5." msgstr "" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:21 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:14 #: of msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +"The learning rate for the clipping norm adaptation. Defaults to 0.2. " +"Andrew et al. recommends to set to 0.2." msgstr "" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:24 #: of msgid "" -":py:obj:`ndarray_to_bytes " -"`\\ \\(ndarray\\)" +"The stddev of the noise added to the count of updates currently below the" +" estimate. Andrew et al. recommends to set to `expected_num_records/20`" msgstr "" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.ndarray_to_bytes:1 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:30 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:23 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:22 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:15 +#: of #, fuzzy -msgid "Serialize NumPy array to bytes." -msgstr "Sérialise le tableau numérique NumPy en octets." +msgid "Create a strategy:" +msgstr "stratégie.du.serveur" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:34 #: of msgid "" -":py:obj:`ndarrays_to_parameters " -"`\\ " -"\\(ndarrays\\)" +"Wrap the strategy with the " +"`DifferentialPrivacyClientSideAdaptiveClipping` wrapper:" msgstr "" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:40 #: of -msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +msgid "On the client, add the `adaptiveclipping_mod` to the client-side mods:" msgstr "" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`num_fit_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`aggregate_evaluate " +"`\\" +" \\(server\\_round\\, results\\, ...\\)" msgstr "" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`parameters_to_ndarrays " -"`\\ " -"\\(parameters\\)" +":py:obj:`aggregate_fit " +"`\\" +" \\(server\\_round\\, results\\, failures\\)" msgstr "" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.parameters_to_ndarrays:1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_fit:1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_fit:1 #: of #, fuzzy -msgid "Convert parameters object to NumPy weights." -msgstr "Convertit l'objet des paramètres en ndarrays NumPy." - -#: ../../source/ref-api/flwr.server.strategy.FedAvgM.rst:2 -#, fuzzy -msgid "FedAvgM" -msgstr "DP-FedAvg" - -#: flwr.server.strategy.fedavgm.FedAvgM:3 of -msgid "Implementation based on https://arxiv.org/abs/1909.06335" -msgstr "" +msgid "Aggregate training results and update clip norms." +msgstr "Résultats globaux de l'évaluation." -#: flwr.server.strategy.fedavgm.FedAvgM:25 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 +#: of msgid "" -"Server-side learning rate used in server-side optimization. Defaults to " -"1.0." +":py:obj:`configure_evaluate " +"`\\" +" \\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.server.strategy.fedavgm.FedAvgM:28 of -msgid "Server-side momentum factor used for FedAvgM. Defaults to 0.0." -msgstr "" - -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 +#: of msgid "" -":py:obj:`aggregate_evaluate " -"`\\ \\(server\\_round\\," -" results\\, ...\\)" +":py:obj:`configure_fit " +"`\\" +" \\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 +#: of msgid "" -":py:obj:`aggregate_fit `\\ " -"\\(server\\_round\\, results\\, failures\\)" +":py:obj:`evaluate " +"`\\" +" \\(server\\_round\\, parameters\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 +#: of msgid "" -":py:obj:`configure_evaluate " -"`\\ \\(server\\_round\\," -" parameters\\, ...\\)" +":py:obj:`initialize_parameters " +"`\\" +" \\(client\\_manager\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`configure_fit `\\ " -"\\(server\\_round\\, parameters\\, ...\\)" -msgstr "" +#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyClientSideFixedClipping.rst:2 +#, fuzzy +msgid "DifferentialPrivacyClientSideFixedClipping" +msgstr "Confidentialité différentielle" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:3 +#: of +msgid "Use `fixedclipping_mod` modifier at the client side." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:5 +#: of msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +"In comparison to `DifferentialPrivacyServerSideFixedClipping`, which " +"performs clipping on the server-side, " +"`DifferentialPrivacyClientSideFixedClipping` expects clipping to happen " +"on the client-side, usually by using the built-in `fixedclipping_mod`." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:12 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:5 +#: of msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +"The noise multiplier for the Gaussian mechanism for model updates. A " +"value of 1.0 or higher is recommended for strong privacy." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:26 +#: of msgid "" -":py:obj:`num_fit_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +"Wrap the strategy with the `DifferentialPrivacyClientSideFixedClipping` " +"wrapper:" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.FedMedian.rst:2 -msgid "FedMedian" +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:32 +#: of +msgid "On the client, add the `fixedclipping_mod` to the client-side mods:" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: of msgid "" ":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" +"`\\" +" \\(server\\_round\\, results\\, ...\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: of msgid "" -":py:obj:`aggregate_fit `\\ " -"\\(server\\_round\\, results\\, failures\\)" +":py:obj:`aggregate_fit " +"`\\" +" \\(server\\_round\\, results\\, failures\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedmedian.FedMedian.aggregate_fit:1 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_fit:1 +#: of #, fuzzy -msgid "Aggregate fit results using median." -msgstr "Résultats globaux de l'évaluation." +msgid "Add noise to the aggregated parameters." +msgstr "Puis sérialise le résultat agrégé :" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: of msgid "" ":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +"`\\" +" \\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: of msgid "" -":py:obj:`configure_fit `\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`configure_fit " +"`\\" +" \\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: of msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" +":py:obj:`evaluate " +"`\\" +" \\(server\\_round\\, parameters\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: of msgid "" ":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +"`\\" +" \\(client\\_manager\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyServerSideAdaptiveClipping.rst:2 +msgid "DifferentialPrivacyServerSideAdaptiveClipping" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:17 +#: of msgid "" -":py:obj:`num_fit_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" -msgstr "" - -#: ../../source/ref-api/flwr.server.strategy.FedOpt.rst:2 -msgid "FedOpt" -msgstr "" - -#: flwr.server.strategy.fedopt.FedOpt:33 of -msgid "Momentum parameter. Defaults to 0.0." +"The standard deviation of the noise added to the count of updates below " +"the estimate. Andrew et al. recommends to set to " +"`expected_num_records/20`" msgstr "" -#: flwr.server.strategy.fedopt.FedOpt:35 of -msgid "Second moment parameter. Defaults to 0.0." +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:27 +#: of +msgid "" +"Wrap the strategy with the DifferentialPrivacyServerSideAdaptiveClipping " +"wrapper" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 +#: of msgid "" ":py:obj:`aggregate_evaluate " -"`\\ \\(server\\_round\\, " -"results\\, ...\\)" +"`\\" +" \\(server\\_round\\, results\\, ...\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 +#: of msgid "" -":py:obj:`aggregate_fit `\\ " -"\\(server\\_round\\, results\\, failures\\)" +":py:obj:`aggregate_fit " +"`\\" +" \\(server\\_round\\, results\\, failures\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 +#: of msgid "" ":py:obj:`configure_evaluate " -"`\\ \\(server\\_round\\, " -"parameters\\, ...\\)" +"`\\" +" \\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 +#: of msgid "" -":py:obj:`configure_fit `\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`configure_fit " +"`\\" +" \\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 +#: of msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" +":py:obj:`evaluate " +"`\\" +" \\(server\\_round\\, parameters\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 +#: of msgid "" ":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +"`\\" +" \\(client\\_manager\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyServerSideFixedClipping.rst:2 +#, fuzzy +msgid "DifferentialPrivacyServerSideFixedClipping" +msgstr "Confidentialité différentielle" + +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:19 +#: of msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +"Wrap the strategy with the DifferentialPrivacyServerSideFixedClipping " +"wrapper" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 +#: of msgid "" -":py:obj:`num_fit_clients `\\" -" \\(num\\_available\\_clients\\)" +":py:obj:`aggregate_evaluate " +"`\\" +" \\(server\\_round\\, results\\, ...\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.FedProx.rst:2 -msgid "FedProx" +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 +#: of +msgid "" +":py:obj:`aggregate_fit " +"`\\" +" \\(server\\_round\\, results\\, failures\\)" msgstr "" -#: flwr.server.strategy.fedprox.FedProx:3 of -msgid "Implementation based on https://arxiv.org/abs/1812.06127" +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_fit:1 +#: of +msgid "Compute the updates, clip, and pass them for aggregation." msgstr "" -#: flwr.server.strategy.fedprox.FedProx:5 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 +#: of msgid "" -"The strategy in itself will not be different than FedAvg, the client " -"needs to be adjusted. A proximal term needs to be added to the loss " -"function during the training:" +":py:obj:`configure_evaluate " +"`\\" +" \\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.server.strategy.fedprox.FedProx:9 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 +#: of msgid "" -"\\\\frac{\\\\mu}{2} || w - w^t ||^2\n" -"\n" -msgstr "" - -#: flwr.server.strategy.fedprox.FedProx:12 of -msgid "" -"Where $w^t$ are the global parameters and $w$ are the local weights the " -"function will be optimized with." -msgstr "" - -#: flwr.server.strategy.fedprox.FedProx:15 of -msgid "In PyTorch, for example, the loss would go from:" +":py:obj:`configure_fit " +"`\\" +" \\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.server.strategy.fedprox.FedProx:21 of -msgid "To:" +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 +#: of +msgid "" +":py:obj:`evaluate " +"`\\" +" \\(server\\_round\\, parameters\\)" msgstr "" -#: flwr.server.strategy.fedprox.FedProx:30 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 +#: of msgid "" -"With `global_params` being a copy of the parameters before the training " -"takes place." +":py:obj:`initialize_parameters " +"`\\" +" \\(client\\_manager\\)" msgstr "" -#: flwr.server.strategy.fedprox.FedProx:65 of -msgid "" -"The weight of the proximal term used in the optimization. 0.0 makes this " -"strategy equivalent to FedAvg, and the higher the coefficient, the more " -"regularization will be used (that is, the client parameters will need to " -"be closer to the server parameters during training)." +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_fit:3 +#: of +msgid "Afterward, add noise to the aggregated parameters." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: ../../source/ref-api/flwr.server.strategy.FaultTolerantFedAvg.rst:2 +#, fuzzy +msgid "FaultTolerantFedAvg" +msgstr "server.strategy.FaultTolerantFedAvg" + +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: of msgid "" ":py:obj:`aggregate_evaluate " -"`\\ \\(server\\_round\\," -" results\\, ...\\)" +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: of msgid "" -":py:obj:`aggregate_fit `\\ " +":py:obj:`aggregate_fit " +"`\\ " "\\(server\\_round\\, results\\, failures\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_fit:1 +#: flwr.server.strategy.fedadagrad.FedAdagrad.aggregate_fit:1 +#: flwr.server.strategy.fedadam.FedAdam.aggregate_fit:1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_fit:1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_fit:1 +#: flwr.server.strategy.fedavgm.FedAvgM.aggregate_fit:1 +#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg.aggregate_fit:1 +#: flwr.server.strategy.fedyogi.FedYogi.aggregate_fit:1 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_fit:1 of +msgid "Aggregate fit results using weighted average." +msgstr "" + +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: of msgid "" ":py:obj:`configure_evaluate " -"`\\ \\(server\\_round\\," -" parameters\\, ...\\)" +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: of msgid "" -":py:obj:`configure_fit `\\ " +":py:obj:`configure_fit " +"`\\ " "\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: of msgid "" -":py:obj:`evaluate `\\ " +":py:obj:`evaluate `\\ " "\\(server\\_round\\, parameters\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: of msgid "" ":py:obj:`initialize_parameters " -"`\\ " +"`\\ " "\\(client\\_manager\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: of msgid "" ":py:obj:`num_evaluation_clients " -"`\\ " +"`\\ " "\\(num\\_available\\_clients\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: of msgid "" ":py:obj:`num_fit_clients " -"`\\ " +"`\\ " "\\(num\\_available\\_clients\\)" msgstr "" -#: flwr.server.strategy.fedprox.FedProx.configure_fit:3 of -msgid "Sends the proximal factor mu to the clients" +#: ../../source/ref-api/flwr.server.strategy.FedAdagrad.rst:2 +#: ../../source/ref-changelog.md:997 +msgid "FedAdagrad" +msgstr "FedAdagrad" + +#: flwr.server.strategy.fedadagrad.FedAdagrad:1 +#: flwr.server.strategy.fedadam.FedAdam:1 +#: flwr.server.strategy.fedyogi.FedYogi:1 of +msgid "Bases: :py:class:`~flwr.server.strategy.fedopt.FedOpt`" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.FedTrimmedAvg.rst:2 -msgid "FedTrimmedAvg" +#: flwr.server.strategy.fedadagrad.FedAdagrad:3 +#: flwr.server.strategy.fedadam.FedAdam:3 flwr.server.strategy.fedopt.FedOpt:3 +#: flwr.server.strategy.fedyogi.FedYogi:3 of +#, fuzzy +msgid "Implementation based on https://arxiv.org/abs/2003.00295v5" msgstr "" +"FedYogi - Stratégie d'apprentissage fédéré utilisant Yogi côté serveur. " +"Mise en oeuvre basée sur https://arxiv.org/abs/2003.00295" -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:3 of -msgid "Implemented based on: https://arxiv.org/abs/1803.01498" +#: flwr.server.strategy.fedadagrad.FedAdagrad:21 +#: flwr.server.strategy.fedadagrad.FedAdagrad:23 +#: flwr.server.strategy.fedadam.FedAdam:25 +#: flwr.server.strategy.fedadam.FedAdam:27 +#: flwr.server.strategy.fedavg.FedAvg:29 flwr.server.strategy.fedavg.FedAvg:31 +#: flwr.server.strategy.fedopt.FedOpt:25 flwr.server.strategy.fedopt.FedOpt:27 +#: flwr.server.strategy.fedprox.FedProx:61 +#: flwr.server.strategy.fedprox.FedProx:63 +#: flwr.server.strategy.fedyogi.FedYogi:28 +#: flwr.server.strategy.fedyogi.FedYogi:30 of +msgid "Metrics aggregation function, optional." msgstr "" -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:25 of -msgid "Fraction to cut off of both tails of the distribution. Defaults to 0.2." +#: flwr.server.strategy.fedadagrad.FedAdagrad:29 +#: flwr.server.strategy.fedadam.FedAdam:29 +#: flwr.server.strategy.fedopt.FedOpt:29 of +msgid "Server-side learning rate. Defaults to 1e-1." +msgstr "" + +#: flwr.server.strategy.fedadagrad.FedAdagrad:31 +#: flwr.server.strategy.fedadam.FedAdam:31 +#: flwr.server.strategy.fedopt.FedOpt:31 of +msgid "Client-side learning rate. Defaults to 1e-1." +msgstr "" + +#: flwr.server.strategy.fedadagrad.FedAdagrad:33 +#: flwr.server.strategy.fedadam.FedAdam:37 +#: flwr.server.strategy.fedopt.FedOpt:37 of +msgid "Controls the algorithm's degree of adaptability. Defaults to 1e-9." msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" ":py:obj:`aggregate_evaluate " -"`\\ " +"`\\ " "\\(server\\_round\\, results\\, ...\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`aggregate_fit " -"`\\ " -"\\(server\\_round\\, results\\, failures\\)" -msgstr "" - -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.aggregate_fit:1 of -msgid "Aggregate fit results using trimmed average." +":py:obj:`aggregate_fit `\\" +" \\(server\\_round\\, results\\, failures\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" ":py:obj:`configure_evaluate " -"`\\ " +"`\\ " "\\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`configure_fit " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`configure_fit `\\" +" \\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`evaluate `\\ " +":py:obj:`evaluate `\\ " "\\(server\\_round\\, parameters\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" ":py:obj:`initialize_parameters " -"`\\ " +"`\\ " "\\(client\\_manager\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" ":py:obj:`num_evaluation_clients " -"`\\ " +"`\\ " "\\(num\\_available\\_clients\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" ":py:obj:`num_fit_clients " -"`\\ " +"`\\ " "\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.FedXgbBagging.rst:2 -msgid "FedXgbBagging" +#: ../../source/ref-api/flwr.server.strategy.FedAdam.rst:2 +#, fuzzy +msgid "FedAdam" +msgstr "FedAdagrad" + +#: flwr.server.strategy.fedadam.FedAdam:33 +#: flwr.server.strategy.fedyogi.FedYogi:36 of +msgid "Momentum parameter. Defaults to 0.9." msgstr "" -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.fedadam.FedAdam:35 +#: flwr.server.strategy.fedyogi.FedYogi:38 of +msgid "Second moment parameter. Defaults to 0.99." +msgstr "" + +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" ":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" +"`\\ \\(server\\_round\\," +" results\\, ...\\)" msgstr "" -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1 -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: of -#, fuzzy -msgid "Aggregate evaluation metrics using average." -msgstr "Résultats globaux de l'évaluation." - -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`aggregate_fit " -"`\\ " +":py:obj:`aggregate_fit `\\ " "\\(server\\_round\\, results\\, failures\\)" msgstr "" -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_fit:1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_fit:1 of -msgid "Aggregate fit results using bagging." -msgstr "" - -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" ":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +"`\\ \\(server\\_round\\," +" parameters\\, ...\\)" msgstr "" -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`configure_fit " -"`\\ " +":py:obj:`configure_fit `\\ " "\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`evaluate `\\ " +":py:obj:`evaluate `\\ " "\\(server\\_round\\, parameters\\)" msgstr "" -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" ":py:obj:`initialize_parameters " -"`\\ " +"`\\ " "\\(client\\_manager\\)" msgstr "" -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" ":py:obj:`num_evaluation_clients " -"`\\ " +"`\\ " "\\(num\\_available\\_clients\\)" msgstr "" -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" ":py:obj:`num_fit_clients " -"`\\ " +"`\\ " "\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.FedXgbCyclic.rst:2 -msgid "FedXgbCyclic" +#: ../../source/ref-api/flwr.server.strategy.FedAvg.rst:2 +#, fuzzy +msgid "FedAvg" +msgstr "DP-FedAvg" + +#: flwr.server.strategy.fedavg.FedAvg:3 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:3 of +msgid "Implementation based on https://arxiv.org/abs/1602.05629" msgstr "" -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg:5 flwr.server.strategy.fedprox.FedProx:37 #: of msgid "" -":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" +"Fraction of clients used during training. In case `min_fit_clients` is " +"larger than `fraction_fit * available_clients`, `min_fit_clients` will " +"still be sampled. Defaults to 1.0." msgstr "" -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg:9 flwr.server.strategy.fedprox.FedProx:41 #: of msgid "" -":py:obj:`aggregate_fit " -"`\\ \\(server\\_round\\," -" results\\, failures\\)" +"Fraction of clients used during validation. In case " +"`min_evaluate_clients` is larger than `fraction_evaluate * " +"available_clients`, `min_evaluate_clients` will still be sampled. " +"Defaults to 1.0." msgstr "" -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.fedavg.FedAvg:33 of +msgid "Enable (True) or disable (False) in-place aggregation of model updates." +msgstr "" + +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +msgid "" +":py:obj:`aggregate_evaluate " +"`\\ \\(server\\_round\\, " +"results\\, ...\\)" +msgstr "" + +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +msgid "" +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" +msgstr "" + +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" ":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +"`\\ \\(server\\_round\\, " +"parameters\\, ...\\)" msgstr "" -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`configure_fit " -"`\\ \\(server\\_round\\," -" parameters\\, ...\\)" +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`evaluate `\\ " +":py:obj:`evaluate `\\ " "\\(server\\_round\\, parameters\\)" msgstr "" -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" ":py:obj:`initialize_parameters " -"`\\ " +"`\\ " "\\(client\\_manager\\)" msgstr "" -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" ":py:obj:`num_evaluation_clients " -"`\\ " +"`\\ " "\\(num\\_available\\_clients\\)" msgstr "" -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`num_fit_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`num_fit_clients `\\" +" \\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.FedXgbNnAvg.rst:2 +#: ../../source/ref-api/flwr.server.strategy.FedAvgAndroid.rst:2 #, fuzzy -msgid "FedXgbNnAvg" -msgstr "DP-FedAvg" - -#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg:5 of -msgid "" -"This strategy is deprecated, but a copy of it is available in Flower " -"Baselines: " -"https://github.com/adap/flower/tree/main/baselines/hfedxgboost." -msgstr "" +msgid "FedAvgAndroid" +msgstr "DPFedAvgAdaptive" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: of msgid "" ":py:obj:`aggregate_evaluate " -"`\\ " +"`\\ " "\\(server\\_round\\, results\\, ...\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: of msgid "" ":py:obj:`aggregate_fit " -"`\\ \\(server\\_round\\, " -"results\\, failures\\)" +"`\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: of +msgid "" +":py:obj:`bytes_to_ndarray " +"`\\ \\(tensor\\)" +msgstr "" + +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.bytes_to_ndarray:1 of +#, fuzzy +msgid "Deserialize NumPy array from bytes." +msgstr "Désérialise le tableau numérique NumPy à partir d'octets." + +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: of msgid "" ":py:obj:`configure_evaluate " -"`\\ " +"`\\ " "\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: of msgid "" ":py:obj:`configure_fit " -"`\\ \\(server\\_round\\, " -"parameters\\, ...\\)" +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: of msgid "" -":py:obj:`evaluate `\\ " +":py:obj:`evaluate `\\ " "\\(server\\_round\\, parameters\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: of msgid "" ":py:obj:`initialize_parameters " -"`\\ " +"`\\ " "\\(client\\_manager\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: of +msgid "" +":py:obj:`ndarray_to_bytes " +"`\\ \\(ndarray\\)" +msgstr "" + +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.ndarray_to_bytes:1 of +#, fuzzy +msgid "Serialize NumPy array to bytes." +msgstr "Sérialise le tableau numérique NumPy en octets." + +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: of +msgid "" +":py:obj:`ndarrays_to_parameters " +"`\\ " +"\\(ndarrays\\)" +msgstr "" + +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: of msgid "" ":py:obj:`num_evaluation_clients " -"`\\ " +"`\\ " "\\(num\\_available\\_clients\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: of msgid "" ":py:obj:`num_fit_clients " -"`\\ " +"`\\ " "\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.FedYogi.rst:2 -msgid "FedYogi" +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: of +msgid "" +":py:obj:`parameters_to_ndarrays " +"`\\ " +"\\(parameters\\)" msgstr "" -#: flwr.server.strategy.fedyogi.FedYogi:32 of -msgid "Server-side learning rate. Defaults to 1e-2." +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.parameters_to_ndarrays:1 +#: of +#, fuzzy +msgid "Convert parameters object to NumPy weights." +msgstr "Convertit l'objet des paramètres en ndarrays NumPy." + +#: ../../source/ref-api/flwr.server.strategy.FedAvgM.rst:2 +#, fuzzy +msgid "FedAvgM" +msgstr "DP-FedAvg" + +#: flwr.server.strategy.fedavgm.FedAvgM:3 of +msgid "Implementation based on https://arxiv.org/abs/1909.06335" msgstr "" -#: flwr.server.strategy.fedyogi.FedYogi:34 of -msgid "Client-side learning rate. Defaults to 0.0316." +#: flwr.server.strategy.fedavgm.FedAvgM:25 of +msgid "" +"Server-side learning rate used in server-side optimization. Defaults to " +"1.0." msgstr "" -#: flwr.server.strategy.fedyogi.FedYogi:40 of -msgid "Controls the algorithm's degree of adaptability. Defaults to 1e-3." +#: flwr.server.strategy.fedavgm.FedAvgM:28 of +msgid "Server-side momentum factor used for FedAvgM. Defaults to 0.0." msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" ":py:obj:`aggregate_evaluate " -"`\\ \\(server\\_round\\," +"`\\ \\(server\\_round\\," " results\\, ...\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`aggregate_fit `\\ " +":py:obj:`aggregate_fit `\\ " "\\(server\\_round\\, results\\, failures\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" ":py:obj:`configure_evaluate " -"`\\ \\(server\\_round\\," +"`\\ \\(server\\_round\\," " parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`configure_fit `\\ " +":py:obj:`configure_fit `\\ " "\\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`evaluate `\\ " +":py:obj:`evaluate `\\ " "\\(server\\_round\\, parameters\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" ":py:obj:`initialize_parameters " -"`\\ " +"`\\ " "\\(client\\_manager\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" ":py:obj:`num_evaluation_clients " -"`\\ " +"`\\ " "\\(num\\_available\\_clients\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" ":py:obj:`num_fit_clients " -"`\\ " +"`\\ " "\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.Krum.rst:2 -msgid "Krum" -msgstr "" - -#: flwr.server.strategy.krum.Krum:3 of -msgid "Implementation based on https://arxiv.org/abs/1703.02757" -msgstr "" - -#: flwr.server.strategy.krum.Krum:17 of -msgid "" -"Number of clients to keep before averaging (MultiKrum). Defaults to 0, in" -" that case classical Krum is applied." +#: ../../source/ref-api/flwr.server.strategy.FedMedian.rst:2 +msgid "FedMedian" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" ":py:obj:`aggregate_evaluate " -"`\\ \\(server\\_round\\, " -"results\\, ...\\)" +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`aggregate_fit `\\ " +":py:obj:`aggregate_fit `\\ " "\\(server\\_round\\, results\\, failures\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.krum.Krum.aggregate_fit:1 of +#: flwr.server.strategy.fedmedian.FedMedian.aggregate_fit:1 of #, fuzzy -msgid "Aggregate fit results using Krum." +msgid "Aggregate fit results using median." msgstr "Résultats globaux de l'évaluation." #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" ":py:obj:`configure_evaluate " -"`\\ \\(server\\_round\\, " -"parameters\\, ...\\)" +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`configure_fit `\\ " +":py:obj:`configure_fit `\\ " "\\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`evaluate `\\ " +":py:obj:`evaluate `\\ " "\\(server\\_round\\, parameters\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" ":py:obj:`initialize_parameters " -"`\\ " +"`\\ " "\\(client\\_manager\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" ":py:obj:`num_evaluation_clients " -"`\\ " +"`\\ " "\\(num\\_available\\_clients\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`num_fit_clients `\\ " +":py:obj:`num_fit_clients " +"`\\ " "\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.QFedAvg.rst:2 -#, fuzzy -msgid "QFedAvg" -msgstr "DP-FedAvg" +#: ../../source/ref-api/flwr.server.strategy.FedOpt.rst:2 +msgid "FedOpt" +msgstr "" -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fedopt.FedOpt:33 of +msgid "Momentum parameter. Defaults to 0.0." +msgstr "" + +#: flwr.server.strategy.fedopt.FedOpt:35 of +msgid "Second moment parameter. Defaults to 0.0." +msgstr "" + +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" ":py:obj:`aggregate_evaluate " -"`\\ \\(server\\_round\\," -" results\\, ...\\)" +"`\\ \\(server\\_round\\, " +"results\\, ...\\)" msgstr "" -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`aggregate_fit `\\ " +":py:obj:`aggregate_fit `\\ " "\\(server\\_round\\, results\\, failures\\)" msgstr "" -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" ":py:obj:`configure_evaluate " -"`\\ \\(server\\_round\\," -" parameters\\, ...\\)" +"`\\ \\(server\\_round\\, " +"parameters\\, ...\\)" msgstr "" -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`configure_fit `\\ " +":py:obj:`configure_fit `\\ " "\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`evaluate `\\ " +":py:obj:`evaluate `\\ " "\\(server\\_round\\, parameters\\)" msgstr "" -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" ":py:obj:`initialize_parameters " -"`\\ " +"`\\ " "\\(client\\_manager\\)" msgstr "" -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" ":py:obj:`num_evaluation_clients " -"`\\ " +"`\\ " "\\(num\\_available\\_clients\\)" msgstr "" -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`num_fit_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`num_fit_clients `\\" +" \\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.Strategy.rst:2 -#, fuzzy -msgid "Strategy" -msgstr "stratégie.du.serveur" - -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" +#: ../../source/ref-api/flwr.server.strategy.FedProx.rst:2 +msgid "FedProx" msgstr "" -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1 -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 -#: of -#, fuzzy -msgid "Aggregate evaluation results." -msgstr "Résultats globaux de l'évaluation." +#: flwr.server.strategy.fedprox.FedProx:3 of +msgid "Implementation based on https://arxiv.org/abs/1812.06127" +msgstr "" -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.fedprox.FedProx:5 of msgid "" -":py:obj:`aggregate_fit `\\ " -"\\(server\\_round\\, results\\, failures\\)" +"The strategy in itself will not be different than FedAvg, the client " +"needs to be adjusted. A proximal term needs to be added to the loss " +"function during the training:" msgstr "" -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 -#: flwr.server.strategy.strategy.Strategy.aggregate_fit:1 of -#, fuzzy -msgid "Aggregate training results." -msgstr "Résultats globaux de l'évaluation." - -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.fedprox.FedProx:9 of msgid "" -":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +"\\\\frac{\\\\mu}{2} || w - w^t ||^2\n" +"\n" msgstr "" -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.fedprox.FedProx:12 of msgid "" -":py:obj:`configure_fit `\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +"Where $w^t$ are the global parameters and $w$ are the local weights the " +"function will be optimized with." msgstr "" -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" +#: flwr.server.strategy.fedprox.FedProx:15 of +msgid "In PyTorch, for example, the loss would go from:" msgstr "" -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 -#: flwr.server.strategy.strategy.Strategy.evaluate:1 of -#, fuzzy -msgid "Evaluate the current model parameters." -msgstr "``get_parameters`` : renvoie les paramètres du modèle local actuel" +#: flwr.server.strategy.fedprox.FedProx:21 of +msgid "To:" +msgstr "" -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.fedprox.FedProx:30 of msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +"With `global_params` being a copy of the parameters before the training " +"takes place." msgstr "" -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 -#: flwr.server.strategy.strategy.Strategy.initialize_parameters:1 of -#, fuzzy -msgid "Initialize the (global) model parameters." -msgstr "Initialise le modèle global" - -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:5 of +#: flwr.server.strategy.fedprox.FedProx:65 of msgid "" -"Successful updates from the previously selected and configured clients. " -"Each pair of `(ClientProxy, FitRes` constitutes a successful update from " -"one of the previously selected clients. Not that not all previously " -"selected clients are necessarily included in this list: a client might " -"drop out and not submit a result. For each client that did not submit an " -"update, there should be an `Exception` in `failures`." +"The weight of the proximal term used in the optimization. 0.0 makes this " +"strategy equivalent to FedAvg, and the higher the coefficient, the more " +"regularization will be used (that is, the client parameters will need to " +"be closer to the server parameters during training)." msgstr "" -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:13 -#: flwr.server.strategy.strategy.Strategy.aggregate_fit:13 of -msgid "Exceptions that occurred while the server was waiting for client updates." +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +msgid "" +":py:obj:`aggregate_evaluate " +"`\\ \\(server\\_round\\," +" results\\, ...\\)" msgstr "" -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:16 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**aggregation_result** -- The aggregated evaluation result. Aggregation " -"typically uses some variant of a weighted average." +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" -#: flwr.server.strategy.strategy.Strategy.aggregate_fit:5 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"Successful updates from the previously selected and configured clients. " -"Each pair of `(ClientProxy, FitRes)` constitutes a successful update from" -" one of the previously selected clients. Not that not all previously " -"selected clients are necessarily included in this list: a client might " -"drop out and not submit a result. For each client that did not submit an " -"update, there should be an `Exception` in `failures`." +":py:obj:`configure_evaluate " +"`\\ \\(server\\_round\\," +" parameters\\, ...\\)" msgstr "" -#: flwr.server.strategy.strategy.Strategy.aggregate_fit:17 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**parameters** -- If parameters are returned, then the server will treat " -"these as the new global model parameters (i.e., it will replace the " -"previous parameters with the ones returned from this method). If `None` " -"is returned (e.g., because there were only failures and no viable " -"results) then the server will no update the previous model parameters, " -"the updates received in this round are discarded, and the global model " -"parameters remain the same." +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.server.strategy.strategy.Strategy.evaluate:3 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"This function can be used to perform centralized (i.e., server-side) " -"evaluation of model parameters." +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" -#: flwr.server.strategy.strategy.Strategy.evaluate:11 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**evaluation_result** -- The evaluation result, usually a Tuple " -"containing loss and a dictionary containing task-specific metrics (e.g., " -"accuracy)." +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" -#: flwr.server.strategy.strategy.Strategy.initialize_parameters:6 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**parameters** -- If parameters are returned, then the server will treat " -"these as the initial global model parameters." +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-api/flwr.server.workflow.rst:2 -#, fuzzy -msgid "workflow" -msgstr "Flux de travail" - -#: ../../source/ref-api/flwr.server.workflow.rst:24::1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`DefaultWorkflow `\\ " -"\\(\\[fit\\_workflow\\, ...\\]\\)" +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-api/flwr.server.workflow.rst:24::1 -#: flwr.server.workflow.default_workflows.DefaultWorkflow:1 of -msgid "Default workflow in Flower." +#: flwr.server.strategy.fedprox.FedProx.configure_fit:3 of +msgid "Sends the proximal factor mu to the clients" msgstr "" -#: ../../source/ref-api/flwr.server.workflow.rst:24::1 -msgid "" -":py:obj:`SecAggPlusWorkflow `\\ " -"\\(num\\_shares\\, ...\\[\\, ...\\]\\)" +#: ../../source/ref-api/flwr.server.strategy.FedTrimmedAvg.rst:2 +msgid "FedTrimmedAvg" msgstr "" -#: ../../source/ref-api/flwr.server.workflow.rst:24::1 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:1 -#: of -msgid "The workflow for the SecAgg+ protocol." +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:3 of +msgid "Implemented based on: https://arxiv.org/abs/1803.01498" msgstr "" -#: ../../source/ref-api/flwr.server.workflow.rst:24::1 -msgid "" -":py:obj:`SecAggWorkflow `\\ " -"\\(reconstruction\\_threshold\\, \\*\\)" +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:25 of +msgid "Fraction to cut off of both tails of the distribution. Defaults to 0.2." msgstr "" -#: ../../source/ref-api/flwr.server.workflow.rst:24::1 -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:1 of -msgid "The workflow for the SecAgg protocol." +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +msgid "" +":py:obj:`aggregate_evaluate " +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" msgstr "" -#: ../../source/ref-api/flwr.server.workflow.DefaultWorkflow.rst:2 -#, fuzzy -msgid "DefaultWorkflow" -msgstr "Flux de travail" +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +msgid "" +":py:obj:`aggregate_fit " +"`\\ " +"\\(server\\_round\\, results\\, failures\\)" +msgstr "" -#: ../../source/ref-api/flwr.server.workflow.SecAggPlusWorkflow.rst:2 -#, fuzzy -msgid "SecAggPlusWorkflow" -msgstr "Flux de travail" +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.aggregate_fit:1 of +msgid "Aggregate fit results using trimmed average." +msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:3 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"The SecAgg+ protocol ensures the secure summation of integer vectors " -"owned by multiple parties, without accessing any individual integer " -"vector. This workflow allows the server to compute the weighted average " -"of model parameters across all clients, ensuring individual contributions" -" remain private. This is achieved by clients sending both, a weighting " -"factor and a weighted version of the locally updated parameters, both of " -"which are masked for privacy. Specifically, each client uploads \"[w, w *" -" params]\" with masks, where weighting factor 'w' is the number of " -"examples ('num_examples') and 'params' represents the model parameters " -"('parameters') from the client's `FitRes`. The server then aggregates " -"these contributions to compute the weighted average of model parameters." +":py:obj:`configure_evaluate " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:14 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"The protocol involves four main stages: - 'setup': Send SecAgg+ " -"configuration to clients and collect their public keys. - 'share keys': " -"Broadcast public keys among clients and collect encrypted secret" +":py:obj:`configure_fit " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:17 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:17 -#: of -msgid "key shares." +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +msgid "" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:18 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:18 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"'collect masked vectors': Forward encrypted secret key shares to target " -"clients and collect masked model parameters." +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:20 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:20 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"'unmask': Collect secret key shares to decrypt and aggregate the model " -"parameters." +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:22 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:22 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"Only the aggregated model parameters are exposed and passed to " -"`Strategy.aggregate_fit`, ensuring individual data privacy." +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:25 -#: of -msgid "" -"The number of shares into which each client's private key is split under " -"the SecAgg+ protocol. If specified as a float, it represents the " -"proportion of all selected clients, and the number of shares will be set " -"dynamically in the run time. A private key can be reconstructed from " -"these shares, allowing for the secure aggregation of model updates. Each " -"client sends one share to each of its neighbors while retaining one." +#: ../../source/ref-api/flwr.server.strategy.FedXgbBagging.rst:2 +msgid "FedXgbBagging" msgstr "" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:25 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:32 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 #: of msgid "" -"The minimum number of shares required to reconstruct a client's private " -"key, or, if specified as a float, it represents the proportion of the " -"total number of shares needed for reconstruction. This threshold ensures " -"privacy by allowing for the recovery of contributions from dropped " -"clients during aggregation, without compromising individual client data." +":py:obj:`aggregate_evaluate " +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:31 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:38 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 #: of -msgid "" -"The maximum value of the weight that can be assigned to any single " -"client's update during the weighted average calculation on the server " -"side, e.g., in the FedAvg algorithm." -msgstr "" +#, fuzzy +msgid "Aggregate evaluation metrics using average." +msgstr "Résultats globaux de l'évaluation." -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:35 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:42 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 #: of msgid "" -"The range within which model parameters are clipped before quantization. " -"This parameter ensures each model parameter is bounded within " -"[-clipping_range, clipping_range], facilitating quantization." +":py:obj:`aggregate_fit " +"`\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:39 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:46 -#: of -msgid "" -"The size of the range into which floating-point model parameters are " -"quantized, mapping each parameter to an integer in [0, " -"quantization_range-1]. This facilitates cryptographic operations on the " -"model updates." +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_fit:1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_fit:1 of +msgid "Aggregate fit results using bagging." msgstr "" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:43 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:50 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 #: of msgid "" -"The range of values from which random mask entries are uniformly sampled " -"([0, modulus_range-1]). `modulus_range` must be less than 4294967296. " -"Please use 2**n values for `modulus_range` to prevent overflow issues." +":py:obj:`configure_evaluate " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:47 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:54 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 #: of msgid "" -"The timeout duration in seconds. If specified, the workflow will wait for" -" replies for this duration each time. If `None`, there is no time limit " -"and the workflow will wait until replies for all messages are received." +":py:obj:`configure_fit " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:61 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 #: of msgid "" -"Generally, higher `num_shares` means more robust to dropouts while " -"increasing the computational costs; higher `reconstruction_threshold` " -"means better privacy guarantees but less tolerance to dropouts." +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:58 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:64 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 #: of -msgid "Too large `max_weight` may compromise the precision of the quantization." +msgid "" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:59 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:65 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 #: of -msgid "`modulus_range` must be 2**n and larger than `quantization_range`." +msgid "" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:66 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 #: of msgid "" -"When `num_shares` is a float, it is interpreted as the proportion of all " -"selected clients, and hence the number of shares will be determined in " -"the runtime. This allows for dynamic adjustment based on the total number" -" of participating clients." +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:69 -#: of -msgid "" -"Similarly, when `reconstruction_threshold` is a float, it is interpreted " -"as the proportion of the number of shares needed for the reconstruction " -"of a private key. This feature enables flexibility in setting the " -"security threshold relative to the number of distributed shares." +#: ../../source/ref-api/flwr.server.strategy.FedXgbCyclic.rst:2 +msgid "FedXgbCyclic" msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:73 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 #: of msgid "" -"`num_shares`, `reconstruction_threshold`, and the quantization parameters" -" (`clipping_range`, `quantization_range`, `modulus_range`) play critical " -"roles in balancing privacy, robustness, and efficiency within the SecAgg+" -" protocol." +":py:obj:`aggregate_evaluate " +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`collect_masked_vectors_stage " -"`\\" -" \\(driver\\, ...\\)" +":py:obj:`aggregate_fit " +"`\\ \\(server\\_round\\," +" results\\, failures\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 #: of -msgid "Execute the 'collect masked vectors' stage." +msgid "" +":py:obj:`configure_evaluate " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`setup_stage " -"`\\ \\(driver\\, " -"context\\, state\\)" +":py:obj:`configure_fit " +"`\\ \\(server\\_round\\," +" parameters\\, ...\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.setup_stage:1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 #: of -msgid "Execute the 'setup' stage." +msgid "" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`share_keys_stage " -"`\\ " -"\\(driver\\, context\\, state\\)" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.share_keys_stage:1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 #: of -msgid "Execute the 'share keys' stage." +msgid "" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`unmask_stage " -"`\\ \\(driver\\, " -"context\\, state\\)" -msgstr "" - -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.unmask_stage:1 -#: of -msgid "Execute the 'unmask' stage." +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-api/flwr.server.workflow.SecAggWorkflow.rst:2 +#: ../../source/ref-api/flwr.server.strategy.FedXgbNnAvg.rst:2 #, fuzzy -msgid "SecAggWorkflow" -msgstr "Flux de travail" +msgid "FedXgbNnAvg" +msgstr "DP-FedAvg" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:1 of +#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg:5 of msgid "" -"Bases: " -":py:class:`~flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow`" +"This strategy is deprecated, but a copy of it is available in Flower " +"Baselines: " +"https://github.com/adap/flower/tree/main/baselines/hfedxgboost." msgstr "" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:3 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"The SecAgg protocol ensures the secure summation of integer vectors owned" -" by multiple parties, without accessing any individual integer vector. " -"This workflow allows the server to compute the weighted average of model " -"parameters across all clients, ensuring individual contributions remain " -"private. This is achieved by clients sending both, a weighting factor and" -" a weighted version of the locally updated parameters, both of which are " -"masked for privacy. Specifically, each client uploads \"[w, w * params]\"" -" with masks, where weighting factor 'w' is the number of examples " -"('num_examples') and 'params' represents the model parameters " -"('parameters') from the client's `FitRes`. The server then aggregates " -"these contributions to compute the weighted average of model parameters." +":py:obj:`aggregate_evaluate " +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:14 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"The protocol involves four main stages: - 'setup': Send SecAgg " -"configuration to clients and collect their public keys. - 'share keys': " -"Broadcast public keys among clients and collect encrypted secret" +":py:obj:`aggregate_fit " +"`\\ \\(server\\_round\\, " +"results\\, failures\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:54 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"Each client's private key is split into N shares under the SecAgg " -"protocol, where N is the number of selected clients." +":py:obj:`configure_evaluate " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:56 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"Generally, higher `reconstruction_threshold` means better privacy " -"guarantees but less tolerance to dropouts." +":py:obj:`configure_fit " +"`\\ \\(server\\_round\\, " +"parameters\\, ...\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:60 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"When `reconstruction_threshold` is a float, it is interpreted as the " -"proportion of the number of all selected clients needed for the " -"reconstruction of a private key. This feature enables flexibility in " -"setting the security threshold relative to the number of selected " -"clients." +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:64 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"`reconstruction_threshold`, and the quantization parameters " -"(`clipping_range`, `quantization_range`, `modulus_range`) play critical " -"roles in balancing privacy, robustness, and efficiency within the SecAgg " -"protocol." +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`collect_masked_vectors_stage " -"`\\ " -"\\(driver\\, ...\\)" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`setup_stage `\\" -" \\(driver\\, context\\, state\\)" +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 -#: of -msgid "" -":py:obj:`share_keys_stage " -"`\\ \\(driver\\, " -"context\\, state\\)" +#: ../../source/ref-api/flwr.server.strategy.FedYogi.rst:2 +msgid "FedYogi" msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 -#: of -msgid "" -":py:obj:`unmask_stage " -"`\\ \\(driver\\, " -"context\\, state\\)" +#: flwr.server.strategy.fedyogi.FedYogi:32 of +msgid "Server-side learning rate. Defaults to 1e-2." msgstr "" -#: ../../source/ref-api/flwr.simulation.rst:2 -#, fuzzy -msgid "simulation" -msgstr "Simulation de moniteur" - -#: ../../source/ref-api/flwr.simulation.rst:19::1 -msgid "" -":py:obj:`start_simulation `\\ \\(\\*\\," -" client\\_fn\\[\\, ...\\]\\)" +#: flwr.server.strategy.fedyogi.FedYogi:34 of +msgid "Client-side learning rate. Defaults to 0.0316." msgstr "" -#: ../../source/ref-api/flwr.simulation.rst:19::1 -#: flwr.simulation.app.start_simulation:1 of -#, fuzzy -msgid "Start a Ray-based Flower simulation server." -msgstr "Simulation de moniteur" +#: flwr.server.strategy.fedyogi.FedYogi:40 of +msgid "Controls the algorithm's degree of adaptability. Defaults to 1e-3." +msgstr "" -#: ../../source/ref-api/flwr.simulation.rst:19::1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`run_simulation_from_cli " -"`\\ \\(\\)" +":py:obj:`aggregate_evaluate " +"`\\ \\(server\\_round\\," +" results\\, ...\\)" msgstr "" -#: ../../source/ref-api/flwr.simulation.rst:19::1 -#: flwr.simulation.run_simulation.run_simulation_from_cli:1 of -msgid "Run Simulation Engine from the CLI." +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +msgid "" +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" -#: ../../source/ref-api/flwr.simulation.rst:19::1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`run_simulation `\\ " -"\\(server\\_app\\, client\\_app\\, ...\\)" +":py:obj:`configure_evaluate " +"`\\ \\(server\\_round\\," +" parameters\\, ...\\)" msgstr "" -#: ../../source/ref-api/flwr.simulation.rst:19::1 -#: flwr.simulation.run_simulation.run_simulation:1 of -msgid "Run a Flower App using the Simulation Engine." +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +msgid "" +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: ../../source/ref-api/flwr.simulation.run_simulation.rst:2 -#, fuzzy -msgid "run\\_simulation" -msgstr "Simulation de moniteur" +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +msgid "" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" +msgstr "" -#: flwr.simulation.run_simulation.run_simulation:3 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"The `ServerApp` to be executed. It will send messages to different " -"`ClientApp` instances running on different (virtual) SuperNodes." +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" -#: flwr.simulation.run_simulation.run_simulation:6 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"The `ClientApp` to be executed by each of the SuperNodes. It will receive" -" messages sent by the `ServerApp`." +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: flwr.simulation.run_simulation.run_simulation:9 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"Number of nodes that run a ClientApp. They can be sampled by a Driver in " -"the ServerApp and receive a Message describing what the ClientApp should " -"perform." +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: flwr.simulation.run_simulation.run_simulation:13 of -msgid "A simulation backend that runs `ClientApp`s." +#: ../../source/ref-api/flwr.server.strategy.Krum.rst:2 +msgid "Krum" msgstr "" -#: flwr.simulation.run_simulation.run_simulation:15 of -msgid "" -"'A dictionary, e.g {\"\": , \"\": } to " -"configure a backend. Values supported in are those included by " -"`flwr.common.typing.ConfigsRecordValues`." +#: flwr.server.strategy.krum.Krum:3 of +msgid "Implementation based on https://arxiv.org/abs/1703.02757" msgstr "" -#: flwr.simulation.run_simulation.run_simulation:19 of +#: flwr.server.strategy.krum.Krum:17 of msgid "" -"A boolean to indicate whether to enable GPU growth on the main thread. " -"This is desirable if you make use of a TensorFlow model on your " -"`ServerApp` while having your `ClientApp` running on the same GPU. " -"Without enabling this, you might encounter an out-of-memory error because" -" TensorFlow, by default, allocates all GPU memory. Read more about how " -"`tf.config.experimental.set_memory_growth()` works in the TensorFlow " -"documentation: https://www.tensorflow.org/api/stable." +"Number of clients to keep before averaging (MultiKrum). Defaults to 0, in" +" that case classical Krum is applied." msgstr "" -#: flwr.simulation.run_simulation.run_simulation:26 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"When diabled, only INFO, WARNING and ERROR log messages will be shown. If" -" enabled, DEBUG-level logs will be displayed." +":py:obj:`aggregate_evaluate " +"`\\ \\(server\\_round\\, " +"results\\, ...\\)" msgstr "" -#: ../../source/ref-api/flwr.simulation.run_simulation_from_cli.rst:2 -#, fuzzy -msgid "run\\_simulation\\_from\\_cli" -msgstr "Simulation de moniteur" +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +msgid "" +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" +msgstr "" -#: ../../source/ref-api/flwr.simulation.start_simulation.rst:2 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.krum.Krum.aggregate_fit:1 of #, fuzzy -msgid "start\\_simulation" -msgstr "démarrer_simulation" +msgid "Aggregate fit results using Krum." +msgstr "Résultats globaux de l'évaluation." -#: flwr.simulation.app.start_simulation:3 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"A function creating client instances. The function must take a single " -"`str` argument called `cid`. It should return a single client instance of" -" type Client. Note that the created client instances are ephemeral and " -"will often be destroyed after a single method invocation. Since client " -"instances are not long-lived, they should not attempt to carry state over" -" method invocations. Any state required by the instance (model, dataset, " -"hyperparameters, ...) should be (re-)created in either the call to " -"`client_fn` or the call to any of the client methods (e.g., load " -"evaluation data in the `evaluate` method itself)." +":py:obj:`configure_evaluate " +"`\\ \\(server\\_round\\, " +"parameters\\, ...\\)" msgstr "" -#: flwr.simulation.app.start_simulation:13 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"The total number of clients in this simulation. This must be set if " -"`clients_ids` is not set and vice-versa." +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.simulation.app.start_simulation:16 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"List `client_id`s for each client. This is only required if `num_clients`" -" is not set. Setting both `num_clients` and `clients_ids` with " -"`len(clients_ids)` not equal to `num_clients` generates an error." +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" -#: flwr.simulation.app.start_simulation:20 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"CPU and GPU resources for a single client. Supported keys are `num_cpus` " -"and `num_gpus`. To understand the GPU utilization caused by `num_gpus`, " -"as well as using custom resources, please consult the Ray documentation." +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" -#: flwr.simulation.app.start_simulation:25 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"An implementation of the abstract base class `flwr.server.Server`. If no " -"instance is provided, then `start_server` will create one." +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: flwr.simulation.app.start_simulation:31 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"An implementation of the abstract base class `flwr.server.Strategy`. If " -"no strategy is provided, then `start_server` will use " -"`flwr.server.strategy.FedAvg`." +":py:obj:`num_fit_clients `\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: flwr.simulation.app.start_simulation:35 of -msgid "" -"An implementation of the abstract base class `flwr.server.ClientManager`." -" If no implementation is provided, then `start_simulation` will use " -"`flwr.server.client_manager.SimpleClientManager`." -msgstr "" +#: ../../source/ref-api/flwr.server.strategy.QFedAvg.rst:2 +#, fuzzy +msgid "QFedAvg" +msgstr "DP-FedAvg" -#: flwr.simulation.app.start_simulation:39 of +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of msgid "" -"Optional dictionary containing arguments for the call to `ray.init`. If " -"ray_init_args is None (the default), Ray will be initialized with the " -"following default args: { \"ignore_reinit_error\": True, " -"\"include_dashboard\": False } An empty dictionary can be used " -"(ray_init_args={}) to prevent any arguments from being passed to " -"ray.init." +":py:obj:`aggregate_evaluate " +"`\\ \\(server\\_round\\," +" results\\, ...\\)" msgstr "" -#: flwr.simulation.app.start_simulation:39 of +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of msgid "" -"Optional dictionary containing arguments for the call to `ray.init`. If " -"ray_init_args is None (the default), Ray will be initialized with the " -"following default args:" -msgstr "" - -#: flwr.simulation.app.start_simulation:43 of -msgid "{ \"ignore_reinit_error\": True, \"include_dashboard\": False }" +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" -#: flwr.simulation.app.start_simulation:45 of +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of msgid "" -"An empty dictionary can be used (ray_init_args={}) to prevent any " -"arguments from being passed to ray.init." +":py:obj:`configure_evaluate " +"`\\ \\(server\\_round\\," +" parameters\\, ...\\)" msgstr "" -#: flwr.simulation.app.start_simulation:48 of +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of msgid "" -"Set to True to prevent `ray.shutdown()` in case " -"`ray.is_initialized()=True`." +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.simulation.app.start_simulation:50 of +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of msgid "" -"Optionally specify the type of actor to use. The actor object, which " -"persists throughout the simulation, will be the process in charge of " -"executing a ClientApp wrapping input argument `client_fn`." +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" -#: flwr.simulation.app.start_simulation:54 of +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of msgid "" -"If you want to create your own Actor classes, you might need to pass some" -" input argument. You can use this dictionary for such purpose." +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" -#: flwr.simulation.app.start_simulation:57 of +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of msgid "" -"(default: \"DEFAULT\") Optional string (\"DEFAULT\" or \"SPREAD\") for " -"the VCE to choose in which node the actor is placed. If you are an " -"advanced user needed more control you can use lower-level scheduling " -"strategies to pin actors to specific compute nodes (e.g. via " -"NodeAffinitySchedulingStrategy). Please note this is an advanced feature." -" For all details, please refer to the Ray documentation: " -"https://docs.ray.io/en/latest/ray-core/scheduling/index.html" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: flwr.simulation.app.start_simulation:66 of -msgid "**hist** -- Object containing metrics from training." +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of +msgid "" +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-changelog.md:1 -msgid "Changelog" -msgstr "Changelog" - -#: ../../source/ref-changelog.md:3 +#: ../../source/ref-api/flwr.server.strategy.Strategy.rst:2 #, fuzzy -msgid "Unreleased" -msgstr "Inédit" - -#: ../../source/ref-changelog.md:5 ../../source/ref-changelog.md:17 -#: ../../source/ref-changelog.md:110 ../../source/ref-changelog.md:210 -#: ../../source/ref-changelog.md:294 ../../source/ref-changelog.md:358 -#: ../../source/ref-changelog.md:416 ../../source/ref-changelog.md:485 -#: ../../source/ref-changelog.md:614 ../../source/ref-changelog.md:656 -#: ../../source/ref-changelog.md:723 ../../source/ref-changelog.md:789 -#: ../../source/ref-changelog.md:834 ../../source/ref-changelog.md:873 -#: ../../source/ref-changelog.md:906 ../../source/ref-changelog.md:956 -msgid "What's new?" -msgstr "Quoi de neuf ?" +msgid "Strategy" +msgstr "stratégie.du.serveur" -#: ../../source/ref-changelog.md:7 ../../source/ref-changelog.md:80 -#: ../../source/ref-changelog.md:192 ../../source/ref-changelog.md:282 -#: ../../source/ref-changelog.md:346 ../../source/ref-changelog.md:404 -#: ../../source/ref-changelog.md:473 ../../source/ref-changelog.md:535 -#: ../../source/ref-changelog.md:554 ../../source/ref-changelog.md:710 -#: ../../source/ref-changelog.md:781 ../../source/ref-changelog.md:818 -#: ../../source/ref-changelog.md:861 -msgid "Incompatible changes" -msgstr "Changements incompatibles" +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 +#: of +msgid "" +":py:obj:`aggregate_evaluate " +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" +msgstr "" -#: ../../source/ref-changelog.md:9 +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1 +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 +#: of #, fuzzy -msgid "v1.7.0 (2024-02-05)" -msgstr "v1.3.0 (2023-02-06)" - -#: ../../source/ref-changelog.md:11 ../../source/ref-changelog.md:104 -#: ../../source/ref-changelog.md:204 ../../source/ref-changelog.md:288 -#: ../../source/ref-changelog.md:352 ../../source/ref-changelog.md:410 -#: ../../source/ref-changelog.md:479 ../../source/ref-changelog.md:548 -msgid "Thanks to our contributors" -msgstr "Merci à nos contributeurs" +msgid "Aggregate evaluation results." +msgstr "Résultats globaux de l'évaluation." -#: ../../source/ref-changelog.md:13 ../../source/ref-changelog.md:106 -#: ../../source/ref-changelog.md:206 ../../source/ref-changelog.md:290 -#: ../../source/ref-changelog.md:354 ../../source/ref-changelog.md:412 +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 +#: of msgid "" -"We would like to give our special thanks to all the contributors who made" -" the new version of Flower possible (in `git shortlog` order):" +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" -"Nous tenons à remercier tout particulièrement tous les contributeurs qui " -"ont rendu possible la nouvelle version de Flower (dans l'ordre `git " -"shortlog`) :" -#: ../../source/ref-changelog.md:15 +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 +#: flwr.server.strategy.strategy.Strategy.aggregate_fit:1 of +#, fuzzy +msgid "Aggregate training results." +msgstr "Résultats globaux de l'évaluation." + +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 +#: of msgid "" -"`Aasheesh Singh`, `Adam Narozniak`, `Aml Hassan Esmil`, `Charles " -"Beauville`, `Daniel J. Beutel`, `Daniel Nata Nugraha`, `Edoardo " -"Gabrielli`, `Gustavo Bertoli`, `HelinLin`, `Heng Pan`, `Javier`, `M S " -"Chaitanya Kumar`, `Mohammad Naseri`, `Nikos Vlachakis`, `Pritam Neog`, " -"`Robert Kuska`, `Robert Steiner`, `Taner Topal`, `Yahia Salaheldin " -"Shaaban`, `Yan Gao`, `Yasar Abbas` " +":py:obj:`configure_evaluate " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:19 -#, fuzzy +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 +#: of msgid "" -"**Introduce stateful clients (experimental)** " -"([#2770](https://github.com/adap/flower/pull/2770), " -"[#2686](https://github.com/adap/flower/pull/2686), " -"[#2696](https://github.com/adap/flower/pull/2696), " -"[#2643](https://github.com/adap/flower/pull/2643), " -"[#2769](https://github.com/adap/flower/pull/2769))" +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -"**([#842](https://github.com/adap/flower/pull/842), " -"[#844](https://github.com/adap/flower/pull/844), " -"[#845](https://github.com/adap/flower/pull/845), " -"[#847](https://github.com/adap/flower/pull/847), " -"[#993](https://github.com/adap/flower/pull/993), " -"[#994](https://github.com/adap/flower/pull/994))" -#: ../../source/ref-changelog.md:21 +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 +#: of msgid "" -"Subclasses of `Client` and `NumPyClient` can now store local state that " -"remains on the client. Let's start with the highlight first: this new " -"feature is compatible with both simulated clients (via " -"`start_simulation`) and networked clients (via `start_client`). It's also" -" the first preview of new abstractions like `Context` and `RecordSet`. " -"Clients can access state of type `RecordSet` via `state: RecordSet = " -"self.context.state`. Changes to this `RecordSet` are preserved across " -"different rounds of execution to enable stateful computations in a " -"unified way across simulation and deployment." +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" -#: ../../source/ref-changelog.md:23 +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 +#: flwr.server.strategy.strategy.Strategy.evaluate:1 of #, fuzzy -msgid "" -"**Improve performance** " -"([#2293](https://github.com/adap/flower/pull/2293))" -msgstr "" -"**Supprimer les stratégies expérimentales** " -"([#1280](https://github.com/adap/flower/pull/1280))" +msgid "Evaluate the current model parameters." +msgstr "``get_parameters`` : renvoie les paramètres du modèle local actuel" -#: ../../source/ref-changelog.md:25 +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 +#: of msgid "" -"Flower is faster than ever. All `FedAvg`-derived strategies now use in-" -"place aggregation to reduce memory consumption. The Flower client " -"serialization/deserialization has been rewritten from the ground up, " -"which results in significant speedups, especially when the client-side " -"training time is short." +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" -#: ../../source/ref-changelog.md:27 +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 +#: flwr.server.strategy.strategy.Strategy.initialize_parameters:1 of #, fuzzy +msgid "Initialize the (global) model parameters." +msgstr "Initialise le modèle global" + +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:5 of msgid "" -"**Support Federated Learning with Apple MLX and Flower** " -"([#2693](https://github.com/adap/flower/pull/2693))" +"Successful updates from the previously selected and configured clients. " +"Each pair of `(ClientProxy, FitRes` constitutes a successful update from " +"one of the previously selected clients. Not that not all previously " +"selected clients are necessarily included in this list: a client might " +"drop out and not submit a result. For each client that did not submit an " +"update, there should be an `Exception` in `failures`." msgstr "" -"**Ajouter un nouvel exemple d'apprentissage fédéré utilisant fastai et " -"Flower** ([#1598](https://github.com/adap/flower/pull/1598))" -#: ../../source/ref-changelog.md:29 -msgid "" -"Flower has official support for federated learning using [Apple " -"MLX](https://ml-explore.github.io/mlx) via the new `quickstart-mlx` code " -"example." +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:13 +#: flwr.server.strategy.strategy.Strategy.aggregate_fit:13 of +msgid "Exceptions that occurred while the server was waiting for client updates." msgstr "" -#: ../../source/ref-changelog.md:31 -#, fuzzy +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:16 of msgid "" -"**Introduce new XGBoost cyclic strategy** " -"([#2666](https://github.com/adap/flower/pull/2666), " -"[#2668](https://github.com/adap/flower/pull/2668))" +"**aggregation_result** -- The aggregated evaluation result. Aggregation " +"typically uses some variant of a weighted average." msgstr "" -"**Introduction du SDK iOS (aperçu)** " -"([#1621](https://github.com/adap/flower/pull/1621), " -"[#1764](https://github.com/adap/flower/pull/1764))" -#: ../../source/ref-changelog.md:33 +#: flwr.server.strategy.strategy.Strategy.aggregate_fit:5 of msgid "" -"A new strategy called `FedXgbCyclic` supports a client-by-client style of" -" training (often called cyclic). The `xgboost-comprehensive` code example" -" shows how to use it in a full project. In addition to that, `xgboost-" -"comprehensive` now also supports simulation mode. With this, Flower " -"offers best-in-class XGBoost support." +"Successful updates from the previously selected and configured clients. " +"Each pair of `(ClientProxy, FitRes)` constitutes a successful update from" +" one of the previously selected clients. Not that not all previously " +"selected clients are necessarily included in this list: a client might " +"drop out and not submit a result. For each client that did not submit an " +"update, there should be an `Exception` in `failures`." msgstr "" -#: ../../source/ref-changelog.md:35 -#, fuzzy +#: flwr.server.strategy.strategy.Strategy.aggregate_fit:17 of msgid "" -"**Support Python 3.11** " -"([#2394](https://github.com/adap/flower/pull/2394))" +"**parameters** -- If parameters are returned, then the server will treat " +"these as the new global model parameters (i.e., it will replace the " +"previous parameters with the ones returned from this method). If `None` " +"is returned (e.g., because there were only failures and no viable " +"results) then the server will no update the previous model parameters, " +"the updates received in this round are discarded, and the global model " +"parameters remain the same." msgstr "" -"**Support Python 3.10** " -"([#1320](https://github.com/adap/flower/pull/1320))" -#: ../../source/ref-changelog.md:37 +#: flwr.server.strategy.strategy.Strategy.evaluate:3 of msgid "" -"Framework tests now run on Python 3.8, 3.9, 3.10, and 3.11. This will " -"ensure better support for users using more recent Python versions." +"This function can be used to perform centralized (i.e., server-side) " +"evaluation of model parameters." msgstr "" -#: ../../source/ref-changelog.md:39 -#, fuzzy +#: flwr.server.strategy.strategy.Strategy.evaluate:11 of msgid "" -"**Update gRPC and ProtoBuf dependencies** " -"([#2814](https://github.com/adap/flower/pull/2814))" +"**evaluation_result** -- The evaluation result, usually a Tuple " +"containing loss and a dictionary containing task-specific metrics (e.g., " +"accuracy)." msgstr "" -"**Ajouter une nouvelle stratégie `FedProx`** " -"([#1619](https://github.com/adap/flower/pull/1619))" -#: ../../source/ref-changelog.md:41 +#: flwr.server.strategy.strategy.Strategy.initialize_parameters:6 of msgid "" -"The `grpcio` and `protobuf` dependencies were updated to their latest " -"versions for improved security and performance." +"**parameters** -- If parameters are returned, then the server will treat " +"these as the initial global model parameters." msgstr "" -#: ../../source/ref-changelog.md:43 +#: ../../source/ref-api/flwr.server.workflow.rst:2 #, fuzzy -msgid "" -"**Introduce Docker image for Flower server** " -"([#2700](https://github.com/adap/flower/pull/2700), " -"[#2688](https://github.com/adap/flower/pull/2688), " -"[#2705](https://github.com/adap/flower/pull/2705), " -"[#2695](https://github.com/adap/flower/pull/2695), " -"[#2747](https://github.com/adap/flower/pull/2747), " -"[#2746](https://github.com/adap/flower/pull/2746), " -"[#2680](https://github.com/adap/flower/pull/2680), " -"[#2682](https://github.com/adap/flower/pull/2682), " -"[#2701](https://github.com/adap/flower/pull/2701))" -msgstr "" -"**([#842](https://github.com/adap/flower/pull/842), " -"[#844](https://github.com/adap/flower/pull/844), " -"[#845](https://github.com/adap/flower/pull/845), " -"[#847](https://github.com/adap/flower/pull/847), " -"[#993](https://github.com/adap/flower/pull/993), " -"[#994](https://github.com/adap/flower/pull/994))" +msgid "workflow" +msgstr "Flux de travail" -#: ../../source/ref-changelog.md:45 +#: ../../source/ref-api/flwr.server.workflow.rst:24::1 msgid "" -"The Flower server can now be run using an official Docker image. A new " -"how-to guide explains [how to run Flower using " -"Docker](https://flower.ai/docs/framework/how-to-run-flower-using-" -"docker.html). An official Flower client Docker image will follow." +":py:obj:`DefaultWorkflow `\\ " +"\\(\\[fit\\_workflow\\, ...\\]\\)" msgstr "" -#: ../../source/ref-changelog.md:47 -#, fuzzy -msgid "" -"**Introduce** `flower-via-docker-compose` **example** " -"([#2626](https://github.com/adap/flower/pull/2626))" +#: ../../source/ref-api/flwr.server.workflow.rst:24::1 +#: flwr.server.workflow.default_workflows.DefaultWorkflow:1 of +msgid "Default workflow in Flower." msgstr "" -"**Introduire une nouvelle ligne de base pour les fleurs : FedAvg " -"FEMNIST** ([#1655](https://github.com/adap/flower/pull/1655))" -#: ../../source/ref-changelog.md:49 -#, fuzzy +#: ../../source/ref-api/flwr.server.workflow.rst:24::1 msgid "" -"**Introduce** `quickstart-sklearn-tabular` **example** " -"([#2719](https://github.com/adap/flower/pull/2719))" +":py:obj:`SecAggPlusWorkflow `\\ " +"\\(num\\_shares\\, ...\\[\\, ...\\]\\)" msgstr "" -"**Ajouter une nouvelle stratégie `FedProx`** " -"([#1619](https://github.com/adap/flower/pull/1619))" -#: ../../source/ref-changelog.md:51 -#, fuzzy -msgid "" -"**Introduce** `custom-metrics` **example** " -"([#1958](https://github.com/adap/flower/pull/1958))" +#: ../../source/ref-api/flwr.server.workflow.rst:24::1 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:1 +#: of +msgid "The workflow for the SecAgg+ protocol." msgstr "" -"**Ajouter une nouvelle stratégie `FedProx`** " -"([#1619](https://github.com/adap/flower/pull/1619))" -#: ../../source/ref-changelog.md:53 -#, fuzzy +#: ../../source/ref-api/flwr.server.workflow.rst:24::1 msgid "" -"**Update code examples to use Flower Datasets** " -"([#2450](https://github.com/adap/flower/pull/2450), " -"[#2456](https://github.com/adap/flower/pull/2456), " -"[#2318](https://github.com/adap/flower/pull/2318), " -"[#2712](https://github.com/adap/flower/pull/2712))" +":py:obj:`SecAggWorkflow `\\ " +"\\(reconstruction\\_threshold\\, \\*\\)" msgstr "" -"Mettre à jour les outils de développement " -"([#1231](https://github.com/adap/flower/pull/1231), " -"[#1276](https://github.com/adap/flower/pull/1276), " -"[#1301](https://github.com/adap/flower/pull/1301), " -"[#1310](https://github.com/adap/flower/pull/1310))" -#: ../../source/ref-changelog.md:55 -msgid "" -"Several code examples were updated to use [Flower " -"Datasets](https://flower.ai/docs/datasets/)." +#: ../../source/ref-api/flwr.server.workflow.rst:24::1 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:1 of +msgid "The workflow for the SecAgg protocol." msgstr "" -#: ../../source/ref-changelog.md:57 +#: ../../source/ref-api/flwr.server.workflow.DefaultWorkflow.rst:2 #, fuzzy -msgid "" -"**General updates to Flower Examples** " -"([#2381](https://github.com/adap/flower/pull/2381), " -"[#2805](https://github.com/adap/flower/pull/2805), " -"[#2782](https://github.com/adap/flower/pull/2782), " -"[#2806](https://github.com/adap/flower/pull/2806), " -"[#2829](https://github.com/adap/flower/pull/2829), " -"[#2825](https://github.com/adap/flower/pull/2825), " -"[#2816](https://github.com/adap/flower/pull/2816), " -"[#2726](https://github.com/adap/flower/pull/2726), " -"[#2659](https://github.com/adap/flower/pull/2659), " -"[#2655](https://github.com/adap/flower/pull/2655))" -msgstr "" -"**Améliorer l'API (expérimentale) du pilote** " -"([#1663](https://github.com/adap/flower/pull/1663), " -"[#1666](https://github.com/adap/flower/pull/1666), " -"[#1667](https://github.com/adap/flower/pull/1667), " -"[#1664](https://github.com/adap/flower/pull/1664), " -"[#1675](https://github.com/adap/flower/pull/1675), " -"[#1676](https://github.com/adap/flower/pull/1676), " -"[#1693](https://github.com/adap/flower/pull/1693), " -"[#1662](https://github.com/adap/flower/pull/1662), " -"[#1794](https://github.com/adap/flower/pull/1794))" - -#: ../../source/ref-changelog.md:59 -msgid "Many Flower code examples received substantial updates." -msgstr "" +msgid "DefaultWorkflow" +msgstr "Flux de travail" -#: ../../source/ref-changelog.md:61 ../../source/ref-changelog.md:154 +#: ../../source/ref-api/flwr.server.workflow.SecAggPlusWorkflow.rst:2 #, fuzzy -msgid "**Update Flower Baselines**" -msgstr "Demande pour une nouvelle Flower Baseline" +msgid "SecAggPlusWorkflow" +msgstr "Flux de travail" -#: ../../source/ref-changelog.md:63 -#, fuzzy +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:3 +#: of msgid "" -"HFedXGBoost ([#2226](https://github.com/adap/flower/pull/2226), " -"[#2771](https://github.com/adap/flower/pull/2771))" +"The SecAgg+ protocol ensures the secure summation of integer vectors " +"owned by multiple parties, without accessing any individual integer " +"vector. This workflow allows the server to compute the weighted average " +"of model parameters across all clients, ensuring individual contributions" +" remain private. This is achieved by clients sending both, a weighting " +"factor and a weighted version of the locally updated parameters, both of " +"which are masked for privacy. Specifically, each client uploads \"[w, w *" +" params]\" with masks, where weighting factor 'w' is the number of " +"examples ('num_examples') and 'params' represents the model parameters " +"('parameters') from the client's `FitRes`. The server then aggregates " +"these contributions to compute the weighted average of model parameters." msgstr "" -"**Nouvel exemple de code JAX** " -"([#906](https://github.com/adap/flower/pull/906), " -"[#1143](https://github.com/adap/flower/pull/1143))" -#: ../../source/ref-changelog.md:64 -#, fuzzy -msgid "FedVSSL ([#2412](https://github.com/adap/flower/pull/2412))" +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:14 +#: of +msgid "" +"The protocol involves four main stages: - 'setup': Send SecAgg+ " +"configuration to clients and collect their public keys. - 'share keys': " +"Broadcast public keys among clients and collect encrypted secret" msgstr "" -"Amélioration de la documentation sur le serveur gRPC " -"([#841](https://github.com/adap/flower/pull/841))" -#: ../../source/ref-changelog.md:65 -#, fuzzy -msgid "FedNova ([#2179](https://github.com/adap/flower/pull/2179))" +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:17 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:17 +#: of +msgid "key shares." msgstr "" -"**Ajouter une nouvelle stratégie `FedProx`** " -"([#1619](https://github.com/adap/flower/pull/1619))" -#: ../../source/ref-changelog.md:66 -#, fuzzy -msgid "HeteroFL ([#2439](https://github.com/adap/flower/pull/2439))" -msgstr "Nouvelle référence API ([#554](https://github.com/adap/flower/pull/554))" +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:18 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:18 +#: of +msgid "" +"'collect masked vectors': Forward encrypted secret key shares to target " +"clients and collect masked model parameters." +msgstr "" -#: ../../source/ref-changelog.md:67 -#, fuzzy -msgid "FedAvgM ([#2246](https://github.com/adap/flower/pull/2246))" -msgstr "Nouvelle référence API ([#554](https://github.com/adap/flower/pull/554))" +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:20 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:20 +#: of +msgid "" +"'unmask': Collect secret key shares to decrypt and aggregate the model " +"parameters." +msgstr "" -#: ../../source/ref-changelog.md:68 -#, fuzzy -msgid "FedPara ([#2722](https://github.com/adap/flower/pull/2722))" +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:22 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:22 +#: of +msgid "" +"Only the aggregated model parameters are exposed and passed to " +"`Strategy.aggregate_fit`, ensuring individual data privacy." msgstr "" -"**Renommé stratégie q-FedAvg** " -"([#802](https://github.com/adap/flower/pull/802))" -#: ../../source/ref-changelog.md:70 -#, fuzzy +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:25 +#: of msgid "" -"**Improve documentation** " -"([#2674](https://github.com/adap/flower/pull/2674), " -"[#2480](https://github.com/adap/flower/pull/2480), " -"[#2826](https://github.com/adap/flower/pull/2826), " -"[#2727](https://github.com/adap/flower/pull/2727), " -"[#2761](https://github.com/adap/flower/pull/2761), " -"[#2900](https://github.com/adap/flower/pull/2900))" +"The number of shares into which each client's private key is split under " +"the SecAgg+ protocol. If specified as a float, it represents the " +"proportion of all selected clients, and the number of shares will be set " +"dynamically in the run time. A private key can be reconstructed from " +"these shares, allowing for the secure aggregation of model updates. Each " +"client sends one share to each of its neighbors while retaining one." msgstr "" -"**Mise à jour de la documentation** " -"([#1629](https://github.com/adap/flower/pull/1629), " -"[#1628](https://github.com/adap/flower/pull/1628), " -"[#1620](https://github.com/adap/flower/pull/1620), " -"[#1618](https://github.com/adap/flower/pull/1618), " -"[#1617](https://github.com/adap/flower/pull/1617), " -"[#1613](https://github.com/adap/flower/pull/1613), " -"[#1614](https://github.com/adap/flower/pull/1614))" -#: ../../source/ref-changelog.md:72 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:25 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:32 +#: of msgid "" -"**Improved testing and development infrastructure** " -"([#2797](https://github.com/adap/flower/pull/2797), " -"[#2676](https://github.com/adap/flower/pull/2676), " -"[#2644](https://github.com/adap/flower/pull/2644), " -"[#2656](https://github.com/adap/flower/pull/2656), " -"[#2848](https://github.com/adap/flower/pull/2848), " -"[#2675](https://github.com/adap/flower/pull/2675), " -"[#2735](https://github.com/adap/flower/pull/2735), " -"[#2767](https://github.com/adap/flower/pull/2767), " -"[#2732](https://github.com/adap/flower/pull/2732), " -"[#2744](https://github.com/adap/flower/pull/2744), " -"[#2681](https://github.com/adap/flower/pull/2681), " -"[#2699](https://github.com/adap/flower/pull/2699), " -"[#2745](https://github.com/adap/flower/pull/2745), " -"[#2734](https://github.com/adap/flower/pull/2734), " -"[#2731](https://github.com/adap/flower/pull/2731), " -"[#2652](https://github.com/adap/flower/pull/2652), " -"[#2720](https://github.com/adap/flower/pull/2720), " -"[#2721](https://github.com/adap/flower/pull/2721), " -"[#2717](https://github.com/adap/flower/pull/2717), " -"[#2864](https://github.com/adap/flower/pull/2864), " -"[#2694](https://github.com/adap/flower/pull/2694), " -"[#2709](https://github.com/adap/flower/pull/2709), " -"[#2658](https://github.com/adap/flower/pull/2658), " -"[#2796](https://github.com/adap/flower/pull/2796), " -"[#2692](https://github.com/adap/flower/pull/2692), " -"[#2657](https://github.com/adap/flower/pull/2657), " -"[#2813](https://github.com/adap/flower/pull/2813), " -"[#2661](https://github.com/adap/flower/pull/2661), " -"[#2398](https://github.com/adap/flower/pull/2398))" +"The minimum number of shares required to reconstruct a client's private " +"key, or, if specified as a float, it represents the proportion of the " +"total number of shares needed for reconstruction. This threshold ensures " +"privacy by allowing for the recovery of contributions from dropped " +"clients during aggregation, without compromising individual client data." msgstr "" -#: ../../source/ref-changelog.md:74 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:31 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:38 +#: of msgid "" -"The Flower testing and development infrastructure has received " -"substantial updates. This makes Flower 1.7 the most tested release ever." +"The maximum value of the weight that can be assigned to any single " +"client's update during the weighted average calculation on the server " +"side, e.g., in the FedAvg algorithm." msgstr "" -#: ../../source/ref-changelog.md:76 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:35 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:42 +#: of msgid "" -"**Update dependencies** " -"([#2753](https://github.com/adap/flower/pull/2753), " -"[#2651](https://github.com/adap/flower/pull/2651), " -"[#2739](https://github.com/adap/flower/pull/2739), " -"[#2837](https://github.com/adap/flower/pull/2837), " -"[#2788](https://github.com/adap/flower/pull/2788), " -"[#2811](https://github.com/adap/flower/pull/2811), " -"[#2774](https://github.com/adap/flower/pull/2774), " -"[#2790](https://github.com/adap/flower/pull/2790), " -"[#2751](https://github.com/adap/flower/pull/2751), " -"[#2850](https://github.com/adap/flower/pull/2850), " -"[#2812](https://github.com/adap/flower/pull/2812), " -"[#2872](https://github.com/adap/flower/pull/2872), " -"[#2736](https://github.com/adap/flower/pull/2736), " -"[#2756](https://github.com/adap/flower/pull/2756), " -"[#2857](https://github.com/adap/flower/pull/2857), " -"[#2757](https://github.com/adap/flower/pull/2757), " -"[#2810](https://github.com/adap/flower/pull/2810), " -"[#2740](https://github.com/adap/flower/pull/2740), " -"[#2789](https://github.com/adap/flower/pull/2789))" +"The range within which model parameters are clipped before quantization. " +"This parameter ensures each model parameter is bounded within " +"[-clipping_range, clipping_range], facilitating quantization." msgstr "" -#: ../../source/ref-changelog.md:78 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:39 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:46 +#: of msgid "" -"**General improvements** " -"([#2803](https://github.com/adap/flower/pull/2803), " -"[#2847](https://github.com/adap/flower/pull/2847), " -"[#2877](https://github.com/adap/flower/pull/2877), " -"[#2690](https://github.com/adap/flower/pull/2690), " -"[#2889](https://github.com/adap/flower/pull/2889), " -"[#2874](https://github.com/adap/flower/pull/2874), " -"[#2819](https://github.com/adap/flower/pull/2819), " -"[#2689](https://github.com/adap/flower/pull/2689), " -"[#2457](https://github.com/adap/flower/pull/2457), " -"[#2870](https://github.com/adap/flower/pull/2870), " -"[#2669](https://github.com/adap/flower/pull/2669), " -"[#2876](https://github.com/adap/flower/pull/2876), " -"[#2885](https://github.com/adap/flower/pull/2885), " -"[#2858](https://github.com/adap/flower/pull/2858), " -"[#2867](https://github.com/adap/flower/pull/2867), " -"[#2351](https://github.com/adap/flower/pull/2351), " -"[#2886](https://github.com/adap/flower/pull/2886), " -"[#2860](https://github.com/adap/flower/pull/2860), " -"[#2828](https://github.com/adap/flower/pull/2828), " -"[#2869](https://github.com/adap/flower/pull/2869), " -"[#2875](https://github.com/adap/flower/pull/2875), " -"[#2733](https://github.com/adap/flower/pull/2733), " -"[#2488](https://github.com/adap/flower/pull/2488), " -"[#2646](https://github.com/adap/flower/pull/2646), " -"[#2879](https://github.com/adap/flower/pull/2879), " -"[#2821](https://github.com/adap/flower/pull/2821), " -"[#2855](https://github.com/adap/flower/pull/2855), " -"[#2800](https://github.com/adap/flower/pull/2800), " -"[#2807](https://github.com/adap/flower/pull/2807), " -"[#2801](https://github.com/adap/flower/pull/2801), " -"[#2804](https://github.com/adap/flower/pull/2804), " -"[#2851](https://github.com/adap/flower/pull/2851), " -"[#2787](https://github.com/adap/flower/pull/2787), " -"[#2852](https://github.com/adap/flower/pull/2852), " -"[#2672](https://github.com/adap/flower/pull/2672), " -"[#2759](https://github.com/adap/flower/pull/2759))" +"The size of the range into which floating-point model parameters are " +"quantized, mapping each parameter to an integer in [0, " +"quantization_range-1]. This facilitates cryptographic operations on the " +"model updates." msgstr "" -#: ../../source/ref-changelog.md:82 -#, fuzzy +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:43 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:50 +#: of msgid "" -"**Deprecate** `start_numpy_client` " -"([#2563](https://github.com/adap/flower/pull/2563), " -"[#2718](https://github.com/adap/flower/pull/2718))" +"The range of values from which random mask entries are uniformly sampled " +"([0, modulus_range-1]). `modulus_range` must be less than 4294967296. " +"Please use 2**n values for `modulus_range` to prevent overflow issues." msgstr "" -"**Nouvelles stratégies intégrées** " -"([#828](https://github.com/adap/flower/pull/828) " -"[#822](https://github.com/adap/flower/pull/822))" -#: ../../source/ref-changelog.md:84 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:47 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:54 +#: of msgid "" -"Until now, clients of type `NumPyClient` needed to be started via " -"`start_numpy_client`. In our efforts to consolidate framework APIs, we " -"have introduced changes, and now all client types should start via " -"`start_client`. To continue using `NumPyClient` clients, you simply need " -"to first call the `.to_client()` method and then pass returned `Client` " -"object to `start_client`. The examples and the documentation have been " -"updated accordingly." +"The timeout duration in seconds. If specified, the workflow will wait for" +" replies for this duration each time. If `None`, there is no time limit " +"and the workflow will wait until replies for all messages are received." msgstr "" -#: ../../source/ref-changelog.md:86 -#, fuzzy +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:61 +#: of msgid "" -"**Deprecate legacy DP wrappers** " -"([#2749](https://github.com/adap/flower/pull/2749))" +"Generally, higher `num_shares` means more robust to dropouts while " +"increasing the computational costs; higher `reconstruction_threshold` " +"means better privacy guarantees but less tolerance to dropouts." msgstr "" -"**Supprimez KerasClient** " -"([#857](https://github.com/adap/flower/pull/857))" -#: ../../source/ref-changelog.md:88 -msgid "" -"Legacy DP wrapper classes are deprecated, but still functional. This is " -"in preparation for an all-new pluggable version of differential privacy " -"support in Flower." +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:58 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:64 +#: of +msgid "Too large `max_weight` may compromise the precision of the quantization." msgstr "" -#: ../../source/ref-changelog.md:90 -#, fuzzy -msgid "" -"**Make optional arg** `--callable` **in** `flower-client` **a required " -"positional arg** ([#2673](https://github.com/adap/flower/pull/2673))" +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:59 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:65 +#: of +msgid "`modulus_range` must be 2**n and larger than `quantization_range`." msgstr "" -"**Log** `Client` **exceptions dans le moteur de client virtuel** " -"([#1493](https://github.com/adap/flower/pull/1493))" -#: ../../source/ref-changelog.md:92 -#, fuzzy +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:66 +#: of msgid "" -"**Rename** `certificates` **to** `root_certificates` **in** `Driver` " -"([#2890](https://github.com/adap/flower/pull/2890))" +"When `num_shares` is a float, it is interpreted as the proportion of all " +"selected clients, and hence the number of shares will be determined in " +"the runtime. This allows for dynamic adjustment based on the total number" +" of participating clients." msgstr "" -"**Rename** `rnd` **to** `server_round` " -"([#1321](https://github.com/adap/flower/pull/1321))" -#: ../../source/ref-changelog.md:94 -#, fuzzy +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:69 +#: of msgid "" -"**Drop experimental** `Task` **fields** " -"([#2866](https://github.com/adap/flower/pull/2866), " -"[#2865](https://github.com/adap/flower/pull/2865))" +"Similarly, when `reconstruction_threshold` is a float, it is interpreted " +"as the proportion of the number of shares needed for the reconstruction " +"of a private key. This feature enables flexibility in setting the " +"security threshold relative to the number of distributed shares." msgstr "" -"**Rename** `Weights` **to** `NDArrays` " -"([#1258](https://github.com/adap/flower/pull/1258), " -"[#1259](https://github.com/adap/flower/pull/1259))" -#: ../../source/ref-changelog.md:96 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:73 +#: of msgid "" -"Experimental fields `sa`, `legacy_server_message` and " -"`legacy_client_message` were removed from `Task` message. The removed " -"fields are superseded by the new `RecordSet` abstraction." +"`num_shares`, `reconstruction_threshold`, and the quantization parameters" +" (`clipping_range`, `quantization_range`, `modulus_range`) play critical " +"roles in balancing privacy, robustness, and efficiency within the SecAgg+" +" protocol." msgstr "" -#: ../../source/ref-changelog.md:98 -#, fuzzy +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: of msgid "" -"**Retire MXNet examples** " -"([#2724](https://github.com/adap/flower/pull/2724))" +":py:obj:`collect_masked_vectors_stage " +"`\\" +" \\(driver\\, ...\\)" msgstr "" -"**Nouvel exemple de code scikit-learn** " -"([#748](https://github.com/adap/flower/pull/748))" -#: ../../source/ref-changelog.md:100 -msgid "" -"The development of the MXNet fremework has ended and the project is now " -"[archived on GitHub](https://github.com/apache/mxnet). Existing MXNet " -"examples won't receive updates." +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: of +msgid "Execute the 'collect masked vectors' stage." msgstr "" -#: ../../source/ref-changelog.md:102 -#, fuzzy -msgid "v1.6.0 (2023-11-28)" -msgstr "v1.4.0 (2023-04-21)" - -#: ../../source/ref-changelog.md:108 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: of msgid "" -"`Aashish Kolluri`, `Adam Narozniak`, `Alessio Mora`, `Barathwaja S`, " -"`Charles Beauville`, `Daniel J. Beutel`, `Daniel Nata Nugraha`, `Gabriel " -"Mota`, `Heng Pan`, `Ivan Agarský`, `JS.KIM`, `Javier`, `Marius Schlegel`," -" `Navin Chandra`, `Nic Lane`, `Peterpan828`, `Qinbin Li`, `Shaz-hash`, " -"`Steve Laskaridis`, `Taner Topal`, `William Lindskog`, `Yan Gao`, " -"`cnxdeveloper`, `k3nfalt` " +":py:obj:`setup_stage " +"`\\ \\(driver\\, " +"context\\, state\\)" msgstr "" -#: ../../source/ref-changelog.md:112 -#, fuzzy -msgid "" -"**Add experimental support for Python 3.12** " -"([#2565](https://github.com/adap/flower/pull/2565))" +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.setup_stage:1 +#: of +msgid "Execute the 'setup' stage." msgstr "" -"**Ajouter la prise en charge expérimentale de Python 3.10 et Python " -"3.11** ([#1135](https://github.com/adap/flower/pull/1135))" -#: ../../source/ref-changelog.md:114 -#, fuzzy +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: of msgid "" -"**Add new XGBoost examples** " -"([#2612](https://github.com/adap/flower/pull/2612), " -"[#2554](https://github.com/adap/flower/pull/2554), " -"[#2617](https://github.com/adap/flower/pull/2617), " -"[#2618](https://github.com/adap/flower/pull/2618), " -"[#2619](https://github.com/adap/flower/pull/2619), " -"[#2567](https://github.com/adap/flower/pull/2567))" +":py:obj:`share_keys_stage " +"`\\ " +"\\(driver\\, context\\, state\\)" msgstr "" -"**([#1520](https://github.com/adap/flower/pull/1520), " -"[#1525](https://github.com/adap/flower/pull/1525), " -"[#1545](https://github.com/adap/flower/pull/1545), " -"[#1546](https://github.com/adap/flower/pull/1546), " -"[#1550](https://github.com/adap/flower/pull/1550), " -"[#1551](https://github.com/adap/flower/pull/1551), " -"[#1567](https://github.com/adap/flower/pull/1567))" -#: ../../source/ref-changelog.md:116 -msgid "" -"We have added a new `xgboost-quickstart` example alongside a new " -"`xgboost-comprehensive` example that goes more in-depth." +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.share_keys_stage:1 +#: of +msgid "Execute the 'share keys' stage." msgstr "" -#: ../../source/ref-changelog.md:118 -#, fuzzy +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: of msgid "" -"**Add Vertical FL example** " -"([#2598](https://github.com/adap/flower/pull/2598))" +":py:obj:`unmask_stage " +"`\\ \\(driver\\, " +"context\\, state\\)" msgstr "" -"**Nouvel exemple de code CoreML pour iOS** " -"([#1289](https://github.com/adap/flower/pull/1289))" -#: ../../source/ref-changelog.md:120 -msgid "" -"We had many questions about Vertical Federated Learning using Flower, so " -"we decided to add an simple example for it on the [Titanic " -"dataset](https://www.kaggle.com/competitions/titanic/data) alongside a " -"tutorial (in the README)." +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.unmask_stage:1 +#: of +msgid "Execute the 'unmask' stage." msgstr "" -#: ../../source/ref-changelog.md:122 +#: ../../source/ref-api/flwr.server.workflow.SecAggWorkflow.rst:2 #, fuzzy +msgid "SecAggWorkflow" +msgstr "Flux de travail" + +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:1 of msgid "" -"**Support custom** `ClientManager` **in** `start_driver()` " -"([#2292](https://github.com/adap/flower/pull/2292))" +"Bases: " +":py:class:`~flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow`" msgstr "" -"Ajout de la prise en charge d'un `ClientManager` personnalisé comme " -"paramètre de `start_simulation` " -"([#1171](https://github.com/adap/flower/pull/1171))" -#: ../../source/ref-changelog.md:124 -#, fuzzy +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:3 of msgid "" -"**Update REST API to support create and delete nodes** " -"([#2283](https://github.com/adap/flower/pull/2283))" +"The SecAgg protocol ensures the secure summation of integer vectors owned" +" by multiple parties, without accessing any individual integer vector. " +"This workflow allows the server to compute the weighted average of model " +"parameters across all clients, ensuring individual contributions remain " +"private. This is achieved by clients sending both, a weighting factor and" +" a weighted version of the locally updated parameters, both of which are " +"masked for privacy. Specifically, each client uploads \"[w, w * params]\"" +" with masks, where weighting factor 'w' is the number of examples " +"('num_examples') and 'params' represents the model parameters " +"('parameters') from the client's `FitRes`. The server then aggregates " +"these contributions to compute the weighted average of model parameters." msgstr "" -"**Nouvelle stratégie expérimentale TensorBoard** " -"([#789](https://github.com/adap/flower/pull/789))" -#: ../../source/ref-changelog.md:126 -#, fuzzy +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:14 of msgid "" -"**Update the Android SDK** " -"([#2187](https://github.com/adap/flower/pull/2187))" +"The protocol involves four main stages: - 'setup': Send SecAgg " +"configuration to clients and collect their public keys. - 'share keys': " +"Broadcast public keys among clients and collect encrypted secret" msgstr "" -"**Introduire une nouvelle ligne de base pour les fleurs : FedAvg " -"FEMNIST** ([#1655](https://github.com/adap/flower/pull/1655))" -#: ../../source/ref-changelog.md:128 -msgid "Add gRPC request-response capability to the Android SDK." +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:54 of +msgid "" +"Each client's private key is split into N shares under the SecAgg " +"protocol, where N is the number of selected clients." msgstr "" -#: ../../source/ref-changelog.md:130 -#, fuzzy +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:56 of msgid "" -"**Update the C++ SDK** " -"([#2537](https://github.com/adap/flower/pull/2537), " -"[#2528](https://github.com/adap/flower/pull/2528), " -"[#2523](https://github.com/adap/flower/pull/2523), " -"[#2522](https://github.com/adap/flower/pull/2522))" +"Generally, higher `reconstruction_threshold` means better privacy " +"guarantees but less tolerance to dropouts." msgstr "" -"Mettre à jour les outils de développement " -"([#1231](https://github.com/adap/flower/pull/1231), " -"[#1276](https://github.com/adap/flower/pull/1276), " -"[#1301](https://github.com/adap/flower/pull/1301), " -"[#1310](https://github.com/adap/flower/pull/1310))" -#: ../../source/ref-changelog.md:132 -msgid "Add gRPC request-response capability to the C++ SDK." +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:60 of +msgid "" +"When `reconstruction_threshold` is a float, it is interpreted as the " +"proportion of the number of all selected clients needed for the " +"reconstruction of a private key. This feature enables flexibility in " +"setting the security threshold relative to the number of selected " +"clients." msgstr "" -#: ../../source/ref-changelog.md:134 -#, fuzzy +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:64 of msgid "" -"**Make HTTPS the new default** " -"([#2591](https://github.com/adap/flower/pull/2591), " -"[#2636](https://github.com/adap/flower/pull/2636))" +"`reconstruction_threshold`, and the quantization parameters " +"(`clipping_range`, `quantization_range`, `modulus_range`) play critical " +"roles in balancing privacy, robustness, and efficiency within the SecAgg " +"protocol." msgstr "" -"**Exemple de code mis à jour** " -"([#1344](https://github.com/adap/flower/pull/1344), " -"[#1347](https://github.com/adap/flower/pull/1347))" -#: ../../source/ref-changelog.md:136 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: of msgid "" -"Flower is moving to HTTPS by default. The new `flower-server` requires " -"passing `--certificates`, but users can enable `--insecure` to use HTTP " -"for prototyping. The same applies to `flower-client`, which can either " -"use user-provided credentials or gRPC-bundled certificates to connect to " -"an HTTPS-enabled server or requires opt-out via passing `--insecure` to " -"enable insecure HTTP connections." +":py:obj:`collect_masked_vectors_stage " +"`\\ " +"\\(driver\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:138 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: of msgid "" -"For backward compatibility, `start_client()` and `start_numpy_client()` " -"will still start in insecure mode by default. In a future release, " -"insecure connections will require user opt-in by passing `insecure=True`." +":py:obj:`setup_stage `\\" +" \\(driver\\, context\\, state\\)" msgstr "" -#: ../../source/ref-changelog.md:140 -#, fuzzy +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: of msgid "" -"**Unify client API** ([#2303](https://github.com/adap/flower/pull/2303), " -"[#2390](https://github.com/adap/flower/pull/2390), " -"[#2493](https://github.com/adap/flower/pull/2493))" +":py:obj:`share_keys_stage " +"`\\ \\(driver\\, " +"context\\, state\\)" msgstr "" -"**Mettre à jour les exemples de code** " -"([#1291](https://github.com/adap/flower/pull/1291), " -"[#1286](https://github.com/adap/flower/pull/1286), " -"[#1282](https://github.com/adap/flower/pull/1282))" -#: ../../source/ref-changelog.md:142 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: of msgid "" -"Using the `client_fn`, Flower clients can interchangeably run as " -"standalone processes (i.e. via `start_client`) or in simulation (i.e. via" -" `start_simulation`) without requiring changes to how the client class is" -" defined and instantiated. The `to_client()` function is introduced to " -"convert a `NumPyClient` to a `Client`." +":py:obj:`unmask_stage " +"`\\ \\(driver\\, " +"context\\, state\\)" msgstr "" -#: ../../source/ref-changelog.md:144 +#: ../../source/ref-api/flwr.simulation.rst:2 #, fuzzy +msgid "simulation" +msgstr "Simulation de moniteur" + +#: ../../source/ref-api/flwr.simulation.rst:18::1 msgid "" -"**Add new** `Bulyan` **strategy** " -"([#1817](https://github.com/adap/flower/pull/1817), " -"[#1891](https://github.com/adap/flower/pull/1891))" +":py:obj:`start_simulation `\\ \\(\\*\\," +" client\\_fn\\[\\, ...\\]\\)" msgstr "" -"**Nouvelles stratégies intégrées** " -"([#828](https://github.com/adap/flower/pull/828) " -"[#822](https://github.com/adap/flower/pull/822))" -#: ../../source/ref-changelog.md:146 +#: ../../source/ref-api/flwr.simulation.rst:18::1 +#: flwr.simulation.app.start_simulation:1 of #, fuzzy +msgid "Start a Ray-based Flower simulation server." +msgstr "Simulation de moniteur" + +#: ../../source/ref-api/flwr.simulation.rst:18::1 msgid "" -"The new `Bulyan` strategy implements Bulyan by [El Mhamdi et al., " -"2018](https://arxiv.org/abs/1802.07927)" +":py:obj:`run_simulation `\\ " +"\\(server\\_app\\, client\\_app\\, ...\\)" msgstr "" -"La nouvelle stratégie `FedMedian` met en œuvre Federated Median " -"(FedMedian) par [Yin et al., 2018] " -"(https://arxiv.org/pdf/1803.01498v1.pdf)." -#: ../../source/ref-changelog.md:148 -#, fuzzy -msgid "" -"**Add new** `XGB Bagging` **strategy** " -"([#2611](https://github.com/adap/flower/pull/2611))" +#: ../../source/ref-api/flwr.simulation.rst:18::1 +#: flwr.simulation.run_simulation.run_simulation:1 of +msgid "Run a Flower App using the Simulation Engine." msgstr "" -"**Ajouter une nouvelle stratégie `FedProx`** " -"([#1619](https://github.com/adap/flower/pull/1619))" -#: ../../source/ref-changelog.md:150 ../../source/ref-changelog.md:152 +#: ../../source/ref-api/flwr.simulation.run_simulation.rst:2 #, fuzzy +msgid "run\\_simulation" +msgstr "Simulation de moniteur" + +#: flwr.simulation.run_simulation.run_simulation:3 of msgid "" -"**Introduce `WorkloadState`** " -"([#2564](https://github.com/adap/flower/pull/2564), " -"[#2632](https://github.com/adap/flower/pull/2632))" +"The `ServerApp` to be executed. It will send messages to different " +"`ClientApp` instances running on different (virtual) SuperNodes." msgstr "" -"**Nouvelles stratégies intégrées** " -"([#828](https://github.com/adap/flower/pull/828) " -"[#822](https://github.com/adap/flower/pull/822))" -#: ../../source/ref-changelog.md:156 -#, fuzzy +#: flwr.simulation.run_simulation.run_simulation:6 of msgid "" -"FedProx ([#2210](https://github.com/adap/flower/pull/2210), " -"[#2286](https://github.com/adap/flower/pull/2286), " -"[#2509](https://github.com/adap/flower/pull/2509))" +"The `ClientApp` to be executed by each of the SuperNodes. It will receive" +" messages sent by the `ServerApp`." msgstr "" -"**Mettre à jour les exemples de code** " -"([#1291](https://github.com/adap/flower/pull/1291), " -"[#1286](https://github.com/adap/flower/pull/1286), " -"[#1282](https://github.com/adap/flower/pull/1282))" -#: ../../source/ref-changelog.md:158 -#, fuzzy +#: flwr.simulation.run_simulation.run_simulation:9 of msgid "" -"Baselines Docs ([#2290](https://github.com/adap/flower/pull/2290), " -"[#2400](https://github.com/adap/flower/pull/2400))" +"Number of nodes that run a ClientApp. They can be sampled by a Driver in " +"the ServerApp and receive a Message describing what the ClientApp should " +"perform." msgstr "" -"**Nouvel exemple de code JAX** " -"([#906](https://github.com/adap/flower/pull/906), " -"[#1143](https://github.com/adap/flower/pull/1143))" -#: ../../source/ref-changelog.md:160 -#, fuzzy -msgid "" -"FedMLB ([#2340](https://github.com/adap/flower/pull/2340), " -"[#2507](https://github.com/adap/flower/pull/2507))" +#: flwr.simulation.run_simulation.run_simulation:13 of +msgid "A simulation backend that runs `ClientApp`s." msgstr "" -"**Exemple de code mis à jour** " -"([#1344](https://github.com/adap/flower/pull/1344), " -"[#1347](https://github.com/adap/flower/pull/1347))" -#: ../../source/ref-changelog.md:162 -#, fuzzy +#: flwr.simulation.run_simulation.run_simulation:15 of msgid "" -"TAMUNA ([#2254](https://github.com/adap/flower/pull/2254), " -"[#2508](https://github.com/adap/flower/pull/2508))" +"'A dictionary, e.g {\"\": , \"\": } to " +"configure a backend. Values supported in are those included by " +"`flwr.common.typing.ConfigsRecordValues`." msgstr "" -"**Nouvelles stratégies intégrées** " -"([#828](https://github.com/adap/flower/pull/828) " -"[#822](https://github.com/adap/flower/pull/822))" - -#: ../../source/ref-changelog.md:164 -#, fuzzy -msgid "FedMeta [#2438](https://github.com/adap/flower/pull/2438)" -msgstr "Nouvelle référence API ([#554](https://github.com/adap/flower/pull/554))" -#: ../../source/ref-changelog.md:166 -#, fuzzy -msgid "FjORD [#2431](https://github.com/adap/flower/pull/2431)" +#: flwr.simulation.run_simulation.run_simulation:19 of +msgid "" +"A boolean to indicate whether to enable GPU growth on the main thread. " +"This is desirable if you make use of a TensorFlow model on your " +"`ServerApp` while having your `ClientApp` running on the same GPU. " +"Without enabling this, you might encounter an out-of-memory error because" +" TensorFlow, by default, allocates all GPU memory. Read more about how " +"`tf.config.experimental.set_memory_growth()` works in the TensorFlow " +"documentation: https://www.tensorflow.org/api/stable." msgstr "" -"Amélioration de la documentation sur le serveur gRPC " -"([#841](https://github.com/adap/flower/pull/841))" -#: ../../source/ref-changelog.md:168 -#, fuzzy -msgid "MOON [#2421](https://github.com/adap/flower/pull/2421)" +#: flwr.simulation.run_simulation.run_simulation:26 of +msgid "" +"When diabled, only INFO, WARNING and ERROR log messages will be shown. If" +" enabled, DEBUG-level logs will be displayed." msgstr "" -"**Ajouter une nouvelle stratégie `FedProx`** " -"([#1619](https://github.com/adap/flower/pull/1619))" -#: ../../source/ref-changelog.md:170 +#: ../../source/ref-api/flwr.simulation.start_simulation.rst:2 #, fuzzy -msgid "DepthFL [#2295](https://github.com/adap/flower/pull/2295)" -msgstr "" -"**Ajouter une nouvelle stratégie `FedProx`** " -"([#1619](https://github.com/adap/flower/pull/1619))" - -#: ../../source/ref-changelog.md:172 -#, fuzzy -msgid "FedPer [#2266](https://github.com/adap/flower/pull/2266)" -msgstr "Nouvelle référence API ([#554](https://github.com/adap/flower/pull/554))" - -#: ../../source/ref-changelog.md:174 -#, fuzzy -msgid "FedWav2vec [#2551](https://github.com/adap/flower/pull/2551)" -msgstr "Nouvelle référence API ([#554](https://github.com/adap/flower/pull/554))" - -#: ../../source/ref-changelog.md:176 -#, fuzzy -msgid "niid-Bench [#2428](https://github.com/adap/flower/pull/2428)" -msgstr "Nouvelle référence API ([#554](https://github.com/adap/flower/pull/554))" +msgid "start\\_simulation" +msgstr "démarrer_simulation" -#: ../../source/ref-changelog.md:178 -#, fuzzy +#: flwr.simulation.app.start_simulation:3 of msgid "" -"FedBN ([#2608](https://github.com/adap/flower/pull/2608), " -"[#2615](https://github.com/adap/flower/pull/2615))" +"A function creating client instances. The function must take a single " +"`str` argument called `cid`. It should return a single client instance of" +" type Client. Note that the created client instances are ephemeral and " +"will often be destroyed after a single method invocation. Since client " +"instances are not long-lived, they should not attempt to carry state over" +" method invocations. Any state required by the instance (model, dataset, " +"hyperparameters, ...) should be (re-)created in either the call to " +"`client_fn` or the call to any of the client methods (e.g., load " +"evaluation data in the `evaluate` method itself)." msgstr "" -"**Nouvelles stratégies intégrées** " -"([#828](https://github.com/adap/flower/pull/828) " -"[#822](https://github.com/adap/flower/pull/822))" -#: ../../source/ref-changelog.md:180 -#, fuzzy +#: flwr.simulation.app.start_simulation:13 of msgid "" -"**General updates to Flower Examples** " -"([#2384](https://github.com/adap/flower/pull/2384), " -"[#2425](https://github.com/adap/flower/pull/2425), " -"[#2526](https://github.com/adap/flower/pull/2526), " -"[#2302](https://github.com/adap/flower/pull/2302), " -"[#2545](https://github.com/adap/flower/pull/2545))" +"The total number of clients in this simulation. This must be set if " +"`clients_ids` is not set and vice-versa." msgstr "" -"Mettre à jour les outils de développement " -"([#1231](https://github.com/adap/flower/pull/1231), " -"[#1276](https://github.com/adap/flower/pull/1276), " -"[#1301](https://github.com/adap/flower/pull/1301), " -"[#1310](https://github.com/adap/flower/pull/1310))" -#: ../../source/ref-changelog.md:182 -#, fuzzy +#: flwr.simulation.app.start_simulation:16 of msgid "" -"**General updates to Flower Baselines** " -"([#2301](https://github.com/adap/flower/pull/2301), " -"[#2305](https://github.com/adap/flower/pull/2305), " -"[#2307](https://github.com/adap/flower/pull/2307), " -"[#2327](https://github.com/adap/flower/pull/2327), " -"[#2435](https://github.com/adap/flower/pull/2435), " -"[#2462](https://github.com/adap/flower/pull/2462), " -"[#2463](https://github.com/adap/flower/pull/2463), " -"[#2461](https://github.com/adap/flower/pull/2461), " -"[#2469](https://github.com/adap/flower/pull/2469), " -"[#2466](https://github.com/adap/flower/pull/2466), " -"[#2471](https://github.com/adap/flower/pull/2471), " -"[#2472](https://github.com/adap/flower/pull/2472), " -"[#2470](https://github.com/adap/flower/pull/2470))" +"List `client_id`s for each client. This is only required if `num_clients`" +" is not set. Setting both `num_clients` and `clients_ids` with " +"`len(clients_ids)` not equal to `num_clients` generates an error." msgstr "" -"**Améliorations générales** " -"([#1491](https://github.com/adap/flower/pull/1491), " -"[#1504](https://github.com/adap/flower/pull/1504), " -"[#1506](https://github.com/adap/flower/pull/1506), " -"[#1514](https://github.com/adap/flower/pull/1514), " -"[#1522](https://github.com/adap/flower/pull/1522), " -"[#1523](https://github.com/adap/flower/pull/1523), " -"[#1526](https://github.com/adap/flower/pull/1526), " -"[#1528](https://github.com/adap/flower/pull/1528), " -"[#1547](https://github.com/adap/flower/pull/1547), " -"[#1549](https://github.com/adap/flower/pull/1549), " -"[#1560](https://github.com/adap/flower/pull/1560), " -"[#1564](https://github.com/adap/flower/pull/1564), " -"[#1566](https://github.com/adap/flower/pull/1566))" -#: ../../source/ref-changelog.md:184 -#, fuzzy +#: flwr.simulation.app.start_simulation:20 of msgid "" -"**General updates to the simulation engine** " -"([#2331](https://github.com/adap/flower/pull/2331), " -"[#2447](https://github.com/adap/flower/pull/2447), " -"[#2448](https://github.com/adap/flower/pull/2448), " -"[#2294](https://github.com/adap/flower/pull/2294))" +"CPU and GPU resources for a single client. Supported keys are `num_cpus` " +"and `num_gpus`. To understand the GPU utilization caused by `num_gpus`, " +"as well as using custom resources, please consult the Ray documentation." msgstr "" -"Mettre à jour les outils de développement " -"([#1231](https://github.com/adap/flower/pull/1231), " -"[#1276](https://github.com/adap/flower/pull/1276), " -"[#1301](https://github.com/adap/flower/pull/1301), " -"[#1310](https://github.com/adap/flower/pull/1310))" -#: ../../source/ref-changelog.md:186 -#, fuzzy +#: flwr.simulation.app.start_simulation:25 of msgid "" -"**General updates to Flower SDKs** " -"([#2288](https://github.com/adap/flower/pull/2288), " -"[#2429](https://github.com/adap/flower/pull/2429), " -"[#2555](https://github.com/adap/flower/pull/2555), " -"[#2543](https://github.com/adap/flower/pull/2543), " -"[#2544](https://github.com/adap/flower/pull/2544), " -"[#2597](https://github.com/adap/flower/pull/2597), " -"[#2623](https://github.com/adap/flower/pull/2623))" +"An implementation of the abstract base class `flwr.server.Server`. If no " +"instance is provided, then `start_server` will create one." msgstr "" -"**Tutoriel amélioré** ([#1468](https://github.com/adap/flower/pull/1468)," -" [#1470](https://github.com/adap/flower/pull/1470), " -"[#1472](https://github.com/adap/flower/pull/1472), " -"[#1473](https://github.com/adap/flower/pull/1473), " -"[#1474](https://github.com/adap/flower/pull/1474), " -"[#1475](https://github.com/adap/flower/pull/1475))" -#: ../../source/ref-changelog.md:188 +#: flwr.simulation.app.start_simulation:31 of msgid "" -"**General improvements** " -"([#2309](https://github.com/adap/flower/pull/2309), " -"[#2310](https://github.com/adap/flower/pull/2310), " -"[#2313](https://github.com/adap/flower/pull/2313), " -"[#2316](https://github.com/adap/flower/pull/2316), " -"[#2317](https://github.com/adap/flower/pull/2317), " -"[#2349](https://github.com/adap/flower/pull/2349), " -"[#2360](https://github.com/adap/flower/pull/2360), " -"[#2402](https://github.com/adap/flower/pull/2402), " -"[#2446](https://github.com/adap/flower/pull/2446), " -"[#2561](https://github.com/adap/flower/pull/2561), " -"[#2273](https://github.com/adap/flower/pull/2273), " -"[#2267](https://github.com/adap/flower/pull/2267), " -"[#2274](https://github.com/adap/flower/pull/2274), " -"[#2275](https://github.com/adap/flower/pull/2275), " -"[#2432](https://github.com/adap/flower/pull/2432), " -"[#2251](https://github.com/adap/flower/pull/2251), " -"[#2321](https://github.com/adap/flower/pull/2321), " -"[#1936](https://github.com/adap/flower/pull/1936), " -"[#2408](https://github.com/adap/flower/pull/2408), " -"[#2413](https://github.com/adap/flower/pull/2413), " -"[#2401](https://github.com/adap/flower/pull/2401), " -"[#2531](https://github.com/adap/flower/pull/2531), " -"[#2534](https://github.com/adap/flower/pull/2534), " -"[#2535](https://github.com/adap/flower/pull/2535), " -"[#2521](https://github.com/adap/flower/pull/2521), " -"[#2553](https://github.com/adap/flower/pull/2553), " -"[#2596](https://github.com/adap/flower/pull/2596))" +"An implementation of the abstract base class `flwr.server.Strategy`. If " +"no strategy is provided, then `start_server` will use " +"`flwr.server.strategy.FedAvg`." msgstr "" -#: ../../source/ref-changelog.md:190 ../../source/ref-changelog.md:280 -#: ../../source/ref-changelog.md:344 ../../source/ref-changelog.md:398 -#: ../../source/ref-changelog.md:465 -msgid "Flower received many improvements under the hood, too many to list here." +#: flwr.simulation.app.start_simulation:35 of +msgid "" +"An implementation of the abstract base class `flwr.server.ClientManager`." +" If no implementation is provided, then `start_simulation` will use " +"`flwr.server.client_manager.SimpleClientManager`." msgstr "" -"Flower a reçu de nombreuses améliorations sous le capot, trop nombreuses " -"pour être énumérées ici." -#: ../../source/ref-changelog.md:194 -#, fuzzy +#: flwr.simulation.app.start_simulation:39 of msgid "" -"**Remove support for Python 3.7** " -"([#2280](https://github.com/adap/flower/pull/2280), " -"[#2299](https://github.com/adap/flower/pull/2299), " -"[#2304](https://github.com/adap/flower/pull/2304), " -"[#2306](https://github.com/adap/flower/pull/2306), " -"[#2355](https://github.com/adap/flower/pull/2355), " -"[#2356](https://github.com/adap/flower/pull/2356))" +"Optional dictionary containing arguments for the call to `ray.init`. If " +"ray_init_args is None (the default), Ray will be initialized with the " +"following default args: { \"ignore_reinit_error\": True, " +"\"include_dashboard\": False } An empty dictionary can be used " +"(ray_init_args={}) to prevent any arguments from being passed to " +"ray.init." msgstr "" -"**Nouvel exemple de code MLCube** " -"([#779](https://github.com/adap/flower/pull/779), " -"[#1034](https://github.com/adap/flower/pull/1034), " -"[#1065](https://github.com/adap/flower/pull/1065), " -"[#1090](https://github.com/adap/flower/pull/1090))" -#: ../../source/ref-changelog.md:196 +#: flwr.simulation.app.start_simulation:39 of msgid "" -"Python 3.7 support was deprecated in Flower 1.5, and this release removes" -" support. Flower now requires Python 3.8." +"Optional dictionary containing arguments for the call to `ray.init`. If " +"ray_init_args is None (the default), Ray will be initialized with the " +"following default args:" msgstr "" -#: ../../source/ref-changelog.md:198 -#, fuzzy -msgid "" -"**Remove experimental argument** `rest` **from** `start_client` " -"([#2324](https://github.com/adap/flower/pull/2324))" +#: flwr.simulation.app.start_simulation:43 of +msgid "{ \"ignore_reinit_error\": True, \"include_dashboard\": False }" msgstr "" -"**Supprimer les stratégies expérimentales** " -"([#1280](https://github.com/adap/flower/pull/1280))" -#: ../../source/ref-changelog.md:200 +#: flwr.simulation.app.start_simulation:45 of msgid "" -"The (still experimental) argument `rest` was removed from `start_client` " -"and `start_numpy_client`. Use `transport=\"rest\"` to opt into the " -"experimental REST API instead." +"An empty dictionary can be used (ray_init_args={}) to prevent any " +"arguments from being passed to ray.init." msgstr "" -#: ../../source/ref-changelog.md:202 -#, fuzzy -msgid "v1.5.0 (2023-08-31)" -msgstr "v1.4.0 (2023-04-21)" - -#: ../../source/ref-changelog.md:208 +#: flwr.simulation.app.start_simulation:48 of msgid "" -"`Adam Narozniak`, `Anass Anhari`, `Charles Beauville`, `Dana-Farber`, " -"`Daniel J. Beutel`, `Daniel Nata Nugraha`, `Edoardo Gabrielli`, `Gustavo " -"Bertoli`, `Heng Pan`, `Javier`, `Mahdi`, `Steven Hé (Sīchàng)`, `Taner " -"Topal`, `achiverram28`, `danielnugraha`, `eunchung`, `ruthgal` " +"Set to True to prevent `ray.shutdown()` in case " +"`ray.is_initialized()=True`." msgstr "" -#: ../../source/ref-changelog.md:212 -#, fuzzy +#: flwr.simulation.app.start_simulation:50 of msgid "" -"**Introduce new simulation engine** " -"([#1969](https://github.com/adap/flower/pull/1969), " -"[#2221](https://github.com/adap/flower/pull/2221), " -"[#2248](https://github.com/adap/flower/pull/2248))" +"Optionally specify the type of actor to use. The actor object, which " +"persists throughout the simulation, will be the process in charge of " +"executing a ClientApp wrapping input argument `client_fn`." msgstr "" -"**Introduire la télémétrie optionnelle** " -"([#1533](https://github.com/adap/flower/pull/1533), " -"[#1544](https://github.com/adap/flower/pull/1544), " -"[#1584](https://github.com/adap/flower/pull/1584))" -#: ../../source/ref-changelog.md:214 +#: flwr.simulation.app.start_simulation:54 of msgid "" -"The new simulation engine has been rewritten from the ground up, yet it " -"remains fully backwards compatible. It offers much improved stability and" -" memory handling, especially when working with GPUs. Simulations " -"transparently adapt to different settings to scale simulation in CPU-" -"only, CPU+GPU, multi-GPU, or multi-node multi-GPU environments." +"If you want to create your own Actor classes, you might need to pass some" +" input argument. You can use this dictionary for such purpose." msgstr "" -#: ../../source/ref-changelog.md:216 +#: flwr.simulation.app.start_simulation:57 of msgid "" -"Comprehensive documentation includes a new [how-to run " -"simulations](https://flower.ai/docs/framework/how-to-run-" -"simulations.html) guide, new [simulation-" -"pytorch](https://flower.ai/docs/examples/simulation-pytorch.html) and " -"[simulation-tensorflow](https://flower.ai/docs/examples/simulation-" -"tensorflow.html) notebooks, and a new [YouTube tutorial " -"series](https://www.youtube.com/watch?v=cRebUIGB5RU&list=PLNG4feLHqCWlnj8a_E1A_n5zr2-8pafTB)." +"(default: \"DEFAULT\") Optional string (\"DEFAULT\" or \"SPREAD\") for " +"the VCE to choose in which node the actor is placed. If you are an " +"advanced user needed more control you can use lower-level scheduling " +"strategies to pin actors to specific compute nodes (e.g. via " +"NodeAffinitySchedulingStrategy). Please note this is an advanced feature." +" For all details, please refer to the Ray documentation: " +"https://docs.ray.io/en/latest/ray-core/scheduling/index.html" +msgstr "" + +#: flwr.simulation.app.start_simulation:66 of +msgid "**hist** -- Object containing metrics from training." msgstr "" -#: ../../source/ref-changelog.md:218 +#: ../../source/ref-changelog.md:1 +msgid "Changelog" +msgstr "Changelog" + +#: ../../source/ref-changelog.md:3 +#, fuzzy +msgid "v1.9.0 (2024-06-10)" +msgstr "v1.3.0 (2023-02-06)" + +#: ../../source/ref-changelog.md:5 ../../source/ref-changelog.md:105 +#: ../../source/ref-changelog.md:169 ../../source/ref-changelog.md:262 +#: ../../source/ref-changelog.md:362 ../../source/ref-changelog.md:446 +#: ../../source/ref-changelog.md:510 ../../source/ref-changelog.md:568 +#: ../../source/ref-changelog.md:637 ../../source/ref-changelog.md:706 +msgid "Thanks to our contributors" +msgstr "Merci à nos contributeurs" + +#: ../../source/ref-changelog.md:7 ../../source/ref-changelog.md:107 +#: ../../source/ref-changelog.md:171 ../../source/ref-changelog.md:264 +#: ../../source/ref-changelog.md:364 ../../source/ref-changelog.md:448 +#: ../../source/ref-changelog.md:512 ../../source/ref-changelog.md:570 msgid "" -"**Restructure Flower Docs** " -"([#1824](https://github.com/adap/flower/pull/1824), " -"[#1865](https://github.com/adap/flower/pull/1865), " -"[#1884](https://github.com/adap/flower/pull/1884), " -"[#1887](https://github.com/adap/flower/pull/1887), " -"[#1919](https://github.com/adap/flower/pull/1919), " -"[#1922](https://github.com/adap/flower/pull/1922), " -"[#1920](https://github.com/adap/flower/pull/1920), " -"[#1923](https://github.com/adap/flower/pull/1923), " -"[#1924](https://github.com/adap/flower/pull/1924), " -"[#1962](https://github.com/adap/flower/pull/1962), " -"[#2006](https://github.com/adap/flower/pull/2006), " -"[#2133](https://github.com/adap/flower/pull/2133), " -"[#2203](https://github.com/adap/flower/pull/2203), " -"[#2215](https://github.com/adap/flower/pull/2215), " -"[#2122](https://github.com/adap/flower/pull/2122), " -"[#2223](https://github.com/adap/flower/pull/2223), " -"[#2219](https://github.com/adap/flower/pull/2219), " -"[#2232](https://github.com/adap/flower/pull/2232), " -"[#2233](https://github.com/adap/flower/pull/2233), " -"[#2234](https://github.com/adap/flower/pull/2234), " -"[#2235](https://github.com/adap/flower/pull/2235), " -"[#2237](https://github.com/adap/flower/pull/2237), " -"[#2238](https://github.com/adap/flower/pull/2238), " -"[#2242](https://github.com/adap/flower/pull/2242), " -"[#2231](https://github.com/adap/flower/pull/2231), " -"[#2243](https://github.com/adap/flower/pull/2243), " -"[#2227](https://github.com/adap/flower/pull/2227))" +"We would like to give our special thanks to all the contributors who made" +" the new version of Flower possible (in `git shortlog` order):" msgstr "" +"Nous tenons à remercier tout particulièrement tous les contributeurs qui " +"ont rendu possible la nouvelle version de Flower (dans l'ordre `git " +"shortlog`) :" -#: ../../source/ref-changelog.md:220 +#: ../../source/ref-changelog.md:9 msgid "" -"Much effort went into a completely restructured Flower docs experience. " -"The documentation on [flower.ai/docs](https://flower.ai/docs) is now " -"divided into Flower Framework, Flower Baselines, Flower Android SDK, " -"Flower iOS SDK, and code example projects." +"`Adam Narozniak`, `Charles Beauville`, `Chong Shen Ng`, `Daniel J. " +"Beutel`, `Daniel Nata Nugraha`, `Heng Pan`, `Javier`, `Mahdi Beitollahi`," +" `Robert Steiner`, `Taner Topal`, `Yan Gao`, `bapic`, `mohammadnaseri` " +msgstr "" + +#: ../../source/ref-changelog.md:11 ../../source/ref-changelog.md:111 +#: ../../source/ref-changelog.md:175 ../../source/ref-changelog.md:268 +#: ../../source/ref-changelog.md:368 ../../source/ref-changelog.md:452 +#: ../../source/ref-changelog.md:516 ../../source/ref-changelog.md:574 +#: ../../source/ref-changelog.md:643 ../../source/ref-changelog.md:772 +#: ../../source/ref-changelog.md:814 ../../source/ref-changelog.md:881 +#: ../../source/ref-changelog.md:947 ../../source/ref-changelog.md:992 +#: ../../source/ref-changelog.md:1031 ../../source/ref-changelog.md:1064 +#: ../../source/ref-changelog.md:1114 +msgid "What's new?" +msgstr "Quoi de neuf ?" + +#: ../../source/ref-changelog.md:13 +#, fuzzy +msgid "" +"**Introduce built-in authentication (preview)** " +"([#2946](https://github.com/adap/flower/pull/2946), " +"[#3388](https://github.com/adap/flower/pull/3388), " +"[#2948](https://github.com/adap/flower/pull/2948), " +"[#2917](https://github.com/adap/flower/pull/2917), " +"[#3386](https://github.com/adap/flower/pull/3386), " +"[#3308](https://github.com/adap/flower/pull/3308), " +"[#3001](https://github.com/adap/flower/pull/3001), " +"[#3409](https://github.com/adap/flower/pull/3409), " +"[#2999](https://github.com/adap/flower/pull/2999), " +"[#2979](https://github.com/adap/flower/pull/2979), " +"[#3389](https://github.com/adap/flower/pull/3389), " +"[#3503](https://github.com/adap/flower/pull/3503), " +"[#3366](https://github.com/adap/flower/pull/3366), " +"[#3357](https://github.com/adap/flower/pull/3357))" msgstr "" +"**Documentation mise à jour** " +"([#1494](https://github.com/adap/flower/pull/1494), " +"[#1496](https://github.com/adap/flower/pull/1496), " +"[#1500](https://github.com/adap/flower/pull/1500), " +"[#1503](https://github.com/adap/flower/pull/1503), " +"[#1505](https://github.com/adap/flower/pull/1505), " +"[#1524](https://github.com/adap/flower/pull/1524), " +"[#1518](https://github.com/adap/flower/pull/1518), " +"[#1519](https://github.com/adap/flower/pull/1519), " +"[#1515](https://github.com/adap/flower/pull/1515))" -#: ../../source/ref-changelog.md:222 -#, fuzzy +#: ../../source/ref-changelog.md:15 msgid "" -"**Introduce Flower Swift SDK** " -"([#1858](https://github.com/adap/flower/pull/1858), " -"[#1897](https://github.com/adap/flower/pull/1897))" +"Flower 1.9 introduces the first build-in version of client node " +"authentication. In previous releases, users often wrote glue code to " +"connect Flower to external authentication systems. With this release, the" +" SuperLink can authenticate SuperNodes using a built-in authentication " +"system. A new [how-to guide](https://flower.ai/docs/framework/how-to-" +"authenticate-supernodes.html) and a new [code " +"example](https://github.com/adap/flower/tree/main/examples/flower-" +"authentication) help you to get started." msgstr "" -"**Introduction du SDK iOS (aperçu)** " -"([#1621](https://github.com/adap/flower/pull/1621), " -"[#1764](https://github.com/adap/flower/pull/1764))" -#: ../../source/ref-changelog.md:224 +#: ../../source/ref-changelog.md:17 msgid "" -"This is the first preview release of the Flower Swift SDK. Flower support" -" on iOS is improving, and alongside the Swift SDK and code example, there" -" is now also an iOS quickstart tutorial." +"This is the first preview release of the Flower-native authentication " +"system. Many additional features are on the roadmap for upcoming Flower " +"releases - stay tuned." msgstr "" -#: ../../source/ref-changelog.md:226 +#: ../../source/ref-changelog.md:19 #, fuzzy msgid "" -"**Introduce Flower Android SDK** " -"([#2131](https://github.com/adap/flower/pull/2131))" +"**Introduce end-to-end Docker support** " +"([#3483](https://github.com/adap/flower/pull/3483), " +"[#3266](https://github.com/adap/flower/pull/3266), " +"[#3390](https://github.com/adap/flower/pull/3390), " +"[#3283](https://github.com/adap/flower/pull/3283), " +"[#3285](https://github.com/adap/flower/pull/3285), " +"[#3391](https://github.com/adap/flower/pull/3391), " +"[#3403](https://github.com/adap/flower/pull/3403), " +"[#3458](https://github.com/adap/flower/pull/3458), " +"[#3533](https://github.com/adap/flower/pull/3533), " +"[#3453](https://github.com/adap/flower/pull/3453), " +"[#3486](https://github.com/adap/flower/pull/3486), " +"[#3290](https://github.com/adap/flower/pull/3290))" msgstr "" -"**Introduire une nouvelle ligne de base pour les fleurs : FedAvg " -"FEMNIST** ([#1655](https://github.com/adap/flower/pull/1655))" +"**Introduire l'API REST (expérimentale)** " +"([#1594](https://github.com/adap/flower/pull/1594), " +"[#1690](https://github.com/adap/flower/pull/1690), " +"[#1695](https://github.com/adap/flower/pull/1695), " +"[#1712](https://github.com/adap/flower/pull/1712), " +"[#1802](https://github.com/adap/flower/pull/1802), " +"[#1770](https://github.com/adap/flower/pull/1770), " +"[#1733](https://github.com/adap/flower/pull/1733))" -#: ../../source/ref-changelog.md:228 +#: ../../source/ref-changelog.md:21 msgid "" -"This is the first preview release of the Flower Kotlin SDK. Flower " -"support on Android is improving, and alongside the Kotlin SDK and code " -"example, there is now also an Android quickstart tutorial." +"Full Flower Next Docker support is here! With the release of Flower 1.9, " +"Flower provides stable Docker images for the Flower SuperLink, the Flower" +" SuperNode, and the Flower `ServerApp`. This set of images enables you to" +" run all Flower components in Docker. Check out the new [how-to " +"guide](https://flower.ai/docs/framework/how-to-run-flower-using-" +"docker.html) to get stated." msgstr "" -#: ../../source/ref-changelog.md:230 +#: ../../source/ref-changelog.md:23 #, fuzzy msgid "" -"**Introduce new end-to-end testing infrastructure** " -"([#1842](https://github.com/adap/flower/pull/1842), " -"[#2071](https://github.com/adap/flower/pull/2071), " -"[#2072](https://github.com/adap/flower/pull/2072), " -"[#2068](https://github.com/adap/flower/pull/2068), " -"[#2067](https://github.com/adap/flower/pull/2067), " -"[#2069](https://github.com/adap/flower/pull/2069), " -"[#2073](https://github.com/adap/flower/pull/2073), " -"[#2070](https://github.com/adap/flower/pull/2070), " -"[#2074](https://github.com/adap/flower/pull/2074), " -"[#2082](https://github.com/adap/flower/pull/2082), " -"[#2084](https://github.com/adap/flower/pull/2084), " -"[#2093](https://github.com/adap/flower/pull/2093), " -"[#2109](https://github.com/adap/flower/pull/2109), " -"[#2095](https://github.com/adap/flower/pull/2095), " -"[#2140](https://github.com/adap/flower/pull/2140), " -"[#2137](https://github.com/adap/flower/pull/2137), " -"[#2165](https://github.com/adap/flower/pull/2165))" -msgstr "" -"**Améliorer l'API (expérimentale) du pilote** " -"([#1663](https://github.com/adap/flower/pull/1663), " -"[#1666](https://github.com/adap/flower/pull/1666), " -"[#1667](https://github.com/adap/flower/pull/1667), " -"[#1664](https://github.com/adap/flower/pull/1664), " -"[#1675](https://github.com/adap/flower/pull/1675), " -"[#1676](https://github.com/adap/flower/pull/1676), " -"[#1693](https://github.com/adap/flower/pull/1693), " -"[#1662](https://github.com/adap/flower/pull/1662), " -"[#1794](https://github.com/adap/flower/pull/1794))" - -#: ../../source/ref-changelog.md:232 -msgid "" -"A new testing infrastructure ensures that new changes stay compatible " -"with existing framework integrations or strategies." +"**Re-architect Flower Next simulation engine** " +"([#3307](https://github.com/adap/flower/pull/3307), " +"[#3355](https://github.com/adap/flower/pull/3355), " +"[#3272](https://github.com/adap/flower/pull/3272), " +"[#3273](https://github.com/adap/flower/pull/3273), " +"[#3417](https://github.com/adap/flower/pull/3417), " +"[#3281](https://github.com/adap/flower/pull/3281), " +"[#3343](https://github.com/adap/flower/pull/3343), " +"[#3326](https://github.com/adap/flower/pull/3326))" msgstr "" +"**Mise à jour de la documentation** " +"([#1629](https://github.com/adap/flower/pull/1629), " +"[#1628](https://github.com/adap/flower/pull/1628), " +"[#1620](https://github.com/adap/flower/pull/1620), " +"[#1618](https://github.com/adap/flower/pull/1618), " +"[#1617](https://github.com/adap/flower/pull/1617), " +"[#1613](https://github.com/adap/flower/pull/1613), " +"[#1614](https://github.com/adap/flower/pull/1614))" -#: ../../source/ref-changelog.md:234 -#, fuzzy -msgid "**Deprecate Python 3.7**" -msgstr "**Créer le PR**" - -#: ../../source/ref-changelog.md:236 +#: ../../source/ref-changelog.md:25 msgid "" -"Since Python 3.7 reached its end of life (EOL) on 2023-06-27, support for" -" Python 3.7 is now deprecated and will be removed in an upcoming release." +"Flower Next simulations now use a new in-memory `Driver` that improves " +"the reliability of simulations, especially in notebook environments. This" +" is a significant step towards a complete overhaul of the Flower Next " +"simulation architecture." msgstr "" -#: ../../source/ref-changelog.md:238 +#: ../../source/ref-changelog.md:27 #, fuzzy msgid "" -"**Add new** `FedTrimmedAvg` **strategy** " -"([#1769](https://github.com/adap/flower/pull/1769), " -"[#1853](https://github.com/adap/flower/pull/1853))" +"**Upgrade simulation engine** " +"([#3354](https://github.com/adap/flower/pull/3354), " +"[#3378](https://github.com/adap/flower/pull/3378), " +"[#3262](https://github.com/adap/flower/pull/3262), " +"[#3435](https://github.com/adap/flower/pull/3435), " +"[#3501](https://github.com/adap/flower/pull/3501), " +"[#3482](https://github.com/adap/flower/pull/3482), " +"[#3494](https://github.com/adap/flower/pull/3494))" msgstr "" -"**Ajouter un nouvel exemple de Federated Analytics avec Pandas** " -"([#1469](https://github.com/adap/flower/pull/1469), " -"[#1535](https://github.com/adap/flower/pull/1535))" +"**Mise à jour de la documentation** " +"([#1629](https://github.com/adap/flower/pull/1629), " +"[#1628](https://github.com/adap/flower/pull/1628), " +"[#1620](https://github.com/adap/flower/pull/1620), " +"[#1618](https://github.com/adap/flower/pull/1618), " +"[#1617](https://github.com/adap/flower/pull/1617), " +"[#1613](https://github.com/adap/flower/pull/1613), " +"[#1614](https://github.com/adap/flower/pull/1614))" -#: ../../source/ref-changelog.md:240 -#, fuzzy +#: ../../source/ref-changelog.md:29 msgid "" -"The new `FedTrimmedAvg` strategy implements Trimmed Mean by [Dong Yin, " -"2018](https://arxiv.org/abs/1803.01498)." +"The Flower Next simulation engine comes with improved and configurable " +"logging. The Ray-based simulation backend in Flower 1.9 was updated to " +"use Ray 2.10." msgstr "" -"La nouvelle stratégie `FedMedian` met en œuvre Federated Median " -"(FedMedian) par [Yin et al., 2018] " -"(https://arxiv.org/pdf/1803.01498v1.pdf)." -#: ../../source/ref-changelog.md:242 +#: ../../source/ref-changelog.md:31 #, fuzzy msgid "" -"**Introduce start_driver** " -"([#1697](https://github.com/adap/flower/pull/1697))" +"**Introduce FedPFT baseline** " +"([#3268](https://github.com/adap/flower/pull/3268))" msgstr "" "**Ajouter une nouvelle stratégie `FedProx`** " "([#1619](https://github.com/adap/flower/pull/1619))" -#: ../../source/ref-changelog.md:244 +#: ../../source/ref-changelog.md:33 msgid "" -"In addition to `start_server` and using the raw Driver API, there is a " -"new `start_driver` function that allows for running `start_server` " -"scripts as a Flower driver with only a single-line code change. Check out" -" the `mt-pytorch` code example to see a working example using " -"`start_driver`." +"FedPFT allows you to perform one-shot Federated Learning by leveraging " +"widely available foundational models, dramatically reducing communication" +" costs while delivering high performing models. This is work led by Mahdi" +" Beitollahi from Huawei Noah's Ark Lab (Montreal, Canada). Read all the " +"details in their paper: \"Parametric Feature Transfer: One-shot Federated" +" Learning with Foundation Models\" " +"([arxiv](https://arxiv.org/abs/2402.01862))" msgstr "" -#: ../../source/ref-changelog.md:246 +#: ../../source/ref-changelog.md:35 #, fuzzy msgid "" -"**Add parameter aggregation to** `mt-pytorch` **code example** " -"([#1785](https://github.com/adap/flower/pull/1785))" +"**Launch additional** `flwr new` **templates for Apple MLX, Hugging Face " +"Transformers, scikit-learn and TensorFlow** " +"([#3291](https://github.com/adap/flower/pull/3291), " +"[#3139](https://github.com/adap/flower/pull/3139), " +"[#3284](https://github.com/adap/flower/pull/3284), " +"[#3251](https://github.com/adap/flower/pull/3251), " +"[#3376](https://github.com/adap/flower/pull/3376), " +"[#3287](https://github.com/adap/flower/pull/3287))" msgstr "" -"**Nouvel exemple de code PyTorch avancé** " -"([#1007](https://github.com/adap/flower/pull/1007))" +"**Nouvel exemple de code MLCube** " +"([#779](https://github.com/adap/flower/pull/779), " +"[#1034](https://github.com/adap/flower/pull/1034), " +"[#1065](https://github.com/adap/flower/pull/1065), " +"[#1090](https://github.com/adap/flower/pull/1090))" -#: ../../source/ref-changelog.md:248 +#: ../../source/ref-changelog.md:37 msgid "" -"The `mt-pytorch` example shows how to aggregate parameters when writing a" -" driver script. The included `driver.py` and `server.py` have been " -"aligned to demonstrate both the low-level way and the high-level way of " -"building server-side logic." +"The `flwr` CLI's `flwr new` command is starting to become everone's " +"favorite way of creating new Flower projects. This release introduces " +"additional `flwr new` templates for Apple MLX, Hugging Face Transformers," +" scikit-learn and TensorFlow. In addition to that, existing templates " +"also received updates." msgstr "" -#: ../../source/ref-changelog.md:250 +#: ../../source/ref-changelog.md:39 #, fuzzy msgid "" -"**Migrate experimental REST API to Starlette** " -"([2171](https://github.com/adap/flower/pull/2171))" +"**Refine** `RecordSet` **API** " +"([#3209](https://github.com/adap/flower/pull/3209), " +"[#3331](https://github.com/adap/flower/pull/3331), " +"[#3334](https://github.com/adap/flower/pull/3334), " +"[#3335](https://github.com/adap/flower/pull/3335), " +"[#3375](https://github.com/adap/flower/pull/3375), " +"[#3368](https://github.com/adap/flower/pull/3368))" msgstr "" -"**Nouvelle stratégie expérimentale TensorBoard** " -"([#789](https://github.com/adap/flower/pull/789))" +"**Mise à jour de la documentation** " +"([#1629](https://github.com/adap/flower/pull/1629), " +"[#1628](https://github.com/adap/flower/pull/1628), " +"[#1620](https://github.com/adap/flower/pull/1620), " +"[#1618](https://github.com/adap/flower/pull/1618), " +"[#1617](https://github.com/adap/flower/pull/1617), " +"[#1613](https://github.com/adap/flower/pull/1613), " +"[#1614](https://github.com/adap/flower/pull/1614))" -#: ../../source/ref-changelog.md:252 +#: ../../source/ref-changelog.md:41 msgid "" -"The (experimental) REST API used to be implemented in " -"[FastAPI](https://fastapi.tiangolo.com/), but it has now been migrated to" -" use [Starlette](https://www.starlette.io/) directly." +"`RecordSet` is part of the Flower Next low-level API preview release. In " +"Flower 1.9, `RecordSet` received a number of usability improvements that " +"make it easier to build `RecordSet`-based `ServerApp`s and `ClientApp`s." msgstr "" -#: ../../source/ref-changelog.md:254 +#: ../../source/ref-changelog.md:43 #, fuzzy msgid "" -"Please note: The REST request-response API is still experimental and will" -" likely change significantly over time." +"**Beautify logging** ([#3379](https://github.com/adap/flower/pull/3379), " +"[#3430](https://github.com/adap/flower/pull/3430), " +"[#3461](https://github.com/adap/flower/pull/3461), " +"[#3360](https://github.com/adap/flower/pull/3360), " +"[#3433](https://github.com/adap/flower/pull/3433))" msgstr "" -"Remarque : l'API REST est encore expérimentale et est susceptible de " -"changer de manière significative au fil du temps." +"Mettre à jour les outils de développement " +"([#1231](https://github.com/adap/flower/pull/1231), " +"[#1276](https://github.com/adap/flower/pull/1276), " +"[#1301](https://github.com/adap/flower/pull/1301), " +"[#1310](https://github.com/adap/flower/pull/1310))" -#: ../../source/ref-changelog.md:256 -#, fuzzy +#: ../../source/ref-changelog.md:45 msgid "" -"**Introduce experimental gRPC request-response API** " -"([#1867](https://github.com/adap/flower/pull/1867), " -"[#1901](https://github.com/adap/flower/pull/1901))" +"Logs received a substantial update. Not only are logs now much nicer to " +"look at, but they are also more configurable." msgstr "" -"**Introduire les enveloppes de confidentialité différentielle (aperçu)** " -"([#1357](https://github.com/adap/flower/pull/1357), " -"[#1460](https://github.com/adap/flower/pull/1460))" -#: ../../source/ref-changelog.md:258 +#: ../../source/ref-changelog.md:47 +#, fuzzy msgid "" -"In addition to the existing gRPC API (based on bidirectional streaming) " -"and the experimental REST API, there is now a new gRPC API that uses a " -"request-response model to communicate with client nodes." +"**Improve reliability** " +"([#3564](https://github.com/adap/flower/pull/3564), " +"[#3561](https://github.com/adap/flower/pull/3561), " +"[#3566](https://github.com/adap/flower/pull/3566), " +"[#3462](https://github.com/adap/flower/pull/3462), " +"[#3225](https://github.com/adap/flower/pull/3225), " +"[#3514](https://github.com/adap/flower/pull/3514), " +"[#3535](https://github.com/adap/flower/pull/3535), " +"[#3372](https://github.com/adap/flower/pull/3372))" msgstr "" +"**Tutoriel amélioré** ([#1468](https://github.com/adap/flower/pull/1468)," +" [#1470](https://github.com/adap/flower/pull/1470), " +"[#1472](https://github.com/adap/flower/pull/1472), " +"[#1473](https://github.com/adap/flower/pull/1473), " +"[#1474](https://github.com/adap/flower/pull/1474), " +"[#1475](https://github.com/adap/flower/pull/1475))" -#: ../../source/ref-changelog.md:260 -#, fuzzy +#: ../../source/ref-changelog.md:49 msgid "" -"Please note: The gRPC request-response API is still experimental and will" -" likely change significantly over time." +"Flower 1.9 includes reliability improvements across many parts of the " +"system. One example is a much improved SuperNode shutdown procedure." msgstr "" -"Remarque : l'API REST est encore expérimentale et est susceptible de " -"changer de manière significative au fil du temps." -#: ../../source/ref-changelog.md:262 +#: ../../source/ref-changelog.md:51 #, fuzzy msgid "" -"**Replace the experimental** `start_client(rest=True)` **with the new** " -"`start_client(transport=\"rest\")` " -"([#1880](https://github.com/adap/flower/pull/1880))" +"**Update Swift and C++ SDKs** " +"([#3321](https://github.com/adap/flower/pull/3321), " +"[#2763](https://github.com/adap/flower/pull/2763))" msgstr "" -"**Initialise** `start_simulation` **avec une liste d'ID de clients** " -"([#860](https://github.com/adap/flower/pull/860))" +"**Exemple de code mis à jour** " +"([#1344](https://github.com/adap/flower/pull/1344), " +"[#1347](https://github.com/adap/flower/pull/1347))" -#: ../../source/ref-changelog.md:264 +#: ../../source/ref-changelog.md:53 msgid "" -"The (experimental) `start_client` argument `rest` was deprecated in " -"favour of a new argument `transport`. `start_client(transport=\"rest\")` " -"will yield the same behaviour as `start_client(rest=True)` did before. " -"All code should migrate to the new argument `transport`. The deprecated " -"argument `rest` will be removed in a future release." +"In the C++ SDK, communication-related code is now separate from main " +"client logic. A new abstract class `Communicator` has been introduced " +"alongside a gRPC implementation of it." msgstr "" -#: ../../source/ref-changelog.md:266 -#, fuzzy +#: ../../source/ref-changelog.md:55 msgid "" -"**Add a new gRPC option** " -"([#2197](https://github.com/adap/flower/pull/2197))" +"**Improve testing, tooling and CI/CD infrastructure** " +"([#3294](https://github.com/adap/flower/pull/3294), " +"[#3282](https://github.com/adap/flower/pull/3282), " +"[#3311](https://github.com/adap/flower/pull/3311), " +"[#2878](https://github.com/adap/flower/pull/2878), " +"[#3333](https://github.com/adap/flower/pull/3333), " +"[#3255](https://github.com/adap/flower/pull/3255), " +"[#3349](https://github.com/adap/flower/pull/3349), " +"[#3400](https://github.com/adap/flower/pull/3400), " +"[#3401](https://github.com/adap/flower/pull/3401), " +"[#3399](https://github.com/adap/flower/pull/3399), " +"[#3346](https://github.com/adap/flower/pull/3346), " +"[#3398](https://github.com/adap/flower/pull/3398), " +"[#3397](https://github.com/adap/flower/pull/3397), " +"[#3347](https://github.com/adap/flower/pull/3347), " +"[#3502](https://github.com/adap/flower/pull/3502), " +"[#3387](https://github.com/adap/flower/pull/3387), " +"[#3542](https://github.com/adap/flower/pull/3542), " +"[#3396](https://github.com/adap/flower/pull/3396), " +"[#3496](https://github.com/adap/flower/pull/3496), " +"[#3465](https://github.com/adap/flower/pull/3465), " +"[#3473](https://github.com/adap/flower/pull/3473), " +"[#3484](https://github.com/adap/flower/pull/3484), " +"[#3521](https://github.com/adap/flower/pull/3521), " +"[#3363](https://github.com/adap/flower/pull/3363), " +"[#3497](https://github.com/adap/flower/pull/3497), " +"[#3464](https://github.com/adap/flower/pull/3464), " +"[#3495](https://github.com/adap/flower/pull/3495), " +"[#3478](https://github.com/adap/flower/pull/3478), " +"[#3271](https://github.com/adap/flower/pull/3271))" msgstr "" -"**Ajouter une nouvelle stratégie `FedProx`** " -"([#1619](https://github.com/adap/flower/pull/1619))" -#: ../../source/ref-changelog.md:268 +#: ../../source/ref-changelog.md:57 msgid "" -"We now start a gRPC server with the `grpc.keepalive_permit_without_calls`" -" option set to 0 by default. This prevents the clients from sending " -"keepalive pings when there is no outstanding stream." +"As always, the Flower tooling, testing, and CI/CD infrastructure has " +"received many updates." msgstr "" -#: ../../source/ref-changelog.md:270 -#, fuzzy +#: ../../source/ref-changelog.md:59 msgid "" -"**Improve example notebooks** " -"([#2005](https://github.com/adap/flower/pull/2005))" +"**Improve documentation** " +"([#3530](https://github.com/adap/flower/pull/3530), " +"[#3539](https://github.com/adap/flower/pull/3539), " +"[#3425](https://github.com/adap/flower/pull/3425), " +"[#3520](https://github.com/adap/flower/pull/3520), " +"[#3286](https://github.com/adap/flower/pull/3286), " +"[#3516](https://github.com/adap/flower/pull/3516), " +"[#3523](https://github.com/adap/flower/pull/3523), " +"[#3545](https://github.com/adap/flower/pull/3545), " +"[#3498](https://github.com/adap/flower/pull/3498), " +"[#3439](https://github.com/adap/flower/pull/3439), " +"[#3440](https://github.com/adap/flower/pull/3440), " +"[#3382](https://github.com/adap/flower/pull/3382), " +"[#3559](https://github.com/adap/flower/pull/3559), " +"[#3432](https://github.com/adap/flower/pull/3432), " +"[#3278](https://github.com/adap/flower/pull/3278), " +"[#3371](https://github.com/adap/flower/pull/3371), " +"[#3519](https://github.com/adap/flower/pull/3519), " +"[#3267](https://github.com/adap/flower/pull/3267), " +"[#3204](https://github.com/adap/flower/pull/3204), " +"[#3274](https://github.com/adap/flower/pull/3274))" +msgstr "" + +#: ../../source/ref-changelog.md:61 +msgid "" +"As always, the Flower documentation has received many updates. Notable " +"new pages include:" msgstr "" -"**Supprimer les stratégies expérimentales** " -"([#1280](https://github.com/adap/flower/pull/1280))" -#: ../../source/ref-changelog.md:272 -#, fuzzy -msgid "There's a new 30min Federated Learning PyTorch tutorial!" -msgstr "Bienvenue au tutoriel sur l'apprentissage fédéré de la fleur !" +#: ../../source/ref-changelog.md:63 +msgid "" +"[How-to upgrate to Flower Next (Flower Next migration " +"guide)](https://flower.ai/docs/framework/how-to-upgrade-to-flower-" +"next.html)" +msgstr "" -#: ../../source/ref-changelog.md:274 +#: ../../source/ref-changelog.md:65 msgid "" -"**Example updates** ([#1772](https://github.com/adap/flower/pull/1772), " -"[#1873](https://github.com/adap/flower/pull/1873), " -"[#1981](https://github.com/adap/flower/pull/1981), " -"[#1988](https://github.com/adap/flower/pull/1988), " -"[#1984](https://github.com/adap/flower/pull/1984), " -"[#1982](https://github.com/adap/flower/pull/1982), " -"[#2112](https://github.com/adap/flower/pull/2112), " -"[#2144](https://github.com/adap/flower/pull/2144), " -"[#2174](https://github.com/adap/flower/pull/2174), " -"[#2225](https://github.com/adap/flower/pull/2225), " -"[#2183](https://github.com/adap/flower/pull/2183))" +"[How-to run Flower using Docker](https://flower.ai/docs/framework/how-to-" +"run-flower-using-docker.html)" msgstr "" -#: ../../source/ref-changelog.md:276 +#: ../../source/ref-changelog.md:67 msgid "" -"Many examples have received significant updates, including simplified " -"advanced-tensorflow and advanced-pytorch examples, improved macOS " -"compatibility of TensorFlow examples, and code examples for simulation. A" -" major upgrade is that all code examples now have a `requirements.txt` " -"(in addition to `pyproject.toml`)." +"[Flower Mods reference](https://flower.ai/docs/framework/ref-" +"api/flwr.client.mod.html#module-flwr.client.mod)" msgstr "" -#: ../../source/ref-changelog.md:278 +#: ../../source/ref-changelog.md:69 #, fuzzy msgid "" -"**General improvements** " -"([#1872](https://github.com/adap/flower/pull/1872), " -"[#1866](https://github.com/adap/flower/pull/1866), " -"[#1884](https://github.com/adap/flower/pull/1884), " -"[#1837](https://github.com/adap/flower/pull/1837), " -"[#1477](https://github.com/adap/flower/pull/1477), " -"[#2171](https://github.com/adap/flower/pull/2171))" +"**General updates to Flower Examples** " +"([#3205](https://github.com/adap/flower/pull/3205), " +"[#3226](https://github.com/adap/flower/pull/3226), " +"[#3211](https://github.com/adap/flower/pull/3211), " +"[#3252](https://github.com/adap/flower/pull/3252), " +"[#3427](https://github.com/adap/flower/pull/3427), " +"[#3410](https://github.com/adap/flower/pull/3410), " +"[#3426](https://github.com/adap/flower/pull/3426), " +"[#3228](https://github.com/adap/flower/pull/3228), " +"[#3342](https://github.com/adap/flower/pull/3342), " +"[#3200](https://github.com/adap/flower/pull/3200), " +"[#3202](https://github.com/adap/flower/pull/3202), " +"[#3394](https://github.com/adap/flower/pull/3394), " +"[#3488](https://github.com/adap/flower/pull/3488), " +"[#3329](https://github.com/adap/flower/pull/3329), " +"[#3526](https://github.com/adap/flower/pull/3526), " +"[#3392](https://github.com/adap/flower/pull/3392), " +"[#3474](https://github.com/adap/flower/pull/3474), " +"[#3269](https://github.com/adap/flower/pull/3269))" msgstr "" "**Mise à jour de la documentation** " -"([#1629](https://github.com/adap/flower/pull/1629), " -"[#1628](https://github.com/adap/flower/pull/1628), " -"[#1620](https://github.com/adap/flower/pull/1620), " -"[#1618](https://github.com/adap/flower/pull/1618), " -"[#1617](https://github.com/adap/flower/pull/1617), " -"[#1613](https://github.com/adap/flower/pull/1613), " -"[#1614](https://github.com/adap/flower/pull/1614))" +"([#1223](https://github.com/adap/flower/pull/1223), " +"[#1209](https://github.com/adap/flower/pull/1209), " +"[#1251](https://github.com/adap/flower/pull/1251), " +"[#1257](https://github.com/adap/flower/pull/1257), " +"[#1267](https://github.com/adap/flower/pull/1267), " +"[#1268](https://github.com/adap/flower/pull/1268), " +"[#1300](https://github.com/adap/flower/pull/1300), " +"[#1304](https://github.com/adap/flower/pull/1304), " +"[#1305](https://github.com/adap/flower/pull/1305), " +"[#1307](https://github.com/adap/flower/pull/1307))" -#: ../../source/ref-changelog.md:284 ../../source/ref-changelog.md:348 -#: ../../source/ref-changelog.md:406 ../../source/ref-changelog.md:475 -#: ../../source/ref-changelog.md:537 -msgid "None" -msgstr "Aucun" +#: ../../source/ref-changelog.md:71 +msgid "As always, Flower code examples have received many updates." +msgstr "" -#: ../../source/ref-changelog.md:286 -msgid "v1.4.0 (2023-04-21)" -msgstr "v1.4.0 (2023-04-21)" +#: ../../source/ref-changelog.md:73 +msgid "" +"**General improvements** " +"([#3532](https://github.com/adap/flower/pull/3532), " +"[#3318](https://github.com/adap/flower/pull/3318), " +"[#3565](https://github.com/adap/flower/pull/3565), " +"[#3296](https://github.com/adap/flower/pull/3296), " +"[#3305](https://github.com/adap/flower/pull/3305), " +"[#3246](https://github.com/adap/flower/pull/3246), " +"[#3224](https://github.com/adap/flower/pull/3224), " +"[#3475](https://github.com/adap/flower/pull/3475), " +"[#3297](https://github.com/adap/flower/pull/3297), " +"[#3317](https://github.com/adap/flower/pull/3317), " +"[#3429](https://github.com/adap/flower/pull/3429), " +"[#3196](https://github.com/adap/flower/pull/3196), " +"[#3534](https://github.com/adap/flower/pull/3534), " +"[#3240](https://github.com/adap/flower/pull/3240), " +"[#3365](https://github.com/adap/flower/pull/3365), " +"[#3407](https://github.com/adap/flower/pull/3407), " +"[#3563](https://github.com/adap/flower/pull/3563), " +"[#3344](https://github.com/adap/flower/pull/3344), " +"[#3330](https://github.com/adap/flower/pull/3330), " +"[#3436](https://github.com/adap/flower/pull/3436), " +"[#3300](https://github.com/adap/flower/pull/3300), " +"[#3327](https://github.com/adap/flower/pull/3327), " +"[#3254](https://github.com/adap/flower/pull/3254), " +"[#3253](https://github.com/adap/flower/pull/3253), " +"[#3419](https://github.com/adap/flower/pull/3419), " +"[#3289](https://github.com/adap/flower/pull/3289), " +"[#3208](https://github.com/adap/flower/pull/3208), " +"[#3245](https://github.com/adap/flower/pull/3245), " +"[#3319](https://github.com/adap/flower/pull/3319), " +"[#3203](https://github.com/adap/flower/pull/3203), " +"[#3423](https://github.com/adap/flower/pull/3423), " +"[#3352](https://github.com/adap/flower/pull/3352), " +"[#3292](https://github.com/adap/flower/pull/3292), " +"[#3261](https://github.com/adap/flower/pull/3261))" +msgstr "" + +#: ../../source/ref-changelog.md:75 ../../source/ref-changelog.md:1058 +msgid "Deprecations" +msgstr "Dépréciations" -#: ../../source/ref-changelog.md:292 +#: ../../source/ref-changelog.md:77 +#, fuzzy +msgid "**Deprecate Python 3.8 support**" +msgstr "**Créer le PR**" + +#: ../../source/ref-changelog.md:79 msgid "" -"`Adam Narozniak`, `Alexander Viala Bellander`, `Charles Beauville`, " -"`Chenyang Ma (Danny)`, `Daniel J. Beutel`, `Edoardo`, `Gautam Jajoo`, " -"`Iacob-Alexandru-Andrei`, `JDRanpariya`, `Jean Charle Yaacoub`, `Kunal " -"Sarkhel`, `L. Jiang`, `Lennart Behme`, `Max Kapsecker`, `Michał`, `Nic " -"Lane`, `Nikolaos Episkopos`, `Ragy`, `Saurav Maheshkar`, `Semo Yang`, " -"`Steve Laskaridis`, `Steven Hé (Sīchàng)`, `Taner Topal`" +"Python 3.8 will stop receiving security fixes in [October " +"2024](https://devguide.python.org/versions/). Support for Python 3.8 is " +"now deprecated and will be removed in an upcoming release." msgstr "" -"`Adam Narozniak`, `Alexander Viala Bellander`, `Charles Beauville`, " -"`Chenyang Ma (Danny)`, `Daniel J. Beutel`, `Edoardo`, `Gautam Jajoo`, " -"`Iacob-Alexandru-Andrei`, `JDRanpariya`, `Jean Charle Yaacoub`, `Kunal " -"Sarkhel`, `L. Jiang`, `Lennart Behme`, `Max Kapsecker`, `Michał`, `Nic " -"Lane`, `Nikolaos Episkopos`, `Ragy`, `Saurav Maheshkar`, `Semo Yang`, " -"`Steve Laskaridis`, `Steven Hé (Sīchàng)`, `Taner Topal`" -#: ../../source/ref-changelog.md:296 +#: ../../source/ref-changelog.md:81 +#, fuzzy msgid "" -"**Introduce support for XGBoost (**`FedXgbNnAvg` **strategy and " -"example)** ([#1694](https://github.com/adap/flower/pull/1694), " -"[#1709](https://github.com/adap/flower/pull/1709), " -"[#1715](https://github.com/adap/flower/pull/1715), " -"[#1717](https://github.com/adap/flower/pull/1717), " -"[#1763](https://github.com/adap/flower/pull/1763), " -"[#1795](https://github.com/adap/flower/pull/1795))" +"**Deprecate (experimental)** `flower-driver-api` **and** `flower-fleet-" +"api` ([#3416](https://github.com/adap/flower/pull/3416), " +"[#3420](https://github.com/adap/flower/pull/3420))" msgstr "" -"**Introduire la prise en charge de XGBoost (**`FedXgbNnAvg` **stratégie " -"et exemple)** ([#1694](https://github.com/adap/flower/pull/1694), " -"[#1709](https://github.com/adap/flower/pull/1709), " -"[#1715](https://github.com/adap/flower/pull/1715), " -"[#1717](https://github.com/adap/flower/pull/1717), " -"[#1763](https://github.com/adap/flower/pull/1763), " -"[#1795](https://github.com/adap/flower/pull/1795))" +"**Rename** `Weights` **to** `NDArrays` " +"([#1258](https://github.com/adap/flower/pull/1258), " +"[#1259](https://github.com/adap/flower/pull/1259))" -#: ../../source/ref-changelog.md:298 +#: ../../source/ref-changelog.md:83 msgid "" -"XGBoost is a tree-based ensemble machine learning algorithm that uses " -"gradient boosting to improve model accuracy. We added a new `FedXgbNnAvg`" -" " -"[strategy](https://github.com/adap/flower/tree/main/src/py/flwr/server/strategy/fedxgb_nn_avg.py)," -" and a [code example](https://github.com/adap/flower/tree/main/examples" -"/xgboost-quickstart) that demonstrates the usage of this new strategy in " -"an XGBoost project." +"Flower 1.9 deprecates the two (experimental) commands `flower-driver-api`" +" and `flower-fleet-api`. Both commands will be removed in an upcoming " +"release. Use `flower-superlink` instead." msgstr "" -"Nous avons ajouté une nouvelle [stratégie] `FedXgbNnAvg` " -"(https://github.com/adap/flower/tree/main/src/py/flwr/server/strategy/fedxgb_nn_avg.py)," -" et un [exemple de code] " -"(https://github.com/adap/flower/tree/main/examples/xgboost-quickstart) " -"qui démontre l'utilisation de cette nouvelle stratégie dans un projet " -"XGBoost." -#: ../../source/ref-changelog.md:300 +#: ../../source/ref-changelog.md:85 +#, fuzzy msgid "" -"**Introduce iOS SDK (preview)** " -"([#1621](https://github.com/adap/flower/pull/1621), " -"[#1764](https://github.com/adap/flower/pull/1764))" +"**Deprecate** `--server` **in favor of** `--superlink` " +"([#3518](https://github.com/adap/flower/pull/3518))" msgstr "" -"**Introduction du SDK iOS (aperçu)** " -"([#1621](https://github.com/adap/flower/pull/1621), " -"[#1764](https://github.com/adap/flower/pull/1764))" +"**Autoriser le passage d'une **instance `Server` à** `start_simulation` " +"([#1281](https://github.com/adap/flower/pull/1281))" -#: ../../source/ref-changelog.md:302 +#: ../../source/ref-changelog.md:87 msgid "" -"This is a major update for anyone wanting to implement Federated Learning" -" on iOS mobile devices. We now have a swift iOS SDK present under " -"[src/swift/flwr](https://github.com/adap/flower/tree/main/src/swift/flwr)" -" that will facilitate greatly the app creating process. To showcase its " -"use, the [iOS " -"example](https://github.com/adap/flower/tree/main/examples/ios) has also " -"been updated!" +"The commands `flower-server-app` and `flower-client-app` should use " +"`--superlink` instead of the now deprecated `--server`. Support for " +"`--server` will be removed in a future release." msgstr "" -"Il s'agit d'une mise à jour majeure pour tous ceux qui souhaitent mettre " -"en œuvre l'apprentissage fédéré sur les appareils mobiles iOS. Nous " -"disposons désormais d'un SDK swift iOS présent sous " -"[src/swift/flwr](https://github.com/adap/flower/tree/main/src/swift/flwr)" -" qui facilitera grandement le processus de création d'applications. Pour " -"présenter son utilisation, l'[exemple " -"iOS](https://github.com/adap/flower/tree/main/examples/ios) a également " -"été mis à jour !" -#: ../../source/ref-changelog.md:304 +#: ../../source/ref-changelog.md:89 ../../source/ref-changelog.md:163 +#: ../../source/ref-changelog.md:238 ../../source/ref-changelog.md:350 +#: ../../source/ref-changelog.md:440 ../../source/ref-changelog.md:504 +#: ../../source/ref-changelog.md:562 ../../source/ref-changelog.md:631 +#: ../../source/ref-changelog.md:693 ../../source/ref-changelog.md:712 +#: ../../source/ref-changelog.md:868 ../../source/ref-changelog.md:939 +#: ../../source/ref-changelog.md:976 ../../source/ref-changelog.md:1019 +msgid "Incompatible changes" +msgstr "Changements incompatibles" + +#: ../../source/ref-changelog.md:91 msgid "" -"**Introduce new \"What is Federated Learning?\" tutorial** " -"([#1657](https://github.com/adap/flower/pull/1657), " -"[#1721](https://github.com/adap/flower/pull/1721))" +"**Replace** `flower-superlink` **CLI option** `--certificates` **with** " +"`--ssl-ca-certfile` **,** `--ssl-certfile` **and** `--ssl-keyfile` " +"([#3512](https://github.com/adap/flower/pull/3512), " +"[#3408](https://github.com/adap/flower/pull/3408))" msgstr "" -"**Introduire un nouveau tutoriel \"Qu'est-ce que l'apprentissage fédéré ?" -" \"** ([#1657](https://github.com/adap/flower/pull/1657), " -"[#1721](https://github.com/adap/flower/pull/1721))" -#: ../../source/ref-changelog.md:306 -#, fuzzy +#: ../../source/ref-changelog.md:93 msgid "" -"A new [entry-level tutorial](https://flower.ai/docs/framework/tutorial-" -"what-is-federated-learning.html) in our documentation explains the basics" -" of Fedetated Learning. It enables anyone who's unfamiliar with Federated" -" Learning to start their journey with Flower. Forward it to anyone who's " -"interested in Federated Learning!" +"SSL-related `flower-superlink` CLI arguments were restructured in an " +"incompatible way. Instead of passing a single `--certificates` flag with " +"three values, you now need to pass three flags (`--ssl-ca-certfile`, " +"`--ssl-certfile` and `--ssl-keyfile`) with one value each. Check out the " +"[SSL connections](https://flower.ai/docs/framework/how-to-enable-ssl-" +"connections.html) documentation page for details." msgstr "" -"Un nouveau [tutoriel d'entrée de gamme] " -"(https://flower.ai/docs/tutorial/Flower-0-What-is-FL.html) dans notre " -"documentation explique les bases de l'apprentissage fédéré. Il permet à " -"tous ceux qui ne connaissent pas l'apprentissage fédéré de commencer leur" -" voyage avec Flower. Fais-le suivre à tous ceux qui s'intéressent à " -"l'apprentissage fédéré !" -#: ../../source/ref-changelog.md:308 +#: ../../source/ref-changelog.md:95 +#, fuzzy msgid "" -"**Introduce new Flower Baseline: FedProx MNIST** " -"([#1513](https://github.com/adap/flower/pull/1513), " -"[#1680](https://github.com/adap/flower/pull/1680), " -"[#1681](https://github.com/adap/flower/pull/1681), " -"[#1679](https://github.com/adap/flower/pull/1679))" +"**Remove SuperLink** `--vce` **option** " +"([#3513](https://github.com/adap/flower/pull/3513))" msgstr "" -"**Introduire une nouvelle fleur Référence : FedProx MNIST** " -"([#1513](https://github.com/adap/flower/pull/1513), " -"[#1680](https://github.com/adap/flower/pull/1680), " -"[#1681](https://github.com/adap/flower/pull/1681), " -"[#1679](https://github.com/adap/flower/pull/1679))" +"**Documentation restructurée** " +"([#1387](https://github.com/adap/flower/pull/1387))" -#: ../../source/ref-changelog.md:310 +#: ../../source/ref-changelog.md:97 msgid "" -"This new baseline replicates the MNIST+CNN task from the paper [Federated" -" Optimization in Heterogeneous Networks (Li et al., " -"2018)](https://arxiv.org/abs/1812.06127). It uses the `FedProx` strategy," -" which aims at making convergence more robust in heterogeneous settings." +"Instead of separately starting a SuperLink and a `ServerApp` for " +"simulation, simulations must now be started using the single `flower-" +"simulation` command." msgstr "" -"Cette nouvelle ligne de base reproduit la tâche MNIST+CNN de l'article " -"[Federated Optimization in Heterogeneous Networks (Li et al., 2018)] " -"(https://arxiv.org/abs/1812.06127). Elle utilise la stratégie `FedProx`, " -"qui vise à rendre la convergence plus robuste dans des contextes " -"hétérogènes." -#: ../../source/ref-changelog.md:312 +#: ../../source/ref-changelog.md:99 +#, fuzzy msgid "" -"**Introduce new Flower Baseline: FedAvg FEMNIST** " -"([#1655](https://github.com/adap/flower/pull/1655))" +"**Merge** `--grpc-rere` **and** `--rest` **SuperLink options** " +"([#3527](https://github.com/adap/flower/pull/3527))" msgstr "" -"**Introduire une nouvelle ligne de base pour les fleurs : FedAvg " -"FEMNIST** ([#1655](https://github.com/adap/flower/pull/1655))" +"**Rename** `rnd` **to** `server_round` " +"([#1321](https://github.com/adap/flower/pull/1321))" -#: ../../source/ref-changelog.md:314 +#: ../../source/ref-changelog.md:101 msgid "" -"This new baseline replicates an experiment evaluating the performance of " -"the FedAvg algorithm on the FEMNIST dataset from the paper [LEAF: A " -"Benchmark for Federated Settings (Caldas et al., " -"2018)](https://arxiv.org/abs/1812.01097)." +"To simplify the usage of `flower-superlink`, previously separate sets of " +"CLI options for gRPC and REST were merged into one unified set of " +"options. Consult the [Flower CLI reference " +"documentation](https://flower.ai/docs/framework/ref-api-cli.html) for " +"details." msgstr "" -"Cette nouvelle ligne de base reproduit une expérience évaluant les " -"performances de l'algorithme FedAvg sur le jeu de données FEMNIST tiré de" -" l'article [LEAF : A Benchmark for Federated Settings (Caldas et al., " -"2018)] (https://arxiv.org/abs/1812.01097)." -#: ../../source/ref-changelog.md:316 -msgid "" -"**Introduce (experimental) REST API** " -"([#1594](https://github.com/adap/flower/pull/1594), " -"[#1690](https://github.com/adap/flower/pull/1690), " -"[#1695](https://github.com/adap/flower/pull/1695), " -"[#1712](https://github.com/adap/flower/pull/1712), " -"[#1802](https://github.com/adap/flower/pull/1802), " -"[#1770](https://github.com/adap/flower/pull/1770), " -"[#1733](https://github.com/adap/flower/pull/1733))" +#: ../../source/ref-changelog.md:103 +#, fuzzy +msgid "v1.8.0 (2024-04-03)" +msgstr "v1.3.0 (2023-02-06)" + +#: ../../source/ref-changelog.md:109 +msgid "" +"`Adam Narozniak`, `Charles Beauville`, `Daniel J. Beutel`, `Daniel Nata " +"Nugraha`, `Danny`, `Gustavo Bertoli`, `Heng Pan`, `Ikko Eltociear " +"Ashimine`, `Jack Cook`, `Javier`, `Raj Parekh`, `Robert Steiner`, " +"`Sebastian van der Voort`, `Taner Topal`, `Yan Gao`, `mohammadnaseri`, " +"`tabdar-khan` " +msgstr "" + +#: ../../source/ref-changelog.md:113 +msgid "" +"**Introduce Flower Next high-level API (stable)** " +"([#3002](https://github.com/adap/flower/pull/3002), " +"[#2934](https://github.com/adap/flower/pull/2934), " +"[#2958](https://github.com/adap/flower/pull/2958), " +"[#3173](https://github.com/adap/flower/pull/3173), " +"[#3174](https://github.com/adap/flower/pull/3174), " +"[#2923](https://github.com/adap/flower/pull/2923), " +"[#2691](https://github.com/adap/flower/pull/2691), " +"[#3079](https://github.com/adap/flower/pull/3079), " +"[#2961](https://github.com/adap/flower/pull/2961), " +"[#2924](https://github.com/adap/flower/pull/2924), " +"[#3166](https://github.com/adap/flower/pull/3166), " +"[#3031](https://github.com/adap/flower/pull/3031), " +"[#3057](https://github.com/adap/flower/pull/3057), " +"[#3000](https://github.com/adap/flower/pull/3000), " +"[#3113](https://github.com/adap/flower/pull/3113), " +"[#2957](https://github.com/adap/flower/pull/2957), " +"[#3183](https://github.com/adap/flower/pull/3183), " +"[#3180](https://github.com/adap/flower/pull/3180), " +"[#3035](https://github.com/adap/flower/pull/3035), " +"[#3189](https://github.com/adap/flower/pull/3189), " +"[#3185](https://github.com/adap/flower/pull/3185), " +"[#3190](https://github.com/adap/flower/pull/3190), " +"[#3191](https://github.com/adap/flower/pull/3191), " +"[#3195](https://github.com/adap/flower/pull/3195), " +"[#3197](https://github.com/adap/flower/pull/3197))" +msgstr "" + +#: ../../source/ref-changelog.md:115 +msgid "" +"The Flower Next high-level API is stable! Flower Next is the future of " +"Flower - all new features (like Flower Mods) will be built on top of it. " +"You can start to migrate your existing projects to Flower Next by using " +"`ServerApp` and `ClientApp` (check out `quickstart-pytorch` or " +"`quickstart-tensorflow`, a detailed migration guide will follow shortly)." +" Flower Next allows you to run multiple projects concurrently (we call " +"this multi-run) and execute the same project in either simulation " +"environments or deployment environments without having to change a single" +" line of code. The best part? It's fully compatible with existing Flower " +"projects that use `Strategy`, `NumPyClient` & co." +msgstr "" + +#: ../../source/ref-changelog.md:117 +#, fuzzy +msgid "" +"**Introduce Flower Next low-level API (preview)** " +"([#3062](https://github.com/adap/flower/pull/3062), " +"[#3034](https://github.com/adap/flower/pull/3034), " +"[#3069](https://github.com/adap/flower/pull/3069))" msgstr "" -"**Introduire l'API REST (expérimentale)** " -"([#1594](https://github.com/adap/flower/pull/1594), " -"[#1690](https://github.com/adap/flower/pull/1690), " -"[#1695](https://github.com/adap/flower/pull/1695), " -"[#1712](https://github.com/adap/flower/pull/1712), " -"[#1802](https://github.com/adap/flower/pull/1802), " -"[#1770](https://github.com/adap/flower/pull/1770), " -"[#1733](https://github.com/adap/flower/pull/1733))" +"**Mettre à jour les exemples de code** " +"([#1291](https://github.com/adap/flower/pull/1291), " +"[#1286](https://github.com/adap/flower/pull/1286), " +"[#1282](https://github.com/adap/flower/pull/1282))" -#: ../../source/ref-changelog.md:318 +#: ../../source/ref-changelog.md:119 msgid "" -"A new REST API has been introduced as an alternative to the gRPC-based " -"communication stack. In this initial version, the REST API only supports " -"anonymous clients." +"In addition to the Flower Next *high-level* API that uses `Strategy`, " +"`NumPyClient` & co, Flower 1.8 also comes with a preview version of the " +"new Flower Next *low-level* API. The low-level API allows for granular " +"control of every aspect of the learning process by sending/receiving " +"individual messages to/from client nodes. The new `ServerApp` supports " +"registering a custom `main` function that allows writing custom training " +"loops for methods like async FL, cyclic training, or federated analytics." +" The new `ClientApp` supports registering `train`, `evaluate` and `query`" +" functions that can access the raw message received from the `ServerApp`." +" New abstractions like `RecordSet`, `Message` and `Context` further " +"enable sending multiple models, multiple sets of config values and " +"metrics, stateful computations on the client node and implementations of " +"custom SMPC protocols, to name just a few." msgstr "" -"Une nouvelle API REST a été introduite comme alternative à la pile de " -"communication basée sur gRPC. Dans cette version initiale, l'API REST ne " -"prend en charge que les clients anonymes." -#: ../../source/ref-changelog.md:320 +#: ../../source/ref-changelog.md:121 +#, fuzzy msgid "" -"Please note: The REST API is still experimental and will likely change " -"significantly over time." +"**Introduce Flower Mods (preview)** " +"([#3054](https://github.com/adap/flower/pull/3054), " +"[#2911](https://github.com/adap/flower/pull/2911), " +"[#3083](https://github.com/adap/flower/pull/3083))" msgstr "" -"Remarque : l'API REST est encore expérimentale et est susceptible de " -"changer de manière significative au fil du temps." +"**Introduire la télémétrie optionnelle** " +"([#1533](https://github.com/adap/flower/pull/1533), " +"[#1544](https://github.com/adap/flower/pull/1544), " +"[#1584](https://github.com/adap/flower/pull/1584))" -#: ../../source/ref-changelog.md:322 +#: ../../source/ref-changelog.md:123 msgid "" -"**Improve the (experimental) Driver API** " -"([#1663](https://github.com/adap/flower/pull/1663), " -"[#1666](https://github.com/adap/flower/pull/1666), " -"[#1667](https://github.com/adap/flower/pull/1667), " -"[#1664](https://github.com/adap/flower/pull/1664), " -"[#1675](https://github.com/adap/flower/pull/1675), " -"[#1676](https://github.com/adap/flower/pull/1676), " -"[#1693](https://github.com/adap/flower/pull/1693), " -"[#1662](https://github.com/adap/flower/pull/1662), " -"[#1794](https://github.com/adap/flower/pull/1794))" +"Flower Modifiers (we call them Mods) can intercept messages and analyze, " +"edit or handle them directly. Mods can be used to develop pluggable " +"modules that work across different projects. Flower 1.8 already includes " +"mods to log the size of a message, the number of parameters sent over the" +" network, differential privacy with fixed clipping and adaptive clipping," +" local differential privacy and secure aggregation protocols SecAgg and " +"SecAgg+. The Flower Mods API is released as a preview, but researchers " +"can already use it to experiment with arbirtrary SMPC protocols." msgstr "" -"**Améliorer l'API (expérimentale) du pilote** " -"([#1663](https://github.com/adap/flower/pull/1663), " -"[#1666](https://github.com/adap/flower/pull/1666), " -"[#1667](https://github.com/adap/flower/pull/1667), " -"[#1664](https://github.com/adap/flower/pull/1664), " -"[#1675](https://github.com/adap/flower/pull/1675), " -"[#1676](https://github.com/adap/flower/pull/1676), " -"[#1693](https://github.com/adap/flower/pull/1693), " -"[#1662](https://github.com/adap/flower/pull/1662), " -"[#1794](https://github.com/adap/flower/pull/1794))" -#: ../../source/ref-changelog.md:324 +#: ../../source/ref-changelog.md:125 +#, fuzzy msgid "" -"The Driver API is still an experimental feature, but this release " -"introduces some major upgrades. One of the main improvements is the " -"introduction of an SQLite database to store server state on disk (instead" -" of in-memory). Another improvement is that tasks (instructions or " -"results) that have been delivered will now be deleted. This greatly " -"improves the memory efficiency of a long-running Flower server." +"**Fine-tune LLMs with LLM FlowerTune** " +"([#3029](https://github.com/adap/flower/pull/3029), " +"[#3089](https://github.com/adap/flower/pull/3089), " +"[#3092](https://github.com/adap/flower/pull/3092), " +"[#3100](https://github.com/adap/flower/pull/3100), " +"[#3114](https://github.com/adap/flower/pull/3114), " +"[#3162](https://github.com/adap/flower/pull/3162), " +"[#3172](https://github.com/adap/flower/pull/3172))" msgstr "" -"L'API du pilote est encore une fonction expérimentale, mais cette version" -" introduit quelques améliorations majeures. L'une des principales " -"améliorations est l'introduction d'une base de données SQLite pour " -"stocker l'état du serveur sur le disque (au lieu de la mémoire). Une " -"autre amélioration est que les tâches (instructions ou résultats) qui ont" -" été livrées seront désormais supprimées, ce qui améliore " -"considérablement l'efficacité de la mémoire d'un serveur Flower " -"fonctionnant depuis longtemps." +"**Tutoriel amélioré** ([#1468](https://github.com/adap/flower/pull/1468)," +" [#1470](https://github.com/adap/flower/pull/1470), " +"[#1472](https://github.com/adap/flower/pull/1472), " +"[#1473](https://github.com/adap/flower/pull/1473), " +"[#1474](https://github.com/adap/flower/pull/1474), " +"[#1475](https://github.com/adap/flower/pull/1475))" -#: ../../source/ref-changelog.md:326 +#: ../../source/ref-changelog.md:127 msgid "" -"**Fix spilling issues related to Ray during simulations** " -"([#1698](https://github.com/adap/flower/pull/1698))" +"We are introducing LLM FlowerTune, an introductory example that " +"demonstrates federated LLM fine-tuning of pre-trained Llama2 models on " +"the Alpaca-GPT4 dataset. The example is built to be easily adapted to use" +" different models and/or datasets. Read our blog post [LLM FlowerTune: " +"Federated LLM Fine-tuning with Flower](https://flower.ai/blog/2024-03-14" +"-llm-flowertune-federated-llm-finetuning-with-flower/) for more details." msgstr "" -"**Répare les problèmes de déversement liés à Ray pendant les " -"simulations** ([#1698](https://github.com/adap/flower/pull/1698))" -#: ../../source/ref-changelog.md:328 +#: ../../source/ref-changelog.md:129 #, fuzzy msgid "" -"While running long simulations, `ray` was sometimes spilling huge amounts" -" of data that would make the training unable to continue. This is now " -"fixed! 🎉" +"**Introduce built-in Differential Privacy (preview)** " +"([#2798](https://github.com/adap/flower/pull/2798), " +"[#2959](https://github.com/adap/flower/pull/2959), " +"[#3038](https://github.com/adap/flower/pull/3038), " +"[#3147](https://github.com/adap/flower/pull/3147), " +"[#2909](https://github.com/adap/flower/pull/2909), " +"[#2893](https://github.com/adap/flower/pull/2893), " +"[#2892](https://github.com/adap/flower/pull/2892), " +"[#3039](https://github.com/adap/flower/pull/3039), " +"[#3074](https://github.com/adap/flower/pull/3074))" msgstr "" -"Lors de l'exécution de longues simulations, `ray` déversait parfois " -"d'énormes quantités de données qui rendaient l'entraînement incapable de " -"continuer. ce problème est maintenant corrigé ! 🎉" +"**([#842](https://github.com/adap/flower/pull/842), " +"[#844](https://github.com/adap/flower/pull/844), " +"[#845](https://github.com/adap/flower/pull/845), " +"[#847](https://github.com/adap/flower/pull/847), " +"[#993](https://github.com/adap/flower/pull/993), " +"[#994](https://github.com/adap/flower/pull/994))" -#: ../../source/ref-changelog.md:330 +#: ../../source/ref-changelog.md:131 msgid "" -"**Add new example using** `TabNet` **and Flower** " -"([#1725](https://github.com/adap/flower/pull/1725))" +"Built-in Differential Privacy is here! Flower supports both central and " +"local differential privacy (DP). Central DP can be configured with either" +" fixed or adaptive clipping. The clipping can happen either on the " +"server-side or the client-side. Local DP does both clipping and noising " +"on the client-side. A new documentation page [explains Differential " +"Privacy approaches](https://flower.ai/docs/framework/explanation-" +"differential-privacy.html) and a new how-to guide describes [how to use " +"the new Differential Privacy components](https://flower.ai/docs/framework" +"/how-to-use-differential-privacy.html) in Flower." msgstr "" -"**Ajouter un nouvel exemple utilisant** `TabNet` **et Flower** " -"([#1725](https://github.com/adap/flower/pull/1725))" -#: ../../source/ref-changelog.md:332 +#: ../../source/ref-changelog.md:133 +#, fuzzy msgid "" -"TabNet is a powerful and flexible framework for training machine learning" -" models on tabular data. We now have a federated example using Flower: " -"[quickstart-tabnet](https://github.com/adap/flower/tree/main/examples" -"/quickstart-tabnet)." +"**Introduce built-in Secure Aggregation (preview)** " +"([#3120](https://github.com/adap/flower/pull/3120), " +"[#3110](https://github.com/adap/flower/pull/3110), " +"[#3108](https://github.com/adap/flower/pull/3108))" msgstr "" -"TabNet est un cadre puissant et flexible pour former des modèles " -"d'apprentissage automatique sur des données tabulaires. Nous avons " -"maintenant un exemple fédéré utilisant Flower : [quickstart-" -"tabnet](https://github.com/adap/flower/tree/main/examples/quickstart-" -"tabnet)." +"**Introduire la télémétrie optionnelle** " +"([#1533](https://github.com/adap/flower/pull/1533), " +"[#1544](https://github.com/adap/flower/pull/1544), " +"[#1584](https://github.com/adap/flower/pull/1584))" -#: ../../source/ref-changelog.md:334 +#: ../../source/ref-changelog.md:135 msgid "" -"**Add new how-to guide for monitoring simulations** " -"([#1649](https://github.com/adap/flower/pull/1649))" +"Built-in Secure Aggregation is here! Flower now supports different secure" +" aggregation protocols out-of-the-box. The best part? You can add secure " +"aggregation to your Flower projects with only a few lines of code. In " +"this initial release, we inlcude support for SecAgg and SecAgg+, but more" +" protocols will be implemented shortly. We'll also add detailed docs that" +" explain secure aggregation and how to use it in Flower. You can already " +"check out the new code example that shows how to use Flower to easily " +"combine Federated Learning, Differential Privacy and Secure Aggregation " +"in the same project." msgstr "" -"**Ajouter un nouveau guide pratique pour le suivi des simulations** " -"([#1649](https://github.com/adap/flower/pull/1649))" -#: ../../source/ref-changelog.md:336 +#: ../../source/ref-changelog.md:137 +#, fuzzy msgid "" -"We now have a documentation guide to help users monitor their performance" -" during simulations." +"**Introduce** `flwr` **CLI (preview)** " +"([#2942](https://github.com/adap/flower/pull/2942), " +"[#3055](https://github.com/adap/flower/pull/3055), " +"[#3111](https://github.com/adap/flower/pull/3111), " +"[#3130](https://github.com/adap/flower/pull/3130), " +"[#3136](https://github.com/adap/flower/pull/3136), " +"[#3094](https://github.com/adap/flower/pull/3094), " +"[#3059](https://github.com/adap/flower/pull/3059), " +"[#3049](https://github.com/adap/flower/pull/3049), " +"[#3142](https://github.com/adap/flower/pull/3142))" msgstr "" -"Nous avons maintenant un guide de documentation pour aider les " -"utilisateurs à surveiller leurs performances pendant les simulations." +"**Mise à jour de la documentation** " +"([#1629](https://github.com/adap/flower/pull/1629), " +"[#1628](https://github.com/adap/flower/pull/1628), " +"[#1620](https://github.com/adap/flower/pull/1620), " +"[#1618](https://github.com/adap/flower/pull/1618), " +"[#1617](https://github.com/adap/flower/pull/1617), " +"[#1613](https://github.com/adap/flower/pull/1613), " +"[#1614](https://github.com/adap/flower/pull/1614))" -#: ../../source/ref-changelog.md:338 +#: ../../source/ref-changelog.md:139 msgid "" -"**Add training metrics to** `History` **object during simulations** " -"([#1696](https://github.com/adap/flower/pull/1696))" +"A new `flwr` CLI command allows creating new Flower projects (`flwr new`)" +" and then running them using the Simulation Engine (`flwr run`)." msgstr "" -"**Ajouter des mesures de formation à** `History` **objet pendant les " -"simulations** ([#1696](https://github.com/adap/flower/pull/1696))" -#: ../../source/ref-changelog.md:340 +#: ../../source/ref-changelog.md:141 +#, fuzzy msgid "" -"The `fit_metrics_aggregation_fn` can be used to aggregate training " -"metrics, but previous releases did not save the results in the `History` " -"object. This is now the case!" +"**Introduce Flower Next Simulation Engine** " +"([#3024](https://github.com/adap/flower/pull/3024), " +"[#3061](https://github.com/adap/flower/pull/3061), " +"[#2997](https://github.com/adap/flower/pull/2997), " +"[#2783](https://github.com/adap/flower/pull/2783), " +"[#3184](https://github.com/adap/flower/pull/3184), " +"[#3075](https://github.com/adap/flower/pull/3075), " +"[#3047](https://github.com/adap/flower/pull/3047), " +"[#2998](https://github.com/adap/flower/pull/2998), " +"[#3009](https://github.com/adap/flower/pull/3009), " +"[#3008](https://github.com/adap/flower/pull/3008))" msgstr "" -"La fonction `fit_metrics_aggregation_fn` peut être utilisée pour agréger " -"les mesures d'entraînement, mais les versions précédentes " -"n'enregistraient pas les résultats dans l'objet `History`. c'est " -"désormais le cas !" +"**Introduire l'API REST (expérimentale)** " +"([#1594](https://github.com/adap/flower/pull/1594), " +"[#1690](https://github.com/adap/flower/pull/1690), " +"[#1695](https://github.com/adap/flower/pull/1695), " +"[#1712](https://github.com/adap/flower/pull/1712), " +"[#1802](https://github.com/adap/flower/pull/1802), " +"[#1770](https://github.com/adap/flower/pull/1770), " +"[#1733](https://github.com/adap/flower/pull/1733))" -#: ../../source/ref-changelog.md:342 +#: ../../source/ref-changelog.md:143 msgid "" -"**General improvements** " -"([#1659](https://github.com/adap/flower/pull/1659), " -"[#1646](https://github.com/adap/flower/pull/1646), " -"[#1647](https://github.com/adap/flower/pull/1647), " -"[#1471](https://github.com/adap/flower/pull/1471), " -"[#1648](https://github.com/adap/flower/pull/1648), " -"[#1651](https://github.com/adap/flower/pull/1651), " -"[#1652](https://github.com/adap/flower/pull/1652), " -"[#1653](https://github.com/adap/flower/pull/1653), " -"[#1659](https://github.com/adap/flower/pull/1659), " -"[#1665](https://github.com/adap/flower/pull/1665), " -"[#1670](https://github.com/adap/flower/pull/1670), " -"[#1672](https://github.com/adap/flower/pull/1672), " -"[#1677](https://github.com/adap/flower/pull/1677), " -"[#1684](https://github.com/adap/flower/pull/1684), " -"[#1683](https://github.com/adap/flower/pull/1683), " -"[#1686](https://github.com/adap/flower/pull/1686), " -"[#1682](https://github.com/adap/flower/pull/1682), " -"[#1685](https://github.com/adap/flower/pull/1685), " -"[#1692](https://github.com/adap/flower/pull/1692), " -"[#1705](https://github.com/adap/flower/pull/1705), " -"[#1708](https://github.com/adap/flower/pull/1708), " -"[#1711](https://github.com/adap/flower/pull/1711), " -"[#1713](https://github.com/adap/flower/pull/1713), " -"[#1714](https://github.com/adap/flower/pull/1714), " -"[#1718](https://github.com/adap/flower/pull/1718), " -"[#1716](https://github.com/adap/flower/pull/1716), " -"[#1723](https://github.com/adap/flower/pull/1723), " -"[#1735](https://github.com/adap/flower/pull/1735), " -"[#1678](https://github.com/adap/flower/pull/1678), " -"[#1750](https://github.com/adap/flower/pull/1750), " -"[#1753](https://github.com/adap/flower/pull/1753), " -"[#1736](https://github.com/adap/flower/pull/1736), " -"[#1766](https://github.com/adap/flower/pull/1766), " -"[#1760](https://github.com/adap/flower/pull/1760), " -"[#1775](https://github.com/adap/flower/pull/1775), " -"[#1776](https://github.com/adap/flower/pull/1776), " -"[#1777](https://github.com/adap/flower/pull/1777), " -"[#1779](https://github.com/adap/flower/pull/1779), " -"[#1784](https://github.com/adap/flower/pull/1784), " -"[#1773](https://github.com/adap/flower/pull/1773), " -"[#1755](https://github.com/adap/flower/pull/1755), " -"[#1789](https://github.com/adap/flower/pull/1789), " -"[#1788](https://github.com/adap/flower/pull/1788), " -"[#1798](https://github.com/adap/flower/pull/1798), " -"[#1799](https://github.com/adap/flower/pull/1799), " -"[#1739](https://github.com/adap/flower/pull/1739), " -"[#1800](https://github.com/adap/flower/pull/1800), " -"[#1804](https://github.com/adap/flower/pull/1804), " -"[#1805](https://github.com/adap/flower/pull/1805))" +"The Flower Simulation Engine can now run Flower Next projects. For " +"notebook environments, there's also a new `run_simulation` function that " +"can run `ServerApp` and `ClientApp`." msgstr "" -"**General improvements** " -"([#1659](https://github.com/adap/flower/pull/1659), " -"[#1646](https://github.com/adap/flower/pull/1646), " -"[#1647](https://github.com/adap/flower/pull/1647), " -"[#1471](https://github.com/adap/flower/pull/1471), " -"[#1648](https://github.com/adap/flower/pull/1648), " -"[#1651](https://github.com/adap/flower/pull/1651), " -"[#1652](https://github.com/adap/flower/pull/1652), " -"[#1653](https://github.com/adap/flower/pull/1653), " -"[#1659](https://github.com/adap/flower/pull/1659), " -"[#1665](https://github.com/adap/flower/pull/1665), " -"[#1670](https://github.com/adap/flower/pull/1670), " -"[#1672](https://github.com/adap/flower/pull/1672), " -"[#1677](https://github.com/adap/flower/pull/1677), " -"[#1684](https://github.com/adap/flower/pull/1684), " -"[#1683](https://github.com/adap/flower/pull/1683), " -"[#1686](https://github.com/adap/flower/pull/1686), " -"[#1682](https://github.com/adap/flower/pull/1682), " -"[#1685](https://github.com/adap/flower/pull/1685), " -"[#1692](https://github.com/adap/flower/pull/1692), " -"[#1705](https://github.com/ada" -#: ../../source/ref-changelog.md:350 -msgid "v1.3.0 (2023-02-06)" -msgstr "v1.3.0 (2023-02-06)" +#: ../../source/ref-changelog.md:145 +#, fuzzy +msgid "" +"**Handle SuperNode connection errors** " +"([#2969](https://github.com/adap/flower/pull/2969))" +msgstr "" +"**Ajouter une nouvelle stratégie `FedProx`** " +"([#1619](https://github.com/adap/flower/pull/1619))" -#: ../../source/ref-changelog.md:356 +#: ../../source/ref-changelog.md:147 msgid "" -"`Adam Narozniak`, `Alexander Viala Bellander`, `Charles Beauville`, " -"`Daniel J. Beutel`, `JDRanpariya`, `Lennart Behme`, `Taner Topal`" +"A SuperNode will now try to reconnect indefinitely to the SuperLink in " +"case of connection errors. The arguments `--max-retries` and `--max-wait-" +"time` can now be passed to the `flower-client-app` command. `--max-" +"retries` will define the number of tentatives the client should make " +"before it gives up trying to reconnect to the SuperLink, and, `--max-" +"wait-time` defines the time before the SuperNode gives up trying to " +"reconnect to the SuperLink." msgstr "" -"`Adam Narozniak`, `Alexander Viala Bellander`, `Charles Beauville`, " -"`Daniel J. Beutel`, `JDRanpariya`, `Lennart Behme`, `Taner Topal`" -#: ../../source/ref-changelog.md:360 +#: ../../source/ref-changelog.md:149 +#, fuzzy msgid "" -"**Add support for** `workload_id` **and** `group_id` **in Driver API** " -"([#1595](https://github.com/adap/flower/pull/1595))" +"**General updates to Flower Baselines** " +"([#2904](https://github.com/adap/flower/pull/2904), " +"[#2482](https://github.com/adap/flower/pull/2482), " +"[#2985](https://github.com/adap/flower/pull/2985), " +"[#2968](https://github.com/adap/flower/pull/2968))" msgstr "" -"**Ajouter la prise en charge de** `workload_id` **et** `group_id` **dans " -"l'API du pilote** ([#1595](https://github.com/adap/flower/pull/1595))" +"**Introduire une nouvelle fleur Référence : FedProx MNIST** " +"([#1513](https://github.com/adap/flower/pull/1513), " +"[#1680](https://github.com/adap/flower/pull/1680), " +"[#1681](https://github.com/adap/flower/pull/1681), " +"[#1679](https://github.com/adap/flower/pull/1679))" -#: ../../source/ref-changelog.md:362 +#: ../../source/ref-changelog.md:151 msgid "" -"The (experimental) Driver API now supports a `workload_id` that can be " -"used to identify which workload a task belongs to. It also supports a new" -" `group_id` that can be used, for example, to indicate the current " -"training round. Both the `workload_id` and `group_id` enable client nodes" -" to decide whether they want to handle a task or not." +"There's a new [FedStar](https://flower.ai/docs/baselines/fedstar.html) " +"baseline. Several other baselined have been updated as well." msgstr "" -"L'API (expérimentale) Driver prend désormais en charge un `workload_id` " -"qui peut être utilisé pour identifier la charge de travail à laquelle une" -" tâche appartient. Elle prend également en charge un nouveau `group_id` " -"qui peut être utilisé, par exemple, pour indiquer le cycle de formation " -"en cours. Le `workload_id` et le `group_id` permettent tous deux aux " -"nœuds clients de décider s'ils veulent traiter une tâche ou non." -#: ../../source/ref-changelog.md:364 +#: ../../source/ref-changelog.md:153 msgid "" -"**Make Driver API and Fleet API address configurable** " -"([#1637](https://github.com/adap/flower/pull/1637))" +"**Improve documentation and translations** " +"([#3050](https://github.com/adap/flower/pull/3050), " +"[#3044](https://github.com/adap/flower/pull/3044), " +"[#3043](https://github.com/adap/flower/pull/3043), " +"[#2986](https://github.com/adap/flower/pull/2986), " +"[#3041](https://github.com/adap/flower/pull/3041), " +"[#3046](https://github.com/adap/flower/pull/3046), " +"[#3042](https://github.com/adap/flower/pull/3042), " +"[#2978](https://github.com/adap/flower/pull/2978), " +"[#2952](https://github.com/adap/flower/pull/2952), " +"[#3167](https://github.com/adap/flower/pull/3167), " +"[#2953](https://github.com/adap/flower/pull/2953), " +"[#3045](https://github.com/adap/flower/pull/3045), " +"[#2654](https://github.com/adap/flower/pull/2654), " +"[#3082](https://github.com/adap/flower/pull/3082), " +"[#2990](https://github.com/adap/flower/pull/2990), " +"[#2989](https://github.com/adap/flower/pull/2989))" msgstr "" -"**Faire en sorte que l'adresse de l'API du conducteur et de l'API de la " -"flotte soit configurable** " -"([#1637](https://github.com/adap/flower/pull/1637))" -#: ../../source/ref-changelog.md:366 +#: ../../source/ref-changelog.md:155 msgid "" -"The (experimental) long-running Flower server (Driver API and Fleet API) " -"can now configure the server address of both Driver API (via `--driver-" -"api-address`) and Fleet API (via `--fleet-api-address`) when starting:" +"As usual, we merged many smaller and larger improvements to the " +"documentation. A special thank you goes to [Sebastian van der " +"Voort](https://github.com/svdvoort) for landing a big documentation PR!" msgstr "" -"Le serveur Flower (expérimental) de longue durée (Driver API et Fleet " -"API) peut maintenant configurer l'adresse du serveur de Driver API (via " -"`--driver-api-address`) et de Fleet API (via `--fleet-api-address`) lors " -"de son démarrage :" -#: ../../source/ref-changelog.md:368 +#: ../../source/ref-changelog.md:157 #, fuzzy msgid "" -"`flower-server --driver-api-address \"0.0.0.0:8081\" --fleet-api-address " -"\"0.0.0.0:8086\"`" +"**General updates to Flower Examples** " +"([3134](https://github.com/adap/flower/pull/3134), " +"[2996](https://github.com/adap/flower/pull/2996), " +"[2930](https://github.com/adap/flower/pull/2930), " +"[2967](https://github.com/adap/flower/pull/2967), " +"[2467](https://github.com/adap/flower/pull/2467), " +"[2910](https://github.com/adap/flower/pull/2910), " +"[#2918](https://github.com/adap/flower/pull/2918), " +"[#2773](https://github.com/adap/flower/pull/2773), " +"[#3063](https://github.com/adap/flower/pull/3063), " +"[#3116](https://github.com/adap/flower/pull/3116), " +"[#3117](https://github.com/adap/flower/pull/3117))" msgstr "" -"``flower-superlink --driver-api-address \"0.0.0.0:8081\" --fleet-api-" -"address \"0.0.0.0:8086\" ``" - -#: ../../source/ref-changelog.md:370 -msgid "Both IPv4 and IPv6 addresses are supported." -msgstr "Les adresses IPv4 et IPv6 sont toutes deux prises en charge." +"**Documentation mise à jour** " +"([#1494](https://github.com/adap/flower/pull/1494), " +"[#1496](https://github.com/adap/flower/pull/1496), " +"[#1500](https://github.com/adap/flower/pull/1500), " +"[#1503](https://github.com/adap/flower/pull/1503), " +"[#1505](https://github.com/adap/flower/pull/1505), " +"[#1524](https://github.com/adap/flower/pull/1524), " +"[#1518](https://github.com/adap/flower/pull/1518), " +"[#1519](https://github.com/adap/flower/pull/1519), " +"[#1515](https://github.com/adap/flower/pull/1515))" -#: ../../source/ref-changelog.md:372 +#: ../../source/ref-changelog.md:159 msgid "" -"**Add new example of Federated Learning using fastai and Flower** " -"([#1598](https://github.com/adap/flower/pull/1598))" +"Two new examples show federated training of a Vision Transformer (ViT) " +"and federated learning in a medical context using the popular MONAI " +"library. `quickstart-pytorch` and `quickstart-tensorflow` demonstrate the" +" new Flower Next `ServerApp` and `ClientApp`. Many other examples " +"received considerable updates as well." msgstr "" -"**Ajouter un nouvel exemple d'apprentissage fédéré utilisant fastai et " -"Flower** ([#1598](https://github.com/adap/flower/pull/1598))" -#: ../../source/ref-changelog.md:374 +#: ../../source/ref-changelog.md:161 msgid "" -"A new code example (`quickstart-fastai`) demonstrates federated learning " -"with [fastai](https://www.fast.ai/) and Flower. You can find it here: " -"[quickstart-fastai](https://github.com/adap/flower/tree/main/examples" -"/quickstart-fastai)." -msgstr "" -"Un nouvel exemple de code (`quickstart-fastai`) démontre l'apprentissage " -"fédéré avec [fastai](https://www.fast.ai/) et Flower. Tu peux le trouver " -"ici : [quickstart-" -"fastai](https://github.com/adap/flower/tree/main/examples/quickstart-" -"fastai)." +"**General improvements** " +"([#3171](https://github.com/adap/flower/pull/3171), " +"[3099](https://github.com/adap/flower/pull/3099), " +"[3003](https://github.com/adap/flower/pull/3003), " +"[3145](https://github.com/adap/flower/pull/3145), " +"[3017](https://github.com/adap/flower/pull/3017), " +"[3085](https://github.com/adap/flower/pull/3085), " +"[3012](https://github.com/adap/flower/pull/3012), " +"[3119](https://github.com/adap/flower/pull/3119), " +"[2991](https://github.com/adap/flower/pull/2991), " +"[2970](https://github.com/adap/flower/pull/2970), " +"[2980](https://github.com/adap/flower/pull/2980), " +"[3086](https://github.com/adap/flower/pull/3086), " +"[2932](https://github.com/adap/flower/pull/2932), " +"[2928](https://github.com/adap/flower/pull/2928), " +"[2941](https://github.com/adap/flower/pull/2941), " +"[2933](https://github.com/adap/flower/pull/2933), " +"[3181](https://github.com/adap/flower/pull/3181), " +"[2973](https://github.com/adap/flower/pull/2973), " +"[2992](https://github.com/adap/flower/pull/2992), " +"[2915](https://github.com/adap/flower/pull/2915), " +"[3040](https://github.com/adap/flower/pull/3040), " +"[3022](https://github.com/adap/flower/pull/3022), " +"[3032](https://github.com/adap/flower/pull/3032), " +"[2902](https://github.com/adap/flower/pull/2902), " +"[2931](https://github.com/adap/flower/pull/2931), " +"[3005](https://github.com/adap/flower/pull/3005), " +"[3132](https://github.com/adap/flower/pull/3132), " +"[3115](https://github.com/adap/flower/pull/3115), " +"[2944](https://github.com/adap/flower/pull/2944), " +"[3064](https://github.com/adap/flower/pull/3064), " +"[3106](https://github.com/adap/flower/pull/3106), " +"[2974](https://github.com/adap/flower/pull/2974), " +"[3178](https://github.com/adap/flower/pull/3178), " +"[2993](https://github.com/adap/flower/pull/2993), " +"[3186](https://github.com/adap/flower/pull/3186), " +"[3091](https://github.com/adap/flower/pull/3091), " +"[3125](https://github.com/adap/flower/pull/3125), " +"[3093](https://github.com/adap/flower/pull/3093), " +"[3013](https://github.com/adap/flower/pull/3013), " +"[3033](https://github.com/adap/flower/pull/3033), " +"[3133](https://github.com/adap/flower/pull/3133), " +"[3068](https://github.com/adap/flower/pull/3068), " +"[2916](https://github.com/adap/flower/pull/2916), " +"[2975](https://github.com/adap/flower/pull/2975), " +"[2984](https://github.com/adap/flower/pull/2984), " +"[2846](https://github.com/adap/flower/pull/2846), " +"[3077](https://github.com/adap/flower/pull/3077), " +"[3143](https://github.com/adap/flower/pull/3143), " +"[2921](https://github.com/adap/flower/pull/2921), " +"[3101](https://github.com/adap/flower/pull/3101), " +"[2927](https://github.com/adap/flower/pull/2927), " +"[2995](https://github.com/adap/flower/pull/2995), " +"[2972](https://github.com/adap/flower/pull/2972), " +"[2912](https://github.com/adap/flower/pull/2912), " +"[3065](https://github.com/adap/flower/pull/3065), " +"[3028](https://github.com/adap/flower/pull/3028), " +"[2922](https://github.com/adap/flower/pull/2922), " +"[2982](https://github.com/adap/flower/pull/2982), " +"[2914](https://github.com/adap/flower/pull/2914), " +"[3179](https://github.com/adap/flower/pull/3179), " +"[3080](https://github.com/adap/flower/pull/3080), " +"[2994](https://github.com/adap/flower/pull/2994), " +"[3187](https://github.com/adap/flower/pull/3187), " +"[2926](https://github.com/adap/flower/pull/2926), " +"[3018](https://github.com/adap/flower/pull/3018), " +"[3144](https://github.com/adap/flower/pull/3144), " +"[3011](https://github.com/adap/flower/pull/3011), " +"[#3152](https://github.com/adap/flower/pull/3152), " +"[#2836](https://github.com/adap/flower/pull/2836), " +"[#2929](https://github.com/adap/flower/pull/2929), " +"[#2943](https://github.com/adap/flower/pull/2943), " +"[#2955](https://github.com/adap/flower/pull/2955), " +"[#2954](https://github.com/adap/flower/pull/2954))" +msgstr "" + +#: ../../source/ref-changelog.md:165 ../../source/ref-changelog.md:442 +#: ../../source/ref-changelog.md:506 ../../source/ref-changelog.md:564 +#: ../../source/ref-changelog.md:633 ../../source/ref-changelog.md:695 +msgid "None" +msgstr "Aucun" -#: ../../source/ref-changelog.md:376 +#: ../../source/ref-changelog.md:167 +#, fuzzy +msgid "v1.7.0 (2024-02-05)" +msgstr "v1.3.0 (2023-02-06)" + +#: ../../source/ref-changelog.md:173 msgid "" -"**Make Android example compatible with** `flwr >= 1.0.0` **and the latest" -" versions of Android** " -"([#1603](https://github.com/adap/flower/pull/1603))" +"`Aasheesh Singh`, `Adam Narozniak`, `Aml Hassan Esmil`, `Charles " +"Beauville`, `Daniel J. Beutel`, `Daniel Nata Nugraha`, `Edoardo " +"Gabrielli`, `Gustavo Bertoli`, `HelinLin`, `Heng Pan`, `Javier`, `M S " +"Chaitanya Kumar`, `Mohammad Naseri`, `Nikos Vlachakis`, `Pritam Neog`, " +"`Robert Kuska`, `Robert Steiner`, `Taner Topal`, `Yahia Salaheldin " +"Shaaban`, `Yan Gao`, `Yasar Abbas` " msgstr "" -"**Rendre l'exemple Android compatible avec** `flwr >= 1.0.0` **et les " -"dernières versions d'Android** " -"([#1603](https://github.com/adap/flower/pull/1603))" -#: ../../source/ref-changelog.md:378 +#: ../../source/ref-changelog.md:177 #, fuzzy msgid "" -"The Android code example has received a substantial update: the project " -"is compatible with Flower 1.0 (and later), the UI received a full " -"refresh, and the project is updated to be compatible with newer Android " -"tooling." +"**Introduce stateful clients (experimental)** " +"([#2770](https://github.com/adap/flower/pull/2770), " +"[#2686](https://github.com/adap/flower/pull/2686), " +"[#2696](https://github.com/adap/flower/pull/2696), " +"[#2643](https://github.com/adap/flower/pull/2643), " +"[#2769](https://github.com/adap/flower/pull/2769))" msgstr "" -"L'exemple de code Android a reçu une mise à jour substantielle : le " -"projet est compatible avec Flower 1.0 et les versions ultérieures, " -"l'interface utilisateur a reçu un rafraîchissement complet, et le projet " -"est mis à jour pour être compatible avec les outils Android les plus " -"récents." +"**([#842](https://github.com/adap/flower/pull/842), " +"[#844](https://github.com/adap/flower/pull/844), " +"[#845](https://github.com/adap/flower/pull/845), " +"[#847](https://github.com/adap/flower/pull/847), " +"[#993](https://github.com/adap/flower/pull/993), " +"[#994](https://github.com/adap/flower/pull/994))" -#: ../../source/ref-changelog.md:380 +#: ../../source/ref-changelog.md:179 msgid "" -"**Add new `FedProx` strategy** " -"([#1619](https://github.com/adap/flower/pull/1619))" +"Subclasses of `Client` and `NumPyClient` can now store local state that " +"remains on the client. Let's start with the highlight first: this new " +"feature is compatible with both simulated clients (via " +"`start_simulation`) and networked clients (via `start_client`). It's also" +" the first preview of new abstractions like `Context` and `RecordSet`. " +"Clients can access state of type `RecordSet` via `state: RecordSet = " +"self.context.state`. Changes to this `RecordSet` are preserved across " +"different rounds of execution to enable stateful computations in a " +"unified way across simulation and deployment." msgstr "" -"**Ajouter une nouvelle stratégie `FedProx`** " -"([#1619](https://github.com/adap/flower/pull/1619))" -#: ../../source/ref-changelog.md:382 +#: ../../source/ref-changelog.md:181 +#, fuzzy msgid "" -"This " -"[strategy](https://github.com/adap/flower/blob/main/src/py/flwr/server/strategy/fedprox.py)" -" is almost identical to " -"[`FedAvg`](https://github.com/adap/flower/blob/main/src/py/flwr/server/strategy/fedavg.py)," -" but helps users replicate what is described in this " -"[paper](https://arxiv.org/abs/1812.06127). It essentially adds a " -"parameter called `proximal_mu` to regularize the local models with " -"respect to the global models." +"**Improve performance** " +"([#2293](https://github.com/adap/flower/pull/2293))" msgstr "" -"Cette " -"[stratégie](https://github.com/adap/flower/blob/main/src/py/flwr/server/strategy/fedprox.py)" -" est presque identique à " -"[`FedAvg`](https://github.com/adap/flower/blob/main/src/py/flwr/server/strategy/fedavg.py)," -" mais aide les utilisateurs à reproduire ce qui est décrit dans cet " -"[article](https://arxiv.org/abs/1812.06127). Elle ajoute essentiellement " -"un paramètre appelé `proximal_mu` pour régulariser les modèles locaux par" -" rapport aux modèles globaux." +"**Supprimer les stratégies expérimentales** " +"([#1280](https://github.com/adap/flower/pull/1280))" -#: ../../source/ref-changelog.md:384 +#: ../../source/ref-changelog.md:183 msgid "" -"**Add new metrics to telemetry events** " -"([#1640](https://github.com/adap/flower/pull/1640))" +"Flower is faster than ever. All `FedAvg`-derived strategies now use in-" +"place aggregation to reduce memory consumption. The Flower client " +"serialization/deserialization has been rewritten from the ground up, " +"which results in significant speedups, especially when the client-side " +"training time is short." msgstr "" -"**Ajouter de nouvelles métriques aux événements de télémétrie** " -"([#1640](https://github.com/adap/flower/pull/1640))" -#: ../../source/ref-changelog.md:386 +#: ../../source/ref-changelog.md:185 +#, fuzzy msgid "" -"An updated event structure allows, for example, the clustering of events " -"within the same workload." +"**Support Federated Learning with Apple MLX and Flower** " +"([#2693](https://github.com/adap/flower/pull/2693))" msgstr "" -"Une structure d'événements mise à jour permet, par exemple, de regrouper " -"des événements au sein d'une même charge de travail." +"**Ajouter un nouvel exemple d'apprentissage fédéré utilisant fastai et " +"Flower** ([#1598](https://github.com/adap/flower/pull/1598))" -#: ../../source/ref-changelog.md:388 +#: ../../source/ref-changelog.md:187 msgid "" -"**Add new custom strategy tutorial section** " -"[#1623](https://github.com/adap/flower/pull/1623)" +"Flower has official support for federated learning using [Apple " +"MLX](https://ml-explore.github.io/mlx) via the new `quickstart-mlx` code " +"example." msgstr "" -"**Ajouter une nouvelle section de tutoriel sur les stratégies " -"personnalisées** [#1623](https://github.com/adap/flower/pull/1623)" -#: ../../source/ref-changelog.md:390 +#: ../../source/ref-changelog.md:189 #, fuzzy msgid "" -"The Flower tutorial now has a new section that covers implementing a " -"custom strategy from scratch: [Open in " -"Colab](https://colab.research.google.com/github/adap/flower/blob/main/doc/source" -"/tutorial-build-a-strategy-from-scratch-pytorch.ipynb)" +"**Introduce new XGBoost cyclic strategy** " +"([#2666](https://github.com/adap/flower/pull/2666), " +"[#2668](https://github.com/adap/flower/pull/2668))" msgstr "" -"Le tutoriel sur les fleurs comporte désormais une nouvelle section qui " -"traite de la mise en œuvre d'une stratégie personnalisée à partir de zéro" -" : [Ouvrir dans " -"Colab](https://colab.research.google.com/github/adap/flower/blob/main/doc/source/tutorial/Flower-3-Building-a" -"-Strategy-PyTorch.ipynb)" +"**Introduction du SDK iOS (aperçu)** " +"([#1621](https://github.com/adap/flower/pull/1621), " +"[#1764](https://github.com/adap/flower/pull/1764))" -#: ../../source/ref-changelog.md:392 +#: ../../source/ref-changelog.md:191 msgid "" -"**Add new custom serialization tutorial section** " -"([#1622](https://github.com/adap/flower/pull/1622))" +"A new strategy called `FedXgbCyclic` supports a client-by-client style of" +" training (often called cyclic). The `xgboost-comprehensive` code example" +" shows how to use it in a full project. In addition to that, `xgboost-" +"comprehensive` now also supports simulation mode. With this, Flower " +"offers best-in-class XGBoost support." msgstr "" -"**Ajouter une nouvelle section de tutoriel sur la sérialisation " -"personnalisée** ([#1622](https://github.com/adap/flower/pull/1622))" -#: ../../source/ref-changelog.md:394 +#: ../../source/ref-changelog.md:193 #, fuzzy msgid "" -"The Flower tutorial now has a new section that covers custom " -"serialization: [Open in " -"Colab](https://colab.research.google.com/github/adap/flower/blob/main/doc/source" -"/tutorial-customize-the-client-pytorch.ipynb)" +"**Support Python 3.11** " +"([#2394](https://github.com/adap/flower/pull/2394))" msgstr "" -"Le tutoriel sur les fleurs comporte désormais une nouvelle section qui " -"traite de la sérialisation personnalisée : [Ouvrir dans " -"Colab](https://colab.research.google.com/github/adap/flower/blob/main/doc/source/tutorial/Flower-4" -"-Client-and-NumPyClient-PyTorch.ipynb)" +"**Support Python 3.10** " +"([#1320](https://github.com/adap/flower/pull/1320))" -#: ../../source/ref-changelog.md:396 +#: ../../source/ref-changelog.md:195 msgid "" -"**General improvements** " -"([#1638](https://github.com/adap/flower/pull/1638), " -"[#1634](https://github.com/adap/flower/pull/1634), " -"[#1636](https://github.com/adap/flower/pull/1636), " -"[#1635](https://github.com/adap/flower/pull/1635), " -"[#1633](https://github.com/adap/flower/pull/1633), " -"[#1632](https://github.com/adap/flower/pull/1632), " -"[#1631](https://github.com/adap/flower/pull/1631), " -"[#1630](https://github.com/adap/flower/pull/1630), " -"[#1627](https://github.com/adap/flower/pull/1627), " -"[#1593](https://github.com/adap/flower/pull/1593), " -"[#1616](https://github.com/adap/flower/pull/1616), " -"[#1615](https://github.com/adap/flower/pull/1615), " -"[#1607](https://github.com/adap/flower/pull/1607), " -"[#1609](https://github.com/adap/flower/pull/1609), " -"[#1608](https://github.com/adap/flower/pull/1608), " -"[#1603](https://github.com/adap/flower/pull/1603), " -"[#1590](https://github.com/adap/flower/pull/1590), " -"[#1580](https://github.com/adap/flower/pull/1580), " -"[#1599](https://github.com/adap/flower/pull/1599), " -"[#1600](https://github.com/adap/flower/pull/1600), " -"[#1601](https://github.com/adap/flower/pull/1601), " -"[#1597](https://github.com/adap/flower/pull/1597), " -"[#1595](https://github.com/adap/flower/pull/1595), " -"[#1591](https://github.com/adap/flower/pull/1591), " -"[#1588](https://github.com/adap/flower/pull/1588), " -"[#1589](https://github.com/adap/flower/pull/1589), " -"[#1587](https://github.com/adap/flower/pull/1587), " -"[#1573](https://github.com/adap/flower/pull/1573), " -"[#1581](https://github.com/adap/flower/pull/1581), " -"[#1578](https://github.com/adap/flower/pull/1578), " -"[#1574](https://github.com/adap/flower/pull/1574), " -"[#1572](https://github.com/adap/flower/pull/1572), " -"[#1586](https://github.com/adap/flower/pull/1586))" +"Framework tests now run on Python 3.8, 3.9, 3.10, and 3.11. This will " +"ensure better support for users using more recent Python versions." msgstr "" -"**General improvements** " -"([#1638](https://github.com/adap/flower/pull/1638), " -"[#1634](https://github.com/adap/flower/pull/1634), " -"[#1636](https://github.com/adap/flower/pull/1636), " -"[#1635](https://github.com/adap/flower/pull/1635), " -"[#1633](https://github.com/adap/flower/pull/1633), " -"[#1632](https://github.com/adap/flower/pull/1632), " -"[#1631](https://github.com/adap/flower/pull/1631), " -"[#1630](https://github.com/adap/flower/pull/1630), " -"[#1627](https://github.com/adap/flower/pull/1627), " -"[#1593](https://github.com/adap/flower/pull/1593), " -"[#1616](https://github.com/adap/flower/pull/1616), " -"[#1615](https://github.com/adap/flower/pull/1615), " -"[#1607](https://github.com/adap/flower/pull/1607), " -"[#1609](https://github.com/adap/flower/pull/1609), " -"[#1608](https://github.com/adap/flower/pull/1608), " -"[#1603](https://github.com/adap/flower/pull/1603), " -"[#1590](https://github.com/adap/flower/pull/1590), " -"[#1580](https://github.com/adap/flower/pull/1580), " -"[#1599](https://github.com/adap/flower/pull/1599), " -"[#1600](https://github.com/ada" -#: ../../source/ref-changelog.md:400 +#: ../../source/ref-changelog.md:197 +#, fuzzy msgid "" -"**Updated documentation** " -"([#1629](https://github.com/adap/flower/pull/1629), " -"[#1628](https://github.com/adap/flower/pull/1628), " -"[#1620](https://github.com/adap/flower/pull/1620), " -"[#1618](https://github.com/adap/flower/pull/1618), " -"[#1617](https://github.com/adap/flower/pull/1617), " -"[#1613](https://github.com/adap/flower/pull/1613), " -"[#1614](https://github.com/adap/flower/pull/1614))" +"**Update gRPC and ProtoBuf dependencies** " +"([#2814](https://github.com/adap/flower/pull/2814))" msgstr "" -"**Mise à jour de la documentation** " -"([#1629](https://github.com/adap/flower/pull/1629), " -"[#1628](https://github.com/adap/flower/pull/1628), " -"[#1620](https://github.com/adap/flower/pull/1620), " -"[#1618](https://github.com/adap/flower/pull/1618), " -"[#1617](https://github.com/adap/flower/pull/1617), " -"[#1613](https://github.com/adap/flower/pull/1613), " -"[#1614](https://github.com/adap/flower/pull/1614))" +"**Ajouter une nouvelle stratégie `FedProx`** " +"([#1619](https://github.com/adap/flower/pull/1619))" -#: ../../source/ref-changelog.md:402 ../../source/ref-changelog.md:469 +#: ../../source/ref-changelog.md:199 msgid "" -"As usual, the documentation has improved quite a bit. It is another step " -"in our effort to make the Flower documentation the best documentation of " -"any project. Stay tuned and as always, feel free to provide feedback!" +"The `grpcio` and `protobuf` dependencies were updated to their latest " +"versions for improved security and performance." msgstr "" -"Comme d'habitude, la documentation s'est beaucoup améliorée. C'est une " -"autre étape dans notre effort pour faire de la documentation de Flower la" -" meilleure documentation de tout projet. Reste à l'écoute et comme " -"toujours, n'hésite pas à nous faire part de tes commentaires !" -#: ../../source/ref-changelog.md:408 -msgid "v1.2.0 (2023-01-13)" -msgstr "v1.2.0 (2023-01-13)" - -#: ../../source/ref-changelog.md:414 +#: ../../source/ref-changelog.md:201 +#, fuzzy msgid "" -"`Adam Narozniak`, `Charles Beauville`, `Daniel J. Beutel`, `Edoardo`, `L." -" Jiang`, `Ragy`, `Taner Topal`, `dannymcy`" +"**Introduce Docker image for Flower server** " +"([#2700](https://github.com/adap/flower/pull/2700), " +"[#2688](https://github.com/adap/flower/pull/2688), " +"[#2705](https://github.com/adap/flower/pull/2705), " +"[#2695](https://github.com/adap/flower/pull/2695), " +"[#2747](https://github.com/adap/flower/pull/2747), " +"[#2746](https://github.com/adap/flower/pull/2746), " +"[#2680](https://github.com/adap/flower/pull/2680), " +"[#2682](https://github.com/adap/flower/pull/2682), " +"[#2701](https://github.com/adap/flower/pull/2701))" msgstr "" -"adam Narozniak`, `Charles Beauville`, `Daniel J. Beutel`, `Edoardo`, `L. " -"Jiang`, `Ragy`, `Taner Topal`, `dannymcy`" +"**([#842](https://github.com/adap/flower/pull/842), " +"[#844](https://github.com/adap/flower/pull/844), " +"[#845](https://github.com/adap/flower/pull/845), " +"[#847](https://github.com/adap/flower/pull/847), " +"[#993](https://github.com/adap/flower/pull/993), " +"[#994](https://github.com/adap/flower/pull/994))" -#: ../../source/ref-changelog.md:418 +#: ../../source/ref-changelog.md:203 msgid "" -"**Introduce new Flower Baseline: FedAvg MNIST** " -"([#1497](https://github.com/adap/flower/pull/1497), " -"[#1552](https://github.com/adap/flower/pull/1552))" +"The Flower server can now be run using an official Docker image. A new " +"how-to guide explains [how to run Flower using " +"Docker](https://flower.ai/docs/framework/how-to-run-flower-using-" +"docker.html). An official Flower client Docker image will follow." msgstr "" -"**Introduire une nouvelle fleur Référence : FedAvg MNIST** " -"([#1497](https://github.com/adap/flower/pull/1497), " -"[#1552](https://github.com/adap/flower/pull/1552))" -#: ../../source/ref-changelog.md:420 +#: ../../source/ref-changelog.md:205 +#, fuzzy msgid "" -"Over the coming weeks, we will be releasing a number of new reference " -"implementations useful especially to FL newcomers. They will typically " -"revisit well known papers from the literature, and be suitable for " -"integration in your own application or for experimentation, in order to " -"deepen your knowledge of FL in general. Today's release is the first in " -"this series. [Read more.](https://flower.ai/blog/2023-01-12-fl-starter-" -"pack-fedavg-mnist-cnn/)" +"**Introduce** `flower-via-docker-compose` **example** " +"([#2626](https://github.com/adap/flower/pull/2626))" msgstr "" -"Au cours des prochaines semaines, nous publierons un certain nombre de " -"nouvelles implémentations de référence utiles en particulier pour les " -"nouveaux venus en FL. Elles revisiteront généralement des articles bien " -"connus de la littérature, et seront adaptées à l'intégration dans votre " -"propre application ou à l'expérimentation, afin d'approfondir votre " -"connaissance de FL en général. La publication d'aujourd'hui est la " -"première de cette série. [Lire la " -"suite.](https://flower.ai/blog/2023-01-12-fl-starter-pack-fedavg-mnist-" -"cnn/)" +"**Introduire une nouvelle ligne de base pour les fleurs : FedAvg " +"FEMNIST** ([#1655](https://github.com/adap/flower/pull/1655))" -#: ../../source/ref-changelog.md:422 +#: ../../source/ref-changelog.md:207 +#, fuzzy msgid "" -"**Improve GPU support in simulations** " -"([#1555](https://github.com/adap/flower/pull/1555))" +"**Introduce** `quickstart-sklearn-tabular` **example** " +"([#2719](https://github.com/adap/flower/pull/2719))" msgstr "" -"**Améliorer la prise en charge des GPU dans les simulations** " -"([#1555](https://github.com/adap/flower/pull/1555))" +"**Ajouter une nouvelle stratégie `FedProx`** " +"([#1619](https://github.com/adap/flower/pull/1619))" -#: ../../source/ref-changelog.md:424 +#: ../../source/ref-changelog.md:209 +#, fuzzy msgid "" -"The Ray-based Virtual Client Engine (`start_simulation`) has been updated" -" to improve GPU support. The update includes some of the hard-earned " -"lessons from scaling simulations in GPU cluster environments. New " -"defaults make running GPU-based simulations substantially more robust." +"**Introduce** `custom-metrics` **example** " +"([#1958](https://github.com/adap/flower/pull/1958))" msgstr "" -"Le moteur client virtuel basé sur Ray (`start_simulation`) a été mis à " -"jour pour améliorer la prise en charge des GPU. La mise à jour inclut " -"certaines des leçons durement apprises lors de la mise à l'échelle des " -"simulations dans des environnements de grappes de GPU. De nouveaux " -"paramètres par défaut rendent l'exécution des simulations basées sur les " -"GPU beaucoup plus robuste." +"**Ajouter une nouvelle stratégie `FedProx`** " +"([#1619](https://github.com/adap/flower/pull/1619))" -#: ../../source/ref-changelog.md:426 +#: ../../source/ref-changelog.md:211 +#, fuzzy msgid "" -"**Improve GPU support in Jupyter Notebook tutorials** " -"([#1527](https://github.com/adap/flower/pull/1527), " -"[#1558](https://github.com/adap/flower/pull/1558))" +"**Update code examples to use Flower Datasets** " +"([#2450](https://github.com/adap/flower/pull/2450), " +"[#2456](https://github.com/adap/flower/pull/2456), " +"[#2318](https://github.com/adap/flower/pull/2318), " +"[#2712](https://github.com/adap/flower/pull/2712))" msgstr "" -"**Améliorer la prise en charge du GPU dans les tutoriels Jupyter " -"Notebook** ([#1527](https://github.com/adap/flower/pull/1527), " -"[#1558](https://github.com/adap/flower/pull/1558))" +"Mettre à jour les outils de développement " +"([#1231](https://github.com/adap/flower/pull/1231), " +"[#1276](https://github.com/adap/flower/pull/1276), " +"[#1301](https://github.com/adap/flower/pull/1301), " +"[#1310](https://github.com/adap/flower/pull/1310))" -#: ../../source/ref-changelog.md:428 +#: ../../source/ref-changelog.md:213 msgid "" -"Some users reported that Jupyter Notebooks have not always been easy to " -"use on GPU instances. We listened and made improvements to all of our " -"Jupyter notebooks! Check out the updated notebooks here:" +"Several code examples were updated to use [Flower " +"Datasets](https://flower.ai/docs/datasets/)." msgstr "" -"Certains utilisateurs ont signalé que les carnets Jupyter n'ont pas " -"toujours été faciles à utiliser sur les instances GPU. Nous les avons " -"écoutés et avons apporté des améliorations à tous nos carnets Jupyter ! " -"Découvre les carnets mis à jour ici :" -#: ../../source/ref-changelog.md:430 +#: ../../source/ref-changelog.md:215 #, fuzzy msgid "" -"[An Introduction to Federated Learning](https://flower.ai/docs/framework" -"/tutorial-get-started-with-flower-pytorch.html)" +"**General updates to Flower Examples** " +"([#2381](https://github.com/adap/flower/pull/2381), " +"[#2805](https://github.com/adap/flower/pull/2805), " +"[#2782](https://github.com/adap/flower/pull/2782), " +"[#2806](https://github.com/adap/flower/pull/2806), " +"[#2829](https://github.com/adap/flower/pull/2829), " +"[#2825](https://github.com/adap/flower/pull/2825), " +"[#2816](https://github.com/adap/flower/pull/2816), " +"[#2726](https://github.com/adap/flower/pull/2726), " +"[#2659](https://github.com/adap/flower/pull/2659), " +"[#2655](https://github.com/adap/flower/pull/2655))" msgstr "" -"[Une introduction à l'apprentissage fédéré] " -"(https://flower.ai/docs/tutorial/Flower-1-Intro-to-FL-PyTorch.html)" +"**Améliorer l'API (expérimentale) du pilote** " +"([#1663](https://github.com/adap/flower/pull/1663), " +"[#1666](https://github.com/adap/flower/pull/1666), " +"[#1667](https://github.com/adap/flower/pull/1667), " +"[#1664](https://github.com/adap/flower/pull/1664), " +"[#1675](https://github.com/adap/flower/pull/1675), " +"[#1676](https://github.com/adap/flower/pull/1676), " +"[#1693](https://github.com/adap/flower/pull/1693), " +"[#1662](https://github.com/adap/flower/pull/1662), " +"[#1794](https://github.com/adap/flower/pull/1794))" -#: ../../source/ref-changelog.md:431 -#, fuzzy -msgid "" -"[Strategies in Federated Learning](https://flower.ai/docs/framework" -"/tutorial-use-a-federated-learning-strategy-pytorch.html)" +#: ../../source/ref-changelog.md:217 +msgid "Many Flower code examples received substantial updates." msgstr "" -"[Stratégies d'apprentissage fédéré] " -"(https://flower.ai/docs/tutorial/Flower-2-Strategies-in-FL-PyTorch.html)" -#: ../../source/ref-changelog.md:432 +#: ../../source/ref-changelog.md:219 ../../source/ref-changelog.md:312 +#, fuzzy +msgid "**Update Flower Baselines**" +msgstr "Demande pour une nouvelle Flower Baseline" + +#: ../../source/ref-changelog.md:221 #, fuzzy msgid "" -"[Building a Strategy](https://flower.ai/docs/framework/tutorial-build-a" -"-strategy-from-scratch-pytorch.html)" +"HFedXGBoost ([#2226](https://github.com/adap/flower/pull/2226), " +"[#2771](https://github.com/adap/flower/pull/2771))" msgstr "" -"[Construire une stratégie] " -"(https://flower.ai/docs/tutorial/Flower-3-Building-a-Strategy-" -"PyTorch.html)" +"**Nouvel exemple de code JAX** " +"([#906](https://github.com/adap/flower/pull/906), " +"[#1143](https://github.com/adap/flower/pull/1143))" -#: ../../source/ref-changelog.md:433 +#: ../../source/ref-changelog.md:222 #, fuzzy -msgid "" -"[Client and NumPyClient](https://flower.ai/docs/framework/tutorial-" -"customize-the-client-pytorch.html)" +msgid "FedVSSL ([#2412](https://github.com/adap/flower/pull/2412))" msgstr "" -"[Client et NumPyClient] (https://flower.ai/docs/tutorial/Flower-4-Client-" -"and-NumPyClient-PyTorch.html)" +"Amélioration de la documentation sur le serveur gRPC " +"([#841](https://github.com/adap/flower/pull/841))" -#: ../../source/ref-changelog.md:435 -msgid "" -"**Introduce optional telemetry** " -"([#1533](https://github.com/adap/flower/pull/1533), " -"[#1544](https://github.com/adap/flower/pull/1544), " -"[#1584](https://github.com/adap/flower/pull/1584))" +#: ../../source/ref-changelog.md:223 +#, fuzzy +msgid "FedNova ([#2179](https://github.com/adap/flower/pull/2179))" msgstr "" -"**Introduire la télémétrie optionnelle** " -"([#1533](https://github.com/adap/flower/pull/1533), " -"[#1544](https://github.com/adap/flower/pull/1544), " -"[#1584](https://github.com/adap/flower/pull/1584))" +"**Ajouter une nouvelle stratégie `FedProx`** " +"([#1619](https://github.com/adap/flower/pull/1619))" -#: ../../source/ref-changelog.md:437 -msgid "" -"After a [request for " -"feedback](https://github.com/adap/flower/issues/1534) from the community," -" the Flower open-source project introduces optional collection of " -"*anonymous* usage metrics to make well-informed decisions to improve " -"Flower. Doing this enables the Flower team to understand how Flower is " -"used and what challenges users might face." +#: ../../source/ref-changelog.md:224 +#, fuzzy +msgid "HeteroFL ([#2439](https://github.com/adap/flower/pull/2439))" +msgstr "Nouvelle référence API ([#554](https://github.com/adap/flower/pull/554))" + +#: ../../source/ref-changelog.md:225 +#, fuzzy +msgid "FedAvgM ([#2246](https://github.com/adap/flower/pull/2246))" +msgstr "Nouvelle référence API ([#554](https://github.com/adap/flower/pull/554))" + +#: ../../source/ref-changelog.md:226 +#, fuzzy +msgid "FedPara ([#2722](https://github.com/adap/flower/pull/2722))" msgstr "" -"À la suite d'une [demande de commentaires] " -"(https://github.com/adap/flower/issues/1534) de la part de la communauté," -" le projet open-source Flower introduit la collecte optionnelle de " -"mesures d'utilisation *anonymes* afin de prendre des décisions éclairées " -"pour améliorer Flower. Cela permet à l'équipe de Flower de comprendre " -"comment Flower est utilisé et quels sont les défis auxquels les " -"utilisateurs peuvent être confrontés." +"**Renommé stratégie q-FedAvg** " +"([#802](https://github.com/adap/flower/pull/802))" -#: ../../source/ref-changelog.md:439 +#: ../../source/ref-changelog.md:228 #, fuzzy msgid "" -"**Flower is a friendly framework for collaborative AI and data science.**" -" Staying true to this statement, Flower makes it easy to disable " -"telemetry for users who do not want to share anonymous usage metrics. " -"[Read more.](https://flower.ai/docs/telemetry.html)." +"**Improve documentation** " +"([#2674](https://github.com/adap/flower/pull/2674), " +"[#2480](https://github.com/adap/flower/pull/2480), " +"[#2826](https://github.com/adap/flower/pull/2826), " +"[#2727](https://github.com/adap/flower/pull/2727), " +"[#2761](https://github.com/adap/flower/pull/2761), " +"[#2900](https://github.com/adap/flower/pull/2900))" msgstr "" -"**Flower est un cadre convivial pour l'IA collaborative et la science des" -" données.** Restant fidèle à cette déclaration, Flower permet de " -"désactiver facilement la télémétrie pour les utilisateurs qui ne " -"souhaitent pas partager des métriques d'utilisation anonymes.[Lire la " -"suite.](https://flower.ai/docs/telemetry.html)." +"**Mise à jour de la documentation** " +"([#1629](https://github.com/adap/flower/pull/1629), " +"[#1628](https://github.com/adap/flower/pull/1628), " +"[#1620](https://github.com/adap/flower/pull/1620), " +"[#1618](https://github.com/adap/flower/pull/1618), " +"[#1617](https://github.com/adap/flower/pull/1617), " +"[#1613](https://github.com/adap/flower/pull/1613), " +"[#1614](https://github.com/adap/flower/pull/1614))" -#: ../../source/ref-changelog.md:441 +#: ../../source/ref-changelog.md:230 msgid "" -"**Introduce (experimental) Driver API** " -"([#1520](https://github.com/adap/flower/pull/1520), " -"[#1525](https://github.com/adap/flower/pull/1525), " -"[#1545](https://github.com/adap/flower/pull/1545), " -"[#1546](https://github.com/adap/flower/pull/1546), " -"[#1550](https://github.com/adap/flower/pull/1550), " -"[#1551](https://github.com/adap/flower/pull/1551), " -"[#1567](https://github.com/adap/flower/pull/1567))" -msgstr "" -"**([#1520](https://github.com/adap/flower/pull/1520), " -"[#1525](https://github.com/adap/flower/pull/1525), " -"[#1545](https://github.com/adap/flower/pull/1545), " -"[#1546](https://github.com/adap/flower/pull/1546), " -"[#1550](https://github.com/adap/flower/pull/1550), " -"[#1551](https://github.com/adap/flower/pull/1551), " -"[#1567](https://github.com/adap/flower/pull/1567))" +"**Improved testing and development infrastructure** " +"([#2797](https://github.com/adap/flower/pull/2797), " +"[#2676](https://github.com/adap/flower/pull/2676), " +"[#2644](https://github.com/adap/flower/pull/2644), " +"[#2656](https://github.com/adap/flower/pull/2656), " +"[#2848](https://github.com/adap/flower/pull/2848), " +"[#2675](https://github.com/adap/flower/pull/2675), " +"[#2735](https://github.com/adap/flower/pull/2735), " +"[#2767](https://github.com/adap/flower/pull/2767), " +"[#2732](https://github.com/adap/flower/pull/2732), " +"[#2744](https://github.com/adap/flower/pull/2744), " +"[#2681](https://github.com/adap/flower/pull/2681), " +"[#2699](https://github.com/adap/flower/pull/2699), " +"[#2745](https://github.com/adap/flower/pull/2745), " +"[#2734](https://github.com/adap/flower/pull/2734), " +"[#2731](https://github.com/adap/flower/pull/2731), " +"[#2652](https://github.com/adap/flower/pull/2652), " +"[#2720](https://github.com/adap/flower/pull/2720), " +"[#2721](https://github.com/adap/flower/pull/2721), " +"[#2717](https://github.com/adap/flower/pull/2717), " +"[#2864](https://github.com/adap/flower/pull/2864), " +"[#2694](https://github.com/adap/flower/pull/2694), " +"[#2709](https://github.com/adap/flower/pull/2709), " +"[#2658](https://github.com/adap/flower/pull/2658), " +"[#2796](https://github.com/adap/flower/pull/2796), " +"[#2692](https://github.com/adap/flower/pull/2692), " +"[#2657](https://github.com/adap/flower/pull/2657), " +"[#2813](https://github.com/adap/flower/pull/2813), " +"[#2661](https://github.com/adap/flower/pull/2661), " +"[#2398](https://github.com/adap/flower/pull/2398))" +msgstr "" -#: ../../source/ref-changelog.md:443 +#: ../../source/ref-changelog.md:232 msgid "" -"Flower now has a new (experimental) Driver API which will enable fully " -"programmable, async, and multi-tenant Federated Learning and Federated " -"Analytics applications. Phew, that's a lot! Going forward, the Driver API" -" will be the abstraction that many upcoming features will be built on - " -"and you can start building those things now, too." +"The Flower testing and development infrastructure has received " +"substantial updates. This makes Flower 1.7 the most tested release ever." msgstr "" -"Flower dispose désormais d'une nouvelle API de pilote (expérimentale) qui" -" permettra de créer des applications Federated Learning et Federated " -"Analytics entièrement programmables, asynchrones et multi-tenant. Ouf, " -"c'est beaucoup ! À l'avenir, l'API de pilote sera l'abstraction sur " -"laquelle de nombreuses fonctionnalités à venir seront construites - et tu" -" peux commencer à construire ces choses dès maintenant, aussi." -#: ../../source/ref-changelog.md:445 +#: ../../source/ref-changelog.md:234 msgid "" -"The Driver API also enables a new execution mode in which the server runs" -" indefinitely. Multiple individual workloads can run concurrently and " -"start and stop their execution independent of the server. This is " -"especially useful for users who want to deploy Flower in production." +"**Update dependencies** " +"([#2753](https://github.com/adap/flower/pull/2753), " +"[#2651](https://github.com/adap/flower/pull/2651), " +"[#2739](https://github.com/adap/flower/pull/2739), " +"[#2837](https://github.com/adap/flower/pull/2837), " +"[#2788](https://github.com/adap/flower/pull/2788), " +"[#2811](https://github.com/adap/flower/pull/2811), " +"[#2774](https://github.com/adap/flower/pull/2774), " +"[#2790](https://github.com/adap/flower/pull/2790), " +"[#2751](https://github.com/adap/flower/pull/2751), " +"[#2850](https://github.com/adap/flower/pull/2850), " +"[#2812](https://github.com/adap/flower/pull/2812), " +"[#2872](https://github.com/adap/flower/pull/2872), " +"[#2736](https://github.com/adap/flower/pull/2736), " +"[#2756](https://github.com/adap/flower/pull/2756), " +"[#2857](https://github.com/adap/flower/pull/2857), " +"[#2757](https://github.com/adap/flower/pull/2757), " +"[#2810](https://github.com/adap/flower/pull/2810), " +"[#2740](https://github.com/adap/flower/pull/2740), " +"[#2789](https://github.com/adap/flower/pull/2789))" msgstr "" -"L'API du pilote permet également un nouveau mode d'exécution dans lequel " -"le serveur s'exécute indéfiniment. Plusieurs charges de travail " -"individuelles peuvent s'exécuter simultanément et démarrer et arrêter " -"leur exécution indépendamment du serveur. Ceci est particulièrement utile" -" pour les utilisateurs qui souhaitent déployer Flower en production." -#: ../../source/ref-changelog.md:447 +#: ../../source/ref-changelog.md:236 msgid "" -"To learn more, check out the `mt-pytorch` code example. We look forward " -"to you feedback!" +"**General improvements** " +"([#2803](https://github.com/adap/flower/pull/2803), " +"[#2847](https://github.com/adap/flower/pull/2847), " +"[#2877](https://github.com/adap/flower/pull/2877), " +"[#2690](https://github.com/adap/flower/pull/2690), " +"[#2889](https://github.com/adap/flower/pull/2889), " +"[#2874](https://github.com/adap/flower/pull/2874), " +"[#2819](https://github.com/adap/flower/pull/2819), " +"[#2689](https://github.com/adap/flower/pull/2689), " +"[#2457](https://github.com/adap/flower/pull/2457), " +"[#2870](https://github.com/adap/flower/pull/2870), " +"[#2669](https://github.com/adap/flower/pull/2669), " +"[#2876](https://github.com/adap/flower/pull/2876), " +"[#2885](https://github.com/adap/flower/pull/2885), " +"[#2858](https://github.com/adap/flower/pull/2858), " +"[#2867](https://github.com/adap/flower/pull/2867), " +"[#2351](https://github.com/adap/flower/pull/2351), " +"[#2886](https://github.com/adap/flower/pull/2886), " +"[#2860](https://github.com/adap/flower/pull/2860), " +"[#2828](https://github.com/adap/flower/pull/2828), " +"[#2869](https://github.com/adap/flower/pull/2869), " +"[#2875](https://github.com/adap/flower/pull/2875), " +"[#2733](https://github.com/adap/flower/pull/2733), " +"[#2488](https://github.com/adap/flower/pull/2488), " +"[#2646](https://github.com/adap/flower/pull/2646), " +"[#2879](https://github.com/adap/flower/pull/2879), " +"[#2821](https://github.com/adap/flower/pull/2821), " +"[#2855](https://github.com/adap/flower/pull/2855), " +"[#2800](https://github.com/adap/flower/pull/2800), " +"[#2807](https://github.com/adap/flower/pull/2807), " +"[#2801](https://github.com/adap/flower/pull/2801), " +"[#2804](https://github.com/adap/flower/pull/2804), " +"[#2851](https://github.com/adap/flower/pull/2851), " +"[#2787](https://github.com/adap/flower/pull/2787), " +"[#2852](https://github.com/adap/flower/pull/2852), " +"[#2672](https://github.com/adap/flower/pull/2672), " +"[#2759](https://github.com/adap/flower/pull/2759))" msgstr "" -"Pour en savoir plus, consulte l'exemple de code `mt-pytorch`. Nous " -"attendons tes commentaires avec impatience !" -#: ../../source/ref-changelog.md:449 +#: ../../source/ref-changelog.md:240 +#, fuzzy msgid "" -"Please note: *The Driver API is still experimental and will likely change" -" significantly over time.*" +"**Deprecate** `start_numpy_client` " +"([#2563](https://github.com/adap/flower/pull/2563), " +"[#2718](https://github.com/adap/flower/pull/2718))" msgstr "" -"Remarque : *L'API du pilote est encore expérimentale et est susceptible " -"de changer de manière significative au fil du temps.*" +"**Nouvelles stratégies intégrées** " +"([#828](https://github.com/adap/flower/pull/828) " +"[#822](https://github.com/adap/flower/pull/822))" -#: ../../source/ref-changelog.md:451 +#: ../../source/ref-changelog.md:242 msgid "" -"**Add new Federated Analytics with Pandas example** " -"([#1469](https://github.com/adap/flower/pull/1469), " -"[#1535](https://github.com/adap/flower/pull/1535))" +"Until now, clients of type `NumPyClient` needed to be started via " +"`start_numpy_client`. In our efforts to consolidate framework APIs, we " +"have introduced changes, and now all client types should start via " +"`start_client`. To continue using `NumPyClient` clients, you simply need " +"to first call the `.to_client()` method and then pass returned `Client` " +"object to `start_client`. The examples and the documentation have been " +"updated accordingly." msgstr "" -"**Ajouter un nouvel exemple de Federated Analytics avec Pandas** " -"([#1469](https://github.com/adap/flower/pull/1469), " -"[#1535](https://github.com/adap/flower/pull/1535))" -#: ../../source/ref-changelog.md:453 +#: ../../source/ref-changelog.md:244 +#, fuzzy msgid "" -"A new code example (`quickstart-pandas`) demonstrates federated analytics" -" with Pandas and Flower. You can find it here: [quickstart-" -"pandas](https://github.com/adap/flower/tree/main/examples/quickstart-" -"pandas)." +"**Deprecate legacy DP wrappers** " +"([#2749](https://github.com/adap/flower/pull/2749))" msgstr "" -"Un nouvel exemple de code (`quickstart-pandas`) démontre l'analyse " -"fédérée avec Pandas et Flower. Tu peux le trouver ici : [quickstart-" -"pandas](https://github.com/adap/flower/tree/main/examples/quickstart-" -"pandas)." +"**Supprimez KerasClient** " +"([#857](https://github.com/adap/flower/pull/857))" -#: ../../source/ref-changelog.md:455 +#: ../../source/ref-changelog.md:246 msgid "" -"**Add new strategies: Krum and MultiKrum** " -"([#1481](https://github.com/adap/flower/pull/1481))" +"Legacy DP wrapper classes are deprecated, but still functional. This is " +"in preparation for an all-new pluggable version of differential privacy " +"support in Flower." msgstr "" -"**Ajouter de nouvelles stratégies : Krum et MultiKrum** " -"([#1481](https://github.com/adap/flower/pull/1481))" -#: ../../source/ref-changelog.md:457 +#: ../../source/ref-changelog.md:248 +#, fuzzy msgid "" -"Edoardo, a computer science student at the Sapienza University of Rome, " -"contributed a new `Krum` strategy that enables users to easily use Krum " -"and MultiKrum in their workloads." +"**Make optional arg** `--callable` **in** `flower-client` **a required " +"positional arg** ([#2673](https://github.com/adap/flower/pull/2673))" msgstr "" -"Edoardo, étudiant en informatique à l'Université Sapienza de Rome, a " -"contribué à une nouvelle stratégie `Krum` qui permet aux utilisateurs " -"d'utiliser facilement Krum et MultiKrum dans leurs charges de travail." +"**Log** `Client` **exceptions dans le moteur de client virtuel** " +"([#1493](https://github.com/adap/flower/pull/1493))" -#: ../../source/ref-changelog.md:459 +#: ../../source/ref-changelog.md:250 +#, fuzzy msgid "" -"**Update C++ example to be compatible with Flower v1.2.0** " -"([#1495](https://github.com/adap/flower/pull/1495))" +"**Rename** `certificates` **to** `root_certificates` **in** `Driver` " +"([#2890](https://github.com/adap/flower/pull/2890))" msgstr "" -"**Mettre à jour l'exemple C++ pour qu'il soit compatible avec Flower " -"v1.2.0** ([#1495](https://github.com/adap/flower/pull/1495))" +"**Rename** `rnd` **to** `server_round` " +"([#1321](https://github.com/adap/flower/pull/1321))" -#: ../../source/ref-changelog.md:461 +#: ../../source/ref-changelog.md:252 +#, fuzzy msgid "" -"The C++ code example has received a substantial update to make it " -"compatible with the latest version of Flower." +"**Drop experimental** `Task` **fields** " +"([#2866](https://github.com/adap/flower/pull/2866), " +"[#2865](https://github.com/adap/flower/pull/2865))" msgstr "" -"L'exemple de code C++ a reçu une mise à jour substantielle pour le rendre" -" compatible avec la dernière version de Flower." +"**Rename** `Weights` **to** `NDArrays` " +"([#1258](https://github.com/adap/flower/pull/1258), " +"[#1259](https://github.com/adap/flower/pull/1259))" -#: ../../source/ref-changelog.md:463 +#: ../../source/ref-changelog.md:254 msgid "" -"**General improvements** " -"([#1491](https://github.com/adap/flower/pull/1491), " -"[#1504](https://github.com/adap/flower/pull/1504), " -"[#1506](https://github.com/adap/flower/pull/1506), " -"[#1514](https://github.com/adap/flower/pull/1514), " -"[#1522](https://github.com/adap/flower/pull/1522), " -"[#1523](https://github.com/adap/flower/pull/1523), " -"[#1526](https://github.com/adap/flower/pull/1526), " -"[#1528](https://github.com/adap/flower/pull/1528), " -"[#1547](https://github.com/adap/flower/pull/1547), " -"[#1549](https://github.com/adap/flower/pull/1549), " -"[#1560](https://github.com/adap/flower/pull/1560), " -"[#1564](https://github.com/adap/flower/pull/1564), " -"[#1566](https://github.com/adap/flower/pull/1566))" +"Experimental fields `sa`, `legacy_server_message` and " +"`legacy_client_message` were removed from `Task` message. The removed " +"fields are superseded by the new `RecordSet` abstraction." msgstr "" -"**Améliorations générales** " -"([#1491](https://github.com/adap/flower/pull/1491), " -"[#1504](https://github.com/adap/flower/pull/1504), " -"[#1506](https://github.com/adap/flower/pull/1506), " -"[#1514](https://github.com/adap/flower/pull/1514), " -"[#1522](https://github.com/adap/flower/pull/1522), " -"[#1523](https://github.com/adap/flower/pull/1523), " -"[#1526](https://github.com/adap/flower/pull/1526), " -"[#1528](https://github.com/adap/flower/pull/1528), " -"[#1547](https://github.com/adap/flower/pull/1547), " -"[#1549](https://github.com/adap/flower/pull/1549), " -"[#1560](https://github.com/adap/flower/pull/1560), " -"[#1564](https://github.com/adap/flower/pull/1564), " -"[#1566](https://github.com/adap/flower/pull/1566))" -#: ../../source/ref-changelog.md:467 +#: ../../source/ref-changelog.md:256 +#, fuzzy msgid "" -"**Updated documentation** " -"([#1494](https://github.com/adap/flower/pull/1494), " -"[#1496](https://github.com/adap/flower/pull/1496), " -"[#1500](https://github.com/adap/flower/pull/1500), " -"[#1503](https://github.com/adap/flower/pull/1503), " -"[#1505](https://github.com/adap/flower/pull/1505), " -"[#1524](https://github.com/adap/flower/pull/1524), " -"[#1518](https://github.com/adap/flower/pull/1518), " -"[#1519](https://github.com/adap/flower/pull/1519), " -"[#1515](https://github.com/adap/flower/pull/1515))" +"**Retire MXNet examples** " +"([#2724](https://github.com/adap/flower/pull/2724))" msgstr "" -"**Documentation mise à jour** " -"([#1494](https://github.com/adap/flower/pull/1494), " -"[#1496](https://github.com/adap/flower/pull/1496), " -"[#1500](https://github.com/adap/flower/pull/1500), " -"[#1503](https://github.com/adap/flower/pull/1503), " -"[#1505](https://github.com/adap/flower/pull/1505), " -"[#1524](https://github.com/adap/flower/pull/1524), " -"[#1518](https://github.com/adap/flower/pull/1518), " -"[#1519](https://github.com/adap/flower/pull/1519), " -"[#1515](https://github.com/adap/flower/pull/1515))" +"**Nouvel exemple de code scikit-learn** " +"([#748](https://github.com/adap/flower/pull/748))" -#: ../../source/ref-changelog.md:471 +#: ../../source/ref-changelog.md:258 msgid "" -"One highlight is the new [first time contributor " -"guide](https://flower.ai/docs/first-time-contributors.html): if you've " -"never contributed on GitHub before, this is the perfect place to start!" +"The development of the MXNet fremework has ended and the project is now " +"[archived on GitHub](https://github.com/apache/mxnet). Existing MXNet " +"examples won't receive updates." msgstr "" -"L'un des points forts est le nouveau [guide du premier contributeur] " -"(https://flower.ai/docs/first-time-contributors.html) : si tu n'as jamais" -" contribué sur GitHub auparavant, c'est l'endroit idéal pour commencer !" -#: ../../source/ref-changelog.md:477 -msgid "v1.1.0 (2022-10-31)" -msgstr "v1.1.0 (2022-10-31)" +#: ../../source/ref-changelog.md:260 +#, fuzzy +msgid "v1.6.0 (2023-11-28)" +msgstr "v1.4.0 (2023-04-21)" -#: ../../source/ref-changelog.md:481 +#: ../../source/ref-changelog.md:266 msgid "" -"We would like to give our **special thanks** to all the contributors who " -"made the new version of Flower possible (in `git shortlog` order):" +"`Aashish Kolluri`, `Adam Narozniak`, `Alessio Mora`, `Barathwaja S`, " +"`Charles Beauville`, `Daniel J. Beutel`, `Daniel Nata Nugraha`, `Gabriel " +"Mota`, `Heng Pan`, `Ivan Agarský`, `JS.KIM`, `Javier`, `Marius Schlegel`," +" `Navin Chandra`, `Nic Lane`, `Peterpan828`, `Qinbin Li`, `Shaz-hash`, " +"`Steve Laskaridis`, `Taner Topal`, `William Lindskog`, `Yan Gao`, " +"`cnxdeveloper`, `k3nfalt` " msgstr "" -"Nous aimerions **remercier tout particulièrement** tous les contributeurs" -" qui ont rendu possible la nouvelle version de Flower (dans l'ordre `git " -"shortlog`) :" -#: ../../source/ref-changelog.md:483 +#: ../../source/ref-changelog.md:270 +#, fuzzy msgid "" -"`Akis Linardos`, `Christopher S`, `Daniel J. Beutel`, `George`, `Jan " -"Schlicht`, `Mohammad Fares`, `Pedro Porto Buarque de Gusmão`, `Philipp " -"Wiesner`, `Rob Luke`, `Taner Topal`, `VasundharaAgarwal`, " -"`danielnugraha`, `edogab33`" +"**Add experimental support for Python 3.12** " +"([#2565](https://github.com/adap/flower/pull/2565))" msgstr "" -"`Akis Linardos`, `Christopher S`, `Daniel J. Beutel`, `George`, `Jan " -"Schlicht`, `Mohammad Fares`, `Pedro Porto Buarque de Gusmão`, `Philipp " -"Wiesner`, `Rob Luke`, `Taner Topal`, `VasundharaAgarwal`, " -"`danielnugraha`, `edogab33`" +"**Ajouter la prise en charge expérimentale de Python 3.10 et Python " +"3.11** ([#1135](https://github.com/adap/flower/pull/1135))" -#: ../../source/ref-changelog.md:487 +#: ../../source/ref-changelog.md:272 +#, fuzzy msgid "" -"**Introduce Differential Privacy wrappers (preview)** " -"([#1357](https://github.com/adap/flower/pull/1357), " -"[#1460](https://github.com/adap/flower/pull/1460))" +"**Add new XGBoost examples** " +"([#2612](https://github.com/adap/flower/pull/2612), " +"[#2554](https://github.com/adap/flower/pull/2554), " +"[#2617](https://github.com/adap/flower/pull/2617), " +"[#2618](https://github.com/adap/flower/pull/2618), " +"[#2619](https://github.com/adap/flower/pull/2619), " +"[#2567](https://github.com/adap/flower/pull/2567))" msgstr "" -"**Introduire les enveloppes de confidentialité différentielle (aperçu)** " -"([#1357](https://github.com/adap/flower/pull/1357), " -"[#1460](https://github.com/adap/flower/pull/1460))" +"**([#1520](https://github.com/adap/flower/pull/1520), " +"[#1525](https://github.com/adap/flower/pull/1525), " +"[#1545](https://github.com/adap/flower/pull/1545), " +"[#1546](https://github.com/adap/flower/pull/1546), " +"[#1550](https://github.com/adap/flower/pull/1550), " +"[#1551](https://github.com/adap/flower/pull/1551), " +"[#1567](https://github.com/adap/flower/pull/1567))" -#: ../../source/ref-changelog.md:489 +#: ../../source/ref-changelog.md:274 msgid "" -"The first (experimental) preview of pluggable Differential Privacy " -"wrappers enables easy configuration and usage of differential privacy " -"(DP). The pluggable DP wrappers enable framework-agnostic **and** " -"strategy-agnostic usage of both client-side DP and server-side DP. Head " -"over to the Flower docs, a new explainer goes into more detail." +"We have added a new `xgboost-quickstart` example alongside a new " +"`xgboost-comprehensive` example that goes more in-depth." msgstr "" -"Le premier aperçu (expérimental) des wrappers enfichables de " -"confidentialité différentielle permet de configurer et d'utiliser " -"facilement la confidentialité différentielle (DP). Les wrappers DP " -"enfichables permettent une utilisation agnostique du cadre **et** de la " -"stratégie à la fois de la DP côté client et de la DP côté serveur. Va " -"voir les documents de Flower, un nouvel explicatif va plus loin dans les " -"détails." -#: ../../source/ref-changelog.md:491 +#: ../../source/ref-changelog.md:276 +#, fuzzy msgid "" -"**New iOS CoreML code example** " -"([#1289](https://github.com/adap/flower/pull/1289))" +"**Add Vertical FL example** " +"([#2598](https://github.com/adap/flower/pull/2598))" msgstr "" "**Nouvel exemple de code CoreML pour iOS** " "([#1289](https://github.com/adap/flower/pull/1289))" -#: ../../source/ref-changelog.md:493 +#: ../../source/ref-changelog.md:278 msgid "" -"Flower goes iOS! A massive new code example shows how Flower clients can " -"be built for iOS. The code example contains both Flower iOS SDK " -"components that can be used for many tasks, and one task example running " -"on CoreML." +"We had many questions about Vertical Federated Learning using Flower, so " +"we decided to add an simple example for it on the [Titanic " +"dataset](https://www.kaggle.com/competitions/titanic/data) alongside a " +"tutorial (in the README)." msgstr "" -"Flower passe à iOS ! Un nouvel exemple de code massif montre comment les " -"clients Flower peuvent être construits pour iOS. L'exemple de code " -"contient à la fois des composants Flower iOS SDK qui peuvent être " -"utilisés pour de nombreuses tâches, et un exemple de tâche fonctionnant " -"sur CoreML." -#: ../../source/ref-changelog.md:495 +#: ../../source/ref-changelog.md:280 +#, fuzzy msgid "" -"**New FedMedian strategy** " -"([#1461](https://github.com/adap/flower/pull/1461))" +"**Support custom** `ClientManager` **in** `start_driver()` " +"([#2292](https://github.com/adap/flower/pull/2292))" msgstr "" -"**Nouvelle stratégie de FedMedian** " -"([#1461](https://github.com/adap/flower/pull/1461))" +"Ajout de la prise en charge d'un `ClientManager` personnalisé comme " +"paramètre de `start_simulation` " +"([#1171](https://github.com/adap/flower/pull/1171))" -#: ../../source/ref-changelog.md:497 +#: ../../source/ref-changelog.md:282 +#, fuzzy msgid "" -"The new `FedMedian` strategy implements Federated Median (FedMedian) by " -"[Yin et al., 2018](https://arxiv.org/pdf/1803.01498v1.pdf)." +"**Update REST API to support create and delete nodes** " +"([#2283](https://github.com/adap/flower/pull/2283))" msgstr "" -"La nouvelle stratégie `FedMedian` met en œuvre Federated Median " -"(FedMedian) par [Yin et al., 2018] " -"(https://arxiv.org/pdf/1803.01498v1.pdf)." +"**Nouvelle stratégie expérimentale TensorBoard** " +"([#789](https://github.com/adap/flower/pull/789))" -#: ../../source/ref-changelog.md:499 +#: ../../source/ref-changelog.md:284 +#, fuzzy msgid "" -"**Log** `Client` **exceptions in Virtual Client Engine** " -"([#1493](https://github.com/adap/flower/pull/1493))" +"**Update the Android SDK** " +"([#2187](https://github.com/adap/flower/pull/2187))" msgstr "" -"**Log** `Client` **exceptions dans le moteur de client virtuel** " -"([#1493](https://github.com/adap/flower/pull/1493))" +"**Introduire une nouvelle ligne de base pour les fleurs : FedAvg " +"FEMNIST** ([#1655](https://github.com/adap/flower/pull/1655))" -#: ../../source/ref-changelog.md:501 -msgid "" -"All `Client` exceptions happening in the VCE are now logged by default " -"and not just exposed to the configured `Strategy` (via the `failures` " -"argument)." +#: ../../source/ref-changelog.md:286 +msgid "Add gRPC request-response capability to the Android SDK." msgstr "" -"Toutes les exceptions `Client` qui se produisent dans le VCE sont " -"maintenant enregistrées par défaut et ne sont pas seulement exposées à la" -" `Stratégie` configurée (via l'argument `failures`)." -#: ../../source/ref-changelog.md:503 +#: ../../source/ref-changelog.md:288 +#, fuzzy msgid "" -"**Improve Virtual Client Engine internals** " -"([#1401](https://github.com/adap/flower/pull/1401), " -"[#1453](https://github.com/adap/flower/pull/1453))" +"**Update the C++ SDK** " +"([#2537](https://github.com/adap/flower/pull/2537), " +"[#2528](https://github.com/adap/flower/pull/2528), " +"[#2523](https://github.com/adap/flower/pull/2523), " +"[#2522](https://github.com/adap/flower/pull/2522))" msgstr "" -"**Améliorer le moteur du client virtuel** " -"([#1401](https://github.com/adap/flower/pull/1401), " -"[#1453](https://github.com/adap/flower/pull/1453))" +"Mettre à jour les outils de développement " +"([#1231](https://github.com/adap/flower/pull/1231), " +"[#1276](https://github.com/adap/flower/pull/1276), " +"[#1301](https://github.com/adap/flower/pull/1301), " +"[#1310](https://github.com/adap/flower/pull/1310))" -#: ../../source/ref-changelog.md:505 +#: ../../source/ref-changelog.md:290 +msgid "Add gRPC request-response capability to the C++ SDK." +msgstr "" + +#: ../../source/ref-changelog.md:292 +#, fuzzy msgid "" -"Some internals of the Virtual Client Engine have been revamped. The VCE " -"now uses Ray 2.0 under the hood, the value type of the `client_resources`" -" dictionary changed to `float` to allow fractions of resources to be " -"allocated." +"**Make HTTPS the new default** " +"([#2591](https://github.com/adap/flower/pull/2591), " +"[#2636](https://github.com/adap/flower/pull/2636))" msgstr "" -"Le VCE utilise maintenant Ray 2.0 sous le capot, le type de valeur du " -"dictionnaire `client_resources` a été remplacé par `float` pour permettre" -" l'allocation de fractions de ressources." +"**Exemple de code mis à jour** " +"([#1344](https://github.com/adap/flower/pull/1344), " +"[#1347](https://github.com/adap/flower/pull/1347))" -#: ../../source/ref-changelog.md:507 +#: ../../source/ref-changelog.md:294 msgid "" -"**Support optional** `Client`**/**`NumPyClient` **methods in Virtual " -"Client Engine**" +"Flower is moving to HTTPS by default. The new `flower-server` requires " +"passing `--certificates`, but users can enable `--insecure` to use HTTP " +"for prototyping. The same applies to `flower-client`, which can either " +"use user-provided credentials or gRPC-bundled certificates to connect to " +"an HTTPS-enabled server or requires opt-out via passing `--insecure` to " +"enable insecure HTTP connections." msgstr "" -"**Support optional** `Client`**/**`NumPyClient` **methods in Virtual " -"Client Engine**" -#: ../../source/ref-changelog.md:509 +#: ../../source/ref-changelog.md:296 msgid "" -"The Virtual Client Engine now has full support for optional `Client` (and" -" `NumPyClient`) methods." +"For backward compatibility, `start_client()` and `start_numpy_client()` " +"will still start in insecure mode by default. In a future release, " +"insecure connections will require user opt-in by passing `insecure=True`." msgstr "" -"Le moteur de client virtuel prend désormais en charge les méthodes " -"optionnelles `Client` (et `NumPyClient`)." -#: ../../source/ref-changelog.md:511 +#: ../../source/ref-changelog.md:298 +#, fuzzy msgid "" -"**Provide type information to packages using** `flwr` " -"([#1377](https://github.com/adap/flower/pull/1377))" +"**Unify client API** ([#2303](https://github.com/adap/flower/pull/2303), " +"[#2390](https://github.com/adap/flower/pull/2390), " +"[#2493](https://github.com/adap/flower/pull/2493))" msgstr "" -"**Fournir des informations de type aux paquets en utilisant** `flwr` " -"([#1377](https://github.com/adap/flower/pull/1377))" +"**Mettre à jour les exemples de code** " +"([#1291](https://github.com/adap/flower/pull/1291), " +"[#1286](https://github.com/adap/flower/pull/1286), " +"[#1282](https://github.com/adap/flower/pull/1282))" -#: ../../source/ref-changelog.md:513 +#: ../../source/ref-changelog.md:300 msgid "" -"The package `flwr` is now bundled with a `py.typed` file indicating that " -"the package is typed. This enables typing support for projects or " -"packages that use `flwr` by enabling them to improve their code using " -"static type checkers like `mypy`." +"Using the `client_fn`, Flower clients can interchangeably run as " +"standalone processes (i.e. via `start_client`) or in simulation (i.e. via" +" `start_simulation`) without requiring changes to how the client class is" +" defined and instantiated. The `to_client()` function is introduced to " +"convert a `NumPyClient` to a `Client`." msgstr "" -"Le paquet `flwr` est maintenant accompagné d'un fichier `py.typed` " -"indiquant que le paquet est typé. Cela permet de prendre en charge le " -"typage pour les projets ou les paquets qui utilisent `flwr` en leur " -"permettant d'améliorer leur code à l'aide de vérificateurs de types " -"statiques comme `mypy`." -#: ../../source/ref-changelog.md:515 +#: ../../source/ref-changelog.md:302 +#, fuzzy msgid "" -"**Updated code example** " -"([#1344](https://github.com/adap/flower/pull/1344), " -"[#1347](https://github.com/adap/flower/pull/1347))" +"**Add new** `Bulyan` **strategy** " +"([#1817](https://github.com/adap/flower/pull/1817), " +"[#1891](https://github.com/adap/flower/pull/1891))" msgstr "" -"**Exemple de code mis à jour** " -"([#1344](https://github.com/adap/flower/pull/1344), " -"[#1347](https://github.com/adap/flower/pull/1347))" +"**Nouvelles stratégies intégrées** " +"([#828](https://github.com/adap/flower/pull/828) " +"[#822](https://github.com/adap/flower/pull/822))" -#: ../../source/ref-changelog.md:517 +#: ../../source/ref-changelog.md:304 +#, fuzzy msgid "" -"The code examples covering scikit-learn and PyTorch Lightning have been " -"updated to work with the latest version of Flower." +"The new `Bulyan` strategy implements Bulyan by [El Mhamdi et al., " +"2018](https://arxiv.org/abs/1802.07927)" msgstr "" -"Les exemples de code couvrant scikit-learn et PyTorch Lightning ont été " -"mis à jour pour fonctionner avec la dernière version de Flower." +"La nouvelle stratégie `FedMedian` met en œuvre Federated Median " +"(FedMedian) par [Yin et al., 2018] " +"(https://arxiv.org/pdf/1803.01498v1.pdf)." -#: ../../source/ref-changelog.md:519 +#: ../../source/ref-changelog.md:306 +#, fuzzy msgid "" -"**Updated documentation** " -"([#1355](https://github.com/adap/flower/pull/1355), " -"[#1558](https://github.com/adap/flower/pull/1558), " -"[#1379](https://github.com/adap/flower/pull/1379), " -"[#1380](https://github.com/adap/flower/pull/1380), " -"[#1381](https://github.com/adap/flower/pull/1381), " -"[#1332](https://github.com/adap/flower/pull/1332), " -"[#1391](https://github.com/adap/flower/pull/1391), " -"[#1403](https://github.com/adap/flower/pull/1403), " -"[#1364](https://github.com/adap/flower/pull/1364), " -"[#1409](https://github.com/adap/flower/pull/1409), " -"[#1419](https://github.com/adap/flower/pull/1419), " -"[#1444](https://github.com/adap/flower/pull/1444), " -"[#1448](https://github.com/adap/flower/pull/1448), " -"[#1417](https://github.com/adap/flower/pull/1417), " -"[#1449](https://github.com/adap/flower/pull/1449), " -"[#1465](https://github.com/adap/flower/pull/1465), " -"[#1467](https://github.com/adap/flower/pull/1467))" +"**Add new** `XGB Bagging` **strategy** " +"([#2611](https://github.com/adap/flower/pull/2611))" msgstr "" -"**Documentation mise à jour** " -"([#1355](https://github.com/adap/flower/pull/1355), " -"[#1558](https://github.com/adap/flower/pull/1558), " -"[#1379](https://github.com/adap/flower/pull/1379), " -"[#1380](https://github.com/adap/flower/pull/1380), " -"[#1381](https://github.com/adap/flower/pull/1381), " -"[#1332](https://github.com/adap/flower/pull/1332), " -"[#1391](https://github.com/adap/flower/pull/1391), " -"[#1403](https://github.com/adap/flower/pull/1403), " -"[#1364](https://github.com/adap/flower/pull/1364), " -"[#1409](https://github.com/adap/flower/pull/1409), " -"[#1419](https://github.com/adap/flower/pull/1419), " -"[#1444](https://github.com/adap/flower/pull/1444), " -"[#1448](https://github.com/adap/flower/pull/1448), " -"[#1417](https://github.com/adap/flower/pull/1417), " -"[#1449](https://github.com/adap/flower/pull/1449), " -"[#1465](https://github.com/adap/flower/pull/1465), " -"[#1467](https://github.com/adap/flower/pull/1467))" +"**Ajouter une nouvelle stratégie `FedProx`** " +"([#1619](https://github.com/adap/flower/pull/1619))" -#: ../../source/ref-changelog.md:521 +#: ../../source/ref-changelog.md:308 ../../source/ref-changelog.md:310 +#, fuzzy msgid "" -"There have been so many documentation updates that it doesn't even make " -"sense to list them individually." +"**Introduce `WorkloadState`** " +"([#2564](https://github.com/adap/flower/pull/2564), " +"[#2632](https://github.com/adap/flower/pull/2632))" msgstr "" -"Il y a eu tellement de mises à jour de la documentation que cela n'a même" -" pas de sens de les énumérer individuellement." +"**Nouvelles stratégies intégrées** " +"([#828](https://github.com/adap/flower/pull/828) " +"[#822](https://github.com/adap/flower/pull/822))" -#: ../../source/ref-changelog.md:523 +#: ../../source/ref-changelog.md:314 +#, fuzzy msgid "" -"**Restructured documentation** " -"([#1387](https://github.com/adap/flower/pull/1387))" +"FedProx ([#2210](https://github.com/adap/flower/pull/2210), " +"[#2286](https://github.com/adap/flower/pull/2286), " +"[#2509](https://github.com/adap/flower/pull/2509))" msgstr "" -"**Documentation restructurée** " -"([#1387](https://github.com/adap/flower/pull/1387))" +"**Mettre à jour les exemples de code** " +"([#1291](https://github.com/adap/flower/pull/1291), " +"[#1286](https://github.com/adap/flower/pull/1286), " +"[#1282](https://github.com/adap/flower/pull/1282))" -#: ../../source/ref-changelog.md:525 +#: ../../source/ref-changelog.md:316 +#, fuzzy msgid "" -"The documentation has been restructured to make it easier to navigate. " -"This is just the first step in a larger effort to make the Flower " -"documentation the best documentation of any project ever. Stay tuned!" +"Baselines Docs ([#2290](https://github.com/adap/flower/pull/2290), " +"[#2400](https://github.com/adap/flower/pull/2400))" msgstr "" -"La documentation a été restructurée pour faciliter la navigation. Ce " -"n'est que la première étape d'un effort plus important visant à faire de " -"la documentation de Flower la meilleure documentation de tous les projets" +"**Nouvel exemple de code JAX** " +"([#906](https://github.com/adap/flower/pull/906), " +"[#1143](https://github.com/adap/flower/pull/1143))" -#: ../../source/ref-changelog.md:527 +#: ../../source/ref-changelog.md:318 +#, fuzzy msgid "" -"**Open in Colab button** " -"([#1389](https://github.com/adap/flower/pull/1389))" +"FedMLB ([#2340](https://github.com/adap/flower/pull/2340), " +"[#2507](https://github.com/adap/flower/pull/2507))" msgstr "" -"**Ouvrir dans le bouton Colab** " -"([#1389](https://github.com/adap/flower/pull/1389))" +"**Exemple de code mis à jour** " +"([#1344](https://github.com/adap/flower/pull/1344), " +"[#1347](https://github.com/adap/flower/pull/1347))" -#: ../../source/ref-changelog.md:529 +#: ../../source/ref-changelog.md:320 +#, fuzzy msgid "" -"The four parts of the Flower Federated Learning Tutorial now come with a " -"new `Open in Colab` button. No need to install anything on your local " -"machine, you can now use and learn about Flower in your browser, it's " -"only a single click away." +"TAMUNA ([#2254](https://github.com/adap/flower/pull/2254), " +"[#2508](https://github.com/adap/flower/pull/2508))" msgstr "" -"Les quatre parties du didacticiel d'apprentissage fédéré Flower sont " -"maintenant accompagnées d'un nouveau bouton \"Ouvrir dans Colab\". Pas " -"besoin d'installer quoi que ce soit sur ta machine locale, tu peux " -"maintenant utiliser et apprendre à connaître Flower dans ton navigateur, " -"il te suffit d'un simple clic." +"**Nouvelles stratégies intégrées** " +"([#828](https://github.com/adap/flower/pull/828) " +"[#822](https://github.com/adap/flower/pull/822))" -#: ../../source/ref-changelog.md:531 -msgid "" -"**Improved tutorial** ([#1468](https://github.com/adap/flower/pull/1468)," -" [#1470](https://github.com/adap/flower/pull/1470), " -"[#1472](https://github.com/adap/flower/pull/1472), " -"[#1473](https://github.com/adap/flower/pull/1473), " -"[#1474](https://github.com/adap/flower/pull/1474), " -"[#1475](https://github.com/adap/flower/pull/1475))" -msgstr "" -"**Tutoriel amélioré** ([#1468](https://github.com/adap/flower/pull/1468)," -" [#1470](https://github.com/adap/flower/pull/1470), " -"[#1472](https://github.com/adap/flower/pull/1472), " -"[#1473](https://github.com/adap/flower/pull/1473), " -"[#1474](https://github.com/adap/flower/pull/1474), " -"[#1475](https://github.com/adap/flower/pull/1475))" +#: ../../source/ref-changelog.md:322 +#, fuzzy +msgid "FedMeta [#2438](https://github.com/adap/flower/pull/2438)" +msgstr "Nouvelle référence API ([#554](https://github.com/adap/flower/pull/554))" -#: ../../source/ref-changelog.md:533 -msgid "" -"The Flower Federated Learning Tutorial has two brand-new parts covering " -"custom strategies (still WIP) and the distinction between `Client` and " -"`NumPyClient`. The existing parts one and two have also been improved " -"(many small changes and fixes)." +#: ../../source/ref-changelog.md:324 +#, fuzzy +msgid "FjORD [#2431](https://github.com/adap/flower/pull/2431)" msgstr "" -"Le tutoriel sur l'apprentissage fédéré des fleurs a deux toutes nouvelles" -" parties couvrant les stratégies personnalisées (encore WIP) et la " -"distinction entre `Client` et `NumPyClient`. Les parties un et deux " -"existantes ont également été améliorées (beaucoup de petits changements " -"et de corrections)." +"Amélioration de la documentation sur le serveur gRPC " +"([#841](https://github.com/adap/flower/pull/841))" -#: ../../source/ref-changelog.md:539 -msgid "v1.0.0 (2022-07-28)" -msgstr "v1.0.0 (2022-07-28)" +#: ../../source/ref-changelog.md:326 +#, fuzzy +msgid "MOON [#2421](https://github.com/adap/flower/pull/2421)" +msgstr "" +"**Ajouter une nouvelle stratégie `FedProx`** " +"([#1619](https://github.com/adap/flower/pull/1619))" -#: ../../source/ref-changelog.md:541 -msgid "Highlights" -msgstr "Points forts" +#: ../../source/ref-changelog.md:328 +#, fuzzy +msgid "DepthFL [#2295](https://github.com/adap/flower/pull/2295)" +msgstr "" +"**Ajouter une nouvelle stratégie `FedProx`** " +"([#1619](https://github.com/adap/flower/pull/1619))" -#: ../../source/ref-changelog.md:543 -msgid "Stable **Virtual Client Engine** (accessible via `start_simulation`)" -msgstr "Moteur de client virtuel stable** (accessible via `start_simulation`)" +#: ../../source/ref-changelog.md:330 +#, fuzzy +msgid "FedPer [#2266](https://github.com/adap/flower/pull/2266)" +msgstr "Nouvelle référence API ([#554](https://github.com/adap/flower/pull/554))" -#: ../../source/ref-changelog.md:544 -msgid "All `Client`/`NumPyClient` methods are now optional" -msgstr "Toutes les méthodes `Client`/`NumPyClient` sont maintenant optionnelles" +#: ../../source/ref-changelog.md:332 +#, fuzzy +msgid "FedWav2vec [#2551](https://github.com/adap/flower/pull/2551)" +msgstr "Nouvelle référence API ([#554](https://github.com/adap/flower/pull/554))" -#: ../../source/ref-changelog.md:545 -msgid "Configurable `get_parameters`" -msgstr "`get_parameters` configurable" +#: ../../source/ref-changelog.md:334 +#, fuzzy +msgid "niid-Bench [#2428](https://github.com/adap/flower/pull/2428)" +msgstr "Nouvelle référence API ([#554](https://github.com/adap/flower/pull/554))" -#: ../../source/ref-changelog.md:546 +#: ../../source/ref-changelog.md:336 +#, fuzzy msgid "" -"Tons of small API cleanups resulting in a more coherent developer " -"experience" +"FedBN ([#2608](https://github.com/adap/flower/pull/2608), " +"[#2615](https://github.com/adap/flower/pull/2615))" msgstr "" -"Des tonnes de petits nettoyages d'API résultant en une expérience plus " -"cohérente pour les développeurs" +"**Nouvelles stratégies intégrées** " +"([#828](https://github.com/adap/flower/pull/828) " +"[#822](https://github.com/adap/flower/pull/822))" -#: ../../source/ref-changelog.md:550 +#: ../../source/ref-changelog.md:338 +#, fuzzy msgid "" -"We would like to give our **special thanks** to all the contributors who " -"made Flower 1.0 possible (in reverse [GitHub " -"Contributors](https://github.com/adap/flower/graphs/contributors) order):" +"**General updates to Flower Examples** " +"([#2384](https://github.com/adap/flower/pull/2384), " +"[#2425](https://github.com/adap/flower/pull/2425), " +"[#2526](https://github.com/adap/flower/pull/2526), " +"[#2302](https://github.com/adap/flower/pull/2302), " +"[#2545](https://github.com/adap/flower/pull/2545))" msgstr "" -"Nous tenons à remercier **particulièrement** tous les contributeurs qui " -"ont rendu Flower 1.0 possible (dans l'ordre inverse de [GitHub " -"Contributors](https://github.com/adap/flower/graphs/contributors)) :" +"Mettre à jour les outils de développement " +"([#1231](https://github.com/adap/flower/pull/1231), " +"[#1276](https://github.com/adap/flower/pull/1276), " +"[#1301](https://github.com/adap/flower/pull/1301), " +"[#1310](https://github.com/adap/flower/pull/1310))" -#: ../../source/ref-changelog.md:552 +#: ../../source/ref-changelog.md:340 +#, fuzzy msgid "" -"[@rtaiello](https://github.com/rtaiello), " -"[@g-pichler](https://github.com/g-pichler), [@rob-" -"luke](https://github.com/rob-luke), [@andreea-zaharia](https://github.com" -"/andreea-zaharia), [@kinshukdua](https://github.com/kinshukdua), " -"[@nfnt](https://github.com/nfnt), " -"[@tatiana-s](https://github.com/tatiana-s), " -"[@TParcollet](https://github.com/TParcollet), " -"[@vballoli](https://github.com/vballoli), " -"[@negedng](https://github.com/negedng), " -"[@RISHIKESHAVAN](https://github.com/RISHIKESHAVAN), " -"[@hei411](https://github.com/hei411), " -"[@SebastianSpeitel](https://github.com/SebastianSpeitel), " -"[@AmitChaulwar](https://github.com/AmitChaulwar), " -"[@Rubiel1](https://github.com/Rubiel1), [@FANTOME-PAN](https://github.com" -"/FANTOME-PAN), [@Rono-BC](https://github.com/Rono-BC), " -"[@lbhm](https://github.com/lbhm), " -"[@sishtiaq](https://github.com/sishtiaq), " -"[@remde](https://github.com/remde), [@Jueun-Park](https://github.com" -"/Jueun-Park), [@architjen](https://github.com/architjen), " -"[@PratikGarai](https://github.com/PratikGarai), " -"[@mrinaald](https://github.com/mrinaald), " -"[@zliel](https://github.com/zliel), " -"[@MeiruiJiang](https://github.com/MeiruiJiang), " -"[@sancarlim](https://github.com/sancarlim), " -"[@gubertoli](https://github.com/gubertoli), " -"[@Vingt100](https://github.com/Vingt100), " -"[@MakGulati](https://github.com/MakGulati), " -"[@cozek](https://github.com/cozek), " -"[@jafermarq](https://github.com/jafermarq), " -"[@sisco0](https://github.com/sisco0), " -"[@akhilmathurs](https://github.com/akhilmathurs), " -"[@CanTuerk](https://github.com/CanTuerk), " -"[@mariaboerner1987](https://github.com/mariaboerner1987), " -"[@pedropgusmao](https://github.com/pedropgusmao), " -"[@tanertopal](https://github.com/tanertopal), " -"[@danieljanes](https://github.com/danieljanes)." +"**General updates to Flower Baselines** " +"([#2301](https://github.com/adap/flower/pull/2301), " +"[#2305](https://github.com/adap/flower/pull/2305), " +"[#2307](https://github.com/adap/flower/pull/2307), " +"[#2327](https://github.com/adap/flower/pull/2327), " +"[#2435](https://github.com/adap/flower/pull/2435), " +"[#2462](https://github.com/adap/flower/pull/2462), " +"[#2463](https://github.com/adap/flower/pull/2463), " +"[#2461](https://github.com/adap/flower/pull/2461), " +"[#2469](https://github.com/adap/flower/pull/2469), " +"[#2466](https://github.com/adap/flower/pull/2466), " +"[#2471](https://github.com/adap/flower/pull/2471), " +"[#2472](https://github.com/adap/flower/pull/2472), " +"[#2470](https://github.com/adap/flower/pull/2470))" msgstr "" -"[@rtaiello](https://github.com/rtaiello), " -"[@g-pichler](https://github.com/g-pichler), [@rob-" -"luke](https://github.com/rob-luke), [@andreea-zaharia](https://github.com" -"/andreea-zaharia), [@kinshukdua](https://github.com/kinshukdua), " -"[@nfnt](https://github.com/nfnt), " -"[@tatiana-s](https://github.com/tatiana-s), " -"[@TParcollet](https://github.com/TParcollet), " -"[@vballoli](https://github.com/vballoli), " -"[@negedng](https://github.com/negedng), " -"[@RISHIKESHAVAN](https://github.com/RISHIKESHAVAN), " -"[@hei411](https://github.com/hei411), " -"[@SebastianSpeitel](https://github.com/SebastianSpeitel), " -"[@AmitChaulwar](https://github.com/AmitChaulwar), " -"[@Rubiel1](https://github.com/Rubiel1), [@FANTOME-PAN](https://github.com" -"/FANTOME-PAN), [@Rono-BC](https://github.com/Rono-BC), " -"[@lbhm](https://github.com/lbhm), " -"[@sishtiaq](https://github.com/sishtiaq), " -"[@remde](https://github.com/remde), [@Jueun-Park](https://github.com" -"/Jueun-Park), [@architjen](https://github.com/architjen), " -"[@PratikGarai](https://github.com/PratikGarai), [@mrinaald](" +"**Améliorations générales** " +"([#1491](https://github.com/adap/flower/pull/1491), " +"[#1504](https://github.com/adap/flower/pull/1504), " +"[#1506](https://github.com/adap/flower/pull/1506), " +"[#1514](https://github.com/adap/flower/pull/1514), " +"[#1522](https://github.com/adap/flower/pull/1522), " +"[#1523](https://github.com/adap/flower/pull/1523), " +"[#1526](https://github.com/adap/flower/pull/1526), " +"[#1528](https://github.com/adap/flower/pull/1528), " +"[#1547](https://github.com/adap/flower/pull/1547), " +"[#1549](https://github.com/adap/flower/pull/1549), " +"[#1560](https://github.com/adap/flower/pull/1560), " +"[#1564](https://github.com/adap/flower/pull/1564), " +"[#1566](https://github.com/adap/flower/pull/1566))" -#: ../../source/ref-changelog.md:556 +#: ../../source/ref-changelog.md:342 +#, fuzzy msgid "" -"**All arguments must be passed as keyword arguments** " -"([#1338](https://github.com/adap/flower/pull/1338))" +"**General updates to the simulation engine** " +"([#2331](https://github.com/adap/flower/pull/2331), " +"[#2447](https://github.com/adap/flower/pull/2447), " +"[#2448](https://github.com/adap/flower/pull/2448), " +"[#2294](https://github.com/adap/flower/pull/2294))" msgstr "" -"**Tous les arguments doivent être passés comme des arguments de mot-clé**" -" ([#1338](https://github.com/adap/flower/pull/1338))" +"Mettre à jour les outils de développement " +"([#1231](https://github.com/adap/flower/pull/1231), " +"[#1276](https://github.com/adap/flower/pull/1276), " +"[#1301](https://github.com/adap/flower/pull/1301), " +"[#1310](https://github.com/adap/flower/pull/1310))" -#: ../../source/ref-changelog.md:558 +#: ../../source/ref-changelog.md:344 #, fuzzy msgid "" -"Pass all arguments as keyword arguments, positional arguments are not " -"longer supported. Code that uses positional arguments (e.g., " -"`start_client(\"127.0.0.1:8080\", FlowerClient())`) must add the keyword " -"for each positional argument (e.g., " -"`start_client(server_address=\"127.0.0.1:8080\", " -"client=FlowerClient())`)." +"**General updates to Flower SDKs** " +"([#2288](https://github.com/adap/flower/pull/2288), " +"[#2429](https://github.com/adap/flower/pull/2429), " +"[#2555](https://github.com/adap/flower/pull/2555), " +"[#2543](https://github.com/adap/flower/pull/2543), " +"[#2544](https://github.com/adap/flower/pull/2544), " +"[#2597](https://github.com/adap/flower/pull/2597), " +"[#2623](https://github.com/adap/flower/pull/2623))" msgstr "" -"Le code qui utilise des arguments positionnels (par exemple, " -"``start_client(\"127.0.0.1:8080\", FlowerClient())`) doit ajouter le mot-" -"clé pour chaque argument positionnel (par exemple, " -"``start_client(server_address=\"127.0.0.1:8080\", " -"client=FlowerClient())`)." +"**Tutoriel amélioré** ([#1468](https://github.com/adap/flower/pull/1468)," +" [#1470](https://github.com/adap/flower/pull/1470), " +"[#1472](https://github.com/adap/flower/pull/1472), " +"[#1473](https://github.com/adap/flower/pull/1473), " +"[#1474](https://github.com/adap/flower/pull/1474), " +"[#1475](https://github.com/adap/flower/pull/1475))" -#: ../../source/ref-changelog.md:560 +#: ../../source/ref-changelog.md:346 msgid "" -"**Introduce configuration object** `ServerConfig` **in** `start_server` " -"**and** `start_simulation` " -"([#1317](https://github.com/adap/flower/pull/1317))" +"**General improvements** " +"([#2309](https://github.com/adap/flower/pull/2309), " +"[#2310](https://github.com/adap/flower/pull/2310), " +"[#2313](https://github.com/adap/flower/pull/2313), " +"[#2316](https://github.com/adap/flower/pull/2316), " +"[#2317](https://github.com/adap/flower/pull/2317), " +"[#2349](https://github.com/adap/flower/pull/2349), " +"[#2360](https://github.com/adap/flower/pull/2360), " +"[#2402](https://github.com/adap/flower/pull/2402), " +"[#2446](https://github.com/adap/flower/pull/2446), " +"[#2561](https://github.com/adap/flower/pull/2561), " +"[#2273](https://github.com/adap/flower/pull/2273), " +"[#2267](https://github.com/adap/flower/pull/2267), " +"[#2274](https://github.com/adap/flower/pull/2274), " +"[#2275](https://github.com/adap/flower/pull/2275), " +"[#2432](https://github.com/adap/flower/pull/2432), " +"[#2251](https://github.com/adap/flower/pull/2251), " +"[#2321](https://github.com/adap/flower/pull/2321), " +"[#1936](https://github.com/adap/flower/pull/1936), " +"[#2408](https://github.com/adap/flower/pull/2408), " +"[#2413](https://github.com/adap/flower/pull/2413), " +"[#2401](https://github.com/adap/flower/pull/2401), " +"[#2531](https://github.com/adap/flower/pull/2531), " +"[#2534](https://github.com/adap/flower/pull/2534), " +"[#2535](https://github.com/adap/flower/pull/2535), " +"[#2521](https://github.com/adap/flower/pull/2521), " +"[#2553](https://github.com/adap/flower/pull/2553), " +"[#2596](https://github.com/adap/flower/pull/2596))" msgstr "" -"**Introduire l'objet de configuration** `ServerConfig` **dans** " -"`start_server` **et** `start_simulation` " -"([#1317](https://github.com/adap/flower/pull/1317))" -#: ../../source/ref-changelog.md:562 -msgid "" -"Instead of a config dictionary `{\"num_rounds\": 3, \"round_timeout\": " -"600.0}`, `start_server` and `start_simulation` now expect a configuration" -" object of type `flwr.server.ServerConfig`. `ServerConfig` takes the same" -" arguments that as the previous config dict, but it makes writing type-" -"safe code easier and the default parameters values more transparent." +#: ../../source/ref-changelog.md:348 ../../source/ref-changelog.md:438 +#: ../../source/ref-changelog.md:502 ../../source/ref-changelog.md:556 +#: ../../source/ref-changelog.md:623 +msgid "Flower received many improvements under the hood, too many to list here." msgstr "" -"Au lieu d'un dictionnaire de configuration `{\"num_rounds\" : 3, " -"\"round_timeout\" : 600.0}`, `start_server` et `start_simulation` " -"attendent maintenant un objet de configuration de type " -"`flwr.server.ServerConfig`. `ServerConfig` prend les mêmes arguments que " -"le dict de configuration précédent, mais il rend l'écriture de code " -"sécurisé plus facile et les valeurs des paramètres par défaut plus " -"transparentes." +"Flower a reçu de nombreuses améliorations sous le capot, trop nombreuses " +"pour être énumérées ici." -#: ../../source/ref-changelog.md:564 +#: ../../source/ref-changelog.md:352 +#, fuzzy msgid "" -"**Rename built-in strategy parameters for clarity** " -"([#1334](https://github.com/adap/flower/pull/1334))" +"**Remove support for Python 3.7** " +"([#2280](https://github.com/adap/flower/pull/2280), " +"[#2299](https://github.com/adap/flower/pull/2299), " +"[#2304](https://github.com/adap/flower/pull/2304), " +"[#2306](https://github.com/adap/flower/pull/2306), " +"[#2355](https://github.com/adap/flower/pull/2355), " +"[#2356](https://github.com/adap/flower/pull/2356))" msgstr "" -"**Renommer les paramètres de la stratégie intégrée pour plus de clarté** " -"([#1334](https://github.com/adap/flower/pull/1334))" +"**Nouvel exemple de code MLCube** " +"([#779](https://github.com/adap/flower/pull/779), " +"[#1034](https://github.com/adap/flower/pull/1034), " +"[#1065](https://github.com/adap/flower/pull/1065), " +"[#1090](https://github.com/adap/flower/pull/1090))" -#: ../../source/ref-changelog.md:566 +#: ../../source/ref-changelog.md:354 msgid "" -"The following built-in strategy parameters were renamed to improve " -"readability and consistency with other API's:" +"Python 3.7 support was deprecated in Flower 1.5, and this release removes" +" support. Flower now requires Python 3.8." msgstr "" -"Les paramètres de stratégie intégrés suivants ont été renommés pour " -"améliorer la lisibilité et la cohérence avec d'autres API :" - -#: ../../source/ref-changelog.md:568 -msgid "`fraction_eval` --> `fraction_evaluate`" -msgstr "`fraction_eval` --> `fraction_evaluate`" - -#: ../../source/ref-changelog.md:569 -msgid "`min_eval_clients` --> `min_evaluate_clients`" -msgstr "`min_eval_clients` --> `min_evaluate_clients`" -#: ../../source/ref-changelog.md:570 -msgid "`eval_fn` --> `evaluate_fn`" -msgstr "`eval_fn` --> `evaluate_fn`" - -#: ../../source/ref-changelog.md:572 +#: ../../source/ref-changelog.md:356 +#, fuzzy msgid "" -"**Update default arguments of built-in strategies** " -"([#1278](https://github.com/adap/flower/pull/1278))" +"**Remove experimental argument** `rest` **from** `start_client` " +"([#2324](https://github.com/adap/flower/pull/2324))" msgstr "" -"**Mettre à jour les arguments par défaut des stratégies intégrées** " -"([#1278](https://github.com/adap/flower/pull/1278))" +"**Supprimer les stratégies expérimentales** " +"([#1280](https://github.com/adap/flower/pull/1280))" -#: ../../source/ref-changelog.md:574 +#: ../../source/ref-changelog.md:358 msgid "" -"All built-in strategies now use `fraction_fit=1.0` and " -"`fraction_evaluate=1.0`, which means they select *all* currently " -"available clients for training and evaluation. Projects that relied on " -"the previous default values can get the previous behaviour by " -"initializing the strategy in the following way:" +"The (still experimental) argument `rest` was removed from `start_client` " +"and `start_numpy_client`. Use `transport=\"rest\"` to opt into the " +"experimental REST API instead." msgstr "" -"Toutes les stratégies intégrées utilisent désormais `fraction_fit=1.0` et" -" `fraction_evaluate=1.0`, ce qui signifie qu'elles sélectionnent *tous* " -"les clients actuellement disponibles pour l'entraînement et l'évaluation." -" Les projets qui s'appuyaient sur les valeurs par défaut précédentes " -"peuvent retrouver le comportement antérieur en initialisant la stratégie " -"de la manière suivante :" - -#: ../../source/ref-changelog.md:576 -msgid "`strategy = FedAvg(fraction_fit=0.1, fraction_evaluate=0.1)`" -msgstr "`stratégie = FedAvg(fraction_fit=0.1, fraction_evaluate=0.1)`" -#: ../../source/ref-changelog.md:578 -msgid "" -"**Add** `server_round` **to** `Strategy.evaluate` " -"([#1334](https://github.com/adap/flower/pull/1334))" -msgstr "" -"**Ajouter** `server_round` **à** `Strategy.evaluate` " -"([#1334](https://github.com/adap/flower/pull/1334))" +#: ../../source/ref-changelog.md:360 +#, fuzzy +msgid "v1.5.0 (2023-08-31)" +msgstr "v1.4.0 (2023-04-21)" -#: ../../source/ref-changelog.md:580 +#: ../../source/ref-changelog.md:366 msgid "" -"The `Strategy` method `evaluate` now receives the current round of " -"federated learning/evaluation as the first parameter." +"`Adam Narozniak`, `Anass Anhari`, `Charles Beauville`, `Dana-Farber`, " +"`Daniel J. Beutel`, `Daniel Nata Nugraha`, `Edoardo Gabrielli`, `Gustavo " +"Bertoli`, `Heng Pan`, `Javier`, `Mahdi`, `Steven Hé (Sīchàng)`, `Taner " +"Topal`, `achiverram28`, `danielnugraha`, `eunchung`, `ruthgal` " msgstr "" -"La méthode `Stratégie` `évaluer` reçoit maintenant le cycle actuel " -"d'apprentissage/évaluation fédéré comme premier paramètre." -#: ../../source/ref-changelog.md:582 +#: ../../source/ref-changelog.md:370 +#, fuzzy msgid "" -"**Add** `server_round` **and** `config` **parameters to** `evaluate_fn` " -"([#1334](https://github.com/adap/flower/pull/1334))" +"**Introduce new simulation engine** " +"([#1969](https://github.com/adap/flower/pull/1969), " +"[#2221](https://github.com/adap/flower/pull/2221), " +"[#2248](https://github.com/adap/flower/pull/2248))" msgstr "" -"**Ajouter** `server_round` **et** `config` **paramètres à** `evaluate_fn`" -" ([#1334](https://github.com/adap/flower/pull/1334))" +"**Introduire la télémétrie optionnelle** " +"([#1533](https://github.com/adap/flower/pull/1533), " +"[#1544](https://github.com/adap/flower/pull/1544), " +"[#1584](https://github.com/adap/flower/pull/1584))" -#: ../../source/ref-changelog.md:584 +#: ../../source/ref-changelog.md:372 msgid "" -"The `evaluate_fn` passed to built-in strategies like `FedAvg` now takes " -"three parameters: (1) The current round of federated learning/evaluation " -"(`server_round`), (2) the model parameters to evaluate (`parameters`), " -"and (3) a config dictionary (`config`)." +"The new simulation engine has been rewritten from the ground up, yet it " +"remains fully backwards compatible. It offers much improved stability and" +" memory handling, especially when working with GPUs. Simulations " +"transparently adapt to different settings to scale simulation in CPU-" +"only, CPU+GPU, multi-GPU, or multi-node multi-GPU environments." msgstr "" -"Le `evaluate_fn` passé aux stratégies intégrées comme `FedAvg` prend " -"maintenant trois paramètres : (1) le cycle actuel " -"d'apprentissage/évaluation fédéré (`server_round`), (2) les paramètres du" -" modèle à évaluer (`parameters`), et (3) un dictionnaire de configuration" -" (`config`)." -#: ../../source/ref-changelog.md:586 +#: ../../source/ref-changelog.md:374 msgid "" -"**Rename** `rnd` **to** `server_round` " -"([#1321](https://github.com/adap/flower/pull/1321))" +"Comprehensive documentation includes a new [how-to run " +"simulations](https://flower.ai/docs/framework/how-to-run-" +"simulations.html) guide, new [simulation-" +"pytorch](https://flower.ai/docs/examples/simulation-pytorch.html) and " +"[simulation-tensorflow](https://flower.ai/docs/examples/simulation-" +"tensorflow.html) notebooks, and a new [YouTube tutorial " +"series](https://www.youtube.com/watch?v=cRebUIGB5RU&list=PLNG4feLHqCWlnj8a_E1A_n5zr2-8pafTB)." msgstr "" -"**Rename** `rnd` **to** `server_round` " -"([#1321](https://github.com/adap/flower/pull/1321))" -#: ../../source/ref-changelog.md:588 +#: ../../source/ref-changelog.md:376 msgid "" -"Several Flower methods and functions (`evaluate_fn`, `configure_fit`, " -"`aggregate_fit`, `configure_evaluate`, `aggregate_evaluate`) receive the " -"current round of federated learning/evaluation as their first parameter. " -"To improve reaability and avoid confusion with *random*, this parameter " -"has been renamed from `rnd` to `server_round`." +"**Restructure Flower Docs** " +"([#1824](https://github.com/adap/flower/pull/1824), " +"[#1865](https://github.com/adap/flower/pull/1865), " +"[#1884](https://github.com/adap/flower/pull/1884), " +"[#1887](https://github.com/adap/flower/pull/1887), " +"[#1919](https://github.com/adap/flower/pull/1919), " +"[#1922](https://github.com/adap/flower/pull/1922), " +"[#1920](https://github.com/adap/flower/pull/1920), " +"[#1923](https://github.com/adap/flower/pull/1923), " +"[#1924](https://github.com/adap/flower/pull/1924), " +"[#1962](https://github.com/adap/flower/pull/1962), " +"[#2006](https://github.com/adap/flower/pull/2006), " +"[#2133](https://github.com/adap/flower/pull/2133), " +"[#2203](https://github.com/adap/flower/pull/2203), " +"[#2215](https://github.com/adap/flower/pull/2215), " +"[#2122](https://github.com/adap/flower/pull/2122), " +"[#2223](https://github.com/adap/flower/pull/2223), " +"[#2219](https://github.com/adap/flower/pull/2219), " +"[#2232](https://github.com/adap/flower/pull/2232), " +"[#2233](https://github.com/adap/flower/pull/2233), " +"[#2234](https://github.com/adap/flower/pull/2234), " +"[#2235](https://github.com/adap/flower/pull/2235), " +"[#2237](https://github.com/adap/flower/pull/2237), " +"[#2238](https://github.com/adap/flower/pull/2238), " +"[#2242](https://github.com/adap/flower/pull/2242), " +"[#2231](https://github.com/adap/flower/pull/2231), " +"[#2243](https://github.com/adap/flower/pull/2243), " +"[#2227](https://github.com/adap/flower/pull/2227))" msgstr "" -"Plusieurs méthodes et fonctions de Flower (`evaluate_fn`, " -"`configure_fit`, `aggregate_fit`, `configure_evaluate`, " -"`aggregate_evaluate`) reçoivent le cycle actuel " -"d'apprentissage/évaluation fédéré comme premier paramètre. Pour améliorer" -" la fiabilité et éviter la confusion avec *random*, ce paramètre a été " -"renommé de `rnd` à `server_round`." -#: ../../source/ref-changelog.md:590 +#: ../../source/ref-changelog.md:378 msgid "" -"**Move** `flwr.dataset` **to** `flwr_baselines` " -"([#1273](https://github.com/adap/flower/pull/1273))" +"Much effort went into a completely restructured Flower docs experience. " +"The documentation on [flower.ai/docs](https://flower.ai/docs) is now " +"divided into Flower Framework, Flower Baselines, Flower Android SDK, " +"Flower iOS SDK, and code example projects." msgstr "" -"**Déplacer** `flwr.dataset` **vers** `flwr_baselines` " -"([#1273](https://github.com/adap/flower/pull/1273))" - -#: ../../source/ref-changelog.md:592 -msgid "The experimental package `flwr.dataset` was migrated to Flower Baselines." -msgstr "Le paquet expérimental `flwr.dataset` a été migré vers Flower Baselines." -#: ../../source/ref-changelog.md:594 +#: ../../source/ref-changelog.md:380 +#, fuzzy msgid "" -"**Remove experimental strategies** " -"([#1280](https://github.com/adap/flower/pull/1280))" +"**Introduce Flower Swift SDK** " +"([#1858](https://github.com/adap/flower/pull/1858), " +"[#1897](https://github.com/adap/flower/pull/1897))" msgstr "" -"**Supprimer les stratégies expérimentales** " -"([#1280](https://github.com/adap/flower/pull/1280))" +"**Introduction du SDK iOS (aperçu)** " +"([#1621](https://github.com/adap/flower/pull/1621), " +"[#1764](https://github.com/adap/flower/pull/1764))" -#: ../../source/ref-changelog.md:596 +#: ../../source/ref-changelog.md:382 msgid "" -"Remove unmaintained experimental strategies (`FastAndSlow`, `FedFSv0`, " -"`FedFSv1`)." +"This is the first preview release of the Flower Swift SDK. Flower support" +" on iOS is improving, and alongside the Swift SDK and code example, there" +" is now also an iOS quickstart tutorial." msgstr "" -"Supprimer les stratégies expérimentales non maintenues (`FastAndSlow`, " -"`FedFSv0`, `FedFSv1`)." -#: ../../source/ref-changelog.md:598 +#: ../../source/ref-changelog.md:384 +#, fuzzy msgid "" -"**Rename** `Weights` **to** `NDArrays` " -"([#1258](https://github.com/adap/flower/pull/1258), " -"[#1259](https://github.com/adap/flower/pull/1259))" +"**Introduce Flower Android SDK** " +"([#2131](https://github.com/adap/flower/pull/2131))" msgstr "" -"**Rename** `Weights` **to** `NDArrays` " -"([#1258](https://github.com/adap/flower/pull/1258), " -"[#1259](https://github.com/adap/flower/pull/1259))" +"**Introduire une nouvelle ligne de base pour les fleurs : FedAvg " +"FEMNIST** ([#1655](https://github.com/adap/flower/pull/1655))" -#: ../../source/ref-changelog.md:600 +#: ../../source/ref-changelog.md:386 msgid "" -"`flwr.common.Weights` was renamed to `flwr.common.NDArrays` to better " -"capture what this type is all about." +"This is the first preview release of the Flower Kotlin SDK. Flower " +"support on Android is improving, and alongside the Kotlin SDK and code " +"example, there is now also an Android quickstart tutorial." msgstr "" -"`flwr.common.Weights` a été renommé en `flwr.common.NDArys` pour mieux " -"rendre compte de la nature de ce type." -#: ../../source/ref-changelog.md:602 +#: ../../source/ref-changelog.md:388 +#, fuzzy msgid "" -"**Remove antiquated** `force_final_distributed_eval` **from** " -"`start_server` ([#1258](https://github.com/adap/flower/pull/1258), " -"[#1259](https://github.com/adap/flower/pull/1259))" +"**Introduce new end-to-end testing infrastructure** " +"([#1842](https://github.com/adap/flower/pull/1842), " +"[#2071](https://github.com/adap/flower/pull/2071), " +"[#2072](https://github.com/adap/flower/pull/2072), " +"[#2068](https://github.com/adap/flower/pull/2068), " +"[#2067](https://github.com/adap/flower/pull/2067), " +"[#2069](https://github.com/adap/flower/pull/2069), " +"[#2073](https://github.com/adap/flower/pull/2073), " +"[#2070](https://github.com/adap/flower/pull/2070), " +"[#2074](https://github.com/adap/flower/pull/2074), " +"[#2082](https://github.com/adap/flower/pull/2082), " +"[#2084](https://github.com/adap/flower/pull/2084), " +"[#2093](https://github.com/adap/flower/pull/2093), " +"[#2109](https://github.com/adap/flower/pull/2109), " +"[#2095](https://github.com/adap/flower/pull/2095), " +"[#2140](https://github.com/adap/flower/pull/2140), " +"[#2137](https://github.com/adap/flower/pull/2137), " +"[#2165](https://github.com/adap/flower/pull/2165))" msgstr "" -"**Supprimez l'ancien** `force_final_distributed_eval` **de** " -"`start_server` ([#1258](https://github.com/adap/flower/pull/1258), " -"[#1259](https://github.com/adap/flower/pull/1259))" +"**Améliorer l'API (expérimentale) du pilote** " +"([#1663](https://github.com/adap/flower/pull/1663), " +"[#1666](https://github.com/adap/flower/pull/1666), " +"[#1667](https://github.com/adap/flower/pull/1667), " +"[#1664](https://github.com/adap/flower/pull/1664), " +"[#1675](https://github.com/adap/flower/pull/1675), " +"[#1676](https://github.com/adap/flower/pull/1676), " +"[#1693](https://github.com/adap/flower/pull/1693), " +"[#1662](https://github.com/adap/flower/pull/1662), " +"[#1794](https://github.com/adap/flower/pull/1794))" -#: ../../source/ref-changelog.md:604 +#: ../../source/ref-changelog.md:390 msgid "" -"The `start_server` parameter `force_final_distributed_eval` has long been" -" a historic artefact, in this release it is finally gone for good." +"A new testing infrastructure ensures that new changes stay compatible " +"with existing framework integrations or strategies." msgstr "" -"Le paramètre `start_server` `force_final_distributed_eval` a longtemps " -"été un artefact historique, dans cette version il a finalement disparu " -"pour de bon." -#: ../../source/ref-changelog.md:606 +#: ../../source/ref-changelog.md:392 +#, fuzzy +msgid "**Deprecate Python 3.7**" +msgstr "**Créer le PR**" + +#: ../../source/ref-changelog.md:394 msgid "" -"**Make** `get_parameters` **configurable** " -"([#1242](https://github.com/adap/flower/pull/1242))" +"Since Python 3.7 reached its end of life (EOL) on 2023-06-27, support for" +" Python 3.7 is now deprecated and will be removed in an upcoming release." msgstr "" -"**Make** `get_parameters` **configurable** " -"([#1242](https://github.com/adap/flower/pull/1242))" -#: ../../source/ref-changelog.md:608 +#: ../../source/ref-changelog.md:396 +#, fuzzy msgid "" -"The `get_parameters` method now accepts a configuration dictionary, just " -"like `get_properties`, `fit`, and `evaluate`." +"**Add new** `FedTrimmedAvg` **strategy** " +"([#1769](https://github.com/adap/flower/pull/1769), " +"[#1853](https://github.com/adap/flower/pull/1853))" msgstr "" -"La méthode `get_parameters` accepte maintenant un dictionnaire de " -"configuration, tout comme `get_properties`, `fit`, et `evaluate`." +"**Ajouter un nouvel exemple de Federated Analytics avec Pandas** " +"([#1469](https://github.com/adap/flower/pull/1469), " +"[#1535](https://github.com/adap/flower/pull/1535))" -#: ../../source/ref-changelog.md:610 +#: ../../source/ref-changelog.md:398 +#, fuzzy msgid "" -"**Replace** `num_rounds` **in** `start_simulation` **with new** `config` " -"**parameter** ([#1281](https://github.com/adap/flower/pull/1281))" +"The new `FedTrimmedAvg` strategy implements Trimmed Mean by [Dong Yin, " +"2018](https://arxiv.org/abs/1803.01498)." msgstr "" -"**Remplace** `num_rounds` **dans** `start_simulation` **avec le nouveau**" -" `config` **paramètre** " -"([#1281](https://github.com/adap/flower/pull/1281))" +"La nouvelle stratégie `FedMedian` met en œuvre Federated Median " +"(FedMedian) par [Yin et al., 2018] " +"(https://arxiv.org/pdf/1803.01498v1.pdf)." -#: ../../source/ref-changelog.md:612 +#: ../../source/ref-changelog.md:400 +#, fuzzy msgid "" -"The `start_simulation` function now accepts a configuration dictionary " -"`config` instead of the `num_rounds` integer. This improves the " -"consistency between `start_simulation` and `start_server` and makes " -"transitioning between the two easier." +"**Introduce start_driver** " +"([#1697](https://github.com/adap/flower/pull/1697))" msgstr "" -"La fonction `start_simulation` accepte maintenant un dictionnaire de " -"configuration `config` au lieu de l'entier `num_rounds`. Cela améliore la" -" cohérence entre `start_simulation` et `start_server` et facilite la " -"transition entre les deux." +"**Ajouter une nouvelle stratégie `FedProx`** " +"([#1619](https://github.com/adap/flower/pull/1619))" -#: ../../source/ref-changelog.md:616 +#: ../../source/ref-changelog.md:402 msgid "" -"**Support Python 3.10** " -"([#1320](https://github.com/adap/flower/pull/1320))" +"In addition to `start_server` and using the raw Driver API, there is a " +"new `start_driver` function that allows for running `start_server` " +"scripts as a Flower driver with only a single-line code change. Check out" +" the `mt-pytorch` code example to see a working example using " +"`start_driver`." msgstr "" -"**Support Python 3.10** " -"([#1320](https://github.com/adap/flower/pull/1320))" -#: ../../source/ref-changelog.md:618 +#: ../../source/ref-changelog.md:404 +#, fuzzy msgid "" -"The previous Flower release introduced experimental support for Python " -"3.10, this release declares Python 3.10 support as stable." +"**Add parameter aggregation to** `mt-pytorch` **code example** " +"([#1785](https://github.com/adap/flower/pull/1785))" msgstr "" -"La version précédente de Flower a introduit la prise en charge " -"expérimentale de Python 3.10, cette version déclare la prise en charge de" -" Python 3.10 comme stable." +"**Nouvel exemple de code PyTorch avancé** " +"([#1007](https://github.com/adap/flower/pull/1007))" -#: ../../source/ref-changelog.md:620 +#: ../../source/ref-changelog.md:406 msgid "" -"**Make all** `Client` **and** `NumPyClient` **methods optional** " -"([#1260](https://github.com/adap/flower/pull/1260), " -"[#1277](https://github.com/adap/flower/pull/1277))" +"The `mt-pytorch` example shows how to aggregate parameters when writing a" +" driver script. The included `driver.py` and `server.py` have been " +"aligned to demonstrate both the low-level way and the high-level way of " +"building server-side logic." msgstr "" -"**Rendre toutes les **méthodes `Client` **et** `NumPyClient` " -"**facultatives** ([#1260](https://github.com/adap/flower/pull/1260), " -"[#1277](https://github.com/adap/flower/pull/1277))" -#: ../../source/ref-changelog.md:622 +#: ../../source/ref-changelog.md:408 +#, fuzzy msgid "" -"The `Client`/`NumPyClient` methods `get_properties`, `get_parameters`, " -"`fit`, and `evaluate` are all optional. This enables writing clients that" -" implement, for example, only `fit`, but no other method. No need to " -"implement `evaluate` when using centralized evaluation!" +"**Migrate experimental REST API to Starlette** " +"([2171](https://github.com/adap/flower/pull/2171))" msgstr "" -"Les méthodes `Client`/`NumPyClient` `get_properties`, `get_parameters`, " -"`fit`, et `evaluate` sont toutes optionnelles. Cela permet d'écrire des " -"clients qui n'implémentent, par exemple, que `fit`, mais aucune autre " -"méthode. Pas besoin d'implémenter `evaluate` quand on utilise " -"l'évaluation centralisée !" +"**Nouvelle stratégie expérimentale TensorBoard** " +"([#789](https://github.com/adap/flower/pull/789))" -#: ../../source/ref-changelog.md:624 +#: ../../source/ref-changelog.md:410 msgid "" -"**Enable passing a** `Server` **instance to** `start_simulation` " -"([#1281](https://github.com/adap/flower/pull/1281))" +"The (experimental) REST API used to be implemented in " +"[FastAPI](https://fastapi.tiangolo.com/), but it has now been migrated to" +" use [Starlette](https://www.starlette.io/) directly." msgstr "" -"**Autoriser le passage d'une **instance `Server` à** `start_simulation` " -"([#1281](https://github.com/adap/flower/pull/1281))" -#: ../../source/ref-changelog.md:626 +#: ../../source/ref-changelog.md:412 +#, fuzzy msgid "" -"Similar to `start_server`, `start_simulation` now accepts a full `Server`" -" instance. This enables users to heavily customize the execution of " -"eperiments and opens the door to running, for example, async FL using the" -" Virtual Client Engine." +"Please note: The REST request-response API is still experimental and will" +" likely change significantly over time." msgstr "" -"Comme pour `start_server`, `start_simulation` accepte maintenant une " -"instance complète de `Server`. Cela permet aux utilisateurs de " -"personnaliser fortement l'exécution des expériences et ouvre la porte à " -"l'exécution, par exemple, de FL asynchrones à l'aide du moteur de client " -"virtuel." +"Remarque : l'API REST est encore expérimentale et est susceptible de " +"changer de manière significative au fil du temps." -#: ../../source/ref-changelog.md:628 +#: ../../source/ref-changelog.md:414 +#, fuzzy msgid "" -"**Update code examples** " -"([#1291](https://github.com/adap/flower/pull/1291), " -"[#1286](https://github.com/adap/flower/pull/1286), " -"[#1282](https://github.com/adap/flower/pull/1282))" +"**Introduce experimental gRPC request-response API** " +"([#1867](https://github.com/adap/flower/pull/1867), " +"[#1901](https://github.com/adap/flower/pull/1901))" msgstr "" -"**Mettre à jour les exemples de code** " -"([#1291](https://github.com/adap/flower/pull/1291), " -"[#1286](https://github.com/adap/flower/pull/1286), " -"[#1282](https://github.com/adap/flower/pull/1282))" +"**Introduire les enveloppes de confidentialité différentielle (aperçu)** " +"([#1357](https://github.com/adap/flower/pull/1357), " +"[#1460](https://github.com/adap/flower/pull/1460))" -#: ../../source/ref-changelog.md:630 +#: ../../source/ref-changelog.md:416 msgid "" -"Many code examples received small or even large maintenance updates, " -"among them are" +"In addition to the existing gRPC API (based on bidirectional streaming) " +"and the experimental REST API, there is now a new gRPC API that uses a " +"request-response model to communicate with client nodes." msgstr "" -"De nombreux exemples de code ont reçu de petites ou même de grandes mises" -" à jour de maintenance" - -#: ../../source/ref-changelog.md:632 -msgid "`scikit-learn`" -msgstr "`scikit-learn`" - -#: ../../source/ref-changelog.md:633 -msgid "`simulation_pytorch`" -msgstr "`simulation_pytorch`" - -#: ../../source/ref-changelog.md:634 -msgid "`quickstart_pytorch`" -msgstr "`quickstart_pytorch` (démarrage rapide)" - -#: ../../source/ref-changelog.md:635 -msgid "`quickstart_simulation`" -msgstr "`quickstart_simulation`" - -#: ../../source/ref-changelog.md:636 -msgid "`quickstart_tensorflow`" -msgstr "`quickstart_tensorflow`" - -#: ../../source/ref-changelog.md:637 -msgid "`advanced_tensorflow`" -msgstr "`advanced_tensorflow` (en anglais)" -#: ../../source/ref-changelog.md:639 +#: ../../source/ref-changelog.md:418 +#, fuzzy msgid "" -"**Remove the obsolete simulation example** " -"([#1328](https://github.com/adap/flower/pull/1328))" +"Please note: The gRPC request-response API is still experimental and will" +" likely change significantly over time." msgstr "" -"**Supprime l'exemple de simulation obsolète** " -"([#1328](https://github.com/adap/flower/pull/1328))" +"Remarque : l'API REST est encore expérimentale et est susceptible de " +"changer de manière significative au fil du temps." -#: ../../source/ref-changelog.md:641 +#: ../../source/ref-changelog.md:420 +#, fuzzy msgid "" -"Removes the obsolete `simulation` example and renames " -"`quickstart_simulation` to `simulation_tensorflow` so it fits withs the " -"naming of `simulation_pytorch`" +"**Replace the experimental** `start_client(rest=True)` **with the new** " +"`start_client(transport=\"rest\")` " +"([#1880](https://github.com/adap/flower/pull/1880))" msgstr "" -"Supprime l'exemple obsolète `simulation` et renomme " -"`quickstart_simulation` en `simulation_tensorflow` pour qu'il corresponde" -" au nom de `simulation_pytorch`" +"**Initialise** `start_simulation` **avec une liste d'ID de clients** " +"([#860](https://github.com/adap/flower/pull/860))" -#: ../../source/ref-changelog.md:643 +#: ../../source/ref-changelog.md:422 msgid "" -"**Update documentation** " -"([#1223](https://github.com/adap/flower/pull/1223), " -"[#1209](https://github.com/adap/flower/pull/1209), " -"[#1251](https://github.com/adap/flower/pull/1251), " -"[#1257](https://github.com/adap/flower/pull/1257), " -"[#1267](https://github.com/adap/flower/pull/1267), " -"[#1268](https://github.com/adap/flower/pull/1268), " -"[#1300](https://github.com/adap/flower/pull/1300), " -"[#1304](https://github.com/adap/flower/pull/1304), " -"[#1305](https://github.com/adap/flower/pull/1305), " -"[#1307](https://github.com/adap/flower/pull/1307))" +"The (experimental) `start_client` argument `rest` was deprecated in " +"favour of a new argument `transport`. `start_client(transport=\"rest\")` " +"will yield the same behaviour as `start_client(rest=True)` did before. " +"All code should migrate to the new argument `transport`. The deprecated " +"argument `rest` will be removed in a future release." msgstr "" -"**Mise à jour de la documentation** " -"([#1223](https://github.com/adap/flower/pull/1223), " -"[#1209](https://github.com/adap/flower/pull/1209), " -"[#1251](https://github.com/adap/flower/pull/1251), " -"[#1257](https://github.com/adap/flower/pull/1257), " -"[#1267](https://github.com/adap/flower/pull/1267), " -"[#1268](https://github.com/adap/flower/pull/1268), " -"[#1300](https://github.com/adap/flower/pull/1300), " -"[#1304](https://github.com/adap/flower/pull/1304), " -"[#1305](https://github.com/adap/flower/pull/1305), " -"[#1307](https://github.com/adap/flower/pull/1307))" -#: ../../source/ref-changelog.md:645 +#: ../../source/ref-changelog.md:424 +#, fuzzy msgid "" -"One substantial documentation update fixes multiple smaller rendering " -"issues, makes titles more succinct to improve navigation, removes a " -"deprecated library, updates documentation dependencies, includes the " -"`flwr.common` module in the API reference, includes support for markdown-" -"based documentation, migrates the changelog from `.rst` to `.md`, and " -"fixes a number of smaller details!" +"**Add a new gRPC option** " +"([#2197](https://github.com/adap/flower/pull/2197))" msgstr "" -"Une mise à jour substantielle de la documentation corrige plusieurs " -"petits problèmes de rendu, rend les titres plus succincts pour améliorer " -"la navigation, supprime une bibliothèque obsolète, met à jour les " -"dépendances de la documentation, inclut le module `flwr.common` dans la " -"référence de l'API, inclut le support de la documentation basée sur le " -"markdown, migre le changelog de `.rst` vers `.md`, et corrige un certain " -"nombre de détails plus petits !" - -#: ../../source/ref-changelog.md:647 ../../source/ref-changelog.md:702 -#: ../../source/ref-changelog.md:771 ../../source/ref-changelog.md:810 -msgid "**Minor updates**" -msgstr "**Mises à jour mineures**" +"**Ajouter une nouvelle stratégie `FedProx`** " +"([#1619](https://github.com/adap/flower/pull/1619))" -#: ../../source/ref-changelog.md:649 +#: ../../source/ref-changelog.md:426 msgid "" -"Add round number to fit and evaluate log messages " -"([#1266](https://github.com/adap/flower/pull/1266))" +"We now start a gRPC server with the `grpc.keepalive_permit_without_calls`" +" option set to 0 by default. This prevents the clients from sending " +"keepalive pings when there is no outstanding stream." msgstr "" -"Ajoute un chiffre rond pour ajuster et évaluer les messages du journal " -"([#1266](https://github.com/adap/flower/pull/1266))" -#: ../../source/ref-changelog.md:650 +#: ../../source/ref-changelog.md:428 +#, fuzzy msgid "" -"Add secure gRPC connection to the `advanced_tensorflow` code example " -"([#847](https://github.com/adap/flower/pull/847))" +"**Improve example notebooks** " +"([#2005](https://github.com/adap/flower/pull/2005))" msgstr "" -"Ajouter une connexion gRPC sécurisée à l'exemple de code " -"`advanced_tensorflow` ([#847](https://github.com/adap/flower/pull/847))" +"**Supprimer les stratégies expérimentales** " +"([#1280](https://github.com/adap/flower/pull/1280))" -#: ../../source/ref-changelog.md:651 -msgid "" -"Update developer tooling " -"([#1231](https://github.com/adap/flower/pull/1231), " -"[#1276](https://github.com/adap/flower/pull/1276), " -"[#1301](https://github.com/adap/flower/pull/1301), " -"[#1310](https://github.com/adap/flower/pull/1310))" -msgstr "" -"Mettre à jour les outils de développement " -"([#1231](https://github.com/adap/flower/pull/1231), " -"[#1276](https://github.com/adap/flower/pull/1276), " -"[#1301](https://github.com/adap/flower/pull/1301), " -"[#1310](https://github.com/adap/flower/pull/1310))" +#: ../../source/ref-changelog.md:430 +#, fuzzy +msgid "There's a new 30min Federated Learning PyTorch tutorial!" +msgstr "Bienvenue au tutoriel sur l'apprentissage fédéré de la fleur !" -#: ../../source/ref-changelog.md:652 +#: ../../source/ref-changelog.md:432 msgid "" -"Rename ProtoBuf messages to improve consistency " -"([#1214](https://github.com/adap/flower/pull/1214), " -"[#1258](https://github.com/adap/flower/pull/1258), " -"[#1259](https://github.com/adap/flower/pull/1259))" +"**Example updates** ([#1772](https://github.com/adap/flower/pull/1772), " +"[#1873](https://github.com/adap/flower/pull/1873), " +"[#1981](https://github.com/adap/flower/pull/1981), " +"[#1988](https://github.com/adap/flower/pull/1988), " +"[#1984](https://github.com/adap/flower/pull/1984), " +"[#1982](https://github.com/adap/flower/pull/1982), " +"[#2112](https://github.com/adap/flower/pull/2112), " +"[#2144](https://github.com/adap/flower/pull/2144), " +"[#2174](https://github.com/adap/flower/pull/2174), " +"[#2225](https://github.com/adap/flower/pull/2225), " +"[#2183](https://github.com/adap/flower/pull/2183))" msgstr "" -"Renomme les messages ProtoBuf pour améliorer la cohérence " -"([#1214](https://github.com/adap/flower/pull/1214), " -"[#1258](https://github.com/adap/flower/pull/1258), " -"[#1259](https://github.com/adap/flower/pull/1259))" -#: ../../source/ref-changelog.md:654 -msgid "v0.19.0 (2022-05-18)" -msgstr "v0.19.0 (2022-05-18)" - -#: ../../source/ref-changelog.md:658 +#: ../../source/ref-changelog.md:434 msgid "" -"**Flower Baselines (preview): FedOpt, FedBN, FedAvgM** " -"([#919](https://github.com/adap/flower/pull/919), " -"[#1127](https://github.com/adap/flower/pull/1127), " -"[#914](https://github.com/adap/flower/pull/914))" +"Many examples have received significant updates, including simplified " +"advanced-tensorflow and advanced-pytorch examples, improved macOS " +"compatibility of TensorFlow examples, and code examples for simulation. A" +" major upgrade is that all code examples now have a `requirements.txt` " +"(in addition to `pyproject.toml`)." msgstr "" -"**Flower Baselines (preview) : FedOpt, FedBN, FedAvgM** " -"([#919](https://github.com/adap/flower/pull/919), " -"[#1127](https://github.com/adap/flower/pull/1127), " -"[#914](https://github.com/adap/flower/pull/914))" -#: ../../source/ref-changelog.md:660 +#: ../../source/ref-changelog.md:436 #, fuzzy msgid "" -"The first preview release of Flower Baselines has arrived! We're " -"kickstarting Flower Baselines with implementations of FedOpt (FedYogi, " -"FedAdam, FedAdagrad), FedBN, and FedAvgM. Check the documentation on how " -"to use [Flower Baselines](https://flower.ai/docs/using-baselines.html). " -"With this first preview release we're also inviting the community to " -"[contribute their own baselines](https://flower.ai/docs/baselines/how-to-" -"contribute-baselines.html)." +"**General improvements** " +"([#1872](https://github.com/adap/flower/pull/1872), " +"[#1866](https://github.com/adap/flower/pull/1866), " +"[#1884](https://github.com/adap/flower/pull/1884), " +"[#1837](https://github.com/adap/flower/pull/1837), " +"[#1477](https://github.com/adap/flower/pull/1477), " +"[#2171](https://github.com/adap/flower/pull/2171))" msgstr "" -"La première version préliminaire de Flower Baselines est arrivée ! Nous " -"démarrons Flower Baselines avec des implémentations de FedOpt (FedYogi, " -"FedAdam, FedAdagrad), FedBN, et FedAvgM. Consultez la documentation sur " -"l'utilisation de [Flower Baselines](https://flower.ai/docs/using-" -"baselines.html). Avec cette première version préliminaire, nous invitons " -"également la communauté à [contribuer à leurs propres lignes de " -"base](https://flower.ai/docs/baselines/how-to-contribute-baselines.html)." +"**Mise à jour de la documentation** " +"([#1629](https://github.com/adap/flower/pull/1629), " +"[#1628](https://github.com/adap/flower/pull/1628), " +"[#1620](https://github.com/adap/flower/pull/1620), " +"[#1618](https://github.com/adap/flower/pull/1618), " +"[#1617](https://github.com/adap/flower/pull/1617), " +"[#1613](https://github.com/adap/flower/pull/1613), " +"[#1614](https://github.com/adap/flower/pull/1614))" -#: ../../source/ref-changelog.md:662 -msgid "" -"**C++ client SDK (preview) and code example** " -"([#1111](https://github.com/adap/flower/pull/1111))" -msgstr "" -"**SDK client C++ (aperçu) et exemple de code** " -"([#1111](https://github.com/adap/flower/pull/1111))" +#: ../../source/ref-changelog.md:444 +msgid "v1.4.0 (2023-04-21)" +msgstr "v1.4.0 (2023-04-21)" -#: ../../source/ref-changelog.md:664 +#: ../../source/ref-changelog.md:450 msgid "" -"Preview support for Flower clients written in C++. The C++ preview " -"includes a Flower client SDK and a quickstart code example that " -"demonstrates a simple C++ client using the SDK." +"`Adam Narozniak`, `Alexander Viala Bellander`, `Charles Beauville`, " +"`Chenyang Ma (Danny)`, `Daniel J. Beutel`, `Edoardo`, `Gautam Jajoo`, " +"`Iacob-Alexandru-Andrei`, `JDRanpariya`, `Jean Charle Yaacoub`, `Kunal " +"Sarkhel`, `L. Jiang`, `Lennart Behme`, `Max Kapsecker`, `Michał`, `Nic " +"Lane`, `Nikolaos Episkopos`, `Ragy`, `Saurav Maheshkar`, `Semo Yang`, " +"`Steve Laskaridis`, `Steven Hé (Sīchàng)`, `Taner Topal`" msgstr "" -"L'aperçu C++ comprend un SDK pour les clients Flower et un exemple de " -"code de démarrage rapide qui démontre un client C++ simple utilisant le " -"SDK." +"`Adam Narozniak`, `Alexander Viala Bellander`, `Charles Beauville`, " +"`Chenyang Ma (Danny)`, `Daniel J. Beutel`, `Edoardo`, `Gautam Jajoo`, " +"`Iacob-Alexandru-Andrei`, `JDRanpariya`, `Jean Charle Yaacoub`, `Kunal " +"Sarkhel`, `L. Jiang`, `Lennart Behme`, `Max Kapsecker`, `Michał`, `Nic " +"Lane`, `Nikolaos Episkopos`, `Ragy`, `Saurav Maheshkar`, `Semo Yang`, " +"`Steve Laskaridis`, `Steven Hé (Sīchàng)`, `Taner Topal`" -#: ../../source/ref-changelog.md:666 +#: ../../source/ref-changelog.md:454 msgid "" -"**Add experimental support for Python 3.10 and Python 3.11** " -"([#1135](https://github.com/adap/flower/pull/1135))" +"**Introduce support for XGBoost (**`FedXgbNnAvg` **strategy and " +"example)** ([#1694](https://github.com/adap/flower/pull/1694), " +"[#1709](https://github.com/adap/flower/pull/1709), " +"[#1715](https://github.com/adap/flower/pull/1715), " +"[#1717](https://github.com/adap/flower/pull/1717), " +"[#1763](https://github.com/adap/flower/pull/1763), " +"[#1795](https://github.com/adap/flower/pull/1795))" msgstr "" -"**Ajouter la prise en charge expérimentale de Python 3.10 et Python " -"3.11** ([#1135](https://github.com/adap/flower/pull/1135))" +"**Introduire la prise en charge de XGBoost (**`FedXgbNnAvg` **stratégie " +"et exemple)** ([#1694](https://github.com/adap/flower/pull/1694), " +"[#1709](https://github.com/adap/flower/pull/1709), " +"[#1715](https://github.com/adap/flower/pull/1715), " +"[#1717](https://github.com/adap/flower/pull/1717), " +"[#1763](https://github.com/adap/flower/pull/1763), " +"[#1795](https://github.com/adap/flower/pull/1795))" -#: ../../source/ref-changelog.md:668 +#: ../../source/ref-changelog.md:456 msgid "" -"Python 3.10 is the latest stable release of Python and Python 3.11 is due" -" to be released in October. This Flower release adds experimental support" -" for both Python versions." +"XGBoost is a tree-based ensemble machine learning algorithm that uses " +"gradient boosting to improve model accuracy. We added a new `FedXgbNnAvg`" +" " +"[strategy](https://github.com/adap/flower/tree/main/src/py/flwr/server/strategy/fedxgb_nn_avg.py)," +" and a [code example](https://github.com/adap/flower/tree/main/examples" +"/xgboost-quickstart) that demonstrates the usage of this new strategy in " +"an XGBoost project." msgstr "" -"Python 3.10 est la dernière version stable de Python et Python 3.11 " -"devrait sortir en octobre. Cette version de Flower ajoute une prise en " -"charge expérimentale pour les deux versions de Python." +"Nous avons ajouté une nouvelle [stratégie] `FedXgbNnAvg` " +"(https://github.com/adap/flower/tree/main/src/py/flwr/server/strategy/fedxgb_nn_avg.py)," +" et un [exemple de code] " +"(https://github.com/adap/flower/tree/main/examples/xgboost-quickstart) " +"qui démontre l'utilisation de cette nouvelle stratégie dans un projet " +"XGBoost." -#: ../../source/ref-changelog.md:670 +#: ../../source/ref-changelog.md:458 msgid "" -"**Aggregate custom metrics through user-provided functions** " -"([#1144](https://github.com/adap/flower/pull/1144))" +"**Introduce iOS SDK (preview)** " +"([#1621](https://github.com/adap/flower/pull/1621), " +"[#1764](https://github.com/adap/flower/pull/1764))" msgstr "" -"**Agréger des mesures personnalisées grâce à des fonctions fournies par " -"l'utilisateur** ([#1144](https://github.com/adap/flower/pull/1144))" +"**Introduction du SDK iOS (aperçu)** " +"([#1621](https://github.com/adap/flower/pull/1621), " +"[#1764](https://github.com/adap/flower/pull/1764))" -#: ../../source/ref-changelog.md:672 +#: ../../source/ref-changelog.md:460 msgid "" -"Custom metrics (e.g., `accuracy`) can now be aggregated without having to" -" customize the strategy. Built-in strategies support two new arguments, " -"`fit_metrics_aggregation_fn` and `evaluate_metrics_aggregation_fn`, that " -"allow passing custom metric aggregation functions." +"This is a major update for anyone wanting to implement Federated Learning" +" on iOS mobile devices. We now have a swift iOS SDK present under " +"[src/swift/flwr](https://github.com/adap/flower/tree/main/src/swift/flwr)" +" that will facilitate greatly the app creating process. To showcase its " +"use, the [iOS " +"example](https://github.com/adap/flower/tree/main/examples/ios) has also " +"been updated!" msgstr "" -"Les stratégies intégrées prennent en charge deux nouveaux arguments, " -"`fit_metrics_aggregation_fn` et `evaluate_metrics_aggregation_fn`, qui " -"permettent de passer des fonctions d'agrégation de métriques " -"personnalisées." +"Il s'agit d'une mise à jour majeure pour tous ceux qui souhaitent mettre " +"en œuvre l'apprentissage fédéré sur les appareils mobiles iOS. Nous " +"disposons désormais d'un SDK swift iOS présent sous " +"[src/swift/flwr](https://github.com/adap/flower/tree/main/src/swift/flwr)" +" qui facilitera grandement le processus de création d'applications. Pour " +"présenter son utilisation, l'[exemple " +"iOS](https://github.com/adap/flower/tree/main/examples/ios) a également " +"été mis à jour !" -#: ../../source/ref-changelog.md:674 +#: ../../source/ref-changelog.md:462 msgid "" -"**User-configurable round timeout** " -"([#1162](https://github.com/adap/flower/pull/1162))" +"**Introduce new \"What is Federated Learning?\" tutorial** " +"([#1657](https://github.com/adap/flower/pull/1657), " +"[#1721](https://github.com/adap/flower/pull/1721))" msgstr "" -"**Temps d'attente configurable par l'utilisateur** " -"([#1162](https://github.com/adap/flower/pull/1162))" +"**Introduire un nouveau tutoriel \"Qu'est-ce que l'apprentissage fédéré ?" +" \"** ([#1657](https://github.com/adap/flower/pull/1657), " +"[#1721](https://github.com/adap/flower/pull/1721))" -#: ../../source/ref-changelog.md:676 +#: ../../source/ref-changelog.md:464 +#, fuzzy msgid "" -"A new configuration value allows the round timeout to be set for " -"`start_server` and `start_simulation`. If the `config` dictionary " -"contains a `round_timeout` key (with a `float` value in seconds), the " -"server will wait *at least* `round_timeout` seconds before it closes the " -"connection." +"A new [entry-level tutorial](https://flower.ai/docs/framework/tutorial-" +"what-is-federated-learning.html) in our documentation explains the basics" +" of Fedetated Learning. It enables anyone who's unfamiliar with Federated" +" Learning to start their journey with Flower. Forward it to anyone who's " +"interested in Federated Learning!" msgstr "" -"Si le dictionnaire `config` contient une clé `round_timeout` (avec une " -"valeur `float` en secondes), le serveur attendra *au moins* " -"`round_timeout` secondes avant de fermer la connexion." +"Un nouveau [tutoriel d'entrée de gamme] " +"(https://flower.ai/docs/tutorial/Flower-0-What-is-FL.html) dans notre " +"documentation explique les bases de l'apprentissage fédéré. Il permet à " +"tous ceux qui ne connaissent pas l'apprentissage fédéré de commencer leur" +" voyage avec Flower. Fais-le suivre à tous ceux qui s'intéressent à " +"l'apprentissage fédéré !" -#: ../../source/ref-changelog.md:678 +#: ../../source/ref-changelog.md:466 msgid "" -"**Enable both federated evaluation and centralized evaluation to be used " -"at the same time in all built-in strategies** " -"([#1091](https://github.com/adap/flower/pull/1091))" +"**Introduce new Flower Baseline: FedProx MNIST** " +"([#1513](https://github.com/adap/flower/pull/1513), " +"[#1680](https://github.com/adap/flower/pull/1680), " +"[#1681](https://github.com/adap/flower/pull/1681), " +"[#1679](https://github.com/adap/flower/pull/1679))" msgstr "" -"**Permettre l'utilisation simultanée de l'évaluation fédérée et de " -"l'évaluation centralisée dans toutes les stratégies intégrées** " -"([#1091](https://github.com/adap/flower/pull/1091))" +"**Introduire une nouvelle fleur Référence : FedProx MNIST** " +"([#1513](https://github.com/adap/flower/pull/1513), " +"[#1680](https://github.com/adap/flower/pull/1680), " +"[#1681](https://github.com/adap/flower/pull/1681), " +"[#1679](https://github.com/adap/flower/pull/1679))" -#: ../../source/ref-changelog.md:680 +#: ../../source/ref-changelog.md:468 msgid "" -"Built-in strategies can now perform both federated evaluation (i.e., " -"client-side) and centralized evaluation (i.e., server-side) in the same " -"round. Federated evaluation can be disabled by setting `fraction_eval` to" -" `0.0`." +"This new baseline replicates the MNIST+CNN task from the paper [Federated" +" Optimization in Heterogeneous Networks (Li et al., " +"2018)](https://arxiv.org/abs/1812.06127). It uses the `FedProx` strategy," +" which aims at making convergence more robust in heterogeneous settings." msgstr "" -"Les stratégies intégrées peuvent maintenant effectuer une évaluation " -"fédérée (c'est-à-dire côté client) et une évaluation centralisée " -"(c'est-à-dire côté serveur) dans le même tour. L'évaluation fédérée peut " -"être désactivée en réglant `fraction_eval` sur `0.0`." +"Cette nouvelle ligne de base reproduit la tâche MNIST+CNN de l'article " +"[Federated Optimization in Heterogeneous Networks (Li et al., 2018)] " +"(https://arxiv.org/abs/1812.06127). Elle utilise la stratégie `FedProx`, " +"qui vise à rendre la convergence plus robuste dans des contextes " +"hétérogènes." -#: ../../source/ref-changelog.md:682 +#: ../../source/ref-changelog.md:470 msgid "" -"**Two new Jupyter Notebook tutorials** " -"([#1141](https://github.com/adap/flower/pull/1141))" +"**Introduce new Flower Baseline: FedAvg FEMNIST** " +"([#1655](https://github.com/adap/flower/pull/1655))" msgstr "" -"**Deux nouveaux tutoriels Jupyter Notebook** " -"([#1141](https://github.com/adap/flower/pull/1141))" +"**Introduire une nouvelle ligne de base pour les fleurs : FedAvg " +"FEMNIST** ([#1655](https://github.com/adap/flower/pull/1655))" -#: ../../source/ref-changelog.md:684 +#: ../../source/ref-changelog.md:472 msgid "" -"Two Jupyter Notebook tutorials (compatible with Google Colab) explain " -"basic and intermediate Flower features:" +"This new baseline replicates an experiment evaluating the performance of " +"the FedAvg algorithm on the FEMNIST dataset from the paper [LEAF: A " +"Benchmark for Federated Settings (Caldas et al., " +"2018)](https://arxiv.org/abs/1812.01097)." msgstr "" -"Deux tutoriels Jupyter Notebook (compatibles avec Google Colab) " -"expliquent les fonctionnalités de base et intermédiaires de Flower :" +"Cette nouvelle ligne de base reproduit une expérience évaluant les " +"performances de l'algorithme FedAvg sur le jeu de données FEMNIST tiré de" +" l'article [LEAF : A Benchmark for Federated Settings (Caldas et al., " +"2018)] (https://arxiv.org/abs/1812.01097)." -#: ../../source/ref-changelog.md:686 +#: ../../source/ref-changelog.md:474 msgid "" -"*An Introduction to Federated Learning*: [Open in " -"Colab](https://colab.research.google.com/github/adap/flower/blob/main/tutorials/Flower-1" -"-Intro-to-FL-PyTorch.ipynb)" +"**Introduce (experimental) REST API** " +"([#1594](https://github.com/adap/flower/pull/1594), " +"[#1690](https://github.com/adap/flower/pull/1690), " +"[#1695](https://github.com/adap/flower/pull/1695), " +"[#1712](https://github.com/adap/flower/pull/1712), " +"[#1802](https://github.com/adap/flower/pull/1802), " +"[#1770](https://github.com/adap/flower/pull/1770), " +"[#1733](https://github.com/adap/flower/pull/1733))" msgstr "" -"*Introduction à l'apprentissage fédéré* : [Open in " -"Colab](https://colab.research.google.com/github/adap/flower/blob/main/tutorials/Flower-1" -"-Intro-to-FL-PyTorch.ipynb)" +"**Introduire l'API REST (expérimentale)** " +"([#1594](https://github.com/adap/flower/pull/1594), " +"[#1690](https://github.com/adap/flower/pull/1690), " +"[#1695](https://github.com/adap/flower/pull/1695), " +"[#1712](https://github.com/adap/flower/pull/1712), " +"[#1802](https://github.com/adap/flower/pull/1802), " +"[#1770](https://github.com/adap/flower/pull/1770), " +"[#1733](https://github.com/adap/flower/pull/1733))" -#: ../../source/ref-changelog.md:688 +#: ../../source/ref-changelog.md:476 msgid "" -"*Using Strategies in Federated Learning*: [Open in " -"Colab](https://colab.research.google.com/github/adap/flower/blob/main/tutorials/Flower-2" -"-Strategies-in-FL-PyTorch.ipynb)" +"A new REST API has been introduced as an alternative to the gRPC-based " +"communication stack. In this initial version, the REST API only supports " +"anonymous clients." msgstr "" -"*Utiliser des stratégies dans l'apprentissage fédéré* : [Open in " -"Colab](https://colab.research.google.com/github/adap/flower/blob/main/tutorials/Flower-2" -"-Strategies-in-FL-PyTorch.ipynb)" +"Une nouvelle API REST a été introduite comme alternative à la pile de " +"communication basée sur gRPC. Dans cette version initiale, l'API REST ne " +"prend en charge que les clients anonymes." -#: ../../source/ref-changelog.md:690 +#: ../../source/ref-changelog.md:478 msgid "" -"**New FedAvgM strategy (Federated Averaging with Server Momentum)** " -"([#1076](https://github.com/adap/flower/pull/1076))" +"Please note: The REST API is still experimental and will likely change " +"significantly over time." msgstr "" -"**Nouvelle stratégie FedAvgM (Federated Averaging with Server Momentum)**" -" ([#1076](https://github.com/adap/flower/pull/1076))" +"Remarque : l'API REST est encore expérimentale et est susceptible de " +"changer de manière significative au fil du temps." -#: ../../source/ref-changelog.md:692 -#, fuzzy +#: ../../source/ref-changelog.md:480 msgid "" -"The new `FedAvgM` strategy implements Federated Averaging with Server " -"Momentum \\[Hsu et al., 2019\\]." +"**Improve the (experimental) Driver API** " +"([#1663](https://github.com/adap/flower/pull/1663), " +"[#1666](https://github.com/adap/flower/pull/1666), " +"[#1667](https://github.com/adap/flower/pull/1667), " +"[#1664](https://github.com/adap/flower/pull/1664), " +"[#1675](https://github.com/adap/flower/pull/1675), " +"[#1676](https://github.com/adap/flower/pull/1676), " +"[#1693](https://github.com/adap/flower/pull/1693), " +"[#1662](https://github.com/adap/flower/pull/1662), " +"[#1794](https://github.com/adap/flower/pull/1794))" msgstr "" -"La nouvelle stratégie `FedAvgM` met en œuvre la moyenne fédérée avec le " -"momentum du serveur [Hsu et al., 2019]." +"**Améliorer l'API (expérimentale) du pilote** " +"([#1663](https://github.com/adap/flower/pull/1663), " +"[#1666](https://github.com/adap/flower/pull/1666), " +"[#1667](https://github.com/adap/flower/pull/1667), " +"[#1664](https://github.com/adap/flower/pull/1664), " +"[#1675](https://github.com/adap/flower/pull/1675), " +"[#1676](https://github.com/adap/flower/pull/1676), " +"[#1693](https://github.com/adap/flower/pull/1693), " +"[#1662](https://github.com/adap/flower/pull/1662), " +"[#1794](https://github.com/adap/flower/pull/1794))" -#: ../../source/ref-changelog.md:694 +#: ../../source/ref-changelog.md:482 msgid "" -"**New advanced PyTorch code example** " -"([#1007](https://github.com/adap/flower/pull/1007))" +"The Driver API is still an experimental feature, but this release " +"introduces some major upgrades. One of the main improvements is the " +"introduction of an SQLite database to store server state on disk (instead" +" of in-memory). Another improvement is that tasks (instructions or " +"results) that have been delivered will now be deleted. This greatly " +"improves the memory efficiency of a long-running Flower server." msgstr "" -"**Nouvel exemple de code PyTorch avancé** " -"([#1007](https://github.com/adap/flower/pull/1007))" +"L'API du pilote est encore une fonction expérimentale, mais cette version" +" introduit quelques améliorations majeures. L'une des principales " +"améliorations est l'introduction d'une base de données SQLite pour " +"stocker l'état du serveur sur le disque (au lieu de la mémoire). Une " +"autre amélioration est que les tâches (instructions ou résultats) qui ont" +" été livrées seront désormais supprimées, ce qui améliore " +"considérablement l'efficacité de la mémoire d'un serveur Flower " +"fonctionnant depuis longtemps." -#: ../../source/ref-changelog.md:696 +#: ../../source/ref-changelog.md:484 msgid "" -"A new code example (`advanced_pytorch`) demonstrates advanced Flower " -"concepts with PyTorch." +"**Fix spilling issues related to Ray during simulations** " +"([#1698](https://github.com/adap/flower/pull/1698))" msgstr "" -"Un nouvel exemple de code (`advanced_pytorch`) démontre des concepts de " -"fleur avancés avec PyTorch." +"**Répare les problèmes de déversement liés à Ray pendant les " +"simulations** ([#1698](https://github.com/adap/flower/pull/1698))" -#: ../../source/ref-changelog.md:698 +#: ../../source/ref-changelog.md:486 +#, fuzzy msgid "" -"**New JAX code example** " -"([#906](https://github.com/adap/flower/pull/906), " -"[#1143](https://github.com/adap/flower/pull/1143))" +"While running long simulations, `ray` was sometimes spilling huge amounts" +" of data that would make the training unable to continue. This is now " +"fixed! 🎉" msgstr "" -"**Nouvel exemple de code JAX** " -"([#906](https://github.com/adap/flower/pull/906), " -"[#1143](https://github.com/adap/flower/pull/1143))" +"Lors de l'exécution de longues simulations, `ray` déversait parfois " +"d'énormes quantités de données qui rendaient l'entraînement incapable de " +"continuer. ce problème est maintenant corrigé ! 🎉" -#: ../../source/ref-changelog.md:700 +#: ../../source/ref-changelog.md:488 msgid "" -"A new code example (`jax_from_centralized_to_federated`) shows federated " -"learning with JAX and Flower." +"**Add new example using** `TabNet` **and Flower** " +"([#1725](https://github.com/adap/flower/pull/1725))" msgstr "" -"Un nouvel exemple de code (`jax_from_centralized_to_federated`) montre " -"l'apprentissage fédéré avec JAX et Flower." +"**Ajouter un nouvel exemple utilisant** `TabNet` **et Flower** " +"([#1725](https://github.com/adap/flower/pull/1725))" -#: ../../source/ref-changelog.md:704 +#: ../../source/ref-changelog.md:490 msgid "" -"New option to keep Ray running if Ray was already initialized in " -"`start_simulation` ([#1177](https://github.com/adap/flower/pull/1177))" +"TabNet is a powerful and flexible framework for training machine learning" +" models on tabular data. We now have a federated example using Flower: " +"[quickstart-tabnet](https://github.com/adap/flower/tree/main/examples" +"/quickstart-tabnet)." msgstr "" -"Nouvelle option pour continuer à faire fonctionner Ray si Ray a déjà été " -"initialisé dans `start_simulation` " -"([#1177](https://github.com/adap/flower/pull/1177))" +"TabNet est un cadre puissant et flexible pour former des modèles " +"d'apprentissage automatique sur des données tabulaires. Nous avons " +"maintenant un exemple fédéré utilisant Flower : [quickstart-" +"tabnet](https://github.com/adap/flower/tree/main/examples/quickstart-" +"tabnet)." -#: ../../source/ref-changelog.md:705 +#: ../../source/ref-changelog.md:492 msgid "" -"Add support for custom `ClientManager` as a `start_simulation` parameter " -"([#1171](https://github.com/adap/flower/pull/1171))" +"**Add new how-to guide for monitoring simulations** " +"([#1649](https://github.com/adap/flower/pull/1649))" msgstr "" -"Ajout de la prise en charge d'un `ClientManager` personnalisé comme " -"paramètre de `start_simulation` " -"([#1171](https://github.com/adap/flower/pull/1171))" +"**Ajouter un nouveau guide pratique pour le suivi des simulations** " +"([#1649](https://github.com/adap/flower/pull/1649))" -#: ../../source/ref-changelog.md:706 -#, fuzzy +#: ../../source/ref-changelog.md:494 msgid "" -"New documentation for [implementing " -"strategies](https://flower.ai/docs/framework/how-to-implement-" -"strategies.html) ([#1097](https://github.com/adap/flower/pull/1097), " -"[#1175](https://github.com/adap/flower/pull/1175))" +"We now have a documentation guide to help users monitor their performance" +" during simulations." msgstr "" -"Nouvelle documentation pour [mettre en œuvre des " -"stratégies](https://flower.ai/docs/framework/how-to-implement-" -"strategies.html) ([#1097](https://github.com/adap/flower/pull/1097), " -"[#1175](https://github.com/adap/flower/pull/1175))" +"Nous avons maintenant un guide de documentation pour aider les " +"utilisateurs à surveiller leurs performances pendant les simulations." -#: ../../source/ref-changelog.md:707 +#: ../../source/ref-changelog.md:496 msgid "" -"New mobile-friendly documentation theme " -"([#1174](https://github.com/adap/flower/pull/1174))" +"**Add training metrics to** `History` **object during simulations** " +"([#1696](https://github.com/adap/flower/pull/1696))" msgstr "" -"Nouveau thème de documentation adapté aux mobiles " -"([#1174](https://github.com/adap/flower/pull/1174))" +"**Ajouter des mesures de formation à** `History` **objet pendant les " +"simulations** ([#1696](https://github.com/adap/flower/pull/1696))" -#: ../../source/ref-changelog.md:708 +#: ../../source/ref-changelog.md:498 msgid "" -"Limit version range for (optional) `ray` dependency to include only " -"compatible releases (`>=1.9.2,<1.12.0`) " -"([#1205](https://github.com/adap/flower/pull/1205))" +"The `fit_metrics_aggregation_fn` can be used to aggregate training " +"metrics, but previous releases did not save the results in the `History` " +"object. This is now the case!" msgstr "" -"Limite la plage de versions pour la dépendance (optionnelle) `ray` pour " -"n'inclure que les versions compatibles (`>=1.9.2,<1.12.0`) " -"([#1205](https://github.com/adap/flower/pull/1205))" +"La fonction `fit_metrics_aggregation_fn` peut être utilisée pour agréger " +"les mesures d'entraînement, mais les versions précédentes " +"n'enregistraient pas les résultats dans l'objet `History`. c'est " +"désormais le cas !" -#: ../../source/ref-changelog.md:712 +#: ../../source/ref-changelog.md:500 msgid "" -"**Remove deprecated support for Python 3.6** " -"([#871](https://github.com/adap/flower/pull/871))" +"**General improvements** " +"([#1659](https://github.com/adap/flower/pull/1659), " +"[#1646](https://github.com/adap/flower/pull/1646), " +"[#1647](https://github.com/adap/flower/pull/1647), " +"[#1471](https://github.com/adap/flower/pull/1471), " +"[#1648](https://github.com/adap/flower/pull/1648), " +"[#1651](https://github.com/adap/flower/pull/1651), " +"[#1652](https://github.com/adap/flower/pull/1652), " +"[#1653](https://github.com/adap/flower/pull/1653), " +"[#1659](https://github.com/adap/flower/pull/1659), " +"[#1665](https://github.com/adap/flower/pull/1665), " +"[#1670](https://github.com/adap/flower/pull/1670), " +"[#1672](https://github.com/adap/flower/pull/1672), " +"[#1677](https://github.com/adap/flower/pull/1677), " +"[#1684](https://github.com/adap/flower/pull/1684), " +"[#1683](https://github.com/adap/flower/pull/1683), " +"[#1686](https://github.com/adap/flower/pull/1686), " +"[#1682](https://github.com/adap/flower/pull/1682), " +"[#1685](https://github.com/adap/flower/pull/1685), " +"[#1692](https://github.com/adap/flower/pull/1692), " +"[#1705](https://github.com/adap/flower/pull/1705), " +"[#1708](https://github.com/adap/flower/pull/1708), " +"[#1711](https://github.com/adap/flower/pull/1711), " +"[#1713](https://github.com/adap/flower/pull/1713), " +"[#1714](https://github.com/adap/flower/pull/1714), " +"[#1718](https://github.com/adap/flower/pull/1718), " +"[#1716](https://github.com/adap/flower/pull/1716), " +"[#1723](https://github.com/adap/flower/pull/1723), " +"[#1735](https://github.com/adap/flower/pull/1735), " +"[#1678](https://github.com/adap/flower/pull/1678), " +"[#1750](https://github.com/adap/flower/pull/1750), " +"[#1753](https://github.com/adap/flower/pull/1753), " +"[#1736](https://github.com/adap/flower/pull/1736), " +"[#1766](https://github.com/adap/flower/pull/1766), " +"[#1760](https://github.com/adap/flower/pull/1760), " +"[#1775](https://github.com/adap/flower/pull/1775), " +"[#1776](https://github.com/adap/flower/pull/1776), " +"[#1777](https://github.com/adap/flower/pull/1777), " +"[#1779](https://github.com/adap/flower/pull/1779), " +"[#1784](https://github.com/adap/flower/pull/1784), " +"[#1773](https://github.com/adap/flower/pull/1773), " +"[#1755](https://github.com/adap/flower/pull/1755), " +"[#1789](https://github.com/adap/flower/pull/1789), " +"[#1788](https://github.com/adap/flower/pull/1788), " +"[#1798](https://github.com/adap/flower/pull/1798), " +"[#1799](https://github.com/adap/flower/pull/1799), " +"[#1739](https://github.com/adap/flower/pull/1739), " +"[#1800](https://github.com/adap/flower/pull/1800), " +"[#1804](https://github.com/adap/flower/pull/1804), " +"[#1805](https://github.com/adap/flower/pull/1805))" msgstr "" -"**Supprime la prise en charge obsolète de Python 3.6** " -"([#871](https://github.com/adap/flower/pull/871))" +"**General improvements** " +"([#1659](https://github.com/adap/flower/pull/1659), " +"[#1646](https://github.com/adap/flower/pull/1646), " +"[#1647](https://github.com/adap/flower/pull/1647), " +"[#1471](https://github.com/adap/flower/pull/1471), " +"[#1648](https://github.com/adap/flower/pull/1648), " +"[#1651](https://github.com/adap/flower/pull/1651), " +"[#1652](https://github.com/adap/flower/pull/1652), " +"[#1653](https://github.com/adap/flower/pull/1653), " +"[#1659](https://github.com/adap/flower/pull/1659), " +"[#1665](https://github.com/adap/flower/pull/1665), " +"[#1670](https://github.com/adap/flower/pull/1670), " +"[#1672](https://github.com/adap/flower/pull/1672), " +"[#1677](https://github.com/adap/flower/pull/1677), " +"[#1684](https://github.com/adap/flower/pull/1684), " +"[#1683](https://github.com/adap/flower/pull/1683), " +"[#1686](https://github.com/adap/flower/pull/1686), " +"[#1682](https://github.com/adap/flower/pull/1682), " +"[#1685](https://github.com/adap/flower/pull/1685), " +"[#1692](https://github.com/adap/flower/pull/1692), " +"[#1705](https://github.com/ada" -#: ../../source/ref-changelog.md:713 -msgid "" -"**Remove deprecated KerasClient** " -"([#857](https://github.com/adap/flower/pull/857))" -msgstr "" -"**Supprimez KerasClient** " -"([#857](https://github.com/adap/flower/pull/857))" +#: ../../source/ref-changelog.md:508 +msgid "v1.3.0 (2023-02-06)" +msgstr "v1.3.0 (2023-02-06)" -#: ../../source/ref-changelog.md:714 +#: ../../source/ref-changelog.md:514 msgid "" -"**Remove deprecated no-op extra installs** " -"([#973](https://github.com/adap/flower/pull/973))" +"`Adam Narozniak`, `Alexander Viala Bellander`, `Charles Beauville`, " +"`Daniel J. Beutel`, `JDRanpariya`, `Lennart Behme`, `Taner Topal`" msgstr "" -"**Supprimer les installations supplémentaires no-op dépréciées** " -"([#973](https://github.com/adap/flower/pull/973))" +"`Adam Narozniak`, `Alexander Viala Bellander`, `Charles Beauville`, " +"`Daniel J. Beutel`, `JDRanpariya`, `Lennart Behme`, `Taner Topal`" -#: ../../source/ref-changelog.md:715 +#: ../../source/ref-changelog.md:518 msgid "" -"**Remove deprecated proto fields from** `FitRes` **and** `EvaluateRes` " -"([#869](https://github.com/adap/flower/pull/869))" +"**Add support for** `workload_id` **and** `group_id` **in Driver API** " +"([#1595](https://github.com/adap/flower/pull/1595))" msgstr "" -"**Supprimez les champs proto obsolètes de** `FitRes` **et** `EvaluateRes`" -" ([#869](https://github.com/adap/flower/pull/869))" +"**Ajouter la prise en charge de** `workload_id` **et** `group_id` **dans " +"l'API du pilote** ([#1595](https://github.com/adap/flower/pull/1595))" -#: ../../source/ref-changelog.md:716 +#: ../../source/ref-changelog.md:520 msgid "" -"**Remove deprecated QffedAvg strategy (replaced by QFedAvg)** " -"([#1107](https://github.com/adap/flower/pull/1107))" +"The (experimental) Driver API now supports a `workload_id` that can be " +"used to identify which workload a task belongs to. It also supports a new" +" `group_id` that can be used, for example, to indicate the current " +"training round. Both the `workload_id` and `group_id` enable client nodes" +" to decide whether they want to handle a task or not." msgstr "" -"**Supprime la stratégie QffedAvg (remplacée par QFedAvg)** " -"([#1107](https://github.com/adap/flower/pull/1107))" +"L'API (expérimentale) Driver prend désormais en charge un `workload_id` " +"qui peut être utilisé pour identifier la charge de travail à laquelle une" +" tâche appartient. Elle prend également en charge un nouveau `group_id` " +"qui peut être utilisé, par exemple, pour indiquer le cycle de formation " +"en cours. Le `workload_id` et le `group_id` permettent tous deux aux " +"nœuds clients de décider s'ils veulent traiter une tâche ou non." -#: ../../source/ref-changelog.md:717 +#: ../../source/ref-changelog.md:522 msgid "" -"**Remove deprecated DefaultStrategy strategy** " -"([#1142](https://github.com/adap/flower/pull/1142))" +"**Make Driver API and Fleet API address configurable** " +"([#1637](https://github.com/adap/flower/pull/1637))" msgstr "" -"**Supprime la stratégie DefaultStrategy qui est obsolète** " -"([#1142](https://github.com/adap/flower/pull/1142))" +"**Faire en sorte que l'adresse de l'API du conducteur et de l'API de la " +"flotte soit configurable** " +"([#1637](https://github.com/adap/flower/pull/1637))" -#: ../../source/ref-changelog.md:718 +#: ../../source/ref-changelog.md:524 msgid "" -"**Remove deprecated support for eval_fn accuracy return value** " -"([#1142](https://github.com/adap/flower/pull/1142))" +"The (experimental) long-running Flower server (Driver API and Fleet API) " +"can now configure the server address of both Driver API (via `--driver-" +"api-address`) and Fleet API (via `--fleet-api-address`) when starting:" msgstr "" -"**Supprimer la prise en charge obsolète de la valeur de retour de la " -"précision eval_fn** ([#1142](https://github.com/adap/flower/pull/1142))" +"Le serveur Flower (expérimental) de longue durée (Driver API et Fleet " +"API) peut maintenant configurer l'adresse du serveur de Driver API (via " +"`--driver-api-address`) et de Fleet API (via `--fleet-api-address`) lors " +"de son démarrage :" -#: ../../source/ref-changelog.md:719 +#: ../../source/ref-changelog.md:526 +#, fuzzy msgid "" -"**Remove deprecated support for passing initial parameters as NumPy " -"ndarrays** ([#1142](https://github.com/adap/flower/pull/1142))" +"`flower-server --driver-api-address \"0.0.0.0:8081\" --fleet-api-address " +"\"0.0.0.0:8086\"`" msgstr "" -"**Supprime la prise en charge obsolète du passage des paramètres initiaux" -" en tant que ndarrays NumPy** " -"([#1142](https://github.com/adap/flower/pull/1142))" +"``flower-superlink --driver-api-address \"0.0.0.0:8081\" --fleet-api-" +"address \"0.0.0.0:8086\" ``" -#: ../../source/ref-changelog.md:721 -msgid "v0.18.0 (2022-02-28)" -msgstr "v0.18.0 (2022-02-28)" +#: ../../source/ref-changelog.md:528 +msgid "Both IPv4 and IPv6 addresses are supported." +msgstr "Les adresses IPv4 et IPv6 sont toutes deux prises en charge." -#: ../../source/ref-changelog.md:725 +#: ../../source/ref-changelog.md:530 msgid "" -"**Improved Virtual Client Engine compatibility with Jupyter Notebook / " -"Google Colab** ([#866](https://github.com/adap/flower/pull/866), " -"[#872](https://github.com/adap/flower/pull/872), " -"[#833](https://github.com/adap/flower/pull/833), " -"[#1036](https://github.com/adap/flower/pull/1036))" +"**Add new example of Federated Learning using fastai and Flower** " +"([#1598](https://github.com/adap/flower/pull/1598))" msgstr "" -"**Amélioration de la compatibilité du moteur de client virtuel avec " -"Jupyter Notebook / Google Colab** " -"([#866](https://github.com/adap/flower/pull/866), " -"[#872](https://github.com/adap/flower/pull/872), " -"[#833](https://github.com/adap/flower/pull/833), " -"[#1036](https://github.com/adap/flower/pull/1036))" +"**Ajouter un nouvel exemple d'apprentissage fédéré utilisant fastai et " +"Flower** ([#1598](https://github.com/adap/flower/pull/1598))" -#: ../../source/ref-changelog.md:727 +#: ../../source/ref-changelog.md:532 msgid "" -"Simulations (using the Virtual Client Engine through `start_simulation`) " -"now work more smoothly on Jupyter Notebooks (incl. Google Colab) after " -"installing Flower with the `simulation` extra (`pip install " -"flwr[simulation]`)." +"A new code example (`quickstart-fastai`) demonstrates federated learning " +"with [fastai](https://www.fast.ai/) and Flower. You can find it here: " +"[quickstart-fastai](https://github.com/adap/flower/tree/main/examples" +"/quickstart-fastai)." msgstr "" -"Les simulations (utilisant le moteur de client virtuel via " -"`start_simulation`) fonctionnent maintenant plus facilement sur les " -"Notebooks Jupyter (y compris Google Colab) après avoir installé Flower " -"avec l'option `simulation` (`pip install flwr[simulation]`)." +"Un nouvel exemple de code (`quickstart-fastai`) démontre l'apprentissage " +"fédéré avec [fastai](https://www.fast.ai/) et Flower. Tu peux le trouver " +"ici : [quickstart-" +"fastai](https://github.com/adap/flower/tree/main/examples/quickstart-" +"fastai)." -#: ../../source/ref-changelog.md:729 +#: ../../source/ref-changelog.md:534 msgid "" -"**New Jupyter Notebook code example** " -"([#833](https://github.com/adap/flower/pull/833))" +"**Make Android example compatible with** `flwr >= 1.0.0` **and the latest" +" versions of Android** " +"([#1603](https://github.com/adap/flower/pull/1603))" msgstr "" -"**Nouvel exemple de code Jupyter Notebook** " -"([#833](https://github.com/adap/flower/pull/833))" +"**Rendre l'exemple Android compatible avec** `flwr >= 1.0.0` **et les " +"dernières versions d'Android** " +"([#1603](https://github.com/adap/flower/pull/1603))" -#: ../../source/ref-changelog.md:731 +#: ../../source/ref-changelog.md:536 +#, fuzzy msgid "" -"A new code example (`quickstart_simulation`) demonstrates Flower " -"simulations using the Virtual Client Engine through Jupyter Notebook " -"(incl. Google Colab)." +"The Android code example has received a substantial update: the project " +"is compatible with Flower 1.0 (and later), the UI received a full " +"refresh, and the project is updated to be compatible with newer Android " +"tooling." msgstr "" -"Un nouvel exemple de code (`quickstart_simulation`) démontre des " -"simulations de Flower en utilisant le moteur de client virtuel via " -"Jupyter Notebook (y compris Google Colab)." - -#: ../../source/ref-changelog.md:733 -msgid "" -"**Client properties (feature preview)** " -"([#795](https://github.com/adap/flower/pull/795))" -msgstr "" -"**Propriétés du client (aperçu des fonctionnalités)** " -"([#795](https://github.com/adap/flower/pull/795))" +"L'exemple de code Android a reçu une mise à jour substantielle : le " +"projet est compatible avec Flower 1.0 et les versions ultérieures, " +"l'interface utilisateur a reçu un rafraîchissement complet, et le projet " +"est mis à jour pour être compatible avec les outils Android les plus " +"récents." -#: ../../source/ref-changelog.md:735 +#: ../../source/ref-changelog.md:538 msgid "" -"Clients can implement a new method `get_properties` to enable server-side" -" strategies to query client properties." +"**Add new `FedProx` strategy** " +"([#1619](https://github.com/adap/flower/pull/1619))" msgstr "" -"Les clients peuvent implémenter une nouvelle méthode `get_properties` " -"pour permettre aux stratégies côté serveur d'interroger les propriétés du" -" client." +"**Ajouter une nouvelle stratégie `FedProx`** " +"([#1619](https://github.com/adap/flower/pull/1619))" -#: ../../source/ref-changelog.md:737 +#: ../../source/ref-changelog.md:540 msgid "" -"**Experimental Android support with TFLite** " -"([#865](https://github.com/adap/flower/pull/865))" +"This " +"[strategy](https://github.com/adap/flower/blob/main/src/py/flwr/server/strategy/fedprox.py)" +" is almost identical to " +"[`FedAvg`](https://github.com/adap/flower/blob/main/src/py/flwr/server/strategy/fedavg.py)," +" but helps users replicate what is described in this " +"[paper](https://arxiv.org/abs/1812.06127). It essentially adds a " +"parameter called `proximal_mu` to regularize the local models with " +"respect to the global models." msgstr "" -"**Support expérimental d'Android avec TFLite** " -"([#865](https://github.com/adap/flower/pull/865))" +"Cette " +"[stratégie](https://github.com/adap/flower/blob/main/src/py/flwr/server/strategy/fedprox.py)" +" est presque identique à " +"[`FedAvg`](https://github.com/adap/flower/blob/main/src/py/flwr/server/strategy/fedavg.py)," +" mais aide les utilisateurs à reproduire ce qui est décrit dans cet " +"[article](https://arxiv.org/abs/1812.06127). Elle ajoute essentiellement " +"un paramètre appelé `proximal_mu` pour régulariser les modèles locaux par" +" rapport aux modèles globaux." -#: ../../source/ref-changelog.md:739 +#: ../../source/ref-changelog.md:542 msgid "" -"Android support has finally arrived in `main`! Flower is both client-" -"agnostic and framework-agnostic by design. One can integrate arbitrary " -"client platforms and with this release, using Flower on Android has " -"become a lot easier." +"**Add new metrics to telemetry events** " +"([#1640](https://github.com/adap/flower/pull/1640))" msgstr "" -"La prise en charge d'Android est enfin arrivée dans `main` ! Flower est à" -" la fois agnostique au niveau du client et du cadre de travail. On peut " -"intégrer des plates-formes client arbitraires et avec cette version, " -"l'utilisation de Flower sur Android est devenue beaucoup plus facile." +"**Ajouter de nouvelles métriques aux événements de télémétrie** " +"([#1640](https://github.com/adap/flower/pull/1640))" -#: ../../source/ref-changelog.md:741 +#: ../../source/ref-changelog.md:544 msgid "" -"The example uses TFLite on the client side, along with a new " -"`FedAvgAndroid` strategy. The Android client and `FedAvgAndroid` are " -"still experimental, but they are a first step towards a fully-fledged " -"Android SDK and a unified `FedAvg` implementation that integrated the new" -" functionality from `FedAvgAndroid`." +"An updated event structure allows, for example, the clustering of events " +"within the same workload." msgstr "" -"L'exemple utilise TFLite du côté client, ainsi qu'une nouvelle stratégie " -"`FedAvgAndroid`. Le client Android et `FedAvgAndroid` sont encore " -"expérimentaux, mais ils constituent un premier pas vers un SDK Android à " -"part entière et une implémentation unifiée de `FedAvg` intégrant la " -"nouvelle fonctionnalité de `FedAvgAndroid`." +"Une structure d'événements mise à jour permet, par exemple, de regrouper " +"des événements au sein d'une même charge de travail." -#: ../../source/ref-changelog.md:743 +#: ../../source/ref-changelog.md:546 msgid "" -"**Make gRPC keepalive time user-configurable and decrease default " -"keepalive time** ([#1069](https://github.com/adap/flower/pull/1069))" +"**Add new custom strategy tutorial section** " +"[#1623](https://github.com/adap/flower/pull/1623)" msgstr "" -"**Rendre le temps de garde gRPC configurable par l'utilisateur et " -"diminuer le temps de garde par défaut** " -"([#1069](https://github.com/adap/flower/pull/1069))" +"**Ajouter une nouvelle section de tutoriel sur les stratégies " +"personnalisées** [#1623](https://github.com/adap/flower/pull/1623)" -#: ../../source/ref-changelog.md:745 +#: ../../source/ref-changelog.md:548 +#, fuzzy msgid "" -"The default gRPC keepalive time has been reduced to increase the " -"compatibility of Flower with more cloud environments (for example, " -"Microsoft Azure). Users can configure the keepalive time to customize the" -" gRPC stack based on specific requirements." +"The Flower tutorial now has a new section that covers implementing a " +"custom strategy from scratch: [Open in " +"Colab](https://colab.research.google.com/github/adap/flower/blob/main/doc/source" +"/tutorial-build-a-strategy-from-scratch-pytorch.ipynb)" msgstr "" -"Le temps de keepalive gRPC par défaut a été réduit pour augmenter la " -"compatibilité de Flower avec davantage d'environnements cloud (par " -"exemple, Microsoft Azure). Les utilisateurs peuvent configurer le temps " -"de keepalive pour personnaliser la pile gRPC en fonction d'exigences " -"spécifiques." +"Le tutoriel sur les fleurs comporte désormais une nouvelle section qui " +"traite de la mise en œuvre d'une stratégie personnalisée à partir de zéro" +" : [Ouvrir dans " +"Colab](https://colab.research.google.com/github/adap/flower/blob/main/doc/source/tutorial/Flower-3-Building-a" +"-Strategy-PyTorch.ipynb)" -#: ../../source/ref-changelog.md:747 +#: ../../source/ref-changelog.md:550 msgid "" -"**New differential privacy example using Opacus and PyTorch** " -"([#805](https://github.com/adap/flower/pull/805))" +"**Add new custom serialization tutorial section** " +"([#1622](https://github.com/adap/flower/pull/1622))" msgstr "" -"**Nouvel exemple de confidentialité différentielle utilisant Opacus et " -"PyTorch** ([#805](https://github.com/adap/flower/pull/805))" +"**Ajouter une nouvelle section de tutoriel sur la sérialisation " +"personnalisée** ([#1622](https://github.com/adap/flower/pull/1622))" -#: ../../source/ref-changelog.md:749 +#: ../../source/ref-changelog.md:552 +#, fuzzy msgid "" -"A new code example (`opacus`) demonstrates differentially-private " -"federated learning with Opacus, PyTorch, and Flower." +"The Flower tutorial now has a new section that covers custom " +"serialization: [Open in " +"Colab](https://colab.research.google.com/github/adap/flower/blob/main/doc/source" +"/tutorial-customize-the-client-pytorch.ipynb)" msgstr "" -"Un nouvel exemple de code (`opacus`) démontre l'apprentissage fédéré " -"différentiellement privé avec Opacus, PyTorch et Flower." +"Le tutoriel sur les fleurs comporte désormais une nouvelle section qui " +"traite de la sérialisation personnalisée : [Ouvrir dans " +"Colab](https://colab.research.google.com/github/adap/flower/blob/main/doc/source/tutorial/Flower-4" +"-Client-and-NumPyClient-PyTorch.ipynb)" -#: ../../source/ref-changelog.md:751 +#: ../../source/ref-changelog.md:554 msgid "" -"**New Hugging Face Transformers code example** " -"([#863](https://github.com/adap/flower/pull/863))" +"**General improvements** " +"([#1638](https://github.com/adap/flower/pull/1638), " +"[#1634](https://github.com/adap/flower/pull/1634), " +"[#1636](https://github.com/adap/flower/pull/1636), " +"[#1635](https://github.com/adap/flower/pull/1635), " +"[#1633](https://github.com/adap/flower/pull/1633), " +"[#1632](https://github.com/adap/flower/pull/1632), " +"[#1631](https://github.com/adap/flower/pull/1631), " +"[#1630](https://github.com/adap/flower/pull/1630), " +"[#1627](https://github.com/adap/flower/pull/1627), " +"[#1593](https://github.com/adap/flower/pull/1593), " +"[#1616](https://github.com/adap/flower/pull/1616), " +"[#1615](https://github.com/adap/flower/pull/1615), " +"[#1607](https://github.com/adap/flower/pull/1607), " +"[#1609](https://github.com/adap/flower/pull/1609), " +"[#1608](https://github.com/adap/flower/pull/1608), " +"[#1603](https://github.com/adap/flower/pull/1603), " +"[#1590](https://github.com/adap/flower/pull/1590), " +"[#1580](https://github.com/adap/flower/pull/1580), " +"[#1599](https://github.com/adap/flower/pull/1599), " +"[#1600](https://github.com/adap/flower/pull/1600), " +"[#1601](https://github.com/adap/flower/pull/1601), " +"[#1597](https://github.com/adap/flower/pull/1597), " +"[#1595](https://github.com/adap/flower/pull/1595), " +"[#1591](https://github.com/adap/flower/pull/1591), " +"[#1588](https://github.com/adap/flower/pull/1588), " +"[#1589](https://github.com/adap/flower/pull/1589), " +"[#1587](https://github.com/adap/flower/pull/1587), " +"[#1573](https://github.com/adap/flower/pull/1573), " +"[#1581](https://github.com/adap/flower/pull/1581), " +"[#1578](https://github.com/adap/flower/pull/1578), " +"[#1574](https://github.com/adap/flower/pull/1574), " +"[#1572](https://github.com/adap/flower/pull/1572), " +"[#1586](https://github.com/adap/flower/pull/1586))" msgstr "" -"**Nouvel exemple de code pour les Transformers à visage embrassant** " -"([#863](https://github.com/adap/flower/pull/863))" +"**General improvements** " +"([#1638](https://github.com/adap/flower/pull/1638), " +"[#1634](https://github.com/adap/flower/pull/1634), " +"[#1636](https://github.com/adap/flower/pull/1636), " +"[#1635](https://github.com/adap/flower/pull/1635), " +"[#1633](https://github.com/adap/flower/pull/1633), " +"[#1632](https://github.com/adap/flower/pull/1632), " +"[#1631](https://github.com/adap/flower/pull/1631), " +"[#1630](https://github.com/adap/flower/pull/1630), " +"[#1627](https://github.com/adap/flower/pull/1627), " +"[#1593](https://github.com/adap/flower/pull/1593), " +"[#1616](https://github.com/adap/flower/pull/1616), " +"[#1615](https://github.com/adap/flower/pull/1615), " +"[#1607](https://github.com/adap/flower/pull/1607), " +"[#1609](https://github.com/adap/flower/pull/1609), " +"[#1608](https://github.com/adap/flower/pull/1608), " +"[#1603](https://github.com/adap/flower/pull/1603), " +"[#1590](https://github.com/adap/flower/pull/1590), " +"[#1580](https://github.com/adap/flower/pull/1580), " +"[#1599](https://github.com/adap/flower/pull/1599), " +"[#1600](https://github.com/ada" -#: ../../source/ref-changelog.md:753 +#: ../../source/ref-changelog.md:558 msgid "" -"A new code example (`quickstart_huggingface`) demonstrates usage of " -"Hugging Face Transformers with Flower." +"**Updated documentation** " +"([#1629](https://github.com/adap/flower/pull/1629), " +"[#1628](https://github.com/adap/flower/pull/1628), " +"[#1620](https://github.com/adap/flower/pull/1620), " +"[#1618](https://github.com/adap/flower/pull/1618), " +"[#1617](https://github.com/adap/flower/pull/1617), " +"[#1613](https://github.com/adap/flower/pull/1613), " +"[#1614](https://github.com/adap/flower/pull/1614))" msgstr "" -"Un nouvel exemple de code (`quickstart_huggingface`) démontre " -"l'utilisation des transformateurs Hugging Face avec Flower." +"**Mise à jour de la documentation** " +"([#1629](https://github.com/adap/flower/pull/1629), " +"[#1628](https://github.com/adap/flower/pull/1628), " +"[#1620](https://github.com/adap/flower/pull/1620), " +"[#1618](https://github.com/adap/flower/pull/1618), " +"[#1617](https://github.com/adap/flower/pull/1617), " +"[#1613](https://github.com/adap/flower/pull/1613), " +"[#1614](https://github.com/adap/flower/pull/1614))" -#: ../../source/ref-changelog.md:755 +#: ../../source/ref-changelog.md:560 ../../source/ref-changelog.md:627 msgid "" -"**New MLCube code example** " -"([#779](https://github.com/adap/flower/pull/779), " -"[#1034](https://github.com/adap/flower/pull/1034), " -"[#1065](https://github.com/adap/flower/pull/1065), " -"[#1090](https://github.com/adap/flower/pull/1090))" +"As usual, the documentation has improved quite a bit. It is another step " +"in our effort to make the Flower documentation the best documentation of " +"any project. Stay tuned and as always, feel free to provide feedback!" msgstr "" -"**Nouvel exemple de code MLCube** " -"([#779](https://github.com/adap/flower/pull/779), " -"[#1034](https://github.com/adap/flower/pull/1034), " -"[#1065](https://github.com/adap/flower/pull/1065), " -"[#1090](https://github.com/adap/flower/pull/1090))" +"Comme d'habitude, la documentation s'est beaucoup améliorée. C'est une " +"autre étape dans notre effort pour faire de la documentation de Flower la" +" meilleure documentation de tout projet. Reste à l'écoute et comme " +"toujours, n'hésite pas à nous faire part de tes commentaires !" -#: ../../source/ref-changelog.md:757 -msgid "" -"A new code example (`quickstart_mlcube`) demonstrates usage of MLCube " -"with Flower." -msgstr "" -"Un nouvel exemple de code (`quickstart_mlcube`) démontre l'utilisation de" -" MLCube avec Flower." +#: ../../source/ref-changelog.md:566 +msgid "v1.2.0 (2023-01-13)" +msgstr "v1.2.0 (2023-01-13)" -#: ../../source/ref-changelog.md:759 +#: ../../source/ref-changelog.md:572 msgid "" -"**SSL-enabled server and client** " -"([#842](https://github.com/adap/flower/pull/842), " -"[#844](https://github.com/adap/flower/pull/844), " -"[#845](https://github.com/adap/flower/pull/845), " -"[#847](https://github.com/adap/flower/pull/847), " -"[#993](https://github.com/adap/flower/pull/993), " -"[#994](https://github.com/adap/flower/pull/994))" +"`Adam Narozniak`, `Charles Beauville`, `Daniel J. Beutel`, `Edoardo`, `L." +" Jiang`, `Ragy`, `Taner Topal`, `dannymcy`" msgstr "" -"**([#842](https://github.com/adap/flower/pull/842), " -"[#844](https://github.com/adap/flower/pull/844), " -"[#845](https://github.com/adap/flower/pull/845), " -"[#847](https://github.com/adap/flower/pull/847), " -"[#993](https://github.com/adap/flower/pull/993), " -"[#994](https://github.com/adap/flower/pull/994))" +"adam Narozniak`, `Charles Beauville`, `Daniel J. Beutel`, `Edoardo`, `L. " +"Jiang`, `Ragy`, `Taner Topal`, `dannymcy`" -#: ../../source/ref-changelog.md:761 +#: ../../source/ref-changelog.md:576 msgid "" -"SSL enables secure encrypted connections between clients and servers. " -"This release open-sources the Flower secure gRPC implementation to make " -"encrypted communication channels accessible to all Flower users." +"**Introduce new Flower Baseline: FedAvg MNIST** " +"([#1497](https://github.com/adap/flower/pull/1497), " +"[#1552](https://github.com/adap/flower/pull/1552))" msgstr "" -"SSL permet d'établir des connexions cryptées et sécurisées entre les " -"clients et les serveurs. Cette version met en open-source " -"l'implémentation gRPC sécurisée de Flower afin de rendre les canaux de " -"communication cryptés accessibles à tous les utilisateurs de Flower." +"**Introduire une nouvelle fleur Référence : FedAvg MNIST** " +"([#1497](https://github.com/adap/flower/pull/1497), " +"[#1552](https://github.com/adap/flower/pull/1552))" -#: ../../source/ref-changelog.md:763 +#: ../../source/ref-changelog.md:578 msgid "" -"**Updated** `FedAdam` **and** `FedYogi` **strategies** " -"([#885](https://github.com/adap/flower/pull/885), " -"[#895](https://github.com/adap/flower/pull/895))" +"Over the coming weeks, we will be releasing a number of new reference " +"implementations useful especially to FL newcomers. They will typically " +"revisit well known papers from the literature, and be suitable for " +"integration in your own application or for experimentation, in order to " +"deepen your knowledge of FL in general. Today's release is the first in " +"this series. [Read more.](https://flower.ai/blog/2023-01-12-fl-starter-" +"pack-fedavg-mnist-cnn/)" msgstr "" -"**Mise à jour** `FedAdam` **et** `FedYogi` **stratégies** " -"([#885](https://github.com/adap/flower/pull/885), " -"[#895](https://github.com/adap/flower/pull/895))" +"Au cours des prochaines semaines, nous publierons un certain nombre de " +"nouvelles implémentations de référence utiles en particulier pour les " +"nouveaux venus en FL. Elles revisiteront généralement des articles bien " +"connus de la littérature, et seront adaptées à l'intégration dans votre " +"propre application ou à l'expérimentation, afin d'approfondir votre " +"connaissance de FL en général. La publication d'aujourd'hui est la " +"première de cette série. [Lire la " +"suite.](https://flower.ai/blog/2023-01-12-fl-starter-pack-fedavg-mnist-" +"cnn/)" -#: ../../source/ref-changelog.md:765 +#: ../../source/ref-changelog.md:580 msgid "" -"`FedAdam` and `FedAdam` match the latest version of the Adaptive " -"Federated Optimization paper." +"**Improve GPU support in simulations** " +"([#1555](https://github.com/adap/flower/pull/1555))" msgstr "" -"`FedAdam` et `FedAdam` correspondent à la dernière version de l'article " -"sur l'optimisation fédérée adaptative." +"**Améliorer la prise en charge des GPU dans les simulations** " +"([#1555](https://github.com/adap/flower/pull/1555))" -#: ../../source/ref-changelog.md:767 +#: ../../source/ref-changelog.md:582 msgid "" -"**Initialize** `start_simulation` **with a list of client IDs** " -"([#860](https://github.com/adap/flower/pull/860))" +"The Ray-based Virtual Client Engine (`start_simulation`) has been updated" +" to improve GPU support. The update includes some of the hard-earned " +"lessons from scaling simulations in GPU cluster environments. New " +"defaults make running GPU-based simulations substantially more robust." msgstr "" -"**Initialise** `start_simulation` **avec une liste d'ID de clients** " -"([#860](https://github.com/adap/flower/pull/860))" +"Le moteur client virtuel basé sur Ray (`start_simulation`) a été mis à " +"jour pour améliorer la prise en charge des GPU. La mise à jour inclut " +"certaines des leçons durement apprises lors de la mise à l'échelle des " +"simulations dans des environnements de grappes de GPU. De nouveaux " +"paramètres par défaut rendent l'exécution des simulations basées sur les " +"GPU beaucoup plus robuste." -#: ../../source/ref-changelog.md:769 +#: ../../source/ref-changelog.md:584 msgid "" -"`start_simulation` can now be called with a list of client IDs " -"(`clients_ids`, type: `List[str]`). Those IDs will be passed to the " -"`client_fn` whenever a client needs to be initialized, which can make it " -"easier to load data partitions that are not accessible through `int` " -"identifiers." +"**Improve GPU support in Jupyter Notebook tutorials** " +"([#1527](https://github.com/adap/flower/pull/1527), " +"[#1558](https://github.com/adap/flower/pull/1558))" msgstr "" -"`start_simulation` peut maintenant être appelé avec une liste " -"d'identifiants de clients (`clients_ids`, type : `List[str]`). Ces " -"identifiants seront passés à `client_fn` chaque fois qu'un client doit " -"être initialisé, ce qui peut faciliter le chargement de partitions de " -"données qui ne sont pas accessibles par des identifiants `int`." +"**Améliorer la prise en charge du GPU dans les tutoriels Jupyter " +"Notebook** ([#1527](https://github.com/adap/flower/pull/1527), " +"[#1558](https://github.com/adap/flower/pull/1558))" -#: ../../source/ref-changelog.md:773 +#: ../../source/ref-changelog.md:586 msgid "" -"Update `num_examples` calculation in PyTorch code examples in " -"([#909](https://github.com/adap/flower/pull/909))" +"Some users reported that Jupyter Notebooks have not always been easy to " +"use on GPU instances. We listened and made improvements to all of our " +"Jupyter notebooks! Check out the updated notebooks here:" msgstr "" -"Mettre à jour le calcul de `num_examples` dans les exemples de code " -"PyTorch dans ([#909](https://github.com/adap/flower/pull/909))" +"Certains utilisateurs ont signalé que les carnets Jupyter n'ont pas " +"toujours été faciles à utiliser sur les instances GPU. Nous les avons " +"écoutés et avons apporté des améliorations à tous nos carnets Jupyter ! " +"Découvre les carnets mis à jour ici :" -#: ../../source/ref-changelog.md:774 +#: ../../source/ref-changelog.md:588 +#, fuzzy msgid "" -"Expose Flower version through `flwr.__version__` " -"([#952](https://github.com/adap/flower/pull/952))" +"[An Introduction to Federated Learning](https://flower.ai/docs/framework" +"/tutorial-get-started-with-flower-pytorch.html)" msgstr "" -"Exposer la version de Flower à travers `flwr.__version__` " -"([#952](https://github.com/adap/flower/pull/952))" +"[Une introduction à l'apprentissage fédéré] " +"(https://flower.ai/docs/tutorial/Flower-1-Intro-to-FL-PyTorch.html)" -#: ../../source/ref-changelog.md:775 +#: ../../source/ref-changelog.md:589 +#, fuzzy msgid "" -"`start_server` in `app.py` now returns a `History` object containing " -"metrics from training ([#974](https://github.com/adap/flower/pull/974))" +"[Strategies in Federated Learning](https://flower.ai/docs/framework" +"/tutorial-use-a-federated-learning-strategy-pytorch.html)" msgstr "" -"`start_server` dans `app.py` renvoie maintenant un objet `History` " -"contenant les métriques de l'entraînement " -"([#974](https://github.com/adap/flower/pull/974))" +"[Stratégies d'apprentissage fédéré] " +"(https://flower.ai/docs/tutorial/Flower-2-Strategies-in-FL-PyTorch.html)" -#: ../../source/ref-changelog.md:776 +#: ../../source/ref-changelog.md:590 +#, fuzzy msgid "" -"Make `max_workers` (used by `ThreadPoolExecutor`) configurable " -"([#978](https://github.com/adap/flower/pull/978))" +"[Building a Strategy](https://flower.ai/docs/framework/tutorial-build-a" +"-strategy-from-scratch-pytorch.html)" msgstr "" -"Rendre `max_workers` (utilisé par `ThreadPoolExecutor`) configurable " -"([#978](https://github.com/adap/flower/pull/978))" +"[Construire une stratégie] " +"(https://flower.ai/docs/tutorial/Flower-3-Building-a-Strategy-" +"PyTorch.html)" -#: ../../source/ref-changelog.md:777 +#: ../../source/ref-changelog.md:591 +#, fuzzy msgid "" -"Increase sleep time after server start to three seconds in all code " -"examples ([#1086](https://github.com/adap/flower/pull/1086))" +"[Client and NumPyClient](https://flower.ai/docs/framework/tutorial-" +"customize-the-client-pytorch.html)" msgstr "" -"Augmente le temps de sommeil après le démarrage du serveur à trois " -"secondes dans tous les exemples de code " -"([#1086](https://github.com/adap/flower/pull/1086))" +"[Client et NumPyClient] (https://flower.ai/docs/tutorial/Flower-4-Client-" +"and-NumPyClient-PyTorch.html)" -#: ../../source/ref-changelog.md:778 +#: ../../source/ref-changelog.md:593 msgid "" -"Added a new FAQ section to the documentation " -"([#948](https://github.com/adap/flower/pull/948))" +"**Introduce optional telemetry** " +"([#1533](https://github.com/adap/flower/pull/1533), " +"[#1544](https://github.com/adap/flower/pull/1544), " +"[#1584](https://github.com/adap/flower/pull/1584))" msgstr "" -"Ajout d'une nouvelle section FAQ à la documentation " -"([#948](https://github.com/adap/flower/pull/948))" +"**Introduire la télémétrie optionnelle** " +"([#1533](https://github.com/adap/flower/pull/1533), " +"[#1544](https://github.com/adap/flower/pull/1544), " +"[#1584](https://github.com/adap/flower/pull/1584))" -#: ../../source/ref-changelog.md:779 +#: ../../source/ref-changelog.md:595 msgid "" -"And many more under-the-hood changes, library updates, documentation " -"changes, and tooling improvements!" +"After a [request for " +"feedback](https://github.com/adap/flower/issues/1534) from the community," +" the Flower open-source project introduces optional collection of " +"*anonymous* usage metrics to make well-informed decisions to improve " +"Flower. Doing this enables the Flower team to understand how Flower is " +"used and what challenges users might face." msgstr "" -"Et bien d'autres changements sous le capot, des mises à jour de la " -"bibliothèque, des modifications de la documentation et des améliorations " -"de l'outillage !" +"À la suite d'une [demande de commentaires] " +"(https://github.com/adap/flower/issues/1534) de la part de la communauté," +" le projet open-source Flower introduit la collecte optionnelle de " +"mesures d'utilisation *anonymes* afin de prendre des décisions éclairées " +"pour améliorer Flower. Cela permet à l'équipe de Flower de comprendre " +"comment Flower est utilisé et quels sont les défis auxquels les " +"utilisateurs peuvent être confrontés." -#: ../../source/ref-changelog.md:783 +#: ../../source/ref-changelog.md:597 +#, fuzzy msgid "" -"**Removed** `flwr_example` **and** `flwr_experimental` **from release " -"build** ([#869](https://github.com/adap/flower/pull/869))" +"**Flower is a friendly framework for collaborative AI and data science.**" +" Staying true to this statement, Flower makes it easy to disable " +"telemetry for users who do not want to share anonymous usage metrics. " +"[Read more.](https://flower.ai/docs/telemetry.html)." msgstr "" -"**Supprimé** `flwr_example` **et** `flwr_experimental` **de la version " -"release build** ([#869](https://github.com/adap/flower/pull/869))" +"**Flower est un cadre convivial pour l'IA collaborative et la science des" +" données.** Restant fidèle à cette déclaration, Flower permet de " +"désactiver facilement la télémétrie pour les utilisateurs qui ne " +"souhaitent pas partager des métriques d'utilisation anonymes.[Lire la " +"suite.](https://flower.ai/docs/telemetry.html)." -#: ../../source/ref-changelog.md:785 +#: ../../source/ref-changelog.md:599 msgid "" -"The packages `flwr_example` and `flwr_experimental` have been deprecated " -"since Flower 0.12.0 and they are not longer included in Flower release " -"builds. The associated extras (`baseline`, `examples-pytorch`, `examples-" -"tensorflow`, `http-logger`, `ops`) are now no-op and will be removed in " -"an upcoming release." +"**Introduce (experimental) Driver API** " +"([#1520](https://github.com/adap/flower/pull/1520), " +"[#1525](https://github.com/adap/flower/pull/1525), " +"[#1545](https://github.com/adap/flower/pull/1545), " +"[#1546](https://github.com/adap/flower/pull/1546), " +"[#1550](https://github.com/adap/flower/pull/1550), " +"[#1551](https://github.com/adap/flower/pull/1551), " +"[#1567](https://github.com/adap/flower/pull/1567))" msgstr "" -"Les paquets `flwr_example` et `flwr_experimental` ont été dépréciés " -"depuis Flower 0.12.0 et ils ne sont plus inclus dans les builds de " -"Flower. Les extras associés (`baseline`, `examples-pytorch`, `examples-" -"tensorflow`, `http-logger`, `ops`) sont maintenant no-op et seront " -"supprimés dans une prochaine version." - -#: ../../source/ref-changelog.md:787 -msgid "v0.17.0 (2021-09-24)" -msgstr "v0.17.0 (2021-09-24)" +"**([#1520](https://github.com/adap/flower/pull/1520), " +"[#1525](https://github.com/adap/flower/pull/1525), " +"[#1545](https://github.com/adap/flower/pull/1545), " +"[#1546](https://github.com/adap/flower/pull/1546), " +"[#1550](https://github.com/adap/flower/pull/1550), " +"[#1551](https://github.com/adap/flower/pull/1551), " +"[#1567](https://github.com/adap/flower/pull/1567))" -#: ../../source/ref-changelog.md:791 +#: ../../source/ref-changelog.md:601 msgid "" -"**Experimental virtual client engine** " -"([#781](https://github.com/adap/flower/pull/781) " -"[#790](https://github.com/adap/flower/pull/790) " -"[#791](https://github.com/adap/flower/pull/791))" +"Flower now has a new (experimental) Driver API which will enable fully " +"programmable, async, and multi-tenant Federated Learning and Federated " +"Analytics applications. Phew, that's a lot! Going forward, the Driver API" +" will be the abstraction that many upcoming features will be built on - " +"and you can start building those things now, too." msgstr "" -"**Moteur expérimental de client virtuel** " -"([#781](https://github.com/adap/flower/pull/781) " -"[#790](https://github.com/adap/flower/pull/790) " -"[#791](https://github.com/adap/flower/pull/791))" +"Flower dispose désormais d'une nouvelle API de pilote (expérimentale) qui" +" permettra de créer des applications Federated Learning et Federated " +"Analytics entièrement programmables, asynchrones et multi-tenant. Ouf, " +"c'est beaucoup ! À l'avenir, l'API de pilote sera l'abstraction sur " +"laquelle de nombreuses fonctionnalités à venir seront construites - et tu" +" peux commencer à construire ces choses dès maintenant, aussi." -#: ../../source/ref-changelog.md:793 +#: ../../source/ref-changelog.md:603 msgid "" -"One of Flower's goals is to enable research at scale. This release " -"enables a first (experimental) peek at a major new feature, codenamed the" -" virtual client engine. Virtual clients enable simulations that scale to " -"a (very) large number of clients on a single machine or compute cluster. " -"The easiest way to test the new functionality is to look at the two new " -"code examples called `quickstart_simulation` and `simulation_pytorch`." +"The Driver API also enables a new execution mode in which the server runs" +" indefinitely. Multiple individual workloads can run concurrently and " +"start and stop their execution independent of the server. This is " +"especially useful for users who want to deploy Flower in production." msgstr "" -"L'un des objectifs de Flower est de permettre la recherche à grande " -"échelle. Cette version donne un premier aperçu (expérimental) d'une " -"nouvelle fonctionnalité majeure, connue sous le nom de code de moteur de " -"client virtuel. Les clients virtuels permettent des simulations qui " -"s'étendent à un (très) grand nombre de clients sur une seule machine ou " -"une grappe de calcul. La façon la plus simple de tester la nouvelle " -"fonctionnalité est de regarder les deux nouveaux exemples de code appelés" -" `quickstart_simulation` et `simulation_pytorch`." +"L'API du pilote permet également un nouveau mode d'exécution dans lequel " +"le serveur s'exécute indéfiniment. Plusieurs charges de travail " +"individuelles peuvent s'exécuter simultanément et démarrer et arrêter " +"leur exécution indépendamment du serveur. Ceci est particulièrement utile" +" pour les utilisateurs qui souhaitent déployer Flower en production." -#: ../../source/ref-changelog.md:795 +#: ../../source/ref-changelog.md:605 msgid "" -"The feature is still experimental, so there's no stability guarantee for " -"the API. It's also not quite ready for prime time and comes with a few " -"known caveats. However, those who are curious are encouraged to try it " -"out and share their thoughts." +"To learn more, check out the `mt-pytorch` code example. We look forward " +"to you feedback!" msgstr "" -"La fonction est encore expérimentale, il n'y a donc aucune garantie de " -"stabilité pour l'API. Elle n'est pas non plus tout à fait prête pour le " -"prime time et s'accompagne de quelques mises en garde connues. Cependant," -" les personnes curieuses sont encouragées à l'essayer et à faire part de " -"leurs réflexions." +"Pour en savoir plus, consulte l'exemple de code `mt-pytorch`. Nous " +"attendons tes commentaires avec impatience !" -#: ../../source/ref-changelog.md:797 +#: ../../source/ref-changelog.md:607 msgid "" -"**New built-in strategies** " -"([#828](https://github.com/adap/flower/pull/828) " -"[#822](https://github.com/adap/flower/pull/822))" +"Please note: *The Driver API is still experimental and will likely change" +" significantly over time.*" msgstr "" -"**Nouvelles stratégies intégrées** " -"([#828](https://github.com/adap/flower/pull/828) " -"[#822](https://github.com/adap/flower/pull/822))" +"Remarque : *L'API du pilote est encore expérimentale et est susceptible " +"de changer de manière significative au fil du temps.*" -#: ../../source/ref-changelog.md:799 +#: ../../source/ref-changelog.md:609 msgid "" -"FedYogi - Federated learning strategy using Yogi on server-side. " -"Implementation based on https://arxiv.org/abs/2003.00295" +"**Add new Federated Analytics with Pandas example** " +"([#1469](https://github.com/adap/flower/pull/1469), " +"[#1535](https://github.com/adap/flower/pull/1535))" msgstr "" -"FedYogi - Stratégie d'apprentissage fédéré utilisant Yogi côté serveur. " -"Mise en oeuvre basée sur https://arxiv.org/abs/2003.00295" +"**Ajouter un nouvel exemple de Federated Analytics avec Pandas** " +"([#1469](https://github.com/adap/flower/pull/1469), " +"[#1535](https://github.com/adap/flower/pull/1535))" -#: ../../source/ref-changelog.md:800 +#: ../../source/ref-changelog.md:611 msgid "" -"FedAdam - Federated learning strategy using Adam on server-side. " -"Implementation based on https://arxiv.org/abs/2003.00295" +"A new code example (`quickstart-pandas`) demonstrates federated analytics" +" with Pandas and Flower. You can find it here: [quickstart-" +"pandas](https://github.com/adap/flower/tree/main/examples/quickstart-" +"pandas)." msgstr "" -"FedAdam - Stratégie d'apprentissage fédéré utilisant Adam côté serveur. " -"Mise en œuvre basée sur https://arxiv.org/abs/2003.00295" +"Un nouvel exemple de code (`quickstart-pandas`) démontre l'analyse " +"fédérée avec Pandas et Flower. Tu peux le trouver ici : [quickstart-" +"pandas](https://github.com/adap/flower/tree/main/examples/quickstart-" +"pandas)." -#: ../../source/ref-changelog.md:802 +#: ../../source/ref-changelog.md:613 msgid "" -"**New PyTorch Lightning code example** " -"([#617](https://github.com/adap/flower/pull/617))" +"**Add new strategies: Krum and MultiKrum** " +"([#1481](https://github.com/adap/flower/pull/1481))" msgstr "" -"**Nouvel exemple de code PyTorch Lightning** " -"([#617](https://github.com/adap/flower/pull/617))" +"**Ajouter de nouvelles stratégies : Krum et MultiKrum** " +"([#1481](https://github.com/adap/flower/pull/1481))" -#: ../../source/ref-changelog.md:804 +#: ../../source/ref-changelog.md:615 msgid "" -"**New Variational Auto-Encoder code example** " -"([#752](https://github.com/adap/flower/pull/752))" +"Edoardo, a computer science student at the Sapienza University of Rome, " +"contributed a new `Krum` strategy that enables users to easily use Krum " +"and MultiKrum in their workloads." msgstr "" -"**Nouvel exemple de code d'autocodage variationnel** " -"([#752](https://github.com/adap/flower/pull/752))" +"Edoardo, étudiant en informatique à l'Université Sapienza de Rome, a " +"contribué à une nouvelle stratégie `Krum` qui permet aux utilisateurs " +"d'utiliser facilement Krum et MultiKrum dans leurs charges de travail." -#: ../../source/ref-changelog.md:806 +#: ../../source/ref-changelog.md:617 msgid "" -"**New scikit-learn code example** " -"([#748](https://github.com/adap/flower/pull/748))" +"**Update C++ example to be compatible with Flower v1.2.0** " +"([#1495](https://github.com/adap/flower/pull/1495))" msgstr "" -"**Nouvel exemple de code scikit-learn** " -"([#748](https://github.com/adap/flower/pull/748))" +"**Mettre à jour l'exemple C++ pour qu'il soit compatible avec Flower " +"v1.2.0** ([#1495](https://github.com/adap/flower/pull/1495))" -#: ../../source/ref-changelog.md:808 +#: ../../source/ref-changelog.md:619 msgid "" -"**New experimental TensorBoard strategy** " -"([#789](https://github.com/adap/flower/pull/789))" +"The C++ code example has received a substantial update to make it " +"compatible with the latest version of Flower." msgstr "" -"**Nouvelle stratégie expérimentale TensorBoard** " -"([#789](https://github.com/adap/flower/pull/789))" +"L'exemple de code C++ a reçu une mise à jour substantielle pour le rendre" +" compatible avec la dernière version de Flower." -#: ../../source/ref-changelog.md:812 +#: ../../source/ref-changelog.md:621 msgid "" -"Improved advanced TensorFlow code example " -"([#769](https://github.com/adap/flower/pull/769))" +"**General improvements** " +"([#1491](https://github.com/adap/flower/pull/1491), " +"[#1504](https://github.com/adap/flower/pull/1504), " +"[#1506](https://github.com/adap/flower/pull/1506), " +"[#1514](https://github.com/adap/flower/pull/1514), " +"[#1522](https://github.com/adap/flower/pull/1522), " +"[#1523](https://github.com/adap/flower/pull/1523), " +"[#1526](https://github.com/adap/flower/pull/1526), " +"[#1528](https://github.com/adap/flower/pull/1528), " +"[#1547](https://github.com/adap/flower/pull/1547), " +"[#1549](https://github.com/adap/flower/pull/1549), " +"[#1560](https://github.com/adap/flower/pull/1560), " +"[#1564](https://github.com/adap/flower/pull/1564), " +"[#1566](https://github.com/adap/flower/pull/1566))" msgstr "" -"Amélioration de l'exemple de code TensorFlow avancé " -"([#769](https://github.com/adap/flower/pull/769))" +"**Améliorations générales** " +"([#1491](https://github.com/adap/flower/pull/1491), " +"[#1504](https://github.com/adap/flower/pull/1504), " +"[#1506](https://github.com/adap/flower/pull/1506), " +"[#1514](https://github.com/adap/flower/pull/1514), " +"[#1522](https://github.com/adap/flower/pull/1522), " +"[#1523](https://github.com/adap/flower/pull/1523), " +"[#1526](https://github.com/adap/flower/pull/1526), " +"[#1528](https://github.com/adap/flower/pull/1528), " +"[#1547](https://github.com/adap/flower/pull/1547), " +"[#1549](https://github.com/adap/flower/pull/1549), " +"[#1560](https://github.com/adap/flower/pull/1560), " +"[#1564](https://github.com/adap/flower/pull/1564), " +"[#1566](https://github.com/adap/flower/pull/1566))" -#: ../../source/ref-changelog.md:813 +#: ../../source/ref-changelog.md:625 msgid "" -"Warning when `min_available_clients` is misconfigured " -"([#830](https://github.com/adap/flower/pull/830))" +"**Updated documentation** " +"([#1494](https://github.com/adap/flower/pull/1494), " +"[#1496](https://github.com/adap/flower/pull/1496), " +"[#1500](https://github.com/adap/flower/pull/1500), " +"[#1503](https://github.com/adap/flower/pull/1503), " +"[#1505](https://github.com/adap/flower/pull/1505), " +"[#1524](https://github.com/adap/flower/pull/1524), " +"[#1518](https://github.com/adap/flower/pull/1518), " +"[#1519](https://github.com/adap/flower/pull/1519), " +"[#1515](https://github.com/adap/flower/pull/1515))" msgstr "" -"Avertissement lorsque `min_available_clients` est mal configuré " -"([#830](https://github.com/adap/flower/pull/830))" +"**Documentation mise à jour** " +"([#1494](https://github.com/adap/flower/pull/1494), " +"[#1496](https://github.com/adap/flower/pull/1496), " +"[#1500](https://github.com/adap/flower/pull/1500), " +"[#1503](https://github.com/adap/flower/pull/1503), " +"[#1505](https://github.com/adap/flower/pull/1505), " +"[#1524](https://github.com/adap/flower/pull/1524), " +"[#1518](https://github.com/adap/flower/pull/1518), " +"[#1519](https://github.com/adap/flower/pull/1519), " +"[#1515](https://github.com/adap/flower/pull/1515))" -#: ../../source/ref-changelog.md:814 +#: ../../source/ref-changelog.md:629 msgid "" -"Improved gRPC server docs " -"([#841](https://github.com/adap/flower/pull/841))" +"One highlight is the new [first time contributor " +"guide](https://flower.ai/docs/first-time-contributors.html): if you've " +"never contributed on GitHub before, this is the perfect place to start!" msgstr "" -"Amélioration de la documentation sur le serveur gRPC " -"([#841](https://github.com/adap/flower/pull/841))" +"L'un des points forts est le nouveau [guide du premier contributeur] " +"(https://flower.ai/docs/first-time-contributors.html) : si tu n'as jamais" +" contribué sur GitHub auparavant, c'est l'endroit idéal pour commencer !" + +#: ../../source/ref-changelog.md:635 +msgid "v1.1.0 (2022-10-31)" +msgstr "v1.1.0 (2022-10-31)" -#: ../../source/ref-changelog.md:815 +#: ../../source/ref-changelog.md:639 msgid "" -"Improved error message in `NumPyClient` " -"([#851](https://github.com/adap/flower/pull/851))" +"We would like to give our **special thanks** to all the contributors who " +"made the new version of Flower possible (in `git shortlog` order):" msgstr "" -"Amélioration du message d'erreur dans `NumPyClient` " -"([#851](https://github.com/adap/flower/pull/851))" +"Nous aimerions **remercier tout particulièrement** tous les contributeurs" +" qui ont rendu possible la nouvelle version de Flower (dans l'ordre `git " +"shortlog`) :" -#: ../../source/ref-changelog.md:816 +#: ../../source/ref-changelog.md:641 msgid "" -"Improved PyTorch quickstart code example " -"([#852](https://github.com/adap/flower/pull/852))" +"`Akis Linardos`, `Christopher S`, `Daniel J. Beutel`, `George`, `Jan " +"Schlicht`, `Mohammad Fares`, `Pedro Porto Buarque de Gusmão`, `Philipp " +"Wiesner`, `Rob Luke`, `Taner Topal`, `VasundharaAgarwal`, " +"`danielnugraha`, `edogab33`" msgstr "" -"Exemple de code de démarrage rapide PyTorch amélioré " -"([#852](https://github.com/adap/flower/pull/852))" +"`Akis Linardos`, `Christopher S`, `Daniel J. Beutel`, `George`, `Jan " +"Schlicht`, `Mohammad Fares`, `Pedro Porto Buarque de Gusmão`, `Philipp " +"Wiesner`, `Rob Luke`, `Taner Topal`, `VasundharaAgarwal`, " +"`danielnugraha`, `edogab33`" -#: ../../source/ref-changelog.md:820 +#: ../../source/ref-changelog.md:645 msgid "" -"**Disabled final distributed evaluation** " -"([#800](https://github.com/adap/flower/pull/800))" +"**Introduce Differential Privacy wrappers (preview)** " +"([#1357](https://github.com/adap/flower/pull/1357), " +"[#1460](https://github.com/adap/flower/pull/1460))" msgstr "" -"**Désactivé l'évaluation finale distribuée** " -"([#800](https://github.com/adap/flower/pull/800))" +"**Introduire les enveloppes de confidentialité différentielle (aperçu)** " +"([#1357](https://github.com/adap/flower/pull/1357), " +"[#1460](https://github.com/adap/flower/pull/1460))" -#: ../../source/ref-changelog.md:822 +#: ../../source/ref-changelog.md:647 msgid "" -"Prior behaviour was to perform a final round of distributed evaluation on" -" all connected clients, which is often not required (e.g., when using " -"server-side evaluation). The prior behaviour can be enabled by passing " -"`force_final_distributed_eval=True` to `start_server`." +"The first (experimental) preview of pluggable Differential Privacy " +"wrappers enables easy configuration and usage of differential privacy " +"(DP). The pluggable DP wrappers enable framework-agnostic **and** " +"strategy-agnostic usage of both client-side DP and server-side DP. Head " +"over to the Flower docs, a new explainer goes into more detail." msgstr "" -"Le comportement précédent consistait à effectuer un dernier tour " -"d'évaluation distribuée sur tous les clients connectés, ce qui n'est " -"souvent pas nécessaire (par exemple, lors de l'utilisation de " -"l'évaluation côté serveur). Le comportement précédent peut être activé en" -" passant `force_final_distributed_eval=True` à `start_server`." +"Le premier aperçu (expérimental) des wrappers enfichables de " +"confidentialité différentielle permet de configurer et d'utiliser " +"facilement la confidentialité différentielle (DP). Les wrappers DP " +"enfichables permettent une utilisation agnostique du cadre **et** de la " +"stratégie à la fois de la DP côté client et de la DP côté serveur. Va " +"voir les documents de Flower, un nouvel explicatif va plus loin dans les " +"détails." -#: ../../source/ref-changelog.md:824 +#: ../../source/ref-changelog.md:649 msgid "" -"**Renamed q-FedAvg strategy** " -"([#802](https://github.com/adap/flower/pull/802))" +"**New iOS CoreML code example** " +"([#1289](https://github.com/adap/flower/pull/1289))" msgstr "" -"**Renommé stratégie q-FedAvg** " -"([#802](https://github.com/adap/flower/pull/802))" +"**Nouvel exemple de code CoreML pour iOS** " +"([#1289](https://github.com/adap/flower/pull/1289))" -#: ../../source/ref-changelog.md:826 +#: ../../source/ref-changelog.md:651 msgid "" -"The strategy named `QffedAvg` was renamed to `QFedAvg` to better reflect " -"the notation given in the original paper (q-FFL is the optimization " -"objective, q-FedAvg is the proposed solver). Note the original (now " -"deprecated) `QffedAvg` class is still available for compatibility reasons" -" (it will be removed in a future release)." +"Flower goes iOS! A massive new code example shows how Flower clients can " +"be built for iOS. The code example contains both Flower iOS SDK " +"components that can be used for many tasks, and one task example running " +"on CoreML." msgstr "" -"La stratégie nommée `QffedAvg` a été renommée en `QFedAvg` pour mieux " -"refléter la notation donnée dans l'article original (q-FFL est l'objectif" -" d'optimisation, q-FedAvg est le solveur proposé). Notez que la classe " -"`QffedAvg` originale (maintenant obsolète) est toujours disponible pour " -"des raisons de compatibilité (elle sera supprimée dans une prochaine " -"version)." +"Flower passe à iOS ! Un nouvel exemple de code massif montre comment les " +"clients Flower peuvent être construits pour iOS. L'exemple de code " +"contient à la fois des composants Flower iOS SDK qui peuvent être " +"utilisés pour de nombreuses tâches, et un exemple de tâche fonctionnant " +"sur CoreML." -#: ../../source/ref-changelog.md:828 +#: ../../source/ref-changelog.md:653 msgid "" -"**Deprecated and renamed code example** `simulation_pytorch` **to** " -"`simulation_pytorch_legacy` " -"([#791](https://github.com/adap/flower/pull/791))" +"**New FedMedian strategy** " +"([#1461](https://github.com/adap/flower/pull/1461))" msgstr "" -"**Exemple de code déprécié et renommé** `simulation_pytorch` **en** " -"`simulation_pytorch_legacy` " -"([#791](https://github.com/adap/flower/pull/791))" +"**Nouvelle stratégie de FedMedian** " +"([#1461](https://github.com/adap/flower/pull/1461))" -#: ../../source/ref-changelog.md:830 +#: ../../source/ref-changelog.md:655 msgid "" -"This example has been replaced by a new example. The new example is based" -" on the experimental virtual client engine, which will become the new " -"default way of doing most types of large-scale simulations in Flower. The" -" existing example was kept for reference purposes, but it might be " -"removed in the future." +"The new `FedMedian` strategy implements Federated Median (FedMedian) by " +"[Yin et al., 2018](https://arxiv.org/pdf/1803.01498v1.pdf)." msgstr "" -"Cet exemple a été remplacé par un nouvel exemple. Le nouvel exemple est " -"basé sur le moteur expérimental du client virtuel, qui deviendra la " -"nouvelle méthode par défaut pour effectuer la plupart des types de " -"simulations à grande échelle dans Flower. L'exemple existant a été " -"conservé à des fins de référence, mais il pourrait être supprimé à " -"l'avenir." - -#: ../../source/ref-changelog.md:832 -msgid "v0.16.0 (2021-05-11)" -msgstr "v0.16.0 (2021-05-11)" +"La nouvelle stratégie `FedMedian` met en œuvre Federated Median " +"(FedMedian) par [Yin et al., 2018] " +"(https://arxiv.org/pdf/1803.01498v1.pdf)." -#: ../../source/ref-changelog.md:836 +#: ../../source/ref-changelog.md:657 msgid "" -"**New built-in strategies** " -"([#549](https://github.com/adap/flower/pull/549))" +"**Log** `Client` **exceptions in Virtual Client Engine** " +"([#1493](https://github.com/adap/flower/pull/1493))" msgstr "" -"**Nouvelles stratégies intégrées** " -"([#549](https://github.com/adap/flower/pull/549))" - -#: ../../source/ref-changelog.md:838 -msgid "(abstract) FedOpt" -msgstr "(résumé) FedOpt" +"**Log** `Client` **exceptions dans le moteur de client virtuel** " +"([#1493](https://github.com/adap/flower/pull/1493))" -#: ../../source/ref-changelog.md:841 +#: ../../source/ref-changelog.md:659 msgid "" -"**Custom metrics for server and strategies** " -"([#717](https://github.com/adap/flower/pull/717))" +"All `Client` exceptions happening in the VCE are now logged by default " +"and not just exposed to the configured `Strategy` (via the `failures` " +"argument)." msgstr "" -"**Métriques personnalisées pour le serveur et les stratégies** " -"([#717](https://github.com/adap/flower/pull/717))" +"Toutes les exceptions `Client` qui se produisent dans le VCE sont " +"maintenant enregistrées par défaut et ne sont pas seulement exposées à la" +" `Stratégie` configurée (via l'argument `failures`)." -#: ../../source/ref-changelog.md:843 +#: ../../source/ref-changelog.md:661 msgid "" -"The Flower server is now fully task-agnostic, all remaining instances of " -"task-specific metrics (such as `accuracy`) have been replaced by custom " -"metrics dictionaries. Flower 0.15 introduced the capability to pass a " -"dictionary containing custom metrics from client to server. As of this " -"release, custom metrics replace task-specific metrics on the server." +"**Improve Virtual Client Engine internals** " +"([#1401](https://github.com/adap/flower/pull/1401), " +"[#1453](https://github.com/adap/flower/pull/1453))" msgstr "" -"Le serveur Flower est maintenant totalement agnostique, toutes les " -"instances restantes de métriques spécifiques à une tâche (telles que " -"`accuracy`) ont été remplacées par des dictionnaires de métriques " -"personnalisées. Flower 0.15 a introduit la possibilité de passer un " -"dictionnaire contenant des métriques personnalisées du client au serveur." -" À partir de cette version, les métriques personnalisées remplacent les " -"métriques spécifiques à une tâche sur le serveur." +"**Améliorer le moteur du client virtuel** " +"([#1401](https://github.com/adap/flower/pull/1401), " +"[#1453](https://github.com/adap/flower/pull/1453))" -#: ../../source/ref-changelog.md:845 -#, fuzzy +#: ../../source/ref-changelog.md:663 msgid "" -"Custom metric dictionaries are now used in two user-facing APIs: they are" -" returned from Strategy methods `aggregate_fit`/`aggregate_evaluate` and " -"they enable evaluation functions passed to built-in strategies (via " -"`eval_fn`) to return more than two evaluation metrics. Strategies can " -"even return *aggregated* metrics dictionaries for the server to keep " -"track of." +"Some internals of the Virtual Client Engine have been revamped. The VCE " +"now uses Ray 2.0 under the hood, the value type of the `client_resources`" +" dictionary changed to `float` to allow fractions of resources to be " +"allocated." msgstr "" -"Les dictionnaires de métriques personnalisés sont maintenant utilisés " -"dans deux API orientées vers l'utilisateur : ils sont renvoyés par les " -"méthodes de stratégie `aggregate_fit`/`aggregate_evaluate` et ils " -"permettent aux fonctions d'évaluation passées aux stratégies intégrées " -"(via `eval_fn`) de renvoyer plus de deux métriques d'évaluation. Les " -"stratégies peuvent même renvoyer des dictionnaires de métriques " -"*agrégées* pour que le serveur puisse en garder la trace." +"Le VCE utilise maintenant Ray 2.0 sous le capot, le type de valeur du " +"dictionnaire `client_resources` a été remplacé par `float` pour permettre" +" l'allocation de fractions de ressources." -#: ../../source/ref-changelog.md:847 -#, fuzzy +#: ../../source/ref-changelog.md:665 msgid "" -"Strategy implementations should migrate their `aggregate_fit` and " -"`aggregate_evaluate` methods to the new return type (e.g., by simply " -"returning an empty `{}`), server-side evaluation functions should migrate" -" from `return loss, accuracy` to `return loss, {\"accuracy\": accuracy}`." +"**Support optional** `Client`**/**`NumPyClient` **methods in Virtual " +"Client Engine**" msgstr "" -"Les implémentations de Stratey doivent migrer leurs méthodes " -"`aggregate_fit` et `aggregate_evaluate` vers le nouveau type de retour " -"(par exemple, en renvoyant simplement un `{}` vide), les fonctions " -"d'évaluation côté serveur doivent migrer de `return loss, accuracy` à " -"`return loss, {\"accuracy\" : accuracy}`." +"**Support optional** `Client`**/**`NumPyClient` **methods in Virtual " +"Client Engine**" -#: ../../source/ref-changelog.md:849 +#: ../../source/ref-changelog.md:667 msgid "" -"Flower 0.15-style return types are deprecated (but still supported), " -"compatibility will be removed in a future release." +"The Virtual Client Engine now has full support for optional `Client` (and" +" `NumPyClient`) methods." msgstr "" -"Les types de retour du style Flower 0.15 sont dépréciés (mais toujours " -"pris en charge), la compatibilité sera supprimée dans une prochaine " -"version." +"Le moteur de client virtuel prend désormais en charge les méthodes " +"optionnelles `Client` (et `NumPyClient`)." -#: ../../source/ref-changelog.md:851 +#: ../../source/ref-changelog.md:669 msgid "" -"**Migration warnings for deprecated functionality** " -"([#690](https://github.com/adap/flower/pull/690))" +"**Provide type information to packages using** `flwr` " +"([#1377](https://github.com/adap/flower/pull/1377))" msgstr "" -"**Avertissements de migration pour les fonctionnalités obsolètes** " -"([#690](https://github.com/adap/flower/pull/690))" +"**Fournir des informations de type aux paquets en utilisant** `flwr` " +"([#1377](https://github.com/adap/flower/pull/1377))" -#: ../../source/ref-changelog.md:853 +#: ../../source/ref-changelog.md:671 msgid "" -"Earlier versions of Flower were often migrated to new APIs, while " -"maintaining compatibility with legacy APIs. This release introduces " -"detailed warning messages if usage of deprecated APIs is detected. The " -"new warning messages often provide details on how to migrate to more " -"recent APIs, thus easing the transition from one release to another." +"The package `flwr` is now bundled with a `py.typed` file indicating that " +"the package is typed. This enables typing support for projects or " +"packages that use `flwr` by enabling them to improve their code using " +"static type checkers like `mypy`." msgstr "" -"Les versions antérieures de Flower ont souvent été migrées vers de " -"nouvelles API, tout en maintenant la compatibilité avec les anciennes " -"API. Cette version introduit des messages d'avertissement détaillés si " -"l'utilisation d'API obsolètes est détectée. Les nouveaux messages " -"d'avertissement fournissent souvent des détails sur la façon de migrer " -"vers des API plus récentes, facilitant ainsi la transition d'une version " -"à l'autre." +"Le paquet `flwr` est maintenant accompagné d'un fichier `py.typed` " +"indiquant que le paquet est typé. Cela permet de prendre en charge le " +"typage pour les projets ou les paquets qui utilisent `flwr` en leur " +"permettant d'améliorer leur code à l'aide de vérificateurs de types " +"statiques comme `mypy`." -#: ../../source/ref-changelog.md:855 +#: ../../source/ref-changelog.md:673 msgid "" -"Improved docs and docstrings " -"([#691](https://github.com/adap/flower/pull/691) " -"[#692](https://github.com/adap/flower/pull/692) " -"[#713](https://github.com/adap/flower/pull/713))" +"**Updated code example** " +"([#1344](https://github.com/adap/flower/pull/1344), " +"[#1347](https://github.com/adap/flower/pull/1347))" msgstr "" -"Amélioration des docs et des docstrings " -"([#691](https://github.com/adap/flower/pull/691) " -"[#692](https://github.com/adap/flower/pull/692) " -"[#713](https://github.com/adap/flower/pull/713))" - -#: ../../source/ref-changelog.md:857 -msgid "MXNet example and documentation" -msgstr "Exemple et documentation MXNet" +"**Exemple de code mis à jour** " +"([#1344](https://github.com/adap/flower/pull/1344), " +"[#1347](https://github.com/adap/flower/pull/1347))" -#: ../../source/ref-changelog.md:859 +#: ../../source/ref-changelog.md:675 msgid "" -"FedBN implementation in example PyTorch: From Centralized To Federated " -"([#696](https://github.com/adap/flower/pull/696) " -"[#702](https://github.com/adap/flower/pull/702) " -"[#705](https://github.com/adap/flower/pull/705))" +"The code examples covering scikit-learn and PyTorch Lightning have been " +"updated to work with the latest version of Flower." msgstr "" -"Mise en œuvre de FedBN dans l'exemple PyTorch : De la centralisation à la" -" fédération ([#696](https://github.com/adap/flower/pull/696) " -"[#702](https://github.com/adap/flower/pull/702) " -"[#705](https://github.com/adap/flower/pull/705))" +"Les exemples de code couvrant scikit-learn et PyTorch Lightning ont été " +"mis à jour pour fonctionner avec la dernière version de Flower." -#: ../../source/ref-changelog.md:863 +#: ../../source/ref-changelog.md:677 msgid "" -"**Serialization-agnostic server** " -"([#721](https://github.com/adap/flower/pull/721))" +"**Updated documentation** " +"([#1355](https://github.com/adap/flower/pull/1355), " +"[#1558](https://github.com/adap/flower/pull/1558), " +"[#1379](https://github.com/adap/flower/pull/1379), " +"[#1380](https://github.com/adap/flower/pull/1380), " +"[#1381](https://github.com/adap/flower/pull/1381), " +"[#1332](https://github.com/adap/flower/pull/1332), " +"[#1391](https://github.com/adap/flower/pull/1391), " +"[#1403](https://github.com/adap/flower/pull/1403), " +"[#1364](https://github.com/adap/flower/pull/1364), " +"[#1409](https://github.com/adap/flower/pull/1409), " +"[#1419](https://github.com/adap/flower/pull/1419), " +"[#1444](https://github.com/adap/flower/pull/1444), " +"[#1448](https://github.com/adap/flower/pull/1448), " +"[#1417](https://github.com/adap/flower/pull/1417), " +"[#1449](https://github.com/adap/flower/pull/1449), " +"[#1465](https://github.com/adap/flower/pull/1465), " +"[#1467](https://github.com/adap/flower/pull/1467))" msgstr "" -"**Serveur agnostique de sérialisation** " -"([#721](https://github.com/adap/flower/pull/721))" +"**Documentation mise à jour** " +"([#1355](https://github.com/adap/flower/pull/1355), " +"[#1558](https://github.com/adap/flower/pull/1558), " +"[#1379](https://github.com/adap/flower/pull/1379), " +"[#1380](https://github.com/adap/flower/pull/1380), " +"[#1381](https://github.com/adap/flower/pull/1381), " +"[#1332](https://github.com/adap/flower/pull/1332), " +"[#1391](https://github.com/adap/flower/pull/1391), " +"[#1403](https://github.com/adap/flower/pull/1403), " +"[#1364](https://github.com/adap/flower/pull/1364), " +"[#1409](https://github.com/adap/flower/pull/1409), " +"[#1419](https://github.com/adap/flower/pull/1419), " +"[#1444](https://github.com/adap/flower/pull/1444), " +"[#1448](https://github.com/adap/flower/pull/1448), " +"[#1417](https://github.com/adap/flower/pull/1417), " +"[#1449](https://github.com/adap/flower/pull/1449), " +"[#1465](https://github.com/adap/flower/pull/1465), " +"[#1467](https://github.com/adap/flower/pull/1467))" -#: ../../source/ref-changelog.md:865 +#: ../../source/ref-changelog.md:679 msgid "" -"The Flower server is now fully serialization-agnostic. Prior usage of " -"class `Weights` (which represents parameters as deserialized NumPy " -"ndarrays) was replaced by class `Parameters` (e.g., in `Strategy`). " -"`Parameters` objects are fully serialization-agnostic and represents " -"parameters as byte arrays, the `tensor_type` attributes indicates how " -"these byte arrays should be interpreted (e.g., for " -"serialization/deserialization)." +"There have been so many documentation updates that it doesn't even make " +"sense to list them individually." msgstr "" -"Le serveur Flower est désormais totalement agnostique en matière de " -"sérialisation. L'utilisation antérieure de la classe `Weights` (qui " -"représente les paramètres sous forme de tableaux NumPy désérialisés) a " -"été remplacée par la classe `Parameters` (par exemple, dans `Strategy`). " -"Les objets `Parameters` sont totalement agnostiques en matière de " -"sérialisation et représentent les paramètres sous forme de tableaux " -"d'octets, les attributs `tensor_type` indiquent comment ces tableaux " -"d'octets doivent être interprétés (par exemple, pour la " -"sérialisation/désérialisation)." +"Il y a eu tellement de mises à jour de la documentation que cela n'a même" +" pas de sens de les énumérer individuellement." -#: ../../source/ref-changelog.md:867 +#: ../../source/ref-changelog.md:681 msgid "" -"Built-in strategies implement this approach by handling serialization and" -" deserialization to/from `Weights` internally. Custom/3rd-party Strategy " -"implementations should update to the slightly changed Strategy method " -"definitions. Strategy authors can consult PR " -"[#721](https://github.com/adap/flower/pull/721) to see how strategies can" -" easily migrate to the new format." +"**Restructured documentation** " +"([#1387](https://github.com/adap/flower/pull/1387))" msgstr "" -"Les stratégies intégrées mettent en œuvre cette approche en gérant en " -"interne la sérialisation et la désérialisation de `Weights`. Les " -"implémentations de stratégies personnalisées ou tierces doivent être " -"mises à jour avec les définitions de méthodes de stratégie légèrement " -"modifiées. Les auteurs de stratégies peuvent consulter le PR " -"[#721](https://github.com/adap/flower/pull/721) pour voir comment les " -"stratégies peuvent facilement migrer vers le nouveau format." +"**Documentation restructurée** " +"([#1387](https://github.com/adap/flower/pull/1387))" -#: ../../source/ref-changelog.md:869 +#: ../../source/ref-changelog.md:683 msgid "" -"Deprecated `flwr.server.Server.evaluate`, use " -"`flwr.server.Server.evaluate_round` instead " -"([#717](https://github.com/adap/flower/pull/717))" +"The documentation has been restructured to make it easier to navigate. " +"This is just the first step in a larger effort to make the Flower " +"documentation the best documentation of any project ever. Stay tuned!" msgstr "" -"Déclassé `flwr.server.Server.evaluate`, utiliser " -"`flwr.server.Server.evaluate_round` à la place " -"([#717](https://github.com/adap/flower/pull/717))" - -#: ../../source/ref-changelog.md:871 -msgid "v0.15.0 (2021-03-12)" -msgstr "v0.15.0 (2021-03-12)" +"La documentation a été restructurée pour faciliter la navigation. Ce " +"n'est que la première étape d'un effort plus important visant à faire de " +"la documentation de Flower la meilleure documentation de tous les projets" -#: ../../source/ref-changelog.md:875 +#: ../../source/ref-changelog.md:685 msgid "" -"**Server-side parameter initialization** " -"([#658](https://github.com/adap/flower/pull/658))" +"**Open in Colab button** " +"([#1389](https://github.com/adap/flower/pull/1389))" msgstr "" -"**Initialisation des paramètres côté serveur** " -"([#658](https://github.com/adap/flower/pull/658))" +"**Ouvrir dans le bouton Colab** " +"([#1389](https://github.com/adap/flower/pull/1389))" -#: ../../source/ref-changelog.md:877 +#: ../../source/ref-changelog.md:687 msgid "" -"Model parameters can now be initialized on the server-side. Server-side " -"parameter initialization works via a new `Strategy` method called " -"`initialize_parameters`." +"The four parts of the Flower Federated Learning Tutorial now come with a " +"new `Open in Colab` button. No need to install anything on your local " +"machine, you can now use and learn about Flower in your browser, it's " +"only a single click away." msgstr "" -"Les paramètres du modèle peuvent maintenant être initialisés côté " -"serveur. L'initialisation des paramètres côté serveur fonctionne via une " -"nouvelle méthode `Strategy` appelée `initialize_parameters`." +"Les quatre parties du didacticiel d'apprentissage fédéré Flower sont " +"maintenant accompagnées d'un nouveau bouton \"Ouvrir dans Colab\". Pas " +"besoin d'installer quoi que ce soit sur ta machine locale, tu peux " +"maintenant utiliser et apprendre à connaître Flower dans ton navigateur, " +"il te suffit d'un simple clic." -#: ../../source/ref-changelog.md:879 +#: ../../source/ref-changelog.md:689 msgid "" -"Built-in strategies support a new constructor argument called " -"`initial_parameters` to set the initial parameters. Built-in strategies " -"will provide these initial parameters to the server on startup and then " -"delete them to free the memory afterwards." +"**Improved tutorial** ([#1468](https://github.com/adap/flower/pull/1468)," +" [#1470](https://github.com/adap/flower/pull/1470), " +"[#1472](https://github.com/adap/flower/pull/1472), " +"[#1473](https://github.com/adap/flower/pull/1473), " +"[#1474](https://github.com/adap/flower/pull/1474), " +"[#1475](https://github.com/adap/flower/pull/1475))" msgstr "" -"Les stratégies intégrées prennent en charge un nouvel argument du " -"constructeur appelé `initial_parameters` pour définir les paramètres " -"initiaux. Les stratégies intégrées fourniront ces paramètres initiaux au " -"serveur au démarrage et les supprimeront ensuite pour libérer la mémoire." +"**Tutoriel amélioré** ([#1468](https://github.com/adap/flower/pull/1468)," +" [#1470](https://github.com/adap/flower/pull/1470), " +"[#1472](https://github.com/adap/flower/pull/1472), " +"[#1473](https://github.com/adap/flower/pull/1473), " +"[#1474](https://github.com/adap/flower/pull/1474), " +"[#1475](https://github.com/adap/flower/pull/1475))" -#: ../../source/ref-changelog.md:898 +#: ../../source/ref-changelog.md:691 msgid "" -"If no initial parameters are provided to the strategy, the server will " -"continue to use the current behaviour (namely, it will ask one of the " -"connected clients for its parameters and use these as the initial global " -"parameters)." +"The Flower Federated Learning Tutorial has two brand-new parts covering " +"custom strategies (still WIP) and the distinction between `Client` and " +"`NumPyClient`. The existing parts one and two have also been improved " +"(many small changes and fixes)." msgstr "" -"Si aucun paramètre initial n'est fourni à la stratégie, le serveur " -"continuera à utiliser le comportement actuel (à savoir qu'il demandera à " -"l'un des clients connectés ses paramètres et les utilisera comme " -"paramètres globaux initiaux)." +"Le tutoriel sur l'apprentissage fédéré des fleurs a deux toutes nouvelles" +" parties couvrant les stratégies personnalisées (encore WIP) et la " +"distinction entre `Client` et `NumPyClient`. Les parties un et deux " +"existantes ont également été améliorées (beaucoup de petits changements " +"et de corrections)." -#: ../../source/ref-changelog.md:900 -msgid "Deprecations" -msgstr "Dépréciations" +#: ../../source/ref-changelog.md:697 +msgid "v1.0.0 (2022-07-28)" +msgstr "v1.0.0 (2022-07-28)" -#: ../../source/ref-changelog.md:902 -msgid "" -"Deprecate `flwr.server.strategy.DefaultStrategy` (migrate to " -"`flwr.server.strategy.FedAvg`, which is equivalent)" -msgstr "" -"Déclasser `flwr.server.strategy.DefaultStrategy` (migrer vers " -"`flwr.server.strategy.FedAvg`, qui est équivalent)" +#: ../../source/ref-changelog.md:699 +msgid "Highlights" +msgstr "Points forts" -#: ../../source/ref-changelog.md:904 -msgid "v0.14.0 (2021-02-18)" -msgstr "v0.14.0 (2021-02-18)" +#: ../../source/ref-changelog.md:701 +msgid "Stable **Virtual Client Engine** (accessible via `start_simulation`)" +msgstr "Moteur de client virtuel stable** (accessible via `start_simulation`)" + +#: ../../source/ref-changelog.md:702 +msgid "All `Client`/`NumPyClient` methods are now optional" +msgstr "Toutes les méthodes `Client`/`NumPyClient` sont maintenant optionnelles" -#: ../../source/ref-changelog.md:908 +#: ../../source/ref-changelog.md:703 +msgid "Configurable `get_parameters`" +msgstr "`get_parameters` configurable" + +#: ../../source/ref-changelog.md:704 msgid "" -"**Generalized** `Client.fit` **and** `Client.evaluate` **return values** " -"([#610](https://github.com/adap/flower/pull/610) " -"[#572](https://github.com/adap/flower/pull/572) " -"[#633](https://github.com/adap/flower/pull/633))" +"Tons of small API cleanups resulting in a more coherent developer " +"experience" msgstr "" -"**Généralisé** `Client.fit` **et** `Client.evaluate` **valeurs de " -"retour** ([#610](https://github.com/adap/flower/pull/610) " -"[#572](https://github.com/adap/flower/pull/572) " -"[#633](https://github.com/adap/flower/pull/633))" +"Des tonnes de petits nettoyages d'API résultant en une expérience plus " +"cohérente pour les développeurs" -#: ../../source/ref-changelog.md:910 +#: ../../source/ref-changelog.md:708 msgid "" -"Clients can now return an additional dictionary mapping `str` keys to " -"values of the following types: `bool`, `bytes`, `float`, `int`, `str`. " -"This means one can return almost arbitrary values from `fit`/`evaluate` " -"and make use of them on the server side!" +"We would like to give our **special thanks** to all the contributors who " +"made Flower 1.0 possible (in reverse [GitHub " +"Contributors](https://github.com/adap/flower/graphs/contributors) order):" msgstr "" -"Les clients peuvent maintenant renvoyer un dictionnaire supplémentaire " -"associant les clés `str` aux valeurs des types suivants : `bool`, " -"`bytes`, `float`, `int`, `str`. Cela signifie que l'on peut renvoyer des " -"valeurs presque arbitraires de `fit`/`evaluate` et les utiliser du côté " -"du serveur !" +"Nous tenons à remercier **particulièrement** tous les contributeurs qui " +"ont rendu Flower 1.0 possible (dans l'ordre inverse de [GitHub " +"Contributors](https://github.com/adap/flower/graphs/contributors)) :" -#: ../../source/ref-changelog.md:912 +#: ../../source/ref-changelog.md:710 msgid "" -"This improvement also allowed for more consistent return types between " -"`fit` and `evaluate`: `evaluate` should now return a tuple `(float, int, " -"dict)` representing the loss, number of examples, and a dictionary " -"holding arbitrary problem-specific values like accuracy." +"[@rtaiello](https://github.com/rtaiello), " +"[@g-pichler](https://github.com/g-pichler), [@rob-" +"luke](https://github.com/rob-luke), [@andreea-zaharia](https://github.com" +"/andreea-zaharia), [@kinshukdua](https://github.com/kinshukdua), " +"[@nfnt](https://github.com/nfnt), " +"[@tatiana-s](https://github.com/tatiana-s), " +"[@TParcollet](https://github.com/TParcollet), " +"[@vballoli](https://github.com/vballoli), " +"[@negedng](https://github.com/negedng), " +"[@RISHIKESHAVAN](https://github.com/RISHIKESHAVAN), " +"[@hei411](https://github.com/hei411), " +"[@SebastianSpeitel](https://github.com/SebastianSpeitel), " +"[@AmitChaulwar](https://github.com/AmitChaulwar), " +"[@Rubiel1](https://github.com/Rubiel1), [@FANTOME-PAN](https://github.com" +"/FANTOME-PAN), [@Rono-BC](https://github.com/Rono-BC), " +"[@lbhm](https://github.com/lbhm), " +"[@sishtiaq](https://github.com/sishtiaq), " +"[@remde](https://github.com/remde), [@Jueun-Park](https://github.com" +"/Jueun-Park), [@architjen](https://github.com/architjen), " +"[@PratikGarai](https://github.com/PratikGarai), " +"[@mrinaald](https://github.com/mrinaald), " +"[@zliel](https://github.com/zliel), " +"[@MeiruiJiang](https://github.com/MeiruiJiang), " +"[@sancarlim](https://github.com/sancarlim), " +"[@gubertoli](https://github.com/gubertoli), " +"[@Vingt100](https://github.com/Vingt100), " +"[@MakGulati](https://github.com/MakGulati), " +"[@cozek](https://github.com/cozek), " +"[@jafermarq](https://github.com/jafermarq), " +"[@sisco0](https://github.com/sisco0), " +"[@akhilmathurs](https://github.com/akhilmathurs), " +"[@CanTuerk](https://github.com/CanTuerk), " +"[@mariaboerner1987](https://github.com/mariaboerner1987), " +"[@pedropgusmao](https://github.com/pedropgusmao), " +"[@tanertopal](https://github.com/tanertopal), " +"[@danieljanes](https://github.com/danieljanes)." msgstr "" -"Cette amélioration a également permis de rendre plus cohérents les types " -"de retour entre `fit` et `evaluate` : `evaluate` devrait maintenant " -"retourner un tuple `(float, int, dict)` représentant la perte, le nombre " -"d'exemples, et un dictionnaire contenant des valeurs arbitraires " -"spécifiques au problème comme la précision." +"[@rtaiello](https://github.com/rtaiello), " +"[@g-pichler](https://github.com/g-pichler), [@rob-" +"luke](https://github.com/rob-luke), [@andreea-zaharia](https://github.com" +"/andreea-zaharia), [@kinshukdua](https://github.com/kinshukdua), " +"[@nfnt](https://github.com/nfnt), " +"[@tatiana-s](https://github.com/tatiana-s), " +"[@TParcollet](https://github.com/TParcollet), " +"[@vballoli](https://github.com/vballoli), " +"[@negedng](https://github.com/negedng), " +"[@RISHIKESHAVAN](https://github.com/RISHIKESHAVAN), " +"[@hei411](https://github.com/hei411), " +"[@SebastianSpeitel](https://github.com/SebastianSpeitel), " +"[@AmitChaulwar](https://github.com/AmitChaulwar), " +"[@Rubiel1](https://github.com/Rubiel1), [@FANTOME-PAN](https://github.com" +"/FANTOME-PAN), [@Rono-BC](https://github.com/Rono-BC), " +"[@lbhm](https://github.com/lbhm), " +"[@sishtiaq](https://github.com/sishtiaq), " +"[@remde](https://github.com/remde), [@Jueun-Park](https://github.com" +"/Jueun-Park), [@architjen](https://github.com/architjen), " +"[@PratikGarai](https://github.com/PratikGarai), [@mrinaald](" -#: ../../source/ref-changelog.md:914 +#: ../../source/ref-changelog.md:714 msgid "" -"In case you wondered: this feature is compatible with existing projects, " -"the additional dictionary return value is optional. New code should " -"however migrate to the new return types to be compatible with upcoming " -"Flower releases (`fit`: `List[np.ndarray], int, Dict[str, Scalar]`, " -"`evaluate`: `float, int, Dict[str, Scalar]`). See the example below for " -"details." +"**All arguments must be passed as keyword arguments** " +"([#1338](https://github.com/adap/flower/pull/1338))" msgstr "" -"Au cas où tu te poserais la question : cette fonctionnalité est " -"compatible avec les projets existants, la valeur de retour supplémentaire" -" du dictionnaire est facultative. Le nouveau code doit cependant migrer " -"vers les nouveaux types de retour pour être compatible avec les " -"prochaines versions de Flower (`fit` : `List[np.ndarray], int, Dict[str, " -"Scalar]`, `evaluate` : `float, int, Dict[str, Scalar]`). Voir l'exemple " -"ci-dessous pour plus de détails." +"**Tous les arguments doivent être passés comme des arguments de mot-clé**" +" ([#1338](https://github.com/adap/flower/pull/1338))" -#: ../../source/ref-changelog.md:916 +#: ../../source/ref-changelog.md:716 +#, fuzzy msgid "" -"*Code example:* note the additional dictionary return values in both " -"`FlwrClient.fit` and `FlwrClient.evaluate`:" +"Pass all arguments as keyword arguments, positional arguments are not " +"longer supported. Code that uses positional arguments (e.g., " +"`start_client(\"127.0.0.1:8080\", FlowerClient())`) must add the keyword " +"for each positional argument (e.g., " +"`start_client(server_address=\"127.0.0.1:8080\", " +"client=FlowerClient())`)." msgstr "" -"*Exemple de code:* note les valeurs de retour du dictionnaire " -"supplémentaires dans `FlwrClient.fit` et `FlwrClient.evaluate` :" +"Le code qui utilise des arguments positionnels (par exemple, " +"``start_client(\"127.0.0.1:8080\", FlowerClient())`) doit ajouter le mot-" +"clé pour chaque argument positionnel (par exemple, " +"``start_client(server_address=\"127.0.0.1:8080\", " +"client=FlowerClient())`)." -#: ../../source/ref-changelog.md:931 +#: ../../source/ref-changelog.md:718 msgid "" -"**Generalized** `config` **argument in** `Client.fit` **and** " -"`Client.evaluate` ([#595](https://github.com/adap/flower/pull/595))" +"**Introduce configuration object** `ServerConfig` **in** `start_server` " +"**and** `start_simulation` " +"([#1317](https://github.com/adap/flower/pull/1317))" msgstr "" -"**Généralisé** `config` **argument dans** `Client.fit` **et** " -"`Client.evaluate` ([#595](https://github.com/adap/flower/pull/595))" +"**Introduire l'objet de configuration** `ServerConfig` **dans** " +"`start_server` **et** `start_simulation` " +"([#1317](https://github.com/adap/flower/pull/1317))" -#: ../../source/ref-changelog.md:933 +#: ../../source/ref-changelog.md:720 msgid "" -"The `config` argument used to be of type `Dict[str, str]`, which means " -"that dictionary values were expected to be strings. The new release " -"generalizes this to enable values of the following types: `bool`, " -"`bytes`, `float`, `int`, `str`." +"Instead of a config dictionary `{\"num_rounds\": 3, \"round_timeout\": " +"600.0}`, `start_server` and `start_simulation` now expect a configuration" +" object of type `flwr.server.ServerConfig`. `ServerConfig` takes the same" +" arguments that as the previous config dict, but it makes writing type-" +"safe code easier and the default parameters values more transparent." msgstr "" -"L'argument `config` était auparavant de type `Dict[str, str]`, ce qui " -"signifie que les valeurs du dictionnaire devaient être des chaînes. La " -"nouvelle version généralise cela pour permettre les valeurs des types " -"suivants : `bool`, `bytes`, `float`, `int`, `str`." +"Au lieu d'un dictionnaire de configuration `{\"num_rounds\" : 3, " +"\"round_timeout\" : 600.0}`, `start_server` et `start_simulation` " +"attendent maintenant un objet de configuration de type " +"`flwr.server.ServerConfig`. `ServerConfig` prend les mêmes arguments que " +"le dict de configuration précédent, mais il rend l'écriture de code " +"sécurisé plus facile et les valeurs des paramètres par défaut plus " +"transparentes." -#: ../../source/ref-changelog.md:935 +#: ../../source/ref-changelog.md:722 msgid "" -"This means one can now pass almost arbitrary values to `fit`/`evaluate` " -"using the `config` dictionary. Yay, no more `str(epochs)` on the server-" -"side and `int(config[\"epochs\"])` on the client side!" +"**Rename built-in strategy parameters for clarity** " +"([#1334](https://github.com/adap/flower/pull/1334))" msgstr "" -"Cela signifie que l'on peut maintenant passer des valeurs presque " -"arbitraires à `fit`/`evaluate` en utilisant le dictionnaire `config`. " -"Yay, plus de `str(epochs)` du côté serveur et `int(config[\"epochs\"])` " -"du côté client !" +"**Renommer les paramètres de la stratégie intégrée pour plus de clarté** " +"([#1334](https://github.com/adap/flower/pull/1334))" -#: ../../source/ref-changelog.md:937 +#: ../../source/ref-changelog.md:724 msgid "" -"*Code example:* note that the `config` dictionary now contains non-`str` " -"values in both `Client.fit` and `Client.evaluate`:" +"The following built-in strategy parameters were renamed to improve " +"readability and consistency with other API's:" msgstr "" -"*Exemple de code:* Notez que le dictionnaire `config` contient maintenant" -" des valeurs autres que `str` dans `Client.fit` et `Client.evaluate` :" +"Les paramètres de stratégie intégrés suivants ont été renommés pour " +"améliorer la lisibilité et la cohérence avec d'autres API :" -#: ../../source/ref-changelog.md:954 -msgid "v0.13.0 (2021-01-08)" -msgstr "v0.13.0 (2021-01-08)" +#: ../../source/ref-changelog.md:726 +msgid "`fraction_eval` --> `fraction_evaluate`" +msgstr "`fraction_eval` --> `fraction_evaluate`" -#: ../../source/ref-changelog.md:958 -msgid "" -"New example: PyTorch From Centralized To Federated " -"([#549](https://github.com/adap/flower/pull/549))" -msgstr "" -"Nouvel exemple : PyTorch de centralisé à fédéré " -"([#549](https://github.com/adap/flower/pull/549))" - -#: ../../source/ref-changelog.md:959 -msgid "Improved documentation" -msgstr "Amélioration de la documentation" - -#: ../../source/ref-changelog.md:960 -msgid "New documentation theme ([#551](https://github.com/adap/flower/pull/551))" -msgstr "" -"Nouveau thème de documentation " -"([#551](https://github.com/adap/flower/pull/551))" +#: ../../source/ref-changelog.md:727 +msgid "`min_eval_clients` --> `min_evaluate_clients`" +msgstr "`min_eval_clients` --> `min_evaluate_clients`" -#: ../../source/ref-changelog.md:961 -msgid "New API reference ([#554](https://github.com/adap/flower/pull/554))" -msgstr "Nouvelle référence API ([#554](https://github.com/adap/flower/pull/554))" +#: ../../source/ref-changelog.md:728 +msgid "`eval_fn` --> `evaluate_fn`" +msgstr "`eval_fn` --> `evaluate_fn`" -#: ../../source/ref-changelog.md:962 +#: ../../source/ref-changelog.md:730 msgid "" -"Updated examples documentation " -"([#549](https://github.com/adap/flower/pull/549))" +"**Update default arguments of built-in strategies** " +"([#1278](https://github.com/adap/flower/pull/1278))" msgstr "" -"Mise à jour de la documentation des exemples " -"([#549](https://github.com/adap/flower/pull/549))" +"**Mettre à jour les arguments par défaut des stratégies intégrées** " +"([#1278](https://github.com/adap/flower/pull/1278))" -#: ../../source/ref-changelog.md:963 +#: ../../source/ref-changelog.md:732 msgid "" -"Removed obsolete documentation " -"([#548](https://github.com/adap/flower/pull/548))" +"All built-in strategies now use `fraction_fit=1.0` and " +"`fraction_evaluate=1.0`, which means they select *all* currently " +"available clients for training and evaluation. Projects that relied on " +"the previous default values can get the previous behaviour by " +"initializing the strategy in the following way:" msgstr "" -"Suppression de la documentation obsolète " -"([#548](https://github.com/adap/flower/pull/548))" +"Toutes les stratégies intégrées utilisent désormais `fraction_fit=1.0` et" +" `fraction_evaluate=1.0`, ce qui signifie qu'elles sélectionnent *tous* " +"les clients actuellement disponibles pour l'entraînement et l'évaluation." +" Les projets qui s'appuyaient sur les valeurs par défaut précédentes " +"peuvent retrouver le comportement antérieur en initialisant la stratégie " +"de la manière suivante :" -#: ../../source/ref-changelog.md:965 -msgid "Bugfix:" -msgstr "Correction de bogues :" +#: ../../source/ref-changelog.md:734 +msgid "`strategy = FedAvg(fraction_fit=0.1, fraction_evaluate=0.1)`" +msgstr "`stratégie = FedAvg(fraction_fit=0.1, fraction_evaluate=0.1)`" -#: ../../source/ref-changelog.md:967 +#: ../../source/ref-changelog.md:736 msgid "" -"`Server.fit` does not disconnect clients when finished, disconnecting the" -" clients is now handled in `flwr.server.start_server` " -"([#553](https://github.com/adap/flower/pull/553) " -"[#540](https://github.com/adap/flower/issues/540))." +"**Add** `server_round` **to** `Strategy.evaluate` " +"([#1334](https://github.com/adap/flower/pull/1334))" msgstr "" -"`Server.fit` ne déconnecte pas les clients lorsqu'il est terminé, la " -"déconnexion des clients est maintenant gérée dans " -"`flwr.server.start_server` " -"([#553](https://github.com/adap/flower/pull/553) " -"[#540](https://github.com/adap/flower/issues/540))." - -#: ../../source/ref-changelog.md:969 -msgid "v0.12.0 (2020-12-07)" -msgstr "v0.12.0 (2020-12-07)" - -#: ../../source/ref-changelog.md:971 ../../source/ref-changelog.md:987 -msgid "Important changes:" -msgstr "Changements importants :" +"**Ajouter** `server_round` **à** `Strategy.evaluate` " +"([#1334](https://github.com/adap/flower/pull/1334))" -#: ../../source/ref-changelog.md:973 +#: ../../source/ref-changelog.md:738 msgid "" -"Added an example for embedded devices " -"([#507](https://github.com/adap/flower/pull/507))" +"The `Strategy` method `evaluate` now receives the current round of " +"federated learning/evaluation as the first parameter." msgstr "" -"Ajout d'un exemple pour les périphériques embarqués " -"([#507](https://github.com/adap/flower/pull/507))" +"La méthode `Stratégie` `évaluer` reçoit maintenant le cycle actuel " +"d'apprentissage/évaluation fédéré comme premier paramètre." -#: ../../source/ref-changelog.md:974 +#: ../../source/ref-changelog.md:740 msgid "" -"Added a new NumPyClient (in addition to the existing KerasClient) " -"([#504](https://github.com/adap/flower/pull/504) " -"[#508](https://github.com/adap/flower/pull/508))" +"**Add** `server_round` **and** `config` **parameters to** `evaluate_fn` " +"([#1334](https://github.com/adap/flower/pull/1334))" msgstr "" -"Ajout d'un nouveau NumPyClient (en plus du KerasClient existant) " -"([#504](https://github.com/adap/flower/pull/504) " -"[#508](https://github.com/adap/flower/pull/508))" +"**Ajouter** `server_round` **et** `config` **paramètres à** `evaluate_fn`" +" ([#1334](https://github.com/adap/flower/pull/1334))" -#: ../../source/ref-changelog.md:975 +#: ../../source/ref-changelog.md:742 msgid "" -"Deprecated `flwr_example` package and started to migrate examples into " -"the top-level `examples` directory " -"([#494](https://github.com/adap/flower/pull/494) " -"[#512](https://github.com/adap/flower/pull/512))" +"The `evaluate_fn` passed to built-in strategies like `FedAvg` now takes " +"three parameters: (1) The current round of federated learning/evaluation " +"(`server_round`), (2) the model parameters to evaluate (`parameters`), " +"and (3) a config dictionary (`config`)." msgstr "" -"Déclassement du paquet `flwr_example` et migration des exemples dans le " -"répertoire de premier niveau `examples` " -"([#494](https://github.com/adap/flower/pull/494) " -"[#512](https://github.com/adap/flower/pull/512))" - -#: ../../source/ref-changelog.md:977 -msgid "v0.11.0 (2020-11-30)" -msgstr "v0.11.0 (2020-11-30)" - -#: ../../source/ref-changelog.md:979 -msgid "Incompatible changes:" -msgstr "Changements incompatibles :" +"Le `evaluate_fn` passé aux stratégies intégrées comme `FedAvg` prend " +"maintenant trois paramètres : (1) le cycle actuel " +"d'apprentissage/évaluation fédéré (`server_round`), (2) les paramètres du" +" modèle à évaluer (`parameters`), et (3) un dictionnaire de configuration" +" (`config`)." -#: ../../source/ref-changelog.md:981 +#: ../../source/ref-changelog.md:744 msgid "" -"Renamed strategy methods " -"([#486](https://github.com/adap/flower/pull/486)) to unify the naming of " -"Flower's public APIs. Other public methods/functions (e.g., every method " -"in `Client`, but also `Strategy.evaluate`) do not use the `on_` prefix, " -"which is why we're removing it from the four methods in Strategy. To " -"migrate rename the following `Strategy` methods accordingly:" +"**Rename** `rnd` **to** `server_round` " +"([#1321](https://github.com/adap/flower/pull/1321))" msgstr "" -"Renommé les méthodes de stratégie " -"([#486](https://github.com/adap/flower/pull/486)) pour unifier le nommage" -" des API publiques de Flower. D'autres méthodes/fonctions publiques (par " -"exemple, toutes les méthodes de `Client`, mais aussi `Strategy.evaluate`)" -" n'utilisent pas le préfixe `on_`, c'est pourquoi nous le supprimons des " -"quatre méthodes de Stratégie. Pour migrer, renommez les méthodes de " -"`Strategy` suivantes en conséquence :" - -#: ../../source/ref-changelog.md:982 -msgid "`on_configure_evaluate` => `configure_evaluate`" -msgstr "`on_configure_evaluate` => `configure_evaluate`" +"**Rename** `rnd` **to** `server_round` " +"([#1321](https://github.com/adap/flower/pull/1321))" -#: ../../source/ref-changelog.md:983 -msgid "`on_aggregate_evaluate` => `aggregate_evaluate`" -msgstr "`on_aggregate_evaluate` => `aggregate_evaluate`" +#: ../../source/ref-changelog.md:746 +msgid "" +"Several Flower methods and functions (`evaluate_fn`, `configure_fit`, " +"`aggregate_fit`, `configure_evaluate`, `aggregate_evaluate`) receive the " +"current round of federated learning/evaluation as their first parameter. " +"To improve reaability and avoid confusion with *random*, this parameter " +"has been renamed from `rnd` to `server_round`." +msgstr "" +"Plusieurs méthodes et fonctions de Flower (`evaluate_fn`, " +"`configure_fit`, `aggregate_fit`, `configure_evaluate`, " +"`aggregate_evaluate`) reçoivent le cycle actuel " +"d'apprentissage/évaluation fédéré comme premier paramètre. Pour améliorer" +" la fiabilité et éviter la confusion avec *random*, ce paramètre a été " +"renommé de `rnd` à `server_round`." -#: ../../source/ref-changelog.md:984 -msgid "`on_configure_fit` => `configure_fit`" -msgstr "`on_configure_fit` => `configure_fit`" +#: ../../source/ref-changelog.md:748 +msgid "" +"**Move** `flwr.dataset` **to** `flwr_baselines` " +"([#1273](https://github.com/adap/flower/pull/1273))" +msgstr "" +"**Déplacer** `flwr.dataset` **vers** `flwr_baselines` " +"([#1273](https://github.com/adap/flower/pull/1273))" -#: ../../source/ref-changelog.md:985 -msgid "`on_aggregate_fit` => `aggregate_fit`" -msgstr "`on_aggregate_fit` => `aggregate_fit`" +#: ../../source/ref-changelog.md:750 +msgid "The experimental package `flwr.dataset` was migrated to Flower Baselines." +msgstr "Le paquet expérimental `flwr.dataset` a été migré vers Flower Baselines." -#: ../../source/ref-changelog.md:989 +#: ../../source/ref-changelog.md:752 msgid "" -"Deprecated `DefaultStrategy` " -"([#479](https://github.com/adap/flower/pull/479)). To migrate use " -"`FedAvg` instead." +"**Remove experimental strategies** " +"([#1280](https://github.com/adap/flower/pull/1280))" msgstr "" -"Déclassé `DefaultStrategy` " -"([#479](https://github.com/adap/flower/pull/479)). Pour migrer, utilisez " -"`FedAvg` à la place." +"**Supprimer les stratégies expérimentales** " +"([#1280](https://github.com/adap/flower/pull/1280))" -#: ../../source/ref-changelog.md:990 +#: ../../source/ref-changelog.md:754 msgid "" -"Simplified examples and baselines " -"([#484](https://github.com/adap/flower/pull/484))." +"Remove unmaintained experimental strategies (`FastAndSlow`, `FedFSv0`, " +"`FedFSv1`)." msgstr "" -"Exemples simplifiés et lignes de base " -"([#484](https://github.com/adap/flower/pull/484))." +"Supprimer les stratégies expérimentales non maintenues (`FastAndSlow`, " +"`FedFSv0`, `FedFSv1`)." -#: ../../source/ref-changelog.md:991 +#: ../../source/ref-changelog.md:756 msgid "" -"Removed presently unused `on_conclude_round` from strategy interface " -"([#483](https://github.com/adap/flower/pull/483))." +"**Rename** `Weights` **to** `NDArrays` " +"([#1258](https://github.com/adap/flower/pull/1258), " +"[#1259](https://github.com/adap/flower/pull/1259))" msgstr "" -"Suppression de `on_conclude_round` actuellement inutilisé de l'interface " -"de stratégie ([#483](https://github.com/adap/flower/pull/483))." +"**Rename** `Weights` **to** `NDArrays` " +"([#1258](https://github.com/adap/flower/pull/1258), " +"[#1259](https://github.com/adap/flower/pull/1259))" -#: ../../source/ref-changelog.md:992 +#: ../../source/ref-changelog.md:758 msgid "" -"Set minimal Python version to 3.6.1 instead of 3.6.9 " -"([#471](https://github.com/adap/flower/pull/471))." +"`flwr.common.Weights` was renamed to `flwr.common.NDArrays` to better " +"capture what this type is all about." msgstr "" -"Fixe la version minimale de Python à 3.6.1 au lieu de 3.6.9 " -"([#471](https://github.com/adap/flower/pull/471))." +"`flwr.common.Weights` a été renommé en `flwr.common.NDArys` pour mieux " +"rendre compte de la nature de ce type." -#: ../../source/ref-changelog.md:993 +#: ../../source/ref-changelog.md:760 msgid "" -"Improved `Strategy` docstrings " -"([#470](https://github.com/adap/flower/pull/470))." +"**Remove antiquated** `force_final_distributed_eval` **from** " +"`start_server` ([#1258](https://github.com/adap/flower/pull/1258), " +"[#1259](https://github.com/adap/flower/pull/1259))" msgstr "" -"Amélioration des docstrings `Stratégie` " -"([#470](https://github.com/adap/flower/pull/470))." - -#: ../../source/ref-example-projects.rst:2 -#, fuzzy -msgid "Example projects" -msgstr "Exemples de PyTorch" +"**Supprimez l'ancien** `force_final_distributed_eval` **de** " +"`start_server` ([#1258](https://github.com/adap/flower/pull/1258), " +"[#1259](https://github.com/adap/flower/pull/1259))" -#: ../../source/ref-example-projects.rst:4 +#: ../../source/ref-changelog.md:762 msgid "" -"Flower comes with a number of usage examples. The examples demonstrate " -"how Flower can be used to federate different kinds of existing machine " -"learning pipelines, usually leveraging popular machine learning " -"frameworks such as `PyTorch `_ or `TensorFlow " -"`_." +"The `start_server` parameter `force_final_distributed_eval` has long been" +" a historic artefact, in this release it is finally gone for good." msgstr "" -"Flower est livré avec un certain nombre d'exemples d'utilisation, qui " -"montrent comment Flower peut être utilisé pour fédérer différents types " -"de pipelines d'apprentissage automatique existants, qui s'appuient " -"généralement sur des frameworks d'apprentissage automatique populaires " -"tels que `PyTorch `_ ou `TensorFlow " -"`_." +"Le paramètre `start_server` `force_final_distributed_eval` a longtemps " +"été un artefact historique, dans cette version il a finalement disparu " +"pour de bon." -#: ../../source/ref-example-projects.rst:10 -#, fuzzy +#: ../../source/ref-changelog.md:764 msgid "" -"The following examples are available as standalone projects. Quickstart " -"TensorFlow/Keras ---------------------------" -msgstr "Les exemples suivants sont disponibles sous forme de projets autonomes." +"**Make** `get_parameters` **configurable** " +"([#1242](https://github.com/adap/flower/pull/1242))" +msgstr "" +"**Make** `get_parameters` **configurable** " +"([#1242](https://github.com/adap/flower/pull/1242))" -#: ../../source/ref-example-projects.rst:14 +#: ../../source/ref-changelog.md:766 msgid "" -"The TensorFlow/Keras quickstart example shows CIFAR-10 image " -"classification with MobileNetV2:" +"The `get_parameters` method now accepts a configuration dictionary, just " +"like `get_properties`, `fit`, and `evaluate`." msgstr "" -"L'exemple de démarrage rapide TensorFlow/Keras montre la classification " -"d'images CIFAR-10 avec MobileNetV2 :" +"La méthode `get_parameters` accepte maintenant un dictionnaire de " +"configuration, tout comme `get_properties`, `fit`, et `evaluate`." -#: ../../source/ref-example-projects.rst:17 -#, fuzzy +#: ../../source/ref-changelog.md:768 msgid "" -"`Quickstart TensorFlow (Code) " -"`_" +"**Replace** `num_rounds` **in** `start_simulation` **with new** `config` " +"**parameter** ([#1281](https://github.com/adap/flower/pull/1281))" msgstr "" -"`Quickstart TensorFlow (Code) " -"`_" +"**Remplace** `num_rounds` **dans** `start_simulation` **avec le nouveau**" +" `config` **paramètre** " +"([#1281](https://github.com/adap/flower/pull/1281))" -#: ../../source/ref-example-projects.rst:18 -#, fuzzy -msgid ":doc:`Quickstart TensorFlow (Tutorial) `" +#: ../../source/ref-changelog.md:770 +msgid "" +"The `start_simulation` function now accepts a configuration dictionary " +"`config` instead of the `num_rounds` integer. This improves the " +"consistency between `start_simulation` and `start_server` and makes " +"transitioning between the two easier." msgstr "" -"`Quickstart TensorFlow (Tutorial) `_" +"La fonction `start_simulation` accepte maintenant un dictionnaire de " +"configuration `config` au lieu de l'entier `num_rounds`. Cela améliore la" +" cohérence entre `start_simulation` et `start_server` et facilite la " +"transition entre les deux." -#: ../../source/ref-example-projects.rst:19 +#: ../../source/ref-changelog.md:774 msgid "" -"`Quickstart TensorFlow (Blog Post) `_" +"**Support Python 3.10** " +"([#1320](https://github.com/adap/flower/pull/1320))" msgstr "" -"`Quickstart TensorFlow (Blog Post) `_" - -#: ../../source/ref-example-projects.rst:23 -#: ../../source/tutorial-quickstart-pytorch.rst:5 -msgid "Quickstart PyTorch" -msgstr "Démarrage rapide de PyTorch" +"**Support Python 3.10** " +"([#1320](https://github.com/adap/flower/pull/1320))" -#: ../../source/ref-example-projects.rst:25 +#: ../../source/ref-changelog.md:776 msgid "" -"The PyTorch quickstart example shows CIFAR-10 image classification with a" -" simple Convolutional Neural Network:" +"The previous Flower release introduced experimental support for Python " +"3.10, this release declares Python 3.10 support as stable." msgstr "" -"L'exemple de démarrage rapide PyTorch montre la classification d'images " -"CIFAR-10 avec un simple réseau neuronal convolutif :" +"La version précédente de Flower a introduit la prise en charge " +"expérimentale de Python 3.10, cette version déclare la prise en charge de" +" Python 3.10 comme stable." -#: ../../source/ref-example-projects.rst:28 -#, fuzzy +#: ../../source/ref-changelog.md:778 msgid "" -"`Quickstart PyTorch (Code) " -"`_" +"**Make all** `Client` **and** `NumPyClient` **methods optional** " +"([#1260](https://github.com/adap/flower/pull/1260), " +"[#1277](https://github.com/adap/flower/pull/1277))" msgstr "" -"`Quickstart PyTorch (Code) " -"`_" +"**Rendre toutes les **méthodes `Client` **et** `NumPyClient` " +"**facultatives** ([#1260](https://github.com/adap/flower/pull/1260), " +"[#1277](https://github.com/adap/flower/pull/1277))" -#: ../../source/ref-example-projects.rst:29 -#, fuzzy -msgid ":doc:`Quickstart PyTorch (Tutorial) `" +#: ../../source/ref-changelog.md:780 +msgid "" +"The `Client`/`NumPyClient` methods `get_properties`, `get_parameters`, " +"`fit`, and `evaluate` are all optional. This enables writing clients that" +" implement, for example, only `fit`, but no other method. No need to " +"implement `evaluate` when using centralized evaluation!" msgstr "" -"`Quickstart PyTorch (Tutorial) `_" +"Les méthodes `Client`/`NumPyClient` `get_properties`, `get_parameters`, " +"`fit`, et `evaluate` sont toutes optionnelles. Cela permet d'écrire des " +"clients qui n'implémentent, par exemple, que `fit`, mais aucune autre " +"méthode. Pas besoin d'implémenter `evaluate` quand on utilise " +"l'évaluation centralisée !" -#: ../../source/ref-example-projects.rst:33 -msgid "PyTorch: From Centralized To Federated" -msgstr "PyTorch : De la centralisation à la fédération" +#: ../../source/ref-changelog.md:782 +msgid "" +"**Enable passing a** `Server` **instance to** `start_simulation` " +"([#1281](https://github.com/adap/flower/pull/1281))" +msgstr "" +"**Autoriser le passage d'une **instance `Server` à** `start_simulation` " +"([#1281](https://github.com/adap/flower/pull/1281))" -#: ../../source/ref-example-projects.rst:35 +#: ../../source/ref-changelog.md:784 msgid "" -"This example shows how a regular PyTorch project can be federated using " -"Flower:" +"Similar to `start_server`, `start_simulation` now accepts a full `Server`" +" instance. This enables users to heavily customize the execution of " +"eperiments and opens the door to running, for example, async FL using the" +" Virtual Client Engine." msgstr "" -"Cet exemple montre comment un projet PyTorch ordinaire peut être fédéré à" -" l'aide de Flower :" +"Comme pour `start_server`, `start_simulation` accepte maintenant une " +"instance complète de `Server`. Cela permet aux utilisateurs de " +"personnaliser fortement l'exécution des expériences et ouvre la porte à " +"l'exécution, par exemple, de FL asynchrones à l'aide du moteur de client " +"virtuel." -#: ../../source/ref-example-projects.rst:37 -#, fuzzy +#: ../../source/ref-changelog.md:786 msgid "" -"`PyTorch: From Centralized To Federated (Code) " -"`_" +"**Update code examples** " +"([#1291](https://github.com/adap/flower/pull/1291), " +"[#1286](https://github.com/adap/flower/pull/1286), " +"[#1282](https://github.com/adap/flower/pull/1282))" msgstr "" -"`PyTorch : De la centralisation à la fédération (Code) " -"`_" +"**Mettre à jour les exemples de code** " +"([#1291](https://github.com/adap/flower/pull/1291), " +"[#1286](https://github.com/adap/flower/pull/1286), " +"[#1282](https://github.com/adap/flower/pull/1282))" -#: ../../source/ref-example-projects.rst:38 -#, fuzzy +#: ../../source/ref-changelog.md:788 msgid "" -":doc:`PyTorch: From Centralized To Federated (Tutorial) `" +"Many code examples received small or even large maintenance updates, " +"among them are" msgstr "" -"`PyTorch : De la centralisation à la fédération (Tutoriel) " -"`_" +"De nombreux exemples de code ont reçu de petites ou même de grandes mises" +" à jour de maintenance" -#: ../../source/ref-example-projects.rst:42 -msgid "Federated Learning on Raspberry Pi and Nvidia Jetson" -msgstr "Apprentissage fédéré sur Raspberry Pi et Nvidia Jetson" +#: ../../source/ref-changelog.md:790 +msgid "`scikit-learn`" +msgstr "`scikit-learn`" -#: ../../source/ref-example-projects.rst:44 +#: ../../source/ref-changelog.md:791 +msgid "`simulation_pytorch`" +msgstr "`simulation_pytorch`" + +#: ../../source/ref-changelog.md:792 +msgid "`quickstart_pytorch`" +msgstr "`quickstart_pytorch` (démarrage rapide)" + +#: ../../source/ref-changelog.md:793 +msgid "`quickstart_simulation`" +msgstr "`quickstart_simulation`" + +#: ../../source/ref-changelog.md:794 +msgid "`quickstart_tensorflow`" +msgstr "`quickstart_tensorflow`" + +#: ../../source/ref-changelog.md:795 +msgid "`advanced_tensorflow`" +msgstr "`advanced_tensorflow` (en anglais)" + +#: ../../source/ref-changelog.md:797 msgid "" -"This example shows how Flower can be used to build a federated learning " -"system that run across Raspberry Pi and Nvidia Jetson:" +"**Remove the obsolete simulation example** " +"([#1328](https://github.com/adap/flower/pull/1328))" msgstr "" -"Cet exemple montre comment Flower peut être utilisé pour construire un " -"système d'apprentissage fédéré qui fonctionne sur Raspberry Pi et Nvidia " -"Jetson :" +"**Supprime l'exemple de simulation obsolète** " +"([#1328](https://github.com/adap/flower/pull/1328))" -#: ../../source/ref-example-projects.rst:46 -#, fuzzy +#: ../../source/ref-changelog.md:799 msgid "" -"`Federated Learning on Raspberry Pi and Nvidia Jetson (Code) " -"`_" +"Removes the obsolete `simulation` example and renames " +"`quickstart_simulation` to `simulation_tensorflow` so it fits withs the " +"naming of `simulation_pytorch`" msgstr "" -"`L'apprentissage fédéré sur Raspberry Pi et Nvidia Jetson (Code) " -"`_" +"Supprime l'exemple obsolète `simulation` et renomme " +"`quickstart_simulation` en `simulation_tensorflow` pour qu'il corresponde" +" au nom de `simulation_pytorch`" -#: ../../source/ref-example-projects.rst:47 +#: ../../source/ref-changelog.md:801 msgid "" -"`Federated Learning on Raspberry Pi and Nvidia Jetson (Blog Post) " -"`_" +"**Update documentation** " +"([#1223](https://github.com/adap/flower/pull/1223), " +"[#1209](https://github.com/adap/flower/pull/1209), " +"[#1251](https://github.com/adap/flower/pull/1251), " +"[#1257](https://github.com/adap/flower/pull/1257), " +"[#1267](https://github.com/adap/flower/pull/1267), " +"[#1268](https://github.com/adap/flower/pull/1268), " +"[#1300](https://github.com/adap/flower/pull/1300), " +"[#1304](https://github.com/adap/flower/pull/1304), " +"[#1305](https://github.com/adap/flower/pull/1305), " +"[#1307](https://github.com/adap/flower/pull/1307))" msgstr "" -"`L'apprentissage fédéré sur Raspberry Pi et Nvidia Jetson (Blog Post) " -"`_" +"**Mise à jour de la documentation** " +"([#1223](https://github.com/adap/flower/pull/1223), " +"[#1209](https://github.com/adap/flower/pull/1209), " +"[#1251](https://github.com/adap/flower/pull/1251), " +"[#1257](https://github.com/adap/flower/pull/1257), " +"[#1267](https://github.com/adap/flower/pull/1267), " +"[#1268](https://github.com/adap/flower/pull/1268), " +"[#1300](https://github.com/adap/flower/pull/1300), " +"[#1304](https://github.com/adap/flower/pull/1304), " +"[#1305](https://github.com/adap/flower/pull/1305), " +"[#1307](https://github.com/adap/flower/pull/1307))" -#: ../../source/ref-faq.rst:4 +#: ../../source/ref-changelog.md:803 msgid "" -"This page collects answers to commonly asked questions about Federated " -"Learning with Flower." +"One substantial documentation update fixes multiple smaller rendering " +"issues, makes titles more succinct to improve navigation, removes a " +"deprecated library, updates documentation dependencies, includes the " +"`flwr.common` module in the API reference, includes support for markdown-" +"based documentation, migrates the changelog from `.rst` to `.md`, and " +"fixes a number of smaller details!" msgstr "" -"Cette page rassemble les réponses aux questions les plus fréquemment " -"posées sur l'apprentissage fédéré avec Flower." +"Une mise à jour substantielle de la documentation corrige plusieurs " +"petits problèmes de rendu, rend les titres plus succincts pour améliorer " +"la navigation, supprime une bibliothèque obsolète, met à jour les " +"dépendances de la documentation, inclut le module `flwr.common` dans la " +"référence de l'API, inclut le support de la documentation basée sur le " +"markdown, migre le changelog de `.rst` vers `.md`, et corrige un certain " +"nombre de détails plus petits !" -#: ../../source/ref-faq.rst -#, fuzzy -msgid ":fa:`eye,mr-1` Can Flower run on Jupyter Notebooks / Google Colab?" -msgstr "" -":fa:`eye,mr-1` Flower peut-il fonctionner sur les ordinateurs portables " -"Juptyter / Google Colab ?" +#: ../../source/ref-changelog.md:805 ../../source/ref-changelog.md:860 +#: ../../source/ref-changelog.md:929 ../../source/ref-changelog.md:968 +msgid "**Minor updates**" +msgstr "**Mises à jour mineures**" -#: ../../source/ref-faq.rst:8 +#: ../../source/ref-changelog.md:807 msgid "" -"Yes, it can! Flower even comes with a few under-the-hood optimizations to" -" make it work even better on Colab. Here's a quickstart example:" +"Add round number to fit and evaluate log messages " +"([#1266](https://github.com/adap/flower/pull/1266))" msgstr "" -"Oui, c'est possible ! Flower est même livré avec quelques optimisations " -"pour qu'il fonctionne encore mieux sur Colab. Voici un exemple de " -"démarrage rapide :" +"Ajoute un chiffre rond pour ajuster et évaluer les messages du journal " +"([#1266](https://github.com/adap/flower/pull/1266))" -#: ../../source/ref-faq.rst:10 -#, fuzzy +#: ../../source/ref-changelog.md:808 msgid "" -"`Flower simulation PyTorch " -"`_" +"Add secure gRPC connection to the `advanced_tensorflow` code example " +"([#847](https://github.com/adap/flower/pull/847))" msgstr "" -"`Flower Quickstart (TensorFlow/Keras) " -"`_" +"Ajouter une connexion gRPC sécurisée à l'exemple de code " +"`advanced_tensorflow` ([#847](https://github.com/adap/flower/pull/847))" -#: ../../source/ref-faq.rst:11 -#, fuzzy +#: ../../source/ref-changelog.md:809 msgid "" -"`Flower simulation TensorFlow/Keras " -"`_" +"Update developer tooling " +"([#1231](https://github.com/adap/flower/pull/1231), " +"[#1276](https://github.com/adap/flower/pull/1276), " +"[#1301](https://github.com/adap/flower/pull/1301), " +"[#1310](https://github.com/adap/flower/pull/1310))" msgstr "" -"`Flower Quickstart (TensorFlow/Keras) " -"`_" - -#: ../../source/ref-faq.rst -msgid ":fa:`eye,mr-1` How can I run Federated Learning on a Raspberry Pi?" -msgstr "" -":fa:`eye,mr-1` Comment puis-je faire fonctionner l'apprentissage fédéré " -"sur un Raspberry Pi ?" +"Mettre à jour les outils de développement " +"([#1231](https://github.com/adap/flower/pull/1231), " +"[#1276](https://github.com/adap/flower/pull/1276), " +"[#1301](https://github.com/adap/flower/pull/1301), " +"[#1310](https://github.com/adap/flower/pull/1310))" -#: ../../source/ref-faq.rst:15 -#, fuzzy +#: ../../source/ref-changelog.md:810 msgid "" -"Find the `blog post about federated learning on embedded device here " -"`_" -" and the corresponding `GitHub code example " -"`_." +"Rename ProtoBuf messages to improve consistency " +"([#1214](https://github.com/adap/flower/pull/1214), " +"[#1258](https://github.com/adap/flower/pull/1258), " +"[#1259](https://github.com/adap/flower/pull/1259))" msgstr "" -"Trouve le `blog post about federated learning on embedded device ici " -"`_" -" et l'exemple de code GitHub correspondant " -"`_." +"Renomme les messages ProtoBuf pour améliorer la cohérence " +"([#1214](https://github.com/adap/flower/pull/1214), " +"[#1258](https://github.com/adap/flower/pull/1258), " +"[#1259](https://github.com/adap/flower/pull/1259))" -#: ../../source/ref-faq.rst -msgid ":fa:`eye,mr-1` Does Flower support federated learning on Android devices?" -msgstr "" -":fa:`eye,mr-1` Est-ce que Flower prend en charge l'apprentissage fédéré " -"sur les appareils Android ?" +#: ../../source/ref-changelog.md:812 +msgid "v0.19.0 (2022-05-18)" +msgstr "v0.19.0 (2022-05-18)" -#: ../../source/ref-faq.rst:19 -#, fuzzy +#: ../../source/ref-changelog.md:816 msgid "" -"Yes, it does. Please take a look at our `blog post " -"`_ or check out the code examples:" +"**Flower Baselines (preview): FedOpt, FedBN, FedAvgM** " +"([#919](https://github.com/adap/flower/pull/919), " +"[#1127](https://github.com/adap/flower/pull/1127), " +"[#914](https://github.com/adap/flower/pull/914))" msgstr "" -"Oui. Jetez un coup d'œil à notre `blog post " -"`_ ou consultez l'`exemple de code Android sur GitHub " -"`_." +"**Flower Baselines (preview) : FedOpt, FedBN, FedAvgM** " +"([#919](https://github.com/adap/flower/pull/919), " +"[#1127](https://github.com/adap/flower/pull/1127), " +"[#914](https://github.com/adap/flower/pull/914))" -#: ../../source/ref-faq.rst:21 +#: ../../source/ref-changelog.md:818 +#, fuzzy msgid "" -"`Android Kotlin example `_" +"The first preview release of Flower Baselines has arrived! We're " +"kickstarting Flower Baselines with implementations of FedOpt (FedYogi, " +"FedAdam, FedAdagrad), FedBN, and FedAvgM. Check the documentation on how " +"to use [Flower Baselines](https://flower.ai/docs/using-baselines.html). " +"With this first preview release we're also inviting the community to " +"[contribute their own baselines](https://flower.ai/docs/baselines/how-to-" +"contribute-baselines.html)." msgstr "" +"La première version préliminaire de Flower Baselines est arrivée ! Nous " +"démarrons Flower Baselines avec des implémentations de FedOpt (FedYogi, " +"FedAdam, FedAdagrad), FedBN, et FedAvgM. Consultez la documentation sur " +"l'utilisation de [Flower Baselines](https://flower.ai/docs/using-" +"baselines.html). Avec cette première version préliminaire, nous invitons " +"également la communauté à [contribuer à leurs propres lignes de " +"base](https://flower.ai/docs/baselines/how-to-contribute-baselines.html)." -#: ../../source/ref-faq.rst:22 -msgid "`Android Java example `_" +#: ../../source/ref-changelog.md:820 +msgid "" +"**C++ client SDK (preview) and code example** " +"([#1111](https://github.com/adap/flower/pull/1111))" msgstr "" +"**SDK client C++ (aperçu) et exemple de code** " +"([#1111](https://github.com/adap/flower/pull/1111))" -#: ../../source/ref-faq.rst -msgid ":fa:`eye,mr-1` Can I combine federated learning with blockchain?" +#: ../../source/ref-changelog.md:822 +msgid "" +"Preview support for Flower clients written in C++. The C++ preview " +"includes a Flower client SDK and a quickstart code example that " +"demonstrates a simple C++ client using the SDK." msgstr "" -":fa:`eye,mr-1` Puis-je combiner l'apprentissage fédéré avec la blockchain" -" ?" +"L'aperçu C++ comprend un SDK pour les clients Flower et un exemple de " +"code de démarrage rapide qui démontre un client C++ simple utilisant le " +"SDK." -#: ../../source/ref-faq.rst:26 +#: ../../source/ref-changelog.md:824 msgid "" -"Yes, of course. A list of available examples using Flower within a " -"blockchain environment is available here:" +"**Add experimental support for Python 3.10 and Python 3.11** " +"([#1135](https://github.com/adap/flower/pull/1135))" msgstr "" -"Oui, bien sûr, une liste d'exemples disponibles utilisant Flower dans un " -"environnement blockchain est disponible ici :" +"**Ajouter la prise en charge expérimentale de Python 3.10 et Python " +"3.11** ([#1135](https://github.com/adap/flower/pull/1135))" -#: ../../source/ref-faq.rst:28 +#: ../../source/ref-changelog.md:826 msgid "" -"`Flower meets Nevermined GitHub Repository `_." +"Python 3.10 is the latest stable release of Python and Python 3.11 is due" +" to be released in October. This Flower release adds experimental support" +" for both Python versions." msgstr "" -"`Flower meets Nevermined GitHub Repository `_." +"Python 3.10 est la dernière version stable de Python et Python 3.11 " +"devrait sortir en octobre. Cette version de Flower ajoute une prise en " +"charge expérimentale pour les deux versions de Python." -#: ../../source/ref-faq.rst:29 +#: ../../source/ref-changelog.md:828 msgid "" -"`Flower meets Nevermined YouTube video " -"`_." +"**Aggregate custom metrics through user-provided functions** " +"([#1144](https://github.com/adap/flower/pull/1144))" msgstr "" -"`Flower rencontre Nevermined vidéo YouTube " -"`_." +"**Agréger des mesures personnalisées grâce à des fonctions fournies par " +"l'utilisateur** ([#1144](https://github.com/adap/flower/pull/1144))" -#: ../../source/ref-faq.rst:30 -#, fuzzy +#: ../../source/ref-changelog.md:830 msgid "" -"`Flower meets KOSMoS `_." +"Custom metrics (e.g., `accuracy`) can now be aggregated without having to" +" customize the strategy. Built-in strategies support two new arguments, " +"`fit_metrics_aggregation_fn` and `evaluate_metrics_aggregation_fn`, that " +"allow passing custom metric aggregation functions." msgstr "" -"`Flower rencontre KOSMoS `_." +"Les stratégies intégrées prennent en charge deux nouveaux arguments, " +"`fit_metrics_aggregation_fn` et `evaluate_metrics_aggregation_fn`, qui " +"permettent de passer des fonctions d'agrégation de métriques " +"personnalisées." -#: ../../source/ref-faq.rst:31 +#: ../../source/ref-changelog.md:832 msgid "" -"`Flower meets Talan blog post `_ ." +"**User-configurable round timeout** " +"([#1162](https://github.com/adap/flower/pull/1162))" msgstr "" -"`Flower meets Talan blog post `_ ." +"**Temps d'attente configurable par l'utilisateur** " +"([#1162](https://github.com/adap/flower/pull/1162))" -#: ../../source/ref-faq.rst:32 +#: ../../source/ref-changelog.md:834 msgid "" -"`Flower meets Talan GitHub Repository " -"`_ ." +"A new configuration value allows the round timeout to be set for " +"`start_server` and `start_simulation`. If the `config` dictionary " +"contains a `round_timeout` key (with a `float` value in seconds), the " +"server will wait *at least* `round_timeout` seconds before it closes the " +"connection." msgstr "" -"`Flower rencontre Talan Dépôt GitHub " -"`_ ." - -#: ../../source/ref-telemetry.md:1 -msgid "Telemetry" -msgstr "Télémétrie" +"Si le dictionnaire `config` contient une clé `round_timeout` (avec une " +"valeur `float` en secondes), le serveur attendra *au moins* " +"`round_timeout` secondes avant de fermer la connexion." -#: ../../source/ref-telemetry.md:3 +#: ../../source/ref-changelog.md:836 msgid "" -"The Flower open-source project collects **anonymous** usage metrics to " -"make well-informed decisions to improve Flower. Doing this enables the " -"Flower team to understand how Flower is used and what challenges users " -"might face." +"**Enable both federated evaluation and centralized evaluation to be used " +"at the same time in all built-in strategies** " +"([#1091](https://github.com/adap/flower/pull/1091))" msgstr "" -"Le projet open-source Flower recueille des mesures d'utilisation " -"**anonymes** afin de prendre des décisions éclairées pour améliorer " -"Flower. Cela permet à l'équipe de Flower de comprendre comment Flower est" -" utilisé et quels sont les défis auxquels les utilisateurs peuvent être " -"confrontés." +"**Permettre l'utilisation simultanée de l'évaluation fédérée et de " +"l'évaluation centralisée dans toutes les stratégies intégrées** " +"([#1091](https://github.com/adap/flower/pull/1091))" -#: ../../source/ref-telemetry.md:5 +#: ../../source/ref-changelog.md:838 msgid "" -"**Flower is a friendly framework for collaborative AI and data science.**" -" Staying true to this statement, Flower makes it easy to disable " -"telemetry for users that do not want to share anonymous usage metrics." +"Built-in strategies can now perform both federated evaluation (i.e., " +"client-side) and centralized evaluation (i.e., server-side) in the same " +"round. Federated evaluation can be disabled by setting `fraction_eval` to" +" `0.0`." msgstr "" -"**Flower est un cadre convivial pour l'IA collaborative et la science des" -" données.** En restant fidèle à cette déclaration, Flower permet de " -"désactiver facilement la télémétrie pour les utilisateurs qui ne " -"souhaitent pas partager des mesures d'utilisation anonymes." - -#: ../../source/ref-telemetry.md:7 -msgid "Principles" -msgstr "Principes" +"Les stratégies intégrées peuvent maintenant effectuer une évaluation " +"fédérée (c'est-à-dire côté client) et une évaluation centralisée " +"(c'est-à-dire côté serveur) dans le même tour. L'évaluation fédérée peut " +"être désactivée en réglant `fraction_eval` sur `0.0`." -#: ../../source/ref-telemetry.md:9 -msgid "We follow strong principles guarding anonymous usage metrics collection:" +#: ../../source/ref-changelog.md:840 +msgid "" +"**Two new Jupyter Notebook tutorials** " +"([#1141](https://github.com/adap/flower/pull/1141))" msgstr "" -"Nous suivons des principes stricts concernant la collecte de données " -"anonymes sur l'utilisation :" +"**Deux nouveaux tutoriels Jupyter Notebook** " +"([#1141](https://github.com/adap/flower/pull/1141))" -#: ../../source/ref-telemetry.md:11 +#: ../../source/ref-changelog.md:842 msgid "" -"**Optional:** You will always be able to disable telemetry; read on to " -"learn “[How to opt-out](#how-to-opt-out)”." +"Two Jupyter Notebook tutorials (compatible with Google Colab) explain " +"basic and intermediate Flower features:" msgstr "" -"**Optionnel:** Tu pourras toujours désactiver la télémétrie ; lis la " -"suite pour apprendre \"[Comment se désengager](#how-to-opt-out)\"." +"Deux tutoriels Jupyter Notebook (compatibles avec Google Colab) " +"expliquent les fonctionnalités de base et intermédiaires de Flower :" -#: ../../source/ref-telemetry.md:12 +#: ../../source/ref-changelog.md:844 msgid "" -"**Anonymous:** The reported usage metrics are anonymous and do not " -"contain any personally identifiable information (PII). See “[Collected " -"metrics](#collected-metrics)” to understand what metrics are being " -"reported." +"*An Introduction to Federated Learning*: [Open in " +"Colab](https://colab.research.google.com/github/adap/flower/blob/main/tutorials/Flower-1" +"-Intro-to-FL-PyTorch.ipynb)" msgstr "" -"**Anonyme:** Les mesures d'utilisation rapportées sont anonymes et ne " -"contiennent aucune information personnelle identifiable (PII). Voir " -"\"[Collected metrics](#collected-metrics)\" pour comprendre quelles " -"mesures sont rapportées." +"*Introduction à l'apprentissage fédéré* : [Open in " +"Colab](https://colab.research.google.com/github/adap/flower/blob/main/tutorials/Flower-1" +"-Intro-to-FL-PyTorch.ipynb)" -#: ../../source/ref-telemetry.md:13 +#: ../../source/ref-changelog.md:846 msgid "" -"**Transparent:** You can easily inspect what anonymous metrics are being " -"reported; see the section “[How to inspect what is being reported](#how-" -"to-inspect-what-is-being-reported)”" +"*Using Strategies in Federated Learning*: [Open in " +"Colab](https://colab.research.google.com/github/adap/flower/blob/main/tutorials/Flower-2" +"-Strategies-in-FL-PyTorch.ipynb)" msgstr "" -"**Transparent:** Tu peux facilement inspecter les métriques anonymes qui " -"sont rapportées ; voir la section \"[Comment inspecter ce qui est " -"rapporté](#how-to-inspect-what-is-being-reported)\"" +"*Utiliser des stratégies dans l'apprentissage fédéré* : [Open in " +"Colab](https://colab.research.google.com/github/adap/flower/blob/main/tutorials/Flower-2" +"-Strategies-in-FL-PyTorch.ipynb)" -#: ../../source/ref-telemetry.md:14 -#, fuzzy +#: ../../source/ref-changelog.md:848 msgid "" -"**Open for feedback:** You can always reach out to us if you have " -"feedback; see the section “[How to contact us](#how-to-contact-us)” for " -"details." +"**New FedAvgM strategy (Federated Averaging with Server Momentum)** " +"([#1076](https://github.com/adap/flower/pull/1076))" msgstr "" -"**Ouvert pour les commentaires:** Tu peux toujours nous contacter si tu " -"as des commentaires ; voir la section \"[Comment nous contacter ](#how-" -"to-contact-us)\" pour plus de détails." - -#: ../../source/ref-telemetry.md:16 -msgid "How to opt-out" -msgstr "Comment se désinscrire" +"**Nouvelle stratégie FedAvgM (Federated Averaging with Server Momentum)**" +" ([#1076](https://github.com/adap/flower/pull/1076))" -#: ../../source/ref-telemetry.md:18 +#: ../../source/ref-changelog.md:850 +#, fuzzy msgid "" -"When Flower starts, it will check for an environment variable called " -"`FLWR_TELEMETRY_ENABLED`. Telemetry can easily be disabled by setting " -"`FLWR_TELEMETRY_ENABLED=0`. Assuming you are starting a Flower server or " -"client, simply do so by prepending your command as in:" +"The new `FedAvgM` strategy implements Federated Averaging with Server " +"Momentum \\[Hsu et al., 2019\\]." msgstr "" -"Lorsque Flower démarre, il vérifie la présence d'une variable " -"d'environnement appelée `FLWR_TELEMETRY_ENABLED`. La télémétrie peut " -"facilement être désactivée en réglant `FLWR_TELEMETRY_ENABLED=0`. En " -"supposant que tu démarres un serveur ou un client Flower, fais-le " -"simplement en faisant précéder ta commande de la façon suivante :" +"La nouvelle stratégie `FedAvgM` met en œuvre la moyenne fédérée avec le " +"momentum du serveur [Hsu et al., 2019]." -#: ../../source/ref-telemetry.md:24 +#: ../../source/ref-changelog.md:852 msgid "" -"Alternatively, you can export `FLWR_TELEMETRY_ENABLED=0` in, for example," -" `.bashrc` (or whatever configuration file applies to your environment) " -"to disable Flower telemetry permanently." +"**New advanced PyTorch code example** " +"([#1007](https://github.com/adap/flower/pull/1007))" msgstr "" -"Tu peux aussi exporter `FLWR_TELEMETRY_ENABLED=0` dans, par exemple, " -"`.bashrc` (ou tout autre fichier de configuration qui s'applique à ton " -"environnement) pour désactiver la télémétrie de la fleur de façon " -"permanente." - -#: ../../source/ref-telemetry.md:26 -msgid "Collected metrics" -msgstr "Mesures collectées" - -#: ../../source/ref-telemetry.md:28 -msgid "Flower telemetry collects the following metrics:" -msgstr "La télémétrie des fleurs recueille les métriques suivantes :" +"**Nouvel exemple de code PyTorch avancé** " +"([#1007](https://github.com/adap/flower/pull/1007))" -#: ../../source/ref-telemetry.md:30 +#: ../../source/ref-changelog.md:854 msgid "" -"**Flower version.** Understand which versions of Flower are currently " -"being used. This helps us to decide whether we should invest effort into " -"releasing a patch version for an older version of Flower or instead use " -"the bandwidth to build new features." +"A new code example (`advanced_pytorch`) demonstrates advanced Flower " +"concepts with PyTorch." msgstr "" -"**Cela nous aide à décider si nous devons investir des efforts dans la " -"publication d'une version corrective pour une version plus ancienne de " -"Flower ou si nous devons plutôt utiliser la bande passante pour " -"développer de nouvelles fonctionnalités." +"Un nouvel exemple de code (`advanced_pytorch`) démontre des concepts de " +"fleur avancés avec PyTorch." -#: ../../source/ref-telemetry.md:32 +#: ../../source/ref-changelog.md:856 msgid "" -"**Operating system.** Enables us to answer questions such as: *Should we " -"create more guides for Linux, macOS, or Windows?*" +"**New JAX code example** " +"([#906](https://github.com/adap/flower/pull/906), " +"[#1143](https://github.com/adap/flower/pull/1143))" msgstr "" -"**Système d'exploitation.** Nous permet de répondre à des questions " -"telles que : *Faudrait-il créer plus de guides pour Linux, macOS ou " -"Windows ?" +"**Nouvel exemple de code JAX** " +"([#906](https://github.com/adap/flower/pull/906), " +"[#1143](https://github.com/adap/flower/pull/1143))" -#: ../../source/ref-telemetry.md:34 +#: ../../source/ref-changelog.md:858 msgid "" -"**Python version.** Knowing the Python version helps us, for example, to " -"decide whether we should invest effort into supporting old versions of " -"Python or stop supporting them and start taking advantage of new Python " -"features." +"A new code example (`jax_from_centralized_to_federated`) shows federated " +"learning with JAX and Flower." msgstr "" -"**Version de Python.** Connaître la version de Python nous aide, par " -"exemple, à décider si nous devons investir des efforts dans la prise en " -"charge des anciennes versions de Python ou cesser de les prendre en " -"charge et commencer à tirer parti des nouvelles fonctionnalités de " -"Python." +"Un nouvel exemple de code (`jax_from_centralized_to_federated`) montre " +"l'apprentissage fédéré avec JAX et Flower." -#: ../../source/ref-telemetry.md:36 +#: ../../source/ref-changelog.md:862 msgid "" -"**Hardware properties.** Understanding the hardware environment that " -"Flower is being used in helps to decide whether we should, for example, " -"put more effort into supporting low-resource environments." +"New option to keep Ray running if Ray was already initialized in " +"`start_simulation` ([#1177](https://github.com/adap/flower/pull/1177))" msgstr "" -"**Comprendre l'environnement matériel dans lequel Flower est utilisé " -"permet de décider si nous devrions, par exemple, faire plus d'efforts " -"pour prendre en charge les environnements à faibles ressources." +"Nouvelle option pour continuer à faire fonctionner Ray si Ray a déjà été " +"initialisé dans `start_simulation` " +"([#1177](https://github.com/adap/flower/pull/1177))" -#: ../../source/ref-telemetry.md:38 +#: ../../source/ref-changelog.md:863 msgid "" -"**Execution mode.** Knowing what execution mode Flower starts in enables " -"us to understand how heavily certain features are being used and better " -"prioritize based on that." +"Add support for custom `ClientManager` as a `start_simulation` parameter " +"([#1171](https://github.com/adap/flower/pull/1171))" msgstr "" -"**Mode d'exécution** Connaître le mode d'exécution dans lequel Flower " -"démarre nous permet de comprendre à quel point certaines fonctionnalités " -"sont utilisées et de mieux établir les priorités en fonction de cela." +"Ajout de la prise en charge d'un `ClientManager` personnalisé comme " +"paramètre de `start_simulation` " +"([#1171](https://github.com/adap/flower/pull/1171))" -#: ../../source/ref-telemetry.md:40 +#: ../../source/ref-changelog.md:864 +#, fuzzy msgid "" -"**Cluster.** Flower telemetry assigns a random in-memory cluster ID each " -"time a Flower workload starts. This allows us to understand which device " -"types not only start Flower workloads but also successfully complete " -"them." +"New documentation for [implementing " +"strategies](https://flower.ai/docs/framework/how-to-implement-" +"strategies.html) ([#1097](https://github.com/adap/flower/pull/1097), " +"[#1175](https://github.com/adap/flower/pull/1175))" msgstr "" -"**Cluster.** La télémétrie Flower attribue un ID de cluster en mémoire " -"aléatoire à chaque fois qu'une charge de travail Flower démarre. Cela " -"nous permet de comprendre quels types d'appareils non seulement démarrent" -" les charges de travail Flower, mais aussi les terminent avec succès." +"Nouvelle documentation pour [mettre en œuvre des " +"stratégies](https://flower.ai/docs/framework/how-to-implement-" +"strategies.html) ([#1097](https://github.com/adap/flower/pull/1097), " +"[#1175](https://github.com/adap/flower/pull/1175))" -#: ../../source/ref-telemetry.md:42 +#: ../../source/ref-changelog.md:865 msgid "" -"**Source.** Flower telemetry tries to store a random source ID in " -"`~/.flwr/source` the first time a telemetry event is generated. The " -"source ID is important to identify whether an issue is recurring or " -"whether an issue is triggered by multiple clusters running concurrently " -"(which often happens in simulation). For example, if a device runs " -"multiple workloads at the same time, and this results in an issue, then, " -"in order to reproduce the issue, multiple workloads must be started at " -"the same time." +"New mobile-friendly documentation theme " +"([#1174](https://github.com/adap/flower/pull/1174))" msgstr "" -"**Source.** La télémétrie de Flower essaie de stocker un ID de source " -"aléatoire dans `~/.flwr/source` la première fois qu'un événement de " -"télémétrie est généré. L'ID de source est important pour identifier si un" -" problème est récurrent ou si un problème est déclenché par plusieurs " -"clusters fonctionnant simultanément (ce qui arrive souvent en " -"simulation). Par exemple, si un périphérique exécute plusieurs charges de" -" travail en même temps, et que cela entraîne un problème, alors, afin de " -"reproduire le problème, plusieurs charges de travail doivent être " -"démarrées en même temps." +"Nouveau thème de documentation adapté aux mobiles " +"([#1174](https://github.com/adap/flower/pull/1174))" -#: ../../source/ref-telemetry.md:44 +#: ../../source/ref-changelog.md:866 msgid "" -"You may delete the source ID at any time. If you wish for all events " -"logged under a specific source ID to be deleted, you can send a deletion " -"request mentioning the source ID to `telemetry@flower.ai`. All events " -"related to that source ID will then be permanently deleted." +"Limit version range for (optional) `ray` dependency to include only " +"compatible releases (`>=1.9.2,<1.12.0`) " +"([#1205](https://github.com/adap/flower/pull/1205))" msgstr "" -"Tu peux supprimer l'identifiant de la source à tout moment. Si tu " -"souhaites que tous les événements enregistrés sous un identifiant de " -"source spécifique soient supprimés, tu peux envoyer une demande de " -"suppression mentionnant l'identifiant de source à `telemetry@flower.ai`. " -"Tous les événements liés à cet identifiant de source seront alors " -"définitivement supprimés." +"Limite la plage de versions pour la dépendance (optionnelle) `ray` pour " +"n'inclure que les versions compatibles (`>=1.9.2,<1.12.0`) " +"([#1205](https://github.com/adap/flower/pull/1205))" -#: ../../source/ref-telemetry.md:46 +#: ../../source/ref-changelog.md:870 msgid "" -"We will not collect any personally identifiable information. If you think" -" any of the metrics collected could be misused in any way, please [get in" -" touch with us](#how-to-contact-us). We will update this page to reflect " -"any changes to the metrics collected and publish changes in the " -"changelog." +"**Remove deprecated support for Python 3.6** " +"([#871](https://github.com/adap/flower/pull/871))" msgstr "" -"Nous ne collecterons aucune information personnelle identifiable. Si tu " -"penses que l'une des métriques collectées pourrait être utilisée à " -"mauvais escient de quelque manière que ce soit, merci de [nous " -"contacter](#commentnouscontacter). Nous mettrons à jour cette page pour " -"refléter toute modification des métriques collectées et nous publierons " -"les changements dans le journal des modifications (changelog)." +"**Supprime la prise en charge obsolète de Python 3.6** " +"([#871](https://github.com/adap/flower/pull/871))" -#: ../../source/ref-telemetry.md:48 +#: ../../source/ref-changelog.md:871 msgid "" -"If you think other metrics would be helpful for us to better guide our " -"decisions, please let us know! We will carefully review them; if we are " -"confident that they do not compromise user privacy, we may add them." +"**Remove deprecated KerasClient** " +"([#857](https://github.com/adap/flower/pull/857))" msgstr "" -"Si tu penses que d'autres mesures nous seraient utiles pour mieux " -"orienter nos décisions, fais-le nous savoir ! Nous les examinerons " -"attentivement ; si nous sommes convaincus qu'elles ne compromettent pas " -"la vie privée des utilisateurs, nous pourrons les ajouter." - -#: ../../source/ref-telemetry.md:50 -msgid "How to inspect what is being reported" -msgstr "Comment inspecter ce qui est rapporté" +"**Supprimez KerasClient** " +"([#857](https://github.com/adap/flower/pull/857))" -#: ../../source/ref-telemetry.md:52 +#: ../../source/ref-changelog.md:872 msgid "" -"We wanted to make it very easy for you to inspect what anonymous usage " -"metrics are reported. You can view all the reported telemetry information" -" by setting the environment variable `FLWR_TELEMETRY_LOGGING=1`. Logging " -"is disabled by default. You may use logging independently from " -"`FLWR_TELEMETRY_ENABLED` so that you can inspect the telemetry feature " -"without sending any metrics." +"**Remove deprecated no-op extra installs** " +"([#973](https://github.com/adap/flower/pull/973))" msgstr "" -"Nous avons voulu qu'il soit très facile pour toi d'inspecter les mesures " -"d'utilisation anonymes qui sont rapportées. Tu peux voir toutes les " -"informations de télémétrie rapportées en définissant la variable " -"d'environnement `FLWR_TELEMETRY_LOGGING=1`. La journalisation est " -"désactivée par défaut. Tu peux utiliser la journalisation indépendamment " -"de `FLWR_TELEMETRY_ENABLED` afin d'inspecter la fonction de télémétrie " -"sans envoyer de mesures." +"**Supprimer les installations supplémentaires no-op dépréciées** " +"([#973](https://github.com/adap/flower/pull/973))" -#: ../../source/ref-telemetry.md:58 +#: ../../source/ref-changelog.md:873 msgid "" -"The inspect Flower telemetry without sending any anonymous usage metrics," -" use both environment variables:" +"**Remove deprecated proto fields from** `FitRes` **and** `EvaluateRes` " +"([#869](https://github.com/adap/flower/pull/869))" msgstr "" -"L'inspecteur Flower telemetry sans envoyer de métriques d'utilisation " -"anonymes, utilise les deux variables d'environnement :" - -#: ../../source/ref-telemetry.md:64 -msgid "How to contact us" -msgstr "Comment nous contacter" +"**Supprimez les champs proto obsolètes de** `FitRes` **et** `EvaluateRes`" +" ([#869](https://github.com/adap/flower/pull/869))" -#: ../../source/ref-telemetry.md:66 +#: ../../source/ref-changelog.md:874 msgid "" -"We want to hear from you. If you have any feedback or ideas on how to " -"improve the way we handle anonymous usage metrics, reach out to us via " -"[Slack](https://flower.ai/join-slack/) (channel `#telemetry`) or email " -"(`telemetry@flower.ai`)." +"**Remove deprecated QffedAvg strategy (replaced by QFedAvg)** " +"([#1107](https://github.com/adap/flower/pull/1107))" msgstr "" -"Si tu as des commentaires ou des idées pour améliorer la façon dont nous " -"traitons les mesures d'utilisation anonymes, contacte-nous via " -"[Slack](https://flower.ai/join-slack/) (canal `#telemetry`) ou par " -"courriel (`telemetry@flower.ai`)." +"**Supprime la stratégie QffedAvg (remplacée par QFedAvg)** " +"([#1107](https://github.com/adap/flower/pull/1107))" -#: ../../source/tutorial-quickstart-android.rst:-1 +#: ../../source/ref-changelog.md:875 msgid "" -"Read this Federated Learning quickstart tutorial for creating an Android " -"app using Flower." +"**Remove deprecated DefaultStrategy strategy** " +"([#1142](https://github.com/adap/flower/pull/1142))" msgstr "" +"**Supprime la stratégie DefaultStrategy qui est obsolète** " +"([#1142](https://github.com/adap/flower/pull/1142))" -#: ../../source/tutorial-quickstart-android.rst:5 -#, fuzzy -msgid "Quickstart Android" -msgstr "Démarrage rapide des Pandas" - -#: ../../source/tutorial-quickstart-android.rst:10 -#, fuzzy +#: ../../source/ref-changelog.md:876 msgid "" -"Let's build a federated learning system using TFLite and Flower on " -"Android!" +"**Remove deprecated support for eval_fn accuracy return value** " +"([#1142](https://github.com/adap/flower/pull/1142))" msgstr "" -"Construisons un système d'apprentissage fédéré en utilisant fastai et " -"Flower !" +"**Supprimer la prise en charge obsolète de la valeur de retour de la " +"précision eval_fn** ([#1142](https://github.com/adap/flower/pull/1142))" -#: ../../source/tutorial-quickstart-android.rst:12 -#, fuzzy +#: ../../source/ref-changelog.md:877 msgid "" -"Please refer to the `full code example " -"`_ to learn " -"more." +"**Remove deprecated support for passing initial parameters as NumPy " +"ndarrays** ([#1142](https://github.com/adap/flower/pull/1142))" msgstr "" -"Réfère-toi à l'exemple de code complet " -"`_ " -"pour en savoir plus." +"**Supprime la prise en charge obsolète du passage des paramètres initiaux" +" en tant que ndarrays NumPy** " +"([#1142](https://github.com/adap/flower/pull/1142))" -#: ../../source/tutorial-quickstart-fastai.rst:-1 +#: ../../source/ref-changelog.md:879 +msgid "v0.18.0 (2022-02-28)" +msgstr "v0.18.0 (2022-02-28)" + +#: ../../source/ref-changelog.md:883 msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with FastAI to train a vision model on CIFAR-10." +"**Improved Virtual Client Engine compatibility with Jupyter Notebook / " +"Google Colab** ([#866](https://github.com/adap/flower/pull/866), " +"[#872](https://github.com/adap/flower/pull/872), " +"[#833](https://github.com/adap/flower/pull/833), " +"[#1036](https://github.com/adap/flower/pull/1036))" msgstr "" +"**Amélioration de la compatibilité du moteur de client virtuel avec " +"Jupyter Notebook / Google Colab** " +"([#866](https://github.com/adap/flower/pull/866), " +"[#872](https://github.com/adap/flower/pull/872), " +"[#833](https://github.com/adap/flower/pull/833), " +"[#1036](https://github.com/adap/flower/pull/1036))" -#: ../../source/tutorial-quickstart-fastai.rst:5 -msgid "Quickstart fastai" -msgstr "Démarrage rapide fastai" - -#: ../../source/tutorial-quickstart-fastai.rst:10 -msgid "Let's build a federated learning system using fastai and Flower!" +#: ../../source/ref-changelog.md:885 +msgid "" +"Simulations (using the Virtual Client Engine through `start_simulation`) " +"now work more smoothly on Jupyter Notebooks (incl. Google Colab) after " +"installing Flower with the `simulation` extra (`pip install " +"flwr[simulation]`)." msgstr "" -"Construisons un système d'apprentissage fédéré en utilisant fastai et " -"Flower !" +"Les simulations (utilisant le moteur de client virtuel via " +"`start_simulation`) fonctionnent maintenant plus facilement sur les " +"Notebooks Jupyter (y compris Google Colab) après avoir installé Flower " +"avec l'option `simulation` (`pip install flwr[simulation]`)." -#: ../../source/tutorial-quickstart-fastai.rst:12 -#, fuzzy +#: ../../source/ref-changelog.md:887 msgid "" -"Please refer to the `full code example " -"`_ " -"to learn more." +"**New Jupyter Notebook code example** " +"([#833](https://github.com/adap/flower/pull/833))" msgstr "" -"Réfère-toi à l'exemple de code complet " -"`_ " -"pour en savoir plus." +"**Nouvel exemple de code Jupyter Notebook** " +"([#833](https://github.com/adap/flower/pull/833))" -#: ../../source/tutorial-quickstart-huggingface.rst:-1 +#: ../../source/ref-changelog.md:889 msgid "" -"Check out this Federating Learning quickstart tutorial for using Flower " -"with HuggingFace Transformers in order to fine-tune an LLM." +"A new code example (`quickstart_simulation`) demonstrates Flower " +"simulations using the Virtual Client Engine through Jupyter Notebook " +"(incl. Google Colab)." msgstr "" +"Un nouvel exemple de code (`quickstart_simulation`) démontre des " +"simulations de Flower en utilisant le moteur de client virtuel via " +"Jupyter Notebook (y compris Google Colab)." -#: ../../source/tutorial-quickstart-huggingface.rst:5 -msgid "Quickstart 🤗 Transformers" -msgstr "Démarrage rapide 🤗 Transformateurs" - -#: ../../source/tutorial-quickstart-huggingface.rst:10 +#: ../../source/ref-changelog.md:891 msgid "" -"Let's build a federated learning system using Hugging Face Transformers " -"and Flower!" +"**Client properties (feature preview)** " +"([#795](https://github.com/adap/flower/pull/795))" msgstr "" -"Construisons un système d'apprentissage fédéré à l'aide des " -"transformateurs Hugging Face et de Flower !" +"**Propriétés du client (aperçu des fonctionnalités)** " +"([#795](https://github.com/adap/flower/pull/795))" -#: ../../source/tutorial-quickstart-huggingface.rst:12 +#: ../../source/ref-changelog.md:893 msgid "" -"We will leverage Hugging Face to federate the training of language models" -" over multiple clients using Flower. More specifically, we will fine-tune" -" a pre-trained Transformer model (distilBERT) for sequence classification" -" over a dataset of IMDB ratings. The end goal is to detect if a movie " -"rating is positive or negative." +"Clients can implement a new method `get_properties` to enable server-side" +" strategies to query client properties." msgstr "" -"Nous nous appuierons sur Hugging Face pour fédérer l'entraînement de " -"modèles de langage sur plusieurs clients à l'aide de Flower. Plus " -"précisément, nous mettrons au point un modèle Transformer pré-entraîné " -"(distilBERT) pour la classification de séquences sur un ensemble de " -"données d'évaluations IMDB. L'objectif final est de détecter si " -"l'évaluation d'un film est positive ou négative." - -#: ../../source/tutorial-quickstart-huggingface.rst:18 -msgid "Dependencies" -msgstr "Dépendances" +"Les clients peuvent implémenter une nouvelle méthode `get_properties` " +"pour permettre aux stratégies côté serveur d'interroger les propriétés du" +" client." -#: ../../source/tutorial-quickstart-huggingface.rst:20 +#: ../../source/ref-changelog.md:895 msgid "" -"To follow along this tutorial you will need to install the following " -"packages: :code:`datasets`, :code:`evaluate`, :code:`flwr`, " -":code:`torch`, and :code:`transformers`. This can be done using " -":code:`pip`:" +"**Experimental Android support with TFLite** " +"([#865](https://github.com/adap/flower/pull/865))" msgstr "" -"Pour suivre ce tutoriel, tu devras installer les paquets suivants : " -":code:`datasets`, :code:`evaluate`, :code:`flwr`, :code:`torch`, et " -":code:`transformers`. Cela peut être fait en utilisant :code:`pip` :" - -#: ../../source/tutorial-quickstart-huggingface.rst:30 -msgid "Standard Hugging Face workflow" -msgstr "Flux de travail standard pour le visage" - -#: ../../source/tutorial-quickstart-huggingface.rst:33 -msgid "Handling the data" -msgstr "Traitement des données" +"**Support expérimental d'Android avec TFLite** " +"([#865](https://github.com/adap/flower/pull/865))" -#: ../../source/tutorial-quickstart-huggingface.rst:35 +#: ../../source/ref-changelog.md:897 msgid "" -"To fetch the IMDB dataset, we will use Hugging Face's :code:`datasets` " -"library. We then need to tokenize the data and create :code:`PyTorch` " -"dataloaders, this is all done in the :code:`load_data` function:" +"Android support has finally arrived in `main`! Flower is both client-" +"agnostic and framework-agnostic by design. One can integrate arbitrary " +"client platforms and with this release, using Flower on Android has " +"become a lot easier." msgstr "" -"Pour récupérer le jeu de données IMDB, nous utiliserons la bibliothèque " -":code:`datasets` de Hugging Face. Nous devons ensuite tokeniser les " -"données et créer des :code:`PyTorch` dataloaders, ce qui est fait dans la" -" fonction :code:`load_data` :" - -#: ../../source/tutorial-quickstart-huggingface.rst:81 -msgid "Training and testing the model" -msgstr "Former et tester le modèle" +"La prise en charge d'Android est enfin arrivée dans `main` ! Flower est à" +" la fois agnostique au niveau du client et du cadre de travail. On peut " +"intégrer des plates-formes client arbitraires et avec cette version, " +"l'utilisation de Flower sur Android est devenue beaucoup plus facile." -#: ../../source/tutorial-quickstart-huggingface.rst:83 +#: ../../source/ref-changelog.md:899 msgid "" -"Once we have a way of creating our trainloader and testloader, we can " -"take care of the training and testing. This is very similar to any " -":code:`PyTorch` training or testing loop:" +"The example uses TFLite on the client side, along with a new " +"`FedAvgAndroid` strategy. The Android client and `FedAvgAndroid` are " +"still experimental, but they are a first step towards a fully-fledged " +"Android SDK and a unified `FedAvg` implementation that integrated the new" +" functionality from `FedAvgAndroid`." msgstr "" -"Une fois que nous avons trouvé un moyen de créer notre trainloader et " -"notre testloader, nous pouvons nous occuper de l'entraînement et du test." -" C'est très similaire à n'importe quelle boucle d'entraînement ou de test" -" :code:`PyTorch` :" - -#: ../../source/tutorial-quickstart-huggingface.rst:121 -msgid "Creating the model itself" -msgstr "Créer le modèle lui-même" +"L'exemple utilise TFLite du côté client, ainsi qu'une nouvelle stratégie " +"`FedAvgAndroid`. Le client Android et `FedAvgAndroid` sont encore " +"expérimentaux, mais ils constituent un premier pas vers un SDK Android à " +"part entière et une implémentation unifiée de `FedAvg` intégrant la " +"nouvelle fonctionnalité de `FedAvgAndroid`." -#: ../../source/tutorial-quickstart-huggingface.rst:123 +#: ../../source/ref-changelog.md:901 msgid "" -"To create the model itself, we will just load the pre-trained distillBERT" -" model using Hugging Face’s :code:`AutoModelForSequenceClassification` :" +"**Make gRPC keepalive time user-configurable and decrease default " +"keepalive time** ([#1069](https://github.com/adap/flower/pull/1069))" msgstr "" -"Pour créer le modèle lui-même, nous allons simplement charger le modèle " -"distillBERT pré-entraîné en utilisant le " -":code:`AutoModelForSequenceClassification` de Hugging Face :" - -#: ../../source/tutorial-quickstart-huggingface.rst:136 -msgid "Federating the example" -msgstr "Fédérer l'exemple" - -#: ../../source/tutorial-quickstart-huggingface.rst:139 -msgid "Creating the IMDBClient" -msgstr "Création du client IMDBC" +"**Rendre le temps de garde gRPC configurable par l'utilisateur et " +"diminuer le temps de garde par défaut** " +"([#1069](https://github.com/adap/flower/pull/1069))" -#: ../../source/tutorial-quickstart-huggingface.rst:141 +#: ../../source/ref-changelog.md:903 msgid "" -"To federate our example to multiple clients, we first need to write our " -"Flower client class (inheriting from :code:`flwr.client.NumPyClient`). " -"This is very easy, as our model is a standard :code:`PyTorch` model:" +"The default gRPC keepalive time has been reduced to increase the " +"compatibility of Flower with more cloud environments (for example, " +"Microsoft Azure). Users can configure the keepalive time to customize the" +" gRPC stack based on specific requirements." msgstr "" -"Pour fédérer notre exemple à plusieurs clients, nous devons d'abord " -"écrire notre classe de client Flower (héritant de " -":code:`flwr.client.NumPyClient`). C'est très facile, car notre modèle est" -" un modèle :code:`PyTorch` standard :" +"Le temps de keepalive gRPC par défaut a été réduit pour augmenter la " +"compatibilité de Flower avec davantage d'environnements cloud (par " +"exemple, Microsoft Azure). Les utilisateurs peuvent configurer le temps " +"de keepalive pour personnaliser la pile gRPC en fonction d'exigences " +"spécifiques." -#: ../../source/tutorial-quickstart-huggingface.rst:169 +#: ../../source/ref-changelog.md:905 msgid "" -"The :code:`get_parameters` function lets the server get the client's " -"parameters. Inversely, the :code:`set_parameters` function allows the " -"server to send its parameters to the client. Finally, the :code:`fit` " -"function trains the model locally for the client, and the " -":code:`evaluate` function tests the model locally and returns the " -"relevant metrics." +"**New differential privacy example using Opacus and PyTorch** " +"([#805](https://github.com/adap/flower/pull/805))" msgstr "" -"La fonction :code:`get_parameters` permet au serveur d'obtenir les " -"paramètres du client. Inversement, la fonction :code:`set_parameters` " -"permet au serveur d'envoyer ses paramètres au client. Enfin, la fonction " -":code:`fit` forme le modèle localement pour le client, et la fonction " -":code:`evaluate` teste le modèle localement et renvoie les mesures " -"correspondantes." - -#: ../../source/tutorial-quickstart-huggingface.rst:175 -msgid "Starting the server" -msgstr "Démarrer le serveur" +"**Nouvel exemple de confidentialité différentielle utilisant Opacus et " +"PyTorch** ([#805](https://github.com/adap/flower/pull/805))" -#: ../../source/tutorial-quickstart-huggingface.rst:177 +#: ../../source/ref-changelog.md:907 msgid "" -"Now that we have a way to instantiate clients, we need to create our " -"server in order to aggregate the results. Using Flower, this can be done " -"very easily by first choosing a strategy (here, we are using " -":code:`FedAvg`, which will define the global weights as the average of " -"all the clients' weights at each round) and then using the " -":code:`flwr.server.start_server` function:" +"A new code example (`opacus`) demonstrates differentially-private " +"federated learning with Opacus, PyTorch, and Flower." msgstr "" -"Maintenant que nous avons un moyen d'instancier les clients, nous devons " -"créer notre serveur afin d'agréger les résultats. Avec Flower, cela peut " -"être fait très facilement en choisissant d'abord une stratégie (ici, nous" -" utilisons :code:`FedAvg`, qui définira les poids globaux comme la " -"moyenne des poids de tous les clients à chaque tour) et en utilisant " -"ensuite la fonction :code:`flwr.server.start_server` :" +"Un nouvel exemple de code (`opacus`) démontre l'apprentissage fédéré " +"différentiellement privé avec Opacus, PyTorch et Flower." -#: ../../source/tutorial-quickstart-huggingface.rst:205 +#: ../../source/ref-changelog.md:909 msgid "" -"The :code:`weighted_average` function is there to provide a way to " -"aggregate the metrics distributed amongst the clients (basically this " -"allows us to display a nice average accuracy and loss for every round)." +"**New Hugging Face Transformers code example** " +"([#863](https://github.com/adap/flower/pull/863))" msgstr "" -"La fonction :code:`weighted_average` est là pour fournir un moyen " -"d'agréger les mesures réparties entre les clients (en gros, cela nous " -"permet d'afficher une belle moyenne de précision et de perte pour chaque " -"tour)." - -#: ../../source/tutorial-quickstart-huggingface.rst:209 -msgid "Putting everything together" -msgstr "Tout assembler" - -#: ../../source/tutorial-quickstart-huggingface.rst:211 -msgid "We can now start client instances using:" -msgstr "Nous pouvons maintenant démarrer des instances de clients en utilisant :" - -#: ../../source/tutorial-quickstart-huggingface.rst:221 -msgid "" -"And they will be able to connect to the server and start the federated " -"training." -msgstr "Et ils pourront se connecter au serveur et démarrer la formation fédérée." +"**Nouvel exemple de code pour les Transformers à visage embrassant** " +"([#863](https://github.com/adap/flower/pull/863))" -#: ../../source/tutorial-quickstart-huggingface.rst:223 -#, fuzzy +#: ../../source/ref-changelog.md:911 msgid "" -"If you want to check out everything put together, you should check out " -"the `full code example `_ ." +"A new code example (`quickstart_huggingface`) demonstrates usage of " +"Hugging Face Transformers with Flower." msgstr "" -"Si tu veux voir tout ce qui est mis ensemble, tu devrais consulter " -"l'exemple de code complet : " -"[https://github.com/adap/flower/tree/main/examples/quickstart-" -"huggingface](https://github.com/adap/flower/tree/main/examples" -"/quickstart-huggingface)." +"Un nouvel exemple de code (`quickstart_huggingface`) démontre " +"l'utilisation des transformateurs Hugging Face avec Flower." -#: ../../source/tutorial-quickstart-huggingface.rst:226 +#: ../../source/ref-changelog.md:913 msgid "" -"Of course, this is a very basic example, and a lot can be added or " -"modified, it was just to showcase how simply we could federate a Hugging " -"Face workflow using Flower." +"**New MLCube code example** " +"([#779](https://github.com/adap/flower/pull/779), " +"[#1034](https://github.com/adap/flower/pull/1034), " +"[#1065](https://github.com/adap/flower/pull/1065), " +"[#1090](https://github.com/adap/flower/pull/1090))" msgstr "" -"Bien sûr, c'est un exemple très basique, et beaucoup de choses peuvent " -"être ajoutées ou modifiées, il s'agissait juste de montrer avec quelle " -"simplicité on pouvait fédérer un flux de travail Hugging Face à l'aide de" -" Flower." +"**Nouvel exemple de code MLCube** " +"([#779](https://github.com/adap/flower/pull/779), " +"[#1034](https://github.com/adap/flower/pull/1034), " +"[#1065](https://github.com/adap/flower/pull/1065), " +"[#1090](https://github.com/adap/flower/pull/1090))" -#: ../../source/tutorial-quickstart-huggingface.rst:229 +#: ../../source/ref-changelog.md:915 msgid "" -"Note that in this example we used :code:`PyTorch`, but we could have very" -" well used :code:`TensorFlow`." +"A new code example (`quickstart_mlcube`) demonstrates usage of MLCube " +"with Flower." msgstr "" -"Notez que dans cet exemple, nous avons utilisé :code:`PyTorch`, mais nous" -" aurions très bien pu utiliser :code:`TensorFlow`." +"Un nouvel exemple de code (`quickstart_mlcube`) démontre l'utilisation de" +" MLCube avec Flower." -#: ../../source/tutorial-quickstart-ios.rst:-1 +#: ../../source/ref-changelog.md:917 msgid "" -"Read this Federated Learning quickstart tutorial for creating an iOS app " -"using Flower to train a neural network on MNIST." +"**SSL-enabled server and client** " +"([#842](https://github.com/adap/flower/pull/842), " +"[#844](https://github.com/adap/flower/pull/844), " +"[#845](https://github.com/adap/flower/pull/845), " +"[#847](https://github.com/adap/flower/pull/847), " +"[#993](https://github.com/adap/flower/pull/993), " +"[#994](https://github.com/adap/flower/pull/994))" msgstr "" +"**([#842](https://github.com/adap/flower/pull/842), " +"[#844](https://github.com/adap/flower/pull/844), " +"[#845](https://github.com/adap/flower/pull/845), " +"[#847](https://github.com/adap/flower/pull/847), " +"[#993](https://github.com/adap/flower/pull/993), " +"[#994](https://github.com/adap/flower/pull/994))" -#: ../../source/tutorial-quickstart-ios.rst:5 -#, fuzzy -msgid "Quickstart iOS" -msgstr "Démarrage rapide XGBoost" - -#: ../../source/tutorial-quickstart-ios.rst:10 -#, fuzzy +#: ../../source/ref-changelog.md:919 msgid "" -"In this tutorial we will learn how to train a Neural Network on MNIST " -"using Flower and CoreML on iOS devices." +"SSL enables secure encrypted connections between clients and servers. " +"This release open-sources the Flower secure gRPC implementation to make " +"encrypted communication channels accessible to all Flower users." msgstr "" -"Dans ce tutoriel, nous allons apprendre, comment former un réseau " -"neuronal convolutif sur MNIST en utilisant Flower et PyTorch." +"SSL permet d'établir des connexions cryptées et sécurisées entre les " +"clients et les serveurs. Cette version met en open-source " +"l'implémentation gRPC sécurisée de Flower afin de rendre les canaux de " +"communication cryptés accessibles à tous les utilisateurs de Flower." -#: ../../source/tutorial-quickstart-ios.rst:12 -#, fuzzy +#: ../../source/ref-changelog.md:921 msgid "" -"First of all, for running the Flower Python server, it is recommended to " -"create a virtual environment and run everything within a :doc:`virtualenv" -" `. For the Flower client " -"implementation in iOS, it is recommended to use Xcode as our IDE." +"**Updated** `FedAdam` **and** `FedYogi` **strategies** " +"([#885](https://github.com/adap/flower/pull/885), " +"[#895](https://github.com/adap/flower/pull/895))" msgstr "" -"Tout d'abord, il est recommandé de créer un environnement virtuel et de " -"tout exécuter au sein d'un `virtualenv `_." +"**Mise à jour** `FedAdam` **et** `FedYogi` **stratégies** " +"([#885](https://github.com/adap/flower/pull/885), " +"[#895](https://github.com/adap/flower/pull/895))" -#: ../../source/tutorial-quickstart-ios.rst:15 -#, fuzzy +#: ../../source/ref-changelog.md:923 msgid "" -"Our example consists of one Python *server* and two iPhone *clients* that" -" all have the same model." +"`FedAdam` and `FedAdam` match the latest version of the Adaptive " +"Federated Optimization paper." msgstr "" -"Notre exemple consiste en un *serveur* et deux *clients* ayant tous le " -"même modèle." +"`FedAdam` et `FedAdam` correspondent à la dernière version de l'article " +"sur l'optimisation fédérée adaptative." -#: ../../source/tutorial-quickstart-ios.rst:17 -#, fuzzy +#: ../../source/ref-changelog.md:925 msgid "" -"*Clients* are responsible for generating individual weight updates for " -"the model based on their local datasets. These updates are then sent to " -"the *server* which will aggregate them to produce a better model. " -"Finally, the *server* sends this improved version of the model back to " -"each *client*. A complete cycle of weight updates is called a *round*." +"**Initialize** `start_simulation` **with a list of client IDs** " +"([#860](https://github.com/adap/flower/pull/860))" msgstr "" -"*Les clients* sont chargés de générer des mises à jour de poids " -"individuelles pour le modèle en fonction de leurs ensembles de données " -"locales. Ces mises à jour sont ensuite envoyées au *serveur* qui les " -"agrège pour produire un meilleur modèle. Enfin, le *serveur* renvoie " -"cette version améliorée du modèle à chaque *client*. Un cycle complet de " -"mises à jour de poids s'appelle un *round*." +"**Initialise** `start_simulation` **avec une liste d'ID de clients** " +"([#860](https://github.com/adap/flower/pull/860))" -#: ../../source/tutorial-quickstart-ios.rst:21 -#, fuzzy +#: ../../source/ref-changelog.md:927 msgid "" -"Now that we have a rough idea of what is going on, let's get started to " -"setup our Flower server environment. We first need to install Flower. You" -" can do this by using pip:" -msgstr "" -"Maintenant que nous avons une idée générale de ce qui se passe, " -"commençons. Nous devons d'abord installer Flower. Tu peux le faire en " -"exécutant :" - -#: ../../source/tutorial-quickstart-ios.rst:27 -msgid "Or Poetry:" +"`start_simulation` can now be called with a list of client IDs " +"(`clients_ids`, type: `List[str]`). Those IDs will be passed to the " +"`client_fn` whenever a client needs to be initialized, which can make it " +"easier to load data partitions that are not accessible through `int` " +"identifiers." msgstr "" +"`start_simulation` peut maintenant être appelé avec une liste " +"d'identifiants de clients (`clients_ids`, type : `List[str]`). Ces " +"identifiants seront passés à `client_fn` chaque fois qu'un client doit " +"être initialisé, ce qui peut faciliter le chargement de partitions de " +"données qui ne sont pas accessibles par des identifiants `int`." -#: ../../source/tutorial-quickstart-ios.rst:34 -#: ../../source/tutorial-quickstart-mxnet.rst:36 -#: ../../source/tutorial-quickstart-pytorch.rst:37 -#: ../../source/tutorial-quickstart-scikitlearn.rst:40 -#: ../../source/tutorial-quickstart-tensorflow.rst:29 -#: ../../source/tutorial-quickstart-xgboost.rst:55 -msgid "Flower Client" -msgstr "Client de la fleur" - -#: ../../source/tutorial-quickstart-ios.rst:36 +#: ../../source/ref-changelog.md:931 msgid "" -"Now that we have all our dependencies installed, let's run a simple " -"distributed training using CoreML as our local training pipeline and " -"MNIST as our dataset. For simplicity reasons we will use the complete " -"Flower client with CoreML, that has been implemented and stored inside " -"the Swift SDK. The client implementation can be seen below:" +"Update `num_examples` calculation in PyTorch code examples in " +"([#909](https://github.com/adap/flower/pull/909))" msgstr "" +"Mettre à jour le calcul de `num_examples` dans les exemples de code " +"PyTorch dans ([#909](https://github.com/adap/flower/pull/909))" -#: ../../source/tutorial-quickstart-ios.rst:72 +#: ../../source/ref-changelog.md:932 msgid "" -"Let's create a new application project in Xcode and add :code:`flwr` as a" -" dependency in your project. For our application, we will store the logic" -" of our app in :code:`FLiOSModel.swift` and the UI elements in " -":code:`ContentView.swift`. We will focus more on :code:`FLiOSModel.swift`" -" in this quickstart. Please refer to the `full code example " -"`_ to learn more " -"about the app." +"Expose Flower version through `flwr.__version__` " +"([#952](https://github.com/adap/flower/pull/952))" msgstr "" +"Exposer la version de Flower à travers `flwr.__version__` " +"([#952](https://github.com/adap/flower/pull/952))" -#: ../../source/tutorial-quickstart-ios.rst:75 -msgid "Import Flower and CoreML related packages in :code:`FLiOSModel.swift`:" +#: ../../source/ref-changelog.md:933 +msgid "" +"`start_server` in `app.py` now returns a `History` object containing " +"metrics from training ([#974](https://github.com/adap/flower/pull/974))" msgstr "" +"`start_server` dans `app.py` renvoie maintenant un objet `History` " +"contenant les métriques de l'entraînement " +"([#974](https://github.com/adap/flower/pull/974))" -#: ../../source/tutorial-quickstart-ios.rst:83 +#: ../../source/ref-changelog.md:934 msgid "" -"Then add the mlmodel to the project simply by drag-and-drop, the mlmodel " -"will be bundled inside the application during deployment to your iOS " -"device. We need to pass the url to access mlmodel and run CoreML machine " -"learning processes, it can be retrieved by calling the function " -":code:`Bundle.main.url`. For the MNIST dataset, we need to preprocess it " -"into :code:`MLBatchProvider` object. The preprocessing is done inside " -":code:`DataLoader.swift`." +"Make `max_workers` (used by `ThreadPoolExecutor`) configurable " +"([#978](https://github.com/adap/flower/pull/978))" msgstr "" +"Rendre `max_workers` (utilisé par `ThreadPoolExecutor`) configurable " +"([#978](https://github.com/adap/flower/pull/978))" -#: ../../source/tutorial-quickstart-ios.rst:99 +#: ../../source/ref-changelog.md:935 msgid "" -"Since CoreML does not allow the model parameters to be seen before " -"training, and accessing the model parameters during or after the training" -" can only be done by specifying the layer name, we need to know this " -"information beforehand, through looking at the model specification, which" -" are written as proto files. The implementation can be seen in " -":code:`MLModelInspect`." +"Increase sleep time after server start to three seconds in all code " +"examples ([#1086](https://github.com/adap/flower/pull/1086))" msgstr "" +"Augmente le temps de sommeil après le démarrage du serveur à trois " +"secondes dans tous les exemples de code " +"([#1086](https://github.com/adap/flower/pull/1086))" -#: ../../source/tutorial-quickstart-ios.rst:102 +#: ../../source/ref-changelog.md:936 msgid "" -"After we have all of the necessary information, let's create our Flower " -"client." +"Added a new FAQ section to the documentation " +"([#948](https://github.com/adap/flower/pull/948))" msgstr "" +"Ajout d'une nouvelle section FAQ à la documentation " +"([#948](https://github.com/adap/flower/pull/948))" -#: ../../source/tutorial-quickstart-ios.rst:117 +#: ../../source/ref-changelog.md:937 msgid "" -"Then start the Flower gRPC client and start communicating to the server " -"by passing our Flower client to the function :code:`startFlwrGRPC`." +"And many more under-the-hood changes, library updates, documentation " +"changes, and tooling improvements!" msgstr "" +"Et bien d'autres changements sous le capot, des mises à jour de la " +"bibliothèque, des modifications de la documentation et des améliorations " +"de l'outillage !" -#: ../../source/tutorial-quickstart-ios.rst:124 +#: ../../source/ref-changelog.md:941 msgid "" -"That's it for the client. We only have to implement :code:`Client` or " -"call the provided :code:`MLFlwrClient` and call :code:`startFlwrGRPC()`. " -"The attribute :code:`hostname` and :code:`port` tells the client which " -"server to connect to. This can be done by entering the hostname and port " -"in the application before clicking the start button to start the " -"federated learning process." +"**Removed** `flwr_example` **and** `flwr_experimental` **from release " +"build** ([#869](https://github.com/adap/flower/pull/869))" msgstr "" +"**Supprimé** `flwr_example` **et** `flwr_experimental` **de la version " +"release build** ([#869](https://github.com/adap/flower/pull/869))" -#: ../../source/tutorial-quickstart-ios.rst:129 -#: ../../source/tutorial-quickstart-mxnet.rst:226 -#: ../../source/tutorial-quickstart-pytorch.rst:203 -#: ../../source/tutorial-quickstart-scikitlearn.rst:157 -#: ../../source/tutorial-quickstart-tensorflow.rst:98 -#: ../../source/tutorial-quickstart-xgboost.rst:309 -msgid "Flower Server" -msgstr "Serveur de Flower" - -#: ../../source/tutorial-quickstart-ios.rst:131 -#: ../../source/tutorial-quickstart-mxnet.rst:228 -#: ../../source/tutorial-quickstart-pytorch.rst:205 -#: ../../source/tutorial-quickstart-tensorflow.rst:100 +#: ../../source/ref-changelog.md:943 msgid "" -"For simple workloads we can start a Flower server and leave all the " -"configuration possibilities at their default values. In a file named " -":code:`server.py`, import Flower and start the server:" +"The packages `flwr_example` and `flwr_experimental` have been deprecated " +"since Flower 0.12.0 and they are not longer included in Flower release " +"builds. The associated extras (`baseline`, `examples-pytorch`, `examples-" +"tensorflow`, `http-logger`, `ops`) are now no-op and will be removed in " +"an upcoming release." msgstr "" -"Pour les charges de travail simples, nous pouvons démarrer un serveur " -"Flower et laisser toutes les possibilités de configuration à leurs " -"valeurs par défaut. Dans un fichier nommé :code:`server.py`, importe " -"Flower et démarre le serveur :" +"Les paquets `flwr_example` et `flwr_experimental` ont été dépréciés " +"depuis Flower 0.12.0 et ils ne sont plus inclus dans les builds de " +"Flower. Les extras associés (`baseline`, `examples-pytorch`, `examples-" +"tensorflow`, `http-logger`, `ops`) sont maintenant no-op et seront " +"supprimés dans une prochaine version." -#: ../../source/tutorial-quickstart-ios.rst:142 -#: ../../source/tutorial-quickstart-mxnet.rst:239 -#: ../../source/tutorial-quickstart-pytorch.rst:216 -#: ../../source/tutorial-quickstart-scikitlearn.rst:215 -#: ../../source/tutorial-quickstart-tensorflow.rst:112 -msgid "Train the model, federated!" -msgstr "Entraîne le modèle, fédéré !" +#: ../../source/ref-changelog.md:945 +msgid "v0.17.0 (2021-09-24)" +msgstr "v0.17.0 (2021-09-24)" -#: ../../source/tutorial-quickstart-ios.rst:144 -#: ../../source/tutorial-quickstart-pytorch.rst:218 -#: ../../source/tutorial-quickstart-tensorflow.rst:114 -#: ../../source/tutorial-quickstart-xgboost.rst:525 +#: ../../source/ref-changelog.md:949 msgid "" -"With both client and server ready, we can now run everything and see " -"federated learning in action. FL systems usually have a server and " -"multiple clients. We therefore have to start the server first:" +"**Experimental virtual client engine** " +"([#781](https://github.com/adap/flower/pull/781) " +"[#790](https://github.com/adap/flower/pull/790) " +"[#791](https://github.com/adap/flower/pull/791))" msgstr "" -"Le client et le serveur étant prêts, nous pouvons maintenant tout " -"exécuter et voir l'apprentissage fédéré en action. Les systèmes FL ont " -"généralement un serveur et plusieurs clients. Nous devons donc commencer " -"par démarrer le serveur :" +"**Moteur expérimental de client virtuel** " +"([#781](https://github.com/adap/flower/pull/781) " +"[#790](https://github.com/adap/flower/pull/790) " +"[#791](https://github.com/adap/flower/pull/791))" -#: ../../source/tutorial-quickstart-ios.rst:152 +#: ../../source/ref-changelog.md:951 msgid "" -"Once the server is running we can start the clients in different " -"terminals. Build and run the client through your Xcode, one through Xcode" -" Simulator and the other by deploying it to your iPhone. To see more " -"about how to deploy your app to iPhone or Simulator visit `here " -"`_." +"One of Flower's goals is to enable research at scale. This release " +"enables a first (experimental) peek at a major new feature, codenamed the" +" virtual client engine. Virtual clients enable simulations that scale to " +"a (very) large number of clients on a single machine or compute cluster. " +"The easiest way to test the new functionality is to look at the two new " +"code examples called `quickstart_simulation` and `simulation_pytorch`." msgstr "" +"L'un des objectifs de Flower est de permettre la recherche à grande " +"échelle. Cette version donne un premier aperçu (expérimental) d'une " +"nouvelle fonctionnalité majeure, connue sous le nom de code de moteur de " +"client virtuel. Les clients virtuels permettent des simulations qui " +"s'étendent à un (très) grand nombre de clients sur une seule machine ou " +"une grappe de calcul. La façon la plus simple de tester la nouvelle " +"fonctionnalité est de regarder les deux nouveaux exemples de code appelés" +" `quickstart_simulation` et `simulation_pytorch`." -#: ../../source/tutorial-quickstart-ios.rst:156 -#, fuzzy +#: ../../source/ref-changelog.md:953 msgid "" -"Congratulations! You've successfully built and run your first federated " -"learning system in your ios device. The full `source code " -"`_ for this " -"example can be found in :code:`examples/ios`." +"The feature is still experimental, so there's no stability guarantee for " +"the API. It's also not quite ready for prime time and comes with a few " +"known caveats. However, those who are curious are encouraged to try it " +"out and share their thoughts." msgstr "" -"Félicitations ! Tu as réussi à construire et à faire fonctionner ton " -"premier système d'apprentissage fédéré. Le code source complet " -"`_ de cet exemple se trouve dans :code:`examples" -"/quickstart-mxnet`." +"La fonction est encore expérimentale, il n'y a donc aucune garantie de " +"stabilité pour l'API. Elle n'est pas non plus tout à fait prête pour le " +"prime time et s'accompagne de quelques mises en garde connues. Cependant," +" les personnes curieuses sont encouragées à l'essayer et à faire part de " +"leurs réflexions." -#: ../../source/tutorial-quickstart-jax.rst:-1 +#: ../../source/ref-changelog.md:955 msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with Jax to train a linear regression model on a scikit-learn dataset." +"**New built-in strategies** " +"([#828](https://github.com/adap/flower/pull/828) " +"[#822](https://github.com/adap/flower/pull/822))" msgstr "" +"**Nouvelles stratégies intégrées** " +"([#828](https://github.com/adap/flower/pull/828) " +"[#822](https://github.com/adap/flower/pull/822))" -#: ../../source/tutorial-quickstart-jax.rst:5 -msgid "Quickstart JAX" -msgstr "Démarrage rapide de JAX" - -#: ../../source/tutorial-quickstart-mxnet.rst:-1 +#: ../../source/ref-changelog.md:957 msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with MXNet to train a Sequential model on MNIST." +"FedYogi - Federated learning strategy using Yogi on server-side. " +"Implementation based on https://arxiv.org/abs/2003.00295" msgstr "" +"FedYogi - Stratégie d'apprentissage fédéré utilisant Yogi côté serveur. " +"Mise en oeuvre basée sur https://arxiv.org/abs/2003.00295" -#: ../../source/tutorial-quickstart-mxnet.rst:5 -msgid "Quickstart MXNet" -msgstr "Démarrage rapide de MXNet" - -#: ../../source/tutorial-quickstart-mxnet.rst:7 +#: ../../source/ref-changelog.md:958 msgid "" -"MXNet is no longer maintained and has been moved into `Attic " -"`_. As a result, we would " -"encourage you to use other ML frameworks alongside Flower, for example, " -"PyTorch. This tutorial might be removed in future versions of Flower." +"FedAdam - Federated learning strategy using Adam on server-side. " +"Implementation based on https://arxiv.org/abs/2003.00295" msgstr "" +"FedAdam - Stratégie d'apprentissage fédéré utilisant Adam côté serveur. " +"Mise en œuvre basée sur https://arxiv.org/abs/2003.00295" -#: ../../source/tutorial-quickstart-mxnet.rst:12 +#: ../../source/ref-changelog.md:960 msgid "" -"In this tutorial, we will learn how to train a :code:`Sequential` model " -"on MNIST using Flower and MXNet." +"**New PyTorch Lightning code example** " +"([#617](https://github.com/adap/flower/pull/617))" msgstr "" -"Dans ce tutoriel, nous allons apprendre à former un modèle " -":code:`Sequential` sur MNIST à l'aide de Flower et de MXNet." +"**Nouvel exemple de code PyTorch Lightning** " +"([#617](https://github.com/adap/flower/pull/617))" -#: ../../source/tutorial-quickstart-mxnet.rst:14 -#: ../../source/tutorial-quickstart-scikitlearn.rst:12 -#, fuzzy +#: ../../source/ref-changelog.md:962 msgid "" -"It is recommended to create a virtual environment and run everything " -"within this :doc:`virtualenv `." +"**New Variational Auto-Encoder code example** " +"([#752](https://github.com/adap/flower/pull/752))" msgstr "" -"Il est recommandé de créer un environnement virtuel et de tout exécuter " -"dans ce `virtualenv `_." +"**Nouvel exemple de code d'autocodage variationnel** " +"([#752](https://github.com/adap/flower/pull/752))" -#: ../../source/tutorial-quickstart-mxnet.rst:16 -#: ../../source/tutorial-quickstart-pytorch.rst:17 -#: ../../source/tutorial-quickstart-scikitlearn.rst:14 +#: ../../source/ref-changelog.md:964 msgid "" -"Our example consists of one *server* and two *clients* all having the " -"same model." +"**New scikit-learn code example** " +"([#748](https://github.com/adap/flower/pull/748))" msgstr "" -"Notre exemple consiste en un *serveur* et deux *clients* ayant tous le " -"même modèle." +"**Nouvel exemple de code scikit-learn** " +"([#748](https://github.com/adap/flower/pull/748))" -#: ../../source/tutorial-quickstart-mxnet.rst:18 -#: ../../source/tutorial-quickstart-scikitlearn.rst:16 +#: ../../source/ref-changelog.md:966 msgid "" -"*Clients* are responsible for generating individual model parameter " -"updates for the model based on their local datasets. These updates are " -"then sent to the *server* which will aggregate them to produce an updated" -" global model. Finally, the *server* sends this improved version of the " -"model back to each *client*. A complete cycle of parameters updates is " -"called a *round*." +"**New experimental TensorBoard strategy** " +"([#789](https://github.com/adap/flower/pull/789))" msgstr "" -"*Les clients* sont chargés de générer des mises à jour individuelles des " -"paramètres du modèle en fonction de leurs ensembles de données locales. " -"Ces mises à jour sont ensuite envoyées au *serveur* qui les agrège pour " -"produire un modèle global mis à jour. Enfin, le *serveur* renvoie cette " -"version améliorée du modèle à chaque *client*. Un cycle complet de mises " -"à jour des paramètres s'appelle un *round*." +"**Nouvelle stratégie expérimentale TensorBoard** " +"([#789](https://github.com/adap/flower/pull/789))" -#: ../../source/tutorial-quickstart-mxnet.rst:22 -#: ../../source/tutorial-quickstart-scikitlearn.rst:20 +#: ../../source/ref-changelog.md:970 msgid "" -"Now that we have a rough idea of what is going on, let's get started. We " -"first need to install Flower. You can do this by running:" +"Improved advanced TensorFlow code example " +"([#769](https://github.com/adap/flower/pull/769))" msgstr "" -"Maintenant que nous avons une idée approximative de ce qui se passe, " -"commençons. Nous devons d'abord installer Flower. Tu peux le faire en " -"lançant :" - -#: ../../source/tutorial-quickstart-mxnet.rst:28 -msgid "Since we want to use MXNet, let's go ahead and install it:" -msgstr "Puisque nous voulons utiliser MXNet, allons-y et installons-le :" +"Amélioration de l'exemple de code TensorFlow avancé " +"([#769](https://github.com/adap/flower/pull/769))" -#: ../../source/tutorial-quickstart-mxnet.rst:38 +#: ../../source/ref-changelog.md:971 msgid "" -"Now that we have all our dependencies installed, let's run a simple " -"distributed training with two clients and one server. Our training " -"procedure and network architecture are based on MXNet´s `Hand-written " -"Digit Recognition tutorial " -"`_." +"Warning when `min_available_clients` is misconfigured " +"([#830](https://github.com/adap/flower/pull/830))" msgstr "" -"Maintenant que toutes nos dépendances sont installées, lançons une " -"formation distribuée simple avec deux clients et un serveur. Notre " -"procédure de formation et l'architecture du réseau sont basées sur le " -"tutoriel de reconnaissance de chiffres écrits à la main du MXNet " -"`_." +"Avertissement lorsque `min_available_clients` est mal configuré " +"([#830](https://github.com/adap/flower/pull/830))" -#: ../../source/tutorial-quickstart-mxnet.rst:40 +#: ../../source/ref-changelog.md:972 msgid "" -"In a file called :code:`client.py`, import Flower and MXNet related " -"packages:" +"Improved gRPC server docs " +"([#841](https://github.com/adap/flower/pull/841))" msgstr "" -"Dans un fichier appelé :code:`client.py`, importe Flower et les paquets " -"liés au MXNet :" - -#: ../../source/tutorial-quickstart-mxnet.rst:55 -msgid "In addition, define the device allocation in MXNet with:" -msgstr "En outre, définis l'attribution de l'appareil dans MXNet avec :" +"Amélioration de la documentation sur le serveur gRPC " +"([#841](https://github.com/adap/flower/pull/841))" -#: ../../source/tutorial-quickstart-mxnet.rst:61 +#: ../../source/ref-changelog.md:973 msgid "" -"We use MXNet to load MNIST, a popular image classification dataset of " -"handwritten digits for machine learning. The MXNet utility " -":code:`mx.test_utils.get_mnist()` downloads the training and test data." +"Improved error message in `NumPyClient` " +"([#851](https://github.com/adap/flower/pull/851))" msgstr "" -"Nous utilisons MXNet pour charger MNIST, un ensemble de données de " -"classification d'images populaire de chiffres manuscrits pour " -"l'apprentissage automatique. L'utilitaire MXNet " -":code:`mx.test_utils.get_mnist()` télécharge les données d'entraînement " -"et de test." +"Amélioration du message d'erreur dans `NumPyClient` " +"([#851](https://github.com/adap/flower/pull/851))" -#: ../../source/tutorial-quickstart-mxnet.rst:75 +#: ../../source/ref-changelog.md:974 msgid "" -"Define the training and loss with MXNet. We train the model by looping " -"over the dataset, measure the corresponding loss, and optimize it." +"Improved PyTorch quickstart code example " +"([#852](https://github.com/adap/flower/pull/852))" msgstr "" -"Définis l'entraînement et la perte avec MXNet. Nous entraînons le modèle " -"en parcourant en boucle l'ensemble des données, nous mesurons la perte " -"correspondante et nous l'optimisons." +"Exemple de code de démarrage rapide PyTorch amélioré " +"([#852](https://github.com/adap/flower/pull/852))" -#: ../../source/tutorial-quickstart-mxnet.rst:113 +#: ../../source/ref-changelog.md:978 msgid "" -"Next, we define the validation of our machine learning model. We loop " -"over the test set and measure both loss and accuracy on the test set." +"**Disabled final distributed evaluation** " +"([#800](https://github.com/adap/flower/pull/800))" msgstr "" -"Ensuite, nous définissons la validation de notre modèle d'apprentissage " -"automatique. Nous effectuons une boucle sur l'ensemble de test et " -"mesurons à la fois la perte et la précision sur l'ensemble de test." +"**Désactivé l'évaluation finale distribuée** " +"([#800](https://github.com/adap/flower/pull/800))" -#: ../../source/tutorial-quickstart-mxnet.rst:137 +#: ../../source/ref-changelog.md:980 msgid "" -"After defining the training and testing of a MXNet machine learning " -"model, we use these functions to implement a Flower client." +"Prior behaviour was to perform a final round of distributed evaluation on" +" all connected clients, which is often not required (e.g., when using " +"server-side evaluation). The prior behaviour can be enabled by passing " +"`force_final_distributed_eval=True` to `start_server`." msgstr "" -"Après avoir défini la formation et le test d'un modèle d'apprentissage " -"automatique MXNet, nous utilisons ces fonctions pour mettre en œuvre un " -"client Flower." - -#: ../../source/tutorial-quickstart-mxnet.rst:139 -msgid "Our Flower clients will use a simple :code:`Sequential` model:" -msgstr "Nos clients Flower utiliseront un modèle simple :code:`Sequential` :" +"Le comportement précédent consistait à effectuer un dernier tour " +"d'évaluation distribuée sur tous les clients connectés, ce qui n'est " +"souvent pas nécessaire (par exemple, lors de l'utilisation de " +"l'évaluation côté serveur). Le comportement précédent peut être activé en" +" passant `force_final_distributed_eval=True` à `start_server`." -#: ../../source/tutorial-quickstart-mxnet.rst:158 +#: ../../source/ref-changelog.md:982 msgid "" -"After loading the dataset with :code:`load_data()` we perform one forward" -" propagation to initialize the model and model parameters with " -":code:`model(init)`. Next, we implement a Flower client." +"**Renamed q-FedAvg strategy** " +"([#802](https://github.com/adap/flower/pull/802))" msgstr "" -"Après avoir chargé l'ensemble de données avec :code:`load_data()`, nous " -"effectuons une propagation vers l'avant pour initialiser le modèle et les" -" paramètres du modèle avec :code:`model(init)`. Ensuite, nous " -"implémentons un client Flower." +"**Renommé stratégie q-FedAvg** " +"([#802](https://github.com/adap/flower/pull/802))" -#: ../../source/tutorial-quickstart-mxnet.rst:160 -#: ../../source/tutorial-quickstart-pytorch.rst:144 -#: ../../source/tutorial-quickstart-tensorflow.rst:54 +#: ../../source/ref-changelog.md:984 msgid "" -"The Flower server interacts with clients through an interface called " -":code:`Client`. When the server selects a particular client for training," -" it sends training instructions over the network. The client receives " -"those instructions and calls one of the :code:`Client` methods to run " -"your code (i.e., to train the neural network we defined earlier)." +"The strategy named `QffedAvg` was renamed to `QFedAvg` to better reflect " +"the notation given in the original paper (q-FFL is the optimization " +"objective, q-FedAvg is the proposed solver). Note the original (now " +"deprecated) `QffedAvg` class is still available for compatibility reasons" +" (it will be removed in a future release)." msgstr "" -"Le serveur Flower interagit avec les clients par le biais d'une interface" -" appelée :code:`Client`. Lorsque le serveur sélectionne un client " -"particulier pour la formation, il envoie des instructions de formation " -"sur le réseau. Le client reçoit ces instructions et appelle l'une des " -"méthodes :code:`Client` pour exécuter ton code (c'est-à-dire pour former " -"le réseau neuronal que nous avons défini plus tôt)." +"La stratégie nommée `QffedAvg` a été renommée en `QFedAvg` pour mieux " +"refléter la notation donnée dans l'article original (q-FFL est l'objectif" +" d'optimisation, q-FedAvg est le solveur proposé). Notez que la classe " +"`QffedAvg` originale (maintenant obsolète) est toujours disponible pour " +"des raisons de compatibilité (elle sera supprimée dans une prochaine " +"version)." -#: ../../source/tutorial-quickstart-mxnet.rst:166 +#: ../../source/ref-changelog.md:986 msgid "" -"Flower provides a convenience class called :code:`NumPyClient` which " -"makes it easier to implement the :code:`Client` interface when your " -"workload uses MXNet. Implementing :code:`NumPyClient` usually means " -"defining the following methods (:code:`set_parameters` is optional " -"though):" +"**Deprecated and renamed code example** `simulation_pytorch` **to** " +"`simulation_pytorch_legacy` " +"([#791](https://github.com/adap/flower/pull/791))" msgstr "" -"Flower fournit une classe de commodité appelée :code:`NumPyClient` qui " -"facilite l'implémentation de l'interface :code:`Client` lorsque ta charge" -" de travail utilise MXNet. L'implémentation de :code:`NumPyClient` " -"signifie généralement la définition des méthodes suivantes " -"(:code:`set_parameters` est cependant facultatif) :" +"**Exemple de code déprécié et renommé** `simulation_pytorch` **en** " +"`simulation_pytorch_legacy` " +"([#791](https://github.com/adap/flower/pull/791))" -#: ../../source/tutorial-quickstart-mxnet.rst:172 -#: ../../source/tutorial-quickstart-pytorch.rst:156 -#: ../../source/tutorial-quickstart-scikitlearn.rst:109 -msgid "return the model weight as a list of NumPy ndarrays" -msgstr "renvoie le poids du modèle sous la forme d'une liste de ndarrays NumPy" - -#: ../../source/tutorial-quickstart-mxnet.rst:173 -#: ../../source/tutorial-quickstart-pytorch.rst:157 -#: ../../source/tutorial-quickstart-scikitlearn.rst:111 -msgid ":code:`set_parameters` (optional)" -msgstr ":code:`set_parameters` (optionnel)" - -#: ../../source/tutorial-quickstart-mxnet.rst:174 -#: ../../source/tutorial-quickstart-pytorch.rst:158 -#: ../../source/tutorial-quickstart-scikitlearn.rst:111 +#: ../../source/ref-changelog.md:988 msgid "" -"update the local model weights with the parameters received from the " -"server" +"This example has been replaced by a new example. The new example is based" +" on the experimental virtual client engine, which will become the new " +"default way of doing most types of large-scale simulations in Flower. The" +" existing example was kept for reference purposes, but it might be " +"removed in the future." msgstr "" -"mettre à jour les poids du modèle local avec les paramètres reçus du " -"serveur" - -#: ../../source/tutorial-quickstart-mxnet.rst:176 -#: ../../source/tutorial-quickstart-pytorch.rst:160 -#: ../../source/tutorial-quickstart-scikitlearn.rst:114 -msgid "set the local model weights" -msgstr "fixe les poids du modèle local" +"Cet exemple a été remplacé par un nouvel exemple. Le nouvel exemple est " +"basé sur le moteur expérimental du client virtuel, qui deviendra la " +"nouvelle méthode par défaut pour effectuer la plupart des types de " +"simulations à grande échelle dans Flower. L'exemple existant a été " +"conservé à des fins de référence, mais il pourrait être supprimé à " +"l'avenir." -#: ../../source/tutorial-quickstart-mxnet.rst:177 -#: ../../source/tutorial-quickstart-pytorch.rst:161 -#: ../../source/tutorial-quickstart-scikitlearn.rst:115 -msgid "train the local model" -msgstr "entraîne le modèle local" +#: ../../source/ref-changelog.md:990 +msgid "v0.16.0 (2021-05-11)" +msgstr "v0.16.0 (2021-05-11)" -#: ../../source/tutorial-quickstart-mxnet.rst:178 -#: ../../source/tutorial-quickstart-pytorch.rst:162 -#: ../../source/tutorial-quickstart-scikitlearn.rst:116 -msgid "receive the updated local model weights" -msgstr "recevoir les poids du modèle local mis à jour" +#: ../../source/ref-changelog.md:994 +msgid "" +"**New built-in strategies** " +"([#549](https://github.com/adap/flower/pull/549))" +msgstr "" +"**Nouvelles stratégies intégrées** " +"([#549](https://github.com/adap/flower/pull/549))" -#: ../../source/tutorial-quickstart-mxnet.rst:180 -#: ../../source/tutorial-quickstart-pytorch.rst:164 -#: ../../source/tutorial-quickstart-scikitlearn.rst:118 -msgid "test the local model" -msgstr "teste le modèle local" +#: ../../source/ref-changelog.md:996 +msgid "(abstract) FedOpt" +msgstr "(résumé) FedOpt" -#: ../../source/tutorial-quickstart-mxnet.rst:182 -msgid "They can be implemented in the following way:" -msgstr "Ils peuvent être mis en œuvre de la manière suivante :" +#: ../../source/ref-changelog.md:999 +msgid "" +"**Custom metrics for server and strategies** " +"([#717](https://github.com/adap/flower/pull/717))" +msgstr "" +"**Métriques personnalisées pour le serveur et les stratégies** " +"([#717](https://github.com/adap/flower/pull/717))" -#: ../../source/tutorial-quickstart-mxnet.rst:212 +#: ../../source/ref-changelog.md:1001 msgid "" -"We can now create an instance of our class :code:`MNISTClient` and add " -"one line to actually run this client:" +"The Flower server is now fully task-agnostic, all remaining instances of " +"task-specific metrics (such as `accuracy`) have been replaced by custom " +"metrics dictionaries. Flower 0.15 introduced the capability to pass a " +"dictionary containing custom metrics from client to server. As of this " +"release, custom metrics replace task-specific metrics on the server." msgstr "" -"Nous pouvons maintenant créer une instance de notre classe " -":code:`MNISTClient` et ajouter une ligne pour exécuter ce client :" +"Le serveur Flower est maintenant totalement agnostique, toutes les " +"instances restantes de métriques spécifiques à une tâche (telles que " +"`accuracy`) ont été remplacées par des dictionnaires de métriques " +"personnalisées. Flower 0.15 a introduit la possibilité de passer un " +"dictionnaire contenant des métriques personnalisées du client au serveur." +" À partir de cette version, les métriques personnalisées remplacent les " +"métriques spécifiques à une tâche sur le serveur." -#: ../../source/tutorial-quickstart-mxnet.rst:219 +#: ../../source/ref-changelog.md:1003 #, fuzzy msgid "" -"That's it for the client. We only have to implement :code:`Client` or " -":code:`NumPyClient` and call :code:`fl.client.start_client()` or " -":code:`fl.client.start_numpy_client()`. The string " -":code:`\"0.0.0.0:8080\"` tells the client which server to connect to. In " -"our case we can run the server and the client on the same machine, " -"therefore we use :code:`\"0.0.0.0:8080\"`. If we run a truly federated " -"workload with the server and clients running on different machines, all " -"that needs to change is the :code:`server_address` we pass to the client." +"Custom metric dictionaries are now used in two user-facing APIs: they are" +" returned from Strategy methods `aggregate_fit`/`aggregate_evaluate` and " +"they enable evaluation functions passed to built-in strategies (via " +"`eval_fn`) to return more than two evaluation metrics. Strategies can " +"even return *aggregated* metrics dictionaries for the server to keep " +"track of." msgstr "" -"C'est tout pour le client. Il nous suffit d'implémenter :code:`Client` ou" -" :code:`NumPyClient` et d'appeler :code:`fl.client.start_client()`. La " -"chaîne :code:`\"0.0.0:8080\"` indique au client à quel serveur se " -"connecter. Dans notre cas, nous pouvons exécuter le serveur et le client " -"sur la même machine, c'est pourquoi nous utilisons " -":code:`\"0.0.0:8080\"`. Si nous exécutons une charge de travail " -"véritablement fédérée avec le serveur et les clients s'exécutant sur des " -"machines différentes, tout ce qui doit changer est :code:`server_address`" -" que nous transmettons au client." +"Les dictionnaires de métriques personnalisés sont maintenant utilisés " +"dans deux API orientées vers l'utilisateur : ils sont renvoyés par les " +"méthodes de stratégie `aggregate_fit`/`aggregate_evaluate` et ils " +"permettent aux fonctions d'évaluation passées aux stratégies intégrées " +"(via `eval_fn`) de renvoyer plus de deux métriques d'évaluation. Les " +"stratégies peuvent même renvoyer des dictionnaires de métriques " +"*agrégées* pour que le serveur puisse en garder la trace." -#: ../../source/tutorial-quickstart-mxnet.rst:241 +#: ../../source/ref-changelog.md:1005 +#, fuzzy msgid "" -"With both client and server ready, we can now run everything and see " -"federated learning in action. Federated learning systems usually have a " -"server and multiple clients. We therefore have to start the server first:" +"Strategy implementations should migrate their `aggregate_fit` and " +"`aggregate_evaluate` methods to the new return type (e.g., by simply " +"returning an empty `{}`), server-side evaluation functions should migrate" +" from `return loss, accuracy` to `return loss, {\"accuracy\": accuracy}`." msgstr "" -"Le client et le serveur étant prêts, nous pouvons maintenant tout " -"exécuter et voir l'apprentissage fédéré en action. Les systèmes " -"d'apprentissage fédéré ont généralement un serveur et plusieurs clients. " -"Nous devons donc commencer par démarrer le serveur :" +"Les implémentations de Stratey doivent migrer leurs méthodes " +"`aggregate_fit` et `aggregate_evaluate` vers le nouveau type de retour " +"(par exemple, en renvoyant simplement un `{}` vide), les fonctions " +"d'évaluation côté serveur doivent migrer de `return loss, accuracy` à " +"`return loss, {\"accuracy\" : accuracy}`." -#: ../../source/tutorial-quickstart-mxnet.rst:249 -#: ../../source/tutorial-quickstart-pytorch.rst:226 -#: ../../source/tutorial-quickstart-scikitlearn.rst:224 -#: ../../source/tutorial-quickstart-tensorflow.rst:122 -#: ../../source/tutorial-quickstart-xgboost.rst:533 +#: ../../source/ref-changelog.md:1007 msgid "" -"Once the server is running we can start the clients in different " -"terminals. Open a new terminal and start the first client:" +"Flower 0.15-style return types are deprecated (but still supported), " +"compatibility will be removed in a future release." msgstr "" -"Une fois que le serveur fonctionne, nous pouvons démarrer les clients " -"dans différents terminaux. Ouvre un nouveau terminal et démarre le " -"premier client :" - -#: ../../source/tutorial-quickstart-mxnet.rst:256 -#: ../../source/tutorial-quickstart-pytorch.rst:233 -#: ../../source/tutorial-quickstart-scikitlearn.rst:231 -#: ../../source/tutorial-quickstart-tensorflow.rst:129 -#: ../../source/tutorial-quickstart-xgboost.rst:540 -msgid "Open another terminal and start the second client:" -msgstr "Ouvre un autre terminal et démarre le deuxième client :" +"Les types de retour du style Flower 0.15 sont dépréciés (mais toujours " +"pris en charge), la compatibilité sera supprimée dans une prochaine " +"version." -#: ../../source/tutorial-quickstart-mxnet.rst:262 -#: ../../source/tutorial-quickstart-pytorch.rst:239 -#: ../../source/tutorial-quickstart-scikitlearn.rst:237 -#: ../../source/tutorial-quickstart-xgboost.rst:546 +#: ../../source/ref-changelog.md:1009 msgid "" -"Each client will have its own dataset. You should now see how the " -"training does in the very first terminal (the one that started the " -"server):" +"**Migration warnings for deprecated functionality** " +"([#690](https://github.com/adap/flower/pull/690))" msgstr "" -"Chaque client aura son propre ensemble de données. Tu devrais maintenant " -"voir comment la formation se déroule dans le tout premier terminal (celui" -" qui a démarré le serveur) :" +"**Avertissements de migration pour les fonctionnalités obsolètes** " +"([#690](https://github.com/adap/flower/pull/690))" -#: ../../source/tutorial-quickstart-mxnet.rst:294 -#, fuzzy +#: ../../source/ref-changelog.md:1011 msgid "" -"Congratulations! You've successfully built and run your first federated " -"learning system. The full `source code " -"`_ for this example can be found in :code:`examples" -"/quickstart-mxnet`." +"Earlier versions of Flower were often migrated to new APIs, while " +"maintaining compatibility with legacy APIs. This release introduces " +"detailed warning messages if usage of deprecated APIs is detected. The " +"new warning messages often provide details on how to migrate to more " +"recent APIs, thus easing the transition from one release to another." msgstr "" -"Félicitations ! Tu as réussi à construire et à faire fonctionner ton " -"premier système d'apprentissage fédéré. Le code source complet " -"`_ de cet exemple se trouve dans :code:`examples" -"/quickstart-mxnet`." +"Les versions antérieures de Flower ont souvent été migrées vers de " +"nouvelles API, tout en maintenant la compatibilité avec les anciennes " +"API. Cette version introduit des messages d'avertissement détaillés si " +"l'utilisation d'API obsolètes est détectée. Les nouveaux messages " +"d'avertissement fournissent souvent des détails sur la façon de migrer " +"vers des API plus récentes, facilitant ainsi la transition d'une version " +"à l'autre." -#: ../../source/tutorial-quickstart-pandas.rst:-1 +#: ../../source/ref-changelog.md:1013 msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with Pandas to perform Federated Analytics." +"Improved docs and docstrings " +"([#691](https://github.com/adap/flower/pull/691) " +"[#692](https://github.com/adap/flower/pull/692) " +"[#713](https://github.com/adap/flower/pull/713))" msgstr "" +"Amélioration des docs et des docstrings " +"([#691](https://github.com/adap/flower/pull/691) " +"[#692](https://github.com/adap/flower/pull/692) " +"[#713](https://github.com/adap/flower/pull/713))" -#: ../../source/tutorial-quickstart-pandas.rst:5 -msgid "Quickstart Pandas" -msgstr "Démarrage rapide des Pandas" - -#: ../../source/tutorial-quickstart-pandas.rst:10 -msgid "Let's build a federated analytics system using Pandas and Flower!" -msgstr "Construisons un système d'analyse fédéré à l'aide de Pandas et de Flower !" +#: ../../source/ref-changelog.md:1015 +msgid "MXNet example and documentation" +msgstr "Exemple et documentation MXNet" -#: ../../source/tutorial-quickstart-pandas.rst:12 -#, fuzzy +#: ../../source/ref-changelog.md:1017 msgid "" -"Please refer to the `full code example " -"`_ " -"to learn more." +"FedBN implementation in example PyTorch: From Centralized To Federated " +"([#696](https://github.com/adap/flower/pull/696) " +"[#702](https://github.com/adap/flower/pull/702) " +"[#705](https://github.com/adap/flower/pull/705))" msgstr "" -"Réfère-toi à l'exemple de code complet " -"`_ " -"pour en savoir plus." +"Mise en œuvre de FedBN dans l'exemple PyTorch : De la centralisation à la" +" fédération ([#696](https://github.com/adap/flower/pull/696) " +"[#702](https://github.com/adap/flower/pull/702) " +"[#705](https://github.com/adap/flower/pull/705))" -#: ../../source/tutorial-quickstart-pytorch.rst:-1 +#: ../../source/ref-changelog.md:1021 msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with PyTorch to train a CNN model on MNIST." +"**Serialization-agnostic server** " +"([#721](https://github.com/adap/flower/pull/721))" msgstr "" +"**Serveur agnostique de sérialisation** " +"([#721](https://github.com/adap/flower/pull/721))" -#: ../../source/tutorial-quickstart-pytorch.rst:13 +#: ../../source/ref-changelog.md:1023 msgid "" -"In this tutorial we will learn how to train a Convolutional Neural " -"Network on CIFAR10 using Flower and PyTorch." +"The Flower server is now fully serialization-agnostic. Prior usage of " +"class `Weights` (which represents parameters as deserialized NumPy " +"ndarrays) was replaced by class `Parameters` (e.g., in `Strategy`). " +"`Parameters` objects are fully serialization-agnostic and represents " +"parameters as byte arrays, the `tensor_type` attributes indicates how " +"these byte arrays should be interpreted (e.g., for " +"serialization/deserialization)." msgstr "" -"Dans ce tutoriel, nous allons apprendre à entraîner un réseau neuronal " -"convolutif sur CIFAR10 à l'aide de Flower et PyTorch." +"Le serveur Flower est désormais totalement agnostique en matière de " +"sérialisation. L'utilisation antérieure de la classe `Weights` (qui " +"représente les paramètres sous forme de tableaux NumPy désérialisés) a " +"été remplacée par la classe `Parameters` (par exemple, dans `Strategy`). " +"Les objets `Parameters` sont totalement agnostiques en matière de " +"sérialisation et représentent les paramètres sous forme de tableaux " +"d'octets, les attributs `tensor_type` indiquent comment ces tableaux " +"d'octets doivent être interprétés (par exemple, pour la " +"sérialisation/désérialisation)." -#: ../../source/tutorial-quickstart-pytorch.rst:15 -#: ../../source/tutorial-quickstart-xgboost.rst:39 -#, fuzzy +#: ../../source/ref-changelog.md:1025 msgid "" -"First of all, it is recommended to create a virtual environment and run " -"everything within a :doc:`virtualenv `." +"Built-in strategies implement this approach by handling serialization and" +" deserialization to/from `Weights` internally. Custom/3rd-party Strategy " +"implementations should update to the slightly changed Strategy method " +"definitions. Strategy authors can consult PR " +"[#721](https://github.com/adap/flower/pull/721) to see how strategies can" +" easily migrate to the new format." msgstr "" -"Tout d'abord, il est recommandé de créer un environnement virtuel et de " -"tout exécuter au sein d'un `virtualenv `_." +"Les stratégies intégrées mettent en œuvre cette approche en gérant en " +"interne la sérialisation et la désérialisation de `Weights`. Les " +"implémentations de stratégies personnalisées ou tierces doivent être " +"mises à jour avec les définitions de méthodes de stratégie légèrement " +"modifiées. Les auteurs de stratégies peuvent consulter le PR " +"[#721](https://github.com/adap/flower/pull/721) pour voir comment les " +"stratégies peuvent facilement migrer vers le nouveau format." -#: ../../source/tutorial-quickstart-pytorch.rst:19 +#: ../../source/ref-changelog.md:1027 msgid "" -"*Clients* are responsible for generating individual weight-updates for " -"the model based on their local datasets. These updates are then sent to " -"the *server* which will aggregate them to produce a better model. " -"Finally, the *server* sends this improved version of the model back to " -"each *client*. A complete cycle of weight updates is called a *round*." +"Deprecated `flwr.server.Server.evaluate`, use " +"`flwr.server.Server.evaluate_round` instead " +"([#717](https://github.com/adap/flower/pull/717))" msgstr "" -"*Les clients* sont chargés de générer des mises à jour de poids " -"individuelles pour le modèle en fonction de leurs ensembles de données " -"locales. Ces mises à jour sont ensuite envoyées au *serveur* qui les " -"agrège pour produire un meilleur modèle. Enfin, le *serveur* renvoie " -"cette version améliorée du modèle à chaque *client*. Un cycle complet de " -"mises à jour de poids s'appelle un *round*." +"Déclassé `flwr.server.Server.evaluate`, utiliser " +"`flwr.server.Server.evaluate_round` à la place " +"([#717](https://github.com/adap/flower/pull/717))" -#: ../../source/tutorial-quickstart-pytorch.rst:23 +#: ../../source/ref-changelog.md:1029 +msgid "v0.15.0 (2021-03-12)" +msgstr "v0.15.0 (2021-03-12)" + +#: ../../source/ref-changelog.md:1033 msgid "" -"Now that we have a rough idea of what is going on, let's get started. We " -"first need to install Flower. You can do this by running :" +"**Server-side parameter initialization** " +"([#658](https://github.com/adap/flower/pull/658))" msgstr "" -"Maintenant que nous avons une idée générale de ce qui se passe, " -"commençons. Nous devons d'abord installer Flower. Tu peux le faire en " -"exécutant :" +"**Initialisation des paramètres côté serveur** " +"([#658](https://github.com/adap/flower/pull/658))" -#: ../../source/tutorial-quickstart-pytorch.rst:29 +#: ../../source/ref-changelog.md:1035 msgid "" -"Since we want to use PyTorch to solve a computer vision task, let's go " -"ahead and install PyTorch and the **torchvision** library:" +"Model parameters can now be initialized on the server-side. Server-side " +"parameter initialization works via a new `Strategy` method called " +"`initialize_parameters`." msgstr "" -"Puisque nous voulons utiliser PyTorch pour résoudre une tâche de vision " -"par ordinateur, allons-y et installons PyTorch et la bibliothèque " -"**torchvision** :" +"Les paramètres du modèle peuvent maintenant être initialisés côté " +"serveur. L'initialisation des paramètres côté serveur fonctionne via une " +"nouvelle méthode `Strategy` appelée `initialize_parameters`." -#: ../../source/tutorial-quickstart-pytorch.rst:39 +#: ../../source/ref-changelog.md:1037 msgid "" -"Now that we have all our dependencies installed, let's run a simple " -"distributed training with two clients and one server. Our training " -"procedure and network architecture are based on PyTorch's `Deep Learning " -"with PyTorch " -"`_." +"Built-in strategies support a new constructor argument called " +"`initial_parameters` to set the initial parameters. Built-in strategies " +"will provide these initial parameters to the server on startup and then " +"delete them to free the memory afterwards." msgstr "" -"Maintenant que nous avons installé toutes nos dépendances, lançons une " -"formation distribuée simple avec deux clients et un serveur. Notre " -"procédure de formation et l'architecture de notre réseau sont basées sur " -"`Deep Learning with PyTorch " -"`_ de" -" PyTorch." +"Les stratégies intégrées prennent en charge un nouvel argument du " +"constructeur appelé `initial_parameters` pour définir les paramètres " +"initiaux. Les stratégies intégrées fourniront ces paramètres initiaux au " +"serveur au démarrage et les supprimeront ensuite pour libérer la mémoire." -#: ../../source/tutorial-quickstart-pytorch.rst:41 +#: ../../source/ref-changelog.md:1056 msgid "" -"In a file called :code:`client.py`, import Flower and PyTorch related " -"packages:" +"If no initial parameters are provided to the strategy, the server will " +"continue to use the current behaviour (namely, it will ask one of the " +"connected clients for its parameters and use these as the initial global " +"parameters)." msgstr "" -"Dans un fichier appelé :code:`client.py`, importe Flower et les paquets " -"liés à PyTorch :" - -#: ../../source/tutorial-quickstart-pytorch.rst:56 -msgid "In addition, we define the device allocation in PyTorch with:" -msgstr "En outre, nous définissons l'attribution des appareils dans PyTorch avec :" +"Si aucun paramètre initial n'est fourni à la stratégie, le serveur " +"continuera à utiliser le comportement actuel (à savoir qu'il demandera à " +"l'un des clients connectés ses paramètres et les utilisera comme " +"paramètres globaux initiaux)." -#: ../../source/tutorial-quickstart-pytorch.rst:62 +#: ../../source/ref-changelog.md:1060 msgid "" -"We use PyTorch to load CIFAR10, a popular colored image classification " -"dataset for machine learning. The PyTorch :code:`DataLoader()` downloads " -"the training and test data that are then normalized." +"Deprecate `flwr.server.strategy.DefaultStrategy` (migrate to " +"`flwr.server.strategy.FedAvg`, which is equivalent)" msgstr "" -"Nous utilisons PyTorch pour charger CIFAR10, un ensemble de données de " -"classification d'images colorées populaire pour l'apprentissage " -"automatique. Le :code:`DataLoader()` de PyTorch télécharge les données " -"d'entraînement et de test qui sont ensuite normalisées." +"Déclasser `flwr.server.strategy.DefaultStrategy` (migrer vers " +"`flwr.server.strategy.FedAvg`, qui est équivalent)" -#: ../../source/tutorial-quickstart-pytorch.rst:78 +#: ../../source/ref-changelog.md:1062 +msgid "v0.14.0 (2021-02-18)" +msgstr "v0.14.0 (2021-02-18)" + +#: ../../source/ref-changelog.md:1066 msgid "" -"Define the loss and optimizer with PyTorch. The training of the dataset " -"is done by looping over the dataset, measure the corresponding loss and " -"optimize it." +"**Generalized** `Client.fit` **and** `Client.evaluate` **return values** " +"([#610](https://github.com/adap/flower/pull/610) " +"[#572](https://github.com/adap/flower/pull/572) " +"[#633](https://github.com/adap/flower/pull/633))" msgstr "" -"Définis la perte et l'optimiseur avec PyTorch L'entraînement de " -"l'ensemble de données se fait en bouclant sur l'ensemble de données, en " -"mesurant la perte correspondante et en l'optimisant." +"**Généralisé** `Client.fit` **et** `Client.evaluate` **valeurs de " +"retour** ([#610](https://github.com/adap/flower/pull/610) " +"[#572](https://github.com/adap/flower/pull/572) " +"[#633](https://github.com/adap/flower/pull/633))" -#: ../../source/tutorial-quickstart-pytorch.rst:94 +#: ../../source/ref-changelog.md:1068 msgid "" -"Define then the validation of the machine learning network. We loop over" -" the test set and measure the loss and accuracy of the test set." +"Clients can now return an additional dictionary mapping `str` keys to " +"values of the following types: `bool`, `bytes`, `float`, `int`, `str`. " +"This means one can return almost arbitrary values from `fit`/`evaluate` " +"and make use of them on the server side!" msgstr "" -"Définis ensuite la validation du réseau d'apprentissage automatique. Nous" -" passons en boucle sur l'ensemble de test et mesurons la perte et la " -"précision de l'ensemble de test." +"Les clients peuvent maintenant renvoyer un dictionnaire supplémentaire " +"associant les clés `str` aux valeurs des types suivants : `bool`, " +"`bytes`, `float`, `int`, `str`. Cela signifie que l'on peut renvoyer des " +"valeurs presque arbitraires de `fit`/`evaluate` et les utiliser du côté " +"du serveur !" -#: ../../source/tutorial-quickstart-pytorch.rst:113 +#: ../../source/ref-changelog.md:1070 msgid "" -"After defining the training and testing of a PyTorch machine learning " -"model, we use the functions for the Flower clients." +"This improvement also allowed for more consistent return types between " +"`fit` and `evaluate`: `evaluate` should now return a tuple `(float, int, " +"dict)` representing the loss, number of examples, and a dictionary " +"holding arbitrary problem-specific values like accuracy." msgstr "" -"Après avoir défini l'entraînement et le test d'un modèle d'apprentissage " -"automatique PyTorch, nous utilisons les fonctions pour les clients " -"Flower." +"Cette amélioration a également permis de rendre plus cohérents les types " +"de retour entre `fit` et `evaluate` : `evaluate` devrait maintenant " +"retourner un tuple `(float, int, dict)` représentant la perte, le nombre " +"d'exemples, et un dictionnaire contenant des valeurs arbitraires " +"spécifiques au problème comme la précision." -#: ../../source/tutorial-quickstart-pytorch.rst:115 +#: ../../source/ref-changelog.md:1072 msgid "" -"The Flower clients will use a simple CNN adapted from 'PyTorch: A 60 " -"Minute Blitz':" +"In case you wondered: this feature is compatible with existing projects, " +"the additional dictionary return value is optional. New code should " +"however migrate to the new return types to be compatible with upcoming " +"Flower releases (`fit`: `List[np.ndarray], int, Dict[str, Scalar]`, " +"`evaluate`: `float, int, Dict[str, Scalar]`). See the example below for " +"details." msgstr "" -"Les clients de Flower utiliseront un CNN simple adapté de \"PyTorch : A " -"60 Minute Blitz\" :" +"Au cas où tu te poserais la question : cette fonctionnalité est " +"compatible avec les projets existants, la valeur de retour supplémentaire" +" du dictionnaire est facultative. Le nouveau code doit cependant migrer " +"vers les nouveaux types de retour pour être compatible avec les " +"prochaines versions de Flower (`fit` : `List[np.ndarray], int, Dict[str, " +"Scalar]`, `evaluate` : `float, int, Dict[str, Scalar]`). Voir l'exemple " +"ci-dessous pour plus de détails." -#: ../../source/tutorial-quickstart-pytorch.rst:142 +#: ../../source/ref-changelog.md:1074 msgid "" -"After loading the data set with :code:`load_data()` we define the Flower " -"interface." +"*Code example:* note the additional dictionary return values in both " +"`FlwrClient.fit` and `FlwrClient.evaluate`:" msgstr "" -"Après avoir chargé l'ensemble des données avec :code:`load_data()`, nous " -"définissons l'interface Flower." +"*Exemple de code:* note les valeurs de retour du dictionnaire " +"supplémentaires dans `FlwrClient.fit` et `FlwrClient.evaluate` :" -#: ../../source/tutorial-quickstart-pytorch.rst:150 +#: ../../source/ref-changelog.md:1089 msgid "" -"Flower provides a convenience class called :code:`NumPyClient` which " -"makes it easier to implement the :code:`Client` interface when your " -"workload uses PyTorch. Implementing :code:`NumPyClient` usually means " -"defining the following methods (:code:`set_parameters` is optional " -"though):" +"**Generalized** `config` **argument in** `Client.fit` **and** " +"`Client.evaluate` ([#595](https://github.com/adap/flower/pull/595))" msgstr "" -"Flower fournit une classe de commodité appelée :code:`NumPyClient` qui " -"facilite la mise en œuvre de l'interface :code:`Client` lorsque ta charge" -" de travail utilise PyTorch. Mettre en œuvre :code:`NumPyClient` signifie" -" généralement définir les méthodes suivantes (:code:`set_parameters` est " -"cependant facultatif) :" - -#: ../../source/tutorial-quickstart-pytorch.rst:166 -msgid "which can be implemented in the following way:" -msgstr "qui peut être mis en œuvre de la manière suivante :" +"**Généralisé** `config` **argument dans** `Client.fit` **et** " +"`Client.evaluate` ([#595](https://github.com/adap/flower/pull/595))" -#: ../../source/tutorial-quickstart-pytorch.rst:189 -#: ../../source/tutorial-quickstart-tensorflow.rst:82 +#: ../../source/ref-changelog.md:1091 msgid "" -"We can now create an instance of our class :code:`CifarClient` and add " -"one line to actually run this client:" +"The `config` argument used to be of type `Dict[str, str]`, which means " +"that dictionary values were expected to be strings. The new release " +"generalizes this to enable values of the following types: `bool`, " +"`bytes`, `float`, `int`, `str`." msgstr "" -"Nous pouvons maintenant créer une instance de notre classe " -":code:`CifarClient` et ajouter une ligne pour exécuter ce client :" +"L'argument `config` était auparavant de type `Dict[str, str]`, ce qui " +"signifie que les valeurs du dictionnaire devaient être des chaînes. La " +"nouvelle version généralise cela pour permettre les valeurs des types " +"suivants : `bool`, `bytes`, `float`, `int`, `str`." -#: ../../source/tutorial-quickstart-pytorch.rst:196 -#: ../../source/tutorial-quickstart-tensorflow.rst:90 -#, fuzzy +#: ../../source/ref-changelog.md:1093 msgid "" -"That's it for the client. We only have to implement :code:`Client` or " -":code:`NumPyClient` and call :code:`fl.client.start_client()`. If you " -"implement a client of type :code:`NumPyClient` you'll need to first call " -"its :code:`to_client()` method. The string :code:`\"[::]:8080\"` tells " -"the client which server to connect to. In our case we can run the server " -"and the client on the same machine, therefore we use " -":code:`\"[::]:8080\"`. If we run a truly federated workload with the " -"server and clients running on different machines, all that needs to " -"change is the :code:`server_address` we point the client at." +"This means one can now pass almost arbitrary values to `fit`/`evaluate` " +"using the `config` dictionary. Yay, no more `str(epochs)` on the server-" +"side and `int(config[\"epochs\"])` on the client side!" msgstr "" -"C'est tout pour le client. Il nous suffit d'implémenter :code:`Client` ou" -" :code:`NumPyClient` et d'appeler :code:`fl.client.start_client()`. La " -"chaîne :code:`\"[: :]:8080\"` indique au client à quel serveur se " -"connecter. Dans notre cas, nous pouvons exécuter le serveur et le client " -"sur la même machine, c'est pourquoi nous utilisons :code:`\"[: " -":]:8080\"`. Si nous exécutons une charge de travail véritablement fédérée" -" avec le serveur et les clients fonctionnant sur des machines " -"différentes, tout ce qui doit changer est l'adresse " -":code:`server_address` vers laquelle nous dirigeons le client." +"Cela signifie que l'on peut maintenant passer des valeurs presque " +"arbitraires à `fit`/`evaluate` en utilisant le dictionnaire `config`. " +"Yay, plus de `str(epochs)` du côté serveur et `int(config[\"epochs\"])` " +"du côté client !" -#: ../../source/tutorial-quickstart-pytorch.rst:271 -#, fuzzy +#: ../../source/ref-changelog.md:1095 msgid "" -"Congratulations! You've successfully built and run your first federated " -"learning system. The full `source code " -"`_ for this example can be found in :code:`examples" -"/quickstart-pytorch`." +"*Code example:* note that the `config` dictionary now contains non-`str` " +"values in both `Client.fit` and `Client.evaluate`:" msgstr "" -"Félicitations ! Tu as réussi à construire et à faire fonctionner ton " -"premier système d'apprentissage fédéré. Le code source complet " -"`_ de cet exemple se trouve dans :code:`examples" -"/quickstart-pytorch`." +"*Exemple de code:* Notez que le dictionnaire `config` contient maintenant" +" des valeurs autres que `str` dans `Client.fit` et `Client.evaluate` :" -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:-1 +#: ../../source/ref-changelog.md:1112 +msgid "v0.13.0 (2021-01-08)" +msgstr "v0.13.0 (2021-01-08)" + +#: ../../source/ref-changelog.md:1116 msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with PyTorch Lightning to train an Auto Encoder model on MNIST." +"New example: PyTorch From Centralized To Federated " +"([#549](https://github.com/adap/flower/pull/549))" msgstr "" +"Nouvel exemple : PyTorch de centralisé à fédéré " +"([#549](https://github.com/adap/flower/pull/549))" -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:5 -msgid "Quickstart PyTorch Lightning" -msgstr "Démarrage rapide de PyTorch Lightning" +#: ../../source/ref-changelog.md:1117 +msgid "Improved documentation" +msgstr "Amélioration de la documentation" -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:10 -#, fuzzy -msgid "" -"Let's build a horizontal federated learning system using PyTorch " -"Lightning and Flower!" +#: ../../source/ref-changelog.md:1118 +msgid "New documentation theme ([#551](https://github.com/adap/flower/pull/551))" msgstr "" -"Construisons un système d'apprentissage fédéré en utilisant PyTorch " -"Lightning et Flower !" +"Nouveau thème de documentation " +"([#551](https://github.com/adap/flower/pull/551))" -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:12 -#, fuzzy +#: ../../source/ref-changelog.md:1119 +msgid "New API reference ([#554](https://github.com/adap/flower/pull/554))" +msgstr "Nouvelle référence API ([#554](https://github.com/adap/flower/pull/554))" + +#: ../../source/ref-changelog.md:1120 msgid "" -"Please refer to the `full code example " -"`_ to learn more." +"Updated examples documentation " +"([#549](https://github.com/adap/flower/pull/549))" msgstr "" -"Réfère-toi à l'exemple de code complet " -"`_ pour en savoir plus." +"Mise à jour de la documentation des exemples " +"([#549](https://github.com/adap/flower/pull/549))" -#: ../../source/tutorial-quickstart-scikitlearn.rst:-1 +#: ../../source/ref-changelog.md:1121 msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with scikit-learn to train a linear regression model." +"Removed obsolete documentation " +"([#548](https://github.com/adap/flower/pull/548))" msgstr "" +"Suppression de la documentation obsolète " +"([#548](https://github.com/adap/flower/pull/548))" -#: ../../source/tutorial-quickstart-scikitlearn.rst:5 -msgid "Quickstart scikit-learn" -msgstr "Démarrage rapide de scikit-learn" +#: ../../source/ref-changelog.md:1123 +msgid "Bugfix:" +msgstr "Correction de bogues :" -#: ../../source/tutorial-quickstart-scikitlearn.rst:10 +#: ../../source/ref-changelog.md:1125 msgid "" -"In this tutorial, we will learn how to train a :code:`Logistic " -"Regression` model on MNIST using Flower and scikit-learn." +"`Server.fit` does not disconnect clients when finished, disconnecting the" +" clients is now handled in `flwr.server.start_server` " +"([#553](https://github.com/adap/flower/pull/553) " +"[#540](https://github.com/adap/flower/issues/540))." msgstr "" -"Dans ce tutoriel, nous allons apprendre à former un :code:`modèle de " -"régression logistique` sur MNIST en utilisant Flower et scikit-learn." +"`Server.fit` ne déconnecte pas les clients lorsqu'il est terminé, la " +"déconnexion des clients est maintenant gérée dans " +"`flwr.server.start_server` " +"([#553](https://github.com/adap/flower/pull/553) " +"[#540](https://github.com/adap/flower/issues/540))." -#: ../../source/tutorial-quickstart-scikitlearn.rst:26 -#, fuzzy -msgid "Since we want to use scikit-learn, let's go ahead and install it:" -msgstr "Puisque nous voulons utiliser scikt-learn, allons-y et installons-le :" +#: ../../source/ref-changelog.md:1127 +msgid "v0.12.0 (2020-12-07)" +msgstr "v0.12.0 (2020-12-07)" -#: ../../source/tutorial-quickstart-scikitlearn.rst:32 -msgid "Or simply install all dependencies using Poetry:" -msgstr "Ou installe simplement toutes les dépendances à l'aide de Poetry :" +#: ../../source/ref-changelog.md:1129 ../../source/ref-changelog.md:1145 +msgid "Important changes:" +msgstr "Changements importants :" -#: ../../source/tutorial-quickstart-scikitlearn.rst:42 +#: ../../source/ref-changelog.md:1131 msgid "" -"Now that we have all our dependencies installed, let's run a simple " -"distributed training with two clients and one server. However, before " -"setting up the client and server, we will define all functionalities that" -" we need for our federated learning setup within :code:`utils.py`. The " -":code:`utils.py` contains different functions defining all the machine " -"learning basics:" +"Added an example for embedded devices " +"([#507](https://github.com/adap/flower/pull/507))" msgstr "" -"Maintenant que toutes nos dépendances sont installées, exécutons une " -"formation distribuée simple avec deux clients et un serveur. Cependant, " -"avant de configurer le client et le serveur, nous allons définir toutes " -"les fonctionnalités dont nous avons besoin pour notre configuration " -"d'apprentissage fédéré dans :code:`utils.py`. Le :code:`utils.py` " -"contient différentes fonctions définissant toutes les bases de " -"l'apprentissage automatique :" - -#: ../../source/tutorial-quickstart-scikitlearn.rst:45 -msgid ":code:`get_model_parameters()`" -msgstr ":code:`get_model_parameters()`" +"Ajout d'un exemple pour les périphériques embarqués " +"([#507](https://github.com/adap/flower/pull/507))" -#: ../../source/tutorial-quickstart-scikitlearn.rst:46 -msgid "Returns the parameters of a :code:`sklearn` LogisticRegression model" +#: ../../source/ref-changelog.md:1132 +msgid "" +"Added a new NumPyClient (in addition to the existing KerasClient) " +"([#504](https://github.com/adap/flower/pull/504) " +"[#508](https://github.com/adap/flower/pull/508))" msgstr "" -"Renvoie les paramètres d'un modèle de régression logistique " -":code:`sklearn`" - -#: ../../source/tutorial-quickstart-scikitlearn.rst:47 -msgid ":code:`set_model_params()`" -msgstr ":code:`set_model_params()`" - -#: ../../source/tutorial-quickstart-scikitlearn.rst:48 -msgid "Sets the parameters of a :code:`sklean` LogisticRegression model" -msgstr "Définit les paramètres d'un modèle de régression logistique :code:`sklean`" +"Ajout d'un nouveau NumPyClient (en plus du KerasClient existant) " +"([#504](https://github.com/adap/flower/pull/504) " +"[#508](https://github.com/adap/flower/pull/508))" -#: ../../source/tutorial-quickstart-scikitlearn.rst:49 -msgid ":code:`set_initial_params()`" -msgstr ":code:`set_initial_params()`" +#: ../../source/ref-changelog.md:1133 +msgid "" +"Deprecated `flwr_example` package and started to migrate examples into " +"the top-level `examples` directory " +"([#494](https://github.com/adap/flower/pull/494) " +"[#512](https://github.com/adap/flower/pull/512))" +msgstr "" +"Déclassement du paquet `flwr_example` et migration des exemples dans le " +"répertoire de premier niveau `examples` " +"([#494](https://github.com/adap/flower/pull/494) " +"[#512](https://github.com/adap/flower/pull/512))" -#: ../../source/tutorial-quickstart-scikitlearn.rst:50 -msgid "Initializes the model parameters that the Flower server will ask for" -msgstr "Initialise les paramètres du modèle que le serveur de Flower demandera" +#: ../../source/ref-changelog.md:1135 +msgid "v0.11.0 (2020-11-30)" +msgstr "v0.11.0 (2020-11-30)" -#: ../../source/tutorial-quickstart-scikitlearn.rst:51 -msgid ":code:`load_mnist()`" -msgstr ":code:`load_mnist()`" +#: ../../source/ref-changelog.md:1137 +msgid "Incompatible changes:" +msgstr "Changements incompatibles :" -#: ../../source/tutorial-quickstart-scikitlearn.rst:52 -msgid "Loads the MNIST dataset using OpenML" -msgstr "Charge l'ensemble de données MNIST à l'aide d'OpenML" +#: ../../source/ref-changelog.md:1139 +msgid "" +"Renamed strategy methods " +"([#486](https://github.com/adap/flower/pull/486)) to unify the naming of " +"Flower's public APIs. Other public methods/functions (e.g., every method " +"in `Client`, but also `Strategy.evaluate`) do not use the `on_` prefix, " +"which is why we're removing it from the four methods in Strategy. To " +"migrate rename the following `Strategy` methods accordingly:" +msgstr "" +"Renommé les méthodes de stratégie " +"([#486](https://github.com/adap/flower/pull/486)) pour unifier le nommage" +" des API publiques de Flower. D'autres méthodes/fonctions publiques (par " +"exemple, toutes les méthodes de `Client`, mais aussi `Strategy.evaluate`)" +" n'utilisent pas le préfixe `on_`, c'est pourquoi nous le supprimons des " +"quatre méthodes de Stratégie. Pour migrer, renommez les méthodes de " +"`Strategy` suivantes en conséquence :" -#: ../../source/tutorial-quickstart-scikitlearn.rst:53 -msgid ":code:`shuffle()`" -msgstr ":code:`shuffle()`" +#: ../../source/ref-changelog.md:1140 +msgid "`on_configure_evaluate` => `configure_evaluate`" +msgstr "`on_configure_evaluate` => `configure_evaluate`" -#: ../../source/tutorial-quickstart-scikitlearn.rst:54 -msgid "Shuffles data and its label" -msgstr "Mélange les données et leur étiquette" +#: ../../source/ref-changelog.md:1141 +msgid "`on_aggregate_evaluate` => `aggregate_evaluate`" +msgstr "`on_aggregate_evaluate` => `aggregate_evaluate`" -#: ../../source/tutorial-quickstart-scikitlearn.rst:56 -msgid ":code:`partition()`" -msgstr ":code:`partition()`" +#: ../../source/ref-changelog.md:1142 +msgid "`on_configure_fit` => `configure_fit`" +msgstr "`on_configure_fit` => `configure_fit`" -#: ../../source/tutorial-quickstart-scikitlearn.rst:56 -msgid "Splits datasets into a number of partitions" -msgstr "Divise les ensembles de données en un certain nombre de partitions" +#: ../../source/ref-changelog.md:1143 +msgid "`on_aggregate_fit` => `aggregate_fit`" +msgstr "`on_aggregate_fit` => `aggregate_fit`" -#: ../../source/tutorial-quickstart-scikitlearn.rst:58 +#: ../../source/ref-changelog.md:1147 msgid "" -"Please check out :code:`utils.py` `here " -"`_ for more details. The pre-defined functions are used in" -" the :code:`client.py` and imported. The :code:`client.py` also requires " -"to import several packages such as Flower and scikit-learn:" +"Deprecated `DefaultStrategy` " +"([#479](https://github.com/adap/flower/pull/479)). To migrate use " +"`FedAvg` instead." msgstr "" -"Tu peux consulter :code:`utils.py` `ici " -"`_ pour plus de détails. Les fonctions prédéfinies sont " -"utilisées dans :code:`client.py` et importées. :code:`client.py` " -"nécessite également d'importer plusieurs paquets tels que Flower et " -"scikit-learn :" +"Déclassé `DefaultStrategy` " +"([#479](https://github.com/adap/flower/pull/479)). Pour migrer, utilisez " +"`FedAvg` à la place." -#: ../../source/tutorial-quickstart-scikitlearn.rst:73 -#, fuzzy +#: ../../source/ref-changelog.md:1148 msgid "" -"We load the MNIST dataset from `OpenML " -"`_, a popular " -"image classification dataset of handwritten digits for machine learning. " -"The utility :code:`utils.load_mnist()` downloads the training and test " -"data. The training set is split afterwards into 10 partitions with " -":code:`utils.partition()`." +"Simplified examples and baselines " +"([#484](https://github.com/adap/flower/pull/484))." msgstr "" -"Nous chargeons l'ensemble de données MNIST de `OpenML " -"`_, un ensemble de données de " -"classification d'images populaires de chiffres manuscrits pour " -"l'apprentissage automatique. L'utilitaire :code:`utils.load_mnist()` " -"télécharge les données d'entraînement et de test. L'ensemble " -"d'entraînement est ensuite divisé en 10 partitions avec " -":code:`utils.partition()`." +"Exemples simplifiés et lignes de base " +"([#484](https://github.com/adap/flower/pull/484))." -#: ../../source/tutorial-quickstart-scikitlearn.rst:85 +#: ../../source/ref-changelog.md:1149 msgid "" -"Next, the logistic regression model is defined and initialized with " -":code:`utils.set_initial_params()`." +"Removed presently unused `on_conclude_round` from strategy interface " +"([#483](https://github.com/adap/flower/pull/483))." msgstr "" -"Ensuite, le modèle de régression logistique est défini et initialisé avec" -" :code:`utils.set_initial_params()`." +"Suppression de `on_conclude_round` actuellement inutilisé de l'interface " +"de stratégie ([#483](https://github.com/adap/flower/pull/483))." -#: ../../source/tutorial-quickstart-scikitlearn.rst:97 +#: ../../source/ref-changelog.md:1150 msgid "" -"The Flower server interacts with clients through an interface called " -":code:`Client`. When the server selects a particular client for training," -" it sends training instructions over the network. The client receives " -"those instructions and calls one of the :code:`Client` methods to run " -"your code (i.e., to fit the logistic regression we defined earlier)." +"Set minimal Python version to 3.6.1 instead of 3.6.9 " +"([#471](https://github.com/adap/flower/pull/471))." msgstr "" -"Le serveur Flower interagit avec les clients par le biais d'une interface" -" appelée :code:`Client`. Lorsque le serveur sélectionne un client " -"particulier pour la formation, il envoie des instructions de formation " -"sur le réseau. Le client reçoit ces instructions et appelle l'une des " -"méthodes :code:`Client` pour exécuter ton code (c'est-à-dire pour ajuster" -" la régression logistique que nous avons définie plus tôt)." +"Fixe la version minimale de Python à 3.6.1 au lieu de 3.6.9 " +"([#471](https://github.com/adap/flower/pull/471))." -#: ../../source/tutorial-quickstart-scikitlearn.rst:103 +#: ../../source/ref-changelog.md:1151 msgid "" -"Flower provides a convenience class called :code:`NumPyClient` which " -"makes it easier to implement the :code:`Client` interface when your " -"workload uses scikit-learn. Implementing :code:`NumPyClient` usually " -"means defining the following methods (:code:`set_parameters` is optional " -"though):" +"Improved `Strategy` docstrings " +"([#470](https://github.com/adap/flower/pull/470))." msgstr "" -"Flower fournit une classe de commodité appelée :code:`NumPyClient` qui " -"facilite la mise en œuvre de l'interface :code:`Client` lorsque ta charge" -" de travail utilise scikit-learn. Mettre en œuvre :code:`NumPyClient` " -"signifie généralement définir les méthodes suivantes " -"(:code:`set_parameters` est cependant facultatif) :" - -#: ../../source/tutorial-quickstart-scikitlearn.rst:112 -msgid "is directly imported with :code:`utils.set_model_params()`" -msgstr "est directement importé avec :code:`utils.set_model_params()`" +"Amélioration des docstrings `Stratégie` " +"([#470](https://github.com/adap/flower/pull/470))." -#: ../../source/tutorial-quickstart-scikitlearn.rst:120 -msgid "The methods can be implemented in the following way:" -msgstr "Les méthodes peuvent être mises en œuvre de la manière suivante :" +#: ../../source/ref-example-projects.rst:2 +#, fuzzy +msgid "Example projects" +msgstr "Exemples de PyTorch" -#: ../../source/tutorial-quickstart-scikitlearn.rst:143 +#: ../../source/ref-example-projects.rst:4 msgid "" -"We can now create an instance of our class :code:`MnistClient` and add " -"one line to actually run this client:" +"Flower comes with a number of usage examples. The examples demonstrate " +"how Flower can be used to federate different kinds of existing machine " +"learning pipelines, usually leveraging popular machine learning " +"frameworks such as `PyTorch `_ or `TensorFlow " +"`_." msgstr "" -"Nous pouvons maintenant créer une instance de notre classe " -":code:`MnistClient` et ajouter une ligne pour exécuter ce client :" +"Flower est livré avec un certain nombre d'exemples d'utilisation, qui " +"montrent comment Flower peut être utilisé pour fédérer différents types " +"de pipelines d'apprentissage automatique existants, qui s'appuient " +"généralement sur des frameworks d'apprentissage automatique populaires " +"tels que `PyTorch `_ ou `TensorFlow " +"`_." -#: ../../source/tutorial-quickstart-scikitlearn.rst:150 +#: ../../source/ref-example-projects.rst:10 #, fuzzy msgid "" -"That's it for the client. We only have to implement :code:`Client` or " -":code:`NumPyClient` and call :code:`fl.client.start_client()`. If you " -"implement a client of type :code:`NumPyClient` you'll need to first call " -"its :code:`to_client()` method. The string :code:`\"0.0.0.0:8080\"` tells" -" the client which server to connect to. In our case we can run the server" -" and the client on the same machine, therefore we use " -":code:`\"0.0.0.0:8080\"`. If we run a truly federated workload with the " -"server and clients running on different machines, all that needs to " -"change is the :code:`server_address` we pass to the client." -msgstr "" -"C'est tout pour le client. Il nous suffit d'implémenter :code:`Client` ou" -" :code:`NumPyClient` et d'appeler :code:`fl.client.start_client()`. La " -"chaîne :code:`\"0.0.0:8080\"` indique au client à quel serveur se " -"connecter. Dans notre cas, nous pouvons exécuter le serveur et le client " -"sur la même machine, c'est pourquoi nous utilisons " -":code:`\"0.0.0:8080\"`. Si nous exécutons une charge de travail " -"véritablement fédérée avec le serveur et les clients s'exécutant sur des " -"machines différentes, tout ce qui doit changer est :code:`server_address`" -" que nous transmettons au client." +"The following examples are available as standalone projects. Quickstart " +"TensorFlow/Keras ---------------------------" +msgstr "Les exemples suivants sont disponibles sous forme de projets autonomes." -#: ../../source/tutorial-quickstart-scikitlearn.rst:159 +#: ../../source/ref-example-projects.rst:14 msgid "" -"The following Flower server is a little bit more advanced and returns an " -"evaluation function for the server-side evaluation. First, we import " -"again all required libraries such as Flower and scikit-learn." +"The TensorFlow/Keras quickstart example shows CIFAR-10 image " +"classification with MobileNetV2:" msgstr "" -"Le serveur Flower suivant est un peu plus avancé et renvoie une fonction " -"d'évaluation pour l'évaluation côté serveur. Tout d'abord, nous importons" -" à nouveau toutes les bibliothèques requises telles que Flower et scikit-" -"learn." - -#: ../../source/tutorial-quickstart-scikitlearn.rst:162 -msgid ":code:`server.py`, import Flower and start the server:" -msgstr ":code:`server.py`, importe Flower et démarre le serveur :" +"L'exemple de démarrage rapide TensorFlow/Keras montre la classification " +"d'images CIFAR-10 avec MobileNetV2 :" -#: ../../source/tutorial-quickstart-scikitlearn.rst:173 +#: ../../source/ref-example-projects.rst:17 +#, fuzzy msgid "" -"The number of federated learning rounds is set in :code:`fit_round()` and" -" the evaluation is defined in :code:`get_evaluate_fn()`. The evaluation " -"function is called after each federated learning round and gives you " -"information about loss and accuracy." +"`Quickstart TensorFlow (Code) " +"`_" msgstr "" -"Le nombre de tours d'apprentissage fédéré est défini dans " -":code:`fit_round()` et l'évaluation est définie dans " -":code:`get_evaluate_fn()`. La fonction d'évaluation est appelée après " -"chaque tour d'apprentissage fédéré et te donne des informations sur la " -"perte et la précision." +"`Quickstart TensorFlow (Code) " +"`_" -#: ../../source/tutorial-quickstart-scikitlearn.rst:198 -msgid "" -"The :code:`main` contains the server-side parameter initialization " -":code:`utils.set_initial_params()` as well as the aggregation strategy " -":code:`fl.server.strategy:FedAvg()`. The strategy is the default one, " -"federated averaging (or FedAvg), with two clients and evaluation after " -"each federated learning round. The server can be started with the command" -" :code:`fl.server.start_server(server_address=\"0.0.0.0:8080\", " -"strategy=strategy, config=fl.server.ServerConfig(num_rounds=3))`." +#: ../../source/ref-example-projects.rst:18 +#, fuzzy +msgid ":doc:`Quickstart TensorFlow (Tutorial) `" msgstr "" -"Le :code:`main` contient l'initialisation des paramètres côté serveur " -":code:`utils.set_initial_params()` ainsi que la stratégie d'agrégation " -":code:`fl.server.strategy:FedAvg()`. La stratégie est celle par défaut, " -"la moyenne fédérée (ou FedAvg), avec deux clients et une évaluation après" -" chaque tour d'apprentissage fédéré. Le serveur peut être démarré avec la" -" commande :code:`fl.server.start_server(server_address=\"0.0.0.0:8080\", " -"strategy=strategy, config=fl.server.ServerConfig(num_rounds=3))`." +"`Quickstart TensorFlow (Tutorial) `_" -#: ../../source/tutorial-quickstart-scikitlearn.rst:217 +#: ../../source/ref-example-projects.rst:19 msgid "" -"With both client and server ready, we can now run everything and see " -"federated learning in action. Federated learning systems usually have a " -"server and multiple clients. We, therefore, have to start the server " -"first:" +"`Quickstart TensorFlow (Blog Post) `_" msgstr "" -"Le client et le serveur étant prêts, nous pouvons maintenant tout lancer " -"et voir l'apprentissage fédéré en action. Les systèmes d'apprentissage " -"fédéré ont généralement un serveur et plusieurs clients. Nous devons donc" -" commencer par lancer le serveur :" +"`Quickstart TensorFlow (Blog Post) `_" -#: ../../source/tutorial-quickstart-scikitlearn.rst:271 -msgid "" -"Congratulations! You've successfully built and run your first federated " -"learning system. The full `source code " -"`_ for this example can be found in :code:`examples/sklearn-logreg-" -"mnist`." -msgstr "" -"Félicitations ! Tu as réussi à construire et à faire fonctionner ton " -"premier système d'apprentissage fédéré. Le code source complet " -"`_ de cet exemple se trouve dans :code:`examples/sklearn-logreg-" -"mnist`." +#: ../../source/ref-example-projects.rst:23 +#: ../../source/tutorial-quickstart-pytorch.rst:5 +msgid "Quickstart PyTorch" +msgstr "Démarrage rapide de PyTorch" -#: ../../source/tutorial-quickstart-tensorflow.rst:-1 +#: ../../source/ref-example-projects.rst:25 msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with TensorFlow to train a MobilNetV2 model on CIFAR-10." -msgstr "" - -#: ../../source/tutorial-quickstart-tensorflow.rst:5 -msgid "Quickstart TensorFlow" -msgstr "Démarrage rapide de TensorFlow" - -#: ../../source/tutorial-quickstart-tensorflow.rst:13 -msgid "Let's build a federated learning system in less than 20 lines of code!" +"The PyTorch quickstart example shows CIFAR-10 image classification with a" +" simple Convolutional Neural Network:" msgstr "" -"Construisons un système d'apprentissage fédéré en moins de 20 lignes de " -"code !" - -#: ../../source/tutorial-quickstart-tensorflow.rst:15 -msgid "Before Flower can be imported we have to install it:" -msgstr "Avant de pouvoir importer une fleur, nous devons l'installer :" +"L'exemple de démarrage rapide PyTorch montre la classification d'images " +"CIFAR-10 avec un simple réseau neuronal convolutif :" -#: ../../source/tutorial-quickstart-tensorflow.rst:21 +#: ../../source/ref-example-projects.rst:28 +#, fuzzy msgid "" -"Since we want to use the Keras API of TensorFlow (TF), we have to install" -" TF as well:" +"`Quickstart PyTorch (Code) " +"`_" msgstr "" -"Comme nous voulons utiliser l'API Keras de TensorFlow (TF), nous devons " -"également installer TF :" +"`Quickstart PyTorch (Code) " +"`_" -#: ../../source/tutorial-quickstart-tensorflow.rst:31 -msgid "Next, in a file called :code:`client.py`, import Flower and TensorFlow:" +#: ../../source/ref-example-projects.rst:29 +#, fuzzy +msgid ":doc:`Quickstart PyTorch (Tutorial) `" msgstr "" -"Ensuite, dans un fichier appelé :code:`client.py`, importe Flower et " -"TensorFlow :" +"`Quickstart PyTorch (Tutorial) `_" -#: ../../source/tutorial-quickstart-tensorflow.rst:38 +#: ../../source/ref-example-projects.rst:33 +msgid "PyTorch: From Centralized To Federated" +msgstr "PyTorch : De la centralisation à la fédération" + +#: ../../source/ref-example-projects.rst:35 msgid "" -"We use the Keras utilities of TF to load CIFAR10, a popular colored image" -" classification dataset for machine learning. The call to " -":code:`tf.keras.datasets.cifar10.load_data()` downloads CIFAR10, caches " -"it locally, and then returns the entire training and test set as NumPy " -"ndarrays." +"This example shows how a regular PyTorch project can be federated using " +"Flower:" msgstr "" -"Nous utilisons les utilitaires Keras de TF pour charger CIFAR10, un " -"ensemble de données de classification d'images colorées populaire pour " -"l'apprentissage automatique. L'appel à " -":code:`tf.keras.datasets.cifar10.load_data()` télécharge CIFAR10, le met " -"en cache localement, puis renvoie l'ensemble d'entraînement et de test " -"sous forme de NumPy ndarrays." +"Cet exemple montre comment un projet PyTorch ordinaire peut être fédéré à" +" l'aide de Flower :" -#: ../../source/tutorial-quickstart-tensorflow.rst:47 +#: ../../source/ref-example-projects.rst:37 +#, fuzzy msgid "" -"Next, we need a model. For the purpose of this tutorial, we use " -"MobilNetV2 with 10 output classes:" +"`PyTorch: From Centralized To Federated (Code) " +"`_" msgstr "" -"Ensuite, nous avons besoin d'un modèle. Pour les besoins de ce tutoriel, " -"nous utilisons MobilNetV2 avec 10 classes de sortie :" +"`PyTorch : De la centralisation à la fédération (Code) " +"`_" -#: ../../source/tutorial-quickstart-tensorflow.rst:60 +#: ../../source/ref-example-projects.rst:38 +#, fuzzy msgid "" -"Flower provides a convenience class called :code:`NumPyClient` which " -"makes it easier to implement the :code:`Client` interface when your " -"workload uses Keras. The :code:`NumPyClient` interface defines three " -"methods which can be implemented in the following way:" +":doc:`PyTorch: From Centralized To Federated (Tutorial) `" msgstr "" -"Flower fournit une classe de commodité appelée :code:`NumPyClient` qui " -"facilite la mise en œuvre de l'interface :code:`Client` lorsque ta charge" -" de travail utilise Keras. L'interface :code:`NumPyClient` définit trois " -"méthodes qui peuvent être mises en œuvre de la manière suivante :" +"`PyTorch : De la centralisation à la fédération (Tutoriel) " +"`_" -#: ../../source/tutorial-quickstart-tensorflow.rst:135 -msgid "Each client will have its own dataset." -msgstr "Chaque client aura son propre ensemble de données." +#: ../../source/ref-example-projects.rst:42 +msgid "Federated Learning on Raspberry Pi and Nvidia Jetson" +msgstr "Apprentissage fédéré sur Raspberry Pi et Nvidia Jetson" -#: ../../source/tutorial-quickstart-tensorflow.rst:137 +#: ../../source/ref-example-projects.rst:44 msgid "" -"You should now see how the training does in the very first terminal (the " -"one that started the server):" +"This example shows how Flower can be used to build a federated learning " +"system that run across Raspberry Pi and Nvidia Jetson:" msgstr "" -"Tu devrais maintenant voir comment la formation se déroule dans le tout " -"premier terminal (celui qui a démarré le serveur) :" +"Cet exemple montre comment Flower peut être utilisé pour construire un " +"système d'apprentissage fédéré qui fonctionne sur Raspberry Pi et Nvidia " +"Jetson :" -#: ../../source/tutorial-quickstart-tensorflow.rst:169 +#: ../../source/ref-example-projects.rst:46 #, fuzzy msgid "" -"Congratulations! You've successfully built and run your first federated " -"learning system. The full `source code " -"`_ for this can be found in :code:`examples" -"/quickstart-tensorflow/client.py`." +"`Federated Learning on Raspberry Pi and Nvidia Jetson (Code) " +"`_" msgstr "" -"Félicitations ! Tu as réussi à construire et à faire fonctionner ton " -"premier système d'apprentissage fédéré. Le `code source complet " -"`_ pour cela se trouve dans :code:`examples" -"/quickstart-tensorflow/client.py`." +"`L'apprentissage fédéré sur Raspberry Pi et Nvidia Jetson (Code) " +"`_" -#: ../../source/tutorial-quickstart-xgboost.rst:-1 +#: ../../source/ref-example-projects.rst:47 msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with XGBoost to train classification models on trees." +"`Federated Learning on Raspberry Pi and Nvidia Jetson (Blog Post) " +"`_" msgstr "" +"`L'apprentissage fédéré sur Raspberry Pi et Nvidia Jetson (Blog Post) " +"`_" -#: ../../source/tutorial-quickstart-xgboost.rst:5 -msgid "Quickstart XGBoost" -msgstr "Démarrage rapide XGBoost" +#: ../../source/ref-faq.rst:4 +msgid "" +"This page collects answers to commonly asked questions about Federated " +"Learning with Flower." +msgstr "" +"Cette page rassemble les réponses aux questions les plus fréquemment " +"posées sur l'apprentissage fédéré avec Flower." -#: ../../source/tutorial-quickstart-xgboost.rst:14 +#: ../../source/ref-faq.rst #, fuzzy -msgid "Federated XGBoost" -msgstr "Formation fédérée" +msgid ":fa:`eye,mr-1` Can Flower run on Jupyter Notebooks / Google Colab?" +msgstr "" +":fa:`eye,mr-1` Flower peut-il fonctionner sur les ordinateurs portables " +"Juptyter / Google Colab ?" -#: ../../source/tutorial-quickstart-xgboost.rst:16 +#: ../../source/ref-faq.rst:8 msgid "" -"EXtreme Gradient Boosting (**XGBoost**) is a robust and efficient " -"implementation of gradient-boosted decision tree (**GBDT**), that " -"maximises the computational boundaries for boosted tree methods. It's " -"primarily designed to enhance both the performance and computational " -"speed of machine learning models. In XGBoost, trees are constructed " -"concurrently, unlike the sequential approach taken by GBDT." +"Yes, it can! Flower even comes with a few under-the-hood optimizations to" +" make it work even better on Colab. Here's a quickstart example:" msgstr "" +"Oui, c'est possible ! Flower est même livré avec quelques optimisations " +"pour qu'il fonctionne encore mieux sur Colab. Voici un exemple de " +"démarrage rapide :" -#: ../../source/tutorial-quickstart-xgboost.rst:20 +#: ../../source/ref-faq.rst:10 +#, fuzzy msgid "" -"Often, for tabular data on medium-sized datasets with fewer than 10k " -"training examples, XGBoost surpasses the results of deep learning " -"techniques." +"`Flower simulation PyTorch " +"`_" msgstr "" +"`Flower Quickstart (TensorFlow/Keras) " +"`_" -#: ../../source/tutorial-quickstart-xgboost.rst:23 +#: ../../source/ref-faq.rst:11 #, fuzzy -msgid "Why federated XGBoost?" -msgstr "Qu'est-ce que l'apprentissage fédéré ?" - -#: ../../source/tutorial-quickstart-xgboost.rst:25 msgid "" -"Indeed, as the demand for data privacy and decentralized learning grows, " -"there's an increasing requirement to implement federated XGBoost systems " -"for specialised applications, like survival analysis and financial fraud " -"detection." +"`Flower simulation TensorFlow/Keras " +"`_" msgstr "" +"`Flower Quickstart (TensorFlow/Keras) " +"`_" -#: ../../source/tutorial-quickstart-xgboost.rst:27 -msgid "" -"Federated learning ensures that raw data remains on the local device, " -"making it an attractive approach for sensitive domains where data " -"security and privacy are paramount. Given the robustness and efficiency " -"of XGBoost, combining it with federated learning offers a promising " -"solution for these specific challenges." +#: ../../source/ref-faq.rst +msgid ":fa:`eye,mr-1` How can I run Federated Learning on a Raspberry Pi?" msgstr "" +":fa:`eye,mr-1` Comment puis-je faire fonctionner l'apprentissage fédéré " +"sur un Raspberry Pi ?" -#: ../../source/tutorial-quickstart-xgboost.rst:30 +#: ../../source/ref-faq.rst:15 +#, fuzzy msgid "" -"In this tutorial we will learn how to train a federated XGBoost model on " -"HIGGS dataset using Flower and :code:`xgboost` package. We use a simple " -"example (`full code xgboost-quickstart " -"`_)" -" with two *clients* and one *server* to demonstrate how federated XGBoost" -" works, and then we dive into a more complex example (`full code xgboost-" -"comprehensive `_) to run various experiments." -msgstr "" - -#: ../../source/tutorial-quickstart-xgboost.rst:37 -msgid "Environment Setup" +"Find the `blog post about federated learning on embedded device here " +"`_" +" and the corresponding `GitHub code example " +"`_." msgstr "" +"Trouve le `blog post about federated learning on embedded device ici " +"`_" +" et l'exemple de code GitHub correspondant " +"`_." -#: ../../source/tutorial-quickstart-xgboost.rst:41 -msgid "" -"We first need to install Flower and Flower Datasets. You can do this by " -"running :" +#: ../../source/ref-faq.rst +msgid ":fa:`eye,mr-1` Does Flower support federated learning on Android devices?" msgstr "" +":fa:`eye,mr-1` Est-ce que Flower prend en charge l'apprentissage fédéré " +"sur les appareils Android ?" -#: ../../source/tutorial-quickstart-xgboost.rst:47 +#: ../../source/ref-faq.rst:19 #, fuzzy msgid "" -"Since we want to use :code:`xgboost` package to build up XGBoost trees, " -"let's go ahead and install :code:`xgboost`:" -msgstr "Puisque nous voulons utiliser scikt-learn, allons-y et installons-le :" +"Yes, it does. Please take a look at our `blog post " +"`_ or check out the code examples:" +msgstr "" +"Oui. Jetez un coup d'œil à notre `blog post " +"`_ ou consultez l'`exemple de code Android sur GitHub " +"`_." -#: ../../source/tutorial-quickstart-xgboost.rst:57 +#: ../../source/ref-faq.rst:21 msgid "" -"*Clients* are responsible for generating individual weight-updates for " -"the model based on their local datasets. Now that we have all our " -"dependencies installed, let's run a simple distributed training with two " -"clients and one server." +"`Android Kotlin example `_" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:60 -#, fuzzy -msgid "" -"In a file called :code:`client.py`, import xgboost, Flower, Flower " -"Datasets and other related functions:" +#: ../../source/ref-faq.rst:22 +msgid "`Android Java example `_" msgstr "" -"Dans un fichier appelé :code:`client.py`, importe Flower et les paquets " -"liés à PyTorch :" -#: ../../source/tutorial-quickstart-xgboost.rst:87 -msgid "Dataset partition and hyper-parameter selection" +#: ../../source/ref-faq.rst +msgid ":fa:`eye,mr-1` Can I combine federated learning with blockchain?" msgstr "" +":fa:`eye,mr-1` Puis-je combiner l'apprentissage fédéré avec la blockchain" +" ?" -#: ../../source/tutorial-quickstart-xgboost.rst:89 +#: ../../source/ref-faq.rst:26 msgid "" -"Prior to local training, we require loading the HIGGS dataset from Flower" -" Datasets and conduct data partitioning for FL:" +"Yes, of course. A list of available examples using Flower within a " +"blockchain environment is available here:" msgstr "" +"Oui, bien sûr, une liste d'exemples disponibles utilisant Flower dans un " +"environnement blockchain est disponible ici :" -#: ../../source/tutorial-quickstart-xgboost.rst:102 +#: ../../source/ref-faq.rst:28 msgid "" -"In this example, we split the dataset into two partitions with uniform " -"distribution (:code:`IidPartitioner(num_partitions=2)`). Then, we load " -"the partition for the given client based on :code:`node_id`:" +"`Flower meets Nevermined GitHub Repository `_." msgstr "" +"`Flower meets Nevermined GitHub Repository `_." -#: ../../source/tutorial-quickstart-xgboost.rst:121 +#: ../../source/ref-faq.rst:29 msgid "" -"After that, we do train/test splitting on the given partition (client's " -"local data), and transform data format for :code:`xgboost` package." +"`Flower meets Nevermined YouTube video " +"`_." msgstr "" +"`Flower rencontre Nevermined vidéo YouTube " +"`_." -#: ../../source/tutorial-quickstart-xgboost.rst:134 +#: ../../source/ref-faq.rst:30 +#, fuzzy msgid "" -"The functions of :code:`train_test_split` and " -":code:`transform_dataset_to_dmatrix` are defined as below:" +"`Flower meets KOSMoS `_." msgstr "" +"`Flower rencontre KOSMoS `_." -#: ../../source/tutorial-quickstart-xgboost.rst:158 -msgid "Finally, we define the hyper-parameters used for XGBoost training." +#: ../../source/ref-faq.rst:31 +msgid "" +"`Flower meets Talan blog post `_ ." msgstr "" +"`Flower meets Talan blog post `_ ." -#: ../../source/tutorial-quickstart-xgboost.rst:174 +#: ../../source/ref-faq.rst:32 msgid "" -"The :code:`num_local_round` represents the number of iterations for local" -" tree boost. We use CPU for the training in default. One can shift it to " -"GPU by setting :code:`tree_method` to :code:`gpu_hist`. We use AUC as " -"evaluation metric." +"`Flower meets Talan GitHub Repository " +"`_ ." msgstr "" +"`Flower rencontre Talan Dépôt GitHub " +"`_ ." -#: ../../source/tutorial-quickstart-xgboost.rst:181 -msgid "Flower client definition for XGBoost" -msgstr "" +#: ../../source/ref-telemetry.md:1 +msgid "Telemetry" +msgstr "Télémétrie" -#: ../../source/tutorial-quickstart-xgboost.rst:183 +#: ../../source/ref-telemetry.md:3 msgid "" -"After loading the dataset we define the Flower client. We follow the " -"general rule to define :code:`XgbClient` class inherited from " -":code:`fl.client.Client`." +"The Flower open-source project collects **anonymous** usage metrics to " +"make well-informed decisions to improve Flower. Doing this enables the " +"Flower team to understand how Flower is used and what challenges users " +"might face." msgstr "" +"Le projet open-source Flower recueille des mesures d'utilisation " +"**anonymes** afin de prendre des décisions éclairées pour améliorer " +"Flower. Cela permet à l'équipe de Flower de comprendre comment Flower est" +" utilisé et quels sont les défis auxquels les utilisateurs peuvent être " +"confrontés." -#: ../../source/tutorial-quickstart-xgboost.rst:193 +#: ../../source/ref-telemetry.md:5 msgid "" -"The :code:`self.bst` is used to keep the Booster objects that remain " -"consistent across rounds, allowing them to store predictions from trees " -"integrated in earlier rounds and maintain other essential data structures" -" for training." +"**Flower is a friendly framework for collaborative AI and data science.**" +" Staying true to this statement, Flower makes it easy to disable " +"telemetry for users that do not want to share anonymous usage metrics." msgstr "" +"**Flower est un cadre convivial pour l'IA collaborative et la science des" +" données.** En restant fidèle à cette déclaration, Flower permet de " +"désactiver facilement la télémétrie pour les utilisateurs qui ne " +"souhaitent pas partager des mesures d'utilisation anonymes." -#: ../../source/tutorial-quickstart-xgboost.rst:196 -msgid "" -"Then, we override :code:`get_parameters`, :code:`fit` and " -":code:`evaluate` methods insides :code:`XgbClient` class as follows." -msgstr "" +#: ../../source/ref-telemetry.md:7 +msgid "Principles" +msgstr "Principes" -#: ../../source/tutorial-quickstart-xgboost.rst:210 -msgid "" -"Unlike neural network training, XGBoost trees are not started from a " -"specified random weights. In this case, we do not use " -":code:`get_parameters` and :code:`set_parameters` to initialise model " -"parameters for XGBoost. As a result, let's return an empty tensor in " -":code:`get_parameters` when it is called by the server at the first " -"round." +#: ../../source/ref-telemetry.md:9 +msgid "We follow strong principles guarding anonymous usage metrics collection:" msgstr "" +"Nous suivons des principes stricts concernant la collecte de données " +"anonymes sur l'utilisation :" -#: ../../source/tutorial-quickstart-xgboost.rst:251 +#: ../../source/ref-telemetry.md:11 msgid "" -"In :code:`fit`, at the first round, we call :code:`xgb.train()` to build " -"up the first set of trees. the returned Booster object and config are " -"stored in :code:`self.bst` and :code:`self.config`, respectively. From " -"the second round, we load the global model sent from server to " -":code:`self.bst`, and then update model weights on local training data " -"with function :code:`local_boost` as follows:" +"**Optional:** You will always be able to disable telemetry; read on to " +"learn “[How to opt-out](#how-to-opt-out)”." msgstr "" +"**Optionnel:** Tu pourras toujours désactiver la télémétrie ; lis la " +"suite pour apprendre \"[Comment se désengager](#how-to-opt-out)\"." -#: ../../source/tutorial-quickstart-xgboost.rst:269 +#: ../../source/ref-telemetry.md:12 msgid "" -"Given :code:`num_local_round`, we update trees by calling " -":code:`self.bst.update` method. After training, the last " -":code:`N=num_local_round` trees will be extracted to send to the server." +"**Anonymous:** The reported usage metrics are anonymous and do not " +"contain any personally identifiable information (PII). See “[Collected " +"metrics](#collected-metrics)” to understand what metrics are being " +"reported." msgstr "" +"**Anonyme:** Les mesures d'utilisation rapportées sont anonymes et ne " +"contiennent aucune information personnelle identifiable (PII). Voir " +"\"[Collected metrics](#collected-metrics)\" pour comprendre quelles " +"mesures sont rapportées." -#: ../../source/tutorial-quickstart-xgboost.rst:291 +#: ../../source/ref-telemetry.md:13 msgid "" -"In :code:`evaluate`, we call :code:`self.bst.eval_set` function to " -"conduct evaluation on valid set. The AUC value will be returned." +"**Transparent:** You can easily inspect what anonymous metrics are being " +"reported; see the section “[How to inspect what is being reported](#how-" +"to-inspect-what-is-being-reported)”" msgstr "" +"**Transparent:** Tu peux facilement inspecter les métriques anonymes qui " +"sont rapportées ; voir la section \"[Comment inspecter ce qui est " +"rapporté](#how-to-inspect-what-is-being-reported)\"" -#: ../../source/tutorial-quickstart-xgboost.rst:294 +#: ../../source/ref-telemetry.md:14 #, fuzzy msgid "" -"Now, we can create an instance of our class :code:`XgbClient` and add one" -" line to actually run this client:" +"**Open for feedback:** You can always reach out to us if you have " +"feedback; see the section “[How to contact us](#how-to-contact-us)” for " +"details." msgstr "" -"Nous pouvons maintenant créer une instance de notre classe " -":code:`MnistClient` et ajouter une ligne pour exécuter ce client :" +"**Ouvert pour les commentaires:** Tu peux toujours nous contacter si tu " +"as des commentaires ; voir la section \"[Comment nous contacter ](#how-" +"to-contact-us)\" pour plus de détails." -#: ../../source/tutorial-quickstart-xgboost.rst:300 -#, fuzzy +#: ../../source/ref-telemetry.md:16 +msgid "How to opt-out" +msgstr "Comment se désinscrire" + +#: ../../source/ref-telemetry.md:18 msgid "" -"That's it for the client. We only have to implement :code:`Client`and " -"call :code:`fl.client.start_client()`. The string :code:`\"[::]:8080\"` " -"tells the client which server to connect to. In our case we can run the " -"server and the client on the same machine, therefore we use " -":code:`\"[::]:8080\"`. If we run a truly federated workload with the " -"server and clients running on different machines, all that needs to " -"change is the :code:`server_address` we point the client at." +"When Flower starts, it will check for an environment variable called " +"`FLWR_TELEMETRY_ENABLED`. Telemetry can easily be disabled by setting " +"`FLWR_TELEMETRY_ENABLED=0`. Assuming you are starting a Flower server or " +"client, simply do so by prepending your command as in:" msgstr "" -"C'est tout pour le client. Il nous suffit d'implémenter :code:`Client` ou" -" :code:`NumPyClient` et d'appeler :code:`fl.client.start_client()`. La " -"chaîne :code:`\"[: :]:8080\"` indique au client à quel serveur se " -"connecter. Dans notre cas, nous pouvons exécuter le serveur et le client " -"sur la même machine, c'est pourquoi nous utilisons :code:`\"[: " -":]:8080\"`. Si nous exécutons une charge de travail véritablement fédérée" -" avec le serveur et les clients fonctionnant sur des machines " -"différentes, tout ce qui doit changer est l'adresse " -":code:`server_address` vers laquelle nous dirigeons le client." +"Lorsque Flower démarre, il vérifie la présence d'une variable " +"d'environnement appelée `FLWR_TELEMETRY_ENABLED`. La télémétrie peut " +"facilement être désactivée en réglant `FLWR_TELEMETRY_ENABLED=0`. En " +"supposant que tu démarres un serveur ou un client Flower, fais-le " +"simplement en faisant précéder ta commande de la façon suivante :" -#: ../../source/tutorial-quickstart-xgboost.rst:311 -#, fuzzy +#: ../../source/ref-telemetry.md:24 msgid "" -"These updates are then sent to the *server* which will aggregate them to " -"produce a better model. Finally, the *server* sends this improved version" -" of the model back to each *client* to finish a complete FL round." +"Alternatively, you can export `FLWR_TELEMETRY_ENABLED=0` in, for example," +" `.bashrc` (or whatever configuration file applies to your environment) " +"to disable Flower telemetry permanently." msgstr "" -"*Les clients* sont chargés de générer des mises à jour de poids " -"individuelles pour le modèle en fonction de leurs ensembles de données " -"locales. Ces mises à jour sont ensuite envoyées au *serveur* qui les " -"agrège pour produire un meilleur modèle. Enfin, le *serveur* renvoie " -"cette version améliorée du modèle à chaque *client*. Un cycle complet de " -"mises à jour de poids s'appelle un *round*." +"Tu peux aussi exporter `FLWR_TELEMETRY_ENABLED=0` dans, par exemple, " +"`.bashrc` (ou tout autre fichier de configuration qui s'applique à ton " +"environnement) pour désactiver la télémétrie de la fleur de façon " +"permanente." -#: ../../source/tutorial-quickstart-xgboost.rst:314 -#, fuzzy -msgid "" -"In a file named :code:`server.py`, import Flower and FedXgbBagging from " -":code:`flwr.server.strategy`." -msgstr "" -"Dans un fichier appelé :code:`client.py`, importe Flower et les paquets " -"liés au MXNet :" +#: ../../source/ref-telemetry.md:26 +msgid "Collected metrics" +msgstr "Mesures collectées" -#: ../../source/tutorial-quickstart-xgboost.rst:316 -msgid "We first define a strategy for XGBoost bagging aggregation." -msgstr "" +#: ../../source/ref-telemetry.md:28 +msgid "Flower telemetry collects the following metrics:" +msgstr "La télémétrie des fleurs recueille les métriques suivantes :" -#: ../../source/tutorial-quickstart-xgboost.rst:339 +#: ../../source/ref-telemetry.md:30 msgid "" -"We use two clients for this example. An " -":code:`evaluate_metrics_aggregation` function is defined to collect and " -"wighted average the AUC values from clients." +"**Flower version.** Understand which versions of Flower are currently " +"being used. This helps us to decide whether we should invest effort into " +"releasing a patch version for an older version of Flower or instead use " +"the bandwidth to build new features." msgstr "" +"**Cela nous aide à décider si nous devons investir des efforts dans la " +"publication d'une version corrective pour une version plus ancienne de " +"Flower ou si nous devons plutôt utiliser la bande passante pour " +"développer de nouvelles fonctionnalités." -#: ../../source/tutorial-quickstart-xgboost.rst:342 -#, fuzzy -msgid "Then, we start the server:" -msgstr "Démarrer le serveur" - -#: ../../source/tutorial-quickstart-xgboost.rst:354 -msgid "Tree-based bagging aggregation" +#: ../../source/ref-telemetry.md:32 +msgid "" +"**Operating system.** Enables us to answer questions such as: *Should we " +"create more guides for Linux, macOS, or Windows?*" msgstr "" +"**Système d'exploitation.** Nous permet de répondre à des questions " +"telles que : *Faudrait-il créer plus de guides pour Linux, macOS ou " +"Windows ?" -#: ../../source/tutorial-quickstart-xgboost.rst:356 +#: ../../source/ref-telemetry.md:34 msgid "" -"You must be curious about how bagging aggregation works. Let's look into " -"the details." +"**Python version.** Knowing the Python version helps us, for example, to " +"decide whether we should invest effort into supporting old versions of " +"Python or stop supporting them and start taking advantage of new Python " +"features." msgstr "" +"**Version de Python.** Connaître la version de Python nous aide, par " +"exemple, à décider si nous devons investir des efforts dans la prise en " +"charge des anciennes versions de Python ou cesser de les prendre en " +"charge et commencer à tirer parti des nouvelles fonctionnalités de " +"Python." -#: ../../source/tutorial-quickstart-xgboost.rst:358 +#: ../../source/ref-telemetry.md:36 msgid "" -"In file :code:`flwr.server.strategy.fedxgb_bagging.py`, we define " -":code:`FedXgbBagging` inherited from :code:`flwr.server.strategy.FedAvg`." -" Then, we override the :code:`aggregate_fit`, :code:`aggregate_evaluate` " -"and :code:`evaluate` methods as follows:" +"**Hardware properties.** Understanding the hardware environment that " +"Flower is being used in helps to decide whether we should, for example, " +"put more effort into supporting low-resource environments." msgstr "" +"**Comprendre l'environnement matériel dans lequel Flower est utilisé " +"permet de décider si nous devrions, par exemple, faire plus d'efforts " +"pour prendre en charge les environnements à faibles ressources." -#: ../../source/tutorial-quickstart-xgboost.rst:454 +#: ../../source/ref-telemetry.md:38 msgid "" -"In :code:`aggregate_fit`, we sequentially aggregate the clients' XGBoost " -"trees by calling :code:`aggregate()` function:" +"**Execution mode.** Knowing what execution mode Flower starts in enables " +"us to understand how heavily certain features are being used and better " +"prioritize based on that." msgstr "" +"**Mode d'exécution** Connaître le mode d'exécution dans lequel Flower " +"démarre nous permet de comprendre à quel point certaines fonctionnalités " +"sont utilisées et de mieux établir les priorités en fonction de cela." -#: ../../source/tutorial-quickstart-xgboost.rst:513 +#: ../../source/ref-telemetry.md:40 msgid "" -"In this function, we first fetch the number of trees and the number of " -"parallel trees for the current and previous model by calling " -":code:`_get_tree_nums`. Then, the fetched information will be aggregated." -" After that, the trees (containing model weights) are aggregated to " -"generate a new tree model." +"**Cluster.** Flower telemetry assigns a random in-memory cluster ID each " +"time a Flower workload starts. This allows us to understand which device " +"types not only start Flower workloads but also successfully complete " +"them." msgstr "" +"**Cluster.** La télémétrie Flower attribue un ID de cluster en mémoire " +"aléatoire à chaque fois qu'une charge de travail Flower démarre. Cela " +"nous permet de comprendre quels types d'appareils non seulement démarrent" +" les charges de travail Flower, mais aussi les terminent avec succès." -#: ../../source/tutorial-quickstart-xgboost.rst:518 +#: ../../source/ref-telemetry.md:42 msgid "" -"After traversal of all clients' models, a new global model is generated, " -"followed by the serialisation, and sending back to each client." +"**Source.** Flower telemetry tries to store a random source ID in " +"`~/.flwr/source` the first time a telemetry event is generated. The " +"source ID is important to identify whether an issue is recurring or " +"whether an issue is triggered by multiple clusters running concurrently " +"(which often happens in simulation). For example, if a device runs " +"multiple workloads at the same time, and this results in an issue, then, " +"in order to reproduce the issue, multiple workloads must be started at " +"the same time." msgstr "" +"**Source.** La télémétrie de Flower essaie de stocker un ID de source " +"aléatoire dans `~/.flwr/source` la première fois qu'un événement de " +"télémétrie est généré. L'ID de source est important pour identifier si un" +" problème est récurrent ou si un problème est déclenché par plusieurs " +"clusters fonctionnant simultanément (ce qui arrive souvent en " +"simulation). Par exemple, si un périphérique exécute plusieurs charges de" +" travail en même temps, et que cela entraîne un problème, alors, afin de " +"reproduire le problème, plusieurs charges de travail doivent être " +"démarrées en même temps." -#: ../../source/tutorial-quickstart-xgboost.rst:523 -msgid "Launch Federated XGBoost!" +#: ../../source/ref-telemetry.md:44 +msgid "" +"You may delete the source ID at any time. If you wish for all events " +"logged under a specific source ID to be deleted, you can send a deletion " +"request mentioning the source ID to `telemetry@flower.ai`. All events " +"related to that source ID will then be permanently deleted." msgstr "" +"Tu peux supprimer l'identifiant de la source à tout moment. Si tu " +"souhaites que tous les événements enregistrés sous un identifiant de " +"source spécifique soient supprimés, tu peux envoyer une demande de " +"suppression mentionnant l'identifiant de source à `telemetry@flower.ai`. " +"Tous les événements liés à cet identifiant de source seront alors " +"définitivement supprimés." -#: ../../source/tutorial-quickstart-xgboost.rst:585 +#: ../../source/ref-telemetry.md:46 msgid "" -"Congratulations! You've successfully built and run your first federated " -"XGBoost system. The AUC values can be checked in " -":code:`metrics_distributed`. One can see that the average AUC increases " -"over FL rounds." +"We will not collect any personally identifiable information. If you think" +" any of the metrics collected could be misused in any way, please [get in" +" touch with us](#how-to-contact-us). We will update this page to reflect " +"any changes to the metrics collected and publish changes in the " +"changelog." msgstr "" +"Nous ne collecterons aucune information personnelle identifiable. Si tu " +"penses que l'une des métriques collectées pourrait être utilisée à " +"mauvais escient de quelque manière que ce soit, merci de [nous " +"contacter](#commentnouscontacter). Nous mettrons à jour cette page pour " +"refléter toute modification des métriques collectées et nous publierons " +"les changements dans le journal des modifications (changelog)." -#: ../../source/tutorial-quickstart-xgboost.rst:590 -#, fuzzy +#: ../../source/ref-telemetry.md:48 msgid "" -"The full `source code `_ for this example can be found in :code:`examples" -"/xgboost-quickstart`." +"If you think other metrics would be helpful for us to better guide our " +"decisions, please let us know! We will carefully review them; if we are " +"confident that they do not compromise user privacy, we may add them." msgstr "" -"Félicitations ! Tu as réussi à construire et à faire fonctionner ton " -"premier système d'apprentissage fédéré. Le code source complet " -"`_ de cet exemple se trouve dans :code:`examples" -"/quickstart-mxnet`." +"Si tu penses que d'autres mesures nous seraient utiles pour mieux " +"orienter nos décisions, fais-le nous savoir ! Nous les examinerons " +"attentivement ; si nous sommes convaincus qu'elles ne compromettent pas " +"la vie privée des utilisateurs, nous pourrons les ajouter." -#: ../../source/tutorial-quickstart-xgboost.rst:594 -msgid "Comprehensive Federated XGBoost" +#: ../../source/ref-telemetry.md:50 +msgid "How to inspect what is being reported" +msgstr "Comment inspecter ce qui est rapporté" + +#: ../../source/ref-telemetry.md:52 +msgid "" +"We wanted to make it very easy for you to inspect what anonymous usage " +"metrics are reported. You can view all the reported telemetry information" +" by setting the environment variable `FLWR_TELEMETRY_LOGGING=1`. Logging " +"is disabled by default. You may use logging independently from " +"`FLWR_TELEMETRY_ENABLED` so that you can inspect the telemetry feature " +"without sending any metrics." msgstr "" +"Nous avons voulu qu'il soit très facile pour toi d'inspecter les mesures " +"d'utilisation anonymes qui sont rapportées. Tu peux voir toutes les " +"informations de télémétrie rapportées en définissant la variable " +"d'environnement `FLWR_TELEMETRY_LOGGING=1`. La journalisation est " +"désactivée par défaut. Tu peux utiliser la journalisation indépendamment " +"de `FLWR_TELEMETRY_ENABLED` afin d'inspecter la fonction de télémétrie " +"sans envoyer de mesures." -#: ../../source/tutorial-quickstart-xgboost.rst:596 +#: ../../source/ref-telemetry.md:58 msgid "" -"Now that you have known how federated XGBoost work with Flower, it's time" -" to run some more comprehensive experiments by customising the " -"experimental settings. In the xgboost-comprehensive example (`full code " -"`_), we provide more options to define various experimental" -" setups, including aggregation strategies, data partitioning and " -"centralised/distributed evaluation. We also support :doc:`Flower " -"simulation ` making it easy to simulate large " -"client cohorts in a resource-aware manner. Let's take a look!" +"The inspect Flower telemetry without sending any anonymous usage metrics," +" use both environment variables:" msgstr "" +"L'inspecteur Flower telemetry sans envoyer de métriques d'utilisation " +"anonymes, utilise les deux variables d'environnement :" -#: ../../source/tutorial-quickstart-xgboost.rst:603 -#, fuzzy -msgid "Cyclic training" -msgstr "Formation centralisée" +#: ../../source/ref-telemetry.md:64 +msgid "How to contact us" +msgstr "Comment nous contacter" -#: ../../source/tutorial-quickstart-xgboost.rst:605 +#: ../../source/ref-telemetry.md:66 msgid "" -"In addition to bagging aggregation, we offer a cyclic training scheme, " -"which performs FL in a client-by-client fashion. Instead of aggregating " -"multiple clients, there is only one single client participating in the " -"training per round in the cyclic training scenario. The trained local " -"XGBoost trees will be passed to the next client as an initialised model " -"for next round's boosting." +"We want to hear from you. If you have any feedback or ideas on how to " +"improve the way we handle anonymous usage metrics, reach out to us via " +"[Slack](https://flower.ai/join-slack/) (channel `#telemetry`) or email " +"(`telemetry@flower.ai`)." msgstr "" +"Si tu as des commentaires ou des idées pour améliorer la façon dont nous " +"traitons les mesures d'utilisation anonymes, contacte-nous via " +"[Slack](https://flower.ai/join-slack/) (canal `#telemetry`) ou par " +"courriel (`telemetry@flower.ai`)." -#: ../../source/tutorial-quickstart-xgboost.rst:609 +#: ../../source/tutorial-quickstart-android.rst:-1 msgid "" -"To do this, we first customise a :code:`ClientManager` in " -":code:`server_utils.py`:" +"Read this Federated Learning quickstart tutorial for creating an Android " +"app using Flower." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:649 +#: ../../source/tutorial-quickstart-android.rst:5 +#, fuzzy +msgid "Quickstart Android" +msgstr "Démarrage rapide des Pandas" + +#: ../../source/tutorial-quickstart-android.rst:10 +#, fuzzy msgid "" -"The customised :code:`ClientManager` samples all available clients in " -"each FL round based on the order of connection to the server. Then, we " -"define a new strategy :code:`FedXgbCyclic` in " -":code:`flwr.server.strategy.fedxgb_cyclic.py`, in order to sequentially " -"select only one client in given round and pass the received model to next" -" client." +"Let's build a federated learning system using TFLite and Flower on " +"Android!" msgstr "" +"Construisons un système d'apprentissage fédéré en utilisant fastai et " +"Flower !" -#: ../../source/tutorial-quickstart-xgboost.rst:690 +#: ../../source/tutorial-quickstart-android.rst:12 +#, fuzzy msgid "" -"Unlike the original :code:`FedAvg`, we don't perform aggregation here. " -"Instead, we just make a copy of the received client model as global model" -" by overriding :code:`aggregate_fit`." +"Please refer to the `full code example " +"`_ to learn " +"more." msgstr "" +"Réfère-toi à l'exemple de code complet " +"`_ " +"pour en savoir plus." -#: ../../source/tutorial-quickstart-xgboost.rst:693 +#: ../../source/tutorial-quickstart-fastai.rst:-1 msgid "" -"Also, the customised :code:`configure_fit` and :code:`configure_evaluate`" -" methods ensure the clients to be sequentially selected given FL round:" +"Check out this Federated Learning quickstart tutorial for using Flower " +"with FastAI to train a vision model on CIFAR-10." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:757 -msgid "Customised data partitioning" -msgstr "" +#: ../../source/tutorial-quickstart-fastai.rst:5 +msgid "Quickstart fastai" +msgstr "Démarrage rapide fastai" -#: ../../source/tutorial-quickstart-xgboost.rst:759 -msgid "" -"In :code:`dataset.py`, we have a function :code:`instantiate_partitioner`" -" to instantiate the data partitioner based on the given " -":code:`num_partitions` and :code:`partitioner_type`. Currently, we " -"provide four supported partitioner type to simulate the uniformity/non-" -"uniformity in data quantity (uniform, linear, square, exponential)." +#: ../../source/tutorial-quickstart-fastai.rst:10 +msgid "Let's build a federated learning system using fastai and Flower!" msgstr "" +"Construisons un système d'apprentissage fédéré en utilisant fastai et " +"Flower !" -#: ../../source/tutorial-quickstart-xgboost.rst:790 +#: ../../source/tutorial-quickstart-fastai.rst:12 #, fuzzy -msgid "Customised centralised/distributed evaluation" -msgstr "Évaluation centralisée" - -#: ../../source/tutorial-quickstart-xgboost.rst:792 msgid "" -"To facilitate centralised evaluation, we define a function in " -":code:`server_utils.py`:" +"Please refer to the `full code example " +"`_ " +"to learn more." msgstr "" +"Réfère-toi à l'exemple de code complet " +"`_ " +"pour en savoir plus." -#: ../../source/tutorial-quickstart-xgboost.rst:824 +#: ../../source/tutorial-quickstart-huggingface.rst:-1 msgid "" -"This function returns a evaluation function which instantiates a " -":code:`Booster` object and loads the global model weights to it. The " -"evaluation is conducted by calling :code:`eval_set()` method, and the " -"tested AUC value is reported." +"Check out this Federating Learning quickstart tutorial for using Flower " +"with HuggingFace Transformers in order to fine-tune an LLM." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:827 +#: ../../source/tutorial-quickstart-huggingface.rst:5 +msgid "Quickstart 🤗 Transformers" +msgstr "Démarrage rapide 🤗 Transformateurs" + +#: ../../source/tutorial-quickstart-huggingface.rst:10 msgid "" -"As for distributed evaluation on the clients, it's same as the quick-" -"start example by overriding the :code:`evaluate()` method insides the " -":code:`XgbClient` class in :code:`client_utils.py`." +"Let's build a federated learning system using Hugging Face Transformers " +"and Flower!" msgstr "" +"Construisons un système d'apprentissage fédéré à l'aide des " +"transformateurs Hugging Face et de Flower !" -#: ../../source/tutorial-quickstart-xgboost.rst:831 -#, fuzzy -msgid "Flower simulation" -msgstr "Simulation de moniteur" - -#: ../../source/tutorial-quickstart-xgboost.rst:832 +#: ../../source/tutorial-quickstart-huggingface.rst:12 msgid "" -"We also provide an example code (:code:`sim.py`) to use the simulation " -"capabilities of Flower to simulate federated XGBoost training on either a" -" single machine or a cluster of machines." +"We will leverage Hugging Face to federate the training of language models" +" over multiple clients using Flower. More specifically, we will fine-tune" +" a pre-trained Transformer model (distilBERT) for sequence classification" +" over a dataset of IMDB ratings. The end goal is to detect if a movie " +"rating is positive or negative." msgstr "" +"Nous nous appuierons sur Hugging Face pour fédérer l'entraînement de " +"modèles de langage sur plusieurs clients à l'aide de Flower. Plus " +"précisément, nous mettrons au point un modèle Transformer pré-entraîné " +"(distilBERT) pour la classification de séquences sur un ensemble de " +"données d'évaluations IMDB. L'objectif final est de détecter si " +"l'évaluation d'un film est positive ou négative." -#: ../../source/tutorial-quickstart-xgboost.rst:866 +#: ../../source/tutorial-quickstart-huggingface.rst:18 +msgid "Dependencies" +msgstr "Dépendances" + +#: ../../source/tutorial-quickstart-huggingface.rst:20 msgid "" -"After importing all required packages, we define a :code:`main()` " -"function to perform the simulation process:" +"To follow along this tutorial you will need to install the following " +"packages: :code:`datasets`, :code:`evaluate`, :code:`flwr`, " +":code:`torch`, and :code:`transformers`. This can be done using " +":code:`pip`:" msgstr "" +"Pour suivre ce tutoriel, tu devras installer les paquets suivants : " +":code:`datasets`, :code:`evaluate`, :code:`flwr`, :code:`torch`, et " +":code:`transformers`. Cela peut être fait en utilisant :code:`pip` :" -#: ../../source/tutorial-quickstart-xgboost.rst:921 +#: ../../source/tutorial-quickstart-huggingface.rst:30 +msgid "Standard Hugging Face workflow" +msgstr "Flux de travail standard pour le visage" + +#: ../../source/tutorial-quickstart-huggingface.rst:33 +msgid "Handling the data" +msgstr "Traitement des données" + +#: ../../source/tutorial-quickstart-huggingface.rst:35 msgid "" -"We first load the dataset and perform data partitioning, and the pre-" -"processed data is stored in a :code:`list`. After the simulation begins, " -"the clients won't need to pre-process their partitions again." +"To fetch the IMDB dataset, we will use Hugging Face's :code:`datasets` " +"library. We then need to tokenize the data and create :code:`PyTorch` " +"dataloaders, this is all done in the :code:`load_data` function:" msgstr "" +"Pour récupérer le jeu de données IMDB, nous utiliserons la bibliothèque " +":code:`datasets` de Hugging Face. Nous devons ensuite tokeniser les " +"données et créer des :code:`PyTorch` dataloaders, ce qui est fait dans la" +" fonction :code:`load_data` :" -#: ../../source/tutorial-quickstart-xgboost.rst:924 -msgid "Then, we define the strategies and other hyper-parameters:" -msgstr "" +#: ../../source/tutorial-quickstart-huggingface.rst:81 +msgid "Training and testing the model" +msgstr "Former et tester le modèle" -#: ../../source/tutorial-quickstart-xgboost.rst:975 +#: ../../source/tutorial-quickstart-huggingface.rst:83 msgid "" -"After that, we start the simulation by calling " -":code:`fl.simulation.start_simulation`:" +"Once we have a way of creating our trainloader and testloader, we can " +"take care of the training and testing. This is very similar to any " +":code:`PyTorch` training or testing loop:" msgstr "" +"Une fois que nous avons trouvé un moyen de créer notre trainloader et " +"notre testloader, nous pouvons nous occuper de l'entraînement et du test." +" C'est très similaire à n'importe quelle boucle d'entraînement ou de test" +" :code:`PyTorch` :" -#: ../../source/tutorial-quickstart-xgboost.rst:995 +#: ../../source/tutorial-quickstart-huggingface.rst:121 +msgid "Creating the model itself" +msgstr "Créer le modèle lui-même" + +#: ../../source/tutorial-quickstart-huggingface.rst:123 msgid "" -"One of key parameters for :code:`start_simulation` is :code:`client_fn` " -"which returns a function to construct a client. We define it as follows:" +"To create the model itself, we will just load the pre-trained distillBERT" +" model using Hugging Face’s :code:`AutoModelForSequenceClassification` :" msgstr "" +"Pour créer le modèle lui-même, nous allons simplement charger le modèle " +"distillBERT pré-entraîné en utilisant le " +":code:`AutoModelForSequenceClassification` de Hugging Face :" -#: ../../source/tutorial-quickstart-xgboost.rst:1038 -msgid "Arguments parser" -msgstr "" +#: ../../source/tutorial-quickstart-huggingface.rst:136 +msgid "Federating the example" +msgstr "Fédérer l'exemple" -#: ../../source/tutorial-quickstart-xgboost.rst:1040 +#: ../../source/tutorial-quickstart-huggingface.rst:139 +msgid "Creating the IMDBClient" +msgstr "Création du client IMDBC" + +#: ../../source/tutorial-quickstart-huggingface.rst:141 msgid "" -"In :code:`utils.py`, we define the arguments parsers for clients, server " -"and simulation, allowing users to specify different experimental " -"settings. Let's first see the sever side:" +"To federate our example to multiple clients, we first need to write our " +"Flower client class (inheriting from :code:`flwr.client.NumPyClient`). " +"This is very easy, as our model is a standard :code:`PyTorch` model:" msgstr "" +"Pour fédérer notre exemple à plusieurs clients, nous devons d'abord " +"écrire notre classe de client Flower (héritant de " +":code:`flwr.client.NumPyClient`). C'est très facile, car notre modèle est" +" un modèle :code:`PyTorch` standard :" -#: ../../source/tutorial-quickstart-xgboost.rst:1086 +#: ../../source/tutorial-quickstart-huggingface.rst:169 msgid "" -"This allows user to specify training strategies / the number of total " -"clients / FL rounds / participating clients / clients for evaluation, and" -" evaluation fashion. Note that with :code:`--centralised-eval`, the sever" -" will do centralised evaluation and all functionalities for client " -"evaluation will be disabled." +"The :code:`get_parameters` function lets the server get the client's " +"parameters. Inversely, the :code:`set_parameters` function allows the " +"server to send its parameters to the client. Finally, the :code:`fit` " +"function trains the model locally for the client, and the " +":code:`evaluate` function tests the model locally and returns the " +"relevant metrics." msgstr "" +"La fonction :code:`get_parameters` permet au serveur d'obtenir les " +"paramètres du client. Inversement, la fonction :code:`set_parameters` " +"permet au serveur d'envoyer ses paramètres au client. Enfin, la fonction " +":code:`fit` forme le modèle localement pour le client, et la fonction " +":code:`evaluate` teste le modèle localement et renvoie les mesures " +"correspondantes." -#: ../../source/tutorial-quickstart-xgboost.rst:1090 -msgid "Then, the argument parser on client side:" -msgstr "" +#: ../../source/tutorial-quickstart-huggingface.rst:175 +msgid "Starting the server" +msgstr "Démarrer le serveur" -#: ../../source/tutorial-quickstart-xgboost.rst:1144 +#: ../../source/tutorial-quickstart-huggingface.rst:177 msgid "" -"This defines various options for client data partitioning. Besides, " -"clients also have an option to conduct evaluation on centralised test set" -" by setting :code:`--centralised-eval`, as well as an option to perform " -"scaled learning rate based on the number of clients by setting :code" -":`--scaled-lr`." +"Now that we have a way to instantiate clients, we need to create our " +"server in order to aggregate the results. Using Flower, this can be done " +"very easily by first choosing a strategy (here, we are using " +":code:`FedAvg`, which will define the global weights as the average of " +"all the clients' weights at each round) and then using the " +":code:`flwr.server.start_server` function:" msgstr "" +"Maintenant que nous avons un moyen d'instancier les clients, nous devons " +"créer notre serveur afin d'agréger les résultats. Avec Flower, cela peut " +"être fait très facilement en choisissant d'abord une stratégie (ici, nous" +" utilisons :code:`FedAvg`, qui définira les poids globaux comme la " +"moyenne des poids de tous les clients à chaque tour) et en utilisant " +"ensuite la fonction :code:`flwr.server.start_server` :" -#: ../../source/tutorial-quickstart-xgboost.rst:1148 -msgid "We also have an argument parser for simulation:" +#: ../../source/tutorial-quickstart-huggingface.rst:205 +msgid "" +"The :code:`weighted_average` function is there to provide a way to " +"aggregate the metrics distributed amongst the clients (basically this " +"allows us to display a nice average accuracy and loss for every round)." msgstr "" +"La fonction :code:`weighted_average` est là pour fournir un moyen " +"d'agréger les mesures réparties entre les clients (en gros, cela nous " +"permet d'afficher une belle moyenne de précision et de perte pour chaque " +"tour)." -#: ../../source/tutorial-quickstart-xgboost.rst:1226 -msgid "This integrates all arguments for both client and server sides." -msgstr "" +#: ../../source/tutorial-quickstart-huggingface.rst:209 +msgid "Putting everything together" +msgstr "Tout assembler" -#: ../../source/tutorial-quickstart-xgboost.rst:1229 -#, fuzzy -msgid "Example commands" -msgstr "Exemples de PyTorch" +#: ../../source/tutorial-quickstart-huggingface.rst:211 +msgid "We can now start client instances using:" +msgstr "Nous pouvons maintenant démarrer des instances de clients en utilisant :" -#: ../../source/tutorial-quickstart-xgboost.rst:1231 +#: ../../source/tutorial-quickstart-huggingface.rst:221 msgid "" -"To run a centralised evaluated experiment with bagging strategy on 5 " -"clients with exponential distribution for 50 rounds, we first start the " -"server as below:" -msgstr "" +"And they will be able to connect to the server and start the federated " +"training." +msgstr "Et ils pourront se connecter au serveur et démarrer la formation fédérée." -#: ../../source/tutorial-quickstart-xgboost.rst:1238 +#: ../../source/tutorial-quickstart-huggingface.rst:223 #, fuzzy -msgid "Then, on each client terminal, we start the clients:" -msgstr "Ouvre un autre terminal et démarre le deuxième client :" +msgid "" +"If you want to check out everything put together, you should check out " +"the `full code example `_ ." +msgstr "" +"Si tu veux voir tout ce qui est mis ensemble, tu devrais consulter " +"l'exemple de code complet : " +"[https://github.com/adap/flower/tree/main/examples/quickstart-" +"huggingface](https://github.com/adap/flower/tree/main/examples" +"/quickstart-huggingface)." -#: ../../source/tutorial-quickstart-xgboost.rst:1244 -msgid "To run the same experiment with Flower simulation:" +#: ../../source/tutorial-quickstart-huggingface.rst:226 +msgid "" +"Of course, this is a very basic example, and a lot can be added or " +"modified, it was just to showcase how simply we could federate a Hugging " +"Face workflow using Flower." msgstr "" +"Bien sûr, c'est un exemple très basique, et beaucoup de choses peuvent " +"être ajoutées ou modifiées, il s'agissait juste de montrer avec quelle " +"simplicité on pouvait fédérer un flux de travail Hugging Face à l'aide de" +" Flower." -#: ../../source/tutorial-quickstart-xgboost.rst:1250 -#, fuzzy +#: ../../source/tutorial-quickstart-huggingface.rst:229 msgid "" -"The full `code `_ for this comprehensive example can be found in" -" :code:`examples/xgboost-comprehensive`." +"Note that in this example we used :code:`PyTorch`, but we could have very" +" well used :code:`TensorFlow`." msgstr "" -"Félicitations ! Tu as réussi à construire et à faire fonctionner ton " -"premier système d'apprentissage fédéré. Le code source complet " -"`_ de cet exemple se trouve dans :code:`examples" -"/quickstart-mxnet`." +"Notez que dans cet exemple, nous avons utilisé :code:`PyTorch`, mais nous" +" aurions très bien pu utiliser :code:`TensorFlow`." -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:9 +#: ../../source/tutorial-quickstart-ios.rst:-1 +msgid "" +"Read this Federated Learning quickstart tutorial for creating an iOS app " +"using Flower to train a neural network on MNIST." +msgstr "" + +#: ../../source/tutorial-quickstart-ios.rst:5 #, fuzzy -msgid "Build a strategy from scratch" -msgstr "Élaborer une stratégie à partir de zéro" +msgid "Quickstart iOS" +msgstr "Démarrage rapide XGBoost" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:11 +#: ../../source/tutorial-quickstart-ios.rst:10 #, fuzzy msgid "" -"Welcome to the third part of the Flower federated learning tutorial. In " -"previous parts of this tutorial, we introduced federated learning with " -"PyTorch and Flower (`part 1 `__) and we learned how strategies " -"can be used to customize the execution on both the server and the clients" -" (`part 2 `__)." +"In this tutorial we will learn how to train a Neural Network on MNIST " +"using Flower and CoreML on iOS devices." msgstr "" -"Bienvenue dans la troisième partie du tutoriel sur l'apprentissage fédéré" -" Flower. Dans les parties précédentes de ce tutoriel, nous avons présenté" -" l'apprentissage fédéré avec PyTorch et Flower (`partie 1 " -"`__) " -"et nous avons appris comment les stratégies peuvent être utilisées pour " -"personnaliser l'exécution à la fois sur le serveur et sur les clients " -"(`partie 2 `__)." +"Dans ce tutoriel, nous allons apprendre, comment former un réseau " +"neuronal convolutif sur MNIST en utilisant Flower et PyTorch." -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:13 +#: ../../source/tutorial-quickstart-ios.rst:12 +#, fuzzy msgid "" -"In this notebook, we'll continue to customize the federated learning " -"system we built previously by creating a custom version of FedAvg (again," -" using `Flower `__ and `PyTorch " -"`__)." +"First of all, for running the Flower Python server, it is recommended to " +"create a virtual environment and run everything within a :doc:`virtualenv" +" `. For the Flower client " +"implementation in iOS, it is recommended to use Xcode as our IDE." msgstr "" -"Dans ce carnet, nous allons continuer à personnaliser le système " -"d'apprentissage fédéré que nous avons construit précédemment en créant " -"une version personnalisée de FedAvg (encore une fois, en utilisant " -"`Flower `__ et `PyTorch `__)." +"Tout d'abord, il est recommandé de créer un environnement virtuel et de " +"tout exécuter au sein d'un `virtualenv `_." -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:15 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:16 -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:15 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:15 +#: ../../source/tutorial-quickstart-ios.rst:15 +#, fuzzy msgid "" -"`Star Flower on GitHub `__ ⭐️ and join " -"the Flower community on Slack to connect, ask questions, and get help: " -"`Join Slack `__ 🌼 We'd love to hear from " -"you in the ``#introductions`` channel! And if anything is unclear, head " -"over to the ``#questions`` channel." +"Our example consists of one Python *server* and two iPhone *clients* that" +" all have the same model." msgstr "" -"`Star Flower on GitHub `__ ⭐️ et " -"rejoignez la communauté Flower sur Slack pour vous connecter, poser des " -"questions et obtenir de l'aide : `Join Slack `__ 🌼 Nous serions ravis d'avoir de vos nouvelles dans le canal " -"``#introductions`` ! Et si quelque chose n'est pas clair, rendez-vous sur" -" le canal ``#questions``." - -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:17 -msgid "Let's build a new ``Strategy`` from scratch!" -msgstr "Construisons une nouvelle ``Stratégie`` à partir de zéro !" +"Notre exemple consiste en un *serveur* et deux *clients* ayant tous le " +"même modèle." -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:29 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:29 -msgid "Preparation" -msgstr "Préparation" +#: ../../source/tutorial-quickstart-ios.rst:17 +#, fuzzy +msgid "" +"*Clients* are responsible for generating individual weight updates for " +"the model based on their local datasets. These updates are then sent to " +"the *server* which will aggregate them to produce a better model. " +"Finally, the *server* sends this improved version of the model back to " +"each *client*. A complete cycle of weight updates is called a *round*." +msgstr "" +"*Les clients* sont chargés de générer des mises à jour de poids " +"individuelles pour le modèle en fonction de leurs ensembles de données " +"locales. Ces mises à jour sont ensuite envoyées au *serveur* qui les " +"agrège pour produire un meilleur modèle. Enfin, le *serveur* renvoie " +"cette version améliorée du modèle à chaque *client*. Un cycle complet de " +"mises à jour de poids s'appelle un *round*." -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:31 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:32 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:31 +#: ../../source/tutorial-quickstart-ios.rst:21 +#, fuzzy msgid "" -"Before we begin with the actual code, let's make sure that we have " -"everything we need." +"Now that we have a rough idea of what is going on, let's get started to " +"setup our Flower server environment. We first need to install Flower. You" +" can do this by using pip:" msgstr "" -"Avant de commencer le code proprement dit, assurons-nous que nous " -"disposons de tout ce dont nous avons besoin." +"Maintenant que nous avons une idée générale de ce qui se passe, " +"commençons. Nous devons d'abord installer Flower. Tu peux le faire en " +"exécutant :" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:43 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:44 -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:43 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:43 -msgid "Installing dependencies" -msgstr "Installation des dépendances" +#: ../../source/tutorial-quickstart-ios.rst:27 +msgid "Or Poetry:" +msgstr "" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:45 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:46 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:45 -msgid "First, we install the necessary packages:" -msgstr "Tout d'abord, nous installons les paquets nécessaires :" +#: ../../source/tutorial-quickstart-ios.rst:34 +#: ../../source/tutorial-quickstart-pytorch.rst:37 +#: ../../source/tutorial-quickstart-scikitlearn.rst:40 +#: ../../source/tutorial-quickstart-tensorflow.rst:29 +#: ../../source/tutorial-quickstart-xgboost.rst:55 +msgid "Flower Client" +msgstr "Client de la fleur" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:65 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:66 -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:65 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:65 +#: ../../source/tutorial-quickstart-ios.rst:36 msgid "" -"Now that we have all dependencies installed, we can import everything we " -"need for this tutorial:" +"Now that we have all our dependencies installed, let's run a simple " +"distributed training using CoreML as our local training pipeline and " +"MNIST as our dataset. For simplicity reasons we will use the complete " +"Flower client with CoreML, that has been implemented and stored inside " +"the Swift SDK. The client implementation can be seen below:" msgstr "" -"Maintenant que toutes les dépendances sont installées, nous pouvons " -"importer tout ce dont nous avons besoin pour ce tutoriel :" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:101 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:102 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:101 +#: ../../source/tutorial-quickstart-ios.rst:72 msgid "" -"It is possible to switch to a runtime that has GPU acceleration enabled " -"(on Google Colab: ``Runtime > Change runtime type > Hardware acclerator: " -"GPU > Save``). Note, however, that Google Colab is not always able to " -"offer GPU acceleration. If you see an error related to GPU availability " -"in one of the following sections, consider switching back to CPU-based " -"execution by setting ``DEVICE = torch.device(\"cpu\")``. If the runtime " -"has GPU acceleration enabled, you should see the output ``Training on " -"cuda``, otherwise it'll say ``Training on cpu``." +"Let's create a new application project in Xcode and add :code:`flwr` as a" +" dependency in your project. For our application, we will store the logic" +" of our app in :code:`FLiOSModel.swift` and the UI elements in " +":code:`ContentView.swift`. We will focus more on :code:`FLiOSModel.swift`" +" in this quickstart. Please refer to the `full code example " +"`_ to learn more " +"about the app." msgstr "" -"Il est possible de passer à un runtime dont l'accélération GPU est " -"activée (sur Google Colab : ``Runtime > Change runtime type > Hardware " -"acclerator : GPU > Save``). Note cependant que Google Colab n'est pas " -"toujours en mesure de proposer l'accélération GPU. Si tu vois une erreur " -"liée à la disponibilité du GPU dans l'une des sections suivantes, " -"envisage de repasser à une exécution basée sur le CPU en définissant " -"``DEVICE = torch.device(\"cpu\")``. Si le runtime a activé l'accélération" -" GPU, tu devrais voir apparaître le résultat ``Training on cuda``, sinon " -"il dira ``Training on cpu``." -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:114 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:115 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:114 -msgid "Data loading" -msgstr "Chargement des données" +#: ../../source/tutorial-quickstart-ios.rst:75 +msgid "Import Flower and CoreML related packages in :code:`FLiOSModel.swift`:" +msgstr "" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:116 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:116 +#: ../../source/tutorial-quickstart-ios.rst:83 msgid "" -"Let's now load the CIFAR-10 training and test set, partition them into " -"ten smaller datasets (each split into training and validation set), and " -"wrap everything in their own ``DataLoader``. We introduce a new parameter" -" ``num_clients`` which allows us to call ``load_datasets`` with different" -" numbers of clients." +"Then add the mlmodel to the project simply by drag-and-drop, the mlmodel " +"will be bundled inside the application during deployment to your iOS " +"device. We need to pass the url to access mlmodel and run CoreML machine " +"learning processes, it can be retrieved by calling the function " +":code:`Bundle.main.url`. For the MNIST dataset, we need to preprocess it " +"into :code:`MLBatchProvider` object. The preprocessing is done inside " +":code:`DataLoader.swift`." msgstr "" -"Chargeons maintenant les ensembles d'entraînement et de test CIFAR-10, " -"divisons-les en dix ensembles de données plus petits (chacun divisé en " -"ensemble d'entraînement et de validation), et enveloppons le tout dans " -"leur propre ``DataLoader``. Nous introduisons un nouveau paramètre " -"``num_clients`` qui nous permet d'appeler ``load_datasets`` avec " -"différents nombres de clients." - -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:167 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:168 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:167 -msgid "Model training/evaluation" -msgstr "Formation/évaluation du modèle" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:169 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:170 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:169 +#: ../../source/tutorial-quickstart-ios.rst:99 msgid "" -"Let's continue with the usual model definition (including " -"``set_parameters`` and ``get_parameters``), training and test functions:" +"Since CoreML does not allow the model parameters to be seen before " +"training, and accessing the model parameters during or after the training" +" can only be done by specifying the layer name, we need to know this " +"information beforehand, through looking at the model specification, which" +" are written as proto files. The implementation can be seen in " +":code:`MLModelInspect`." msgstr "" -"Continuons avec la définition habituelle du modèle (y compris " -"``set_parameters`` et ``get_parameters``), les fonctions d'entraînement " -"et de test :" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:258 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:258 -msgid "Flower client" -msgstr "Client de Flower" +#: ../../source/tutorial-quickstart-ios.rst:102 +msgid "" +"After we have all of the necessary information, let's create our Flower " +"client." +msgstr "" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:260 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:260 +#: ../../source/tutorial-quickstart-ios.rst:117 msgid "" -"To implement the Flower client, we (again) create a subclass of " -"``flwr.client.NumPyClient`` and implement the three methods " -"``get_parameters``, ``fit``, and ``evaluate``. Here, we also pass the " -"``cid`` to the client and use it log additional details:" +"Then start the Flower gRPC client and start communicating to the server " +"by passing our Flower client to the function :code:`startFlwrGRPC`." msgstr "" -"Pour mettre en œuvre le client Flower, nous créons (à nouveau) une sous-" -"classe de ``flwr.client.NumPyClient`` et mettons en œuvre les trois " -"méthodes ``get_parameters``, ``fit`` et ``evaluate``. Ici, nous " -"transmettons également le ``cid`` au client et l'utilisons pour consigner" -" des détails supplémentaires :" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:308 -msgid "Let's test what we have so far before we continue:" -msgstr "Testons ce que nous avons jusqu'à présent avant de continuer :" +#: ../../source/tutorial-quickstart-ios.rst:124 +msgid "" +"That's it for the client. We only have to implement :code:`Client` or " +"call the provided :code:`MLFlwrClient` and call :code:`startFlwrGRPC()`. " +"The attribute :code:`hostname` and :code:`port` tells the client which " +"server to connect to. This can be done by entering the hostname and port " +"in the application before clicking the start button to start the " +"federated learning process." +msgstr "" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:339 -msgid "Build a Strategy from scratch" -msgstr "Élaborer une stratégie à partir de zéro" +#: ../../source/tutorial-quickstart-ios.rst:129 +#: ../../source/tutorial-quickstart-pytorch.rst:203 +#: ../../source/tutorial-quickstart-scikitlearn.rst:167 +#: ../../source/tutorial-quickstart-tensorflow.rst:98 +#: ../../source/tutorial-quickstart-xgboost.rst:309 +msgid "Flower Server" +msgstr "Serveur de Flower" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:341 +#: ../../source/tutorial-quickstart-ios.rst:131 +#: ../../source/tutorial-quickstart-pytorch.rst:205 +#: ../../source/tutorial-quickstart-tensorflow.rst:100 msgid "" -"Let’s overwrite the ``configure_fit`` method such that it passes a higher" -" learning rate (potentially also other hyperparameters) to the optimizer " -"of a fraction of the clients. We will keep the sampling of the clients as" -" it is in ``FedAvg`` and then change the configuration dictionary (one of" -" the ``FitIns`` attributes)." +"For simple workloads we can start a Flower server and leave all the " +"configuration possibilities at their default values. In a file named " +":code:`server.py`, import Flower and start the server:" msgstr "" -"Remplaçons la méthode ``configure_fit`` de façon à ce qu'elle transmette " -"un taux d'apprentissage plus élevé (potentiellement aussi d'autres " -"hyperparamètres) à l'optimiseur d'une fraction des clients. Nous " -"garderons l'échantillonnage des clients tel qu'il est dans ``FedAvg`` et " -"changerons ensuite le dictionnaire de configuration (l'un des attributs " -"``FitIns``)." +"Pour les charges de travail simples, nous pouvons démarrer un serveur " +"Flower et laisser toutes les possibilités de configuration à leurs " +"valeurs par défaut. Dans un fichier nommé :code:`server.py`, importe " +"Flower et démarre le serveur :" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:507 +#: ../../source/tutorial-quickstart-ios.rst:142 +#: ../../source/tutorial-quickstart-pytorch.rst:216 +#: ../../source/tutorial-quickstart-scikitlearn.rst:230 +#: ../../source/tutorial-quickstart-tensorflow.rst:112 +msgid "Train the model, federated!" +msgstr "Entraîne le modèle, fédéré !" + +#: ../../source/tutorial-quickstart-ios.rst:144 +#: ../../source/tutorial-quickstart-pytorch.rst:218 +#: ../../source/tutorial-quickstart-tensorflow.rst:114 +#: ../../source/tutorial-quickstart-xgboost.rst:525 msgid "" -"The only thing left is to use the newly created custom Strategy " -"``FedCustom`` when starting the experiment:" +"With both client and server ready, we can now run everything and see " +"federated learning in action. FL systems usually have a server and " +"multiple clients. We therefore have to start the server first:" msgstr "" -"Il ne reste plus qu'à utiliser la stratégie personnalisée nouvellement " -"créée ``FedCustom`` lors du démarrage de l'expérience :" - -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:534 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:932 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:697 -msgid "Recap" -msgstr "Récapitulation" +"Le client et le serveur étant prêts, nous pouvons maintenant tout " +"exécuter et voir l'apprentissage fédéré en action. Les systèmes FL ont " +"généralement un serveur et plusieurs clients. Nous devons donc commencer " +"par démarrer le serveur :" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:536 +#: ../../source/tutorial-quickstart-ios.rst:152 msgid "" -"In this notebook, we’ve seen how to implement a custom strategy. A custom" -" strategy enables granular control over client node configuration, result" -" aggregation, and more. To define a custom strategy, you only have to " -"overwrite the abstract methods of the (abstract) base class ``Strategy``." -" To make custom strategies even more powerful, you can pass custom " -"functions to the constructor of your new class (``__init__``) and then " -"call these functions whenever needed." +"Once the server is running we can start the clients in different " +"terminals. Build and run the client through your Xcode, one through Xcode" +" Simulator and the other by deploying it to your iPhone. To see more " +"about how to deploy your app to iPhone or Simulator visit `here " +"`_." msgstr "" -"Dans ce carnet, nous avons vu comment mettre en place une stratégie " -"personnalisée. Une stratégie personnalisée permet un contrôle granulaire " -"sur la configuration des nœuds clients, l'agrégation des résultats, et " -"bien plus encore. Pour définir une stratégie personnalisée, il te suffit " -"d'écraser les méthodes abstraites de la classe de base (abstraite) " -"``Strategy``. Pour rendre les stratégies personnalisées encore plus " -"puissantes, tu peux passer des fonctions personnalisées au constructeur " -"de ta nouvelle classe (``__init__``) et appeler ensuite ces fonctions à " -"chaque fois que c'est nécessaire." -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:550 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:948 -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:729 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:715 -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:369 +#: ../../source/tutorial-quickstart-ios.rst:156 +#, fuzzy msgid "" -"Before you continue, make sure to join the Flower community on Slack: " -"`Join Slack `__" +"Congratulations! You've successfully built and run your first federated " +"learning system in your ios device. The full `source code " +"`_ for this " +"example can be found in :code:`examples/ios`." msgstr "" -"Avant de continuer, n'oublie pas de rejoindre la communauté Flower sur " -"Slack : `Join Slack `__" +"Félicitations ! Tu as réussi à construire et à faire fonctionner ton " +"premier système d'apprentissage fédéré. Le code source complet " +"`_ de cet exemple se trouve dans :code:`examples" +"/quickstart-mxnet`." -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:552 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:950 -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:731 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:717 -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:371 +#: ../../source/tutorial-quickstart-jax.rst:-1 msgid "" -"There's a dedicated ``#questions`` channel if you need help, but we'd " -"also love to hear who you are in ``#introductions``!" +"Check out this Federated Learning quickstart tutorial for using Flower " +"with Jax to train a linear regression model on a scikit-learn dataset." msgstr "" -"Il existe un canal dédié aux ``questions`` si vous avez besoin d'aide, " -"mais nous aimerions aussi savoir qui vous êtes dans ``#introductions`` !" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:554 -#, fuzzy +#: ../../source/tutorial-quickstart-jax.rst:5 +msgid "Quickstart JAX" +msgstr "Démarrage rapide de JAX" + +#: ../../source/tutorial-quickstart-pandas.rst:-1 msgid "" -"The `Flower Federated Learning Tutorial - Part 4 " -"`__ introduces ``Client``, the flexible API underlying " -"``NumPyClient``." +"Check out this Federated Learning quickstart tutorial for using Flower " +"with Pandas to perform Federated Analytics." msgstr "" -"Le `Tutoriel d'apprentissage fédéré Flower - Partie 4 " -"`__ présente ``Client``, l'API flexible qui sous-tend " -"``NumPyClient``." -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:9 -#, fuzzy -msgid "Customize the client" -msgstr "Création du client IMDBC" +#: ../../source/tutorial-quickstart-pandas.rst:5 +msgid "Quickstart Pandas" +msgstr "Démarrage rapide des Pandas" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:11 +#: ../../source/tutorial-quickstart-pandas.rst:10 +msgid "Let's build a federated analytics system using Pandas and Flower!" +msgstr "Construisons un système d'analyse fédéré à l'aide de Pandas et de Flower !" + +#: ../../source/tutorial-quickstart-pandas.rst:12 #, fuzzy msgid "" -"Welcome to the fourth part of the Flower federated learning tutorial. In " -"the previous parts of this tutorial, we introduced federated learning " -"with PyTorch and Flower (`part 1 `__), we learned how " -"strategies can be used to customize the execution on both the server and " -"the clients (`part 2 `__), and we built our own " -"custom strategy from scratch (`part 3 `__)." +"Please refer to the `full code example " +"`_ " +"to learn more." msgstr "" -"Bienvenue dans la quatrième partie du tutoriel sur l'apprentissage fédéré" -" Flower. Dans les parties précédentes de ce tutoriel, nous avons présenté" -" l'apprentissage fédéré avec PyTorch et Flower (`partie 1 " -"`__), " -"nous avons appris comment les stratégies peuvent être utilisées pour " -"personnaliser l'exécution à la fois sur le serveur et les clients " -"(`partie 2 `__), et nous avons construit notre propre stratégie " -"personnalisée à partir de zéro (`partie 3 - WIP " -"`__)." +"Réfère-toi à l'exemple de code complet " +"`_ " +"pour en savoir plus." -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:14 +#: ../../source/tutorial-quickstart-pytorch.rst:-1 msgid "" -"In this notebook, we revisit ``NumPyClient`` and introduce a new " -"baseclass for building clients, simply named ``Client``. In previous " -"parts of this tutorial, we've based our client on ``NumPyClient``, a " -"convenience class which makes it easy to work with machine learning " -"libraries that have good NumPy interoperability. With ``Client``, we gain" -" a lot of flexibility that we didn't have before, but we'll also have to " -"do a few things the we didn't have to do before." +"Check out this Federated Learning quickstart tutorial for using Flower " +"with PyTorch to train a CNN model on MNIST." msgstr "" -"Dans ce carnet, nous revisitons `NumPyClient`` et introduisons une " -"nouvelle classe de base pour construire des clients, simplement appelée " -"`Client``. Dans les parties précédentes de ce tutoriel, nous avons basé " -"notre client sur ``NumPyClient``, une classe de commodité qui facilite le" -" travail avec les bibliothèques d'apprentissage automatique qui ont une " -"bonne interopérabilité NumPy. Avec ``Client``, nous gagnons beaucoup de " -"flexibilité que nous n'avions pas auparavant, mais nous devrons également" -" faire quelques choses que nous n'avions pas à faire auparavant." -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:18 +#: ../../source/tutorial-quickstart-pytorch.rst:13 msgid "" -"Let's go deeper and see what it takes to move from ``NumPyClient`` to " -"``Client``!" +"In this tutorial we will learn how to train a Convolutional Neural " +"Network on CIFAR10 using Flower and PyTorch." msgstr "" -"Allons plus loin et voyons ce qu'il faut faire pour passer de " -"``NumPyClient`` à ``Client`` !" - -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:30 -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:29 -msgid "Step 0: Preparation" -msgstr "Étape 0 : Préparation" +"Dans ce tutoriel, nous allons apprendre à entraîner un réseau neuronal " +"convolutif sur CIFAR10 à l'aide de Flower et PyTorch." -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:117 +#: ../../source/tutorial-quickstart-pytorch.rst:15 +#: ../../source/tutorial-quickstart-xgboost.rst:39 +#, fuzzy msgid "" -"Let's now load the CIFAR-10 training and test set, partition them into " -"ten smaller datasets (each split into training and validation set), and " -"wrap everything in their own ``DataLoader``." +"First of all, it is recommended to create a virtual environment and run " +"everything within a :doc:`virtualenv `." msgstr "" -"Chargeons maintenant les ensembles d'entraînement et de test CIFAR-10, " -"divisons-les en dix ensembles de données plus petits (chacun divisé en " -"ensemble d'entraînement et de validation) et enveloppons le tout dans " -"leur propre ``DataLoader``." - -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:259 -msgid "Step 1: Revisiting NumPyClient" -msgstr "Étape 1 : Revoir NumPyClient" +"Tout d'abord, il est recommandé de créer un environnement virtuel et de " +"tout exécuter au sein d'un `virtualenv `_." -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:261 +#: ../../source/tutorial-quickstart-pytorch.rst:17 +#: ../../source/tutorial-quickstart-scikitlearn.rst:14 msgid "" -"So far, we've implemented our client by subclassing " -"``flwr.client.NumPyClient``. The three methods we implemented are " -"``get_parameters``, ``fit``, and ``evaluate``. Finally, we wrap the " -"creation of instances of this class in a function called ``client_fn``:" +"Our example consists of one *server* and two *clients* all having the " +"same model." msgstr "" -"Jusqu'à présent, nous avons implémenté notre client en sous-classant " -"``flwr.client.NumPyClient``. Les trois méthodes que nous avons " -"implémentées sont ``get_parameters``, ``fit`` et ``evaluate``. Enfin, " -"nous enveloppons la création d'instances de cette classe dans une " -"fonction appelée ``client_fn`` :" +"Notre exemple consiste en un *serveur* et deux *clients* ayant tous le " +"même modèle." -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:309 +#: ../../source/tutorial-quickstart-pytorch.rst:19 msgid "" -"We've seen this before, there's nothing new so far. The only *tiny* " -"difference compared to the previous notebook is naming, we've changed " -"``FlowerClient`` to ``FlowerNumPyClient`` and ``client_fn`` to " -"``numpyclient_fn``. Let's run it to see the output we get:" +"*Clients* are responsible for generating individual weight-updates for " +"the model based on their local datasets. These updates are then sent to " +"the *server* which will aggregate them to produce a better model. " +"Finally, the *server* sends this improved version of the model back to " +"each *client*. A complete cycle of weight updates is called a *round*." msgstr "" -"Nous avons déjà vu cela auparavant, il n'y a rien de nouveau jusqu'à " -"présent. La seule *petite* différence par rapport au carnet précédent est" -" le nommage, nous avons changé ``FlowerClient`` en ``FlowerNumPyClient`` " -"et ``client_fn`` en ``numpyclient_fn``. Exécutons-le pour voir la sortie " -"que nous obtenons :" +"*Les clients* sont chargés de générer des mises à jour de poids " +"individuelles pour le modèle en fonction de leurs ensembles de données " +"locales. Ces mises à jour sont ensuite envoyées au *serveur* qui les " +"agrège pour produire un meilleur modèle. Enfin, le *serveur* renvoie " +"cette version améliorée du modèle à chaque *client*. Un cycle complet de " +"mises à jour de poids s'appelle un *round*." -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:339 +#: ../../source/tutorial-quickstart-pytorch.rst:23 msgid "" -"This works as expected, two clients are training for three rounds of " -"federated learning." +"Now that we have a rough idea of what is going on, let's get started. We " +"first need to install Flower. You can do this by running :" msgstr "" -"Cela fonctionne comme prévu, deux clients s'entraînent pour trois tours " -"d'apprentissage fédéré." +"Maintenant que nous avons une idée générale de ce qui se passe, " +"commençons. Nous devons d'abord installer Flower. Tu peux le faire en " +"exécutant :" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:341 +#: ../../source/tutorial-quickstart-pytorch.rst:29 msgid "" -"Let's dive a little bit deeper and discuss how Flower executes this " -"simulation. Whenever a client is selected to do some work, " -"``start_simulation`` calls the function ``numpyclient_fn`` to create an " -"instance of our ``FlowerNumPyClient`` (along with loading the model and " -"the data)." +"Since we want to use PyTorch to solve a computer vision task, let's go " +"ahead and install PyTorch and the **torchvision** library:" msgstr "" -"Plongeons un peu plus profondément et discutons de la façon dont Flower " -"exécute cette simulation. Chaque fois qu'un client est sélectionné pour " -"effectuer un travail, ``start_simulation`` appelle la fonction " -"``numpyclient_fn`` pour créer une instance de notre ``FlowerNumPyClient``" -" (en même temps qu'il charge le modèle et les données)." +"Puisque nous voulons utiliser PyTorch pour résoudre une tâche de vision " +"par ordinateur, allons-y et installons PyTorch et la bibliothèque " +"**torchvision** :" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:343 +#: ../../source/tutorial-quickstart-pytorch.rst:39 msgid "" -"But here's the perhaps surprising part: Flower doesn't actually use the " -"``FlowerNumPyClient`` object directly. Instead, it wraps the object to " -"makes it look like a subclass of ``flwr.client.Client``, not " -"``flwr.client.NumPyClient``. In fact, the Flower core framework doesn't " -"know how to handle ``NumPyClient``'s, it only knows how to handle " -"``Client``'s. ``NumPyClient`` is just a convenience abstraction built on " -"top of ``Client``." +"Now that we have all our dependencies installed, let's run a simple " +"distributed training with two clients and one server. Our training " +"procedure and network architecture are based on PyTorch's `Deep Learning " +"with PyTorch " +"`_." msgstr "" -"Mais voici la partie la plus surprenante : Flower n'utilise pas " -"directement l'objet `FlowerNumPyClient`. Au lieu de cela, il enveloppe " -"l'objet pour le faire ressembler à une sous-classe de " -"`flwr.client.Client`, et non de `flwr.client.NumPyClient`. En fait, le " -"noyau de Flower ne sait pas comment gérer les `NumPyClient`, il sait " -"seulement comment gérer les `Client`. `NumPyClient` est juste une " -"abstraction de commodité construite au dessus de `Client`." +"Maintenant que nous avons installé toutes nos dépendances, lançons une " +"formation distribuée simple avec deux clients et un serveur. Notre " +"procédure de formation et l'architecture de notre réseau sont basées sur " +"`Deep Learning with PyTorch " +"`_ de" +" PyTorch." -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:345 +#: ../../source/tutorial-quickstart-pytorch.rst:41 msgid "" -"Instead of building on top of ``NumPyClient``, we can directly build on " -"top of ``Client``." +"In a file called :code:`client.py`, import Flower and PyTorch related " +"packages:" msgstr "" -"Au lieu de construire par-dessus `NumPyClient``, nous pouvons construire " -"directement par-dessus `Client``." +"Dans un fichier appelé :code:`client.py`, importe Flower et les paquets " +"liés à PyTorch :" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:357 -msgid "Step 2: Moving from ``NumPyClient`` to ``Client``" -msgstr "Étape 2 : Passer de ``NumPyClient`` à ``Client``" +#: ../../source/tutorial-quickstart-pytorch.rst:56 +msgid "In addition, we define the device allocation in PyTorch with:" +msgstr "En outre, nous définissons l'attribution des appareils dans PyTorch avec :" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:359 +#: ../../source/tutorial-quickstart-pytorch.rst:62 msgid "" -"Let's try to do the same thing using ``Client`` instead of " -"``NumPyClient``." +"We use PyTorch to load CIFAR10, a popular colored image classification " +"dataset for machine learning. The PyTorch :code:`DataLoader()` downloads " +"the training and test data that are then normalized." msgstr "" -"Essayons de faire la même chose en utilisant ``Client`` au lieu de " -"``NumPyClient``." +"Nous utilisons PyTorch pour charger CIFAR10, un ensemble de données de " +"classification d'images colorées populaire pour l'apprentissage " +"automatique. Le :code:`DataLoader()` de PyTorch télécharge les données " +"d'entraînement et de test qui sont ensuite normalisées." -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:465 +#: ../../source/tutorial-quickstart-pytorch.rst:78 msgid "" -"Before we discuss the code in more detail, let's try to run it! Gotta " -"make sure our new ``Client``-based client works, right?" +"Define the loss and optimizer with PyTorch. The training of the dataset " +"is done by looping over the dataset, measure the corresponding loss and " +"optimize it." msgstr "" -"Avant de discuter du code plus en détail, essayons de l'exécuter ! Nous " -"devons nous assurer que notre nouveau client basé sur le ``Client`` " -"fonctionne, n'est-ce pas ?" +"Définis la perte et l'optimiseur avec PyTorch L'entraînement de " +"l'ensemble de données se fait en bouclant sur l'ensemble de données, en " +"mesurant la perte correspondante et en l'optimisant." -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:490 +#: ../../source/tutorial-quickstart-pytorch.rst:94 msgid "" -"That's it, we're now using ``Client``. It probably looks similar to what " -"we've done with ``NumPyClient``. So what's the difference?" +"Define then the validation of the machine learning network. We loop over" +" the test set and measure the loss and accuracy of the test set." msgstr "" -"Voilà, nous utilisons maintenant ``Client``. Cela ressemble probablement " -"à ce que nous avons fait avec ``NumPyClient``. Alors quelle est la " -"différence ?" +"Définis ensuite la validation du réseau d'apprentissage automatique. Nous" +" passons en boucle sur l'ensemble de test et mesurons la perte et la " +"précision de l'ensemble de test." -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:492 +#: ../../source/tutorial-quickstart-pytorch.rst:113 msgid "" -"First of all, it's more code. But why? The difference comes from the fact" -" that ``Client`` expects us to take care of parameter serialization and " -"deserialization. For Flower to be able to send parameters over the " -"network, it eventually needs to turn these parameters into ``bytes``. " -"Turning parameters (e.g., NumPy ``ndarray``'s) into raw bytes is called " -"serialization. Turning raw bytes into something more useful (like NumPy " -"``ndarray``'s) is called deserialization. Flower needs to do both: it " -"needs to serialize parameters on the server-side and send them to the " -"client, the client needs to deserialize them to use them for local " -"training, and then serialize the updated parameters again to send them " -"back to the server, which (finally!) deserializes them again in order to " -"aggregate them with the updates received from other clients." +"After defining the training and testing of a PyTorch machine learning " +"model, we use the functions for the Flower clients." msgstr "" -"First of all, it's more code. But why? The difference comes from the fact" -" that ``Client`` expects us to take care of parameter serialization and " -"deserialization. For Flower to be able to send parameters over the " -"network, it eventually needs to turn these parameters into ``bytes``. " -"Turning parameters (e.g., NumPy ``ndarray``'s) into raw bytes is called " -"serialization. Turning raw bytes into something more useful (like NumPy " -"``ndarray``'s) is called deserialization. Flower needs to do both: it " -"needs to serialize parameters on the server-side and send them to the " -"client, the client needs to deserialize them to use them for local " -"training, and then serialize the updated parameters again to send them " -"back to the server, which (finally!) deserializes them again in order to " -"aggregate them with the updates received from other clients." +"Après avoir défini l'entraînement et le test d'un modèle d'apprentissage " +"automatique PyTorch, nous utilisons les fonctions pour les clients " +"Flower." -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:495 +#: ../../source/tutorial-quickstart-pytorch.rst:115 msgid "" -"The only *real* difference between Client and NumPyClient is that " -"NumPyClient takes care of serialization and deserialization for you. It " -"can do so because it expects you to return parameters as NumPy ndarray's," -" and it knows how to handle these. This makes working with machine " -"learning libraries that have good NumPy support (most of them) a breeze." +"The Flower clients will use a simple CNN adapted from 'PyTorch: A 60 " +"Minute Blitz':" msgstr "" -"La seule *vraie* différence entre Client et NumPyClient est que " -"NumPyClient s'occupe de la sérialisation et de la désérialisation pour " -"toi. Il peut le faire parce qu'il s'attend à ce que tu renvoies des " -"paramètres sous forme de NumPy ndarray, et il sait comment les gérer. " -"Cela permet de travailler avec des bibliothèques d'apprentissage " -"automatique qui ont une bonne prise en charge de NumPy (la plupart " -"d'entre elles) en un clin d'œil." +"Les clients de Flower utiliseront un CNN simple adapté de \"PyTorch : A " +"60 Minute Blitz\" :" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:497 +#: ../../source/tutorial-quickstart-pytorch.rst:142 msgid "" -"In terms of API, there's one major difference: all methods in Client take" -" exactly one argument (e.g., ``FitIns`` in ``Client.fit``) and return " -"exactly one value (e.g., ``FitRes`` in ``Client.fit``). The methods in " -"``NumPyClient`` on the other hand have multiple arguments (e.g., " -"``parameters`` and ``config`` in ``NumPyClient.fit``) and multiple return" -" values (e.g., ``parameters``, ``num_example``, and ``metrics`` in " -"``NumPyClient.fit``) if there are multiple things to handle. These " -"``*Ins`` and ``*Res`` objects in ``Client`` wrap all the individual " -"values you're used to from ``NumPyClient``." +"After loading the data set with :code:`load_data()` we define the Flower " +"interface." msgstr "" -"In terms of API, there's one major difference: all methods in Client take" -" exactly one argument (e.g., ``FitIns`` in ``Client.fit``) and return " -"exactly one value (e.g., ``FitRes`` in ``Client.fit``). The methods in " -"``NumPyClient`` on the other hand have multiple arguments (e.g., " -"``parameters`` and ``config`` in ``NumPyClient.fit``) and multiple return" -" values (e.g., ``parameters``, ``num_example``, and ``metrics`` in " -"``NumPyClient.fit``) if there are multiple things to handle. These " -"``*Ins`` and ``*Res`` objects in ``Client`` wrap all the individual " -"values you're used to from ``NumPyClient``." - -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:510 -msgid "Step 3: Custom serialization" -msgstr "Étape 3 : Sérialisation personnalisée" +"Après avoir chargé l'ensemble des données avec :code:`load_data()`, nous " +"définissons l'interface Flower." -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:512 +#: ../../source/tutorial-quickstart-pytorch.rst:144 +#: ../../source/tutorial-quickstart-tensorflow.rst:54 msgid "" -"Here we will explore how to implement custom serialization with a simple " -"example." +"The Flower server interacts with clients through an interface called " +":code:`Client`. When the server selects a particular client for training," +" it sends training instructions over the network. The client receives " +"those instructions and calls one of the :code:`Client` methods to run " +"your code (i.e., to train the neural network we defined earlier)." msgstr "" -"Nous allons ici explorer comment mettre en œuvre une sérialisation " -"personnalisée à l'aide d'un exemple simple." +"Le serveur Flower interagit avec les clients par le biais d'une interface" +" appelée :code:`Client`. Lorsque le serveur sélectionne un client " +"particulier pour la formation, il envoie des instructions de formation " +"sur le réseau. Le client reçoit ces instructions et appelle l'une des " +"méthodes :code:`Client` pour exécuter ton code (c'est-à-dire pour former " +"le réseau neuronal que nous avons défini plus tôt)." -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:514 +#: ../../source/tutorial-quickstart-pytorch.rst:150 msgid "" -"But first what is serialization? Serialization is just the process of " -"converting an object into raw bytes, and equally as important, " -"deserialization is the process of converting raw bytes back into an " -"object. This is very useful for network communication. Indeed, without " -"serialization, you could not just a Python object through the internet." +"Flower provides a convenience class called :code:`NumPyClient` which " +"makes it easier to implement the :code:`Client` interface when your " +"workload uses PyTorch. Implementing :code:`NumPyClient` usually means " +"defining the following methods (:code:`set_parameters` is optional " +"though):" msgstr "" -"Mais d'abord, qu'est-ce que la sérialisation ? La sérialisation est " -"simplement le processus de conversion d'un objet en octets bruts, et tout" -" aussi important, la désérialisation est le processus de reconversion des" -" octets bruts en objet. Ceci est très utile pour la communication réseau." -" En effet, sans la sérialisation, tu ne pourrais pas faire passer un " -"objet Python par Internet." +"Flower fournit une classe de commodité appelée :code:`NumPyClient` qui " +"facilite la mise en œuvre de l'interface :code:`Client` lorsque ta charge" +" de travail utilise PyTorch. Mettre en œuvre :code:`NumPyClient` signifie" +" généralement définir les méthodes suivantes (:code:`set_parameters` est " +"cependant facultatif) :" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:516 -msgid "" -"Federated Learning relies heavily on internet communication for training " -"by sending Python objects back and forth between the clients and the " -"server. This means that serialization is an essential part of Federated " -"Learning." -msgstr "" -"L'apprentissage fédéré s'appuie fortement sur la communication Internet " -"pour la formation en envoyant des objets Python dans les deux sens entre " -"les clients et le serveur, ce qui signifie que la sérialisation est un " -"élément essentiel de l'apprentissage fédéré." +#: ../../source/tutorial-quickstart-pytorch.rst:156 +#: ../../source/tutorial-quickstart-scikitlearn.rst:119 +msgid "return the model weight as a list of NumPy ndarrays" +msgstr "renvoie le poids du modèle sous la forme d'une liste de ndarrays NumPy" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:518 +#: ../../source/tutorial-quickstart-pytorch.rst:157 +#: ../../source/tutorial-quickstart-scikitlearn.rst:121 +msgid ":code:`set_parameters` (optional)" +msgstr ":code:`set_parameters` (optionnel)" + +#: ../../source/tutorial-quickstart-pytorch.rst:158 +#: ../../source/tutorial-quickstart-scikitlearn.rst:121 msgid "" -"In the following section, we will write a basic example where instead of " -"sending a serialized version of our ``ndarray``\\ s containing our " -"parameters, we will first convert the ``ndarray`` into sparse matrices, " -"before sending them. This technique can be used to save bandwidth, as in " -"certain cases where the weights of a model are sparse (containing many 0 " -"entries), converting them to a sparse matrix can greatly improve their " -"bytesize." +"update the local model weights with the parameters received from the " +"server" msgstr "" -"Dans la section suivante, nous allons écrire un exemple de base où, au " -"lieu d'envoyer une version sérialisée de nos ``ndarray`` contenant nos " -"paramètres, nous allons d'abord convertir les ``ndarray`` en matrices " -"éparses, avant de les envoyer. Cette technique peut être utilisée pour " -"économiser de la bande passante, car dans certains cas où les poids d'un " -"modèle sont épars (contenant de nombreuses entrées 0), les convertir en " -"une matrice éparse peut grandement améliorer leur taille en octets." +"mettre à jour les poids du modèle local avec les paramètres reçus du " +"serveur" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:521 -msgid "Our custom serialization/deserialization functions" -msgstr "Nos fonctions de sérialisation/désérialisation personnalisées" +#: ../../source/tutorial-quickstart-pytorch.rst:160 +#: ../../source/tutorial-quickstart-scikitlearn.rst:124 +msgid "set the local model weights" +msgstr "fixe les poids du modèle local" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:523 +#: ../../source/tutorial-quickstart-pytorch.rst:161 +#: ../../source/tutorial-quickstart-scikitlearn.rst:125 +msgid "train the local model" +msgstr "entraîne le modèle local" + +#: ../../source/tutorial-quickstart-pytorch.rst:162 +#: ../../source/tutorial-quickstart-scikitlearn.rst:126 +msgid "receive the updated local model weights" +msgstr "recevoir les poids du modèle local mis à jour" + +#: ../../source/tutorial-quickstart-pytorch.rst:164 +#: ../../source/tutorial-quickstart-scikitlearn.rst:128 +msgid "test the local model" +msgstr "teste le modèle local" + +#: ../../source/tutorial-quickstart-pytorch.rst:166 +msgid "which can be implemented in the following way:" +msgstr "qui peut être mis en œuvre de la manière suivante :" + +#: ../../source/tutorial-quickstart-pytorch.rst:189 +#: ../../source/tutorial-quickstart-tensorflow.rst:82 msgid "" -"This is where the real serialization/deserialization will happen, " -"especially in ``ndarray_to_sparse_bytes`` for serialization and " -"``sparse_bytes_to_ndarray`` for deserialization." +"We can now create an instance of our class :code:`CifarClient` and add " +"one line to actually run this client:" msgstr "" -"C'est là que la véritable sérialisation/désérialisation se produira, en " -"particulier dans ``ndarray_to_sparse_bytes`` pour la sérialisation et " -"``sparse_bytes_to_ndarray`` pour la désérialisation." +"Nous pouvons maintenant créer une instance de notre classe " +":code:`CifarClient` et ajouter une ligne pour exécuter ce client :" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:525 +#: ../../source/tutorial-quickstart-pytorch.rst:196 +#: ../../source/tutorial-quickstart-tensorflow.rst:90 +#, fuzzy msgid "" -"Note that we imported the ``scipy.sparse`` library in order to convert " -"our arrays." +"That's it for the client. We only have to implement :code:`Client` or " +":code:`NumPyClient` and call :code:`fl.client.start_client()`. If you " +"implement a client of type :code:`NumPyClient` you'll need to first call " +"its :code:`to_client()` method. The string :code:`\"[::]:8080\"` tells " +"the client which server to connect to. In our case we can run the server " +"and the client on the same machine, therefore we use " +":code:`\"[::]:8080\"`. If we run a truly federated workload with the " +"server and clients running on different machines, all that needs to " +"change is the :code:`server_address` we point the client at." msgstr "" -"Notez que nous avons importé la bibliothèque ``scipy.sparse`` afin de " -"convertir nos tableaux." - -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:613 -msgid "Client-side" -msgstr "Côté client" +"C'est tout pour le client. Il nous suffit d'implémenter :code:`Client` ou" +" :code:`NumPyClient` et d'appeler :code:`fl.client.start_client()`. La " +"chaîne :code:`\"[: :]:8080\"` indique au client à quel serveur se " +"connecter. Dans notre cas, nous pouvons exécuter le serveur et le client " +"sur la même machine, c'est pourquoi nous utilisons :code:`\"[: " +":]:8080\"`. Si nous exécutons une charge de travail véritablement fédérée" +" avec le serveur et les clients fonctionnant sur des machines " +"différentes, tout ce qui doit changer est l'adresse " +":code:`server_address` vers laquelle nous dirigeons le client." -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:615 +#: ../../source/tutorial-quickstart-pytorch.rst:226 +#: ../../source/tutorial-quickstart-scikitlearn.rst:239 +#: ../../source/tutorial-quickstart-tensorflow.rst:122 +#: ../../source/tutorial-quickstart-xgboost.rst:533 msgid "" -"To be able to serialize our ``ndarray``\\ s into sparse parameters, we " -"will just have to call our custom functions in our " -"``flwr.client.Client``." +"Once the server is running we can start the clients in different " +"terminals. Open a new terminal and start the first client:" msgstr "" -"Pour pouvoir sérialiser nos ``ndarray`` en paramètres sparse, il nous " -"suffira d'appeler nos fonctions personnalisées dans notre " -"``flwr.client.Client``." +"Une fois que le serveur fonctionne, nous pouvons démarrer les clients " +"dans différents terminaux. Ouvre un nouveau terminal et démarre le " +"premier client :" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:617 +#: ../../source/tutorial-quickstart-pytorch.rst:233 +#: ../../source/tutorial-quickstart-scikitlearn.rst:246 +#: ../../source/tutorial-quickstart-tensorflow.rst:129 +#: ../../source/tutorial-quickstart-xgboost.rst:540 +msgid "Open another terminal and start the second client:" +msgstr "Ouvre un autre terminal et démarre le deuxième client :" + +#: ../../source/tutorial-quickstart-pytorch.rst:239 +#: ../../source/tutorial-quickstart-scikitlearn.rst:252 +#: ../../source/tutorial-quickstart-xgboost.rst:546 msgid "" -"Indeed, in ``get_parameters`` we need to serialize the parameters we got " -"from our network using our custom ``ndarrays_to_sparse_parameters`` " -"defined above." +"Each client will have its own dataset. You should now see how the " +"training does in the very first terminal (the one that started the " +"server):" msgstr "" -"En effet, dans ``get_parameters`` nous devons sérialiser les paramètres " -"que nous avons obtenus de notre réseau en utilisant nos " -"``ndarrays_to_sparse_parameters`` personnalisés définis ci-dessus." +"Chaque client aura son propre ensemble de données. Tu devrais maintenant " +"voir comment la formation se déroule dans le tout premier terminal (celui" +" qui a démarré le serveur) :" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:619 +#: ../../source/tutorial-quickstart-pytorch.rst:271 +#, fuzzy msgid "" -"In ``fit``, we first need to deserialize the parameters coming from the " -"server using our custom ``sparse_parameters_to_ndarrays`` and then we " -"need to serialize our local results with " -"``ndarrays_to_sparse_parameters``." +"Congratulations! You've successfully built and run your first federated " +"learning system. The full `source code " +"`_ for this example can be found in :code:`examples" +"/quickstart-pytorch`." msgstr "" -"Dans ``fit``, nous devons d'abord désérialiser les paramètres provenant " -"du serveur en utilisant notre ``sparse_parameters_to_ndarrays`` " -"personnalisé, puis nous devons sérialiser nos résultats locaux avec " -"``ndarrays_to_sparse_parameters``." +"Félicitations ! Tu as réussi à construire et à faire fonctionner ton " +"premier système d'apprentissage fédéré. Le code source complet " +"`_ de cet exemple se trouve dans :code:`examples" +"/quickstart-pytorch`." -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:621 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:-1 msgid "" -"In ``evaluate``, we will only need to deserialize the global parameters " -"with our custom function." +"Check out this Federated Learning quickstart tutorial for using Flower " +"with PyTorch Lightning to train an Auto Encoder model on MNIST." msgstr "" -"Dans ``evaluate``, nous n'aurons besoin que de désérialiser les " -"paramètres globaux avec notre fonction personnalisée." -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:725 -msgid "Server-side" -msgstr "Côté serveur" +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:5 +msgid "Quickstart PyTorch Lightning" +msgstr "Démarrage rapide de PyTorch Lightning" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:727 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:10 +#, fuzzy msgid "" -"For this example, we will just use ``FedAvg`` as a strategy. To change " -"the serialization and deserialization here, we only need to reimplement " -"the ``evaluate`` and ``aggregate_fit`` functions of ``FedAvg``. The other" -" functions of the strategy will be inherited from the super class " -"``FedAvg``." +"Let's build a horizontal federated learning system using PyTorch " +"Lightning and Flower!" msgstr "" -"Pour cet exemple, nous utiliserons simplement ``FedAvg`` comme stratégie." -" Pour modifier la sérialisation et la désérialisation ici, il suffit de " -"réimplémenter les fonctions ``evaluate`` et ``aggregate_fit`` de " -"``FedAvg``. Les autres fonctions de la stratégie seront héritées de la " -"super-classe ``FedAvg``." +"Construisons un système d'apprentissage fédéré en utilisant PyTorch " +"Lightning et Flower !" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:729 -msgid "As you can see only one line as change in ``evaluate``:" -msgstr "Comme tu peux le voir, seule une ligne a été modifiée dans ``evaluate`` :" +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:12 +#, fuzzy +msgid "" +"Please refer to the `full code example " +"`_ to learn more." +msgstr "" +"Réfère-toi à l'exemple de code complet " +"`_ pour en savoir plus." -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:735 +#: ../../source/tutorial-quickstart-scikitlearn.rst:-1 msgid "" -"And for ``aggregate_fit``, we will first deserialize every result we " -"received:" +"Check out this Federated Learning quickstart tutorial for using Flower " +"with scikit-learn to train a linear regression model." msgstr "" -"Et pour ``aggregate_fit``, nous allons d'abord désérialiser chaque " -"résultat que nous avons reçu :" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:744 -msgid "And then serialize the aggregated result:" -msgstr "Puis sérialise le résultat agrégé :" +#: ../../source/tutorial-quickstart-scikitlearn.rst:5 +msgid "Quickstart scikit-learn" +msgstr "Démarrage rapide de scikit-learn" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:903 -msgid "We can now run our custom serialization example!" -msgstr "" -"Nous pouvons maintenant exécuter notre exemple de sérialisation " -"personnalisée !" - -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:934 +#: ../../source/tutorial-quickstart-scikitlearn.rst:10 msgid "" -"In this part of the tutorial, we've seen how we can build clients by " -"subclassing either ``NumPyClient`` or ``Client``. ``NumPyClient`` is a " -"convenience abstraction that makes it easier to work with machine " -"learning libraries that have good NumPy interoperability. ``Client`` is a" -" more flexible abstraction that allows us to do things that are not " -"possible in ``NumPyClient``. In order to do so, it requires us to handle " -"parameter serialization and deserialization ourselves." +"In this tutorial, we will learn how to train a :code:`Logistic " +"Regression` model on MNIST using Flower and scikit-learn." msgstr "" -"Dans cette partie du tutoriel, nous avons vu comment construire des " -"clients en sous-classant soit ``NumPyClient``, soit ``Client``. " -"``NumPyClient`` est une abstraction de commodité qui facilite le travail " -"avec les bibliothèques d'apprentissage automatique qui ont une bonne " -"interopérabilité NumPy. ``Client`` est une abstraction plus flexible qui " -"nous permet de faire des choses qui ne sont pas possibles dans " -"``NumPyClient``. Pour ce faire, elle nous oblige à gérer nous-mêmes la " -"sérialisation et la désérialisation des paramètres." +"Dans ce tutoriel, nous allons apprendre à former un :code:`modèle de " +"régression logistique` sur MNIST en utilisant Flower et scikit-learn." -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:952 +#: ../../source/tutorial-quickstart-scikitlearn.rst:12 +#, fuzzy msgid "" -"This is the final part of the Flower tutorial (for now!), " -"congratulations! You're now well equipped to understand the rest of the " -"documentation. There are many topics we didn't cover in the tutorial, we " -"recommend the following resources:" +"It is recommended to create a virtual environment and run everything " +"within this :doc:`virtualenv `." msgstr "" -"C'est la dernière partie du tutoriel Flower (pour l'instant !), " -"félicitations ! Tu es maintenant bien équipé pour comprendre le reste de " -"la documentation. Il y a de nombreux sujets que nous n'avons pas abordés " -"dans le tutoriel, nous te recommandons les ressources suivantes :" - -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:954 -msgid "`Read Flower Docs `__" -msgstr "`Lire les docs sur les fleurs `__" +"Il est recommandé de créer un environnement virtuel et de tout exécuter " +"dans ce `virtualenv `_." -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:955 +#: ../../source/tutorial-quickstart-scikitlearn.rst:16 msgid "" -"`Check out Flower Code Examples " -"`__" +"*Clients* are responsible for generating individual model parameter " +"updates for the model based on their local datasets. These updates are " +"then sent to the *server* which will aggregate them to produce an updated" +" global model. Finally, the *server* sends this improved version of the " +"model back to each *client*. A complete cycle of parameters updates is " +"called a *round*." msgstr "" -"`Check out Flower Code Examples " -"`__" +"*Les clients* sont chargés de générer des mises à jour individuelles des " +"paramètres du modèle en fonction de leurs ensembles de données locales. " +"Ces mises à jour sont ensuite envoyées au *serveur* qui les agrège pour " +"produire un modèle global mis à jour. Enfin, le *serveur* renvoie cette " +"version améliorée du modèle à chaque *client*. Un cycle complet de mises " +"à jour des paramètres s'appelle un *round*." -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:956 -#, fuzzy +#: ../../source/tutorial-quickstart-scikitlearn.rst:20 msgid "" -"`Use Flower Baselines for your research " -"`__" +"Now that we have a rough idea of what is going on, let's get started. We " +"first need to install Flower. You can do this by running:" msgstr "" -"`Utilise les lignes de base des fleurs pour ta recherche " -"`__" +"Maintenant que nous avons une idée approximative de ce qui se passe, " +"commençons. Nous devons d'abord installer Flower. Tu peux le faire en " +"lançant :" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:957 +#: ../../source/tutorial-quickstart-scikitlearn.rst:26 #, fuzzy -msgid "" -"`Watch Flower Summit 2023 videos `__" -msgstr "" -"`Regardez les vidéos du Flower Summit 2022 `__" - -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:9 -msgid "Get started with Flower" -msgstr "" +msgid "Since we want to use scikit-learn, let's go ahead and install it:" +msgstr "Puisque nous voulons utiliser scikt-learn, allons-y et installons-le :" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:11 -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:11 -msgid "Welcome to the Flower federated learning tutorial!" -msgstr "Bienvenue au tutoriel sur l'apprentissage fédéré de la fleur !" +#: ../../source/tutorial-quickstart-scikitlearn.rst:32 +msgid "Or simply install all dependencies using Poetry:" +msgstr "Ou installe simplement toutes les dépendances à l'aide de Poetry :" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:13 -#, fuzzy +#: ../../source/tutorial-quickstart-scikitlearn.rst:42 msgid "" -"In this notebook, we'll build a federated learning system using Flower, " -"`Flower Datasets `__ and PyTorch. In " -"part 1, we use PyTorch for the model training pipeline and data loading. " -"In part 2, we continue to federate the PyTorch-based pipeline using " -"Flower." +"Now that we have all our dependencies installed, let's run a simple " +"distributed training with two clients and one server. However, before " +"setting up the client and server, we will define all functionalities that" +" we need for our federated learning setup within :code:`utils.py`. The " +":code:`utils.py` contains different functions defining all the machine " +"learning basics:" msgstr "" -"Dans ce carnet, nous allons construire un système d'apprentissage fédéré " -"en utilisant Flower et PyTorch. Dans la première partie, nous utilisons " -"PyTorch pour le pipeline d'entraînement des modèles et le chargement des " -"données. Dans la deuxième partie, nous continuons à fédérer le pipeline " -"basé sur PyTorch en utilisant Flower." +"Maintenant que toutes nos dépendances sont installées, exécutons une " +"formation distribuée simple avec deux clients et un serveur. Cependant, " +"avant de configurer le client et le serveur, nous allons définir toutes " +"les fonctionnalités dont nous avons besoin pour notre configuration " +"d'apprentissage fédéré dans :code:`utils.py`. Le :code:`utils.py` " +"contient différentes fonctions définissant toutes les bases de " +"l'apprentissage automatique :" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:17 -msgid "Let's get stated!" -msgstr "Allons-y, déclarons-le !" +#: ../../source/tutorial-quickstart-scikitlearn.rst:45 +msgid ":code:`get_model_parameters()`" +msgstr ":code:`get_model_parameters()`" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:31 -msgid "" -"Before we begin with any actual code, let's make sure that we have " -"everything we need." +#: ../../source/tutorial-quickstart-scikitlearn.rst:46 +msgid "Returns the parameters of a :code:`sklearn` LogisticRegression model" msgstr "" -"Avant de commencer à coder, assurons-nous que nous disposons de tout ce " -"dont nous avons besoin." +"Renvoie les paramètres d'un modèle de régression logistique " +":code:`sklearn`" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:45 -#, fuzzy -msgid "" -"Next, we install the necessary packages for PyTorch (``torch`` and " -"``torchvision``), Flower Datasets (``flwr-datasets``) and Flower " -"(``flwr``):" -msgstr "" -"Ensuite, nous installons les paquets nécessaires pour PyTorch (``torch`` " -"et ``torchvision``) et Flower (``flwr``) :" +#: ../../source/tutorial-quickstart-scikitlearn.rst:47 +msgid ":code:`set_model_params()`" +msgstr ":code:`set_model_params()`" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:105 +#: ../../source/tutorial-quickstart-scikitlearn.rst:48 #, fuzzy -msgid "" -"It is possible to switch to a runtime that has GPU acceleration enabled " -"(on Google Colab: ``Runtime > Change runtime type > Hardware accelerator:" -" GPU > Save``). Note, however, that Google Colab is not always able to " -"offer GPU acceleration. If you see an error related to GPU availability " -"in one of the following sections, consider switching back to CPU-based " -"execution by setting ``DEVICE = torch.device(\"cpu\")``. If the runtime " -"has GPU acceleration enabled, you should see the output ``Training on " -"cuda``, otherwise it'll say ``Training on cpu``." -msgstr "" -"Il est possible de passer à un runtime dont l'accélération GPU est " -"activée (sur Google Colab : ``Runtime > Change runtime type > Hardware " -"acclerator : GPU > Save``). Note cependant que Google Colab n'est pas " -"toujours en mesure de proposer l'accélération GPU. Si tu vois une erreur " -"liée à la disponibilité du GPU dans l'une des sections suivantes, " -"envisage de repasser à une exécution basée sur le CPU en définissant " -"``DEVICE = torch.device(\"cpu\")``. Si le runtime a activé l'accélération" -" GPU, tu devrais voir apparaître le résultat ``Training on cuda``, sinon " -"il dira ``Training on cpu``." +msgid "Sets the parameters of a :code:`sklearn` LogisticRegression model" +msgstr "Définit les paramètres d'un modèle de régression logistique :code:`sklean`" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:118 -msgid "Loading the data" -msgstr "Chargement des données" +#: ../../source/tutorial-quickstart-scikitlearn.rst:50 +msgid ":code:`set_initial_params()`" +msgstr ":code:`set_initial_params()`" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:120 -#, fuzzy +#: ../../source/tutorial-quickstart-scikitlearn.rst:50 +msgid "Initializes the model parameters that the Flower server will ask for" +msgstr "Initialise les paramètres du modèle que le serveur de Flower demandera" + +#: ../../source/tutorial-quickstart-scikitlearn.rst:52 msgid "" -"Federated learning can be applied to many different types of tasks across" -" different domains. In this tutorial, we introduce federated learning by " -"training a simple convolutional neural network (CNN) on the popular " -"CIFAR-10 dataset. CIFAR-10 can be used to train image classifiers that " -"distinguish between images from ten different classes: 'airplane', " -"'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', and " -"'truck'." +"Please check out :code:`utils.py` `here " +"`_ for more details. The pre-defined functions are used in" +" the :code:`client.py` and imported. The :code:`client.py` also requires " +"to import several packages such as Flower and scikit-learn:" msgstr "" -"L'apprentissage fédéré peut être appliqué à de nombreux types de tâches " -"dans différents domaines. Dans ce tutoriel, nous présentons " -"l'apprentissage fédéré en formant un simple réseau neuronal " -"convolutionnel (CNN) sur l'ensemble de données populaire CIFAR-10. " -"CIFAR-10 peut être utilisé pour former des classificateurs d'images qui " -"font la distinction entre les images de dix classes différentes :" +"Tu peux consulter :code:`utils.py` `ici " +"`_ pour plus de détails. Les fonctions prédéfinies sont " +"utilisées dans :code:`client.py` et importées. :code:`client.py` " +"nécessite également d'importer plusieurs paquets tels que Flower et " +"scikit-learn :" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:131 +#: ../../source/tutorial-quickstart-scikitlearn.rst:67 msgid "" -"We simulate having multiple datasets from multiple organizations (also " -"called the \"cross-silo\" setting in federated learning) by splitting the" -" original CIFAR-10 dataset into multiple partitions. Each partition will " -"represent the data from a single organization. We're doing this purely " -"for experimentation purposes, in the real world there's no need for data " -"splitting because each organization already has their own data (so the " -"data is naturally partitioned)." +"Prior to local training, we need to load the MNIST dataset, a popular " +"image classification dataset of handwritten digits for machine learning, " +"and partition the dataset for FL. This can be conveniently achieved using" +" `Flower Datasets `_. The " +":code:`FederatedDataset.load_partition()` method loads the partitioned " +"training set for each partition ID defined in the :code:`--partition-id` " +"argument." msgstr "" -"Nous simulons le fait d'avoir plusieurs ensembles de données provenant de" -" plusieurs organisations (également appelé le paramètre \"cross-silo\" " -"dans l'apprentissage fédéré) en divisant l'ensemble de données CIFAR-10 " -"original en plusieurs partitions. Chaque partition représentera les " -"données d'une seule organisation. Nous faisons cela purement à des fins " -"d'expérimentation, dans le monde réel, il n'y a pas besoin de diviser les" -" données parce que chaque organisation a déjà ses propres données (les " -"données sont donc naturellement partitionnées)." -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:133 -#, fuzzy +#: ../../source/tutorial-quickstart-scikitlearn.rst:95 msgid "" -"Each organization will act as a client in the federated learning system. " -"So having ten organizations participate in a federation means having ten " -"clients connected to the federated learning server." +"Next, the logistic regression model is defined and initialized with " +":code:`utils.set_initial_params()`." msgstr "" -"Chaque organisation agira comme un client dans le système d'apprentissage" -" fédéré. Ainsi, le fait que dix organisations participent à une " -"fédération signifie que dix clients sont connectés au serveur " -"d'apprentissage fédéré :" +"Ensuite, le modèle de régression logistique est défini et initialisé avec" +" :code:`utils.set_initial_params()`." -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:144 +#: ../../source/tutorial-quickstart-scikitlearn.rst:107 msgid "" -"Let's now create the Federated Dataset abstraction that from ``flwr-" -"datasets`` that partitions the CIFAR-10. We will create small training " -"and test set for each edge device and wrap each of them into a PyTorch " -"``DataLoader``:" +"The Flower server interacts with clients through an interface called " +":code:`Client`. When the server selects a particular client for training," +" it sends training instructions over the network. The client receives " +"those instructions and calls one of the :code:`Client` methods to run " +"your code (i.e., to fit the logistic regression we defined earlier)." msgstr "" +"Le serveur Flower interagit avec les clients par le biais d'une interface" +" appelée :code:`Client`. Lorsque le serveur sélectionne un client " +"particulier pour la formation, il envoie des instructions de formation " +"sur le réseau. Le client reçoit ces instructions et appelle l'une des " +"méthodes :code:`Client` pour exécuter ton code (c'est-à-dire pour ajuster" +" la régression logistique que nous avons définie plus tôt)." -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:198 +#: ../../source/tutorial-quickstart-scikitlearn.rst:113 msgid "" -"We now have a list of ten training sets and ten validation sets " -"(``trainloaders`` and ``valloaders``) representing the data of ten " -"different organizations. Each ``trainloader``/``valloader`` pair contains" -" 4500 training examples and 500 validation examples. There's also a " -"single ``testloader`` (we did not split the test set). Again, this is " -"only necessary for building research or educational systems, actual " -"federated learning systems have their data naturally distributed across " -"multiple partitions." +"Flower provides a convenience class called :code:`NumPyClient` which " +"makes it easier to implement the :code:`Client` interface when your " +"workload uses scikit-learn. Implementing :code:`NumPyClient` usually " +"means defining the following methods (:code:`set_parameters` is optional " +"though):" msgstr "" -"Nous avons maintenant une liste de dix ensembles de formation et dix " -"ensembles de validation (``trainloaders`` et ``valloaders``) représentant" -" les données de dix organisations différentes. Chaque paire " -"``trainloader`/``valloader`` contient 4500 exemples de formation et 500 " -"exemples de validation. Il y a également un seul ``testloader`` (nous " -"n'avons pas divisé l'ensemble de test). Encore une fois, cela n'est " -"nécessaire que pour construire des systèmes de recherche ou d'éducation, " -"les systèmes d'apprentissage fédérés actuels ont leurs données " -"naturellement distribuées à travers plusieurs partitions." +"Flower fournit une classe de commodité appelée :code:`NumPyClient` qui " +"facilite la mise en œuvre de l'interface :code:`Client` lorsque ta charge" +" de travail utilise scikit-learn. Mettre en œuvre :code:`NumPyClient` " +"signifie généralement définir les méthodes suivantes " +"(:code:`set_parameters` est cependant facultatif) :" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:201 +#: ../../source/tutorial-quickstart-scikitlearn.rst:122 +msgid "is directly imported with :code:`utils.set_model_params()`" +msgstr "est directement importé avec :code:`utils.set_model_params()`" + +#: ../../source/tutorial-quickstart-scikitlearn.rst:130 +msgid "The methods can be implemented in the following way:" +msgstr "Les méthodes peuvent être mises en œuvre de la manière suivante :" + +#: ../../source/tutorial-quickstart-scikitlearn.rst:153 msgid "" -"Let's take a look at the first batch of images and labels in the first " -"training set (i.e., ``trainloaders[0]``) before we move on:" +"We can now create an instance of our class :code:`MnistClient` and add " +"one line to actually run this client:" msgstr "" -"Jetons un coup d'œil au premier lot d'images et d'étiquettes du premier " -"ensemble d'entraînement (c'est-à-dire ``trainloaders[0]``) avant de " -"poursuivre :" +"Nous pouvons maintenant créer une instance de notre classe " +":code:`MnistClient` et ajouter une ligne pour exécuter ce client :" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:240 +#: ../../source/tutorial-quickstart-scikitlearn.rst:160 +#, fuzzy msgid "" -"The output above shows a random batch of images from the first " -"``trainloader`` in our list of ten ``trainloaders``. It also prints the " -"labels associated with each image (i.e., one of the ten possible labels " -"we've seen above). If you run the cell again, you should see another " -"batch of images." +"That's it for the client. We only have to implement :code:`Client` or " +":code:`NumPyClient` and call :code:`fl.client.start_client()`. If you " +"implement a client of type :code:`NumPyClient` you'll need to first call " +"its :code:`to_client()` method. The string :code:`\"0.0.0.0:8080\"` tells" +" the client which server to connect to. In our case we can run the server" +" and the client on the same machine, therefore we use " +":code:`\"0.0.0.0:8080\"`. If we run a truly federated workload with the " +"server and clients running on different machines, all that needs to " +"change is the :code:`server_address` we pass to the client." msgstr "" -"La sortie ci-dessus montre un lot aléatoire d'images provenant du premier" -" ``chargeur de formation`` de notre liste de dix ``chargeurs de " -"formation``. Elle imprime également les étiquettes associées à chaque " -"image (c'est-à-dire l'une des dix étiquettes possibles que nous avons " -"vues ci-dessus). Si tu exécutes à nouveau la cellule, tu devrais voir un " -"autre lot d'images." - -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:252 -msgid "Step 1: Centralized Training with PyTorch" -msgstr "Étape 1 : Formation centralisée avec PyTorch" +"C'est tout pour le client. Il nous suffit d'implémenter :code:`Client` ou" +" :code:`NumPyClient` et d'appeler :code:`fl.client.start_client()`. La " +"chaîne :code:`\"0.0.0:8080\"` indique au client à quel serveur se " +"connecter. Dans notre cas, nous pouvons exécuter le serveur et le client " +"sur la même machine, c'est pourquoi nous utilisons " +":code:`\"0.0.0:8080\"`. Si nous exécutons une charge de travail " +"véritablement fédérée avec le serveur et les clients s'exécutant sur des " +"machines différentes, tout ce qui doit changer est :code:`server_address`" +" que nous transmettons au client." -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:263 +#: ../../source/tutorial-quickstart-scikitlearn.rst:169 msgid "" -"Next, we're going to use PyTorch to define a simple convolutional neural " -"network. This introduction assumes basic familiarity with PyTorch, so it " -"doesn't cover the PyTorch-related aspects in full detail. If you want to " -"dive deeper into PyTorch, we recommend `DEEP LEARNING WITH PYTORCH: A 60 " -"MINUTE BLITZ " -"`__." +"The following Flower server is a little bit more advanced and returns an " +"evaluation function for the server-side evaluation. First, we import " +"again all required libraries such as Flower and scikit-learn." msgstr "" -"Ensuite, nous allons utiliser PyTorch pour définir un simple réseau " -"neuronal convolutif. Cette introduction suppose une familiarité de base " -"avec PyTorch, elle ne couvre donc pas en détail les aspects liés à " -"PyTorch. Si tu veux plonger plus profondément dans PyTorch, nous te " -"recommandons `DEEP LEARNING WITH PYTORCH : A 60 MINUTE BLITZ " -"`__." +"Le serveur Flower suivant est un peu plus avancé et renvoie une fonction " +"d'évaluation pour l'évaluation côté serveur. Tout d'abord, nous importons" +" à nouveau toutes les bibliothèques requises telles que Flower et scikit-" +"learn." -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:275 -msgid "Defining the model" -msgstr "Définir le modèle" +#: ../../source/tutorial-quickstart-scikitlearn.rst:172 +msgid ":code:`server.py`, import Flower and start the server:" +msgstr ":code:`server.py`, importe Flower et démarre le serveur :" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:277 +#: ../../source/tutorial-quickstart-scikitlearn.rst:185 +#, fuzzy msgid "" -"We use the simple CNN described in the `PyTorch tutorial " -"`__:" +"The number of federated learning rounds is set in :code:`fit_round()` and" +" the evaluation is defined in :code:`get_evaluate_fn()`. The evaluation " +"function is called after each federated learning round and gives you " +"information about loss and accuracy. Note that we also make use of Flower" +" Datasets here to load the test split of the MNIST dataset for server-" +"side evaluation." msgstr "" -"Nous utilisons le CNN simple décrit dans le tutoriel `PyTorch " -"`__ :" +"Le nombre de tours d'apprentissage fédéré est défini dans " +":code:`fit_round()` et l'évaluation est définie dans " +":code:`get_evaluate_fn()`. La fonction d'évaluation est appelée après " +"chaque tour d'apprentissage fédéré et te donne des informations sur la " +"perte et la précision." -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:314 -msgid "Let's continue with the usual training and test functions:" -msgstr "Poursuivons avec les fonctions habituelles de formation et de test :" +#: ../../source/tutorial-quickstart-scikitlearn.rst:213 +msgid "" +"The :code:`main` contains the server-side parameter initialization " +":code:`utils.set_initial_params()` as well as the aggregation strategy " +":code:`fl.server.strategy:FedAvg()`. The strategy is the default one, " +"federated averaging (or FedAvg), with two clients and evaluation after " +"each federated learning round. The server can be started with the command" +" :code:`fl.server.start_server(server_address=\"0.0.0.0:8080\", " +"strategy=strategy, config=fl.server.ServerConfig(num_rounds=3))`." +msgstr "" +"Le :code:`main` contient l'initialisation des paramètres côté serveur " +":code:`utils.set_initial_params()` ainsi que la stratégie d'agrégation " +":code:`fl.server.strategy:FedAvg()`. La stratégie est celle par défaut, " +"la moyenne fédérée (ou FedAvg), avec deux clients et une évaluation après" +" chaque tour d'apprentissage fédéré. Le serveur peut être démarré avec la" +" commande :code:`fl.server.start_server(server_address=\"0.0.0.0:8080\", " +"strategy=strategy, config=fl.server.ServerConfig(num_rounds=3))`." -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:374 -msgid "Training the model" -msgstr "Entraîne le modèle" +#: ../../source/tutorial-quickstart-scikitlearn.rst:232 +msgid "" +"With both client and server ready, we can now run everything and see " +"federated learning in action. Federated learning systems usually have a " +"server and multiple clients. We, therefore, have to start the server " +"first:" +msgstr "" +"Le client et le serveur étant prêts, nous pouvons maintenant tout lancer " +"et voir l'apprentissage fédéré en action. Les systèmes d'apprentissage " +"fédéré ont généralement un serveur et plusieurs clients. Nous devons donc" +" commencer par lancer le serveur :" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:376 +#: ../../source/tutorial-quickstart-scikitlearn.rst:286 msgid "" -"We now have all the basic building blocks we need: a dataset, a model, a " -"training function, and a test function. Let's put them together to train " -"the model on the dataset of one of our organizations " -"(``trainloaders[0]``). This simulates the reality of most machine " -"learning projects today: each organization has their own data and trains " -"models only on this internal data:" +"Congratulations! You've successfully built and run your first federated " +"learning system. The full `source code " +"`_ for this example can be found in :code:`examples/sklearn-logreg-" +"mnist`." msgstr "" -"Nous avons maintenant tous les éléments de base dont nous avons besoin : " -"un ensemble de données, un modèle, une fonction d'entraînement et une " -"fonction de test. Assemblons-les pour entraîner le modèle sur l'ensemble " -"de données de l'une de nos organisations (``trainloaders[0]``). Cela " -"simule la réalité de la plupart des projets d'apprentissage automatique " -"aujourd'hui : chaque organisation possède ses propres données et entraîne" -" les modèles uniquement sur ces données internes :" +"Félicitations ! Tu as réussi à construire et à faire fonctionner ton " +"premier système d'apprentissage fédéré. Le code source complet " +"`_ de cet exemple se trouve dans :code:`examples/sklearn-logreg-" +"mnist`." -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:406 +#: ../../source/tutorial-quickstart-tensorflow.rst:-1 msgid "" -"Training the simple CNN on our CIFAR-10 split for 5 epochs should result " -"in a test set accuracy of about 41%, which is not good, but at the same " -"time, it doesn't really matter for the purposes of this tutorial. The " -"intent was just to show a simplistic centralized training pipeline that " -"sets the stage for what comes next - federated learning!" +"Check out this Federated Learning quickstart tutorial for using Flower " +"with TensorFlow to train a MobilNetV2 model on CIFAR-10." msgstr "" -"L'entraînement du CNN simple sur notre fractionnement CIFAR-10 pendant 5 " -"époques devrait se traduire par une précision de l'ensemble de test " -"d'environ 41 %, ce qui n'est pas bon, mais en même temps, cela n'a pas " -"vraiment d'importance pour les besoins de ce tutoriel. L'intention était " -"juste de montrer un pipeline d'entraînement centralisé simpliste qui " -"prépare le terrain pour ce qui vient ensuite - l'apprentissage fédéré !" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:418 -msgid "Step 2: Federated Learning with Flower" -msgstr "Étape 2 : Apprentissage fédéré avec Flower" +#: ../../source/tutorial-quickstart-tensorflow.rst:5 +msgid "Quickstart TensorFlow" +msgstr "Démarrage rapide de TensorFlow" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:420 -msgid "" -"Step 1 demonstrated a simple centralized training pipeline. All data was " -"in one place (i.e., a single ``trainloader`` and a single ``valloader``)." -" Next, we'll simulate a situation where we have multiple datasets in " -"multiple organizations and where we train a model over these " -"organizations using federated learning." +#: ../../source/tutorial-quickstart-tensorflow.rst:13 +msgid "Let's build a federated learning system in less than 20 lines of code!" msgstr "" -"L'étape 1 a montré un simple pipeline de formation centralisé. Toutes les" -" données étaient au même endroit (c'est-à-dire un seul ``trainloader`` et" -" un seul ``valloader``). Ensuite, nous allons simuler une situation où " -"nous avons plusieurs ensembles de données dans plusieurs organisations et" -" où nous formons un modèle sur ces organisations à l'aide de " -"l'apprentissage fédéré." +"Construisons un système d'apprentissage fédéré en moins de 20 lignes de " +"code !" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:432 -msgid "Updating model parameters" -msgstr "Mise à jour des paramètres du modèle" +#: ../../source/tutorial-quickstart-tensorflow.rst:15 +msgid "Before Flower can be imported we have to install it:" +msgstr "Avant de pouvoir importer une fleur, nous devons l'installer :" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:434 +#: ../../source/tutorial-quickstart-tensorflow.rst:21 msgid "" -"In federated learning, the server sends the global model parameters to " -"the client, and the client updates the local model with the parameters " -"received from the server. It then trains the model on the local data " -"(which changes the model parameters locally) and sends the " -"updated/changed model parameters back to the server (or, alternatively, " -"it sends just the gradients back to the server, not the full model " -"parameters)." +"Since we want to use the Keras API of TensorFlow (TF), we have to install" +" TF as well:" msgstr "" -"Dans l'apprentissage fédéré, le serveur envoie les paramètres du modèle " -"global au client, et le client met à jour le modèle local avec les " -"paramètres reçus du serveur. Il entraîne ensuite le modèle sur les " -"données locales (ce qui modifie les paramètres du modèle localement) et " -"renvoie les paramètres du modèle mis à jour/changés au serveur (ou, " -"alternativement, il renvoie seulement les gradients au serveur, et non " -"pas les paramètres complets du modèle)." +"Comme nous voulons utiliser l'API Keras de TensorFlow (TF), nous devons " +"également installer TF :" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:436 -msgid "" -"We need two helper functions to update the local model with parameters " -"received from the server and to get the updated model parameters from the" -" local model: ``set_parameters`` and ``get_parameters``. The following " -"two functions do just that for the PyTorch model above." +#: ../../source/tutorial-quickstart-tensorflow.rst:31 +msgid "Next, in a file called :code:`client.py`, import Flower and TensorFlow:" msgstr "" -"Nous avons besoin de deux fonctions d'aide pour mettre à jour le modèle " -"local avec les paramètres reçus du serveur et pour obtenir les paramètres" -" mis à jour du modèle local : ``set_parameters`` et ``get_parameters``. " -"Les deux fonctions suivantes font exactement cela pour le modèle PyTorch " -"ci-dessus." +"Ensuite, dans un fichier appelé :code:`client.py`, importe Flower et " +"TensorFlow :" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:438 +#: ../../source/tutorial-quickstart-tensorflow.rst:38 msgid "" -"The details of how this works are not really important here (feel free to" -" consult the PyTorch documentation if you want to learn more). In " -"essence, we use ``state_dict`` to access PyTorch model parameter tensors." -" The parameter tensors are then converted to/from a list of NumPy " -"ndarray's (which Flower knows how to serialize/deserialize):" +"We use the Keras utilities of TF to load CIFAR10, a popular colored image" +" classification dataset for machine learning. The call to " +":code:`tf.keras.datasets.cifar10.load_data()` downloads CIFAR10, caches " +"it locally, and then returns the entire training and test set as NumPy " +"ndarrays." msgstr "" -"Les détails de ce fonctionnement ne sont pas vraiment importants ici " -"(n'hésite pas à consulter la documentation PyTorch si tu veux en savoir " -"plus). En substance, nous utilisons ``state_dict`` pour accéder aux " -"tenseurs de paramètres du modèle PyTorch. Les tenseurs de paramètres sont" -" ensuite convertis en/depuis une liste de ndarray NumPy (que Flower sait " -"sérialiser/désérialiser) :" - -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:466 -msgid "Implementing a Flower client" -msgstr "Mise en place d'un client Flower" +"Nous utilisons les utilitaires Keras de TF pour charger CIFAR10, un " +"ensemble de données de classification d'images colorées populaire pour " +"l'apprentissage automatique. L'appel à " +":code:`tf.keras.datasets.cifar10.load_data()` télécharge CIFAR10, le met " +"en cache localement, puis renvoie l'ensemble d'entraînement et de test " +"sous forme de NumPy ndarrays." -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:468 +#: ../../source/tutorial-quickstart-tensorflow.rst:47 msgid "" -"With that out of the way, let's move on to the interesting part. " -"Federated learning systems consist of a server and multiple clients. In " -"Flower, we create clients by implementing subclasses of " -"``flwr.client.Client`` or ``flwr.client.NumPyClient``. We use " -"``NumPyClient`` in this tutorial because it is easier to implement and " -"requires us to write less boilerplate." +"Next, we need a model. For the purpose of this tutorial, we use " +"MobilNetV2 with 10 output classes:" msgstr "" -"Ceci étant dit, passons à la partie intéressante. Les systèmes " -"d'apprentissage fédérés se composent d'un serveur et de plusieurs " -"clients. Dans Flower, nous créons des clients en mettant en œuvre des " -"sous-classes de ``flwr.client.Client`` ou de ``flwr.client.NumPyClient``." -" Nous utilisons ``NumPyClient`` dans ce tutoriel parce qu'il est plus " -"facile à mettre en œuvre et qu'il nous oblige à rédiger moins de modèles " -"de chaudière." +"Ensuite, nous avons besoin d'un modèle. Pour les besoins de ce tutoriel, " +"nous utilisons MobilNetV2 avec 10 classes de sortie :" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:470 +#: ../../source/tutorial-quickstart-tensorflow.rst:60 msgid "" -"To implement the Flower client, we create a subclass of " -"``flwr.client.NumPyClient`` and implement the three methods " -"``get_parameters``, ``fit``, and ``evaluate``:" +"Flower provides a convenience class called :code:`NumPyClient` which " +"makes it easier to implement the :code:`Client` interface when your " +"workload uses Keras. The :code:`NumPyClient` interface defines three " +"methods which can be implemented in the following way:" msgstr "" -"Pour mettre en œuvre le client Flower, nous créons une sous-classe de " -"``flwr.client.NumPyClient`` et mettons en œuvre les trois méthodes " -"``get_parameters``, ``fit`` et ``evaluate`` :" +"Flower fournit une classe de commodité appelée :code:`NumPyClient` qui " +"facilite la mise en œuvre de l'interface :code:`Client` lorsque ta charge" +" de travail utilise Keras. L'interface :code:`NumPyClient` définit trois " +"méthodes qui peuvent être mises en œuvre de la manière suivante :" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:472 -msgid "``get_parameters``: Return the current local model parameters" -msgstr "``get_parameters`` : renvoie les paramètres du modèle local actuel" +#: ../../source/tutorial-quickstart-tensorflow.rst:135 +msgid "Each client will have its own dataset." +msgstr "Chaque client aura son propre ensemble de données." -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:473 +#: ../../source/tutorial-quickstart-tensorflow.rst:137 msgid "" -"``fit``: Receive model parameters from the server, train the model " -"parameters on the local data, and return the (updated) model parameters " -"to the server" +"You should now see how the training does in the very first terminal (the " +"one that started the server):" msgstr "" -"``fit`` : reçoit les paramètres du modèle du serveur, entraîne les " -"paramètres du modèle sur les données locales et renvoie les paramètres du" -" modèle (mis à jour) au serveur" +"Tu devrais maintenant voir comment la formation se déroule dans le tout " +"premier terminal (celui qui a démarré le serveur) :" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:474 +#: ../../source/tutorial-quickstart-tensorflow.rst:169 +#, fuzzy msgid "" -"``evaluate``: Receive model parameters from the server, evaluate the " -"model parameters on the local data, and return the evaluation result to " -"the server" +"Congratulations! You've successfully built and run your first federated " +"learning system. The full `source code " +"`_ for this can be found in :code:`examples" +"/quickstart-tensorflow/client.py`." msgstr "" -"``evaluate`` : reçoit les paramètres du modèle du serveur, évalue les " -"paramètres du modèle sur les données locales et renvoie le résultat de " -"l'évaluation au serveur" +"Félicitations ! Tu as réussi à construire et à faire fonctionner ton " +"premier système d'apprentissage fédéré. Le `code source complet " +"`_ pour cela se trouve dans :code:`examples" +"/quickstart-tensorflow/client.py`." -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:476 +#: ../../source/tutorial-quickstart-xgboost.rst:-1 msgid "" -"We mentioned that our clients will use the previously defined PyTorch " -"components for model training and evaluation. Let's see a simple Flower " -"client implementation that brings everything together:" +"Check out this Federated Learning quickstart tutorial for using Flower " +"with XGBoost to train classification models on trees." msgstr "" -"Nous avons mentionné que nos clients utiliseront les composants PyTorch " -"définis précédemment pour la formation et l'évaluation des modèles. " -"Voyons une simple mise en œuvre du client Flower qui réunit tout cela :" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:513 -msgid "" -"Our class ``FlowerClient`` defines how local training/evaluation will be " -"performed and allows Flower to call the local training/evaluation through" -" ``fit`` and ``evaluate``. Each instance of ``FlowerClient`` represents a" -" *single client* in our federated learning system. Federated learning " -"systems have multiple clients (otherwise, there's not much to federate), " -"so each client will be represented by its own instance of " -"``FlowerClient``. If we have, for example, three clients in our workload," -" then we'd have three instances of ``FlowerClient``. Flower calls " -"``FlowerClient.fit`` on the respective instance when the server selects a" -" particular client for training (and ``FlowerClient.evaluate`` for " -"evaluation)." -msgstr "" -"Our class ``FlowerClient`` defines how local training/evaluation will be " -"performed and allows Flower to call the local training/evaluation through" -" ``fit`` and ``evaluate``. Each instance of ``FlowerClient`` represents a" -" *single client* in our federated learning system. Federated learning " -"systems have multiple clients (otherwise, there's not much to federate), " -"so each client will be represented by its own instance of " -"``FlowerClient``. If we have, for example, three clients in our workload," -" then we'd have three instances of ``FlowerClient``. Flower calls " -"``FlowerClient.fit`` on the respective instance when the server selects a" -" particular client for training (and ``FlowerClient.evaluate`` for " -"evaluation)." +#: ../../source/tutorial-quickstart-xgboost.rst:5 +msgid "Quickstart XGBoost" +msgstr "Démarrage rapide XGBoost" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:517 -msgid "Using the Virtual Client Engine" -msgstr "Utilisation du moteur du client virtuel" +#: ../../source/tutorial-quickstart-xgboost.rst:14 +#, fuzzy +msgid "Federated XGBoost" +msgstr "Formation fédérée" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:519 +#: ../../source/tutorial-quickstart-xgboost.rst:16 msgid "" -"In this notebook, we want to simulate a federated learning system with 10" -" clients on a single machine. This means that the server and all 10 " -"clients will live on a single machine and share resources such as CPU, " -"GPU, and memory. Having 10 clients would mean having 10 instances of " -"``FlowerClient`` in memory. Doing this on a single machine can quickly " -"exhaust the available memory resources, even if only a subset of these " -"clients participates in a single round of federated learning." +"EXtreme Gradient Boosting (**XGBoost**) is a robust and efficient " +"implementation of gradient-boosted decision tree (**GBDT**), that " +"maximises the computational boundaries for boosted tree methods. It's " +"primarily designed to enhance both the performance and computational " +"speed of machine learning models. In XGBoost, trees are constructed " +"concurrently, unlike the sequential approach taken by GBDT." msgstr "" -"Dans ce carnet, nous voulons simuler un système d'apprentissage fédéré " -"avec 10 clients sur une seule machine. Cela signifie que le serveur et " -"les 10 clients vivront sur une seule machine et partageront des " -"ressources telles que le CPU, le GPU et la mémoire. Avoir 10 clients " -"signifierait avoir 10 instances de ``FlowerClient`` en mémoire. Faire " -"cela sur une seule machine peut rapidement épuiser les ressources mémoire" -" disponibles, même si seulement un sous-ensemble de ces clients participe" -" à un seul tour d'apprentissage fédéré." -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:521 +#: ../../source/tutorial-quickstart-xgboost.rst:20 msgid "" -"In addition to the regular capabilities where server and clients run on " -"multiple machines, Flower, therefore, provides special simulation " -"capabilities that create ``FlowerClient`` instances only when they are " -"actually necessary for training or evaluation. To enable the Flower " -"framework to create clients when necessary, we need to implement a " -"function called ``client_fn`` that creates a ``FlowerClient`` instance on" -" demand. Flower calls ``client_fn`` whenever it needs an instance of one " -"particular client to call ``fit`` or ``evaluate`` (those instances are " -"usually discarded after use, so they should not keep any local state). " -"Clients are identified by a client ID, or short ``cid``. The ``cid`` can " -"be used, for example, to load different local data partitions for " -"different clients, as can be seen below:" +"Often, for tabular data on medium-sized datasets with fewer than 10k " +"training examples, XGBoost surpasses the results of deep learning " +"techniques." msgstr "" -"In addition to the regular capabilities where server and clients run on " -"multiple machines, Flower, therefore, provides special simulation " -"capabilities that create ``FlowerClient`` instances only when they are " -"actually necessary for training or evaluation. To enable the Flower " -"framework to create clients when necessary, we need to implement a " -"function called ``client_fn`` that creates a ``FlowerClient`` instance on" -" demand. Flower calls ``client_fn`` whenever it needs an instance of one " -"particular client to call ``fit`` or ``evaluate`` (those instances are " -"usually discarded after use, so they should not keep any local state). " -"Clients are identified by a client ID, or short ``cid``. The ``cid`` can " -"be used, for example, to load different local data partitions for " -"different clients, as can be seen below:" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:556 -msgid "Starting the training" -msgstr "Commencer la formation" +#: ../../source/tutorial-quickstart-xgboost.rst:23 +#, fuzzy +msgid "Why federated XGBoost?" +msgstr "Qu'est-ce que l'apprentissage fédéré ?" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:558 +#: ../../source/tutorial-quickstart-xgboost.rst:25 msgid "" -"We now have the class ``FlowerClient`` which defines client-side " -"training/evaluation and ``client_fn`` which allows Flower to create " -"``FlowerClient`` instances whenever it needs to call ``fit`` or " -"``evaluate`` on one particular client. The last step is to start the " -"actual simulation using ``flwr.simulation.start_simulation``." +"Indeed, as the demand for data privacy and decentralized learning grows, " +"there's an increasing requirement to implement federated XGBoost systems " +"for specialised applications, like survival analysis and financial fraud " +"detection." msgstr "" -"Nous avons maintenant la classe ``FlowerClient`` qui définit " -"l'entraînement/évaluation côté client et ``client_fn`` qui permet à " -"Flower de créer des instances de ``FlowerClient`` chaque fois qu'il a " -"besoin d'appeler ``fit`` ou ``evaluate`` sur un client particulier. La " -"dernière étape consiste à démarrer la simulation réelle en utilisant " -"``flwr.simulation.start_simulation``." -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:560 +#: ../../source/tutorial-quickstart-xgboost.rst:27 msgid "" -"The function ``start_simulation`` accepts a number of arguments, amongst " -"them the ``client_fn`` used to create ``FlowerClient`` instances, the " -"number of clients to simulate (``num_clients``), the number of federated " -"learning rounds (``num_rounds``), and the strategy. The strategy " -"encapsulates the federated learning approach/algorithm, for example, " -"*Federated Averaging* (FedAvg)." +"Federated learning ensures that raw data remains on the local device, " +"making it an attractive approach for sensitive domains where data " +"security and privacy are paramount. Given the robustness and efficiency " +"of XGBoost, combining it with federated learning offers a promising " +"solution for these specific challenges." msgstr "" -"La fonction ``start_simulation`` accepte un certain nombre d'arguments, " -"parmi lesquels le ``client_fn`` utilisé pour créer les instances " -"``FlowerClient``, le nombre de clients à simuler (``num_clients``), le " -"nombre de tours d'apprentissage fédéré (``num_rounds``), et la stratégie." -" La stratégie encapsule l'approche/algorithme d'apprentissage fédéré, par" -" exemple, *Federated Averaging* (FedAvg)." -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:562 +#: ../../source/tutorial-quickstart-xgboost.rst:30 msgid "" -"Flower has a number of built-in strategies, but we can also use our own " -"strategy implementations to customize nearly all aspects of the federated" -" learning approach. For this example, we use the built-in ``FedAvg`` " -"implementation and customize it using a few basic parameters. The last " -"step is the actual call to ``start_simulation`` which - you guessed it - " -"starts the simulation:" +"In this tutorial we will learn how to train a federated XGBoost model on " +"HIGGS dataset using Flower and :code:`xgboost` package. We use a simple " +"example (`full code xgboost-quickstart " +"`_)" +" with two *clients* and one *server* to demonstrate how federated XGBoost" +" works, and then we dive into a more complex example (`full code xgboost-" +"comprehensive `_) to run various experiments." msgstr "" -"Flower dispose d'un certain nombre de stratégies intégrées, mais nous " -"pouvons également utiliser nos propres implémentations de stratégies pour" -" personnaliser presque tous les aspects de l'approche de l'apprentissage " -"fédéré. Pour cet exemple, nous utilisons l'implémentation intégrée " -"``FedAvg`` et nous la personnalisons en utilisant quelques paramètres de " -"base. La dernière étape est l'appel à ``start_simulation`` qui - tu l'as " -"deviné - démarre la simulation :" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:608 -msgid "Behind the scenes" -msgstr "Dans les coulisses" +#: ../../source/tutorial-quickstart-xgboost.rst:37 +msgid "Environment Setup" +msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:610 -msgid "So how does this work? How does Flower execute this simulation?" +#: ../../source/tutorial-quickstart-xgboost.rst:41 +msgid "" +"We first need to install Flower and Flower Datasets. You can do this by " +"running :" msgstr "" -"Alors, comment cela fonctionne-t-il ? Comment Flower exécute-t-il cette " -"simulation ?" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:612 -#, python-format +#: ../../source/tutorial-quickstart-xgboost.rst:47 +#, fuzzy msgid "" -"When we call ``start_simulation``, we tell Flower that there are 10 " -"clients (``num_clients=10``). Flower then goes ahead an asks the " -"``FedAvg`` strategy to select clients. ``FedAvg`` knows that it should " -"select 100% of the available clients (``fraction_fit=1.0``), so it goes " -"ahead and selects 10 random clients (i.e., 100% of 10)." +"Since we want to use :code:`xgboost` package to build up XGBoost trees, " +"let's go ahead and install :code:`xgboost`:" +msgstr "Puisque nous voulons utiliser scikt-learn, allons-y et installons-le :" + +#: ../../source/tutorial-quickstart-xgboost.rst:57 +msgid "" +"*Clients* are responsible for generating individual weight-updates for " +"the model based on their local datasets. Now that we have all our " +"dependencies installed, let's run a simple distributed training with two " +"clients and one server." msgstr "" -"Lorsque nous appelons ``start_simulation``, nous disons à Flower qu'il y " -"a 10 clients (``num_clients=10``). Flower demande alors à la stratégie " -"``FedAvg`` de sélectionner des clients. ``FedAvg` sait qu'il doit " -"sélectionner 100% des clients disponibles (``fraction_fit=1.0``), alors " -"il choisit 10 clients au hasard (c'est à dire 100% de 10)." -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:614 +#: ../../source/tutorial-quickstart-xgboost.rst:60 +#, fuzzy msgid "" -"Flower then asks the selected 10 clients to train the model. When the " -"server receives the model parameter updates from the clients, it hands " -"those updates over to the strategy (*FedAvg*) for aggregation. The " -"strategy aggregates those updates and returns the new global model, which" -" then gets used in the next round of federated learning." +"In a file called :code:`client.py`, import xgboost, Flower, Flower " +"Datasets and other related functions:" msgstr "" -"Flower demande ensuite aux 10 clients sélectionnés d'entraîner le modèle." -" Lorsque le serveur reçoit les mises à jour des paramètres du modèle de " -"la part des clients, il les transmet à la stratégie (*FedAvg*) pour " -"qu'elle les agrège. La stratégie agrège ces mises à jour et renvoie le " -"nouveau modèle global, qui est ensuite utilisé dans le prochain cycle " -"d'apprentissage fédéré." +"Dans un fichier appelé :code:`client.py`, importe Flower et les paquets " +"liés à PyTorch :" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:626 -msgid "Where's the accuracy?" -msgstr "Où est la précision ?" +#: ../../source/tutorial-quickstart-xgboost.rst:87 +msgid "Dataset partition and hyper-parameter selection" +msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:628 +#: ../../source/tutorial-quickstart-xgboost.rst:89 msgid "" -"You may have noticed that all metrics except for ``losses_distributed`` " -"are empty. Where did the ``{\"accuracy\": float(accuracy)}`` go?" +"Prior to local training, we require loading the HIGGS dataset from Flower" +" Datasets and conduct data partitioning for FL:" msgstr "" -"Tu as peut-être remarqué que toutes les mesures, à l'exception de " -"``pertes_distribuées``, sont vides. Où est passée la ``{\"précision\" : " -"float(précision)}`` ?" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:630 +#: ../../source/tutorial-quickstart-xgboost.rst:102 msgid "" -"Flower can automatically aggregate losses returned by individual clients," -" but it cannot do the same for metrics in the generic metrics dictionary " -"(the one with the ``accuracy`` key). Metrics dictionaries can contain " -"very different kinds of metrics and even key/value pairs that are not " -"metrics at all, so the framework does not (and can not) know how to " -"handle these automatically." +"In this example, we split the dataset into two partitions with uniform " +"distribution (:code:`IidPartitioner(num_partitions=2)`). Then, we load " +"the partition for the given client based on :code:`node_id`:" msgstr "" -"Flower peut automatiquement agréger les pertes renvoyées par les clients " -"individuels, mais il ne peut pas faire la même chose pour les mesures " -"dans le dictionnaire de mesures générique (celui avec la clé " -"``accuracy``). Les dictionnaires de mesures peuvent contenir des types de" -" mesures très différents et même des paires clé/valeur qui ne sont pas " -"des mesures du tout, donc le cadre ne sait pas (et ne peut pas) savoir " -"comment les gérer automatiquement." -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:632 +#: ../../source/tutorial-quickstart-xgboost.rst:121 msgid "" -"As users, we need to tell the framework how to handle/aggregate these " -"custom metrics, and we do so by passing metric aggregation functions to " -"the strategy. The strategy will then call these functions whenever it " -"receives fit or evaluate metrics from clients. The two possible functions" -" are ``fit_metrics_aggregation_fn`` and " -"``evaluate_metrics_aggregation_fn``." +"After that, we do train/test splitting on the given partition (client's " +"local data), and transform data format for :code:`xgboost` package." msgstr "" -"En tant qu'utilisateurs, nous devons indiquer au framework comment " -"gérer/agréger ces métriques personnalisées, et nous le faisons en passant" -" des fonctions d'agrégation de métriques à la stratégie. La stratégie " -"appellera alors ces fonctions chaque fois qu'elle recevra des métriques " -"d'ajustement ou d'évaluation de la part des clients. Les deux fonctions " -"possibles sont ``fit_metrics_aggregation_fn`` et " -"``evaluate_metrics_aggregation_fn``." -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:634 +#: ../../source/tutorial-quickstart-xgboost.rst:134 msgid "" -"Let's create a simple weighted averaging function to aggregate the " -"``accuracy`` metric we return from ``evaluate``:" +"The functions of :code:`train_test_split` and " +":code:`transform_dataset_to_dmatrix` are defined as below:" msgstr "" -"Créons une simple fonction de calcul de la moyenne pondérée pour agréger " -"la mesure de \"précision\" que nous renvoie ``evaluate`` :" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:660 -msgid "" -"The only thing left to do is to tell the strategy to call this function " -"whenever it receives evaluation metric dictionaries from the clients:" +#: ../../source/tutorial-quickstart-xgboost.rst:158 +msgid "Finally, we define the hyper-parameters used for XGBoost training." msgstr "" -"La seule chose qui reste à faire est d'indiquer à la stratégie d'appeler " -"cette fonction chaque fois qu'elle reçoit des dictionnaires de métriques " -"d'évaluation de la part des clients :" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:697 +#: ../../source/tutorial-quickstart-xgboost.rst:174 msgid "" -"We now have a full system that performs federated training and federated " -"evaluation. It uses the ``weighted_average`` function to aggregate custom" -" evaluation metrics and calculates a single ``accuracy`` metric across " -"all clients on the server side." +"The :code:`num_local_round` represents the number of iterations for local" +" tree boost. We use CPU for the training in default. One can shift it to " +"GPU by setting :code:`tree_method` to :code:`gpu_hist`. We use AUC as " +"evaluation metric." msgstr "" -"Nous avons maintenant un système complet qui effectue la formation " -"fédérée et l'évaluation fédérée. Il utilise la fonction ``moyenne " -"pondérée`` pour agréger les mesures d'évaluation personnalisées et " -"calcule une seule mesure de ``précision`` pour tous les clients du côté " -"du serveur." -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:699 -msgid "" -"The other two categories of metrics (``losses_centralized`` and " -"``metrics_centralized``) are still empty because they only apply when " -"centralized evaluation is being used. Part two of the Flower tutorial " -"will cover centralized evaluation." +#: ../../source/tutorial-quickstart-xgboost.rst:181 +msgid "Flower client definition for XGBoost" msgstr "" -"Les deux autres catégories de mesures (``pertes_centralisées`` et " -"``métriques_centralisées``) sont toujours vides car elles ne s'appliquent" -" que lorsque l'évaluation centralisée est utilisée. La deuxième partie du" -" tutoriel sur les fleurs couvrira l'évaluation centralisée." -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:711 -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:351 -msgid "Final remarks" -msgstr "Remarques finales" +#: ../../source/tutorial-quickstart-xgboost.rst:183 +msgid "" +"After loading the dataset we define the Flower client. We follow the " +"general rule to define :code:`XgbClient` class inherited from " +":code:`fl.client.Client`." +msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:713 +#: ../../source/tutorial-quickstart-xgboost.rst:193 msgid "" -"Congratulations, you just trained a convolutional neural network, " -"federated over 10 clients! With that, you understand the basics of " -"federated learning with Flower. The same approach you've seen can be used" -" with other machine learning frameworks (not just PyTorch) and tasks (not" -" just CIFAR-10 images classification), for example NLP with Hugging Face " -"Transformers or speech with SpeechBrain." +"The :code:`self.bst` is used to keep the Booster objects that remain " +"consistent across rounds, allowing them to store predictions from trees " +"integrated in earlier rounds and maintain other essential data structures" +" for training." msgstr "" -"Félicitations, tu viens d'entraîner un réseau neuronal convolutif, fédéré" -" sur 10 clients ! Avec ça, tu comprends les bases de l'apprentissage " -"fédéré avec Flower. La même approche que tu as vue peut être utilisée " -"avec d'autres cadres d'apprentissage automatique (pas seulement PyTorch) " -"et d'autres tâches (pas seulement la classification des images CIFAR-10)," -" par exemple le NLP avec Hugging Face Transformers ou la parole avec " -"SpeechBrain." -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:715 +#: ../../source/tutorial-quickstart-xgboost.rst:196 msgid "" -"In the next notebook, we're going to cover some more advanced concepts. " -"Want to customize your strategy? Initialize parameters on the server " -"side? Or evaluate the aggregated model on the server side? We'll cover " -"all this and more in the next tutorial." +"Then, we override :code:`get_parameters`, :code:`fit` and " +":code:`evaluate` methods insides :code:`XgbClient` class as follows." msgstr "" -"Dans le prochain cahier, nous allons aborder des concepts plus avancés. " -"Tu veux personnaliser ta stratégie ? Initialiser des paramètres côté " -"serveur ? Ou évaluer le modèle agrégé côté serveur ? Nous aborderons tout" -" cela et bien plus encore dans le prochain tutoriel." -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:733 -#, fuzzy +#: ../../source/tutorial-quickstart-xgboost.rst:210 msgid "" -"The `Flower Federated Learning Tutorial - Part 2 " -"`__ goes into more depth about strategies and all " -"the advanced things you can build with them." +"Unlike neural network training, XGBoost trees are not started from a " +"specified random weights. In this case, we do not use " +":code:`get_parameters` and :code:`set_parameters` to initialise model " +"parameters for XGBoost. As a result, let's return an empty tensor in " +":code:`get_parameters` when it is called by the server at the first " +"round." msgstr "" -"Le `Tutoriel d'apprentissage fédéré Flower - Partie 2 " -"`__ va plus en profondeur sur les stratégies et toutes les " -"choses avancées que tu peux construire avec elles." -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:9 -#, fuzzy -msgid "Use a federated learning strategy" -msgstr "Stratégie de moyenne fédérée." +#: ../../source/tutorial-quickstart-xgboost.rst:251 +msgid "" +"In :code:`fit`, at the first round, we call :code:`xgb.train()` to build " +"up the first set of trees. the returned Booster object and config are " +"stored in :code:`self.bst` and :code:`self.config`, respectively. From " +"the second round, we load the global model sent from server to " +":code:`self.bst`, and then update model weights on local training data " +"with function :code:`local_boost` as follows:" +msgstr "" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:11 -#, fuzzy +#: ../../source/tutorial-quickstart-xgboost.rst:269 msgid "" -"Welcome to the next part of the federated learning tutorial. In previous " -"parts of this tutorial, we introduced federated learning with PyTorch and" -" Flower (`part 1 `__)." +"Given :code:`num_local_round`, we update trees by calling " +":code:`self.bst.update` method. After training, the last " +":code:`N=num_local_round` trees will be extracted to send to the server." msgstr "" -"Bienvenue dans la prochaine partie du tutoriel sur l'apprentissage " -"fédéré. Dans les parties précédentes de ce tutoriel, nous avons présenté " -"l'apprentissage fédéré avec PyTorch et Flower (`partie 1 " -"`__)." -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:13 +#: ../../source/tutorial-quickstart-xgboost.rst:291 msgid "" -"In this notebook, we'll begin to customize the federated learning system " -"we built in the introductory notebook (again, using `Flower " -"`__ and `PyTorch `__)." +"In :code:`evaluate`, we call :code:`self.bst.eval_set` function to " +"conduct evaluation on valid set. The AUC value will be returned." msgstr "" -"Dans ce carnet, nous allons commencer à personnaliser le système " -"d'apprentissage fédéré que nous avons construit dans le carnet " -"d'introduction (toujours en utilisant `Flower `__ et " -"`PyTorch `__)." -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:17 +#: ../../source/tutorial-quickstart-xgboost.rst:294 #, fuzzy -msgid "Let's move beyond FedAvg with Flower strategies!" -msgstr "Dépassons FedAvg avec les stratégies florales !" - -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:309 -msgid "Strategy customization" -msgstr "Personnalisation de la stratégie" - -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:311 msgid "" -"So far, everything should look familiar if you've worked through the " -"introductory notebook. With that, we're ready to introduce a number of " -"new features." +"Now, we can create an instance of our class :code:`XgbClient` and add one" +" line to actually run this client:" msgstr "" -"Jusqu'à présent, tout devrait te sembler familier si tu as travaillé sur " -"le cahier d'introduction. Avec cela, nous sommes prêts à présenter un " -"certain nombre de nouvelles fonctionnalités." - -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:323 -msgid "Server-side parameter **initialization**" -msgstr "Paramètres côté serveur **initialisation**" +"Nous pouvons maintenant créer une instance de notre classe " +":code:`MnistClient` et ajouter une ligne pour exécuter ce client :" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:325 +#: ../../source/tutorial-quickstart-xgboost.rst:300 +#, fuzzy msgid "" -"Flower, by default, initializes the global model by asking one random " -"client for the initial parameters. In many cases, we want more control " -"over parameter initialization though. Flower therefore allows you to " -"directly pass the initial parameters to the Strategy:" +"That's it for the client. We only have to implement :code:`Client`and " +"call :code:`fl.client.start_client()`. The string :code:`\"[::]:8080\"` " +"tells the client which server to connect to. In our case we can run the " +"server and the client on the same machine, therefore we use " +":code:`\"[::]:8080\"`. If we run a truly federated workload with the " +"server and clients running on different machines, all that needs to " +"change is the :code:`server_address` we point the client at." msgstr "" -"Flower, par défaut, initialise le modèle global en demandant à un client " -"aléatoire les paramètres initiaux. Dans de nombreux cas, nous voulons " -"cependant avoir plus de contrôle sur l'initialisation des paramètres. " -"Flower te permet donc de passer directement les paramètres initiaux à la " -"Stratégie :" +"C'est tout pour le client. Il nous suffit d'implémenter :code:`Client` ou" +" :code:`NumPyClient` et d'appeler :code:`fl.client.start_client()`. La " +"chaîne :code:`\"[: :]:8080\"` indique au client à quel serveur se " +"connecter. Dans notre cas, nous pouvons exécuter le serveur et le client " +"sur la même machine, c'est pourquoi nous utilisons :code:`\"[: " +":]:8080\"`. Si nous exécutons une charge de travail véritablement fédérée" +" avec le serveur et les clients fonctionnant sur des machines " +"différentes, tout ce qui doit changer est l'adresse " +":code:`server_address` vers laquelle nous dirigeons le client." -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:370 +#: ../../source/tutorial-quickstart-xgboost.rst:311 +#, fuzzy msgid "" -"Passing ``initial_parameters`` to the ``FedAvg`` strategy prevents Flower" -" from asking one of the clients for the initial parameters. If we look " -"closely, we can see that the logs do not show any calls to the " -"``FlowerClient.get_parameters`` method." +"These updates are then sent to the *server* which will aggregate them to " +"produce a better model. Finally, the *server* sends this improved version" +" of the model back to each *client* to finish a complete FL round." msgstr "" -"Le fait de passer ``initial_parameters`` à la stratégie ``FedAvg`` " -"empêche Flower de demander les paramètres initiaux à l'un des clients. Si" -" nous regardons de près, nous pouvons voir que les journaux ne montrent " -"aucun appel à la méthode ``FlowerClient.get_parameters``." - -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:382 -msgid "Starting with a customized strategy" -msgstr "Commencer par une stratégie personnalisée" +"*Les clients* sont chargés de générer des mises à jour de poids " +"individuelles pour le modèle en fonction de leurs ensembles de données " +"locales. Ces mises à jour sont ensuite envoyées au *serveur* qui les " +"agrège pour produire un meilleur modèle. Enfin, le *serveur* renvoie " +"cette version améliorée du modèle à chaque *client*. Un cycle complet de " +"mises à jour de poids s'appelle un *round*." -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:384 +#: ../../source/tutorial-quickstart-xgboost.rst:314 +#, fuzzy msgid "" -"We've seen the function ``start_simulation`` before. It accepts a number " -"of arguments, amongst them the ``client_fn`` used to create " -"``FlowerClient`` instances, the number of clients to simulate " -"``num_clients``, the number of rounds ``num_rounds``, and the strategy." +"In a file named :code:`server.py`, import Flower and FedXgbBagging from " +":code:`flwr.server.strategy`." msgstr "" -"Elle accepte un certain nombre d'arguments, parmi lesquels le " -"``client_fn`` utilisé pour créer les instances de ``FlowerClient``, le " -"nombre de clients à simuler ``num_clients``, le nombre de rounds " -"``num_rounds``, et la stratégie." +"Dans un fichier appelé :code:`client.py`, importe Flower et les paquets " +"liés au MXNet :" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:386 +#: ../../source/tutorial-quickstart-xgboost.rst:316 +msgid "We first define a strategy for XGBoost bagging aggregation." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:339 msgid "" -"The strategy encapsulates the federated learning approach/algorithm, for " -"example, ``FedAvg`` or ``FedAdagrad``. Let's try to use a different " -"strategy this time:" +"We use two clients for this example. An " +":code:`evaluate_metrics_aggregation` function is defined to collect and " +"wighted average the AUC values from clients." msgstr "" -"La stratégie englobe l'approche/l'algorithme d'apprentissage fédéré, par " -"exemple, ``FedAvg`` ou ``FedAdagrad``. Essayons d'utiliser une stratégie " -"différente cette fois-ci :" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:424 -msgid "Server-side parameter **evaluation**" -msgstr "Paramètre côté serveur **évaluation**" +#: ../../source/tutorial-quickstart-xgboost.rst:342 +#, fuzzy +msgid "Then, we start the server:" +msgstr "Démarrer le serveur" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:426 -msgid "" -"Flower can evaluate the aggregated model on the server-side or on the " -"client-side. Client-side and server-side evaluation are similar in some " -"ways, but different in others." +#: ../../source/tutorial-quickstart-xgboost.rst:354 +msgid "Tree-based bagging aggregation" msgstr "" -"Flower peut évaluer le modèle agrégé côté serveur ou côté client. Les " -"évaluations côté client et côté serveur sont similaires à certains " -"égards, mais différentes à d'autres." -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:428 +#: ../../source/tutorial-quickstart-xgboost.rst:356 msgid "" -"**Centralized Evaluation** (or *server-side evaluation*) is conceptually " -"simple: it works the same way that evaluation in centralized machine " -"learning does. If there is a server-side dataset that can be used for " -"evaluation purposes, then that's great. We can evaluate the newly " -"aggregated model after each round of training without having to send the " -"model to clients. We're also fortunate in the sense that our entire " -"evaluation dataset is available at all times." +"You must be curious about how bagging aggregation works. Let's look into " +"the details." msgstr "" -"**L'évaluation centralisée** (ou *évaluation côté serveur*) est " -"conceptuellement simple : elle fonctionne de la même manière que " -"l'évaluation dans l'apprentissage automatique centralisé. S'il existe un " -"ensemble de données côté serveur qui peut être utilisé à des fins " -"d'évaluation, alors c'est parfait. Nous pouvons évaluer le modèle " -"nouvellement agrégé après chaque cycle de formation sans avoir à envoyer " -"le modèle aux clients. Nous avons également la chance que l'ensemble de " -"notre ensemble de données d'évaluation soit disponible à tout moment." -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:430 +#: ../../source/tutorial-quickstart-xgboost.rst:358 msgid "" -"**Federated Evaluation** (or *client-side evaluation*) is more complex, " -"but also more powerful: it doesn't require a centralized dataset and " -"allows us to evaluate models over a larger set of data, which often " -"yields more realistic evaluation results. In fact, many scenarios require" -" us to use **Federated Evaluation** if we want to get representative " -"evaluation results at all. But this power comes at a cost: once we start " -"to evaluate on the client side, we should be aware that our evaluation " -"dataset can change over consecutive rounds of learning if those clients " -"are not always available. Moreover, the dataset held by each client can " -"also change over consecutive rounds. This can lead to evaluation results " -"that are not stable, so even if we would not change the model, we'd see " -"our evaluation results fluctuate over consecutive rounds." +"In file :code:`flwr.server.strategy.fedxgb_bagging.py`, we define " +":code:`FedXgbBagging` inherited from :code:`flwr.server.strategy.FedAvg`." +" Then, we override the :code:`aggregate_fit`, :code:`aggregate_evaluate` " +"and :code:`evaluate` methods as follows:" msgstr "" -"**L'évaluation fédérée** (ou évaluation côté client) est plus complexe, " -"mais aussi plus puissante : elle ne nécessite pas d'ensemble de données " -"centralisé et nous permet d'évaluer les modèles sur un plus grand " -"ensemble de données, ce qui donne souvent des résultats d'évaluation plus" -" réalistes. En fait, de nombreux scénarios exigent que nous utilisions " -"l'évaluation fédérée** si nous voulons obtenir des résultats d'évaluation" -" représentatifs. Mais cette puissance a un coût : une fois que nous " -"commençons à évaluer côté client, nous devons savoir que notre ensemble " -"de données d'évaluation peut changer au cours des cycles d'apprentissage " -"consécutifs si ces clients ne sont pas toujours disponibles. De plus, " -"l'ensemble de données détenu par chaque client peut également changer au " -"cours des cycles consécutifs. Cela peut conduire à des résultats " -"d'évaluation qui ne sont pas stables, donc même si nous ne changions pas " -"le modèle, nous verrions nos résultats d'évaluation fluctuer au cours des" -" cycles consécutifs." -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:433 +#: ../../source/tutorial-quickstart-xgboost.rst:454 msgid "" -"We've seen how federated evaluation works on the client side (i.e., by " -"implementing the ``evaluate`` method in ``FlowerClient``). Now let's see " -"how we can evaluate aggregated model parameters on the server-side:" +"In :code:`aggregate_fit`, we sequentially aggregate the clients' XGBoost " +"trees by calling :code:`aggregate()` function:" msgstr "" -"Nous avons vu comment l'évaluation fédérée fonctionne du côté client " -"(c'est-à-dire en implémentant la méthode ``evaluate`` dans " -"``FlowerClient``). Voyons maintenant comment nous pouvons évaluer les " -"paramètres du modèle agrégé du côté serveur :" - -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:490 -msgid "Sending/receiving arbitrary values to/from clients" -msgstr "Envoi/réception de valeurs arbitraires vers/depuis les clients" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:492 -#, fuzzy +#: ../../source/tutorial-quickstart-xgboost.rst:513 msgid "" -"In some situations, we want to configure client-side execution (training," -" evaluation) from the server-side. One example for that is the server " -"asking the clients to train for a certain number of local epochs. Flower " -"provides a way to send configuration values from the server to the " -"clients using a dictionary. Let's look at an example where the clients " -"receive values from the server through the ``config`` parameter in " -"``fit`` (``config`` is also available in ``evaluate``). The ``fit`` " -"method receives the configuration dictionary through the ``config`` " -"parameter and can then read values from this dictionary. In this example," -" it reads ``server_round`` and ``local_epochs`` and uses those values to " -"improve the logging and configure the number of local training epochs:" +"In this function, we first fetch the number of trees and the number of " +"parallel trees for the current and previous model by calling " +":code:`_get_tree_nums`. Then, the fetched information will be aggregated." +" After that, the trees (containing model weights) are aggregated to " +"generate a new tree model." msgstr "" -"In some situations, we want to configure client-side execution (training," -" evaluation) from the server-side. One example for that is the server " -"asking the clients to train for a certain number of local epochs. Flower " -"provides a way to send configuration values from the server to the " -"clients using a dictionary. Let's look at an example where the clients " -"receive values from the server through the ``config`` parameter in " -"``fit`` (``config`` is also available in ``evaluate``). The ``fit`` " -"method receives the configuration dictionary through the ``config`` " -"parameter and can then read values from this dictionary. In this example," -" it reads ``server_round`` and ``local_epochs`` and uses those values to " -"improve the logging and configure the number of local training epochs:" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:546 +#: ../../source/tutorial-quickstart-xgboost.rst:518 msgid "" -"So how can we send this config dictionary from server to clients? The " -"built-in Flower Strategies provide way to do this, and it works similarly" -" to the way server-side evaluation works. We provide a function to the " -"strategy, and the strategy calls this function for every round of " -"federated learning:" +"After traversal of all clients' models, a new global model is generated, " +"followed by the serialisation, and sending back to each client." msgstr "" -"Comment pouvons-nous donc envoyer ce dictionnaire de configuration du " -"serveur aux clients ? Les stratégies de Flower intégrées fournissent un " -"moyen de le faire, et cela fonctionne de la même façon que l'évaluation " -"côté serveur. Nous fournissons une fonction à la stratégie, et la " -"stratégie appelle cette fonction pour chaque cycle d'apprentissage fédéré" -" :" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:576 -msgid "" -"Next, we'll just pass this function to the FedAvg strategy before " -"starting the simulation:" +#: ../../source/tutorial-quickstart-xgboost.rst:523 +msgid "Launch Federated XGBoost!" msgstr "" -"Ensuite, nous allons simplement passer cette fonction à la stratégie " -"FedAvg avant de commencer la simulation :" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:613 +#: ../../source/tutorial-quickstart-xgboost.rst:585 msgid "" -"As we can see, the client logs now include the current round of federated" -" learning (which they read from the ``config`` dictionary). We can also " -"configure local training to run for one epoch during the first and second" -" round of federated learning, and then for two epochs during the third " -"round." +"Congratulations! You've successfully built and run your first federated " +"XGBoost system. The AUC values can be checked in " +":code:`metrics_distributed`. One can see that the average AUC increases " +"over FL rounds." msgstr "" -"Comme nous pouvons le voir, les journaux des clients incluent maintenant " -"le cycle actuel d'apprentissage fédéré (qu'ils lisent depuis le " -"dictionnaire ``config``). Nous pouvons également configurer " -"l'apprentissage local pour qu'il s'exécute pendant une époque au cours du" -" premier et du deuxième cycle d'apprentissage fédéré, puis pendant deux " -"époques au cours du troisième cycle." -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:615 +#: ../../source/tutorial-quickstart-xgboost.rst:590 +#, fuzzy msgid "" -"Clients can also return arbitrary values to the server. To do so, they " -"return a dictionary from ``fit`` and/or ``evaluate``. We have seen and " -"used this concept throughout this notebook without mentioning it " -"explicitly: our ``FlowerClient`` returns a dictionary containing a custom" -" key/value pair as the third return value in ``evaluate``." +"The full `source code `_ for this example can be found in :code:`examples" +"/xgboost-quickstart`." msgstr "" -"Les clients peuvent également renvoyer des valeurs arbitraires au " -"serveur. Pour ce faire, ils renvoient un dictionnaire depuis ``fit`` " -"et/ou ``evaluate``. Nous avons vu et utilisé ce concept tout au long de " -"ce carnet sans le mentionner explicitement : notre ``FlowerClient`` " -"renvoie un dictionnaire contenant une paire clé/valeur personnalisée en " -"tant que troisième valeur de retour dans ``evaluate``." +"Félicitations ! Tu as réussi à construire et à faire fonctionner ton " +"premier système d'apprentissage fédéré. Le code source complet " +"`_ de cet exemple se trouve dans :code:`examples" +"/quickstart-mxnet`." -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:627 -msgid "Scaling federated learning" -msgstr "Mise à l'échelle de l'apprentissage fédéré" +#: ../../source/tutorial-quickstart-xgboost.rst:594 +msgid "Comprehensive Federated XGBoost" +msgstr "" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:629 +#: ../../source/tutorial-quickstart-xgboost.rst:596 msgid "" -"As a last step in this notebook, let's see how we can use Flower to " -"experiment with a large number of clients." +"Now that you have known how federated XGBoost work with Flower, it's time" +" to run some more comprehensive experiments by customising the " +"experimental settings. In the xgboost-comprehensive example (`full code " +"`_), we provide more options to define various experimental" +" setups, including aggregation strategies, data partitioning and " +"centralised/distributed evaluation. We also support :doc:`Flower " +"simulation ` making it easy to simulate large " +"client cohorts in a resource-aware manner. Let's take a look!" msgstr "" -"Comme dernière étape de ce carnet, voyons comment nous pouvons utiliser " -"Flower pour expérimenter avec un grand nombre de clients." -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:651 -#, python-format +#: ../../source/tutorial-quickstart-xgboost.rst:603 +#, fuzzy +msgid "Cyclic training" +msgstr "Formation centralisée" + +#: ../../source/tutorial-quickstart-xgboost.rst:605 msgid "" -"We now have 1000 partitions, each holding 45 training and 5 validation " -"examples. Given that the number of training examples on each client is " -"quite small, we should probably train the model a bit longer, so we " -"configure the clients to perform 3 local training epochs. We should also " -"adjust the fraction of clients selected for training during each round " -"(we don't want all 1000 clients participating in every round), so we " -"adjust ``fraction_fit`` to ``0.05``, which means that only 5% of " -"available clients (so 50 clients) will be selected for training each " -"round:" +"In addition to bagging aggregation, we offer a cyclic training scheme, " +"which performs FL in a client-by-client fashion. Instead of aggregating " +"multiple clients, there is only one single client participating in the " +"training per round in the cyclic training scenario. The trained local " +"XGBoost trees will be passed to the next client as an initialised model " +"for next round's boosting." msgstr "" -"Nous avons maintenant 1000 partitions, chacune contenant 45 exemples " -"d'entraînement et 5 exemples de validation. Etant donné que le nombre " -"d'exemples d'entraînement sur chaque client est assez faible, nous " -"devrions probablement entraîner le modèle un peu plus longtemps, nous " -"configurons donc les clients pour qu'ils effectuent 3 époques " -"d'entraînement local. Nous devrions également ajuster la fraction de " -"clients sélectionnés pour l'entraînement à chaque tour (nous ne voulons " -"pas que les 1000 clients participent à chaque tour), nous ajustons donc " -"``fraction_fit`` à ``0.05``, ce qui signifie que seulement 5% des clients" -" disponibles (donc 50 clients) seront sélectionnés pour l'entraînement à " -"chaque tour :" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:699 +#: ../../source/tutorial-quickstart-xgboost.rst:609 msgid "" -"In this notebook, we've seen how we can gradually enhance our system by " -"customizing the strategy, initializing parameters on the server side, " -"choosing a different strategy, and evaluating models on the server-side. " -"That's quite a bit of flexibility with so little code, right?" +"To do this, we first customise a :code:`ClientManager` in " +":code:`server_utils.py`:" msgstr "" -"Dans ce carnet, nous avons vu comment nous pouvons progressivement " -"améliorer notre système en personnalisant la stratégie, en initialisant " -"les paramètres côté serveur, en choisissant une stratégie différente et " -"en évaluant les modèles côté serveur. C'est une sacrée flexibilité avec " -"si peu de code, n'est-ce pas ?" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:701 +#: ../../source/tutorial-quickstart-xgboost.rst:649 msgid "" -"In the later sections, we've seen how we can communicate arbitrary values" -" between server and clients to fully customize client-side execution. " -"With that capability, we built a large-scale Federated Learning " -"simulation using the Flower Virtual Client Engine and ran an experiment " -"involving 1000 clients in the same workload - all in a Jupyter Notebook!" +"The customised :code:`ClientManager` samples all available clients in " +"each FL round based on the order of connection to the server. Then, we " +"define a new strategy :code:`FedXgbCyclic` in " +":code:`flwr.server.strategy.fedxgb_cyclic.py`, in order to sequentially " +"select only one client in given round and pass the received model to next" +" client." msgstr "" -"Dans les sections ultérieures, nous avons vu comment nous pouvons " -"communiquer des valeurs arbitraires entre le serveur et les clients pour " -"personnaliser entièrement l'exécution côté client. Grâce à cette " -"capacité, nous avons construit une simulation d'apprentissage fédéré à " -"grande échelle en utilisant le moteur de client virtuel Flower et nous " -"avons mené une expérience impliquant 1000 clients dans la même charge de " -"travail - le tout dans un carnet Jupyter !" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:719 -#, fuzzy +#: ../../source/tutorial-quickstart-xgboost.rst:690 msgid "" -"The `Flower Federated Learning Tutorial - Part 3 " -"`__ shows how to build a fully custom ``Strategy`` from " -"scratch." +"Unlike the original :code:`FedAvg`, we don't perform aggregation here. " +"Instead, we just make a copy of the received client model as global model" +" by overriding :code:`aggregate_fit`." msgstr "" -"Le `Tutoriel d'apprentissage fédéré Flower - Partie 3 [WIP] " -"`__ montre comment construire une ``Stratégie`` entièrement " -"personnalisée à partir de zéro." - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:9 -msgid "What is Federated Learning?" -msgstr "Qu'est-ce que l'apprentissage fédéré ?" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:13 -#, fuzzy +#: ../../source/tutorial-quickstart-xgboost.rst:693 msgid "" -"In this tutorial, you will learn what federated learning is, build your " -"first system in Flower, and gradually extend it. If you work through all " -"parts of the tutorial, you will be able to build advanced federated " -"learning systems that approach the current state of the art in the field." +"Also, the customised :code:`configure_fit` and :code:`configure_evaluate`" +" methods ensure the clients to be sequentially selected given FL round:" msgstr "" -"Dans ce tutoriel, tu apprendras ce qu'est l'apprentissage fédéré, tu " -"construiras ton premier système dans Flower, et tu l'étendras " -"progressivement. Si tu travailles sur toutes les parties du tutoriel, tu " -"seras capable de construire des systèmes d'apprentissage fédéré avancés " -"qui se rapprochent de l'état actuel de l'art dans le domaine." -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:15 -msgid "" -"🧑‍🏫 This tutorial starts at zero and expects no familiarity with " -"federated learning. Only a basic understanding of data science and Python" -" programming is assumed." +#: ../../source/tutorial-quickstart-xgboost.rst:757 +msgid "Customised data partitioning" msgstr "" -"🧑‍🏫 Ce tutoriel part de zéro et n'attend aucune familiarité avec " -"l'apprentissage fédéré. Seule une compréhension de base de la science des" -" données et de la programmation Python est supposée." -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:17 -#, fuzzy +#: ../../source/tutorial-quickstart-xgboost.rst:759 msgid "" -"`Star Flower on GitHub `__ ⭐️ and join " -"the open-source Flower community on Slack to connect, ask questions, and " -"get help: `Join Slack `__ 🌼 We'd love to " -"hear from you in the ``#introductions`` channel! And if anything is " -"unclear, head over to the ``#questions`` channel." +"In :code:`dataset.py`, we have a function :code:`instantiate_partitioner`" +" to instantiate the data partitioner based on the given " +":code:`num_partitions` and :code:`partitioner_type`. Currently, we " +"provide four supported partitioner type to simulate the uniformity/non-" +"uniformity in data quantity (uniform, linear, square, exponential)." msgstr "" -"`Star Flower on GitHub `__ ⭐️ et " -"rejoignez la communauté Flower sur Slack pour vous connecter, poser des " -"questions et obtenir de l'aide : `Join Slack `__ 🌼 Nous serions ravis d'avoir de vos nouvelles dans le canal " -"``#introductions`` ! Et si quelque chose n'est pas clair, rendez-vous sur" -" le canal ``#questions``." -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:19 +#: ../../source/tutorial-quickstart-xgboost.rst:790 #, fuzzy -msgid "Let's get started!" -msgstr "Allons-y, déclarons-le !" - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:31 -msgid "Classic machine learning" -msgstr "Apprentissage automatique classique" +msgid "Customised centralised/distributed evaluation" +msgstr "Évaluation centralisée" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:33 +#: ../../source/tutorial-quickstart-xgboost.rst:792 msgid "" -"Before we begin to discuss federated learning, let us quickly recap how " -"most machine learning works today." +"To facilitate centralised evaluation, we define a function in " +":code:`server_utils.py`:" msgstr "" -"Avant de commencer à discuter de l'apprentissage fédéré, récapitulons " -"rapidement la façon dont la plupart des apprentissages automatiques " -"fonctionnent aujourd'hui." -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:35 +#: ../../source/tutorial-quickstart-xgboost.rst:824 msgid "" -"In machine learning, we have a model, and we have data. The model could " -"be a neural network (as depicted here), or something else, like classical" -" linear regression." +"This function returns a evaluation function which instantiates a " +":code:`Booster` object and loads the global model weights to it. The " +"evaluation is conducted by calling :code:`eval_set()` method, and the " +"tested AUC value is reported." msgstr "" -"Dans l'apprentissage automatique, nous avons un modèle et des données. Le" -" modèle peut être un réseau neuronal (comme illustré ici), ou quelque " -"chose d'autre, comme la régression linéaire classique." -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:41 -msgid "|2b5c62c529f6416f840c594cce062fbb|" +#: ../../source/tutorial-quickstart-xgboost.rst:827 +msgid "" +"As for distributed evaluation on the clients, it's same as the quick-" +"start example by overriding the :code:`evaluate()` method insides the " +":code:`XgbClient` class in :code:`client_utils.py`." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:109 -msgid "Model and data" -msgstr "Modèle et données" +#: ../../source/tutorial-quickstart-xgboost.rst:831 +#, fuzzy +msgid "Flower simulation" +msgstr "Simulation de moniteur" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:47 +#: ../../source/tutorial-quickstart-xgboost.rst:832 msgid "" -"We train the model using the data to perform a useful task. A task could " -"be to detect objects in images, transcribe an audio recording, or play a " -"game like Go." +"We also provide an example code (:code:`sim.py`) to use the simulation " +"capabilities of Flower to simulate federated XGBoost training on either a" +" single machine or a cluster of machines." msgstr "" -"Nous entraînons le modèle en utilisant les données pour effectuer une " -"tâche utile. Une tâche peut consister à détecter des objets dans des " -"images, à transcrire un enregistrement audio ou à jouer à un jeu comme le" -" Go." -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:53 -msgid "|90b334680cb7467d9a04d39b8e8dca9f|" +#: ../../source/tutorial-quickstart-xgboost.rst:866 +msgid "" +"After importing all required packages, we define a :code:`main()` " +"function to perform the simulation process:" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:111 -msgid "Train model using data" -msgstr "Entraîne le modèle à l'aide des données" - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:59 -#, fuzzy +#: ../../source/tutorial-quickstart-xgboost.rst:921 msgid "" -"Now, in practice, the training data we work with doesn't originate on the" -" machine we train the model on. It gets created somewhere else." +"We first load the dataset and perform data partitioning, and the pre-" +"processed data is stored in a :code:`list`. After the simulation begins, " +"the clients won't need to pre-process their partitions again." msgstr "" -"Dans la pratique, les données d'entraînement avec lesquelles nous " -"travaillons ne proviennent pas de la machine sur laquelle nous entraînons" -" le modèle. Elles sont créées ailleurs." -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:61 -#, fuzzy +#: ../../source/tutorial-quickstart-xgboost.rst:924 +msgid "Then, we define the strategies and other hyper-parameters:" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:975 msgid "" -"It originates on a smartphone by the user interacting with an app, a car " -"collecting sensor data, a laptop receiving input via the keyboard, or a " -"smart speaker listening to someone trying to sing a song." -msgstr "" -"Elle prend naissance sur un smartphone par l'interaction de l'utilisateur" -" avec une application, une voiture qui collecte des données de capteurs, " -"un ordinateur portable qui reçoit des entrées via le clavier, ou un haut-" -"parleur intelligent qui écoute quelqu'un qui essaie de chanter une " -"chanson." - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:67 -msgid "|65764ceee89f4335bfd93fd0b115e831|" +"After that, we start the simulation by calling " +":code:`fl.simulation.start_simulation`:" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:113 -msgid "Data on a phone" -msgstr "Données sur un téléphone" - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:73 +#: ../../source/tutorial-quickstart-xgboost.rst:995 msgid "" -"What's also important to mention, this \"somewhere else\" is usually not " -"just one place, it's many places. It could be several devices all running" -" the same app. But it could also be several organizations, all generating" -" data for the same task." +"One of key parameters for :code:`start_simulation` is :code:`client_fn` " +"which returns a function to construct a client. We define it as follows:" msgstr "" -"Il est également important de mentionner que cet \"ailleurs\" n'est " -"généralement pas un seul endroit, mais plusieurs. Il peut s'agir de " -"plusieurs appareils fonctionnant tous avec la même application. Mais il " -"peut également s'agir de plusieurs organisations, qui génèrent toutes des" -" données pour la même tâche." -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:79 -msgid "|d97319ec28bb407ea0ab9705e38f3bcf|" +#: ../../source/tutorial-quickstart-xgboost.rst:1038 +msgid "Arguments parser" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:115 -msgid "Data is on many devices" -msgstr "Les données se trouvent sur de nombreux appareils" - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:85 +#: ../../source/tutorial-quickstart-xgboost.rst:1040 msgid "" -"So to use machine learning, or any kind of data analysis, the approach " -"that has been used in the past was to collect all data on a central " -"server. This server can be somewhere in a data center, or somewhere in " -"the cloud." -msgstr "" -"Ainsi, pour utiliser l'apprentissage automatique, ou tout autre type " -"d'analyse de données, l'approche utilisée par le passé consistait à " -"collecter toutes les données sur un serveur central. Ce serveur peut se " -"trouver quelque part dans un centre de données, ou quelque part dans le " -"cloud." - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:91 -msgid "|11e95ac83a8548d8b3505b4663187d07|" +"In :code:`utils.py`, we define the arguments parsers for clients, server " +"and simulation, allowing users to specify different experimental " +"settings. Let's first see the sever side:" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:117 -msgid "Central data collection" -msgstr "Collecte centralisée des données" - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:97 -#, fuzzy +#: ../../source/tutorial-quickstart-xgboost.rst:1086 msgid "" -"Once all the data is collected in one place, we can finally use machine " -"learning algorithms to train our model on the data. This is the machine " -"learning approach that we've basically always relied on." +"This allows user to specify training strategies / the number of total " +"clients / FL rounds / participating clients / clients for evaluation, and" +" evaluation fashion. Note that with :code:`--centralised-eval`, the sever" +" will do centralised evaluation and all functionalities for client " +"evaluation will be disabled." msgstr "" -"Une fois que toutes les données sont rassemblées en un seul endroit, nous" -" pouvons enfin utiliser des algorithmes d'apprentissage automatique pour " -"entraîner notre modèle sur les données. C'est l'approche d'apprentissage " -"automatique sur laquelle nous nous sommes fondamentalement toujours " -"appuyés." -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:103 -msgid "|1dab2f3a23674abc8a6731f20fa10730|" +#: ../../source/tutorial-quickstart-xgboost.rst:1090 +msgid "Then, the argument parser on client side:" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:119 -msgid "Central model training" -msgstr "Formation au modèle central" - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:130 -msgid "Challenges of classical machine learning" -msgstr "Les défis de l'apprentissage automatique classique" - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:132 +#: ../../source/tutorial-quickstart-xgboost.rst:1144 msgid "" -"The classic machine learning approach we've just seen can be used in some" -" cases. Great examples include categorizing holiday photos, or analyzing " -"web traffic. Cases, where all the data is naturally available on a " -"centralized server." -msgstr "" -"L'approche classique de l'apprentissage automatique que nous venons de " -"voir peut être utilisée dans certains cas. Parmi les grands exemples, on " -"peut citer la catégorisation des photos de vacances, ou l'analyse du " -"trafic web. Des cas, où toutes les données sont naturellement disponibles" -" sur un serveur centralisé." - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:138 -msgid "|7f0ee162da38450788493a21627306f7|" +"This defines various options for client data partitioning. Besides, " +"clients also have an option to conduct evaluation on centralised test set" +" by setting :code:`--centralised-eval`, as well as an option to perform " +"scaled learning rate based on the number of clients by setting :code" +":`--scaled-lr`." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:173 -msgid "Centralized possible" -msgstr "Possibilité de centralisation" - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:144 -msgid "" -"But the approach can not be used in many other cases. Cases, where the " -"data is not available on a centralized server, or cases where the data " -"available on one server is not enough to train a good model." +#: ../../source/tutorial-quickstart-xgboost.rst:1148 +msgid "We also have an argument parser for simulation:" msgstr "" -"Mais cette approche ne peut pas être utilisée dans de nombreux autres cas" -" : lorsque les données ne sont pas disponibles sur un serveur centralisé," -" ou lorsque les données disponibles sur un serveur ne sont pas " -"suffisantes pour former un bon modèle." -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:150 -msgid "|296a1fb72c514b23b3d8905ff0ff98c6|" +#: ../../source/tutorial-quickstart-xgboost.rst:1226 +msgid "This integrates all arguments for both client and server sides." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:175 -msgid "Centralized impossible" -msgstr "Impossible de centraliser" - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:156 +#: ../../source/tutorial-quickstart-xgboost.rst:1229 #, fuzzy +msgid "Example commands" +msgstr "Exemples de PyTorch" + +#: ../../source/tutorial-quickstart-xgboost.rst:1231 msgid "" -"There are many reasons why the classic centralized machine learning " -"approach does not work for a large number of highly important real-world " -"use cases. Those reasons include:" +"To run a centralised evaluated experiment with bagging strategy on 5 " +"clients with exponential distribution for 50 rounds, we first start the " +"server as below:" msgstr "" -"Il existe de nombreuses raisons pour lesquelles l'approche classique " -"centralisée de l'apprentissage automatique ne fonctionne pas pour un " -"grand nombre de cas d'utilisation très importants dans le monde réel, " -"notamment :" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:158 +#: ../../source/tutorial-quickstart-xgboost.rst:1238 #, fuzzy -msgid "" -"**Regulations**: GDPR (Europe), CCPA (California), PIPEDA (Canada), LGPD " -"(Brazil), PDPL (Argentina), KVKK (Turkey), POPI (South Africa), FSS " -"(Russia), CDPR (China), PDPB (India), PIPA (Korea), APPI (Japan), PDP " -"(Indonesia), PDPA (Singapore), APP (Australia), and other regulations " -"protect sensitive data from being moved. In fact, those regulations " -"sometimes even prevent single organizations from combining their own " -"users' data for artificial intelligence training because those users live" -" in different parts of the world, and their data is governed by different" -" data protection regulations." -msgstr "" -"**Réglementations** : GDPR (Europe), CCPA (Californie), PIPEDA (Canada), " -"LGPD (Brésil), PDPL (Argentine), KVKK (Turquie), POPI (Afrique du Sud), " -"FSS (Russie), CDPR (Chine), PDPB (Inde), PIPA (Corée), APPI (Japon), PDP " -"(Indonésie), PDPA (Singapour), APP (Australie), et d'autres " -"réglementations protègent les données sensibles contre le déplacement. En" -" fait, ces réglementations empêchent même parfois des organisations " -"individuelles de combiner les données de leurs propres utilisateurs pour " -"la formation à l'intelligence artificielle parce que ces utilisateurs " -"vivent dans différentes parties du monde, et que leurs données sont " -"régies par des réglementations différentes en matière de protection des " -"données." +msgid "Then, on each client terminal, we start the clients:" +msgstr "Ouvre un autre terminal et démarre le deuxième client :" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:160 -msgid "" -"**User preference**: In addition to regulation, there are use cases where" -" users just expect that no data leaves their device, ever. If you type " -"your passwords and credit card info into the digital keyboard of your " -"phone, you don't expect those passwords to end up on the server of the " -"company that developed that keyboard, do you? In fact, that use case was " -"the reason federated learning was invented in the first place." +#: ../../source/tutorial-quickstart-xgboost.rst:1244 +msgid "To run the same experiment with Flower simulation:" msgstr "" -"**Préférence de l'utilisateur** : En plus de la réglementation, il existe" -" des cas d'utilisation où les utilisateurs s'attendent tout simplement à " -"ce qu'aucune donnée ne quitte leur appareil, jamais. Si tu tapes tes mots" -" de passe et tes informations de carte de crédit sur le clavier numérique" -" de ton téléphone, tu ne t'attends pas à ce que ces mots de passe " -"finissent sur le serveur de l'entreprise qui a développé ce clavier, n" -"'est-ce pas ? En fait, ce cas d'utilisation est la raison pour laquelle " -"l'apprentissage fédéré a été inventé en premier lieu." -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:161 +#: ../../source/tutorial-quickstart-xgboost.rst:1250 #, fuzzy msgid "" -"**Data volume**: Some sensors, like cameras, produce such a high data " -"volume that it is neither feasible nor economic to collect all the data " -"(due to, for example, bandwidth or communication efficiency). Think about" -" a national rail service with hundreds of train stations across the " -"country. If each of these train stations is outfitted with a number of " -"security cameras, the volume of raw on-device data they produce requires " -"incredibly powerful and exceedingly expensive infrastructure to process " -"and store. And most of the data isn't even useful." +"The full `code `_ for this comprehensive example can be found in" +" :code:`examples/xgboost-comprehensive`." msgstr "" -"**volume de données** : certains capteurs, comme les caméras, produisent " -"un volume de données si important qu'il n'est ni possible ni économique " -"de collecter toutes les données (en raison, par exemple, de la bande " -"passante ou de l'efficacité des communications). Pensez à un service " -"ferroviaire national comptant des centaines de gares à travers le pays. " -"Si chacune de ces gares est équipée d'un certain nombre de caméras de " -"sécurité, le volume de données brutes sur les appareils qu'elles " -"produisent nécessite une infrastructure incroyablement puissante et " -"excessivement coûteuse pour les traiter et les stocker. Et la plupart de " -"ces données ne sont même pas utiles." +"Félicitations ! Tu as réussi à construire et à faire fonctionner ton " +"premier système d'apprentissage fédéré. Le code source complet " +"`_ de cet exemple se trouve dans :code:`examples" +"/quickstart-mxnet`." -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:164 -msgid "Examples where centralized machine learning does not work include:" -msgstr "" -"Voici quelques exemples où l'apprentissage automatique centralisé ne " -"fonctionne pas :" +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:9 +#, fuzzy +msgid "Build a strategy from scratch" +msgstr "Élaborer une stratégie à partir de zéro" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:166 +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:11 #, fuzzy msgid "" -"Sensitive healthcare records from multiple hospitals to train cancer " -"detection models" +"Welcome to the third part of the Flower federated learning tutorial. In " +"previous parts of this tutorial, we introduced federated learning with " +"PyTorch and Flower (`part 1 `__) and we learned how strategies " +"can be used to customize the execution on both the server and the clients" +" (`part 2 `__)." msgstr "" -"Des dossiers médicaux sensibles provenant de plusieurs hôpitaux pour " -"former des modèles de détection du cancer" +"Bienvenue dans la troisième partie du tutoriel sur l'apprentissage fédéré" +" Flower. Dans les parties précédentes de ce tutoriel, nous avons présenté" +" l'apprentissage fédéré avec PyTorch et Flower (`partie 1 " +"`__) " +"et nous avons appris comment les stratégies peuvent être utilisées pour " +"personnaliser l'exécution à la fois sur le serveur et sur les clients " +"(`partie 2 `__)." -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:167 +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:13 msgid "" -"Financial information from different organizations to detect financial " -"fraud" +"In this notebook, we'll continue to customize the federated learning " +"system we built previously by creating a custom version of FedAvg (again," +" using `Flower `__ and `PyTorch " +"`__)." msgstr "" -"Informations financières provenant de différentes organisations pour " -"détecter les fraudes financières" +"Dans ce carnet, nous allons continuer à personnaliser le système " +"d'apprentissage fédéré que nous avons construit précédemment en créant " +"une version personnalisée de FedAvg (encore une fois, en utilisant " +"`Flower `__ et `PyTorch `__)." -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:168 -msgid "Location data from your electric car to make better range prediction" -msgstr "" -"Les données de localisation de ta voiture électrique pour mieux prédire " -"l'autonomie" - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:169 -msgid "End-to-end encrypted messages to train better auto-complete models" -msgstr "" -"Messages cryptés de bout en bout pour former de meilleurs modèles " -"d'autocomplétion" - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:171 -#, fuzzy +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:15 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:16 +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:15 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:15 msgid "" -"The popularity of privacy-enhancing systems like the `Brave " -"`__ browser or the `Signal `__ " -"messenger shows that users care about privacy. In fact, they choose the " -"privacy-enhancing version over other alternatives, if such an alternative" -" exists. But what can we do to apply machine learning and data science to" -" these cases to utilize private data? After all, these are all areas that" -" would benefit significantly from recent advances in AI." +"`Star Flower on GitHub `__ ⭐️ and join " +"the Flower community on Slack to connect, ask questions, and get help: " +"`Join Slack `__ 🌼 We'd love to hear from " +"you in the ``#introductions`` channel! And if anything is unclear, head " +"over to the ``#questions`` channel." msgstr "" -"La popularité des systèmes améliorant la confidentialité comme le " -"navigateur `Brave `__ ou le messager `Signal " -"`__ montre que les utilisateurs se soucient de la " -"confidentialité. En fait, ils choisissent la version améliorant la " -"confidentialité plutôt que d'autres alternatives, si une telle " -"alternative existe. Mais que pouvons-nous faire pour appliquer " -"l'apprentissage automatique et la science des données à ces cas afin " -"d'utiliser les données privées ? Après tout, ce sont tous des domaines " -"qui bénéficieraient de manière significative des récentes avancées en " -"matière d'IA." +"`Star Flower on GitHub `__ ⭐️ et " +"rejoignez la communauté Flower sur Slack pour vous connecter, poser des " +"questions et obtenir de l'aide : `Join Slack `__ 🌼 Nous serions ravis d'avoir de vos nouvelles dans le canal " +"``#introductions`` ! Et si quelque chose n'est pas clair, rendez-vous sur" +" le canal ``#questions``." -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:186 -msgid "Federated learning" -msgstr "Apprentissage fédéré" +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:17 +msgid "Let's build a new ``Strategy`` from scratch!" +msgstr "Construisons une nouvelle ``Stratégie`` à partir de zéro !" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:188 +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:29 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:29 +msgid "Preparation" +msgstr "Préparation" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:31 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:32 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:31 msgid "" -"Federated learning simply reverses this approach. It enables machine " -"learning on distributed data by moving the training to the data, instead " -"of moving the data to the training. Here's the single-sentence " -"explanation:" +"Before we begin with the actual code, let's make sure that we have " +"everything we need." msgstr "" -"L'apprentissage fédéré inverse simplement cette approche. Il permet " -"l'apprentissage automatique sur des données distribuées en déplaçant la " -"formation vers les données, au lieu de déplacer les données vers la " -"formation. Voici l'explication en une seule phrase :" +"Avant de commencer le code proprement dit, assurons-nous que nous " +"disposons de tout ce dont nous avons besoin." -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:190 -msgid "Central machine learning: move the data to the computation" -msgstr "Apprentissage automatique central : déplace les données vers le calcul" +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:43 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:44 +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:43 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:43 +msgid "Installing dependencies" +msgstr "Installation des dépendances" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:191 -msgid "Federated (machine) learning: move the computation to the data" -msgstr "Apprentissage (machine) fédéré : déplacer le calcul vers les données" +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:45 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:46 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:45 +msgid "First, we install the necessary packages:" +msgstr "Tout d'abord, nous installons les paquets nécessaires :" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:193 +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:65 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:66 +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:65 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:65 msgid "" -"By doing so, it enables us to use machine learning (and other data " -"science approaches) in areas where it wasn't possible before. We can now " -"train excellent medical AI models by enabling different hospitals to work" -" together. We can solve financial fraud by training AI models on the data" -" of different financial institutions. We can build novel privacy-" -"enhancing applications (such as secure messaging) that have better built-" -"in AI than their non-privacy-enhancing alternatives. And those are just a" -" few of the examples that come to mind. As we deploy federated learning, " -"we discover more and more areas that can suddenly be reinvented because " -"they now have access to vast amounts of previously inaccessible data." +"Now that we have all dependencies installed, we can import everything we " +"need for this tutorial:" msgstr "" -"Ce faisant, il nous permet d'utiliser l'apprentissage automatique (et " -"d'autres approches de science des données) dans des domaines où cela " -"n'était pas possible auparavant. Nous pouvons désormais former " -"d'excellents modèles d'IA médicale en permettant à différents hôpitaux de" -" travailler ensemble. Nous pouvons résoudre les fraudes financières en " -"formant des modèles d'IA sur les données de différentes institutions " -"financières. Nous pouvons créer de nouvelles applications d'amélioration " -"de la confidentialité (telles que la messagerie sécurisée) qui ont une " -"meilleure IA intégrée que leurs alternatives d'amélioration de la " -"confidentialité. Et ce ne sont là que quelques exemples qui me viennent à" -" l'esprit. Au fur et à mesure que nous déployons l'apprentissage fédéré, " -"nous découvrons de plus en plus de domaines qui peuvent soudainement être" -" réinventés parce qu'ils ont maintenant accès à de vastes quantités de " -"données auparavant inaccessibles." +"Maintenant que toutes les dépendances sont installées, nous pouvons " +"importer tout ce dont nous avons besoin pour ce tutoriel :" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:196 +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:101 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:102 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:101 msgid "" -"So how does federated learning work, exactly? Let's start with an " -"intuitive explanation." +"It is possible to switch to a runtime that has GPU acceleration enabled " +"(on Google Colab: ``Runtime > Change runtime type > Hardware acclerator: " +"GPU > Save``). Note, however, that Google Colab is not always able to " +"offer GPU acceleration. If you see an error related to GPU availability " +"in one of the following sections, consider switching back to CPU-based " +"execution by setting ``DEVICE = torch.device(\"cpu\")``. If the runtime " +"has GPU acceleration enabled, you should see the output ``Training on " +"cuda``, otherwise it'll say ``Training on cpu``." msgstr "" -"Comment fonctionne l'apprentissage fédéré ? Commençons par une " -"explication intuitive." - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:199 -msgid "Federated learning in five steps" -msgstr "L'apprentissage fédéré en cinq étapes" +"Il est possible de passer à un runtime dont l'accélération GPU est " +"activée (sur Google Colab : ``Runtime > Change runtime type > Hardware " +"acclerator : GPU > Save``). Note cependant que Google Colab n'est pas " +"toujours en mesure de proposer l'accélération GPU. Si tu vois une erreur " +"liée à la disponibilité du GPU dans l'une des sections suivantes, " +"envisage de repasser à une exécution basée sur le CPU en définissant " +"``DEVICE = torch.device(\"cpu\")``. Si le runtime a activé l'accélération" +" GPU, tu devrais voir apparaître le résultat ``Training on cuda``, sinon " +"il dira ``Training on cpu``." -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:202 -msgid "Step 0: Initialize global model" -msgstr "Étape 0 : Initialisation du modèle global" +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:114 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:115 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:114 +msgid "Data loading" +msgstr "Chargement des données" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:204 +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:116 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:116 msgid "" -"We start by initializing the model on the server. This is exactly the " -"same in classic centralized learning: we initialize the model parameters," -" either randomly or from a previously saved checkpoint." -msgstr "" -"Nous commençons par initialiser le modèle sur le serveur. C'est " -"exactement la même chose dans l'apprentissage centralisé classique : nous" -" initialisons les paramètres du modèle, soit de façon aléatoire, soit à " -"partir d'un point de contrôle précédemment sauvegardé." - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:210 -msgid "|5b1408eec0d746cdb91162a9107b6089|" +"Let's now load the CIFAR-10 training and test set, partition them into " +"ten smaller datasets (each split into training and validation set), and " +"wrap everything in their own ``DataLoader``. We introduce a new parameter" +" ``num_clients`` which allows us to call ``load_datasets`` with different" +" numbers of clients." msgstr "" +"Chargeons maintenant les ensembles d'entraînement et de test CIFAR-10, " +"divisons-les en dix ensembles de données plus petits (chacun divisé en " +"ensemble d'entraînement et de validation), et enveloppons le tout dans " +"leur propre ``DataLoader``. Nous introduisons un nouveau paramètre " +"``num_clients`` qui nous permet d'appeler ``load_datasets`` avec " +"différents nombres de clients." -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:307 -msgid "Initialize global model" -msgstr "Initialise le modèle global" +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:167 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:168 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:167 +msgid "Model training/evaluation" +msgstr "Formation/évaluation du modèle" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:217 +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:169 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:170 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:169 msgid "" -"Step 1: Send model to a number of connected organizations/devices (client" -" nodes)" +"Let's continue with the usual model definition (including " +"``set_parameters`` and ``get_parameters``), training and test functions:" msgstr "" -"Étape 1 : envoyer le modèle à un certain nombre d'organisations/appareils" -" connectés (nœuds clients)" +"Continuons avec la définition habituelle du modèle (y compris " +"``set_parameters`` et ``get_parameters``), les fonctions d'entraînement " +"et de test :" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:219 -#, fuzzy +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:258 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:258 +msgid "Flower client" +msgstr "Client de Flower" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:260 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:260 msgid "" -"Next, we send the parameters of the global model to the connected client " -"nodes (think: edge devices like smartphones or servers belonging to " -"organizations). This is to ensure that each participating node starts " -"their local training using the same model parameters. We often use only a" -" few of the connected nodes instead of all nodes. The reason for this is " -"that selecting more and more client nodes has diminishing returns." +"To implement the Flower client, we (again) create a subclass of " +"``flwr.client.NumPyClient`` and implement the three methods " +"``get_parameters``, ``fit``, and ``evaluate``. Here, we also pass the " +"``cid`` to the client and use it log additional details:" msgstr "" -"Ensuite, nous envoyons les paramètres du modèle global aux nœuds clients " -"connectés (par exemple, les appareils périphériques comme les smartphones" -" ou les serveurs appartenant à des organisations). Cela permet de " -"s'assurer que chaque nœud participant commence sa formation locale en " -"utilisant les mêmes paramètres de modèle. Nous n'utilisons souvent que " -"quelques-uns des nœuds connectés au lieu de tous les nœuds. La raison en " -"est que la sélection d'un nombre croissant de nœuds clients a des " -"rendements décroissants." +"Pour mettre en œuvre le client Flower, nous créons (à nouveau) une sous-" +"classe de ``flwr.client.NumPyClient`` et mettons en œuvre les trois " +"méthodes ``get_parameters``, ``fit`` et ``evaluate``. Ici, nous " +"transmettons également le ``cid`` au client et l'utilisons pour consigner" +" des détails supplémentaires :" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:225 -msgid "|aef19f4b122c4e8d9f4c57f99bcd5dd2|" -msgstr "" +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:308 +msgid "Let's test what we have so far before we continue:" +msgstr "Testons ce que nous avons jusqu'à présent avant de continuer :" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:309 -msgid "Send global model" -msgstr "Envoyer le modèle global" +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:339 +msgid "Build a Strategy from scratch" +msgstr "Élaborer une stratégie à partir de zéro" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:232 +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:341 msgid "" -"Step 2: Train model locally on the data of each organization/device " -"(client node)" +"Let’s overwrite the ``configure_fit`` method such that it passes a higher" +" learning rate (potentially also other hyperparameters) to the optimizer " +"of a fraction of the clients. We will keep the sampling of the clients as" +" it is in ``FedAvg`` and then change the configuration dictionary (one of" +" the ``FitIns`` attributes)." msgstr "" -"Étape 2 : Entraîne le modèle localement sur les données de chaque " -"organisation/appareil (nœud client)" +"Remplaçons la méthode ``configure_fit`` de façon à ce qu'elle transmette " +"un taux d'apprentissage plus élevé (potentiellement aussi d'autres " +"hyperparamètres) à l'optimiseur d'une fraction des clients. Nous " +"garderons l'échantillonnage des clients tel qu'il est dans ``FedAvg`` et " +"changerons ensuite le dictionnaire de configuration (l'un des attributs " +"``FitIns``)." -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:234 +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:507 msgid "" -"Now that all (selected) client nodes have the latest version of the " -"global model parameters, they start the local training. They use their " -"own local dataset to train their own local model. They don't train the " -"model until full convergence, but they only train for a little while. " -"This could be as little as one epoch on the local data, or even just a " -"few steps (mini-batches)." +"The only thing left is to use the newly created custom Strategy " +"``FedCustom`` when starting the experiment:" msgstr "" -"Maintenant que tous les nœuds clients (sélectionnés) disposent de la " -"dernière version des paramètres du modèle global, ils commencent " -"l'entraînement local. Ils utilisent leur propre ensemble de données " -"locales pour entraîner leur propre modèle local. Ils n'entraînent pas le " -"modèle jusqu'à la convergence totale, mais ils ne s'entraînent que " -"pendant un petit moment. Il peut s'agir d'une seule époque sur les " -"données locales, ou même de quelques étapes (mini-batchs)." +"Il ne reste plus qu'à utiliser la stratégie personnalisée nouvellement " +"créée ``FedCustom`` lors du démarrage de l'expérience :" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:240 -msgid "|2881a86d8fc54ba29d96b29fc2819f4a|" -msgstr "" +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:534 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:932 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:697 +msgid "Recap" +msgstr "Récapitulation" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:311 -msgid "Train on local data" -msgstr "Forme-toi aux données locales" +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:536 +msgid "" +"In this notebook, we’ve seen how to implement a custom strategy. A custom" +" strategy enables granular control over client node configuration, result" +" aggregation, and more. To define a custom strategy, you only have to " +"overwrite the abstract methods of the (abstract) base class ``Strategy``." +" To make custom strategies even more powerful, you can pass custom " +"functions to the constructor of your new class (``__init__``) and then " +"call these functions whenever needed." +msgstr "" +"Dans ce carnet, nous avons vu comment mettre en place une stratégie " +"personnalisée. Une stratégie personnalisée permet un contrôle granulaire " +"sur la configuration des nœuds clients, l'agrégation des résultats, et " +"bien plus encore. Pour définir une stratégie personnalisée, il te suffit " +"d'écraser les méthodes abstraites de la classe de base (abstraite) " +"``Strategy``. Pour rendre les stratégies personnalisées encore plus " +"puissantes, tu peux passer des fonctions personnalisées au constructeur " +"de ta nouvelle classe (``__init__``) et appeler ensuite ces fonctions à " +"chaque fois que c'est nécessaire." -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:247 -msgid "Step 3: Return model updates back to the server" -msgstr "Étape 3 : Renvoyer les mises à jour du modèle au serveur" +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:550 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:948 +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:729 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:715 +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:369 +msgid "" +"Before you continue, make sure to join the Flower community on Slack: " +"`Join Slack `__" +msgstr "" +"Avant de continuer, n'oublie pas de rejoindre la communauté Flower sur " +"Slack : `Join Slack `__" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:249 +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:552 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:950 +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:731 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:717 +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:371 msgid "" -"After local training, each client node has a slightly different version " -"of the model parameters they originally received. The parameters are all " -"different because each client node has different examples in its local " -"dataset. The client nodes then send those model updates back to the " -"server. The model updates they send can either be the full model " -"parameters or just the gradients that were accumulated during local " -"training." +"There's a dedicated ``#questions`` channel if you need help, but we'd " +"also love to hear who you are in ``#introductions``!" msgstr "" -"Après l'entraînement local, chaque nœud client possède une version " -"légèrement différente des paramètres du modèle qu'il a reçus à l'origine." -" Les paramètres sont tous différents parce que chaque nœud client a des " -"exemples différents dans son ensemble de données local. Les nœuds clients" -" renvoient ensuite ces mises à jour du modèle au serveur. Les mises à " -"jour du modèle qu'ils envoient peuvent être soit les paramètres complets " -"du modèle, soit seulement les gradients qui ont été accumulés au cours de" -" l'entraînement local." +"Il existe un canal dédié aux ``questions`` si vous avez besoin d'aide, " +"mais nous aimerions aussi savoir qui vous êtes dans ``#introductions`` !" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:255 -msgid "|ec1fe880237247e0975f52766775ab84|" +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:554 +#, fuzzy +msgid "" +"The `Flower Federated Learning Tutorial - Part 4 " +"`__ introduces ``Client``, the flexible API underlying " +"``NumPyClient``." msgstr "" +"Le `Tutoriel d'apprentissage fédéré Flower - Partie 4 " +"`__ présente ``Client``, l'API flexible qui sous-tend " +"``NumPyClient``." -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:313 -msgid "Send model updates" -msgstr "Envoyer les mises à jour du modèle" +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:9 +#, fuzzy +msgid "Customize the client" +msgstr "Création du client IMDBC" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:262 -msgid "Step 4: Aggregate model updates into a new global model" +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:11 +#, fuzzy +msgid "" +"Welcome to the fourth part of the Flower federated learning tutorial. In " +"the previous parts of this tutorial, we introduced federated learning " +"with PyTorch and Flower (`part 1 `__), we learned how " +"strategies can be used to customize the execution on both the server and " +"the clients (`part 2 `__), and we built our own " +"custom strategy from scratch (`part 3 `__)." msgstr "" -"Étape 4 : Agréger les mises à jour des modèles dans un nouveau modèle " -"global" +"Bienvenue dans la quatrième partie du tutoriel sur l'apprentissage fédéré" +" Flower. Dans les parties précédentes de ce tutoriel, nous avons présenté" +" l'apprentissage fédéré avec PyTorch et Flower (`partie 1 " +"`__), " +"nous avons appris comment les stratégies peuvent être utilisées pour " +"personnaliser l'exécution à la fois sur le serveur et les clients " +"(`partie 2 `__), et nous avons construit notre propre stratégie " +"personnalisée à partir de zéro (`partie 3 - WIP " +"`__)." -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:264 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:14 msgid "" -"The server receives model updates from the selected client nodes. If it " -"selected 100 client nodes, it now has 100 slightly different versions of " -"the original global model, each trained on the local data of one client. " -"But didn't we want to have one model that contains the learnings from the" -" data of all 100 client nodes?" +"In this notebook, we revisit ``NumPyClient`` and introduce a new " +"baseclass for building clients, simply named ``Client``. In previous " +"parts of this tutorial, we've based our client on ``NumPyClient``, a " +"convenience class which makes it easy to work with machine learning " +"libraries that have good NumPy interoperability. With ``Client``, we gain" +" a lot of flexibility that we didn't have before, but we'll also have to " +"do a few things the we didn't have to do before." msgstr "" -"Le serveur reçoit les mises à jour du modèle des nœuds clients " -"sélectionnés. S'il a sélectionné 100 nœuds clients, il dispose maintenant" -" de 100 versions légèrement différentes du modèle global original, " -"chacune ayant été formée sur les données locales d'un client. Mais ne " -"voulions-nous pas avoir un seul modèle qui contienne les apprentissages " -"des données de l'ensemble des 100 nœuds clients ?" +"Dans ce carnet, nous revisitons `NumPyClient`` et introduisons une " +"nouvelle classe de base pour construire des clients, simplement appelée " +"`Client``. Dans les parties précédentes de ce tutoriel, nous avons basé " +"notre client sur ``NumPyClient``, une classe de commodité qui facilite le" +" travail avec les bibliothèques d'apprentissage automatique qui ont une " +"bonne interopérabilité NumPy. Avec ``Client``, nous gagnons beaucoup de " +"flexibilité que nous n'avions pas auparavant, mais nous devrons également" +" faire quelques choses que nous n'avions pas à faire auparavant." -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:266 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:18 msgid "" -"In order to get one single model, we have to combine all the model " -"updates we received from the client nodes. This process is called " -"*aggregation*, and there are many different ways to do it. The most basic" -" way to do it is called *Federated Averaging* (`McMahan et al., 2016 " -"`__), often abbreviated as *FedAvg*. " -"*FedAvg* takes the 100 model updates and, as the name suggests, averages " -"them. To be more precise, it takes the *weighted average* of the model " -"updates, weighted by the number of examples each client used for " -"training. The weighting is important to make sure that each data example " -"has the same \"influence\" on the resulting global model. If one client " -"has 10 examples, and another client has 100 examples, then - without " -"weighting - each of the 10 examples would influence the global model ten " -"times as much as each of the 100 examples." +"Let's go deeper and see what it takes to move from ``NumPyClient`` to " +"``Client``!" msgstr "" -"In order to get one single model, we have to combine all the model " -"updates we received from the client nodes. This process is called " -"*aggregation*, and there are many different ways to do it. The most basic" -" way to do it is called *Federated Averaging* (`McMahan et al., 2016 " -"`__), often abbreviated as *FedAvg*. " -"*FedAvg* takes the 100 model updates and, as the name suggests, averages " -"them. To be more precise, it takes the *weighted average* of the model " -"updates, weighted by the number of examples each client used for " -"training. The weighting is important to make sure that each data example " -"has the same \"influence\" on the resulting global model. If one client " -"has 10 examples, and another client has 100 examples, then - without " -"weighting - each of the 10 examples would influence the global model ten " -"times as much as each of the 100 examples." +"Allons plus loin et voyons ce qu'il faut faire pour passer de " +"``NumPyClient`` à ``Client`` !" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:273 -msgid "|9fdf048ed58d4467b2718cdf4aaf1ec3|" -msgstr "" +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:30 +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:29 +msgid "Step 0: Preparation" +msgstr "Étape 0 : Préparation" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:315 -msgid "Aggregate model updates" -msgstr "Mises à jour globales du modèle" +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:117 +msgid "" +"Let's now load the CIFAR-10 training and test set, partition them into " +"ten smaller datasets (each split into training and validation set), and " +"wrap everything in their own ``DataLoader``." +msgstr "" +"Chargeons maintenant les ensembles d'entraînement et de test CIFAR-10, " +"divisons-les en dix ensembles de données plus petits (chacun divisé en " +"ensemble d'entraînement et de validation) et enveloppons le tout dans " +"leur propre ``DataLoader``." -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:280 -msgid "Step 5: Repeat steps 1 to 4 until the model converges" -msgstr "Étape 5 : répète les étapes 1 à 4 jusqu'à ce que le modèle converge" +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:259 +msgid "Step 1: Revisiting NumPyClient" +msgstr "Étape 1 : Revoir NumPyClient" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:282 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:261 msgid "" -"Steps 1 to 4 are what we call a single round of federated learning. The " -"global model parameters get sent to the participating client nodes (step " -"1), the client nodes train on their local data (step 2), they send their " -"updated models to the server (step 3), and the server then aggregates the" -" model updates to get a new version of the global model (step 4)." +"So far, we've implemented our client by subclassing " +"``flwr.client.NumPyClient``. The three methods we implemented are " +"``get_parameters``, ``fit``, and ``evaluate``. Finally, we wrap the " +"creation of instances of this class in a function called ``client_fn``:" msgstr "" -"Les étapes 1 à 4 constituent ce que nous appelons un cycle unique " -"d'apprentissage fédéré. Les paramètres du modèle global sont envoyés aux " -"nœuds clients participants (étape 1), les nœuds clients s'entraînent sur " -"leurs données locales (étape 2), ils envoient leurs modèles mis à jour au" -" serveur (étape 3), et le serveur agrège ensuite les mises à jour du " -"modèle pour obtenir une nouvelle version du modèle global (étape 4)." +"Jusqu'à présent, nous avons implémenté notre client en sous-classant " +"``flwr.client.NumPyClient``. Les trois méthodes que nous avons " +"implémentées sont ``get_parameters``, ``fit`` et ``evaluate``. Enfin, " +"nous enveloppons la création d'instances de cette classe dans une " +"fonction appelée ``client_fn`` :" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:284 -#, fuzzy +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:309 msgid "" -"During a single round, each client node that participates in that " -"iteration only trains for a little while. This means that after the " -"aggregation step (step 4), we have a model that has been trained on all " -"the data of all participating client nodes, but only for a little while. " -"We then have to repeat this training process over and over again to " -"eventually arrive at a fully trained model that performs well across the " -"data of all client nodes." +"We've seen this before, there's nothing new so far. The only *tiny* " +"difference compared to the previous notebook is naming, we've changed " +"``FlowerClient`` to ``FlowerNumPyClient`` and ``client_fn`` to " +"``numpyclient_fn``. Let's run it to see the output we get:" msgstr "" -"Au cours d'un seul tour, chaque nœud client qui participe à cette " -"itération ne s'entraîne que pendant un petit moment. Cela signifie " -"qu'après l'étape d'agrégation (étape 4), nous avons un modèle qui a été " -"entraîné sur toutes les données de tous les nœuds clients participants, " -"mais seulement pendant un petit moment. Nous devons ensuite répéter ce " -"processus d'entraînement encore et encore pour finalement arriver à un " -"modèle entièrement entraîné qui fonctionne bien sur l'ensemble des " -"données de tous les nœuds clients." +"Nous avons déjà vu cela auparavant, il n'y a rien de nouveau jusqu'à " +"présent. La seule *petite* différence par rapport au carnet précédent est" +" le nommage, nous avons changé ``FlowerClient`` en ``FlowerNumPyClient`` " +"et ``client_fn`` en ``numpyclient_fn``. Exécutons-le pour voir la sortie " +"que nous obtenons :" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:289 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:339 msgid "" -"Congratulations, you now understand the basics of federated learning. " -"There's a lot more to discuss, of course, but that was federated learning" -" in a nutshell. In later parts of this tutorial, we will go into more " -"detail. Interesting questions include: How can we select the best client " -"nodes that should participate in the next round? What's the best way to " -"aggregate model updates? How can we handle failing client nodes " -"(stragglers)?" +"This works as expected, two clients are training for three rounds of " +"federated learning." msgstr "" -"Félicitations, tu comprends maintenant les bases de l'apprentissage " -"fédéré. Il y a bien sûr beaucoup plus à discuter, mais c'était " -"l'apprentissage fédéré en quelques mots. Dans les parties suivantes de ce" -" tutoriel, nous irons plus en détail. Les questions intéressantes " -"comprennent : comment pouvons-nous sélectionner les meilleurs nœuds " -"clients qui devraient participer au prochain tour ? Quelle est la " -"meilleure façon d'agréger les mises à jour du modèle ? Comment pouvons-" -"nous gérer les nœuds clients qui échouent (stragglers) ?" +"Cela fonctionne comme prévu, deux clients s'entraînent pour trois tours " +"d'apprentissage fédéré." -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:294 -#, fuzzy +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:341 msgid "" -"Just like we can train a model on the decentralized data of different " -"client nodes, we can also evaluate the model on that data to receive " -"valuable metrics. This is called federated evaluation, sometimes " -"abbreviated as FE. In fact, federated evaluation is an integral part of " -"most federated learning systems." +"Let's dive a little bit deeper and discuss how Flower executes this " +"simulation. Whenever a client is selected to do some work, " +"``start_simulation`` calls the function ``numpyclient_fn`` to create an " +"instance of our ``FlowerNumPyClient`` (along with loading the model and " +"the data)." msgstr "" -"Tout comme nous pouvons former un modèle sur les données décentralisées " -"de différents nœuds clients, nous pouvons également évaluer le modèle sur" -" ces données pour recevoir des mesures précieuses. C'est ce qu'on appelle" -" l'évaluation fédérée, parfois abrégée en FE. En fait, l'évaluation " -"fédérée fait partie intégrante de la plupart des systèmes d'apprentissage" -" fédéré." - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:297 -msgid "Federated analytics" -msgstr "Analyses fédérées" +"Plongeons un peu plus profondément et discutons de la façon dont Flower " +"exécute cette simulation. Chaque fois qu'un client est sélectionné pour " +"effectuer un travail, ``start_simulation`` appelle la fonction " +"``numpyclient_fn`` pour créer une instance de notre ``FlowerNumPyClient``" +" (en même temps qu'il charge le modèle et les données)." -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:299 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:343 msgid "" -"In many cases, machine learning isn't necessary to derive value from " -"data. Data analysis can yield valuable insights, but again, there's often" -" not enough data to get a clear answer. What's the average age at which " -"people develop a certain type of health condition? Federated analytics " -"enables such queries over multiple client nodes. It is usually used in " -"conjunction with other privacy-enhancing technologies like secure " -"aggregation to prevent the server from seeing the results submitted by " -"individual client nodes." +"But here's the perhaps surprising part: Flower doesn't actually use the " +"``FlowerNumPyClient`` object directly. Instead, it wraps the object to " +"makes it look like a subclass of ``flwr.client.Client``, not " +"``flwr.client.NumPyClient``. In fact, the Flower core framework doesn't " +"know how to handle ``NumPyClient``'s, it only knows how to handle " +"``Client``'s. ``NumPyClient`` is just a convenience abstraction built on " +"top of ``Client``." msgstr "" -"Dans de nombreux cas, l'apprentissage automatique n'est pas nécessaire " -"pour tirer de la valeur des données. L'analyse des données peut donner " -"des indications précieuses, mais là encore, il n'y a souvent pas assez de" -" données pour obtenir une réponse claire. Quel est l'âge moyen auquel les" -" gens développent un certain type de problème de santé ? L'analyse " -"fédérée permet de telles requêtes sur plusieurs nœuds clients. Elle est " -"généralement utilisée en conjonction avec d'autres technologies de " -"renforcement de la confidentialité, comme l'agrégation sécurisée, pour " -"empêcher le serveur de voir les résultats soumis par les nœuds clients " -"individuels." +"Mais voici la partie la plus surprenante : Flower n'utilise pas " +"directement l'objet `FlowerNumPyClient`. Au lieu de cela, il enveloppe " +"l'objet pour le faire ressembler à une sous-classe de " +"`flwr.client.Client`, et non de `flwr.client.NumPyClient`. En fait, le " +"noyau de Flower ne sait pas comment gérer les `NumPyClient`, il sait " +"seulement comment gérer les `Client`. `NumPyClient` est juste une " +"abstraction de commodité construite au dessus de `Client`." -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:305 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:345 msgid "" -"Differential privacy (DP) is often mentioned in the context of Federated " -"Learning. It is a privacy-preserving method used when analyzing and " -"sharing statistical data, ensuring the privacy of individual " -"participants. DP achieves this by adding statistical noise to the model " -"updates, ensuring any individual participants’ information cannot be " -"distinguished or re-identified. This technique can be considered an " -"optimization that provides a quantifiable privacy protection measure." +"Instead of building on top of ``NumPyClient``, we can directly build on " +"top of ``Client``." msgstr "" +"Au lieu de construire par-dessus `NumPyClient``, nous pouvons construire " +"directement par-dessus `Client``." -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:326 -msgid "Flower" -msgstr "Fleur" +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:357 +msgid "Step 2: Moving from ``NumPyClient`` to ``Client``" +msgstr "Étape 2 : Passer de ``NumPyClient`` à ``Client``" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:328 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:359 msgid "" -"Federated learning, federated evaluation, and federated analytics require" -" infrastructure to move machine learning models back and forth, train and" -" evaluate them on local data, and then aggregate the updated models. " -"Flower provides the infrastructure to do exactly that in an easy, " -"scalable, and secure way. In short, Flower presents a unified approach to" -" federated learning, analytics, and evaluation. It allows the user to " -"federate any workload, any ML framework, and any programming language." +"Let's try to do the same thing using ``Client`` instead of " +"``NumPyClient``." msgstr "" -"L'apprentissage fédéré, l'évaluation fédérée et l'analyse fédérée " -"nécessitent une infrastructure pour déplacer les modèles d'apprentissage " -"automatique dans les deux sens, les entraîner et les évaluer sur des " -"données locales, puis agréger les modèles mis à jour. Flower fournit " -"l'infrastructure pour faire exactement cela de manière simple, évolutive " -"et sécurisée. En bref, Flower présente une approche unifiée de " -"l'apprentissage, de l'analyse et de l'évaluation fédérés. Il permet à " -"l'utilisateur de fédérer n'importe quelle charge de travail, n'importe " -"quel cadre de ML et n'importe quel langage de programmation." +"Essayons de faire la même chose en utilisant ``Client`` au lieu de " +"``NumPyClient``." -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:334 -msgid "|ff726bc5505e432388ee2fdd6ef420b9|" +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:465 +msgid "" +"Before we discuss the code in more detail, let's try to run it! Gotta " +"make sure our new ``Client``-based client works, right?" msgstr "" +"Avant de discuter du code plus en détail, essayons de l'exécuter ! Nous " +"devons nous assurer que notre nouveau client basé sur le ``Client`` " +"fonctionne, n'est-ce pas ?" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:340 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:490 msgid "" -"Flower federated learning server and client nodes (car, scooter, personal" -" computer, roomba, and phone)" +"That's it, we're now using ``Client``. It probably looks similar to what " +"we've done with ``NumPyClient``. So what's the difference?" msgstr "" -"Serveur d'apprentissage fédéré de Flower et nœuds clients (voiture, " -"scooter, ordinateur personnel, roomba et téléphone)" +"Voilà, nous utilisons maintenant ``Client``. Cela ressemble probablement " +"à ce que nous avons fait avec ``NumPyClient``. Alors quelle est la " +"différence ?" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:353 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:492 msgid "" -"Congratulations, you just learned the basics of federated learning and " -"how it relates to the classic (centralized) machine learning!" +"First of all, it's more code. But why? The difference comes from the fact" +" that ``Client`` expects us to take care of parameter serialization and " +"deserialization. For Flower to be able to send parameters over the " +"network, it eventually needs to turn these parameters into ``bytes``. " +"Turning parameters (e.g., NumPy ``ndarray``'s) into raw bytes is called " +"serialization. Turning raw bytes into something more useful (like NumPy " +"``ndarray``'s) is called deserialization. Flower needs to do both: it " +"needs to serialize parameters on the server-side and send them to the " +"client, the client needs to deserialize them to use them for local " +"training, and then serialize the updated parameters again to send them " +"back to the server, which (finally!) deserializes them again in order to " +"aggregate them with the updates received from other clients." msgstr "" -"Félicitations, tu viens d'apprendre les bases de l'apprentissage fédéré " -"et son rapport avec l'apprentissage automatique classique (centralisé) !" +"First of all, it's more code. But why? The difference comes from the fact" +" that ``Client`` expects us to take care of parameter serialization and " +"deserialization. For Flower to be able to send parameters over the " +"network, it eventually needs to turn these parameters into ``bytes``. " +"Turning parameters (e.g., NumPy ``ndarray``'s) into raw bytes is called " +"serialization. Turning raw bytes into something more useful (like NumPy " +"``ndarray``'s) is called deserialization. Flower needs to do both: it " +"needs to serialize parameters on the server-side and send them to the " +"client, the client needs to deserialize them to use them for local " +"training, and then serialize the updated parameters again to send them " +"back to the server, which (finally!) deserializes them again in order to " +"aggregate them with the updates received from other clients." -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:355 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:495 msgid "" -"In the next part of this tutorial, we are going to build a first " -"federated learning system with Flower." +"The only *real* difference between Client and NumPyClient is that " +"NumPyClient takes care of serialization and deserialization for you. It " +"can do so because it expects you to return parameters as NumPy ndarray's," +" and it knows how to handle these. This makes working with machine " +"learning libraries that have good NumPy support (most of them) a breeze." msgstr "" -"Dans la prochaine partie de ce tutoriel, nous allons construire un " -"premier système d'apprentissage fédéré avec Flower." +"La seule *vraie* différence entre Client et NumPyClient est que " +"NumPyClient s'occupe de la sérialisation et de la désérialisation pour " +"toi. Il peut le faire parce qu'il s'attend à ce que tu renvoies des " +"paramètres sous forme de NumPy ndarray, et il sait comment les gérer. " +"Cela permet de travailler avec des bibliothèques d'apprentissage " +"automatique qui ont une bonne prise en charge de NumPy (la plupart " +"d'entre elles) en un clin d'œil." -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:373 -#, fuzzy +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:497 msgid "" -"The `Flower Federated Learning Tutorial - Part 1 " -"`__ shows how to build a simple federated learning system " -"with PyTorch and Flower." +"In terms of API, there's one major difference: all methods in Client take" +" exactly one argument (e.g., ``FitIns`` in ``Client.fit``) and return " +"exactly one value (e.g., ``FitRes`` in ``Client.fit``). The methods in " +"``NumPyClient`` on the other hand have multiple arguments (e.g., " +"``parameters`` and ``config`` in ``NumPyClient.fit``) and multiple return" +" values (e.g., ``parameters``, ``num_example``, and ``metrics`` in " +"``NumPyClient.fit``) if there are multiple things to handle. These " +"``*Ins`` and ``*Res`` objects in ``Client`` wrap all the individual " +"values you're used to from ``NumPyClient``." msgstr "" -"Le `Tutoriel d'apprentissage fédéré Flower - Partie 1 " -"`__ " -"montre comment construire un système d'apprentissage fédéré simple avec " -"PyTorch et Flower." - -#~ msgid "Flower CLI commands" -#~ msgstr "Commandes CLI Flower" +"In terms of API, there's one major difference: all methods in Client take" +" exactly one argument (e.g., ``FitIns`` in ``Client.fit``) and return " +"exactly one value (e.g., ``FitRes`` in ``Client.fit``). The methods in " +"``NumPyClient`` on the other hand have multiple arguments (e.g., " +"``parameters`` and ``config`` in ``NumPyClient.fit``) and multiple return" +" values (e.g., ``parameters``, ``num_example``, and ``metrics`` in " +"``NumPyClient.fit``) if there are multiple things to handle. These " +"``*Ins`` and ``*Res`` objects in ``Client`` wrap all the individual " +"values you're used to from ``NumPyClient``." -#~ msgid "Contributor guide" -#~ msgstr "Guide pour les contributeurs" +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:510 +msgid "Step 3: Custom serialization" +msgstr "Étape 3 : Sérialisation personnalisée" -#~ msgid "API Reference - Flower CLI commands" -#~ msgstr "Référence API - Commandes CLI pour Flower" +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:512 +msgid "" +"Here we will explore how to implement custom serialization with a simple " +"example." +msgstr "" +"Nous allons ici explorer comment mettre en œuvre une sérialisation " +"personnalisée à l'aide d'un exemple simple." -#~ msgid "API Reference - flwr (Python package)" -#~ msgstr "Référence API - flwr (paquetage Python)" +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:514 +msgid "" +"But first what is serialization? Serialization is just the process of " +"converting an object into raw bytes, and equally as important, " +"deserialization is the process of converting raw bytes back into an " +"object. This is very useful for network communication. Indeed, without " +"serialization, you could not just a Python object through the internet." +msgstr "" +"Mais d'abord, qu'est-ce que la sérialisation ? La sérialisation est " +"simplement le processus de conversion d'un objet en octets bruts, et tout" +" aussi important, la désérialisation est le processus de reconversion des" +" octets bruts en objet. Ceci est très utile pour la communication réseau." +" En effet, sans la sérialisation, tu ne pourrais pas faire passer un " +"objet Python par Internet." + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:516 +msgid "" +"Federated Learning relies heavily on internet communication for training " +"by sending Python objects back and forth between the clients and the " +"server. This means that serialization is an essential part of Federated " +"Learning." +msgstr "" +"L'apprentissage fédéré s'appuie fortement sur la communication Internet " +"pour la formation en envoyant des objets Python dans les deux sens entre " +"les clients et le serveur, ce qui signifie que la sérialisation est un " +"élément essentiel de l'apprentissage fédéré." + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:518 +msgid "" +"In the following section, we will write a basic example where instead of " +"sending a serialized version of our ``ndarray``\\ s containing our " +"parameters, we will first convert the ``ndarray`` into sparse matrices, " +"before sending them. This technique can be used to save bandwidth, as in " +"certain cases where the weights of a model are sparse (containing many 0 " +"entries), converting them to a sparse matrix can greatly improve their " +"bytesize." +msgstr "" +"Dans la section suivante, nous allons écrire un exemple de base où, au " +"lieu d'envoyer une version sérialisée de nos ``ndarray`` contenant nos " +"paramètres, nous allons d'abord convertir les ``ndarray`` en matrices " +"éparses, avant de les envoyer. Cette technique peut être utilisée pour " +"économiser de la bande passante, car dans certains cas où les poids d'un " +"modèle sont épars (contenant de nombreuses entrées 0), les convertir en " +"une matrice éparse peut grandement améliorer leur taille en octets." + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:521 +msgid "Our custom serialization/deserialization functions" +msgstr "Nos fonctions de sérialisation/désérialisation personnalisées" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:523 +msgid "" +"This is where the real serialization/deserialization will happen, " +"especially in ``ndarray_to_sparse_bytes`` for serialization and " +"``sparse_bytes_to_ndarray`` for deserialization." +msgstr "" +"C'est là que la véritable sérialisation/désérialisation se produira, en " +"particulier dans ``ndarray_to_sparse_bytes`` pour la sérialisation et " +"``sparse_bytes_to_ndarray`` pour la désérialisation." + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:525 +msgid "" +"Note that we imported the ``scipy.sparse`` library in order to convert " +"our arrays." +msgstr "" +"Notez que nous avons importé la bibliothèque ``scipy.sparse`` afin de " +"convertir nos tableaux." + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:613 +msgid "Client-side" +msgstr "Côté client" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:615 +msgid "" +"To be able to serialize our ``ndarray``\\ s into sparse parameters, we " +"will just have to call our custom functions in our " +"``flwr.client.Client``." +msgstr "" +"Pour pouvoir sérialiser nos ``ndarray`` en paramètres sparse, il nous " +"suffira d'appeler nos fonctions personnalisées dans notre " +"``flwr.client.Client``." + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:617 +msgid "" +"Indeed, in ``get_parameters`` we need to serialize the parameters we got " +"from our network using our custom ``ndarrays_to_sparse_parameters`` " +"defined above." +msgstr "" +"En effet, dans ``get_parameters`` nous devons sérialiser les paramètres " +"que nous avons obtenus de notre réseau en utilisant nos " +"``ndarrays_to_sparse_parameters`` personnalisés définis ci-dessus." + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:619 +msgid "" +"In ``fit``, we first need to deserialize the parameters coming from the " +"server using our custom ``sparse_parameters_to_ndarrays`` and then we " +"need to serialize our local results with " +"``ndarrays_to_sparse_parameters``." +msgstr "" +"Dans ``fit``, nous devons d'abord désérialiser les paramètres provenant " +"du serveur en utilisant notre ``sparse_parameters_to_ndarrays`` " +"personnalisé, puis nous devons sérialiser nos résultats locaux avec " +"``ndarrays_to_sparse_parameters``." + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:621 +msgid "" +"In ``evaluate``, we will only need to deserialize the global parameters " +"with our custom function." +msgstr "" +"Dans ``evaluate``, nous n'aurons besoin que de désérialiser les " +"paramètres globaux avec notre fonction personnalisée." + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:725 +msgid "Server-side" +msgstr "Côté serveur" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:727 +msgid "" +"For this example, we will just use ``FedAvg`` as a strategy. To change " +"the serialization and deserialization here, we only need to reimplement " +"the ``evaluate`` and ``aggregate_fit`` functions of ``FedAvg``. The other" +" functions of the strategy will be inherited from the super class " +"``FedAvg``." +msgstr "" +"Pour cet exemple, nous utiliserons simplement ``FedAvg`` comme stratégie." +" Pour modifier la sérialisation et la désérialisation ici, il suffit de " +"réimplémenter les fonctions ``evaluate`` et ``aggregate_fit`` de " +"``FedAvg``. Les autres fonctions de la stratégie seront héritées de la " +"super-classe ``FedAvg``." + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:729 +msgid "As you can see only one line as change in ``evaluate``:" +msgstr "Comme tu peux le voir, seule une ligne a été modifiée dans ``evaluate`` :" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:735 +msgid "" +"And for ``aggregate_fit``, we will first deserialize every result we " +"received:" +msgstr "" +"Et pour ``aggregate_fit``, nous allons d'abord désérialiser chaque " +"résultat que nous avons reçu :" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:744 +msgid "And then serialize the aggregated result:" +msgstr "Puis sérialise le résultat agrégé :" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:903 +msgid "We can now run our custom serialization example!" +msgstr "" +"Nous pouvons maintenant exécuter notre exemple de sérialisation " +"personnalisée !" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:934 +msgid "" +"In this part of the tutorial, we've seen how we can build clients by " +"subclassing either ``NumPyClient`` or ``Client``. ``NumPyClient`` is a " +"convenience abstraction that makes it easier to work with machine " +"learning libraries that have good NumPy interoperability. ``Client`` is a" +" more flexible abstraction that allows us to do things that are not " +"possible in ``NumPyClient``. In order to do so, it requires us to handle " +"parameter serialization and deserialization ourselves." +msgstr "" +"Dans cette partie du tutoriel, nous avons vu comment construire des " +"clients en sous-classant soit ``NumPyClient``, soit ``Client``. " +"``NumPyClient`` est une abstraction de commodité qui facilite le travail " +"avec les bibliothèques d'apprentissage automatique qui ont une bonne " +"interopérabilité NumPy. ``Client`` est une abstraction plus flexible qui " +"nous permet de faire des choses qui ne sont pas possibles dans " +"``NumPyClient``. Pour ce faire, elle nous oblige à gérer nous-mêmes la " +"sérialisation et la désérialisation des paramètres." + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:952 +msgid "" +"This is the final part of the Flower tutorial (for now!), " +"congratulations! You're now well equipped to understand the rest of the " +"documentation. There are many topics we didn't cover in the tutorial, we " +"recommend the following resources:" +msgstr "" +"C'est la dernière partie du tutoriel Flower (pour l'instant !), " +"félicitations ! Tu es maintenant bien équipé pour comprendre le reste de " +"la documentation. Il y a de nombreux sujets que nous n'avons pas abordés " +"dans le tutoriel, nous te recommandons les ressources suivantes :" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:954 +msgid "`Read Flower Docs `__" +msgstr "`Lire les docs sur les fleurs `__" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:955 +msgid "" +"`Check out Flower Code Examples " +"`__" +msgstr "" +"`Check out Flower Code Examples " +"`__" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:956 +#, fuzzy +msgid "" +"`Use Flower Baselines for your research " +"`__" +msgstr "" +"`Utilise les lignes de base des fleurs pour ta recherche " +"`__" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:957 +#, fuzzy +msgid "" +"`Watch Flower Summit 2023 videos `__" +msgstr "" +"`Regardez les vidéos du Flower Summit 2022 `__" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:9 +msgid "Get started with Flower" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:11 +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:11 +msgid "Welcome to the Flower federated learning tutorial!" +msgstr "Bienvenue au tutoriel sur l'apprentissage fédéré de la fleur !" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:13 +#, fuzzy +msgid "" +"In this notebook, we'll build a federated learning system using Flower, " +"`Flower Datasets `__ and PyTorch. In " +"part 1, we use PyTorch for the model training pipeline and data loading. " +"In part 2, we continue to federate the PyTorch-based pipeline using " +"Flower." +msgstr "" +"Dans ce carnet, nous allons construire un système d'apprentissage fédéré " +"en utilisant Flower et PyTorch. Dans la première partie, nous utilisons " +"PyTorch pour le pipeline d'entraînement des modèles et le chargement des " +"données. Dans la deuxième partie, nous continuons à fédérer le pipeline " +"basé sur PyTorch en utilisant Flower." + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:17 +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:19 +#, fuzzy +msgid "Let's get started!" +msgstr "Allons-y, déclarons-le !" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:31 +msgid "" +"Before we begin with any actual code, let's make sure that we have " +"everything we need." +msgstr "" +"Avant de commencer à coder, assurons-nous que nous disposons de tout ce " +"dont nous avons besoin." + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:45 +#, fuzzy +msgid "" +"Next, we install the necessary packages for PyTorch (``torch`` and " +"``torchvision``), Flower Datasets (``flwr-datasets``) and Flower " +"(``flwr``):" +msgstr "" +"Ensuite, nous installons les paquets nécessaires pour PyTorch (``torch`` " +"et ``torchvision``) et Flower (``flwr``) :" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:105 +#, fuzzy +msgid "" +"It is possible to switch to a runtime that has GPU acceleration enabled " +"(on Google Colab: ``Runtime > Change runtime type > Hardware accelerator:" +" GPU > Save``). Note, however, that Google Colab is not always able to " +"offer GPU acceleration. If you see an error related to GPU availability " +"in one of the following sections, consider switching back to CPU-based " +"execution by setting ``DEVICE = torch.device(\"cpu\")``. If the runtime " +"has GPU acceleration enabled, you should see the output ``Training on " +"cuda``, otherwise it'll say ``Training on cpu``." +msgstr "" +"Il est possible de passer à un runtime dont l'accélération GPU est " +"activée (sur Google Colab : ``Runtime > Change runtime type > Hardware " +"acclerator : GPU > Save``). Note cependant que Google Colab n'est pas " +"toujours en mesure de proposer l'accélération GPU. Si tu vois une erreur " +"liée à la disponibilité du GPU dans l'une des sections suivantes, " +"envisage de repasser à une exécution basée sur le CPU en définissant " +"``DEVICE = torch.device(\"cpu\")``. Si le runtime a activé l'accélération" +" GPU, tu devrais voir apparaître le résultat ``Training on cuda``, sinon " +"il dira ``Training on cpu``." + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:118 +msgid "Loading the data" +msgstr "Chargement des données" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:120 +#, fuzzy +msgid "" +"Federated learning can be applied to many different types of tasks across" +" different domains. In this tutorial, we introduce federated learning by " +"training a simple convolutional neural network (CNN) on the popular " +"CIFAR-10 dataset. CIFAR-10 can be used to train image classifiers that " +"distinguish between images from ten different classes: 'airplane', " +"'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', and " +"'truck'." +msgstr "" +"L'apprentissage fédéré peut être appliqué à de nombreux types de tâches " +"dans différents domaines. Dans ce tutoriel, nous présentons " +"l'apprentissage fédéré en formant un simple réseau neuronal " +"convolutionnel (CNN) sur l'ensemble de données populaire CIFAR-10. " +"CIFAR-10 peut être utilisé pour former des classificateurs d'images qui " +"font la distinction entre les images de dix classes différentes :" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:131 +msgid "" +"We simulate having multiple datasets from multiple organizations (also " +"called the \"cross-silo\" setting in federated learning) by splitting the" +" original CIFAR-10 dataset into multiple partitions. Each partition will " +"represent the data from a single organization. We're doing this purely " +"for experimentation purposes, in the real world there's no need for data " +"splitting because each organization already has their own data (so the " +"data is naturally partitioned)." +msgstr "" +"Nous simulons le fait d'avoir plusieurs ensembles de données provenant de" +" plusieurs organisations (également appelé le paramètre \"cross-silo\" " +"dans l'apprentissage fédéré) en divisant l'ensemble de données CIFAR-10 " +"original en plusieurs partitions. Chaque partition représentera les " +"données d'une seule organisation. Nous faisons cela purement à des fins " +"d'expérimentation, dans le monde réel, il n'y a pas besoin de diviser les" +" données parce que chaque organisation a déjà ses propres données (les " +"données sont donc naturellement partitionnées)." + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:133 +#, fuzzy +msgid "" +"Each organization will act as a client in the federated learning system. " +"So having ten organizations participate in a federation means having ten " +"clients connected to the federated learning server." +msgstr "" +"Chaque organisation agira comme un client dans le système d'apprentissage" +" fédéré. Ainsi, le fait que dix organisations participent à une " +"fédération signifie que dix clients sont connectés au serveur " +"d'apprentissage fédéré :" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:144 +msgid "" +"Let's now create the Federated Dataset abstraction that from ``flwr-" +"datasets`` that partitions the CIFAR-10. We will create small training " +"and test set for each edge device and wrap each of them into a PyTorch " +"``DataLoader``:" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:198 +#, fuzzy +msgid "" +"We now have a list of ten training sets and ten validation sets " +"(``trainloaders`` and ``valloaders``) representing the data of ten " +"different organizations. Each ``trainloader``/``valloader`` pair contains" +" 4000 training examples and 1000 validation examples. There's also a " +"single ``testloader`` (we did not split the test set). Again, this is " +"only necessary for building research or educational systems, actual " +"federated learning systems have their data naturally distributed across " +"multiple partitions." +msgstr "" +"Nous avons maintenant une liste de dix ensembles de formation et dix " +"ensembles de validation (``trainloaders`` et ``valloaders``) représentant" +" les données de dix organisations différentes. Chaque paire " +"``trainloader`/``valloader`` contient 4500 exemples de formation et 500 " +"exemples de validation. Il y a également un seul ``testloader`` (nous " +"n'avons pas divisé l'ensemble de test). Encore une fois, cela n'est " +"nécessaire que pour construire des systèmes de recherche ou d'éducation, " +"les systèmes d'apprentissage fédérés actuels ont leurs données " +"naturellement distribuées à travers plusieurs partitions." + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:201 +msgid "" +"Let's take a look at the first batch of images and labels in the first " +"training set (i.e., ``trainloaders[0]``) before we move on:" +msgstr "" +"Jetons un coup d'œil au premier lot d'images et d'étiquettes du premier " +"ensemble d'entraînement (c'est-à-dire ``trainloaders[0]``) avant de " +"poursuivre :" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:240 +msgid "" +"The output above shows a random batch of images from the first " +"``trainloader`` in our list of ten ``trainloaders``. It also prints the " +"labels associated with each image (i.e., one of the ten possible labels " +"we've seen above). If you run the cell again, you should see another " +"batch of images." +msgstr "" +"La sortie ci-dessus montre un lot aléatoire d'images provenant du premier" +" ``chargeur de formation`` de notre liste de dix ``chargeurs de " +"formation``. Elle imprime également les étiquettes associées à chaque " +"image (c'est-à-dire l'une des dix étiquettes possibles que nous avons " +"vues ci-dessus). Si tu exécutes à nouveau la cellule, tu devrais voir un " +"autre lot d'images." + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:252 +msgid "Step 1: Centralized Training with PyTorch" +msgstr "Étape 1 : Formation centralisée avec PyTorch" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:263 +msgid "" +"Next, we're going to use PyTorch to define a simple convolutional neural " +"network. This introduction assumes basic familiarity with PyTorch, so it " +"doesn't cover the PyTorch-related aspects in full detail. If you want to " +"dive deeper into PyTorch, we recommend `DEEP LEARNING WITH PYTORCH: A 60 " +"MINUTE BLITZ " +"`__." +msgstr "" +"Ensuite, nous allons utiliser PyTorch pour définir un simple réseau " +"neuronal convolutif. Cette introduction suppose une familiarité de base " +"avec PyTorch, elle ne couvre donc pas en détail les aspects liés à " +"PyTorch. Si tu veux plonger plus profondément dans PyTorch, nous te " +"recommandons `DEEP LEARNING WITH PYTORCH : A 60 MINUTE BLITZ " +"`__." + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:275 +msgid "Defining the model" +msgstr "Définir le modèle" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:277 +msgid "" +"We use the simple CNN described in the `PyTorch tutorial " +"`__:" +msgstr "" +"Nous utilisons le CNN simple décrit dans le tutoriel `PyTorch " +"`__ :" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:314 +msgid "Let's continue with the usual training and test functions:" +msgstr "Poursuivons avec les fonctions habituelles de formation et de test :" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:374 +msgid "Training the model" +msgstr "Entraîne le modèle" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:376 +msgid "" +"We now have all the basic building blocks we need: a dataset, a model, a " +"training function, and a test function. Let's put them together to train " +"the model on the dataset of one of our organizations " +"(``trainloaders[0]``). This simulates the reality of most machine " +"learning projects today: each organization has their own data and trains " +"models only on this internal data:" +msgstr "" +"Nous avons maintenant tous les éléments de base dont nous avons besoin : " +"un ensemble de données, un modèle, une fonction d'entraînement et une " +"fonction de test. Assemblons-les pour entraîner le modèle sur l'ensemble " +"de données de l'une de nos organisations (``trainloaders[0]``). Cela " +"simule la réalité de la plupart des projets d'apprentissage automatique " +"aujourd'hui : chaque organisation possède ses propres données et entraîne" +" les modèles uniquement sur ces données internes :" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:406 +msgid "" +"Training the simple CNN on our CIFAR-10 split for 5 epochs should result " +"in a test set accuracy of about 41%, which is not good, but at the same " +"time, it doesn't really matter for the purposes of this tutorial. The " +"intent was just to show a simplistic centralized training pipeline that " +"sets the stage for what comes next - federated learning!" +msgstr "" +"L'entraînement du CNN simple sur notre fractionnement CIFAR-10 pendant 5 " +"époques devrait se traduire par une précision de l'ensemble de test " +"d'environ 41 %, ce qui n'est pas bon, mais en même temps, cela n'a pas " +"vraiment d'importance pour les besoins de ce tutoriel. L'intention était " +"juste de montrer un pipeline d'entraînement centralisé simpliste qui " +"prépare le terrain pour ce qui vient ensuite - l'apprentissage fédéré !" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:418 +msgid "Step 2: Federated Learning with Flower" +msgstr "Étape 2 : Apprentissage fédéré avec Flower" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:420 +msgid "" +"Step 1 demonstrated a simple centralized training pipeline. All data was " +"in one place (i.e., a single ``trainloader`` and a single ``valloader``)." +" Next, we'll simulate a situation where we have multiple datasets in " +"multiple organizations and where we train a model over these " +"organizations using federated learning." +msgstr "" +"L'étape 1 a montré un simple pipeline de formation centralisé. Toutes les" +" données étaient au même endroit (c'est-à-dire un seul ``trainloader`` et" +" un seul ``valloader``). Ensuite, nous allons simuler une situation où " +"nous avons plusieurs ensembles de données dans plusieurs organisations et" +" où nous formons un modèle sur ces organisations à l'aide de " +"l'apprentissage fédéré." + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:432 +msgid "Updating model parameters" +msgstr "Mise à jour des paramètres du modèle" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:434 +msgid "" +"In federated learning, the server sends the global model parameters to " +"the client, and the client updates the local model with the parameters " +"received from the server. It then trains the model on the local data " +"(which changes the model parameters locally) and sends the " +"updated/changed model parameters back to the server (or, alternatively, " +"it sends just the gradients back to the server, not the full model " +"parameters)." +msgstr "" +"Dans l'apprentissage fédéré, le serveur envoie les paramètres du modèle " +"global au client, et le client met à jour le modèle local avec les " +"paramètres reçus du serveur. Il entraîne ensuite le modèle sur les " +"données locales (ce qui modifie les paramètres du modèle localement) et " +"renvoie les paramètres du modèle mis à jour/changés au serveur (ou, " +"alternativement, il renvoie seulement les gradients au serveur, et non " +"pas les paramètres complets du modèle)." + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:436 +msgid "" +"We need two helper functions to update the local model with parameters " +"received from the server and to get the updated model parameters from the" +" local model: ``set_parameters`` and ``get_parameters``. The following " +"two functions do just that for the PyTorch model above." +msgstr "" +"Nous avons besoin de deux fonctions d'aide pour mettre à jour le modèle " +"local avec les paramètres reçus du serveur et pour obtenir les paramètres" +" mis à jour du modèle local : ``set_parameters`` et ``get_parameters``. " +"Les deux fonctions suivantes font exactement cela pour le modèle PyTorch " +"ci-dessus." + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:438 +msgid "" +"The details of how this works are not really important here (feel free to" +" consult the PyTorch documentation if you want to learn more). In " +"essence, we use ``state_dict`` to access PyTorch model parameter tensors." +" The parameter tensors are then converted to/from a list of NumPy " +"ndarray's (which Flower knows how to serialize/deserialize):" +msgstr "" +"Les détails de ce fonctionnement ne sont pas vraiment importants ici " +"(n'hésite pas à consulter la documentation PyTorch si tu veux en savoir " +"plus). En substance, nous utilisons ``state_dict`` pour accéder aux " +"tenseurs de paramètres du modèle PyTorch. Les tenseurs de paramètres sont" +" ensuite convertis en/depuis une liste de ndarray NumPy (que Flower sait " +"sérialiser/désérialiser) :" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:466 +msgid "Implementing a Flower client" +msgstr "Mise en place d'un client Flower" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:468 +msgid "" +"With that out of the way, let's move on to the interesting part. " +"Federated learning systems consist of a server and multiple clients. In " +"Flower, we create clients by implementing subclasses of " +"``flwr.client.Client`` or ``flwr.client.NumPyClient``. We use " +"``NumPyClient`` in this tutorial because it is easier to implement and " +"requires us to write less boilerplate." +msgstr "" +"Ceci étant dit, passons à la partie intéressante. Les systèmes " +"d'apprentissage fédérés se composent d'un serveur et de plusieurs " +"clients. Dans Flower, nous créons des clients en mettant en œuvre des " +"sous-classes de ``flwr.client.Client`` ou de ``flwr.client.NumPyClient``." +" Nous utilisons ``NumPyClient`` dans ce tutoriel parce qu'il est plus " +"facile à mettre en œuvre et qu'il nous oblige à rédiger moins de modèles " +"de chaudière." + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:470 +msgid "" +"To implement the Flower client, we create a subclass of " +"``flwr.client.NumPyClient`` and implement the three methods " +"``get_parameters``, ``fit``, and ``evaluate``:" +msgstr "" +"Pour mettre en œuvre le client Flower, nous créons une sous-classe de " +"``flwr.client.NumPyClient`` et mettons en œuvre les trois méthodes " +"``get_parameters``, ``fit`` et ``evaluate`` :" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:472 +msgid "``get_parameters``: Return the current local model parameters" +msgstr "``get_parameters`` : renvoie les paramètres du modèle local actuel" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:473 +msgid "" +"``fit``: Receive model parameters from the server, train the model " +"parameters on the local data, and return the (updated) model parameters " +"to the server" +msgstr "" +"``fit`` : reçoit les paramètres du modèle du serveur, entraîne les " +"paramètres du modèle sur les données locales et renvoie les paramètres du" +" modèle (mis à jour) au serveur" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:474 +msgid "" +"``evaluate``: Receive model parameters from the server, evaluate the " +"model parameters on the local data, and return the evaluation result to " +"the server" +msgstr "" +"``evaluate`` : reçoit les paramètres du modèle du serveur, évalue les " +"paramètres du modèle sur les données locales et renvoie le résultat de " +"l'évaluation au serveur" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:476 +msgid "" +"We mentioned that our clients will use the previously defined PyTorch " +"components for model training and evaluation. Let's see a simple Flower " +"client implementation that brings everything together:" +msgstr "" +"Nous avons mentionné que nos clients utiliseront les composants PyTorch " +"définis précédemment pour la formation et l'évaluation des modèles. " +"Voyons une simple mise en œuvre du client Flower qui réunit tout cela :" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:513 +msgid "" +"Our class ``FlowerClient`` defines how local training/evaluation will be " +"performed and allows Flower to call the local training/evaluation through" +" ``fit`` and ``evaluate``. Each instance of ``FlowerClient`` represents a" +" *single client* in our federated learning system. Federated learning " +"systems have multiple clients (otherwise, there's not much to federate), " +"so each client will be represented by its own instance of " +"``FlowerClient``. If we have, for example, three clients in our workload," +" then we'd have three instances of ``FlowerClient``. Flower calls " +"``FlowerClient.fit`` on the respective instance when the server selects a" +" particular client for training (and ``FlowerClient.evaluate`` for " +"evaluation)." +msgstr "" +"Our class ``FlowerClient`` defines how local training/evaluation will be " +"performed and allows Flower to call the local training/evaluation through" +" ``fit`` and ``evaluate``. Each instance of ``FlowerClient`` represents a" +" *single client* in our federated learning system. Federated learning " +"systems have multiple clients (otherwise, there's not much to federate), " +"so each client will be represented by its own instance of " +"``FlowerClient``. If we have, for example, three clients in our workload," +" then we'd have three instances of ``FlowerClient``. Flower calls " +"``FlowerClient.fit`` on the respective instance when the server selects a" +" particular client for training (and ``FlowerClient.evaluate`` for " +"evaluation)." + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:517 +msgid "Using the Virtual Client Engine" +msgstr "Utilisation du moteur du client virtuel" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:519 +msgid "" +"In this notebook, we want to simulate a federated learning system with 10" +" clients on a single machine. This means that the server and all 10 " +"clients will live on a single machine and share resources such as CPU, " +"GPU, and memory. Having 10 clients would mean having 10 instances of " +"``FlowerClient`` in memory. Doing this on a single machine can quickly " +"exhaust the available memory resources, even if only a subset of these " +"clients participates in a single round of federated learning." +msgstr "" +"Dans ce carnet, nous voulons simuler un système d'apprentissage fédéré " +"avec 10 clients sur une seule machine. Cela signifie que le serveur et " +"les 10 clients vivront sur une seule machine et partageront des " +"ressources telles que le CPU, le GPU et la mémoire. Avoir 10 clients " +"signifierait avoir 10 instances de ``FlowerClient`` en mémoire. Faire " +"cela sur une seule machine peut rapidement épuiser les ressources mémoire" +" disponibles, même si seulement un sous-ensemble de ces clients participe" +" à un seul tour d'apprentissage fédéré." + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:521 +msgid "" +"In addition to the regular capabilities where server and clients run on " +"multiple machines, Flower, therefore, provides special simulation " +"capabilities that create ``FlowerClient`` instances only when they are " +"actually necessary for training or evaluation. To enable the Flower " +"framework to create clients when necessary, we need to implement a " +"function called ``client_fn`` that creates a ``FlowerClient`` instance on" +" demand. Flower calls ``client_fn`` whenever it needs an instance of one " +"particular client to call ``fit`` or ``evaluate`` (those instances are " +"usually discarded after use, so they should not keep any local state). " +"Clients are identified by a client ID, or short ``cid``. The ``cid`` can " +"be used, for example, to load different local data partitions for " +"different clients, as can be seen below:" +msgstr "" +"In addition to the regular capabilities where server and clients run on " +"multiple machines, Flower, therefore, provides special simulation " +"capabilities that create ``FlowerClient`` instances only when they are " +"actually necessary for training or evaluation. To enable the Flower " +"framework to create clients when necessary, we need to implement a " +"function called ``client_fn`` that creates a ``FlowerClient`` instance on" +" demand. Flower calls ``client_fn`` whenever it needs an instance of one " +"particular client to call ``fit`` or ``evaluate`` (those instances are " +"usually discarded after use, so they should not keep any local state). " +"Clients are identified by a client ID, or short ``cid``. The ``cid`` can " +"be used, for example, to load different local data partitions for " +"different clients, as can be seen below:" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:556 +msgid "Starting the training" +msgstr "Commencer la formation" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:558 +msgid "" +"We now have the class ``FlowerClient`` which defines client-side " +"training/evaluation and ``client_fn`` which allows Flower to create " +"``FlowerClient`` instances whenever it needs to call ``fit`` or " +"``evaluate`` on one particular client. The last step is to start the " +"actual simulation using ``flwr.simulation.start_simulation``." +msgstr "" +"Nous avons maintenant la classe ``FlowerClient`` qui définit " +"l'entraînement/évaluation côté client et ``client_fn`` qui permet à " +"Flower de créer des instances de ``FlowerClient`` chaque fois qu'il a " +"besoin d'appeler ``fit`` ou ``evaluate`` sur un client particulier. La " +"dernière étape consiste à démarrer la simulation réelle en utilisant " +"``flwr.simulation.start_simulation``." + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:560 +msgid "" +"The function ``start_simulation`` accepts a number of arguments, amongst " +"them the ``client_fn`` used to create ``FlowerClient`` instances, the " +"number of clients to simulate (``num_clients``), the number of federated " +"learning rounds (``num_rounds``), and the strategy. The strategy " +"encapsulates the federated learning approach/algorithm, for example, " +"*Federated Averaging* (FedAvg)." +msgstr "" +"La fonction ``start_simulation`` accepte un certain nombre d'arguments, " +"parmi lesquels le ``client_fn`` utilisé pour créer les instances " +"``FlowerClient``, le nombre de clients à simuler (``num_clients``), le " +"nombre de tours d'apprentissage fédéré (``num_rounds``), et la stratégie." +" La stratégie encapsule l'approche/algorithme d'apprentissage fédéré, par" +" exemple, *Federated Averaging* (FedAvg)." + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:562 +msgid "" +"Flower has a number of built-in strategies, but we can also use our own " +"strategy implementations to customize nearly all aspects of the federated" +" learning approach. For this example, we use the built-in ``FedAvg`` " +"implementation and customize it using a few basic parameters. The last " +"step is the actual call to ``start_simulation`` which - you guessed it - " +"starts the simulation:" +msgstr "" +"Flower dispose d'un certain nombre de stratégies intégrées, mais nous " +"pouvons également utiliser nos propres implémentations de stratégies pour" +" personnaliser presque tous les aspects de l'approche de l'apprentissage " +"fédéré. Pour cet exemple, nous utilisons l'implémentation intégrée " +"``FedAvg`` et nous la personnalisons en utilisant quelques paramètres de " +"base. La dernière étape est l'appel à ``start_simulation`` qui - tu l'as " +"deviné - démarre la simulation :" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:608 +msgid "Behind the scenes" +msgstr "Dans les coulisses" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:610 +msgid "So how does this work? How does Flower execute this simulation?" +msgstr "" +"Alors, comment cela fonctionne-t-il ? Comment Flower exécute-t-il cette " +"simulation ?" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:612 +#, python-format +msgid "" +"When we call ``start_simulation``, we tell Flower that there are 10 " +"clients (``num_clients=10``). Flower then goes ahead an asks the " +"``FedAvg`` strategy to select clients. ``FedAvg`` knows that it should " +"select 100% of the available clients (``fraction_fit=1.0``), so it goes " +"ahead and selects 10 random clients (i.e., 100% of 10)." +msgstr "" +"Lorsque nous appelons ``start_simulation``, nous disons à Flower qu'il y " +"a 10 clients (``num_clients=10``). Flower demande alors à la stratégie " +"``FedAvg`` de sélectionner des clients. ``FedAvg` sait qu'il doit " +"sélectionner 100% des clients disponibles (``fraction_fit=1.0``), alors " +"il choisit 10 clients au hasard (c'est à dire 100% de 10)." + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:614 +msgid "" +"Flower then asks the selected 10 clients to train the model. When the " +"server receives the model parameter updates from the clients, it hands " +"those updates over to the strategy (*FedAvg*) for aggregation. The " +"strategy aggregates those updates and returns the new global model, which" +" then gets used in the next round of federated learning." +msgstr "" +"Flower demande ensuite aux 10 clients sélectionnés d'entraîner le modèle." +" Lorsque le serveur reçoit les mises à jour des paramètres du modèle de " +"la part des clients, il les transmet à la stratégie (*FedAvg*) pour " +"qu'elle les agrège. La stratégie agrège ces mises à jour et renvoie le " +"nouveau modèle global, qui est ensuite utilisé dans le prochain cycle " +"d'apprentissage fédéré." + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:626 +msgid "Where's the accuracy?" +msgstr "Où est la précision ?" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:628 +msgid "" +"You may have noticed that all metrics except for ``losses_distributed`` " +"are empty. Where did the ``{\"accuracy\": float(accuracy)}`` go?" +msgstr "" +"Tu as peut-être remarqué que toutes les mesures, à l'exception de " +"``pertes_distribuées``, sont vides. Où est passée la ``{\"précision\" : " +"float(précision)}`` ?" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:630 +msgid "" +"Flower can automatically aggregate losses returned by individual clients," +" but it cannot do the same for metrics in the generic metrics dictionary " +"(the one with the ``accuracy`` key). Metrics dictionaries can contain " +"very different kinds of metrics and even key/value pairs that are not " +"metrics at all, so the framework does not (and can not) know how to " +"handle these automatically." +msgstr "" +"Flower peut automatiquement agréger les pertes renvoyées par les clients " +"individuels, mais il ne peut pas faire la même chose pour les mesures " +"dans le dictionnaire de mesures générique (celui avec la clé " +"``accuracy``). Les dictionnaires de mesures peuvent contenir des types de" +" mesures très différents et même des paires clé/valeur qui ne sont pas " +"des mesures du tout, donc le cadre ne sait pas (et ne peut pas) savoir " +"comment les gérer automatiquement." + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:632 +msgid "" +"As users, we need to tell the framework how to handle/aggregate these " +"custom metrics, and we do so by passing metric aggregation functions to " +"the strategy. The strategy will then call these functions whenever it " +"receives fit or evaluate metrics from clients. The two possible functions" +" are ``fit_metrics_aggregation_fn`` and " +"``evaluate_metrics_aggregation_fn``." +msgstr "" +"En tant qu'utilisateurs, nous devons indiquer au framework comment " +"gérer/agréger ces métriques personnalisées, et nous le faisons en passant" +" des fonctions d'agrégation de métriques à la stratégie. La stratégie " +"appellera alors ces fonctions chaque fois qu'elle recevra des métriques " +"d'ajustement ou d'évaluation de la part des clients. Les deux fonctions " +"possibles sont ``fit_metrics_aggregation_fn`` et " +"``evaluate_metrics_aggregation_fn``." + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:634 +msgid "" +"Let's create a simple weighted averaging function to aggregate the " +"``accuracy`` metric we return from ``evaluate``:" +msgstr "" +"Créons une simple fonction de calcul de la moyenne pondérée pour agréger " +"la mesure de \"précision\" que nous renvoie ``evaluate`` :" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:660 +msgid "" +"The only thing left to do is to tell the strategy to call this function " +"whenever it receives evaluation metric dictionaries from the clients:" +msgstr "" +"La seule chose qui reste à faire est d'indiquer à la stratégie d'appeler " +"cette fonction chaque fois qu'elle reçoit des dictionnaires de métriques " +"d'évaluation de la part des clients :" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:697 +msgid "" +"We now have a full system that performs federated training and federated " +"evaluation. It uses the ``weighted_average`` function to aggregate custom" +" evaluation metrics and calculates a single ``accuracy`` metric across " +"all clients on the server side." +msgstr "" +"Nous avons maintenant un système complet qui effectue la formation " +"fédérée et l'évaluation fédérée. Il utilise la fonction ``moyenne " +"pondérée`` pour agréger les mesures d'évaluation personnalisées et " +"calcule une seule mesure de ``précision`` pour tous les clients du côté " +"du serveur." + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:699 +msgid "" +"The other two categories of metrics (``losses_centralized`` and " +"``metrics_centralized``) are still empty because they only apply when " +"centralized evaluation is being used. Part two of the Flower tutorial " +"will cover centralized evaluation." +msgstr "" +"Les deux autres catégories de mesures (``pertes_centralisées`` et " +"``métriques_centralisées``) sont toujours vides car elles ne s'appliquent" +" que lorsque l'évaluation centralisée est utilisée. La deuxième partie du" +" tutoriel sur les fleurs couvrira l'évaluation centralisée." + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:711 +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:351 +msgid "Final remarks" +msgstr "Remarques finales" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:713 +msgid "" +"Congratulations, you just trained a convolutional neural network, " +"federated over 10 clients! With that, you understand the basics of " +"federated learning with Flower. The same approach you've seen can be used" +" with other machine learning frameworks (not just PyTorch) and tasks (not" +" just CIFAR-10 images classification), for example NLP with Hugging Face " +"Transformers or speech with SpeechBrain." +msgstr "" +"Félicitations, tu viens d'entraîner un réseau neuronal convolutif, fédéré" +" sur 10 clients ! Avec ça, tu comprends les bases de l'apprentissage " +"fédéré avec Flower. La même approche que tu as vue peut être utilisée " +"avec d'autres cadres d'apprentissage automatique (pas seulement PyTorch) " +"et d'autres tâches (pas seulement la classification des images CIFAR-10)," +" par exemple le NLP avec Hugging Face Transformers ou la parole avec " +"SpeechBrain." + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:715 +msgid "" +"In the next notebook, we're going to cover some more advanced concepts. " +"Want to customize your strategy? Initialize parameters on the server " +"side? Or evaluate the aggregated model on the server side? We'll cover " +"all this and more in the next tutorial." +msgstr "" +"Dans le prochain cahier, nous allons aborder des concepts plus avancés. " +"Tu veux personnaliser ta stratégie ? Initialiser des paramètres côté " +"serveur ? Ou évaluer le modèle agrégé côté serveur ? Nous aborderons tout" +" cela et bien plus encore dans le prochain tutoriel." + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:733 +#, fuzzy +msgid "" +"The `Flower Federated Learning Tutorial - Part 2 " +"`__ goes into more depth about strategies and all " +"the advanced things you can build with them." +msgstr "" +"Le `Tutoriel d'apprentissage fédéré Flower - Partie 2 " +"`__ va plus en profondeur sur les stratégies et toutes les " +"choses avancées que tu peux construire avec elles." + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:9 +#, fuzzy +msgid "Use a federated learning strategy" +msgstr "Stratégie de moyenne fédérée." + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:11 +#, fuzzy +msgid "" +"Welcome to the next part of the federated learning tutorial. In previous " +"parts of this tutorial, we introduced federated learning with PyTorch and" +" Flower (`part 1 `__)." +msgstr "" +"Bienvenue dans la prochaine partie du tutoriel sur l'apprentissage " +"fédéré. Dans les parties précédentes de ce tutoriel, nous avons présenté " +"l'apprentissage fédéré avec PyTorch et Flower (`partie 1 " +"`__)." + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:13 +msgid "" +"In this notebook, we'll begin to customize the federated learning system " +"we built in the introductory notebook (again, using `Flower " +"`__ and `PyTorch `__)." +msgstr "" +"Dans ce carnet, nous allons commencer à personnaliser le système " +"d'apprentissage fédéré que nous avons construit dans le carnet " +"d'introduction (toujours en utilisant `Flower `__ et " +"`PyTorch `__)." + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:17 +#, fuzzy +msgid "Let's move beyond FedAvg with Flower strategies!" +msgstr "Dépassons FedAvg avec les stratégies florales !" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:309 +msgid "Strategy customization" +msgstr "Personnalisation de la stratégie" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:311 +msgid "" +"So far, everything should look familiar if you've worked through the " +"introductory notebook. With that, we're ready to introduce a number of " +"new features." +msgstr "" +"Jusqu'à présent, tout devrait te sembler familier si tu as travaillé sur " +"le cahier d'introduction. Avec cela, nous sommes prêts à présenter un " +"certain nombre de nouvelles fonctionnalités." + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:323 +msgid "Server-side parameter **initialization**" +msgstr "Paramètres côté serveur **initialisation**" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:325 +msgid "" +"Flower, by default, initializes the global model by asking one random " +"client for the initial parameters. In many cases, we want more control " +"over parameter initialization though. Flower therefore allows you to " +"directly pass the initial parameters to the Strategy:" +msgstr "" +"Flower, par défaut, initialise le modèle global en demandant à un client " +"aléatoire les paramètres initiaux. Dans de nombreux cas, nous voulons " +"cependant avoir plus de contrôle sur l'initialisation des paramètres. " +"Flower te permet donc de passer directement les paramètres initiaux à la " +"Stratégie :" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:370 +msgid "" +"Passing ``initial_parameters`` to the ``FedAvg`` strategy prevents Flower" +" from asking one of the clients for the initial parameters. If we look " +"closely, we can see that the logs do not show any calls to the " +"``FlowerClient.get_parameters`` method." +msgstr "" +"Le fait de passer ``initial_parameters`` à la stratégie ``FedAvg`` " +"empêche Flower de demander les paramètres initiaux à l'un des clients. Si" +" nous regardons de près, nous pouvons voir que les journaux ne montrent " +"aucun appel à la méthode ``FlowerClient.get_parameters``." + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:382 +msgid "Starting with a customized strategy" +msgstr "Commencer par une stratégie personnalisée" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:384 +msgid "" +"We've seen the function ``start_simulation`` before. It accepts a number " +"of arguments, amongst them the ``client_fn`` used to create " +"``FlowerClient`` instances, the number of clients to simulate " +"``num_clients``, the number of rounds ``num_rounds``, and the strategy." +msgstr "" +"Elle accepte un certain nombre d'arguments, parmi lesquels le " +"``client_fn`` utilisé pour créer les instances de ``FlowerClient``, le " +"nombre de clients à simuler ``num_clients``, le nombre de rounds " +"``num_rounds``, et la stratégie." + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:386 +msgid "" +"The strategy encapsulates the federated learning approach/algorithm, for " +"example, ``FedAvg`` or ``FedAdagrad``. Let's try to use a different " +"strategy this time:" +msgstr "" +"La stratégie englobe l'approche/l'algorithme d'apprentissage fédéré, par " +"exemple, ``FedAvg`` ou ``FedAdagrad``. Essayons d'utiliser une stratégie " +"différente cette fois-ci :" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:424 +msgid "Server-side parameter **evaluation**" +msgstr "Paramètre côté serveur **évaluation**" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:426 +msgid "" +"Flower can evaluate the aggregated model on the server-side or on the " +"client-side. Client-side and server-side evaluation are similar in some " +"ways, but different in others." +msgstr "" +"Flower peut évaluer le modèle agrégé côté serveur ou côté client. Les " +"évaluations côté client et côté serveur sont similaires à certains " +"égards, mais différentes à d'autres." + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:428 +msgid "" +"**Centralized Evaluation** (or *server-side evaluation*) is conceptually " +"simple: it works the same way that evaluation in centralized machine " +"learning does. If there is a server-side dataset that can be used for " +"evaluation purposes, then that's great. We can evaluate the newly " +"aggregated model after each round of training without having to send the " +"model to clients. We're also fortunate in the sense that our entire " +"evaluation dataset is available at all times." +msgstr "" +"**L'évaluation centralisée** (ou *évaluation côté serveur*) est " +"conceptuellement simple : elle fonctionne de la même manière que " +"l'évaluation dans l'apprentissage automatique centralisé. S'il existe un " +"ensemble de données côté serveur qui peut être utilisé à des fins " +"d'évaluation, alors c'est parfait. Nous pouvons évaluer le modèle " +"nouvellement agrégé après chaque cycle de formation sans avoir à envoyer " +"le modèle aux clients. Nous avons également la chance que l'ensemble de " +"notre ensemble de données d'évaluation soit disponible à tout moment." + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:430 +msgid "" +"**Federated Evaluation** (or *client-side evaluation*) is more complex, " +"but also more powerful: it doesn't require a centralized dataset and " +"allows us to evaluate models over a larger set of data, which often " +"yields more realistic evaluation results. In fact, many scenarios require" +" us to use **Federated Evaluation** if we want to get representative " +"evaluation results at all. But this power comes at a cost: once we start " +"to evaluate on the client side, we should be aware that our evaluation " +"dataset can change over consecutive rounds of learning if those clients " +"are not always available. Moreover, the dataset held by each client can " +"also change over consecutive rounds. This can lead to evaluation results " +"that are not stable, so even if we would not change the model, we'd see " +"our evaluation results fluctuate over consecutive rounds." +msgstr "" +"**L'évaluation fédérée** (ou évaluation côté client) est plus complexe, " +"mais aussi plus puissante : elle ne nécessite pas d'ensemble de données " +"centralisé et nous permet d'évaluer les modèles sur un plus grand " +"ensemble de données, ce qui donne souvent des résultats d'évaluation plus" +" réalistes. En fait, de nombreux scénarios exigent que nous utilisions " +"l'évaluation fédérée** si nous voulons obtenir des résultats d'évaluation" +" représentatifs. Mais cette puissance a un coût : une fois que nous " +"commençons à évaluer côté client, nous devons savoir que notre ensemble " +"de données d'évaluation peut changer au cours des cycles d'apprentissage " +"consécutifs si ces clients ne sont pas toujours disponibles. De plus, " +"l'ensemble de données détenu par chaque client peut également changer au " +"cours des cycles consécutifs. Cela peut conduire à des résultats " +"d'évaluation qui ne sont pas stables, donc même si nous ne changions pas " +"le modèle, nous verrions nos résultats d'évaluation fluctuer au cours des" +" cycles consécutifs." + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:433 +msgid "" +"We've seen how federated evaluation works on the client side (i.e., by " +"implementing the ``evaluate`` method in ``FlowerClient``). Now let's see " +"how we can evaluate aggregated model parameters on the server-side:" +msgstr "" +"Nous avons vu comment l'évaluation fédérée fonctionne du côté client " +"(c'est-à-dire en implémentant la méthode ``evaluate`` dans " +"``FlowerClient``). Voyons maintenant comment nous pouvons évaluer les " +"paramètres du modèle agrégé du côté serveur :" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:490 +msgid "Sending/receiving arbitrary values to/from clients" +msgstr "Envoi/réception de valeurs arbitraires vers/depuis les clients" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:492 +#, fuzzy +msgid "" +"In some situations, we want to configure client-side execution (training," +" evaluation) from the server-side. One example for that is the server " +"asking the clients to train for a certain number of local epochs. Flower " +"provides a way to send configuration values from the server to the " +"clients using a dictionary. Let's look at an example where the clients " +"receive values from the server through the ``config`` parameter in " +"``fit`` (``config`` is also available in ``evaluate``). The ``fit`` " +"method receives the configuration dictionary through the ``config`` " +"parameter and can then read values from this dictionary. In this example," +" it reads ``server_round`` and ``local_epochs`` and uses those values to " +"improve the logging and configure the number of local training epochs:" +msgstr "" +"In some situations, we want to configure client-side execution (training," +" evaluation) from the server-side. One example for that is the server " +"asking the clients to train for a certain number of local epochs. Flower " +"provides a way to send configuration values from the server to the " +"clients using a dictionary. Let's look at an example where the clients " +"receive values from the server through the ``config`` parameter in " +"``fit`` (``config`` is also available in ``evaluate``). The ``fit`` " +"method receives the configuration dictionary through the ``config`` " +"parameter and can then read values from this dictionary. In this example," +" it reads ``server_round`` and ``local_epochs`` and uses those values to " +"improve the logging and configure the number of local training epochs:" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:546 +msgid "" +"So how can we send this config dictionary from server to clients? The " +"built-in Flower Strategies provide way to do this, and it works similarly" +" to the way server-side evaluation works. We provide a function to the " +"strategy, and the strategy calls this function for every round of " +"federated learning:" +msgstr "" +"Comment pouvons-nous donc envoyer ce dictionnaire de configuration du " +"serveur aux clients ? Les stratégies de Flower intégrées fournissent un " +"moyen de le faire, et cela fonctionne de la même façon que l'évaluation " +"côté serveur. Nous fournissons une fonction à la stratégie, et la " +"stratégie appelle cette fonction pour chaque cycle d'apprentissage fédéré" +" :" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:576 +msgid "" +"Next, we'll just pass this function to the FedAvg strategy before " +"starting the simulation:" +msgstr "" +"Ensuite, nous allons simplement passer cette fonction à la stratégie " +"FedAvg avant de commencer la simulation :" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:613 +msgid "" +"As we can see, the client logs now include the current round of federated" +" learning (which they read from the ``config`` dictionary). We can also " +"configure local training to run for one epoch during the first and second" +" round of federated learning, and then for two epochs during the third " +"round." +msgstr "" +"Comme nous pouvons le voir, les journaux des clients incluent maintenant " +"le cycle actuel d'apprentissage fédéré (qu'ils lisent depuis le " +"dictionnaire ``config``). Nous pouvons également configurer " +"l'apprentissage local pour qu'il s'exécute pendant une époque au cours du" +" premier et du deuxième cycle d'apprentissage fédéré, puis pendant deux " +"époques au cours du troisième cycle." + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:615 +msgid "" +"Clients can also return arbitrary values to the server. To do so, they " +"return a dictionary from ``fit`` and/or ``evaluate``. We have seen and " +"used this concept throughout this notebook without mentioning it " +"explicitly: our ``FlowerClient`` returns a dictionary containing a custom" +" key/value pair as the third return value in ``evaluate``." +msgstr "" +"Les clients peuvent également renvoyer des valeurs arbitraires au " +"serveur. Pour ce faire, ils renvoient un dictionnaire depuis ``fit`` " +"et/ou ``evaluate``. Nous avons vu et utilisé ce concept tout au long de " +"ce carnet sans le mentionner explicitement : notre ``FlowerClient`` " +"renvoie un dictionnaire contenant une paire clé/valeur personnalisée en " +"tant que troisième valeur de retour dans ``evaluate``." + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:627 +msgid "Scaling federated learning" +msgstr "Mise à l'échelle de l'apprentissage fédéré" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:629 +msgid "" +"As a last step in this notebook, let's see how we can use Flower to " +"experiment with a large number of clients." +msgstr "" +"Comme dernière étape de ce carnet, voyons comment nous pouvons utiliser " +"Flower pour expérimenter avec un grand nombre de clients." + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:651 +#, python-format +msgid "" +"We now have 1000 partitions, each holding 45 training and 5 validation " +"examples. Given that the number of training examples on each client is " +"quite small, we should probably train the model a bit longer, so we " +"configure the clients to perform 3 local training epochs. We should also " +"adjust the fraction of clients selected for training during each round " +"(we don't want all 1000 clients participating in every round), so we " +"adjust ``fraction_fit`` to ``0.05``, which means that only 5% of " +"available clients (so 50 clients) will be selected for training each " +"round:" +msgstr "" +"Nous avons maintenant 1000 partitions, chacune contenant 45 exemples " +"d'entraînement et 5 exemples de validation. Etant donné que le nombre " +"d'exemples d'entraînement sur chaque client est assez faible, nous " +"devrions probablement entraîner le modèle un peu plus longtemps, nous " +"configurons donc les clients pour qu'ils effectuent 3 époques " +"d'entraînement local. Nous devrions également ajuster la fraction de " +"clients sélectionnés pour l'entraînement à chaque tour (nous ne voulons " +"pas que les 1000 clients participent à chaque tour), nous ajustons donc " +"``fraction_fit`` à ``0.05``, ce qui signifie que seulement 5% des clients" +" disponibles (donc 50 clients) seront sélectionnés pour l'entraînement à " +"chaque tour :" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:699 +msgid "" +"In this notebook, we've seen how we can gradually enhance our system by " +"customizing the strategy, initializing parameters on the server side, " +"choosing a different strategy, and evaluating models on the server-side. " +"That's quite a bit of flexibility with so little code, right?" +msgstr "" +"Dans ce carnet, nous avons vu comment nous pouvons progressivement " +"améliorer notre système en personnalisant la stratégie, en initialisant " +"les paramètres côté serveur, en choisissant une stratégie différente et " +"en évaluant les modèles côté serveur. C'est une sacrée flexibilité avec " +"si peu de code, n'est-ce pas ?" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:701 +msgid "" +"In the later sections, we've seen how we can communicate arbitrary values" +" between server and clients to fully customize client-side execution. " +"With that capability, we built a large-scale Federated Learning " +"simulation using the Flower Virtual Client Engine and ran an experiment " +"involving 1000 clients in the same workload - all in a Jupyter Notebook!" +msgstr "" +"Dans les sections ultérieures, nous avons vu comment nous pouvons " +"communiquer des valeurs arbitraires entre le serveur et les clients pour " +"personnaliser entièrement l'exécution côté client. Grâce à cette " +"capacité, nous avons construit une simulation d'apprentissage fédéré à " +"grande échelle en utilisant le moteur de client virtuel Flower et nous " +"avons mené une expérience impliquant 1000 clients dans la même charge de " +"travail - le tout dans un carnet Jupyter !" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:719 +#, fuzzy +msgid "" +"The `Flower Federated Learning Tutorial - Part 3 " +"`__ shows how to build a fully custom ``Strategy`` from " +"scratch." +msgstr "" +"Le `Tutoriel d'apprentissage fédéré Flower - Partie 3 [WIP] " +"`__ montre comment construire une ``Stratégie`` entièrement " +"personnalisée à partir de zéro." + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:9 +msgid "What is Federated Learning?" +msgstr "Qu'est-ce que l'apprentissage fédéré ?" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:13 +#, fuzzy +msgid "" +"In this tutorial, you will learn what federated learning is, build your " +"first system in Flower, and gradually extend it. If you work through all " +"parts of the tutorial, you will be able to build advanced federated " +"learning systems that approach the current state of the art in the field." +msgstr "" +"Dans ce tutoriel, tu apprendras ce qu'est l'apprentissage fédéré, tu " +"construiras ton premier système dans Flower, et tu l'étendras " +"progressivement. Si tu travailles sur toutes les parties du tutoriel, tu " +"seras capable de construire des systèmes d'apprentissage fédéré avancés " +"qui se rapprochent de l'état actuel de l'art dans le domaine." + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:15 +msgid "" +"🧑‍🏫 This tutorial starts at zero and expects no familiarity with " +"federated learning. Only a basic understanding of data science and Python" +" programming is assumed." +msgstr "" +"🧑‍🏫 Ce tutoriel part de zéro et n'attend aucune familiarité avec " +"l'apprentissage fédéré. Seule une compréhension de base de la science des" +" données et de la programmation Python est supposée." + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:17 +#, fuzzy +msgid "" +"`Star Flower on GitHub `__ ⭐️ and join " +"the open-source Flower community on Slack to connect, ask questions, and " +"get help: `Join Slack `__ 🌼 We'd love to " +"hear from you in the ``#introductions`` channel! And if anything is " +"unclear, head over to the ``#questions`` channel." +msgstr "" +"`Star Flower on GitHub `__ ⭐️ et " +"rejoignez la communauté Flower sur Slack pour vous connecter, poser des " +"questions et obtenir de l'aide : `Join Slack `__ 🌼 Nous serions ravis d'avoir de vos nouvelles dans le canal " +"``#introductions`` ! Et si quelque chose n'est pas clair, rendez-vous sur" +" le canal ``#questions``." + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:31 +msgid "Classic machine learning" +msgstr "Apprentissage automatique classique" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:33 +msgid "" +"Before we begin to discuss federated learning, let us quickly recap how " +"most machine learning works today." +msgstr "" +"Avant de commencer à discuter de l'apprentissage fédéré, récapitulons " +"rapidement la façon dont la plupart des apprentissages automatiques " +"fonctionnent aujourd'hui." + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:35 +msgid "" +"In machine learning, we have a model, and we have data. The model could " +"be a neural network (as depicted here), or something else, like classical" +" linear regression." +msgstr "" +"Dans l'apprentissage automatique, nous avons un modèle et des données. Le" +" modèle peut être un réseau neuronal (comme illustré ici), ou quelque " +"chose d'autre, comme la régression linéaire classique." + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:41 +msgid "|93b02017c78049bbbd5ae456dcb2c91b|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:109 +msgid "Model and data" +msgstr "Modèle et données" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:47 +msgid "" +"We train the model using the data to perform a useful task. A task could " +"be to detect objects in images, transcribe an audio recording, or play a " +"game like Go." +msgstr "" +"Nous entraînons le modèle en utilisant les données pour effectuer une " +"tâche utile. Une tâche peut consister à détecter des objets dans des " +"images, à transcrire un enregistrement audio ou à jouer à un jeu comme le" +" Go." + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:53 +msgid "|01471150fd5144c080a176b43e92a3ff|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:111 +msgid "Train model using data" +msgstr "Entraîne le modèle à l'aide des données" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:59 +#, fuzzy +msgid "" +"Now, in practice, the training data we work with doesn't originate on the" +" machine we train the model on. It gets created somewhere else." +msgstr "" +"Dans la pratique, les données d'entraînement avec lesquelles nous " +"travaillons ne proviennent pas de la machine sur laquelle nous entraînons" +" le modèle. Elles sont créées ailleurs." + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:61 +#, fuzzy +msgid "" +"It originates on a smartphone by the user interacting with an app, a car " +"collecting sensor data, a laptop receiving input via the keyboard, or a " +"smart speaker listening to someone trying to sing a song." +msgstr "" +"Elle prend naissance sur un smartphone par l'interaction de l'utilisateur" +" avec une application, une voiture qui collecte des données de capteurs, " +"un ordinateur portable qui reçoit des entrées via le clavier, ou un haut-" +"parleur intelligent qui écoute quelqu'un qui essaie de chanter une " +"chanson." + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:67 +msgid "|9bc21c7dbd17444a8f070c60786e3484|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:113 +msgid "Data on a phone" +msgstr "Données sur un téléphone" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:73 +msgid "" +"What's also important to mention, this \"somewhere else\" is usually not " +"just one place, it's many places. It could be several devices all running" +" the same app. But it could also be several organizations, all generating" +" data for the same task." +msgstr "" +"Il est également important de mentionner que cet \"ailleurs\" n'est " +"généralement pas un seul endroit, mais plusieurs. Il peut s'agir de " +"plusieurs appareils fonctionnant tous avec la même application. Mais il " +"peut également s'agir de plusieurs organisations, qui génèrent toutes des" +" données pour la même tâche." + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:79 +msgid "|3047bbce54b34099ae559963d0420d79|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:115 +msgid "Data is on many devices" +msgstr "Les données se trouvent sur de nombreux appareils" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:85 +msgid "" +"So to use machine learning, or any kind of data analysis, the approach " +"that has been used in the past was to collect all data on a central " +"server. This server can be somewhere in a data center, or somewhere in " +"the cloud." +msgstr "" +"Ainsi, pour utiliser l'apprentissage automatique, ou tout autre type " +"d'analyse de données, l'approche utilisée par le passé consistait à " +"collecter toutes les données sur un serveur central. Ce serveur peut se " +"trouver quelque part dans un centre de données, ou quelque part dans le " +"cloud." + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:91 +msgid "|e9f8ce948593444fb838d2f354c7ec5d|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:117 +msgid "Central data collection" +msgstr "Collecte centralisée des données" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:97 +#, fuzzy +msgid "" +"Once all the data is collected in one place, we can finally use machine " +"learning algorithms to train our model on the data. This is the machine " +"learning approach that we've basically always relied on." +msgstr "" +"Une fois que toutes les données sont rassemblées en un seul endroit, nous" +" pouvons enfin utiliser des algorithmes d'apprentissage automatique pour " +"entraîner notre modèle sur les données. C'est l'approche d'apprentissage " +"automatique sur laquelle nous nous sommes fondamentalement toujours " +"appuyés." + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:103 +msgid "|c24c1478b30e4f74839208628a842d1e|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:119 +msgid "Central model training" +msgstr "Formation au modèle central" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:130 +msgid "Challenges of classical machine learning" +msgstr "Les défis de l'apprentissage automatique classique" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:132 +msgid "" +"The classic machine learning approach we've just seen can be used in some" +" cases. Great examples include categorizing holiday photos, or analyzing " +"web traffic. Cases, where all the data is naturally available on a " +"centralized server." +msgstr "" +"L'approche classique de l'apprentissage automatique que nous venons de " +"voir peut être utilisée dans certains cas. Parmi les grands exemples, on " +"peut citer la catégorisation des photos de vacances, ou l'analyse du " +"trafic web. Des cas, où toutes les données sont naturellement disponibles" +" sur un serveur centralisé." + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:138 +msgid "|1b3613d7a58847b59e1d3180802dbc09|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:173 +msgid "Centralized possible" +msgstr "Possibilité de centralisation" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:144 +msgid "" +"But the approach can not be used in many other cases. Cases, where the " +"data is not available on a centralized server, or cases where the data " +"available on one server is not enough to train a good model." +msgstr "" +"Mais cette approche ne peut pas être utilisée dans de nombreux autres cas" +" : lorsque les données ne sont pas disponibles sur un serveur centralisé," +" ou lorsque les données disponibles sur un serveur ne sont pas " +"suffisantes pour former un bon modèle." + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:150 +msgid "|9980b5213db547d0b8024a50992b9e3f|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:175 +msgid "Centralized impossible" +msgstr "Impossible de centraliser" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:156 +#, fuzzy +msgid "" +"There are many reasons why the classic centralized machine learning " +"approach does not work for a large number of highly important real-world " +"use cases. Those reasons include:" +msgstr "" +"Il existe de nombreuses raisons pour lesquelles l'approche classique " +"centralisée de l'apprentissage automatique ne fonctionne pas pour un " +"grand nombre de cas d'utilisation très importants dans le monde réel, " +"notamment :" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:158 +#, fuzzy +msgid "" +"**Regulations**: GDPR (Europe), CCPA (California), PIPEDA (Canada), LGPD " +"(Brazil), PDPL (Argentina), KVKK (Turkey), POPI (South Africa), FSS " +"(Russia), CDPR (China), PDPB (India), PIPA (Korea), APPI (Japan), PDP " +"(Indonesia), PDPA (Singapore), APP (Australia), and other regulations " +"protect sensitive data from being moved. In fact, those regulations " +"sometimes even prevent single organizations from combining their own " +"users' data for artificial intelligence training because those users live" +" in different parts of the world, and their data is governed by different" +" data protection regulations." +msgstr "" +"**Réglementations** : GDPR (Europe), CCPA (Californie), PIPEDA (Canada), " +"LGPD (Brésil), PDPL (Argentine), KVKK (Turquie), POPI (Afrique du Sud), " +"FSS (Russie), CDPR (Chine), PDPB (Inde), PIPA (Corée), APPI (Japon), PDP " +"(Indonésie), PDPA (Singapour), APP (Australie), et d'autres " +"réglementations protègent les données sensibles contre le déplacement. En" +" fait, ces réglementations empêchent même parfois des organisations " +"individuelles de combiner les données de leurs propres utilisateurs pour " +"la formation à l'intelligence artificielle parce que ces utilisateurs " +"vivent dans différentes parties du monde, et que leurs données sont " +"régies par des réglementations différentes en matière de protection des " +"données." + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:160 +msgid "" +"**User preference**: In addition to regulation, there are use cases where" +" users just expect that no data leaves their device, ever. If you type " +"your passwords and credit card info into the digital keyboard of your " +"phone, you don't expect those passwords to end up on the server of the " +"company that developed that keyboard, do you? In fact, that use case was " +"the reason federated learning was invented in the first place." +msgstr "" +"**Préférence de l'utilisateur** : En plus de la réglementation, il existe" +" des cas d'utilisation où les utilisateurs s'attendent tout simplement à " +"ce qu'aucune donnée ne quitte leur appareil, jamais. Si tu tapes tes mots" +" de passe et tes informations de carte de crédit sur le clavier numérique" +" de ton téléphone, tu ne t'attends pas à ce que ces mots de passe " +"finissent sur le serveur de l'entreprise qui a développé ce clavier, n" +"'est-ce pas ? En fait, ce cas d'utilisation est la raison pour laquelle " +"l'apprentissage fédéré a été inventé en premier lieu." + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:161 +#, fuzzy +msgid "" +"**Data volume**: Some sensors, like cameras, produce such a high data " +"volume that it is neither feasible nor economic to collect all the data " +"(due to, for example, bandwidth or communication efficiency). Think about" +" a national rail service with hundreds of train stations across the " +"country. If each of these train stations is outfitted with a number of " +"security cameras, the volume of raw on-device data they produce requires " +"incredibly powerful and exceedingly expensive infrastructure to process " +"and store. And most of the data isn't even useful." +msgstr "" +"**volume de données** : certains capteurs, comme les caméras, produisent " +"un volume de données si important qu'il n'est ni possible ni économique " +"de collecter toutes les données (en raison, par exemple, de la bande " +"passante ou de l'efficacité des communications). Pensez à un service " +"ferroviaire national comptant des centaines de gares à travers le pays. " +"Si chacune de ces gares est équipée d'un certain nombre de caméras de " +"sécurité, le volume de données brutes sur les appareils qu'elles " +"produisent nécessite une infrastructure incroyablement puissante et " +"excessivement coûteuse pour les traiter et les stocker. Et la plupart de " +"ces données ne sont même pas utiles." + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:164 +msgid "Examples where centralized machine learning does not work include:" +msgstr "" +"Voici quelques exemples où l'apprentissage automatique centralisé ne " +"fonctionne pas :" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:166 +#, fuzzy +msgid "" +"Sensitive healthcare records from multiple hospitals to train cancer " +"detection models" +msgstr "" +"Des dossiers médicaux sensibles provenant de plusieurs hôpitaux pour " +"former des modèles de détection du cancer" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:167 +msgid "" +"Financial information from different organizations to detect financial " +"fraud" +msgstr "" +"Informations financières provenant de différentes organisations pour " +"détecter les fraudes financières" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:168 +msgid "Location data from your electric car to make better range prediction" +msgstr "" +"Les données de localisation de ta voiture électrique pour mieux prédire " +"l'autonomie" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:169 +msgid "End-to-end encrypted messages to train better auto-complete models" +msgstr "" +"Messages cryptés de bout en bout pour former de meilleurs modèles " +"d'autocomplétion" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:171 +#, fuzzy +msgid "" +"The popularity of privacy-enhancing systems like the `Brave " +"`__ browser or the `Signal `__ " +"messenger shows that users care about privacy. In fact, they choose the " +"privacy-enhancing version over other alternatives, if such an alternative" +" exists. But what can we do to apply machine learning and data science to" +" these cases to utilize private data? After all, these are all areas that" +" would benefit significantly from recent advances in AI." +msgstr "" +"La popularité des systèmes améliorant la confidentialité comme le " +"navigateur `Brave `__ ou le messager `Signal " +"`__ montre que les utilisateurs se soucient de la " +"confidentialité. En fait, ils choisissent la version améliorant la " +"confidentialité plutôt que d'autres alternatives, si une telle " +"alternative existe. Mais que pouvons-nous faire pour appliquer " +"l'apprentissage automatique et la science des données à ces cas afin " +"d'utiliser les données privées ? Après tout, ce sont tous des domaines " +"qui bénéficieraient de manière significative des récentes avancées en " +"matière d'IA." + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:186 +msgid "Federated learning" +msgstr "Apprentissage fédéré" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:188 +msgid "" +"Federated learning simply reverses this approach. It enables machine " +"learning on distributed data by moving the training to the data, instead " +"of moving the data to the training. Here's the single-sentence " +"explanation:" +msgstr "" +"L'apprentissage fédéré inverse simplement cette approche. Il permet " +"l'apprentissage automatique sur des données distribuées en déplaçant la " +"formation vers les données, au lieu de déplacer les données vers la " +"formation. Voici l'explication en une seule phrase :" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:190 +msgid "Central machine learning: move the data to the computation" +msgstr "Apprentissage automatique central : déplace les données vers le calcul" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:191 +msgid "Federated (machine) learning: move the computation to the data" +msgstr "Apprentissage (machine) fédéré : déplacer le calcul vers les données" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:193 +msgid "" +"By doing so, it enables us to use machine learning (and other data " +"science approaches) in areas where it wasn't possible before. We can now " +"train excellent medical AI models by enabling different hospitals to work" +" together. We can solve financial fraud by training AI models on the data" +" of different financial institutions. We can build novel privacy-" +"enhancing applications (such as secure messaging) that have better built-" +"in AI than their non-privacy-enhancing alternatives. And those are just a" +" few of the examples that come to mind. As we deploy federated learning, " +"we discover more and more areas that can suddenly be reinvented because " +"they now have access to vast amounts of previously inaccessible data." +msgstr "" +"Ce faisant, il nous permet d'utiliser l'apprentissage automatique (et " +"d'autres approches de science des données) dans des domaines où cela " +"n'était pas possible auparavant. Nous pouvons désormais former " +"d'excellents modèles d'IA médicale en permettant à différents hôpitaux de" +" travailler ensemble. Nous pouvons résoudre les fraudes financières en " +"formant des modèles d'IA sur les données de différentes institutions " +"financières. Nous pouvons créer de nouvelles applications d'amélioration " +"de la confidentialité (telles que la messagerie sécurisée) qui ont une " +"meilleure IA intégrée que leurs alternatives d'amélioration de la " +"confidentialité. Et ce ne sont là que quelques exemples qui me viennent à" +" l'esprit. Au fur et à mesure que nous déployons l'apprentissage fédéré, " +"nous découvrons de plus en plus de domaines qui peuvent soudainement être" +" réinventés parce qu'ils ont maintenant accès à de vastes quantités de " +"données auparavant inaccessibles." + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:196 +msgid "" +"So how does federated learning work, exactly? Let's start with an " +"intuitive explanation." +msgstr "" +"Comment fonctionne l'apprentissage fédéré ? Commençons par une " +"explication intuitive." + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:199 +msgid "Federated learning in five steps" +msgstr "L'apprentissage fédéré en cinq étapes" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:202 +msgid "Step 0: Initialize global model" +msgstr "Étape 0 : Initialisation du modèle global" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:204 +msgid "" +"We start by initializing the model on the server. This is exactly the " +"same in classic centralized learning: we initialize the model parameters," +" either randomly or from a previously saved checkpoint." +msgstr "" +"Nous commençons par initialiser le modèle sur le serveur. C'est " +"exactement la même chose dans l'apprentissage centralisé classique : nous" +" initialisons les paramètres du modèle, soit de façon aléatoire, soit à " +"partir d'un point de contrôle précédemment sauvegardé." + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:210 +msgid "|c7afb4c92d154bfaa5e8cb9a150e17f1|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:307 +msgid "Initialize global model" +msgstr "Initialise le modèle global" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:217 +msgid "" +"Step 1: Send model to a number of connected organizations/devices (client" +" nodes)" +msgstr "" +"Étape 1 : envoyer le modèle à un certain nombre d'organisations/appareils" +" connectés (nœuds clients)" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:219 +#, fuzzy +msgid "" +"Next, we send the parameters of the global model to the connected client " +"nodes (think: edge devices like smartphones or servers belonging to " +"organizations). This is to ensure that each participating node starts " +"their local training using the same model parameters. We often use only a" +" few of the connected nodes instead of all nodes. The reason for this is " +"that selecting more and more client nodes has diminishing returns." +msgstr "" +"Ensuite, nous envoyons les paramètres du modèle global aux nœuds clients " +"connectés (par exemple, les appareils périphériques comme les smartphones" +" ou les serveurs appartenant à des organisations). Cela permet de " +"s'assurer que chaque nœud participant commence sa formation locale en " +"utilisant les mêmes paramètres de modèle. Nous n'utilisons souvent que " +"quelques-uns des nœuds connectés au lieu de tous les nœuds. La raison en " +"est que la sélection d'un nombre croissant de nœuds clients a des " +"rendements décroissants." + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:225 +msgid "|032eb6fed6924ac387b9f13854919196|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:309 +msgid "Send global model" +msgstr "Envoyer le modèle global" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:232 +msgid "" +"Step 2: Train model locally on the data of each organization/device " +"(client node)" +msgstr "" +"Étape 2 : Entraîne le modèle localement sur les données de chaque " +"organisation/appareil (nœud client)" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:234 +msgid "" +"Now that all (selected) client nodes have the latest version of the " +"global model parameters, they start the local training. They use their " +"own local dataset to train their own local model. They don't train the " +"model until full convergence, but they only train for a little while. " +"This could be as little as one epoch on the local data, or even just a " +"few steps (mini-batches)." +msgstr "" +"Maintenant que tous les nœuds clients (sélectionnés) disposent de la " +"dernière version des paramètres du modèle global, ils commencent " +"l'entraînement local. Ils utilisent leur propre ensemble de données " +"locales pour entraîner leur propre modèle local. Ils n'entraînent pas le " +"modèle jusqu'à la convergence totale, mais ils ne s'entraînent que " +"pendant un petit moment. Il peut s'agir d'une seule époque sur les " +"données locales, ou même de quelques étapes (mini-batchs)." + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:240 +msgid "|fbf225add7fd4df5a9bf25a95597d954|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:311 +msgid "Train on local data" +msgstr "Forme-toi aux données locales" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:247 +msgid "Step 3: Return model updates back to the server" +msgstr "Étape 3 : Renvoyer les mises à jour du modèle au serveur" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:249 +msgid "" +"After local training, each client node has a slightly different version " +"of the model parameters they originally received. The parameters are all " +"different because each client node has different examples in its local " +"dataset. The client nodes then send those model updates back to the " +"server. The model updates they send can either be the full model " +"parameters or just the gradients that were accumulated during local " +"training." +msgstr "" +"Après l'entraînement local, chaque nœud client possède une version " +"légèrement différente des paramètres du modèle qu'il a reçus à l'origine." +" Les paramètres sont tous différents parce que chaque nœud client a des " +"exemples différents dans son ensemble de données local. Les nœuds clients" +" renvoient ensuite ces mises à jour du modèle au serveur. Les mises à " +"jour du modèle qu'ils envoient peuvent être soit les paramètres complets " +"du modèle, soit seulement les gradients qui ont été accumulés au cours de" +" l'entraînement local." + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:255 +msgid "|7efbe3d29d8349b89594e8947e910525|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:313 +msgid "Send model updates" +msgstr "Envoyer les mises à jour du modèle" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:262 +msgid "Step 4: Aggregate model updates into a new global model" +msgstr "" +"Étape 4 : Agréger les mises à jour des modèles dans un nouveau modèle " +"global" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:264 +msgid "" +"The server receives model updates from the selected client nodes. If it " +"selected 100 client nodes, it now has 100 slightly different versions of " +"the original global model, each trained on the local data of one client. " +"But didn't we want to have one model that contains the learnings from the" +" data of all 100 client nodes?" +msgstr "" +"Le serveur reçoit les mises à jour du modèle des nœuds clients " +"sélectionnés. S'il a sélectionné 100 nœuds clients, il dispose maintenant" +" de 100 versions légèrement différentes du modèle global original, " +"chacune ayant été formée sur les données locales d'un client. Mais ne " +"voulions-nous pas avoir un seul modèle qui contienne les apprentissages " +"des données de l'ensemble des 100 nœuds clients ?" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:266 +msgid "" +"In order to get one single model, we have to combine all the model " +"updates we received from the client nodes. This process is called " +"*aggregation*, and there are many different ways to do it. The most basic" +" way to do it is called *Federated Averaging* (`McMahan et al., 2016 " +"`__), often abbreviated as *FedAvg*. " +"*FedAvg* takes the 100 model updates and, as the name suggests, averages " +"them. To be more precise, it takes the *weighted average* of the model " +"updates, weighted by the number of examples each client used for " +"training. The weighting is important to make sure that each data example " +"has the same \"influence\" on the resulting global model. If one client " +"has 10 examples, and another client has 100 examples, then - without " +"weighting - each of the 10 examples would influence the global model ten " +"times as much as each of the 100 examples." +msgstr "" +"In order to get one single model, we have to combine all the model " +"updates we received from the client nodes. This process is called " +"*aggregation*, and there are many different ways to do it. The most basic" +" way to do it is called *Federated Averaging* (`McMahan et al., 2016 " +"`__), often abbreviated as *FedAvg*. " +"*FedAvg* takes the 100 model updates and, as the name suggests, averages " +"them. To be more precise, it takes the *weighted average* of the model " +"updates, weighted by the number of examples each client used for " +"training. The weighting is important to make sure that each data example " +"has the same \"influence\" on the resulting global model. If one client " +"has 10 examples, and another client has 100 examples, then - without " +"weighting - each of the 10 examples would influence the global model ten " +"times as much as each of the 100 examples." + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:273 +msgid "|329fb3c04c744eda83bb51fa444c2266|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:315 +msgid "Aggregate model updates" +msgstr "Mises à jour globales du modèle" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:280 +msgid "Step 5: Repeat steps 1 to 4 until the model converges" +msgstr "Étape 5 : répète les étapes 1 à 4 jusqu'à ce que le modèle converge" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:282 +msgid "" +"Steps 1 to 4 are what we call a single round of federated learning. The " +"global model parameters get sent to the participating client nodes (step " +"1), the client nodes train on their local data (step 2), they send their " +"updated models to the server (step 3), and the server then aggregates the" +" model updates to get a new version of the global model (step 4)." +msgstr "" +"Les étapes 1 à 4 constituent ce que nous appelons un cycle unique " +"d'apprentissage fédéré. Les paramètres du modèle global sont envoyés aux " +"nœuds clients participants (étape 1), les nœuds clients s'entraînent sur " +"leurs données locales (étape 2), ils envoient leurs modèles mis à jour au" +" serveur (étape 3), et le serveur agrège ensuite les mises à jour du " +"modèle pour obtenir une nouvelle version du modèle global (étape 4)." + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:284 +#, fuzzy +msgid "" +"During a single round, each client node that participates in that " +"iteration only trains for a little while. This means that after the " +"aggregation step (step 4), we have a model that has been trained on all " +"the data of all participating client nodes, but only for a little while. " +"We then have to repeat this training process over and over again to " +"eventually arrive at a fully trained model that performs well across the " +"data of all client nodes." +msgstr "" +"Au cours d'un seul tour, chaque nœud client qui participe à cette " +"itération ne s'entraîne que pendant un petit moment. Cela signifie " +"qu'après l'étape d'agrégation (étape 4), nous avons un modèle qui a été " +"entraîné sur toutes les données de tous les nœuds clients participants, " +"mais seulement pendant un petit moment. Nous devons ensuite répéter ce " +"processus d'entraînement encore et encore pour finalement arriver à un " +"modèle entièrement entraîné qui fonctionne bien sur l'ensemble des " +"données de tous les nœuds clients." + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:289 +msgid "" +"Congratulations, you now understand the basics of federated learning. " +"There's a lot more to discuss, of course, but that was federated learning" +" in a nutshell. In later parts of this tutorial, we will go into more " +"detail. Interesting questions include: How can we select the best client " +"nodes that should participate in the next round? What's the best way to " +"aggregate model updates? How can we handle failing client nodes " +"(stragglers)?" +msgstr "" +"Félicitations, tu comprends maintenant les bases de l'apprentissage " +"fédéré. Il y a bien sûr beaucoup plus à discuter, mais c'était " +"l'apprentissage fédéré en quelques mots. Dans les parties suivantes de ce" +" tutoriel, nous irons plus en détail. Les questions intéressantes " +"comprennent : comment pouvons-nous sélectionner les meilleurs nœuds " +"clients qui devraient participer au prochain tour ? Quelle est la " +"meilleure façon d'agréger les mises à jour du modèle ? Comment pouvons-" +"nous gérer les nœuds clients qui échouent (stragglers) ?" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:294 +#, fuzzy +msgid "" +"Just like we can train a model on the decentralized data of different " +"client nodes, we can also evaluate the model on that data to receive " +"valuable metrics. This is called federated evaluation, sometimes " +"abbreviated as FE. In fact, federated evaluation is an integral part of " +"most federated learning systems." +msgstr "" +"Tout comme nous pouvons former un modèle sur les données décentralisées " +"de différents nœuds clients, nous pouvons également évaluer le modèle sur" +" ces données pour recevoir des mesures précieuses. C'est ce qu'on appelle" +" l'évaluation fédérée, parfois abrégée en FE. En fait, l'évaluation " +"fédérée fait partie intégrante de la plupart des systèmes d'apprentissage" +" fédéré." + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:297 +msgid "Federated analytics" +msgstr "Analyses fédérées" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:299 +msgid "" +"In many cases, machine learning isn't necessary to derive value from " +"data. Data analysis can yield valuable insights, but again, there's often" +" not enough data to get a clear answer. What's the average age at which " +"people develop a certain type of health condition? Federated analytics " +"enables such queries over multiple client nodes. It is usually used in " +"conjunction with other privacy-enhancing technologies like secure " +"aggregation to prevent the server from seeing the results submitted by " +"individual client nodes." +msgstr "" +"Dans de nombreux cas, l'apprentissage automatique n'est pas nécessaire " +"pour tirer de la valeur des données. L'analyse des données peut donner " +"des indications précieuses, mais là encore, il n'y a souvent pas assez de" +" données pour obtenir une réponse claire. Quel est l'âge moyen auquel les" +" gens développent un certain type de problème de santé ? L'analyse " +"fédérée permet de telles requêtes sur plusieurs nœuds clients. Elle est " +"généralement utilisée en conjonction avec d'autres technologies de " +"renforcement de la confidentialité, comme l'agrégation sécurisée, pour " +"empêcher le serveur de voir les résultats soumis par les nœuds clients " +"individuels." + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:305 +msgid "" +"Differential privacy (DP) is often mentioned in the context of Federated " +"Learning. It is a privacy-preserving method used when analyzing and " +"sharing statistical data, ensuring the privacy of individual " +"participants. DP achieves this by adding statistical noise to the model " +"updates, ensuring any individual participants’ information cannot be " +"distinguished or re-identified. This technique can be considered an " +"optimization that provides a quantifiable privacy protection measure." +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:326 +msgid "Flower" +msgstr "Fleur" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:328 +msgid "" +"Federated learning, federated evaluation, and federated analytics require" +" infrastructure to move machine learning models back and forth, train and" +" evaluate them on local data, and then aggregate the updated models. " +"Flower provides the infrastructure to do exactly that in an easy, " +"scalable, and secure way. In short, Flower presents a unified approach to" +" federated learning, analytics, and evaluation. It allows the user to " +"federate any workload, any ML framework, and any programming language." +msgstr "" +"L'apprentissage fédéré, l'évaluation fédérée et l'analyse fédérée " +"nécessitent une infrastructure pour déplacer les modèles d'apprentissage " +"automatique dans les deux sens, les entraîner et les évaluer sur des " +"données locales, puis agréger les modèles mis à jour. Flower fournit " +"l'infrastructure pour faire exactement cela de manière simple, évolutive " +"et sécurisée. En bref, Flower présente une approche unifiée de " +"l'apprentissage, de l'analyse et de l'évaluation fédérés. Il permet à " +"l'utilisateur de fédérer n'importe quelle charge de travail, n'importe " +"quel cadre de ML et n'importe quel langage de programmation." + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:334 +msgid "|c00bf2750bc24d229737a0fe1395f0fc|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:340 +msgid "" +"Flower federated learning server and client nodes (car, scooter, personal" +" computer, roomba, and phone)" +msgstr "" +"Serveur d'apprentissage fédéré de Flower et nœuds clients (voiture, " +"scooter, ordinateur personnel, roomba et téléphone)" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:353 +msgid "" +"Congratulations, you just learned the basics of federated learning and " +"how it relates to the classic (centralized) machine learning!" +msgstr "" +"Félicitations, tu viens d'apprendre les bases de l'apprentissage fédéré " +"et son rapport avec l'apprentissage automatique classique (centralisé) !" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:355 +msgid "" +"In the next part of this tutorial, we are going to build a first " +"federated learning system with Flower." +msgstr "" +"Dans la prochaine partie de ce tutoriel, nous allons construire un " +"premier système d'apprentissage fédéré avec Flower." + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:373 +#, fuzzy +msgid "" +"The `Flower Federated Learning Tutorial - Part 1 " +"`__ shows how to build a simple federated learning system " +"with PyTorch and Flower." +msgstr "" +"Le `Tutoriel d'apprentissage fédéré Flower - Partie 1 " +"`__ " +"montre comment construire un système d'apprentissage fédéré simple avec " +"PyTorch et Flower." + +#~ msgid "Flower CLI commands" +#~ msgstr "Commandes CLI Flower" + +#~ msgid "Contributor guide" +#~ msgstr "Guide pour les contributeurs" + +#~ msgid "API Reference - Flower CLI commands" +#~ msgstr "Référence API - Commandes CLI pour Flower" + +#~ msgid "API Reference - flwr (Python package)" +#~ msgstr "Référence API - flwr (paquetage Python)" + +#~ msgid "Flower client." +#~ msgstr "Client de Flower" + +#~ msgid "Abstract base class for Flower clients." +#~ msgstr "" + +#~ msgid "Evaluate the provided parameters using the locally held dataset." +#~ msgstr "évaluer le modèle mis à jour sur l'ensemble de test local" + +#~ msgid "Parameters" +#~ msgstr "Paramètres du modèle." + +#~ msgid "" +#~ "The evaluation instructions containing " +#~ "(global) model parameters received from " +#~ "the server and a dictionary of " +#~ "configuration values used to customize " +#~ "the local evaluation process." +#~ msgstr "" + +#~ msgid "Returns" +#~ msgstr "Ressources" + +#~ msgid "" +#~ "The evaluation result containing the " +#~ "loss on the local dataset and " +#~ "other details such as the number " +#~ "of local data examples used for " +#~ "evaluation." +#~ msgstr "" + +#~ msgid "Return type" +#~ msgstr "" + +#~ msgid "Refine the provided parameters using the locally held dataset." +#~ msgstr "" + +#~ msgid "" +#~ "The training instructions containing (global)" +#~ " model parameters received from the " +#~ "server and a dictionary of configuration" +#~ " values used to customize the local" +#~ " training process." +#~ msgstr "" + +#~ msgid "" +#~ "The training result containing updated " +#~ "parameters and other details such as " +#~ "the number of local training examples" +#~ " used for training." +#~ msgstr "" + +#~ msgid "Return the current local model parameters." +#~ msgstr "``get_parameters`` : renvoie les paramètres du modèle local actuel" + +#~ msgid "" +#~ "The get parameters instructions received " +#~ "from the server containing a dictionary" +#~ " of configuration values." +#~ msgstr "" + +#~ msgid "The current local model parameters." +#~ msgstr "``get_parameters`` : renvoie les paramètres du modèle local actuel" + +#~ msgid "Return set of client's properties." +#~ msgstr "" + +#~ msgid "" +#~ "The get properties instructions received " +#~ "from the server containing a dictionary" +#~ " of configuration values." +#~ msgstr "" + +#~ msgid "The current client properties." +#~ msgstr "" + +#~ msgid "Start a Flower client node which connects to a Flower server." +#~ msgstr "" + +#~ msgid "" +#~ "The IPv4 or IPv6 address of the" +#~ " server. If the Flower server runs" +#~ " on the same machine on port " +#~ "8080, then `server_address` would be " +#~ "`\"[::]:8080\"`." +#~ msgstr "" + +#~ msgid "An implementation of the abstract base class `flwr.client.Client`." +#~ msgstr "" + +#~ msgid "" +#~ "The maximum length of gRPC messages " +#~ "that can be exchanged with the " +#~ "Flower server. The default should be " +#~ "sufficient for most models. Users who" +#~ " train very large models might need" +#~ " to increase this value. Note that" +#~ " the Flower server needs to be " +#~ "started with the same value (see " +#~ "`flwr.server.start_server`), otherwise it will " +#~ "not know about the increased limit " +#~ "and block larger messages." +#~ msgstr "" + +#~ msgid "" +#~ "The PEM-encoded root certificates as " +#~ "a byte string or a path string." +#~ " If provided, a secure connection " +#~ "using the certificates will be " +#~ "established to an SSL-enabled Flower " +#~ "server." +#~ msgstr "" + +#~ msgid "" +#~ "DEPRECATED - USE 'transport' INSTEAD. " +#~ "Defines whether or not the client " +#~ "is interacting with the server using " +#~ "the experimental REST API. This feature" +#~ " is experimental, it might change " +#~ "considerably in future versions of " +#~ "Flower." +#~ msgstr "" +#~ "DÉPRÉCIÉ - UTILISER 'transport' À LA " +#~ "PLACE Définit si le client interagit " +#~ "ou non avec le serveur à l'aide" +#~ " de l'API REST expérimentale. Cette " +#~ "fonctionnalité est expérimentale, elle " +#~ "pourrait changer considérablement dans les " +#~ "futures versions de Flower." + +#~ msgid "" +#~ "Configure the transport layer. Allowed " +#~ "values: - 'grpc-bidi': gRPC, " +#~ "bidirectional streaming - 'grpc-rere': " +#~ "gRPC, request-response (experimental) - " +#~ "'rest': HTTP (experimental)" +#~ msgstr "" +#~ "Valeurs autorisées : - 'grpc-bidi' " +#~ ": gRPC, flux bidirectionnel - 'grpc-" +#~ "rere' : gRPC, requête-réponse " +#~ "(expérimental) - 'rest' : HTTP " +#~ "(expérimental)" + +#~ msgid "Starting a gRPC client with an insecure server connection:" +#~ msgstr "" + +#~ msgid "Starting an SSL-enabled gRPC client:" +#~ msgstr "" + +#~ msgid "Abstract base class for Flower clients using NumPy." +#~ msgstr "" + +#~ msgid "The current (global) model parameters." +#~ msgstr "``get_parameters`` : renvoie les paramètres du modèle local actuel" + +#~ msgid "" +#~ "Configuration parameters which allow the " +#~ "server to influence evaluation on the" +#~ " client. It can be used to " +#~ "communicate arbitrary values from the " +#~ "server to the client, for example, " +#~ "to influence the number of examples " +#~ "used for evaluation." +#~ msgstr "" + +#~ msgid "" +#~ "* **loss** (*float*) -- The evaluation" +#~ " loss of the model on the local" +#~ " dataset. * **num_examples** (*int*) -- " +#~ "The number of examples used for " +#~ "evaluation. * **metrics** (*Dict[str, " +#~ "Scalar]*) -- A dictionary mapping " +#~ "arbitrary string keys to values of " +#~ "type bool, bytes, float, int, or " +#~ "str. It can be used to " +#~ "communicate arbitrary values back to the" +#~ " server." +#~ msgstr "" + +#~ msgid "" +#~ "**loss** (*float*) -- The evaluation " +#~ "loss of the model on the local " +#~ "dataset." +#~ msgstr "" + +#~ msgid "**num_examples** (*int*) -- The number of examples used for evaluation." +#~ msgstr "" + +#~ msgid "" +#~ "**metrics** (*Dict[str, Scalar]*) -- A " +#~ "dictionary mapping arbitrary string keys " +#~ "to values of type bool, bytes, " +#~ "float, int, or str. It can be " +#~ "used to communicate arbitrary values " +#~ "back to the server." +#~ msgstr "" + +#~ msgid "" +#~ "The previous return type format (int," +#~ " float, float) and the extended " +#~ "format (int, float, float, Dict[str, " +#~ "Scalar]) have been deprecated and " +#~ "removed since Flower 0.19." +#~ msgstr "" + +#~ msgid "Train the provided parameters using the locally held dataset." +#~ msgstr "entraîne le modèle sur l'ensemble d'apprentissage local" + +#~ msgid "" +#~ "Configuration parameters which allow the " +#~ "server to influence training on the " +#~ "client. It can be used to " +#~ "communicate arbitrary values from the " +#~ "server to the client, for example, " +#~ "to set the number of (local) " +#~ "training epochs." +#~ msgstr "" + +#~ msgid "" +#~ "* **parameters** (*NDArrays*) -- The " +#~ "locally updated model parameters. * " +#~ "**num_examples** (*int*) -- The number " +#~ "of examples used for training. * " +#~ "**metrics** (*Dict[str, Scalar]*) -- A " +#~ "dictionary mapping arbitrary string keys " +#~ "to values of type bool, bytes, " +#~ "float, int, or str. It can be " +#~ "used to communicate arbitrary values " +#~ "back to the server." +#~ msgstr "" + +#~ msgid "**parameters** (*NDArrays*) -- The locally updated model parameters." +#~ msgstr "``get_parameters`` : renvoie les paramètres du modèle local actuel" + +#~ msgid "**num_examples** (*int*) -- The number of examples used for training." +#~ msgstr "" + +#~ msgid "" +#~ "Configuration parameters requested by the " +#~ "server. This can be used to tell" +#~ " the client which parameters are " +#~ "needed along with some Scalar " +#~ "attributes." +#~ msgstr "" + +#~ msgid "" +#~ "**parameters** -- The local model " +#~ "parameters as a list of NumPy " +#~ "ndarrays." +#~ msgstr "renvoie le poids du modèle sous la forme d'une liste de ndarrays NumPy" + +#~ msgid "Return a client's set of properties." +#~ msgstr "Renvoie l'ensemble des propriétés d'un client." + +#~ msgid "" +#~ "Configuration parameters requested by the " +#~ "server. This can be used to tell" +#~ " the client which properties are " +#~ "needed along with some Scalar " +#~ "attributes." +#~ msgstr "" + +#~ msgid "" +#~ "**properties** -- A dictionary mapping " +#~ "arbitrary string keys to values of " +#~ "type bool, bytes, float, int, or " +#~ "str. It can be used to communicate" +#~ " arbitrary property values back to " +#~ "the server." +#~ msgstr "" + +#~ msgid "Start a Flower NumPyClient which connects to a gRPC server." +#~ msgstr "" + +#~ msgid "An implementation of the abstract base class `flwr.client.NumPyClient`." +#~ msgstr "" + +#~ msgid "Starting a client with an insecure server connection:" +#~ msgstr "" + +#~ msgid "Starting a SSL-enabled client:" +#~ msgstr "" + +#~ msgid "Start a Ray-based Flower simulation server." +#~ msgstr "Simulation de moniteur" + +#~ msgid "" +#~ "A function creating client instances. " +#~ "The function must take a single " +#~ "`str` argument called `cid`. It should" +#~ " return a single client instance of" +#~ " type ClientLike. Note that the " +#~ "created client instances are ephemeral " +#~ "and will often be destroyed after " +#~ "a single method invocation. Since client" +#~ " instances are not long-lived, they" +#~ " should not attempt to carry state" +#~ " over method invocations. Any state " +#~ "required by the instance (model, " +#~ "dataset, hyperparameters, ...) should be " +#~ "(re-)created in either the call to " +#~ "`client_fn` or the call to any of" +#~ " the client methods (e.g., load " +#~ "evaluation data in the `evaluate` method" +#~ " itself)." +#~ msgstr "" +#~ "Une fonction créant des instances de " +#~ "client. La fonction doit prendre un " +#~ "seul argument `str` appelé `cid`. Elle" +#~ " doit retourner une seule instance de" +#~ " client de type ClientLike. Notez que" +#~ " les instances de client créées sont" +#~ " éphémères et seront souvent détruites " +#~ "après une seule invocation de méthode." +#~ " Puisque les instances de client ne" +#~ " sont pas de longue durée, elles " +#~ "ne doivent pas essayer de transporter" +#~ " l'état sur les invocations de " +#~ "méthode. Tout état requis par l'instance" +#~ " (modèle, jeu de données, hyperparamètres," +#~ " ...) doit être (re)créé dans l'appel" +#~ " à `client_fn` ou dans l'appel à " +#~ "n'importe quelle méthode de client (par" +#~ " exemple, charger les données d'évaluation" +#~ " dans la méthode `evaluate` elle-" +#~ "même)." + +#~ msgid "" +#~ "The total number of clients in " +#~ "this simulation. This must be set " +#~ "if `clients_ids` is not set and " +#~ "vice-versa." +#~ msgstr "" + +#~ msgid "" +#~ "List `client_id`s for each client. This" +#~ " is only required if `num_clients` is" +#~ " not set. Setting both `num_clients` " +#~ "and `clients_ids` with `len(clients_ids)` not" +#~ " equal to `num_clients` generates an " +#~ "error." +#~ msgstr "" + +#~ msgid "" +#~ "CPU and GPU resources for a single" +#~ " client. Supported keys are `num_cpus` " +#~ "and `num_gpus`. Example: `{\"num_cpus\": 4," +#~ " \"num_gpus\": 1}`. To understand the " +#~ "GPU utilization caused by `num_gpus`, " +#~ "consult the Ray documentation on GPU " +#~ "support." +#~ msgstr "" + +#~ msgid "" +#~ "An implementation of the abstract base" +#~ " class `flwr.server.Server`. If no instance" +#~ " is provided, then `start_server` will " +#~ "create one." +#~ msgstr "" + +#~ msgid "" +#~ "Currently supported values are `num_rounds`" +#~ " (int, default: 1) and `round_timeout` " +#~ "in seconds (float, default: None)." +#~ msgstr "" + +#~ msgid "" +#~ "An implementation of the abstract base" +#~ " class `flwr.server.Strategy`. If no " +#~ "strategy is provided, then `start_server` " +#~ "will use `flwr.server.strategy.FedAvg`." +#~ msgstr "" + +#~ msgid "" +#~ "An implementation of the abstract base" +#~ " class `flwr.server.ClientManager`. If no " +#~ "implementation is provided, then " +#~ "`start_simulation` will use " +#~ "`flwr.server.client_manager.SimpleClientManager`." +#~ msgstr "" + +#~ msgid "" +#~ "Optional dictionary containing arguments for" +#~ " the call to `ray.init`. If " +#~ "ray_init_args is None (the default), Ray" +#~ " will be initialized with the " +#~ "following default args: { " +#~ "\"ignore_reinit_error\": True, \"include_dashboard\": " +#~ "False } An empty dictionary can " +#~ "be used (ray_init_args={}) to prevent " +#~ "any arguments from being passed to " +#~ "ray.init." +#~ msgstr "" + +#~ msgid "" +#~ "Optional dictionary containing arguments for" +#~ " the call to `ray.init`. If " +#~ "ray_init_args is None (the default), Ray" +#~ " will be initialized with the " +#~ "following default args:" +#~ msgstr "" + +#~ msgid "{ \"ignore_reinit_error\": True, \"include_dashboard\": False }" +#~ msgstr "" + +#~ msgid "" +#~ "An empty dictionary can be used " +#~ "(ray_init_args={}) to prevent any arguments" +#~ " from being passed to ray.init." +#~ msgstr "" + +#~ msgid "" +#~ "Set to True to prevent `ray.shutdown()`" +#~ " in case `ray.is_initialized()=True`." +#~ msgstr "" + +#~ msgid "**hist** -- Object containing metrics from training." +#~ msgstr "" + +#~ msgid "Flower server." +#~ msgstr "Serveur de Flower" + +#~ msgid "Start a Flower server using the gRPC transport layer." +#~ msgstr "" + +#~ msgid "The IPv4 or IPv6 address of the server. Defaults to `\"[::]:8080\"`." +#~ msgstr "" + +#~ msgid "" +#~ "A server implementation, either " +#~ "`flwr.server.Server` or a subclass thereof." +#~ " If no instance is provided, then " +#~ "`start_server` will create one." +#~ msgstr "" + +#~ msgid "" +#~ "An implementation of the abstract base" +#~ " class `flwr.server.strategy.Strategy`. If no " +#~ "strategy is provided, then `start_server` " +#~ "will use `flwr.server.strategy.FedAvg`." +#~ msgstr "" + +#~ msgid "" +#~ "An implementation of the abstract base" +#~ " class `flwr.server.ClientManager`. If no " +#~ "implementation is provided, then " +#~ "`start_server` will use " +#~ "`flwr.server.client_manager.SimpleClientManager`." +#~ msgstr "" + +#~ msgid "" +#~ "The maximum length of gRPC messages " +#~ "that can be exchanged with the " +#~ "Flower clients. The default should be" +#~ " sufficient for most models. Users " +#~ "who train very large models might " +#~ "need to increase this value. Note " +#~ "that the Flower clients need to be" +#~ " started with the same value (see " +#~ "`flwr.client.start_client`), otherwise clients will" +#~ " not know about the increased limit" +#~ " and block larger messages." +#~ msgstr "" + +#~ msgid "" +#~ "Tuple containing root certificate, server " +#~ "certificate, and private key to start" +#~ " a secure SSL-enabled server. The " +#~ "tuple is expected to have three " +#~ "bytes elements in the following order:" +#~ " * CA certificate. * server " +#~ "certificate. * server private key." +#~ msgstr "" + +#~ msgid "" +#~ "Tuple containing root certificate, server " +#~ "certificate, and private key to start" +#~ " a secure SSL-enabled server. The " +#~ "tuple is expected to have three " +#~ "bytes elements in the following order:" +#~ msgstr "" + +#~ msgid "CA certificate." +#~ msgstr "Certificats" + +#~ msgid "server certificate." +#~ msgstr "Certificats" + +#~ msgid "server private key." +#~ msgstr "stratégie.du.serveur" + +#~ msgid "**hist** -- Object containing training and evaluation metrics." +#~ msgstr "" + +#~ msgid "Starting an insecure server:" +#~ msgstr "Démarrer le serveur" + +#~ msgid "Starting an SSL-enabled server:" +#~ msgstr "Démarrer le serveur" + +#~ msgid "Contains the strategy abstraction and different implementations." +#~ msgstr "" + +#~ msgid "Abstract base class for server strategy implementations." +#~ msgstr "" + +#~ msgid "The current round of federated learning." +#~ msgstr "Qu'est-ce que l'apprentissage fédéré ?" + +#~ msgid "" +#~ "Successful updates from the previously " +#~ "selected and configured clients. Each " +#~ "pair of `(ClientProxy, FitRes` constitutes " +#~ "a successful update from one of " +#~ "the previously selected clients. Not " +#~ "that not all previously selected clients" +#~ " are necessarily included in this " +#~ "list: a client might drop out and" +#~ " not submit a result. For each " +#~ "client that did not submit an " +#~ "update, there should be an `Exception`" +#~ " in `failures`." +#~ msgstr "" + +#~ msgid "" +#~ "Exceptions that occurred while the " +#~ "server was waiting for client updates." +#~ msgstr "" + +#~ msgid "" +#~ "**aggregation_result** -- The aggregated " +#~ "evaluation result. Aggregation typically uses" +#~ " some variant of a weighted average." +#~ msgstr "" + +#~ msgid "Aggregate training results." +#~ msgstr "Résultats globaux de l'évaluation." + +#~ msgid "" +#~ "Successful updates from the previously " +#~ "selected and configured clients. Each " +#~ "pair of `(ClientProxy, FitRes)` constitutes" +#~ " a successful update from one of " +#~ "the previously selected clients. Not " +#~ "that not all previously selected clients" +#~ " are necessarily included in this " +#~ "list: a client might drop out and" +#~ " not submit a result. For each " +#~ "client that did not submit an " +#~ "update, there should be an `Exception`" +#~ " in `failures`." +#~ msgstr "" + +#~ msgid "" +#~ "**parameters** -- If parameters are " +#~ "returned, then the server will treat " +#~ "these as the new global model " +#~ "parameters (i.e., it will replace the" +#~ " previous parameters with the ones " +#~ "returned from this method). If `None`" +#~ " is returned (e.g., because there " +#~ "were only failures and no viable " +#~ "results) then the server will no " +#~ "update the previous model parameters, " +#~ "the updates received in this round " +#~ "are discarded, and the global model " +#~ "parameters remain the same." +#~ msgstr "" + +#~ msgid "Configure the next round of evaluation." +#~ msgstr "Configuration de l'évaluation côté serveur" + +#~ msgid "The client manager which holds all currently connected clients." +#~ msgstr "" + +#~ msgid "" +#~ "**evaluate_configuration** -- A list of " +#~ "tuples. Each tuple in the list " +#~ "identifies a `ClientProxy` and the " +#~ "`EvaluateIns` for this particular " +#~ "`ClientProxy`. If a particular `ClientProxy`" +#~ " is not included in this list, " +#~ "it means that this `ClientProxy` will" +#~ " not participate in the next round" +#~ " of federated evaluation." +#~ msgstr "" + +#~ msgid "Configure the next round of training." +#~ msgstr "" + +#~ msgid "" +#~ "**fit_configuration** -- A list of " +#~ "tuples. Each tuple in the list " +#~ "identifies a `ClientProxy` and the " +#~ "`FitIns` for this particular `ClientProxy`." +#~ " If a particular `ClientProxy` is not" +#~ " included in this list, it means " +#~ "that this `ClientProxy` will not " +#~ "participate in the next round of " +#~ "federated learning." +#~ msgstr "" + +#~ msgid "Evaluate the current model parameters." +#~ msgstr "``get_parameters`` : renvoie les paramètres du modèle local actuel" + +#~ msgid "" +#~ "This function can be used to " +#~ "perform centralized (i.e., server-side) " +#~ "evaluation of model parameters." +#~ msgstr "" + +#~ msgid "" +#~ "**evaluation_result** -- The evaluation " +#~ "result, usually a Tuple containing loss" +#~ " and a dictionary containing task-" +#~ "specific metrics (e.g., accuracy)." +#~ msgstr "" + +#~ msgid "Initialize the (global) model parameters." +#~ msgstr "Initialise le modèle global" + +#~ msgid "" +#~ "**parameters** -- If parameters are " +#~ "returned, then the server will treat " +#~ "these as the initial global model " +#~ "parameters." +#~ msgstr "" + +#~ msgid "Configurable FedAvg strategy implementation." +#~ msgstr "Configuration de l'évaluation fédérée" + +#~ msgid "Implementation based on https://arxiv.org/abs/1602.05629" +#~ msgstr "" + +#~ msgid "" +#~ "Fraction of clients used during " +#~ "training. In case `min_fit_clients` is " +#~ "larger than `fraction_fit * " +#~ "available_clients`, `min_fit_clients` will still " +#~ "be sampled. Defaults to 1.0." +#~ msgstr "" + +#~ msgid "" +#~ "Fraction of clients used during " +#~ "validation. In case `min_evaluate_clients` is" +#~ " larger than `fraction_evaluate * " +#~ "available_clients`, `min_evaluate_clients` will " +#~ "still be sampled. Defaults to 1.0." +#~ msgstr "" + +#~ msgid "Minimum number of clients used during training. Defaults to 2." +#~ msgstr "" + +#~ msgid "Minimum number of clients used during validation. Defaults to 2." +#~ msgstr "" + +#~ msgid "Minimum number of total clients in the system. Defaults to 2." +#~ msgstr "" + +#~ msgid "Optional function used for validation. Defaults to None." +#~ msgstr "" + +#~ msgid "Function used to configure training. Defaults to None." +#~ msgstr "" + +#~ msgid "Function used to configure validation. Defaults to None." +#~ msgstr "" + +#~ msgid "Whether or not accept rounds containing failures. Defaults to True." +#~ msgstr "" + +#~ msgid "Initial global model parameters." +#~ msgstr "Initialise le modèle global" + +#~ msgid "Metrics aggregation function, optional." +#~ msgstr "" + +#~ msgid "Aggregate evaluation losses using weighted average." +#~ msgstr "Résultats globaux de l'évaluation." + +#~ msgid "Aggregate fit results using weighted average." +#~ msgstr "" + +#~ msgid "Evaluate model parameters using an evaluation function." +#~ msgstr "" + +#~ msgid "Initialize global model parameters." +#~ msgstr "Initialise le modèle global" + +#~ msgid "Use a fraction of available clients for evaluation." +#~ msgstr "" + +#~ msgid "Return the sample size and the required number of available clients." +#~ msgstr "" + +#~ msgid "Configurable FedAvg with Momentum strategy implementation." +#~ msgstr "" + +#~ msgid "Federated Averaging with Momentum strategy." +#~ msgstr "Stratégie de moyenne fédérée." + +#~ msgid "Implementation based on https://arxiv.org/pdf/1909.06335.pdf" +#~ msgstr "" + +#~ msgid "Fraction of clients used during training. Defaults to 0.1." +#~ msgstr "" -#~ msgid "Flower client." -#~ msgstr "Client de Flower" +#~ msgid "Fraction of clients used during validation. Defaults to 0.1." +#~ msgstr "" -#~ msgid "Abstract base class for Flower clients." +#~ msgid "" +#~ "Server-side learning rate used in " +#~ "server-side optimization. Defaults to 1.0." #~ msgstr "" -#~ msgid "Evaluate the provided parameters using the locally held dataset." -#~ msgstr "évaluer le modèle mis à jour sur l'ensemble de test local" +#~ msgid "Server-side momentum factor used for FedAvgM. Defaults to 0.0." +#~ msgstr "" -#~ msgid "Parameters" -#~ msgstr "Paramètres du modèle." +#~ msgid "Configurable QFedAvg strategy implementation." +#~ msgstr "" -#~ msgid "" -#~ "The evaluation instructions containing " -#~ "(global) model parameters received from " -#~ "the server and a dictionary of " -#~ "configuration values used to customize " -#~ "the local evaluation process." +#~ msgid "Configurable fault-tolerant FedAvg strategy implementation." #~ msgstr "" -#~ msgid "Returns" -#~ msgstr "Ressources" +#~ msgid "Configurable FedAdagrad strategy implementation." +#~ msgstr "" -#~ msgid "" -#~ "The evaluation result containing the " -#~ "loss on the local dataset and " -#~ "other details such as the number " -#~ "of local data examples used for " -#~ "evaluation." +#~ msgid "Federated Optim strategy interface." #~ msgstr "" -#~ msgid "Return type" +#~ msgid "Implementation based on https://arxiv.org/abs/2003.00295v5" #~ msgstr "" +#~ "FedYogi - Stratégie d'apprentissage fédéré " +#~ "utilisant Yogi côté serveur. Mise en " +#~ "oeuvre basée sur https://arxiv.org/abs/2003.00295" -#~ msgid "Refine the provided parameters using the locally held dataset." +#~ msgid "Fraction of clients used during training. Defaults to 1.0." #~ msgstr "" -#~ msgid "" -#~ "The training instructions containing (global)" -#~ " model parameters received from the " -#~ "server and a dictionary of configuration" -#~ " values used to customize the local" -#~ " training process." +#~ msgid "Fraction of clients used during validation. Defaults to 1.0." #~ msgstr "" -#~ msgid "" -#~ "The training result containing updated " -#~ "parameters and other details such as " -#~ "the number of local training examples" -#~ " used for training." +#~ msgid "Server-side learning rate. Defaults to 1e-1." #~ msgstr "" -#~ msgid "Return the current local model parameters." -#~ msgstr "``get_parameters`` : renvoie les paramètres du modèle local actuel" +#~ msgid "Client-side learning rate. Defaults to 1e-1." +#~ msgstr "" -#~ msgid "" -#~ "The get parameters instructions received " -#~ "from the server containing a dictionary" -#~ " of configuration values." +#~ msgid "Momentum parameter. Defaults to 0.0." #~ msgstr "" -#~ msgid "The current local model parameters." -#~ msgstr "``get_parameters`` : renvoie les paramètres du modèle local actuel" +#~ msgid "Second moment parameter. Defaults to 0.0." +#~ msgstr "" -#~ msgid "Return set of client's properties." +#~ msgid "Controls the algorithm's degree of adaptability. Defaults to 1e-9." #~ msgstr "" -#~ msgid "" -#~ "The get properties instructions received " -#~ "from the server containing a dictionary" -#~ " of configuration values." +#~ msgid "Configurable FedProx strategy implementation." #~ msgstr "" -#~ msgid "The current client properties." +#~ msgid "Federated Optimization strategy." +#~ msgstr "Stratégie de moyenne fédérée." + +#~ msgid "Implementation based on https://arxiv.org/abs/1812.06127" #~ msgstr "" -#~ msgid "Start a Flower client node which connects to a Flower server." +#~ msgid "" +#~ "The strategy in itself will not be" +#~ " different than FedAvg, the client " +#~ "needs to be adjusted. A proximal " +#~ "term needs to be added to the " +#~ "loss function during the training:" #~ msgstr "" #~ msgid "" -#~ "The IPv4 or IPv6 address of the" -#~ " server. If the Flower server runs" -#~ " on the same machine on port " -#~ "8080, then `server_address` would be " -#~ "`\"[::]:8080\"`." +#~ "\\\\frac{\\\\mu}{2} || w - w^t ||^2\n" +#~ "\n" +#~ msgstr "\\\\frac{\\Nmu}{2} || w - w^t ||^2" + +#~ msgid "" +#~ "Where $w^t$ are the global parameters" +#~ " and $w$ are the local weights " +#~ "the function will be optimized with." #~ msgstr "" -#~ msgid "An implementation of the abstract base class `flwr.client.Client`." +#~ msgid "In PyTorch, for example, the loss would go from:" +#~ msgstr "" + +#~ msgid "To:" #~ msgstr "" #~ msgid "" -#~ "The maximum length of gRPC messages " -#~ "that can be exchanged with the " -#~ "Flower server. The default should be " -#~ "sufficient for most models. Users who" -#~ " train very large models might need" -#~ " to increase this value. Note that" -#~ " the Flower server needs to be " -#~ "started with the same value (see " -#~ "`flwr.server.start_server`), otherwise it will " -#~ "not know about the increased limit " -#~ "and block larger messages." +#~ "With `global_params` being a copy of " +#~ "the parameters before the training takes" +#~ " place." #~ msgstr "" #~ msgid "" -#~ "The PEM-encoded root certificates as " -#~ "a byte string or a path string." -#~ " If provided, a secure connection " -#~ "using the certificates will be " -#~ "established to an SSL-enabled Flower " -#~ "server." +#~ "The weight of the proximal term " +#~ "used in the optimization. 0.0 makes " +#~ "this strategy equivalent to FedAvg, and" +#~ " the higher the coefficient, the more" +#~ " regularization will be used (that " +#~ "is, the client parameters will need " +#~ "to be closer to the server " +#~ "parameters during training)." +#~ msgstr "" + +#~ msgid "Sends the proximal factor mu to the clients" +#~ msgstr "" + +#~ msgid "FedAdagrad strategy - Adaptive Federated Optimization using Adagrad." +#~ msgstr "" +#~ "Stratégie FedAdagrad - Optimisation fédérée" +#~ " adaptative à l'aide d'Adagrad." + +#~ msgid "Paper: https://arxiv.org/abs/2003.00295" #~ msgstr "" +#~ msgid "Federated learning strategy using Adagrad on server-side." +#~ msgstr "" +#~ "Construisons un système d'apprentissage fédéré" +#~ " en utilisant fastai et Flower !" + +#~ msgid "FedAdam - Adaptive Federated Optimization using Adam." +#~ msgstr "FedAdam - Optimisation fédérée adaptative utilisant Adam." + +#~ msgid "Momentum parameter. Defaults to 0.9." +#~ msgstr "" + +#~ msgid "Second moment parameter. Defaults to 0.99." +#~ msgstr "" + +#~ msgid "FedYogi [Reddi et al., 2020] strategy." +#~ msgstr "Stratégie FedYogi [Reddi et al., 2020]." + +#~ msgid "Adaptive Federated Optimization using Yogi." +#~ msgstr "Optimisation fédérée adaptative à l'aide de Yogi." + +#~ msgid "Federated learning strategy using Yogi on server-side." +#~ msgstr "L'apprentissage fédéré en cinq étapes" + +#~ msgid "Differential Privacy Wrappers in Flower" +#~ msgstr "Les enveloppes différentielles de confidentialité dans les fleurs" + +#~ msgid "Evaluation" +#~ msgstr "Solution" + +#~ msgid "Code examples" +#~ msgstr "Exemple de code complet" + #~ msgid "" -#~ "DEPRECATED - USE 'transport' INSTEAD. " -#~ "Defines whether or not the client " -#~ "is interacting with the server using " -#~ "the experimental REST API. This feature" -#~ " is experimental, it might change " -#~ "considerably in future versions of " -#~ "Flower." +#~ "Flower Quickstart (PyTorch): coming soon " +#~ "(the TensorFlow/Keras example can easily " +#~ "be changed to make it work with" +#~ " PyTorch)" #~ msgstr "" -#~ "DÉPRÉCIÉ - UTILISER 'transport' À LA " -#~ "PLACE Définit si le client interagit " -#~ "ou non avec le serveur à l'aide" -#~ " de l'API REST expérimentale. Cette " -#~ "fonctionnalité est expérimentale, elle " -#~ "pourrait changer considérablement dans les " -#~ "futures versions de Flower." -#~ msgid "" -#~ "Configure the transport layer. Allowed " -#~ "values: - 'grpc-bidi': gRPC, " -#~ "bidirectional streaming - 'grpc-rere': " -#~ "gRPC, request-response (experimental) - " -#~ "'rest': HTTP (experimental)" -#~ msgstr "" -#~ "Valeurs autorisées : - 'grpc-bidi' " -#~ ": gRPC, flux bidirectionnel - 'grpc-" -#~ "rere' : gRPC, requête-réponse " -#~ "(expérimental) - 'rest' : HTTP " -#~ "(expérimental)" +#~ msgid "First time contributors" +#~ msgstr "Bonnes premières contributions" + +#~ msgid "First MXNet 1.6 example (MNIST)" +#~ msgstr "" + +#~ msgid "ImageNet (PyTorch/TensorFlow)" +#~ msgstr "" + +#~ msgid "LSTM (PyTorch/TensorFlow)" +#~ msgstr "" + +#~ msgid "Transformer (PyTorch/TensorFlow)" +#~ msgstr "" + +#~ msgid "BERT (PyTorch/TensorFlow)" +#~ msgstr "" + +#~ msgid "Logging" +#~ msgstr "Enregistrement" + +#~ msgid "|cce04c6f539b421a91f5dba40287193f|" +#~ msgstr "|cce04c6f539b421a91f5dba40287193f|" + +#~ msgid "|e392aef42ba248e19e35446f95a6d1ca|" +#~ msgstr "|e392aef42ba248e19e35446f95a6d1ca|" + +#~ msgid "|7e028f44defe4f31a02debc729f2010d|" +#~ msgstr "|7e028f44defe4f31a02debc729f2010d|" + +#~ msgid "|b89f7b7ae05e4ecd92baa69b7a9fe1be|" +#~ msgstr "|b89f7b7ae05e4ecd92baa69b7a9fe1be|" + +#~ msgid "|9c0445ce962744e1a1c0a4abc697a334|" +#~ msgstr "|9c0445ce962744e1a1c0a4abc697a334|" + +#~ msgid "|a3246766a6db412888131b3bcdad0971|" +#~ msgstr "|a3246766a6db412888131b3bcdad0971|" + +#~ msgid "|db6f2bee32f143b8a5085b6a8ce1acd1|" +#~ msgstr "|db6f2bee32f143b8a5085b6a8ce1acd1|" + +#~ msgid "|405653bc8f874e9595fd59cc82b3d48c|" +#~ msgstr "|405653bc8f874e9595fd59cc82b3d48c|" + +#~ msgid "|073a728154ed406e8fe54e1d9f18dcb9|" +#~ msgstr "|073a728154ed406e8fe54e1d9f18dcb9|" + +#~ msgid "|50e80ea4f22945848b65ed7eed35e0e1|" +#~ msgstr "|50e80ea4f22945848b65ed7eed35e0e1|" + +#~ msgid "|f3cf9148d85e4b68b66b6c255b25e327|" +#~ msgstr "|f3cf9148d85e4b68b66b6c255b25e327|" + +#~ msgid "|1fedb4f8714947e1b13f03696180c741|" +#~ msgstr "|1fedb4f8714947e1b13f03696180c741|" + +#~ msgid "|a32d4ad1ccb34461942d75c7b2b51d65|" +#~ msgstr "|a32d4ad1ccb34461942d75c7b2b51d65|" + +#~ msgid "|3531696c52904cd3b9944034ab959d48|" +#~ msgstr "|3531696c52904cd3b9944034ab959d48|" + +#~ msgid "An Introduction to Federated Learning" +#~ msgstr "Mise à l'échelle de l'apprentissage fédéré" -#~ msgid "Starting a gRPC client with an insecure server connection:" -#~ msgstr "" +#~ msgid "Strategies in Federated Learning" +#~ msgstr "Mise à l'échelle de l'apprentissage fédéré" -#~ msgid "Starting an SSL-enabled gRPC client:" -#~ msgstr "" +#~ msgid "Building a Strategy" +#~ msgstr "Stratégies intégrées" -#~ msgid "Abstract base class for Flower clients using NumPy." -#~ msgstr "" +#~ msgid "Client and NumPyClient" +#~ msgstr "NumPyClient" -#~ msgid "The current (global) model parameters." -#~ msgstr "``get_parameters`` : renvoie les paramètres du modèle local actuel" +#~ msgid "Strategies" +#~ msgstr "Stratégies personnalisées" -#~ msgid "" -#~ "Configuration parameters which allow the " -#~ "server to influence evaluation on the" -#~ " client. It can be used to " -#~ "communicate arbitrary values from the " -#~ "server to the client, for example, " -#~ "to influence the number of examples " -#~ "used for evaluation." +#~ msgid "SSL-enabled Server and Client" #~ msgstr "" -#~ msgid "" -#~ "* **loss** (*float*) -- The evaluation" -#~ " loss of the model on the local" -#~ " dataset. * **num_examples** (*int*) -- " -#~ "The number of examples used for " -#~ "evaluation. * **metrics** (*Dict[str, " -#~ "Scalar]*) -- A dictionary mapping " -#~ "arbitrary string keys to values of " -#~ "type bool, bytes, float, int, or " -#~ "str. It can be used to " -#~ "communicate arbitrary values back to the" -#~ " server." -#~ msgstr "" +#~ msgid "About these documents" +#~ msgstr "À propos de ces documents" -#~ msgid "" -#~ "**loss** (*float*) -- The evaluation " -#~ "loss of the model on the local " -#~ "dataset." -#~ msgstr "" +#~ msgid "Index" +#~ msgstr "Index" -#~ msgid "**num_examples** (*int*) -- The number of examples used for evaluation." -#~ msgstr "" +#~ msgid "Search" +#~ msgstr "Recherche" -#~ msgid "" -#~ "**metrics** (*Dict[str, Scalar]*) -- A " -#~ "dictionary mapping arbitrary string keys " -#~ "to values of type bool, bytes, " -#~ "float, int, or str. It can be " -#~ "used to communicate arbitrary values " -#~ "back to the server." +#~ msgid "Copyright" +#~ msgstr "Droits d'auteur" + +#~ msgid "Save Progress" #~ msgstr "" #~ msgid "" -#~ "The previous return type format (int," -#~ " float, float) and the extended " -#~ "format (int, float, float, Dict[str, " -#~ "Scalar]) have been deprecated and " -#~ "removed since Flower 0.19." +#~ "The Flower server does not prescribe " +#~ "a way to persist model updates or" +#~ " evaluation results. Flower does not " +#~ "(yet) automatically save model updates " +#~ "on the server-side. It's on the" +#~ " roadmap to provide a built-in " +#~ "way of doing this." #~ msgstr "" -#~ msgid "Train the provided parameters using the locally held dataset." -#~ msgstr "entraîne le modèle sur l'ensemble d'apprentissage local" +#~ msgid "Release Process" +#~ msgstr "Publier Flower" -#~ msgid "" -#~ "Configuration parameters which allow the " -#~ "server to influence training on the " -#~ "client. It can be used to " -#~ "communicate arbitrary values from the " -#~ "server to the client, for example, " -#~ "to set the number of (local) " -#~ "training epochs." -#~ msgstr "" +#~ msgid "Virtual Env Installation" +#~ msgstr "Virtualenv avec Anaconda" + +#~ msgid "Install development versions" +#~ msgstr "Installer les versions de développement de Flower" + +#~ msgid "Set up a virtual env" +#~ msgstr "Mettre en place un environment virtuel" #~ msgid "" -#~ "* **parameters** (*NDArrays*) -- The " -#~ "locally updated model parameters. * " -#~ "**num_examples** (*int*) -- The number " -#~ "of examples used for training. * " -#~ "**metrics** (*Dict[str, Scalar]*) -- A " -#~ "dictionary mapping arbitrary string keys " -#~ "to values of type bool, bytes, " -#~ "float, int, or str. It can be " -#~ "used to communicate arbitrary values " -#~ "back to the server." +#~ "Note that, in order to build the" +#~ " documentation locally (with ``poetry run" +#~ " make html``, like described below), " +#~ "`Pandoc _` needs " +#~ "to be installed on the system." #~ msgstr "" +#~ "Notez que, pour construire la " +#~ "documentation localement (avec ``poetry run" +#~ " make html``, comme décrit ci-" +#~ "dessous), ``Pandoc _`" +#~ " doit être installé sur le système." -#~ msgid "**parameters** (*NDArrays*) -- The locally updated model parameters." -#~ msgstr "``get_parameters`` : renvoie les paramètres du modèle local actuel" +#~ msgid "Llama 2 fine-tuning, with Hugging Face Transformers and PyTorch" +#~ msgstr "Un fine-tuning de LLaMA 2 avec Hugging Face et PyTorch" -#~ msgid "**num_examples** (*int*) -- The number of examples used for training." -#~ msgstr "" +#~ msgid "XGBoost" +#~ msgstr "XGBoost" -#~ msgid "" -#~ "Configuration parameters requested by the " -#~ "server. This can be used to tell" -#~ " the client which parameters are " -#~ "needed along with some Scalar " -#~ "attributes." +#~ msgid "Android ONNX on-device training" #~ msgstr "" +#~ "Utiliser Android ONNX pour faire du " +#~ "training directement sur le téléphone" -#~ msgid "" -#~ "**parameters** -- The local model " -#~ "parameters as a list of NumPy " -#~ "ndarrays." -#~ msgstr "renvoie le poids du modèle sous la forme d'une liste de ndarrays NumPy" +#~ msgid "Contribute on GitHub" +#~ msgstr "Contribuer sur GitHub" -#~ msgid "Return a client's set of properties." -#~ msgstr "Renvoie l'ensemble des propriétés d'un client." +#~ msgid "How to write a good PR title" +#~ msgstr "Comment écrire un bon titre de PR" #~ msgid "" -#~ "Configuration parameters requested by the " -#~ "server. This can be used to tell" -#~ " the client which properties are " -#~ "needed along with some Scalar " -#~ "attributes." +#~ "A well-crafted PR title helps team" +#~ " members quickly understand the purpose " +#~ "and scope of the changes being " +#~ "proposed. Here's a guide to help " +#~ "you write a good GitHub PR title:" #~ msgstr "" +#~ "Un titre de PR bien choisi permet" +#~ " aux autres développeurs de rapidement " +#~ "comprendre l'intérêt et le scope des " +#~ "changements proposés. Voici un guide " +#~ "pour vous aider à écrire des bons" +#~ " titres de PR :" #~ msgid "" -#~ "**properties** -- A dictionary mapping " -#~ "arbitrary string keys to values of " -#~ "type bool, bytes, float, int, or " -#~ "str. It can be used to communicate" -#~ " arbitrary property values back to " -#~ "the server." +#~ "1. Be Clear and Concise: Provide a" +#~ " clear summary of the changes in " +#~ "a concise manner. 1. Use Actionable " +#~ "Verbs: Start with verbs like \"Add,\"" +#~ " \"Update,\" or \"Fix\" to indicate " +#~ "the purpose. 1. Include Relevant " +#~ "Information: Mention the affected feature " +#~ "or module for context. 1. Keep it" +#~ " Short: Avoid lengthy titles for easy" +#~ " readability. 1. Use Proper Capitalization" +#~ " and Punctuation: Follow grammar rules " +#~ "for clarity." #~ msgstr "" +#~ "1. Soyez clair et concis : Donnez" +#~ " un résumé clair des changements de" +#~ " manière concise. 1. Utilisez des " +#~ "verbes actionnables : Commencez par des" +#~ " verbes comme \"Add\", \"Update\", ou " +#~ "\"Fix\" pour indiquer le but. 1. " +#~ "Inclure des renseignements pertinents : " +#~ "Mentionner la caractéristique ou le " +#~ "module concerné pour le contexte. 1. " +#~ "Gardez le court : Évitez les longs" +#~ " titres pour une lisibilité facile. " +#~ "1. Utiliser une bonne capitalisation et" +#~ " une ponctuation : Suivre les règles" +#~ " de grammaire pour la clarté." -#~ msgid "Start a Flower NumPyClient which connects to a gRPC server." +#~ msgid "" +#~ "Let's start with a few examples " +#~ "for titles that should be avoided " +#~ "because they do not provide meaningful" +#~ " information:" #~ msgstr "" +#~ "Commençons par quelques exemples de " +#~ "titres qui devraient être évités parce" +#~ " qu'ils ne fournissent pas d'information" +#~ " significative :" -#~ msgid "An implementation of the abstract base class `flwr.client.NumPyClient`." -#~ msgstr "" +#~ msgid "Implement Algorithm" +#~ msgstr "Implement Algorithm" -#~ msgid "Starting a client with an insecure server connection:" -#~ msgstr "" +#~ msgid "Database" +#~ msgstr "Base de données" -#~ msgid "Starting a SSL-enabled client:" -#~ msgstr "" +#~ msgid "Add my_new_file.py to codebase" +#~ msgstr "Add my_new_file.py to codebase" -#~ msgid "Start a Ray-based Flower simulation server." -#~ msgstr "Simulation de moniteur" +#~ msgid "Improve code in module" +#~ msgstr "Improve code in module" + +#~ msgid "Change SomeModule" +#~ msgstr "Change SomeModule" #~ msgid "" -#~ "A function creating client instances. " -#~ "The function must take a single " -#~ "`str` argument called `cid`. It should" -#~ " return a single client instance of" -#~ " type ClientLike. Note that the " -#~ "created client instances are ephemeral " -#~ "and will often be destroyed after " -#~ "a single method invocation. Since client" -#~ " instances are not long-lived, they" -#~ " should not attempt to carry state" -#~ " over method invocations. Any state " -#~ "required by the instance (model, " -#~ "dataset, hyperparameters, ...) should be " -#~ "(re-)created in either the call to " -#~ "`client_fn` or the call to any of" -#~ " the client methods (e.g., load " -#~ "evaluation data in the `evaluate` method" -#~ " itself)." +#~ "Here are a few positive examples " +#~ "which provide helpful information without " +#~ "repeating how they do it, as that" +#~ " is already visible in the \"Files" +#~ " changed\" section of the PR:" #~ msgstr "" -#~ "Une fonction créant des instances de " -#~ "client. La fonction doit prendre un " -#~ "seul argument `str` appelé `cid`. Elle" -#~ " doit retourner une seule instance de" -#~ " client de type ClientLike. Notez que" -#~ " les instances de client créées sont" -#~ " éphémères et seront souvent détruites " -#~ "après une seule invocation de méthode." -#~ " Puisque les instances de client ne" -#~ " sont pas de longue durée, elles " -#~ "ne doivent pas essayer de transporter" -#~ " l'état sur les invocations de " -#~ "méthode. Tout état requis par l'instance" -#~ " (modèle, jeu de données, hyperparamètres," -#~ " ...) doit être (re)créé dans l'appel" -#~ " à `client_fn` ou dans l'appel à " -#~ "n'importe quelle méthode de client (par" -#~ " exemple, charger les données d'évaluation" -#~ " dans la méthode `evaluate` elle-" -#~ "même)." +#~ "Voici quelques bons exemples qui " +#~ "fournissent de l'information utile sans " +#~ "répéter comment ils le font, comme " +#~ "cela est déjà visible dans la " +#~ "section \"Files changed\" de la PR " +#~ ":" + +#~ msgid "Update docs banner to mention Flower Summit 2023" +#~ msgstr "Update docs banner to mention Flower Summit 2023" + +#~ msgid "Remove unnecessary XGBoost dependency" +#~ msgstr "Remove unnecessary XGBoost dependency" + +#~ msgid "Remove redundant attributes in strategies subclassing FedAvg" +#~ msgstr "Remove redundant attributes in strategies subclassing FedAvg" + +#~ msgid "Add CI job to deploy the staging system when the `main` branch changes" +#~ msgstr "" +#~ "Ajoute une tâche CI pour déployer " +#~ "le système de mise en scène " +#~ "lorsque la branche `main` change" #~ msgid "" -#~ "The total number of clients in " -#~ "this simulation. This must be set " -#~ "if `clients_ids` is not set and " -#~ "vice-versa." +#~ "Add new amazing library which will " +#~ "be used to improve the simulation " +#~ "engine" #~ msgstr "" +#~ "Add new amazing library which will " +#~ "be used to improve the simulation " +#~ "engine" + +#~ msgid "Differential privacy" +#~ msgstr "Confidentialité différentielle" #~ msgid "" -#~ "List `client_id`s for each client. This" -#~ " is only required if `num_clients` is" -#~ " not set. Setting both `num_clients` " -#~ "and `clients_ids` with `len(clients_ids)` not" -#~ " equal to `num_clients` generates an " -#~ "error." +#~ "The Flower server does not prescribe " +#~ "a way to aggregate evaluation results," +#~ " but it enables the user to " +#~ "fully customize result aggregation." #~ msgstr "" +#~ msgid "Configure logging" +#~ msgstr "Configurer les clients" + #~ msgid "" -#~ "CPU and GPU resources for a single" -#~ " client. Supported keys are `num_cpus` " -#~ "and `num_gpus`. Example: `{\"num_cpus\": 4," -#~ " \"num_gpus\": 1}`. To understand the " -#~ "GPU utilization caused by `num_gpus`, " -#~ "consult the Ray documentation on GPU " -#~ "support." +#~ "The Flower logger keeps track of " +#~ "all core events that take place in" +#~ " federated learning workloads. It presents" +#~ " information by default following a " +#~ "standard message format:" #~ msgstr "" +#~ "L'enregistreur de Flower garde la trace" +#~ " de tous les événements principaux " +#~ "qui ont lieu dans les charges de" +#~ " travail de l'apprentissage fédéré. Il " +#~ "présente les informations par défaut en" +#~ " suivant un format de message " +#~ "standard :" #~ msgid "" -#~ "An implementation of the abstract base" -#~ " class `flwr.server.Server`. If no instance" -#~ " is provided, then `start_server` will " -#~ "create one." +#~ "containing relevant information including: log" +#~ " message level (e.g. :code:`INFO`, " +#~ ":code:`DEBUG`), a timestamp, the line " +#~ "where the logging took place from, " +#~ "as well as the log message itself." +#~ " In this way, the logger would " +#~ "typically display information on your " +#~ "terminal as follows:" #~ msgstr "" +#~ "contenant des informations pertinentes, " +#~ "notamment : le niveau du message " +#~ "de journal (par exemple :code:`INFO`, " +#~ ":code:`DEBUG`), un horodatage, la ligne " +#~ "à partir de laquelle l'enregistrement a" +#~ " eu lieu, ainsi que le message " +#~ "de journal lui-même. De cette " +#~ "façon, le logger afficherait typiquement " +#~ "des informations sur ton terminal comme" +#~ " suit :" + +#~ msgid "Saving log to file" +#~ msgstr "Enregistrement du journal dans un fichier" #~ msgid "" -#~ "Currently supported values are `num_rounds`" -#~ " (int, default: 1) and `round_timeout` " -#~ "in seconds (float, default: None)." +#~ "By default, the Flower log is " +#~ "outputted to the terminal where you " +#~ "launch your Federated Learning workload " +#~ "from. This applies for both gRPC-" +#~ "based federation (i.e. when you do " +#~ ":code:`fl.server.start_server`) and when using " +#~ "the :code:`VirtualClientEngine` (i.e. when you" +#~ " do :code:`fl.simulation.start_simulation`). In " +#~ "some situations you might want to " +#~ "save this log to disk. You can " +#~ "do so by calling the " +#~ "`fl.common.logger.configure() " +#~ "`_" +#~ " function. For example:" #~ msgstr "" +#~ "Par défaut, le journal de Flower " +#~ "est affiché dans le terminal à " +#~ "partir duquel tu as lancé ta " +#~ "charge de travail d'apprentissage fédéré. " +#~ "Cela s'applique à la fois à la " +#~ "fédération basée sur gRPC (c'est-à-dire " +#~ "lorsque tu fais :code:`fl.server.start_server`) " +#~ "et à l'utilisation du " +#~ ":code:`VirtualClientEngine` (c'est-à-dire lorsque tu" +#~ " fais :code:`fl.simulation.start_simulation`). Dans " +#~ "certaines situations, tu peux vouloir " +#~ "sauvegarder ce journal sur le disque." +#~ " Tu peux le faire en appelant " +#~ "la fonction `fl.common.logger.configure() " +#~ "`_." +#~ " Par exemple :" #~ msgid "" -#~ "An implementation of the abstract base" -#~ " class `flwr.server.Strategy`. If no " -#~ "strategy is provided, then `start_server` " -#~ "will use `flwr.server.strategy.FedAvg`." +#~ "With the above, Flower will record " +#~ "the log you see on your terminal" +#~ " to :code:`log.txt`. This file will " +#~ "be created in the same directory " +#~ "as were you are running the code" +#~ " from. If we inspect we see the" +#~ " log above is also recorded but " +#~ "prefixing with :code:`identifier` each line:" #~ msgstr "" +#~ "Avec ce qui précède, Flower enregistrera" +#~ " le journal que tu vois sur ton" +#~ " terminal dans :code:`log.txt`. Ce fichier" +#~ " sera créé dans le même répertoire" +#~ " que celui à partir duquel tu " +#~ "exécutes le code. Si nous inspectons," +#~ " nous voyons que le journal ci-" +#~ "dessus est également enregistré, mais en" +#~ " préfixant chaque ligne avec " +#~ ":code:`identifier` :" + +#~ msgid "Log your own messages" +#~ msgstr "Enregistrer tes propres messages" #~ msgid "" -#~ "An implementation of the abstract base" -#~ " class `flwr.server.ClientManager`. If no " -#~ "implementation is provided, then " -#~ "`start_simulation` will use " -#~ "`flwr.server.client_manager.SimpleClientManager`." +#~ "You might expand the information shown" +#~ " by default with the Flower logger" +#~ " by adding more messages relevant to" +#~ " your application. You can achieve " +#~ "this easily as follows." #~ msgstr "" +#~ "Tu peux élargir les informations " +#~ "affichées par défaut avec le logger " +#~ "Flower en ajoutant d'autres messages " +#~ "pertinents pour ton application. Tu peux" +#~ " y parvenir facilement en procédant " +#~ "comme suit." #~ msgid "" -#~ "Optional dictionary containing arguments for" -#~ " the call to `ray.init`. If " -#~ "ray_init_args is None (the default), Ray" -#~ " will be initialized with the " -#~ "following default args: { " -#~ "\"ignore_reinit_error\": True, \"include_dashboard\": " -#~ "False } An empty dictionary can " -#~ "be used (ray_init_args={}) to prevent " -#~ "any arguments from being passed to " -#~ "ray.init." +#~ "In this way your logger will show," +#~ " in addition to the default messages," +#~ " the ones introduced by the clients" +#~ " as specified above." #~ msgstr "" +#~ "De cette façon, ton logger affichera," +#~ " en plus des messages par défaut, " +#~ "ceux introduits par les clients comme" +#~ " spécifié ci-dessus." + +#~ msgid "Log to a remote service" +#~ msgstr "Se connecter à un service distant" #~ msgid "" -#~ "Optional dictionary containing arguments for" -#~ " the call to `ray.init`. If " -#~ "ray_init_args is None (the default), Ray" -#~ " will be initialized with the " -#~ "following default args:" +#~ "The :code:`fl.common.logger.configure` function, " +#~ "also allows specifying a host to " +#~ "which logs can be pushed (via " +#~ ":code:`POST`) through a native Python " +#~ ":code:`logging.handler.HTTPHandler`. This is a " +#~ "particularly useful feature in " +#~ ":code:`gRPC`-based Federated Learning workloads " +#~ "where otherwise gathering logs from all" +#~ " entities (i.e. the server and the" +#~ " clients) might be cumbersome. Note " +#~ "that in Flower simulation, the server" +#~ " automatically displays all logs. You " +#~ "can still specify a :code:`HTTPHandler` " +#~ "should you wish to backup or " +#~ "analyze the logs somewhere else." #~ msgstr "" +#~ "La fonction :code:`fl.common.logger.configure` " +#~ "permet également de spécifier un hôte" +#~ " vers lequel les journaux peuvent " +#~ "être envoyés (via :code:`POST`) par " +#~ "l'intermédiaire d'un :code:`logging.handler.HTTPHandler`" +#~ " natif de Python. Il s'agit d'une " +#~ "fonction particulièrement utile dans les " +#~ "charges de travail d'apprentissage fédéré " +#~ "basées sur :code:`gRPC` où la collecte" +#~ " des journaux de toutes les entités" +#~ " (c'est-à-dire le serveur et les " +#~ "clients) pourrait s'avérer fastidieuse. Notez" +#~ " que dans la simulation Flower, le" +#~ " serveur affiche automatiquement tous les" +#~ " journaux. Vous pouvez toujours spécifier" +#~ " un :code:`HTTPHandler` si vous souhaitez" +#~ " sauvegarder ou analyser les journaux " +#~ "à un autre endroit." -#~ msgid "{ \"ignore_reinit_error\": True, \"include_dashboard\": False }" +#~ msgid "Enable SSL connections" +#~ msgstr "Collecte centralisée des données" + +#~ msgid "Python version" +#~ msgstr "Version Python" + +#~ msgid "" +#~ "Flower requires at least `Python 3.7 " +#~ "`_, but `Python 3.8" +#~ " `_ or above is " +#~ "recommended." #~ msgstr "" +#~ "Flower nécessite `Python 3.7 " +#~ "`_ ou plus, nous " +#~ "recommandons `Python 3.8 " +#~ "`_." + +#~ msgid "Run simulations" +#~ msgstr "Simulation de moniteur" #~ msgid "" -#~ "An empty dictionary can be used " -#~ "(ray_init_args={}) to prevent any arguments" -#~ " from being passed to ray.init." +#~ "Simulating Federated Learning workloads is " +#~ "useful for a multitude of use-" +#~ "cases: you might want to run your" +#~ " workload on a large cohort of " +#~ "clients but without having to source," +#~ " configure and mange a large number" +#~ " of physical devices; you might want" +#~ " to run your FL workloads as " +#~ "fast as possible on the compute " +#~ "systems you have access to without " +#~ "having to go through a complex " +#~ "setup process; you might want to " +#~ "validate your algorithm on different " +#~ "scenarios at varying levels of data " +#~ "and system heterogeneity, client availability," +#~ " privacy budgets, etc. These are " +#~ "among some of the use-cases where" +#~ " simulating FL workloads makes sense. " +#~ "Flower can accommodate these scenarios " +#~ "by means of its `VirtualClientEngine " +#~ "`_ or VCE." #~ msgstr "" #~ msgid "" -#~ "Set to True to prevent `ray.shutdown()`" -#~ " in case `ray.is_initialized()=True`." -#~ msgstr "" - -#~ msgid "**hist** -- Object containing metrics from training." -#~ msgstr "" - -#~ msgid "Flower server." -#~ msgstr "Serveur de Flower" - -#~ msgid "Start a Flower server using the gRPC transport layer." -#~ msgstr "" - -#~ msgid "The IPv4 or IPv6 address of the server. Defaults to `\"[::]:8080\"`." +#~ "The :code:`VirtualClientEngine` schedules, launches" +#~ " and manages `virtual` clients. These " +#~ "clients are identical to `non-virtual`" +#~ " clients (i.e. the ones you launch" +#~ " via the command `flwr.client.start_client " +#~ "`_) in the" +#~ " sense that they can be configure " +#~ "by creating a class inheriting, for " +#~ "example, from `flwr.client.NumPyClient `_ and therefore" +#~ " behave in an identical way. In " +#~ "addition to that, clients managed by " +#~ "the :code:`VirtualClientEngine` are:" #~ msgstr "" #~ msgid "" -#~ "A server implementation, either " -#~ "`flwr.server.Server` or a subclass thereof." -#~ " If no instance is provided, then " -#~ "`start_server` will create one." +#~ "resource-aware: this means that each " +#~ "client gets assigned a portion of " +#~ "the compute and memory on your " +#~ "system. You as a user can control" +#~ " this at the beginning of the " +#~ "simulation and allows you to control " +#~ "the degree of parallelism of your " +#~ "Flower FL simulation. The fewer the " +#~ "resources per client, the more clients" +#~ " can run concurrently on the same " +#~ "hardware." #~ msgstr "" #~ msgid "" -#~ "An implementation of the abstract base" -#~ " class `flwr.server.strategy.Strategy`. If no " -#~ "strategy is provided, then `start_server` " -#~ "will use `flwr.server.strategy.FedAvg`." +#~ "self-managed: this means that you " +#~ "as a user do not need to " +#~ "launch clients manually, instead this " +#~ "gets delegated to :code:`VirtualClientEngine`'s " +#~ "internals." #~ msgstr "" #~ msgid "" -#~ "An implementation of the abstract base" -#~ " class `flwr.server.ClientManager`. If no " -#~ "implementation is provided, then " -#~ "`start_server` will use " -#~ "`flwr.server.client_manager.SimpleClientManager`." +#~ "ephemeral: this means that a client " +#~ "is only materialized when it is " +#~ "required in the FL process (e.g. " +#~ "to do `fit() `_). The object is" +#~ " destroyed afterwards, releasing the " +#~ "resources it was assigned and allowing" +#~ " in this way other clients to " +#~ "participate." #~ msgstr "" #~ msgid "" -#~ "The maximum length of gRPC messages " -#~ "that can be exchanged with the " -#~ "Flower clients. The default should be" -#~ " sufficient for most models. Users " -#~ "who train very large models might " -#~ "need to increase this value. Note " -#~ "that the Flower clients need to be" -#~ " started with the same value (see " -#~ "`flwr.client.start_client`), otherwise clients will" -#~ " not know about the increased limit" -#~ " and block larger messages." +#~ "The :code:`VirtualClientEngine` implements `virtual`" +#~ " clients using `Ray `_, " +#~ "an open-source framework for scalable" +#~ " Python workloads. In particular, Flower's" +#~ " :code:`VirtualClientEngine` makes use of " +#~ "`Actors `_ to spawn `virtual` clients" +#~ " and run their workload." #~ msgstr "" -#~ msgid "" -#~ "Tuple containing root certificate, server " -#~ "certificate, and private key to start" -#~ " a secure SSL-enabled server. The " -#~ "tuple is expected to have three " -#~ "bytes elements in the following order:" -#~ " * CA certificate. * server " -#~ "certificate. * server private key." +#~ msgid "Launch your Flower simulation" #~ msgstr "" #~ msgid "" -#~ "Tuple containing root certificate, server " -#~ "certificate, and private key to start" -#~ " a secure SSL-enabled server. The " -#~ "tuple is expected to have three " -#~ "bytes elements in the following order:" -#~ msgstr "" - -#~ msgid "CA certificate." -#~ msgstr "Certificats" - -#~ msgid "server certificate." -#~ msgstr "Certificats" - -#~ msgid "server private key." -#~ msgstr "stratégie.du.serveur" - -#~ msgid "**hist** -- Object containing training and evaluation metrics." +#~ "Running Flower simulations still require " +#~ "you to define your client class, a" +#~ " strategy, and utility functions to " +#~ "download and load (and potentially " +#~ "partition) your dataset. With that out" +#~ " of the way, launching your " +#~ "simulation is done with `start_simulation " +#~ "`_ " +#~ "and a minimal example looks as " +#~ "follows:" #~ msgstr "" -#~ msgid "Starting an insecure server:" -#~ msgstr "Démarrer le serveur" - -#~ msgid "Starting an SSL-enabled server:" -#~ msgstr "Démarrer le serveur" - -#~ msgid "Contains the strategy abstraction and different implementations." -#~ msgstr "" +#~ msgid "VirtualClientEngine resources" +#~ msgstr "Moteur de client virtuel" -#~ msgid "Abstract base class for server strategy implementations." +#~ msgid "" +#~ "By default the VCE has access to" +#~ " all system resources (i.e. all CPUs," +#~ " all GPUs, etc) since that is " +#~ "also the default behavior when starting" +#~ " Ray. However, in some settings you" +#~ " might want to limit how many " +#~ "of your system resources are used " +#~ "for simulation. You can do this " +#~ "via the :code:`ray_init_args` input argument" +#~ " to :code:`start_simulation` which the VCE" +#~ " internally passes to Ray's " +#~ ":code:`ray.init` command. For a complete " +#~ "list of settings you can configure " +#~ "check the `ray.init `_ " +#~ "documentation. Do not set " +#~ ":code:`ray_init_args` if you want the " +#~ "VCE to use all your system's CPUs" +#~ " and GPUs." #~ msgstr "" -#~ msgid "The current round of federated learning." -#~ msgstr "Qu'est-ce que l'apprentissage fédéré ?" - -#~ msgid "" -#~ "Successful updates from the previously " -#~ "selected and configured clients. Each " -#~ "pair of `(ClientProxy, FitRes` constitutes " -#~ "a successful update from one of " -#~ "the previously selected clients. Not " -#~ "that not all previously selected clients" -#~ " are necessarily included in this " -#~ "list: a client might drop out and" -#~ " not submit a result. For each " -#~ "client that did not submit an " -#~ "update, there should be an `Exception`" -#~ " in `failures`." +#~ msgid "Assigning client resources" #~ msgstr "" #~ msgid "" -#~ "Exceptions that occurred while the " -#~ "server was waiting for client updates." +#~ "By default the :code:`VirtualClientEngine` " +#~ "assigns a single CPU core (and " +#~ "nothing else) to each virtual client." +#~ " This means that if your system " +#~ "has 10 cores, that many virtual " +#~ "clients can be concurrently running." #~ msgstr "" #~ msgid "" -#~ "**aggregation_result** -- The aggregated " -#~ "evaluation result. Aggregation typically uses" -#~ " some variant of a weighted average." +#~ "More often than not, you would " +#~ "probably like to adjust the resources" +#~ " your clients get assigned based on" +#~ " the complexity (i.e. compute and " +#~ "memory footprint) of your FL workload." +#~ " You can do so when starting " +#~ "your simulation by setting the argument" +#~ " `client_resources` to `start_simulation `_. Two " +#~ "keys are internally used by Ray to" +#~ " schedule and spawn workloads (in our" +#~ " case Flower clients):" #~ msgstr "" -#~ msgid "Aggregate training results." -#~ msgstr "Résultats globaux de l'évaluation." - -#~ msgid "" -#~ "Successful updates from the previously " -#~ "selected and configured clients. Each " -#~ "pair of `(ClientProxy, FitRes)` constitutes" -#~ " a successful update from one of " -#~ "the previously selected clients. Not " -#~ "that not all previously selected clients" -#~ " are necessarily included in this " -#~ "list: a client might drop out and" -#~ " not submit a result. For each " -#~ "client that did not submit an " -#~ "update, there should be an `Exception`" -#~ " in `failures`." +#~ msgid ":code:`num_cpus` indicates the number of CPU cores a client would get." #~ msgstr "" #~ msgid "" -#~ "**parameters** -- If parameters are " -#~ "returned, then the server will treat " -#~ "these as the new global model " -#~ "parameters (i.e., it will replace the" -#~ " previous parameters with the ones " -#~ "returned from this method). If `None`" -#~ " is returned (e.g., because there " -#~ "were only failures and no viable " -#~ "results) then the server will no " -#~ "update the previous model parameters, " -#~ "the updates received in this round " -#~ "are discarded, and the global model " -#~ "parameters remain the same." +#~ ":code:`num_gpus` indicates the **ratio** of" +#~ " GPU memory a client gets assigned." #~ msgstr "" -#~ msgid "Configure the next round of evaluation." -#~ msgstr "Configuration de l'évaluation côté serveur" - -#~ msgid "The client manager which holds all currently connected clients." +#~ msgid "Let's see a few examples:" #~ msgstr "" #~ msgid "" -#~ "**evaluate_configuration** -- A list of " -#~ "tuples. Each tuple in the list " -#~ "identifies a `ClientProxy` and the " -#~ "`EvaluateIns` for this particular " -#~ "`ClientProxy`. If a particular `ClientProxy`" -#~ " is not included in this list, " -#~ "it means that this `ClientProxy` will" -#~ " not participate in the next round" -#~ " of federated evaluation." -#~ msgstr "" - -#~ msgid "Configure the next round of training." +#~ "While the :code:`client_resources` can be " +#~ "used to control the degree of " +#~ "concurrency in your FL simulation, this" +#~ " does not stop you from running " +#~ "dozens, hundreds or even thousands of" +#~ " clients in the same round and " +#~ "having orders of magnitude more " +#~ "`dormant` (i.e. not participating in a" +#~ " round) clients. Let's say you want" +#~ " to have 100 clients per round " +#~ "but your system can only accommodate " +#~ "8 clients concurrently. The " +#~ ":code:`VirtualClientEngine` will schedule 100 " +#~ "jobs to run (each simulating a " +#~ "client sampled by the strategy) and " +#~ "then will execute them in a " +#~ "resource-aware manner in batches of " +#~ "8." #~ msgstr "" #~ msgid "" -#~ "**fit_configuration** -- A list of " -#~ "tuples. Each tuple in the list " -#~ "identifies a `ClientProxy` and the " -#~ "`FitIns` for this particular `ClientProxy`." -#~ " If a particular `ClientProxy` is not" -#~ " included in this list, it means " -#~ "that this `ClientProxy` will not " -#~ "participate in the next round of " -#~ "federated learning." +#~ "To understand all the intricate details" +#~ " on how resources are used to " +#~ "schedule FL clients and how to " +#~ "define custom resources, please take a" +#~ " look at the `Ray documentation " +#~ "`_." #~ msgstr "" -#~ msgid "Evaluate the current model parameters." -#~ msgstr "``get_parameters`` : renvoie les paramètres du modèle local actuel" +#~ msgid "Simulation examples" +#~ msgstr "Exemples de PyTorch" #~ msgid "" -#~ "This function can be used to " -#~ "perform centralized (i.e., server-side) " -#~ "evaluation of model parameters." +#~ "A few ready-to-run complete " +#~ "examples for Flower simulation in " +#~ "Tensorflow/Keras and PyTorch are provided " +#~ "in the `Flower repository " +#~ "`_. You can run " +#~ "them on Google Colab too:" #~ msgstr "" #~ msgid "" -#~ "**evaluation_result** -- The evaluation " -#~ "result, usually a Tuple containing loss" -#~ " and a dictionary containing task-" -#~ "specific metrics (e.g., accuracy)." +#~ "`Tensorflow/Keras Simulation " +#~ "`_: 100 clients collaboratively " +#~ "train a MLP model on MNIST." #~ msgstr "" - -#~ msgid "Initialize the (global) model parameters." -#~ msgstr "Initialise le modèle global" +#~ "`Quickstart TensorFlow (Code) " +#~ "`_" #~ msgid "" -#~ "**parameters** -- If parameters are " -#~ "returned, then the server will treat " -#~ "these as the initial global model " -#~ "parameters." -#~ msgstr "" - -#~ msgid "Configurable FedAvg strategy implementation." -#~ msgstr "Configuration de l'évaluation fédérée" - -#~ msgid "Implementation based on https://arxiv.org/abs/1602.05629" +#~ "`PyTorch Simulation " +#~ "`_: 100 clients collaboratively train" +#~ " a CNN model on MNIST." #~ msgstr "" +#~ "`Quickstart PyTorch (Code) " +#~ "`_" #~ msgid "" -#~ "Fraction of clients used during " -#~ "training. In case `min_fit_clients` is " -#~ "larger than `fraction_fit * " -#~ "available_clients`, `min_fit_clients` will still " -#~ "be sampled. Defaults to 1.0." +#~ "Flower's :code:`VirtualClientEngine` allows you " +#~ "to run FL simulations across multiple" +#~ " compute nodes. Before starting your " +#~ "multi-node simulation ensure that you:" #~ msgstr "" -#~ msgid "" -#~ "Fraction of clients used during " -#~ "validation. In case `min_evaluate_clients` is" -#~ " larger than `fraction_evaluate * " -#~ "available_clients`, `min_evaluate_clients` will " -#~ "still be sampled. Defaults to 1.0." +#~ msgid "Have the same Python environment in all nodes." #~ msgstr "" -#~ msgid "Minimum number of clients used during training. Defaults to 2." +#~ msgid "Have a copy of your code (e.g. your entire repo) in all nodes." #~ msgstr "" -#~ msgid "Minimum number of clients used during validation. Defaults to 2." +#~ msgid "" +#~ "Have a copy of your dataset in " +#~ "all nodes (more about this in " +#~ ":ref:`simulation considerations `)" #~ msgstr "" -#~ msgid "Minimum number of total clients in the system. Defaults to 2." +#~ msgid "" +#~ "Pass :code:`ray_init_args={\"address\"=\"auto\"}` to " +#~ "`start_simulation `_ so the " +#~ ":code:`VirtualClientEngine` attaches to a " +#~ "running Ray instance." #~ msgstr "" -#~ msgid "Optional function used for validation. Defaults to None." +#~ msgid "" +#~ "Start Ray on you head node: on " +#~ "the terminal type :code:`ray start " +#~ "--head`. This command will print a " +#~ "few lines, one of which indicates " +#~ "how to attach other nodes to the" +#~ " head node." #~ msgstr "" -#~ msgid "Function used to configure training. Defaults to None." +#~ msgid "" +#~ "Attach other nodes to the head " +#~ "node: copy the command shown after " +#~ "starting the head and execute it " +#~ "on terminal of a new node: for " +#~ "example :code:`ray start " +#~ "--address='192.168.1.132:6379'`" #~ msgstr "" -#~ msgid "Function used to configure validation. Defaults to None." +#~ msgid "" +#~ "With all the above done, you can" +#~ " run your code from the head " +#~ "node as you would if the " +#~ "simulation was running on a single " +#~ "node." #~ msgstr "" -#~ msgid "Whether or not accept rounds containing failures. Defaults to True." +#~ msgid "" +#~ "Once your simulation is finished, if " +#~ "you'd like to dismantle your cluster " +#~ "you simply need to run the command" +#~ " :code:`ray stop` in each node's " +#~ "terminal (including the head node)." #~ msgstr "" -#~ msgid "Initial global model parameters." -#~ msgstr "Initialise le modèle global" - -#~ msgid "Metrics aggregation function, optional." +#~ msgid "Multi-node simulation good-to-know" #~ msgstr "" -#~ msgid "Aggregate evaluation losses using weighted average." -#~ msgstr "Résultats globaux de l'évaluation." - -#~ msgid "Aggregate fit results using weighted average." +#~ msgid "" +#~ "Here we list a few interesting " +#~ "functionality when running multi-node FL" +#~ " simulations:" #~ msgstr "" -#~ msgid "Evaluate model parameters using an evaluation function." +#~ msgid "" +#~ "User :code:`ray status` to check all " +#~ "nodes connected to your head node " +#~ "as well as the total resources " +#~ "available to the :code:`VirtualClientEngine`." #~ msgstr "" -#~ msgid "Initialize global model parameters." -#~ msgstr "Initialise le modèle global" - -#~ msgid "Use a fraction of available clients for evaluation." +#~ msgid "" +#~ "When attaching a new node to the" +#~ " head, all its resources (i.e. all" +#~ " CPUs, all GPUs) will be visible " +#~ "by the head node. This means that" +#~ " the :code:`VirtualClientEngine` can schedule " +#~ "as many `virtual` clients as that " +#~ "node can possible run. In some " +#~ "settings you might want to exclude " +#~ "certain resources from the simulation. " +#~ "You can do this by appending " +#~ "`--num-cpus=` and/or `--num-" +#~ "gpus=` in any :code:`ray " +#~ "start` command (including when starting " +#~ "the head)" #~ msgstr "" -#~ msgid "Return the sample size and the required number of available clients." +#~ msgid "Considerations for simulations" +#~ msgstr "Simulation de moniteur" + +#~ msgid "" +#~ "We are actively working on these " +#~ "fronts so to make it trivial to" +#~ " run any FL workload with Flower " +#~ "simulation." #~ msgstr "" -#~ msgid "Configurable FedAvg with Momentum strategy implementation." +#~ msgid "" +#~ "The current VCE allows you to run" +#~ " Federated Learning workloads in simulation" +#~ " mode whether you are prototyping " +#~ "simple scenarios on your personal laptop" +#~ " or you want to train a complex" +#~ " FL pipeline across multiple high-" +#~ "performance GPU nodes. While we add " +#~ "more capabilities to the VCE, the " +#~ "points below highlight some of the " +#~ "considerations to keep in mind when " +#~ "designing your FL pipeline with Flower." +#~ " We also highlight a couple of " +#~ "current limitations in our implementation." #~ msgstr "" -#~ msgid "Federated Averaging with Momentum strategy." -#~ msgstr "Stratégie de moyenne fédérée." +#~ msgid "GPU resources" +#~ msgstr "Ressources" -#~ msgid "Implementation based on https://arxiv.org/pdf/1909.06335.pdf" +#~ msgid "" +#~ "The VCE assigns a share of GPU " +#~ "memory to a client that specifies " +#~ "the key :code:`num_gpus` in " +#~ ":code:`client_resources`. This being said, Ray" +#~ " (used internally by the VCE) is " +#~ "by default:" #~ msgstr "" -#~ msgid "Fraction of clients used during training. Defaults to 0.1." +#~ msgid "" +#~ "not aware of the total VRAM " +#~ "available on the GPUs. This means " +#~ "that if you set :code:`num_gpus=0.5` and" +#~ " you have two GPUs in your " +#~ "system with different (e.g. 32GB and " +#~ "8GB) VRAM amounts, they both would " +#~ "run 2 clients concurrently." #~ msgstr "" -#~ msgid "Fraction of clients used during validation. Defaults to 0.1." +#~ msgid "" +#~ "not aware of other unrelated (i.e. " +#~ "not created by the VCE) workloads " +#~ "are running on the GPU. Two " +#~ "takeaways from this are:" #~ msgstr "" #~ msgid "" -#~ "Server-side learning rate used in " -#~ "server-side optimization. Defaults to 1.0." +#~ "Your Flower server might need a " +#~ "GPU to evaluate the `global model` " +#~ "after aggregation (by instance when " +#~ "making use of the `evaluate method " +#~ "`_)" #~ msgstr "" -#~ msgid "Server-side momentum factor used for FedAvgM. Defaults to 0.0." +#~ msgid "" +#~ "If you want to run several " +#~ "independent Flower simulations on the " +#~ "same machine you need to mask-out" +#~ " your GPUs with " +#~ ":code:`CUDA_VISIBLE_DEVICES=\"\"` when launching" +#~ " your experiment." #~ msgstr "" -#~ msgid "Configurable QFedAvg strategy implementation." +#~ msgid "" +#~ "In addition, the GPU resource limits " +#~ "passed to :code:`client_resources` are not " +#~ "`enforced` (i.e. they can be exceeded)" +#~ " which can result in the situation" +#~ " of client using more VRAM than " +#~ "the ratio specified when starting the" +#~ " simulation." #~ msgstr "" -#~ msgid "Configurable fault-tolerant FedAvg strategy implementation." -#~ msgstr "" +#~ msgid "TensorFlow with GPUs" +#~ msgstr "Exemples de TensorFlow" -#~ msgid "Configurable FedAdagrad strategy implementation." +#~ msgid "" +#~ "When `using a GPU with TensorFlow " +#~ "`_ nearly your " +#~ "entire GPU memory of all your GPUs" +#~ " visible to the process will be " +#~ "mapped. This is done by TensorFlow " +#~ "for optimization purposes. However, in " +#~ "settings such as FL simulations where" +#~ " we want to split the GPU into" +#~ " multiple `virtual` clients, this is " +#~ "not a desirable mechanism. Luckily we" +#~ " can disable this default behavior by" +#~ " `enabling memory growth " +#~ "`_." #~ msgstr "" -#~ msgid "Federated Optim strategy interface." +#~ msgid "" +#~ "This would need to be done in " +#~ "the main process (which is where " +#~ "the server would run) and in each" +#~ " Actor created by the VCE. By " +#~ "means of :code:`actor_kwargs` we can " +#~ "pass the reserved key `\"on_actor_init_fn\"`" +#~ " in order to specify a function " +#~ "to be executed upon actor " +#~ "initialization. In this case, to enable" +#~ " GPU growth for TF workloads. It " +#~ "would look as follows:" #~ msgstr "" -#~ msgid "Implementation based on https://arxiv.org/abs/2003.00295v5" +#~ msgid "" +#~ "This is precisely the mechanism used " +#~ "in `Tensorflow/Keras Simulation " +#~ "`_ example." #~ msgstr "" -#~ "FedYogi - Stratégie d'apprentissage fédéré " -#~ "utilisant Yogi côté serveur. Mise en " -#~ "oeuvre basée sur https://arxiv.org/abs/2003.00295" +#~ "`Quickstart TensorFlow (Code) " +#~ "`_" -#~ msgid "Fraction of clients used during training. Defaults to 1.0." +#~ msgid "Multi-node setups" #~ msgstr "" -#~ msgid "Fraction of clients used during validation. Defaults to 1.0." +#~ msgid "" +#~ "The VCE does not currently offer a" +#~ " way to control on which node a" +#~ " particular `virtual` client is executed." +#~ " In other words, if more than a" +#~ " single node have the resources " +#~ "needed by a client to run, then" +#~ " any of those nodes could get " +#~ "the client workload scheduled onto. " +#~ "Later in the FL process (i.e. in" +#~ " a different round) the same client" +#~ " could be executed by a different " +#~ "node. Depending on how your clients " +#~ "access their datasets, this might " +#~ "require either having a copy of " +#~ "all dataset partitions on all nodes " +#~ "or a dataset serving mechanism (e.g. " +#~ "using nfs, a database) to circumvent " +#~ "data duplication." #~ msgstr "" -#~ msgid "Server-side learning rate. Defaults to 1e-1." +#~ msgid "" +#~ "By definition virtual clients are " +#~ "`stateless` due to their ephemeral " +#~ "nature. A client state can be " +#~ "implemented as part of the Flower " +#~ "client class but users need to " +#~ "ensure this saved to persistent storage" +#~ " (e.g. a database, disk) and that " +#~ "can be retrieve later by the same" +#~ " client regardless on which node it" +#~ " is running from. This is related " +#~ "to the point above also since, in" +#~ " some way, the client's dataset could" +#~ " be seen as a type of `state`." #~ msgstr "" -#~ msgid "Client-side learning rate. Defaults to 1e-1." -#~ msgstr "" +#~ msgid "Save and load model checkpoints" +#~ msgstr "Sauvegarde et chargement des points de contrôle PyTorch" -#~ msgid "Momentum parameter. Defaults to 0.0." +#~ msgid "" +#~ "Flower does not automatically save model" +#~ " updates on the server-side. This " +#~ "how-to guide describes the steps " +#~ "to save (and load) model checkpoints " +#~ "in Flower." #~ msgstr "" -#~ msgid "Second moment parameter. Defaults to 0.0." +#~ msgid "Legacy example guides" #~ msgstr "" -#~ msgid "Controls the algorithm's degree of adaptability. Defaults to 1e-9." -#~ msgstr "" +#~ msgid "Contributor tutorials" +#~ msgstr "Configuration du contributeur" -#~ msgid "Configurable FedProx strategy implementation." -#~ msgstr "" +#~ msgid "Contributor explanations" +#~ msgstr "Explications" -#~ msgid "Federated Optimization strategy." -#~ msgstr "Stratégie de moyenne fédérée." +#~ msgid "Flower Framework Documentation" +#~ msgstr "Documentation de Flower" -#~ msgid "Implementation based on https://arxiv.org/abs/1812.06127" -#~ msgstr "" +#~ msgid "PyTorch" +#~ msgstr "Exemples de PyTorch" -#~ msgid "" -#~ "The strategy in itself will not be" -#~ " different than FedAvg, the client " -#~ "needs to be adjusted. A proximal " -#~ "term needs to be added to the " -#~ "loss function during the training:" -#~ msgstr "" +#~ msgid "TensorFlow" +#~ msgstr "TensorFlow" -#~ msgid "" -#~ "\\\\frac{\\\\mu}{2} || w - w^t ||^2\n" -#~ "\n" -#~ msgstr "\\\\frac{\\Nmu}{2} || w - w^t ||^2" +#~ msgid "Flower CLI reference" +#~ msgstr "Client de Flower" -#~ msgid "" -#~ "Where $w^t$ are the global parameters" -#~ " and $w$ are the local weights " -#~ "the function will be optimized with." -#~ msgstr "" +#~ msgid "flwr (Python API reference)" +#~ msgstr "Référence pour l'API" -#~ msgid "In PyTorch, for example, the loss would go from:" -#~ msgstr "" +#~ msgid "Unreleased" +#~ msgstr "Inédit" -#~ msgid "To:" -#~ msgstr "" +#~ msgid "**Deprecate Python 3.7**" +#~ msgstr "**Deprecate Python 3.7**" #~ msgid "" -#~ "With `global_params` being a copy of " -#~ "the parameters before the training takes" -#~ " place." +#~ "Since Python 3.7 reached its end " +#~ "of life (EOL) on 2023-06-27, support " +#~ "for Python 3.7 is now deprecated " +#~ "and will be removed in an upcoming" +#~ " release." #~ msgstr "" +#~ "Étant donné que Python 3.7 a " +#~ "atteint sa fin de vie (EOL) le " +#~ "2023-06-27, la prise en charge de " +#~ "Python 3.7 est désormais dépréciée et" +#~ " sera supprimée dans une prochaine " +#~ "version." #~ msgid "" -#~ "The weight of the proximal term " -#~ "used in the optimization. 0.0 makes " -#~ "this strategy equivalent to FedAvg, and" -#~ " the higher the coefficient, the more" -#~ " regularization will be used (that " -#~ "is, the client parameters will need " -#~ "to be closer to the server " -#~ "parameters during training)." -#~ msgstr "" - -#~ msgid "Sends the proximal factor mu to the clients" +#~ "**Add new** `FedTrimmedAvg` **strategy** " +#~ "([#1769](https://github.com/adap/flower/pull/1769), " +#~ "[#1853](https://github.com/adap/flower/pull/1853))" #~ msgstr "" +#~ "**Ajouter un nouveau** `FedTrimmedAvg` " +#~ "**stratégie** " +#~ "([#1769](https://github.com/adap/flower/pull/1769), " +#~ "[#1853](https://github.com/adap/flower/pull/1853))" -#~ msgid "FedAdagrad strategy - Adaptive Federated Optimization using Adagrad." +#~ msgid "" +#~ "The new `FedTrimmedAvg` strategy implements" +#~ " Trimmed Mean by [Dong Yin, " +#~ "2018](https://arxiv.org/abs/1803.01498)" #~ msgstr "" -#~ "Stratégie FedAdagrad - Optimisation fédérée" -#~ " adaptative à l'aide d'Adagrad." +#~ "La nouvelle stratégie `FedTrimmedAvg` met " +#~ "en œuvre la moyenne trimmée par " +#~ "[Dong Yin, 2018](https://arxiv.org/abs/1803.01498)" -#~ msgid "Paper: https://arxiv.org/abs/2003.00295" +#~ msgid "" +#~ "**Add parameter aggregation to** `mt-" +#~ "pytorch` **code example** " +#~ "([#1785](https://github.com/adap/flower/pull/1785))" #~ msgstr "" +#~ "**Ajouter l'agrégation des paramètres à** " +#~ "`mt-pytorch` **exemple de code** " +#~ "([#1785](https://github.com/adap/flower/pull/1785))" -#~ msgid "Federated learning strategy using Adagrad on server-side." +#~ msgid "" +#~ "The `mt-pytorch` example shows how " +#~ "to aggregate parameters when writing a" +#~ " driver script. The included `driver.py`" +#~ " and `server.py` have been aligned to" +#~ " demonstrate both the low-level way" +#~ " and the high-level way of " +#~ "building server-side logic." #~ msgstr "" -#~ "Construisons un système d'apprentissage fédéré" -#~ " en utilisant fastai et Flower !" - -#~ msgid "FedAdam - Adaptive Federated Optimization using Adam." -#~ msgstr "FedAdam - Optimisation fédérée adaptative utilisant Adam." +#~ "L'exemple `mt-pytorch` montre comment " +#~ "agréger des paramètres lors de " +#~ "l'écriture d'un script de pilote. Les" +#~ " fichiers `driver.py` et `server.py` inclus" +#~ " ont été alignés pour démontrer à " +#~ "la fois la manière de bas niveau" +#~ " et la manière de haut niveau " +#~ "de construire la logique côté serveur." -#~ msgid "Momentum parameter. Defaults to 0.9." +#~ msgid "" +#~ "**Introduce (experimental) gRPC request-" +#~ "response API** " +#~ "([#1867](https://github.com/adap/flower/pull/1867), " +#~ "[#1901](https://github.com/adap/flower/pull/1901))" #~ msgstr "" +#~ "**Introduire l'API demande-réponse gRPC " +#~ "(expérimentale)** " +#~ "([#1867](https://github.com/adap/flower/pull/1867), " +#~ "[#1901](https://github.com/adap/flower/pull/1901))" -#~ msgid "Second moment parameter. Defaults to 0.99." +#~ msgid "" +#~ "In addition to the existing gRPC " +#~ "API (based on bidirectional streaming) " +#~ "and the experimental REST API, there " +#~ "is now a new gRPC API that " +#~ "uses a request-response model to " +#~ "communicate with client nodes." #~ msgstr "" - -#~ msgid "FedYogi [Reddi et al., 2020] strategy." -#~ msgstr "Stratégie FedYogi [Reddi et al., 2020]." - -#~ msgid "Adaptive Federated Optimization using Yogi." -#~ msgstr "Optimisation fédérée adaptative à l'aide de Yogi." - -#~ msgid "Federated learning strategy using Yogi on server-side." -#~ msgstr "L'apprentissage fédéré en cinq étapes" - -#~ msgid "Differential Privacy Wrappers in Flower" -#~ msgstr "Les enveloppes différentielles de confidentialité dans les fleurs" - -#~ msgid "Evaluation" -#~ msgstr "Solution" - -#~ msgid "Code examples" -#~ msgstr "Exemple de code complet" +#~ "En plus de l'API gRPC existante " +#~ "(basée sur un flux bidirectionnel) et" +#~ " de l'API REST expérimentale, il " +#~ "existe désormais une nouvelle API gRPC" +#~ " qui utilise un modèle demande-" +#~ "réponse pour communiquer avec les nœuds" +#~ " clients." #~ msgid "" -#~ "Flower Quickstart (PyTorch): coming soon " -#~ "(the TensorFlow/Keras example can easily " -#~ "be changed to make it work with" -#~ " PyTorch)" +#~ "Please note: The gRPC request-response" +#~ " API is still experimental and will" +#~ " likely change significantly over time." #~ msgstr "" +#~ "Remarque : l'API requête-réponse gRPC" +#~ " est encore expérimentale et est " +#~ "susceptible de changer de manière " +#~ "significative au fil du temps." -#~ msgid "First time contributors" -#~ msgstr "Bonnes premières contributions" - -#~ msgid "First MXNet 1.6 example (MNIST)" +#~ msgid "" +#~ "**Replace the eperimental** " +#~ "`start_client(rest=True)` **with the new** " +#~ "`start_client(transport=\"rest\")` " +#~ "([#1880](https://github.com/adap/flower/pull/1880))" #~ msgstr "" +#~ "**Remplacez le fichier expérimental** " +#~ "`start_client(rest=True) **par le nouveau** " +#~ "`start_client(transport=\"rest\")` " +#~ "([#1880](https://github.com/adap/flower/pull/1880))" -#~ msgid "ImageNet (PyTorch/TensorFlow)" +#~ msgid "" +#~ "The (experimental) `start_client` argument " +#~ "`rest` was deprecated in favor of " +#~ "a new argument `transport`. " +#~ "`start_client(transport=\"rest\")` will yield the" +#~ " same behaviour as `start_client(rest=True)` " +#~ "did before. All code should migrate " +#~ "to the new argument `transport`. The " +#~ "deprecated argument `rest` will be " +#~ "removed in a future release." #~ msgstr "" -#~ msgid "LSTM (PyTorch/TensorFlow)" +#~ msgid "" +#~ "**Migrate experimental REST API to " +#~ "Starlette** ([2171](https://github.com/adap/flower/pull/2171))" #~ msgstr "" +#~ "**Migrer l'API REST expérimentale vers " +#~ "Starlette** ([2171](https://github.com/adap/flower/pull/2171))" -#~ msgid "Transformer (PyTorch/TensorFlow)" +#~ msgid "" +#~ "The (experimental) REST API used to " +#~ "be implemented in " +#~ "[FastAPI](https://fastapi.tiangolo.com/), but it has" +#~ " now been migrated to use " +#~ "[Starlette](https://www.starlette.io/) directly." #~ msgstr "" +#~ "L'API REST (expérimentale) était auparavant" +#~ " implémentée dans " +#~ "[FastAPI](https://fastapi.tiangolo.com/), mais elle " +#~ "a maintenant été migrée pour utiliser" +#~ " directement [Starlette](https://www.starlette.io/)." -#~ msgid "BERT (PyTorch/TensorFlow)" +#~ msgid "" +#~ "**Add a new gRPC option** " +#~ "([#2197](https://github.com/adap/flower/pull/2197))" #~ msgstr "" +#~ "**Ajouter une nouvelle option gRPC** " +#~ "([#2197](https://github.com/adap/flower/pull/2197))" -#~ msgid "Logging" -#~ msgstr "Enregistrement" - -#~ msgid "|cce04c6f539b421a91f5dba40287193f|" -#~ msgstr "|cce04c6f539b421a91f5dba40287193f|" - -#~ msgid "|e392aef42ba248e19e35446f95a6d1ca|" -#~ msgstr "|e392aef42ba248e19e35446f95a6d1ca|" - -#~ msgid "|7e028f44defe4f31a02debc729f2010d|" -#~ msgstr "|7e028f44defe4f31a02debc729f2010d|" - -#~ msgid "|b89f7b7ae05e4ecd92baa69b7a9fe1be|" -#~ msgstr "|b89f7b7ae05e4ecd92baa69b7a9fe1be|" - -#~ msgid "|9c0445ce962744e1a1c0a4abc697a334|" -#~ msgstr "|9c0445ce962744e1a1c0a4abc697a334|" - -#~ msgid "|a3246766a6db412888131b3bcdad0971|" -#~ msgstr "|a3246766a6db412888131b3bcdad0971|" - -#~ msgid "|db6f2bee32f143b8a5085b6a8ce1acd1|" -#~ msgstr "|db6f2bee32f143b8a5085b6a8ce1acd1|" - -#~ msgid "|405653bc8f874e9595fd59cc82b3d48c|" -#~ msgstr "|405653bc8f874e9595fd59cc82b3d48c|" - -#~ msgid "|073a728154ed406e8fe54e1d9f18dcb9|" -#~ msgstr "|073a728154ed406e8fe54e1d9f18dcb9|" - -#~ msgid "|50e80ea4f22945848b65ed7eed35e0e1|" -#~ msgstr "|50e80ea4f22945848b65ed7eed35e0e1|" - -#~ msgid "|f3cf9148d85e4b68b66b6c255b25e327|" -#~ msgstr "|f3cf9148d85e4b68b66b6c255b25e327|" - -#~ msgid "|1fedb4f8714947e1b13f03696180c741|" -#~ msgstr "|1fedb4f8714947e1b13f03696180c741|" - -#~ msgid "|a32d4ad1ccb34461942d75c7b2b51d65|" -#~ msgstr "|a32d4ad1ccb34461942d75c7b2b51d65|" - -#~ msgid "|3531696c52904cd3b9944034ab959d48|" -#~ msgstr "|3531696c52904cd3b9944034ab959d48|" - -#~ msgid "An Introduction to Federated Learning" -#~ msgstr "Mise à l'échelle de l'apprentissage fédéré" +#~ msgid "" +#~ "We now start a gRPC server with" +#~ " the `grpc.keepalive_permit_without_calls` option " +#~ "set to 0 by default. This prevents" +#~ " the clients from sending keepalive " +#~ "pings when there is no outstanding " +#~ "stream." +#~ msgstr "" +#~ "Nous démarrons maintenant un serveur " +#~ "gRPC avec l'option " +#~ "`grpc.keepalive_permit_without_calls` réglée sur 0" +#~ " par défaut, ce qui empêche les " +#~ "clients d'envoyer des pings de maintien" +#~ " lorsqu'il n'y a pas de flux en" +#~ " attente." -#~ msgid "Strategies in Federated Learning" -#~ msgstr "Mise à l'échelle de l'apprentissage fédéré" +#~ msgid "" +#~ "**General improvements** " +#~ "([#1872](https://github.com/adap/flower/pull/1872), " +#~ "[#1866](https://github.com/adap/flower/pull/1866), " +#~ "[#1884](https://github.com/adap/flower/pull/1884))" +#~ msgstr "" +#~ "**Mettre à jour les exemples de " +#~ "code** ([#1291](https://github.com/adap/flower/pull/1291), " +#~ "[#1286](https://github.com/adap/flower/pull/1286), " +#~ "[#1282](https://github.com/adap/flower/pull/1282))" -#~ msgid "Building a Strategy" -#~ msgstr "Stratégies intégrées" +#~ msgid "Example projects" +#~ msgstr "Exemples" -#~ msgid "Client and NumPyClient" -#~ msgstr "NumPyClient" +#~ msgid "" +#~ "`Flower simulation PyTorch " +#~ "`_" +#~ msgstr "" +#~ "`Flower Quickstart (TensorFlow/Keras) " +#~ "`_" -#~ msgid "Strategies" -#~ msgstr "Stratégies personnalisées" +#~ msgid "" +#~ "`Android Kotlin example " +#~ "`_" +#~ msgstr "" -#~ msgid "SSL-enabled Server and Client" +#~ msgid "`Android Java example `_" #~ msgstr "" -#~ msgid "About these documents" -#~ msgstr "À propos de ces documents" +#~ msgid "Build a strategy from scratch" +#~ msgstr "Élaborer une stratégie à partir de zéro" -#~ msgid "Index" -#~ msgstr "Index" +#~ msgid "Customize the client" +#~ msgstr "Création du client IMDBC" -#~ msgid "Search" -#~ msgstr "Recherche" +#~ msgid "Get started with Flower" +#~ msgstr "" -#~ msgid "Copyright" -#~ msgstr "Droits d'auteur" +#~ msgid "Quickstart Android" +#~ msgstr "Démarrage rapide d'Android" -#~ msgid "Save Progress" +#~ msgid "" +#~ "Let's build a federated learning system" +#~ " using TFLite and Flower on Android!" #~ msgstr "" +#~ "Construisons un système d'apprentissage fédéré" +#~ " en utilisant TFLite et Flower sur" +#~ " Android !" #~ msgid "" -#~ "The Flower server does not prescribe " -#~ "a way to persist model updates or" -#~ " evaluation results. Flower does not " -#~ "(yet) automatically save model updates " -#~ "on the server-side. It's on the" -#~ " roadmap to provide a built-in " -#~ "way of doing this." +#~ "Please refer to the `full code " +#~ "example " +#~ "`_ to" +#~ " learn more." #~ msgstr "" +#~ "Réfère-toi à l'exemple de code " +#~ "complet " +#~ "`_ " +#~ "pour en savoir plus." -#~ msgid "Release Process" -#~ msgstr "Publier Flower" +#~ msgid "Quickstart iOS" +#~ msgstr "Démarrage rapide iOS" -#~ msgid "Virtual Env Installation" -#~ msgstr "Virtualenv avec Anaconda" +#~ msgid "" +#~ "In this tutorial we will learn how" +#~ " to train a Neural Network on " +#~ "MNIST using Flower and CoreML on " +#~ "iOS devices." +#~ msgstr "" +#~ "Dans ce tutoriel, nous allons apprendre" +#~ " à former un réseau neuronal sur " +#~ "MNIST en utilisant Flower et CoreML " +#~ "sur les appareils iOS." -#~ msgid "Install development versions" -#~ msgstr "Installer les versions de développement de Flower" +#~ msgid "" +#~ "First of all, for running the " +#~ "Flower Python server, it is recommended" +#~ " to create a virtual environment and" +#~ " run everything within a `virtualenv " +#~ "`_. " +#~ "For the Flower client implementation in" +#~ " iOS, it is recommended to use " +#~ "Xcode as our IDE." +#~ msgstr "" +#~ "Tout d'abord, pour l'exécution du " +#~ "serveur Flower Python, il est recommandé" +#~ " de créer un environnement virtuel et" +#~ " de tout exécuter au sein d'un " +#~ "`virtualenv `_. Pour l'implémentation du client" +#~ " Flower dans iOS, il est recommandé" +#~ " d'utiliser Xcode comme notre IDE." -#~ msgid "Set up a virtual env" -#~ msgstr "Mettre en place un environment virtuel" +#~ msgid "" +#~ "Our example consists of one Python " +#~ "*server* and two iPhone *clients* that" +#~ " all have the same model." +#~ msgstr "" +#~ "Notre exemple se compose d'un *serveur*" +#~ " Python et de deux *clients* iPhone" +#~ " qui ont tous le même modèle." #~ msgid "" -#~ "Note that, in order to build the" -#~ " documentation locally (with ``poetry run" -#~ " make html``, like described below), " -#~ "`Pandoc _` needs " -#~ "to be installed on the system." +#~ "*Clients* are responsible for generating " +#~ "individual weight updates for the model" +#~ " based on their local datasets. These" +#~ " updates are then sent to the " +#~ "*server* which will aggregate them to" +#~ " produce a better model. Finally, the" +#~ " *server* sends this improved version " +#~ "of the model back to each " +#~ "*client*. A complete cycle of weight " +#~ "updates is called a *round*." #~ msgstr "" -#~ "Notez que, pour construire la " -#~ "documentation localement (avec ``poetry run" -#~ " make html``, comme décrit ci-" -#~ "dessous), ``Pandoc _`" -#~ " doit être installé sur le système." +#~ "*Les clients* sont chargés de générer" +#~ " des mises à jour de poids " +#~ "individuelles pour le modèle en fonction" +#~ " de leurs ensembles de données " +#~ "locaux. Ces mises à jour sont " +#~ "ensuite envoyées au *serveur* qui les" +#~ " agrège pour produire un meilleur " +#~ "modèle. Enfin, le *serveur* renvoie " +#~ "cette version améliorée du modèle à " +#~ "chaque *client*. Un cycle complet de " +#~ "mises à jour de poids s'appelle un" +#~ " *round*." -#~ msgid "Llama 2 fine-tuning, with Hugging Face Transformers and PyTorch" -#~ msgstr "Un fine-tuning de LLaMA 2 avec Hugging Face et PyTorch" +#~ msgid "" +#~ "Now that we have a rough idea " +#~ "of what is going on, let's get " +#~ "started to setup our Flower server " +#~ "environment. We first need to install" +#~ " Flower. You can do this by " +#~ "using pip:" +#~ msgstr "" +#~ "Maintenant que nous avons une idée " +#~ "approximative de ce qui se passe, " +#~ "commençons à configurer notre environnement" +#~ " de serveur Flower. Nous devons " +#~ "d'abord installer Flower, ce que tu " +#~ "peux faire à l'aide de pip :" -#~ msgid "XGBoost" -#~ msgstr "XGBoost" +#~ msgid "Or Poetry:" +#~ msgstr "Ou de la poésie :" -#~ msgid "Android ONNX on-device training" +#~ msgid "" +#~ "Now that we have all our " +#~ "dependencies installed, let's run a " +#~ "simple distributed training using CoreML " +#~ "as our local training pipeline and " +#~ "MNIST as our dataset. For simplicity " +#~ "reasons we will use the complete " +#~ "Flower client with CoreML, that has " +#~ "been implemented and stored inside the" +#~ " Swift SDK. The client implementation " +#~ "can be seen below:" #~ msgstr "" -#~ "Utiliser Android ONNX pour faire du " -#~ "training directement sur le téléphone" +#~ "Maintenant que toutes nos dépendances " +#~ "sont installées, exécutons une simple " +#~ "formation distribuée en utilisant CoreML " +#~ "comme pipeline de formation local et " +#~ "MNIST comme ensemble de données. Pour" +#~ " des raisons de simplicité, nous " +#~ "utiliserons le client Flower complet " +#~ "avec CoreML, qui a été mis en " +#~ "œuvre et stocké à l'intérieur du " +#~ "SDK Swift. La mise en œuvre du " +#~ "client peut être vue ci-dessous :" -#~ msgid "Contribute on GitHub" -#~ msgstr "Contribuer sur GitHub" +#~ msgid "" +#~ "Let's create a new application project" +#~ " in Xcode and add :code:`flwr` as " +#~ "a dependency in your project. For " +#~ "our application, we will store the " +#~ "logic of our app in " +#~ ":code:`FLiOSModel.swift` and the UI elements" +#~ " in :code:`ContentView.swift`. We will " +#~ "focus more on :code:`FLiOSModel.swift` in " +#~ "this quickstart. Please refer to the " +#~ "`full code example " +#~ "`_ to " +#~ "learn more about the app." +#~ msgstr "" +#~ "Créons un nouveau projet d'application " +#~ "dans Xcode et ajoutons :code:`flwr` " +#~ "comme dépendance dans ton projet. Pour" +#~ " notre application, nous stockerons la " +#~ "logique de notre application dans " +#~ ":code:`FLiOSModel.swift` et les éléments de" +#~ " l'interface utilisateur dans " +#~ ":code:`ContentView.swift`.Nous nous concentrerons " +#~ "davantage sur :code:`FLiOSModel.swift` dans ce" +#~ " quickstart. N'hésite pas à te " +#~ "référer à l'`exemple de code complet " +#~ "`_ pour" +#~ " en savoir plus sur l'application." -#~ msgid "How to write a good PR title" -#~ msgstr "Comment rédiger un bon titre de RP" +#~ msgid "Import Flower and CoreML related packages in :code:`FLiOSModel.swift`:" +#~ msgstr "" +#~ "Importe les paquets liés à Flower " +#~ "et CoreML dans :code:`FLiOSModel.swift` :" #~ msgid "" -#~ "A well-crafted PR title helps team" -#~ " members quickly understand the purpose " -#~ "and scope of the changes being " -#~ "proposed. Here's a guide to help " -#~ "you write a good GitHub PR title:" +#~ "Then add the mlmodel to the " +#~ "project simply by drag-and-drop, " +#~ "the mlmodel will be bundled inside " +#~ "the application during deployment to " +#~ "your iOS device. We need to pass" +#~ " the url to access mlmodel and " +#~ "run CoreML machine learning processes, " +#~ "it can be retrieved by calling the" +#~ " function :code:`Bundle.main.url`. For the " +#~ "MNIST dataset, we need to preprocess " +#~ "it into :code:`MLBatchProvider` object. The" +#~ " preprocessing is done inside " +#~ ":code:`DataLoader.swift`." #~ msgstr "" -#~ "Un titre de RP bien rédigé aide" -#~ " les membres de l'équipe à comprendre" -#~ " rapidement l'objectif et la portée " -#~ "des changements proposés. Voici un guide" -#~ " pour t'aider à rédiger un bon " -#~ "titre de RP GitHub :" +#~ "Ensuite, ajoute le mlmodel au projet " +#~ "simplement par glisser-déposer, le " +#~ "mlmodel sera regroupé à l'intérieur de" +#~ " l'application lors du déploiement sur " +#~ "ton appareil iOS. Nous devons passer " +#~ "l'url pour accéder au mlmodel et " +#~ "exécuter les processus d'apprentissage " +#~ "automatique CoreML, elle peut être " +#~ "récupérée en appelant la fonction " +#~ ":code:`Bundle.main.url`. Pour l'ensemble de " +#~ "données MNIST, nous devons le prétraiter" +#~ " dans l'objet :code:`MLBatchProvider`. Le " +#~ "prétraitement est effectué à l'intérieur " +#~ "de :code:`DataLoader.swift`." #~ msgid "" -#~ "1. Be Clear and Concise: Provide a" -#~ " clear summary of the changes in " -#~ "a concise manner. 1. Use Actionable " -#~ "Verbs: Start with verbs like \"Add,\"" -#~ " \"Update,\" or \"Fix\" to indicate " -#~ "the purpose. 1. Include Relevant " -#~ "Information: Mention the affected feature " -#~ "or module for context. 1. Keep it" -#~ " Short: Avoid lengthy titles for easy" -#~ " readability. 1. Use Proper Capitalization" -#~ " and Punctuation: Follow grammar rules " -#~ "for clarity." +#~ "Since CoreML does not allow the " +#~ "model parameters to be seen before " +#~ "training, and accessing the model " +#~ "parameters during or after the training" +#~ " can only be done by specifying " +#~ "the layer name, we need to know" +#~ " this informations beforehand, through " +#~ "looking at the model specification, " +#~ "which are written as proto files. " +#~ "The implementation can be seen in " +#~ ":code:`MLModelInspect`." #~ msgstr "" -#~ "1. Être clair et concis : fournir" -#~ " un résumé clair des changements de" -#~ " manière concise. 1. Utiliser des " -#~ "verbes d'action : commencer par des " -#~ "verbes comme \" Ajouter \", \" " -#~ "Mettre à jour \" ou \" Réparer " -#~ "\" pour indiquer l'objectif. 1. Inclure" -#~ " des informations pertinentes : mentionner" -#~ " la fonctionnalité ou le module " -#~ "affecté pour le contexte. 1. Faire " -#~ "court : éviter les titres trop " -#~ "longs pour faciliter la lecture. 1. " -#~ "Utiliser des majuscules et une " -#~ "ponctuation correctes : suivre les " -#~ "règles de grammaire pour plus de " -#~ "clarté." #~ msgid "" -#~ "Let's start with a few examples " -#~ "for titles that should be avoided " -#~ "because they do not provide meaningful" -#~ " information:" +#~ "After we have all of the necessary" +#~ " informations, let's create our Flower " +#~ "client." #~ msgstr "" -#~ "Commençons par quelques exemples de " -#~ "titres à éviter car ils ne " -#~ "fournissent pas d'informations significatives " -#~ ":" - -#~ msgid "Implement Algorithm" -#~ msgstr "Implémenter l'algorithme" - -#~ msgid "Database" -#~ msgstr "Base de données" - -#~ msgid "Add my_new_file.py to codebase" -#~ msgstr "Ajoute my_new_file.py à la base de code" - -#~ msgid "Improve code in module" -#~ msgstr "Améliore le code dans le module" - -#~ msgid "Change SomeModule" -#~ msgstr "Change SomeModule" #~ msgid "" -#~ "Here are a few positive examples " -#~ "which provide helpful information without " -#~ "repeating how they do it, as that" -#~ " is already visible in the \"Files" -#~ " changed\" section of the PR:" +#~ "Then start the Flower gRPC client " +#~ "and start communicating to the server" +#~ " by passing our Flower client to " +#~ "the function :code:`startFlwrGRPC`." #~ msgstr "" -#~ "Voici quelques exemples positifs qui " -#~ "fournissent des informations utiles sans " -#~ "répéter comment ils procèdent, car cela" -#~ " est déjà visible dans la section " -#~ "\"Fichiers modifiés\" du RP :" +#~ "Lance ensuite le client Flower gRPC " +#~ "et commence à communiquer avec le " +#~ "serveur en passant notre client Flower" +#~ " à la fonction :code:`startFlwrGRPC`." -#~ msgid "Update docs banner to mention Flower Summit 2023" +#~ msgid "" +#~ "That's it for the client. We only" +#~ " have to implement :code:`Client` or " +#~ "call the provided :code:`MLFlwrClient` and " +#~ "call :code:`startFlwrGRPC()`. The attribute " +#~ ":code:`hostname` and :code:`port` tells the" +#~ " client which server to connect to." +#~ " This can be done by entering " +#~ "the hostname and port in the " +#~ "application before clicking the start " +#~ "button to start the federated learning" +#~ " process." #~ msgstr "" -#~ "Mettre à jour la bannière des docs" -#~ " pour mentionner le sommet des fleurs" -#~ " 2023" - -#~ msgid "Remove unnecessary XGBoost dependency" -#~ msgstr "Supprime les dépendances inutiles de XGBoost" +#~ "C'est tout pour le client. Il nous" +#~ " suffit d'implémenter :code:`Client` ou " +#~ "d'appeler le :code:`MLFlwrClient` fourni et" +#~ " d'appeler :code:`startFlwrGRPC()`. L'attribut " +#~ ":code:`hostname` et :code:`port` indique au" +#~ " client à quel serveur se connecter." +#~ " Pour ce faire, il suffit d'entrer" +#~ " le nom d'hôte et le port dans" +#~ " l'application avant de cliquer sur " +#~ "le bouton de démarrage pour lancer " +#~ "le processus d'apprentissage fédéré." -#~ msgid "Remove redundant attributes in strategies subclassing FedAvg" +#~ msgid "" +#~ "Once the server is running we can" +#~ " start the clients in different " +#~ "terminals. Build and run the client " +#~ "through your Xcode, one through Xcode" +#~ " Simulator and the other by deploying" +#~ " it to your iPhone. To see more" +#~ " about how to deploy your app " +#~ "to iPhone or Simulator visit `here " +#~ "`_." #~ msgstr "" -#~ "Supprimer les attributs redondants dans " -#~ "les stratégies sous-classant FedAvg" +#~ "Une fois que le serveur fonctionne, " +#~ "nous pouvons démarrer les clients dans" +#~ " différents terminaux. Construis et exécute" +#~ " le client grâce à ton Xcode, " +#~ "l'un via le simulateur Xcode et " +#~ "l'autre en le déployant sur ton " +#~ "iPhone. Pour en savoir plus sur la" +#~ " façon de déployer ton application " +#~ "sur l'iPhone ou le simulateur, visite" +#~ " `ici `_." -#~ msgid "Add CI job to deploy the staging system when the `main` branch changes" +#~ msgid "" +#~ "Congratulations! You've successfully built and" +#~ " run your first federated learning " +#~ "system in your ios device. The " +#~ "full `source code " +#~ "`_ for" +#~ " this example can be found in " +#~ ":code:`examples/ios`." #~ msgstr "" -#~ "Ajoute une tâche CI pour déployer " -#~ "le système de mise en scène " -#~ "lorsque la branche `main` change" +#~ "Félicitations ! Tu as réussi à " +#~ "construire et à faire fonctionner ton" +#~ " premier système d'apprentissage fédéré " +#~ "dans ton appareil ios. Le `code " +#~ "source complet " +#~ "`_ de " +#~ "cet exemple se trouve dans " +#~ ":code:`examples/ios`." #~ msgid "" -#~ "Add new amazing library which will " -#~ "be used to improve the simulation " -#~ "engine" +#~ "`Star Flower on GitHub " +#~ "`__ ⭐️ and join " +#~ "the open-source Flower community on " +#~ "Slack to connect, ask questions, and " +#~ "get help: `Join Slack `__ 🌼 We'd love to hear" +#~ " from you in the ``#introductions`` " +#~ "channel! And if anything is unclear, " +#~ "head over to the ``#questions`` channel." #~ msgstr "" -#~ "Ajoute une nouvelle bibliothèque étonnante " -#~ "qui sera utilisée pour améliorer le " -#~ "moteur de simulation" - -#~ msgid "Differential privacy" -#~ msgstr "Confidentialité différentielle" +#~ "`Star Flower on GitHub " +#~ "`__ ⭐️ et rejoignez" +#~ " la communauté open-source Flower sur" +#~ " Slack pour vous connecter, poser des" +#~ " questions et obtenir de l'aide : " +#~ "`Join Slack `__ " +#~ "🌼 Nous serions ravis d'avoir de " +#~ "vos nouvelles dans le canal " +#~ "``#introductions`` ! Et si quelque chose" +#~ " n'est pas clair, dirigez-vous vers" +#~ " le canal ``#questions``." -#~ msgid "" -#~ "The Flower server does not prescribe " -#~ "a way to aggregate evaluation results," -#~ " but it enables the user to " -#~ "fully customize result aggregation." +#~ msgid "|bd48315a61c14495babefe3c7918b493|" #~ msgstr "" -#~ msgid "Configure logging" -#~ msgstr "Configurer les clients" - -#~ msgid "" -#~ "The Flower logger keeps track of " -#~ "all core events that take place in" -#~ " federated learning workloads. It presents" -#~ " information by default following a " -#~ "standard message format:" +#~ msgid "|c00d9e5b0d324d96b86da8a78b05b14b|" #~ msgstr "" -#~ "L'enregistreur de Flower garde la trace" -#~ " de tous les événements principaux " -#~ "qui ont lieu dans les charges de" -#~ " travail de l'apprentissage fédéré. Il " -#~ "présente les informations par défaut en" -#~ " suivant un format de message " -#~ "standard :" -#~ msgid "" -#~ "containing relevant information including: log" -#~ " message level (e.g. :code:`INFO`, " -#~ ":code:`DEBUG`), a timestamp, the line " -#~ "where the logging took place from, " -#~ "as well as the log message itself." -#~ " In this way, the logger would " -#~ "typically display information on your " -#~ "terminal as follows:" +#~ msgid "|faae2ee10f4149c9907563c4f48ec6ea|" #~ msgstr "" -#~ "contenant des informations pertinentes, " -#~ "notamment : le niveau du message " -#~ "de journal (par exemple :code:`INFO`, " -#~ ":code:`DEBUG`), un horodatage, la ligne " -#~ "à partir de laquelle l'enregistrement a" -#~ " eu lieu, ainsi que le message " -#~ "de journal lui-même. De cette " -#~ "façon, le logger afficherait typiquement " -#~ "des informations sur ton terminal comme" -#~ " suit :" - -#~ msgid "Saving log to file" -#~ msgstr "Enregistrement du journal dans un fichier" -#~ msgid "" -#~ "By default, the Flower log is " -#~ "outputted to the terminal where you " -#~ "launch your Federated Learning workload " -#~ "from. This applies for both gRPC-" -#~ "based federation (i.e. when you do " -#~ ":code:`fl.server.start_server`) and when using " -#~ "the :code:`VirtualClientEngine` (i.e. when you" -#~ " do :code:`fl.simulation.start_simulation`). In " -#~ "some situations you might want to " -#~ "save this log to disk. You can " -#~ "do so by calling the " -#~ "`fl.common.logger.configure() " -#~ "`_" -#~ " function. For example:" +#~ msgid "|13a655510351455292f145a61d6c15d6|" #~ msgstr "" -#~ "Par défaut, le journal de Flower " -#~ "est affiché dans le terminal à " -#~ "partir duquel tu as lancé ta " -#~ "charge de travail d'apprentissage fédéré. " -#~ "Cela s'applique à la fois à la " -#~ "fédération basée sur gRPC (c'est-à-dire " -#~ "lorsque tu fais :code:`fl.server.start_server`) " -#~ "et à l'utilisation du " -#~ ":code:`VirtualClientEngine` (c'est-à-dire lorsque tu" -#~ " fais :code:`fl.simulation.start_simulation`). Dans " -#~ "certaines situations, tu peux vouloir " -#~ "sauvegarder ce journal sur le disque." -#~ " Tu peux le faire en appelant " -#~ "la fonction `fl.common.logger.configure() " -#~ "`_." -#~ " Par exemple :" -#~ msgid "" -#~ "With the above, Flower will record " -#~ "the log you see on your terminal" -#~ " to :code:`log.txt`. This file will " -#~ "be created in the same directory " -#~ "as were you are running the code" -#~ " from. If we inspect we see the" -#~ " log above is also recorded but " -#~ "prefixing with :code:`identifier` each line:" +#~ msgid "|13949884182846e3a91433190a936ba9|" #~ msgstr "" -#~ "Avec ce qui précède, Flower enregistrera" -#~ " le journal que tu vois sur ton" -#~ " terminal dans :code:`log.txt`. Ce fichier" -#~ " sera créé dans le même répertoire" -#~ " que celui à partir duquel tu " -#~ "exécutes le code. Si nous inspectons," -#~ " nous voyons que le journal ci-" -#~ "dessus est également enregistré, mais en" -#~ " préfixant chaque ligne avec " -#~ ":code:`identifier` :" - -#~ msgid "Log your own messages" -#~ msgstr "Enregistrer tes propres messages" -#~ msgid "" -#~ "You might expand the information shown" -#~ " by default with the Flower logger" -#~ " by adding more messages relevant to" -#~ " your application. You can achieve " -#~ "this easily as follows." +#~ msgid "|9bf26cc650b146e88b4745df040ece37|" #~ msgstr "" -#~ "Tu peux élargir les informations " -#~ "affichées par défaut avec le logger " -#~ "Flower en ajoutant d'autres messages " -#~ "pertinents pour ton application. Tu peux" -#~ " y parvenir facilement en procédant " -#~ "comme suit." -#~ msgid "" -#~ "In this way your logger will show," -#~ " in addition to the default messages," -#~ " the ones introduced by the clients" -#~ " as specified above." +#~ msgid "|1590915480fc41708bd43e48af9582f9|" #~ msgstr "" -#~ "De cette façon, ton logger affichera," -#~ " en plus des messages par défaut, " -#~ "ceux introduits par les clients comme" -#~ " spécifié ci-dessus." -#~ msgid "Log to a remote service" -#~ msgstr "Se connecter à un service distant" +#~ msgid "|e5ee96d702b64256b97b8ca99db10787|" +#~ msgstr "" -#~ msgid "" -#~ "The :code:`fl.common.logger.configure` function, " -#~ "also allows specifying a host to " -#~ "which logs can be pushed (via " -#~ ":code:`POST`) through a native Python " -#~ ":code:`logging.handler.HTTPHandler`. This is a " -#~ "particularly useful feature in " -#~ ":code:`gRPC`-based Federated Learning workloads " -#~ "where otherwise gathering logs from all" -#~ " entities (i.e. the server and the" -#~ " clients) might be cumbersome. Note " -#~ "that in Flower simulation, the server" -#~ " automatically displays all logs. You " -#~ "can still specify a :code:`HTTPHandler` " -#~ "should you wish to backup or " -#~ "analyze the logs somewhere else." +#~ msgid "|84840b244edd47c481278ce534c126cd|" #~ msgstr "" -#~ "La fonction :code:`fl.common.logger.configure` " -#~ "permet également de spécifier un hôte" -#~ " vers lequel les journaux peuvent " -#~ "être envoyés (via :code:`POST`) par " -#~ "l'intermédiaire d'un :code:`logging.handler.HTTPHandler`" -#~ " natif de Python. Il s'agit d'une " -#~ "fonction particulièrement utile dans les " -#~ "charges de travail d'apprentissage fédéré " -#~ "basées sur :code:`gRPC` où la collecte" -#~ " des journaux de toutes les entités" -#~ " (c'est-à-dire le serveur et les " -#~ "clients) pourrait s'avérer fastidieuse. Notez" -#~ " que dans la simulation Flower, le" -#~ " serveur affiche automatiquement tous les" -#~ " journaux. Vous pouvez toujours spécifier" -#~ " un :code:`HTTPHandler` si vous souhaitez" -#~ " sauvegarder ou analyser les journaux " -#~ "à un autre endroit." -#~ msgid "Enable SSL connections" -#~ msgstr "Collecte centralisée des données" +#~ msgid "|f33f5ebb3a844a2ba54bb6be3571b172|" +#~ msgstr "" -#~ msgid "Python version" -#~ msgstr "Version Python" +#~ msgid "|5645db4ba9c945518d51ff234f35c797|" +#~ msgstr "" -#~ msgid "" -#~ "Flower requires at least `Python 3.7 " -#~ "`_, but `Python 3.8" -#~ " `_ or above is " -#~ "recommended." +#~ msgid "|317af8d28fcc479ab981047d058c4751|" #~ msgstr "" -#~ "Flower nécessite `Python 3.7 " -#~ "`_ ou plus, nous " -#~ "recommandons `Python 3.8 " -#~ "`_." -#~ msgid "Run simulations" -#~ msgstr "Simulation de moniteur" +#~ msgid "|8bfd0e697a494d5385662debafade6bf|" +#~ msgstr "" #~ msgid "" -#~ "Simulating Federated Learning workloads is " -#~ "useful for a multitude of use-" -#~ "cases: you might want to run your" -#~ " workload on a large cohort of " -#~ "clients but without having to source," -#~ " configure and mange a large number" -#~ " of physical devices; you might want" -#~ " to run your FL workloads as " -#~ "fast as possible on the compute " -#~ "systems you have access to without " -#~ "having to go through a complex " -#~ "setup process; you might want to " -#~ "validate your algorithm on different " -#~ "scenarios at varying levels of data " -#~ "and system heterogeneity, client availability," -#~ " privacy budgets, etc. These are " -#~ "among some of the use-cases where" -#~ " simulating FL workloads makes sense. " -#~ "Flower can accommodate these scenarios " -#~ "by means of its `VirtualClientEngine " -#~ "`_ or VCE." +#~ "Differential privacy (DP) is often " +#~ "mentioned in the context of Federated" +#~ " Learning. It is a privacy-preserving" +#~ " method used when analyzing and " +#~ "sharing statistical data, ensuring the " +#~ "privacy of individual participants. DP " +#~ "achieves this by adding statistical " +#~ "noise to the model updates, ensuring " +#~ "any individual participants’ information " +#~ "cannot be distinguished or re-" +#~ "identified. This technique can be " +#~ "considered an optimization that provides " +#~ "a quantifiable privacy protection measure." #~ msgstr "" +#~ "La confidentialité différentielle (DP) est " +#~ "souvent mentionnée dans le contexte de" +#~ " l'apprentissage fédéré. Il s'agit d'une" +#~ " méthode de préservation de la vie" +#~ " privée utilisée lors de l'analyse et" +#~ " du partage de données statistiques, " +#~ "garantissant la confidentialité des " +#~ "participants individuels. La DP y " +#~ "parvient en ajoutant un bruit " +#~ "statistique aux mises à jour du " +#~ "modèle, garantissant que toute information " +#~ "sur les participants individuels ne peut" +#~ " être distinguée ou réidentifiée. Cette " +#~ "technique peut être considérée comme une" +#~ " optimisation qui fournit une mesure " +#~ "quantifiable de protection de la vie " +#~ "privée." -#~ msgid "" -#~ "The :code:`VirtualClientEngine` schedules, launches" -#~ " and manages `virtual` clients. These " -#~ "clients are identical to `non-virtual`" -#~ " clients (i.e. the ones you launch" -#~ " via the command `flwr.client.start_client " -#~ "`_) in the" -#~ " sense that they can be configure " -#~ "by creating a class inheriting, for " -#~ "example, from `flwr.client.NumPyClient `_ and therefore" -#~ " behave in an identical way. In " -#~ "addition to that, clients managed by " -#~ "the :code:`VirtualClientEngine` are:" +#~ msgid "|e5dc001d27ad460caeab669e957b3c36|" +#~ msgstr "" + +#~ msgid "API Reference - Flower binaries" #~ msgstr "" +#~ msgid "API Reference - flwr" +#~ msgstr "Référence pour l'API" + #~ msgid "" -#~ "resource-aware: this means that each " -#~ "client gets assigned a portion of " -#~ "the compute and memory on your " -#~ "system. You as a user can control" -#~ " this at the beginning of the " -#~ "simulation and allows you to control " -#~ "the degree of parallelism of your " -#~ "Flower FL simulation. The fewer the " -#~ "resources per client, the more clients" -#~ " can run concurrently on the same " -#~ "hardware." +#~ "Defines whether or not the client " +#~ "is interacting with the server using " +#~ "the experimental REST API. This feature" +#~ " is experimental, it might change " +#~ "considerably in future versions of " +#~ "Flower." +#~ msgstr "" + +#~ msgid "Returns a client's set of properties." #~ msgstr "" #~ msgid "" -#~ "self-managed: this means that you " -#~ "as a user do not need to " -#~ "launch clients manually, instead this " -#~ "gets delegated to :code:`VirtualClientEngine`'s " -#~ "internals." +#~ "Defines whether or not the client " +#~ "is interacting with the server using " +#~ "the experimental REST API. This feature" +#~ " is experimental, it might be change" +#~ " considerably in future versions of " +#~ "Flower." #~ msgstr "" #~ msgid "" -#~ "ephemeral: this means that a client " -#~ "is only materialized when it is " -#~ "required in the FL process (e.g. " -#~ "to do `fit() `_). The object is" -#~ " destroyed afterwards, releasing the " -#~ "resources it was assigned and allowing" -#~ " in this way other clients to " -#~ "participate." +#~ "A function creating client instances. " +#~ "The function must take a single " +#~ "str argument called `cid`. It should " +#~ "return a single client instance of " +#~ "type ClientLike. Note that the created" +#~ " client instances are ephemeral and " +#~ "will often be destroyed after a " +#~ "single method invocation. Since client " +#~ "instances are not long-lived, they " +#~ "should not attempt to carry state " +#~ "over method invocations. Any state " +#~ "required by the instance (model, " +#~ "dataset,hyperparameters, ...) should be " +#~ "(re-)created in either the call to " +#~ "`client_fn` or the call to any of" +#~ " the client methods (e.g., load " +#~ "evaluation data in the `evaluate` method" +#~ " itself)." #~ msgstr "" #~ msgid "" -#~ "The :code:`VirtualClientEngine` implements `virtual`" -#~ " clients using `Ray `_, " -#~ "an open-source framework for scalable" -#~ " Python workloads. In particular, Flower's" -#~ " :code:`VirtualClientEngine` makes use of " -#~ "`Actors `_ to spawn `virtual` clients" -#~ " and run their workload." +#~ "A function creating client instances. " +#~ "The function must take a single " +#~ "str argument called `cid`. It should " +#~ "return a single client instance of " +#~ "type ClientLike. Note that the created" +#~ " client instances are ephemeral and " +#~ "will often be destroyed after a " +#~ "single method invocation. Since client " +#~ "instances are not long-lived, they " +#~ "should not" #~ msgstr "" -#~ msgid "Launch your Flower simulation" +#~ msgid "attempt to carry state over method invocations. Any state required by" #~ msgstr "" #~ msgid "" -#~ "Running Flower simulations still require " -#~ "you to define your client class, a" -#~ " strategy, and utility functions to " -#~ "download and load (and potentially " -#~ "partition) your dataset. With that out" -#~ " of the way, launching your " -#~ "simulation is done with `start_simulation " -#~ "`_ " -#~ "and a minimal example looks as " -#~ "follows:" +#~ "the instance (model, dataset,hyperparameters, " +#~ "...) should be (re-)created in either" +#~ " the call to `client_fn` or the " +#~ "call to any of the client methods" +#~ " (e.g., load evaluation data in the" +#~ " `evaluate` method itself)." #~ msgstr "" -#~ msgid "VirtualClientEngine resources" -#~ msgstr "Moteur de client virtuel" - #~ msgid "" -#~ "By default the VCE has access to" -#~ " all system resources (i.e. all CPUs," -#~ " all GPUs, etc) since that is " -#~ "also the default behavior when starting" -#~ " Ray. However, in some settings you" -#~ " might want to limit how many " -#~ "of your system resources are used " -#~ "for simulation. You can do this " -#~ "via the :code:`ray_init_args` input argument" -#~ " to :code:`start_simulation` which the VCE" -#~ " internally passes to Ray's " -#~ ":code:`ray.init` command. For a complete " -#~ "list of settings you can configure " -#~ "check the `ray.init `_ " -#~ "documentation. Do not set " -#~ ":code:`ray_init_args` if you want the " -#~ "VCE to use all your system's CPUs" -#~ " and GPUs." +#~ "\\frac{\\mu}{2} || w - w^t ||^2\n" +#~ "\n" #~ msgstr "" -#~ msgid "Assigning client resources" +#~ msgid "" +#~ "Adaptive Federated Optimization using Adagrad" +#~ " (FedAdagrad) [Reddi et al., 2020] " +#~ "strategy." #~ msgstr "" #~ msgid "" -#~ "By default the :code:`VirtualClientEngine` " -#~ "assigns a single CPU core (and " -#~ "nothing else) to each virtual client." -#~ " This means that if your system " -#~ "has 10 cores, that many virtual " -#~ "clients can be concurrently running." +#~ "Adaptive Federated Optimization using Adam " +#~ "(FedAdam) [Reddi et al., 2020] strategy." #~ msgstr "" #~ msgid "" -#~ "More often than not, you would " -#~ "probably like to adjust the resources" -#~ " your clients get assigned based on" -#~ " the complexity (i.e. compute and " -#~ "memory footprint) of your FL workload." -#~ " You can do so when starting " -#~ "your simulation by setting the argument" -#~ " `client_resources` to `start_simulation `_. Two " -#~ "keys are internally used by Ray to" -#~ " schedule and spawn workloads (in our" -#~ " case Flower clients):" +#~ "Adaptive Federated Optimization using Yogi " +#~ "(FedYogi) [Reddi et al., 2020] strategy." #~ msgstr "" -#~ msgid ":code:`num_cpus` indicates the number of CPU cores a client would get." -#~ msgstr "" +#~ msgid "Contributing Baselines" +#~ msgstr "Configuration du contributeur" #~ msgid "" -#~ ":code:`num_gpus` indicates the **ratio** of" -#~ " GPU memory a client gets assigned." -#~ msgstr "" - -#~ msgid "Let's see a few examples:" +#~ "Do you have a new federated " +#~ "learning paper and want to add a" +#~ " new baseline to Flower? Or do " +#~ "you want to add an experiment to" +#~ " an existing baseline paper? Great, " +#~ "we really appreciate your contribution." #~ msgstr "" #~ msgid "" -#~ "While the :code:`client_resources` can be " -#~ "used to control the degree of " -#~ "concurrency in your FL simulation, this" -#~ " does not stop you from running " -#~ "dozens, hundreds or even thousands of" -#~ " clients in the same round and " -#~ "having orders of magnitude more " -#~ "`dormant` (i.e. not participating in a" -#~ " round) clients. Let's say you want" -#~ " to have 100 clients per round " -#~ "but your system can only accommodate " -#~ "8 clients concurrently. The " -#~ ":code:`VirtualClientEngine` will schedule 100 " -#~ "jobs to run (each simulating a " -#~ "client sampled by the strategy) and " -#~ "then will execute them in a " -#~ "resource-aware manner in batches of " -#~ "8." +#~ "The goal of Flower Baselines is to" +#~ " reproduce experiments from popular papers" +#~ " to accelerate researchers by enabling " +#~ "faster comparisons to new strategies, " +#~ "datasets, models, and federated pipelines " +#~ "in general." #~ msgstr "" #~ msgid "" -#~ "To understand all the intricate details" -#~ " on how resources are used to " -#~ "schedule FL clients and how to " -#~ "define custom resources, please take a" -#~ " look at the `Ray documentation " -#~ "`_." +#~ "Before you start to work on a " +#~ "new baseline or experiment, please check" +#~ " the `Flower Issues " +#~ "`_ or `Flower " +#~ "Pull Requests `_ " +#~ "to see if someone else is already" +#~ " working on it. Please open a " +#~ "new issue if you are planning to" +#~ " work on a new baseline or " +#~ "experiment with a short description of" +#~ " the corresponding paper and the " +#~ "experiment you want to contribute." #~ msgstr "" -#~ msgid "Simulation examples" -#~ msgstr "Exemples de PyTorch" +#~ msgid "TL;DR: Adding a new Flower Baseline" +#~ msgstr "" #~ msgid "" -#~ "A few ready-to-run complete " -#~ "examples for Flower simulation in " -#~ "Tensorflow/Keras and PyTorch are provided " -#~ "in the `Flower repository " -#~ "`_. You can run " -#~ "them on Google Colab too:" +#~ "Let's say you want to contribute " +#~ "the code of your most recent " +#~ "Federated Learning publication, *FedAweseome*. " +#~ "There are only three steps necessary " +#~ "to create a new *FedAweseome* Flower " +#~ "Baseline:" +#~ msgstr "" + +#~ msgid "**Get the Flower source code on your machine**" #~ msgstr "" #~ msgid "" -#~ "`Tensorflow/Keras Simulation " -#~ "`_: 100 clients collaboratively " -#~ "train a MLP model on MNIST." +#~ "Fork the Flower codebase: got to " +#~ "the `Flower GitHub repo " +#~ "`_ and fork the " +#~ "code (click the *Fork* button in " +#~ "the top-right corner and follow " +#~ "the instructions)" #~ msgstr "" -#~ "`Quickstart TensorFlow (Code) " -#~ "`_" #~ msgid "" -#~ "`PyTorch Simulation " -#~ "`_: 100 clients collaboratively train" -#~ " a CNN model on MNIST." +#~ "Clone the (forked) Flower source code:" +#~ " :code:`git clone " +#~ "git@github.com:[your_github_username]/flower.git`" #~ msgstr "" -#~ "`Quickstart PyTorch (Code) " -#~ "`_" #~ msgid "" -#~ "Flower's :code:`VirtualClientEngine` allows you " -#~ "to run FL simulations across multiple" -#~ " compute nodes. Before starting your " -#~ "multi-node simulation ensure that you:" +#~ "Open the code in your favorite " +#~ "editor (e.g., using VSCode: ``cd flower" +#~ " ; code .``)" #~ msgstr "" -#~ msgid "Have the same Python environment in all nodes." +#~ msgid "**Add the FedAwesome code**" #~ msgstr "" -#~ msgid "Have a copy of your code (e.g. your entire repo) in all nodes." +#~ msgid "" +#~ "Add your :code:`FedAwesome` code under " +#~ ":code:`baselines/flwr_baselines/publications/[fedawesome]`" #~ msgstr "" -#~ msgid "" -#~ "Have a copy of your dataset in " -#~ "all nodes (more about this in " -#~ ":ref:`simulation considerations `)" +#~ msgid "Add a `pyproject.toml` with all necessary dependencies" #~ msgstr "" -#~ msgid "" -#~ "Pass :code:`ray_init_args={\"address\"=\"auto\"}` to " -#~ "`start_simulation `_ so the " -#~ ":code:`VirtualClientEngine` attaches to a " -#~ "running Ray instance." +#~ msgid "Add a `README.md` describing how to use your baseline" #~ msgstr "" -#~ msgid "" -#~ "Start Ray on you head node: on " -#~ "the terminal type :code:`ray start " -#~ "--head`. This command will print a " -#~ "few lines, one of which indicates " -#~ "how to attach other nodes to the" -#~ " head node." +#~ msgid "**Open a pull request**" #~ msgstr "" -#~ msgid "" -#~ "Attach other nodes to the head " -#~ "node: copy the command shown after " -#~ "starting the head and execute it " -#~ "on terminal of a new node: for " -#~ "example :code:`ray start " -#~ "--address='192.168.1.132:6379'`" +#~ msgid "Stage your changes: :code:`git add .`" #~ msgstr "" #~ msgid "" -#~ "With all the above done, you can" -#~ " run your code from the head " -#~ "node as you would if the " -#~ "simulation was running on a single " -#~ "node." +#~ "Commit & push: :code:`git commit -m " +#~ "\"Create new FedAweseome baseline\" ; " +#~ "git push`" #~ msgstr "" #~ msgid "" -#~ "Once your simulation is finished, if " -#~ "you'd like to dismantle your cluster " -#~ "you simply need to run the command" -#~ " :code:`ray stop` in each node's " -#~ "terminal (including the head node)." +#~ "Open a pull request: go to *your*" +#~ " fork of the Flower codebase and " +#~ "create a pull request that targets " +#~ "the Flower ``main``` branch" #~ msgstr "" -#~ msgid "Multi-node simulation good-to-know" -#~ msgstr "" +#~ msgid "Further reading:" +#~ msgstr "Aide supplémentaire" #~ msgid "" -#~ "Here we list a few interesting " -#~ "functionality when running multi-node FL" -#~ " simulations:" +#~ "`GitHub docs: About forks " +#~ "`_" #~ msgstr "" #~ msgid "" -#~ "User :code:`ray status` to check all " -#~ "nodes connected to your head node " -#~ "as well as the total resources " -#~ "available to the :code:`VirtualClientEngine`." +#~ "`GitHub docs: Creating a pull request" +#~ " `_" #~ msgstr "" #~ msgid "" -#~ "When attaching a new node to the" -#~ " head, all its resources (i.e. all" -#~ " CPUs, all GPUs) will be visible " -#~ "by the head node. This means that" -#~ " the :code:`VirtualClientEngine` can schedule " -#~ "as many `virtual` clients as that " -#~ "node can possible run. In some " -#~ "settings you might want to exclude " -#~ "certain resources from the simulation. " -#~ "You can do this by appending " -#~ "`--num-cpus=` and/or `--num-" -#~ "gpus=` in any :code:`ray " -#~ "start` command (including when starting " -#~ "the head)" +#~ "`GitHub docs: Creating a pull request" +#~ " from a fork `_" #~ msgstr "" -#~ msgid "Considerations for simulations" -#~ msgstr "Simulation de moniteur" +#~ msgid "Requirements" +#~ msgstr "Changements nécessaires" #~ msgid "" -#~ "We are actively working on these " -#~ "fronts so to make it trivial to" -#~ " run any FL workload with Flower " -#~ "simulation." +#~ "Contributing a new baseline is really" +#~ " easy. You only have to make " +#~ "sure that your federated learning " +#~ "experiments are running with Flower. As" +#~ " soon as you have created a " +#~ "Flower-based experiment, you can contribute" +#~ " it." #~ msgstr "" #~ msgid "" -#~ "The current VCE allows you to run" -#~ " Federated Learning workloads in simulation" -#~ " mode whether you are prototyping " -#~ "simple scenarios on your personal laptop" -#~ " or you want to train a complex" -#~ " FL pipeline across multiple high-" -#~ "performance GPU nodes. While we add " -#~ "more capabilities to the VCE, the " -#~ "points below highlight some of the " -#~ "considerations to keep in mind when " -#~ "designing your FL pipeline with Flower." -#~ " We also highlight a couple of " -#~ "current limitations in our implementation." +#~ "It is recommended (but not required) " +#~ "to use `Hydra `_ to " +#~ "execute the experiment." #~ msgstr "" -#~ msgid "GPU resources" -#~ msgstr "Ressources" - #~ msgid "" -#~ "The VCE assigns a share of GPU " -#~ "memory to a client that specifies " -#~ "the key :code:`num_gpus` in " -#~ ":code:`client_resources`. This being said, Ray" -#~ " (used internally by the VCE) is " -#~ "by default:" +#~ "Please make sure to add your " +#~ "baseline or experiment to the " +#~ "corresponding directory as explained in " +#~ "`Executing Baseline `_. Give your baseline the " +#~ "unique identifier. For example, :code:`fedbn`" +#~ " refers to the paper \"FedBN: " +#~ "Federated Learning on non-IID Features" +#~ " via Local Batch Normalization\" and " +#~ "creates the corresponding directory " +#~ ":code:`flower/baselines/flwr_baselines/publications/fedbn`. Then" +#~ " you create the experiment directory " +#~ "with the experiment name. For example," +#~ " the experiment that measures the " +#~ "convergence has the directory " +#~ ":code:`flower/baselines/flwr_baselines/publications/fedbn/convergence_rate`." +#~ " This directory contains all your " +#~ "code and a :code:`README.md` with a " +#~ "link to the paper, the paper's " +#~ "abstract, and a detailed description of" +#~ " how to execute the experiments." #~ msgstr "" #~ msgid "" -#~ "not aware of the total VRAM " -#~ "available on the GPUs. This means " -#~ "that if you set :code:`num_gpus=0.5` and" -#~ " you have two GPUs in your " -#~ "system with different (e.g. 32GB and " -#~ "8GB) VRAM amounts, they both would " -#~ "run 2 clients concurrently." +#~ "Please also check if :code:`pyproject.toml`" +#~ " and :code:`requirements.txt` (all in the" +#~ " directory `baselines " +#~ "`_ contain" +#~ " all required Python packages (libraries," +#~ " frameworks, ...). If the required " +#~ "Python package is not yet listed, " +#~ "please add it to :code:`pyproject.toml`. " +#~ "If you need a different version of" +#~ " a package already listed, please try" +#~ " to ensure your experiment runs with" +#~ " the existing version listed in " +#~ ":code:`pyproject.toml` (or :code:`requirements.txt`). " +#~ "If that doesn't work, open a " +#~ "GitHub Issue and request the version " +#~ "change." #~ msgstr "" #~ msgid "" -#~ "not aware of other unrelated (i.e. " -#~ "not created by the VCE) workloads " -#~ "are running on the GPU. Two " -#~ "takeaways from this are:" +#~ "The experiment also needs to contain " +#~ "a file with a downloader for the" +#~ " dataset - if possible automatic. " +#~ "This can be included in one of " +#~ "the files or as an extra file." #~ msgstr "" #~ msgid "" -#~ "Your Flower server might need a " -#~ "GPU to evaluate the `global model` " -#~ "after aggregation (by instance when " -#~ "making use of the `evaluate method " -#~ "`_)" +#~ "Finally, please add plots for all " +#~ "experimental results your code is " +#~ "running to the :code:`experiment` directory" +#~ " and include them in :code:`README.md`. " +#~ "Doing this helps others and enables " +#~ "them to recognize your contributions " +#~ "quickly." #~ msgstr "" #~ msgid "" -#~ "If you want to run several " -#~ "independent Flower simulations on the " -#~ "same machine you need to mask-out" -#~ " your GPUs with " -#~ ":code:`CUDA_VISIBLE_DEVICES=\"\"` when launching" -#~ " your experiment." +#~ "We are aware that a few libraries" +#~ " are available only via Conda. " +#~ "However, we want to encourage you " +#~ "to ensure that your code also runs" +#~ " well outside of Conda to make " +#~ "it more accessible to the broader " +#~ "research community." #~ msgstr "" -#~ msgid "" -#~ "In addition, the GPU resource limits " -#~ "passed to :code:`client_resources` are not " -#~ "`enforced` (i.e. they can be exceeded)" -#~ " which can result in the situation" -#~ " of client using more VRAM than " -#~ "the ratio specified when starting the" -#~ " simulation." +#~ msgid "Here is a checklist for adding a new baseline:" #~ msgstr "" -#~ msgid "TensorFlow with GPUs" -#~ msgstr "Exemples de TensorFlow" - #~ msgid "" -#~ "When `using a GPU with TensorFlow " -#~ "`_ nearly your " -#~ "entire GPU memory of all your GPUs" -#~ " visible to the process will be " -#~ "mapped. This is done by TensorFlow " -#~ "for optimization purposes. However, in " -#~ "settings such as FL simulations where" -#~ " we want to split the GPU into" -#~ " multiple `virtual` clients, this is " -#~ "not a desirable mechanism. Luckily we" -#~ " can disable this default behavior by" -#~ " `enabling memory growth " -#~ "`_." +#~ "add required Python packages to " +#~ ":code:`pyproject.toml` or :code:`requirements.txt`" #~ msgstr "" #~ msgid "" -#~ "This would need to be done in " -#~ "the main process (which is where " -#~ "the server would run) and in each" -#~ " Actor created by the VCE. By " -#~ "means of :code:`actor_kwargs` we can " -#~ "pass the reserved key `\"on_actor_init_fn\"`" -#~ " in order to specify a function " -#~ "to be executed upon actor " -#~ "initialization. In this case, to enable" -#~ " GPU growth for TF workloads. It " -#~ "would look as follows:" +#~ "add all required code under " +#~ ":code:`baselines/flwr_baselines/publications/[new_publication]`" +#~ msgstr "" + +#~ msgid "add a dataset downloader" +#~ msgstr "" + +#~ msgid "add an experiment plot" #~ msgstr "" -#~ msgid "" -#~ "This is precisely the mechanism used " -#~ "in `Tensorflow/Keras Simulation " -#~ "`_ example." +#~ msgid "add a :code:`README.md`" #~ msgstr "" -#~ "`Quickstart TensorFlow (Code) " -#~ "`_" -#~ msgid "Multi-node setups" +#~ msgid "Usability" #~ msgstr "" #~ msgid "" -#~ "The VCE does not currently offer a" -#~ " way to control on which node a" -#~ " particular `virtual` client is executed." -#~ " In other words, if more than a" -#~ " single node have the resources " -#~ "needed by a client to run, then" -#~ " any of those nodes could get " -#~ "the client workload scheduled onto. " -#~ "Later in the FL process (i.e. in" -#~ " a different round) the same client" -#~ " could be executed by a different " -#~ "node. Depending on how your clients " -#~ "access their datasets, this might " -#~ "require either having a copy of " -#~ "all dataset partitions on all nodes " -#~ "or a dataset serving mechanism (e.g. " -#~ "using nfs, a database) to circumvent " -#~ "data duplication." +#~ "Flower is known and loved for its" +#~ " usability. Therefore, make sure that " +#~ "your baseline or experiment can be " +#~ "executed with a single command such " +#~ "as :code:`./run.sh` or :code:`python3 " +#~ "main.py`. How you organize the " +#~ "experiments and the related code " +#~ "structure is up to you as an " +#~ "author, but please keep in mind to" +#~ " make sure that other users can " +#~ "easily understand and execute your " +#~ "baseline." #~ msgstr "" -#~ msgid "" -#~ "By definition virtual clients are " -#~ "`stateless` due to their ephemeral " -#~ "nature. A client state can be " -#~ "implemented as part of the Flower " -#~ "client class but users need to " -#~ "ensure this saved to persistent storage" -#~ " (e.g. a database, disk) and that " -#~ "can be retrieve later by the same" -#~ " client regardless on which node it" -#~ " is running from. This is related " -#~ "to the point above also since, in" -#~ " some way, the client's dataset could" -#~ " be seen as a type of `state`." +#~ msgid "We look forward to your contribution!" +#~ msgstr "Exemple de première contribution" + +#~ msgid "flwr" +#~ msgstr "Fleur" + +#~ msgid "binaries" #~ msgstr "" -#~ msgid "Save and load model checkpoints" -#~ msgstr "Sauvegarde et chargement des points de contrôle PyTorch" +#~ msgid "Flower Baselines" +#~ msgstr "Demande pour une nouvelle Flower Baseline" #~ msgid "" -#~ "Flower does not automatically save model" -#~ " updates on the server-side. This " -#~ "how-to guide describes the steps " -#~ "to save (and load) model checkpoints " -#~ "in Flower." +#~ "Flower Baselines are a collection of " +#~ "organised scripts used to reproduce " +#~ "results from well-known publications or" +#~ " benchmarks. You can check which " +#~ "baselines already exist and/or contribute " +#~ "your own baseline." #~ msgstr "" -#~ msgid "Legacy example guides" +#~ msgid "Flower requires `Python 3.7 `_ or above." +#~ msgstr "`Python 3.7 `_ ou plus" + +#~ msgid "|9e234df38403464899ad3aee36bf1b95|" #~ msgstr "" -#~ msgid "Contributor tutorials" -#~ msgstr "Configuration du contributeur" +#~ msgid "|081158351506446f9f772cb45ee68523|" +#~ msgstr "" -#~ msgid "Contributor explanations" -#~ msgstr "Explications" +#~ msgid "|e9325042b79c45ed96b5a8d2f6f3cdc9|" +#~ msgstr "" -#~ msgid "Flower Framework Documentation" -#~ msgstr "Documentation de Flower" +#~ msgid "|11b83bb107344db78a37266e080c4a7a|" +#~ msgstr "" -#~ msgid "PyTorch" -#~ msgstr "Exemples de PyTorch" +#~ msgid "|cd764bcf6d174a9cb62880ace9a8a6bd|" +#~ msgstr "" -#~ msgid "TensorFlow" -#~ msgstr "TensorFlow" +#~ msgid "|5c520984cced41e38f6bb4af416c3f84|" +#~ msgstr "" -#~ msgid "Flower CLI reference" -#~ msgstr "Client de Flower" +#~ msgid "|66941b0608644cf1a2269a194d3bc0dd|" +#~ msgstr "" -#~ msgid "flwr (Python API reference)" -#~ msgstr "Référence pour l'API" +#~ msgid "|4b149f3a095b402bb8890275aabc9298|" +#~ msgstr "" -#~ msgid "Unreleased" -#~ msgstr "Inédit" +#~ msgid "|675cf7d3d53a4817b5d47529c0758158|" +#~ msgstr "" -#~ msgid "**Deprecate Python 3.7**" -#~ msgstr "**Deprecate Python 3.7**" +#~ msgid "|7ca594e16ae7477790c2e3cf096ec7cd|" +#~ msgstr "" -#~ msgid "" -#~ "Since Python 3.7 reached its end " -#~ "of life (EOL) on 2023-06-27, support " -#~ "for Python 3.7 is now deprecated " -#~ "and will be removed in an upcoming" -#~ " release." +#~ msgid "|d669336577b545a081d5d74169a9bc4d|" #~ msgstr "" -#~ "Étant donné que Python 3.7 a " -#~ "atteint sa fin de vie (EOL) le " -#~ "2023-06-27, la prise en charge de " -#~ "Python 3.7 est désormais dépréciée et" -#~ " sera supprimée dans une prochaine " -#~ "version." -#~ msgid "" -#~ "**Add new** `FedTrimmedAvg` **strategy** " -#~ "([#1769](https://github.com/adap/flower/pull/1769), " -#~ "[#1853](https://github.com/adap/flower/pull/1853))" +#~ msgid "|00b3d6cde1ff410ba54eff58da4e033a|" #~ msgstr "" -#~ "**Ajouter un nouveau** `FedTrimmedAvg` " -#~ "**stratégie** " -#~ "([#1769](https://github.com/adap/flower/pull/1769), " -#~ "[#1853](https://github.com/adap/flower/pull/1853))" -#~ msgid "" -#~ "The new `FedTrimmedAvg` strategy implements" -#~ " Trimmed Mean by [Dong Yin, " -#~ "2018](https://arxiv.org/abs/1803.01498)" +#~ msgid "|29a11f5353084c1995c538f7edef71a5|" #~ msgstr "" -#~ "La nouvelle stratégie `FedTrimmedAvg` met " -#~ "en œuvre la moyenne trimmée par " -#~ "[Dong Yin, 2018](https://arxiv.org/abs/1803.01498)" -#~ msgid "" -#~ "**Add parameter aggregation to** `mt-" -#~ "pytorch` **code example** " -#~ "([#1785](https://github.com/adap/flower/pull/1785))" +#~ msgid "|d62eda312fd44726bb5db2b761fe7e0d|" #~ msgstr "" -#~ "**Ajouter l'agrégation des paramètres à** " -#~ "`mt-pytorch` **exemple de code** " -#~ "([#1785](https://github.com/adap/flower/pull/1785))" -#~ msgid "" -#~ "The `mt-pytorch` example shows how " -#~ "to aggregate parameters when writing a" -#~ " driver script. The included `driver.py`" -#~ " and `server.py` have been aligned to" -#~ " demonstrate both the low-level way" -#~ " and the high-level way of " -#~ "building server-side logic." +#~ msgid "Using Baselines" #~ msgstr "" -#~ "L'exemple `mt-pytorch` montre comment " -#~ "agréger des paramètres lors de " -#~ "l'écriture d'un script de pilote. Les" -#~ " fichiers `driver.py` et `server.py` inclus" -#~ " ont été alignés pour démontrer à " -#~ "la fois la manière de bas niveau" -#~ " et la manière de haut niveau " -#~ "de construire la logique côté serveur." -#~ msgid "" -#~ "**Introduce (experimental) gRPC request-" -#~ "response API** " -#~ "([#1867](https://github.com/adap/flower/pull/1867), " -#~ "[#1901](https://github.com/adap/flower/pull/1901))" +#~ msgid "Structure" #~ msgstr "" -#~ "**Introduire l'API demande-réponse gRPC " -#~ "(expérimentale)** " -#~ "([#1867](https://github.com/adap/flower/pull/1867), " -#~ "[#1901](https://github.com/adap/flower/pull/1901))" #~ msgid "" -#~ "In addition to the existing gRPC " -#~ "API (based on bidirectional streaming) " -#~ "and the experimental REST API, there " -#~ "is now a new gRPC API that " -#~ "uses a request-response model to " -#~ "communicate with client nodes." +#~ "All baselines are available in the " +#~ "directory `baselines " +#~ "`_. This " +#~ "directory has two different files:" #~ msgstr "" -#~ "En plus de l'API gRPC existante " -#~ "(basée sur un flux bidirectionnel) et" -#~ " de l'API REST expérimentale, il " -#~ "existe désormais une nouvelle API gRPC" -#~ " qui utilise un modèle demande-" -#~ "réponse pour communiquer avec les nœuds" -#~ " clients." #~ msgid "" -#~ "Please note: The gRPC request-response" -#~ " API is still experimental and will" -#~ " likely change significantly over time." +#~ "Both files contain all the information" +#~ " about required Python packages (libraries," +#~ " frameworks, ...) and their versions. " +#~ "You can install each library separately" +#~ " by using :code: `pip install` or " +#~ "you can use Poetry and run " +#~ "code:`poetry install` in the directory " +#~ "where you find the :code:`pyproject.toml` " +#~ "file. After installing all requirements, " +#~ "you can start to run your " +#~ "baseline." #~ msgstr "" -#~ "Remarque : l'API requête-réponse gRPC" -#~ " est encore expérimentale et est " -#~ "susceptible de changer de manière " -#~ "significative au fil du temps." #~ msgid "" -#~ "**Replace the eperimental** " -#~ "`start_client(rest=True)` **with the new** " -#~ "`start_client(transport=\"rest\")` " -#~ "([#1880](https://github.com/adap/flower/pull/1880))" +#~ "Go to the baseline that you want" +#~ " to execute. The directories and " +#~ "files are structured so that you " +#~ "can first find the paper with " +#~ "their unique identifier such that, for" +#~ " example, :code:`FedProx` refers to the " +#~ "paper \"Federated Optimization in " +#~ "Heterogeneous Networks\". The :code:`fedprox` " +#~ "section contains all available experiments " +#~ "from that paper." #~ msgstr "" -#~ "**Remplacez le fichier expérimental** " -#~ "`start_client(rest=True) **par le nouveau** " -#~ "`start_client(transport=\"rest\")` " -#~ "([#1880](https://github.com/adap/flower/pull/1880))" #~ msgid "" -#~ "The (experimental) `start_client` argument " -#~ "`rest` was deprecated in favor of " -#~ "a new argument `transport`. " -#~ "`start_client(transport=\"rest\")` will yield the" -#~ " same behaviour as `start_client(rest=True)` " -#~ "did before. All code should migrate " -#~ "to the new argument `transport`. The " -#~ "deprecated argument `rest` will be " -#~ "removed in a future release." +#~ "The experiment area contains a " +#~ ":code:`README.md` covering the corresponding " +#~ "paper, its abstract, and goal as " +#~ "well as a detailed description of " +#~ "how to run the baseline. Please " +#~ "use the :code:`README.md` to see how " +#~ "to execute each individual baseline." +#~ msgstr "" + +#~ msgid "Available Baselines" #~ msgstr "" #~ msgid "" -#~ "**Migrate experimental REST API to " -#~ "Starlette** ([2171](https://github.com/adap/flower/pull/2171))" +#~ "The following table lists all currently" +#~ " available baselines and the corresponding" +#~ " papers. If you want to add a" +#~ " new baseline or experiment, please " +#~ "check the `Contributing Baselines " +#~ "`_ section." #~ msgstr "" -#~ "**Migrer l'API REST expérimentale vers " -#~ "Starlette** ([2171](https://github.com/adap/flower/pull/2171))" -#~ msgid "" -#~ "The (experimental) REST API used to " -#~ "be implemented in " -#~ "[FastAPI](https://fastapi.tiangolo.com/), but it has" -#~ " now been migrated to use " -#~ "[Starlette](https://www.starlette.io/) directly." +#~ msgid "Paper" #~ msgstr "" -#~ "L'API REST (expérimentale) était auparavant" -#~ " implémentée dans " -#~ "[FastAPI](https://fastapi.tiangolo.com/), mais elle " -#~ "a maintenant été migrée pour utiliser" -#~ " directement [Starlette](https://www.starlette.io/)." -#~ msgid "" -#~ "**Add a new gRPC option** " -#~ "([#2197](https://github.com/adap/flower/pull/2197))" +#~ msgid "Experiment" #~ msgstr "" -#~ "**Ajouter une nouvelle option gRPC** " -#~ "([#2197](https://github.com/adap/flower/pull/2197))" -#~ msgid "" -#~ "We now start a gRPC server with" -#~ " the `grpc.keepalive_permit_without_calls` option " -#~ "set to 0 by default. This prevents" -#~ " the clients from sending keepalive " -#~ "pings when there is no outstanding " -#~ "stream." +#~ msgid "Directory" #~ msgstr "" -#~ "Nous démarrons maintenant un serveur " -#~ "gRPC avec l'option " -#~ "`grpc.keepalive_permit_without_calls` réglée sur 0" -#~ " par défaut, ce qui empêche les " -#~ "clients d'envoyer des pings de maintien" -#~ " lorsqu'il n'y a pas de flux en" -#~ " attente." -#~ msgid "" -#~ "**General improvements** " -#~ "([#1872](https://github.com/adap/flower/pull/1872), " -#~ "[#1866](https://github.com/adap/flower/pull/1866), " -#~ "[#1884](https://github.com/adap/flower/pull/1884))" +#~ msgid "`FedAvg `_" #~ msgstr "" -#~ "**Mettre à jour les exemples de " -#~ "code** ([#1291](https://github.com/adap/flower/pull/1291), " -#~ "[#1286](https://github.com/adap/flower/pull/1286), " -#~ "[#1282](https://github.com/adap/flower/pull/1282))" -#~ msgid "Example projects" -#~ msgstr "Exemples" +#~ msgid "MNIST" +#~ msgstr "" -#~ msgid "" -#~ "`Flower simulation PyTorch " -#~ "`_" +#~ msgid ":code:`flower/baselines/flwr_baselines/publications/fedavg_mnist/`" #~ msgstr "" -#~ "`Flower Quickstart (TensorFlow/Keras) " -#~ "`_" -#~ msgid "" -#~ "`Android Kotlin example " -#~ "`_" +#~ msgid "`FedProx `_" #~ msgstr "" -#~ msgid "`Android Java example `_" +#~ msgid ":code:`flower/baselines/flwr_baselines/publications/fedprox_mnist/`" #~ msgstr "" -#~ msgid "Build a strategy from scratch" -#~ msgstr "Élaborer une stratégie à partir de zéro" +#~ msgid "`FedOpt `_" +#~ msgstr "" -#~ msgid "Customize the client" -#~ msgstr "Création du client IMDBC" +#~ msgid "sparse gradient task" +#~ msgstr "" -#~ msgid "Get started with Flower" +#~ msgid ":code:`flower/baselines/flwr_baselines/publications/adaptive_federated_optimization`" #~ msgstr "" -#~ msgid "Quickstart Android" -#~ msgstr "Démarrage rapide d'Android" +#~ msgid "`FedBN `_" +#~ msgstr "" -#~ msgid "" -#~ "Let's build a federated learning system" -#~ " using TFLite and Flower on Android!" +#~ msgid "convergence rate" +#~ msgstr "" + +#~ msgid ":code:`flower/baselines/flwr_baselines/publications/fedbn/convergence_rate`" #~ msgstr "" -#~ "Construisons un système d'apprentissage fédéré" -#~ " en utilisant TFLite et Flower sur" -#~ " Android !" #~ msgid "" -#~ "Please refer to the `full code " -#~ "example " -#~ "`_ to" -#~ " learn more." +#~ "Flower requires `Python 3.7 " +#~ "`_ or above, we " +#~ "recommend `Python 3.8 " +#~ "`_." #~ msgstr "" -#~ "Réfère-toi à l'exemple de code " -#~ "complet " -#~ "`_ " -#~ "pour en savoir plus." +#~ "Flower nécessite `Python 3.7 " +#~ "`_ ou plus, nous " +#~ "recommandons `Python 3.8 " +#~ "`_." -#~ msgid "Quickstart iOS" -#~ msgstr "Démarrage rapide iOS" +#~ msgid "|6baade94cd14454e82ead34fcc29a182|" +#~ msgstr "" -#~ msgid "" -#~ "In this tutorial we will learn how" -#~ " to train a Neural Network on " -#~ "MNIST using Flower and CoreML on " -#~ "iOS devices." +#~ msgid "|1209ecd819104c458d396cf665c7ed4f|" #~ msgstr "" -#~ "Dans ce tutoriel, nous allons apprendre" -#~ " à former un réseau neuronal sur " -#~ "MNIST en utilisant Flower et CoreML " -#~ "sur les appareils iOS." -#~ msgid "" -#~ "First of all, for running the " -#~ "Flower Python server, it is recommended" -#~ " to create a virtual environment and" -#~ " run everything within a `virtualenv " -#~ "`_. " -#~ "For the Flower client implementation in" -#~ " iOS, it is recommended to use " -#~ "Xcode as our IDE." +#~ msgid "|c088b02349304344a53f3ce1464225fb|" #~ msgstr "" -#~ "Tout d'abord, pour l'exécution du " -#~ "serveur Flower Python, il est recommandé" -#~ " de créer un environnement virtuel et" -#~ " de tout exécuter au sein d'un " -#~ "`virtualenv `_. Pour l'implémentation du client" -#~ " Flower dans iOS, il est recommandé" -#~ " d'utiliser Xcode comme notre IDE." -#~ msgid "" -#~ "Our example consists of one Python " -#~ "*server* and two iPhone *clients* that" -#~ " all have the same model." +#~ msgid "|b54d50afc82a4a57a55997a9eaeb735b|" #~ msgstr "" -#~ "Notre exemple se compose d'un *serveur*" -#~ " Python et de deux *clients* iPhone" -#~ " qui ont tous le même modèle." -#~ msgid "" -#~ "*Clients* are responsible for generating " -#~ "individual weight updates for the model" -#~ " based on their local datasets. These" -#~ " updates are then sent to the " -#~ "*server* which will aggregate them to" -#~ " produce a better model. Finally, the" -#~ " *server* sends this improved version " -#~ "of the model back to each " -#~ "*client*. A complete cycle of weight " -#~ "updates is called a *round*." +#~ msgid "|d17b57e97b714a25b43790d4b832fd87|" #~ msgstr "" -#~ "*Les clients* sont chargés de générer" -#~ " des mises à jour de poids " -#~ "individuelles pour le modèle en fonction" -#~ " de leurs ensembles de données " -#~ "locaux. Ces mises à jour sont " -#~ "ensuite envoyées au *serveur* qui les" -#~ " agrège pour produire un meilleur " -#~ "modèle. Enfin, le *serveur* renvoie " -#~ "cette version améliorée du modèle à " -#~ "chaque *client*. Un cycle complet de " -#~ "mises à jour de poids s'appelle un" -#~ " *round*." -#~ msgid "" -#~ "Now that we have a rough idea " -#~ "of what is going on, let's get " -#~ "started to setup our Flower server " -#~ "environment. We first need to install" -#~ " Flower. You can do this by " -#~ "using pip:" +#~ msgid "|38966d05301a4854aa73c8c5033bfaab|" #~ msgstr "" -#~ "Maintenant que nous avons une idée " -#~ "approximative de ce qui se passe, " -#~ "commençons à configurer notre environnement" -#~ " de serveur Flower. Nous devons " -#~ "d'abord installer Flower, ce que tu " -#~ "peux faire à l'aide de pip :" -#~ msgid "Or Poetry:" -#~ msgstr "Ou de la poésie :" +#~ msgid "|231d55f7926d4a5db02dcd724ec62529|" +#~ msgstr "" -#~ msgid "" -#~ "Now that we have all our " -#~ "dependencies installed, let's run a " -#~ "simple distributed training using CoreML " -#~ "as our local training pipeline and " -#~ "MNIST as our dataset. For simplicity " -#~ "reasons we will use the complete " -#~ "Flower client with CoreML, that has " -#~ "been implemented and stored inside the" -#~ " Swift SDK. The client implementation " -#~ "can be seen below:" +#~ msgid "|fb44f2e13a1b4b69b7a72234eedd13f4|" #~ msgstr "" -#~ "Maintenant que toutes nos dépendances " -#~ "sont installées, exécutons une simple " -#~ "formation distribuée en utilisant CoreML " -#~ "comme pipeline de formation local et " -#~ "MNIST comme ensemble de données. Pour" -#~ " des raisons de simplicité, nous " -#~ "utiliserons le client Flower complet " -#~ "avec CoreML, qui a été mis en " -#~ "œuvre et stocké à l'intérieur du " -#~ "SDK Swift. La mise en œuvre du " -#~ "client peut être vue ci-dessous :" -#~ msgid "" -#~ "Let's create a new application project" -#~ " in Xcode and add :code:`flwr` as " -#~ "a dependency in your project. For " -#~ "our application, we will store the " -#~ "logic of our app in " -#~ ":code:`FLiOSModel.swift` and the UI elements" -#~ " in :code:`ContentView.swift`. We will " -#~ "focus more on :code:`FLiOSModel.swift` in " -#~ "this quickstart. Please refer to the " -#~ "`full code example " -#~ "`_ to " -#~ "learn more about the app." +#~ msgid "|1cfc77af5d164030942e84d14268c256|" #~ msgstr "" -#~ "Créons un nouveau projet d'application " -#~ "dans Xcode et ajoutons :code:`flwr` " -#~ "comme dépendance dans ton projet. Pour" -#~ " notre application, nous stockerons la " -#~ "logique de notre application dans " -#~ ":code:`FLiOSModel.swift` et les éléments de" -#~ " l'interface utilisateur dans " -#~ ":code:`ContentView.swift`.Nous nous concentrerons " -#~ "davantage sur :code:`FLiOSModel.swift` dans ce" -#~ " quickstart. N'hésite pas à te " -#~ "référer à l'`exemple de code complet " -#~ "`_ pour" -#~ " en savoir plus sur l'application." -#~ msgid "Import Flower and CoreML related packages in :code:`FLiOSModel.swift`:" +#~ msgid "|0d50828231a64bc08223544a2d2fa216|" +#~ msgstr "" + +#~ msgid "|904387757ceb42fbaa1875f3e8061113|" +#~ msgstr "" + +#~ msgid "|68608e1b7c4842458c528b431c715f5a|" +#~ msgstr "" + +#~ msgid "|2adb106bda97480bb4b33eac472e321e|" +#~ msgstr "" + +#~ msgid "|025f0a6f7a6145cba4bf8fa0e2495851|" #~ msgstr "" -#~ "Importe les paquets liés à Flower " -#~ "et CoreML dans :code:`FLiOSModel.swift` :" + +#~ msgid "Before the release" +#~ msgstr "Avant la sortie" #~ msgid "" -#~ "Then add the mlmodel to the " -#~ "project simply by drag-and-drop, " -#~ "the mlmodel will be bundled inside " -#~ "the application during deployment to " -#~ "your iOS device. We need to pass" -#~ " the url to access mlmodel and " -#~ "run CoreML machine learning processes, " -#~ "it can be retrieved by calling the" -#~ " function :code:`Bundle.main.url`. For the " -#~ "MNIST dataset, we need to preprocess " -#~ "it into :code:`MLBatchProvider` object. The" -#~ " preprocessing is done inside " -#~ ":code:`DataLoader.swift`." +#~ "Update the changelog (``changelog.md``) with" +#~ " all relevant changes that happened " +#~ "after the last release. If the " +#~ "last release was tagged ``v1.2.0``, you" +#~ " can use the following URL to " +#~ "see all commits that got merged " +#~ "into ``main`` since then:" #~ msgstr "" -#~ "Ensuite, ajoute le mlmodel au projet " -#~ "simplement par glisser-déposer, le " -#~ "mlmodel sera regroupé à l'intérieur de" -#~ " l'application lors du déploiement sur " -#~ "ton appareil iOS. Nous devons passer " -#~ "l'url pour accéder au mlmodel et " -#~ "exécuter les processus d'apprentissage " -#~ "automatique CoreML, elle peut être " -#~ "récupérée en appelant la fonction " -#~ ":code:`Bundle.main.url`. Pour l'ensemble de " -#~ "données MNIST, nous devons le prétraiter" -#~ " dans l'objet :code:`MLBatchProvider`. Le " -#~ "prétraitement est effectué à l'intérieur " -#~ "de :code:`DataLoader.swift`." +#~ "Mettez à jour le journal des " +#~ "modifications (``changelog.md``) avec tous les" +#~ " changements pertinents qui se sont " +#~ "produits après la dernière version. Si" +#~ " la dernière version a été étiquetée" +#~ " ``v1.2.0``, vous pouvez utiliser l'URL " +#~ "suivante pour voir tous les commits " +#~ "qui ont été fusionnés dans ``main`` " +#~ "depuis lors :" #~ msgid "" -#~ "Since CoreML does not allow the " -#~ "model parameters to be seen before " -#~ "training, and accessing the model " -#~ "parameters during or after the training" -#~ " can only be done by specifying " -#~ "the layer name, we need to know" -#~ " this informations beforehand, through " -#~ "looking at the model specification, " -#~ "which are written as proto files. " -#~ "The implementation can be seen in " -#~ ":code:`MLModelInspect`." +#~ "`GitHub: Compare v1.2.0...main " +#~ "`_" #~ msgstr "" +#~ "`GitHub : Compare v1.2.0...main " +#~ "`_" #~ msgid "" -#~ "After we have all of the necessary" -#~ " informations, let's create our Flower " -#~ "client." +#~ "Thank the authors who contributed since" +#~ " the last release. This command helps" +#~ " extract them: ``git log --format='%aN' " +#~ "v1.1.0..HEAD | sort -u``. The command" +#~ " has the same order as ``git " +#~ "shortlog``." #~ msgstr "" +#~ "Remerciez les auteurs qui ont contribué" +#~ " depuis la dernière version. Cette " +#~ "commande permet de les extraire : " +#~ "``git log --format='%aN' v1.1.0..HEAD | " +#~ "sort -u``. La commande a le même" +#~ " ordre que ``git shortlog``." #~ msgid "" -#~ "Then start the Flower gRPC client " -#~ "and start communicating to the server" -#~ " by passing our Flower client to " -#~ "the function :code:`startFlwrGRPC`." +#~ "Update the ``changelog.md`` section header " +#~ "``Unreleased`` to contain the version " +#~ "number and date for the release " +#~ "you are building. Create a pull " +#~ "request with the change." #~ msgstr "" -#~ "Lance ensuite le client Flower gRPC " -#~ "et commence à communiquer avec le " -#~ "serveur en passant notre client Flower" -#~ " à la fonction :code:`startFlwrGRPC`." +#~ "Mettez à jour l'en-tête de section" +#~ " ``changelog.md`` ``Unreleased`` pour qu'il " +#~ "contienne le numéro de version et " +#~ "la date de la version que vous " +#~ "construisez. Créez une demande de " +#~ "traction avec le changement." #~ msgid "" -#~ "That's it for the client. We only" -#~ " have to implement :code:`Client` or " -#~ "call the provided :code:`MLFlwrClient` and " -#~ "call :code:`startFlwrGRPC()`. The attribute " -#~ ":code:`hostname` and :code:`port` tells the" -#~ " client which server to connect to." -#~ " This can be done by entering " -#~ "the hostname and port in the " -#~ "application before clicking the start " -#~ "button to start the federated learning" -#~ " process." +#~ "Tag the release commit with the " +#~ "version number as soon as the PR" +#~ " is merged: ``git tag v0.12.3``, then" +#~ " ``git push --tags``" #~ msgstr "" -#~ "C'est tout pour le client. Il nous" -#~ " suffit d'implémenter :code:`Client` ou " -#~ "d'appeler le :code:`MLFlwrClient` fourni et" -#~ " d'appeler :code:`startFlwrGRPC()`. L'attribut " -#~ ":code:`hostname` et :code:`port` indique au" -#~ " client à quel serveur se connecter." -#~ " Pour ce faire, il suffit d'entrer" -#~ " le nom d'hôte et le port dans" -#~ " l'application avant de cliquer sur " -#~ "le bouton de démarrage pour lancer " -#~ "le processus d'apprentissage fédéré." +#~ "Marquez le commit de la version " +#~ "avec le numéro de version dès que" +#~ " le PR est fusionné : ``git tag" +#~ " v0.12.3``, puis ``git push --tags``" #~ msgid "" -#~ "Once the server is running we can" -#~ " start the clients in different " -#~ "terminals. Build and run the client " -#~ "through your Xcode, one through Xcode" -#~ " Simulator and the other by deploying" -#~ " it to your iPhone. To see more" -#~ " about how to deploy your app " -#~ "to iPhone or Simulator visit `here " -#~ "`_." +#~ "Build the release with ``./dev/build.sh``, " +#~ "then publish it with ``./dev/publish.sh``" #~ msgstr "" -#~ "Une fois que le serveur fonctionne, " -#~ "nous pouvons démarrer les clients dans" -#~ " différents terminaux. Construis et exécute" -#~ " le client grâce à ton Xcode, " -#~ "l'un via le simulateur Xcode et " -#~ "l'autre en le déployant sur ton " -#~ "iPhone. Pour en savoir plus sur la" -#~ " façon de déployer ton application " -#~ "sur l'iPhone ou le simulateur, visite" -#~ " `ici `_." +#~ "Construisez la version avec " +#~ "``./dev/build.sh``, puis publiez-la avec " +#~ "``./dev/publish.sh``" #~ msgid "" -#~ "Congratulations! You've successfully built and" -#~ " run your first federated learning " -#~ "system in your ios device. The " -#~ "full `source code " -#~ "`_ for" -#~ " this example can be found in " -#~ ":code:`examples/ios`." +#~ "Create an entry in GitHub releases " +#~ "with the release notes for the " +#~ "previously tagged commit and attach the" +#~ " build artifacts (:code:`.whl` and " +#~ ":code:`.tar.gz`)." #~ msgstr "" -#~ "Félicitations ! Tu as réussi à " -#~ "construire et à faire fonctionner ton" -#~ " premier système d'apprentissage fédéré " -#~ "dans ton appareil ios. Le `code " -#~ "source complet " -#~ "`_ de " -#~ "cet exemple se trouve dans " -#~ ":code:`examples/ios`." +#~ "Crée une entrée dans GitHub releases " +#~ "avec les notes de version pour le" +#~ " commit précédemment étiqueté et attache" +#~ " les artefacts de construction " +#~ "(:code:`.whl` et :code:`.tar.gz`)." #~ msgid "" -#~ "`Star Flower on GitHub " -#~ "`__ ⭐️ and join " -#~ "the open-source Flower community on " -#~ "Slack to connect, ask questions, and " -#~ "get help: `Join Slack `__ 🌼 We'd love to hear" -#~ " from you in the ``#introductions`` " -#~ "channel! And if anything is unclear, " -#~ "head over to the ``#questions`` channel." +#~ "Second, create a virtual environment " +#~ "(and activate it). If you chose to" +#~ " use :code:`pyenv` (with the :code" +#~ ":`pyenv-virtualenv` plugin) and already " +#~ "have it installed , you can use" +#~ " the following convenience script (by " +#~ "default it will use :code:`Python " +#~ "3.8.17`, but you can change it by" +#~ " providing a specific :code:``)::" #~ msgstr "" -#~ "`Star Flower on GitHub " -#~ "`__ ⭐️ et rejoignez" -#~ " la communauté open-source Flower sur" -#~ " Slack pour vous connecter, poser des" -#~ " questions et obtenir de l'aide : " -#~ "`Join Slack `__ " -#~ "🌼 Nous serions ravis d'avoir de " -#~ "vos nouvelles dans le canal " -#~ "``#introductions`` ! Et si quelque chose" -#~ " n'est pas clair, dirigez-vous vers" -#~ " le canal ``#questions``." +#~ "Deuxièmement, créer un environnement virtuel" +#~ " (et l'activer). Si vous choisissez " +#~ "d'utiliser :code:`pyenv` (avec le plugin " +#~ ":code:`pyenv-virtualenv`) et que vous " +#~ "l'avez déjà installé, vous pouvez " +#~ "utiliser le script suivant (par défaut" +#~ " il utilisera :code:`Python 3.8.17`, mais" +#~ " vous pouvez le changer en " +#~ "fournissant une :code:`` spécifique)::" -#~ msgid "|bd48315a61c14495babefe3c7918b493|" -#~ msgstr "" +#~ msgid "server.strategy.FedAvg" +#~ msgstr "serveur.stratégie.FedAvg" -#~ msgid "|c00d9e5b0d324d96b86da8a78b05b14b|" -#~ msgstr "" +#~ msgid "server.strategy.FedAvgM" +#~ msgstr "stratégie.serveur.FedAvgM" -#~ msgid "|faae2ee10f4149c9907563c4f48ec6ea|" -#~ msgstr "" +#~ msgid "server.strategy.QFedAvg" +#~ msgstr "server.strategy.QFedAvg" -#~ msgid "|13a655510351455292f145a61d6c15d6|" -#~ msgstr "" +#~ msgid "server.strategy.FedOpt" +#~ msgstr "serveur.stratégie.FedOpt" -#~ msgid "|13949884182846e3a91433190a936ba9|" -#~ msgstr "" +#~ msgid "server.strategy.FedProx" +#~ msgstr "serveur.stratégie.FedProx" -#~ msgid "|9bf26cc650b146e88b4745df040ece37|" -#~ msgstr "" +#~ msgid "server.strategy.FedAdagrad" +#~ msgstr "serveur.stratégie.FedAdagrad" -#~ msgid "|1590915480fc41708bd43e48af9582f9|" +#~ msgid "server.strategy.FedAdam" +#~ msgstr "serveur.stratégie.FedAdam" + +#~ msgid "server.strategy.FedYogi" +#~ msgstr "serveur.stratégie.FedYogi" + +#~ msgid "" +#~ "`achiverram28`, `Adam Narozniak`, `Anass " +#~ "Anhari`, `Charles Beauville`, `Dana-Farber`," +#~ " `Daniel J. Beutel`, `Daniel Nata " +#~ "Nugraha`, `Edoardo Gabrielli`, `eunchung`, " +#~ "`Gustavo Bertoli`, `Heng Pan`, `Javier`, " +#~ "`Mahdi`, `Ruth Galindo`, `Steven Hé " +#~ "(Sīchàng)`, `Taner Topal`" #~ msgstr "" -#~ msgid "|e5ee96d702b64256b97b8ca99db10787|" +#~ msgid "" +#~ "Let's now load the CIFAR-10 training " +#~ "and test set, partition them into " +#~ "ten smaller datasets (each split into" +#~ " training and validation set), and " +#~ "wrap the resulting partitions by " +#~ "creating a PyTorch ``DataLoader`` for " +#~ "each of them:" #~ msgstr "" +#~ "Chargeons maintenant l'ensemble de formation" +#~ " et de test CIFAR-10, partitionnons-" +#~ "les en dix ensembles de données " +#~ "plus petits (chacun divisé en ensemble" +#~ " de formation et de validation), et" +#~ " enveloppons les partitions résultantes en" +#~ " créant un PyTorch ``DataLoader`` pour " +#~ "chacun d'entre eux :" -#~ msgid "|84840b244edd47c481278ce534c126cd|" +#~ msgid "" +#~ "Let's build a horizontal federated " +#~ "learning system using XGBoost and " +#~ "Flower!" #~ msgstr "" +#~ "Construisons un système d'apprentissage fédéré" +#~ " horizontal en utilisant XGBoost et " +#~ "Flower !" -#~ msgid "|f33f5ebb3a844a2ba54bb6be3571b172|" +#~ msgid "" +#~ "Please refer to the `full code " +#~ "example `_ to learn " +#~ "more." #~ msgstr "" +#~ "Réfère-toi à l'exemple de code " +#~ "complet `_ pour en " +#~ "savoir plus." -#~ msgid "|5645db4ba9c945518d51ff234f35c797|" +#~ msgid "|3ff4c820a01d4a5abb022617de537c54|" #~ msgstr "" -#~ msgid "|317af8d28fcc479ab981047d058c4751|" +#~ msgid "|7f1889391ad448e2a65920165f0d798c|" #~ msgstr "" -#~ msgid "|8bfd0e697a494d5385662debafade6bf|" +#~ msgid "|a171dc4a0d044e70b5d585cc10ace0e0|" #~ msgstr "" -#~ msgid "" -#~ "Differential privacy (DP) is often " -#~ "mentioned in the context of Federated" -#~ " Learning. It is a privacy-preserving" -#~ " method used when analyzing and " -#~ "sharing statistical data, ensuring the " -#~ "privacy of individual participants. DP " -#~ "achieves this by adding statistical " -#~ "noise to the model updates, ensuring " -#~ "any individual participants’ information " -#~ "cannot be distinguished or re-" -#~ "identified. This technique can be " -#~ "considered an optimization that provides " -#~ "a quantifiable privacy protection measure." +#~ msgid "|fe518aa0d86341f7b2fc87bd6e3bbf0c|" #~ msgstr "" -#~ "La confidentialité différentielle (DP) est " -#~ "souvent mentionnée dans le contexte de" -#~ " l'apprentissage fédéré. Il s'agit d'une" -#~ " méthode de préservation de la vie" -#~ " privée utilisée lors de l'analyse et" -#~ " du partage de données statistiques, " -#~ "garantissant la confidentialité des " -#~ "participants individuels. La DP y " -#~ "parvient en ajoutant un bruit " -#~ "statistique aux mises à jour du " -#~ "modèle, garantissant que toute information " -#~ "sur les participants individuels ne peut" -#~ " être distinguée ou réidentifiée. Cette " -#~ "technique peut être considérée comme une" -#~ " optimisation qui fournit une mesure " -#~ "quantifiable de protection de la vie " -#~ "privée." -#~ msgid "|e5dc001d27ad460caeab669e957b3c36|" +#~ msgid "|6abfdf0dade44469ae9f08c8dc7d148c|" #~ msgstr "" -#~ msgid "API Reference - Flower binaries" +#~ msgid "|b4f147db24bb4da9a786e1d6676a1c2d|" #~ msgstr "" -#~ msgid "API Reference - flwr" -#~ msgstr "Référence pour l'API" - -#~ msgid "" -#~ "Defines whether or not the client " -#~ "is interacting with the server using " -#~ "the experimental REST API. This feature" -#~ " is experimental, it might change " -#~ "considerably in future versions of " -#~ "Flower." +#~ msgid "|5c62032f589a457bb37b5fee5b2adbde|" #~ msgstr "" -#~ msgid "Returns a client's set of properties." +#~ msgid "|f154df1846dd44f79a94f1dc3ae8b088|" #~ msgstr "" -#~ msgid "" -#~ "Defines whether or not the client " -#~ "is interacting with the server using " -#~ "the experimental REST API. This feature" -#~ " is experimental, it might be change" -#~ " considerably in future versions of " -#~ "Flower." +#~ msgid "|9d20be8160f7451fb0f33b194506503f|" #~ msgstr "" -#~ msgid "" -#~ "A function creating client instances. " -#~ "The function must take a single " -#~ "str argument called `cid`. It should " -#~ "return a single client instance of " -#~ "type ClientLike. Note that the created" -#~ " client instances are ephemeral and " -#~ "will often be destroyed after a " -#~ "single method invocation. Since client " -#~ "instances are not long-lived, they " -#~ "should not attempt to carry state " -#~ "over method invocations. Any state " -#~ "required by the instance (model, " -#~ "dataset,hyperparameters, ...) should be " -#~ "(re-)created in either the call to " -#~ "`client_fn` or the call to any of" -#~ " the client methods (e.g., load " -#~ "evaluation data in the `evaluate` method" -#~ " itself)." +#~ msgid "|3d949f76988443c59990d2e64f05c386|" #~ msgstr "" -#~ msgid "" -#~ "A function creating client instances. " -#~ "The function must take a single " -#~ "str argument called `cid`. It should " -#~ "return a single client instance of " -#~ "type ClientLike. Note that the created" -#~ " client instances are ephemeral and " -#~ "will often be destroyed after a " -#~ "single method invocation. Since client " -#~ "instances are not long-lived, they " -#~ "should not" +#~ msgid "|526c6d9140f6404f8a226d9056327b3b|" #~ msgstr "" -#~ msgid "attempt to carry state over method invocations. Any state required by" +#~ msgid "|a5f6af14cd7c4550929b17f83b4f63c7|" #~ msgstr "" -#~ msgid "" -#~ "the instance (model, dataset,hyperparameters, " -#~ "...) should be (re-)created in either" -#~ " the call to `client_fn` or the " -#~ "call to any of the client methods" -#~ " (e.g., load evaluation data in the" -#~ " `evaluate` method itself)." +#~ msgid "|bcd571c4f4ee4803a54f71b5c20448cb|" #~ msgstr "" -#~ msgid "" -#~ "\\frac{\\mu}{2} || w - w^t ||^2\n" -#~ "\n" +#~ msgid "|c76452ae1ed84965be7ef23c72b95845|" #~ msgstr "" #~ msgid "" -#~ "Adaptive Federated Optimization using Adagrad" -#~ " (FedAdagrad) [Reddi et al., 2020] " -#~ "strategy." +#~ "Please follow the first section on " +#~ "`Run Flower using Docker " +#~ "`_ which covers this" +#~ " step in more detail." #~ msgstr "" #~ msgid "" -#~ "Adaptive Federated Optimization using Adam " -#~ "(FedAdam) [Reddi et al., 2020] strategy." +#~ "Since `Flower 1.5 `_ we have " +#~ "introduced translations to our doc " +#~ "pages, but, as you might have " +#~ "noticed, the translations are often " +#~ "imperfect. If you speak languages other" +#~ " than English, you might be able " +#~ "to help us in our effort to " +#~ "make Federated Learning accessible to as" +#~ " many people as possible by " +#~ "contributing to those translations! This " +#~ "might also be a great opportunity " +#~ "for those wanting to become open " +#~ "source contributors with little prerequistes." #~ msgstr "" #~ msgid "" -#~ "Adaptive Federated Optimization using Yogi " -#~ "(FedYogi) [Reddi et al., 2020] strategy." +#~ "You input your translation in the " +#~ "textbox at the top and then, once" +#~ " you are happy with it, you " +#~ "either press ``Save and continue`` (to" +#~ " save the translation and go to " +#~ "the next untranslated string), ``Save " +#~ "and stay`` (to save the translation " +#~ "and stay on the same page), " +#~ "``Suggest`` (to add your translation to" +#~ " suggestions for other users to " +#~ "view), or ``Skip`` (to go to the" +#~ " next untranslated string without saving" +#~ " anything)." #~ msgstr "" -#~ msgid "Contributing Baselines" -#~ msgstr "Configuration du contributeur" - #~ msgid "" -#~ "Do you have a new federated " -#~ "learning paper and want to add a" -#~ " new baseline to Flower? Or do " -#~ "you want to add an experiment to" -#~ " an existing baseline paper? Great, " -#~ "we really appreciate your contribution." +#~ "If the section is completely empty " +#~ "(without any token) or non-existant, " +#~ "the changelog will just contain the " +#~ "title of the PR for the changelog" +#~ " entry, without any description." #~ msgstr "" -#~ msgid "" -#~ "The goal of Flower Baselines is to" -#~ " reproduce experiments from popular papers" -#~ " to accelerate researchers by enabling " -#~ "faster comparisons to new strategies, " -#~ "datasets, models, and federated pipelines " -#~ "in general." -#~ msgstr "" +#~ msgid "Example: Walk-Through PyTorch & MNIST" +#~ msgstr "Exemple : PyTorch et MNIST" #~ msgid "" -#~ "Before you start to work on a " -#~ "new baseline or experiment, please check" -#~ " the `Flower Issues " -#~ "`_ or `Flower " -#~ "Pull Requests `_ " -#~ "to see if someone else is already" -#~ " working on it. Please open a " -#~ "new issue if you are planning to" -#~ " work on a new baseline or " -#~ "experiment with a short description of" -#~ " the corresponding paper and the " -#~ "experiment you want to contribute." -#~ msgstr "" - -#~ msgid "TL;DR: Adding a new Flower Baseline" +#~ "In this tutorial we will learn, " +#~ "how to train a Convolutional Neural " +#~ "Network on MNIST using Flower and " +#~ "PyTorch." #~ msgstr "" +#~ "Dans ce tutoriel, nous allons apprendre," +#~ " comment former un réseau neuronal " +#~ "convolutif sur MNIST en utilisant Flower" +#~ " et PyTorch." #~ msgid "" -#~ "Let's say you want to contribute " -#~ "the code of your most recent " -#~ "Federated Learning publication, *FedAweseome*. " -#~ "There are only three steps necessary " -#~ "to create a new *FedAweseome* Flower " -#~ "Baseline:" -#~ msgstr "" - -#~ msgid "**Get the Flower source code on your machine**" +#~ "Since we want to use PyTorch to" +#~ " solve a computer vision task, let's" +#~ " go ahead an install PyTorch and " +#~ "the **torchvision** library:" #~ msgstr "" +#~ "Puisque nous voulons utiliser PyTorch " +#~ "pour résoudre une tâche de vision " +#~ "par ordinateur, installons PyTorch et la" +#~ " bibliothèque **torchvision** :" -#~ msgid "" -#~ "Fork the Flower codebase: got to " -#~ "the `Flower GitHub repo " -#~ "`_ and fork the " -#~ "code (click the *Fork* button in " -#~ "the top-right corner and follow " -#~ "the instructions)" -#~ msgstr "" +#~ msgid "Ready... Set... Train!" +#~ msgstr "Prêts... prêts... entraînez-vous !" #~ msgid "" -#~ "Clone the (forked) Flower source code:" -#~ " :code:`git clone " -#~ "git@github.com:[your_github_username]/flower.git`" +#~ "Now that we have all our " +#~ "dependencies installed, let's run a " +#~ "simple distributed training with two " +#~ "clients and one server. Our training " +#~ "procedure and network architecture are " +#~ "based on PyTorch's `Basic MNIST Example" +#~ " `_. " +#~ "This will allow you see how easy" +#~ " it is to wrap your code with" +#~ " Flower and begin training in a " +#~ "federated way. We provide you with " +#~ "two helper scripts, namely *run-" +#~ "server.sh*, and *run-clients.sh*. Don't " +#~ "be afraid to look inside, they are" +#~ " simple enough =)." #~ msgstr "" +#~ "Maintenant que nous avons installé " +#~ "toutes nos dépendances, lançons un " +#~ "simple entraînement distribué avec deux " +#~ "clients et un serveur. Notre procédure" +#~ " d'entraînement et l'architecture de notre" +#~ " réseau sont basées sur l'exemple " +#~ "MNIST de base de PyTorch " +#~ "`_. Cela" +#~ " te permettra de voir à quel " +#~ "point il est facile d'envelopper ton " +#~ "code avec Flower et de commencer " +#~ "l'entraînement de manière fédérée. Nous " +#~ "te fournissons deux scripts d'aide, à" +#~ " savoir *run-server.sh*, et *run-" +#~ "clients.sh*. N'aie pas peur de regarder" +#~ " à l'intérieur, ils sont assez " +#~ "simples =)." #~ msgid "" -#~ "Open the code in your favorite " -#~ "editor (e.g., using VSCode: ``cd flower" -#~ " ; code .``)" -#~ msgstr "" +#~ "Go ahead and launch on a terminal" +#~ " the *run-server.sh* script first as" +#~ " follows:" +#~ msgstr "Lance sur un terminal le script *run-server.sh* d'abord comme suit :" -#~ msgid "**Add the FedAwesome code**" -#~ msgstr "" +#~ msgid "Now that the server is up and running, go ahead and launch the clients." +#~ msgstr "Maintenant que le serveur est opérationnel, vas-y et lance les clients." #~ msgid "" -#~ "Add your :code:`FedAwesome` code under " -#~ ":code:`baselines/flwr_baselines/publications/[fedawesome]`" -#~ msgstr "" - -#~ msgid "Add a `pyproject.toml` with all necessary dependencies" +#~ "Et voilà! You should be seeing the" +#~ " training procedure and, after a few" +#~ " iterations, the test accuracy for " +#~ "each client." #~ msgstr "" +#~ "Et voilà ! Tu devrais voir la " +#~ "procédure d'entraînement et, après quelques" +#~ " itérations, la précision du test " +#~ "pour chaque client." -#~ msgid "Add a `README.md` describing how to use your baseline" -#~ msgstr "" +#~ msgid "Now, let's see what is really happening inside." +#~ msgstr "Maintenant, voyons ce qui se passe réellement à l'intérieur." -#~ msgid "**Open a pull request**" +#~ msgid "" +#~ "Inside the server helper script *run-" +#~ "server.sh* you will find the following" +#~ " code that basically runs the " +#~ ":code:`server.py`" #~ msgstr "" +#~ "Dans le script d'aide au serveur " +#~ "*run-server.sh*, tu trouveras le code " +#~ "suivant qui exécute le fichier " +#~ ":code:`server.py`" -#~ msgid "Stage your changes: :code:`git add .`" +#~ msgid "" +#~ "We can go a bit deeper and " +#~ "see that :code:`server.py` simply launches " +#~ "a server that will coordinate three " +#~ "rounds of training. Flower Servers are" +#~ " very customizable, but for simple " +#~ "workloads, we can start a server " +#~ "using the :ref:`start_server ` function and leave " +#~ "all the configuration possibilities at " +#~ "their default values, as seen below." #~ msgstr "" +#~ "Nous pouvons aller un peu plus " +#~ "loin et voir que :code:`server.py` lance" +#~ " simplement un serveur qui coordonnera " +#~ "trois tours de formation. Flower Les " +#~ "serveurs sont très personnalisables, mais " +#~ "pour les charges de travail simples, " +#~ "nous pouvons démarrer un serveur à " +#~ "l'aide de la fonction :ref:`start_server " +#~ "` et " +#~ "laisser toutes les possibilités de " +#~ "configuration à leurs valeurs par " +#~ "défaut, comme on peut le voir " +#~ "ci-dessous." #~ msgid "" -#~ "Commit & push: :code:`git commit -m " -#~ "\"Create new FedAweseome baseline\" ; " -#~ "git push`" +#~ "Next, let's take a look at the " +#~ "*run-clients.sh* file. You will see " +#~ "that it contains the main loop " +#~ "that starts a set of *clients*." #~ msgstr "" +#~ "Ensuite, jetons un coup d'œil au " +#~ "fichier *run-clients.sh*. Tu verras " +#~ "qu'il contient la boucle principale qui" +#~ " démarre un ensemble de *clients*." #~ msgid "" -#~ "Open a pull request: go to *your*" -#~ " fork of the Flower codebase and " -#~ "create a pull request that targets " -#~ "the Flower ``main``` branch" +#~ "**cid**: is the client ID. It is" +#~ " an integer that uniquely identifies " +#~ "client identifier." #~ msgstr "" +#~ "**cid** : c'est l'identifiant du client." +#~ " C'est un nombre entier qui identifie" +#~ " de façon unique l'identifiant du " +#~ "client." -#~ msgid "Further reading:" -#~ msgstr "Aide supplémentaire" +#~ msgid "**sever_address**: String that identifies IP and port of the server." +#~ msgstr "**sever_address** : Chaîne qui identifie l'IP et le port du serveur." #~ msgid "" -#~ "`GitHub docs: About forks " -#~ "`_" +#~ "**nb_clients**: This defines the number " +#~ "of clients being created. This piece " +#~ "of information is not required by " +#~ "the client, but it helps us " +#~ "partition the original MNIST dataset to" +#~ " make sure that every client is " +#~ "working on unique subsets of both " +#~ "*training* and *test* sets." #~ msgstr "" +#~ "**Cette information n'est pas requise " +#~ "par le client, mais elle nous aide" +#~ " à partitionner l'ensemble de données " +#~ "MNIST original pour nous assurer que " +#~ "chaque client travaille sur des sous-" +#~ "ensembles uniques des ensembles *formation*" +#~ " et *test*." #~ msgid "" -#~ "`GitHub docs: Creating a pull request" -#~ " `_" +#~ "Again, we can go deeper and look" +#~ " inside :code:`flwr_example/quickstart-" +#~ "pytorch/client.py`. After going through the" +#~ " argument parsing code at the " +#~ "beginning of our :code:`main` function, " +#~ "you will find a call to " +#~ ":code:`mnist.load_data`. This function is " +#~ "responsible for partitioning the original " +#~ "MNIST datasets (*training* and *test*) " +#~ "and returning a :code:`torch.utils.data.DataLoader`" +#~ " s for each of them. We then" +#~ " instantiate a :code:`PytorchMNISTClient` object" +#~ " with our client ID, our DataLoaders," +#~ " the number of epochs in each " +#~ "round, and which device we want to" +#~ " use for training (CPU or GPU)." #~ msgstr "" +#~ "Encore une fois, nous pouvons aller " +#~ "plus loin et regarder dans " +#~ ":code:`flwr_example/quickstart-pytorch/client.py`. Après" +#~ " avoir parcouru le code d'analyse des" +#~ " arguments au début de notre fonction" +#~ " :code:`main`, tu trouveras un appel " +#~ "à :code:`mnist.load_data`. Cette fonction est" +#~ " responsable du partitionnement des " +#~ "ensembles de données MNIST originaux " +#~ "(*training* et *test*) et renvoie un " +#~ ":code:`torch.utils.data.DataLoader` s pour chacun" +#~ " d'entre eux. Nous instancions ensuite " +#~ "un objet :code:`PytorchMNISTClient` avec notre" +#~ " ID client, nos DataLoaders, le " +#~ "nombre d'époques dans chaque tour et " +#~ "le périphérique que nous voulons " +#~ "utiliser pour l'entraînement (CPU ou " +#~ "GPU)." #~ msgid "" -#~ "`GitHub docs: Creating a pull request" -#~ " from a fork `_" +#~ "The :code:`PytorchMNISTClient` object when " +#~ "finally passed to :code:`fl.client.start_client` " +#~ "along with the server's address as " +#~ "the training process begins." #~ msgstr "" +#~ "L'objet :code:`PytorchMNISTClient` est finalement" +#~ " transmis à :code:`fl.client.start_client` avec" +#~ " l'adresse du serveur lorsque le " +#~ "processus de formation commence." -#~ msgid "Requirements" -#~ msgstr "Changements nécessaires" +#~ msgid "A Closer Look" +#~ msgstr "Regarder de plus près" #~ msgid "" -#~ "Contributing a new baseline is really" -#~ " easy. You only have to make " -#~ "sure that your federated learning " -#~ "experiments are running with Flower. As" -#~ " soon as you have created a " -#~ "Flower-based experiment, you can contribute" -#~ " it." +#~ "Now, let's look closely into the " +#~ ":code:`PytorchMNISTClient` inside :code:`flwr_example" +#~ ".quickstart-pytorch.mnist` and see what it" +#~ " is doing:" #~ msgstr "" +#~ "Maintenant, examinons de près le " +#~ ":code:`PytorchMNISTClient` à l'intérieur du " +#~ ":code:`flwr_example.quickstart-pytorch.mnist` et " +#~ "voyons ce qu'il fait :" #~ msgid "" -#~ "It is recommended (but not required) " -#~ "to use `Hydra `_ to " -#~ "execute the experiment." +#~ "The first thing to notice is that" +#~ " :code:`PytorchMNISTClient` instantiates a CNN" +#~ " model inside its constructor" #~ msgstr "" +#~ "La première chose à remarquer est " +#~ "que :code:`PytorchMNISTClient` instancie un " +#~ "modèle CNN dans son constructeur" #~ msgid "" -#~ "Please make sure to add your " -#~ "baseline or experiment to the " -#~ "corresponding directory as explained in " -#~ "`Executing Baseline `_. Give your baseline the " -#~ "unique identifier. For example, :code:`fedbn`" -#~ " refers to the paper \"FedBN: " -#~ "Federated Learning on non-IID Features" -#~ " via Local Batch Normalization\" and " -#~ "creates the corresponding directory " -#~ ":code:`flower/baselines/flwr_baselines/publications/fedbn`. Then" -#~ " you create the experiment directory " -#~ "with the experiment name. For example," -#~ " the experiment that measures the " -#~ "convergence has the directory " -#~ ":code:`flower/baselines/flwr_baselines/publications/fedbn/convergence_rate`." -#~ " This directory contains all your " -#~ "code and a :code:`README.md` with a " -#~ "link to the paper, the paper's " -#~ "abstract, and a detailed description of" -#~ " how to execute the experiments." +#~ "The code for the CNN is available" +#~ " under :code:`quickstart-pytorch.mnist` and " +#~ "it is reproduced below. It is the" +#~ " same network found in `Basic MNIST" +#~ " Example " +#~ "`_." #~ msgstr "" +#~ "Le code du CNN est disponible sous" +#~ " :code:`quickstart-pytorch.mnist` et il est" +#~ " reproduit ci-dessous. Il s'agit du" +#~ " même réseau que celui que l'on " +#~ "trouve dans `Exemple basique de MNIST" +#~ " `_." #~ msgid "" -#~ "Please also check if :code:`pyproject.toml`" -#~ " and :code:`requirements.txt` (all in the" -#~ " directory `baselines " -#~ "`_ contain" -#~ " all required Python packages (libraries," -#~ " frameworks, ...). If the required " -#~ "Python package is not yet listed, " -#~ "please add it to :code:`pyproject.toml`. " -#~ "If you need a different version of" -#~ " a package already listed, please try" -#~ " to ensure your experiment runs with" -#~ " the existing version listed in " -#~ ":code:`pyproject.toml` (or :code:`requirements.txt`). " -#~ "If that doesn't work, open a " -#~ "GitHub Issue and request the version " -#~ "change." +#~ "The second thing to notice is that" +#~ " :code:`PytorchMNISTClient` class inherits from" +#~ " the :code:`fl.client.Client`, and hence it" +#~ " must implement the following methods:" #~ msgstr "" +#~ "La deuxième chose à noter est que" +#~ " la classe :code:`PytorchMNISTClient` hérite " +#~ "de :code:`fl.client.Client`, et qu'elle doit" +#~ " donc implémenter les méthodes suivantes" +#~ " :" #~ msgid "" -#~ "The experiment also needs to contain " -#~ "a file with a downloader for the" -#~ " dataset - if possible automatic. " -#~ "This can be included in one of " -#~ "the files or as an extra file." +#~ "When comparing the abstract class to " +#~ "its derived class :code:`PytorchMNISTClient` " +#~ "you will notice that :code:`fit` calls" +#~ " a :code:`train` function and that " +#~ ":code:`evaluate` calls a :code:`test`: " +#~ "function." #~ msgstr "" +#~ "En comparant la classe abstraite à " +#~ "sa classe dérivée :code:`PytorchMNISTClient`, " +#~ "tu remarqueras que :code:`fit` appelle " +#~ "une fonction :code:`train` et que " +#~ ":code:`evaluate` appelle une fonction " +#~ ":code:`test` :." #~ msgid "" -#~ "Finally, please add plots for all " -#~ "experimental results your code is " -#~ "running to the :code:`experiment` directory" -#~ " and include them in :code:`README.md`. " -#~ "Doing this helps others and enables " -#~ "them to recognize your contributions " -#~ "quickly." +#~ "These functions can both be found " +#~ "inside the same :code:`quickstart-" +#~ "pytorch.mnist` module:" #~ msgstr "" +#~ "Ces fonctions se trouvent toutes deux" +#~ " dans le même module :code:`quickstart-" +#~ "pytorch.mnist` :" #~ msgid "" -#~ "We are aware that a few libraries" -#~ " are available only via Conda. " -#~ "However, we want to encourage you " -#~ "to ensure that your code also runs" -#~ " well outside of Conda to make " -#~ "it more accessible to the broader " -#~ "research community." +#~ "Observe that these functions encapsulate " +#~ "regular training and test loops and " +#~ "provide :code:`fit` and :code:`evaluate` with" +#~ " final statistics for each round. You" +#~ " could substitute them with your " +#~ "custom train and test loops and " +#~ "change the network architecture, and the" +#~ " entire example would still work " +#~ "flawlessly. As a matter of fact, " +#~ "why not try and modify the code" +#~ " to an example of your liking?" #~ msgstr "" +#~ "Observe que ces fonctions encapsulent " +#~ "les boucles d'entraînement et de test" +#~ " habituelles et fournissent à :code:`fit`" +#~ " et :code:`evaluate` les statistiques " +#~ "finales pour chaque tour. Tu pourrais" +#~ " les remplacer par tes boucles " +#~ "d'entraînement et de test personnalisées " +#~ "et changer l'architecture du réseau, et" +#~ " l'ensemble de l'exemple fonctionnerait " +#~ "toujours parfaitement. En fait, pourquoi " +#~ "ne pas essayer de modifier le code" +#~ " pour en faire un exemple qui " +#~ "te plairait ?" -#~ msgid "Here is a checklist for adding a new baseline:" +#~ msgid "Give It a Try" +#~ msgstr "Fais un essai" + +#~ msgid "" +#~ "Looking through the quickstart code " +#~ "description above will have given a " +#~ "good understanding of how *clients* and" +#~ " *servers* work in Flower, how to " +#~ "run a simple experiment, and the " +#~ "internals of a client wrapper. Here " +#~ "are a few things you could try " +#~ "on your own and get more " +#~ "experience with Flower:" #~ msgstr "" +#~ "En parcourant la description du code " +#~ "de démarrage rapide ci-dessus, tu " +#~ "auras acquis une bonne compréhension du" +#~ " fonctionnement des *clients* et des " +#~ "*serveurs* dans Flower, de l'exécution " +#~ "d'une expérience simple et de la " +#~ "structure interne d'un wrapper client. " +#~ "Voici quelques exemples que tu peux " +#~ "essayer par toi-même pour acquérir " +#~ "plus d'expérience avec Flower :" #~ msgid "" -#~ "add required Python packages to " -#~ ":code:`pyproject.toml` or :code:`requirements.txt`" +#~ "Try and change :code:`PytorchMNISTClient` so" +#~ " it can accept different architectures." #~ msgstr "" +#~ "Essaie de modifier :code:`PytorchMNISTClient` " +#~ "pour qu'il puisse accepter différentes " +#~ "architectures." #~ msgid "" -#~ "add all required code under " -#~ ":code:`baselines/flwr_baselines/publications/[new_publication]`" +#~ "Modify the :code:`train` function so " +#~ "that it accepts different optimizers" #~ msgstr "" +#~ "Modifie la fonction :code:`train` pour " +#~ "qu'elle accepte différents optimiseurs" -#~ msgid "add a dataset downloader" +#~ msgid "" +#~ "Modify the :code:`test` function so that" +#~ " it proves not only the top-1 " +#~ "(regular accuracy) but also the top-5" +#~ " accuracy?" #~ msgstr "" +#~ "Modifie la fonction :code:`test` pour " +#~ "qu'elle prouve non seulement le top-1" +#~ " (précision normale) mais aussi le " +#~ "top-5 ?" -#~ msgid "add an experiment plot" +#~ msgid "" +#~ "Go larger! Try to adapt the code" +#~ " to larger images and datasets. Why" +#~ " not try training on ImageNet with" +#~ " a ResNet-50?" #~ msgstr "" +#~ "Essaie d'adapter le code à des " +#~ "images et à des ensembles de " +#~ "données plus grands. Pourquoi ne pas " +#~ "essayer de s'entraîner sur ImageNet avec" +#~ " un ResNet-50 ?" -#~ msgid "add a :code:`README.md`" +#~ msgid "You are ready now. Enjoy learning in a federated way!" +#~ msgstr "Tu es prêt maintenant. Profite de l'apprentissage de manière fédérée !" + +#~ msgid "" +#~ "Flower provides differential privacy (DP) " +#~ "wrapper classes for the easy integration" +#~ " of the central DP guarantees " +#~ "provided by DP-FedAvg into training " +#~ "pipelines defined in any of the " +#~ "various ML frameworks that Flower is " +#~ "compatible with." #~ msgstr "" +#~ "Flower fournit des classes d'enveloppe " +#~ "de confidentialité différentielle (DP) pour" +#~ " l'intégration facile des garanties " +#~ "centrales de DP fournies par DP-" +#~ "FedAvg dans les pipelines de formation" +#~ " définis dans n'importe lequel des " +#~ "divers cadres de ML avec lesquels " +#~ "Flower est compatible." -#~ msgid "Usability" +#~ msgid "" +#~ "Please note that these components are" +#~ " still experimental; the correct " +#~ "configuration of DP for a specific " +#~ "task is still an unsolved problem." #~ msgstr "" +#~ "Note que ces composants sont encore " +#~ "expérimentaux, la configuration correcte du" +#~ " DP pour une tâche spécifique est " +#~ "encore un problème non résolu." #~ msgid "" -#~ "Flower is known and loved for its" -#~ " usability. Therefore, make sure that " -#~ "your baseline or experiment can be " -#~ "executed with a single command such " -#~ "as :code:`./run.sh` or :code:`python3 " -#~ "main.py`. How you organize the " -#~ "experiments and the related code " -#~ "structure is up to you as an " -#~ "author, but please keep in mind to" -#~ " make sure that other users can " -#~ "easily understand and execute your " -#~ "baseline." +#~ "The name DP-FedAvg is misleading " +#~ "since it can be applied on top " +#~ "of any FL algorithm that conforms " +#~ "to the general structure prescribed by" +#~ " the FedOpt family of algorithms." #~ msgstr "" +#~ "Le nom DP-FedAvg est trompeur car" +#~ " il peut être appliqué à n'importe" +#~ " quel algorithme FL qui se conforme" +#~ " à la structure générale prescrite " +#~ "par la famille d'algorithmes FedOpt." -#~ msgid "We look forward to your contribution!" -#~ msgstr "Exemple de première contribution" +#~ msgid "DP-FedAvg" +#~ msgstr "DP-FedAvg" -#~ msgid "flwr" -#~ msgstr "Fleur" +#~ msgid "" +#~ "DP-FedAvg, originally proposed by " +#~ "McMahan et al. [mcmahan]_ and extended" +#~ " by Andrew et al. [andrew]_, is " +#~ "essentially FedAvg with the following " +#~ "modifications." +#~ msgstr "" +#~ "DP-FedAvg, proposé à l'origine par " +#~ "McMahan et al. [mcmahan]_ et étendu " +#~ "par Andrew et al. [andrew]_, est " +#~ "essentiellement FedAvg avec les modifications" +#~ " suivantes." -#~ msgid "binaries" +#~ msgid "" +#~ "**Clipping** : The influence of each " +#~ "client's update is bounded by clipping" +#~ " it. This is achieved by enforcing" +#~ " a cap on the L2 norm of " +#~ "the update, scaling it down if " +#~ "needed." #~ msgstr "" +#~ "**Clipping** : L'influence de la mise" +#~ " à jour de chaque client est " +#~ "limitée en l'écrêtant. Ceci est réalisé" +#~ " en imposant un plafond à la " +#~ "norme L2 de la mise à jour, " +#~ "en la réduisant si nécessaire." -#~ msgid "Flower Baselines" -#~ msgstr "Demande pour une nouvelle Flower Baseline" +#~ msgid "" +#~ "**Noising** : Gaussian noise, calibrated " +#~ "to the clipping threshold, is added " +#~ "to the average computed at the " +#~ "server." +#~ msgstr "" +#~ "**Bruit** : un bruit gaussien, calibré" +#~ " sur le seuil d'écrêtage, est ajouté" +#~ " à la moyenne calculée au niveau " +#~ "du serveur." #~ msgid "" -#~ "Flower Baselines are a collection of " -#~ "organised scripts used to reproduce " -#~ "results from well-known publications or" -#~ " benchmarks. You can check which " -#~ "baselines already exist and/or contribute " -#~ "your own baseline." +#~ "The distribution of the update norm " +#~ "has been shown to vary from " +#~ "task-to-task and to evolve as " +#~ "training progresses. This variability is " +#~ "crucial in understanding its impact on" +#~ " differential privacy guarantees, emphasizing " +#~ "the need for an adaptive approach " +#~ "[andrew]_ that continuously adjusts the " +#~ "clipping threshold to track a " +#~ "prespecified quantile of the update norm" +#~ " distribution." #~ msgstr "" +#~ "Il a été démontré que la " +#~ "distribution de la norme de mise à" +#~ " jour varie d'une tâche à l'autre " +#~ "et évolue au fur et à mesure " +#~ "de la formation. C'est pourquoi nous " +#~ "utilisons une approche adaptative [andrew]_" +#~ " qui ajuste continuellement le seuil " +#~ "d'écrêtage pour suivre un quantile " +#~ "prédéfini de la distribution de la " +#~ "norme de mise à jour." -#~ msgid "Flower requires `Python 3.7 `_ or above." -#~ msgstr "`Python 3.7 `_ ou plus" +#~ msgid "Simplifying Assumptions" +#~ msgstr "Simplifier les hypothèses" -#~ msgid "|9e234df38403464899ad3aee36bf1b95|" +#~ msgid "" +#~ "We make (and attempt to enforce) a" +#~ " number of assumptions that must be" +#~ " satisfied to ensure that the " +#~ "training process actually realizes the " +#~ ":math:`(\\epsilon, \\delta)` guarantees the " +#~ "user has in mind when configuring " +#~ "the setup." #~ msgstr "" +#~ "Nous formulons (et tentons d'appliquer) " +#~ "un certain nombre d'hypothèses qui " +#~ "doivent être satisfaites pour que le " +#~ "processus de formation réalise réellement " +#~ "les garanties :math:`(\\epsilon, \\delta)` que" +#~ " l'utilisateur a à l'esprit lorsqu'il " +#~ "configure l'installation." -#~ msgid "|081158351506446f9f772cb45ee68523|" +#~ msgid "" +#~ "**Fixed-size subsampling** :Fixed-size " +#~ "subsamples of the clients must be " +#~ "taken at each round, as opposed to" +#~ " variable-sized Poisson subsamples." #~ msgstr "" +#~ "**Sous-échantillonnage de taille fixe** " +#~ ":Des sous-échantillons de taille fixe" +#~ " des clients doivent être prélevés à" +#~ " chaque tour, par opposition aux " +#~ "sous-échantillons de Poisson de taille " +#~ "variable." -#~ msgid "|e9325042b79c45ed96b5a8d2f6f3cdc9|" +#~ msgid "" +#~ "**Unweighted averaging** : The contributions" +#~ " from all the clients must weighted" +#~ " equally in the aggregate to " +#~ "eliminate the requirement for the server" +#~ " to know in advance the sum of" +#~ " the weights of all clients available" +#~ " for selection." #~ msgstr "" +#~ "**Moyenne non pondérée** : Les " +#~ "contributions de tous les clients " +#~ "doivent être pondérées de façon égale" +#~ " dans l'ensemble afin que le serveur" +#~ " n'ait pas à connaître à l'avance " +#~ "la somme des poids de tous les " +#~ "clients disponibles pour la sélection." -#~ msgid "|11b83bb107344db78a37266e080c4a7a|" +#~ msgid "" +#~ "**No client failures** : The set " +#~ "of available clients must stay constant" +#~ " across all rounds of training. In" +#~ " other words, clients cannot drop out" +#~ " or fail." #~ msgstr "" +#~ "**Aucune défaillance de client** : " +#~ "L'ensemble des clients disponibles doit " +#~ "rester constant pendant toutes les " +#~ "séries de formation. En d'autres termes," +#~ " les clients ne peuvent pas " +#~ "abandonner ou échouer." -#~ msgid "|cd764bcf6d174a9cb62880ace9a8a6bd|" +#~ msgid "" +#~ "The first two are useful for " +#~ "eliminating a multitude of complications " +#~ "associated with calibrating the noise to" +#~ " the clipping threshold, while the " +#~ "third one is required to comply " +#~ "with the assumptions of the privacy " +#~ "analysis." #~ msgstr "" +#~ "Les deux premiers sont utiles pour " +#~ "éliminer une multitude de complications " +#~ "liées au calibrage du bruit en " +#~ "fonction du seuil d'écrêtage, tandis que" +#~ " le troisième est nécessaire pour se" +#~ " conformer aux hypothèses de l'analyse " +#~ "de la vie privée." -#~ msgid "|5c520984cced41e38f6bb4af416c3f84|" +#~ msgid "" +#~ "These restrictions are in line with " +#~ "constraints imposed by Andrew et al. " +#~ "[andrew]_." #~ msgstr "" +#~ "Ces restrictions sont conformes aux " +#~ "contraintes imposées par Andrew et al." +#~ " [andrew]_." -#~ msgid "|66941b0608644cf1a2269a194d3bc0dd|" -#~ msgstr "" +#~ msgid "Customizable Responsibility for Noise injection" +#~ msgstr "Responsabilité personnalisable pour l'injection de bruit" -#~ msgid "|4b149f3a095b402bb8890275aabc9298|" +#~ msgid "" +#~ "In contrast to other implementations " +#~ "where the addition of noise is " +#~ "performed at the server, you can " +#~ "configure the site of noise injection" +#~ " to better match your threat model." +#~ " We provide users with the " +#~ "flexibility to set up the training " +#~ "such that each client independently adds" +#~ " a small amount of noise to the" +#~ " clipped update, with the result that" +#~ " simply aggregating the noisy updates " +#~ "is equivalent to the explicit addition" +#~ " of noise to the non-noisy " +#~ "aggregate at the server." #~ msgstr "" +#~ "Contrairement à d'autres implémentations où" +#~ " l'ajout de bruit est effectué au " +#~ "niveau du serveur, tu peux configurer" +#~ " le site d'injection de bruit pour" +#~ " qu'il corresponde mieux à ton modèle" +#~ " de menace. Nous offrons aux " +#~ "utilisateurs la possibilité de configurer " +#~ "l'entraînement de telle sorte que chaque" +#~ " client ajoute indépendamment une petite" +#~ " quantité de bruit à la mise à" +#~ " jour écrêtée, ce qui fait que " +#~ "le simple fait d'agréger les mises " +#~ "à jour bruyantes équivaut à l'ajout " +#~ "explicite de bruit à l'agrégat non " +#~ "bruyant au niveau du serveur." -#~ msgid "|675cf7d3d53a4817b5d47529c0758158|" +#~ msgid "" +#~ "To be precise, if we let :math:`m`" +#~ " be the number of clients sampled " +#~ "each round and :math:`\\sigma_\\Delta` be " +#~ "the scale of the total Gaussian " +#~ "noise that needs to be added to" +#~ " the sum of the model updates, " +#~ "we can use simple maths to show" +#~ " that this is equivalent to each " +#~ "client adding noise with scale " +#~ ":math:`\\sigma_\\Delta/\\sqrt{m}`." #~ msgstr "" +#~ "Pour être précis, si nous laissons " +#~ ":math:`m` être le nombre de clients " +#~ "échantillonnés à chaque tour et " +#~ ":math:\\sigma_\\Delta` être l'échelle du bruit" +#~ " gaussien total qui doit être ajouté" +#~ " à la somme des mises à jour" +#~ " du modèle, nous pouvons utiliser des" +#~ " mathématiques simples pour montrer que " +#~ "cela équivaut à ce que chaque " +#~ "client ajoute du bruit avec l'échelle" +#~ " :math:\\sigma_\\Delta/\\sqrt{m}`." -#~ msgid "|7ca594e16ae7477790c2e3cf096ec7cd|" -#~ msgstr "" +#~ msgid "Wrapper-based approach" +#~ msgstr "Approche basée sur l'enveloppe" -#~ msgid "|d669336577b545a081d5d74169a9bc4d|" +#~ msgid "" +#~ "Introducing DP to an existing workload" +#~ " can be thought of as adding an" +#~ " extra layer of security around it." +#~ " This inspired us to provide the " +#~ "additional server and client-side logic" +#~ " needed to make the training process" +#~ " differentially private as wrappers for " +#~ "instances of the :code:`Strategy` and " +#~ ":code:`NumPyClient` abstract classes respectively." +#~ " This wrapper-based approach has the" +#~ " advantage of being easily composable " +#~ "with other wrappers that someone might" +#~ " contribute to the Flower library in" +#~ " the future, e.g., for secure " +#~ "aggregation. Using Inheritance instead can " +#~ "be tedious because that would require" +#~ " the creation of new sub- classes " +#~ "every time a new class implementing " +#~ ":code:`Strategy` or :code:`NumPyClient` is " +#~ "defined." #~ msgstr "" +#~ "L'introduction du DP dans une charge " +#~ "de travail existante peut être " +#~ "considérée comme l'ajout d'une couche de" +#~ " sécurité supplémentaire autour d'elle. " +#~ "Cela nous a incités à fournir la" +#~ " logique supplémentaire côté serveur et " +#~ "côté client nécessaire pour rendre le" +#~ " processus de formation différentiellement " +#~ "privé en tant qu'enveloppes pour les " +#~ "instances des classes abstraites " +#~ ":code:`Strategy` et :code:`NumPyClient` " +#~ "respectivement. Cette approche basée sur " +#~ "l'enveloppe a l'avantage d'être facilement " +#~ "composable avec d'autres enveloppes que " +#~ "quelqu'un pourrait contribuer à la " +#~ "bibliothèque Flower à l'avenir, par " +#~ "exemple, pour l'agrégation sécurisée. " +#~ "L'utilisation de l'héritage à la place" +#~ " peut être fastidieuse car cela " +#~ "nécessiterait la création de nouvelles " +#~ "sous-classes chaque fois qu'une nouvelle" +#~ " classe mettant en œuvre :code:`Strategy`" +#~ " ou :code:`NumPyClient` est définie." -#~ msgid "|00b3d6cde1ff410ba54eff58da4e033a|" +#~ msgid "" +#~ "The first version of our solution " +#~ "was to define a decorator whose " +#~ "constructor accepted, among other things, " +#~ "a boolean-valued variable indicating " +#~ "whether adaptive clipping was to be " +#~ "enabled or not. We quickly realized " +#~ "that this would clutter its " +#~ ":code:`__init__()` function with variables " +#~ "corresponding to hyperparameters of adaptive" +#~ " clipping that would remain unused " +#~ "when it was disabled. A cleaner " +#~ "implementation could be achieved by " +#~ "splitting the functionality into two " +#~ "decorators, :code:`DPFedAvgFixed` and " +#~ ":code:`DPFedAvgAdaptive`, with the latter sub-" +#~ " classing the former. The constructors " +#~ "for both classes accept a boolean " +#~ "parameter :code:`server_side_noising`, which, as " +#~ "the name suggests, determines where " +#~ "noising is to be performed." #~ msgstr "" +#~ "La première version de notre solution" +#~ " consistait à définir un décorateur " +#~ "dont le constructeur acceptait, entre " +#~ "autres, une variable à valeur booléenne" +#~ " indiquant si l'écrêtage adaptatif devait" +#~ " être activé ou non. Nous nous " +#~ "sommes rapidement rendu compte que cela" +#~ " encombrerait sa fonction :code:`__init__()` " +#~ "avec des variables correspondant aux " +#~ "hyperparamètres de l'écrêtage adaptatif qui" +#~ " resteraient inutilisées lorsque celui-ci" +#~ " était désactivé. Une implémentation plus" +#~ " propre pourrait être obtenue en " +#~ "divisant la fonctionnalité en deux " +#~ "décorateurs, :code:`DPFedAvgFixed` et " +#~ ":code:`DPFedAvgAdaptive`, le second sous-" +#~ "classant le premier. Les constructeurs " +#~ "des deux classes acceptent un paramètre" +#~ " booléen :code:`server_side_noising` qui, comme" +#~ " son nom l'indique, détermine l'endroit " +#~ "où le noising doit être effectué." -#~ msgid "|29a11f5353084c1995c538f7edef71a5|" +#~ msgid "" +#~ "The server-side capabilities required " +#~ "for the original version of DP-" +#~ "FedAvg, i.e., the one which performed" +#~ " fixed clipping, can be completely " +#~ "captured with the help of wrapper " +#~ "logic for just the following two " +#~ "methods of the :code:`Strategy` abstract " +#~ "class." #~ msgstr "" +#~ "Les capacités côté serveur requises pour" +#~ " la version originale de DP-FedAvg," +#~ " c'est-à-dire celle qui effectue un " +#~ "écrêtage fixe, peuvent être entièrement " +#~ "capturées à l'aide d'une logique " +#~ "d'enveloppement pour les deux méthodes " +#~ "suivantes de la classe abstraite " +#~ ":code:`Strategy`." -#~ msgid "|d62eda312fd44726bb5db2b761fe7e0d|" +#~ msgid "" +#~ ":code:`configure_fit()` : The config " +#~ "dictionary being sent by the wrapped " +#~ ":code:`Strategy` to each client needs to" +#~ " be augmented with an additional " +#~ "value equal to the clipping threshold" +#~ " (keyed under :code:`dpfedavg_clip_norm`) and," +#~ " if :code:`server_side_noising=true`, another one" +#~ " equal to the scale of the " +#~ "Gaussian noise that needs to be " +#~ "added at the client (keyed under " +#~ ":code:`dpfedavg_noise_stddev`). This entails " +#~ "*post*-processing of the results returned " +#~ "by the wrappee's implementation of " +#~ ":code:`configure_fit()`." #~ msgstr "" +#~ ":code:`configure_fit()` : Le dictionnaire de" +#~ " configuration envoyé par la " +#~ ":code:`Strategy` enveloppée à chaque client" +#~ " doit être augmenté d'une valeur " +#~ "supplémentaire égale au seuil d'écrêtage " +#~ "(indiqué sous :code:`dpfedavg_clip_norm`) et, " +#~ "si :code:`server_side_noising=true`, d'une autre " +#~ "égale à l'échelle du bruit gaussien " +#~ "qui doit être ajouté au client " +#~ "(indiqué sous :code:`dpfedavg_noise_stddev`)." -#~ msgid "Using Baselines" +#~ msgid "" +#~ ":code:`aggregate_fit()`: We check whether any" +#~ " of the sampled clients dropped out" +#~ " or failed to upload an update " +#~ "before the round timed out. In " +#~ "that case, we need to abort the" +#~ " current round, discarding any successful" +#~ " updates that were received, and move" +#~ " on to the next one. On the " +#~ "other hand, if all clients responded " +#~ "successfully, we must force the " +#~ "averaging of the updates to happen " +#~ "in an unweighted manner by intercepting" +#~ " the :code:`parameters` field of " +#~ ":code:`FitRes` for each received update " +#~ "and setting it to 1. Furthermore, " +#~ "if :code:`server_side_noising=true`, each update " +#~ "is perturbed with an amount of " +#~ "noise equal to what it would have" +#~ " been subjected to had client-side" +#~ " noising being enabled. This entails " +#~ "*pre*-processing of the arguments to " +#~ "this method before passing them on " +#~ "to the wrappee's implementation of " +#~ ":code:`aggregate_fit()`." #~ msgstr "" +#~ ":code:`aggregate_fit()`: We check whether any" +#~ " of the sampled clients dropped out" +#~ " or failed to upload an update " +#~ "before the round timed out. In " +#~ "that case, we need to abort the" +#~ " current round, discarding any successful" +#~ " updates that were received, and move" +#~ " on to the next one. On the " +#~ "other hand, if all clients responded " +#~ "successfully, we must force the " +#~ "averaging of the updates to happen " +#~ "in an unweighted manner by intercepting" +#~ " the :code:`parameters` field of " +#~ ":code:`FitRes` for each received update " +#~ "and setting it to 1. Furthermore, " +#~ "if :code:`server_side_noising=true`, each update " +#~ "is perturbed with an amount of " +#~ "noise equal to what it would have" +#~ " been subjected to had client-side" +#~ " noising being enabled. This entails " +#~ "*pre*-processing of the arguments to " +#~ "this method before passing them on " +#~ "to the wrappee's implementation of " +#~ ":code:`aggregate_fit()`." -#~ msgid "Structure" +#~ msgid "" +#~ "We can't directly change the aggregation" +#~ " function of the wrapped strategy to" +#~ " force it to add noise to the" +#~ " aggregate, hence we simulate client-" +#~ "side noising to implement server-side" +#~ " noising." #~ msgstr "" +#~ "Nous ne pouvons pas modifier directement" +#~ " la fonction d'agrégation de la " +#~ "stratégie enveloppée pour la forcer à" +#~ " ajouter du bruit à l'agrégat, c'est" +#~ " pourquoi nous simulons le bruit côté" +#~ " client pour mettre en œuvre le " +#~ "bruit côté serveur." #~ msgid "" -#~ "All baselines are available in the " -#~ "directory `baselines " -#~ "`_. This " -#~ "directory has two different files:" +#~ "These changes have been put together " +#~ "into a class called :code:`DPFedAvgFixed`, " +#~ "whose constructor accepts the strategy " +#~ "being decorated, the clipping threshold " +#~ "and the number of clients sampled " +#~ "every round as compulsory arguments. The" +#~ " user is expected to specify the " +#~ "clipping threshold since the order of" +#~ " magnitude of the update norms is " +#~ "highly dependent on the model being " +#~ "trained and providing a default value" +#~ " would be misleading. The number of" +#~ " clients sampled at every round is" +#~ " required to calculate the amount of" +#~ " noise that must be added to " +#~ "each individual update, either by the" +#~ " server or the clients." #~ msgstr "" +#~ "Ces modifications ont été regroupées " +#~ "dans une classe appelée :code:`DPFedAvgFixed`," +#~ " dont le constructeur accepte la " +#~ "stratégie décorée, le seuil d'écrêtage " +#~ "et le nombre de clients échantillonnés" +#~ " à chaque tour comme arguments " +#~ "obligatoires. L'utilisateur est censé " +#~ "spécifier le seuil d'écrêtage car " +#~ "l'ordre de grandeur des normes de " +#~ "mise à jour dépend fortement du " +#~ "modèle formé et fournir une valeur " +#~ "par défaut serait trompeur. Le nombre" +#~ " de clients échantillonnés à chaque " +#~ "tour est nécessaire pour calculer la " +#~ "quantité de bruit qui doit être " +#~ "ajoutée à chaque mise à jour " +#~ "individuelle, que ce soit par le " +#~ "serveur ou par les clients." #~ msgid "" -#~ "Both files contain all the information" -#~ " about required Python packages (libraries," -#~ " frameworks, ...) and their versions. " -#~ "You can install each library separately" -#~ " by using :code: `pip install` or " -#~ "you can use Poetry and run " -#~ "code:`poetry install` in the directory " -#~ "where you find the :code:`pyproject.toml` " -#~ "file. After installing all requirements, " -#~ "you can start to run your " -#~ "baseline." +#~ "The additional functionality required to " +#~ "facilitate adaptive clipping has been " +#~ "provided in :code:`DPFedAvgAdaptive`, a " +#~ "subclass of :code:`DPFedAvgFixed`. It " +#~ "overrides the above-mentioned methods to" +#~ " do the following." #~ msgstr "" +#~ "La fonctionnalité supplémentaire nécessaire " +#~ "pour faciliter l'écrêtage adaptatif a " +#~ "été fournie dans :code:`DPFedAvgAdaptive`, une" +#~ " sous-classe de :code:`DPFedAvgFixed`. Elle" +#~ " remplace les méthodes mentionnées ci-" +#~ "dessus pour effectuer les opérations " +#~ "suivantes." #~ msgid "" -#~ "Go to the baseline that you want" -#~ " to execute. The directories and " -#~ "files are structured so that you " -#~ "can first find the paper with " -#~ "their unique identifier such that, for" -#~ " example, :code:`FedProx` refers to the " -#~ "paper \"Federated Optimization in " -#~ "Heterogeneous Networks\". The :code:`fedprox` " -#~ "section contains all available experiments " -#~ "from that paper." +#~ ":code:`configure_fit()` : It intercepts the" +#~ " config dict returned by " +#~ ":code:`super.configure_fit()` to add the " +#~ "key-value pair " +#~ ":code:`dpfedavg_adaptive_clip_enabled:True` to it, " +#~ "which the client interprets as an " +#~ "instruction to include an indicator bit" +#~ " (1 if update norm <= clipping " +#~ "threshold, 0 otherwise) in the results" +#~ " returned by it." #~ msgstr "" +#~ ":code:`configure_fit()` : Il intercepte le " +#~ "dict de configuration renvoyé par " +#~ ":code:`super.configure_fit()` pour y ajouter " +#~ "la paire clé-valeur " +#~ ":code:`dpfedavg_adaptive_clip_enabled:True`, que le " +#~ "client interprète comme une instruction " +#~ "d'inclure un bit indicateur (1 si " +#~ "la norme de mise à jour <= " +#~ "seuil d'écrêtage, 0 sinon) dans les " +#~ "résultats qu'il renvoie." #~ msgid "" -#~ "The experiment area contains a " -#~ ":code:`README.md` covering the corresponding " -#~ "paper, its abstract, and goal as " -#~ "well as a detailed description of " -#~ "how to run the baseline. Please " -#~ "use the :code:`README.md` to see how " -#~ "to execute each individual baseline." +#~ ":code:`aggregate_fit()` : It follows a " +#~ "call to :code:`super.aggregate_fit()` with one" +#~ " to :code:`__update_clip_norm__()`, a procedure" +#~ " which adjusts the clipping threshold " +#~ "on the basis of the indicator bits" +#~ " received from the sampled clients." #~ msgstr "" +#~ ":code:`aggregate_fit()` : Il fait suivre " +#~ "un appel à :code:`super.aggregate_fit()` d'un" +#~ " appel à :code:`__update_clip_norm__()`, une " +#~ "procédure qui ajuste le seuil d'écrêtage" +#~ " sur la base des bits indicateurs " +#~ "reçus des clients échantillonnés." -#~ msgid "Available Baselines" +#~ msgid "" +#~ "The client-side capabilities required " +#~ "can be completely captured through " +#~ "wrapper logic for just the :code:`fit()`" +#~ " method of the :code:`NumPyClient` abstract" +#~ " class. To be precise, we need " +#~ "to *post-process* the update computed" +#~ " by the wrapped client to clip " +#~ "it, if necessary, to the threshold " +#~ "value supplied by the server as " +#~ "part of the config dictionary. In " +#~ "addition to this, it may need to" +#~ " perform some extra work if either" +#~ " (or both) of the following keys " +#~ "are also present in the dict." #~ msgstr "" +#~ "Les capacités requises côté client " +#~ "peuvent être entièrement capturées par " +#~ "une logique de wrapper pour la " +#~ "seule méthode :code:`fit()` de la classe" +#~ " abstraite :code:`NumPyClient`. Pour être " +#~ "précis, nous devons *post-traiter* la" +#~ " mise à jour calculée par le " +#~ "client wrapped pour l'écrêter, si " +#~ "nécessaire, à la valeur seuil fournie" +#~ " par le serveur dans le cadre " +#~ "du dictionnaire de configuration. En " +#~ "plus de cela, il peut avoir besoin" +#~ " d'effectuer un travail supplémentaire si" +#~ " l'une des clés suivantes (ou les " +#~ "deux) est également présente dans le " +#~ "dict." #~ msgid "" -#~ "The following table lists all currently" -#~ " available baselines and the corresponding" -#~ " papers. If you want to add a" -#~ " new baseline or experiment, please " -#~ "check the `Contributing Baselines " -#~ "`_ section." +#~ ":code:`dpfedavg_noise_stddev` : Generate and " +#~ "add the specified amount of noise " +#~ "to the clipped update." #~ msgstr "" +#~ ":code:`dpfedavg_noise_stddev` : Génère et " +#~ "ajoute la quantité de bruit spécifiée" +#~ " à la mise à jour de " +#~ "l'écrêtage." -#~ msgid "Paper" +#~ msgid "" +#~ ":code:`dpfedavg_adaptive_clip_enabled` : Augment the" +#~ " metrics dict in the :code:`FitRes` " +#~ "object being returned to the server " +#~ "with an indicator bit, calculated as " +#~ "described earlier." #~ msgstr "" +#~ ":code:`dpfedavg_adaptive_clip_enabled` : Complète " +#~ "les métriques dict dans l'objet " +#~ ":code:`FitRes` renvoyé au serveur avec " +#~ "un bit indicateur, calculé comme décrit" +#~ " précédemment." -#~ msgid "Experiment" -#~ msgstr "" +#~ msgid "Performing the :math:`(\\epsilon, \\delta)` analysis" +#~ msgstr "Effectuer l'analyse :math:`(\\epsilon, \\delta)`" -#~ msgid "Directory" +#~ msgid "" +#~ "Assume you have trained for :math:`n`" +#~ " rounds with sampling fraction :math:`q`" +#~ " and noise multiplier :math:`z`. In " +#~ "order to calculate the :math:`\\epsilon` " +#~ "value this would result in for a" +#~ " particular :math:`\\delta`, the following " +#~ "script may be used." #~ msgstr "" +#~ "Supposons que tu te sois entraîné " +#~ "pendant :math:`n` tours avec la fraction" +#~ " d'échantillonnage :math:`q` et le " +#~ "multiplicateur de bruit :math:`z`. Afin " +#~ "de calculer la valeur :math:`epsilon` " +#~ "qui en résulterait pour un " +#~ ":math:`\\delta` particulier, le script suivant" +#~ " peut être utilisé." -#~ msgid "`FedAvg `_" +#~ msgid "" +#~ "`How to run Flower using Docker " +#~ "`_" #~ msgstr "" -#~ msgid "MNIST" +#~ msgid "Enjoy building more robust and flexible ``ClientApp``s with mods!" #~ msgstr "" -#~ msgid ":code:`flower/baselines/flwr_baselines/publications/fedavg_mnist/`" +#~ msgid "" +#~ ":py:obj:`ClientApp `\\ " +#~ "\\(client\\_fn\\[\\, mods\\]\\)" #~ msgstr "" -#~ msgid "`FedProx `_" +#~ msgid ":py:obj:`flwr.server.driver `\\" #~ msgstr "" -#~ msgid ":code:`flower/baselines/flwr_baselines/publications/fedprox_mnist/`" -#~ msgstr "" +#~ msgid "Flower driver SDK." +#~ msgstr "Serveur de Flower" -#~ msgid "`FedOpt `_" -#~ msgstr "" +#~ msgid "driver" +#~ msgstr "serveur" -#~ msgid "sparse gradient task" +#~ msgid "" +#~ ":py:obj:`start_driver `\\ " +#~ "\\(\\*\\[\\, server\\_address\\, server\\, ...\\]\\)" #~ msgstr "" -#~ msgid ":code:`flower/baselines/flwr_baselines/publications/adaptive_federated_optimization`" +#~ msgid "" +#~ ":py:obj:`Driver `\\ " +#~ "\\(\\[driver\\_service\\_address\\, ...\\]\\)" #~ msgstr "" -#~ msgid "`FedBN `_" +#~ msgid "" +#~ ":py:obj:`GrpcDriver `\\ " +#~ "\\(\\[driver\\_service\\_address\\, ...\\]\\)" #~ msgstr "" -#~ msgid "convergence rate" +#~ msgid "`GrpcDriver` provides access to the gRPC Driver API/service." #~ msgstr "" -#~ msgid ":code:`flower/baselines/flwr_baselines/publications/fedbn/convergence_rate`" +#~ msgid ":py:obj:`get_nodes `\\ \\(\\)" #~ msgstr "" #~ msgid "" -#~ "Flower requires `Python 3.7 " -#~ "`_ or above, we " -#~ "recommend `Python 3.8 " -#~ "`_." +#~ ":py:obj:`pull_task_res " +#~ "`\\ \\(task\\_ids\\)" #~ msgstr "" -#~ "Flower nécessite `Python 3.7 " -#~ "`_ ou plus, nous " -#~ "recommandons `Python 3.8 " -#~ "`_." -#~ msgid "|6baade94cd14454e82ead34fcc29a182|" +#~ msgid "Get task results." #~ msgstr "" -#~ msgid "|1209ecd819104c458d396cf665c7ed4f|" +#~ msgid "" +#~ ":py:obj:`push_task_ins " +#~ "`\\ " +#~ "\\(task\\_ins\\_list\\)" #~ msgstr "" -#~ msgid "|c088b02349304344a53f3ce1464225fb|" +#~ msgid "Schedule tasks." #~ msgstr "" -#~ msgid "|b54d50afc82a4a57a55997a9eaeb735b|" +#~ msgid "GrpcDriver" #~ msgstr "" -#~ msgid "|d17b57e97b714a25b43790d4b832fd87|" +#~ msgid ":py:obj:`connect `\\ \\(\\)" #~ msgstr "" -#~ msgid "|38966d05301a4854aa73c8c5033bfaab|" +#~ msgid "Connect to the Driver API." #~ msgstr "" -#~ msgid "|231d55f7926d4a5db02dcd724ec62529|" +#~ msgid "" +#~ ":py:obj:`create_run " +#~ "`\\ \\(req\\)" #~ msgstr "" -#~ msgid "|fb44f2e13a1b4b69b7a72234eedd13f4|" -#~ msgstr "" +#~ msgid "Request for run ID." +#~ msgstr "Demande pour une nouvelle Flower Baseline" -#~ msgid "|1cfc77af5d164030942e84d14268c256|" +#~ msgid "" +#~ ":py:obj:`disconnect " +#~ "`\\ \\(\\)" #~ msgstr "" -#~ msgid "|0d50828231a64bc08223544a2d2fa216|" +#~ msgid "Disconnect from the Driver API." #~ msgstr "" -#~ msgid "|904387757ceb42fbaa1875f3e8061113|" +#~ msgid "" +#~ ":py:obj:`get_nodes `\\" +#~ " \\(req\\)" #~ msgstr "" -#~ msgid "|68608e1b7c4842458c528b431c715f5a|" -#~ msgstr "" +#~ msgid "Get client IDs." +#~ msgstr "Moteur client Edge" -#~ msgid "|2adb106bda97480bb4b33eac472e321e|" +#~ msgid "" +#~ ":py:obj:`pull_task_res " +#~ "`\\ \\(req\\)" #~ msgstr "" -#~ msgid "|025f0a6f7a6145cba4bf8fa0e2495851|" +#~ msgid "" +#~ ":py:obj:`push_task_ins " +#~ "`\\ \\(req\\)" #~ msgstr "" -#~ msgid "Before the release" -#~ msgstr "Avant la sortie" - #~ msgid "" -#~ "Update the changelog (``changelog.md``) with" -#~ " all relevant changes that happened " -#~ "after the last release. If the " -#~ "last release was tagged ``v1.2.0``, you" -#~ " can use the following URL to " -#~ "see all commits that got merged " -#~ "into ``main`` since then:" +#~ "Optionally specify the type of actor " +#~ "to use. The actor object, which " +#~ "persists throughout the simulation, will " +#~ "be the process in charge of " +#~ "running the clients' jobs (i.e. their" +#~ " `fit()` method)." #~ msgstr "" -#~ "Mettez à jour le journal des " -#~ "modifications (``changelog.md``) avec tous les" -#~ " changements pertinents qui se sont " -#~ "produits après la dernière version. Si" -#~ " la dernière version a été étiquetée" -#~ " ``v1.2.0``, vous pouvez utiliser l'URL " -#~ "suivante pour voir tous les commits " -#~ "qui ont été fusionnés dans ``main`` " -#~ "depuis lors :" #~ msgid "" -#~ "`GitHub: Compare v1.2.0...main " -#~ "`_" +#~ "Much effort went into a completely " +#~ "restructured Flower docs experience. The " +#~ "documentation on [flower.ai/docs](flower.ai/docs) is" +#~ " now divided into Flower Framework, " +#~ "Flower Baselines, Flower Android SDK, " +#~ "Flower iOS SDK, and code example " +#~ "projects." #~ msgstr "" -#~ "`GitHub : Compare v1.2.0...main " -#~ "`_" #~ msgid "" -#~ "Thank the authors who contributed since" -#~ " the last release. This command helps" -#~ " extract them: ``git log --format='%aN' " -#~ "v1.1.0..HEAD | sort -u``. The command" -#~ " has the same order as ``git " -#~ "shortlog``." +#~ "Flower usage examples used to be " +#~ "bundled with Flower in a package " +#~ "called ``flwr_example``. We are migrating " +#~ "those examples to standalone projects to" +#~ " make them easier to use. All " +#~ "new examples are based in the " +#~ "directory `examples " +#~ "`_." #~ msgstr "" -#~ "Remerciez les auteurs qui ont contribué" -#~ " depuis la dernière version. Cette " -#~ "commande permet de les extraire : " -#~ "``git log --format='%aN' v1.1.0..HEAD | " -#~ "sort -u``. La commande a le même" -#~ " ordre que ``git shortlog``." +#~ "Les exemples d'utilisation de Flower " +#~ "étaient auparavant regroupés avec Flower " +#~ "dans un paquet appelé ``flwr_example``. " +#~ "Nous migrons ces exemples vers des " +#~ "projets autonomes pour les rendre plus" +#~ " faciles à utiliser. Tous les " +#~ "nouveaux exemples sont basés dans le " +#~ "répertoire ``examples " +#~ "`_." + +#~ msgid "Quickstart TensorFlow/Keras" +#~ msgstr "Démarrage rapide de TensorFlow/Keras" + +#~ msgid "Legacy Examples (`flwr_example`)" +#~ msgstr "Exemples hérités (`flwr_example`)" #~ msgid "" -#~ "Update the ``changelog.md`` section header " -#~ "``Unreleased`` to contain the version " -#~ "number and date for the release " -#~ "you are building. Create a pull " -#~ "request with the change." +#~ "The useage examples in `flwr_example` " +#~ "are deprecated and will be removed " +#~ "in the future. New examples are " +#~ "provided as standalone projects in " +#~ "`examples `_." #~ msgstr "" -#~ "Mettez à jour l'en-tête de section" -#~ " ``changelog.md`` ``Unreleased`` pour qu'il " -#~ "contienne le numéro de version et " -#~ "la date de la version que vous " -#~ "construisez. Créez une demande de " -#~ "traction avec le changement." +#~ "Les exemples d'utilisation dans `flwr_example`" +#~ " sont obsolètes et seront supprimés à" +#~ " l'avenir. De nouveaux exemples sont " +#~ "fournis en tant que projets autonomes" +#~ " dans `examples " +#~ "`_." + +#~ msgid "Extra Dependencies" +#~ msgstr "Dépendances supplémentaires" #~ msgid "" -#~ "Tag the release commit with the " -#~ "version number as soon as the PR" -#~ " is merged: ``git tag v0.12.3``, then" -#~ " ``git push --tags``" +#~ "The core Flower framework keeps a " +#~ "minimal set of dependencies. The " +#~ "examples demonstrate Flower in the " +#~ "context of different machine learning " +#~ "frameworks, so additional dependencies need" +#~ " to be installed before an example" +#~ " can be run." #~ msgstr "" -#~ "Marquez le commit de la version " -#~ "avec le numéro de version dès que" -#~ " le PR est fusionné : ``git tag" -#~ " v0.12.3``, puis ``git push --tags``" +#~ "Le noyau du framework Flower conserve" +#~ " un ensemble minimal de dépendances. " +#~ "Les exemples démontrent Flower dans le" +#~ " contexte de différents frameworks " +#~ "d'apprentissage automatique, de sorte que " +#~ "des dépendances supplémentaires doivent être" +#~ " installées avant qu'un exemple puisse " +#~ "être exécuté." + +#~ msgid "For PyTorch examples::" +#~ msgstr "Pour les exemples de PyTorch: :" + +#~ msgid "For TensorFlow examples::" +#~ msgstr "Pour les exemples de TensorFlow : :" + +#~ msgid "For both PyTorch and TensorFlow examples::" +#~ msgstr "Pour les exemples PyTorch et TensorFlow: :" #~ msgid "" -#~ "Build the release with ``./dev/build.sh``, " -#~ "then publish it with ``./dev/publish.sh``" +#~ "Please consult :code:`pyproject.toml` for a" +#~ " full list of possible extras " +#~ "(section :code:`[tool.poetry.extras]`)." #~ msgstr "" -#~ "Construisez la version avec " -#~ "``./dev/build.sh``, puis publiez-la avec " -#~ "``./dev/publish.sh``" +#~ "Tu peux consulter :code:`pyproject.toml` pour" +#~ " une liste complète des extras " +#~ "possibles (section :code:`[tool.poetry.extras]`)." + +#~ msgid "PyTorch Examples" +#~ msgstr "Exemples de PyTorch" #~ msgid "" -#~ "Create an entry in GitHub releases " -#~ "with the release notes for the " -#~ "previously tagged commit and attach the" -#~ " build artifacts (:code:`.whl` and " -#~ ":code:`.tar.gz`)." +#~ "Our PyTorch examples are based on " +#~ "PyTorch 1.7. They should work with " +#~ "other releases as well. So far, we" +#~ " provide the following examples." #~ msgstr "" -#~ "Crée une entrée dans GitHub releases " -#~ "avec les notes de version pour le" -#~ " commit précédemment étiqueté et attache" -#~ " les artefacts de construction " -#~ "(:code:`.whl` et :code:`.tar.gz`)." +#~ "Nos exemples PyTorch sont basés sur " +#~ "PyTorch 1.7. Ils devraient fonctionner " +#~ "avec d'autres versions également. Jusqu'à " +#~ "présent, nous fournissons les exemples " +#~ "suivants." + +#~ msgid "CIFAR-10 Image Classification" +#~ msgstr "Classification d'images CIFAR-10" #~ msgid "" -#~ "Second, create a virtual environment " -#~ "(and activate it). If you chose to" -#~ " use :code:`pyenv` (with the :code" -#~ ":`pyenv-virtualenv` plugin) and already " -#~ "have it installed , you can use" -#~ " the following convenience script (by " -#~ "default it will use :code:`Python " -#~ "3.8.17`, but you can change it by" -#~ " providing a specific :code:``)::" +#~ "`CIFAR-10 and CIFAR-100 " +#~ "`_ are " +#~ "popular RGB image datasets. The Flower" +#~ " CIFAR-10 example uses PyTorch to " +#~ "train a simple CNN classifier in a" +#~ " federated learning setup with two " +#~ "clients." #~ msgstr "" -#~ "Deuxièmement, créer un environnement virtuel" -#~ " (et l'activer). Si vous choisissez " -#~ "d'utiliser :code:`pyenv` (avec le plugin " -#~ ":code:`pyenv-virtualenv`) et que vous " -#~ "l'avez déjà installé, vous pouvez " -#~ "utiliser le script suivant (par défaut" -#~ " il utilisera :code:`Python 3.8.17`, mais" -#~ " vous pouvez le changer en " -#~ "fournissant une :code:`` spécifique)::" +#~ "`CIFAR-10 et CIFAR-100 " +#~ "`_ sont des" +#~ " ensembles de données d'images RVB " +#~ "populaires. L'exemple Flower CIFAR-10 utilise" +#~ " PyTorch pour former un classificateur " +#~ "CNN simple dans une configuration " +#~ "d'apprentissage fédéré avec deux clients." -#~ msgid "server.strategy.FedAvg" -#~ msgstr "serveur.stratégie.FedAvg" +#~ msgid "First, start a Flower server:" +#~ msgstr "Tout d'abord, démarre un serveur Flower :" -#~ msgid "server.strategy.FedAvgM" -#~ msgstr "stratégie.serveur.FedAvgM" +#~ msgid "$ ./src/py/flwr_example/pytorch_cifar/run-server.sh" +#~ msgstr "$ ./src/py/flwr_example/pytorch_cifar/run-server.sh" -#~ msgid "server.strategy.QFedAvg" -#~ msgstr "server.strategy.QFedAvg" +#~ msgid "Then, start the two clients in a new terminal window:" +#~ msgstr "" +#~ "Ensuite, démarre les deux clients dans" +#~ " une nouvelle fenêtre de terminal :" -#~ msgid "server.strategy.FedOpt" -#~ msgstr "serveur.stratégie.FedOpt" +#~ msgid "$ ./src/py/flwr_example/pytorch_cifar/run-clients.sh" +#~ msgstr "$ ./src/py/flwr_example/pytorch_cifar/run-clients.sh" -#~ msgid "server.strategy.FedProx" -#~ msgstr "serveur.stratégie.FedProx" +#~ msgid "For more details, see :code:`src/py/flwr_example/pytorch_cifar`." +#~ msgstr "Pour plus de détails, voir :code:`src/py/flwr_example/pytorch_cifar`." -#~ msgid "server.strategy.FedAdagrad" -#~ msgstr "serveur.stratégie.FedAdagrad" +#~ msgid "ImageNet-2012 Image Classification" +#~ msgstr "ImageNet-2012 Classification des images" -#~ msgid "server.strategy.FedAdam" -#~ msgstr "serveur.stratégie.FedAdam" +#~ msgid "" +#~ "`ImageNet-2012 `_ is " +#~ "one of the major computer vision " +#~ "datasets. The Flower ImageNet example " +#~ "uses PyTorch to train a ResNet-18 " +#~ "classifier in a federated learning setup" +#~ " with ten clients." +#~ msgstr "" +#~ "`ImageNet-2012 `_ est " +#~ "l'un des principaux ensembles de données" +#~ " de vision par ordinateur. L'exemple " +#~ "Flower ImageNet utilise PyTorch pour " +#~ "entraîner un classificateur ResNet-18 dans " +#~ "une configuration d'apprentissage fédéré avec" +#~ " dix clients." -#~ msgid "server.strategy.FedYogi" -#~ msgstr "serveur.stratégie.FedYogi" +#~ msgid "$ ./src/py/flwr_example/pytorch_imagenet/run-server.sh" +#~ msgstr "$ ./src/py/flwr_example/pytorch_imagenet/run-server.sh" -#~ msgid "" -#~ "`achiverram28`, `Adam Narozniak`, `Anass " -#~ "Anhari`, `Charles Beauville`, `Dana-Farber`," -#~ " `Daniel J. Beutel`, `Daniel Nata " -#~ "Nugraha`, `Edoardo Gabrielli`, `eunchung`, " -#~ "`Gustavo Bertoli`, `Heng Pan`, `Javier`, " -#~ "`Mahdi`, `Ruth Galindo`, `Steven Hé " -#~ "(Sīchàng)`, `Taner Topal`" +#~ msgid "$ ./src/py/flwr_example/pytorch_imagenet/run-clients.sh" +#~ msgstr "$ ./src/py/flwr_example/pytorch_imagenet/run-clients.sh" + +#~ msgid "For more details, see :code:`src/py/flwr_example/pytorch_imagenet`." #~ msgstr "" +#~ "Pour plus de détails, voir " +#~ ":code:`src/py/flwr_example/pytorch_imagenet`." + +#~ msgid "TensorFlow Examples" +#~ msgstr "Exemples de TensorFlow" #~ msgid "" -#~ "Let's now load the CIFAR-10 training " -#~ "and test set, partition them into " -#~ "ten smaller datasets (each split into" -#~ " training and validation set), and " -#~ "wrap the resulting partitions by " -#~ "creating a PyTorch ``DataLoader`` for " -#~ "each of them:" +#~ "Our TensorFlow examples are based on " +#~ "TensorFlow 2.0 or newer. So far, " +#~ "we provide the following examples." #~ msgstr "" -#~ "Chargeons maintenant l'ensemble de formation" -#~ " et de test CIFAR-10, partitionnons-" -#~ "les en dix ensembles de données " -#~ "plus petits (chacun divisé en ensemble" -#~ " de formation et de validation), et" -#~ " enveloppons les partitions résultantes en" -#~ " créant un PyTorch ``DataLoader`` pour " -#~ "chacun d'entre eux :" +#~ "Nos exemples TensorFlow sont basés sur" +#~ " TensorFlow 2.0 ou une version plus" +#~ " récente. Jusqu'à présent, nous te " +#~ "proposons les exemples suivants." + +#~ msgid "Fashion-MNIST Image Classification" +#~ msgstr "Classification d'images Fashion-MNIST" #~ msgid "" -#~ "Let's build a horizontal federated " -#~ "learning system using XGBoost and " -#~ "Flower!" +#~ "`Fashion-MNIST `_ is often used as " +#~ "the \"Hello, world!\" of machine " +#~ "learning. We follow this tradition and" +#~ " provide an example which samples " +#~ "random local datasets from Fashion-MNIST" +#~ " and trains a simple image " +#~ "classification model over those partitions." #~ msgstr "" -#~ "Construisons un système d'apprentissage fédéré" -#~ " horizontal en utilisant XGBoost et " -#~ "Flower !" +#~ "nous suivons cette tradition et " +#~ "fournissons un exemple qui échantillonne " +#~ "des ensembles de données locales " +#~ "aléatoires de Fashion-MNIST et entraîne" +#~ " un modèle simple de classification " +#~ "d'images sur ces partitions." + +#~ msgid "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-server.sh" +#~ msgstr "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-server.sh" + +#~ msgid "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-clients.sh" +#~ msgstr "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-clients.sh" #~ msgid "" -#~ "Please refer to the `full code " -#~ "example `_ to learn " -#~ "more." +#~ "For more details, see " +#~ ":code:`src/py/flwr_example/tensorflow_fashion_mnist`." #~ msgstr "" -#~ "Réfère-toi à l'exemple de code " -#~ "complet `_ pour en " -#~ "savoir plus." +#~ "Pour plus de détails, voir " +#~ ":code:`src/py/flwr_example/tensorflow_fashion_mnist`." -#~ msgid "|3ff4c820a01d4a5abb022617de537c54|" +#~ msgid "" +#~ "MXNet is no longer maintained and " +#~ "has been moved into `Attic " +#~ "`_. As a " +#~ "result, we would encourage you to " +#~ "use other ML frameworks alongise Flower," +#~ " for example, PyTorch. This tutorial " +#~ "might be removed in future versions " +#~ "of Flower." #~ msgstr "" -#~ msgid "|7f1889391ad448e2a65920165f0d798c|" +#~ msgid "" +#~ "Now that you have known how " +#~ "federated XGBoost work with Flower, it's" +#~ " time to run some more comprehensive" +#~ " experiments by customising the " +#~ "experimental settings. In the xgboost-" +#~ "comprehensive example (`full code " +#~ "`_), we provide more options " +#~ "to define various experimental setups, " +#~ "including aggregation strategies, data " +#~ "partitioning and centralised/distributed evaluation." +#~ " We also support `Flower simulation " +#~ "`_ making it easy to " +#~ "simulate large client cohorts in a " +#~ "resource-aware manner. Let's take a " +#~ "look!" #~ msgstr "" -#~ msgid "|a171dc4a0d044e70b5d585cc10ace0e0|" +#~ msgid "|31e4b1afa87c4b968327bbeafbf184d4|" #~ msgstr "" -#~ msgid "|fe518aa0d86341f7b2fc87bd6e3bbf0c|" +#~ msgid "|c9d935b4284e4c389a33d86b33e07c0a|" #~ msgstr "" -#~ msgid "|6abfdf0dade44469ae9f08c8dc7d148c|" +#~ msgid "|00727b5faffb468f84dd1b03ded88638|" #~ msgstr "" -#~ msgid "|b4f147db24bb4da9a786e1d6676a1c2d|" +#~ msgid "|daf0cf0ff4c24fd29439af78416cf47b|" #~ msgstr "" -#~ msgid "|5c62032f589a457bb37b5fee5b2adbde|" +#~ msgid "|9f093007080d471d94ca90d3e9fde9b6|" #~ msgstr "" -#~ msgid "|f154df1846dd44f79a94f1dc3ae8b088|" +#~ msgid "|46a26e6150e0479fbd3dfd655f36eb13|" #~ msgstr "" -#~ msgid "|9d20be8160f7451fb0f33b194506503f|" +#~ msgid "|3daba297595c4c7fb845d90404a6179a|" #~ msgstr "" -#~ msgid "|3d949f76988443c59990d2e64f05c386|" +#~ msgid "|5769874fa9c4455b80b2efda850d39d7|" #~ msgstr "" -#~ msgid "|526c6d9140f6404f8a226d9056327b3b|" +#~ msgid "|ba47ffb421814b0f8f9fa5719093d839|" #~ msgstr "" -#~ msgid "|a5f6af14cd7c4550929b17f83b4f63c7|" +#~ msgid "|aeac5bf79cbf497082e979834717e01b|" #~ msgstr "" -#~ msgid "|bcd571c4f4ee4803a54f71b5c20448cb|" +#~ msgid "|ce27ed4bbe95459dba016afc42486ba2|" #~ msgstr "" -#~ msgid "|c76452ae1ed84965be7ef23c72b95845|" +#~ msgid "|ae94a7f71dda443cbec2385751427d41|" #~ msgstr "" -#~ msgid "" -#~ "Please follow the first section on " -#~ "`Run Flower using Docker " -#~ "`_ which covers this" -#~ " step in more detail." +#~ msgid "|e61fce4d43d243e7bb08bdde97d81ce6|" +#~ msgstr "" + +#~ msgid "|08cb60859b07461588fe44e55810b050|" #~ msgstr "" #~ msgid "" -#~ "Since `Flower 1.5 `_ we have " -#~ "introduced translations to our doc " -#~ "pages, but, as you might have " -#~ "noticed, the translations are often " -#~ "imperfect. If you speak languages other" -#~ " than English, you might be able " -#~ "to help us in our effort to " -#~ "make Federated Learning accessible to as" -#~ " many people as possible by " -#~ "contributing to those translations! This " -#~ "might also be a great opportunity " -#~ "for those wanting to become open " -#~ "source contributors with little prerequistes." +#~ "Flower provides pre-made docker images" +#~ " on `Docker Hub " +#~ "`_ that include" +#~ " all necessary dependencies for running " +#~ "the server. You can also build " +#~ "your own custom docker images from " +#~ "scratch with a different version of " +#~ "Python or Ubuntu if that is what" +#~ " you need. In this guide, we " +#~ "will explain what images exist and " +#~ "how to build them locally." #~ msgstr "" #~ msgid "" -#~ "You input your translation in the " -#~ "textbox at the top and then, once" -#~ " you are happy with it, you " -#~ "either press ``Save and continue`` (to" -#~ " save the translation and go to " -#~ "the next untranslated string), ``Save " -#~ "and stay`` (to save the translation " -#~ "and stay on the same page), " -#~ "``Suggest`` (to add your translation to" -#~ " suggestions for other users to " -#~ "view), or ``Skip`` (to go to the" -#~ " next untranslated string without saving" -#~ " anything)." +#~ "Currently, Flower provides two images, a" +#~ " base image and a server image. " +#~ "There will also be a client image" +#~ " soon. The base image, as the " +#~ "name suggests, contains basic dependencies " +#~ "that both the server and the " +#~ "client need. This includes system " +#~ "dependencies, Python and Python tools. " +#~ "The server image is based on the" +#~ " base image, but it additionally " +#~ "installs the Flower server using " +#~ "``pip``." #~ msgstr "" #~ msgid "" -#~ "If the section is completely empty " -#~ "(without any token) or non-existant, " -#~ "the changelog will just contain the " -#~ "title of the PR for the changelog" -#~ " entry, without any description." +#~ "Both, base and server image are " +#~ "configured via build arguments. Through " +#~ "build arguments, we can make our " +#~ "build more flexible. For example, in " +#~ "the base image, we can specify the" +#~ " version of Python to install using" +#~ " the ``PYTHON_VERSION`` build argument. " +#~ "Some of the build arguments have " +#~ "default values, others must be specified" +#~ " when building the image. All " +#~ "available build arguments for each image" +#~ " are listed in one of the " +#~ "tables below." #~ msgstr "" -#~ msgid "Example: Walk-Through PyTorch & MNIST" -#~ msgstr "Exemple : PyTorch et MNIST" +#~ msgid "Defaults to ``flwr/server``." +#~ msgstr "" -#~ msgid "" -#~ "In this tutorial we will learn, " -#~ "how to train a Convolutional Neural " -#~ "Network on MNIST using Flower and " -#~ "PyTorch." +#~ msgid "``BASE_IMAGE_TAG``" #~ msgstr "" -#~ "Dans ce tutoriel, nous allons apprendre," -#~ " comment former un réseau neuronal " -#~ "convolutif sur MNIST en utilisant Flower" -#~ " et PyTorch." -#~ msgid "" -#~ "Since we want to use PyTorch to" -#~ " solve a computer vision task, let's" -#~ " go ahead an install PyTorch and " -#~ "the **torchvision** library:" +#~ msgid "The image tag of the base image." #~ msgstr "" -#~ "Puisque nous voulons utiliser PyTorch " -#~ "pour résoudre une tâche de vision " -#~ "par ordinateur, installons PyTorch et la" -#~ " bibliothèque **torchvision** :" -#~ msgid "Ready... Set... Train!" -#~ msgstr "Prêts... prêts... entraînez-vous !" +#~ msgid "Defaults to ``py3.11-ubuntu22.04``." +#~ msgstr "" #~ msgid "" -#~ "Now that we have all our " -#~ "dependencies installed, let's run a " -#~ "simple distributed training with two " -#~ "clients and one server. Our training " -#~ "procedure and network architecture are " -#~ "based on PyTorch's `Basic MNIST Example" -#~ " `_. " -#~ "This will allow you see how easy" -#~ " it is to wrap your code with" -#~ " Flower and begin training in a " -#~ "federated way. We provide you with " -#~ "two helper scripts, namely *run-" -#~ "server.sh*, and *run-clients.sh*. Don't " -#~ "be afraid to look inside, they are" -#~ " simple enough =)." +#~ "The following example creates a server" +#~ " image with the official Flower base" +#~ " image py3.11-ubuntu22.04 and Flower 1.7.0:" #~ msgstr "" -#~ "Maintenant que nous avons installé " -#~ "toutes nos dépendances, lançons un " -#~ "simple entraînement distribué avec deux " -#~ "clients et un serveur. Notre procédure" -#~ " d'entraînement et l'architecture de notre" -#~ " réseau sont basées sur l'exemple " -#~ "MNIST de base de PyTorch " -#~ "`_. Cela" -#~ " te permettra de voir à quel " -#~ "point il est facile d'envelopper ton " -#~ "code avec Flower et de commencer " -#~ "l'entraînement de manière fédérée. Nous " -#~ "te fournissons deux scripts d'aide, à" -#~ " savoir *run-server.sh*, et *run-" -#~ "clients.sh*. N'aie pas peur de regarder" -#~ " à l'intérieur, ils sont assez " -#~ "simples =)." #~ msgid "" -#~ "Go ahead and launch on a terminal" -#~ " the *run-server.sh* script first as" -#~ " follows:" -#~ msgstr "Lance sur un terminal le script *run-server.sh* d'abord comme suit :" +#~ "The name of image is ``flwr_server`` " +#~ "and the tag ``0.1.0``. Remember that " +#~ "the build arguments as well as the" +#~ " name and tag can be adapted to" +#~ " your needs. These values serve as" +#~ " examples only." +#~ msgstr "" -#~ msgid "Now that the server is up and running, go ahead and launch the clients." -#~ msgstr "Maintenant que le serveur est opérationnel, vas-y et lance les clients." +#~ msgid "" +#~ "If you want to use your own " +#~ "base image instead of the official " +#~ "Flower base image, all you need to" +#~ " do is set the ``BASE_REPOSITORY`` " +#~ "and ``BASE_IMAGE_TAG`` build arguments. The" +#~ " value of ``BASE_REPOSITORY`` must match" +#~ " the name of your image and the" +#~ " value of ``BASE_IMAGE_TAG`` must match " +#~ "the tag of your image." +#~ msgstr "" #~ msgid "" -#~ "Et voilà! You should be seeing the" -#~ " training procedure and, after a few" -#~ " iterations, the test accuracy for " -#~ "each client." +#~ "It is important to follow the " +#~ "instructions described in comments. For " +#~ "instance, in order to not break " +#~ "how our changelog system works, you " +#~ "should read the information above the" +#~ " ``Changelog entry`` section carefully. You" +#~ " can also checkout some examples and" +#~ " details in the :ref:`changelogentry` " +#~ "appendix." #~ msgstr "" -#~ "Et voilà ! Tu devrais voir la " -#~ "procédure d'entraînement et, après quelques" -#~ " itérations, la précision du test " -#~ "pour chaque client." -#~ msgid "Now, let's see what is really happening inside." -#~ msgstr "Maintenant, voyons ce qui se passe réellement à l'intérieur." +#~ msgid "Open a PR (as shown above)" +#~ msgstr "Ouvre un RP (comme indiqué ci-dessus)" #~ msgid "" -#~ "Inside the server helper script *run-" -#~ "server.sh* you will find the following" -#~ " code that basically runs the " -#~ ":code:`server.py`" -#~ msgstr "" -#~ "Dans le script d'aide au serveur " -#~ "*run-server.sh*, tu trouveras le code " -#~ "suivant qui exécute le fichier " -#~ ":code:`server.py`" +#~ "Add CI job to deploy the staging" +#~ " system when the ``main`` branch " +#~ "changes" +#~ msgstr "Add CI job to deploy the staging system when the `main` branch changes" + +#~ msgid "Changelog entry" +#~ msgstr "Changelog" #~ msgid "" -#~ "We can go a bit deeper and " -#~ "see that :code:`server.py` simply launches " -#~ "a server that will coordinate three " -#~ "rounds of training. Flower Servers are" -#~ " very customizable, but for simple " -#~ "workloads, we can start a server " -#~ "using the :ref:`start_server ` function and leave " -#~ "all the configuration possibilities at " -#~ "their default values, as seen below." +#~ "When opening a new PR, inside its" +#~ " description, there should be a " +#~ "``Changelog entry`` header." #~ msgstr "" -#~ "Nous pouvons aller un peu plus " -#~ "loin et voir que :code:`server.py` lance" -#~ " simplement un serveur qui coordonnera " -#~ "trois tours de formation. Flower Les " -#~ "serveurs sont très personnalisables, mais " -#~ "pour les charges de travail simples, " -#~ "nous pouvons démarrer un serveur à " -#~ "l'aide de la fonction :ref:`start_server " -#~ "` et " -#~ "laisser toutes les possibilités de " -#~ "configuration à leurs valeurs par " -#~ "défaut, comme on peut le voir " -#~ "ci-dessous." #~ msgid "" -#~ "Next, let's take a look at the " -#~ "*run-clients.sh* file. You will see " -#~ "that it contains the main loop " -#~ "that starts a set of *clients*." +#~ "Above this header you should see " +#~ "the following comment that explains how" +#~ " to write your changelog entry:" #~ msgstr "" -#~ "Ensuite, jetons un coup d'œil au " -#~ "fichier *run-clients.sh*. Tu verras " -#~ "qu'il contient la boucle principale qui" -#~ " démarre un ensemble de *clients*." #~ msgid "" -#~ "**cid**: is the client ID. It is" -#~ " an integer that uniquely identifies " -#~ "client identifier." +#~ "Inside the following 'Changelog entry' " +#~ "section, you should put the description" +#~ " of your changes that will be " +#~ "added to the changelog alongside your" +#~ " PR title." #~ msgstr "" -#~ "**cid** : c'est l'identifiant du client." -#~ " C'est un nombre entier qui identifie" -#~ " de façon unique l'identifiant du " -#~ "client." - -#~ msgid "**sever_address**: String that identifies IP and port of the server." -#~ msgstr "**sever_address** : Chaîne qui identifie l'IP et le port du serveur." #~ msgid "" -#~ "**nb_clients**: This defines the number " -#~ "of clients being created. This piece " -#~ "of information is not required by " -#~ "the client, but it helps us " -#~ "partition the original MNIST dataset to" -#~ " make sure that every client is " -#~ "working on unique subsets of both " -#~ "*training* and *test* sets." +#~ "If the section is completely empty " +#~ "(without any token) or non-existent, " +#~ "the changelog will just contain the " +#~ "title of the PR for the changelog" +#~ " entry, without any description." #~ msgstr "" -#~ "**Cette information n'est pas requise " -#~ "par le client, mais elle nous aide" -#~ " à partitionner l'ensemble de données " -#~ "MNIST original pour nous assurer que " -#~ "chaque client travaille sur des sous-" -#~ "ensembles uniques des ensembles *formation*" -#~ " et *test*." #~ msgid "" -#~ "Again, we can go deeper and look" -#~ " inside :code:`flwr_example/quickstart-" -#~ "pytorch/client.py`. After going through the" -#~ " argument parsing code at the " -#~ "beginning of our :code:`main` function, " -#~ "you will find a call to " -#~ ":code:`mnist.load_data`. This function is " -#~ "responsible for partitioning the original " -#~ "MNIST datasets (*training* and *test*) " -#~ "and returning a :code:`torch.utils.data.DataLoader`" -#~ " s for each of them. We then" -#~ " instantiate a :code:`PytorchMNISTClient` object" -#~ " with our client ID, our DataLoaders," -#~ " the number of epochs in each " -#~ "round, and which device we want to" -#~ " use for training (CPU or GPU)." +#~ "If the section contains some text " +#~ "other than tokens, it will use it" +#~ " to add a description to the " +#~ "change." #~ msgstr "" -#~ "Encore une fois, nous pouvons aller " -#~ "plus loin et regarder dans " -#~ ":code:`flwr_example/quickstart-pytorch/client.py`. Après" -#~ " avoir parcouru le code d'analyse des" -#~ " arguments au début de notre fonction" -#~ " :code:`main`, tu trouveras un appel " -#~ "à :code:`mnist.load_data`. Cette fonction est" -#~ " responsable du partitionnement des " -#~ "ensembles de données MNIST originaux " -#~ "(*training* et *test*) et renvoie un " -#~ ":code:`torch.utils.data.DataLoader` s pour chacun" -#~ " d'entre eux. Nous instancions ensuite " -#~ "un objet :code:`PytorchMNISTClient` avec notre" -#~ " ID client, nos DataLoaders, le " -#~ "nombre d'époques dans chaque tour et " -#~ "le périphérique que nous voulons " -#~ "utiliser pour l'entraînement (CPU ou " -#~ "GPU)." #~ msgid "" -#~ "The :code:`PytorchMNISTClient` object when " -#~ "finally passed to :code:`fl.client.start_client` " -#~ "along with the server's address as " -#~ "the training process begins." +#~ "If the section contains one of the" +#~ " following tokens it will ignore any" +#~ " other text and put the PR " +#~ "under the corresponding section of the" +#~ " changelog:" #~ msgstr "" -#~ "L'objet :code:`PytorchMNISTClient` est finalement" -#~ " transmis à :code:`fl.client.start_client` avec" -#~ " l'adresse du serveur lorsque le " -#~ "processus de formation commence." - -#~ msgid "A Closer Look" -#~ msgstr "Regarder de plus près" -#~ msgid "" -#~ "Now, let's look closely into the " -#~ ":code:`PytorchMNISTClient` inside :code:`flwr_example" -#~ ".quickstart-pytorch.mnist` and see what it" -#~ " is doing:" +#~ msgid " is for classifying a PR as a general improvement." #~ msgstr "" -#~ "Maintenant, examinons de près le " -#~ ":code:`PytorchMNISTClient` à l'intérieur du " -#~ ":code:`flwr_example.quickstart-pytorch.mnist` et " -#~ "voyons ce qu'il fait :" -#~ msgid "" -#~ "The first thing to notice is that" -#~ " :code:`PytorchMNISTClient` instantiates a CNN" -#~ " model inside its constructor" +#~ msgid " is to not add the PR to the changelog" #~ msgstr "" -#~ "La première chose à remarquer est " -#~ "que :code:`PytorchMNISTClient` instancie un " -#~ "modèle CNN dans son constructeur" -#~ msgid "" -#~ "The code for the CNN is available" -#~ " under :code:`quickstart-pytorch.mnist` and " -#~ "it is reproduced below. It is the" -#~ " same network found in `Basic MNIST" -#~ " Example " -#~ "`_." +#~ msgid " is to add a general baselines change to the PR" +#~ msgstr "" + +#~ msgid " is to add a general examples change to the PR" #~ msgstr "" -#~ "Le code du CNN est disponible sous" -#~ " :code:`quickstart-pytorch.mnist` et il est" -#~ " reproduit ci-dessous. Il s'agit du" -#~ " même réseau que celui que l'on " -#~ "trouve dans `Exemple basique de MNIST" -#~ " `_." -#~ msgid "" -#~ "The second thing to notice is that" -#~ " :code:`PytorchMNISTClient` class inherits from" -#~ " the :code:`fl.client.Client`, and hence it" -#~ " must implement the following methods:" +#~ msgid " is to add a general sdk change to the PR" #~ msgstr "" -#~ "La deuxième chose à noter est que" -#~ " la classe :code:`PytorchMNISTClient` hérite " -#~ "de :code:`fl.client.Client`, et qu'elle doit" -#~ " donc implémenter les méthodes suivantes" -#~ " :" -#~ msgid "" -#~ "When comparing the abstract class to " -#~ "its derived class :code:`PytorchMNISTClient` " -#~ "you will notice that :code:`fit` calls" -#~ " a :code:`train` function and that " -#~ ":code:`evaluate` calls a :code:`test`: " -#~ "function." +#~ msgid " is to add a general simulations change to the PR" #~ msgstr "" -#~ "En comparant la classe abstraite à " -#~ "sa classe dérivée :code:`PytorchMNISTClient`, " -#~ "tu remarqueras que :code:`fit` appelle " -#~ "une fonction :code:`train` et que " -#~ ":code:`evaluate` appelle une fonction " -#~ ":code:`test` :." -#~ msgid "" -#~ "These functions can both be found " -#~ "inside the same :code:`quickstart-" -#~ "pytorch.mnist` module:" +#~ msgid "Note that only one token should be used." #~ msgstr "" -#~ "Ces fonctions se trouvent toutes deux" -#~ " dans le même module :code:`quickstart-" -#~ "pytorch.mnist` :" #~ msgid "" -#~ "Observe that these functions encapsulate " -#~ "regular training and test loops and " -#~ "provide :code:`fit` and :code:`evaluate` with" -#~ " final statistics for each round. You" -#~ " could substitute them with your " -#~ "custom train and test loops and " -#~ "change the network architecture, and the" -#~ " entire example would still work " -#~ "flawlessly. As a matter of fact, " -#~ "why not try and modify the code" -#~ " to an example of your liking?" +#~ "Its content must have a specific " +#~ "format. We will break down what " +#~ "each possibility does:" #~ msgstr "" -#~ "Observe que ces fonctions encapsulent " -#~ "les boucles d'entraînement et de test" -#~ " habituelles et fournissent à :code:`fit`" -#~ " et :code:`evaluate` les statistiques " -#~ "finales pour chaque tour. Tu pourrais" -#~ " les remplacer par tes boucles " -#~ "d'entraînement et de test personnalisées " -#~ "et changer l'architecture du réseau, et" -#~ " l'ensemble de l'exemple fonctionnerait " -#~ "toujours parfaitement. En fait, pourquoi " -#~ "ne pas essayer de modifier le code" -#~ " pour en faire un exemple qui " -#~ "te plairait ?" - -#~ msgid "Give It a Try" -#~ msgstr "Fais un essai" #~ msgid "" -#~ "Looking through the quickstart code " -#~ "description above will have given a " -#~ "good understanding of how *clients* and" -#~ " *servers* work in Flower, how to " -#~ "run a simple experiment, and the " -#~ "internals of a client wrapper. Here " -#~ "are a few things you could try " -#~ "on your own and get more " -#~ "experience with Flower:" +#~ "If the ``### Changelog entry`` section" +#~ " contains nothing or doesn't exist, " +#~ "the following text will be added " +#~ "to the changelog::" #~ msgstr "" -#~ "En parcourant la description du code " -#~ "de démarrage rapide ci-dessus, tu " -#~ "auras acquis une bonne compréhension du" -#~ " fonctionnement des *clients* et des " -#~ "*serveurs* dans Flower, de l'exécution " -#~ "d'une expérience simple et de la " -#~ "structure interne d'un wrapper client. " -#~ "Voici quelques exemples que tu peux " -#~ "essayer par toi-même pour acquérir " -#~ "plus d'expérience avec Flower :" #~ msgid "" -#~ "Try and change :code:`PytorchMNISTClient` so" -#~ " it can accept different architectures." +#~ "If the ``### Changelog entry`` section" +#~ " contains a description (and no " +#~ "token), the following text will be " +#~ "added to the changelog::" #~ msgstr "" -#~ "Essaie de modifier :code:`PytorchMNISTClient` " -#~ "pour qu'il puisse accepter différentes " -#~ "architectures." #~ msgid "" -#~ "Modify the :code:`train` function so " -#~ "that it accepts different optimizers" +#~ "If the ``### Changelog entry`` section" +#~ " contains ````, nothing will change" +#~ " in the changelog." #~ msgstr "" -#~ "Modifie la fonction :code:`train` pour " -#~ "qu'elle accepte différents optimiseurs" #~ msgid "" -#~ "Modify the :code:`test` function so that" -#~ " it proves not only the top-1 " -#~ "(regular accuracy) but also the top-5" -#~ " accuracy?" +#~ "If the ``### Changelog entry`` section" +#~ " contains ````, the following text" +#~ " will be added to the changelog::" #~ msgstr "" -#~ "Modifie la fonction :code:`test` pour " -#~ "qu'elle prouve non seulement le top-1" -#~ " (précision normale) mais aussi le " -#~ "top-5 ?" #~ msgid "" -#~ "Go larger! Try to adapt the code" -#~ " to larger images and datasets. Why" -#~ " not try training on ImageNet with" -#~ " a ResNet-50?" +#~ "If the ``### Changelog entry`` section" +#~ " contains ````, the following " +#~ "text will be added to the " +#~ "changelog::" #~ msgstr "" -#~ "Essaie d'adapter le code à des " -#~ "images et à des ensembles de " -#~ "données plus grands. Pourquoi ne pas " -#~ "essayer de s'entraîner sur ImageNet avec" -#~ " un ResNet-50 ?" -#~ msgid "You are ready now. Enjoy learning in a federated way!" -#~ msgstr "Tu es prêt maintenant. Profite de l'apprentissage de manière fédérée !" +#~ msgid "" +#~ "If the ``### Changelog entry`` section" +#~ " contains ````, the following " +#~ "text will be added to the " +#~ "changelog::" +#~ msgstr "" #~ msgid "" -#~ "Flower provides differential privacy (DP) " -#~ "wrapper classes for the easy integration" -#~ " of the central DP guarantees " -#~ "provided by DP-FedAvg into training " -#~ "pipelines defined in any of the " -#~ "various ML frameworks that Flower is " -#~ "compatible with." +#~ "If the ``### Changelog entry`` section" +#~ " contains ````, the following text " +#~ "will be added to the changelog::" #~ msgstr "" -#~ "Flower fournit des classes d'enveloppe " -#~ "de confidentialité différentielle (DP) pour" -#~ " l'intégration facile des garanties " -#~ "centrales de DP fournies par DP-" -#~ "FedAvg dans les pipelines de formation" -#~ " définis dans n'importe lequel des " -#~ "divers cadres de ML avec lesquels " -#~ "Flower est compatible." #~ msgid "" -#~ "Please note that these components are" -#~ " still experimental; the correct " -#~ "configuration of DP for a specific " -#~ "task is still an unsolved problem." +#~ "If the ``### Changelog entry`` section" +#~ " contains ````, the following " +#~ "text will be added to the " +#~ "changelog::" #~ msgstr "" -#~ "Note que ces composants sont encore " -#~ "expérimentaux, la configuration correcte du" -#~ " DP pour une tâche spécifique est " -#~ "encore un problème non résolu." #~ msgid "" -#~ "The name DP-FedAvg is misleading " -#~ "since it can be applied on top " -#~ "of any FL algorithm that conforms " -#~ "to the general structure prescribed by" -#~ " the FedOpt family of algorithms." +#~ "Note that only one token must be" +#~ " provided, otherwise, only the first " +#~ "action (in the order listed above), " +#~ "will be performed." #~ msgstr "" -#~ "Le nom DP-FedAvg est trompeur car" -#~ " il peut être appliqué à n'importe" -#~ " quel algorithme FL qui se conforme" -#~ " à la structure générale prescrite " -#~ "par la famille d'algorithmes FedOpt." -#~ msgid "DP-FedAvg" -#~ msgstr "DP-FedAvg" +#~ msgid "Example: MXNet - Run MXNet Federated" +#~ msgstr "Exemple : MXNet - Exécuter MXNet Federated" #~ msgid "" -#~ "DP-FedAvg, originally proposed by " -#~ "McMahan et al. [mcmahan]_ and extended" -#~ " by Andrew et al. [andrew]_, is " -#~ "essentially FedAvg with the following " -#~ "modifications." +#~ "This tutorial will show you how to" +#~ " use Flower to build a federated " +#~ "version of an existing MXNet workload." +#~ " We are using MXNet to train a" +#~ " Sequential model on the MNIST " +#~ "dataset. We will structure the example" +#~ " similar to our `PyTorch - From " +#~ "Centralized To Federated " +#~ "`_ walkthrough. " +#~ "MXNet and PyTorch are very similar " +#~ "and a very good comparison between " +#~ "MXNet and PyTorch is given `here " +#~ "`_. First, " +#~ "we build a centralized training approach" +#~ " based on the `Handwritten Digit " +#~ "Recognition " +#~ "`_" +#~ " tutorial. Then, we build upon the" +#~ " centralized training code to run the" +#~ " training in a federated fashion." #~ msgstr "" -#~ "DP-FedAvg, proposé à l'origine par " -#~ "McMahan et al. [mcmahan]_ et étendu " -#~ "par Andrew et al. [andrew]_, est " -#~ "essentiellement FedAvg avec les modifications" -#~ " suivantes." +#~ "Ce tutoriel te montrera comment utiliser" +#~ " Flower pour construire une version " +#~ "fédérée d'une charge de travail MXNet" +#~ " existante. Nous utilisons MXNet pour " +#~ "former un modèle séquentiel sur " +#~ "l'ensemble de données MNIST. Nous " +#~ "structurerons l'exemple de la même " +#~ "manière que notre présentation `PyTorch " +#~ "- De la centralisation à la " +#~ "fédération `_. " +#~ "MXNet et PyTorch sont très similaires" +#~ " et une très bonne comparaison entre" +#~ " MXNet et PyTorch est donnée ici " +#~ "`_. Tout " +#~ "d'abord, nous construisons une approche " +#~ "de formation centralisée basée sur le" +#~ " tutoriel `Handandwritten Digit Recognition " +#~ "`_." +#~ " Ensuite, nous nous basons sur le " +#~ "code de formation centralisé pour " +#~ "exécuter la formation de manière " +#~ "fédérée." + +#~ msgid "" +#~ "Before we start setting up our " +#~ "MXNet example, we install the " +#~ ":code:`mxnet` and :code:`flwr` packages:" +#~ msgstr "" +#~ "Avant de commencer à configurer notre" +#~ " exemple MXNet, nous installons les " +#~ "paquets :code:`mxnet` et :code:`flwr` :" + +#~ msgid "MNIST Training with MXNet" +#~ msgstr "Formation MNIST avec MXNet" + +#~ msgid "" +#~ "We begin with a brief description " +#~ "of the centralized training code based" +#~ " on a :code:`Sequential` model. If " +#~ "you want a more in-depth " +#~ "explanation of what's going on then " +#~ "have a look at the official `MXNet" +#~ " tutorial " +#~ "`_." +#~ msgstr "" +#~ "Nous commençons par une brève " +#~ "description du code d'entraînement centralisé" +#~ " basé sur un modèle :code:`Sequential`. " +#~ "Si tu veux une explication plus " +#~ "approfondie de ce qui se passe, " +#~ "jette un coup d'œil au tutoriel " +#~ "officiel `MXNet " +#~ "`_." + +#~ msgid "" +#~ "Let's create a new file " +#~ "called:code:`mxnet_mnist.py` with all the " +#~ "components required for a traditional " +#~ "(centralized) MNIST training. First, the " +#~ "MXNet package :code:`mxnet` needs to be" +#~ " imported. You can see that we " +#~ "do not yet import the :code:`flwr` " +#~ "package for federated learning. This " +#~ "will be done later." +#~ msgstr "" +#~ "Créons un nouveau fichier appelé " +#~ ":code:`mxnet_mnist.py` avec tous les " +#~ "composants requis pour un apprentissage " +#~ "MNIST traditionnel (centralisé). Tout d'abord," +#~ " le package MXNet :code:`mxnet` doit " +#~ "être importé. Tu peux voir que " +#~ "nous n'avons pas encore importé le " +#~ "package :code:`flwr` pour l'apprentissage " +#~ "fédéré. Cela sera fait plus tard." + +#~ msgid "" +#~ "The :code:`load_data()` function loads the " +#~ "MNIST training and test sets." +#~ msgstr "" +#~ "La fonction :code:`load_data()` charge les " +#~ "ensembles d'entraînement et de test " +#~ "MNIST." + +#~ msgid "" +#~ "As already mentioned, we will use " +#~ "the MNIST dataset for this machine " +#~ "learning workload. The model architecture " +#~ "(a very simple :code:`Sequential` model) " +#~ "is defined in :code:`model()`." +#~ msgstr "" +#~ "Comme nous l'avons déjà mentionné, nous" +#~ " utiliserons l'ensemble de données MNIST" +#~ " pour cette charge de travail " +#~ "d'apprentissage automatique. L'architecture du " +#~ "modèle (un modèle :code:`Séquentiel` très " +#~ "simple) est définie dans :code:`model()`." + +#~ msgid "" +#~ "We now need to define the training" +#~ " (function :code:`train()`) which loops " +#~ "over the training set and measures " +#~ "the loss for each batch of " +#~ "training examples." +#~ msgstr "" +#~ "Nous devons maintenant définir la " +#~ "formation (fonction :code:`train()`) qui passe" +#~ " en boucle sur l'ensemble de la " +#~ "formation et mesure la perte pour " +#~ "chaque lot d'exemples de formation." + +#~ msgid "" +#~ "The evaluation of the model is " +#~ "defined in function :code:`test()`. The " +#~ "function loops over all test samples " +#~ "and measures the loss and accuracy " +#~ "of the model based on the test " +#~ "dataset." +#~ msgstr "" +#~ "L'évaluation du modèle est définie dans" +#~ " la fonction :code:`test()`. Cette fonction" +#~ " passe en boucle sur tous les " +#~ "échantillons de test et mesure la " +#~ "perte et la précision du modèle en" +#~ " fonction de l'ensemble des données " +#~ "de test." + +#~ msgid "" +#~ "Having defined the data loading, model" +#~ " architecture, training, and evaluation we" +#~ " can put everything together and " +#~ "train our model on MNIST. Note " +#~ "that the GPU/CPU device for the " +#~ "training and testing is defined within" +#~ " the :code:`ctx` (context)." +#~ msgstr "" +#~ "Après avoir défini le chargement des " +#~ "données, l'architecture du modèle, " +#~ "l'entraînement et l'évaluation, nous pouvons" +#~ " tout assembler et entraîner notre " +#~ "modèle sur MNIST. Note que le " +#~ "dispositif GPU/CPU pour l'entraînement et " +#~ "le test est défini dans le " +#~ ":code:`ctx` (contexte)." + +#~ msgid "You can now run your (centralized) MXNet machine learning workload:" +#~ msgstr "" +#~ "Tu peux maintenant exécuter ta charge" +#~ " de travail (centralisée) d'apprentissage " +#~ "automatique MXNet :" + +#~ msgid "" +#~ "So far this should all look fairly" +#~ " familiar if you've used MXNet (or" +#~ " even PyTorch) before. Let's take the" +#~ " next step and use what we've " +#~ "built to create a simple federated " +#~ "learning system consisting of one server" +#~ " and two clients." +#~ msgstr "" +#~ "Jusqu'à présent, tout cela devrait te" +#~ " sembler assez familier si tu as " +#~ "déjà utilisé MXNet (ou même PyTorch)." +#~ " Passons à l'étape suivante et " +#~ "utilisons ce que nous avons construit" +#~ " pour créer un simple système " +#~ "d'apprentissage fédéré composé d'un serveur" +#~ " et de deux clients." + +#~ msgid "MXNet meets Flower" +#~ msgstr "MXNet rencontre Flower" + +#~ msgid "" +#~ "So far, it was not easily possible" +#~ " to use MXNet workloads for federated" +#~ " learning because federated learning is " +#~ "not supported in MXNet. Since Flower " +#~ "is fully agnostic towards the underlying" +#~ " machine learning framework, it can " +#~ "be used to federated arbitrary machine" +#~ " learning workloads. This section will " +#~ "show you how Flower can be used" +#~ " to federate our centralized MXNet " +#~ "workload." +#~ msgstr "" +#~ "Jusqu'à présent, il n'était pas facile" +#~ " d'utiliser les charges de travail " +#~ "MXNet pour l'apprentissage fédéré car " +#~ "l'apprentissage fédéré n'est pas pris en" +#~ " charge dans MXNet. Comme Flower est" +#~ " totalement agnostique vis-à-vis du cadre" +#~ " d'apprentissage automatique sous-jacent, " +#~ "il peut être utilisé pour fédérer " +#~ "des charges de travail d'apprentissage " +#~ "automatique arbitraires. Cette section te " +#~ "montrera comment Flower peut être " +#~ "utilisé pour fédérer notre charge de " +#~ "travail MXNet centralisée." + +#~ msgid "" +#~ "The concept to federate an existing " +#~ "workload is always the same and " +#~ "easy to understand. We have to " +#~ "start a *server* and then use the" +#~ " code in :code:`mxnet_mnist.py` for the " +#~ "*clients* that are connected to the " +#~ "*server*. The *server* sends model " +#~ "parameters to the clients. The *clients*" +#~ " run the training and update the " +#~ "parameters. The updated parameters are " +#~ "sent back to the *server* which " +#~ "averages all received parameter updates. " +#~ "This describes one round of the " +#~ "federated learning process and we repeat" +#~ " this for multiple rounds." +#~ msgstr "" +#~ "Le concept pour fédérer une charge " +#~ "de travail existante est toujours le " +#~ "même et facile à comprendre. Nous " +#~ "devons démarrer un *serveur* et ensuite" +#~ " utiliser le code dans " +#~ ":code:`mxnet_mnist.py` pour les *clients* qui" +#~ " sont connectés au *serveur*. Le " +#~ "*serveur* envoie les paramètres du " +#~ "modèle aux clients. Les *clients* " +#~ "exécutent la formation et mettent à " +#~ "jour les paramètres. Les paramètres mis" +#~ " à jour sont renvoyés au *serveur*" +#~ " qui fait la moyenne de toutes " +#~ "les mises à jour de paramètres " +#~ "reçues. Ceci décrit un tour du " +#~ "processus d'apprentissage fédéré et nous " +#~ "répétons cette opération pour plusieurs " +#~ "tours." + +#~ msgid "" +#~ "Finally, we will define our *client* " +#~ "logic in :code:`client.py` and build " +#~ "upon the previously defined MXNet " +#~ "training in :code:`mxnet_mnist.py`. Our " +#~ "*client* needs to import :code:`flwr`, " +#~ "but also :code:`mxnet` to update the " +#~ "parameters on our MXNet model:" +#~ msgstr "" +#~ "Enfin, nous allons définir la logique" +#~ " de notre *client* dans :code:`client.py`" +#~ " et nous appuyer sur l'entraînement " +#~ "MXNet défini précédemment dans " +#~ ":code:`mxnet_mnist.py`. Notre *client* doit " +#~ "importer :code:`flwr`, mais aussi " +#~ ":code:`mxnet` pour mettre à jour les " +#~ "paramètres de notre modèle MXNet :" + +#~ msgid "" +#~ "Implementing a Flower *client* basically " +#~ "means implementing a subclass of either" +#~ " :code:`flwr.client.Client` or " +#~ ":code:`flwr.client.NumPyClient`. Our implementation " +#~ "will be based on " +#~ ":code:`flwr.client.NumPyClient` and we'll call " +#~ "it :code:`MNISTClient`. :code:`NumPyClient` is " +#~ "slightly easier to implement than " +#~ ":code:`Client` if you use a framework" +#~ " with good NumPy interoperability (like " +#~ "PyTorch or MXNet) because it avoids " +#~ "some of the boilerplate that would " +#~ "otherwise be necessary. :code:`MNISTClient` " +#~ "needs to implement four methods, two " +#~ "methods for getting/setting model parameters," +#~ " one method for training the model," +#~ " and one method for testing the " +#~ "model:" +#~ msgstr "" +#~ "Implementing a Flower *client* basically " +#~ "means implementing a subclass of either" +#~ " :code:`flwr.client.Client` or " +#~ ":code:`flwr.client.NumPyClient`. Our implementation " +#~ "will be based on " +#~ ":code:`flwr.client.NumPyClient` and we'll call " +#~ "it :code:`MNISTClient`. :code:`NumPyClient` is " +#~ "slightly easier to implement than " +#~ ":code:`Client` if you use a framework" +#~ " with good NumPy interoperability (like " +#~ "PyTorch or MXNet) because it avoids " +#~ "some of the boilerplate that would " +#~ "otherwise be necessary. :code:`MNISTClient` " +#~ "needs to implement four methods, two " +#~ "methods for getting/setting model parameters," +#~ " one method for training the model," +#~ " and one method for testing the " +#~ "model:" + +#~ msgid "transform MXNet :code:`NDArray`'s to NumPy :code:`ndarray`'s" +#~ msgstr "transforme les :code:`NDArray` du MXNet en :code:`ndarray` de NumPy" + +#~ msgid "" +#~ "The challenging part is to transform " +#~ "the MXNet parameters from :code:`NDArray` " +#~ "to :code:`NumPy Arrays` to make it " +#~ "readable for Flower." +#~ msgstr "" +#~ "La partie la plus difficile est de" +#~ " transformer les paramètres MXNet de " +#~ ":code:`NDArray` en :code:`NumPy Arrays` pour" +#~ " les rendre lisibles pour Flower." + +#~ msgid "" +#~ "The two :code:`NumPyClient` methods " +#~ ":code:`fit` and :code:`evaluate` make use " +#~ "of the functions :code:`train()` and " +#~ ":code:`test()` previously defined in " +#~ ":code:`mxnet_mnist.py`. So what we really " +#~ "do here is we tell Flower through" +#~ " our :code:`NumPyClient` subclass which of" +#~ " our already defined functions to " +#~ "call for training and evaluation. We " +#~ "included type annotations to give you" +#~ " a better understanding of the data" +#~ " types that get passed around." +#~ msgstr "" +#~ "Les deux méthodes :code:`NumPyClient` " +#~ ":code:`fit` et :code:`evaluate` utilisent les" +#~ " fonctions :code:`train()` et :code:`test()` " +#~ "définies précédemment dans :code:`mxnet_mnist.py`." +#~ " Ce que nous faisons vraiment ici," +#~ " c'est que nous indiquons à Flower," +#~ " par le biais de notre sous-" +#~ "classe :code:`NumPyClient`, laquelle de nos" +#~ " fonctions déjà définies doit être " +#~ "appelée pour l'entraînement et l'évaluation." +#~ " Nous avons inclus des annotations de" +#~ " type pour te donner une meilleure" +#~ " compréhension des types de données " +#~ "qui sont transmis." + +#~ msgid "" +#~ "Having defined data loading, model " +#~ "architecture, training, and evaluation we " +#~ "can put everything together and train" +#~ " our :code:`Sequential` model on MNIST." +#~ msgstr "" +#~ "Après avoir défini le chargement des " +#~ "données, l'architecture du modèle, la " +#~ "formation et l'évaluation, nous pouvons " +#~ "tout rassembler et former notre modèle" +#~ " :code:`Sequential` sur MNIST." + +#~ msgid "" +#~ "in each window (make sure that the" +#~ " server is still running before you" +#~ " do so) and see your MXNet " +#~ "project run federated learning across " +#~ "two clients. Congratulations!" +#~ msgstr "" +#~ "dans chaque fenêtre (assure-toi que " +#~ "le serveur est toujours en cours " +#~ "d'exécution avant de le faire) et " +#~ "tu verras ton projet MXNet exécuter " +#~ "l'apprentissage fédéré sur deux clients. " +#~ "Félicitations !" + +#~ msgid "" +#~ "The full source code for this " +#~ "example: `MXNet: From Centralized To " +#~ "Federated (Code) " +#~ "`_. Our " +#~ "example is of course somewhat over-" +#~ "simplified because both clients load the" +#~ " exact same dataset, which isn't " +#~ "realistic. You're now prepared to " +#~ "explore this topic further. How about" +#~ " using a CNN or using a " +#~ "different dataset? How about adding more" +#~ " clients?" +#~ msgstr "" +#~ "Le code source complet de cet " +#~ "exemple : `MXNet : From Centralized " +#~ "To Federated (Code) " +#~ "`_. Notre " +#~ "exemple est bien sûr un peu trop" +#~ " simplifié parce que les deux clients" +#~ " chargent exactement le même ensemble " +#~ "de données, ce qui n'est pas " +#~ "réaliste. Tu es maintenant prêt à " +#~ "explorer ce sujet plus en profondeur." +#~ " Pourquoi ne pas utiliser un CNN " +#~ "ou un ensemble de données différent " +#~ "? Pourquoi ne pas ajouter d'autres " +#~ "clients ?" + +#~ msgid "with the following command sequence:" +#~ msgstr "avec la séquence de commandes suivante :" + +#~ msgid "" +#~ "In case you are a researcher you" +#~ " might be just fine using the " +#~ "self-signed certificates generated using " +#~ "the scripts which are part of this" +#~ " guide." +#~ msgstr "" +#~ "Si tu es un chercheur, tu peux " +#~ "très bien utiliser les certificats " +#~ "auto-signés générés à l'aide des " +#~ "scripts qui font partie de ce " +#~ "guide." + +#~ msgid "" +#~ "We are now going to show how " +#~ "to write a sever which uses the" +#~ " previously generated scripts." +#~ msgstr "" +#~ "Nous allons maintenant montrer comment " +#~ "écrire un serveur qui utilise les " +#~ "scripts générés précédemment." + +#~ msgid "" +#~ "When providing certificates, the server " +#~ "expects a tuple of three certificates." +#~ " :code:`Path` can be used to easily" +#~ " read the contents of those files " +#~ "into byte strings, which is the " +#~ "data type :code:`start_server` expects." +#~ msgstr "" +#~ "Lorsqu'il fournit des certificats, le " +#~ "serveur attend un tuple de trois " +#~ "certificats. :code:`Path` peut être utilisé" +#~ " pour lire facilement le contenu de" +#~ " ces fichiers en chaînes d'octets, ce" +#~ " qui est le type de données " +#~ "attendu par :code:`start_server`." + +#~ msgid "" +#~ "The simplest way to get started " +#~ "with Flower is by using the " +#~ "pre-made Docker images, which you can" +#~ " find on `Docker Hub " +#~ "`_." +#~ msgstr "" + +#~ msgid "Flower server" +#~ msgstr "Serveur de Flower" #~ msgid "" -#~ "**Clipping** : The influence of each " -#~ "client's update is bounded by clipping" -#~ " it. This is achieved by enforcing" -#~ " a cap on the L2 norm of " -#~ "the update, scaling it down if " -#~ "needed." +#~ "The command will pull the Docker " +#~ "image with the tag " +#~ "``1.7.0-py3.11-ubuntu22.04`` from Docker Hub. " +#~ "The tag contains the information which" +#~ " Flower, Python and Ubuntu is used." +#~ " In this case, it uses Flower " +#~ "1.7.0, Python 3.11 and Ubuntu 22.04. " +#~ "The ``--rm`` flag tells Docker to " +#~ "remove the container after it exits." #~ msgstr "" -#~ "**Clipping** : L'influence de la mise" -#~ " à jour de chaque client est " -#~ "limitée en l'écrêtant. Ceci est réalisé" -#~ " en imposant un plafond à la " -#~ "norme L2 de la mise à jour, " -#~ "en la réduisant si nécessaire." #~ msgid "" -#~ "**Noising** : Gaussian noise, calibrated " -#~ "to the clipping threshold, is added " -#~ "to the average computed at the " -#~ "server." +#~ "By default, the Flower server keeps " +#~ "state in-memory. When using the " +#~ "Docker flag ``--rm``, the state is " +#~ "not persisted between container starts. " +#~ "We will show below how to save " +#~ "the state in a file on your " +#~ "host system." #~ msgstr "" -#~ "**Bruit** : un bruit gaussien, calibré" -#~ " sur le seuil d'écrêtage, est ajouté" -#~ " à la moyenne calculée au niveau " -#~ "du serveur." #~ msgid "" -#~ "The distribution of the update norm " -#~ "has been shown to vary from " -#~ "task-to-task and to evolve as " -#~ "training progresses. This variability is " -#~ "crucial in understanding its impact on" -#~ " differential privacy guarantees, emphasizing " -#~ "the need for an adaptive approach " -#~ "[andrew]_ that continuously adjusts the " -#~ "clipping threshold to track a " -#~ "prespecified quantile of the update norm" -#~ " distribution." +#~ "The ``-p :`` flag tells " +#~ "Docker to map the ports " +#~ "``9091``/``9092`` of the host to " +#~ "``9091``/``9092`` of the container, allowing" +#~ " you to access the Driver API " +#~ "on ``http://localhost:9091`` and the Fleet " +#~ "API on ``http://localhost:9092``. Lastly, any" +#~ " flag that comes after the tag " +#~ "is passed to the Flower server. " +#~ "Here, we are passing the flag " +#~ "``--insecure``." #~ msgstr "" -#~ "Il a été démontré que la " -#~ "distribution de la norme de mise à" -#~ " jour varie d'une tâche à l'autre " -#~ "et évolue au fur et à mesure " -#~ "de la formation. C'est pourquoi nous " -#~ "utilisons une approche adaptative [andrew]_" -#~ " qui ajuste continuellement le seuil " -#~ "d'écrêtage pour suivre un quantile " -#~ "prédéfini de la distribution de la " -#~ "norme de mise à jour." -#~ msgid "Simplifying Assumptions" -#~ msgstr "Simplifier les hypothèses" +#~ msgid "" +#~ "The ``--insecure`` flag enables insecure " +#~ "communication (using HTTP, not HTTPS) " +#~ "and should only be used for " +#~ "testing purposes. We strongly recommend " +#~ "enabling `SSL `_ when " +#~ "deploying to a production environment." +#~ msgstr "" #~ msgid "" -#~ "We make (and attempt to enforce) a" -#~ " number of assumptions that must be" -#~ " satisfied to ensure that the " -#~ "training process actually realizes the " -#~ ":math:`(\\epsilon, \\delta)` guarantees the " -#~ "user has in mind when configuring " -#~ "the setup." +#~ "You can use ``--help`` to view all" +#~ " available flags that the server " +#~ "supports:" #~ msgstr "" -#~ "Nous formulons (et tentons d'appliquer) " -#~ "un certain nombre d'hypothèses qui " -#~ "doivent être satisfaites pour que le " -#~ "processus de formation réalise réellement " -#~ "les garanties :math:`(\\epsilon, \\delta)` que" -#~ " l'utilisateur a à l'esprit lorsqu'il " -#~ "configure l'installation." #~ msgid "" -#~ "**Fixed-size subsampling** :Fixed-size " -#~ "subsamples of the clients must be " -#~ "taken at each round, as opposed to" -#~ " variable-sized Poisson subsamples." +#~ "If you want to persist the state" +#~ " of the server on your host " +#~ "system, all you need to do is " +#~ "specify a path where you want to" +#~ " save the file on your host " +#~ "system and a name for the database" +#~ " file. In the example below, we " +#~ "tell Docker via the flag ``-v`` to" +#~ " mount the user's home directory " +#~ "(``~/`` on your host) into the " +#~ "``/app/`` directory of the container. " +#~ "Furthermore, we use the flag " +#~ "``--database`` to specify the name of" +#~ " the database file." #~ msgstr "" -#~ "**Sous-échantillonnage de taille fixe** " -#~ ":Des sous-échantillons de taille fixe" -#~ " des clients doivent être prélevés à" -#~ " chaque tour, par opposition aux " -#~ "sous-échantillons de Poisson de taille " -#~ "variable." #~ msgid "" -#~ "**Unweighted averaging** : The contributions" -#~ " from all the clients must weighted" -#~ " equally in the aggregate to " -#~ "eliminate the requirement for the server" -#~ " to know in advance the sum of" -#~ " the weights of all clients available" -#~ " for selection." +#~ "As soon as the server starts, the" +#~ " file ``state.db`` is created in the" +#~ " user's home directory on your host" +#~ " system. If the file already exists," +#~ " the server tries to restore the " +#~ "state from the file. To start the" +#~ " server with an empty database, " +#~ "simply remove the ``state.db`` file." #~ msgstr "" -#~ "**Moyenne non pondérée** : Les " -#~ "contributions de tous les clients " -#~ "doivent être pondérées de façon égale" -#~ " dans l'ensemble afin que le serveur" -#~ " n'ait pas à connaître à l'avance " -#~ "la somme des poids de tous les " -#~ "clients disponibles pour la sélection." #~ msgid "" -#~ "**No client failures** : The set " -#~ "of available clients must stay constant" -#~ " across all rounds of training. In" -#~ " other words, clients cannot drop out" -#~ " or fail." +#~ "To enable SSL, you will need a " +#~ "CA certificate, a server certificate and" +#~ " a server private key." #~ msgstr "" -#~ "**Aucune défaillance de client** : " -#~ "L'ensemble des clients disponibles doit " -#~ "rester constant pendant toutes les " -#~ "séries de formation. En d'autres termes," -#~ " les clients ne peuvent pas " -#~ "abandonner ou échouer." #~ msgid "" -#~ "The first two are useful for " -#~ "eliminating a multitude of complications " -#~ "associated with calibrating the noise to" -#~ " the clipping threshold, while the " -#~ "third one is required to comply " -#~ "with the assumptions of the privacy " -#~ "analysis." +#~ "For testing purposes, you can generate" +#~ " your own self-signed certificates. " +#~ "The `Enable SSL connections " +#~ "`_ page contains " +#~ "a section that will guide you " +#~ "through the process." #~ msgstr "" -#~ "Les deux premiers sont utiles pour " -#~ "éliminer une multitude de complications " -#~ "liées au calibrage du bruit en " -#~ "fonction du seuil d'écrêtage, tandis que" -#~ " le troisième est nécessaire pour se" -#~ " conformer aux hypothèses de l'analyse " -#~ "de la vie privée." #~ msgid "" -#~ "These restrictions are in line with " -#~ "constraints imposed by Andrew et al. " -#~ "[andrew]_." +#~ "Assuming all files we need are in" +#~ " the local ``certificates`` directory, we" +#~ " can use the flag ``-v`` to " +#~ "mount the local directory into the " +#~ "``/app/`` directory of the container. " +#~ "This allows the server to access " +#~ "the files within the container. Finally," +#~ " we pass the names of the " +#~ "certificates to the server with the " +#~ "``--certificates`` flag." #~ msgstr "" -#~ "Ces restrictions sont conformes aux " -#~ "contraintes imposées par Andrew et al." -#~ " [andrew]_." -#~ msgid "Customizable Responsibility for Noise injection" -#~ msgstr "Responsabilité personnalisable pour l'injection de bruit" +#~ msgid "Using a different Flower or Python version" +#~ msgstr "" #~ msgid "" -#~ "In contrast to other implementations " -#~ "where the addition of noise is " -#~ "performed at the server, you can " -#~ "configure the site of noise injection" -#~ " to better match your threat model." -#~ " We provide users with the " -#~ "flexibility to set up the training " -#~ "such that each client independently adds" -#~ " a small amount of noise to the" -#~ " clipped update, with the result that" -#~ " simply aggregating the noisy updates " -#~ "is equivalent to the explicit addition" -#~ " of noise to the non-noisy " -#~ "aggregate at the server." +#~ "If you want to use a different " +#~ "version of Flower or Python, you " +#~ "can do so by changing the tag. " +#~ "All versions we provide are available" +#~ " on `Docker Hub " +#~ "`_." #~ msgstr "" -#~ "Contrairement à d'autres implémentations où" -#~ " l'ajout de bruit est effectué au " -#~ "niveau du serveur, tu peux configurer" -#~ " le site d'injection de bruit pour" -#~ " qu'il corresponde mieux à ton modèle" -#~ " de menace. Nous offrons aux " -#~ "utilisateurs la possibilité de configurer " -#~ "l'entraînement de telle sorte que chaque" -#~ " client ajoute indépendamment une petite" -#~ " quantité de bruit à la mise à" -#~ " jour écrêtée, ce qui fait que " -#~ "le simple fait d'agréger les mises " -#~ "à jour bruyantes équivaut à l'ajout " -#~ "explicite de bruit à l'agrégat non " -#~ "bruyant au niveau du serveur." #~ msgid "" -#~ "To be precise, if we let :math:`m`" -#~ " be the number of clients sampled " -#~ "each round and :math:`\\sigma_\\Delta` be " -#~ "the scale of the total Gaussian " -#~ "noise that needs to be added to" -#~ " the sum of the model updates, " -#~ "we can use simple maths to show" -#~ " that this is equivalent to each " -#~ "client adding noise with scale " -#~ ":math:`\\sigma_\\Delta/\\sqrt{m}`." +#~ "The following command returns the " +#~ "current image hash referenced by the " +#~ "``server:1.7.0-py3.11-ubuntu22.04`` tag:" #~ msgstr "" -#~ "Pour être précis, si nous laissons " -#~ ":math:`m` être le nombre de clients " -#~ "échantillonnés à chaque tour et " -#~ ":math:\\sigma_\\Delta` être l'échelle du bruit" -#~ " gaussien total qui doit être ajouté" -#~ " à la somme des mises à jour" -#~ " du modèle, nous pouvons utiliser des" -#~ " mathématiques simples pour montrer que " -#~ "cela équivaut à ce que chaque " -#~ "client ajoute du bruit avec l'échelle" -#~ " :math:\\sigma_\\Delta/\\sqrt{m}`." -#~ msgid "Wrapper-based approach" -#~ msgstr "Approche basée sur l'enveloppe" +#~ msgid "Next, we can pin the hash when running a new server container:" +#~ msgstr "" + +#~ msgid "flower-driver-api" +#~ msgstr "flower-driver-api" + +#~ msgid "flower-fleet-api" +#~ msgstr "flower-fleet-api" #~ msgid "" -#~ "Introducing DP to an existing workload" -#~ " can be thought of as adding an" -#~ " extra layer of security around it." -#~ " This inspired us to provide the " -#~ "additional server and client-side logic" -#~ " needed to make the training process" -#~ " differentially private as wrappers for " -#~ "instances of the :code:`Strategy` and " -#~ ":code:`NumPyClient` abstract classes respectively." -#~ " This wrapper-based approach has the" -#~ " advantage of being easily composable " -#~ "with other wrappers that someone might" -#~ " contribute to the Flower library in" -#~ " the future, e.g., for secure " -#~ "aggregation. Using Inheritance instead can " -#~ "be tedious because that would require" -#~ " the creation of new sub- classes " -#~ "every time a new class implementing " -#~ ":code:`Strategy` or :code:`NumPyClient` is " -#~ "defined." +#~ "Bases: :py:class:`~flwr.common.record.typeddict.TypedDict`\\ " +#~ "[:py:class:`str`, :py:obj:`~typing.Union`\\ " +#~ "[:py:class:`int`, :py:class:`float`, :py:class:`str`, " +#~ ":py:class:`bytes`, :py:class:`bool`, " +#~ ":py:class:`~typing.List`\\ [:py:class:`int`], " +#~ ":py:class:`~typing.List`\\ [:py:class:`float`], " +#~ ":py:class:`~typing.List`\\ [:py:class:`str`], " +#~ ":py:class:`~typing.List`\\ [:py:class:`bytes`], " +#~ ":py:class:`~typing.List`\\ [:py:class:`bool`]]]" #~ msgstr "" -#~ "L'introduction du DP dans une charge " -#~ "de travail existante peut être " -#~ "considérée comme l'ajout d'une couche de" -#~ " sécurité supplémentaire autour d'elle. " -#~ "Cela nous a incités à fournir la" -#~ " logique supplémentaire côté serveur et " -#~ "côté client nécessaire pour rendre le" -#~ " processus de formation différentiellement " -#~ "privé en tant qu'enveloppes pour les " -#~ "instances des classes abstraites " -#~ ":code:`Strategy` et :code:`NumPyClient` " -#~ "respectivement. Cette approche basée sur " -#~ "l'enveloppe a l'avantage d'être facilement " -#~ "composable avec d'autres enveloppes que " -#~ "quelqu'un pourrait contribuer à la " -#~ "bibliothèque Flower à l'avenir, par " -#~ "exemple, pour l'agrégation sécurisée. " -#~ "L'utilisation de l'héritage à la place" -#~ " peut être fastidieuse car cela " -#~ "nécessiterait la création de nouvelles " -#~ "sous-classes chaque fois qu'une nouvelle" -#~ " classe mettant en œuvre :code:`Strategy`" -#~ " ou :code:`NumPyClient` est définie." #~ msgid "" -#~ "The first version of our solution " -#~ "was to define a decorator whose " -#~ "constructor accepted, among other things, " -#~ "a boolean-valued variable indicating " -#~ "whether adaptive clipping was to be " -#~ "enabled or not. We quickly realized " -#~ "that this would clutter its " -#~ ":code:`__init__()` function with variables " -#~ "corresponding to hyperparameters of adaptive" -#~ " clipping that would remain unused " -#~ "when it was disabled. A cleaner " -#~ "implementation could be achieved by " -#~ "splitting the functionality into two " -#~ "decorators, :code:`DPFedAvgFixed` and " -#~ ":code:`DPFedAvgAdaptive`, with the latter sub-" -#~ " classing the former. The constructors " -#~ "for both classes accept a boolean " -#~ "parameter :code:`server_side_noising`, which, as " -#~ "the name suggests, determines where " -#~ "noising is to be performed." +#~ ":py:obj:`create_error_reply " +#~ "`\\ \\(error\\, " +#~ "ttl\\)" #~ msgstr "" -#~ "La première version de notre solution" -#~ " consistait à définir un décorateur " -#~ "dont le constructeur acceptait, entre " -#~ "autres, une variable à valeur booléenne" -#~ " indiquant si l'écrêtage adaptatif devait" -#~ " être activé ou non. Nous nous " -#~ "sommes rapidement rendu compte que cela" -#~ " encombrerait sa fonction :code:`__init__()` " -#~ "avec des variables correspondant aux " -#~ "hyperparamètres de l'écrêtage adaptatif qui" -#~ " resteraient inutilisées lorsque celui-ci" -#~ " était désactivé. Une implémentation plus" -#~ " propre pourrait être obtenue en " -#~ "divisant la fonctionnalité en deux " -#~ "décorateurs, :code:`DPFedAvgFixed` et " -#~ ":code:`DPFedAvgAdaptive`, le second sous-" -#~ "classant le premier. Les constructeurs " -#~ "des deux classes acceptent un paramètre" -#~ " booléen :code:`server_side_noising` qui, comme" -#~ " son nom l'indique, détermine l'endroit " -#~ "où le noising doit être effectué." #~ msgid "" -#~ "The server-side capabilities required " -#~ "for the original version of DP-" -#~ "FedAvg, i.e., the one which performed" -#~ " fixed clipping, can be completely " -#~ "captured with the help of wrapper " -#~ "logic for just the following two " -#~ "methods of the :code:`Strategy` abstract " -#~ "class." +#~ ":py:obj:`create_reply `\\ " +#~ "\\(content\\, ttl\\)" #~ msgstr "" -#~ "Les capacités côté serveur requises pour" -#~ " la version originale de DP-FedAvg," -#~ " c'est-à-dire celle qui effectue un " -#~ "écrêtage fixe, peuvent être entièrement " -#~ "capturées à l'aide d'une logique " -#~ "d'enveloppement pour les deux méthodes " -#~ "suivantes de la classe abstraite " -#~ ":code:`Strategy`." #~ msgid "" -#~ ":code:`configure_fit()` : The config " -#~ "dictionary being sent by the wrapped " -#~ ":code:`Strategy` to each client needs to" -#~ " be augmented with an additional " -#~ "value equal to the clipping threshold" -#~ " (keyed under :code:`dpfedavg_clip_norm`) and," -#~ " if :code:`server_side_noising=true`, another one" -#~ " equal to the scale of the " -#~ "Gaussian noise that needs to be " -#~ "added at the client (keyed under " -#~ ":code:`dpfedavg_noise_stddev`). This entails " -#~ "*post*-processing of the results returned " -#~ "by the wrappee's implementation of " -#~ ":code:`configure_fit()`." +#~ "Bases: :py:class:`~flwr.common.record.typeddict.TypedDict`\\ " +#~ "[:py:class:`str`, :py:obj:`~typing.Union`\\ " +#~ "[:py:class:`int`, :py:class:`float`, " +#~ ":py:class:`~typing.List`\\ [:py:class:`int`], " +#~ ":py:class:`~typing.List`\\ [:py:class:`float`]]]" +#~ msgstr "" + +#~ msgid "Run Flower server (Driver API and Fleet API)." #~ msgstr "" -#~ ":code:`configure_fit()` : Le dictionnaire de" -#~ " configuration envoyé par la " -#~ ":code:`Strategy` enveloppée à chaque client" -#~ " doit être augmenté d'une valeur " -#~ "supplémentaire égale au seuil d'écrêtage " -#~ "(indiqué sous :code:`dpfedavg_clip_norm`) et, " -#~ "si :code:`server_side_noising=true`, d'une autre " -#~ "égale à l'échelle du bruit gaussien " -#~ "qui doit être ajouté au client " -#~ "(indiqué sous :code:`dpfedavg_noise_stddev`)." #~ msgid "" -#~ ":code:`aggregate_fit()`: We check whether any" -#~ " of the sampled clients dropped out" -#~ " or failed to upload an update " -#~ "before the round timed out. In " -#~ "that case, we need to abort the" -#~ " current round, discarding any successful" -#~ " updates that were received, and move" -#~ " on to the next one. On the " -#~ "other hand, if all clients responded " -#~ "successfully, we must force the " -#~ "averaging of the updates to happen " -#~ "in an unweighted manner by intercepting" -#~ " the :code:`parameters` field of " -#~ ":code:`FitRes` for each received update " -#~ "and setting it to 1. Furthermore, " -#~ "if :code:`server_side_noising=true`, each update " -#~ "is perturbed with an amount of " -#~ "noise equal to what it would have" -#~ " been subjected to had client-side" -#~ " noising being enabled. This entails " -#~ "*pre*-processing of the arguments to " -#~ "this method before passing them on " -#~ "to the wrappee's implementation of " -#~ ":code:`aggregate_fit()`." +#~ ":py:obj:`start_driver `\\ " +#~ "\\(\\*\\[\\, server\\_address\\, server\\, ...\\]\\)" #~ msgstr "" -#~ ":code:`aggregate_fit()`: We check whether any" -#~ " of the sampled clients dropped out" -#~ " or failed to upload an update " -#~ "before the round timed out. In " -#~ "that case, we need to abort the" -#~ " current round, discarding any successful" -#~ " updates that were received, and move" -#~ " on to the next one. On the " -#~ "other hand, if all clients responded " -#~ "successfully, we must force the " -#~ "averaging of the updates to happen " -#~ "in an unweighted manner by intercepting" -#~ " the :code:`parameters` field of " -#~ ":code:`FitRes` for each received update " -#~ "and setting it to 1. Furthermore, " -#~ "if :code:`server_side_noising=true`, each update " -#~ "is perturbed with an amount of " -#~ "noise equal to what it would have" -#~ " been subjected to had client-side" -#~ " noising being enabled. This entails " -#~ "*pre*-processing of the arguments to " -#~ "this method before passing them on " -#~ "to the wrappee's implementation of " -#~ ":code:`aggregate_fit()`." + +#~ msgid "Start a Flower Driver API server." +#~ msgstr "Tout d'abord, démarre un serveur Flower :" #~ msgid "" -#~ "We can't directly change the aggregation" -#~ " function of the wrapped strategy to" -#~ " force it to add noise to the" -#~ " aggregate, hence we simulate client-" -#~ "side noising to implement server-side" -#~ " noising." +#~ ":py:obj:`Driver `\\ " +#~ "\\(\\[driver\\_service\\_address\\, ...\\]\\)" +#~ msgstr "" +#~ "Flower 1.0 : ``start_server(..., " +#~ "config=flwr.server.ServerConfig(num_rounds=3, " +#~ "round_timeout=600.0), ...)``" + +#~ msgid "`Driver` class provides an interface to the Driver API." #~ msgstr "" -#~ "Nous ne pouvons pas modifier directement" -#~ " la fonction d'agrégation de la " -#~ "stratégie enveloppée pour la forcer à" -#~ " ajouter du bruit à l'agrégat, c'est" -#~ " pourquoi nous simulons le bruit côté" -#~ " client pour mettre en œuvre le " -#~ "bruit côté serveur." #~ msgid "" -#~ "These changes have been put together " -#~ "into a class called :code:`DPFedAvgFixed`, " -#~ "whose constructor accepts the strategy " -#~ "being decorated, the clipping threshold " -#~ "and the number of clients sampled " -#~ "every round as compulsory arguments. The" -#~ " user is expected to specify the " -#~ "clipping threshold since the order of" -#~ " magnitude of the update norms is " -#~ "highly dependent on the model being " -#~ "trained and providing a default value" -#~ " would be misleading. The number of" -#~ " clients sampled at every round is" -#~ " required to calculate the amount of" -#~ " noise that must be added to " -#~ "each individual update, either by the" -#~ " server or the clients." +#~ "The IPv4 or IPv6 address of the" +#~ " Driver API server. Defaults to " +#~ "`\"[::]:9091\"`." +#~ msgstr "" + +#~ msgid "Disconnect from the SuperLink if connected." #~ msgstr "" -#~ "Ces modifications ont été regroupées " -#~ "dans une classe appelée :code:`DPFedAvgFixed`," -#~ " dont le constructeur accepte la " -#~ "stratégie décorée, le seuil d'écrêtage " -#~ "et le nombre de clients échantillonnés" -#~ " à chaque tour comme arguments " -#~ "obligatoires. L'utilisateur est censé " -#~ "spécifier le seuil d'écrêtage car " -#~ "l'ordre de grandeur des normes de " -#~ "mise à jour dépend fortement du " -#~ "modèle formé et fournir une valeur " -#~ "par défaut serait trompeur. Le nombre" -#~ " de clients échantillonnés à chaque " -#~ "tour est nécessaire pour calculer la " -#~ "quantité de bruit qui doit être " -#~ "ajoutée à chaque mise à jour " -#~ "individuelle, que ce soit par le " -#~ "serveur ou par les clients." #~ msgid "" -#~ "The additional functionality required to " -#~ "facilitate adaptive clipping has been " -#~ "provided in :code:`DPFedAvgAdaptive`, a " -#~ "subclass of :code:`DPFedAvgFixed`. It " -#~ "overrides the above-mentioned methods to" -#~ " do the following." +#~ ":py:obj:`create_message `\\" +#~ " \\(content\\, message\\_type\\, ...\\)" #~ msgstr "" -#~ "La fonctionnalité supplémentaire nécessaire " -#~ "pour faciliter l'écrêtage adaptatif a " -#~ "été fournie dans :code:`DPFedAvgAdaptive`, une" -#~ " sous-classe de :code:`DPFedAvgFixed`. Elle" -#~ " remplace les méthodes mentionnées ci-" -#~ "dessus pour effectuer les opérations " -#~ "suivantes." #~ msgid "" -#~ ":code:`configure_fit()` : It intercepts the" -#~ " config dict returned by " -#~ ":code:`super.configure_fit()` to add the " -#~ "key-value pair " -#~ ":code:`dpfedavg_adaptive_clip_enabled:True` to it, " -#~ "which the client interprets as an " -#~ "instruction to include an indicator bit" -#~ " (1 if update norm <= clipping " -#~ "threshold, 0 otherwise) in the results" -#~ " returned by it." +#~ "Time-to-live for the round trip" +#~ " of this message, i.e., the time " +#~ "from sending this message to receiving" +#~ " a reply. It specifies the duration" +#~ " for which the message and its " +#~ "potential reply are considered valid." #~ msgstr "" -#~ ":code:`configure_fit()` : Il intercepte le " -#~ "dict de configuration renvoyé par " -#~ ":code:`super.configure_fit()` pour y ajouter " -#~ "la paire clé-valeur " -#~ ":code:`dpfedavg_adaptive_clip_enabled:True`, que le " -#~ "client interprète comme une instruction " -#~ "d'inclure un bit indicateur (1 si " -#~ "la norme de mise à jour <= " -#~ "seuil d'écrêtage, 0 sinon) dans les " -#~ "résultats qu'il renvoie." + +#~ msgid "start\\_driver" +#~ msgstr "start_client" #~ msgid "" -#~ ":code:`aggregate_fit()` : It follows a " -#~ "call to :code:`super.aggregate_fit()` with one" -#~ " to :code:`__update_clip_norm__()`, a procedure" -#~ " which adjusts the clipping threshold " -#~ "on the basis of the indicator bits" -#~ " received from the sampled clients." +#~ "The IPv4 or IPv6 address of the" +#~ " Driver API server. Defaults to " +#~ "`\"[::]:8080\"`." #~ msgstr "" -#~ ":code:`aggregate_fit()` : Il fait suivre " -#~ "un appel à :code:`super.aggregate_fit()` d'un" -#~ " appel à :code:`__update_clip_norm__()`, une " -#~ "procédure qui ajuste le seuil d'écrêtage" -#~ " sur la base des bits indicateurs " -#~ "reçus des clients échantillonnés." #~ msgid "" -#~ "The client-side capabilities required " -#~ "can be completely captured through " -#~ "wrapper logic for just the :code:`fit()`" -#~ " method of the :code:`NumPyClient` abstract" -#~ " class. To be precise, we need " -#~ "to *post-process* the update computed" -#~ " by the wrapped client to clip " -#~ "it, if necessary, to the threshold " -#~ "value supplied by the server as " -#~ "part of the config dictionary. In " -#~ "addition to this, it may need to" -#~ " perform some extra work if either" -#~ " (or both) of the following keys " -#~ "are also present in the dict." +#~ "A server implementation, either " +#~ "`flwr.server.Server` or a subclass thereof." +#~ " If no instance is provided, then " +#~ "`start_driver` will create one." #~ msgstr "" -#~ "Les capacités requises côté client " -#~ "peuvent être entièrement capturées par " -#~ "une logique de wrapper pour la " -#~ "seule méthode :code:`fit()` de la classe" -#~ " abstraite :code:`NumPyClient`. Pour être " -#~ "précis, nous devons *post-traiter* la" -#~ " mise à jour calculée par le " -#~ "client wrapped pour l'écrêter, si " -#~ "nécessaire, à la valeur seuil fournie" -#~ " par le serveur dans le cadre " -#~ "du dictionnaire de configuration. En " -#~ "plus de cela, il peut avoir besoin" -#~ " d'effectuer un travail supplémentaire si" -#~ " l'une des clés suivantes (ou les " -#~ "deux) est également présente dans le " -#~ "dict." #~ msgid "" -#~ ":code:`dpfedavg_noise_stddev` : Generate and " -#~ "add the specified amount of noise " -#~ "to the clipped update." +#~ "An implementation of the class " +#~ "`flwr.server.ClientManager`. If no implementation" +#~ " is provided, then `start_driver` will " +#~ "use `flwr.server.SimpleClientManager`." +#~ msgstr "" + +#~ msgid "The Driver object to use." +#~ msgstr "" + +#~ msgid "Starting a driver that connects to an insecure server:" +#~ msgstr "" + +#~ msgid "Starting a driver that connects to an SSL-enabled server:" #~ msgstr "" -#~ ":code:`dpfedavg_noise_stddev` : Génère et " -#~ "ajoute la quantité de bruit spécifiée" -#~ " à la mise à jour de " -#~ "l'écrêtage." #~ msgid "" -#~ ":code:`dpfedavg_adaptive_clip_enabled` : Augment the" -#~ " metrics dict in the :code:`FitRes` " -#~ "object being returned to the server " -#~ "with an indicator bit, calculated as " -#~ "described earlier." +#~ ":py:obj:`run_simulation_from_cli " +#~ "`\\ \\(\\)" #~ msgstr "" -#~ ":code:`dpfedavg_adaptive_clip_enabled` : Complète " -#~ "les métriques dict dans l'objet " -#~ ":code:`FitRes` renvoyé au serveur avec " -#~ "un bit indicateur, calculé comme décrit" -#~ " précédemment." -#~ msgid "Performing the :math:`(\\epsilon, \\delta)` analysis" -#~ msgstr "Effectuer l'analyse :math:`(\\epsilon, \\delta)`" +#~ msgid "Run Simulation Engine from the CLI." +#~ msgstr "" + +#~ msgid "run\\_simulation\\_from\\_cli" +#~ msgstr "Simulation de moniteur" #~ msgid "" -#~ "Assume you have trained for :math:`n`" -#~ " rounds with sampling fraction :math:`q`" -#~ " and noise multiplier :math:`z`. In " -#~ "order to calculate the :math:`\\epsilon` " -#~ "value this would result in for a" -#~ " particular :math:`\\delta`, the following " -#~ "script may be used." +#~ "Check out this Federated Learning " +#~ "quickstart tutorial for using Flower " +#~ "with MXNet to train a Sequential " +#~ "model on MNIST." #~ msgstr "" -#~ "Supposons que tu te sois entraîné " -#~ "pendant :math:`n` tours avec la fraction" -#~ " d'échantillonnage :math:`q` et le " -#~ "multiplicateur de bruit :math:`z`. Afin " -#~ "de calculer la valeur :math:`epsilon` " -#~ "qui en résulterait pour un " -#~ ":math:`\\delta` particulier, le script suivant" -#~ " peut être utilisé." + +#~ msgid "Quickstart MXNet" +#~ msgstr "Démarrage rapide de MXNet" #~ msgid "" -#~ "`How to run Flower using Docker " -#~ "`_" +#~ "MXNet is no longer maintained and " +#~ "has been moved into `Attic " +#~ "`_. As a " +#~ "result, we would encourage you to " +#~ "use other ML frameworks alongside " +#~ "Flower, for example, PyTorch. This " +#~ "tutorial might be removed in future " +#~ "versions of Flower." #~ msgstr "" -#~ msgid "Enjoy building more robust and flexible ``ClientApp``s with mods!" +#~ msgid "" +#~ "In this tutorial, we will learn " +#~ "how to train a :code:`Sequential` model" +#~ " on MNIST using Flower and MXNet." #~ msgstr "" +#~ "Dans ce tutoriel, nous allons apprendre" +#~ " à former un modèle :code:`Sequential` " +#~ "sur MNIST à l'aide de Flower et" +#~ " de MXNet." + +#~ msgid "Since we want to use MXNet, let's go ahead and install it:" +#~ msgstr "Puisque nous voulons utiliser MXNet, allons-y et installons-le :" #~ msgid "" -#~ ":py:obj:`ClientApp `\\ " -#~ "\\(client\\_fn\\[\\, mods\\]\\)" +#~ "Now that we have all our " +#~ "dependencies installed, let's run a " +#~ "simple distributed training with two " +#~ "clients and one server. Our training " +#~ "procedure and network architecture are " +#~ "based on MXNet´s `Hand-written Digit " +#~ "Recognition tutorial " +#~ "`_." #~ msgstr "" +#~ "Maintenant que toutes nos dépendances " +#~ "sont installées, lançons une formation " +#~ "distribuée simple avec deux clients et" +#~ " un serveur. Notre procédure de " +#~ "formation et l'architecture du réseau " +#~ "sont basées sur le tutoriel de " +#~ "reconnaissance de chiffres écrits à la" +#~ " main du MXNet " +#~ "`_." + +#~ msgid "" +#~ "In a file called :code:`client.py`, " +#~ "import Flower and MXNet related " +#~ "packages:" +#~ msgstr "" +#~ "Dans un fichier appelé :code:`client.py`, " +#~ "importe Flower et les paquets liés " +#~ "au MXNet :" + +#~ msgid "In addition, define the device allocation in MXNet with:" +#~ msgstr "En outre, définis l'attribution de l'appareil dans MXNet avec :" + +#~ msgid "" +#~ "We use MXNet to load MNIST, a " +#~ "popular image classification dataset of " +#~ "handwritten digits for machine learning. " +#~ "The MXNet utility :code:`mx.test_utils.get_mnist()`" +#~ " downloads the training and test " +#~ "data." +#~ msgstr "" +#~ "Nous utilisons MXNet pour charger MNIST," +#~ " un ensemble de données de " +#~ "classification d'images populaire de chiffres" +#~ " manuscrits pour l'apprentissage automatique. " +#~ "L'utilitaire MXNet :code:`mx.test_utils.get_mnist()` " +#~ "télécharge les données d'entraînement et " +#~ "de test." + +#~ msgid "" +#~ "Define the training and loss with " +#~ "MXNet. We train the model by " +#~ "looping over the dataset, measure the" +#~ " corresponding loss, and optimize it." +#~ msgstr "" +#~ "Définis l'entraînement et la perte avec" +#~ " MXNet. Nous entraînons le modèle en" +#~ " parcourant en boucle l'ensemble des " +#~ "données, nous mesurons la perte " +#~ "correspondante et nous l'optimisons." + +#~ msgid "" +#~ "Next, we define the validation of " +#~ "our machine learning model. We loop " +#~ "over the test set and measure both" +#~ " loss and accuracy on the test " +#~ "set." +#~ msgstr "" +#~ "Ensuite, nous définissons la validation " +#~ "de notre modèle d'apprentissage automatique." +#~ " Nous effectuons une boucle sur " +#~ "l'ensemble de test et mesurons à " +#~ "la fois la perte et la précision" +#~ " sur l'ensemble de test." + +#~ msgid "" +#~ "After defining the training and testing" +#~ " of a MXNet machine learning model," +#~ " we use these functions to implement" +#~ " a Flower client." +#~ msgstr "" +#~ "Après avoir défini la formation et " +#~ "le test d'un modèle d'apprentissage " +#~ "automatique MXNet, nous utilisons ces " +#~ "fonctions pour mettre en œuvre un " +#~ "client Flower." + +#~ msgid "Our Flower clients will use a simple :code:`Sequential` model:" +#~ msgstr "Nos clients Flower utiliseront un modèle simple :code:`Sequential` :" + +#~ msgid "" +#~ "After loading the dataset with " +#~ ":code:`load_data()` we perform one forward " +#~ "propagation to initialize the model and" +#~ " model parameters with :code:`model(init)`. " +#~ "Next, we implement a Flower client." +#~ msgstr "" +#~ "Après avoir chargé l'ensemble de données" +#~ " avec :code:`load_data()`, nous effectuons " +#~ "une propagation vers l'avant pour " +#~ "initialiser le modèle et les paramètres" +#~ " du modèle avec :code:`model(init)`. " +#~ "Ensuite, nous implémentons un client " +#~ "Flower." -#~ msgid ":py:obj:`flwr.server.driver `\\" +#~ msgid "" +#~ "Flower provides a convenience class " +#~ "called :code:`NumPyClient` which makes it " +#~ "easier to implement the :code:`Client` " +#~ "interface when your workload uses MXNet." +#~ " Implementing :code:`NumPyClient` usually means" +#~ " defining the following methods " +#~ "(:code:`set_parameters` is optional though):" #~ msgstr "" +#~ "Flower fournit une classe de commodité" +#~ " appelée :code:`NumPyClient` qui facilite " +#~ "l'implémentation de l'interface :code:`Client` " +#~ "lorsque ta charge de travail utilise " +#~ "MXNet. L'implémentation de :code:`NumPyClient` " +#~ "signifie généralement la définition des " +#~ "méthodes suivantes (:code:`set_parameters` est " +#~ "cependant facultatif) :" -#~ msgid "Flower driver SDK." -#~ msgstr "Serveur de Flower" +#~ msgid "They can be implemented in the following way:" +#~ msgstr "Ils peuvent être mis en œuvre de la manière suivante :" -#~ msgid "driver" -#~ msgstr "serveur" +#~ msgid "" +#~ "We can now create an instance of" +#~ " our class :code:`MNISTClient` and add " +#~ "one line to actually run this " +#~ "client:" +#~ msgstr "" +#~ "Nous pouvons maintenant créer une " +#~ "instance de notre classe :code:`MNISTClient`" +#~ " et ajouter une ligne pour exécuter" +#~ " ce client :" #~ msgid "" -#~ ":py:obj:`start_driver `\\ " -#~ "\\(\\*\\[\\, server\\_address\\, server\\, ...\\]\\)" +#~ "That's it for the client. We only" +#~ " have to implement :code:`Client` or " +#~ ":code:`NumPyClient` and call " +#~ ":code:`fl.client.start_client()` or " +#~ ":code:`fl.client.start_numpy_client()`. The string " +#~ ":code:`\"0.0.0.0:8080\"` tells the client " +#~ "which server to connect to. In our" +#~ " case we can run the server and" +#~ " the client on the same machine, " +#~ "therefore we use :code:`\"0.0.0.0:8080\"`. If" +#~ " we run a truly federated workload" +#~ " with the server and clients running" +#~ " on different machines, all that " +#~ "needs to change is the " +#~ ":code:`server_address` we pass to the " +#~ "client." #~ msgstr "" +#~ "C'est tout pour le client. Il nous" +#~ " suffit d'implémenter :code:`Client` ou " +#~ ":code:`NumPyClient` et d'appeler " +#~ ":code:`fl.client.start_client()`. La chaîne " +#~ ":code:`\"0.0.0:8080\"` indique au client à " +#~ "quel serveur se connecter. Dans notre" +#~ " cas, nous pouvons exécuter le " +#~ "serveur et le client sur la même" +#~ " machine, c'est pourquoi nous utilisons " +#~ ":code:`\"0.0.0:8080\"`. Si nous exécutons une" +#~ " charge de travail véritablement fédérée" +#~ " avec le serveur et les clients " +#~ "s'exécutant sur des machines différentes, " +#~ "tout ce qui doit changer est " +#~ ":code:`server_address` que nous transmettons " +#~ "au client." + +#~ msgid "" +#~ "With both client and server ready, " +#~ "we can now run everything and see" +#~ " federated learning in action. Federated" +#~ " learning systems usually have a " +#~ "server and multiple clients. We " +#~ "therefore have to start the server " +#~ "first:" +#~ msgstr "" +#~ "Le client et le serveur étant " +#~ "prêts, nous pouvons maintenant tout " +#~ "exécuter et voir l'apprentissage fédéré " +#~ "en action. Les systèmes d'apprentissage " +#~ "fédéré ont généralement un serveur et" +#~ " plusieurs clients. Nous devons donc " +#~ "commencer par démarrer le serveur :" #~ msgid "" -#~ ":py:obj:`Driver `\\ " -#~ "\\(\\[driver\\_service\\_address\\, ...\\]\\)" +#~ "Congratulations! You've successfully built and" +#~ " run your first federated learning " +#~ "system. The full `source code " +#~ "`_ for this example can " +#~ "be found in :code:`examples/quickstart-mxnet`." #~ msgstr "" +#~ "Félicitations ! Tu as réussi à " +#~ "construire et à faire fonctionner ton" +#~ " premier système d'apprentissage fédéré. Le" +#~ " code source complet " +#~ "`_ de cet exemple se " +#~ "trouve dans :code:`examples/quickstart-mxnet`." + +#~ msgid ":code:`load_mnist()`" +#~ msgstr ":code:`load_mnist()`" + +#~ msgid "Loads the MNIST dataset using OpenML" +#~ msgstr "Charge l'ensemble de données MNIST à l'aide d'OpenML" + +#~ msgid ":code:`shuffle()`" +#~ msgstr ":code:`shuffle()`" + +#~ msgid "Shuffles data and its label" +#~ msgstr "Mélange les données et leur étiquette" + +#~ msgid ":code:`partition()`" +#~ msgstr ":code:`partition()`" + +#~ msgid "Splits datasets into a number of partitions" +#~ msgstr "Divise les ensembles de données en un certain nombre de partitions" #~ msgid "" -#~ ":py:obj:`GrpcDriver `\\ " -#~ "\\(\\[driver\\_service\\_address\\, ...\\]\\)" +#~ "We load the MNIST dataset from " +#~ "`OpenML " +#~ "`_, a" +#~ " popular image classification dataset of" +#~ " handwritten digits for machine learning." +#~ " The utility :code:`utils.load_mnist()` downloads" +#~ " the training and test data. The " +#~ "training set is split afterwards into" +#~ " 10 partitions with :code:`utils.partition()`." #~ msgstr "" +#~ "Nous chargeons l'ensemble de données " +#~ "MNIST de `OpenML `_," +#~ " un ensemble de données de " +#~ "classification d'images populaires de chiffres" +#~ " manuscrits pour l'apprentissage automatique. " +#~ "L'utilitaire :code:`utils.load_mnist()` télécharge " +#~ "les données d'entraînement et de test." +#~ " L'ensemble d'entraînement est ensuite " +#~ "divisé en 10 partitions avec " +#~ ":code:`utils.partition()`." -#~ msgid "`GrpcDriver` provides access to the gRPC Driver API/service." +#~ msgid "Let's get stated!" +#~ msgstr "Allons-y, déclarons-le !" + +#~ msgid "|2b5c62c529f6416f840c594cce062fbb|" #~ msgstr "" -#~ msgid ":py:obj:`get_nodes `\\ \\(\\)" +#~ msgid "|90b334680cb7467d9a04d39b8e8dca9f|" #~ msgstr "" -#~ msgid "" -#~ ":py:obj:`pull_task_res " -#~ "`\\ \\(task\\_ids\\)" +#~ msgid "|65764ceee89f4335bfd93fd0b115e831|" #~ msgstr "" -#~ msgid "Get task results." +#~ msgid "|d97319ec28bb407ea0ab9705e38f3bcf|" +#~ msgstr "" + +#~ msgid "|11e95ac83a8548d8b3505b4663187d07|" +#~ msgstr "" + +#~ msgid "|1dab2f3a23674abc8a6731f20fa10730|" +#~ msgstr "" + +#~ msgid "|7f0ee162da38450788493a21627306f7|" +#~ msgstr "" + +#~ msgid "|296a1fb72c514b23b3d8905ff0ff98c6|" +#~ msgstr "" + +#~ msgid "|5b1408eec0d746cdb91162a9107b6089|" +#~ msgstr "" + +#~ msgid "|aef19f4b122c4e8d9f4c57f99bcd5dd2|" +#~ msgstr "" + +#~ msgid "|2881a86d8fc54ba29d96b29fc2819f4a|" +#~ msgstr "" + +#~ msgid "|ec1fe880237247e0975f52766775ab84|" +#~ msgstr "" + +#~ msgid "|9fdf048ed58d4467b2718cdf4aaf1ec3|" +#~ msgstr "" + +#~ msgid "|ff726bc5505e432388ee2fdd6ef420b9|" #~ msgstr "" #~ msgid "" -#~ ":py:obj:`push_task_ins " -#~ "`\\ " -#~ "\\(task\\_ins\\_list\\)" +#~ "Flower provides pre-made docker images" +#~ " on `Docker Hub `_" +#~ " that include all necessary dependencies" +#~ " for running the SuperLink. You can" +#~ " also build your own custom docker" +#~ " images from scratch with a different" +#~ " version of Python or Ubuntu if " +#~ "that is what you need. In this " +#~ "guide, we will explain what images " +#~ "exist and how to build them " +#~ "locally." #~ msgstr "" -#~ msgid "Schedule tasks." +#~ msgid "" +#~ "Currently, Flower provides two images, a" +#~ " ``base`` image and a ``superlink`` " +#~ "image. The base image, as the name" +#~ " suggests, contains basic dependencies that" +#~ " the SuperLink needs. This includes " +#~ "system dependencies, Python and Python " +#~ "tools. The SuperLink image is based " +#~ "on the base image, but it " +#~ "additionally installs the SuperLink using " +#~ "``pip``." #~ msgstr "" -#~ msgid "GrpcDriver" +#~ msgid "" +#~ "Both, base and SuperLink image are " +#~ "configured via build arguments. Through " +#~ "build arguments, we can make our " +#~ "build more flexible. For example, in " +#~ "the base image, we can specify the" +#~ " version of Python to install using" +#~ " the ``PYTHON_VERSION`` build argument. " +#~ "Some of the build arguments have " +#~ "default values, others must be specified" +#~ " when building the image. All " +#~ "available build arguments for each image" +#~ " are listed in one of the " +#~ "tables below." +#~ msgstr "" + +#~ msgid "``3.11``" +#~ msgstr "1.0.0rc1" + +#~ msgid "``UBUNTU_VERSION``" #~ msgstr "" -#~ msgid ":py:obj:`connect `\\ \\(\\)" +#~ msgid "Version of the official Ubuntu Docker image." #~ msgstr "" -#~ msgid "Connect to the Driver API." +#~ msgid "Defaults to ``22.04``." #~ msgstr "" #~ msgid "" -#~ ":py:obj:`create_run " -#~ "`\\ \\(req\\)" +#~ "The following example creates a base " +#~ "image with Python 3.11.0, pip 23.0.1 " +#~ "and setuptools 69.0.2:" #~ msgstr "" -#~ msgid "Request for run ID." -#~ msgstr "Demande pour une nouvelle Flower Baseline" +#~ msgid "Building the SuperLink image" +#~ msgstr "Démarrer le serveur" -#~ msgid "" -#~ ":py:obj:`disconnect " -#~ "`\\ \\(\\)" +#~ msgid "Defaults to ``flwr/base``." #~ msgstr "" -#~ msgid "Disconnect from the Driver API." +#~ msgid "The Python version of the base image." +#~ msgstr "Évaluer la réponse d'un client." + +#~ msgid "Defaults to ``py3.11``." #~ msgstr "" -#~ msgid "" -#~ ":py:obj:`get_nodes `\\" -#~ " \\(req\\)" +#~ msgid "Defaults to ``ubuntu22.04``." #~ msgstr "" -#~ msgid "Get client IDs." -#~ msgstr "Moteur client Edge" +#~ msgid "The PyPI package to install." +#~ msgstr "" + +#~ msgid "Defaults to ``flwr``." +#~ msgstr "Flux de travail" #~ msgid "" -#~ ":py:obj:`pull_task_res " -#~ "`\\ \\(req\\)" +#~ "The following example creates a " +#~ "SuperLink image with the official Flower" +#~ " base image py3.11-ubuntu22.04 and Flower" +#~ " 1.8.0:" #~ msgstr "" #~ msgid "" -#~ ":py:obj:`push_task_ins " -#~ "`\\ \\(req\\)" +#~ "The name of image is ``flwr_superlink``" +#~ " and the tag ``0.1.0``. Remember that" +#~ " the build arguments as well as " +#~ "the name and tag can be adapted" +#~ " to your needs. These values serve" +#~ " as examples only." #~ msgstr "" #~ msgid "" -#~ "Optionally specify the type of actor " -#~ "to use. The actor object, which " -#~ "persists throughout the simulation, will " -#~ "be the process in charge of " -#~ "running the clients' jobs (i.e. their" -#~ " `fit()` method)." +#~ "If you want to use your own " +#~ "base image instead of the official " +#~ "Flower base image, all you need to" +#~ " do is set the ``BASE_REPOSITORY``, " +#~ "``PYTHON_VERSION`` and ``UBUNTU_VERSION`` build " +#~ "arguments." #~ msgstr "" +#~ msgid "Creating New Messages" +#~ msgstr "Création de nouveaux messages" + #~ msgid "" -#~ "Much effort went into a completely " -#~ "restructured Flower docs experience. The " -#~ "documentation on [flower.ai/docs](flower.ai/docs) is" -#~ " now divided into Flower Framework, " -#~ "Flower Baselines, Flower Android SDK, " -#~ "Flower iOS SDK, and code example " -#~ "projects." +#~ "This is a simple guide for " +#~ "creating a new type of message " +#~ "between the server and clients in " +#~ "Flower." #~ msgstr "" +#~ "Voici un guide simple pour créer " +#~ "un nouveau type de message entre " +#~ "le serveur et les clients dans " +#~ "Flower." #~ msgid "" -#~ "Flower usage examples used to be " -#~ "bundled with Flower in a package " -#~ "called ``flwr_example``. We are migrating " -#~ "those examples to standalone projects to" -#~ " make them easier to use. All " -#~ "new examples are based in the " -#~ "directory `examples " -#~ "`_." +#~ "Let's suppose we have the following " +#~ "example functions in :code:`server.py` and " +#~ ":code:`numpy_client.py`..." #~ msgstr "" -#~ "Les exemples d'utilisation de Flower " -#~ "étaient auparavant regroupés avec Flower " -#~ "dans un paquet appelé ``flwr_example``. " -#~ "Nous migrons ces exemples vers des " -#~ "projets autonomes pour les rendre plus" -#~ " faciles à utiliser. Tous les " -#~ "nouveaux exemples sont basés dans le " -#~ "répertoire ``examples " -#~ "`_." +#~ "Supposons que nous ayons les fonctions" +#~ " suivantes dans :code:`server.py` et " +#~ ":code:`numpy_client.py`..." -#~ msgid "Quickstart TensorFlow/Keras" -#~ msgstr "Démarrage rapide de TensorFlow/Keras" +#~ msgid "Server's side:" +#~ msgstr "Côté serveur :" -#~ msgid "Legacy Examples (`flwr_example`)" -#~ msgstr "Exemples hérités (`flwr_example`)" +#~ msgid "Client's side:" +#~ msgstr "Côté client :" #~ msgid "" -#~ "The useage examples in `flwr_example` " -#~ "are deprecated and will be removed " -#~ "in the future. New examples are " -#~ "provided as standalone projects in " -#~ "`examples `_." +#~ "Let's now see what we need to " +#~ "implement in order to get this " +#~ "simple function between the server and" +#~ " client to work!" #~ msgstr "" -#~ "Les exemples d'utilisation dans `flwr_example`" -#~ " sont obsolètes et seront supprimés à" -#~ " l'avenir. De nouveaux exemples sont " -#~ "fournis en tant que projets autonomes" -#~ " dans `examples " -#~ "`_." +#~ "Voyons maintenant ce que nous devons " +#~ "mettre en œuvre pour que cette " +#~ "simple fonction entre le serveur et " +#~ "le client fonctionne !" -#~ msgid "Extra Dependencies" -#~ msgstr "Dépendances supplémentaires" +#~ msgid "Message Types for Protocol Buffers" +#~ msgstr "Types de messages pour les tampons de protocole" #~ msgid "" -#~ "The core Flower framework keeps a " -#~ "minimal set of dependencies. The " -#~ "examples demonstrate Flower in the " -#~ "context of different machine learning " -#~ "frameworks, so additional dependencies need" -#~ " to be installed before an example" -#~ " can be run." +#~ "The first thing we need to do " +#~ "is to define a message type for" +#~ " the RPC system in :code:`transport.proto`." +#~ " Note that we have to do it " +#~ "for both the request and response " +#~ "messages. For more details on the " +#~ "syntax of proto3, please see the " +#~ "`official documentation `_." #~ msgstr "" -#~ "Le noyau du framework Flower conserve" -#~ " un ensemble minimal de dépendances. " -#~ "Les exemples démontrent Flower dans le" -#~ " contexte de différents frameworks " -#~ "d'apprentissage automatique, de sorte que " -#~ "des dépendances supplémentaires doivent être" -#~ " installées avant qu'un exemple puisse " -#~ "être exécuté." - -#~ msgid "For PyTorch examples::" -#~ msgstr "Pour les exemples de PyTorch: :" +#~ "La première chose à faire est de" +#~ " définir un type de message pour " +#~ "le système RPC dans :code:`transport.proto`." +#~ " Notez que nous devons le faire " +#~ "à la fois pour les messages de " +#~ "demande et de réponse. Pour plus " +#~ "de détails sur la syntaxe de " +#~ "proto3, veuillez consulter la `documentation" +#~ " officielle `_." -#~ msgid "For TensorFlow examples::" -#~ msgstr "Pour les exemples de TensorFlow : :" +#~ msgid "Within the :code:`ServerMessage` block:" +#~ msgstr "Dans le bloc :code:`ServerMessage` :" -#~ msgid "For both PyTorch and TensorFlow examples::" -#~ msgstr "Pour les exemples PyTorch et TensorFlow: :" +#~ msgid "Within the ClientMessage block:" +#~ msgstr "Dans le bloc ClientMessage :" #~ msgid "" -#~ "Please consult :code:`pyproject.toml` for a" -#~ " full list of possible extras " -#~ "(section :code:`[tool.poetry.extras]`)." +#~ "Make sure to also add a field " +#~ "of the newly created message type " +#~ "in :code:`oneof msg`." #~ msgstr "" -#~ "Tu peux consulter :code:`pyproject.toml` pour" -#~ " une liste complète des extras " -#~ "possibles (section :code:`[tool.poetry.extras]`)." +#~ "Veille à ajouter également un champ " +#~ "du type de message nouvellement créé " +#~ "dans :code:`oneof msg`." -#~ msgid "PyTorch Examples" -#~ msgstr "Exemples de PyTorch" +#~ msgid "Once that is done, we will compile the file with:" +#~ msgstr "Une fois que c'est fait, nous compilerons le fichier avec :" -#~ msgid "" -#~ "Our PyTorch examples are based on " -#~ "PyTorch 1.7. They should work with " -#~ "other releases as well. So far, we" -#~ " provide the following examples." -#~ msgstr "" -#~ "Nos exemples PyTorch sont basés sur " -#~ "PyTorch 1.7. Ils devraient fonctionner " -#~ "avec d'autres versions également. Jusqu'à " -#~ "présent, nous fournissons les exemples " -#~ "suivants." +#~ msgid "If it compiles successfully, you should see the following message:" +#~ msgstr "S'il se compile avec succès, tu devrais voir le message suivant :" -#~ msgid "CIFAR-10 Image Classification" -#~ msgstr "Classification d'images CIFAR-10" +#~ msgid "Serialization and Deserialization Functions" +#~ msgstr "Fonctions de sérialisation et de désérialisation" #~ msgid "" -#~ "`CIFAR-10 and CIFAR-100 " -#~ "`_ are " -#~ "popular RGB image datasets. The Flower" -#~ " CIFAR-10 example uses PyTorch to " -#~ "train a simple CNN classifier in a" -#~ " federated learning setup with two " -#~ "clients." +#~ "Our next step is to add functions" +#~ " to serialize and deserialize Python " +#~ "datatypes to or from our defined " +#~ "RPC message types. You should add " +#~ "these functions in :code:`serde.py`." #~ msgstr "" -#~ "`CIFAR-10 et CIFAR-100 " -#~ "`_ sont des" -#~ " ensembles de données d'images RVB " -#~ "populaires. L'exemple Flower CIFAR-10 utilise" -#~ " PyTorch pour former un classificateur " -#~ "CNN simple dans une configuration " -#~ "d'apprentissage fédéré avec deux clients." +#~ "La prochaine étape consiste à ajouter" +#~ " des fonctions pour sérialiser et " +#~ "désérialiser les types de données Python" +#~ " vers ou à partir des types de" +#~ " messages RPC définis. Tu dois " +#~ "ajouter ces fonctions dans :code:`serde.py`." -#~ msgid "First, start a Flower server:" -#~ msgstr "Tout d'abord, démarre un serveur Flower :" +#~ msgid "The four functions:" +#~ msgstr "Les quatre fonctions :" -#~ msgid "$ ./src/py/flwr_example/pytorch_cifar/run-server.sh" -#~ msgstr "$ ./src/py/flwr_example/pytorch_cifar/run-server.sh" +#~ msgid "Sending the Message from the Server" +#~ msgstr "Envoi du message à partir du serveur" -#~ msgid "Then, start the two clients in a new terminal window:" +#~ msgid "" +#~ "Now write the request function in " +#~ "your Client Proxy class (e.g., " +#~ ":code:`grpc_client_proxy.py`) using the serde " +#~ "functions you just created:" #~ msgstr "" -#~ "Ensuite, démarre les deux clients dans" -#~ " une nouvelle fenêtre de terminal :" - -#~ msgid "$ ./src/py/flwr_example/pytorch_cifar/run-clients.sh" -#~ msgstr "$ ./src/py/flwr_example/pytorch_cifar/run-clients.sh" - -#~ msgid "For more details, see :code:`src/py/flwr_example/pytorch_cifar`." -#~ msgstr "Pour plus de détails, voir :code:`src/py/flwr_example/pytorch_cifar`." +#~ "Écris maintenant la fonction de demande" +#~ " dans ta classe Client Proxy (par " +#~ "exemple, :code:`grpc_client_proxy.py`) en utilisant" +#~ " les fonctions serde que tu viens " +#~ "de créer :" -#~ msgid "ImageNet-2012 Image Classification" -#~ msgstr "ImageNet-2012 Classification des images" +#~ msgid "Receiving the Message by the Client" +#~ msgstr "Réception du message par le client" #~ msgid "" -#~ "`ImageNet-2012 `_ is " -#~ "one of the major computer vision " -#~ "datasets. The Flower ImageNet example " -#~ "uses PyTorch to train a ResNet-18 " -#~ "classifier in a federated learning setup" -#~ " with ten clients." +#~ "Last step! Modify the code in " +#~ ":code:`message_handler.py` to check the field" +#~ " of your message and call the " +#~ ":code:`example_response` function. Remember to " +#~ "use the serde functions!" #~ msgstr "" -#~ "`ImageNet-2012 `_ est " -#~ "l'un des principaux ensembles de données" -#~ " de vision par ordinateur. L'exemple " -#~ "Flower ImageNet utilise PyTorch pour " -#~ "entraîner un classificateur ResNet-18 dans " -#~ "une configuration d'apprentissage fédéré avec" -#~ " dix clients." +#~ "Dernière étape ! Modifie le code " +#~ "dans :code:`message_handler.py` pour vérifier " +#~ "le champ de ton message et appeler" +#~ " la fonction :code:`example_response`. N'oublie" +#~ " pas d'utiliser les fonctions serde !" -#~ msgid "$ ./src/py/flwr_example/pytorch_imagenet/run-server.sh" -#~ msgstr "$ ./src/py/flwr_example/pytorch_imagenet/run-server.sh" +#~ msgid "Within the handle function:" +#~ msgstr "Dans le cadre de la fonction de poignée :" -#~ msgid "$ ./src/py/flwr_example/pytorch_imagenet/run-clients.sh" -#~ msgstr "$ ./src/py/flwr_example/pytorch_imagenet/run-clients.sh" +#~ msgid "And add a new function:" +#~ msgstr "Et ajoute une nouvelle fonction :" -#~ msgid "For more details, see :code:`src/py/flwr_example/pytorch_imagenet`." +#~ msgid "Hopefully, when you run your program you will get the intended result!" #~ msgstr "" -#~ "Pour plus de détails, voir " -#~ ":code:`src/py/flwr_example/pytorch_imagenet`." +#~ "Avec un peu de chance, lorsque tu" +#~ " exécuteras ton programme, tu obtiendras" +#~ " le résultat escompté !" -#~ msgid "TensorFlow Examples" -#~ msgstr "Exemples de TensorFlow" +#~ msgid "" +#~ "The simplest way to get started " +#~ "with Flower is by using the " +#~ "pre-made Docker images, which you can" +#~ " find on `Docker Hub " +#~ "`__." +#~ msgstr "" #~ msgid "" -#~ "Our TensorFlow examples are based on " -#~ "TensorFlow 2.0 or newer. So far, " -#~ "we provide the following examples." +#~ "If you want to persist the state" +#~ " of the SuperLink on your host " +#~ "system, all you need to do is " +#~ "specify a path where you want to" +#~ " save the file on your host " +#~ "system and a name for the database" +#~ " file. In the example below, we " +#~ "tell Docker via the flag ``--volume``" +#~ " to mount the user's home directory" +#~ " (``~/`` on your host) into the " +#~ "``/app/`` directory of the container. " +#~ "Furthermore, we use the flag " +#~ "``--database`` to specify the name of" +#~ " the database file." #~ msgstr "" -#~ "Nos exemples TensorFlow sont basés sur" -#~ " TensorFlow 2.0 ou une version plus" -#~ " récente. Jusqu'à présent, nous te " -#~ "proposons les exemples suivants." -#~ msgid "Fashion-MNIST Image Classification" -#~ msgstr "Classification d'images Fashion-MNIST" +#~ msgid "" +#~ "As soon as the SuperLink starts, " +#~ "the file ``state.db`` is created in " +#~ "the user's home directory on your " +#~ "host system. If the file already " +#~ "exists, the SuperLink tries to restore" +#~ " the state from the file. To " +#~ "start the SuperLink with an empty " +#~ "database, simply remove the ``state.db`` " +#~ "file." +#~ msgstr "" #~ msgid "" -#~ "`Fashion-MNIST `_ is often used as " -#~ "the \"Hello, world!\" of machine " -#~ "learning. We follow this tradition and" -#~ " provide an example which samples " -#~ "random local datasets from Fashion-MNIST" -#~ " and trains a simple image " -#~ "classification model over those partitions." +#~ "Assuming all files we need are in" +#~ " the local ``certificates`` directory, we" +#~ " can use the flag ``--volume`` to " +#~ "mount the local directory into the " +#~ "``/app/`` directory of the container. " +#~ "This allows the SuperLink to access " +#~ "the files within the container. Finally," +#~ " we pass the names of the " +#~ "certificates to the SuperLink with the" +#~ " ``--certificates`` flag." #~ msgstr "" -#~ "nous suivons cette tradition et " -#~ "fournissons un exemple qui échantillonne " -#~ "des ensembles de données locales " -#~ "aléatoires de Fashion-MNIST et entraîne" -#~ " un modèle simple de classification " -#~ "d'images sur ces partitions." -#~ msgid "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-server.sh" -#~ msgstr "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-server.sh" +#~ msgid "" +#~ "``--server 192.168.1.100:9092``: This option " +#~ "specifies the address of the SuperLinks" +#~ " Fleet" +#~ msgstr "" -#~ msgid "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-clients.sh" -#~ msgstr "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-clients.sh" +#~ msgid "" +#~ "Assuming the certificate already exists " +#~ "locally, we can use the flag " +#~ "``--volume`` to mount the local " +#~ "certificate into the container's ``/app/`` " +#~ "directory. This allows the SuperNode to" +#~ " access the certificate within the " +#~ "container. Use the ``--certificates`` flag " +#~ "when starting the container." +#~ msgstr "" #~ msgid "" -#~ "For more details, see " -#~ ":code:`src/py/flwr_example/tensorflow_fashion_mnist`." +#~ "``--server 192.168.1.100:9091``: This option " +#~ "specifies the address of the SuperLinks" +#~ " Driver" #~ msgstr "" -#~ "Pour plus de détails, voir " -#~ ":code:`src/py/flwr_example/tensorflow_fashion_mnist`." #~ msgid "" -#~ "MXNet is no longer maintained and " -#~ "has been moved into `Attic " -#~ "`_. As a " -#~ "result, we would encourage you to " -#~ "use other ML frameworks alongise Flower," -#~ " for example, PyTorch. This tutorial " -#~ "might be removed in future versions " -#~ "of Flower." +#~ "Assuming the certificate already exists " +#~ "locally, we can use the flag " +#~ "``--volume`` to mount the local " +#~ "certificate into the container's ``/app/`` " +#~ "directory. This allows the ServerApp to" +#~ " access the certificate within the " +#~ "container. Use the ``--certificates`` flag " +#~ "when starting the container." #~ msgstr "" #~ msgid "" -#~ "Now that you have known how " -#~ "federated XGBoost work with Flower, it's" -#~ " time to run some more comprehensive" -#~ " experiments by customising the " -#~ "experimental settings. In the xgboost-" -#~ "comprehensive example (`full code " -#~ "`_), we provide more options " -#~ "to define various experimental setups, " -#~ "including aggregation strategies, data " -#~ "partitioning and centralised/distributed evaluation." -#~ " We also support `Flower simulation " -#~ "`_ making it easy to " -#~ "simulate large client cohorts in a " -#~ "resource-aware manner. Let's take a " -#~ "look!" +#~ "If you want to use a different " +#~ "version of Flower, for example Flower" +#~ " nightly, you can do so by " +#~ "changing the tag. All available versions" +#~ " are on `Docker Hub " +#~ "`__." #~ msgstr "" -#~ msgid "|31e4b1afa87c4b968327bbeafbf184d4|" +#~ msgid "" +#~ "Here's another example to start with " +#~ "HTTPS. Use the ``--certificates`` command " +#~ "line argument to pass paths to (CA" +#~ " certificate, server certificate, and " +#~ "server private key)." #~ msgstr "" -#~ msgid "|c9d935b4284e4c389a33d86b33e07c0a|" +#~ msgid ":py:obj:`run_driver_api `\\ \\(\\)" #~ msgstr "" -#~ msgid "|00727b5faffb468f84dd1b03ded88638|" +#~ msgid "Run Flower server (Driver API)." +#~ msgstr "flower-driver-api" + +#~ msgid ":py:obj:`run_fleet_api `\\ \\(\\)" #~ msgstr "" -#~ msgid "|daf0cf0ff4c24fd29439af78416cf47b|" +#~ msgid "Run Flower server (Fleet API)." +#~ msgstr "flower-fleet-api" + +#~ msgid "|d8bf04f23d9b46d8a23cc6f4887d7873|" #~ msgstr "" -#~ msgid "|9f093007080d471d94ca90d3e9fde9b6|" +#~ msgid "|5aa1711387d74d0f8b9c499e1a51627e|" #~ msgstr "" -#~ msgid "|46a26e6150e0479fbd3dfd655f36eb13|" +#~ msgid "|2bc8e069228d4873804061ff4a95048c|" #~ msgstr "" -#~ msgid "|3daba297595c4c7fb845d90404a6179a|" +#~ msgid "|c258488766324dc9a6807f0e7c4fd5f4|" #~ msgstr "" -#~ msgid "|5769874fa9c4455b80b2efda850d39d7|" +#~ msgid "|d5f962c3f4ec48529efda980868c14b0|" #~ msgstr "" -#~ msgid "|ba47ffb421814b0f8f9fa5719093d839|" +#~ msgid "|a5eccea18d4c43a68b54b65043cabef8|" #~ msgstr "" -#~ msgid "|aeac5bf79cbf497082e979834717e01b|" +#~ msgid "|f17662f7df2d42f68cac70a1fdeda8a7|" #~ msgstr "" -#~ msgid "|ce27ed4bbe95459dba016afc42486ba2|" +#~ msgid "|241fc906441a4f038c625a19d30d01b2|" #~ msgstr "" -#~ msgid "|ae94a7f71dda443cbec2385751427d41|" +#~ msgid "|0aa5aa05810b44b6a835cecce28f3137|" #~ msgstr "" -#~ msgid "|e61fce4d43d243e7bb08bdde97d81ce6|" +#~ msgid "|c742940dd4bf4de09d8d0d5e8d179638|" #~ msgstr "" -#~ msgid "|08cb60859b07461588fe44e55810b050|" +#~ msgid "|1f169ab4601a47e1a226f1628f4ebddb|" +#~ msgstr "" + +#~ msgid "|12cfa9cde14440ecb8c8f6c1d7185bec|" +#~ msgstr "" + +#~ msgid "|72939caf6e294b0986fee6dde96614d7|" +#~ msgstr "" + +#~ msgid "|83a8daee45da4a98b8d6f24ae098fc50|" #~ msgstr "" diff --git a/doc/locales/fr/LC_MESSAGES/sphinx.po b/doc/locales/fr/LC_MESSAGES/sphinx.po index 28669f60bab6..d1978d701446 100644 --- a/doc/locales/fr/LC_MESSAGES/sphinx.po +++ b/doc/locales/fr/LC_MESSAGES/sphinx.po @@ -8,7 +8,7 @@ msgid "" msgstr "" "Project-Id-Version: Flower \n" "Report-Msgid-Bugs-To: \n" -"POT-Creation-Date: 2023-08-29 16:41+0200\n" +"POT-Creation-Date: 2024-05-28 11:47+0200\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: fr\n" @@ -17,21 +17,26 @@ msgstr "" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" -"Generated-By: Babel 2.12.1\n" +"Generated-By: Babel 2.15.0\n" -#: ../../source/_templates/base.html:13 +#: ../../source/_templates/base.html:18 msgid "About these documents" msgstr "" -#: ../../source/_templates/base.html:16 +#: ../../source/_templates/base.html:21 msgid "Index" msgstr "" -#: ../../source/_templates/base.html:19 +#: ../../source/_templates/base.html:24 +#: ../../source/_templates/sidebar/search.html:2 msgid "Search" msgstr "" -#: ../../source/_templates/base.html:22 +#: ../../source/_templates/base.html:27 msgid "Copyright" msgstr "" +#: ../../source/_templates/sidebar/search.html:2 +msgid "Type / to search" +msgstr "" + diff --git a/doc/locales/ko/LC_MESSAGES/framework-docs.po b/doc/locales/ko/LC_MESSAGES/framework-docs.po index 8ef7cf887247..3c41a8647c35 100644 --- a/doc/locales/ko/LC_MESSAGES/framework-docs.po +++ b/doc/locales/ko/LC_MESSAGES/framework-docs.po @@ -3,671 +3,646 @@ # This file is distributed under the same license as the Flower package. # FIRST AUTHOR , 2024. # -#, fuzzy msgid "" msgstr "" "Project-Id-Version: Flower main\n" "Report-Msgid-Bugs-To: \n" -"POT-Creation-Date: 2024-05-13 09:48+0200\n" -"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" -"Last-Translator: FULL NAME \n" +"POT-Creation-Date: 2024-06-17 16:09+0200\n" +"PO-Revision-Date: 2024-06-23 14:41+0000\n" +"Last-Translator: 박태현 \n" +"Language-Team: Korean \n" "Language: ko\n" -"Language-Team: ko \n" -"Plural-Forms: nplurals=1; plural=0;\n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" -"Generated-By: Babel 2.14.0\n" +"Plural-Forms: nplurals=1; plural=0;\n" +"X-Generator: Weblate 5.6-rc\n" +"Generated-By: Babel 2.15.0\n" #: ../../source/contributor-explanation-architecture.rst:2 msgid "Flower Architecture" -msgstr "" +msgstr "Flower 아키텍처" #: ../../source/contributor-explanation-architecture.rst:5 msgid "Edge Client Engine" -msgstr "" +msgstr "엣지 클라이언트 엔진" #: ../../source/contributor-explanation-architecture.rst:7 msgid "" -"`Flower `_ core framework architecture with Edge " -"Client Engine" +"`Flower `_ core framework architecture with Edge Client " +"Engine" msgstr "" +"`Flower `_의 핵심 프레임워크 아키텍처와 엣지 클라이언트 엔" +"진" #: ../../source/contributor-explanation-architecture.rst:13 msgid "Virtual Client Engine" -msgstr "" +msgstr "가상 클라이언트 엔진" #: ../../source/contributor-explanation-architecture.rst:15 msgid "" "`Flower `_ core framework architecture with Virtual " "Client Engine" msgstr "" +"`Flower `_의 핵심 프레임워크 아키텍처와 가상 클라이언트 엔" +"진" #: ../../source/contributor-explanation-architecture.rst:21 msgid "Virtual Client Engine and Edge Client Engine in the same workload" -msgstr "" +msgstr "동일 작업에서 가상 클라이언트 엔진과 엣지 클라이언트 엔진" #: ../../source/contributor-explanation-architecture.rst:23 msgid "" -"`Flower `_ core framework architecture with both " -"Virtual Client Engine and Edge Client Engine" +"`Flower `_ core framework architecture with both Virtual " +"Client Engine and Edge Client Engine" msgstr "" +"`Flower `_의 핵심 프레임워크 아키텍처와 가상 및 엣지 클라" +"이언트 엔진" #: ../../source/contributor-how-to-build-docker-images.rst:2 msgid "How to build Docker Flower images locally" -msgstr "" +msgstr "Docker Flower 이미지를 Locally 구축하는 방법" #: ../../source/contributor-how-to-build-docker-images.rst:4 msgid "" -"Flower provides pre-made docker images on `Docker Hub " -"`_ that include all necessary dependencies" -" for running the SuperLink. You can also build your own custom docker " -"images from scratch with a different version of Python or Ubuntu if that " -"is what you need. In this guide, we will explain what images exist and " -"how to build them locally." +"Flower provides pre-made docker images on `Docker Hub `_ that include all necessary dependencies for running the " +"SuperLink, SuperNode or ServerApp. You can also build your own custom docker " +"images from scratch with a different version of Python or Linux distribution " +"(Ubuntu/Alpine) if that is what you need. In this guide, we will explain " +"what images exist and how to build them locally." msgstr "" +"Flower는 'Docker Hub '_에서 미리 만들어진 " +"Docker 이미지들을 제공합니다. 해당 이미지들은 SuperLink, ServerNode 또는 " +"ServerApp을 실행하는 데 필요한 모든 dependencies를 포함합니다. 필요한 경우 다" +"른 버전의 Python이나 Linux 배포판(Ubuntu/Alpine)을 사용해 처음부터 사용자 정" +"의 Docker 이미지를 빌드할 수도 있습니다. 이 가이드에서는 존재하는 이미지들과 " +"이들을 로컬에서 빌드하는 방법에 대해 설명하겠습니다." -#: ../../source/contributor-how-to-build-docker-images.rst:9 +#: ../../source/contributor-how-to-build-docker-images.rst:10 msgid "" "Before we can start, we need to meet a few prerequisites in our local " "development environment." -msgstr "" +msgstr "시작하기 전에, 로컬 개발 환경에서 몇 가지 전제 조건을 충족해야 합니다." -#: ../../source/contributor-how-to-build-docker-images.rst:11 -#: ../../source/how-to-run-flower-using-docker.rst:129 +#: ../../source/contributor-how-to-build-docker-images.rst:12 msgid "Clone the flower repository." -msgstr "" +msgstr "Flower 레포지토리를 복제합니다." -#: ../../source/contributor-how-to-build-docker-images.rst:17 -#: ../../source/how-to-run-flower-using-docker.rst:135 +#: ../../source/contributor-how-to-build-docker-images.rst:18 +#: ../../source/how-to-run-flower-using-docker.rst:165 msgid "Verify the Docker daemon is running." -msgstr "" +msgstr "Docker 데몬이 실행 중인지 확인하십시오." -#: ../../source/contributor-how-to-build-docker-images.rst:19 -#: ../../source/how-to-run-flower-using-docker.rst:137 +#: ../../source/contributor-how-to-build-docker-images.rst:20 +#: ../../source/how-to-run-flower-using-docker.rst:167 msgid "" -"Please follow the first section on :doc:`Run Flower using Docker ` which covers this step in more detail." +"Please follow the first section on :doc:`Run Flower using Docker ` which covers this step in more detail." msgstr "" +":doc:Run Flower using Docker 의 첫 번째 섹션" +"을 따라 주십시오. 해당 부분을 더 자세히 설명해 줍니다." -#: ../../source/contributor-how-to-build-docker-images.rst:23 +#: ../../source/contributor-how-to-build-docker-images.rst:25 msgid "" -"Currently, Flower provides two images, a ``base`` image and a " -"``superlink`` image. The base image, as the name suggests, contains basic" -" dependencies that the SuperLink needs. This includes system " -"dependencies, Python and Python tools. The SuperLink image is based on " -"the base image, but it additionally installs the SuperLink using ``pip``." +"The build instructions that assemble the images are located in the " +"respective Dockerfiles. You can find them in the subdirectories of ``src/" +"docker``." msgstr "" +"이미지들을 조합하는 빌드 명령어들은 해당 Dockerfile에 있습니다. \"src/" +"docker\" 의 하위 디렉토리에서 찾을 수 있습니다." #: ../../source/contributor-how-to-build-docker-images.rst:28 msgid "" -"The build instructions that assemble the images are located in the " -"respective Dockerfiles. You can find them in the subdirectories of " -"``src/docker``." -msgstr "" +"Flower Docker images are configured via build arguments. Through build " +"arguments, we can make the creation of images more flexible. For example, in " +"the base image, we can specify the version of Python to install using the " +"``PYTHON_VERSION`` build argument. Some of the build arguments have default " +"values, others must be specified when building the image. All available " +"build arguments for each image are listed in one of the tables below." +msgstr "" +"Flower Docker는 빌드 전달인자를 통해 구성됩니다. 빌드 argument들을 통해, " +"이미지를 보다 유연하게 생성할 수 있습니다. 예를 들어, base 이미지에서 " +"\"PYTHON_VERSION\" 빌드 전달인자를 사용하여 Python 버전을 지정할 수 " +"있습니다. 일부 빌드 전달인자들은 기본값이며, 이미지를 빌드할 때 지정해야 " +"합니다. 각 이미지에 사용할 수 있는 모든 빌드 전달인자는 아래 표 중에 " +"있습니다." + +#: ../../source/contributor-how-to-build-docker-images.rst:35 +msgid "Building the base image" +msgstr "기본 이미지 빌드" -#: ../../source/contributor-how-to-build-docker-images.rst:31 -msgid "" -"Both, base and SuperLink image are configured via build arguments. " -"Through build arguments, we can make our build more flexible. For " -"example, in the base image, we can specify the version of Python to " -"install using the ``PYTHON_VERSION`` build argument. Some of the build " -"arguments have default values, others must be specified when building the" -" image. All available build arguments for each image are listed in one of" -" the tables below." -msgstr "" +#: ../../source/contributor-how-to-build-docker-images.rst:41 +#: ../../source/contributor-how-to-build-docker-images.rst:98 +msgid "Build argument" +msgstr "빌드 전달인자" -#: ../../source/contributor-how-to-build-docker-images.rst:38 -msgid "Building the base image" -msgstr "" +#: ../../source/contributor-how-to-build-docker-images.rst:42 +#: ../../source/contributor-how-to-build-docker-images.rst:99 +msgid "Description" +msgstr "설명" + +#: ../../source/contributor-how-to-build-docker-images.rst:43 +#: ../../source/contributor-how-to-build-docker-images.rst:100 +msgid "Required" +msgstr "필수" #: ../../source/contributor-how-to-build-docker-images.rst:44 -#: ../../source/contributor-how-to-build-docker-images.rst:86 -msgid "Build argument" -msgstr "" +#: ../../source/contributor-how-to-build-docker-images.rst:101 +msgid "Example" +msgstr "예시" #: ../../source/contributor-how-to-build-docker-images.rst:45 -#: ../../source/contributor-how-to-build-docker-images.rst:87 -msgid "Description" -msgstr "" +msgid "``DISTRO``" +msgstr "``DISTRO``" #: ../../source/contributor-how-to-build-docker-images.rst:46 -#: ../../source/contributor-how-to-build-docker-images.rst:88 -msgid "Required" -msgstr "" +msgid "The Linux distribution to use as the base image." +msgstr "기본 이미지 사용을 위한 Linux 배포판." #: ../../source/contributor-how-to-build-docker-images.rst:47 -#: ../../source/contributor-how-to-build-docker-images.rst:89 -msgid "Example" -msgstr "" +#: ../../source/contributor-how-to-build-docker-images.rst:51 +#: ../../source/contributor-how-to-build-docker-images.rst:55 +#: ../../source/contributor-how-to-build-docker-images.rst:71 +#: ../../source/contributor-how-to-build-docker-images.rst:104 +msgid "No" +msgstr "아니오" #: ../../source/contributor-how-to-build-docker-images.rst:48 -#: ../../source/contributor-how-to-build-docker-images.rst:94 -msgid "``PYTHON_VERSION``" -msgstr "" +msgid "``ubuntu``" +msgstr "``ubuntu``" #: ../../source/contributor-how-to-build-docker-images.rst:49 -msgid "Version of ``python`` to be installed." -msgstr "" +msgid "``DISTRO_VERSION``" +msgstr "``DISTRO_VERSION``" #: ../../source/contributor-how-to-build-docker-images.rst:50 -#: ../../source/contributor-how-to-build-docker-images.rst:54 -#: ../../source/contributor-how-to-build-docker-images.rst:58 -#: ../../source/contributor-how-to-build-docker-images.rst:108 -msgid "Yes" -msgstr "" - -#: ../../source/contributor-how-to-build-docker-images.rst:51 -msgid "``3.11``" -msgstr "" +msgid "Version of the Linux distribution." +msgstr "Linux 배포판 버전." #: ../../source/contributor-how-to-build-docker-images.rst:52 -msgid "``PIP_VERSION``" -msgstr "" +msgid "``22.04``" +msgstr "``22.04``" #: ../../source/contributor-how-to-build-docker-images.rst:53 -msgid "Version of ``pip`` to be installed." -msgstr "" +msgid "``PYTHON_VERSION``" +msgstr "``PYTHON_VERSION``" -#: ../../source/contributor-how-to-build-docker-images.rst:55 -msgid "``23.0.1``" -msgstr "" +#: ../../source/contributor-how-to-build-docker-images.rst:54 +msgid "Version of ``python`` to be installed." +msgstr "설치 된 ``python`` 버전." #: ../../source/contributor-how-to-build-docker-images.rst:56 -msgid "``SETUPTOOLS_VERSION``" -msgstr "" +msgid "``3.11`` or ``3.11.1``" +msgstr "``3.11`` 또는 ``3.11.1``" #: ../../source/contributor-how-to-build-docker-images.rst:57 -msgid "Version of ``setuptools`` to be installed." -msgstr "" +msgid "``PIP_VERSION``" +msgstr "``PIP_VERSION``" + +#: ../../source/contributor-how-to-build-docker-images.rst:58 +msgid "Version of ``pip`` to be installed." +msgstr "설치 된 ``pip`` 버전." #: ../../source/contributor-how-to-build-docker-images.rst:59 -msgid "``69.0.2``" -msgstr "" +#: ../../source/contributor-how-to-build-docker-images.rst:63 +#: ../../source/contributor-how-to-build-docker-images.rst:67 +#: ../../source/contributor-how-to-build-docker-images.rst:108 +msgid "Yes" +msgstr "예" #: ../../source/contributor-how-to-build-docker-images.rst:60 -#: ../../source/contributor-how-to-build-docker-images.rst:98 -msgid "``UBUNTU_VERSION``" -msgstr "" +msgid "``23.0.1``" +msgstr "``23.0.1``" #: ../../source/contributor-how-to-build-docker-images.rst:61 -msgid "Version of the official Ubuntu Docker image." -msgstr "" +msgid "``SETUPTOOLS_VERSION``" +msgstr "``SETUPTOOLS_VERSION``" #: ../../source/contributor-how-to-build-docker-images.rst:62 -msgid "Defaults to ``22.04``." -msgstr "" +msgid "Version of ``setuptools`` to be installed." +msgstr "설치 된 ``setuptools`` 버전." -#: ../../source/contributor-how-to-build-docker-images.rst:65 -msgid "" -"The following example creates a base image with Python 3.11.0, pip 23.0.1" -" and setuptools 69.0.2:" -msgstr "" +#: ../../source/contributor-how-to-build-docker-images.rst:64 +msgid "``69.0.2``" +msgstr "``69.0.2``" -#: ../../source/contributor-how-to-build-docker-images.rst:76 -msgid "" -"The name of image is ``flwr_base`` and the tag ``0.1.0``. Remember that " -"the build arguments as well as the name and tag can be adapted to your " -"needs. These values serve as examples only." -msgstr "" +#: ../../source/contributor-how-to-build-docker-images.rst:65 +msgid "``FLWR_VERSION``" +msgstr "``FLWR_VERSION``" -#: ../../source/contributor-how-to-build-docker-images.rst:80 -msgid "Building the SuperLink image" -msgstr "" +#: ../../source/contributor-how-to-build-docker-images.rst:66 +msgid "Version of Flower to be installed." +msgstr "설치 된 Flower 버전." -#: ../../source/contributor-how-to-build-docker-images.rst:90 -msgid "``BASE_REPOSITORY``" -msgstr "" +#: ../../source/contributor-how-to-build-docker-images.rst:68 +msgid "``1.8.0``" +msgstr "``1.8.0``" -#: ../../source/contributor-how-to-build-docker-images.rst:91 -msgid "The repository name of the base image." -msgstr "" +#: ../../source/contributor-how-to-build-docker-images.rst:69 +msgid "``FLWR_PACKAGE``" +msgstr "``FLWR_PACKAGE``" -#: ../../source/contributor-how-to-build-docker-images.rst:92 -msgid "Defaults to ``flwr/base``." -msgstr "" +#: ../../source/contributor-how-to-build-docker-images.rst:70 +msgid "The Flower package to be installed." +msgstr "설치 할 Flower 패키지." -#: ../../source/contributor-how-to-build-docker-images.rst:95 -msgid "The Python version of the base image." -msgstr "" +#: ../../source/contributor-how-to-build-docker-images.rst:72 +msgid "``flwr`` or ``flwr-nightly``" +msgstr "``flwr`` 또는 ``flwr-nightly``" -#: ../../source/contributor-how-to-build-docker-images.rst:96 -msgid "Defaults to ``py3.11``." +#: ../../source/contributor-how-to-build-docker-images.rst:75 +msgid "" +"The following example creates a base Ubuntu/Alpine image with Python 3.11.0, " +"pip 23.0.1, setuptools 69.0.2 and Flower 1.8.0:" msgstr "" +"다음 예시에서는 Python 3.11.0, pip 23.0.1, setuptools 및 Flower 1.8.0으로 기" +"본 Ubuntu/Alpine 이미지를 만듭니다:" -#: ../../source/contributor-how-to-build-docker-images.rst:99 -msgid "The Ubuntu version of the base image." +#: ../../source/contributor-how-to-build-docker-images.rst:88 +msgid "" +"The name of image is ``flwr_base`` and the tag ``0.1.0``. Remember that the " +"build arguments as well as the name and tag can be adapted to your needs. " +"These values serve as examples only." msgstr "" +"이미지의 이름은 ``flwr_base``이고 태그는 ``0.1.0``입니다. 필요에 따라 빌드 " +"전달인자들 뿐만 아니라 이름과 태그도 정할 수 있습니다. 이 값들은 예시일 " +"뿐입니다." -#: ../../source/contributor-how-to-build-docker-images.rst:100 -msgid "Defaults to ``ubuntu22.04``." -msgstr "" +#: ../../source/contributor-how-to-build-docker-images.rst:92 +msgid "Building the SuperLink/SuperNode or ServerApp image" +msgstr "SuperLink/SuperNode 또는 ServerApp 이미지 빌드" #: ../../source/contributor-how-to-build-docker-images.rst:102 -msgid "``FLWR_PACKAGE``" -msgstr "" +msgid "``BASE_REPOSITORY``" +msgstr "``BASE_REPOSITORY``" #: ../../source/contributor-how-to-build-docker-images.rst:103 -msgid "The PyPI package to install." -msgstr "" +msgid "The repository name of the base image." +msgstr "기본 이미지의 레포지토리 이름." -#: ../../source/contributor-how-to-build-docker-images.rst:104 -msgid "Defaults to ``flwr``." -msgstr "" +#: ../../source/contributor-how-to-build-docker-images.rst:105 +msgid "``flwr/base``" +msgstr "``flwr/base``" #: ../../source/contributor-how-to-build-docker-images.rst:106 -msgid "``FLWR_VERSION``" -msgstr "" +msgid "``BASE_IMAGE``" +msgstr "``BASE_IMAGE``" #: ../../source/contributor-how-to-build-docker-images.rst:107 -msgid "Version of Flower to be installed." -msgstr "" +msgid "The Tag of the Flower base image." +msgstr "Flower 기본 이미지의 태그." #: ../../source/contributor-how-to-build-docker-images.rst:109 -msgid "``1.8.0``" -msgstr "" +msgid "``1.8.0-py3.10-ubuntu22.04``" +msgstr "``1.8.0-py3.10-ubuntu22.04``" -#: ../../source/contributor-how-to-build-docker-images.rst:112 +#: ../../source/contributor-how-to-build-docker-images.rst:111 msgid "" -"The following example creates a SuperLink image with the official Flower " -"base image py3.11-ubuntu22.04 and Flower 1.8.0:" +"The following example creates a SuperLink/SuperNode or ServerApp image with " +"the official Flower base image:" msgstr "" +"다음 예시에서는 공식 Flower 기본 이미지로 SuperLink/SuperNode 또는 ServerApp" +"이미지를 만듭니다:" #: ../../source/contributor-how-to-build-docker-images.rst:122 msgid "" -"The name of image is ``flwr_superlink`` and the tag ``0.1.0``. Remember " -"that the build arguments as well as the name and tag can be adapted to " -"your needs. These values serve as examples only." -msgstr "" - -#: ../../source/contributor-how-to-build-docker-images.rst:125 -msgid "" -"If you want to use your own base image instead of the official Flower " -"base image, all you need to do is set the ``BASE_REPOSITORY``, " -"``PYTHON_VERSION`` and ``UBUNTU_VERSION`` build arguments. .. code-" -"block:: bash" -msgstr "" - -#: ../../source/contributor-how-to-build-docker-images.rst:129 -msgid "$ cd src/docker/superlink/ $ docker build \\" -msgstr "" - -#: ../../source/contributor-how-to-build-docker-images.rst:131 -msgid "" -"--build-arg BASE_REPOSITORY=flwr_base \\ --build-arg PYTHON_VERSION=3.11 " -"\\ --build-arg UBUNTU_VERSION=ubuntu22.04 \\ --build-arg " -"FLWR_VERSION=1.8.0 \\ -t flwr_superlink:0.1.0 ." +"If you want to use your own base image instead of the official Flower base " +"image, all you need to do is set the ``BASE_REPOSITORY`` build argument." msgstr "" +"공식 Flower 기본 이미지 대신 자체 기본 이미지를 사용 하길 원한다면, " +"``BASE_REPOSITORY`` 빌드 전달인자들을 설정해야 합니다." -#: ../../source/contributor-how-to-build-docker-images.rst:137 +#: ../../source/contributor-how-to-build-docker-images.rst:133 msgid "After creating the image, we can test whether the image is working:" -msgstr "" +msgstr "이미지 생성 후에, 이미지가 작동하는지 테스트할 수 있습니다:" #: ../../source/contributor-how-to-contribute-translations.rst:2 msgid "Contribute translations" -msgstr "" +msgstr "번역 기여" #: ../../source/contributor-how-to-contribute-translations.rst:4 msgid "" -"Since `Flower 1.5 `_ we have introduced translations to " -"our doc pages, but, as you might have noticed, the translations are often" -" imperfect. If you speak languages other than English, you might be able " -"to help us in our effort to make Federated Learning accessible to as many" -" people as possible by contributing to those translations! This might " -"also be a great opportunity for those wanting to become open source " -"contributors with little prerequisites." -msgstr "" +"Since `Flower 1.5 `_ we have introduced translations to our doc pages, " +"but, as you might have noticed, the translations are often imperfect. If you " +"speak languages other than English, you might be able to help us in our " +"effort to make Federated Learning accessible to as many people as possible " +"by contributing to those translations! This might also be a great " +"opportunity for those wanting to become open source contributors with little " +"prerequisites." +msgstr "" +"`Flower 1.5 `_ 부터 문서 페이지에 번역을 도입했지만, 아시다시피 " +"번역이 불안전한 경우가 많습니다. 만일 영어 이외의 언어를 사용한다면, 많은 " +"사람들이 연합 학습에 접근할 수 있도록 번역 작업에 기여함으로써 저희의 노력에 " +"도움을 주실 수 있습니다! 이는 전제 조건이 거의 없는 오픈 소스 기여자가 " +"되고자 하는 사람들에게 좋은 기회가 될 수도 있습니다." #: ../../source/contributor-how-to-contribute-translations.rst:13 msgid "" -"Our translation project is publicly available over on `Weblate " -"`_, this " -"where most of the work will happen." +"Our translation project is publicly available over on `Weblate `_, this where most of " +"the work will happen." msgstr "" +"번역 프로젝트는 `Weblate `_에서 공개적으로 진행되며, 대부분의 작업이 이곳에서 이루어집니다." #: ../../source/contributor-how-to-contribute-translations.rst:18 msgid "Contribute to existing languages" -msgstr "" +msgstr "기존 언어에 기여하기" #: ../../source/contributor-how-to-contribute-translations.rst:23 msgid "" -"The first thing you will need to do in order to contribute is to create a" -" free Weblate account on this `page " -"`_. More information about" -" profile settings can be found `here " +"The first thing you will need to do in order to contribute is to create a " +"free Weblate account on this `page `_. More information about profile settings can be found `here " "`_." msgstr "" +"기여를 하기 위해 가장 먼저 해야 할 일은 해당 `page `_에서 무료 Weblate 계정을 만드는 것입니다. 프로필 설" +"정에 대한 자세한 정보는 `here `_를 참조하세요." #: ../../source/contributor-how-to-contribute-translations.rst:29 msgid "" -"Once you are signed in to Weblate, you can navigate to the `Flower " -"Framework project `_. Here, you should see the different existing languages" -" that can be found on the website." +"Once you are signed in to Weblate, you can navigate to the `Flower Framework " +"project `_. " +"Here, you should see the different existing languages that can be found on " +"the website." msgstr "" +"Weblate에 로그인한 후, `Flower Framework project `_로 이동할 수 있습니다. 여기에서 웹사이트에 " +"있는 다양한 기존 언어들을 확인할 수 있습니다." #: ../../source/contributor-how-to-contribute-translations.rst:34 msgid "" -"Once you have selected the language you want to contribute to, you should" -" see a similar interface to this:" -msgstr "" +"Once you have selected the language you want to contribute to, you should " +"see a similar interface to this:" +msgstr "기여하고자 하는 언어를 선택하면, 다음과 같은 인터페이스가 나타납니다:" #: ../../source/contributor-how-to-contribute-translations.rst:39 msgid "" "The most straight forward option here is to click on the ``Translate`` " -"button on the top right (in the ``Translation status`` section). This " -"will automatically bring you to the translation interface for " -"untranslated strings." +"button on the top right (in the ``Translation status`` section). This will " +"automatically bring you to the translation interface for untranslated " +"strings." msgstr "" +"여기서 가장 간단한 옵션은 오른쪽 상단(``Translation status`` 부분)에 있는 " +"``Translate`` 버튼을 클릭하는 것 입니다. 번역되지 않은 문장에 대한 번역 인터" +"페이스로 자동으로 이동합니다." #: ../../source/contributor-how-to-contribute-translations.rst:43 msgid "This is what the interface looks like:" -msgstr "" +msgstr "인터페이스는 다음과 같습니다:" #: ../../source/contributor-how-to-contribute-translations.rst:47 msgid "" -"You input your translation in the text box at the top and then, once you " -"are happy with it, you either press ``Save and continue`` (to save the " -"translation and go to the next untranslated string), ``Save and stay`` " -"(to save the translation and stay on the same page), ``Suggest`` (to add " -"your translation to suggestions for other users to view), or ``Skip`` (to" -" go to the next untranslated string without saving anything)." +"You input your translation in the text box at the top and then, once you are " +"happy with it, you either press ``Save and continue`` (to save the " +"translation and go to the next untranslated string), ``Save and stay`` (to " +"save the translation and stay on the same page), ``Suggest`` (to add your " +"translation to suggestions for other users to view), or ``Skip`` (to go to " +"the next untranslated string without saving anything)." msgstr "" +"번역문을 상단의 텍스트 상자에 입력한 후, 번역이 만족스러우면 ``Save and " +"continue``(번역을 저장하고 다음 미번역 문장으로 이동), ``Save and stay``(번역" +"을 저장하고 해당 페이지에 머무르기), ``Suggest`` (다른 사용자가 볼 수 있도록 " +"번역을 제안 항목에 추가), ``Skip``(아무것도 저장하지 않고 다음 미번역 문장으" +"로 이동) 중 하나를 선택하면 됩니다." #: ../../source/contributor-how-to-contribute-translations.rst:54 msgid "" "In order to help with the translations, you can see on the bottom the " "``Nearby strings``, the ``Comments`` (from other contributors), the " "``Automatic suggestions`` (from machine translation engines), the " -"translations in ``Other languages``, and the ``History`` of translations " -"for this string." +"translations in ``Other languages``, and the ``History`` of translations for " +"this string." msgstr "" +"번역에 도움을 주기위해 하단에서 `주변 문자열``, ``의견``(다른 기여자의), ``자" +"동 제안``(기계 번역의), ``다른 언어``의 번역 및 해당 문장의 번역``히스토리``" +"를 볼 수 있습니다." #: ../../source/contributor-how-to-contribute-translations.rst:59 msgid "" -"On the right, under the ``String information`` section, you can also " -"click the link under ``Source string location`` in order to view the " -"source of the doc file containing the string." +"On the right, under the ``String information`` section, you can also click " +"the link under ``Source string location`` in order to view the source of the " +"doc file containing the string." msgstr "" +"오른쪽의 ``문자열 정보``에서 ``원본 문자열 위치``를 클릭하여 해당 문장이 포함" +"된 문서의 파일 소스를 볼 수도 있습니다." #: ../../source/contributor-how-to-contribute-translations.rst:63 msgid "" -"For more information about translating using Weblate, you can check out " -"this `in-depth guide " -"`_." +"For more information about translating using Weblate, you can check out this " +"`in-depth guide `_." msgstr "" +"Weblate를 통한 번역에 대한 자세한 정보는 `in-depth guide `_를 확인하세요." #: ../../source/contributor-how-to-contribute-translations.rst:67 msgid "Add new languages" -msgstr "" +msgstr "새 언어 추가" #: ../../source/contributor-how-to-contribute-translations.rst:69 msgid "" -"If you want to add a new language, you will first have to contact us, " -"either on `Slack `_, or by opening an issue" -" on our `GitHub repo `_." -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:2 -msgid "Creating New Messages" -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:4 -msgid "" -"This is a simple guide for creating a new type of message between the " -"server and clients in Flower." -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:6 -msgid "" -"Let's suppose we have the following example functions in " -":code:`server.py` and :code:`numpy_client.py`..." -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:8 -msgid "Server's side:" -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:17 -msgid "Client's side:" -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:26 -msgid "" -"Let's now see what we need to implement in order to get this simple " -"function between the server and client to work!" -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:30 -msgid "Message Types for Protocol Buffers" -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:32 -msgid "" -"The first thing we need to do is to define a message type for the RPC " -"system in :code:`transport.proto`. Note that we have to do it for both " -"the request and response messages. For more details on the syntax of " -"proto3, please see the `official documentation `_." -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:35 -msgid "Within the :code:`ServerMessage` block:" -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:52 -msgid "Within the ClientMessage block:" -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:70 -msgid "" -"Make sure to also add a field of the newly created message type in " -":code:`oneof msg`." -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:72 -msgid "Once that is done, we will compile the file with:" -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:78 -msgid "If it compiles successfully, you should see the following message:" -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:87 -msgid "Serialization and Deserialization Functions" -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:89 -msgid "" -"Our next step is to add functions to serialize and deserialize Python " -"datatypes to or from our defined RPC message types. You should add these " -"functions in :code:`serde.py`." -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:91 -msgid "The four functions:" -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:112 -msgid "Sending the Message from the Server" -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:114 -msgid "" -"Now write the request function in your Client Proxy class (e.g., " -":code:`grpc_client_proxy.py`) using the serde functions you just created:" -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:128 -msgid "Receiving the Message by the Client" -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:130 -msgid "" -"Last step! Modify the code in :code:`message_handler.py` to check the " -"field of your message and call the :code:`example_response` function. " -"Remember to use the serde functions!" -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:132 -msgid "Within the handle function:" -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:139 -msgid "And add a new function:" -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:149 -msgid "Hopefully, when you run your program you will get the intended result!" +"If you want to add a new language, you will first have to contact us, either " +"on `Slack `_, or by opening an issue on our " +"`GitHub repo `_." msgstr "" +"새 언어를 추가하려면, `Slack `에 문의하거나 " +"`GitHub repo `_에서 issue에 들어가 문의 해야 " +"합니다." #: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:2 msgid "Develop in VSCode Dev Containers" -msgstr "" +msgstr "VSCode Dev Container에서 개발" #: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:4 msgid "" -"When working on the Flower framework we want to ensure that all " -"contributors use the same developer environment to format code or run " -"tests. For this purpose we are using the VSCode Remote Containers " -"extension. What is it? Read the following quote:" +"When working on the Flower framework we want to ensure that all contributors " +"use the same developer environment to format code or run tests. For this " +"purpose we are using the VSCode Remote Containers extension. What is it? " +"Read the following quote:" msgstr "" +"Flower 프레임워크 작업시, 모든 기여자들이 코드 포맷팅이나 테스트 실행을 위해 " +"동일한 개발 환경을 사용하길 원합니다. 이를 위해 VSCode Remote Containers 확장" +"을 사용하고 있습니다. 그것이 무엇인지 알아보기 위해 다음 인용문을 읽어보세요:" #: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:7 msgid "" -"The Visual Studio Code Remote - Containers extension lets you use a " -"Docker container as a fully-featured development environment. It allows " -"you to open any folder inside (or mounted into) a container and take " -"advantage of Visual Studio Code's full feature set. A " -":code:`devcontainer.json` file in your project tells VS Code how to " -"access (or create) a development container with a well-defined tool and " -"runtime stack. This container can be used to run an application or to " -"separate tools, libraries, or runtimes needed for working with a " -"codebase." -msgstr "" +"The Visual Studio Code Remote - Containers extension lets you use a Docker " +"container as a fully-featured development environment. It allows you to open " +"any folder inside (or mounted into) a container and take advantage of Visual " +"Studio Code's full feature set. A :code:`devcontainer.json` file in your " +"project tells VS Code how to access (or create) a development container with " +"a well-defined tool and runtime stack. This container can be used to run an " +"application or to separate tools, libraries, or runtimes needed for working " +"with a codebase." +msgstr "" +"Visual Studio Code Remote - 컨테이너 확장을 사용하면 Docker 컨테이너를 모든 " +"기능을 갖춘 개발 환경으로 사용할 수 있습니다. 이 확장 기능을 사용하면 컨테이" +"너 내부(또는 컨테이너에 마운트된)의 모든 폴더를 열고 Visual Studio Code의 모" +"든 기능을 활용할 수 있습니다. 프로젝트에 있는 :code:`devcontainer.json` 파일" +"은 잘 정의된 도구와 런타임 스택을 사용하여 개발 컨테이너에 액세스(또는 생성)" +"하는 방법을 VS Code에 알려줍니다. 이 컨테이너는 애플리케이션을 실행하거나 코" +"드베이스 작업에 필요한 도구, 라이브러리 또는 런타임을 분리하는 데 사용할 수 " +"있습니다." #: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:9 msgid "" -"Workspace files are mounted from the local file system or copied or " -"cloned into the container. Extensions are installed and run inside the " -"container, where they have full access to the tools, platform, and file " -"system. This means that you can seamlessly switch your entire development" -" environment just by connecting to a different container." +"Workspace files are mounted from the local file system or copied or cloned " +"into the container. Extensions are installed and run inside the container, " +"where they have full access to the tools, platform, and file system. This " +"means that you can seamlessly switch your entire development environment " +"just by connecting to a different container." msgstr "" +"작업 공간 파일은 로컬 파일 시스템에서 마운트되거나 컨테이너에 복사 또는 클론" +"됩니다. 확장 프로그램은 컨테이너 내부에 설치되고 실행되며, 도구, 플랫폼 및 파" +"일 시스템에 완전한 접근 권한을 갖습니다. 이는 다른 컨테이너에 연결하는 것만으" +"로 전체 개발 환경을 원활하게 전환할 수 있음을 의미합니다." #: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:11 msgid "" -"Source: `Official VSCode documentation " -"`_" -msgstr "" +"Source: `Official VSCode documentation `_" +msgstr "출처 : 공식 VSCode 문서" #: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:15 msgid "Getting started" -msgstr "" +msgstr "시작하기" #: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:17 msgid "" -"Configuring and setting up the :code:`Dockerfile` as well the " -"configuration for the devcontainer can be a bit more involved. The good " -"thing is you don't have to do it. Usually it should be enough to install " -"`Docker `_ on your system and " -"ensure its available on your command line. Additionally, install the " -"`VSCode Containers Extension `_." +"Configuring and setting up the :code:`Dockerfile` as well the configuration " +"for the devcontainer can be a bit more involved. The good thing is you don't " +"have to do it. Usually it should be enough to install `Docker `_ on your system and ensure its available on " +"your command line. Additionally, install the `VSCode Containers Extension " +"`_." msgstr "" +"`Dockerfile`을 설정하고 구성하는 것과 개발 컨테이너 구성은 약간 복잡할 수 있" +"습니다. 다행히도, 이를 직접 할 필요는 없습니다. 일반적으로 시스템에 `Docker " +"`_를 설치하고 커맨드 라인에서 사용" +"할 수 있는지 확인하는 것으로 충분합니다. 추가로 `VSCode Containers Extension " +"`_을 설치하세요." #: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:19 msgid "" -"Now you should be good to go. When starting VSCode, it will ask you to " -"run in the container environment and - if you confirm - automatically " -"build the container and use it. To manually instruct VSCode to use the " -"devcontainer, you can, after installing the extension, click the green " -"area in the bottom left corner of your VSCode window and select the " -"option *(Re)Open Folder in Container*." +"Now you should be good to go. When starting VSCode, it will ask you to run " +"in the container environment and - if you confirm - automatically build the " +"container and use it. To manually instruct VSCode to use the devcontainer, " +"you can, after installing the extension, click the green area in the bottom " +"left corner of your VSCode window and select the option *(Re)Open Folder in " +"Container*." msgstr "" +"이제 준비가 완료되었습니다. VSCode를 시작하면 컨테이너 환경에서 실행할지를 묻" +"고, 확인하면 자동으로 컨테이너를 빌드하고 사용할 것입니다. VSCode에 수동으로 " +"개발 컨테이너를 사용하도록 지시하려면, 확장을 설치한 후, VSCode 창의 왼쪽 하" +"단에 있는 초록색 부을 클릭하고 *(Re)Open Folder in Container* 옵션을 선택하세" +"요." #: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:21 msgid "" -"In some cases your setup might be more involved. For those cases consult " -"the following sources:" +"In some cases your setup might be more involved. For those cases consult the " +"following sources:" msgstr "" +"경우에 따라 설정이 더 복잡할 수도 있습니다. 이러한 경우에는 다음 소스를 참조" +"하세요:" #: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:23 msgid "" -"`Developing inside a Container " -"`_" +"`Developing inside a Container `_" msgstr "" +"`컨테이너 내부 개발`_" #: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:24 msgid "" -"`Remote development in Containers " -"`_" +"`Remote development in Containers `_" msgstr "" +"`컨테이너 원격 개발`_" #: ../../source/contributor-how-to-install-development-versions.rst:2 msgid "Install development versions" -msgstr "" +msgstr "개발 버전 설치하기" #: ../../source/contributor-how-to-install-development-versions.rst:5 msgid "Install development versions of Flower" -msgstr "" +msgstr "Flower 개발 버전 설치하기" #: ../../source/contributor-how-to-install-development-versions.rst:8 msgid "Using Poetry (recommended)" -msgstr "" +msgstr "Poetry 사용하기(권장)" #: ../../source/contributor-how-to-install-development-versions.rst:10 msgid "" -"Install a ``flwr`` pre-release from PyPI: update the ``flwr`` dependency " -"in ``pyproject.toml`` and then reinstall (don't forget to delete " -"``poetry.lock`` (``rm poetry.lock``) before running ``poetry install``)." +"Install a ``flwr`` pre-release from PyPI: update the ``flwr`` dependency in " +"``pyproject.toml`` and then reinstall (don't forget to delete ``poetry." +"lock`` (``rm poetry.lock``) before running ``poetry install``)." msgstr "" +"PyPI에서 ``flwr`` 사전 릴리스 설치하기: ``pyproject.toml``에서 ``flwr``의 " +"의존성을 업데이트한 다음, 재설치하세요(``poetry 설치``이전에 ``poetry.lock`` " +"(``rm poetry.lock``)를 제거하는 것을 잊지 마세요)." #: ../../source/contributor-how-to-install-development-versions.rst:12 msgid "" "``flwr = { version = \"1.0.0a0\", allow-prereleases = true }`` (without " "extras)" msgstr "" +"``flwr = { version = \"1.0.0a0\", allow-prereleases = true }`` (extras 제외)" #: ../../source/contributor-how-to-install-development-versions.rst:13 msgid "" "``flwr = { version = \"1.0.0a0\", allow-prereleases = true, extras = " "[\"simulation\"] }`` (with extras)" msgstr "" +"``flwr = { version = \"1.0.0a0\", allow-prereleases = true, extras = " +"[\"simulation\"] }`` (extras 포함)" #: ../../source/contributor-how-to-install-development-versions.rst:15 msgid "" -"Install ``flwr`` from a local copy of the Flower source code via " -"``pyproject.toml``:" +"Install ``flwr`` from a local copy of the Flower source code via ``pyproject." +"toml``:" msgstr "" +"``pyproject.toml``을 통해 Flower 소스 코드의 로컬 복사본에서 ``flwr``을 설치" +"하세요:" #: ../../source/contributor-how-to-install-development-versions.rst:17 msgid "``flwr = { path = \"../../\", develop = true }`` (without extras)" -msgstr "" +msgstr "``flwr = { path = \"../../\", develop = true }`` (extras 제외)" #: ../../source/contributor-how-to-install-development-versions.rst:18 msgid "" -"``flwr = { path = \"../../\", develop = true, extras = [\"simulation\"] " -"}`` (with extras)" +"``flwr = { path = \"../../\", develop = true, extras = [\"simulation\"] }`` " +"(with extras)" msgstr "" +"``flwr = { path = \"../../\", develop = true, extras = [\"simulation\"] }`` " +"(extras 포함)" #: ../../source/contributor-how-to-install-development-versions.rst:20 msgid "Install ``flwr`` from a local wheel file via ``pyproject.toml``:" -msgstr "" +msgstr "``pyproject.toml``을 통해 로컬 wheel file에서 ``flwr``을 설치하세요:" #: ../../source/contributor-how-to-install-development-versions.rst:22 msgid "" -"``flwr = { path = \"../../dist/flwr-1.8.0-py3-none-any.whl\" }`` (without" -" extras)" +"``flwr = { path = \"../../dist/flwr-1.8.0-py3-none-any.whl\" }`` (without " +"extras)" msgstr "" +"``flwr = { path = \"../../dist/flwr-1.8.0-py3-none-any.whl\" }`` (extras 제" +"외)" #: ../../source/contributor-how-to-install-development-versions.rst:23 msgid "" "``flwr = { path = \"../../dist/flwr-1.8.0-py3-none-any.whl\", extras = " "[\"simulation\"] }`` (with extras)" msgstr "" +"``flwr = { path = \"../../dist/flwr-1.8.0-py3-none-any.whl\", extras = " +"[\"simulation\"] }`` (extras 포함)" #: ../../source/contributor-how-to-install-development-versions.rst:25 msgid "" @@ -675,130 +650,155 @@ msgid "" "Dependency Specification `_" msgstr "" +"자세한 내용은 Poetry 문서를 참고하세요: `Poetry Dependency Specification " +"`_" #: ../../source/contributor-how-to-install-development-versions.rst:28 msgid "Using pip (recommended on Colab)" -msgstr "" +msgstr "pip 사용하기(Colab에서 권장)" #: ../../source/contributor-how-to-install-development-versions.rst:30 msgid "Install a ``flwr`` pre-release from PyPI:" -msgstr "" +msgstr "PyPI에서 ``flwr`` 사전 릴리즈를 설치하기:" #: ../../source/contributor-how-to-install-development-versions.rst:32 msgid "``pip install -U --pre flwr`` (without extras)" -msgstr "" +msgstr "``pip install -U --pre flwr`` (extras 제외)" #: ../../source/contributor-how-to-install-development-versions.rst:33 msgid "``pip install -U --pre flwr[simulation]`` (with extras)" -msgstr "" +msgstr "``pip install -U --pre flwr[simulation]`` (extras 포함)" #: ../../source/contributor-how-to-install-development-versions.rst:35 msgid "" "Python packages can be installed from git repositories. Use one of the " "following commands to install the Flower directly from GitHub." msgstr "" +"Python 패키지는 git 저장소에서 설치할 수 있습니다. 다음 명령어 중 하나를 사용" +"하여 GitHub에서 직접 Flower를 설치하세요." #: ../../source/contributor-how-to-install-development-versions.rst:37 msgid "Install ``flwr`` from the default GitHub branch (``main``):" -msgstr "" +msgstr "기본 GitHub branch (``main``)에서 ``flwr`` 를 설치하기:" #: ../../source/contributor-how-to-install-development-versions.rst:39 msgid "" -"``pip install flwr@git+https://github.com/adap/flower.git`` (without " -"extras)" +"``pip install flwr@git+https://github.com/adap/flower.git`` (without extras)" msgstr "" +"``pip install flwr@git+https://github.com/adap/flower.git`` (extras 제외)" #: ../../source/contributor-how-to-install-development-versions.rst:40 msgid "" "``pip install flwr[simulation]@git+https://github.com/adap/flower.git`` " "(with extras)" msgstr "" +"``pip install flwr[simulation]@git+https://github.com/adap/flower.git`` " +"(extras 포함)" #: ../../source/contributor-how-to-install-development-versions.rst:42 msgid "Install ``flwr`` from a specific GitHub branch (``branch-name``):" -msgstr "" +msgstr "특정 GitHub branch (``branch-name``)에서 ``flwr`` 설치하기:" #: ../../source/contributor-how-to-install-development-versions.rst:44 msgid "" "``pip install flwr@git+https://github.com/adap/flower.git@branch-name`` " "(without extras)" msgstr "" +"``pip install flwr@git+https://github.com/adap/flower.git@branch-name`` " +"(extras 제외)" #: ../../source/contributor-how-to-install-development-versions.rst:45 msgid "" -"``pip install flwr[simulation]@git+https://github.com/adap/flower.git" -"@branch-name`` (with extras)" +"``pip install flwr[simulation]@git+https://github.com/adap/flower.git@branch-" +"name`` (with extras)" msgstr "" +"``pip install flwr[simulation]@git+https://github.com/adap/flower.git@branch-" +"name`` (extras 포함)" #: ../../source/contributor-how-to-install-development-versions.rst:49 msgid "Open Jupyter Notebooks on Google Colab" -msgstr "" +msgstr "Google Colab에서 Jupyter Notebooks 열기" #: ../../source/contributor-how-to-install-development-versions.rst:51 msgid "" "Open the notebook ``doc/source/tutorial-series-get-started-with-flower-" "pytorch.ipynb``:" msgstr "" +"``doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb``notebook" +"을 엽니다:" #: ../../source/contributor-how-to-install-development-versions.rst:53 msgid "" -"https://colab.research.google.com/github/adap/flower/blob/main/doc/source" -"/tutorial-series-get-started-with-flower-pytorch.ipynb" +"https://colab.research.google.com/github/adap/flower/blob/main/doc/source/" +"tutorial-series-get-started-with-flower-pytorch.ipynb" msgstr "" +"https://colab.research.google.com/github/adap/flower/blob/main/doc/source/" +"tutorial-series-get-started-with-flower-pytorch.ipynb" #: ../../source/contributor-how-to-install-development-versions.rst:55 msgid "" -"Open a development version of the same notebook from branch `branch-name`" -" by changing ``main`` to ``branch-name`` (right after ``blob``):" +"Open a development version of the same notebook from branch `branch-name` by " +"changing ``main`` to ``branch-name`` (right after ``blob``):" msgstr "" +"``main``을 ``branch-name``(``blob`` 바로 뒤)으로 변경하여 동일한 notebook의 " +"개발 버전을 브랜치 `branch-name`에서 엽니다 :" #: ../../source/contributor-how-to-install-development-versions.rst:57 msgid "" -"https://colab.research.google.com/github/adap/flower/blob/branch-" -"name/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb" +"https://colab.research.google.com/github/adap/flower/blob/branch-name/doc/" +"source/tutorial-series-get-started-with-flower-pytorch.ipynb" msgstr "" +"https://colab.research.google.com/github/adap/flower/blob/branch-name/doc/" +"source/tutorial-series-get-started-with-flower-pytorch.ipynb" #: ../../source/contributor-how-to-install-development-versions.rst:59 msgid "Install a `whl` on Google Colab:" -msgstr "" +msgstr "Google Colab에서 `whl` 설치하기:" #: ../../source/contributor-how-to-install-development-versions.rst:61 msgid "" -"In the vertical icon grid on the left hand side, select ``Files`` > " -"``Upload to session storage``" +"In the vertical icon grid on the left hand side, select ``Files`` > ``Upload " +"to session storage``" msgstr "" +"왼쪽의 수직 아이콘 그리드에서 ``Files`` > ``Upload to session storage``를 선" +"택하세요" #: ../../source/contributor-how-to-install-development-versions.rst:62 msgid "Upload the whl (e.g., ``flwr-1.8.0-py3-none-any.whl``)" -msgstr "" +msgstr "whl (예:``flwr-1.8.0-py3-none-any.whl``)을 업로드하세요" #: ../../source/contributor-how-to-install-development-versions.rst:63 msgid "" -"Change ``!pip install -q 'flwr[simulation]' torch torchvision " -"matplotlib`` to ``!pip install -q 'flwr-1.8.0-py3-none-" -"any.whl[simulation]' torch torchvision matplotlib``" +"Change ``!pip install -q 'flwr[simulation]' torch torchvision matplotlib`` " +"to ``!pip install -q 'flwr-1.8.0-py3-none-any.whl[simulation]' torch " +"torchvision matplotlib``" msgstr "" +"``!pip install -q 'flwr[simulation]' torch torchvision matplotlib``를 ``!pip " +"install -q 'flwr-1.8.0-py3-none-any.whl[simulation]' torch torchvision " +"matplotlib``로 바꾸세요" #: ../../source/contributor-how-to-release-flower.rst:2 msgid "Release Flower" -msgstr "" +msgstr "Flower 릴리즈 하기" #: ../../source/contributor-how-to-release-flower.rst:4 msgid "" "This document describes the current release process. It may or may not " "change in the future." msgstr "" +"이 문서는 현재 릴리즈 과정을 설명합니다. 이는 앞으로 변경될 수도 있습니다." #: ../../source/contributor-how-to-release-flower.rst:7 msgid "During the release" -msgstr "" +msgstr "릴리즈 동안에" #: ../../source/contributor-how-to-release-flower.rst:9 msgid "" -"The version number of a release is stated in ``pyproject.toml``. To " -"release a new version of Flower, the following things need to happen (in " -"that order):" +"The version number of a release is stated in ``pyproject.toml``. To release " +"a new version of Flower, the following things need to happen (in that order):" msgstr "" +"릴리즈의 버전 번호는 ``pyproject.toml``에 명시되어 있습니다. Flower의 새 버전" +"을 릴리즈하려면 다음 작업이 순서대로 수행되어야 합니다:" #: ../../source/contributor-how-to-release-flower.rst:11 msgid "" @@ -806,1739 +806,1999 @@ msgid "" "order to add every new change to the changelog (feel free to make manual " "changes to the changelog afterwards until it looks good)." msgstr "" +"모든 새로운 변경 사항을 변경 로그에 추가하기 위해``python3 src/py/flwr_tool/" +"update_changelog.py ``을 실행합니다 (변경 로그가 만족스러워질 " +"때까지 수동으로 변경해도 됩니다)." #: ../../source/contributor-how-to-release-flower.rst:12 msgid "" -"Once the changelog has been updated with all the changes, run ``./dev" -"/prepare-release-changelog.sh v``, where ```` " -"is the version stated in ``pyproject.toml`` (notice the ``v`` added " -"before it). This will replace the ``Unreleased`` header of the changelog " -"by the version and current date, and it will add a thanking message for " -"the contributors. Open a pull request with those changes." +"Once the changelog has been updated with all the changes, run ``./dev/" +"prepare-release-changelog.sh v``, where ```` is " +"the version stated in ``pyproject.toml`` (notice the ``v`` added before it). " +"This will replace the ``Unreleased`` header of the changelog by the version " +"and current date, and it will add a thanking message for the contributors. " +"Open a pull request with those changes." msgstr "" +"모든 변경 사항으로 변경 로그가 업데이트되면,``./dev/prepare-release-" +"changelog.sh v``을 실행합니다. 여기서 ````은 " +"``pyproject.toml``에 명시된 버전 번호입니다 (앞에 ``v``가 추가된 것을 주의하" +"세요). 이 명령어는 변경 로그의 ``Unreleased``헤더를 해당 버전과 현재 날짜로 " +"교체하고, 기여자들에게 감사 메시지가 추가됩니다. 이러한 변경 사항으로 pull " +"request합니다." #: ../../source/contributor-how-to-release-flower.rst:13 msgid "" "Once the pull request is merged, tag the release commit with the version " -"number as soon as the PR is merged: ``git tag v`` (notice " -"the ``v`` added before the version number), then ``git push --tags``. " -"This will create a draft release on GitHub containing the correct " -"artifacts and the relevant part of the changelog." +"number as soon as the PR is merged: ``git tag v`` (notice the " +"``v`` added before the version number), then ``git push --tags``. This will " +"create a draft release on GitHub containing the correct artifacts and the " +"relevant part of the changelog." msgstr "" +"pull request가 병합되면, PR이 병합되는 즉시 버전 번호로 릴리즈 커밋에 태그를 " +"지정합니다:``git tag v`` (버전 번호 앞에 ``v``가 추가된 것을 확" +"인), 그 다음 ``git push --tags``. 이렇게 하면 올바른 아티팩트와 변경 로그의 " +"관련 부분이 포함된 초안 릴리즈가 GitHub에 생성됩니다." #: ../../source/contributor-how-to-release-flower.rst:14 -msgid "Check the draft release on GitHub, and if everything is good, publish it." -msgstr "" +msgid "" +"Check the draft release on GitHub, and if everything is good, publish it." +msgstr "GitHub에서 릴리즈 초안을 확인하고, 모든 것이 양호하면 게시하세요." + +#: ../../source/contributor-how-to-release-flower.rst:15 +msgid "Trigger the CI for building the Docker images." +msgstr "Docker 이미지 빌드를 위해 CI를 트리거합니다." #: ../../source/contributor-how-to-release-flower.rst:17 -msgid "After the release" +msgid "" +"To trigger the workflow, a collaborator must create a ``workflow_dispatch`` " +"event in the GitHub CI. This can be done either through the UI or via the " +"GitHub CLI. The event requires only one input, the Flower version, to be " +"released." msgstr "" +"워크플로우를 트리거하려면 공동 작업자가 GitHub CI에서 ``workflow_dispatch``" +"를 생성해야 합니다. 이 작업은 UI 또는 GitHub CLI 를 통해 수행할 수 있습니다. " +"이벤트는 Flower 버전 한 가지 입력만 필요합니다." -#: ../../source/contributor-how-to-release-flower.rst:19 -msgid "Create a pull request which contains the following changes:" +#: ../../source/contributor-how-to-release-flower.rst:21 +msgid "**Via the UI**" +msgstr "**UI를 통해서**" + +#: ../../source/contributor-how-to-release-flower.rst:23 +msgid "" +"Go to the ``Build docker images`` workflow `page `_." msgstr "" +"``Build docker images`` 워크플로우 `페이지 `_로 이동합니다." -#: ../../source/contributor-how-to-release-flower.rst:21 -msgid "Increase the minor version in ``pyproject.toml`` by one." +#: ../../source/contributor-how-to-release-flower.rst:24 +msgid "" +"Click on the ``Run workflow`` button and type the new version of Flower in " +"the ``Version of Flower`` input field." msgstr "" +"``Run workflow`` 버튼을 누르고 ``Version of Flower``에 Flower의 새버전을 입력" +"합니다." -#: ../../source/contributor-how-to-release-flower.rst:22 -msgid "Update all files which contain the current version number if necessary." +#: ../../source/contributor-how-to-release-flower.rst:25 +msgid "Click on the **green** ``Run workflow`` button." +msgstr "**초록색**의 ``Run workflow``버튼을 클릭합니다." + +#: ../../source/contributor-how-to-release-flower.rst:29 +msgid "**Via the GitHub CI**" +msgstr "**GitHub CI를 통해서**" + +#: ../../source/contributor-how-to-release-flower.rst:31 +msgid "" +"Make sure you are logged in via ``gh auth login`` and that the current " +"working directory is the root of the Flower repository." msgstr "" +"``gh auth login``을 통해 로그인 했는지, 현재 작업 디렉토리가 Flower 리포지토" +"리의 root인지 확인하세요." -#: ../../source/contributor-how-to-release-flower.rst:23 -msgid "Add a new ``Unreleased`` section in ``changelog.md``." +#: ../../source/contributor-how-to-release-flower.rst:32 +msgid "" +"Trigger the workflow via ``gh workflow run docker-images.yml -f flwr-" +"version=``." msgstr "" +"``gh workflow run docker-images.yml -f flwr-version=``을 통해 워" +"크플로우 를 트리거합니다." -#: ../../source/contributor-how-to-release-flower.rst:25 +#: ../../source/contributor-how-to-release-flower.rst:35 +msgid "After the release" +msgstr "릴리즈 후에" + +#: ../../source/contributor-how-to-release-flower.rst:37 +msgid "Create a pull request which contains the following changes:" +msgstr "다음 변경 사항이 포함된 pull request를 만듭니다:" + +#: ../../source/contributor-how-to-release-flower.rst:39 +msgid "Increase the minor version in ``pyproject.toml`` by one." +msgstr "``pyproject.toml``의 마이너 버전을 하나씩 늘립니다." + +#: ../../source/contributor-how-to-release-flower.rst:40 +msgid "Update all files which contain the current version number if necessary." +msgstr "필요한 경우 현재 버전 번호가 포함된 모든 파일을 업데이트합니다." + +#: ../../source/contributor-how-to-release-flower.rst:41 +msgid "Add a new ``Unreleased`` section in ``changelog.md``." +msgstr "``changelog.md``에 ``Unreleased`` 섹션을 새로 추가합니다." + +#: ../../source/contributor-how-to-release-flower.rst:43 msgid "" -"Merge the pull request on the same day (i.e., before a new nightly " -"release gets published to PyPI)." +"Merge the pull request on the same day (i.e., before a new nightly release " +"gets published to PyPI)." msgstr "" +"pull request를 같은 날(즉, 새로운 nightly 릴리즈가 PyPI에 게시되기 전에) 병합" +"하세요." -#: ../../source/contributor-how-to-release-flower.rst:28 +#: ../../source/contributor-how-to-release-flower.rst:46 msgid "Publishing a pre-release" -msgstr "" +msgstr "사전 릴리즈 게시" -#: ../../source/contributor-how-to-release-flower.rst:31 +#: ../../source/contributor-how-to-release-flower.rst:49 msgid "Pre-release naming" -msgstr "" +msgstr "사전 릴리즈 이름" -#: ../../source/contributor-how-to-release-flower.rst:33 +#: ../../source/contributor-how-to-release-flower.rst:51 msgid "" -"PyPI supports pre-releases (alpha, beta, release candidate). Pre-releases" -" MUST use one of the following naming patterns:" +"PyPI supports pre-releases (alpha, beta, release candidate). Pre-releases " +"MUST use one of the following naming patterns:" msgstr "" +"PyPI는 사전 릴리즈(알파, 베타, 릴리스 후보)를 지원합니다. 사전 릴리즈는 반드" +"시 다음 명명 패턴 중 하나를 사용해야 합니다:" -#: ../../source/contributor-how-to-release-flower.rst:35 +#: ../../source/contributor-how-to-release-flower.rst:53 msgid "Alpha: ``MAJOR.MINOR.PATCHaN``" -msgstr "" +msgstr "Alpha: ``MAJOR.MINOR.PATCHaN``" -#: ../../source/contributor-how-to-release-flower.rst:36 +#: ../../source/contributor-how-to-release-flower.rst:54 msgid "Beta: ``MAJOR.MINOR.PATCHbN``" -msgstr "" +msgstr "Beta: ``MAJOR.MINOR.PATCHbN``" -#: ../../source/contributor-how-to-release-flower.rst:37 +#: ../../source/contributor-how-to-release-flower.rst:55 msgid "Release candidate (RC): ``MAJOR.MINOR.PATCHrcN``" -msgstr "" +msgstr "Release candidate (RC): ``MAJOR.MINOR.PATCHrcN``" -#: ../../source/contributor-how-to-release-flower.rst:39 +#: ../../source/contributor-how-to-release-flower.rst:57 msgid "Examples include:" -msgstr "" +msgstr "예시:" -#: ../../source/contributor-how-to-release-flower.rst:41 +#: ../../source/contributor-how-to-release-flower.rst:59 msgid "``1.0.0a0``" -msgstr "" +msgstr "``1.0.0a0``" -#: ../../source/contributor-how-to-release-flower.rst:42 +#: ../../source/contributor-how-to-release-flower.rst:60 msgid "``1.0.0b0``" -msgstr "" +msgstr "``1.0.0b0``" -#: ../../source/contributor-how-to-release-flower.rst:43 +#: ../../source/contributor-how-to-release-flower.rst:61 msgid "``1.0.0rc0``" -msgstr "" +msgstr "``1.0.0rc0``" -#: ../../source/contributor-how-to-release-flower.rst:44 +#: ../../source/contributor-how-to-release-flower.rst:62 msgid "``1.0.0rc1``" -msgstr "" +msgstr "``1.0.0rc1``" -#: ../../source/contributor-how-to-release-flower.rst:46 +#: ../../source/contributor-how-to-release-flower.rst:64 msgid "" "This is in line with PEP-440 and the recommendations from the Python " "Packaging Authority (PyPA):" msgstr "" +"이는 PEP-440 및 Python Packaging Authority (PyPA)의 권장 사항과 일치합니다:" -#: ../../source/contributor-how-to-release-flower.rst:49 +#: ../../source/contributor-how-to-release-flower.rst:67 msgid "`PEP-440 `_" -msgstr "" +msgstr "`PEP-440 `_" -#: ../../source/contributor-how-to-release-flower.rst:50 +#: ../../source/contributor-how-to-release-flower.rst:68 msgid "" -"`PyPA Choosing a versioning scheme " -"`_" +"`PyPA Choosing a versioning scheme `_" msgstr "" +"`PyPA 버전 관리 체계 선택하기 `_" -#: ../../source/contributor-how-to-release-flower.rst:52 +#: ../../source/contributor-how-to-release-flower.rst:70 msgid "" -"Note that the approach defined by PyPA is not compatible with SemVer " -"2.0.0 spec, for details consult the `Semantic Versioning Specification " -"`_ (specifically item " -"11 on precedence)." +"Note that the approach defined by PyPA is not compatible with SemVer 2.0.0 " +"spec, for details consult the `Semantic Versioning Specification `_ (specifically item 11 on " +"precedence)." msgstr "" +"PyPA에서 정의한 접근 방식은 SemVer 2.0.0 사양과 호환되지 않으며, 자세한 내용" +"은`Semantic Versioning 관리 사양 `_ (특히 항목 11이 우선순위)을 참조하세요." -#: ../../source/contributor-how-to-release-flower.rst:55 +#: ../../source/contributor-how-to-release-flower.rst:73 msgid "Pre-release classification" -msgstr "" +msgstr "사전 릴리즈 분류" -#: ../../source/contributor-how-to-release-flower.rst:57 -msgid "Should the next pre-release be called alpha, beta, or release candidate?" -msgstr "" +#: ../../source/contributor-how-to-release-flower.rst:75 +msgid "" +"Should the next pre-release be called alpha, beta, or release candidate?" +msgstr "다음 사전 릴리즈를 알파, 베타 또는 릴리스 후보라고 불러야 하나요?" -#: ../../source/contributor-how-to-release-flower.rst:59 +#: ../../source/contributor-how-to-release-flower.rst:77 msgid "" -"RC: feature complete, no known issues (apart from issues that are " -"classified as \"won't fix\" for the next stable release) - if no issues " -"surface this will become the next stable release" +"RC: feature complete, no known issues (apart from issues that are classified " +"as \"won't fix\" for the next stable release) - if no issues surface this " +"will become the next stable release" msgstr "" +"RC: 기능 완료, 알려진 문제 없음(다음 stable 릴리즈에서 \"수정되지 않음\"으로 " +"분류된 문제 제외) - 문제가 나타나지 않으면 다음 stable 릴리즈가 됩니다" -#: ../../source/contributor-how-to-release-flower.rst:60 +#: ../../source/contributor-how-to-release-flower.rst:78 msgid "Beta: feature complete, allowed to have known issues" -msgstr "" +msgstr "베타: 기능 완료, 알려진 문제 발생 가능" -#: ../../source/contributor-how-to-release-flower.rst:61 +#: ../../source/contributor-how-to-release-flower.rst:79 msgid "Alpha: not feature complete, allowed to have known issues" -msgstr "" +msgstr "알파: 기능 미완성, 알려진 문제가 있을 수 있음" #: ../../source/contributor-how-to-set-up-a-virtual-env.rst:2 msgid "Set up a virtual env" -msgstr "" +msgstr "가상 환경 설정" #: ../../source/contributor-how-to-set-up-a-virtual-env.rst:4 msgid "" "It is recommended to run your Python setup within a virtual environment. " "This guide shows three different examples how to create a virtual " -"environment with pyenv virtualenv, poetry, or Anaconda. You can follow " -"the instructions or choose your preferred setup." +"environment with pyenv virtualenv, poetry, or Anaconda. You can follow the " +"instructions or choose your preferred setup." msgstr "" +"가상 환경 내에서 파이썬 설정을 실행하는 것이 좋습니다. 이 가이드에서는 pyenv " +"virtualenv, poetry 또는 Anaconda를 사용하여 가상 환경을 만드는 세 가지 예제" +"를 보여줍니다. 안내를 따르거나 원하는 설정을 선택할 수 있습니다." #: ../../source/contributor-how-to-set-up-a-virtual-env.rst:9 msgid "Python Version" -msgstr "" +msgstr "Python 버전" #: ../../source/contributor-how-to-set-up-a-virtual-env.rst:11 #: ../../source/how-to-install-flower.rst:8 msgid "" -"Flower requires at least `Python 3.8 `_, " -"but `Python 3.10 `_ or above is " -"recommended." +"Flower requires at least `Python 3.8 `_, but " +"`Python 3.10 `_ or above is recommended." msgstr "" +"Flower는 `Python 3.8 `_이상이 필요하지만, " +"`Python 3.10 `_이상을 권장합니다." #: ../../source/contributor-how-to-set-up-a-virtual-env.rst:14 msgid "" -"Due to a known incompatibility with `ray " -"`_, we currently recommend utilizing at " -"most `Python 3.11 `_ for running Flower " -"simulations." +"Due to a known incompatibility with `ray `_, " +"we currently recommend utilizing at most `Python 3.11 `_ for running Flower simulations." msgstr "" +"`Ray `__와 호환되지 않는 것으로 알려져 있으므" +"로, 현재 Flower 시뮬레이션을 실행할 때는 최대 `Python 3.11 `_을 사용하는 것이 좋습니다." #: ../../source/contributor-how-to-set-up-a-virtual-env.rst:19 msgid "Virtualenv with Pyenv/Virtualenv" -msgstr "" +msgstr "Pyenv/Virtualenv를 사용한 가상 환경" #: ../../source/contributor-how-to-set-up-a-virtual-env.rst:21 msgid "" -"One of the recommended virtual environment is `pyenv " -"`_/`virtualenv `_. Please see `Flower examples " -"`_ for details." +"One of the recommended virtual environment is `pyenv `_/`virtualenv `_. " +"Please see `Flower examples `_ for details." msgstr "" +"권장 가상 환경 중 하나는 `pyenv `_/" +"`virtualenv `_입니다. 자세한 내용" +"은 `Flower examples `_를 " +"참조하세요." #: ../../source/contributor-how-to-set-up-a-virtual-env.rst:23 msgid "" "Once Pyenv is set up, you can use it to install `Python Version 3.10 " "`_ or above:" msgstr "" +"Pyenv가 설정되면 이를 사용하여 'Python 버전 3.10 `_ 이상'을 설치할 수 있습니다:" #: ../../source/contributor-how-to-set-up-a-virtual-env.rst:29 msgid "Create the virtualenv with:" -msgstr "" +msgstr "가상 환경을 만듭니다:" #: ../../source/contributor-how-to-set-up-a-virtual-env.rst:36 msgid "Activate the virtualenv by running the following command:" -msgstr "" +msgstr "다음 명령을 실행하여 가상 환경을 활성화합니다:" #: ../../source/contributor-how-to-set-up-a-virtual-env.rst:44 msgid "Virtualenv with Poetry" -msgstr "" +msgstr "Poetry를 사용한 가상 환경" #: ../../source/contributor-how-to-set-up-a-virtual-env.rst:46 msgid "" -"The Flower examples are based on `Poetry `_ to manage dependencies. After installing Poetry you " -"simply create a virtual environment with:" +"The Flower examples are based on `Poetry `_ " +"to manage dependencies. After installing Poetry you simply create a virtual " +"environment with:" msgstr "" +"Flower examples은 의존성을 관리하기 위해 `Poetry `_를 기반으로 합니다. Poetry를 설치한 후 가상 환경을 생성하기만 하면 " +"됩니다:" #: ../../source/contributor-how-to-set-up-a-virtual-env.rst:52 msgid "" -"If you open a new terminal you can activate the previously created " -"virtual environment with the following command:" +"If you open a new terminal you can activate the previously created virtual " +"environment with the following command:" msgstr "" +"새 터미널을 열면 다음 명령을 사용하여 이전에 생성한 가상 환경을 활성화할 수 " +"있습니다:" #: ../../source/contributor-how-to-set-up-a-virtual-env.rst:60 msgid "Virtualenv with Anaconda" -msgstr "" +msgstr "Anaconda를 사용한 가상 환경" #: ../../source/contributor-how-to-set-up-a-virtual-env.rst:62 msgid "" -"If you prefer to use Anaconda for your virtual environment then install " -"and setup the `conda `_ package. After setting it up you can " -"create a virtual environment with:" +"If you prefer to use Anaconda for your virtual environment then install and " +"setup the `conda `_ package. After setting it up you can create a virtual " +"environment with:" msgstr "" +"가상 환경에서 Anaconda를 사용하려면 `conda `_ 패키지를 설치 및 " +"설정하세요. 설정 후 다음을 사용하여 가상 환경을 만들 수 있습니다:" #: ../../source/contributor-how-to-set-up-a-virtual-env.rst:68 msgid "and activate the virtual environment with:" -msgstr "" +msgstr "그 후 가상 환경을 활성화합니다:" #: ../../source/contributor-how-to-set-up-a-virtual-env.rst:76 msgid "And then?" -msgstr "" +msgstr "그다음은?" #: ../../source/contributor-how-to-set-up-a-virtual-env.rst:78 msgid "" -"As soon as you created your virtual environment you clone one of the " -"`Flower examples `_." +"As soon as you created your virtual environment you clone one of the `Flower " +"examples `_." msgstr "" +"가상 환경을 생성하자마자 'Flower examples `_ 중 하나를 클론합니다." #: ../../source/contributor-how-to-write-documentation.rst:2 msgid "Write documentation" -msgstr "" +msgstr "문서 작성" #: ../../source/contributor-how-to-write-documentation.rst:6 msgid "Project layout" -msgstr "" +msgstr "프로젝트 레이아웃" #: ../../source/contributor-how-to-write-documentation.rst:8 msgid "" -"The Flower documentation lives in the ``doc`` directory. The Sphinx-based" -" documentation system supports both reStructuredText (``.rst`` files) and" -" Markdown (``.md`` files)." +"The Flower documentation lives in the ``doc`` directory. The Sphinx-based " +"documentation system supports both reStructuredText (``.rst`` files) and " +"Markdown (``.md`` files)." msgstr "" +"Flower 문서는 ``doc`` 디렉토리에 있습니다. Sphinx 기반 문서 시스템은 " +"reStructuredText 텍스트(``.rst`` 파일)와 Markdown(``.md`` 파일)을 모두 지원합" +"니다." #: ../../source/contributor-how-to-write-documentation.rst:10 #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:169 msgid "" -"Note that, in order to build the documentation locally (with ``poetry run" -" make html``, like described below), `Pandoc " -"`_ needs to be installed on the " -"system." +"Note that, in order to build the documentation locally (with ``poetry run " +"make html``, like described below), `Pandoc `_ needs to be installed on the system." msgstr "" +"로컬에서 문서를 작성하려면(아래 설명과 같이 ``poetry run make html``로) " +"`Pandoc `_이 시스템에 설치되어 있어야 합" +"니다." #: ../../source/contributor-how-to-write-documentation.rst:14 msgid "Edit an existing page" -msgstr "" +msgstr "기존 페이지 편집" #: ../../source/contributor-how-to-write-documentation.rst:16 msgid "Edit an existing ``.rst`` (or ``.md``) file under ``doc/source/``" -msgstr "" +msgstr "doc/source/``에서 기존 ``.rst``(또는 ``.md``) 파일을 편집합니다" #: ../../source/contributor-how-to-write-documentation.rst:17 #: ../../source/contributor-how-to-write-documentation.rst:27 msgid "Compile the docs: ``cd doc``, then ``poetry run make html``" msgstr "" +"문서를 컴파일합니다: ``cd doc``, ``poetry run make html`` 순으로 컴파일합니다" #: ../../source/contributor-how-to-write-documentation.rst:18 #: ../../source/contributor-how-to-write-documentation.rst:28 msgid "Open ``doc/build/html/index.html`` in the browser to check the result" -msgstr "" +msgstr "브라우저에서 ``doc/build/html/index.html``을 열어 결과를 확인합니다" #: ../../source/contributor-how-to-write-documentation.rst:22 msgid "Create a new page" -msgstr "" +msgstr "새 페이지 만들기" #: ../../source/contributor-how-to-write-documentation.rst:24 msgid "Add new ``.rst`` file under ``doc/source/``" -msgstr "" +msgstr "``doc/source/`에 새 ``.rst`` 을 추가합니다" #: ../../source/contributor-how-to-write-documentation.rst:25 msgid "Add content to the new ``.rst`` file" -msgstr "" +msgstr "새 '.rst' 파일에 내용을 추가합니다" #: ../../source/contributor-how-to-write-documentation.rst:26 msgid "Link to the new rst from ``index.rst``" -msgstr "" +msgstr "``index.rst``에서 새 rst로 연결합니다" #: ../../source/contributor-ref-good-first-contributions.rst:2 msgid "Good first contributions" -msgstr "" +msgstr "훌륭한 첫 번째 기여" #: ../../source/contributor-ref-good-first-contributions.rst:4 msgid "" -"We welcome contributions to Flower! However, it is not always easy to " -"know where to start. We therefore put together a few recommendations on " -"where to start to increase your chances of getting your PR accepted into " -"the Flower codebase." +"We welcome contributions to Flower! However, it is not always easy to know " +"where to start. We therefore put together a few recommendations on where to " +"start to increase your chances of getting your PR accepted into the Flower " +"codebase." msgstr "" +"Flower에 대한 기여를 환영합니다! 하지만 어디서부터 시작해야 할지 알기란 쉽지 " +"않습니다. 그래서 저희는 여러분의 PR이 Flower 코드베이스에 채택될 가능성을 높" +"이기 위해 어디서부터 시작해야 하는지 몇 가지 권장 사항을 정리해 보았습니다." #: ../../source/contributor-ref-good-first-contributions.rst:11 msgid "Where to start" -msgstr "" +msgstr "시작 위치" #: ../../source/contributor-ref-good-first-contributions.rst:13 msgid "" -"Until the Flower core library matures it will be easier to get PR's " -"accepted if they only touch non-core areas of the codebase. Good " -"candidates to get started are:" +"Until the Flower core library matures it will be easier to get PR's accepted " +"if they only touch non-core areas of the codebase. Good candidates to get " +"started are:" msgstr "" +"Flower 코어 라이브러리가 완성될 때까지는 코드베이스의 비핵심 영역만 건드리는 " +"것이 PR을 승인받기가 더 쉬울 것입니다. 시작하기에 좋은 후보자는 다음과 같습니" +"다:" #: ../../source/contributor-ref-good-first-contributions.rst:17 msgid "Documentation: What's missing? What could be expressed more clearly?" -msgstr "" +msgstr "문서: 무엇이 누락되었나요? 무엇을 더 명확하게 표현할 수 있을까요?" #: ../../source/contributor-ref-good-first-contributions.rst:18 msgid "Baselines: See below." -msgstr "" +msgstr "Baselines: 아래를 참조하세요." #: ../../source/contributor-ref-good-first-contributions.rst:19 msgid "Examples: See below." -msgstr "" +msgstr "예시: 아래를 참조하세요." #: ../../source/contributor-ref-good-first-contributions.rst:23 msgid "Request for Flower Baselines" -msgstr "" +msgstr "Flower Baselines 요청" #: ../../source/contributor-ref-good-first-contributions.rst:25 msgid "" -"If you are not familiar with Flower Baselines, you should probably check-" -"out our `contributing guide for baselines " -"`_." +"If you are not familiar with Flower Baselines, you should probably check-out " +"our `contributing guide for baselines `_." msgstr "" +"Flower Baseline에 익숙하지 않다면 ' Baseline 기여 가이드 `_를 확인해보세요." #: ../../source/contributor-ref-good-first-contributions.rst:27 msgid "" -"You should then check out the open `issues " -"`_" -" for baseline requests. If you find a baseline that you'd like to work on" -" and that has no assignees, feel free to assign it to yourself and start " -"working on it!" +"You should then check out the open `issues `_ for baseline " +"requests. If you find a baseline that you'd like to work on and that has no " +"assignees, feel free to assign it to yourself and start working on it!" msgstr "" +"그런 다음 오픈 된 `issues `_에서 baseline " +"요청을 확인해야 합니다. 작업하고 싶은 기준선을 찾았지만 담당자가 없는 경우, " +"자유롭게 자신에게 할당하고 작업을 시작하세요!" #: ../../source/contributor-ref-good-first-contributions.rst:31 msgid "" -"Otherwise, if you don't find a baseline you'd like to work on, be sure to" -" open a new issue with the baseline request template!" +"Otherwise, if you don't find a baseline you'd like to work on, be sure to " +"open a new issue with the baseline request template!" msgstr "" +"그렇지 않으면 작업하고 싶은 baseline을 찾지 못하면 baseline 요청 템플릿으로 " +"새 이슈를 열어야 합니다!" #: ../../source/contributor-ref-good-first-contributions.rst:34 msgid "Request for examples" -msgstr "" +msgstr "예시 요청" #: ../../source/contributor-ref-good-first-contributions.rst:36 msgid "" "We wish we had more time to write usage examples because we believe they " -"help users to get started with building what they want to build. Here are" -" a few ideas where we'd be happy to accept a PR:" +"help users to get started with building what they want to build. Here are a " +"few ideas where we'd be happy to accept a PR:" msgstr "" +"사용 예시는 사용자가 원하는 것을 구축하는 데 도움이 된다고 생각하기 때문에 " +"더 많은 시간을 할애하여 작성할 수 있었으면 합니다. 다음은 저희가 기꺼이 PR을 " +"수락할 수 있는 몇 가지 아이디어입니다:" #: ../../source/contributor-ref-good-first-contributions.rst:40 msgid "Llama 2 fine-tuning, with Hugging Face Transformers and PyTorch" -msgstr "" +msgstr "Llama 2 미세 조정, Hugging Face Transformer와 파이토치 포함" #: ../../source/contributor-ref-good-first-contributions.rst:41 msgid "XGBoost" -msgstr "" +msgstr "XGBoost" #: ../../source/contributor-ref-good-first-contributions.rst:42 msgid "Android ONNX on-device training" -msgstr "" +msgstr "Android ONNX 온디바이스 훈련" #: ../../source/contributor-ref-secure-aggregation-protocols.rst:2 msgid "Secure Aggregation Protocols" -msgstr "" +msgstr "Secure Aggregation 프로토콜" #: ../../source/contributor-ref-secure-aggregation-protocols.rst:4 msgid "" -"Include SecAgg, SecAgg+, and LightSecAgg protocol. The LightSecAgg " -"protocol has not been implemented yet, so its diagram and abstraction may" -" not be accurate in practice. The SecAgg protocol can be considered as a " -"special case of the SecAgg+ protocol." +"Include SecAgg, SecAgg+, and LightSecAgg protocol. The LightSecAgg protocol " +"has not been implemented yet, so its diagram and abstraction may not be " +"accurate in practice. The SecAgg protocol can be considered as a special " +"case of the SecAgg+ protocol." msgstr "" +"SecAgg, SecAgg+, LightSecAgg 프로토콜을 포함합니다. LightSecAgg 프로토콜은 아" +"직 구현되지 않았기 때문에 다이어그램과 추상화가 실제로는 정확하지 않을 수 있" +"습니다. SecAgg 프로토콜은 SecAgg+ 프로토콜의 특수한 경우로 간주할 수 있습니" +"다." #: ../../source/contributor-ref-secure-aggregation-protocols.rst:8 msgid "The :code:`SecAgg+` abstraction" -msgstr "" +msgstr "The :code:`SecAgg+` 추상화" #: ../../source/contributor-ref-secure-aggregation-protocols.rst:10 #: ../../source/contributor-ref-secure-aggregation-protocols.rst:161 msgid "" "In this implementation, each client will be assigned with a unique index " -"(int) for secure aggregation, and thus many python dictionaries used have" -" keys of int type rather than ClientProxy type." +"(int) for secure aggregation, and thus many python dictionaries used have " +"keys of int type rather than ClientProxy type." msgstr "" +"구현에서는 각 클라이언트에 secure aggregation를 위한 고유 인덱스(int)가 할당" +"되므로 사용되는 많은 파이썬 dictionaries에는 ClientProxy 타입이 아닌 int 타입" +"의 키가 있습니다." #: ../../source/contributor-ref-secure-aggregation-protocols.rst:65 #: ../../source/contributor-ref-secure-aggregation-protocols.rst:198 msgid "" -"The Flower server will execute and process received results in the " -"following order:" -msgstr "" +"The Flower server will execute and process received results in the following " +"order:" +msgstr "Flower 서버는 수신된 결과를 다음 순서로 실행하고 처리합니다:" #: ../../source/contributor-ref-secure-aggregation-protocols.rst:159 msgid "The :code:`LightSecAgg` abstraction" -msgstr "" +msgstr "The :code:`LightSecAgg` 추상" #: ../../source/contributor-ref-secure-aggregation-protocols.rst:271 msgid "Types" -msgstr "" +msgstr "타입" #: ../../source/contributor-tutorial-contribute-on-github.rst:2 msgid "Contribute on GitHub" -msgstr "" +msgstr "GitHub에서 기여하기" #: ../../source/contributor-tutorial-contribute-on-github.rst:4 msgid "" -"This guide is for people who want to get involved with Flower, but who " -"are not used to contributing to GitHub projects." +"This guide is for people who want to get involved with Flower, but who are " +"not used to contributing to GitHub projects." msgstr "" +"이 가이드는 Flower에 참여하고 싶지만 GitHub 프로젝트에 기여하는 데 익숙하지 " +"않은 분들을 위한 것입니다." #: ../../source/contributor-tutorial-contribute-on-github.rst:6 msgid "" -"If you're familiar with how contributing on GitHub works, you can " -"directly checkout our :doc:`getting started guide for contributors " -"`." +"If you're familiar with how contributing on GitHub works, you can directly " +"checkout our :doc:`getting started guide for contributors `." msgstr "" +"깃허브에서 기여하는 방식에 익숙하다면 :doc:`기여자를 위한 시작 가이드" +"`를 직접 확인하세요." #: ../../source/contributor-tutorial-contribute-on-github.rst:10 msgid "Setting up the repository" -msgstr "" +msgstr "레포지토리 설정하기" #: ../../source/contributor-tutorial-contribute-on-github.rst:21 msgid "**Create a GitHub account and setup Git**" -msgstr "" +msgstr "**GitHub 계정을 만들고 Git을 설정합니다**" #: ../../source/contributor-tutorial-contribute-on-github.rst:13 msgid "" "Git is a distributed version control tool. This allows for an entire " "codebase's history to be stored and every developer's machine. It is a " "software that will need to be installed on your local machine, you can " -"follow this `guide `_ to set it up." +"follow this `guide `_ to set it up." msgstr "" +"Git은 분산 버전 관리 도구입니다. 이를 통해 전체 코드베이스의 히스토리와 모든 " +"개발자의 컴퓨터를 저장할 수 있습니다. 로컬 컴퓨터에 설치해야 하는 소프트웨어" +"로, 이 `가이드 `_를 따라 설정할 수 있습니다." #: ../../source/contributor-tutorial-contribute-on-github.rst:16 msgid "" "GitHub, itself, is a code hosting platform for version control and " -"collaboration. It allows for everyone to collaborate and work from " -"anywhere on remote repositories." +"collaboration. It allows for everyone to collaborate and work from anywhere " +"on remote repositories." msgstr "" +"GitHub는 그 자체로 버전 관리 및 협업을 위한 코드 호스팅 플랫폼입니다. 누구나 " +"원격 레포지토리에서 어디서든 협업하고 작업할 수 있습니다." #: ../../source/contributor-tutorial-contribute-on-github.rst:18 msgid "" "If you haven't already, you will need to create an account on `GitHub " "`_." msgstr "" +"아직 계정을 만들지 않았다면 `GitHub `_에서 계정을 " +"만들어야 합니다." #: ../../source/contributor-tutorial-contribute-on-github.rst:20 msgid "" -"The idea behind the generic Git and GitHub workflow boils down to this: " -"you download code from a remote repository on GitHub, make changes " -"locally and keep track of them using Git and then you upload your new " -"history back to GitHub." +"The idea behind the generic Git and GitHub workflow boils down to this: you " +"download code from a remote repository on GitHub, make changes locally and " +"keep track of them using Git and then you upload your new history back to " +"GitHub." msgstr "" +"일반적인 Git 및 GitHub 워크플로우의 기본 개념은 다음과 같이 요약됩니다. " +"GitHub의 원격 레포지토리에서 코드를 다운로드하고 로컬에서 변경한 후 Git을 사" +"용하여 추적한 다음 새 기록을 다시 GitHub에 업로드하는 것입니다." #: ../../source/contributor-tutorial-contribute-on-github.rst:32 msgid "**Forking the Flower repository**" -msgstr "" +msgstr "**Flower 레포지토리 포크하기**" #: ../../source/contributor-tutorial-contribute-on-github.rst:24 msgid "" -"A fork is a personal copy of a GitHub repository. To create one for " -"Flower, you must navigate to ``_ (while " -"connected to your GitHub account) and click the ``Fork`` button situated " -"on the top right of the page." +"A fork is a personal copy of a GitHub repository. To create one for Flower, " +"you must navigate to ``_ (while connected to " +"your GitHub account) and click the ``Fork`` button situated on the top right " +"of the page." msgstr "" +"포크는 GitHub 리포지토리의 개인 복사본입니다. Flower용 포크를 만들려면 " +"``_로 이동하여(GitHub 계정에 연결된 상태에" +"서) 페이지 오른쪽 상단에 있는 ``포크`` 버튼을 클릭해야 합니다." #: ../../source/contributor-tutorial-contribute-on-github.rst:29 msgid "" "You can change the name if you want, but this is not necessary as this " -"version of Flower will be yours and will sit inside your own account " -"(i.e., in your own list of repositories). Once created, you should see on" -" the top left corner that you are looking at your own version of Flower." +"version of Flower will be yours and will sit inside your own account (i.e., " +"in your own list of repositories). Once created, you should see on the top " +"left corner that you are looking at your own version of Flower." msgstr "" +"원하는 경우 이름을 변경할 수 있지만, 이 버전의 Flower는 자신의 계정(즉, 자신" +"의 리포지토리 목록)에 위치하게 되므로 변경할 필요는 없습니다. 만들기가 완료되" +"면 왼쪽 상단에Flower 버전이 표시되는 것을 볼 수 있습니다." #: ../../source/contributor-tutorial-contribute-on-github.rst:47 msgid "**Cloning your forked repository**" -msgstr "" +msgstr "**포크된 레포지토리 클론하기**" #: ../../source/contributor-tutorial-contribute-on-github.rst:35 msgid "" "The next step is to download the forked repository on your machine to be " -"able to make changes to it. On your forked repository page, you should " -"first click on the ``Code`` button on the right, this will give you the " -"ability to copy the HTTPS link of the repository." +"able to make changes to it. On your forked repository page, you should first " +"click on the ``Code`` button on the right, this will give you the ability to " +"copy the HTTPS link of the repository." msgstr "" +"다음 단계는 컴퓨터에서 포크된 레포지토리를 변경할 수 있도록 다운로드하는 것입" +"니다. 포크된 포지토리 페이지에서 먼저 오른쪽의 ``Code`` 버튼을 클릭하면 레포" +"지토리의 HTTPS 링크를 복사할 수 있습니다." #: ../../source/contributor-tutorial-contribute-on-github.rst:41 msgid "" "Once you copied the \\, you can open a terminal on your machine, " "navigate to the place you want to download the repository to and type:" msgstr "" +"\\를 복사한 후에는 컴퓨터에서 터미널을 열고 레포지토리를 다운로드할 위" +"치로 이동하여 입력하면 됩니다:" #: ../../source/contributor-tutorial-contribute-on-github.rst:47 msgid "" -"This will create a ``flower/`` (or the name of your fork if you renamed " -"it) folder in the current working directory." +"This will create a ``flower/`` (or the name of your fork if you renamed it) " +"folder in the current working directory." msgstr "" +"현재 작업 디렉터리에``flower/``(또는 포크 이름을 변경한 경우 포크 이름) 폴더" +"가 생성됩니다." #: ../../source/contributor-tutorial-contribute-on-github.rst:66 msgid "**Add origin**" -msgstr "" +msgstr "**origin 추가**" #: ../../source/contributor-tutorial-contribute-on-github.rst:50 msgid "You can then go into the repository folder:" -msgstr "" +msgstr "그런 다음 레포지토리 폴더로 이동할 수 있습니다:" #: ../../source/contributor-tutorial-contribute-on-github.rst:56 msgid "" -"And here we will need to add an origin to our repository. The origin is " -"the \\ of the remote fork repository. To obtain it, we can do as " -"previously mentioned by going to our fork repository on our GitHub " -"account and copying the link." +"And here we will need to add an origin to our repository. The origin is the " +"\\ of the remote fork repository. To obtain it, we can do as " +"previously mentioned by going to our fork repository on our GitHub account " +"and copying the link." msgstr "" +"여기에 레포지토리에 origin을 추가해야 합니다. origin은 원격 포크 레포지토리" +"의 \\입니다. origin을 얻으려면 앞서 설명한 대로 GitHub 계정의 포크 레" +"포지토리로 이동하여 링크를 복사하면 됩니다." #: ../../source/contributor-tutorial-contribute-on-github.rst:61 msgid "" "Once the \\ is copied, we can type the following command in our " "terminal:" -msgstr "" +msgstr "\\ 이 복사되면 터미널에 다음 명령을 입력하면 됩니다:" #: ../../source/contributor-tutorial-contribute-on-github.rst:90 msgid "**Add upstream**" -msgstr "" +msgstr "**Upstream 추가하기**" #: ../../source/contributor-tutorial-contribute-on-github.rst:69 msgid "" "Now we will add an upstream address to our repository. Still in the same " "directory, we must run the following command:" msgstr "" +"이제 레포지토리에 upstream 주소를 추가하겠습니다. 여전히 같은 디렉터리에서 다" +"음 명령을 실행해야 합니다:" #: ../../source/contributor-tutorial-contribute-on-github.rst:76 -msgid "The following diagram visually explains what we did in the previous steps:" -msgstr "" +msgid "" +"The following diagram visually explains what we did in the previous steps:" +msgstr "다음 다이어그램은 이전 단계에서 수행한 작업을 시각적으로 설명합니다:" #: ../../source/contributor-tutorial-contribute-on-github.rst:80 msgid "" -"The upstream is the GitHub remote address of the parent repository (in " -"this case Flower), i.e. the one we eventually want to contribute to and " -"therefore need an up-to-date history of. The origin is just the GitHub " -"remote address of the forked repository we created, i.e. the copy (fork) " -"in our own account." +"The upstream is the GitHub remote address of the parent repository (in this " +"case Flower), i.e. the one we eventually want to contribute to and therefore " +"need an up-to-date history of. The origin is just the GitHub remote address " +"of the forked repository we created, i.e. the copy (fork) in our own account." msgstr "" +"upstream은 부모 레포지토리(이 경우 Flower)의 GitHub 원격 주소, 즉 우리가 최종" +"적으로 기여하고 싶고 따라서 최신 기록이 필요한 레포지토리입니다. origin은 우" +"리가 만든 포크된 레포지토리의 GitHub 원격 주소, 즉 우리 계정에 있는 사본(포" +"크)입니다." #: ../../source/contributor-tutorial-contribute-on-github.rst:84 msgid "" "To make sure our local version of the fork is up-to-date with the latest " "changes from the Flower repository, we can execute the following command:" msgstr "" +"로컬 버전의 포크가 Flower 레포지토리의 최신 변경 사항으로 최신 상태인지 확인" +"하려면 다음 명령을 실행하면 됩니다:" #: ../../source/contributor-tutorial-contribute-on-github.rst:93 msgid "Setting up the coding environment" -msgstr "" +msgstr "코딩 환경 설정" #: ../../source/contributor-tutorial-contribute-on-github.rst:95 msgid "" "This can be achieved by following this :doc:`getting started guide for " -"contributors ` (note " -"that you won't need to clone the repository). Once you are able to write " -"code and test it, you can finally start making changes!" +"contributors ` (note that " +"you won't need to clone the repository). Once you are able to write code and " +"test it, you can finally start making changes!" msgstr "" +":doc:'기여자를 위한 시작 가이드 '를 참조하세요(레포지토리를 복제할 필요는 없습니다). 코드를 " +"작성하고 테스트할 수 있게 되면 드디어 변경을 시작할 수 있습니다!" #: ../../source/contributor-tutorial-contribute-on-github.rst:100 msgid "Making changes" -msgstr "" +msgstr "변경하기" #: ../../source/contributor-tutorial-contribute-on-github.rst:102 msgid "" -"Before making any changes make sure you are up-to-date with your " -"repository:" -msgstr "" +"Before making any changes make sure you are up-to-date with your repository:" +msgstr "변경하기 전에 레포지토리를 최신 상태로 유지하세요:" #: ../../source/contributor-tutorial-contribute-on-github.rst:108 msgid "And with Flower's repository:" -msgstr "" +msgstr "Flower의 레포지토리도 있습니다:" #: ../../source/contributor-tutorial-contribute-on-github.rst:122 msgid "**Create a new branch**" -msgstr "" +msgstr "**새 브랜치 만들기**" #: ../../source/contributor-tutorial-contribute-on-github.rst:115 msgid "" -"To make the history cleaner and easier to work with, it is good practice " -"to create a new branch for each feature/project that needs to be " -"implemented." +"To make the history cleaner and easier to work with, it is good practice to " +"create a new branch for each feature/project that needs to be implemented." msgstr "" +"히스토리를 더 깔끔하고 작업하기 쉽게 만들려면 구현해야 하는 각 기능/프로젝트" +"에 대해 새 브랜치를 만드는 것이 좋습니다." #: ../../source/contributor-tutorial-contribute-on-github.rst:118 msgid "" -"To do so, just run the following command inside the repository's " -"directory:" -msgstr "" +"To do so, just run the following command inside the repository's directory:" +msgstr "이렇게 하려면 레포지토리 디렉토리에서 다음 명령을 실행하면 됩니다:" #: ../../source/contributor-tutorial-contribute-on-github.rst:125 msgid "**Make changes**" -msgstr "" +msgstr "**변경하기**" #: ../../source/contributor-tutorial-contribute-on-github.rst:125 -msgid "Write great code and create wonderful changes using your favorite editor!" -msgstr "" +msgid "" +"Write great code and create wonderful changes using your favorite editor!" +msgstr "선호하는 편집기를 사용하여 멋진 코드를 작성하고 훌륭한 변화를 만들어 보세요!" #: ../../source/contributor-tutorial-contribute-on-github.rst:138 msgid "**Test and format your code**" -msgstr "" +msgstr "**코드 테스트 및 서식 지정**" #: ../../source/contributor-tutorial-contribute-on-github.rst:128 msgid "" -"Don't forget to test and format your code! Otherwise your code won't be " -"able to be merged into the Flower repository. This is done so the " -"codebase stays consistent and easy to understand." +"Don't forget to test and format your code! Otherwise your code won't be able " +"to be merged into the Flower repository. This is done so the codebase stays " +"consistent and easy to understand." msgstr "" +"코드를 테스트하고 서식을 지정하는 것을 잊지 마세요! 그렇지 않으면 코드를 " +"Flower 레포지토리에 병합할 수 없습니다. 이는 코드베이스가 일관성을 유지하고 " +"이해하기 쉽도록 하기 위한 것입니다." #: ../../source/contributor-tutorial-contribute-on-github.rst:131 msgid "To do so, we have written a few scripts that you can execute:" -msgstr "" +msgstr "이를 위해 실행할 수 있는 몇 가지 스크립트를 작성했습니다:" #: ../../source/contributor-tutorial-contribute-on-github.rst:150 msgid "**Stage changes**" -msgstr "" +msgstr "**Stage 변경**" #: ../../source/contributor-tutorial-contribute-on-github.rst:141 msgid "" -"Before creating a commit that will update your history, you must specify " -"to Git which files it needs to take into account." +"Before creating a commit that will update your history, you must specify to " +"Git which files it needs to take into account." msgstr "" +"기록을 업데이트할 커밋을 만들기 전에 어떤 파일을 고려해야 하는지 Git에 지정해" +"야 합니다." #: ../../source/contributor-tutorial-contribute-on-github.rst:143 msgid "This can be done with:" -msgstr "" +msgstr "이 작업을 수행할 수 있습니다:" #: ../../source/contributor-tutorial-contribute-on-github.rst:149 msgid "" -"To check which files have been modified compared to the last version " -"(last commit) and to see which files are staged for commit, you can use " -"the :code:`git status` command." +"To check which files have been modified compared to the last version (last " +"commit) and to see which files are staged for commit, you can use the :code:" +"`git status` command." msgstr "" +"마지막 버전(마지막 커밋)과 비교하여 수정된 파일을 확인하고 커밋을 위해 스테이" +"징된 파일을 확인하려면 :code:`git status` 명령을 사용하면 됩니다." #: ../../source/contributor-tutorial-contribute-on-github.rst:160 msgid "**Commit changes**" -msgstr "" +msgstr "**Commit 변경**" #: ../../source/contributor-tutorial-contribute-on-github.rst:153 msgid "" "Once you have added all the files you wanted to commit using :code:`git " "add`, you can finally create your commit using this command:" msgstr "" +":code:`git add`를 사용하여 커밋하려는 모든 파일을 추가한 후, 마지막으로 이 명" +"령을 사용하여 커밋을 생성할 수 있습니다:" #: ../../source/contributor-tutorial-contribute-on-github.rst:159 msgid "" -"The \\ is there to explain to others what the commit " -"does. It should be written in an imperative style and be concise. An " -"example would be :code:`git commit -m \"Add images to README\"`." +"The \\ is there to explain to others what the commit does. " +"It should be written in an imperative style and be concise. An example would " +"be :code:`git commit -m \"Add images to README\"`." msgstr "" +"커밋의 내용을 다른 사람에게 설명하기 위해 \\가 있습니다. 명" +"령형 스타일로 작성해야 하며 간결해야 합니다. 예를 들면 :code:`git commit -m " +"\"Add images to README\"`." #: ../../source/contributor-tutorial-contribute-on-github.rst:171 msgid "**Push the changes to the fork**" -msgstr "" +msgstr "**변경 사항을 포크에 푸시**" #: ../../source/contributor-tutorial-contribute-on-github.rst:163 msgid "" -"Once we have committed our changes, we have effectively updated our local" -" history, but GitHub has no way of knowing this unless we push our " -"changes to our origin's remote address:" +"Once we have committed our changes, we have effectively updated our local " +"history, but GitHub has no way of knowing this unless we push our changes to " +"our origin's remote address:" msgstr "" +"변경 사항을 커밋하면 로컬 히스토리를 효과적으로 업데이트한 것이지만, 변경 사" +"항을 원본의 원격 주소로 푸시하지 않는 한 GitHub는 이를 알 방법이 없습니다:" #: ../../source/contributor-tutorial-contribute-on-github.rst:170 msgid "" "Once this is done, you will see on the GitHub that your forked repo was " "updated with the changes you have made." msgstr "" +"이 작업이 완료되면 변경한 내용으로 포크된 레포지토리가 업데이트된 것을 GitHub" +"에서 확인할 수 있습니다." #: ../../source/contributor-tutorial-contribute-on-github.rst:174 msgid "Creating and merging a pull request (PR)" -msgstr "" +msgstr "pull request(PR) 만들기 및 병합하기" -#: ../../source/contributor-tutorial-contribute-on-github.rst:205 +#: ../../source/contributor-tutorial-contribute-on-github.rst:206 msgid "**Create the PR**" -msgstr "" +msgstr "**PR 만들기**" #: ../../source/contributor-tutorial-contribute-on-github.rst:177 msgid "" -"Once you have pushed changes, on the GitHub webpage of your repository " -"you should see the following message:" +"Once you have pushed changes, on the GitHub webpage of your repository you " +"should see the following message:" msgstr "" +"변경 사항을 푸시하고 나면 레포지토리의 GitHub 웹페이지에 다음 메시지가 표시됩" +"니다:" #: ../../source/contributor-tutorial-contribute-on-github.rst:181 msgid "Otherwise you can always find this option in the ``Branches`` page." msgstr "" +"그렇지 않으면 언제든지 ``Branches`` 페이지에서 이 옵션을 찾을 수 있습니다." #: ../../source/contributor-tutorial-contribute-on-github.rst:183 msgid "" "Once you click the ``Compare & pull request`` button, you should see " "something similar to this:" msgstr "" +"``Compare & pull request`` 버튼을 클릭하면 이와 비슷한 화면이 표시됩니다:" #: ../../source/contributor-tutorial-contribute-on-github.rst:187 -msgid "At the top you have an explanation of which branch will be merged where:" -msgstr "" +msgid "" +"At the top you have an explanation of which branch will be merged where:" +msgstr "상단에는 어느 지점이 어디에 병합될 것인지에 대한 설명이 있습니다:" #: ../../source/contributor-tutorial-contribute-on-github.rst:191 msgid "" -"In this example you can see that the request is to merge the branch " -"``doc-fixes`` from my forked repository to branch ``main`` from the " -"Flower repository." +"In this example you can see that the request is to merge the branch ``doc-" +"fixes`` from my forked repository to branch ``main`` from the Flower " +"repository." msgstr "" +"이 예제에서는 내 포크된 레포지토리의 ``doc-fixes`` 브랜치를 Flower 레포지토리" +"의 ``main`` 브랜치에 병합하라는 요청을 볼 수 있습니다." #: ../../source/contributor-tutorial-contribute-on-github.rst:193 msgid "" -"The input box in the middle is there for you to describe what your PR " -"does and to link it to existing issues. We have placed comments (that " -"won't be rendered once the PR is opened) to guide you through the " -"process." +"The title should be changed to adhere to the :ref:`pr_title_format` " +"guidelines, otherwise it won't be possible to merge the PR. So in this case, " +"a correct title might be ``docs(framework:skip) Fix typos``." msgstr "" +"제목은 :ref:`pr_title_format` 가이드라인을 준수하도록 변경해야 하며, 그렇지 " +"않으면 PR을 병합할 수 없습니다. 따라서 이 경우 올바른 제목은 " +"``docs(framework:skip) Fix typos``이 될 수 있습니다." #: ../../source/contributor-tutorial-contribute-on-github.rst:196 msgid "" -"It is important to follow the instructions described in comments. For " -"instance, in order to not break how our changelog system works, you " -"should read the information above the ``Changelog entry`` section " -"carefully. You can also checkout some examples and details in the " -":ref:`changelogentry` appendix." +"The input box in the middle is there for you to describe what your PR does " +"and to link it to existing issues. We have placed comments (that won't be " +"rendered once the PR is opened) to guide you through the process." msgstr "" +"가운데에 있는 입력 상자는 PR의 기능을 설명하고 기존 이슈에 연결할 수 있는 곳" +"입니다. 프로세스를 안내하기 위해 코멘트(PR이 열리면 렌더링되지 않음)를 배치했" +"습니다." + +#: ../../source/contributor-tutorial-contribute-on-github.rst:199 +msgid "It is important to follow the instructions described in comments." +msgstr "코멘트에 설명된 지침을 따르는 것이 중요합니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:200 +#: ../../source/contributor-tutorial-contribute-on-github.rst:201 msgid "" "At the bottom you will find the button to open the PR. This will notify " -"reviewers that a new PR has been opened and that they should look over it" -" to merge or to request changes." +"reviewers that a new PR has been opened and that they should look over it to " +"merge or to request changes." msgstr "" +"하단에는 PR을 여는 버튼이 있습니다. 이렇게 하면 검토자에게 새 PR이 열렸으며 " +"병합하거나 변경을 요청하기 위해 검토해야 함을 알립니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:203 +#: ../../source/contributor-tutorial-contribute-on-github.rst:204 msgid "" -"If your PR is not yet ready for review, and you don't want to notify " -"anyone, you have the option to create a draft pull request:" +"If your PR is not yet ready for review, and you don't want to notify anyone, " +"you have the option to create a draft pull request:" msgstr "" +"PR이 아직 검토할 준비가 되지 않았고 다른 사람에게 알리고 싶지 않은 경우 pull " +"request 초안을 만드는 옵션이 있습니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:208 +#: ../../source/contributor-tutorial-contribute-on-github.rst:209 msgid "**Making new changes**" -msgstr "" +msgstr "**new changes 만들기**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:208 +#: ../../source/contributor-tutorial-contribute-on-github.rst:209 msgid "" "Once the PR has been opened (as draft or not), you can still push new " -"commits to it the same way we did before, by making changes to the branch" -" associated with the PR." +"commits to it the same way we did before, by making changes to the branch " +"associated with the PR." msgstr "" +"PR이 초안으로 열렸든 아니든, PR과 연결된 브랜치를 변경하여 이전과 같은 방식으" +"로 새 커밋을 푸시할 수 있습니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:230 +#: ../../source/contributor-tutorial-contribute-on-github.rst:231 msgid "**Review the PR**" -msgstr "" +msgstr "**PR 검토하기**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:211 +#: ../../source/contributor-tutorial-contribute-on-github.rst:212 msgid "" -"Once the PR has been opened or once the draft PR has been marked as " -"ready, a review from code owners will be automatically requested:" +"Once the PR has been opened or once the draft PR has been marked as ready, a " +"review from code owners will be automatically requested:" msgstr "" +"PR이 열리거나 초안 PR이 준비됨으로 표시되면 코드 소유자의 검토가 자동으로 요" +"청됩니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:215 +#: ../../source/contributor-tutorial-contribute-on-github.rst:216 msgid "" -"Code owners will then look into the code, ask questions, request changes " -"or validate the PR." +"Code owners will then look into the code, ask questions, request changes or " +"validate the PR." msgstr "" +"그러면 코드 소유자는 코드를 살펴보고, 질문하고, 변경을 요청하거나 PR의 유효성" +"을 검사합니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:217 +#: ../../source/contributor-tutorial-contribute-on-github.rst:218 msgid "Merging will be blocked if there are ongoing requested changes." -msgstr "" +msgstr "진행 중인 변경 요청이 있는 경우 병합이 차단됩니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:221 +#: ../../source/contributor-tutorial-contribute-on-github.rst:222 msgid "" -"To resolve them, just push the necessary changes to the branch associated" -" with the PR:" +"To resolve them, just push the necessary changes to the branch associated " +"with the PR:" msgstr "" +"이를 해결하려면 PR과 연결된 브랜치에 필요한 변경 사항을 푸시하면 됩니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:225 +#: ../../source/contributor-tutorial-contribute-on-github.rst:226 msgid "And resolve the conversation:" -msgstr "" +msgstr "그리고 소통을 통해 해결하세요:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:229 +#: ../../source/contributor-tutorial-contribute-on-github.rst:230 msgid "" -"Once all the conversations have been resolved, you can re-request a " -"review." -msgstr "" +"Once all the conversations have been resolved, you can re-request a review." +msgstr "모든 대화가 해결되면 검토를 다시 요청할 수 있습니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:250 +#: ../../source/contributor-tutorial-contribute-on-github.rst:251 msgid "**Once the PR is merged**" -msgstr "" +msgstr "**PR이 병합되면**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:233 +#: ../../source/contributor-tutorial-contribute-on-github.rst:234 msgid "" -"If all the automatic tests have passed and reviewers have no more changes" -" to request, they can approve the PR and merge it." +"If all the automatic tests have passed and reviewers have no more changes to " +"request, they can approve the PR and merge it." msgstr "" +"모든 자동 테스트가 통과되고 검토자가 더 이상 요청할 변경 사항이 없는 경우 PR" +"을 승인하고 병합할 수 있습니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:237 +#: ../../source/contributor-tutorial-contribute-on-github.rst:238 msgid "" "Once it is merged, you can delete the branch on GitHub (a button should " "appear to do so) and also delete it locally by doing:" msgstr "" +"병합이 완료되면 GitHub에서 브랜치를 삭제할 수 있으며(삭제 버튼이 표시되어야 " +"함), 로컬에서도 삭제할 수 있습니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:244 +#: ../../source/contributor-tutorial-contribute-on-github.rst:245 msgid "Then you should update your forked repository by doing:" -msgstr "" +msgstr "그런 다음 다음을 수행하여 포크된 레포지토리를 업데이트해야 합니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:253 +#: ../../source/contributor-tutorial-contribute-on-github.rst:254 msgid "Example of first contribution" -msgstr "" +msgstr "첫 번째 기여의 예" -#: ../../source/contributor-tutorial-contribute-on-github.rst:256 +#: ../../source/contributor-tutorial-contribute-on-github.rst:257 msgid "Problem" -msgstr "" +msgstr "문제" -#: ../../source/contributor-tutorial-contribute-on-github.rst:258 +#: ../../source/contributor-tutorial-contribute-on-github.rst:259 msgid "" -"For our documentation, we've started to use the `Diàtaxis framework " -"`_." +"For our documentation, we've started to use the `Diàtaxis framework `_." msgstr "" +"저희 문서에는 'Diàtaxis 프레임워크 `_'를 사용하기 시작" +"했습니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:260 +#: ../../source/contributor-tutorial-contribute-on-github.rst:261 msgid "" -"Our \"How to\" guides should have titles that continue the sentence \"How" -" to …\", for example, \"How to upgrade to Flower 1.0\"." +"Our \"How to\" guides should have titles that continue the sentence \"How to " +"…\", for example, \"How to upgrade to Flower 1.0\"." msgstr "" +"'How to' 가이드의 제목은 \"How to …\"라는 문장을 이어가는 제목이어야 합니다" +"(예: \"How to upgrade to Flower 1.0\")." -#: ../../source/contributor-tutorial-contribute-on-github.rst:262 +#: ../../source/contributor-tutorial-contribute-on-github.rst:263 msgid "" "Most of our guides do not follow this new format yet, and changing their " "title is (unfortunately) more involved than one might think." msgstr "" +"대부분의 가이드는 아직 이 새로운 형식을 따르지 않으며, 안타깝게도 제목을 변경" +"하는 작업은 생각보다 복잡합니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:264 +#: ../../source/contributor-tutorial-contribute-on-github.rst:265 msgid "" -"This issue is about changing the title of a doc from present continuous " -"to present simple." +"This issue is about changing the title of a doc from present continuous to " +"present simple." msgstr "" +"이번 이슈는 문서 제목을 현재 연속형에서 현재 단순형으로 변경하는 것에 관한 것" +"입니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:266 +#: ../../source/contributor-tutorial-contribute-on-github.rst:267 msgid "" "Let's take the example of \"Saving Progress\" which we changed to \"Save " "Progress\". Does this pass our check?" msgstr "" +"\"How to saving progress\"을 \"How to save progress\"으로 변경한 예를 들어 보" +"겠습니다. 이것이 우리의 점검을 통과했나요?" -#: ../../source/contributor-tutorial-contribute-on-github.rst:268 +#: ../../source/contributor-tutorial-contribute-on-github.rst:269 msgid "Before: \"How to saving progress\" ❌" -msgstr "" +msgstr "Before: \"How to saving progress\" ❌" -#: ../../source/contributor-tutorial-contribute-on-github.rst:270 +#: ../../source/contributor-tutorial-contribute-on-github.rst:271 msgid "After: \"How to save progress\" ✅" -msgstr "" +msgstr "After: \"How to save progress\" ✅" -#: ../../source/contributor-tutorial-contribute-on-github.rst:273 +#: ../../source/contributor-tutorial-contribute-on-github.rst:274 msgid "Solution" -msgstr "" +msgstr "해결법" -#: ../../source/contributor-tutorial-contribute-on-github.rst:275 +#: ../../source/contributor-tutorial-contribute-on-github.rst:276 msgid "" "This is a tiny change, but it'll allow us to test your end-to-end setup. " "After cloning and setting up the Flower repo, here's what you should do:" msgstr "" +"이것은 사소한 변경이지만 end-to-end 설정을 테스트할 수 있습니다. Flower " +"레포지토리를 복제하고 설정한 후에는 다음과 같이 하세요:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:277 +#: ../../source/contributor-tutorial-contribute-on-github.rst:278 msgid "Find the source file in ``doc/source``" -msgstr "" +msgstr "``doc/source``에서 소스 파일을 찾습니다" -#: ../../source/contributor-tutorial-contribute-on-github.rst:278 +#: ../../source/contributor-tutorial-contribute-on-github.rst:279 msgid "" "Make the change in the ``.rst`` file (beware, the dashes under the title " "should be the same length as the title itself)" msgstr "" +"``.rst`` 파일에서 변경합니다(제목 아래의 대시는 제목 자체의 길이와 같아야 합" +"니다)" -#: ../../source/contributor-tutorial-contribute-on-github.rst:279 +#: ../../source/contributor-tutorial-contribute-on-github.rst:280 msgid "" -"Build the docs and `check the result `_" +"Build the docs and `check the result `_" msgstr "" +"문서를 빌드하고 '결과 확인 `_'합니다" -#: ../../source/contributor-tutorial-contribute-on-github.rst:282 +#: ../../source/contributor-tutorial-contribute-on-github.rst:283 msgid "Rename file" -msgstr "" +msgstr "파일 이름 바꾸기" -#: ../../source/contributor-tutorial-contribute-on-github.rst:284 +#: ../../source/contributor-tutorial-contribute-on-github.rst:285 msgid "" -"You might have noticed that the file name still reflects the old wording." -" If we just change the file, then we break all existing links to it - it " -"is **very important** to avoid that, breaking links can harm our search " -"engine ranking." +"You might have noticed that the file name still reflects the old wording. If " +"we just change the file, then we break all existing links to it - it is " +"**very important** to avoid that, breaking links can harm our search engine " +"ranking." msgstr "" +"파일 이름에 여전히 이전 문구가 반영되어 있는 것을 보셨을 것입니다. 파일만 변" +"경하면 파일에 대한 기존 링크가 모두 끊어지는데, 링크를 끊으면 검색 엔진 순위" +"에 영향을 줄 수 있으므로 이를 방지하는 것이 **매우 중요**합니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:287 +#: ../../source/contributor-tutorial-contribute-on-github.rst:288 msgid "Here's how to change the file name:" -msgstr "" +msgstr "파일 이름을 변경하는 방법은 다음과 같습니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:289 +#: ../../source/contributor-tutorial-contribute-on-github.rst:290 msgid "Change the file name to ``save-progress.rst``" -msgstr "" +msgstr "파일 이름을 ``save-progress.rst``로 변경합니다" -#: ../../source/contributor-tutorial-contribute-on-github.rst:290 +#: ../../source/contributor-tutorial-contribute-on-github.rst:291 msgid "Add a redirect rule to ``doc/source/conf.py``" -msgstr "" +msgstr "'doc/source/conf.py'에 리디렉션 규칙을 추가합니다" -#: ../../source/contributor-tutorial-contribute-on-github.rst:292 +#: ../../source/contributor-tutorial-contribute-on-github.rst:293 msgid "" -"This will cause a redirect from ``saving-progress.html`` to ``save-" -"progress.html``, old links will continue to work." +"This will cause a redirect from ``saving-progress.html`` to ``save-progress." +"html``, old links will continue to work." msgstr "" +"이렇게 하면 ``saving-progress.html``에서 ``save-progress.html``로 리디렉션되" +"며, 이전 링크는 계속 작동합니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:295 +#: ../../source/contributor-tutorial-contribute-on-github.rst:296 msgid "Apply changes in the index file" -msgstr "" +msgstr "인덱스 파일에 변경 사항 적용" -#: ../../source/contributor-tutorial-contribute-on-github.rst:297 +#: ../../source/contributor-tutorial-contribute-on-github.rst:298 msgid "" "For the lateral navigation bar to work properly, it is very important to " "update the ``index.rst`` file as well. This is where we define the whole " "arborescence of the navbar." msgstr "" +"횡방향 내비게이션 바가 제대로 작동하려면 ``index.rst`` 파일도 업데이트하는 것" +"이 매우 중요합니다. 이 파일은 탐색 모음의 전체 배열을 정의하는 곳입니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:300 +#: ../../source/contributor-tutorial-contribute-on-github.rst:301 msgid "Find and modify the file name in ``index.rst``" -msgstr "" +msgstr "``index.rst``에서 파일 이름을 찾아 수정합니다" -#: ../../source/contributor-tutorial-contribute-on-github.rst:303 +#: ../../source/contributor-tutorial-contribute-on-github.rst:304 msgid "Open PR" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:305 -msgid "" -"Commit the changes (commit messages are always imperative: \"Do " -"something\", in this case \"Change …\")" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:306 -msgid "Push the changes to your fork" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:307 -msgid "Open a PR (as shown above)" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:308 -msgid "Wait for it to be approved!" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:309 -msgid "Congrats! 🥳 You're now officially a Flower contributor!" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:313 -msgid "How to write a good PR title" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:315 -msgid "" -"A well-crafted PR title helps team members quickly understand the purpose" -" and scope of the changes being proposed. Here's a guide to help you " -"write a good GitHub PR title:" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:317 -msgid "" -"1. Be Clear and Concise: Provide a clear summary of the changes in a " -"concise manner. 1. Use Actionable Verbs: Start with verbs like \"Add,\" " -"\"Update,\" or \"Fix\" to indicate the purpose. 1. Include Relevant " -"Information: Mention the affected feature or module for context. 1. Keep " -"it Short: Avoid lengthy titles for easy readability. 1. Use Proper " -"Capitalization and Punctuation: Follow grammar rules for clarity." -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:323 -msgid "" -"Let's start with a few examples for titles that should be avoided because" -" they do not provide meaningful information:" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:325 -msgid "Implement Algorithm" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:326 -msgid "Database" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:327 -msgid "Add my_new_file.py to codebase" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:328 -msgid "Improve code in module" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:329 -msgid "Change SomeModule" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:331 -msgid "" -"Here are a few positive examples which provide helpful information " -"without repeating how they do it, as that is already visible in the " -"\"Files changed\" section of the PR:" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:333 -msgid "Update docs banner to mention Flower Summit 2023" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:334 -msgid "Remove unnecessary XGBoost dependency" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:335 -msgid "Remove redundant attributes in strategies subclassing FedAvg" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:336 -msgid "Add CI job to deploy the staging system when the ``main`` branch changes" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:337 -msgid "" -"Add new amazing library which will be used to improve the simulation " -"engine" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:341 -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:548 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:946 -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:727 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:713 -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:367 -msgid "Next steps" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:343 -msgid "" -"Once you have made your first PR, and want to contribute more, be sure to" -" check out the following :" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:345 -msgid "" -":doc:`Good first contributions `, where you should particularly look into the " -":code:`baselines` contributions." -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:349 -#: ../../source/fed/0000-20200102-fed-template.md:60 -msgid "Appendix" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:354 -msgid "Changelog entry" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:356 -msgid "" -"When opening a new PR, inside its description, there should be a " -"``Changelog entry`` header." -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:358 -msgid "" -"Above this header you should see the following comment that explains how " -"to write your changelog entry:" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:360 -msgid "" -"Inside the following 'Changelog entry' section, you should put the " -"description of your changes that will be added to the changelog alongside" -" your PR title." -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:363 -msgid "" -"If the section is completely empty (without any token) or non-existent, " -"the changelog will just contain the title of the PR for the changelog " -"entry, without any description." -msgstr "" +msgstr "PR 열기" -#: ../../source/contributor-tutorial-contribute-on-github.rst:366 +#: ../../source/contributor-tutorial-contribute-on-github.rst:306 msgid "" -"If the section contains some text other than tokens, it will use it to " -"add a description to the change." +"Commit the changes (commit messages are always imperative: \"Do something\", " +"in this case \"Change …\")" msgstr "" +"변경 사항을 커밋합니다(커밋 메시지는 항상 필수 메시지입니다:\"Do " +"something\"(이 경우 는 \"Change …\" )" + +#: ../../source/contributor-tutorial-contribute-on-github.rst:307 +msgid "Push the changes to your fork" +msgstr "변경 사항을 포크에 푸시합니다" -#: ../../source/contributor-tutorial-contribute-on-github.rst:368 +#: ../../source/contributor-tutorial-contribute-on-github.rst:308 msgid "" -"If the section contains one of the following tokens it will ignore any " -"other text and put the PR under the corresponding section of the " -"changelog:" +"Open a PR (as shown above) with title ``docs(framework) Update how-to guide " +"title``" msgstr "" +"``docs(framework) Update how-to guide title`` 제목으로 PR(위와 같이)을 엽니다" -#: ../../source/contributor-tutorial-contribute-on-github.rst:370 -msgid " is for classifying a PR as a general improvement." -msgstr "" +#: ../../source/contributor-tutorial-contribute-on-github.rst:309 +msgid "Wait for it to be approved!" +msgstr "승인될 때까지 기다리세요!" -#: ../../source/contributor-tutorial-contribute-on-github.rst:372 -msgid " is to not add the PR to the changelog" -msgstr "" +#: ../../source/contributor-tutorial-contribute-on-github.rst:310 +msgid "Congrats! 🥳 You're now officially a Flower contributor!" +msgstr "축하합니다! 이제 공식적으로 Flower 기여자가 되셨습니다!" -#: ../../source/contributor-tutorial-contribute-on-github.rst:374 -msgid " is to add a general baselines change to the PR" -msgstr "" +#: ../../source/contributor-tutorial-contribute-on-github.rst:314 +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:548 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:946 +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:727 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:713 +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:367 +msgid "Next steps" +msgstr "다음 단계" -#: ../../source/contributor-tutorial-contribute-on-github.rst:376 -msgid " is to add a general examples change to the PR" -msgstr "" +#: ../../source/contributor-tutorial-contribute-on-github.rst:316 +msgid "" +"Once you have made your first PR, and want to contribute more, be sure to " +"check out the following :" +msgstr "첫 번째 PR을 작성하고 더 많은 기여를 하고 싶다면 다음을 확인하세요:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:378 -msgid " is to add a general sdk change to the PR" +#: ../../source/contributor-tutorial-contribute-on-github.rst:318 +msgid "" +":doc:`Good first contributions `, " +"where you should particularly look into the :code:`baselines` contributions." msgstr "" +":doc:`훌륭한 첫 번째 기여 `, 특히 :" +"code:`baselines` 기여를 살펴봐야 합니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:380 -msgid " is to add a general simulations change to the PR" -msgstr "" +#: ../../source/contributor-tutorial-contribute-on-github.rst:322 +#: ../../source/fed/0000-20200102-fed-template.md:60 +msgid "Appendix" +msgstr "부록" -#: ../../source/contributor-tutorial-contribute-on-github.rst:382 -msgid "Note that only one token should be used." -msgstr "" +#: ../../source/contributor-tutorial-contribute-on-github.rst:327 +msgid "PR title format" +msgstr "PR 제목 형식" -#: ../../source/contributor-tutorial-contribute-on-github.rst:384 -msgid "" -"Its content must have a specific format. We will break down what each " -"possibility does:" -msgstr "" +#: ../../source/contributor-tutorial-contribute-on-github.rst:329 +msgid "We enforce the following PR title format:" +msgstr "다음과 같은 PR 제목 형식을 적용합니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:386 +#: ../../source/contributor-tutorial-contribute-on-github.rst:335 msgid "" -"If the ``### Changelog entry`` section contains nothing or doesn't exist," -" the following text will be added to the changelog::" +"(or ``(:skip) `` to ignore the PR in the changelog)" msgstr "" +"(또는 ``(:skip) ``를 사용하면 변경 로그에서 PR을 무시" +"합니다.)" -#: ../../source/contributor-tutorial-contribute-on-github.rst:390 +#: ../../source/contributor-tutorial-contribute-on-github.rst:337 msgid "" -"If the ``### Changelog entry`` section contains a description (and no " -"token), the following text will be added to the changelog::" +"Where ```` needs to be in ``{ci, fix, feat, docs, refactor, break}``, " +"```` should be in ``{framework, baselines, datasets, examples, or " +"'*' when modifying multiple projects which requires the ':skip' flag to be " +"used}``, and ```` starts with a capitalised verb in the imperative " +"mood." msgstr "" +"여기서 ````은 ``{ci, fix, feat, docs, refactor, break}``, ````" +"는 ``{framework, baselines, datasets, examples, or '*' ':skip' 플래그를 사용" +"해야 하는 여러 프로젝트를 수정하는 경우}``로 입력해야 하며, ````는 " +"대문자로 시작해야 합니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:396 -msgid "" -"If the ``### Changelog entry`` section contains ````, nothing will " -"change in the changelog." -msgstr "" +#: ../../source/contributor-tutorial-contribute-on-github.rst:341 +msgid "Valid examples:" +msgstr "유효한 예시입니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:398 -msgid "" -"If the ``### Changelog entry`` section contains ````, the " -"following text will be added to the changelog::" -msgstr "" +#: ../../source/contributor-tutorial-contribute-on-github.rst:343 +msgid "``feat(framework) Add flwr build CLI command``" +msgstr "``feat(framework) Add flwr build CLI command``" -#: ../../source/contributor-tutorial-contribute-on-github.rst:402 -msgid "" -"If the ``### Changelog entry`` section contains ````, the " -"following text will be added to the changelog::" -msgstr "" +#: ../../source/contributor-tutorial-contribute-on-github.rst:344 +msgid "``refactor(examples:skip) Improve quickstart-pytorch logging``" +msgstr "``refactor(examples:skip) Improve quickstart-pytorch logging``" -#: ../../source/contributor-tutorial-contribute-on-github.rst:406 -msgid "" -"If the ``### Changelog entry`` section contains ````, the " -"following text will be added to the changelog::" -msgstr "" +#: ../../source/contributor-tutorial-contribute-on-github.rst:345 +msgid "``ci(*:skip) Enforce PR title format``" +msgstr "``ci(*:skip) Enforce PR title format``" -#: ../../source/contributor-tutorial-contribute-on-github.rst:410 -msgid "" -"If the ``### Changelog entry`` section contains ````, the following " -"text will be added to the changelog::" -msgstr "" +#: ../../source/contributor-tutorial-contribute-on-github.rst:347 +msgid "Invalid examples:" +msgstr "잘못된 예시입니다:" + +#: ../../source/contributor-tutorial-contribute-on-github.rst:349 +msgid "``feat(framework): Add flwr build CLI command`` (extra ``:``)" +msgstr "``feat(framework): Add flwr build CLI command`` ( ``:``제외)" -#: ../../source/contributor-tutorial-contribute-on-github.rst:414 +#: ../../source/contributor-tutorial-contribute-on-github.rst:350 msgid "" -"If the ``### Changelog entry`` section contains ````, the " -"following text will be added to the changelog::" +"``feat(*) Add flwr build CLI command`` (missing ``skip`` flag along with " +"``*``)" msgstr "" +"``feat(*) Add flwr build CLI command`` (``skip`` flag와 함께 ``*``누락)" -#: ../../source/contributor-tutorial-contribute-on-github.rst:418 -msgid "" -"Note that only one token must be provided, otherwise, only the first " -"action (in the order listed above), will be performed." +#: ../../source/contributor-tutorial-contribute-on-github.rst:351 +msgid "``feat(skip) Add flwr build CLI command`` (missing ````)" +msgstr "``feat(skip) Add flwr build CLI command`` (````누락)" + +#: ../../source/contributor-tutorial-contribute-on-github.rst:352 +msgid "``feat(framework) add flwr build CLI command`` (non capitalised verb)" msgstr "" +"``feat(framework) add flwr build CLI command`` (대문자로 표기되지 않은 동사)" + +#: ../../source/contributor-tutorial-contribute-on-github.rst:353 +msgid "``feat(framework) Add flwr build CLI command.`` (dot at the end)" +msgstr "``feat(framework) Add flwr build CLI command.`` (끝에 마침표)" + +#: ../../source/contributor-tutorial-contribute-on-github.rst:354 +msgid "``Add flwr build CLI command.`` (missing ``()``)" +msgstr "``Add flwr build CLI command.`` ( ``()``누락)" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:2 msgid "Get started as a contributor" -msgstr "" +msgstr "기여자로 시작하기" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:5 -#: ../../source/how-to-run-flower-using-docker.rst:123 +#: ../../source/how-to-run-flower-using-docker.rst:153 msgid "Prerequisites" -msgstr "" +msgstr "전제 조건" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:7 msgid "`Python 3.8 `_ or above" -msgstr "" +msgstr "Python 3.8 `_ 이상" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:8 msgid "`Poetry 1.3 `_ or above" -msgstr "" +msgstr "`Poetry 1.3 `_ _ 이상" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:9 msgid "(Optional) `pyenv `_" -msgstr "" +msgstr "(선택 사항) `pyenv `_" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:10 -msgid "(Optional) `pyenv-virtualenv `_" +msgid "" +"(Optional) `pyenv-virtualenv `_" msgstr "" +"(선택 사항) `pyenv-virtualenv `_" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:12 msgid "" "Flower uses :code:`pyproject.toml` to manage dependencies and configure " -"development tools (the ones which support it). Poetry is a build tool " -"which supports `PEP 517 `_." +"development tools (the ones which support it). Poetry is a build tool which " +"supports `PEP 517 `_." msgstr "" +"Flower는 dependencies을 관리하고 개발 도구(이를 지원하는 도구)를 구성하기 위" +"해 :code:`pyproject.toml`을 사용합니다. Poetry는 `PEP 517 `_을 지원하는 빌드 도구입니다." #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:18 msgid "Developer Machine Setup" -msgstr "" +msgstr "개발자 머신 설정" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:21 msgid "Preliminarities" -msgstr "" +msgstr "사전 준비" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:22 msgid "Some system-wide dependencies are needed." -msgstr "" +msgstr "일부 시스템 전체에 대한 의존성이 필요합니다." #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:25 msgid "For macOS" -msgstr "" +msgstr "macOS의 경우" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:27 msgid "" -"Install `homebrew `_. Don't forget the post-" -"installation actions to add `brew` to your PATH." +"Install `homebrew `_. Don't forget the post-installation " +"actions to add `brew` to your PATH." msgstr "" +"`homebrew `_를 설치합니다. 설치 후 `brew`를 PATH에 추가하" +"는 작업을 잊지 마세요." #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:28 msgid "" -"Install `xz` (to install different Python versions) and `pandoc` to build" -" the docs::" +"Install `xz` (to install different Python versions) and `pandoc` to build " +"the docs::" msgstr "" +"xz`(다른 Python 버전을 설치하려면)와 `pandoc`을 설치하여 문서를 빌드합니다::" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:34 msgid "For Ubuntu" -msgstr "" +msgstr "Ubuntu의 경우" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:35 msgid "" -"Ensure you system (Ubuntu 22.04+) is up-to-date, and you have all " -"necessary packages::" +"Ensure you system (Ubuntu 22.04+) is up-to-date, and you have all necessary " +"packages::" msgstr "" +"시스템(우분투 22.04 이상)이 최신 상태이고 필요한 패키지가 모두 설치되어 있는" +"지 확인하세요:" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:44 msgid "Create Flower Dev Environment" -msgstr "" +msgstr "Flower 개발 환경 만들기" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:46 msgid "" "1. Clone the `Flower repository `_ from " "GitHub::" msgstr "" +"1. GitHub: 에서 ``Flower 레포지토리 `_를 복제" +"합니다::" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:52 msgid "" -"Let's create the Python environment for all-things Flower. If you wish to" -" use :code:`pyenv`, we provide two convenience scripts that you can use. " -"If you prefer using something else than :code:`pyenv`, create a new " +"Let's create the Python environment for all-things Flower. If you wish to " +"use :code:`pyenv`, we provide two convenience scripts that you can use. If " +"you prefer using something else than :code:`pyenv`, create a new " "environment, activate and skip to the last point where all packages are " "installed." msgstr "" +"Flower의 모든 것을 위한 파이썬 환경을 만들어 보겠습니다.:code:`pyenv`를 사용" +"하고자 하는 경우 사용할 수 있는 두 가지 편의 스크립트를 제공합니다.:code:" +"`pyenv`가 아닌 다른 것을 사용하려면 새 환경을 생성하고 활성화한 후 모든 패키" +"지가 설치된 마지막 지점으로 건너뛰세요." #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:54 msgid "" -"If you don't have :code:`pyenv` installed, the following script that will" -" install it, set it up, and create the virtual environment (with " -":code:`Python 3.8.17` by default)::" +"If you don't have :code:`pyenv` installed, the following script that will " +"install it, set it up, and create the virtual environment (with :code:" +"`Python 3.8.17` by default)::" msgstr "" +":code:`pyenv`가 설치되어 있지 않은 경우 다음 스크립트를 사용하여 설치, 설정 " +"및 가상 환경을 생성합니다(기본적으로 :code:`Python 3.8.17` 사용):" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:58 msgid "" "If you already have :code:`pyenv` installed (along with the :code:`pyenv-" -"virtualenv` plugin), you can use the following convenience script (with " -":code:`Python 3.8.17` by default)::" +"virtualenv` plugin), you can use the following convenience script (with :" +"code:`Python 3.8.17` by default)::" msgstr "" +":code:`pyenv`가 이미 설치되어 있는 경우( :code:`pyenv-virtualenv` 플러그인과 " +"함께) 다음과 같은 편의 스크립트를 사용할 수 있습니다(기본적으로 코드:`Python " +"3.8.17` 사용):" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:62 msgid "" -"3. Install the Flower package in development mode (think :code:`pip " -"install -e`) along with all necessary dependencies::" +"3. Install the Flower package in development mode (think :code:`pip install -" +"e`) along with all necessary dependencies::" msgstr "" +"3. 필요한 모든 dependencies와 함께 개발 모드에서 Flower 패키지를 설치합니다" +"(예:code:`pip install -e`)::" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:69 msgid "Convenience Scripts" -msgstr "" +msgstr "편의 스크립트" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:71 msgid "" "The Flower repository contains a number of convenience scripts to make " -"recurring development tasks easier and less error-prone. See the " -":code:`/dev` subdirectory for a full list. The following scripts are " -"amongst the most important ones:" +"recurring development tasks easier and less error-prone. See the :code:`/" +"dev` subdirectory for a full list. The following scripts are amongst the " +"most important ones:" msgstr "" +"Flower 레포지토리에는 반복적인 개발 작업을 더 쉽고 오류를 줄이기 위한 여러 가" +"지 편의 스크립트가 포함되어 있습니다. 전체 목록은 :code:`/dev` 하위 디렉터리" +"를 참조하세요. 다음 스크립트는 가장 중요한 스크립트 중 하나입니다:" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:77 msgid "Create/Delete Virtual Environment" -msgstr "" +msgstr "가상 환경 생성/삭제" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:85 msgid "Compile ProtoBuf Definitions" -msgstr "" +msgstr "ProtoBuf 정의 컴파일" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:92 msgid "Auto-Format Code" -msgstr "" +msgstr "자동 포맷 코드" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:99 msgid "Run Linters and Tests" -msgstr "" +msgstr "린터 및 테스트 실행" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:106 msgid "Add a pre-commit hook" -msgstr "" +msgstr "사전 커밋 훅 추가" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:108 msgid "" -"Developers may integrate a pre-commit hook into their workflow utilizing " -"the `pre-commit `_ library. The pre-" -"commit hook is configured to execute two primary operations: " -"``./dev/format.sh`` and ``./dev/test.sh`` scripts." +"Developers may integrate a pre-commit hook into their workflow utilizing the " +"`pre-commit `_ library. The pre-commit hook " +"is configured to execute two primary operations: ``./dev/format.sh`` and ``./" +"dev/test.sh`` scripts." msgstr "" +"개발자는 `pre-commit `_ 라이브러리를 사용하" +"여 사전 커밋 훅을 워크플로에 통합할 수 있습니다. 사전 커밋 훅은 두 가지 기본 " +"작업을 실행하도록 구성됩니다:``./dev/format.sh`` 및 ``./dev/test.sh`` 스크립" +"트." #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:110 msgid "There are multiple ways developers can use this:" -msgstr "" +msgstr "개발자가 이것을 사용할 수 있는 여러가지 방법이 있습니다:" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:112 -msgid "Install the pre-commit hook to your local git directory by simply running:" -msgstr "" +msgid "" +"Install the pre-commit hook to your local git directory by simply running:" +msgstr "간단하게 실행하여 로컬 git 디렉터리에 사전 커밋 훅을 설치하세요:" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:118 msgid "" -"Each ``git commit`` will trigger the execution of formatting and " -"linting/test scripts." -msgstr "" +"Each ``git commit`` will trigger the execution of formatting and linting/" +"test scripts." +msgstr "각 ``git 커밋``은 포맷 및 린팅/테스트 스크립트의 실행을 트리거합니다." #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:119 msgid "" -"If in a hurry, bypass the hook using ``--no-verify`` with the ``git " -"commit`` command. ::" +"If in a hurry, bypass the hook using ``--no-verify`` with the ``git commit`` " +"command. ::" msgstr "" +"급한 경우 ``git commit`` 명령과 함께 `--no-verify``를 사용하여 훅을 넘기세요:" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:124 msgid "" "For developers who prefer not to install the hook permanently, it is " -"possible to execute a one-time check prior to committing changes by using" -" the following command:" +"possible to execute a one-time check prior to committing changes by using " +"the following command:" msgstr "" +"훅을 영구적으로 설치하지 않으려는 개발자의 경우 다음 명령을 사용하여 변경 사" +"항을 커밋하기 전에 일회성 검사를 실행할 수 있습니다:" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:130 msgid "" "This executes the formatting and linting checks/tests on all the files " "without modifying the default behavior of ``git commit``." msgstr "" +"이렇게 하면 ``git commit``의 기본 동작을 수정하지 않고 모든 파일에 대해 포맷 " +"및 린팅 검사/테스트를 실행합니다." #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:133 msgid "Run Github Actions (CI) locally" -msgstr "" +msgstr "로컬에서 Github Action(CI) 실행하기" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:135 msgid "" -"Developers could run the full set of Github Actions workflows under their" -" local environment by using `Act `_. " -"Please refer to the installation instructions under the linked repository" -" and run the next command under Flower main cloned repository folder::" +"Developers could run the full set of Github Actions workflows under their " +"local environment by using `Act `_. Please " +"refer to the installation instructions under the linked repository and run " +"the next command under Flower main cloned repository folder::" msgstr "" +"개발자는 `Act `_를 사용하여 로컬 환경에서 전" +"체 Github Actions 워크플로우 세트를 실행할 수 있습니다. 링크된 레포지토리 아" +"래의 설치 지침을 참조하여 Flower 메인 클론 레포지토리 폴더 아래에서 다음 명령" +"을 실행하세요::" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:142 msgid "" "The Flower default workflow would run by setting up the required Docker " "machines underneath." msgstr "" +"Flower 기본 워크플로우는 아래에 필요한 Docker 머신을 설정하여 실행합니다." #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:147 msgid "Build Release" -msgstr "" +msgstr "릴리즈 빌드" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:149 msgid "" -"Flower uses Poetry to build releases. The necessary command is wrapped in" -" a simple script::" +"Flower uses Poetry to build releases. The necessary command is wrapped in a " +"simple script::" msgstr "" +"Flower는 Poetry를 사용하여 릴리즈를 빌드합니다. 필요한 명령은 간단한 스크립트" +"로 래핑됩니다::" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:154 msgid "" -"The resulting :code:`.whl` and :code:`.tar.gz` releases will be stored in" -" the :code:`/dist` subdirectory." +"The resulting :code:`.whl` and :code:`.tar.gz` releases will be stored in " +"the :code:`/dist` subdirectory." msgstr "" +"결과물인 :code:`.whl` 및 :code:`.tar.gz` 릴리즈는 :code:`/dist` 하위 디렉터리" +"에 저장됩니다." #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:159 msgid "Build Documentation" -msgstr "" +msgstr "문서 빌드" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:161 msgid "" -"Flower's documentation uses `Sphinx `_. " -"There's no convenience script to re-build the documentation yet, but it's" -" pretty easy::" +"Flower's documentation uses `Sphinx `_. There's " +"no convenience script to re-build the documentation yet, but it's pretty " +"easy::" msgstr "" +"Flower의 문서는 `Sphinx `_를 사용합니다. 아직 문" +"서를 다시 작성할 수 있는 편리한 스크립트는 없지만 다음과 같이 쉽게 작성할 수 " +"있습니다:" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:167 msgid "This will generate HTML documentation in ``doc/build/html``." -msgstr "" +msgstr "그러면 ``doc/build/html``에 HTML 문서가 생성됩니다." #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:2 msgid "Example: FedBN in PyTorch - From Centralized To Federated" -msgstr "" +msgstr "예시: PyTorch에서 FedBN - 중앙 집중식에서 연합식으로" #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:4 msgid "" -"This tutorial will show you how to use Flower to build a federated " -"version of an existing machine learning workload with `FedBN " -"`_, a federated training strategy " -"designed for non-iid data. We are using PyTorch to train a Convolutional " -"Neural Network(with Batch Normalization layers) on the CIFAR-10 dataset. " -"When applying FedBN, only few changes needed compared to :doc:`Example: " -"PyTorch - From Centralized To Federated `." -msgstr "" +"This tutorial will show you how to use Flower to build a federated version " +"of an existing machine learning workload with `FedBN `_, a federated training strategy designed for non-iid data. We " +"are using PyTorch to train a Convolutional Neural Network(with Batch " +"Normalization layers) on the CIFAR-10 dataset. When applying FedBN, only few " +"changes needed compared to :doc:`Example: PyTorch - From Centralized To " +"Federated `." +msgstr "" +"이 튜토리얼에서는 non-iid data를 위해 설계된 federated 훈련 전략인 `FedBN " +"`_으로 기존 머신러닝 워크로드의 federated " +"버전을 구축하기 위해 Flower를 사용하는 방법을 보여드립니다. 우리는 PyTorch를 " +"사용하여 CIFAR-10 데이터 세트에서 컨볼루션 신경망(일괄 정규화 레이어 포함)을 " +"훈련하고 있습니다. FedBN을 적용할 때, :doc:`예제: 파이토치 -중앙 집중식에서 " +"연합식으로 ` 와 비교했을 때 " +"몇 가지 사항만 변경 하면 됩니다." #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:9 #: ../../source/example-pytorch-from-centralized-to-federated.rst:10 msgid "Centralized Training" -msgstr "" +msgstr "중앙 집중식 훈련" #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:10 msgid "" -"All files are revised based on :doc:`Example: PyTorch - From Centralized " -"To Federated `. The only " -"thing to do is modifying the file called :code:`cifar.py`, revised part " -"is shown below:" +"All files are revised based on :doc:`Example: PyTorch - From Centralized To " +"Federated `. The only thing " +"to do is modifying the file called :code:`cifar.py`, revised part is shown " +"below:" msgstr "" +"모든 파일은 :doc:`예제: 파이토치 -중앙 집중식에서 연합식으로 `를 기반으로 수정합니다. :code:`cifar." +"py`라는 파일을 수정하기만 하면 되며, 수정된 부분은 아래와 같습니다:" #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:13 msgid "" "The model architecture defined in class Net() is added with Batch " "Normalization layers accordingly." msgstr "" +"Net() 클래스에 정의된 모델 아키텍처는 그에 따라 배치 정규화 레이어가 추가됩니" +"다." #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:41 #: ../../source/example-pytorch-from-centralized-to-federated.rst:157 msgid "You can now run your machine learning workload:" -msgstr "" +msgstr "이제 머신 러닝 워크로드를 실행할 수 있습니다:" #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:47 msgid "" -"So far this should all look fairly familiar if you've used PyTorch " -"before. Let's take the next step and use what we've built to create a " -"federated learning system within FedBN, the system consists of one server" -" and two clients." +"So far this should all look fairly familiar if you've used PyTorch before. " +"Let's take the next step and use what we've built to create a federated " +"learning system within FedBN, the system consists of one server and two " +"clients." msgstr "" +"지금까지는 파이토치를 사용해 본 적이 있다면 상당히 익숙하게 보일 것입니다. " +"다음 단계로 넘어가서 우리가 구축한 것을 사용하여 FedBN 내에서 하나의 서버와 " +"두 개의 클라이언트로 구성된 연합학습 시스템을 만들어 보겠습니다." #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:51 #: ../../source/example-pytorch-from-centralized-to-federated.rst:167 msgid "Federated Training" -msgstr "" +msgstr "연합 훈련" #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:53 msgid "" "If you have read :doc:`Example: PyTorch - From Centralized To Federated " -"`, the following parts are" -" easy to follow, only :code:`get_parameters` and :code:`set_parameters` " -"function in :code:`client.py` needed to revise. If not, please read the " -":doc:`Example: PyTorch - From Centralized To Federated `. first." -msgstr "" +"`, the following parts are " +"easy to follow, only :code:`get_parameters` and :code:`set_parameters` " +"function in :code:`client.py` needed to revise. If not, please read the :doc:" +"`Example: PyTorch - From Centralized To Federated `. first." +msgstr "" +":doc:`예제: 파이토치 - 중앙 집중식에서 연합식으로 `를 읽었다면, 다음 부분은 쉽게 따라할 수 있으며 " +":code:`client.py`의 :code:`get_parameters`와 :code:`set_parameters` 함수만 " +"수정해야 합니다. 그렇지 않은 경우 :doc:`예제: 파이토치 - 중앙 집중식에서 " +"연합식으로 `를 먼저 " +"읽어보세요." #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:56 msgid "" -"Our example consists of one *server* and two *clients*. In FedBN, " -":code:`server.py` keeps unchanged, we can start the server directly." +"Our example consists of one *server* and two *clients*. In FedBN, :code:" +"`server.py` keeps unchanged, we can start the server directly." msgstr "" +"이 예제는 하나의 *서버*와 두 개의 *클라이언트*로 구성됩니다. FedBN에서 :code:" +"`server.py`는 변경되지 않고 그대로 유지되므로 서버를 바로 시작할 수 있습니다." #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:62 msgid "" -"Finally, we will revise our *client* logic by changing " -":code:`get_parameters` and :code:`set_parameters` in :code:`client.py`, " -"we will exclude batch normalization parameters from model parameter list " -"when sending to or receiving from the server." +"Finally, we will revise our *client* logic by changing :code:" +"`get_parameters` and :code:`set_parameters` in :code:`client.py`, we will " +"exclude batch normalization parameters from model parameter list when " +"sending to or receiving from the server." msgstr "" +"마지막으로, :code:`client.py`에서 :code:`get_parameters` 및 :code:" +"`set_parameters`를 변경하여 *client* 로직을 수정할 것입니다. 서버로 보내거나 " +"서버에서 받을 때 모델 파라미터 목록에서 배치 정규화 파라미터를 제외할 수 있습" +"니다." #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:85 msgid "Now, you can now open two additional terminal windows and run" -msgstr "" +msgstr "이제 두 개의 터미널 창을 추가로 열고 다음을 실행할 수 있습니다" #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:91 msgid "" -"in each window (make sure that the server is still running before you do " -"so) and see your (previously centralized) PyTorch project run federated " -"learning with FedBN strategy across two clients. Congratulations!" +"in each window (make sure that the server is still running before you do so) " +"and see your (previously centralized) PyTorch project run federated learning " +"with FedBN strategy across two clients. Congratulations!" msgstr "" +"를 입력하고(클릭하기 전에 서버가 계속 실행 중인지 확인하세요), (이전에 중앙 " +"집중된) PyTorch 프로젝트가 두 클라이언트에서 FedBN으로 연합 학습을 실행하는 " +"것을 확인합니다. 축하합니다!" #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:94 #: ../../source/example-jax-from-centralized-to-federated.rst:277 #: ../../source/example-pytorch-from-centralized-to-federated.rst:310 #: ../../source/tutorial-quickstart-jax.rst:283 msgid "Next Steps" -msgstr "" +msgstr "다음 단계" #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:96 msgid "" -"The full source code for this example can be found `here " -"`_. Our example is of course somewhat over-" -"simplified because both clients load the exact same dataset, which isn't " -"realistic. You're now prepared to explore this topic further. How about " -"using different subsets of CIFAR-10 on each client? How about adding more" -" clients?" +"The full source code for this example can be found `here `_. Our " +"example is of course somewhat over-simplified because both clients load the " +"exact same dataset, which isn't realistic. You're now prepared to explore " +"this topic further. How about using different subsets of CIFAR-10 on each " +"client? How about adding more clients?" msgstr "" +"이 예제의 전체 소스 코드는 '여기 `_'에서 확인할 수 있습니다. 물" +"론 이 예제는 두 클라이언트가 완전히 동일한 데이터 세트를 로드하기 때문에 다" +"소 지나치게 단순화되어 있으며, 이는 현실적이지 않습니다. 이제 이 주제를 더 자" +"세히 살펴볼 준비가 되셨습니다. 각 클라이언트에서 서로 다른 CIFAR-10의 하위 집" +"합을 사용해 보는 것은 어떨까요? 클라이언트를 더 추가하는 것은 어떨까요?" #: ../../source/example-jax-from-centralized-to-federated.rst:2 msgid "Example: JAX - Run JAX Federated" -msgstr "" +msgstr "예시: JAX - JAX Federated 실행" #: ../../source/example-jax-from-centralized-to-federated.rst:4 #: ../../source/tutorial-quickstart-jax.rst:10 msgid "" -"This tutorial will show you how to use Flower to build a federated " -"version of an existing JAX workload. We are using JAX to train a linear " -"regression model on a scikit-learn dataset. We will structure the example" -" similar to our `PyTorch - From Centralized To Federated " -"`_ walkthrough. First, we build a centralized " -"training approach based on the `Linear Regression with JAX " -"`_" -" tutorial`. Then, we build upon the centralized training code to run the " -"training in a federated fashion." -msgstr "" +"This tutorial will show you how to use Flower to build a federated version " +"of an existing JAX workload. We are using JAX to train a linear regression " +"model on a scikit-learn dataset. We will structure the example similar to " +"our `PyTorch - From Centralized To Federated `_ walkthrough. " +"First, we build a centralized training approach based on the `Linear " +"Regression with JAX `_ tutorial`. Then, we build upon the centralized " +"training code to run the training in a federated fashion." +msgstr "" +"이 튜토리얼에서는 Flower를 사용하여 기존 JAX 워크로드의 연합 버전을 구축하는 " +"방법을 보여드립니다. JAX를 사용해 scikit-learn 데이터 세트에서 선형 회귀 " +"모델을 훈련하고 있습니다. 예제는 '파이토치 - Centralized에서 Federated으로 " +"`_ 워크스루와 유사하게 구성하겠습니다. 먼저, `JAX를 사용한 선형 " +"회귀 `_ 튜토리얼`을 기반으로 centralized 학습 접근 방식을 구축합니다. 그런 " +"다음 centralized 트레이닝 코드를 기반으로 federated 방식으로 트레이닝을 " +"실행합니다." #: ../../source/example-jax-from-centralized-to-federated.rst:10 #: ../../source/tutorial-quickstart-jax.rst:16 msgid "" -"Before we start building our JAX example, we need install the packages " -":code:`jax`, :code:`jaxlib`, :code:`scikit-learn`, and :code:`flwr`:" +"Before we start building our JAX example, we need install the packages :code:" +"`jax`, :code:`jaxlib`, :code:`scikit-learn`, and :code:`flwr`:" msgstr "" +"JAX 예제 빌드를 시작하기 전에 :code:`jax`, :code:`jaxlib`, :code:`scikit-" +"learn`, :code:`flwr` 패키지를 설치해야 합니다:" #: ../../source/example-jax-from-centralized-to-federated.rst:18 #: ../../source/tutorial-quickstart-jax.rst:24 msgid "Linear Regression with JAX" -msgstr "" +msgstr "JAX를 사용한 선형 회귀" #: ../../source/example-jax-from-centralized-to-federated.rst:20 #: ../../source/tutorial-quickstart-jax.rst:26 msgid "" -"We begin with a brief description of the centralized training code based " -"on a :code:`Linear Regression` model. If you want a more in-depth " -"explanation of what's going on then have a look at the official `JAX " -"documentation `_." +"We begin with a brief description of the centralized training code based on " +"a :code:`Linear Regression` model. If you want a more in-depth explanation " +"of what's going on then have a look at the official `JAX documentation " +"`_." msgstr "" +"먼저 :code:`선형 회귀` 모델을 기반으로 하는 중앙 집중식 훈련 코드에 대한 " +"간략한 설명부터 시작하겠습니다. 더 자세한 설명을 원하시면 공식 `JAX 문서 " +"`_를 참조하세요." #: ../../source/example-jax-from-centralized-to-federated.rst:23 #: ../../source/tutorial-quickstart-jax.rst:29 msgid "" "Let's create a new file called :code:`jax_training.py` with all the " "components required for a traditional (centralized) linear regression " -"training. First, the JAX packages :code:`jax` and :code:`jaxlib` need to " -"be imported. In addition, we need to import :code:`sklearn` since we use " -":code:`make_regression` for the dataset and :code:`train_test_split` to " -"split the dataset into a training and test set. You can see that we do " -"not yet import the :code:`flwr` package for federated learning. This will" -" be done later." -msgstr "" +"training. First, the JAX packages :code:`jax` and :code:`jaxlib` need to be " +"imported. In addition, we need to import :code:`sklearn` since we use :code:" +"`make_regression` for the dataset and :code:`train_test_split` to split the " +"dataset into a training and test set. You can see that we do not yet import " +"the :code:`flwr` package for federated learning. This will be done later." +msgstr "" +"전통적인(중앙 집중식) 선형 회귀 훈련에 필요한 모든 구성 요소가 포함된 " +":code:`jax_training.py`라는 새 파일을 생성해 보겠습니다. 먼저, JAX 패키지인 " +":code:`jax`와 :code:`jaxlib`를 가져와야 합니다. 또한 데이터 세트에 " +":code:`make_regression`을 사용하고 데이터 세트를 학습 및 테스트 세트로 " +"분할하기 위해 :code:`train_test_split`을 사용하므로 :code:`sklearn`을 " +"가져와야 합니다. 연합 학습을 위해 아직 :code:`flwr` 패키지를 가져오지 않은 " +"것을 볼 수 있습니다. 이 작업은 나중에 수행됩니다." #: ../../source/example-jax-from-centralized-to-federated.rst:37 #: ../../source/tutorial-quickstart-jax.rst:43 msgid "" -"The :code:`load_data()` function loads the mentioned training and test " -"sets." +"The :code:`load_data()` function loads the mentioned training and test sets." msgstr "" +"code:`load_data()` 함수는 앞서 언급한 트레이닝 및 테스트 세트를 로드합니다." #: ../../source/example-jax-from-centralized-to-federated.rst:47 #: ../../source/tutorial-quickstart-jax.rst:53 msgid "" -"The model architecture (a very simple :code:`Linear Regression` model) is" -" defined in :code:`load_model()`." +"The model architecture (a very simple :code:`Linear Regression` model) is " +"defined in :code:`load_model()`." msgstr "" +"모델 아키텍처(매우 간단한 :code:`선형 회귀` 모델)는 :code:`load_model()`에 정" +"의되어 있습니다." #: ../../source/example-jax-from-centralized-to-federated.rst:59 #: ../../source/tutorial-quickstart-jax.rst:65 msgid "" -"We now need to define the training (function :code:`train()`), which " -"loops over the training set and measures the loss (function " -":code:`loss_fn()`) for each batch of training examples. The loss function" -" is separate since JAX takes derivatives with a :code:`grad()` function " -"(defined in the :code:`main()` function and called in :code:`train()`)." +"We now need to define the training (function :code:`train()`), which loops " +"over the training set and measures the loss (function :code:`loss_fn()`) for " +"each batch of training examples. The loss function is separate since JAX " +"takes derivatives with a :code:`grad()` function (defined in the :code:" +"`main()` function and called in :code:`train()`)." msgstr "" +"이제 훈련 집합을 반복하고 각 훈련 예제 배치에 대해 손실을 측정하는(함수 :" +"code:`loss_fn()`) 훈련(함수 :code:`train()`)을 정의해야 합니다. JAX는 :code:" +"`grad()` 함수(:code:`main()` 함수에 정의되고 :code:`train()`에서 호출됨)로 파" +"생물을 취하므로 손실 함수는 분리되어 있습니다." #: ../../source/example-jax-from-centralized-to-federated.rst:77 #: ../../source/tutorial-quickstart-jax.rst:83 msgid "" -"The evaluation of the model is defined in the function " -":code:`evaluation()`. The function takes all test examples and measures " -"the loss of the linear regression model." +"The evaluation of the model is defined in the function :code:`evaluation()`. " +"The function takes all test examples and measures the loss of the linear " +"regression model." msgstr "" +"모델의 평가는 :code:`evaluation()` 함수에 정의되어 있습니다. 이 함수는 모든 " +"테스트 예제를 가져와 선형 회귀 모델의 손실을 측정합니다." #: ../../source/example-jax-from-centralized-to-federated.rst:88 #: ../../source/tutorial-quickstart-jax.rst:94 msgid "" "Having defined the data loading, model architecture, training, and " -"evaluation we can put everything together and train our model using JAX. " -"As already mentioned, the :code:`jax.grad()` function is defined in " -":code:`main()` and passed to :code:`train()`." +"evaluation we can put everything together and train our model using JAX. As " +"already mentioned, the :code:`jax.grad()` function is defined in :code:" +"`main()` and passed to :code:`train()`." msgstr "" +"데이터 로딩, 모델 아키텍처, 훈련 및 평가를 정의했으므로 이제 모든 것을 종합하" +"여 JAX를 사용 모델을 훈련할 수 있습니다. 이미 언급했듯이 :code:`jax.grad()` " +"함수는 :code:`main()`에 정의되어 :code:`train()`에 전달됩니다." #: ../../source/example-jax-from-centralized-to-federated.rst:105 #: ../../source/tutorial-quickstart-jax.rst:111 msgid "You can now run your (centralized) JAX linear regression workload:" -msgstr "" +msgstr "이제 (중앙 집중식) JAX 선형 회귀 워크로드를 실행할 수 있습니다:" #: ../../source/example-jax-from-centralized-to-federated.rst:111 #: ../../source/tutorial-quickstart-jax.rst:117 msgid "" -"So far this should all look fairly familiar if you've used JAX before. " -"Let's take the next step and use what we've built to create a simple " -"federated learning system consisting of one server and two clients." +"So far this should all look fairly familiar if you've used JAX before. Let's " +"take the next step and use what we've built to create a simple federated " +"learning system consisting of one server and two clients." msgstr "" +"지금까지는 JAX를 사용해 본 적이 있다면 이 모든 것이 상당히 익숙해 보일 것입니" +"다. 다음 단계로 넘어가서 우리가 구축한 것을 사용하여 하나의 서버와 두 개의 클" +"라이언트로 구성된 간단한 연합 학습 시스템을 만들어 보겠습니다." #: ../../source/example-jax-from-centralized-to-federated.rst:115 #: ../../source/tutorial-quickstart-jax.rst:121 msgid "JAX meets Flower" -msgstr "" +msgstr "JAX와 Flower의 만남" #: ../../source/example-jax-from-centralized-to-federated.rst:117 #: ../../source/tutorial-quickstart-jax.rst:123 msgid "" -"The concept of federating an existing workload is always the same and " -"easy to understand. We have to start a *server* and then use the code in " -":code:`jax_training.py` for the *clients* that are connected to the " -"*server*. The *server* sends model parameters to the clients. The " -"*clients* run the training and update the parameters. The updated " -"parameters are sent back to the *server*, which averages all received " -"parameter updates. This describes one round of the federated learning " -"process, and we repeat this for multiple rounds." -msgstr "" +"The concept of federating an existing workload is always the same and easy " +"to understand. We have to start a *server* and then use the code in :code:" +"`jax_training.py` for the *clients* that are connected to the *server*. The " +"*server* sends model parameters to the clients. The *clients* run the " +"training and update the parameters. The updated parameters are sent back to " +"the *server*, which averages all received parameter updates. This describes " +"one round of the federated learning process, and we repeat this for multiple " +"rounds." +msgstr "" +"기존 워크로드를 연합하는 개념은 항상 동일하고 이해하기 쉽습니다. 서버*를 " +"시작한 다음 *서버*에 연결된 *클라이언트*에 대해 :code:`jax_training.py`의 " +"코드를 사용해야 합니다. *서버*는 모델 파라미터를 클라이언트로 전송합니다. " +"클라이언트는 학습을 실행하고 파라미터를 업데이트합니다. 업데이트된 " +"파라미터는 *서버*로 다시 전송되며, 수신된 모든 파라미터 업데이트의 평균을 " +"구합니다. 이는 연합 학습 프로세스의 한 라운드를 설명하며, 이 과정을 여러 " +"라운드에 걸쳐 반복합니다." #: ../../source/example-jax-from-centralized-to-federated.rst:123 #: ../../source/example-pytorch-from-centralized-to-federated.rst:181 #: ../../source/tutorial-quickstart-jax.rst:129 msgid "" -"Our example consists of one *server* and two *clients*. Let's set up " -":code:`server.py` first. The *server* needs to import the Flower package " -":code:`flwr`. Next, we use the :code:`start_server` function to start a " -"server and tell it to perform three rounds of federated learning." +"Our example consists of one *server* and two *clients*. Let's set up :code:" +"`server.py` first. The *server* needs to import the Flower package :code:" +"`flwr`. Next, we use the :code:`start_server` function to start a server and " +"tell it to perform three rounds of federated learning." msgstr "" +"이 예제는 하나의 *서버*와 두 개의 *클라이언트*로 구성됩니다. 먼저 " +":code:`server.py`를 설정해 보겠습니다. *server*는 Flower 패키지 :code:`flwr`" +"를 가져와야 합니다. 다음으로, :code:`start_server` 함수를 사용하여 서버를 " +"시작하고 세 차례의 연합 학습을 수행하도록 지시합니다." #: ../../source/example-jax-from-centralized-to-federated.rst:133 #: ../../source/example-pytorch-from-centralized-to-federated.rst:191 #: ../../source/tutorial-quickstart-jax.rst:139 msgid "We can already start the *server*:" -msgstr "" +msgstr "이미 *서버*를 시작할 수 있습니다:" #: ../../source/example-jax-from-centralized-to-federated.rst:139 #: ../../source/tutorial-quickstart-jax.rst:145 msgid "" -"Finally, we will define our *client* logic in :code:`client.py` and build" -" upon the previously defined JAX training in :code:`jax_training.py`. Our" -" *client* needs to import :code:`flwr`, but also :code:`jax` and " -":code:`jaxlib` to update the parameters on our JAX model:" +"Finally, we will define our *client* logic in :code:`client.py` and build " +"upon the previously defined JAX training in :code:`jax_training.py`. Our " +"*client* needs to import :code:`flwr`, but also :code:`jax` and :code:" +"`jaxlib` to update the parameters on our JAX model:" msgstr "" +"마지막으로, :code:`client.py`에서 *client* 로직을 정의하고 :code:" +"`jax_training.py`에서 이전에 정의한 JAX 교육을 기반으로 빌드합니다. *클라이언" +"트*는 :code:`flwr`을 가져와야 하며, JAX 모델의 파라미터를 업데이트하기 위해 :" +"code:`jax` 및 :code:`jaxlib`도 가져와야 합니다:" #: ../../source/example-jax-from-centralized-to-federated.rst:154 #: ../../source/tutorial-quickstart-jax.rst:160 msgid "" -"Implementing a Flower *client* basically means implementing a subclass of" -" either :code:`flwr.client.Client` or :code:`flwr.client.NumPyClient`. " -"Our implementation will be based on :code:`flwr.client.NumPyClient` and " -"we'll call it :code:`FlowerClient`. :code:`NumPyClient` is slightly " -"easier to implement than :code:`Client` if you use a framework with good " -"NumPy interoperability (like JAX) because it avoids some of the " -"boilerplate that would otherwise be necessary. :code:`FlowerClient` needs" -" to implement four methods, two methods for getting/setting model " -"parameters, one method for training the model, and one method for testing" -" the model:" -msgstr "" +"Implementing a Flower *client* basically means implementing a subclass of " +"either :code:`flwr.client.Client` or :code:`flwr.client.NumPyClient`. Our " +"implementation will be based on :code:`flwr.client.NumPyClient` and we'll " +"call it :code:`FlowerClient`. :code:`NumPyClient` is slightly easier to " +"implement than :code:`Client` if you use a framework with good NumPy " +"interoperability (like JAX) because it avoids some of the boilerplate that " +"would otherwise be necessary. :code:`FlowerClient` needs to implement four " +"methods, two methods for getting/setting model parameters, one method for " +"training the model, and one method for testing the model:" +msgstr "" +"Flower *클라이언트*를 구현한다는 것은 기본적으로 :code:`flwr.client.Client` " +"또는 :code:`flwr.client.NumPyClient`의 서브클래스를 구현하는 것을 의미합니" +"다. 구현은 :code:`flwr.client.NumPyClient`를 기반으로 하며, 이를 :code:" +"`FlowerClient`라고 부를 것입니다. :code:`NumPyClient`는 필요한 일부 보일러플" +"레이를 피할 수 있기 때문에 NumPy 상호 운용성이 좋은 프레임워크(예: JAX)를 사" +"용하는 경우 :code:`Client`보다 구현하기가 약간 더 쉽습니다. code:" +"`FlowerClient`는 모델 매개변수를 가져오거나 설정하는 메서드 2개, 모델 학습을 " +"위한 메서드 1개, 모델 테스트를 위한 메서드 1개 등 총 4개의 메서드를 구현해야 " +"합니다:" #: ../../source/example-jax-from-centralized-to-federated.rst:161 #: ../../source/tutorial-quickstart-jax.rst:167 msgid ":code:`set_parameters (optional)`" -msgstr "" +msgstr ":code:`set_parameters (선택사항)`" #: ../../source/example-jax-from-centralized-to-federated.rst:160 #: ../../source/example-pytorch-from-centralized-to-federated.rst:219 #: ../../source/tutorial-quickstart-jax.rst:166 msgid "" -"set the model parameters on the local model that are received from the " -"server" -msgstr "" +"set the model parameters on the local model that are received from the server" +msgstr "서버에서 수신한 로컬 모델의 모델 파라미터를 설정합니다" #: ../../source/example-jax-from-centralized-to-federated.rst:161 #: ../../source/tutorial-quickstart-jax.rst:167 msgid "transform parameters to NumPy :code:`ndarray`'s" -msgstr "" +msgstr "매개 변수를 NumPy :code:`ndarray`로 변환" #: ../../source/example-jax-from-centralized-to-federated.rst:162 #: ../../source/example-pytorch-from-centralized-to-federated.rst:220 #: ../../source/tutorial-quickstart-jax.rst:168 msgid "" -"loop over the list of model parameters received as NumPy " -":code:`ndarray`'s (think list of neural network layers)" +"loop over the list of model parameters received as NumPy :code:`ndarray`'s " +"(think list of neural network layers)" msgstr "" +"(신경망 레이어 목록으로 생각하면 됩니다) NumPy :code:`ndarray`로 받은 모델 파" +"라미터 목록에 대해 반복합니다" #: ../../source/example-jax-from-centralized-to-federated.rst:163 #: ../../source/example-pytorch-from-centralized-to-federated.rst:221 @@ -2546,15 +2806,17 @@ msgstr "" #: ../../source/tutorial-quickstart-pytorch.rst:155 #: ../../source/tutorial-quickstart-scikitlearn.rst:118 msgid ":code:`get_parameters`" -msgstr "" +msgstr ":code:`get_parameters`" #: ../../source/example-jax-from-centralized-to-federated.rst:164 #: ../../source/example-pytorch-from-centralized-to-federated.rst:222 #: ../../source/tutorial-quickstart-jax.rst:170 msgid "" -"get the model parameters and return them as a list of NumPy " -":code:`ndarray`'s (which is what :code:`flwr.client.NumPyClient` expects)" +"get the model parameters and return them as a list of NumPy :code:" +"`ndarray`'s (which is what :code:`flwr.client.NumPyClient` expects)" msgstr "" +"모델 매개변수를 가져와서 NumPy :code:`ndarray`의 목록으로 반환합니다(이는 :" +"code:`flwr.client.NumPyClient`가 기대하는 바와 같습니다)" #: ../../source/example-jax-from-centralized-to-federated.rst:167 #: ../../source/example-pytorch-from-centralized-to-federated.rst:225 @@ -2562,7 +2824,7 @@ msgstr "" #: ../../source/tutorial-quickstart-pytorch.rst:161 #: ../../source/tutorial-quickstart-scikitlearn.rst:125 msgid ":code:`fit`" -msgstr "" +msgstr ":code:`fit`" #: ../../source/example-jax-from-centralized-to-federated.rst:166 #: ../../source/example-jax-from-centralized-to-federated.rst:170 @@ -2571,20 +2833,20 @@ msgstr "" #: ../../source/tutorial-quickstart-jax.rst:172 #: ../../source/tutorial-quickstart-jax.rst:176 msgid "" -"update the parameters of the local model with the parameters received " -"from the server" -msgstr "" +"update the parameters of the local model with the parameters received from " +"the server" +msgstr "서버에서 받은 파라미터로 로컬 모델의 파라미터를 업데이트합니다" #: ../../source/example-jax-from-centralized-to-federated.rst:167 #: ../../source/example-pytorch-from-centralized-to-federated.rst:225 #: ../../source/tutorial-quickstart-jax.rst:173 msgid "train the model on the local training set" -msgstr "" +msgstr "로컬 훈련 세트에서 모델을 훈련합니다" #: ../../source/example-jax-from-centralized-to-federated.rst:168 #: ../../source/tutorial-quickstart-jax.rst:174 msgid "get the updated local model parameters and return them to the server" -msgstr "" +msgstr "업데이트된 로컬 모델 파라미터를 가져와 서버로 반환합니다" #: ../../source/example-jax-from-centralized-to-federated.rst:172 #: ../../source/example-pytorch-from-centralized-to-federated.rst:230 @@ -2592,108 +2854,140 @@ msgstr "" #: ../../source/tutorial-quickstart-pytorch.rst:164 #: ../../source/tutorial-quickstart-scikitlearn.rst:128 msgid ":code:`evaluate`" -msgstr "" +msgstr ":code:`evaluate`" #: ../../source/example-jax-from-centralized-to-federated.rst:171 #: ../../source/example-pytorch-from-centralized-to-federated.rst:229 #: ../../source/tutorial-quickstart-jax.rst:177 msgid "evaluate the updated model on the local test set" -msgstr "" +msgstr "로컬 테스트 세트에서 업데이트된 모델을 평가합니다" #: ../../source/example-jax-from-centralized-to-federated.rst:172 #: ../../source/tutorial-quickstart-jax.rst:178 msgid "return the local loss to the server" -msgstr "" +msgstr "로컬 손실을 서버로 반환합니다" #: ../../source/example-jax-from-centralized-to-federated.rst:174 #: ../../source/tutorial-quickstart-jax.rst:180 msgid "" -"The challenging part is to transform the JAX model parameters from " -":code:`DeviceArray` to :code:`NumPy ndarray` to make them compatible with" -" `NumPyClient`." +"The challenging part is to transform the JAX model parameters from :code:" +"`DeviceArray` to :code:`NumPy ndarray` to make them compatible with " +"`NumPyClient`." msgstr "" +"어려운 부분은 JAX 모델 매개변수를 :code:`DeviceArray`에서 :code:`NumPy " +"ndarray`로 변환하여 `NumPyClient`와 호환되도록 하는 것입니다." #: ../../source/example-jax-from-centralized-to-federated.rst:176 #: ../../source/tutorial-quickstart-jax.rst:182 msgid "" -"The two :code:`NumPyClient` methods :code:`fit` and :code:`evaluate` make" -" use of the functions :code:`train()` and :code:`evaluate()` previously " +"The two :code:`NumPyClient` methods :code:`fit` and :code:`evaluate` make " +"use of the functions :code:`train()` and :code:`evaluate()` previously " "defined in :code:`jax_training.py`. So what we really do here is we tell " -"Flower through our :code:`NumPyClient` subclass which of our already " -"defined functions to call for training and evaluation. We included type " -"annotations to give you a better understanding of the data types that get" -" passed around." +"Flower through our :code:`NumPyClient` subclass which of our already defined " +"functions to call for training and evaluation. We included type annotations " +"to give you a better understanding of the data types that get passed around." msgstr "" +"두 개의 :code:`NumPyClient` 메서드인 :code:`fit`과 :code:`evaluate`는 이전" +"에 :code:`jax_training.py`에 정의된 함수 :code:`train()`과 :code:`evaluate()`" +"를 사용합니다. 따라서 여기서 우리가 실제로 하는 일은 이미 정의된 함수 중 훈련" +"과 평가를 위해 호출할 함수를 :code:`NumPyClient` 서브클래스를 통해 Flower에" +"게 알려주는 것입니다. 전달되는 데이터 유형을 더 잘 이해할 수 있도록 유형 " +"type annotation을 포함했습니다." #: ../../source/example-jax-from-centralized-to-federated.rst:245 #: ../../source/tutorial-quickstart-jax.rst:251 msgid "Having defined the federation process, we can run it." -msgstr "" +msgstr "연합 프로세스를 정의했으면 이제 실행할 수 있습니다." #: ../../source/example-jax-from-centralized-to-federated.rst:268 #: ../../source/example-pytorch-from-centralized-to-federated.rst:301 #: ../../source/tutorial-quickstart-jax.rst:274 msgid "And that's it. You can now open two additional terminal windows and run" -msgstr "" +msgstr "여기까지입니다. 이제 두 개의 터미널 창을 추가로 열고 다음을 실행할 수 " +"있습니다" #: ../../source/example-jax-from-centralized-to-federated.rst:274 #: ../../source/tutorial-quickstart-jax.rst:280 msgid "" -"in each window (make sure that the server is still running before you do " -"so) and see your JAX project run federated learning across two clients. " +"in each window (make sure that the server is still running before you do so) " +"and see your JAX project run federated learning across two clients. " "Congratulations!" msgstr "" +"를 입력하고(그 전에 서버가 계속 실행 중인지 확인하세요) 두 클라이언트에서 " +"연합 학습을 실행하는 JAX 프로젝트를 확인합니다. 축하합니다!" #: ../../source/example-jax-from-centralized-to-federated.rst:279 #: ../../source/tutorial-quickstart-jax.rst:285 msgid "" "The source code of this example was improved over time and can be found " -"here: `Quickstart JAX `_. Our example is somewhat over-simplified because both " +"here: `Quickstart JAX `_. Our example is somewhat over-simplified because both " "clients load the same dataset." msgstr "" +"이 예제의 소스 코드는 시간이 지남에 따라 개선되었으며 여기에서 확인할 수 있습" +"니다: 'Quickstart JAX `_. 두 클라이언트가 동일한 데이터 세트를 로드하기 때문에 이 예" +"제는 다소 단순화되어 있습니다." #: ../../source/example-jax-from-centralized-to-federated.rst:282 #: ../../source/tutorial-quickstart-jax.rst:288 msgid "" -"You're now prepared to explore this topic further. How about using a more" -" sophisticated model or using a different dataset? How about adding more " +"You're now prepared to explore this topic further. How about using a more " +"sophisticated model or using a different dataset? How about adding more " "clients?" msgstr "" +"이제 이 주제를 더 자세히 살펴볼 준비가 되었습니다. 더 정교한 모델을 사용하거" +"나 다른 데이터 집합을 사용해 보는 것은 어떨까요? 클라이언트를 더 추가하는 것" +"은 어떨까요?" #: ../../source/example-pytorch-from-centralized-to-federated.rst:2 msgid "Example: PyTorch - From Centralized To Federated" -msgstr "" +msgstr "예제: 파이토치 - 중앙 집중식에서 연합식으로" #: ../../source/example-pytorch-from-centralized-to-federated.rst:4 msgid "" -"This tutorial will show you how to use Flower to build a federated " -"version of an existing machine learning workload. We are using PyTorch to" -" train a Convolutional Neural Network on the CIFAR-10 dataset. First, we " -"introduce this machine learning task with a centralized training approach" -" based on the `Deep Learning with PyTorch " +"This tutorial will show you how to use Flower to build a federated version " +"of an existing machine learning workload. We are using PyTorch to train a " +"Convolutional Neural Network on the CIFAR-10 dataset. First, we introduce " +"this machine learning task with a centralized training approach based on the " +"`Deep Learning with PyTorch `_ tutorial. Then, we build upon the centralized " +"training code to run the training in a federated fashion." +msgstr "" +"이 튜토리얼에서는 Flower를 사용해 기존 머신 러닝 워크로드의 연합 버전을 " +"구축하는 방법을 보여드립니다. 여기서는 PyTorch를 사용해 CIFAR-10 데이터 " +"세트에서 컨볼루션 신경망을 훈련합니다. 먼저, 'PyTorch로 딥 러닝 " "`_ " -"tutorial. Then, we build upon the centralized training code to run the " -"training in a federated fashion." -msgstr "" +"튜토리얼을 기반으로 centralized 학습 접근 방식을 사용하여 이 머신 러닝 " +"작업을 소개합니다. 그런 다음 centralized 훈련 코드를 기반으로 연합 방식 " +"훈련을 실행합니다." #: ../../source/example-pytorch-from-centralized-to-federated.rst:12 msgid "" -"We begin with a brief description of the centralized CNN training code. " -"If you want a more in-depth explanation of what's going on then have a " -"look at the official `PyTorch tutorial " -"`_." +"We begin with a brief description of the centralized CNN training code. If " +"you want a more in-depth explanation of what's going on then have a look at " +"the official `PyTorch tutorial `_." msgstr "" +"중앙 집중식 CNN 트레이닝 코드에 대한 간략한 설명부터 시작하겠습니다. 무슨 " +"일이 일어나고 있는지 더 자세히 설명하려면 공식 `PyTorch 튜토리얼 " +"`_을 " +"참조하세요." #: ../../source/example-pytorch-from-centralized-to-federated.rst:15 msgid "" "Let's create a new file called :code:`cifar.py` with all the components " -"required for a traditional (centralized) training on CIFAR-10. First, all" -" required packages (such as :code:`torch` and :code:`torchvision`) need " -"to be imported. You can see that we do not import any package for " -"federated learning. You can keep all these imports as they are even when " -"we add the federated learning components at a later point." -msgstr "" +"required for a traditional (centralized) training on CIFAR-10. First, all " +"required packages (such as :code:`torch` and :code:`torchvision`) need to be " +"imported. You can see that we do not import any package for federated " +"learning. You can keep all these imports as they are even when we add the " +"federated learning components at a later point." +msgstr "" +"CIFAR-10에 대한 기존 (중앙 집중식) 교육에 필요한 모든 구성 요소가 포함된 " +":code:`cifar.py`라는 새 파일을 생성해 보겠습니다. 먼저, 필요한 모든 " +"패키지(예: :code:`torch` 및 :code:`torchvision`)를 가져와야 합니다. 연합 " +"학습을 위한 패키지를 가져오지 않는 것을 확인 할 수 있습니. 나중에 연합 학습 " +"구성 요소를 추가할 때에도 이러한 모든 가져오기를 그대로 유지할 수 있습니다." #: ../../source/example-pytorch-from-centralized-to-federated.rst:32 msgid "" @@ -2701,163 +2995,230 @@ msgid "" "learning workload. The model architecture (a very simple Convolutional " "Neural Network) is defined in :code:`class Net()`." msgstr "" +"이미 언급했듯이 이 머신 러닝 워크로드에는 CIFAR-10 데이터 세트를 사용합니다. " +"모델 아키텍처(매우 간단한 컨볼루션 신경망)는 :code:`class Net()`에 정의되어 " +"있습니다." #: ../../source/example-pytorch-from-centralized-to-federated.rst:56 msgid "" -"The :code:`load_data()` function loads the CIFAR-10 training and test " -"sets. The :code:`transform` normalized the data after loading." +"The :code:`load_data()` function loads the CIFAR-10 training and test sets. " +"The :code:`transform` normalized the data after loading." msgstr "" +":code:`load_data()` 함수는 CIFAR-10 훈련 및 테스트 세트를 로드합니다. :code:" +"`transform`은 로드 후 데이터를 정규화합니다." #: ../../source/example-pytorch-from-centralized-to-federated.rst:74 msgid "" -"We now need to define the training (function :code:`train()`) which loops" -" over the training set, measures the loss, backpropagates it, and then " -"takes one optimizer step for each batch of training examples." +"We now need to define the training (function :code:`train()`) which loops " +"over the training set, measures the loss, backpropagates it, and then takes " +"one optimizer step for each batch of training examples." msgstr "" +"이제 학습 집합을 반복하고, 손실을 측정하고, 이를 역전파한 다음 각 학습 예제 " +"배치에 대해 하나의 최적화 단계를 수행하는 학습(함수 :code:`train()`)을 정의해" +"야 합니다." #: ../../source/example-pytorch-from-centralized-to-federated.rst:76 msgid "" -"The evaluation of the model is defined in the function :code:`test()`. " -"The function loops over all test samples and measures the loss of the " -"model based on the test dataset." +"The evaluation of the model is defined in the function :code:`test()`. The " +"function loops over all test samples and measures the loss of the model " +"based on the test dataset." msgstr "" +"모델 평가는 :code:`test()` 함수에 정의되어 있습니다. 이 함수는 모든 테스트 샘" +"플을 반복하고 테스트 데이터 세트에 따라 모델의 손실을 측정합니다." #: ../../source/example-pytorch-from-centralized-to-federated.rst:136 msgid "" "Having defined the data loading, model architecture, training, and " "evaluation we can put everything together and train our CNN on CIFAR-10." msgstr "" +"데이터 로딩, 모델 아키텍처, 훈련 및 평가를 정의했으면 모든 것을 종합하여 " +"CIFAR-10에서 CNN을 훈련할 수 있습니다." #: ../../source/example-pytorch-from-centralized-to-federated.rst:163 msgid "" -"So far, this should all look fairly familiar if you've used PyTorch " -"before. Let's take the next step and use what we've built to create a " -"simple federated learning system consisting of one server and two " -"clients." +"So far, this should all look fairly familiar if you've used PyTorch before. " +"Let's take the next step and use what we've built to create a simple " +"federated learning system consisting of one server and two clients." msgstr "" +"지금까지는 파이토치를 사용해 본 적이 있다면 상당히 익숙하게 보일 것입니다. 다" +"음 단계로 넘어가서 구축한 것을 사용하여 하나의 서버와 두 개의 클라이언트로 구" +"성된 간단한 연합 학습 시스템을 만들어 보겠습니다." #: ../../source/example-pytorch-from-centralized-to-federated.rst:169 msgid "" -"The simple machine learning project discussed in the previous section " -"trains the model on a single dataset (CIFAR-10), we call this centralized" -" learning. This concept of centralized learning, as shown in the previous" -" section, is probably known to most of you, and many of you have used it " -"previously. Normally, if you'd want to run machine learning workloads in " -"a federated fashion, then you'd have to change most of your code and set " -"everything up from scratch. This can be a considerable effort." -msgstr "" +"The simple machine learning project discussed in the previous section trains " +"the model on a single dataset (CIFAR-10), we call this centralized learning. " +"This concept of centralized learning, as shown in the previous section, is " +"probably known to most of you, and many of you have used it previously. " +"Normally, if you'd want to run machine learning workloads in a federated " +"fashion, then you'd have to change most of your code and set everything up " +"from scratch. This can be a considerable effort." +msgstr "" +"이전 섹션에서 설명한 간단한 머신 러닝 프로젝트는 단일 데이터 세트(CIFAR-10)" +"로 모델을 학습시키는데, 이를 중앙 집중식 학습이라고 부릅니다. 이전 섹션에서 " +"설명한 중앙 집중식 학습의 개념은 대부분 알고 계실 것이며, 많은 분들이 이전에 " +"사용해 보셨을 것입니다. 일반적으로 머신 러닝 워크로드를 연합 방식으로 " +"실행하려면 대부분의 코드를 변경하고 모든 것을 처음부터 다시 설정해야 합니다. " +"이는 상당한 노력이 필요할 수 있습니다." #: ../../source/example-pytorch-from-centralized-to-federated.rst:173 msgid "" -"However, with Flower you can evolve your pre-existing code into a " -"federated learning setup without the need for a major rewrite." +"However, with Flower you can evolve your pre-existing code into a federated " +"learning setup without the need for a major rewrite." msgstr "" +"하지만 Flower를 사용하면 대대적인 재작성 없이도 기존 코드를 연합 학습 설정으" +"로 발전시킬 수 있습니다." #: ../../source/example-pytorch-from-centralized-to-federated.rst:175 msgid "" -"The concept is easy to understand. We have to start a *server* and then " -"use the code in :code:`cifar.py` for the *clients* that are connected to " -"the *server*. The *server* sends model parameters to the clients. The " -"*clients* run the training and update the parameters. The updated " -"parameters are sent back to the *server* which averages all received " -"parameter updates. This describes one round of the federated learning " -"process and we repeat this for multiple rounds." -msgstr "" +"The concept is easy to understand. We have to start a *server* and then use " +"the code in :code:`cifar.py` for the *clients* that are connected to the " +"*server*. The *server* sends model parameters to the clients. The *clients* " +"run the training and update the parameters. The updated parameters are sent " +"back to the *server* which averages all received parameter updates. This " +"describes one round of the federated learning process and we repeat this for " +"multiple rounds." +msgstr "" +"개념은 이해하기 쉽습니다. *서버*를 시작한 다음 *서버*에 연결된 *클라이언트*" +"에 대해 :code:`cifar.py`의 코드를 사용해야 합니다. *서버*는 모델 파라미터를 " +"클라이언트로 전송합니다. *클라이언트*는 학습을 실행하고 파라미터를 " +"업데이트합니다. 업데이트된 파라미터는 *서버*로 다시 전송되며, *서버*는 " +"수신된 모든 파라미터 업데이트의 평균을 구합니다. 이것은 연합 학습 프로세스의 " +"한 라운드를 설명하며 여러 라운드에 걸쳐 이 과정을 반복합니다." #: ../../source/example-pytorch-from-centralized-to-federated.rst:197 msgid "" -"Finally, we will define our *client* logic in :code:`client.py` and build" -" upon the previously defined centralized training in :code:`cifar.py`. " -"Our *client* needs to import :code:`flwr`, but also :code:`torch` to " -"update the parameters on our PyTorch model:" +"Finally, we will define our *client* logic in :code:`client.py` and build " +"upon the previously defined centralized training in :code:`cifar.py`. Our " +"*client* needs to import :code:`flwr`, but also :code:`torch` to update the " +"parameters on our PyTorch model:" msgstr "" +"마지막으로, :code:`client.py`에서 *client* 로직을 정의하고 :code:`cifar.py`" +"에서 이전에 정의한 중앙 집중식 학습을 기반으로 구축합니다. *클라이언트*는 " +":code:`flwr`을 가져와야 하며, PyTorch 모델의 파라미터를 업데이트하기 위해 " +":code:`torch`도 가져와야 합니다:" #: ../../source/example-pytorch-from-centralized-to-federated.rst:213 msgid "" -"Implementing a Flower *client* basically means implementing a subclass of" -" either :code:`flwr.client.Client` or :code:`flwr.client.NumPyClient`. " -"Our implementation will be based on :code:`flwr.client.NumPyClient` and " -"we'll call it :code:`CifarClient`. :code:`NumPyClient` is slightly easier" -" to implement than :code:`Client` if you use a framework with good NumPy " -"interoperability (like PyTorch or TensorFlow/Keras) because it avoids " -"some of the boilerplate that would otherwise be necessary. " -":code:`CifarClient` needs to implement four methods, two methods for " -"getting/setting model parameters, one method for training the model, and " -"one method for testing the model:" -msgstr "" +"Implementing a Flower *client* basically means implementing a subclass of " +"either :code:`flwr.client.Client` or :code:`flwr.client.NumPyClient`. Our " +"implementation will be based on :code:`flwr.client.NumPyClient` and we'll " +"call it :code:`CifarClient`. :code:`NumPyClient` is slightly easier to " +"implement than :code:`Client` if you use a framework with good NumPy " +"interoperability (like PyTorch or TensorFlow/Keras) because it avoids some " +"of the boilerplate that would otherwise be necessary. :code:`CifarClient` " +"needs to implement four methods, two methods for getting/setting model " +"parameters, one method for training the model, and one method for testing " +"the model:" +msgstr "" +"Flower *클라이언트*를 구현한다는 것은 기본적으로 :code:`flwr.client.Client` " +"또는 :code:`flwr.client.NumPyClient`의 서브클래스를 구현하는 것을 의미합니" +"다. 우리의 구현은 :code:`flwr.client.NumPyClient`를 기반으로 하며, 이를 :" +"code:`CifarClient`라고 부를 것입니다. :code:`NumPyClient`는 파이토치나 텐서플" +"로우/Keras처럼 NumPy 상호운용성이 좋은 프레임워크를 사용하는 경우 필요한 일" +"부 보일러플레이트를 피하기 때문에 :code:`Client`보다 구현하기가 조금 더 쉽습" +"니다. code:`CifarClient`는 모델 파라미터를 가져오거나 설정하는 메서드 2개, 모" +"델 학습을 위한 메서드 1개, 모델 테스트를 위한 메서드 1개 등 네 가지 메서드를 " +"구현해야 합니다:" #: ../../source/example-pytorch-from-centralized-to-federated.rst:219 msgid ":code:`set_parameters`" -msgstr "" +msgstr ":code:`set_parameters`" #: ../../source/example-pytorch-from-centralized-to-federated.rst:226 msgid "get the updated local model weights and return them to the server" -msgstr "" +msgstr "업데이트된 로컬 모델 가중치를 가져와 서버로 반환합니다" #: ../../source/example-pytorch-from-centralized-to-federated.rst:230 msgid "return the local loss and accuracy to the server" -msgstr "" +msgstr "로컬 손실 및 정확도를 서버에 반환합니다" #: ../../source/example-pytorch-from-centralized-to-federated.rst:232 msgid "" -"The two :code:`NumPyClient` methods :code:`fit` and :code:`evaluate` make" -" use of the functions :code:`train()` and :code:`test()` previously " -"defined in :code:`cifar.py`. So what we really do here is we tell Flower " -"through our :code:`NumPyClient` subclass which of our already defined " -"functions to call for training and evaluation. We included type " -"annotations to give you a better understanding of the data types that get" -" passed around." +"The two :code:`NumPyClient` methods :code:`fit` and :code:`evaluate` make " +"use of the functions :code:`train()` and :code:`test()` previously defined " +"in :code:`cifar.py`. So what we really do here is we tell Flower through " +"our :code:`NumPyClient` subclass which of our already defined functions to " +"call for training and evaluation. We included type annotations to give you a " +"better understanding of the data types that get passed around." msgstr "" +"두 개의 :code:`NumPyClient` 메서드인 :code:`fit`과 :code:`evaluate`는 이전" +"에 :code:`cifar.py`에 정의된 함수인 :code:`train()`과 :code:`test()`를 활용합" +"니다. 따라서 여기서 실제로 하는 일은 :code:`NumPyClient` 서브클래스를 통해 이" +"미 정의된 함수 중 훈련과 평가를 위해 호출할 함수를 Flower에 알려주는 것입니" +"다. 전달되는 데이터 유형을 더 잘 이해할 수 있도록 type annotations을 포함했습" +"니다." #: ../../source/example-pytorch-from-centralized-to-federated.rst:280 msgid "" "All that's left to do it to define a function that loads both model and " -"data, creates a :code:`CifarClient`, and starts this client. You load " -"your data and model by using :code:`cifar.py`. Start :code:`CifarClient` " -"with the function :code:`fl.client.start_client()` by pointing it at the " -"same IP address we used in :code:`server.py`:" +"data, creates a :code:`CifarClient`, and starts this client. You load your " +"data and model by using :code:`cifar.py`. Start :code:`CifarClient` with the " +"function :code:`fl.client.start_client()` by pointing it at the same IP " +"address we used in :code:`server.py`:" msgstr "" +"이제 모델과 데이터를 모두 로드하는 함수를 정의하고, :code:`CifarClient`를 생" +"성하고, 이 클라이언트를 시작하는 작업만 남았습니다. 코드:`cifar.py`를 사용하" +"여 데이터와 모델을 로드합니다. :code:`server.py`에서 사용한 것과 동일한 IP 주" +"소를 지정하여 :code:`fl.client.start_client()` 함수로 :code:`CifarClient`를 " +"시작합니다:" #: ../../source/example-pytorch-from-centralized-to-federated.rst:307 msgid "" -"in each window (make sure that the server is running before you do so) " -"and see your (previously centralized) PyTorch project run federated " -"learning across two clients. Congratulations!" +"in each window (make sure that the server is running before you do so) and " +"see your (previously centralized) PyTorch project run federated learning " +"across two clients. Congratulations!" msgstr "" +"를 입력하고(그 전에 서버가 실행 중인지 확인하세요) (이전에는 중앙 집중식) " +"PyTorch 프로젝트가 두 클라이언트에서 연합 학습을 실행하는 것을 확인합니다. " +"축하합니다!" #: ../../source/example-pytorch-from-centralized-to-federated.rst:312 msgid "" "The full source code for this example: `PyTorch: From Centralized To " -"Federated (Code) `_. Our example is, of course, " -"somewhat over-simplified because both clients load the exact same " -"dataset, which isn't realistic. You're now prepared to explore this topic" -" further. How about using different subsets of CIFAR-10 on each client? " -"How about adding more clients?" +"Federated (Code) `_. Our example is, of course, somewhat over-" +"simplified because both clients load the exact same dataset, which isn't " +"realistic. You're now prepared to explore this topic further. How about " +"using different subsets of CIFAR-10 on each client? How about adding more " +"clients?" msgstr "" +"이 예제의 전체 소스 코드: `파이토치: 중앙 Centralized에서 Federated으로 (코" +"드) `_. 물론 이 예제는 두 클라이언트가 완전히 동일한 데" +"이터 세트를 로드하기 때문에 다소 지나치게 단순화되어 있으며, 이는 현실적이지 " +"않습니다. 이제 이 주제를 더 자세히 살펴볼 준비가 되셨습니다. 각 클라이언트에" +"서 서로 다른 CIFAR-10의 하위 집합을 사용해 보는 것은 어떨까요? 클라이언트를 " +"더 추가하는 것은 어떨까요?" #: ../../source/explanation-differential-privacy.rst:2 #: ../../source/explanation-differential-privacy.rst:11 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:303 msgid "Differential Privacy" -msgstr "" +msgstr "차분 프라이버시" #: ../../source/explanation-differential-privacy.rst:3 msgid "" -"The information in datasets like healthcare, financial transactions, user" -" preferences, etc., is valuable and has the potential for scientific " -"breakthroughs and provides important business insights. However, such " -"data is also sensitive and there is a risk of compromising individual " -"privacy." +"The information in datasets like healthcare, financial transactions, user " +"preferences, etc., is valuable and has the potential for scientific " +"breakthroughs and provides important business insights. However, such data " +"is also sensitive and there is a risk of compromising individual privacy." msgstr "" +"의료, 금융 거래, 사용자 선호도 등과 같은 데이터 세트의 정보는 가치 있고 과학" +"적 혁신의 잠재력을 지니고 있으며 중요한 비즈니스 인사이트를 제공합니다. 그러" +"나 이러한 데이터는 또한 민감한 정보이며 개인의 프라이버시를 침해할 위험이 있" +"습니다." #: ../../source/explanation-differential-privacy.rst:6 msgid "" "Traditional methods like anonymization alone would not work because of " -"attacks like Re-identification and Data Linkage. That's where " -"differential privacy comes in. It provides the possibility of analyzing " -"data while ensuring the privacy of individuals." +"attacks like Re-identification and Data Linkage. That's where differential " +"privacy comes in. It provides the possibility of analyzing data while " +"ensuring the privacy of individuals." msgstr "" +"익명화와 같은 기존 방법만으로는 재식별 및 데이터 연결과 같은 공격으로 인해 효" +"과가 없습니다. 그래서 차분 프라이버시가 등장했습니다. 차등 개인정보 보호는 개" +"인의 프라이버시를 보장하면서 데이터를 분석할 수 있는 가능성을 제공합니다." #: ../../source/explanation-differential-privacy.rst:12 msgid "" @@ -2865,226 +3226,309 @@ msgid "" "instance, Alice's data). Differential Privacy (DP) guarantees that any " "analysis (M), like calculating the average income, will produce nearly " "identical results for both datasets (O and O' would be similar). This " -"preserves group patterns while obscuring individual details, ensuring the" -" individual's information remains hidden in the crowd." +"preserves group patterns while obscuring individual details, ensuring the " +"individual's information remains hidden in the crowd." msgstr "" +"하나의 레코드(예: 앨리스의 데이터)를 제외하고는 동일한 두 개의 데이터 세트가 " +"있다고 상상해 보세요. 차분 프라이버(DP)는 평균 소득 계산과 같은 모든 분석(M)" +"이 두 데이터 세트에 대해 거의 동일한 결과를 산출하도록 보장합니다(O와 O' 는 " +"비슷할 것입니다). 이렇게 하면 그룹 패턴은 보존하면서 개별 세부 정보는 가려져 " +"개인의 정보가 군중 속에 숨겨집니다." #: ../../source/explanation-differential-privacy.rst:-1 msgid "DP Intro" -msgstr "" +msgstr "DP 소개" #: ../../source/explanation-differential-privacy.rst:22 msgid "" "One of the most commonly used mechanisms to achieve DP is adding enough " "noise to the output of the analysis to mask the contribution of each " -"individual in the data while preserving the overall accuracy of the " -"analysis." +"individual in the data while preserving the overall accuracy of the analysis." msgstr "" +"DP를 달성하기 위해 가장 일반적으로 사용되는 메커니즘 중 하나는 분석의 전반적" +"인 정확도를 유지하면서 데이터에서 각 개인의 기여도를 가릴 수 있도록 분석 결과" +"에 충분한 노이즈를 추가하는 것입니다." #: ../../source/explanation-differential-privacy.rst:25 msgid "Formal Definition" -msgstr "" +msgstr "공식 정의" #: ../../source/explanation-differential-privacy.rst:26 msgid "" "Differential Privacy (DP) provides statistical guarantees against the " "information an adversary can infer through the output of a randomized " -"algorithm. It provides an unconditional upper bound on the influence of a" -" single individual on the output of the algorithm by adding noise [1]. A " -"randomized mechanism M provides (:math:`\\epsilon`, " -":math:`\\delta`)-differential privacy if for any two neighboring " -"databases, D :sub:`1` and D :sub:`2`, that differ in only a single " -"record, and for all possible outputs S ⊆ Range(A):" -msgstr "" +"algorithm. It provides an unconditional upper bound on the influence of a " +"single individual on the output of the algorithm by adding noise [1]. A " +"randomized mechanism M provides (:math:`\\epsilon`, :math:`\\delta`)-" +"differential privacy if for any two neighboring databases, D :sub:`1` and D :" +"sub:`2`, that differ in only a single record, and for all possible outputs S " +"⊆ Range(A):" +msgstr "" +"차분 프라이버시(DP)는 공격자가 무작위 알고리즘의 출력을 통해 유추할 수 있는 " +"정보에 대해 통계적 보장을 제공합니다. 이는 노이즈를 추가하여 알고리즘의 출력" +"에 대한 한 개인의 영향력에 대한 무조건적인 상한선을 제공합니다[1]. 무작위 메" +"커니즘 M은 하나의 레코드만 다른 두 개의 인접 데이터베이스인 D:sub:`1`과 D:" +"sub:`2`의 경우, 가능한 모든 출력 S ⊆ Range(A)에 대해 (:math:`\\epsilon`, :" +"math:`\\delta`)-차분 프라이버시를 제공합니다:" #: ../../source/explanation-differential-privacy.rst:32 msgid "" "\\small\n" "P[M(D_{1} \\in A)] \\leq e^{\\delta} P[M(D_{2} \\in A)] + \\delta" msgstr "" +"\\small\n" +"P[M(D_{1} \\in A)] \\leq e^{\\delta} P[M(D_{2} \\in A)] + \\delta" #: ../../source/explanation-differential-privacy.rst:38 msgid "" "The :math:`\\epsilon` parameter, also known as the privacy budget, is a " "metric of privacy loss. It also controls the privacy-utility trade-off; " "lower :math:`\\epsilon` values indicate higher levels of privacy but are " -"likely to reduce utility as well. The :math:`\\delta` parameter accounts " -"for a small probability on which the upper bound :math:`\\epsilon` does " -"not hold. The amount of noise needed to achieve differential privacy is " -"proportional to the sensitivity of the output, which measures the maximum" -" change in the output due to the inclusion or removal of a single record." -msgstr "" +"likely to reduce utility as well. The :math:`\\delta` parameter accounts for " +"a small probability on which the upper bound :math:`\\epsilon` does not " +"hold. The amount of noise needed to achieve differential privacy is " +"proportional to the sensitivity of the output, which measures the maximum " +"change in the output due to the inclusion or removal of a single record." +msgstr "" +"프라이버시 예산이라고도 하는 :math:`\\epsilon` 매개변수는 프라이버시 손실을 " +"측정하는 지표입니다. 이 매개변수는 프라이버시와 효용의 균형을 제어하며, :" +"math:`\\epsilon` 값이 낮을수록 프라이버시 수준이 높지만 효용도 감소할 가능성" +"이 높습니다. math:`\\delta` 매개변수는 상한값인 :math:`\\epsilon`이 적용되지 " +"않는 작은 확률을 설명합니다. 차분 프라이버시를 달성하는 데 필요한 노이즈의 양" +"은 출력의 감도에 비례하며, 이는 단일 레코드의 포함 또는 제거로 인한 출력의 최" +"대 변화를 측정합니다." #: ../../source/explanation-differential-privacy.rst:45 msgid "Differential Privacy in Machine Learning" -msgstr "" +msgstr "머신 러닝의 차분 프라이버시" #: ../../source/explanation-differential-privacy.rst:46 msgid "" "DP can be utilized in machine learning to preserve the privacy of the " "training data. Differentially private machine learning algorithms are " -"designed in a way to prevent the algorithm to learn any specific " -"information about any individual data points and subsequently prevent the" -" model from revealing sensitive information. Depending on the stage at " -"which noise is introduced, various methods exist for applying DP to " -"machine learning algorithms. One approach involves adding noise to the " -"training data (either to the features or labels), while another method " -"entails injecting noise into the gradients of the loss function during " -"model training. Additionally, such noise can be incorporated into the " -"model's output." -msgstr "" +"designed in a way to prevent the algorithm to learn any specific information " +"about any individual data points and subsequently prevent the model from " +"revealing sensitive information. Depending on the stage at which noise is " +"introduced, various methods exist for applying DP to machine learning " +"algorithms. One approach involves adding noise to the training data (either " +"to the features or labels), while another method entails injecting noise " +"into the gradients of the loss function during model training. Additionally, " +"such noise can be incorporated into the model's output." +msgstr "" +"머신 러닝에서 DP를 활용하여 학습 데이터의 개인정보를 보호할 수 있습니다. 차" +"분 비공개 머신 러닝 알고리즘은 알고리즘이 개별 데이터 포인트에 대한 특정 정보" +"를 학습하지 못하도록 하여 모델이 민감한 정보를 노출하지 않도록 하는 방식으로 " +"설계되었습니다. 노이즈가 도입되는 단계에 따라 머신 러닝 알고리즘에 DP를 적용" +"하는 다양한 방법이 존재합니다. 한 가지 방법은 학습 데이터(특징 또는 레이블)" +"에 노이즈를 추가하는 것이고, 다른 방법은 모델 학습 중에 손실 함수의 기울기에 " +"노이즈를 주입하는 것입니다. 또한 이러한 노이즈를 모델의 출력에 통합할 수도 있" +"습니다." #: ../../source/explanation-differential-privacy.rst:53 msgid "Differential Privacy in Federated Learning" -msgstr "" +msgstr "연합 학습의 차분 프라이버시" #: ../../source/explanation-differential-privacy.rst:54 msgid "" "Federated learning is a data minimization approach that allows multiple " "parties to collaboratively train a model without sharing their raw data. " "However, federated learning also introduces new privacy challenges. The " -"model updates between parties and the central server can leak information" -" about the local data. These leaks can be exploited by attacks such as " +"model updates between parties and the central server can leak information " +"about the local data. These leaks can be exploited by attacks such as " "membership inference and property inference attacks, or model inversion " "attacks." msgstr "" +"연합 학습은 여러 당사자가 원시 데이터를 공유하지 않고도 공동으로 모델을 " +"학습할 수 있는 데이터 최소화 접근 방식입니다. 그러나 연합 학습은 새로운 " +"개인정보 보호 문제를 야기하기도 합니다. 당사자와 중앙 서버 간의 모델 " +"업데이트는 로컬 데이터에 대한 정보를 유출할 수 있습니다. 이러한 유출은 " +"멤버십 추론 및 속성 추론 공격이나 모델 반전 공격과 같은 공격에 악용될 수 " +"있습니다." #: ../../source/explanation-differential-privacy.rst:58 msgid "" -"DP can play a crucial role in federated learning to provide privacy for " -"the clients' data." -msgstr "" +"DP can play a crucial role in federated learning to provide privacy for the " +"clients' data." +msgstr "DP는 연합 학습에서 클라이언트의 데이터에 대한 개인 정보 보호를 제공하는 데 " +"중요한 역할을 할 수 있습니다." #: ../../source/explanation-differential-privacy.rst:60 msgid "" -"Depending on the granularity of privacy provision or the location of " -"noise addition, different forms of DP exist in federated learning. In " -"this explainer, we focus on two approaches of DP utilization in federated" -" learning based on where the noise is added: at the server (also known as" -" the center) or at the client (also known as the local)." +"Depending on the granularity of privacy provision or the location of noise " +"addition, different forms of DP exist in federated learning. In this " +"explainer, we focus on two approaches of DP utilization in federated " +"learning based on where the noise is added: at the server (also known as the " +"center) or at the client (also known as the local)." msgstr "" +"개인 정보 제공의 세분성 또는 노이즈 추가 위치에 따라 연합 학습에는 다양한 " +"형태의 DP가 존재합니다. 이 설명에서는 노이즈가 추가되는 위치에 따라 서버(" +"중앙이라고도 함) 또는 클라이언트(로컬이라고도 함)에서의 연합 학습에서 DP를 " +"활용하는 두 가지 접근 방식에 중점을 둡니다." #: ../../source/explanation-differential-privacy.rst:63 msgid "" -"**Central Differential Privacy**: DP is applied by the server and the " -"goal is to prevent the aggregated model from leaking information about " -"each client's data." +"**Central Differential Privacy**: DP is applied by the server and the goal " +"is to prevent the aggregated model from leaking information about each " +"client's data." msgstr "" +"**중앙 차분 프라이버시**: DP는 서버에서 적용되며 집계된 모델이 각 클라이언트" +"의 데이터에 대한 정보를 유출하는 것을 방지하는 것이 목표입니다." #: ../../source/explanation-differential-privacy.rst:65 msgid "" "**Local Differential Privacy**: DP is applied on the client side before " -"sending any information to the server and the goal is to prevent the " -"updates that are sent to the server from leaking any information about " -"the client's data." +"sending any information to the server and the goal is to prevent the updates " +"that are sent to the server from leaking any information about the client's " +"data." msgstr "" +"**로컬 차분 개인정보 보호**: DP는 정보를 서버로 보내기 전에 클라이언트 측에" +"서 적용되며, 서버로 전송되는 업데이트가 클라이언트 데이터에 대한 정보를 유출" +"하는 것을 방지하는 것이 목표입니다." #: ../../source/explanation-differential-privacy.rst:-1 #: ../../source/explanation-differential-privacy.rst:68 #: ../../source/how-to-use-differential-privacy.rst:11 msgid "Central Differential Privacy" -msgstr "" +msgstr "중앙 차분 프라이버시" #: ../../source/explanation-differential-privacy.rst:69 msgid "" -"In this approach, which is also known as user-level DP, the central " -"server is responsible for adding noise to the globally aggregated " -"parameters. It should be noted that trust in the server is required." +"In this approach, which is also known as user-level DP, the central server " +"is responsible for adding noise to the globally aggregated parameters. It " +"should be noted that trust in the server is required." msgstr "" +"사용자 수준 DP라고도 하는 이 접근 방식에서는 중앙 서버가 전역적으로 집계된 매" +"개변수에 노이즈를 추가하는 역할을 담당합니다. 서버에 대한 신뢰가 필요하다는 " +"점에 유의해야 합니다." #: ../../source/explanation-differential-privacy.rst:76 msgid "" -"While there are various ways to implement central DP in federated " -"learning, we concentrate on the algorithms proposed by [2] and [3]. The " -"overall approach is to clip the model updates sent by the clients and add" -" some amount of noise to the aggregated model. In each iteration, a " -"random set of clients is chosen with a specific probability for training." -" Each client performs local training on its own data. The update of each " -"client is then clipped by some value `S` (sensitivity `S`). This would " -"limit the impact of any individual client which is crucial for privacy " -"and often beneficial for robustness. A common approach to achieve this is" -" by restricting the `L2` norm of the clients' model updates, ensuring " -"that larger updates are scaled down to fit within the norm `S`." -msgstr "" +"While there are various ways to implement central DP in federated learning, " +"we concentrate on the algorithms proposed by [2] and [3]. The overall " +"approach is to clip the model updates sent by the clients and add some " +"amount of noise to the aggregated model. In each iteration, a random set of " +"clients is chosen with a specific probability for training. Each client " +"performs local training on its own data. The update of each client is then " +"clipped by some value `S` (sensitivity `S`). This would limit the impact of " +"any individual client which is crucial for privacy and often beneficial for " +"robustness. A common approach to achieve this is by restricting the `L2` " +"norm of the clients' model updates, ensuring that larger updates are scaled " +"down to fit within the norm `S`." +msgstr "" +"연합 학습에서 중앙 DP를 구현하는 방법은 여러 가지가 있지만, 여기서는 [2]와 " +"[3]에서 제안한 알고리즘에 집중합니다. 전반적인 접근 방식은 클라이언트가 " +"전송한 모델 업데이트를 잘라내고 집계된 모델에 약간의 노이즈를 추가하는 " +"것입니다. 각 반복에서 특정 확률로 훈련할 무작위 클라이언트 세트가 " +"선택됩니다. 각 클라이언트는 자체 데이터에 대해 로컬 학습을 수행합니다. 그런 " +"다음 각 클라이언트의 업데이트는 특정 값 `S`(민감도 `S`)에 의해 잘립니다. " +"이렇게 하면 개별 클라이언트의 영향을 제한할 수 있어 개인정보 보호에 중요하고 " +"견고성에 도움이 되는 경우가 많습니다. 이를 달성하기 위한 일반적인 접근 " +"방식은 클라이언트 모델 업데이트의 `L2` 규범을 제한하여 더 큰 업데이트가 규범 " +"`S`에 맞도록 축소되도록 하는 것입니다." #: ../../source/explanation-differential-privacy.rst:-1 msgid "clipping" -msgstr "" +msgstr "클리핑" #: ../../source/explanation-differential-privacy.rst:89 msgid "" -"Afterwards, the Gaussian mechanism is used to add noise in order to " -"distort the sum of all clients' updates. The amount of noise is scaled to" -" the sensitivity value to obtain a privacy guarantee. The Gaussian " -"mechanism is used with a noise sampled from `N (0, σ²)` where `σ = ( " -"noise_scale * S ) / (number of sampled clients)`." +"Afterwards, the Gaussian mechanism is used to add noise in order to distort " +"the sum of all clients' updates. The amount of noise is scaled to the " +"sensitivity value to obtain a privacy guarantee. The Gaussian mechanism is " +"used with a noise sampled from `N (0, σ²)` where `σ = ( noise_scale * S ) / " +"(number of sampled clients)`." msgstr "" +"그 후 가우시안 메커니즘을 사용하여 모든 클라이언트의 업데이트 합계를 왜곡하" +"기 위해 노이즈를 추가합니다. 노이즈의 양은 감도 값에 따라 조정되어 프라이버" +"시 보장을 얻습니다. 가우시안 메커니즘은 `N (0, σ²)`에서 샘플링된 노이즈와 함" +"께 사용됩니다. 여기서 `σ = (noise_scale * S) / (샘플링된 클라이언트 수)`입니" +"다." #: ../../source/explanation-differential-privacy.rst:94 msgid "Clipping" -msgstr "" +msgstr "클리핑" #: ../../source/explanation-differential-privacy.rst:96 msgid "" -"There are two forms of clipping commonly used in Central DP: Fixed " -"Clipping and Adaptive Clipping." -msgstr "" +"There are two forms of clipping commonly used in Central DP: Fixed Clipping " +"and Adaptive Clipping." +msgstr "중앙 DP에서 일반적으로 사용되는 클리핑에는 고정 클리핑과 조정 클리핑의 두 " +"가지 형태가 있습니다." #: ../../source/explanation-differential-privacy.rst:98 msgid "" -"**Fixed Clipping** : A predefined fix threshold is set for the magnitude " -"of clients' updates. Any update exceeding this threshold is clipped back " -"to the threshold value." +"**Fixed Clipping** : A predefined fix threshold is set for the magnitude of " +"clients' updates. Any update exceeding this threshold is clipped back to the " +"threshold value." msgstr "" +"**고정 클리핑** : 클라이언트의 업데이트 크기에 대해 미리 정의된 고정 임계값" +"이 설정됩니다. 이 임계값을 초과하는 모든 업데이트는 임계값으로 다시 클리핑됩" +"니다." #: ../../source/explanation-differential-privacy.rst:100 msgid "" -"**Adaptive Clipping** : The clipping threshold dynamically adjusts based " -"on the observed update distribution [4]. It means that the clipping value" -" is tuned during the rounds with respect to the quantile of the update " -"norm distribution." +"**Adaptive Clipping** : The clipping threshold dynamically adjusts based on " +"the observed update distribution [4]. It means that the clipping value is " +"tuned during the rounds with respect to the quantile of the update norm " +"distribution." msgstr "" +"**조정 클리핑** : 클리핑 임계값은 관찰된 업데이트 분포에 따라 동적으로 " +"조정됩니다[4]. 즉, 클리핑 값은 업데이트 표준 분포의 사분위수에 따라 라운드가 " +"진행되는 동안 조정됩니다." #: ../../source/explanation-differential-privacy.rst:102 msgid "" -"The choice between fixed and adaptive clipping depends on various factors" -" such as privacy requirements, data distribution, model complexity, and " +"The choice between fixed and adaptive clipping depends on various factors " +"such as privacy requirements, data distribution, model complexity, and " "others." msgstr "" +"고정 클리핑과 조정 클리핑 중 선택은 개인정보 보호 요구 사항, 데이터 배포, 모" +"델 복잡성 등 다양한 요인에 따라 달라집니다." #: ../../source/explanation-differential-privacy.rst:-1 #: ../../source/explanation-differential-privacy.rst:105 #: ../../source/how-to-use-differential-privacy.rst:96 msgid "Local Differential Privacy" -msgstr "" +msgstr "로컬 차분 프라이버시" #: ../../source/explanation-differential-privacy.rst:107 msgid "" "In this approach, each client is responsible for performing DP. Local DP " -"avoids the need for a fully trusted aggregator, but it should be noted " -"that local DP leads to a decrease in accuracy but better privacy in " -"comparison to central DP." +"avoids the need for a fully trusted aggregator, but it should be noted that " +"local DP leads to a decrease in accuracy but better privacy in comparison to " +"central DP." msgstr "" +"이 접근 방식에서는 각 클라이언트가 DP를 수행할 책임이 있습니다. 로컬 DP는 완" +"전히 신뢰할 수 있는 애그리게이터가 필요하지 않지만, 로컬 DP는 중앙 DP에 비해 " +"정확도는 떨어져도 개인 정보 보호는 더 우수하다는 점에 유의해야 합니다." #: ../../source/explanation-differential-privacy.rst:116 msgid "In this explainer, we focus on two forms of achieving Local DP:" -msgstr "" +msgstr "이 설명에서는 로컬 DP를 달성하는 두 가지 형태에 중점을 둡니다:" #: ../../source/explanation-differential-privacy.rst:118 msgid "" "Each client adds noise to the local updates before sending them to the " -"server. To achieve (:math:`\\epsilon`, :math:`\\delta`)-DP, considering " -"the sensitivity of the local model to be ∆, Gaussian noise is applied " -"with a noise scale of σ where:" +"server. To achieve (:math:`\\epsilon`, :math:`\\delta`)-DP, considering the " +"sensitivity of the local model to be ∆, Gaussian noise is applied with a " +"noise scale of σ where:" msgstr "" +"각 클라이언트는 로컬 업데이트를 서버로 보내기 전에 로컬 업데이트에 노이즈를 " +"추가합니다. 로컬 모델의 감도를 ∆로 간주하여 가우시안 노이즈가 σ의 노이즈 스케" +"일로 적용되어 (:math:`\\epsilon`, :math:`\\delta`)-DP를 달성하기 위해, 여기" +"서 σ는 노이즈 스케일입니다:" #: ../../source/explanation-differential-privacy.rst:120 msgid "" "\\small\n" -"\\frac{∆ \\times \\sqrt{2 \\times " -"\\log\\left(\\frac{1.25}{\\delta}\\right)}}{\\epsilon}\n" +"\\frac{∆ \\times \\sqrt{2 \\times \\log\\left(\\frac{1.25}{\\delta}\\right)}}" +"{\\epsilon}\n" "\n" msgstr "" +"\\small\n" +"\\frac{∆ \\times \\sqrt{2 \\times \\log\\left(\\frac{1.25}{\\delta}\\right)}}" +"{\\epsilon}\n" +"\n" #: ../../source/explanation-differential-privacy.rst:125 msgid "" @@ -3092,56 +3536,63 @@ msgid "" "training (DP-SGD). More specifically, in this approach, gradients are " "clipped and an amount of calibrated noise is injected into the gradients." msgstr "" +"각 클라이언트는 로컬 트레이닝(DP-SGD) 중에 모델의 gradient에 노이즈를 추가합" +"니다. 보다 구체적으로, 이 접근 방식에서는 gradient이 클리핑되고 보정된 노이즈" +"가 gradient에 주입됩니다." #: ../../source/explanation-differential-privacy.rst:128 msgid "" "Please note that these two approaches are providing privacy at different " "levels." msgstr "" +"이 두 가지 접근 방식은 서로 다른 수준의 개인정보 보호 기능을 제공한다는 점에 " +"유의하세요." #: ../../source/explanation-differential-privacy.rst:131 msgid "**References:**" -msgstr "" +msgstr "**참고:**" #: ../../source/explanation-differential-privacy.rst:133 msgid "[1] Dwork et al. The Algorithmic Foundations of Differential Privacy." -msgstr "" +msgstr "[1] Dwork 외. 차분 프라이버시의 알고리즘적 기초." #: ../../source/explanation-differential-privacy.rst:135 msgid "" -"[2] McMahan et al. Learning Differentially Private Recurrent Language " -"Models." -msgstr "" +"[2] McMahan et al. Learning Differentially Private Recurrent Language Models." +msgstr "[2] McMahan 외. 차분적 개인 반복 언어 모델 학습." #: ../../source/explanation-differential-privacy.rst:137 msgid "" -"[3] Geyer et al. Differentially Private Federated Learning: A Client " -"Level Perspective." -msgstr "" +"[3] Geyer et al. Differentially Private Federated Learning: A Client Level " +"Perspective." +msgstr "[3] Geyer 외. 차분적 개인 연합 학습: 고객 수준의 관점." #: ../../source/explanation-differential-privacy.rst:139 -msgid "[4] Galen et al. Differentially Private Learning with Adaptive Clipping." -msgstr "" +msgid "" +"[4] Galen et al. Differentially Private Learning with Adaptive Clipping." +msgstr "[4] Galen 외. 조정형 클리핑을 통한 차분적 개인 학습." #: ../../source/explanation-federated-evaluation.rst:2 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:292 msgid "Federated evaluation" -msgstr "" +msgstr "연합 평가" #: ../../source/explanation-federated-evaluation.rst:4 msgid "" "There are two main approaches to evaluating models in federated learning " -"systems: centralized (or server-side) evaluation and federated (or " -"client-side) evaluation." +"systems: centralized (or server-side) evaluation and federated (or client-" +"side) evaluation." msgstr "" +"연합 학습 시스템에서 모델을 평가하는 데는 중앙 집중식(또는 서버 측) 평가와 " +"연합(또는 클라이언트 측) 평가라는 두 가지 주요 접근 방식이 있습니다." #: ../../source/explanation-federated-evaluation.rst:8 msgid "Centralized Evaluation" -msgstr "" +msgstr "중앙 집중식 평가" #: ../../source/explanation-federated-evaluation.rst:11 msgid "Built-In Strategies" -msgstr "" +msgstr "기본 제공 전략" #: ../../source/explanation-federated-evaluation.rst:13 msgid "" @@ -3150,177 +3601,206 @@ msgid "" "function that can take the current global model parameters as input and " "return evaluation results:" msgstr "" +"모든 기본 제공 전략은 초기화 중에 평가 함수를 제공하여 중앙 집중식 평가를 " +"지원합니다. 평가 함수는 현재 글로벌 모델 파라미터를 입력으로 받아 평가 " +"결과를 반환할 수 있는 모든 함수입니다:" #: ../../source/explanation-federated-evaluation.rst:58 msgid "Custom Strategies" -msgstr "" +msgstr "사용자 정의 전략" #: ../../source/explanation-federated-evaluation.rst:60 msgid "" -"The :code:`Strategy` abstraction provides a method called " -":code:`evaluate` that can directly be used to evaluate the current global" -" model parameters. The current server implementation calls " -":code:`evaluate` after parameter aggregation and before federated " -"evaluation (see next paragraph)." +"The :code:`Strategy` abstraction provides a method called :code:`evaluate` " +"that can directly be used to evaluate the current global model parameters. " +"The current server implementation calls :code:`evaluate` after parameter " +"aggregation and before federated evaluation (see next paragraph)." msgstr "" +"코드:`전략` 추상화는 현재 전역 모델 파라미터를 평가하는 데 직접 사용할 수 있" +"는 :코드:`평가`라는 메서드를 제공합니다. 현재 서버 구현에서는 매개변수 집계 " +"후와 연합 평가 전에 :code:`evaluate`를 호출합니다(다음 단락 참조)." #: ../../source/explanation-federated-evaluation.rst:65 msgid "Federated Evaluation" -msgstr "" +msgstr "연합 평가" #: ../../source/explanation-federated-evaluation.rst:68 msgid "Implementing Federated Evaluation" -msgstr "" +msgstr "연합 평가 구현" #: ../../source/explanation-federated-evaluation.rst:70 msgid "" -"Client-side evaluation happens in the :code:`Client.evaluate` method and " -"can be configured from the server side." +"Client-side evaluation happens in the :code:`Client.evaluate` method and can " +"be configured from the server side." msgstr "" +"클라이언트 측 평가는 :code:`Client.evaluate` 메서드에서 이루어지며 서버 측에" +"서 구성할 수 있습니다." #: ../../source/explanation-federated-evaluation.rst:101 msgid "Configuring Federated Evaluation" -msgstr "" +msgstr "연합 평가 구성" #: ../../source/explanation-federated-evaluation.rst:103 msgid "" "Federated evaluation can be configured from the server side. Built-in " "strategies support the following arguments:" -msgstr "" +msgstr "연합 평가는 서버 측에서 구성할 수 있습니다. 기본 제공 전략은 다음 인수를 " +"지원합니다:" #: ../../source/explanation-federated-evaluation.rst:105 msgid "" -":code:`fraction_evaluate`: a :code:`float` defining the fraction of " -"clients that will be selected for evaluation. If " -":code:`fraction_evaluate` is set to :code:`0.1` and :code:`100` clients " -"are connected to the server, then :code:`10` will be randomly selected " -"for evaluation. If :code:`fraction_evaluate` is set to :code:`0.0`, " -"federated evaluation will be disabled." +":code:`fraction_evaluate`: a :code:`float` defining the fraction of clients " +"that will be selected for evaluation. If :code:`fraction_evaluate` is set " +"to :code:`0.1` and :code:`100` clients are connected to the server, then :" +"code:`10` will be randomly selected for evaluation. If :code:" +"`fraction_evaluate` is set to :code:`0.0`, federated evaluation will be " +"disabled." msgstr "" +":code:`fraction_evaluate`: 평가를 위해 선택될 클라이언트의 비율을 정의하는 " +":code:`float`입니다. 코드:`fraction_evaluate`가 :code:`0.1`로 설정되어 있고 " +":code:`100` 클라이언트가 서버에 연결되어 있는 경우 :code:`10`이 평가를 위해 " +"무작위로 선택됩니다. code:`fraction_evaluate`가 :code:`0.0`으로 설정된 경우 " +"연합 평가가 비활성화됩니다." #: ../../source/explanation-federated-evaluation.rst:106 msgid "" -":code:`min_evaluate_clients`: an :code:`int`: the minimum number of " -"clients to be selected for evaluation. If :code:`fraction_evaluate` is " -"set to :code:`0.1`, :code:`min_evaluate_clients` is set to 20, and " -":code:`100` clients are connected to the server, then :code:`20` clients " -"will be selected for evaluation." +":code:`min_evaluate_clients`: an :code:`int`: the minimum number of clients " +"to be selected for evaluation. If :code:`fraction_evaluate` is set to :code:" +"`0.1`, :code:`min_evaluate_clients` is set to 20, and :code:`100` clients " +"are connected to the server, then :code:`20` clients will be selected for " +"evaluation." msgstr "" +":code:`min_evaluate_clients`: 평가를 위해 선택할 최소 클라이언트 수. :code:" +"`int`. 코드:`fraction_evaluate`가 :code:`0.1`로 설정되어 있고 :code:" +"`fraction_evaluate`가 20으로 설정되어 있으며 :code:`100` 클라이언트가 서버에 " +"연결되어 있는 경우 :code:`20` 클라이언트가 평가를 위해 선택됩니다." #: ../../source/explanation-federated-evaluation.rst:107 msgid "" ":code:`min_available_clients`: an :code:`int` that defines the minimum " -"number of clients which need to be connected to the server before a round" -" of federated evaluation can start. If fewer than " -":code:`min_available_clients` are connected to the server, the server " -"will wait until more clients are connected before it continues to sample " -"clients for evaluation." +"number of clients which need to be connected to the server before a round of " +"federated evaluation can start. If fewer than :code:`min_available_clients` " +"are connected to the server, the server will wait until more clients are " +"connected before it continues to sample clients for evaluation." msgstr "" +":code:`min_available_clients`: federated 평가 단계를 시작하기 전에 서버에 연" +"결해야 하는 최소 클라이언트 수를 정의하는 :code:`int`입니다. 서버에 연결된 클" +"라이언트가 :code:`min_available_clients`보다 적으면 서버는 더 많은 클라이언트" +"가 연결될 때까지 기다렸다가 평가를 위한 클라이언트 샘플링을 계속합니다." #: ../../source/explanation-federated-evaluation.rst:108 msgid "" ":code:`on_evaluate_config_fn`: a function that returns a configuration " -"dictionary which will be sent to the selected clients. The function will " -"be called during each round and provides a convenient way to customize " -"client-side evaluation from the server side, for example, to configure " -"the number of validation steps performed." +"dictionary which will be sent to the selected clients. The function will be " +"called during each round and provides a convenient way to customize client-" +"side evaluation from the server side, for example, to configure the number " +"of validation steps performed." msgstr "" +"code:`on_evaluate_config_fn`: 선택한 클라이언트로 전송할 구성 사전을 반환하" +"는 함수입니다. 이 함수는 각 단계 중에 호출되며, 서버 측에서 클라이언트 측 평" +"가를 사용자 지정하는 편리한 방법을 제공합니다(예: 수행되는 유효성 검사 단계 " +"수 구성)." #: ../../source/explanation-federated-evaluation.rst:135 msgid "Evaluating Local Model Updates During Training" -msgstr "" +msgstr "훈련 중 로컬 모델 업데이트 평가" #: ../../source/explanation-federated-evaluation.rst:137 msgid "" -"Model parameters can also be evaluated during training. " -":code:`Client.fit` can return arbitrary evaluation results as a " -"dictionary:" +"Model parameters can also be evaluated during training. :code:`Client.fit` " +"can return arbitrary evaluation results as a dictionary:" msgstr "" +"모델 파라미터는 훈련 중에도 평가할 수 있습니다. :code:`Client.fit`은 임의의 " +"평가 결과를 dictionary로 반환할 수 있습니다:" #: ../../source/explanation-federated-evaluation.rst:177 msgid "Full Code Example" -msgstr "" +msgstr "전체 코드 예제" #: ../../source/explanation-federated-evaluation.rst:179 msgid "" -"For a full code example that uses both centralized and federated " -"evaluation, see the *Advanced TensorFlow Example* (the same approach can " -"be applied to workloads implemented in any other framework): " -"https://github.com/adap/flower/tree/main/examples/advanced-tensorflow" +"For a full code example that uses both centralized and federated evaluation, " +"see the *Advanced TensorFlow Example* (the same approach can be applied to " +"workloads implemented in any other framework): https://github.com/adap/" +"flower/tree/main/examples/advanced-tensorflow" msgstr "" +"연합 평가와 중앙 집중식 평가를 모두 사용하는 전체 코드 예제는 *고급 " +"텐서플로우 예제*(다른 프레임워크에서 구현된 워크로드에도 동일한 접근 방식을 " +"적용할 수 있음)를 참조하세요: https://github.com/adap/flower/tree/main/" +"examples/advanced-tensorflow" #: ../../source/fed/0000-20200102-fed-template.md:10 msgid "FED Template" -msgstr "" +msgstr "FED 템플릿" #: ../../source/fed/0000-20200102-fed-template.md:12 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:12 msgid "Table of Contents" -msgstr "" +msgstr "목차" #: ../../source/fed/0000-20200102-fed-template.md:14 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:14 msgid "[Table of Contents](#table-of-contents)" -msgstr "" +msgstr "[목차](#목차)" #: ../../source/fed/0000-20200102-fed-template.md:15 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:15 msgid "[Summary](#summary)" -msgstr "" +msgstr "[요약](#요약)" #: ../../source/fed/0000-20200102-fed-template.md:16 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:16 msgid "[Motivation](#motivation)" -msgstr "" +msgstr "[동기](#동기)" #: ../../source/fed/0000-20200102-fed-template.md:17 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:17 msgid "[Goals](#goals)" -msgstr "" +msgstr "[목표](#목표)" #: ../../source/fed/0000-20200102-fed-template.md:18 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:18 msgid "[Non-Goals](#non-goals)" -msgstr "" +msgstr "[비목표](#비목표)" #: ../../source/fed/0000-20200102-fed-template.md:19 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:19 msgid "[Proposal](#proposal)" -msgstr "" +msgstr "[제안](#제안)" #: ../../source/fed/0000-20200102-fed-template.md:20 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:23 msgid "[Drawbacks](#drawbacks)" -msgstr "" +msgstr "[단점](#단점)" #: ../../source/fed/0000-20200102-fed-template.md:21 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:24 msgid "[Alternatives Considered](#alternatives-considered)" -msgstr "" +msgstr "[고려되는 대안](#고려되는 대안)" #: ../../source/fed/0000-20200102-fed-template.md:22 msgid "[Appendix](#appendix)" -msgstr "" +msgstr "[부록](#부록)" #: ../../source/fed/0000-20200102-fed-template.md:24 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:28 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:76 msgid "Summary" -msgstr "" +msgstr "요약" #: ../../source/fed/0000-20200102-fed-template.md:26 msgid "\\[TODO - sentence 1: summary of the problem\\]" -msgstr "" +msgstr "\\[TODO - 문장 1: 문제 요약\\]" #: ../../source/fed/0000-20200102-fed-template.md:28 msgid "\\[TODO - sentence 2: summary of the solution\\]" -msgstr "" +msgstr "\\[TODO - 문장 2: 솔루션 요약\\]" #: ../../source/fed/0000-20200102-fed-template.md:30 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:47 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:77 msgid "Motivation" -msgstr "" +msgstr "동기" #: ../../source/fed/0000-20200102-fed-template.md:32 #: ../../source/fed/0000-20200102-fed-template.md:36 @@ -3330,990 +3810,1228 @@ msgstr "" #: ../../source/fed/0000-20200102-fed-template.md:54 #: ../../source/fed/0000-20200102-fed-template.md:58 msgid "\\[TODO\\]" -msgstr "" +msgstr "\\[TODO\\]" #: ../../source/fed/0000-20200102-fed-template.md:34 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:53 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:78 msgid "Goals" -msgstr "" +msgstr "목표" #: ../../source/fed/0000-20200102-fed-template.md:38 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:59 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:79 msgid "Non-Goals" -msgstr "" +msgstr "비목표" #: ../../source/fed/0000-20200102-fed-template.md:42 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:65 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:80 msgid "Proposal" -msgstr "" +msgstr "제안" #: ../../source/fed/0000-20200102-fed-template.md:46 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:85 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:129 msgid "Drawbacks" -msgstr "" +msgstr "단점" #: ../../source/fed/0000-20200102-fed-template.md:50 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:86 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:135 msgid "Alternatives Considered" -msgstr "" +msgstr "고려되는 대안" #: ../../source/fed/0000-20200102-fed-template.md:52 msgid "\\[Alternative 1\\]" -msgstr "" +msgstr "\\[대안 1\\]" #: ../../source/fed/0000-20200102-fed-template.md:56 msgid "\\[Alternative 2\\]" -msgstr "" +msgstr "\\[대안 2\\]" #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:10 msgid "Flower Enhancement Doc" -msgstr "" +msgstr "Flower Enhancement Doc" #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:20 msgid "[Enhancement Doc Template](#enhancement-doc-template)" -msgstr "" +msgstr "[Enhancement Doc 템플릿](#enhancement-doc-템플릿)" #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:21 msgid "[Metadata](#metadata)" -msgstr "" +msgstr "[Metadata](#metadata)" #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:22 msgid "[Workflow](#workflow)" -msgstr "" +msgstr "[워크플로우](#워크플로우)" #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:25 msgid "[GitHub Issues](#github-issues)" -msgstr "" +msgstr "[GitHub Issues](#github-issues)" #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:26 msgid "[Google Docs](#google-docs)" -msgstr "" +msgstr "[Google Docs](#google-docs)" #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:30 msgid "A Flower Enhancement is a standardized development process to" -msgstr "" +msgstr "Flower Enhancement는 다음과 같은 표준화된 개발 프로세스입니다" #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:32 msgid "provide a common structure for proposing larger changes" -msgstr "" +msgstr "더 큰 변경 사항을 제안하기 위한 공통 구조를 제공합니다" #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:33 msgid "ensure that the motivation for a change is clear" -msgstr "" +msgstr "변화의 동기가 분명한지 확인합니다" #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:34 msgid "persist project information in a version control system" -msgstr "" +msgstr "버전 관리 시스템에서 프로젝트 정보를 유지합니다" #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:35 msgid "document the motivation for impactful user-facing changes" -msgstr "" +msgstr "사용자에게 영향력 있는 변화에 대한 동기를 문서화합니다" #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:36 msgid "reserve GitHub issues for tracking work in flight" -msgstr "" +msgstr "운행 중 작업 추적을 위한 깃허브 이슈를 예약합니다" #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:37 msgid "" -"ensure community participants can successfully drive changes to " -"completion across one or more releases while stakeholders are adequately " -"represented throughout the process" +"ensure community participants can successfully drive changes to completion " +"across one or more releases while stakeholders are adequately represented " +"throughout the process" msgstr "" +"커뮤니티 참여자가 하나 이상의 릴리즈에서 변경 사항을 성공적으로 완료할 수 있" +"도록 하는 동시에 이해 관계자가 프로세스 전반에 걸쳐 적절히 대표되도록 보장합" +"니다" #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:39 msgid "Hence, an Enhancement Doc combines aspects of" -msgstr "" +msgstr "따라서 Enhancement 문서에는 다음과 같은 측면이 결합되어 있습니다" #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:41 msgid "a feature, and effort-tracking document" -msgstr "" +msgstr "기능 및 effort-tracking 문서" #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:42 msgid "a product requirements document" -msgstr "" +msgstr "제품 요구 사항 문서" #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:43 msgid "a design document" -msgstr "" +msgstr "디자인 문서" #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:45 msgid "" "into one file, which is created incrementally in collaboration with the " "community." -msgstr "" +msgstr "를 하나의 파일로 통합하여 커뮤니티와 협력해 점진적으로 생성합니다." #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:49 msgid "" "For far-fetching changes or features proposed to Flower, an abstraction " -"beyond a single GitHub issue or pull request is required to understand " -"and communicate upcoming changes to the project." +"beyond a single GitHub issue or pull request is required to understand and " +"communicate upcoming changes to the project." msgstr "" +"Flower에 제안된 변경 사항이나 기능을 멀리 가져오는 경우, 프로젝트의 향후 변" +"경 사항을 이해하고 전달하기 위해 단일 GitHub 이슈 또는 pull request를 넘어서" +"는 abstraction이 필요합니다." #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:51 msgid "" -"The purpose of this process is to reduce the amount of \"tribal " -"knowledge\" in our community. By moving decisions from Slack threads, " -"video calls, and hallway conversations into a well-tracked artifact, this" -" process aims to enhance communication and discoverability." +"The purpose of this process is to reduce the amount of \"tribal knowledge\" " +"in our community. By moving decisions from Slack threads, video calls, and " +"hallway conversations into a well-tracked artifact, this process aims to " +"enhance communication and discoverability." msgstr "" +"이 프로세스의 목적은 커뮤니티 내 '부족한 지식'의 양을 줄이는 것입니다. 이 프" +"로세스는 Slack 스레드, 영상 통화, 복도 대화에서 나온 의사 결정을 잘 추적된 아" +"티팩트로 옮김으로써 커뮤니케이션과 검색 가능성을 향상시키는 것을 목표로 합니" +"다." #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:55 msgid "" -"Roughly any larger, user-facing enhancement should follow the Enhancement" -" process. If an enhancement would be described in either written or " -"verbal communication to anyone besides the author or developer, then " -"consider creating an Enhancement Doc." +"Roughly any larger, user-facing enhancement should follow the Enhancement " +"process. If an enhancement would be described in either written or verbal " +"communication to anyone besides the author or developer, then consider " +"creating an Enhancement Doc." msgstr "" +"대략적으로 사용자를 대상으로 하는 대규모 개선 사항은 개선 프로세스를 따라야 " +"합니다. 개선 사항을 작성자나 개발자 이외의 다른 사람에게 서면 또는 구두로 설" +"명해야 하는 경우에는 개선 문서 작성을 고려하세요." #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:57 msgid "" -"Similarly, any technical effort (refactoring, major architectural change)" -" that will impact a large section of the development community should " -"also be communicated widely. The Enhancement process is suited for this " -"even if it will have zero impact on the typical user or operator." +"Similarly, any technical effort (refactoring, major architectural change) " +"that will impact a large section of the development community should also be " +"communicated widely. The Enhancement process is suited for this even if it " +"will have zero impact on the typical user or operator." msgstr "" +"마찬가지로 개발 커뮤니티의 많은 부분에 영향을 미치는 기술적 노력(리팩토링, 주" +"요 아키텍처 변경)도 널리 알려야 합니다. 개선 프로세스는 일반 사용자나 운영자" +"에게 전혀 영향을 미치지 않더라도 이를 위해 적합합니다." #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:61 msgid "" -"For small changes and additions, going through the Enhancement process " -"would be time-consuming and unnecessary. This includes, for example, " -"adding new Federated Learning algorithms, as these only add features " -"without changing how Flower works or is used." +"For small changes and additions, going through the Enhancement process would " +"be time-consuming and unnecessary. This includes, for example, adding new " +"Federated Learning algorithms, as these only add features without changing " +"how Flower works or is used." msgstr "" +"작은 변경 및 추가의 경우, 개선 프로세스를 거치는 것은 시간이 많이 걸리고 " +"불필요합니다. 예를 들어, 새로운 연합 학습 알고리즘을 추가하는 것은 Flower의 " +"작동 방식이나 사용 방식을 변경하지 않고 기능만 추가하는 것이기 때문입니다." #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:63 msgid "" "Enhancements are different from feature requests, as they are already " -"providing a laid-out path for implementation and are championed by " -"members of the community." +"providing a laid-out path for implementation and are championed by members " +"of the community." msgstr "" +"기능 개선은 이미 구현할 수 있는 경로가 마련되어 있고 커뮤니티 구성원들이 지지" +"하는 것이므로 기능 요청과는 다릅니다." #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:67 msgid "" "An Enhancement is captured in a Markdown file that follows a defined " -"template and a workflow to review and store enhancement docs for " -"reference — the Enhancement Doc." +"template and a workflow to review and store enhancement docs for reference " +"— the Enhancement Doc." msgstr "" +"개선 사항은 정의된 템플릿과 참조용으로 Enhancement Doc.를 검토하고 저장하는 " +"워크플로우를 따르는 Markdown 파일에 캡처됩니다." #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:69 msgid "Enhancement Doc Template" -msgstr "" +msgstr "Enhancement Doc 템플릿" #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:71 msgid "" "Each enhancement doc is provided as a Markdown file having the following " "structure" -msgstr "" +msgstr "각 개선 사항 문서는 다음과 같은 구조의 Markdown 파일로 제공됩니다" #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:73 msgid "Metadata (as [described below](#metadata) in form of a YAML preamble)" -msgstr "" +msgstr "Metadata ([아래 설명](#metadata) YAML preamble 형식)" #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:74 msgid "Title (same as in metadata)" -msgstr "" +msgstr "Title (metadata와 같게)" #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:75 msgid "Table of Contents (if needed)" -msgstr "" +msgstr "Table of Contents (필요시)" #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:81 msgid "Notes/Constraints/Caveats (optional)" -msgstr "" +msgstr "Notes/Constraints/Caveats (선택 사항)" #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:82 msgid "Design Details (optional)" -msgstr "" +msgstr "Design Details (선택 사항)" #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:83 msgid "Graduation Criteria" -msgstr "" +msgstr "졸업 기준" #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:84 msgid "Upgrade/Downgrade Strategy (if applicable)" -msgstr "" +msgstr "업그레이드/다운그레이드 전략(해당되는 경우)" #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:88 msgid "As a reference, this document follows the above structure." -msgstr "" +msgstr "참고로 이 문서는 위의 구조를 따릅니다." #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:90 #: ../../source/ref-api/flwr.common.Metadata.rst:2 msgid "Metadata" -msgstr "" +msgstr "Metadata" #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:92 msgid "" -"**fed-number** (Required) The `fed-number` of the last Flower Enhancement" -" Doc + 1. With this number, it becomes easy to reference other proposals." +"**fed-number** (Required) The `fed-number` of the last Flower Enhancement " +"Doc + 1. With this number, it becomes easy to reference other proposals." msgstr "" +"**피드 번호** (필수) 마지막 Flower Enhancement 문서의 `피드 번호` + 1. 이 번" +"호를 사용하면 다른 제안을 쉽게 참조할 수 있습니다." #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:94 msgid "**title** (Required) The title of the proposal in plain language." -msgstr "" +msgstr "**제목** (필수) 제안서의 제목을 평이한 언어로 입력합니다." #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:96 msgid "" -"**status** (Required) The current status of the proposal. See " -"[workflow](#workflow) for the possible states." +"**status** (Required) The current status of the proposal. See [workflow]" +"(#workflow) for the possible states." msgstr "" +"**상태** (필수) 제안의 현재 상태입니다. 가능한 상태는 [워크플로](#워크플로)" +"를 참조하세요." #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:98 msgid "" -"**authors** (Required) A list of authors of the proposal. This is simply " -"the GitHub ID." -msgstr "" +"**authors** (Required) A list of authors of the proposal. This is simply the " +"GitHub ID." +msgstr "**저자** (필수) 제안서의 작성자 목록입니다. 간단히 GitHub ID입니다." #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:100 msgid "" -"**creation-date** (Required) The date that the proposal was first " -"submitted in a PR." -msgstr "" +"**creation-date** (Required) The date that the proposal was first submitted " +"in a PR." +msgstr "**생성 날짜** (필수) PR에서 제안서를 처음 제출한 날짜입니다." #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:102 msgid "" "**last-updated** (Optional) The date that the proposal was last changed " "significantly." msgstr "" +"**마지막 업데이트** (선택 사항) 제안서가 마지막으로 크게 변경된 날짜입니다." #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:104 msgid "" -"**see-also** (Optional) A list of other proposals that are relevant to " -"this one." -msgstr "" +"**see-also** (Optional) A list of other proposals that are relevant to this " +"one." +msgstr "**함께 보기** (선택 사항) 이 제안과 관련된 다른 제안 목록입니다." #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:106 msgid "**replaces** (Optional) A list of proposals that this one replaces." -msgstr "" +msgstr "**대체** (선택 사항) 이 제안이 대체하는 제안 목록입니다." #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:108 -msgid "**superseded-by** (Optional) A list of proposals that this one supersedes." -msgstr "" +msgid "" +"**superseded-by** (Optional) A list of proposals that this one supersedes." +msgstr "**대체됨** (선택 사항) 이 제안이 대체하는 제안의 목록입니다." #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:111 msgid "Workflow" -msgstr "" +msgstr "워크플로우" #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:113 msgid "" "The idea forming the enhancement should already have been discussed or " -"pitched in the community. As such, it needs a champion, usually the " -"author, who shepherds the enhancement. This person also has to find " -"committers to Flower willing to review the proposal." +"pitched in the community. As such, it needs a champion, usually the author, " +"who shepherds the enhancement. This person also has to find committers to " +"Flower willing to review the proposal." msgstr "" +"개선 사항을 구성하는 아이디어는 이미 커뮤니티에서 논의되었거나 제안된 적이 있" +"어야 합니다. 따라서 개선 사항을 주도하는 사(보통 작성자)이 필요합니다. 이 사" +"람은 또한 제안을 검토할 의향이 있는 Flower 커미터를 찾아야 합니다." #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:115 msgid "" "New enhancements are checked in with a file name in the form of `NNNN-" -"YYYYMMDD-enhancement-title.md`, with `NNNN` being the Flower Enhancement " -"Doc number, to `enhancements`. All enhancements start in `provisional` " -"state as part of a pull request. Discussions are done as part of the pull" -" request review." +"YYYYMMDD-enhancement-title.md`, with `NNNN` being the Flower Enhancement Doc " +"number, to `enhancements`. All enhancements start in `provisional` state as " +"part of a pull request. Discussions are done as part of the pull request " +"review." msgstr "" +"새 개선 사항은 `NNNN-YYYYMMDD-enhancement-title.md` 형식의 파일 이름으로 체크" +"인되며, `NNNN`은 Flower 개선 문서 번호이고 `enhancements`에 해당합니다. 모든 " +"개선 사항은 pull request의 일부로 `잠정` 상태에서 시작됩니다. 토론은 pull " +"request 검토의 일부로 이루어집니다." #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:117 msgid "" -"Once an enhancement has been reviewed and approved, its status is changed" -" to `implementable`. The actual implementation is then done in separate " -"pull requests. These pull requests should mention the respective " -"enhancement as part of their description. After the implementation is " -"done, the proposal status is changed to `implemented`." +"Once an enhancement has been reviewed and approved, its status is changed to " +"`implementable`. The actual implementation is then done in separate pull " +"requests. These pull requests should mention the respective enhancement as " +"part of their description. After the implementation is done, the proposal " +"status is changed to `implemented`." msgstr "" +"개선 사항이 검토 및 승인되면 상태가 '구현 가능'으로 변경됩니다. 그런 다음 실" +"제 구현은 별도의 pull requests를 통해 이루어집니다. 이러한 pull requests는 설" +"명의 일부로 해당 개선 사항을 언급해야 합니다. 구현이 완료되면 제안 상태는 '구" +"현됨'으로 변경됩니다." #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:119 msgid "" -"Under certain conditions, other states are possible. An Enhancement has " -"the following states:" +"Under certain conditions, other states are possible. An Enhancement has the " +"following states:" msgstr "" +"특정 조건에서는 다른 상태도 가능합니다. 개선에는 다음과 같은 상태가 있습니다:" #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:121 msgid "" "`provisional`: The enhancement has been proposed and is actively being " -"defined. This is the starting state while the proposal is being fleshed " -"out and actively defined and discussed." +"defined. This is the starting state while the proposal is being fleshed out " +"and actively defined and discussed." msgstr "" +"'잠정적': 개선 사항이 제안되어 활발히 정의되고 있습니다. 제안이 구체화되고 활" +"발하게 정의 및 논의되는 동안의 시작 단계입니다." #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:122 msgid "`implementable`: The enhancement has been reviewed and approved." -msgstr "" +msgstr "`구현 가능`: 개선 사항이 검토 및 승인되었습니다." #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:123 msgid "" "`implemented`: The enhancement has been implemented and is no longer " "actively changed." -msgstr "" +msgstr "`구현됨`: 개선 사항이 구현되었으며 더 이상 활발히 변경되지 않습니다." #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:124 -msgid "`deferred`: The enhancement is proposed but not actively being worked on." -msgstr "" +msgid "" +"`deferred`: The enhancement is proposed but not actively being worked on." +msgstr "'지연됨': 개선 사항이 제안되었지만 아직 활발히 작업 중이 아닙니다." #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:125 msgid "" -"`rejected`: The authors and reviewers have decided that this enhancement " -"is not moving forward." +"`rejected`: The authors and reviewers have decided that this enhancement is " +"not moving forward." msgstr "" +"`거부됨`: 작성자와 검토자는 이 개선 사항을 더 이상 진행하지 않기로 결정했습니" +"다." #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:126 msgid "`withdrawn`: The authors have withdrawn the enhancement." -msgstr "" +msgstr "`철회`: 작성자가 개선 사항을 철회했습니다." #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:127 msgid "`replaced`: The enhancement has been replaced by a new enhancement." -msgstr "" +msgstr "'대체됨': 개선 사항이 새로운 개선 사항으로 대체되었습니다." #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:131 msgid "" -"Adding an additional process to the ones already provided by GitHub " -"(Issues and Pull Requests) adds more complexity and can be a barrier for " -"potential first-time contributors." +"Adding an additional process to the ones already provided by GitHub (Issues " +"and Pull Requests) adds more complexity and can be a barrier for potential " +"first-time contributors." msgstr "" +"GitHub에서 이미 제공하는 프로세스(이슈 및 Pull Requests)에 추가 프로세스를 추" +"가하면 더 복잡해지고 잠재적인 처음인 기여자에게는 장벽이 될 수 있습니다." #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:133 msgid "" "Expanding the proposal template beyond the single-sentence description " -"currently required in the features issue template may be a heavy burden " -"for non-native English speakers." +"currently required in the features issue template may be a heavy burden for " +"non-native English speakers." msgstr "" +"현재 기능 이슈 템플릿에서 요구되는 한 문장 설명 이상으로 제안서 템플릿을 확장" +"하는 것은 영어가 모국어가 아닌 사용자에게는 큰 부담이 될 수 있습니다." #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:137 msgid "GitHub Issues" -msgstr "" +msgstr "GitHub 이슈" #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:139 msgid "" "Using GitHub Issues for these kinds of enhancements is doable. One could " -"use, for example, tags, to differentiate and filter them from other " -"issues. The main issue is in discussing and reviewing an enhancement: " -"GitHub issues only have a single thread for comments. Enhancements " -"usually have multiple threads of discussion at the same time for various " -"parts of the doc. Managing these multiple discussions can be confusing " -"when using GitHub Issues." -msgstr "" +"use, for example, tags, to differentiate and filter them from other issues. " +"The main issue is in discussing and reviewing an enhancement: GitHub issues " +"only have a single thread for comments. Enhancements usually have multiple " +"threads of discussion at the same time for various parts of the doc. " +"Managing these multiple discussions can be confusing when using GitHub " +"Issues." +msgstr "" +"이러한 종류의 개선을 위해 GitHub 이슈를 사용하면 가능합니다. 예를 들어 태그" +"를 사용하여 다른 이슈와 구별하고 필터링할 수 있습니다. 주요 이슈는 개선 사항" +"에 대해 토론하고 검토하는 것입니다: GitHub 이슈에는 댓글 스레드가 하나만 있습" +"니다. 개선 사항에는 일반적으로 문서의 여러 부분에 대해 동시에 여러 개의 토론 " +"스레드가 있습니다. GitHub 이슈를 사용할 때 이러한 여러 토론을 관리하면 혼란스" +"러울 수 있습니다." #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:141 msgid "Google Docs" -msgstr "" +msgstr "Google 문서 도구" #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:143 msgid "" -"Google Docs allow for multiple threads of discussions. But as Google Docs" -" are hosted outside the project, their discoverability by the community " -"needs to be taken care of. A list of links to all proposals has to be " -"managed and made available for the community. Compared to shipping " -"proposals as part of Flower's repository, the potential for missing links" -" is much higher." +"Google Docs allow for multiple threads of discussions. But as Google Docs " +"are hosted outside the project, their discoverability by the community needs " +"to be taken care of. A list of links to all proposals has to be managed and " +"made available for the community. Compared to shipping proposals as part of " +"Flower's repository, the potential for missing links is much higher." msgstr "" +"Google 문서는 여러 스레드의 토론을 허용합니다. 하지만 Google 문서는 프로젝트 " +"외부에서 호스팅되므로 커뮤니티에서 검색할 수 있도록 관리해야 합니다. 모든 제" +"안에 대한 링크 목록을 관리하고 커뮤니티에 제공해야 합니다. Flower 저장소의 일" +"부로 제안서를 보낼 때와 비교하면 링크가 누락될 가능성이 훨씬 더 높습니다." #: ../../source/fed/index.md:1 msgid "FED - Flower Enhancement Doc" -msgstr "" +msgstr "FED - Flower 개선 문서" #: ../../source/how-to-aggregate-evaluation-results.rst:2 msgid "Aggregate evaluation results" -msgstr "" +msgstr "종합 평가 결과" #: ../../source/how-to-aggregate-evaluation-results.rst:4 msgid "" -"The Flower server does not prescribe a way to aggregate evaluation " -"results, but it enables the user to fully customize result aggregation." +"The Flower server does not prescribe a way to aggregate evaluation results, " +"but it enables the user to fully customize result aggregation." msgstr "" +"Flower 서버는 평가 결과를 집계하는 방법을 규정하고 있지 않지만 사용자가 결과 " +"집계를 완전히 사용자 지정할 수 있습니다." #: ../../source/how-to-aggregate-evaluation-results.rst:8 msgid "Aggregate Custom Evaluation Results" -msgstr "" +msgstr "사용자 지정 평가 결과 집계" #: ../../source/how-to-aggregate-evaluation-results.rst:10 msgid "" -"The same :code:`Strategy`-customization approach can be used to aggregate" -" custom evaluation results coming from individual clients. Clients can " -"return custom metrics to the server by returning a dictionary:" +"The same :code:`Strategy`-customization approach can be used to aggregate " +"custom evaluation results coming from individual clients. Clients can return " +"custom metrics to the server by returning a dictionary:" msgstr "" +"동일한 :code:`Strategy`-사용자 지정 방식을 사용하여 개별 클라이언트로부터 오" +"는 사용자 지정 평가 결과를 집계할 수 있습니다. 클라이언트는 dictionary를 반환" +"하여 사용자 지정 지표를 서버에 반환할 수 있습니다:" #: ../../source/how-to-aggregate-evaluation-results.rst:36 msgid "" "The server can then use a customized strategy to aggregate the metrics " "provided in these dictionaries:" msgstr "" +"그런 다음 서버는 사용자 지정 전략을 사용하여 이러한 dictionaries에서 제공하" +"는 메트릭을 집계할 수 있습니다:" #: ../../source/how-to-authenticate-supernodes.rst:2 msgid "Authenticate SuperNodes" -msgstr "" +msgstr "SuperNodes 인증하기" #: ../../source/how-to-authenticate-supernodes.rst:4 msgid "" -"Flower has built-in support for authenticated SuperNodes that you can use" -" to verify the identities of each SuperNode connecting to a SuperLink. " -"Flower node authentication works similar to how GitHub SSH authentication" -" works:" +"Flower has built-in support for authenticated SuperNodes that you can use to " +"verify the identities of each SuperNode connecting to a SuperLink. Flower " +"node authentication works similar to how GitHub SSH authentication works:" msgstr "" +"Flower는 SuperLink에 연결하는 각 SuperNodes의 신원을 확인하는 데 사용할 수 있" +"는 인증된 SuperNodes에 대한 기본 지원을 제공합니다. Flower 노드 인증은 " +"GitHub SSH 인증 방식과 유사하게 작동합니다:" #: ../../source/how-to-authenticate-supernodes.rst:7 msgid "SuperLink (server) stores a list of known (client) node public keys" -msgstr "" +msgstr "SuperLink(서버)는 알려진 (클라이언트) 노드 공개키 목록을 저장합니다" #: ../../source/how-to-authenticate-supernodes.rst:8 msgid "" -"Using ECDH, both SuperNode and SuperLink independently derive a shared " -"secret" +"Using ECDH, both SuperNode and SuperLink independently derive a shared secret" msgstr "" +"SuperNode와 SuperLink는 ECDH를 사용하여 독립적으로 공유된 비밀을 도출합니다" #: ../../source/how-to-authenticate-supernodes.rst:9 msgid "" "Shared secret is used to compute the HMAC value of the message sent from " "SuperNode to SuperLink as a token" msgstr "" +"비밀 공유는 SuperNode에서 SuperLink로 토큰으로 전송된 메시지의 HMAC 값을 계산" +"하는 데 사용됩니다" #: ../../source/how-to-authenticate-supernodes.rst:10 msgid "SuperLink verifies the token" -msgstr "" +msgstr "SuperLink가 토큰을 확인합니다" #: ../../source/how-to-authenticate-supernodes.rst:12 msgid "" -"We recommend you to check out the complete `code example " -"`_ demonstrating federated learning with Flower in an " -"authenticated setting." +"We recommend you to check out the complete `code example `_ demonstrating " +"federated learning with Flower in an authenticated setting." msgstr "" +"인증된 환경에서 Flower로 연합 학습을 시연하는 전체 '코드 예제 `_를 확인하는 것이 " +"좋습니다." #: ../../source/how-to-authenticate-supernodes.rst:15 msgid "" -"This guide covers a preview feature that might change in future versions " -"of Flower." +"This guide covers a preview feature that might change in future versions of " +"Flower." msgstr "" +"이 가이드에서는 향후 버전의 Flower에서 변경될 수 있는 미리보기 기능에 대해 설" +"명합니다." #: ../../source/how-to-authenticate-supernodes.rst:18 msgid "" -"For increased security, node authentication can only be used when " -"encrypted connections (SSL/TLS) are enabled." +"For increased security, node authentication can only be used when encrypted " +"connections (SSL/TLS) are enabled." msgstr "" +"보안을 강화하기 위해 노드 인증은 암호화된 연결(SSL/TLS)을 사용하도록 설정한 " +"경우에만 사용할 수 있습니다." #: ../../source/how-to-authenticate-supernodes.rst:21 msgid "Enable node authentication in :code:`SuperLink`" -msgstr "" +msgstr ":code:`SuperLink`에서 노드 인증 활성화" #: ../../source/how-to-authenticate-supernodes.rst:23 msgid "" "To enable node authentication, first you need to configure SSL/TLS " -"connections to secure the SuperLink<>SuperNode communication. You can " -"find the complete guide `here `_. After configuring secure connections, you" -" can enable client authentication in a long-running Flower " -":code:`SuperLink`. Use the following terminal command to start a Flower " -":code:`SuperNode` that has both secure connections and node " -"authentication enabled:" -msgstr "" +"connections to secure the SuperLink<>SuperNode communication. You can find " +"the complete guide `here `_. After configuring secure connections, you can enable " +"client authentication in a long-running Flower :code:`SuperLink`. Use the " +"following terminal command to start a Flower :code:`SuperNode` that has both " +"secure connections and node authentication enabled:" +msgstr "" +"노드 인증을 활성화하려면 먼저 SuperLink<>SuperNode 통신을 보호하기 위해 SSL/" +"TLS 연결을 구성해야 합니다. 전체 가이드는 `여기 `_에서 확인할 수 있습니다. 보안 " +"연결을 구성한 후, 장기 실행하는 Flower :code:`SuperLink`에서 클라이언트 인증" +"을 활성화할 수 있습니다. 다음 터미널 명령을 사용하여 보안 연결과 노드 인증이 " +"모두 활성화된 Flower :code:`SuperNode`를 시작하세요:" -#: ../../source/how-to-authenticate-supernodes.rst:34 -msgid "Let's break down the :code:`--require-client-authentication` flag:" -msgstr "" +#: ../../source/how-to-authenticate-supernodes.rst:38 +msgid "Let's break down the authentication flags:" +msgstr "인증 플래그를 세분화해 보겠습니다:" -#: ../../source/how-to-authenticate-supernodes.rst:36 +#: ../../source/how-to-authenticate-supernodes.rst:40 msgid "" -"The first argument is a path to a CSV file storing all known node public " -"keys. You need to store all known node public keys that are allowed to " -"participate in a federation in one CSV file (:code:`.csv`)." +"The first flag :code:`--auth-list-public-keys` expects a path to a CSV file " +"storing all known node public keys. You need to store all known node public " +"keys that are allowed to participate in a federation in one CSV file (:code:" +"`.csv`)." msgstr "" +"첫 번째 플래그 :code:`--auth-list-public-keys`는 알려진 모든 노드 공개키를 저" +"장하는 CSV 파일의 경로를 기대합니다. federation에 참여하도록 허용된 모든 알려" +"진 노드 공개 키를 하나의 CSV 파일(:code:`.csv`)에 저장해야 합니다." -#: ../../source/how-to-authenticate-supernodes.rst:38 +#: ../../source/how-to-authenticate-supernodes.rst:42 msgid "" "A valid CSV file storing known node public keys should list the keys in " "OpenSSH format, separated by commas and without any comments. For an " -"example, refer to our code sample, which contains a CSV file with two " -"known node public keys." +"example, refer to our code sample, which contains a CSV file with two known " +"node public keys." msgstr "" +"알려진 노드 공개키를 저장하는 유효한 CSV 파일은 쉼표로 구분하고 주석 없이 " +"OpenSSH 형식으로 키를 나열해야 합니다. 예를 들어, 두 개의 알려진 노드 공개키" +"가 포함된 CSV 파일이 포함된 코드 샘플을 참조하세요." -#: ../../source/how-to-authenticate-supernodes.rst:40 +#: ../../source/how-to-authenticate-supernodes.rst:44 msgid "" -"The second and third arguments are paths to the server's private and " -"public keys. For development purposes, you can generate a private and " -"public key pair using :code:`ssh-keygen -t ecdsa -b 384`." +"The second and third flags :code:`--auth-superlink-private-key` and :code:`--" +"auth-superlink-public-key` expect paths to the server's private and public " +"keys. For development purposes, you can generate a private and public key " +"pair using :code:`ssh-keygen -t ecdsa -b 384`." msgstr "" +"두 번째 및 세 번째 플래그 :code:`--auth-superlink-private-key` 및 :code:`--" +"auth-superlink-public-key`는 서버의 개인 및 공개 키의 경로를 예상합니다. 개" +"발 목적으로 :code:`ssh-keygen -t ecdsa -b 384`를 사용하여 개인 및 공개 키 쌍" +"을 생성할 수 있습니다." -#: ../../source/how-to-authenticate-supernodes.rst:43 +#: ../../source/how-to-authenticate-supernodes.rst:47 msgid "" "In Flower 1.9, there is no support for dynamically removing, editing, or " -"adding known node public keys to the SuperLink. To change the set of " -"known nodes, you need to shut the server down, edit the CSV file, and " -"start the server again. Support for dynamically changing the set of known" -" nodes is on the roadmap to be released in Flower 1.10 (ETA: June)." -msgstr "" - -#: ../../source/how-to-authenticate-supernodes.rst:49 +"adding known node public keys to the SuperLink. To change the set of known " +"nodes, you need to shut the server down, edit the CSV file, and start the " +"server again. Support for dynamically changing the set of known nodes is on " +"the roadmap to be released in Flower 1.10 (ETA: June)." +msgstr "" +"Flower 1.9에서는 알려진 노드 공개키를 SuperLink에 동적으로 제거, 편집 또는 추" +"가하는 기능이 지원되지 않습니다. 알려진 노드 집합을 변경하려면 서버를 종료하" +"고 CSV 파일을 편집한 다음 서버를 다시 시작해야 합니다. 알려진 노드 집합을 동" +"적으로 변경하는 기능은 Flower 1.10(출시 예정일: 6월)에서 로드맵에 포함되어 있" +"습니다." + +#: ../../source/how-to-authenticate-supernodes.rst:53 msgid "Enable node authentication in :code:`SuperNode`" -msgstr "" +msgstr ":code:`SuperNode`에서 노드 인증을 활성화합니다" -#: ../../source/how-to-authenticate-supernodes.rst:51 +#: ../../source/how-to-authenticate-supernodes.rst:55 msgid "" "Similar to the long-running Flower server (:code:`SuperLink`), you can " -"easily enable node authentication in the long-running Flower client " -"(:code:`SuperNode`). Use the following terminal command to start an " -"authenticated :code:`SuperNode`:" -msgstr "" - -#: ../../source/how-to-authenticate-supernodes.rst:61 -msgid "" -"The :code:`--authentication-keys` flag expects two arguments: a path to " -"the node's private key file and a path to the node's public key file. For" -" development purposes, you can generate a private and public key pair " -"using :code:`ssh-keygen -t ecdsa -b 384`." -msgstr "" - -#: ../../source/how-to-authenticate-supernodes.rst:65 -msgid "Security notice" +"easily enable node authentication in the long-running Flower client (:code:" +"`SuperNode`). Use the following terminal command to start an authenticated :" +"code:`SuperNode`:" msgstr "" +"장기 실행 중인 Flower 서버(:code:`SuperLink`)와 마찬가지로, 장기 실행 중인 " +"Flower 클라이언트(:code:`SuperNode`)에서도 노드 인증을 쉽게 활성화할 수 있습" +"니다. 다음 터미널 명령을 사용하여 인증된 :code:`SuperNode`를 시작하세요:" -#: ../../source/how-to-authenticate-supernodes.rst:67 +#: ../../source/how-to-authenticate-supernodes.rst:66 msgid "" -"The system's security relies on the credentials of the SuperLink and each" -" SuperNode. Therefore, it is imperative to safeguard and safely store the" -" credentials to avoid security risks such as Public Key Infrastructure " -"(PKI) impersonation attacks. The node authentication mechanism also " -"involves human interaction, so please ensure that all of the " -"communication is done in a secure manner, using trusted communication " -"methods." +"The :code:`--auth-supernode-private-key` flag expects a path to the node's " +"private key file and the :code:`--auth-supernode-public-key` flag expects a " +"path to the node's public key file. For development purposes, you can " +"generate a private and public key pair using :code:`ssh-keygen -t ecdsa -b " +"384`." msgstr "" +":code:`--auth-supernode-private-key` 플래그는 노드의 개인 키 파일 경로를, :" +"code:`--auth-supernode-public-key` 플래그는 노드의 공개 키 파일 경로를 예상합" +"니다. 개발 목적으로 :code:`ssh-keygen -t ecdsa -b 384`를 사용하여 개인 및 공" +"개 키 쌍을 생성할 수 있습니다." + +#: ../../source/how-to-authenticate-supernodes.rst:70 +msgid "Security notice" +msgstr "보안 공지" #: ../../source/how-to-authenticate-supernodes.rst:72 -#: ../../source/how-to-enable-ssl-connections.rst:65 +msgid "" +"The system's security relies on the credentials of the SuperLink and each " +"SuperNode. Therefore, it is imperative to safeguard and safely store the " +"credentials to avoid security risks such as Public Key Infrastructure (PKI) " +"impersonation attacks. The node authentication mechanism also involves human " +"interaction, so please ensure that all of the communication is done in a " +"secure manner, using trusted communication methods." +msgstr "" +"시스템의 보안은 SuperLink와 각SuperNode의 자격 증명에 의존합니다. 따라서 공개" +"키 기반구조(PKI) 사칭 공격과 같은 보안 위험을 피하기 위해 자격 증명을 보호하" +"고 안전하게 보관하는 것이 필수적입니다. 노드 인증 메커니즘에는 사람의 상호 작" +"용도 포함되므로 모든 통신이 신뢰할 수 있는 통신 방법을 사용하여 안전한 방식으" +"로 이루어지도록 하세요." + +#: ../../source/how-to-authenticate-supernodes.rst:77 +#: ../../source/how-to-enable-ssl-connections.rst:68 #: ../../source/how-to-use-built-in-mods.rst:85 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:287 msgid "Conclusion" -msgstr "" +msgstr "결론" -#: ../../source/how-to-authenticate-supernodes.rst:74 +#: ../../source/how-to-authenticate-supernodes.rst:79 msgid "" -"You should now have learned how to start a long-running Flower server " -"(:code:`SuperLink`) and client (:code:`SuperNode`) with node " -"authentication enabled. You should also know the significance of the " -"private key and store it safely to minimize security risks." +"You should now have learned how to start a long-running Flower server (:code:" +"`SuperLink`) and client (:code:`SuperNode`) with node authentication " +"enabled. You should also know the significance of the private key and store " +"it safely to minimize security risks." msgstr "" +"이제 노드 인증이 활성화된 상태에서 장기간 실행되는 Flower 서버(:code:" +"`SuperLink`)와 클라이언트(:code:`SuperNode`)를 시작하는 방법을 배웠을 것입니" +"다. 또한 보안 위험을 최소화하기 위해 개인키의 중요성을 알고 안전하게 보관해" +"야 합니다." #: ../../source/how-to-configure-clients.rst:2 msgid "Configure clients" -msgstr "" +msgstr "클라이언트 구성" #: ../../source/how-to-configure-clients.rst:4 msgid "" "Along with model parameters, Flower can send configuration values to " -"clients. Configuration values can be used for various purposes. They are," -" for example, a popular way to control client-side hyperparameters from " -"the server." +"clients. Configuration values can be used for various purposes. They are, " +"for example, a popular way to control client-side hyperparameters from the " +"server." msgstr "" +"모델 파라미터와 함께 Flower는 설정 값을 클라이언트에 전송할 수 있습니다. 구" +"성 값은 다양한 용도로 사용할 수 있습니다. 예를 들어 서버에서 클라이언트 측 하" +"이퍼파라미터를 제어하는 데 널리 사용되는 방법입니다." #: ../../source/how-to-configure-clients.rst:7 msgid "Configuration values" -msgstr "" +msgstr "구성 값" #: ../../source/how-to-configure-clients.rst:9 msgid "" -"Configuration values are represented as a dictionary with ``str`` keys " -"and values of type ``bool``, ``bytes``, ``double`` (64-bit precision " -"float), ``int``, or ``str`` (or equivalent types in different languages)." -" Here is an example of a configuration dictionary in Python:" +"Configuration values are represented as a dictionary with ``str`` keys and " +"values of type ``bool``, ``bytes``, ``double`` (64-bit precision float), " +"``int``, or ``str`` (or equivalent types in different languages). Here is an " +"example of a configuration dictionary in Python:" msgstr "" +"구성 값은 ``str`` 키와 ``bool``, ``bytes``, ``double``(64비트 정밀도 정수), " +"``int`` 또는 ``str``(또는 다른 언어의 동등한 유형) 유형의 값으로 구성된 사전" +"으로 표현됩니다. 다음은 Python의 구성 사전 예제입니다:" #: ../../source/how-to-configure-clients.rst:20 msgid "" "Flower serializes these configuration dictionaries (or *config dict* for " -"short) to their ProtoBuf representation, transports them to the client " -"using gRPC, and then deserializes them back to Python dictionaries." +"short) to their ProtoBuf representation, transports them to the client using " +"gRPC, and then deserializes them back to Python dictionaries." msgstr "" +"Flower는 이러한 구성 dictionaries(또는 줄여서 *config dict*)를 ProtoBuf 표현" +"으로 직렬화하고, gRPC를 사용하여 클라이언트로 전송한 다음 다시 Python " +"dictionaries로 역직렬화합니다." #: ../../source/how-to-configure-clients.rst:24 msgid "" -"Currently, there is no support for directly sending collection types " -"(e.g., ``Set``, ``List``, ``Map``) as values in configuration " -"dictionaries. There are several workarounds to send collections as values" -" by converting them to one of the supported value types (and converting " -"them back on the client-side)." +"Currently, there is no support for directly sending collection types (e.g., " +"``Set``, ``List``, ``Map``) as values in configuration dictionaries. There " +"are several workarounds to send collections as values by converting them to " +"one of the supported value types (and converting them back on the client-" +"side)." msgstr "" +"현재 구성 사전에서 컬렉션 유형(예: ``Set``, ``List``, ``Map``)을 값으로 직접 " +"전송하는 기능은 지원되지 않습니다. 컬렉션을 지원되는 값 유형 중 하나로 변환" +"한 다음 클라이언트 측에서 다시 변환하여 값으로 보내는 몇 가지 해결 방법이 있" +"습니다." #: ../../source/how-to-configure-clients.rst:26 msgid "" "One can, for example, convert a list of floating-point numbers to a JSON " -"string, then send the JSON string using the configuration dictionary, and" -" then convert the JSON string back to a list of floating-point numbers on" -" the client." +"string, then send the JSON string using the configuration dictionary, and " +"then convert the JSON string back to a list of floating-point numbers on the " +"client." msgstr "" +"예를 들어 부동 소수점 숫자 목록을 JSON 문자열로 변환한 다음 구성 dictionary" +"을 사용하여 JSON 문자열을 전송한 다음 클라이언트에서 다시 부동 소수점 숫자 목" +"록으로 변환할 수 있습니다." #: ../../source/how-to-configure-clients.rst:30 msgid "Configuration through built-in strategies" -msgstr "" +msgstr "기본 제공 전략을 통한 구성" #: ../../source/how-to-configure-clients.rst:32 msgid "" -"The easiest way to send configuration values to clients is to use a " -"built-in strategy like :code:`FedAvg`. Built-in strategies support so-" -"called configuration functions. A configuration function is a function " -"that the built-in strategy calls to get the configuration dictionary for " -"the current round. It then forwards the configuration dictionary to all " -"the clients selected during that round." +"The easiest way to send configuration values to clients is to use a built-in " +"strategy like :code:`FedAvg`. Built-in strategies support so-called " +"configuration functions. A configuration function is a function that the " +"built-in strategy calls to get the configuration dictionary for the current " +"round. It then forwards the configuration dictionary to all the clients " +"selected during that round." msgstr "" +"클라이언트에 구성 값을 보내는 가장 쉬운 방법은 :code:`FedAvg`와 같은 기본 제" +"공 전략을 사용하는 것입니다. 기본 제공 전략은 소위 구성 함수를 지원합니다. 구" +"성 함수는 내장 전략이 현재 단계의 구성 사전을 가져오기 위해 호출하는 함수입니" +"다. 그런 다음 해당 단계 동안 선택된 모든 클라이언트에 구성 사전을 전달합니다." #: ../../source/how-to-configure-clients.rst:34 msgid "" "Let's start with a simple example. Imagine we want to send (a) the batch " -"size that the client should use, (b) the current global round of " -"federated learning, and (c) the number of epochs to train on the client-" -"side. Our configuration function could look like this:" +"size that the client should use, (b) the current global round of federated " +"learning, and (c) the number of epochs to train on the client-side. Our " +"configuration function could look like this:" msgstr "" +"간단한 예부터 시작하겠습니다. (a) 클라이언트가 사용해야 하는 배치 크기, (b) " +"현재 글로벌 연합 라운드, (c) 클라이언트 측에서 학습할 에포크 수를 전송하고 " +"싶다고 가정해 보겠습니다. 구성 함수는 다음과 같습니다:" #: ../../source/how-to-configure-clients.rst:47 msgid "" "To make the built-in strategies use this function, we can pass it to " -"``FedAvg`` during initialization using the parameter " -":code:`on_fit_config_fn`:" +"``FedAvg`` during initialization using the parameter :code:" +"`on_fit_config_fn`:" msgstr "" +"기본 제공 전략이 이 함수를 사용하도록 하려면 초기화 중에 매개 변수 :code:" +"`on_fit_config_fn`을 사용하여 ``FedAvg``에 이 함수를 전달하면 됩니다:" #: ../../source/how-to-configure-clients.rst:56 -msgid "One the client side, we receive the configuration dictionary in ``fit``:" -msgstr "" +msgid "" +"One the client side, we receive the configuration dictionary in ``fit``:" +msgstr "클라이언트 측에서는 ``fit``으로 구성 dictionary을 받습니다:" #: ../../source/how-to-configure-clients.rst:67 msgid "" "There is also an `on_evaluate_config_fn` to configure evaluation, which " -"works the same way. They are separate functions because one might want to" -" send different configuration values to `evaluate` (for example, to use a" -" different batch size)." +"works the same way. They are separate functions because one might want to " +"send different configuration values to `evaluate` (for example, to use a " +"different batch size)." msgstr "" +"평가를 구성하는 `on_evaluate_config_fn`도 있으며, 같은 방식으로 작동합니다. " +"다른 배치 크기를 사용하기 위해 다른 구성 값을 `evaluate`로 보내려고 할 수 있" +"기 때문에 이 함수는 별도의 함수입니다." #: ../../source/how-to-configure-clients.rst:69 msgid "" -"The built-in strategies call this function every round (that is, every " -"time `Strategy.configure_fit` or `Strategy.configure_evaluate` runs). " -"Calling `on_evaluate_config_fn` every round allows us to vary/change the " -"config dict over consecutive rounds. If we wanted to implement a " -"hyperparameter schedule, for example, to increase the number of local " -"epochs during later rounds, we could do the following:" +"The built-in strategies call this function every round (that is, every time " +"`Strategy.configure_fit` or `Strategy.configure_evaluate` runs). Calling " +"`on_evaluate_config_fn` every round allows us to vary/change the config dict " +"over consecutive rounds. If we wanted to implement a hyperparameter " +"schedule, for example, to increase the number of local epochs during later " +"rounds, we could do the following:" msgstr "" +"기본 제공 전략은 매 라운드마다 이 함수를 호출합니다(즉, `Strategy." +"configure_fit` 또는 `Strategy.configure_evaluate`가 실행될 때마다). 매 라운드" +"마다 `on_evaluate_config_fn`을 호출하면 연속된 라운드에서 config dict를 변경/" +"변경할 수 있습니다. 예를 들어 이후 라운드에서 로컬 에포크 수를 늘리기 위해 하" +"이퍼파라미터 일정을 구현하려면 다음과 같이 할 수 있습니다:" #: ../../source/how-to-configure-clients.rst:82 msgid "The :code:`FedAvg` strategy will call this function *every round*." -msgstr "" +msgstr ":code:`FedAvg` 전략은 이 함수를 *매 라운드마다* 호출합니다." #: ../../source/how-to-configure-clients.rst:85 msgid "Configuring individual clients" -msgstr "" +msgstr "개별 클라이언트 구성" #: ../../source/how-to-configure-clients.rst:87 msgid "" "In some cases, it is necessary to send different configuration values to " "different clients." msgstr "" +"경우에 따라 다른 구성 값을 다른 클라이언트에 보내야 하는 경우도 있습니다." #: ../../source/how-to-configure-clients.rst:89 msgid "" -"This can be achieved by customizing an existing strategy or by " -":doc:`implementing a custom strategy from scratch `. Here's a nonsensical example that customizes :code:`FedAvg`" -" by adding a custom ``\"hello\": \"world\"`` configuration key/value pair" -" to the config dict of a *single client* (only the first client in the " -"list, the other clients in this round to not receive this \"special\" " -"config value):" +"This can be achieved by customizing an existing strategy or by :doc:" +"`implementing a custom strategy from scratch `. " +"Here's a nonsensical example that customizes :code:`FedAvg` by adding a " +"custom ``\"hello\": \"world\"`` configuration key/value pair to the config " +"dict of a *single client* (only the first client in the list, the other " +"clients in this round to not receive this \"special\" config value):" msgstr "" +"이는 기존 전략을 사용자 지정하거나 :doc:`implementing a custom strategy from " +"scratch `를 통해 수행할 수 있습니다. 다음은 사용" +"자 지정 ``\"hello\"'를 추가하여 :code:`FedAvg`를 사용자 지정하는 무의미한 예" +"입니다: \"world\"`` 구성 키/값 쌍을 *단일 클라이언트*의 config dict에 추가합" +"니다(목록의 첫 번째 클라이언트만, 이 라운드의 다른 클라이언트는 이 \"특별한" +"\" 구성 값을 수신하지 않음):" #: ../../source/how-to-configure-logging.rst:2 msgid "Configure logging" -msgstr "" +msgstr "로깅 구성" #: ../../source/how-to-configure-logging.rst:4 msgid "" "The Flower logger keeps track of all core events that take place in " -"federated learning workloads. It presents information by default " -"following a standard message format:" +"federated learning workloads. It presents information by default following a " +"standard message format:" msgstr "" +"Flower 로거는 federated 학습 워크로드에서 발생하는 모든 핵심 이벤트를 추적합" +"니다. 기본적으로 표준 메시지 형식에 따라 정보를 표시합니다:" #: ../../source/how-to-configure-logging.rst:13 msgid "" -"containing relevant information including: log message level (e.g. " -":code:`INFO`, :code:`DEBUG`), a timestamp, the line where the logging " -"took place from, as well as the log message itself. In this way, the " -"logger would typically display information on your terminal as follows:" +"containing relevant information including: log message level (e.g. :code:" +"`INFO`, :code:`DEBUG`), a timestamp, the line where the logging took place " +"from, as well as the log message itself. In this way, the logger would " +"typically display information on your terminal as follows:" msgstr "" +"로그 메시지 수준(예: :code:`INFO`, :code:`DEBUG`), 타임스탬프, 로깅이 발생한 " +"줄, 로그 메시지 자체 등 관련 정보를 포함합니다. 이러한 방식으로 로거는 일반적" +"으로 다음과 같은 정보를 터미널에 표시합니다:" #: ../../source/how-to-configure-logging.rst:34 msgid "Saving log to file" -msgstr "" +msgstr "파일에 로그 저장" #: ../../source/how-to-configure-logging.rst:36 msgid "" "By default, the Flower log is outputted to the terminal where you launch " "your Federated Learning workload from. This applies for both gRPC-based " -"federation (i.e. when you do :code:`fl.server.start_server`) and when " -"using the :code:`VirtualClientEngine` (i.e. when you do " -":code:`fl.simulation.start_simulation`). In some situations you might " -"want to save this log to disk. You can do so by calling the " -"`fl.common.logger.configure() " -"`_" -" function. For example:" -msgstr "" +"federation (i.e. when you do :code:`fl.server.start_server`) and when using " +"the :code:`VirtualClientEngine` (i.e. when you do :code:`fl.simulation." +"start_simulation`). In some situations you might want to save this log to " +"disk. You can do so by calling the `fl.common.logger.configure() `_ function. " +"For example:" +msgstr "" +"기본적으로 Flower 로그는 Federated 학습 워크로드를 실행하는 터미널에 출력됩니" +"다. 이는 gRPC 기반 페더레이션(즉,:code:`fl.simulation.start_simulation`를 실" +"행하는 경우)과 :code:`VirtualClientEngine`을 사용하는 경우(즉, :코드:`fl." +"simulation.start_simulation`을 실행하는 경우) 모두에 적용됩니다. 경우에 따라 " +"이 로그를 디스크에 저장하고 싶을 수도 있습니다. 이 경우 `fl.common.logger." +"configure() `_ 함수를 호출하여 저장할 수 있습니다. 예를 들어:" #: ../../source/how-to-configure-logging.rst:53 msgid "" -"With the above, Flower will record the log you see on your terminal to " -":code:`log.txt`. This file will be created in the same directory as were " -"you are running the code from. If we inspect we see the log above is also" -" recorded but prefixing with :code:`identifier` each line:" +"With the above, Flower will record the log you see on your terminal to :code:" +"`log.txt`. This file will be created in the same directory as were you are " +"running the code from. If we inspect we see the log above is also recorded " +"but prefixing with :code:`identifier` each line:" msgstr "" +"위와 같이 하면 Flower는 터미널에 표시되는 로그를 :code:`log.txt`에 기록합니" +"다. 이 파일은 코드를 실행한 디렉터리와 동일한 디렉터리에 생성됩니다. 검사해보" +"면 위의 로그도 기록되지만 각 줄 앞에 :code:`identifier` 접두사가 붙는 것을 확" +"인할 수 있습니다:" #: ../../source/how-to-configure-logging.rst:74 msgid "Log your own messages" -msgstr "" +msgstr "나만의 메시지 기록" #: ../../source/how-to-configure-logging.rst:76 msgid "" -"You might expand the information shown by default with the Flower logger " -"by adding more messages relevant to your application. You can achieve " -"this easily as follows." +"You might expand the information shown by default with the Flower logger by " +"adding more messages relevant to your application. You can achieve this " +"easily as follows." msgstr "" +"애플리케이션과 관련된 메시지를 더 추가하여 Flower 로거에 기본적으로 표시되는 " +"정보를 확장할 수 있습니다. 다음과 같이 쉽게 추가할 수 있습니다." #: ../../source/how-to-configure-logging.rst:102 msgid "" -"In this way your logger will show, in addition to the default messages, " -"the ones introduced by the clients as specified above." +"In this way your logger will show, in addition to the default messages, the " +"ones introduced by the clients as specified above." msgstr "" +"이렇게 하면 로거에 기본 메시지 외에 위에서 지정한 대로 클라이언트가 소개한 메" +"시지가 표시됩니다." #: ../../source/how-to-configure-logging.rst:128 msgid "Log to a remote service" -msgstr "" +msgstr "원격 서비스에 로그인" #: ../../source/how-to-configure-logging.rst:130 msgid "" -"The :code:`fl.common.logger.configure` function, also allows specifying a" -" host to which logs can be pushed (via :code:`POST`) through a native " -"Python :code:`logging.handler.HTTPHandler`. This is a particularly useful" -" feature in :code:`gRPC`-based Federated Learning workloads where " -"otherwise gathering logs from all entities (i.e. the server and the " -"clients) might be cumbersome. Note that in Flower simulation, the server " -"automatically displays all logs. You can still specify a " -":code:`HTTPHandler` should you wish to backup or analyze the logs " -"somewhere else." -msgstr "" +"The :code:`fl.common.logger.configure` function, also allows specifying a " +"host to which logs can be pushed (via :code:`POST`) through a native Python :" +"code:`logging.handler.HTTPHandler`. This is a particularly useful feature " +"in :code:`gRPC`-based Federated Learning workloads where otherwise gathering " +"logs from all entities (i.e. the server and the clients) might be " +"cumbersome. Note that in Flower simulation, the server automatically " +"displays all logs. You can still specify a :code:`HTTPHandler` should you " +"wish to backup or analyze the logs somewhere else." +msgstr "" +"또한 :code:`fl.common.logger.configure` 함수를 사용하면 네이티브 Python :" +"code:`logging.handler.HTTPHandler`를 통해 로그를 푸시할 수 있는 호스트를 지정" +"할 수 있습니다(:code:`POST`를 통해). 이는 모든 엔티티(예: 서버 및 클라이언트)" +"에서 로그를 수집하는 것이 번거로울 수 있는 :code:`gRPC` 기반 Federated 학습 " +"워크로드에서 특히 유용한 기능입니다. Flower 시뮬레이션에서는 서버가 모든 로그" +"를 자동으로 표시합니다. 로그를 다른 곳에 백업하거나 분석하려는 경우 :code:" +"`HTTPHandler`를 지정할 수 있습니다." #: ../../source/how-to-enable-ssl-connections.rst:2 msgid "Enable SSL connections" -msgstr "" +msgstr "SSL 연결 사용" #: ../../source/how-to-enable-ssl-connections.rst:4 msgid "" -"This guide describes how to a SSL-enabled secure Flower server " -"(:code:`SuperLink`) can be started and how a Flower client " -"(:code:`SuperNode`) can establish a secure connections to it." +"This guide describes how to a SSL-enabled secure Flower server (:code:" +"`SuperLink`) can be started and how a Flower client (:code:`SuperNode`) can " +"establish a secure connections to it." msgstr "" +"이 가이드에서는 SSL을 지원하는 보안 Flower 서버(:코드:`SuperLink`)를 시작하" +"는 방법과 Flower 클라이언트(:코드:`SuperNode`)가 이 서버에 보안 연결을 설정하" +"는 방법을 설명합니다." #: ../../source/how-to-enable-ssl-connections.rst:7 msgid "" -"A complete code example demonstrating a secure connection can be found " -"`here `_." +"A complete code example demonstrating a secure connection can be found `here " +"`_." msgstr "" +"보안 연결을 보여주는 전체 코드 예제는 '여기 `_'에서 확인할 수 있습니다." #: ../../source/how-to-enable-ssl-connections.rst:10 msgid "" -"The code example comes with a :code:`README.md` file which explains how " -"to start it. Although it is already SSL-enabled, it might be less " -"descriptive on how it does so. Stick to this guide for a deeper " -"introduction to the topic." +"The code example comes with a :code:`README.md` file which explains how to " +"start it. Although it is already SSL-enabled, it might be less descriptive " +"on how it does so. Stick to this guide for a deeper introduction to the " +"topic." msgstr "" +"코드 예제에는 시작 방법을 설명하는 :code:`README.md` 파일이 함께 제공됩니다. " +"이미 SSL을 사용하도록 설정되어 있지만 그 방법에 대한 설명이 부족할 수 있습니" +"다. 이 가이드를 참고하여 이 주제에 대해 자세히 알아보세요." #: ../../source/how-to-enable-ssl-connections.rst:16 msgid "Certificates" -msgstr "" +msgstr "인증서" #: ../../source/how-to-enable-ssl-connections.rst:18 msgid "" "Using SSL-enabled connections requires certificates to be passed to the " -"server and client. For the purpose of this guide we are going to generate" -" self-signed certificates. As this can become quite complex we are going " -"to ask you to run the script in :code:`examples/advanced-" -"tensorflow/certificates/generate.sh` with the following command sequence:" +"server and client. For the purpose of this guide we are going to generate " +"self-signed certificates. As this can become quite complex we are going to " +"ask you to run the script in :code:`examples/advanced-tensorflow/" +"certificates/generate.sh` with the following command sequence:" msgstr "" +"SSL 사용 연결을 사용하려면 서버와 클라이언트에 인증서를 전달해야 합니다. 이 " +"가이드에서는 자체 서명된 인증서를 생성하겠습니다. 이 과정은 상당히 복잡할 수 " +"있으므로 다음 명령 시퀀스를 사용하여 :code:`examples/advanced-tensorflow/" +"certificates/generate.sh`에서 스크립트를 실행하도록 요청하겠습니다:" #: ../../source/how-to-enable-ssl-connections.rst:29 msgid "" -"This will generate the certificates in :code:`examples/advanced-" -"tensorflow/.cache/certificates`." +"This will generate the certificates in :code:`examples/advanced-tensorflow/." +"cache/certificates`." msgstr "" +"이렇게 하면 :code:`examples/advanced-tensorflow/.cache/certificates`에 인증서" +"가 생성됩니다." #: ../../source/how-to-enable-ssl-connections.rst:31 msgid "" -"The approach for generating SSL certificates in the context of this " -"example can serve as an inspiration and starting point, but it should not" -" be used as a reference for production environments. Please refer to " -"other sources regarding the issue of correctly generating certificates " -"for production environments. For non-critical prototyping or research " -"projects, it might be sufficient to use the self-signed certificates " -"generated using the scripts mentioned in this guide." +"The approach for generating SSL certificates in the context of this example " +"can serve as an inspiration and starting point, but it should not be used as " +"a reference for production environments. Please refer to other sources " +"regarding the issue of correctly generating certificates for production " +"environments. For non-critical prototyping or research projects, it might be " +"sufficient to use the self-signed certificates generated using the scripts " +"mentioned in this guide." msgstr "" +"이 예의 맥락에서 SSL 인증서를 생성하는 접근 방식은 영감과 출발점이 될 수 있지" +"만 프로덕션 환경에 대한 참조로 사용해서는 안 됩니다. 프로덕션 환경용 인증서" +"를 올바르게 생성하는 문제에 대해서는 다른 출처를 참조하세요. 중요하지 않은 프" +"로토타이핑 또는 연구 프로젝트의 경우, 이 가이드에 언급된 스크립트를 사용하여 " +"생성한 자체 서명 인증서를 사용하는 것으로 충분할 수 있습니다." #: ../../source/how-to-enable-ssl-connections.rst:39 msgid "Server (SuperLink)" -msgstr "" +msgstr "서버(SuperLink)" #: ../../source/how-to-enable-ssl-connections.rst:41 msgid "" -"Use the following terminal command to start a sever (SuperLink) that uses" -" the previously generated certificates:" +"Use the following terminal command to start a sever (SuperLink) that uses " +"the previously generated certificates:" msgstr "" +"다음 터미널 명령을 사용하여 이전에 생성한 인증서를 사용하는 서버(SuperLink)" +"를 시작합니다:" -#: ../../source/how-to-enable-ssl-connections.rst:47 +#: ../../source/how-to-enable-ssl-connections.rst:50 msgid "" "When providing certificates, the server expects a tuple of three " -"certificates paths: CA certificate, server certificate and server private" -" key." +"certificates paths: CA certificate, server certificate and server private " +"key." msgstr "" +"인증서를 제공할 때 서버는 세 가지 인증서 경로의 튜플을 기대합니다: CA 인증" +"서, 서버 인증서 및 서버 개인 키입니다." -#: ../../source/how-to-enable-ssl-connections.rst:51 +#: ../../source/how-to-enable-ssl-connections.rst:54 msgid "Client (SuperNode)" -msgstr "" +msgstr "클라이언트(SuperNode)" -#: ../../source/how-to-enable-ssl-connections.rst:53 +#: ../../source/how-to-enable-ssl-connections.rst:56 msgid "" -"Use the following terminal command to start a client (SuperNode) that " -"uses the previously generated certificates:" +"Use the following terminal command to start a client (SuperNode) that uses " +"the previously generated certificates:" msgstr "" +"다음 터미널 명령을 사용하여 이전에 생성한 인증서를 사용하는 클라이언트" +"(SuperNode)를 시작합니다:" -#: ../../source/how-to-enable-ssl-connections.rst:61 +#: ../../source/how-to-enable-ssl-connections.rst:64 msgid "" -"When setting :code:`root_certificates`, the client expects a file path to" -" PEM-encoded root certificates." +"When setting :code:`root_certificates`, the client expects a file path to " +"PEM-encoded root certificates." msgstr "" +"코드:`root_certificates`를 설정하면 클라이언트는 PEM 인코딩된 루트 인증서의 " +"파일 경로를 예상합니다." -#: ../../source/how-to-enable-ssl-connections.rst:67 +#: ../../source/how-to-enable-ssl-connections.rst:70 msgid "" -"You should now have learned how to generate self-signed certificates " -"using the given script, start an SSL-enabled server and have a client " -"establish a secure connection to it." +"You should now have learned how to generate self-signed certificates using " +"the given script, start an SSL-enabled server and have a client establish a " +"secure connection to it." msgstr "" +"이제 주어진 스크립트를 사용하여 자체 서명 인증서를 생성하고, SSL 사용 서버를 " +"시작하고, 클라이언트가 보안 연결을 설정하는 방법을 배웠을 것입니다." -#: ../../source/how-to-enable-ssl-connections.rst:72 +#: ../../source/how-to-enable-ssl-connections.rst:75 msgid "Additional resources" -msgstr "" +msgstr "추가 리소스" -#: ../../source/how-to-enable-ssl-connections.rst:74 +#: ../../source/how-to-enable-ssl-connections.rst:77 msgid "" -"These additional sources might be relevant if you would like to dive " -"deeper into the topic of certificates:" -msgstr "" +"These additional sources might be relevant if you would like to dive deeper " +"into the topic of certificates:" +msgstr "인증서에 대해 더 자세히 알아보고 싶다면 이러한 추가 자료를 참고하세요:" -#: ../../source/how-to-enable-ssl-connections.rst:76 +#: ../../source/how-to-enable-ssl-connections.rst:79 msgid "`Let's Encrypt `_" -msgstr "" +msgstr "'암호화하세요 `_'" -#: ../../source/how-to-enable-ssl-connections.rst:77 +#: ../../source/how-to-enable-ssl-connections.rst:80 msgid "`certbot `_" -msgstr "" +msgstr "`인증봇 `_" #: ../../source/how-to-implement-strategies.rst:2 msgid "Implement strategies" -msgstr "" +msgstr "전략 구현" #: ../../source/how-to-implement-strategies.rst:4 msgid "" -"The strategy abstraction enables implementation of fully custom " -"strategies. A strategy is basically the federated learning algorithm that" -" runs on the server. Strategies decide how to sample clients, how to " -"configure clients for training, how to aggregate updates, and how to " -"evaluate models. Flower provides a few built-in strategies which are " -"based on the same API described below." +"The strategy abstraction enables implementation of fully custom strategies. " +"A strategy is basically the federated learning algorithm that runs on the " +"server. Strategies decide how to sample clients, how to configure clients " +"for training, how to aggregate updates, and how to evaluate models. Flower " +"provides a few built-in strategies which are based on the same API described " +"below." msgstr "" +"전략 추상화를 통해 완전한 맞춤형 전략을 구현할 수 있습니다. 전략은 " +"기본적으로 서버에서 실행되는 연합 학습 알고리즘입니다. 전략은 클라이언트를 " +"샘플링하는 방법, 학습을 위해 클라이언트를 구성하는 방법, 업데이트를 집계하는 " +"방법, 모델을 평가하는 방법을 결정합니다. Flower는 아래에 설명된 것과 동일한 " +"API를 기반으로 하는 몇 가지 기본 제공 전략을 제공합니다." #: ../../source/how-to-implement-strategies.rst:11 msgid "The :code:`Strategy` abstraction" -msgstr "" +msgstr ":code:`Strategy` 추상화" #: ../../source/how-to-implement-strategies.rst:13 msgid "" -"All strategy implementation are derived from the abstract base class " -":code:`flwr.server.strategy.Strategy`, both built-in implementations and " -"third party implementations. This means that custom strategy " -"implementations have the exact same capabilities at their disposal as " -"built-in ones." +"All strategy implementation are derived from the abstract base class :code:" +"`flwr.server.strategy.Strategy`, both built-in implementations and third " +"party implementations. This means that custom strategy implementations have " +"the exact same capabilities at their disposal as built-in ones." msgstr "" +"모든 전략 구현은 기본 제공 구현과 타사 구현 모두 추상 기본 클래스인 :code:" +"`flwr.server.strategy.Strategy`에서 파생됩니다. 즉, 사용자 정의 전략 구현은 " +"기본 제공 구현과 완전히 동일한 기능을 사용할 수 있습니다." #: ../../source/how-to-implement-strategies.rst:18 msgid "" "The strategy abstraction defines a few abstract methods that need to be " "implemented:" -msgstr "" +msgstr "전략 추상화에서는 구현해야 하는 몇 가지 추상적인 메서드를 정의합니다:" #: ../../source/how-to-implement-strategies.rst:75 msgid "" -"Creating a new strategy means implementing a new :code:`class` (derived " -"from the abstract base class :code:`Strategy`) that implements for the " -"previously shown abstract methods:" +"Creating a new strategy means implementing a new :code:`class` (derived from " +"the abstract base class :code:`Strategy`) that implements for the previously " +"shown abstract methods:" msgstr "" +"새 전략을 생성한다는 것은 이전에 표시된 추상 메서드에 대해 구현하는 새로운 :" +"code:`class`(추상 기본 클래스 :code:`Strategy`에서 파생됨)를 구현하는 것을 의" +"미합니다:" #: ../../source/how-to-implement-strategies.rst:100 msgid "The Flower server calls these methods in the following order:" -msgstr "" +msgstr "Flower 서버는 다음 순서로 이러한 메서드를 호출합니다:" #: ../../source/how-to-implement-strategies.rst:177 msgid "The following sections describe each of those methods in more detail." -msgstr "" +msgstr "다음 섹션에서는 이러한 각 방법에 대해 자세히 설명합니다." #: ../../source/how-to-implement-strategies.rst:180 msgid "The :code:`initialize_parameters` method" -msgstr "" +msgstr ":code:`initialize_parameters` 메서드" #: ../../source/how-to-implement-strategies.rst:182 msgid "" -":code:`initialize_parameters` is called only once, at the very beginning " -"of an execution. It is responsible for providing the initial global model" -" parameters in a serialized form (i.e., as a :code:`Parameters` object)." +":code:`initialize_parameters` is called only once, at the very beginning of " +"an execution. It is responsible for providing the initial global model " +"parameters in a serialized form (i.e., as a :code:`Parameters` object)." msgstr "" +"code:`initialize_parameters`는 실행을 처음 시작할 때 한 번만 호출됩니다. 이 " +"함수는 초기 전역 모델 파라미터를 직렬화된 형식(즉, :code:`Parameters` 객체)으" +"로 제공하는 역할을 합니다." #: ../../source/how-to-implement-strategies.rst:184 msgid "" -"Built-in strategies return user-provided initial parameters. The " -"following example shows how initial parameters can be passed to " -":code:`FedAvg`:" +"Built-in strategies return user-provided initial parameters. The following " +"example shows how initial parameters can be passed to :code:`FedAvg`:" msgstr "" +"기본 제공 전략은 사용자가 제공한 초기 매개 변수를 반환합니다. 다음 예는 초기 " +"매개 변수를 :code:`FedAvg`에 전달하는 방법을 보여줍니다:" #: ../../source/how-to-implement-strategies.rst:209 msgid "" "The Flower server will call :code:`initialize_parameters`, which either " -"returns the parameters that were passed to :code:`initial_parameters`, or" -" :code:`None`. If no parameters are returned from " -":code:`initialize_parameters` (i.e., :code:`None`), the server will " -"randomly select one client and ask it to provide its parameters. This is " -"a convenience feature and not recommended in practice, but it can be " -"useful for prototyping. In practice, it is recommended to always use " -"server-side parameter initialization." -msgstr "" +"returns the parameters that were passed to :code:`initial_parameters`, or :" +"code:`None`. If no parameters are returned from :code:" +"`initialize_parameters` (i.e., :code:`None`), the server will randomly " +"select one client and ask it to provide its parameters. This is a " +"convenience feature and not recommended in practice, but it can be useful " +"for prototyping. In practice, it is recommended to always use server-side " +"parameter initialization." +msgstr "" +"Flower 서버는 :code:`initialize_parameters`를 호출하여 :code:" +"`initial_parameters`에 전달된 파라미터를 반환하거나 :code:`None`을 반환합니" +"다. :code:`initial_parameters`에서 반환되는 매개변수가 없는 경우(즉, :code:" +"`None`) 서버는 무작위로 클라이언트 하나를 선택하여 해당 클라이언트에 매개변수" +"를 제공하도록 요청합니다. 이는 편의 기능이며 실제로는 권장하지 않지만 프로토" +"타이핑에는 유용할 수 있습니다. 실제로는 항상 서버 측 매개변수 초기화를 사용하" +"는 것이 좋습니다." #: ../../source/how-to-implement-strategies.rst:213 msgid "" "Server-side parameter initialization is a powerful mechanism. It can be " -"used, for example, to resume training from a previously saved checkpoint." -" It is also the fundamental capability needed to implement hybrid " -"approaches, for example, to fine-tune a pre-trained model using federated" -" learning." +"used, for example, to resume training from a previously saved checkpoint. It " +"is also the fundamental capability needed to implement hybrid approaches, " +"for example, to fine-tune a pre-trained model using federated learning." msgstr "" +"서버 측 파라미터 초기화는 강력한 메커니즘입니다. 예를 들어 이전에 저장한 " +"체크포인트에서 학습을 재개하는 데 사용할 수 있습니다. 또한 연합 학습을 " +"사용하여 사전 학습된 모델을 미세 조정하는 등 하이브리드 접근 방식을 구현하는 " +"데 필요한 기본 기능입니다." #: ../../source/how-to-implement-strategies.rst:216 msgid "The :code:`configure_fit` method" -msgstr "" +msgstr ":code:`configure_fit` 메서드" #: ../../source/how-to-implement-strategies.rst:218 msgid "" -":code:`configure_fit` is responsible for configuring the upcoming round " -"of training. What does *configure* mean in this context? Configuring a " -"round means selecting clients and deciding what instructions to send to " -"these clients. The signature of :code:`configure_fit` makes this clear:" +":code:`configure_fit` is responsible for configuring the upcoming round of " +"training. What does *configure* mean in this context? Configuring a round " +"means selecting clients and deciding what instructions to send to these " +"clients. The signature of :code:`configure_fit` makes this clear:" msgstr "" +":code:`configure_fit`은 다가오는 학 라운드를 구성하는 역할을 합니다. 이 문맥" +"에서 *구성*은 무엇을 의미하나요? 라운드를 구성한다는 것은 클라이언트를 선택하" +"고 이 클라이언트에게 어떤 지침을 보낼지 결정하는 것을 의미합니다. code:" +"`configure_fit`의 시그니처를 보면 이를 명확히 알 수 있습니다:" #: ../../source/how-to-implement-strategies.rst:231 msgid "" "The return value is a list of tuples, each representing the instructions " -"that will be sent to a particular client. Strategy implementations " -"usually perform the following steps in :code:`configure_fit`:" +"that will be sent to a particular client. Strategy implementations usually " +"perform the following steps in :code:`configure_fit`:" msgstr "" +"반환 값은 튜플 목록으로, 각 튜플은 특정 클라이언트로 전송될 명령어를 " +"나타냅니다. 전략 구현은 일반적으로 :code:`configure_fit`에서 다음 단계를 " +"수행합니다:" #: ../../source/how-to-implement-strategies.rst:233 #: ../../source/how-to-implement-strategies.rst:280 @@ -4321,379 +5039,475 @@ msgid "" "Use the :code:`client_manager` to randomly sample all (or a subset of) " "available clients (each represented as a :code:`ClientProxy` object)" msgstr "" +":code:`client_manager`를 사용하여 사용 가능한 모든 클라이언트(또는 그 하위 집" +"합)를 무작위로 샘플링합니다(각각 :code:`ClientProxy` 개체로 표시됨)" #: ../../source/how-to-implement-strategies.rst:234 msgid "" "Pair each :code:`ClientProxy` with the same :code:`FitIns` holding the " "current global model :code:`parameters` and :code:`config` dict" msgstr "" +"각 :code:`ClientProxy`를 현재 글로벌 모델 :code:`parameters` 및 :code:" +"`config` dict를 보유한 동일한 :code:`FitIns`와 쌍을 이룹니다" #: ../../source/how-to-implement-strategies.rst:236 msgid "" "More sophisticated implementations can use :code:`configure_fit` to " -"implement custom client selection logic. A client will only participate " -"in a round if the corresponding :code:`ClientProxy` is included in the " -"list returned from :code:`configure_fit`." +"implement custom client selection logic. A client will only participate in a " +"round if the corresponding :code:`ClientProxy` is included in the list " +"returned from :code:`configure_fit`." msgstr "" +"보다 정교한 구현은 :code:`configure_fit`을 사용하여 사용자 지정 클라이언트 선" +"택 로직을 구현할 수 있습니다. 클라이언트는 :code:`configure_fit`에서 반환된 " +"목록에 해당 :code:`ClientProxy`가 포함된 경우에만 라운드에 참여합니다." #: ../../source/how-to-implement-strategies.rst:240 msgid "" "The structure of this return value provides a lot of flexibility to the " "user. Since instructions are defined on a per-client basis, different " -"instructions can be sent to each client. This enables custom strategies " -"to train, for example, different models on different clients, or use " -"different hyperparameters on different clients (via the :code:`config` " -"dict)." +"instructions can be sent to each client. This enables custom strategies to " +"train, for example, different models on different clients, or use different " +"hyperparameters on different clients (via the :code:`config` dict)." msgstr "" +"이 반환 값의 구조는 사용자에게 많은 유연성을 제공합니다. instructions은 " +"클라이언트별로 정의되므로 각 클라이언트에 서로 다른 명령어를 전송할 수 " +"있습니다. 이를 통해 예를 들어 클라이언트마다 다른 모델을 학습시키거나 " +"클라이언트마다 다른 하이퍼파라미터를 사용하는 사용자 지정 전략을 사용할 수 " +"있습니다(:code:`config` dict를 통해)." #: ../../source/how-to-implement-strategies.rst:243 msgid "The :code:`aggregate_fit` method" -msgstr "" +msgstr ":code:`aggregate_fit` 메서드" #: ../../source/how-to-implement-strategies.rst:245 msgid "" -":code:`aggregate_fit` is responsible for aggregating the results returned" -" by the clients that were selected and asked to train in " -":code:`configure_fit`." +":code:`aggregate_fit` is responsible for aggregating the results returned by " +"the clients that were selected and asked to train in :code:`configure_fit`." msgstr "" +"code:`aggregate_fit`은 :code:`configure_fit`에서 훈련하도록 선택되고 요청된 " +"클라이언트가 반환한 결과를 집계하는 역할을 담당합니다." #: ../../source/how-to-implement-strategies.rst:258 msgid "" "Of course, failures can happen, so there is no guarantee that the server " -"will get results from all the clients it sent instructions to (via " -":code:`configure_fit`). :code:`aggregate_fit` therefore receives a list " -"of :code:`results`, but also a list of :code:`failures`." +"will get results from all the clients it sent instructions to (via :code:" +"`configure_fit`). :code:`aggregate_fit` therefore receives a list of :code:" +"`results`, but also a list of :code:`failures`." msgstr "" +"물론 실패가 발생할 수 있으므로 서버가 명령을 보낸 모든 클라이언트로부터 결과" +"를 얻을 수 있다는 보장은 없습니다(:code:`configure_fit`을 통해). 따라서 :" +"code:`aggregate_fit`은 :code:`results` 목록뿐만 아니라 :code:`failures` 목록" +"도 받습니다." #: ../../source/how-to-implement-strategies.rst:260 msgid "" -":code:`aggregate_fit` returns an optional :code:`Parameters` object and a" -" dictionary of aggregated metrics. The :code:`Parameters` return value is" -" optional because :code:`aggregate_fit` might decide that the results " +":code:`aggregate_fit` returns an optional :code:`Parameters` object and a " +"dictionary of aggregated metrics. The :code:`Parameters` return value is " +"optional because :code:`aggregate_fit` might decide that the results " "provided are not sufficient for aggregation (e.g., too many failures)." msgstr "" +"code:`aggregate_fit`은 선택적 :code:`Parameters` 개체와 집계된 메트릭의 " +"dictionary를 반환합니다. :code:`Parameters` 반환 값은 :code:`aggregate_fit`" +"이 제공된 결과가 집계에 충분하지 않다고 판단할 수 있으므로(예: 실패 수가 너" +"무 많음) 선택 사항입니다." #: ../../source/how-to-implement-strategies.rst:263 msgid "The :code:`configure_evaluate` method" -msgstr "" +msgstr ":code:`configure_evaluate` 메서드" #: ../../source/how-to-implement-strategies.rst:265 msgid "" -":code:`configure_evaluate` is responsible for configuring the upcoming " -"round of evaluation. What does *configure* mean in this context? " -"Configuring a round means selecting clients and deciding what " -"instructions to send to these clients. The signature of " -":code:`configure_evaluate` makes this clear:" +":code:`configure_evaluate` is responsible for configuring the upcoming round " +"of evaluation. What does *configure* mean in this context? Configuring a " +"round means selecting clients and deciding what instructions to send to " +"these clients. The signature of :code:`configure_evaluate` makes this clear:" msgstr "" +":code:`configure_evaluate`는 다가오는 평가 라운드를 구성하는 역할을 합니다. " +"이 문맥에서 *구성*은 무엇을 의미하나요? 라운드를 구성한다는 것은 클라이언트" +"를 선택하고 이러한 클라이언트에 전송할 지침을 결정하는 것을 의미합니다. :" +"code:`configure_evaluate`의 시그니처를 보면 이를 명확히 알 수 있습니다:" #: ../../source/how-to-implement-strategies.rst:278 msgid "" "The return value is a list of tuples, each representing the instructions " -"that will be sent to a particular client. Strategy implementations " -"usually perform the following steps in :code:`configure_evaluate`:" +"that will be sent to a particular client. Strategy implementations usually " +"perform the following steps in :code:`configure_evaluate`:" msgstr "" +"반환 값은 튜플 목록으로, 각 튜플은 특정 클라이언트로 전송될 명령어를 " +"나타냅니다. 전략 구현은 일반적으로 :code:`configure_evaluate`에서 다음 " +"단계를 수행합니다:" #: ../../source/how-to-implement-strategies.rst:281 msgid "" -"Pair each :code:`ClientProxy` with the same :code:`EvaluateIns` holding " -"the current global model :code:`parameters` and :code:`config` dict" +"Pair each :code:`ClientProxy` with the same :code:`EvaluateIns` holding the " +"current global model :code:`parameters` and :code:`config` dict" msgstr "" +"각 :code:`ClientProxy`를 현재 글로벌 모델 :code:`parameters` 및 :code:" +"`config` dict를 보유한 동일한 :code:`EvaluateIns`와 쌍을 이룹니다" #: ../../source/how-to-implement-strategies.rst:283 msgid "" "More sophisticated implementations can use :code:`configure_evaluate` to " -"implement custom client selection logic. A client will only participate " -"in a round if the corresponding :code:`ClientProxy` is included in the " -"list returned from :code:`configure_evaluate`." +"implement custom client selection logic. A client will only participate in a " +"round if the corresponding :code:`ClientProxy` is included in the list " +"returned from :code:`configure_evaluate`." msgstr "" +"보다 정교한 구현은 :code:`configure_evaluate`를 사용하여 사용자 지정 클라이언" +"트 선택 로직을 구현할 수 있습니다. 클라이언트는 :code:`configure_evaluate`에" +"서 반환된 목록에 해당 :code:`ClientProxy`가 포함된 경우에만 라운드에 참여합니" +"다." #: ../../source/how-to-implement-strategies.rst:287 msgid "" "The structure of this return value provides a lot of flexibility to the " "user. Since instructions are defined on a per-client basis, different " -"instructions can be sent to each client. This enables custom strategies " -"to evaluate, for example, different models on different clients, or use " -"different hyperparameters on different clients (via the :code:`config` " -"dict)." +"instructions can be sent to each client. This enables custom strategies to " +"evaluate, for example, different models on different clients, or use " +"different hyperparameters on different clients (via the :code:`config` dict)." msgstr "" +"이 반환 값의 구조는 사용자에게 많은 유연성을 제공합니다. 명령어는 " +"클라이언트별로 정의되므로 각 클라이언트에 서로 다른 명령어를 전송할 수 " +"있습니다. 이를 통해 사용자 지정 전략을 통해 예를 들어 클라이언트마다 다른 " +"모델을 평가하거나 클라이언트마다 다른 하이퍼파라미터를 사용할 수 " +"있습니다(:code:`config` dict를 통해)." #: ../../source/how-to-implement-strategies.rst:291 msgid "The :code:`aggregate_evaluate` method" -msgstr "" +msgstr ":code:`aggregate_evaluate` 메서드" #: ../../source/how-to-implement-strategies.rst:293 msgid "" ":code:`aggregate_evaluate` is responsible for aggregating the results " -"returned by the clients that were selected and asked to evaluate in " -":code:`configure_evaluate`." +"returned by the clients that were selected and asked to evaluate in :code:" +"`configure_evaluate`." msgstr "" +"code:`aggregate_evaluate`는 :code:`configure_evaluate`에서 선택되어 평가를 요" +"청한 클라이언트가 반환한 결과를 집계하는 역할을 담당합니다." #: ../../source/how-to-implement-strategies.rst:306 msgid "" "Of course, failures can happen, so there is no guarantee that the server " -"will get results from all the clients it sent instructions to (via " -":code:`configure_evaluate`). :code:`aggregate_evaluate` therefore " -"receives a list of :code:`results`, but also a list of :code:`failures`." +"will get results from all the clients it sent instructions to (via :code:" +"`configure_evaluate`). :code:`aggregate_evaluate` therefore receives a list " +"of :code:`results`, but also a list of :code:`failures`." msgstr "" +"물론 실패가 발생할 수 있으므로 서버가 명령을 보낸 모든 클라이언트로부터 결과" +"를 얻을 수 있다는 보장은 없습니다(:code:`configure_evaluate`를 통해). 따라" +"서 :code:`aggregate_evaluate`는 :code:`results` 목록뿐만 아니라 :code:" +"`failures` 목록도 받습니다." #: ../../source/how-to-implement-strategies.rst:308 msgid "" -":code:`aggregate_evaluate` returns an optional :code:`float` (loss) and a" -" dictionary of aggregated metrics. The :code:`float` return value is " -"optional because :code:`aggregate_evaluate` might decide that the results" -" provided are not sufficient for aggregation (e.g., too many failures)." +":code:`aggregate_evaluate` returns an optional :code:`float` (loss) and a " +"dictionary of aggregated metrics. The :code:`float` return value is optional " +"because :code:`aggregate_evaluate` might decide that the results provided " +"are not sufficient for aggregation (e.g., too many failures)." msgstr "" +"code:`aggregate_evaluate`는 선택적 :code:`float`(손실)와 집계된 메트릭의 " +"dictionary를 반환합니다. code:`float` 반환 값은 :code:`aggregate_evaluate`가 " +"제공된 결과가 집계에 충분하지 않다고 판단할 수 있으므로(예: 실패 수가 너무 많" +"음) 선택 사항입니다." #: ../../source/how-to-implement-strategies.rst:311 msgid "The :code:`evaluate` method" -msgstr "" +msgstr ":code:`evaluate` 메서드" #: ../../source/how-to-implement-strategies.rst:313 msgid "" ":code:`evaluate` is responsible for evaluating model parameters on the " -"server-side. Having :code:`evaluate` in addition to " -":code:`configure_evaluate`/:code:`aggregate_evaluate` enables strategies " -"to perform both servers-side and client-side (federated) evaluation." +"server-side. Having :code:`evaluate` in addition to :code:" +"`configure_evaluate`/:code:`aggregate_evaluate` enables strategies to " +"perform both servers-side and client-side (federated) evaluation." msgstr "" +":code:`evaluate`는 서버 측에서 모델 매개변수를 평가하는 역할을 담당합니다. " +"code:`configure_evaluate`/:code:`aggregate_evaluate`와 함께 :code:`evaluate`" +"를 사용하면 서버 측과 클라이언트 측(federated) 평가를 모두 수행할 수 있는 전" +"략을 사용할 수 있습니다." #: ../../source/how-to-implement-strategies.rst:323 msgid "" -"The return value is again optional because the strategy might not need to" -" implement server-side evaluation or because the user-defined " -":code:`evaluate` method might not complete successfully (e.g., it might " -"fail to load the server-side evaluation data)." +"The return value is again optional because the strategy might not need to " +"implement server-side evaluation or because the user-defined :code:" +"`evaluate` method might not complete successfully (e.g., it might fail to " +"load the server-side evaluation data)." msgstr "" +"반환 값은 전략에서 서버 측 평가를 구현할 필요가 없거나 사용자 정의 :code:" +"`evaluate` 메서드가 성공적으로 완료되지 않을 수 있기 때문에(예: 서버 측 평가 " +"데이터를 로드하지 못할 수 있음) 다시 선택 사항으로 설정할 수 있습니다." #: ../../source/how-to-install-flower.rst:2 msgid "Install Flower" -msgstr "" +msgstr "Flower 설치" #: ../../source/how-to-install-flower.rst:6 msgid "Python version" -msgstr "" +msgstr "Python 버전" #: ../../source/how-to-install-flower.rst:12 msgid "Install stable release" -msgstr "" +msgstr "안정적인 릴리즈 설치" #: ../../source/how-to-install-flower.rst:15 -#: ../../source/how-to-upgrade-to-flower-next.rst:45 +#: ../../source/how-to-upgrade-to-flower-next.rst:46 msgid "Using pip" -msgstr "" +msgstr "pip 사용" #: ../../source/how-to-install-flower.rst:17 msgid "" -"Stable releases are available on `PyPI " -"`_::" +"Stable releases are available on `PyPI `_::" msgstr "" +"안정적인 릴리즈는 `PyPI `_:: 에서 확인할 수 " +"있습니다::" #: ../../source/how-to-install-flower.rst:21 msgid "" "For simulations that use the Virtual Client Engine, ``flwr`` should be " "installed with the ``simulation`` extra::" msgstr "" +"가상 클라이언트 엔진을 사용하는 시뮬레이션의 경우 ``flwr``을 " +"``simulation``extra와 함께 설치해야 합니다:" #: ../../source/how-to-install-flower.rst:27 msgid "Using conda (or mamba)" -msgstr "" +msgstr "conda(또는 mamba) 사용" #: ../../source/how-to-install-flower.rst:29 msgid "Flower can also be installed from the ``conda-forge`` channel." -msgstr "" +msgstr "Flower은 'conda-forge' 채널에서도 설치할 수 있습니다." #: ../../source/how-to-install-flower.rst:31 msgid "" -"If you have not added ``conda-forge`` to your channels, you will first " -"need to run the following::" -msgstr "" +"If you have not added ``conda-forge`` to your channels, you will first need " +"to run the following::" +msgstr "채널에 'conda-forge'를 추가하지 않은 경우 먼저 다음을 실행해야 합니다:" #: ../../source/how-to-install-flower.rst:36 msgid "" -"Once the ``conda-forge`` channel has been enabled, ``flwr`` can be " -"installed with ``conda``::" +"Once the ``conda-forge`` channel has been enabled, ``flwr`` can be installed " +"with ``conda``::" msgstr "" +"conda-forge`` 채널이 활성화되면 ``flwr``을 ``conda``로 설치할 수 있습니다::" #: ../../source/how-to-install-flower.rst:40 msgid "or with ``mamba``::" -msgstr "" +msgstr "또는 ``mamba``::" #: ../../source/how-to-install-flower.rst:46 msgid "Verify installation" -msgstr "" +msgstr "설치 확인" #: ../../source/how-to-install-flower.rst:48 msgid "" "The following command can be used to verify if Flower was successfully " -"installed. If everything worked, it should print the version of Flower to" -" the command line::" +"installed. If everything worked, it should print the version of Flower to " +"the command line::" msgstr "" +"다음 명령을 사용하여 Flower가 성공적으로 설치되었는지 확인할 수 있습니다. 모" +"든 것이 정상적으로 작동하면 명령줄에 Flower의 버전이 출력됩니다:" #: ../../source/how-to-install-flower.rst:55 msgid "Advanced installation options" -msgstr "" +msgstr "고급 설치 옵션" #: ../../source/how-to-install-flower.rst:58 msgid "Install via Docker" -msgstr "" +msgstr "Docker를 통해 설치" #: ../../source/how-to-install-flower.rst:60 msgid ":doc:`How to run Flower using Docker `" msgstr "" +":doc:`Docker를 사용하여 Flower를 실행하는 방법 `" #: ../../source/how-to-install-flower.rst:63 msgid "Install pre-release" -msgstr "" +msgstr "사전 릴리즈 설치" #: ../../source/how-to-install-flower.rst:65 msgid "" -"New (possibly unstable) versions of Flower are sometimes available as " -"pre-release versions (alpha, beta, release candidate) before the stable " -"release happens::" +"New (possibly unstable) versions of Flower are sometimes available as pre-" +"release versions (alpha, beta, release candidate) before the stable release " +"happens::" msgstr "" +"새(불안정할 수 있는) 버전의 Flower는 안정 버전이 출시되기 전에 사전 릴리즈 버" +"전(알파, 베타, 릴리즈 후보)으로 제공되는 경우가 있습니다:" #: ../../source/how-to-install-flower.rst:69 msgid "" -"For simulations that use the Virtual Client Engine, ``flwr`` pre-releases" -" should be installed with the ``simulation`` extra::" +"For simulations that use the Virtual Client Engine, ``flwr`` pre-releases " +"should be installed with the ``simulation`` extra::" msgstr "" +"가상 클라이언트 엔진을 사용하는 시뮬레이션의 경우 ``flwr`` 사전 릴리즈를 " +"``simulation`` extra와 함께 설치해야 합니다:" #: ../../source/how-to-install-flower.rst:74 msgid "Install nightly release" -msgstr "" +msgstr "야간 릴리즈 설치" #: ../../source/how-to-install-flower.rst:76 msgid "" -"The latest (potentially unstable) changes in Flower are available as " -"nightly releases::" +"The latest (potentially unstable) changes in Flower are available as nightly " +"releases::" msgstr "" +"Flower의 최신 (불안정할 수 있는) 변경 사항은 다음과 같이 야간 릴리즈로 제공됩" +"니다:" #: ../../source/how-to-install-flower.rst:80 msgid "" -"For simulations that use the Virtual Client Engine, ``flwr-nightly`` " -"should be installed with the ``simulation`` extra::" +"For simulations that use the Virtual Client Engine, ``flwr-nightly`` should " +"be installed with the ``simulation`` extra::" msgstr "" +"가상 클라이언트 엔진을 사용하는 시뮬레이션의 경우, ``flwr-nightly``를 " +"``simulation`` extr와 함께 설치해야 합니다::" #: ../../source/how-to-monitor-simulation.rst:2 msgid "Monitor simulation" -msgstr "" +msgstr "모니터 시뮬레이션" #: ../../source/how-to-monitor-simulation.rst:4 msgid "" -"Flower allows you to monitor system resources while running your " -"simulation. Moreover, the Flower simulation engine is powerful and " -"enables you to decide how to allocate resources per client manner and " -"constrain the total usage. Insights from resource consumption can help " -"you make smarter decisions and speed up the execution time." +"Flower allows you to monitor system resources while running your simulation. " +"Moreover, the Flower simulation engine is powerful and enables you to decide " +"how to allocate resources per client manner and constrain the total usage. " +"Insights from resource consumption can help you make smarter decisions and " +"speed up the execution time." msgstr "" +"Flower를 사용하면 시뮬레이션을 실행하는 동안 시스템 리소스를 모니터링할 수 있" +"습니다. 또한 Flower 시뮬레이션 엔진은 강력하며 클라이언트별 리소스 할당 방법" +"을 결정하고 총 사용량을 제한할 수 있습니다. 리소스 소비에 대한 인사이트를 통" +"해 더 현명한 결정을 내리고 실행 시간을 단축할 수 있습니다." #: ../../source/how-to-monitor-simulation.rst:6 msgid "" -"The specific instructions assume you are using macOS and have the " -"`Homebrew `_ package manager installed." +"The specific instructions assume you are using macOS and have the `Homebrew " +"`_ package manager installed." msgstr "" +"구체적인 지침은 macOS를 사용 중이고 'Homebrew `_ 패키지 관" +"리자가 설치되어 있다고 가정합니다." #: ../../source/how-to-monitor-simulation.rst:10 msgid "Downloads" -msgstr "" +msgstr "다운로드" #: ../../source/how-to-monitor-simulation.rst:16 msgid "" -"`Prometheus `_ is used for data collection, while" -" `Grafana `_ will enable you to visualize the " -"collected data. They are both well integrated with `Ray " -"`_ which Flower uses under the hood." +"`Prometheus `_ is used for data collection, while " +"`Grafana `_ will enable you to visualize the collected " +"data. They are both well integrated with `Ray `_ which " +"Flower uses under the hood." msgstr "" +"`Prometheus `_는 데이터 수집에 사용되며, `Grafana " +"`_는 수집된 데이터를 시각화할 수 있게 해줍니다. 이 두 " +"도구는 모두 Flower가 내부적으로 사용하는 `Ray `_와 잘 통" +"합되어 있습니다." #: ../../source/how-to-monitor-simulation.rst:18 msgid "" "Overwrite the configuration files (depending on your device, it might be " "installed on a different path)." -msgstr "" +msgstr "구성 파일을 덮어씁니다(장치에 따라 다른 경로에 설치되어 있을 수 있음)." #: ../../source/how-to-monitor-simulation.rst:20 msgid "If you are on an M1 Mac, it should be:" -msgstr "" +msgstr "M1 Mac을 사용 중이라면:" #: ../../source/how-to-monitor-simulation.rst:27 msgid "On the previous generation Intel Mac devices, it should be:" -msgstr "" +msgstr "이전 세대 Intel Mac 장치에서는:" #: ../../source/how-to-monitor-simulation.rst:34 msgid "" -"Open the respective configuration files and change them. Depending on " -"your device, use one of the two following commands:" +"Open the respective configuration files and change them. Depending on your " +"device, use one of the two following commands:" msgstr "" +"각 구성 파일을 열고 변경합니다. 장치에 따라 다음 두 명령 중 하나를 사용합니" +"다:" #: ../../source/how-to-monitor-simulation.rst:44 msgid "" -"and then delete all the text in the file and paste a new Prometheus " -"config you see below. You may adjust the time intervals to your " -"requirements:" +"and then delete all the text in the file and paste a new Prometheus config " +"you see below. You may adjust the time intervals to your requirements:" msgstr "" +"를 입력한 다음 파일의 모든 텍스트를 삭제하고 아래에 표시된 새 Prometheus 설정" +"을 붙여넣습니다. 요구 사항에 따라 시간 간격을 조정할 수 있습니다:" #: ../../source/how-to-monitor-simulation.rst:59 msgid "" -"Now after you have edited the Prometheus configuration, do the same with " -"the Grafana configuration files. Open those using one of the following " -"commands as before:" +"Now after you have edited the Prometheus configuration, do the same with the " +"Grafana configuration files. Open those using one of the following commands " +"as before:" msgstr "" +"이제 Prometheus 구성을 편집한 후 Grafana 구성 파일에 대해서도 동일한 작업을 " +"수행합니다. 이전과 마찬가지로 다음 명령 중 하나를 사용하여 파일을 엽니다:" #: ../../source/how-to-monitor-simulation.rst:69 msgid "" "Your terminal editor should open and allow you to apply the following " "configuration as before." msgstr "" +"터미널 편집기가 열리면 이전과 마찬가지로 다음 구성을 적용할 수 있습니다." #: ../../source/how-to-monitor-simulation.rst:84 msgid "" -"Congratulations, you just downloaded all the necessary software needed " -"for metrics tracking. Now, let’s start it." +"Congratulations, you just downloaded all the necessary software needed for " +"metrics tracking. Now, let’s start it." msgstr "" +"축하합니다. 매트릭 트레킹에 필요한 모든 소프트웨어를 다운로드하셨습니다. 이" +"제 시작해 보겠습니다." #: ../../source/how-to-monitor-simulation.rst:88 msgid "Tracking metrics" -msgstr "" +msgstr "매트릭 트래킹" #: ../../source/how-to-monitor-simulation.rst:90 msgid "" "Before running your Flower simulation, you have to start the monitoring " "tools you have just installed and configured." msgstr "" +"Flower 시뮬레이션을 실행하기 전에 방금 설치 및 구성한 모니터링 도구를 시작해" +"야 합니다." #: ../../source/how-to-monitor-simulation.rst:97 msgid "" -"Please include the following argument in your Python code when starting a" -" simulation." -msgstr "" +"Please include the following argument in your Python code when starting a " +"simulation." +msgstr "시뮬레이션을 시작할 때 Python 코드에 다음 전달인자를 포함하세요." #: ../../source/how-to-monitor-simulation.rst:108 msgid "Now, you are ready to start your workload." -msgstr "" +msgstr "이제 워크로드를 시작할 준비가 되었습니다." #: ../../source/how-to-monitor-simulation.rst:110 msgid "" -"Shortly after the simulation starts, you should see the following logs in" -" your terminal:" +"Shortly after the simulation starts, you should see the following logs in " +"your terminal:" msgstr "" +"시뮬레이션이 시작되고 얼마 지나지 않아 터미널에 다음 로그가 표시됩니다:" #: ../../source/how-to-monitor-simulation.rst:117 msgid "You can look at everything at ``_ ." -msgstr "" +msgstr "``_ 에서 모든 것을 볼 수 있습니다." #: ../../source/how-to-monitor-simulation.rst:119 msgid "" -"It's a Ray Dashboard. You can navigate to Metrics (on the left panel, the" -" lowest option)." +"It's a Ray Dashboard. You can navigate to Metrics (on the left panel, the " +"lowest option)." msgstr "" +"Ray 대시보드입니다. 메트릭(왼쪽 패널의 가장 아래 옵션)으로 이동할 수 있습니" +"다." #: ../../source/how-to-monitor-simulation.rst:121 msgid "" -"Or alternatively, you can just see them in Grafana by clicking on the " -"right-up corner, “View in Grafana”. Please note that the Ray dashboard is" -" only accessible during the simulation. After the simulation ends, you " -"can only use Grafana to explore the metrics. You can start Grafana by " -"going to ``http://localhost:3000/``." +"Or alternatively, you can just see them in Grafana by clicking on the right-" +"up corner, “View in Grafana”. Please note that the Ray dashboard is only " +"accessible during the simulation. After the simulation ends, you can only " +"use Grafana to explore the metrics. You can start Grafana by going to " +"``http://localhost:3000/``." msgstr "" +"또는 오른쪽 위 모서리인 \"Grafana에서 보기\"를 클릭하여 Grafana에서 바로 확인" +"할 수도 있습니다. Ray 대시보드는 시뮬레이션 중에만 액세스할 수 있다는 점에 유" +"의하세요. 시뮬레이션이 종료된 후에는 Grafana를 사용하여 메트릭을 탐색할 수만 " +"있습니다. ``http://localhost:3000/``로 이동하여 Grafana를 시작할 수 있습니다." #: ../../source/how-to-monitor-simulation.rst:123 msgid "" @@ -4701,764 +5515,1278 @@ msgid "" "important as they will otherwise block, for example port :code:`3000` on " "your machine as long as they are running." msgstr "" +"시각화를 완료한 후에는 Prometheus와 Grafana를 중지합니다. 그렇지 않으면 실행 " +"중인 동안 컴퓨터에서 포트 :code:`3000` 등을 차단하므로 이 작업이 중요합니다." #: ../../source/how-to-monitor-simulation.rst:132 msgid "Resource allocation" -msgstr "" +msgstr "리소스 할당" #: ../../source/how-to-monitor-simulation.rst:134 msgid "" -"You must understand how the Ray library works to efficiently allocate " -"system resources to simulation clients on your own." +"You must understand how the Ray library works to efficiently allocate system " +"resources to simulation clients on your own." msgstr "" +"Ray 라이브러리가 어떻게 작동하는지 이해해야 시뮬레이션 클라이언트에 시스템 리" +"소스를 효율적으로 할당할 수 있습니다." #: ../../source/how-to-monitor-simulation.rst:136 msgid "" "Initially, the simulation (which Ray handles under the hood) starts by " "default with all the available resources on the system, which it shares " -"among the clients. It doesn't mean it divides it equally among all of " -"them, nor that the model training happens at all of them simultaneously. " -"You will learn more about that in the later part of this blog. You can " -"check the system resources by running the following:" +"among the clients. It doesn't mean it divides it equally among all of them, " +"nor that the model training happens at all of them simultaneously. You will " +"learn more about that in the later part of this blog. You can check the " +"system resources by running the following:" msgstr "" +"처음에 시뮬레이션(Ray가 내부에서 처리하는)은 기본적으로 시스템에서 사용 가능" +"한 모든 리소스를 사용하여 시작되며, 이 리소스는 클라이언트 간에 공유됩니다. " +"그렇다고 해서 모든 클라이언트에게 균등하게 분배하거나 모든 클라이언트에서 동" +"시에 모델 학습이 이루어지는 것은 아닙니다. 이에 대한 자세한 내용은 이 블로그" +"의 뒷부분에서 설명합니다. 다음을 실행하여 시스템 리소스를 확인할 수 있습니다:" #: ../../source/how-to-monitor-simulation.rst:143 msgid "In Google Colab, the result you see might be similar to this:" -msgstr "" +msgstr "Google Colab에서는 이와 유사한 결과가 표시될 수 있습니다:" #: ../../source/how-to-monitor-simulation.rst:155 msgid "" -"However, you can overwrite the defaults. When starting a simulation, do " -"the following (you don't need to overwrite all of them):" +"However, you can overwrite the defaults. When starting a simulation, do the " +"following (you don't need to overwrite all of them):" msgstr "" +"그러나 기본값을 덮어쓸 수 있습니다. 시뮬레이션을 시작할 때 다음을 수행합니다" +"(모두 덮어쓸 필요는 없음):" #: ../../source/how-to-monitor-simulation.rst:175 msgid "Let’s also specify the resource for a single client." -msgstr "" +msgstr "단일 클라이언트에 대한 리소스도 지정해 보겠습니다." #: ../../source/how-to-monitor-simulation.rst:205 msgid "" -"Now comes the crucial part. Ray will start a new client only when it has " -"all the required resources (such that they run in parallel) when the " -"resources allow." +"Now comes the crucial part. Ray will start a new client only when it has all " +"the required resources (such that they run in parallel) when the resources " +"allow." msgstr "" +"이제 중요한 부분이 나옵니다. Ray는 리소스가 허용하는 경우에만 필요한 모든 리" +"소스가 있을 때(병렬로 실행되는 등) 새 클라이언트를 시작합니다." #: ../../source/how-to-monitor-simulation.rst:207 msgid "" -"In the example above, only one client will be run, so your clients won't " -"run concurrently. Setting :code:`client_num_gpus = 0.5` would allow " -"running two clients and therefore enable them to run concurrently. Be " -"careful not to require more resources than available. If you specified " -":code:`client_num_gpus = 2`, the simulation wouldn't start (even if you " -"had 2 GPUs but decided to set 1 in :code:`ray_init_args`)." +"In the example above, only one client will be run, so your clients won't run " +"concurrently. Setting :code:`client_num_gpus = 0.5` would allow running two " +"clients and therefore enable them to run concurrently. Be careful not to " +"require more resources than available. If you specified :code:" +"`client_num_gpus = 2`, the simulation wouldn't start (even if you had 2 GPUs " +"but decided to set 1 in :code:`ray_init_args`)." msgstr "" +"위의 예에서는 하나의 클라이언트만 실행되므로 클라이언트가 동시에 실행되지 않" +"습니다. :code:`client_num_gpus = 0.5` 를 설정하면 두 개의 클라이언트를 실행" +"할 수 있으므로 동시에 실행할 수 있습니다. 사용 가능한 리소스보다 더 많은 리소" +"스를 요구하지 않도록 주의하세요. :code:`client_num_gpus = 2`를 지정하면 시뮬" +"레이션이 시작되지 않습니다(GPU가 2개이지만 :code:`ray_init_args`에서 1개를 설" +"정한 경우에도 마찬가지입니다)." #: ../../source/how-to-monitor-simulation.rst:212 ../../source/ref-faq.rst:2 msgid "FAQ" -msgstr "" +msgstr "자주 묻는 질문" #: ../../source/how-to-monitor-simulation.rst:214 msgid "Q: I don't see any metrics logged." -msgstr "" +msgstr "질문: 기록된 메트릭이 보이지 않습니다." #: ../../source/how-to-monitor-simulation.rst:216 msgid "" -"A: The timeframe might not be properly set. The setting is in the top " -"right corner (\"Last 30 minutes\" by default). Please change the " -"timeframe to reflect the period when the simulation was running." +"A: The timeframe might not be properly set. The setting is in the top right " +"corner (\"Last 30 minutes\" by default). Please change the timeframe to " +"reflect the period when the simulation was running." msgstr "" +"A: 기간이 제대로 설정되지 않았을 수 있습니다. 설정은 오른쪽 상단에 있습니다" +"(기본값은 '지난 30분'). 시뮬레이션이 실행된 기간을 반영하도록 기간을 변경해 " +"주세요." #: ../../source/how-to-monitor-simulation.rst:218 msgid "" -"Q: I see “Grafana server not detected. Please make sure the Grafana " -"server is running and refresh this page” after going to the Metrics tab " -"in Ray Dashboard." +"Q: I see “Grafana server not detected. Please make sure the Grafana server " +"is running and refresh this page” after going to the Metrics tab in Ray " +"Dashboard." msgstr "" +"질문: \"Grafana 서버가 감지되지 않았습니다. Ray 대시보드의 메트릭 탭으로 이동" +"한 후 Grafana 서버가 실행 중인지 확인하고 이 페이지를 새로고침하세요.\"라는 " +"메시지가 표시됩니다." #: ../../source/how-to-monitor-simulation.rst:220 msgid "" -"A: You probably don't have Grafana running. Please check the running " -"services" +"A: You probably don't have Grafana running. Please check the running services" msgstr "" +"A: Grafana가 실행되고 있지 않을 수 있습니다. 실행 중인 서비스를 확인하세요" #: ../../source/how-to-monitor-simulation.rst:226 msgid "" "Q: I see \"This site can't be reached\" when going to " "``_." msgstr "" +"Q: ``_로 이동할 때 \"이 사이트에 연결할 수 없습니다." +"\"라는 메시지가 표시됩니다." #: ../../source/how-to-monitor-simulation.rst:228 msgid "" -"A: Either the simulation has already finished, or you still need to start" -" Prometheus." -msgstr "" +"A: Either the simulation has already finished, or you still need to start " +"Prometheus." +msgstr "A: 시뮬레이션이 이미 완료되었거나 아직 Prometheus를 시작해야 합니다." #: ../../source/how-to-monitor-simulation.rst:232 msgid "Resources" -msgstr "" +msgstr "리소스" #: ../../source/how-to-monitor-simulation.rst:234 msgid "" "Ray Dashboard: ``_" msgstr "" +"Ray 대시보드: ``_" #: ../../source/how-to-monitor-simulation.rst:236 msgid "Ray Metrics: ``_" -msgstr "" +msgstr "Ray 메트릭: ``_" #: ../../source/how-to-run-flower-using-docker.rst:2 msgid "Run Flower using Docker" -msgstr "" +msgstr "Docker를 사용하여 Flower 실행" #: ../../source/how-to-run-flower-using-docker.rst:4 msgid "" -"The simplest way to get started with Flower is by using the pre-made " -"Docker images, which you can find on `Docker Hub " -"`_." +"The simplest way to get started with Flower is by using the pre-made Docker " +"images, which you can find on `Docker Hub `__. Supported architectures include ``amd64`` and ``arm64v8``." msgstr "" +"Flower를 시작하는 가장 간단한 방법은 `Docker Hub `__에서 찾을 수 있는 미리 만들어진 Docker 이미지를 사용하는 것입니다. 지" +"원되는 아키텍처는 ``amd64`` 및 ``arm64v8``입니다." -#: ../../source/how-to-run-flower-using-docker.rst:7 +#: ../../source/how-to-run-flower-using-docker.rst:8 msgid "Before you start, make sure that the Docker daemon is running:" +msgstr "시작하기 전에 Docker daemon이 실행 중인지 확인하세요:" + +#: ../../source/how-to-run-flower-using-docker.rst:15 +msgid "" +"If you do not see the version of Docker but instead get an error saying that " +"the command was not found, you will need to install Docker first. You can " +"find installation instruction `here `_." msgstr "" +"전이 표시되지 않고 대신 명령을 찾을 수 없다는 오류가 표시되는 경우 먼저 " +"Docker를 설치해야 합니다. `여기 `_에서 " +"설치 지침을 찾을 수 있습니다." -#: ../../source/how-to-run-flower-using-docker.rst:14 +#: ../../source/how-to-run-flower-using-docker.rst:21 msgid "" -"If you do not see the version of Docker but instead get an error saying " -"that the command was not found, you will need to install Docker first. " -"You can find installation instruction `here `_." +"On Linux, Docker commands require ``sudo`` privilege. If you want to avoid " +"using ``sudo``, you can follow the `Post-installation steps `_ on the official Docker " +"website." msgstr "" +"Linux에서 Docker 명령을 실행하려면 ``sudo`` 권한이 필요합니다. ``sudo`` 를 " +"사용하지 않으려면 공식 Docker 웹사이트의 `Post-installation steps " +"`_를 따르세요." -#: ../../source/how-to-run-flower-using-docker.rst:20 +#: ../../source/how-to-run-flower-using-docker.rst:27 msgid "" -"On Linux, Docker commands require ``sudo`` privilege. If you want to " -"avoid using ``sudo``, you can follow the `Post-installation steps " -"`_ on the " -"official Docker website." +"To ensure optimal performance and compatibility, the SuperLink, SuperNode " +"and ServerApp image must have the same version when running together. This " +"guarantees seamless integration and avoids potential conflicts or issues " +"that may arise from using different versions." msgstr "" +"최적의 성능과 호환성을 보장하려면 SuperLink, SuperNode 및 ServerApp 이미지를 " +"함께 실행할 때 버전이 동일해야 합니다. 이렇게 하면 원활한 통합을 보장하고 서" +"로 다른 버전을 사용할 때 발생할 수 있는 잠재적인 충돌이나 문제를 방지할 수 있" +"습니다." -#: ../../source/how-to-run-flower-using-docker.rst:25 +#: ../../source/how-to-run-flower-using-docker.rst:32 msgid "Flower SuperLink" -msgstr "" +msgstr "Flower SuperLink" -#: ../../source/how-to-run-flower-using-docker.rst:28 +#: ../../source/how-to-run-flower-using-docker.rst:35 msgid "Quickstart" -msgstr "" +msgstr "빠른 시작" -#: ../../source/how-to-run-flower-using-docker.rst:30 +#: ../../source/how-to-run-flower-using-docker.rst:37 msgid "If you're looking to try out Flower, you can use the following command:" -msgstr "" +msgstr "Flower를 사용해보고 싶다면 다음 명령을 사용하면 됩니다:" -#: ../../source/how-to-run-flower-using-docker.rst:36 +#: ../../source/how-to-run-flower-using-docker.rst:43 msgid "" -"The command pulls the Docker image with the tag ``1.8.0`` from Docker " -"Hub. The tag specifies the Flower version. In this case, Flower 1.8.0. " -"The ``--rm`` flag tells Docker to remove the container after it exits." +"The command pulls the Docker image with the tag ``1.8.0`` from Docker Hub. " +"The tag specifies the Flower version. In this case, Flower 1.8.0. The ``--" +"rm`` flag tells Docker to remove the container after it exits." msgstr "" +"이 명령은 Docker Hub에서 ``1.8.0`` 태그가 있는 Docker 이미지를 가져옵니다. " +"이 태그는 Flower 버전을 지정합니다. 이 경우, Flower 1.8.0입니다. '`--rm`` 플" +"래그는 컨테이너가 종료된 후 컨테이너를 제거하도록 Docker에 지시합니다." -#: ../../source/how-to-run-flower-using-docker.rst:42 +#: ../../source/how-to-run-flower-using-docker.rst:49 msgid "" "By default, the Flower SuperLink keeps state in-memory. When using the " -"Docker flag ``--rm``, the state is not persisted between container " -"starts. We will show below how to save the state in a file on your host " -"system." +"Docker flag ``--rm``, the state is not persisted between container starts. " +"We will show below how to save the state in a file on your host system." msgstr "" +"기본적으로 Flower SuperLink는 상태를 in-memory에 유지합니다. Docker 플래그 " +"`--rm``을 사용하는 경우 컨테이너 시작 사이에 상태가 유지되지 않습니다. 아래에" +"서 호스트 시스템의 파일에 상태를 저장하는 방법을 보여드리겠습니다." -#: ../../source/how-to-run-flower-using-docker.rst:46 +#: ../../source/how-to-run-flower-using-docker.rst:53 msgid "" -"The ``-p :`` flag tells Docker to map the ports " -"``9091``/``9092`` of the host to ``9091``/``9092`` of the container, " -"allowing you to access the Driver API on ``http://localhost:9091`` and " -"the Fleet API on ``http://localhost:9092``. Lastly, any flag that comes " -"after the tag is passed to the Flower SuperLink. Here, we are passing the" -" flag ``--insecure``." +"The ``-p :`` flag tells Docker to map the ports ``9091``/" +"``9092`` of the host to ``9091``/``9092`` of the container, allowing you to " +"access the Driver API on ``http://localhost:9091`` and the Fleet API on " +"``http://localhost:9092``. Lastly, any flag that comes after the tag is " +"passed to the Flower SuperLink. Here, we are passing the flag ``--insecure``." msgstr "" +"``-p :`` 플래그는 호스트의 포트 ``9091``/``9092``를 컨테이너" +"의 ``9091``/``9092``에 매핑하여 ``http://localhost:9091``의 드라이버 API와 " +"``http://localhost:9092``의 Fleet API에 액세스할 수 있도록 Docker에 지시합니" +"다. 마지막으로, 태그 뒤에 오는 모든 플래그는 Flower SuperLink에 전달됩니다. " +"여기서는 ``--insecure``플래그를 전달합니다." -#: ../../source/how-to-run-flower-using-docker.rst:53 -#: ../../source/how-to-run-flower-using-docker.rst:216 +#: ../../source/how-to-run-flower-using-docker.rst:60 +#: ../../source/how-to-run-flower-using-docker.rst:259 +#: ../../source/how-to-run-flower-using-docker.rst:376 msgid "" "The ``--insecure`` flag enables insecure communication (using HTTP, not " -"HTTPS) and should only be used for testing purposes. We strongly " -"recommend enabling `SSL `_ when " -"deploying to a production environment." +"HTTPS) and should only be used for testing purposes. We strongly recommend " +"enabling `SSL `__ when deploying to a " +"production environment." msgstr "" +"``--insecure`` 플래그는 안전하지 않은 통신(HTTPS가 아닌 HTTP 사용)을 활성화하" +"며 테스트 목적으로만 사용해야 합니다. 프로덕션 환경에 배포할 때는 `SSL " +"`__을 활성화할 것을 강력히 권장합니" +"다." -#: ../../source/how-to-run-flower-using-docker.rst:58 +#: ../../source/how-to-run-flower-using-docker.rst:65 msgid "" "You can use ``--help`` to view all available flags that the SuperLink " "supports:" msgstr "" +"'`--help``을 사용하면 SuperLink가 지원하는 모든 플래그를 볼 수 있습니다:" -#: ../../source/how-to-run-flower-using-docker.rst:65 +#: ../../source/how-to-run-flower-using-docker.rst:72 msgid "Mounting a volume to store the state on the host system" -msgstr "" - -#: ../../source/how-to-run-flower-using-docker.rst:67 -msgid "" -"If you want to persist the state of the SuperLink on your host system, " -"all you need to do is specify a path where you want to save the file on " -"your host system and a name for the database file. In the example below, " -"we tell Docker via the flag ``-v`` to mount the user's home directory " -"(``~/`` on your host) into the ``/app/`` directory of the container. " -"Furthermore, we use the flag ``--database`` to specify the name of the " -"database file." -msgstr "" +msgstr "호스트 시스템에 상태를 저장할 볼륨 마운트하기" + +#: ../../source/how-to-run-flower-using-docker.rst:74 +msgid "" +"If you want to persist the state of the SuperLink on your host system, all " +"you need to do is specify a directory where you want to save the file on " +"your host system and a name for the database file. By default, the SuperLink " +"container runs with a non-root user called ``app`` with the user ID " +"``49999``. It is recommended to create new directory and change the user ID " +"of the directory to ``49999`` to ensure the mounted directory has the proper " +"permissions. If you later want to delete the directory, you can change the " +"user ID back to the current user ID by running ``sudo chown -R $USER:$(id -" +"gn) state``." +msgstr "" +"호스트 시스템에서 SuperLink의 상태를 유지하려면 호스트 시스템에서 파일을 저장" +"할 디렉터리와 데이터베이스 파일의 이름을 지정하기만 하면 됩니다. 기본적으로 " +"SuperLink 컨테이너는 사용자 ID가 ``49999``인 ``app``이라는 루트가 아닌 사용자" +"로 실행됩니다. 마운트된 디렉터리에 적절한 권한이 있는지 확인하려면 새 디렉터" +"리를 생성하고 디렉터리의 사용자 ID를 ``49999``로 변경하는 것이 좋습니다. 나중" +"에 디렉터리를 삭제하려면 ``sudo chown -R $USER:$(id -gn) state``를 실행하여 " +"사용자 ID를 현재 사용자 ID로 다시 변경할 수 있습니다." + +#: ../../source/how-to-run-flower-using-docker.rst:82 +msgid "" +"In the example below, we create a new directory, change the user ID and tell " +"Docker via the flag ``--volume`` to mount the local ``state`` directory into " +"the ``/app/state`` directory of the container. Furthermore, we use the flag " +"``--database`` to specify the name of the database file." +msgstr "" +"아래 예에서는 새 디렉터리를 생성하고, 사용자 ID를 변경하고, 플래그 ``--" +"volume``을 통해 Docker에게 로컬 ``state`` 디렉터리를 컨테이너의 ``/app/" +"state`` 디렉터리에 마운트하도록 지시합니다. 또한 ``--database`` 플래그를 사용" +"하여 데이터베이스 파일의 이름을 지정합니다." -#: ../../source/how-to-run-flower-using-docker.rst:80 +#: ../../source/how-to-run-flower-using-docker.rst:95 msgid "" "As soon as the SuperLink starts, the file ``state.db`` is created in the " -"user's home directory on your host system. If the file already exists, " -"the SuperLink tries to restore the state from the file. To start the " -"SuperLink with an empty database, simply remove the ``state.db`` file." -msgstr "" - -#: ../../source/how-to-run-flower-using-docker.rst:85 -#: ../../source/how-to-run-flower-using-docker.rst:234 +"``state`` directory on your host system. If the file already exists, the " +"SuperLink tries to restore the state from the file. To start the SuperLink " +"with an empty database, simply remove the ``state.db`` file." +msgstr "" +"SuperLink가 시작되자마자 호스트 시스템의 ``state`` 디렉터리에 ``state.db`` 파" +"일이 생성됩니다. 파일이 이미 존재하는 경우 SuperLink는 파일에서 상태를 복원하" +"려고 시도합니다. 빈 데이터베이스로 SuperLink를 시작하려면 ``state.db`` 파일" +"을 제거하면 됩니다." + +#: ../../source/how-to-run-flower-using-docker.rst:100 +#: ../../source/how-to-run-flower-using-docker.rst:281 +#: ../../source/how-to-run-flower-using-docker.rst:397 msgid "Enabling SSL for secure connections" +msgstr "보안 연결을 위한 SSL 사용 설정" + +#: ../../source/how-to-run-flower-using-docker.rst:102 +msgid "" +"To enable SSL, you will need a PEM-encoded root certificate, a PEM-encoded " +"private key and a PEM-encoded certificate chain." msgstr "" +"SSL을 사용하려면 PEM으로 인코딩된 루트 인증서, PEM으로 인코딩된 개인 키 및 " +"PEM으로 인코딩된 인증서 체인이 필요합니다." -#: ../../source/how-to-run-flower-using-docker.rst:87 +#: ../../source/how-to-run-flower-using-docker.rst:106 msgid "" -"To enable SSL, you will need a PEM-encoded root certificate, a PEM-" -"encoded private key and a PEM-encoded certificate chain." +"For testing purposes, you can generate your own self-signed certificates. " +"The `Enable SSL connections `__ page contains a section that will " +"guide you through the process." msgstr "" +"테스트 목적으로 자체 서명된 인증서를 생성할 수 있습니다. 'SSL 연결 사용 " +"`__ 페이지에 프로세스를 안내하는 섹션이 있습니다." -#: ../../source/how-to-run-flower-using-docker.rst:91 +#: ../../source/how-to-run-flower-using-docker.rst:110 msgid "" -"For testing purposes, you can generate your own self-signed certificates." -" The `Enable SSL connections `_ page contains a section that " -"will guide you through the process." +"Assuming all files we need are in the local ``certificates`` directory, we " +"can use the flag ``--volume`` to mount the local directory into the ``/app/" +"certificates/`` directory of the container. This allows the SuperLink to " +"access the files within the container. The ``ro`` stands for ``read-only``. " +"Docker volumes default to ``read-write``; that option tells Docker to make " +"the volume ``read-only`` instead. Finally, we pass the names of the " +"certificates and key file to the SuperLink with the ``--ssl-ca-certfile``, " +"``--ssl-certfile`` and ``--ssl-keyfile`` flag." msgstr "" +"필요한 모든 파일이 로컬``certificates`` 디렉터리에 있다고 가정하면, ``--" +"volume``플래그를 사용하여 로컬 디렉터리를 컨테이너의 ``/app/certificates/`` " +"디렉터리에 마운트할 수 있습니다. 이렇게 하면 SuperLink 가 컨테이너 내의 파일" +"에 액세스할 수 있습니다. ``ro``는 ``read-only``을 의미합니다. Docker 볼륨은 " +"기본적으로 ``read-write``로 설정되어 있는데, 이 옵션을 사용하면 볼륨을 " +"``read-only``으로 만들 수 있습니다. 마지막으로 인증서 및 키 파일의 이름을 " +"``--ssl-ca-certfile``, ``--ssl-certfile`` 및 ``--ssl-keyfile`` 플래그와 함께 " +"SuperLink에 전달합니다." -#: ../../source/how-to-run-flower-using-docker.rst:95 +#: ../../source/how-to-run-flower-using-docker.rst:128 msgid "" -"Assuming all files we need are in the local ``certificates`` directory, " -"we can use the flag ``-v`` to mount the local directory into the " -"``/app/`` directory of the container. This allows the SuperLink to access" -" the files within the container. Finally, we pass the names of the " -"certificates to the SuperLink with the ``--certificates`` flag." +"Because Flower containers, by default, run with a non-root user ``app``, the " +"mounted files and directories must have the proper permissions for the user " +"ID ``49999``. For example, to change the user ID of all files in the " +"``certificates/`` directory, you can run ``sudo chown -R 49999:49999 " +"certificates/*``." msgstr "" +"기본적으로 Flower 컨테이너는 루트가 아닌 사용자 ``app``로 실행되므로 마운트" +"된 파일과 디렉터리에 사용자 ID ``49999``에 대한 적절한 권한이 있어야 합니다. " +"예를 들어, ``certificates/`` 디렉터리에 있는 모든 파일의 사용자 ID를 변경하려" +"면 ``sudo chown -R 49999:49999 certificates/*``를 실행하면 됩니다." -#: ../../source/how-to-run-flower-using-docker.rst:107 +#: ../../source/how-to-run-flower-using-docker.rst:134 msgid "Flower SuperNode" -msgstr "" +msgstr "Flower SuperNode" -#: ../../source/how-to-run-flower-using-docker.rst:109 +#: ../../source/how-to-run-flower-using-docker.rst:136 msgid "" -"The SuperNode Docker image comes with a pre-installed version of Flower " -"and serves as a base for building your own SuperNode image." +"The SuperNode Docker image comes with a pre-installed version of Flower and " +"serves as a base for building your own SuperNode image." msgstr "" +"SuperNode Docker 이미지는 Flower의 사전 설치된 버전과 함께 제공되며, 자체 " +"SuperNode 이미지를 구축하기 위한 기반 역할을 합니다." -#: ../../source/how-to-run-flower-using-docker.rst:114 +#: ../../source/how-to-run-flower-using-docker.rst:141 msgid "" "The SuperNode Docker image currently works only with the 1.9.0-nightly " -"release. A stable version will be available when Flower 1.9.0 (stable) " -"gets released (ETA: May). A SuperNode nightly image must be paired with " -"the corresponding SuperLink nightly image released on the same day. To " -"ensure the versions are in sync, using the concrete tag, e.g., " +"release. A stable version will be available when Flower 1.9.0 (stable) gets " +"released (ETA: May). A SuperNode nightly image must be paired with the " +"corresponding SuperLink and ServerApp nightly images released on the same " +"day. To ensure the versions are in sync, using the concrete tag, e.g., " "``1.9.0.dev20240501`` instead of ``nightly`` is recommended." msgstr "" +"SuperNode Docker 이미지는 현재 1.9.0 야간 릴리스에서만 작동합니다. 안정 버전" +"은 Flower 1.9.0(안정)이 출시되면 사용할 수 있습니다(예상 출시일: 5월). " +"SuperNode 야간 이미지는 같은 날 릴리스된 해당 SuperLink 및 서버앱 야간 이미지" +"와 페어링되어야 합니다. 버전이 동기화되도록 하려면 ``nightly`` 대신 ``1.9.0." +"dev20240501``과 같은 구체적인 태그를 사용하는 것이 좋습니다." -#: ../../source/how-to-run-flower-using-docker.rst:119 +#: ../../source/how-to-run-flower-using-docker.rst:147 msgid "" -"We will use the ``app-pytorch`` example, which you can find in the Flower" -" repository, to illustrate how you can dockerize your client-app." +"We will use the ``quickstart-pytorch`` example, which you can find in the " +"Flower repository, to illustrate how you can dockerize your ClientApp." msgstr "" +"Flower 레포지토리에서 찾을 수 있는 ``quickstart-pytorch`` 예제를 사용하여 " +"ClientApp을 도커라이즈하는 방법을 설명하겠습니다." -#: ../../source/how-to-run-flower-using-docker.rst:125 +#: ../../source/how-to-run-flower-using-docker.rst:155 msgid "" "Before we can start, we need to meet a few prerequisites in our local " -"development environment. You can skip the first part if you want to run " -"your client-app instead of the ``app-pytorch`` example." +"development environment. You can skip the first part if you want to run your " +"ClientApp instead of the ``quickstart-pytorch`` example." msgstr "" +"시작하기 전에 로컬 개발 환경에서 몇 가지 전제 조건을 충족해야 합니다. " +"'quickstart-pytorch' 예제 대신 ClientApp을 실행하려는 경우 첫 번째 부분을 건" +"너뛸 수 있습니다." -#: ../../source/how-to-run-flower-using-docker.rst:143 +#: ../../source/how-to-run-flower-using-docker.rst:159 +msgid "Clone the Flower repository." +msgstr "플라워 레포지토리를 클론합니다." + +#: ../../source/how-to-run-flower-using-docker.rst:173 msgid "Creating a SuperNode Dockerfile" -msgstr "" +msgstr "SuperNode Dockerfile 만들기" -#: ../../source/how-to-run-flower-using-docker.rst:145 +#: ../../source/how-to-run-flower-using-docker.rst:175 +#: ../../source/how-to-run-flower-using-docker.rst:311 msgid "Let's assume the following project layout:" +msgstr "다음과 같은 프로젝트 레이아웃을 가정해 보겠습니다:" + +#: ../../source/how-to-run-flower-using-docker.rst:184 +msgid "" +"First, we need to create a ``requirements.txt`` file in the directory where " +"the ``ClientApp`` code is located. In the file, we list all the dependencies " +"that the ClientApp requires." msgstr "" +"먼저 ``ClientApp`` 코드가 있는 디렉토리에 ``requirements.txt`` 파일을 " +"만들어야 합니다. 이 파일에는 클라이언트 앱에 필요한 모든 의존성을 나열합니다." -#: ../../source/how-to-run-flower-using-docker.rst:156 +#: ../../source/how-to-run-flower-using-docker.rst:196 msgid "" -"First, we need to create a Dockerfile in the directory where the " -"``ClientApp`` code is located. If you use the ``app-pytorch`` example, " -"create a new file called ``Dockerfile`` in ``examples/app-pytorch``." +"Note that `flwr `__ is already installed in " +"the ``flwr/supernode`` base image, so you only need to include other package " +"dependencies in your ``requirements.txt``, such as ``torch``, " +"``tensorflow``, etc." msgstr "" +"`flwr `__ 는 이미 ``flwr/supernode`` 기본 이" +"미지에 설치되어 있으므로, ``torch``, ``tensorflow`` 등과 같은 다른 패키지 " +"dependencies만 ``requirements.txt``에 포함시키면 됩니다." -#: ../../source/how-to-run-flower-using-docker.rst:160 +#: ../../source/how-to-run-flower-using-docker.rst:200 msgid "" -"The ``Dockerfile`` contains the instructions that assemble the SuperNode " -"image." +"Next, we create a Dockerfile. If you use the ``quickstart-pytorch`` example, " +"create a new file called ``Dockerfile.supernode`` in ``examples/quickstart-" +"pytorch``." msgstr "" +"다음으로, Dockerfile을 생성합니다.``quickstart-pytorch`` 예제를 사용하는 경" +"우 ``examples/quickstart-pytorch``에 ``Dockerfile.supernode``라는 새 파일을 " +"생성합니다." -#: ../../source/how-to-run-flower-using-docker.rst:173 +#: ../../source/how-to-run-flower-using-docker.rst:203 msgid "" -"In the first two lines, we instruct Docker to use the SuperNode image " -"tagged ``nightly`` as a base image and set our working directory to " -"``/app``. The following instructions will now be executed in the ``/app``" -" directory. Next, we install the ``ClientApp`` dependencies by copying " -"the ``requirements.txt`` file into the image and run ``pip install``. In " -"the last two lines, we copy the ``ClientApp`` code (``client.py`` and " -"``task.py``) into the image and set the entry point to ``flower-client-" -"app``." +"The ``Dockerfile.supernode`` contains the instructions that assemble the " +"SuperNode image." msgstr "" +"``Dockerfile.supernode``에는 SuperNode 이미지를 조립하는 지침이 포함되어 있습" +"니다." -#: ../../source/how-to-run-flower-using-docker.rst:181 -msgid "Building the SuperNode Docker image" +#: ../../source/how-to-run-flower-using-docker.rst:217 +msgid "" +"In the first two lines, we instruct Docker to use the SuperNode image tagged " +"``nightly`` as a base image and set our working directory to ``/app``. The " +"following instructions will now be executed in the ``/app`` directory. Next, " +"we install the ClientApp dependencies by copying the ``requirements.txt`` " +"file into the image and run ``pip install``. In the last two lines, we copy " +"the ``client.py`` module into the image and set the entry point to ``flower-" +"client-app`` with the argument ``client:app``. The argument is the object " +"reference of the ClientApp (``:``) that will be run " +"inside the ClientApp." msgstr "" +"처음 두 줄에서는 ``nightly`` 태그가 붙은 SuperNode 이미지를 기본 이미지로 사" +"용하고 작업 디렉터리를 ``/app``로 설정하도록 Docker에 지시합니다. 이제 ``/" +"app`` 디렉토리에서 다음 명령이 실행됩니다. 다음으로, ``requirements.txt`` 파" +"일을 이미지에 복사하여 ClientApp dependencies 요소를 설치하고 ``pip install``" +"을 실행합니다. 마지막 두 줄에서 ``client.py`` 모듈을 이미지에 복사하고 " +"``client:app`` 인수를 사용하여 진입점을 ``flower-client-app``로 설정합니다. " +"인수는 클라이언트앱 내부에서 실행될 클라이언트앱의 객체 참조 (``:" +"``) 입니다." + +#: ../../source/how-to-run-flower-using-docker.rst:226 +msgid "Building the SuperNode Docker image" +msgstr "SuperNode Docker 이미지 빌드" -#: ../../source/how-to-run-flower-using-docker.rst:183 +#: ../../source/how-to-run-flower-using-docker.rst:228 msgid "" -"Next, we build the SuperNode Docker image by running the following " -"command in the directory where Dockerfile and client-app code are " -"located." +"Next, we build the SuperNode Docker image by running the following command " +"in the directory where Dockerfile and ClientApp code are located." msgstr "" +"다음으로, Dockerfile 및 ClientApp 코드가 있는 디렉터리에서 다음 명령을 실행하" +"여 SuperNode Docker 이미지를 빌드합니다." -#: ../../source/how-to-run-flower-using-docker.rst:190 +#: ../../source/how-to-run-flower-using-docker.rst:235 msgid "" "We gave the image the name ``flwr_supernode``, and the tag ``0.0.1``. " "Remember that the here chosen values only serve as an example. You can " "change them to your needs." msgstr "" +"이미지에 ``flwr_supernode``라는 이름을 붙이고 ``0.0.1`` 태그를 붙였습니다. 여" +"기서 선택한 값은 예시일 뿐이라는 점을 기억하세요. 필요에 따라 변경할 수 있습" +"니다." -#: ../../source/how-to-run-flower-using-docker.rst:195 +#: ../../source/how-to-run-flower-using-docker.rst:240 msgid "Running the SuperNode Docker image" -msgstr "" +msgstr "SuperNode Docker 이미지 실행" -#: ../../source/how-to-run-flower-using-docker.rst:197 +#: ../../source/how-to-run-flower-using-docker.rst:242 msgid "Now that we have built the SuperNode image, we can finally run it." -msgstr "" +msgstr "이제 SuperNode 이미지를 빌드했으니 이제 실행할 수 있습니다." -#: ../../source/how-to-run-flower-using-docker.rst:205 +#: ../../source/how-to-run-flower-using-docker.rst:250 +#: ../../source/how-to-run-flower-using-docker.rst:367 msgid "Let's break down each part of this command:" -msgstr "" +msgstr "이 명령의 각 부분을 자세히 살펴보겠습니다:" -#: ../../source/how-to-run-flower-using-docker.rst:207 +#: ../../source/how-to-run-flower-using-docker.rst:252 +#: ../../source/how-to-run-flower-using-docker.rst:369 msgid "``docker run``: This is the command to run a new Docker container." -msgstr "" +msgstr "``docker run``: 새 Docker 컨테이너를 실행하는 명령입니다." -#: ../../source/how-to-run-flower-using-docker.rst:208 +#: ../../source/how-to-run-flower-using-docker.rst:253 +#: ../../source/how-to-run-flower-using-docker.rst:370 msgid "" -"``--rm``: This option specifies that the container should be " -"automatically removed when it stops." +"``--rm``: This option specifies that the container should be automatically " +"removed when it stops." msgstr "" +"``--rm``: 이 옵션은 컨테이너가 중지될 때 자동으로 제거되도록 지정합니다." -#: ../../source/how-to-run-flower-using-docker.rst +#: ../../source/how-to-run-flower-using-docker.rst:254 msgid "``flwr_supernode:0.0.1``: The name the tag of the Docker image to use." -msgstr "" +msgstr "``flwr_supernode:0.0.1``: 사용할 Docker 이미지의 태그 이름입니다." + +#: ../../source/how-to-run-flower-using-docker.rst:255 +#: ../../source/how-to-run-flower-using-docker.rst:372 +msgid "``--insecure``: This option enables insecure communication." +msgstr "``--insecure``: 이 옵션은 보안되지 않은 통신을 활성화합니다." #: ../../source/how-to-run-flower-using-docker.rst msgid "" -"``client:app``: The object reference of the ``ClientApp`` " -"(``:``)." +"``--superlink 192.168.1.100:9092``: This option specifies the address of the " +"SuperLinks Fleet" msgstr "" +"``--superlink 192.168.1.100:9092``: 이 옵션은 SuperLinks Fleet의 주소를 지정" +"합니다" #: ../../source/how-to-run-flower-using-docker.rst +msgid "API to connect to. Remember to update it with your SuperLink IP." +msgstr "" +"API에 연결할 수 있습니다. SuperLink IP로 업데이트하는 것을 잊지 마세요." + +#: ../../source/how-to-run-flower-using-docker.rst:269 msgid "" -"It points to the ``ClientApp`` that will be run inside the SuperNode " -"container." +"To test running Flower locally, you can create a `bridge network `__, use the ``--network`` argument and pass the name of the Docker " +"network to run your SuperNodes." msgstr "" +"로컬에서 Flower를 실행하는 것을 테스트하려면 `bridge network `__를 생성하고 ``--network`` argument를 사용하고 SuperNodes를 실행" +"할 Docker 네트워크의 이름을 전달하면 됩니다." -#: ../../source/how-to-run-flower-using-docker.rst:212 -msgid "``--insecure``: This option enables insecure communication." +#: ../../source/how-to-run-flower-using-docker.rst:273 +msgid "" +"Any argument that comes after the tag is passed to the Flower SuperNode " +"binary. To see all available flags that the SuperNode supports, run:" msgstr "" +"태그 뒤에 오는 모든 argument는 Flower SuperNode 바이너리에 전달됩니다. " +"SuperNode가 지원하는 사용 가능한 모든 플래그를 보려면 실행하세요:" -#: ../../source/how-to-run-flower-using-docker.rst +#: ../../source/how-to-run-flower-using-docker.rst:283 +msgid "" +"To enable SSL, we will need to mount a PEM-encoded root certificate into " +"your SuperNode container." +msgstr "" +"SSL을 사용하려면 PEM 인코딩된 루트 인증서를 SuperNode 컨테이너에 마운트해야 " +"합니다." + +#: ../../source/how-to-run-flower-using-docker.rst:285 +msgid "" +"Assuming the certificate already exists locally, we can use the flag ``--" +"volume`` to mount the local certificate into the container's ``/app/`` " +"directory. This allows the SuperNode to access the certificate within the " +"container. Use the ``--root-certificates`` flag when starting the container." +msgstr "" +"인증서가 이미 로컬에 존재한다고 가정하면, ``--volume`` 플래그를 사용하여 로" +"컬 인증서를 컨테이너의 ``/app/`` 디렉터리에 마운트할 수 있습니다. 이렇게 하" +"면 SuperNode가 컨테이너 내의 인증서에 액세스할 수 있습니다. 컨테이너를 시작" +"할 때 ``--root-certificates`` 플래그를 사용하세요." + +#: ../../source/how-to-run-flower-using-docker.rst:297 +msgid "Flower ServerApp" +msgstr "Flower 서버앱" + +#: ../../source/how-to-run-flower-using-docker.rst:299 +msgid "" +"The procedure for building and running a ServerApp image is almost identical " +"to the SuperNode image." +msgstr "" +"ServerApp 이미지를 빌드하고 실행하는 절차는 SuperNode 이미지와 거의 동일합니" +"다." + +#: ../../source/how-to-run-flower-using-docker.rst:301 +msgid "" +"Similar to the SuperNode image, the ServerApp Docker image comes with a pre-" +"installed version of Flower and serves as a base for building your own " +"ServerApp image." +msgstr "" +"SuperNode 이미지와 마찬가지로 ServerApp Docker 이미지는 Flower의 사전 설치된 " +"버전과 함께 제공되며, 자체 ServerApp 이미지를 구축하기 위한 기본 역할을 합니" +"다." + +#: ../../source/how-to-run-flower-using-docker.rst:304 +msgid "" +"We will use the same ``quickstart-pytorch`` example as we do in the Flower " +"SuperNode section. If you have not already done so, please follow the " +"`SuperNode Prerequisites`_ before proceeding." +msgstr "" +"여기서는 Flower SuperNode 섹션에서와 동일한`quickstart-pytorch`` 예제를 사용" +"하겠습니다. 아직 수행하지 않았다면 계속 진행하기 전에 `SuperNode " +"Prerequisites`_ 을 따르세요." + +#: ../../source/how-to-run-flower-using-docker.rst:309 +msgid "Creating a ServerApp Dockerfile" +msgstr "ServerApp Dockerfile 만들기" + +#: ../../source/how-to-run-flower-using-docker.rst:320 +msgid "" +"First, we need to create a Dockerfile in the directory where the " +"``ServerApp`` code is located. If you use the ``quickstart-pytorch`` " +"example, create a new file called ``Dockerfile.serverapp`` in ``examples/" +"quickstart-pytorch``." +msgstr "" +"먼저, ``ServerApp`` 코드가 있는 디렉토리에 Docker파일을 생성해야 합니다. " +"``quickstart-pytorch`` 예제를 사용하는 경우 ``examples/quickstart-pytorch``" +"에 ``Dockerfile.serverapp``이라는 새 파일을 생성합니다." + +#: ../../source/how-to-run-flower-using-docker.rst:324 +msgid "" +"The ``Dockerfile.serverapp`` contains the instructions that assemble the " +"ServerApp image." +msgstr "" +"``Dockerfile.serverapp``에는 ServerApp 이미지를 합치는 지침이 포함되어 있습니" +"다." + +#: ../../source/how-to-run-flower-using-docker.rst:335 +msgid "" +"In the first two lines, we instruct Docker to use the ServerApp image tagged " +"``1.8.0`` as a base image and set our working directory to ``/app``. The " +"following instructions will now be executed in the ``/app`` directory. In " +"the last two lines, we copy the ``server.py`` module into the image and set " +"the entry point to ``flower-server-app`` with the argument ``server:app``. " +"The argument is the object reference of the ServerApp (``:" +"``) that will be run inside the ServerApp container." +msgstr "" +"처음 두 줄에서는 ``1.8.0`` 태그가 붙은 ServerApp 이미지를 기본 이미지로 사용" +"하고 작업 디렉터리를 ``/app``로 설정하도록 Docker에 지시합니다. 이제 ``/" +"app`` 디렉토리에서 다음 명령이 실행됩니다. 마지막 두 줄에서는 ``server.py`` " +"모듈을 이미지에 복사하고 ``server:app`` argument를 사용하여 진입점을 " +"``flower-server-app``로 설정합니다. 인수는 ServerApp 컨테이너 내에서 실행될 " +"ServerApp의 객체 참조(``:``)입니다." + +#: ../../source/how-to-run-flower-using-docker.rst:343 +msgid "Building the ServerApp Docker image" +msgstr "ServerApp Docker 이미지 빌드" + +#: ../../source/how-to-run-flower-using-docker.rst:345 +msgid "" +"Next, we build the ServerApp Docker image by running the following command " +"in the directory where Dockerfile and ServerApp code are located." +msgstr "" +"다음으로, Docker파일과 ServerApp 코드가 있는 디렉터리에서 다음 명령을 실행하" +"여 ServerApp Docker 이미지를 빌드합니다." + +#: ../../source/how-to-run-flower-using-docker.rst:352 msgid "" -"``--server 192.168.1.100:9092``: This option specifies the address of the" -" SuperLinks Fleet" +"We gave the image the name ``flwr_serverapp``, and the tag ``0.0.1``. " +"Remember that the here chosen values only serve as an example. You can " +"change them to your needs." msgstr "" +"이미지에``flwr_serverapp``이라는 이름을 붙이고 ``0.0.1``이라는 태그를 붙였습" +"니다. 여기서 선택한 값은 예시일 뿐이라는 점을 기억하세요. 필요에 따라 변경할 " +"수 있습니다." + +#: ../../source/how-to-run-flower-using-docker.rst:357 +msgid "Running the ServerApp Docker image" +msgstr "ServerApp Docker 이미지 실행" + +#: ../../source/how-to-run-flower-using-docker.rst:359 +msgid "Now that we have built the ServerApp image, we can finally run it." +msgstr "이제 ServerApp 이미지를 빌드했으니 이제 실행할 수 있습니다." + +#: ../../source/how-to-run-flower-using-docker.rst:371 +msgid "``flwr_serverapp:0.0.1``: The name the tag of the Docker image to use." +msgstr "``flwr_serverapp:0.0.1``: 사용할 Docker 이미지의 태그 이름입니다." #: ../../source/how-to-run-flower-using-docker.rst -msgid "API to connect to. Remember to update it with your SuperLink IP." +msgid "" +"``--superlink 192.168.1.100:9091``: This option specifies the address of the " +"SuperLinks Driver" msgstr "" +"``--superlink 192.168.1.100:9091``: 이 옵션은 SuperLinks 드라이버의 주소를 지" +"정합니다" -#: ../../source/how-to-run-flower-using-docker.rst:226 +#: ../../source/how-to-run-flower-using-docker.rst:385 msgid "" -"Any argument that comes after the tag is passed to the Flower SuperNode " -"binary. To see all available flags that the SuperNode supports, run:" +"To test running Flower locally, you can create a `bridge network `__, use the ``--network`` argument and pass the name of the Docker " +"network to run your ServerApps." +msgstr "" +"로컬에서 Flower를 실행하는 것을 테스트하려면 `bridge network `__,를 생성하고 ``--network`` argument를 사용하여 ServerApp을 실행" +"할 Docker 네트워크의 이름을 전달하면 됩니다." + +#: ../../source/how-to-run-flower-using-docker.rst:389 +msgid "" +"Any argument that comes after the tag is passed to the Flower ServerApp " +"binary. To see all available flags that the ServerApp supports, run:" msgstr "" +"태그 뒤에 오는 모든 argument는 Flower ServerApp 바이너리에 전달됩니다. " +"ServerApp에서 지원하는 사용 가능한 모든 플래그를 보려면 실행하세요:" -#: ../../source/how-to-run-flower-using-docker.rst:236 +#: ../../source/how-to-run-flower-using-docker.rst:399 msgid "" "To enable SSL, we will need to mount a PEM-encoded root certificate into " -"your SuperNode container." +"your ServerApp container." msgstr "" +"SSL을 사용하려면 PEM 인코딩된 루트 인증서를 ServerApp 컨테이너에 마운트해야 " +"합니다." -#: ../../source/how-to-run-flower-using-docker.rst:238 +#: ../../source/how-to-run-flower-using-docker.rst:401 msgid "" -"Assuming the certificate already exists locally, we can use the flag " -"``-v`` to mount the local certificate into the container's ``/app/`` " -"directory. This allows the SuperNode to access the certificate within the" -" container. Use the ``--certificates`` flag when starting the container." +"Assuming the certificate already exists locally, we can use the flag ``--" +"volume`` to mount the local certificate into the container's ``/app/`` " +"directory. This allows the ServerApp to access the certificate within the " +"container. Use the ``--root-certificates`` flags when starting the container." msgstr "" +"인증서가 이미 로컬에 존재한다고 가정하면, ``--volume`` 플래그를 사용하여 로" +"컬 인증서를 컨테이너의 ``/app/`` 디렉터리에 마운트할 수 있습니다. 이렇게 하" +"면 ServerApp이 컨테이너 내의 인증서에 액세스할 수 있습니다. 컨테이너를 시작" +"할 때 ``--root-certificates`` 플래그를 사용하세요." -#: ../../source/how-to-run-flower-using-docker.rst:249 +#: ../../source/how-to-run-flower-using-docker.rst:412 msgid "Advanced Docker options" +msgstr "고급 Docker 옵션" + +#: ../../source/how-to-run-flower-using-docker.rst:415 +msgid "Run with root user privileges" +msgstr "루트 사용자 권한으로 실행" + +#: ../../source/how-to-run-flower-using-docker.rst:417 +msgid "" +"Flower Docker images, by default, run with a non-root user (username/" +"groupname: ``app``, UID/GID: ``49999``). Using root user is not recommended " +"unless it is necessary for specific tasks during the build process. Always " +"make sure to run the container as a non-root user in production to maintain " +"security best practices." msgstr "" +"기본적으로 Flower Docker 이미지는 루트 사용자가 아닌 사용자(사용자명/그룹명:" +"``app``, UID/GID: ``49999``)로 실행됩니다. 빌드 프로세스 중 특정 작업에 필요" +"한 경우가 아니라면 루트 사용자를 사용하지 않는 것이 좋습니다. 보안 모범 사례" +"를 유지하려면 항상 프로덕션 환경에서 루트 사용자가 아닌 사용자로 컨테이너를 " +"실행해야 합니다." -#: ../../source/how-to-run-flower-using-docker.rst:252 -msgid "Using a different Flower version" +#: ../../source/how-to-run-flower-using-docker.rst:422 +msgid "**Run a container with root user privileges**" +msgstr "**루트 사용자 권한으로 컨테이너 실행하기**" + +#: ../../source/how-to-run-flower-using-docker.rst:424 +msgid "" +"Run the Docker image with the ``-u`` flag and specify ``root`` as the " +"username:" msgstr "" +"``-u`` 플래그를 사용하여 Docker 이미지를 실행하고 사용자 이름으로 ``root``를 " +"지정합니다:" -#: ../../source/how-to-run-flower-using-docker.rst:254 +#: ../../source/how-to-run-flower-using-docker.rst:430 +msgid "This command will run the Docker container with root user privileges." +msgstr "이 명령은 루트 사용자 권한으로 Docker 컨테이너를 실행합니다." + +#: ../../source/how-to-run-flower-using-docker.rst:432 +msgid "**Run the build process with root user privileges**" +msgstr "**루트 사용자 권한으로 빌드 프로세스를 실행합니다**" + +#: ../../source/how-to-run-flower-using-docker.rst:434 +msgid "" +"If you want to switch to the root user during the build process of the " +"Docker image to install missing system dependencies, you can use the ``USER " +"root`` directive within your Dockerfile." +msgstr "" +"Docker 이미지 빌드 과정에서 루트 사용자로 전환하여 누락된 시스템 의존성을 " +"설치하려면 Dockerfile 내에서 ``USER root`` 지시어를 사용할 수 있습니다." + +#: ../../source/how-to-run-flower-using-docker.rst:454 +msgid "Using a different Flower version" +msgstr "다른 Flower 버전 사용" + +#: ../../source/how-to-run-flower-using-docker.rst:456 msgid "" "If you want to use a different version of Flower, for example Flower " -"nightly, you can do so by changing the tag. All available versions are on" -" `Docker Hub `_." +"nightly, you can do so by changing the tag. All available versions are on " +"`Docker Hub `__." msgstr "" +"다른 버전의 Flower를 사용하려면 태그를 변경하여 사용할 수 있습니다(예: " +"Flower nightly). 사용 가능한 모든 버전은 `Docker Hub `__에 있습니다." -#: ../../source/how-to-run-flower-using-docker.rst:259 +#: ../../source/how-to-run-flower-using-docker.rst:460 msgid "Pinning a Docker image to a specific version" -msgstr "" +msgstr "특정 버전에 Docker 이미지 고정하기" -#: ../../source/how-to-run-flower-using-docker.rst:261 +#: ../../source/how-to-run-flower-using-docker.rst:462 msgid "" "It may happen that we update the images behind the tags. Such updates " "usually include security updates of system dependencies that should not " -"change the functionality of Flower. However, if you want to ensure that " -"you always use the same image, you can specify the hash of the image " -"instead of the tag." +"change the functionality of Flower. However, if you want to ensure that you " +"always use the same image, you can specify the hash of the image instead of " +"the tag." msgstr "" +"태그 뒤에 있는 이미지가 업데이트될 수 있습니다. 이러한 업데이트에는 " +"일반적으로 Flower의 기능을 변경해서는 안 되는 시스템 의존성에 대한 보안 " +"업데이트가 포함됩니다. 그러나 항상 동일한 이미지를 사용하려면 태그 대신 " +"이미지의 해시를 지정할 수 있습니다." -#: ../../source/how-to-run-flower-using-docker.rst:266 +#: ../../source/how-to-run-flower-using-docker.rst:467 msgid "" "The following command returns the current image hash referenced by the " "``superlink:1.8.0`` tag:" msgstr "" +"다음 명령은 ``superlink:1.8.0`` 태그가 참조하는 현재 이미지 해시를 반환합니" +"다:" -#: ../../source/how-to-run-flower-using-docker.rst:273 +#: ../../source/how-to-run-flower-using-docker.rst:474 msgid "Next, we can pin the hash when running a new SuperLink container:" -msgstr "" +msgstr "다음으로, 새 SuperLink 컨테이너를 실행할 때 해시를 고정할 수 있습니다:" -#: ../../source/how-to-run-flower-using-docker.rst:282 +#: ../../source/how-to-run-flower-using-docker.rst:483 msgid "Setting environment variables" -msgstr "" +msgstr "환경 변수 설정" -#: ../../source/how-to-run-flower-using-docker.rst:284 +#: ../../source/how-to-run-flower-using-docker.rst:485 msgid "" "To set a variable inside a Docker container, you can use the ``-e " "=`` flag." msgstr "" +"Docker 컨테이너 내에서 변수를 설정하려면 ``-e =`` 플래그를 사용" +"하면 됩니다." #: ../../source/how-to-run-simulations.rst:2 msgid "Run simulations" -msgstr "" +msgstr "시뮬레이션 실행" #: ../../source/how-to-run-simulations.rst:8 msgid "" "Simulating Federated Learning workloads is useful for a multitude of use-" -"cases: you might want to run your workload on a large cohort of clients " -"but without having to source, configure and mange a large number of " -"physical devices; you might want to run your FL workloads as fast as " -"possible on the compute systems you have access to without having to go " -"through a complex setup process; you might want to validate your " -"algorithm on different scenarios at varying levels of data and system " -"heterogeneity, client availability, privacy budgets, etc. These are among" -" some of the use-cases where simulating FL workloads makes sense. Flower " -"can accommodate these scenarios by means of its `VirtualClientEngine " -"`_ or " -"VCE." -msgstr "" +"cases: you might want to run your workload on a large cohort of clients but " +"without having to source, configure and mange a large number of physical " +"devices; you might want to run your FL workloads as fast as possible on the " +"compute systems you have access to without having to go through a complex " +"setup process; you might want to validate your algorithm on different " +"scenarios at varying levels of data and system heterogeneity, client " +"availability, privacy budgets, etc. These are among some of the use-cases " +"where simulating FL workloads makes sense. Flower can accommodate these " +"scenarios by means of its `VirtualClientEngine `_ or VCE." +msgstr "" +"Federated 학습 워크로드 시뮬레이션은 다양한 사용 사례에 유용합니다. 대규모 클" +"라이언트 집단에서 워크로드를 실행하되 많은 수의 물리적 장치를 소싱, 구성 및 " +"관리할 필요가 없는 경우, 복잡한 설정 과정을 거치지 않고도 액세스 가능한 컴퓨" +"팅 시스템에서 최대한 빠르게 FL 워크로드를 실행하려는 경우, 다양한 수준의 데이" +"터 및 시스템 이질성, 클라이언트 가용성, 개인정보 예산 등의 다양한 시나리오에" +"서 알고리즘을 검증하려는 경우 등 여러 가지 사용 사례에 유용합니다. 이러한 사" +"례는 FL 워크로드 시뮬레이션이 적합한 사용 사례 중 일부입니다. Flower는 " +"`VirtualClientEngine `_ 또는 VCE를 통해 이러한 시나리오를 수용할 수 있습니다." #: ../../source/how-to-run-simulations.rst:10 msgid "" -"The :code:`VirtualClientEngine` schedules, launches and manages `virtual`" -" clients. These clients are identical to `non-virtual` clients (i.e. the " -"ones you launch via the command `flwr.client.start_client `_) in the sense that they can be configure by " -"creating a class inheriting, for example, from `flwr.client.NumPyClient " -"`_ and therefore behave in an " -"identical way. In addition to that, clients managed by the " -":code:`VirtualClientEngine` are:" -msgstr "" +"The :code:`VirtualClientEngine` schedules, launches and manages `virtual` " +"clients. These clients are identical to `non-virtual` clients (i.e. the ones " +"you launch via the command `flwr.client.start_client `_) in the sense that they can be configure by creating a " +"class inheriting, for example, from `flwr.client.NumPyClient `_ and therefore behave in an identical way. In " +"addition to that, clients managed by the :code:`VirtualClientEngine` are:" +msgstr "" +":code:`VirtualClientEngine`은 `virtual` 클라이언트를 예약, 실행 및 관리합니" +"다. 이러한 클라이언트는 `non-virtual` 클라이언트(예: `flwr.client." +"start_client `_ 명령을 통해 실행하는 클라이언" +"트)와 동일하며, `flwr.client.NumPyClient `_에서 상속하는 클래스 생성으로 구성될 수 있으므로 동일한 방식으" +"로 동작합니다. 그 외에도 :code:`VirtualClientEngine`에 의해 관리되는 클라이언" +"트는 다음과 같습니다:" #: ../../source/how-to-run-simulations.rst:12 msgid "" -"resource-aware: this means that each client gets assigned a portion of " -"the compute and memory on your system. You as a user can control this at " -"the beginning of the simulation and allows you to control the degree of " +"resource-aware: this means that each client gets assigned a portion of the " +"compute and memory on your system. You as a user can control this at the " +"beginning of the simulation and allows you to control the degree of " "parallelism of your Flower FL simulation. The fewer the resources per " "client, the more clients can run concurrently on the same hardware." msgstr "" +"resource-aware: 이는 각 클라이언트가 시스템에서 컴퓨팅 및 메모리의 일부를 할" +"당받는다는 것을 의미합니다. 사용자는 시뮬레이션을 시작할 때 이를 제어할 수 있" +"으며, 이를 통해 Flower FL 시뮬레이션의 병렬 처리 정도를 제어할 수 있습니다. " +"클라이언트당 리소스가 적을수록 동일한 하드웨어에서 더 많은 클라이언트를 동시" +"에 실행할 수 있습니다." #: ../../source/how-to-run-simulations.rst:13 msgid "" -"self-managed: this means that you as a user do not need to launch clients" -" manually, instead this gets delegated to :code:`VirtualClientEngine`'s " +"self-managed: this means that you as a user do not need to launch clients " +"manually, instead this gets delegated to :code:`VirtualClientEngine`'s " "internals." msgstr "" +"self-managed: 이는 사용자가 클라이언트를 수동으로 실행할 필요가 없으며, 대" +"신 :code:`VirtualClientEngine`의 내부에 위임된다는 의미입니다." #: ../../source/how-to-run-simulations.rst:14 msgid "" -"ephemeral: this means that a client is only materialized when it is " -"required in the FL process (e.g. to do `fit() `_). The object is destroyed afterwards," -" releasing the resources it was assigned and allowing in this way other " -"clients to participate." +"ephemeral: this means that a client is only materialized when it is required " +"in the FL process (e.g. to do `fit() `_). The object is destroyed afterwards, releasing the resources it was " +"assigned and allowing in this way other clients to participate." msgstr "" +"ephemeral: 이는 클라이언트가 FL 프로세스에서 필요할 때만 구체화됨을 의미합니" +"다(예: `fit() `_을 수행하기 위해). " +"객체는 나중에 소멸되어 할당된 리소스를 해제하고 다른 클라이언트가 참여할 수 " +"있도록 허용합니다." #: ../../source/how-to-run-simulations.rst:16 msgid "" "The :code:`VirtualClientEngine` implements `virtual` clients using `Ray " "`_, an open-source framework for scalable Python " -"workloads. In particular, Flower's :code:`VirtualClientEngine` makes use " -"of `Actors `_ to " -"spawn `virtual` clients and run their workload." +"workloads. In particular, Flower's :code:`VirtualClientEngine` makes use of " +"`Actors `_ to spawn " +"`virtual` clients and run their workload." msgstr "" +":code:`VirtualClientEngine`은 확장 가능한 파이썬 워크로드를 위한 오픈 소스 프" +"레임워크인 `Ray `_를 사용하여 `virtual` 클라이언트를 구" +"현합니다. 특히 Flower의 :code:`VirtualClientEngine`은 `Actors `_를 사용하여 `virtual` 클라이언트를 생" +"성하고 해당 워크로드를 실행합니다." #: ../../source/how-to-run-simulations.rst:20 msgid "Launch your Flower simulation" -msgstr "" +msgstr "Flower 시뮬레이션 시작" #: ../../source/how-to-run-simulations.rst:22 msgid "" -"Running Flower simulations still require you to define your client class," -" a strategy, and utility functions to download and load (and potentially " -"partition) your dataset. With that out of the way, launching your " -"simulation is done with `start_simulation `_ and a minimal example looks" -" as follows:" +"Running Flower simulations still require you to define your client class, a " +"strategy, and utility functions to download and load (and potentially " +"partition) your dataset. With that out of the way, launching your simulation " +"is done with `start_simulation `_ and a minimal example looks as follows:" msgstr "" +"Flower 시뮬레이션을 실행하려면 여전히 클라이언트 클래스, 전략 및 유틸리티 함" +"수를 정의하여 데이터 세트를 다운로드하고 로드(및 파티션)해야 합니다. 이 작업" +"을 마친 후 시뮬레이션을 시작하려면 `start_simulation `_을 사용하면 되며, 최소한의 예시는 다음과 같습니" +"다:" #: ../../source/how-to-run-simulations.rst:44 msgid "VirtualClientEngine resources" -msgstr "" +msgstr "VirtualClientEngine 리소스" #: ../../source/how-to-run-simulations.rst:45 msgid "" -"By default the VCE has access to all system resources (i.e. all CPUs, all" -" GPUs, etc) since that is also the default behavior when starting Ray. " -"However, in some settings you might want to limit how many of your system" -" resources are used for simulation. You can do this via the " -":code:`ray_init_args` input argument to :code:`start_simulation` which " -"the VCE internally passes to Ray's :code:`ray.init` command. For a " -"complete list of settings you can configure check the `ray.init " -"`_" -" documentation. Do not set :code:`ray_init_args` if you want the VCE to " -"use all your system's CPUs and GPUs." -msgstr "" +"By default the VCE has access to all system resources (i.e. all CPUs, all " +"GPUs, etc) since that is also the default behavior when starting Ray. " +"However, in some settings you might want to limit how many of your system " +"resources are used for simulation. You can do this via the :code:" +"`ray_init_args` input argument to :code:`start_simulation` which the VCE " +"internally passes to Ray's :code:`ray.init` command. For a complete list of " +"settings you can configure check the `ray.init `_ documentation. Do not set :" +"code:`ray_init_args` if you want the VCE to use all your system's CPUs and " +"GPUs." +msgstr "" +"기본적으로 VCE는 모든 시스템 리소스(예: 모든 CPU, 모든 GPU 등)에 액세스할 수 " +"있으며, 이는 Ray를 시작할 때의 기본 동작이기도 합니다. 그러나 일부 설정에서" +"는 시뮬레이션에 사용되는 시스템 리소스의 수를 제한하고 싶을 수 있습니다. 이 " +"설정은 VCE가 내부적으로 Ray의 :code:`ray.init` 명령에 전달하는 :code:" +"`start_simulation`에 대한 :code:`ray_init_args` 입력 인수를 통해 수행할 수 있" +"습니다. 구성할 수 있는 전체 설정 목록은 `ray.init `_ 설명서를 확인하세요. VCE가 " +"시스템의 모든 CPU와 GPU를 사용하도록 하려면 :code:`ray_init_args`를 설정하지 " +"마세요." #: ../../source/how-to-run-simulations.rst:62 msgid "Assigning client resources" -msgstr "" +msgstr "클라이언트 리소스 할당" #: ../../source/how-to-run-simulations.rst:63 msgid "" -"By default the :code:`VirtualClientEngine` assigns a single CPU core (and" -" nothing else) to each virtual client. This means that if your system has" -" 10 cores, that many virtual clients can be concurrently running." +"By default the :code:`VirtualClientEngine` assigns a single CPU core (and " +"nothing else) to each virtual client. This means that if your system has 10 " +"cores, that many virtual clients can be concurrently running." msgstr "" +"기본적으로 :code:`VirtualClientEngine`은 각 가상 클라이언트에 단일 CPU 코어" +"를 할당합니다(그 외에는 아무것도 할당하지 않음). 즉, 시스템에 코어가 10개인 " +"경우 그만큼 많은 가상 클라이언트를 동시에 실행할 수 있습니다." #: ../../source/how-to-run-simulations.rst:65 msgid "" -"More often than not, you would probably like to adjust the resources your" -" clients get assigned based on the complexity (i.e. compute and memory " -"footprint) of your FL workload. You can do so when starting your " -"simulation by setting the argument `client_resources` to " -"`start_simulation `_." -" Two keys are internally used by Ray to schedule and spawn workloads (in " -"our case Flower clients):" +"More often than not, you would probably like to adjust the resources your " +"clients get assigned based on the complexity (i.e. compute and memory " +"footprint) of your FL workload. You can do so when starting your simulation " +"by setting the argument `client_resources` to `start_simulation `_. Two keys are internally used " +"by Ray to schedule and spawn workloads (in our case Flower clients):" msgstr "" +"대부분의 경우 FL 워크로드의 복잡성(즉, 컴퓨팅 및 메모리 사용량)에 따라 클라이" +"언트에 할당되는 리소스를 조정하고 싶을 것입니다. 시뮬레이션을 시작할 때 " +"`client_resources` argument를 `start_simulation `_로 설정하여 이를 수행할 수 있습니다. Ray는 내부" +"적으로 두 개의 키를 사용하여 워크로드(이 경우 Flower 클라이언트)를 스케줄링하" +"고 스폰합니다:" #: ../../source/how-to-run-simulations.rst:67 msgid ":code:`num_cpus` indicates the number of CPU cores a client would get." msgstr "" +":code:`num_cpus`는 클라이언트에서 사용할 수 있는 CPU 코어 수를 나타냅니다." #: ../../source/how-to-run-simulations.rst:68 msgid "" ":code:`num_gpus` indicates the **ratio** of GPU memory a client gets " "assigned." msgstr "" +":code:`num_gpus`는 클라이언트에 할당되는 GPU 메모리의 **비율**을 나타냅니다." #: ../../source/how-to-run-simulations.rst:70 msgid "Let's see a few examples:" -msgstr "" +msgstr "몇 가지 예를 살펴보겠습니다:" #: ../../source/how-to-run-simulations.rst:89 msgid "" "While the :code:`client_resources` can be used to control the degree of " "concurrency in your FL simulation, this does not stop you from running " -"dozens, hundreds or even thousands of clients in the same round and " -"having orders of magnitude more `dormant` (i.e. not participating in a " -"round) clients. Let's say you want to have 100 clients per round but your" -" system can only accommodate 8 clients concurrently. The " -":code:`VirtualClientEngine` will schedule 100 jobs to run (each " -"simulating a client sampled by the strategy) and then will execute them " -"in a resource-aware manner in batches of 8." -msgstr "" +"dozens, hundreds or even thousands of clients in the same round and having " +"orders of magnitude more `dormant` (i.e. not participating in a round) " +"clients. Let's say you want to have 100 clients per round but your system " +"can only accommodate 8 clients concurrently. The :code:`VirtualClientEngine` " +"will schedule 100 jobs to run (each simulating a client sampled by the " +"strategy) and then will execute them in a resource-aware manner in batches " +"of 8." +msgstr "" +"code:`client_resources`를 사용하여 FL 시뮬레이션의 동시성 정도를 제어할 수 있" +"지만, 동일한 라운드에서 수십, 수백 또는 수천 개의 클라이언트를 실행하고 훨씬 " +"더 많은 '휴면'(즉, 라운드에 참여하지 않는) 클라이언트를 보유하는 것을 막을 수" +"는 없습니다. 라운드당 100명의 클라이언트를 받고 싶지만 시스템이 동시에 8명의 " +"클라이언트만 수용할 수 있다고 가정해 봅시다. code:`VirtualClientEngine`은 실" +"행할 100개의 작업(각각 전략에서 샘플링한 클라이언트를 시뮬레이션)을 예약한 다" +"음 리소스 인식 방식으로 8개씩 일괄적으로 실행합니다." #: ../../source/how-to-run-simulations.rst:91 msgid "" "To understand all the intricate details on how resources are used to " -"schedule FL clients and how to define custom resources, please take a " -"look at the `Ray documentation `_." +"schedule FL clients and how to define custom resources, please take a look " +"at the `Ray documentation `_." msgstr "" +"리소스가 FL 클라이언트를 예약하는 데 사용되는 방법과 사용자 지정 리소스를 정" +"의하는 방법에 대한 모든 복잡한 세부 사항을 이해하려면 'Ray 문서 '를 참조하세요." #: ../../source/how-to-run-simulations.rst:94 msgid "Simulation examples" -msgstr "" +msgstr "시뮬레이션 예제" #: ../../source/how-to-run-simulations.rst:96 msgid "" -"A few ready-to-run complete examples for Flower simulation in " -"Tensorflow/Keras and PyTorch are provided in the `Flower repository " -"`_. You can run them on Google Colab too:" +"A few ready-to-run complete examples for Flower simulation in Tensorflow/" +"Keras and PyTorch are provided in the `Flower repository `_. You can run them on Google Colab too:" msgstr "" +"Tensorflow/Keras와 파이토치에서 바로 실행할 수 있는 몇 가지 Flower 시뮬레이" +"션 예제는 `Flower 레포지토리 `_에서 제공됩니" +"다. Google Colab에서도 실행할 수 있습니다:" #: ../../source/how-to-run-simulations.rst:98 msgid "" -"`Tensorflow/Keras Simulation " -"`_: 100 clients collaboratively train a MLP model on MNIST." +"`Tensorflow/Keras Simulation `_: 100 clients collaboratively train a MLP " +"model on MNIST." msgstr "" +"`Tensorflow/Keras 시뮬레이션 `_: 100개의 클라이언트가 공동으로 MNIST에서 " +"MLP 모델을 훈련합니다." #: ../../source/how-to-run-simulations.rst:99 msgid "" -"`PyTorch Simulation `_: 100 clients collaboratively train a CNN model on " +"`PyTorch Simulation `_: 100 clients collaboratively train a CNN model on " "MNIST." msgstr "" +"파이토치 시뮬레이션 `_: 100개의 클라이언트가 공동으로 MNIST에서 CNN 모델을 훈" +"련합니다." #: ../../source/how-to-run-simulations.rst:104 msgid "Multi-node Flower simulations" -msgstr "" +msgstr "멀티 노드 Flower 시뮬레이션" #: ../../source/how-to-run-simulations.rst:106 msgid "" -"Flower's :code:`VirtualClientEngine` allows you to run FL simulations " -"across multiple compute nodes. Before starting your multi-node simulation" -" ensure that you:" +"Flower's :code:`VirtualClientEngine` allows you to run FL simulations across " +"multiple compute nodes. Before starting your multi-node simulation ensure " +"that you:" msgstr "" +"Flower의 :code:`VirtualClientEngine`을 사용하면 여러 컴퓨팅 노드에서 FL 시뮬" +"레이션을 실행할 수 있습니다. 멀티 노드 시뮬레이션을 시작하기 전에 다음 사항" +"을 확인하세요:" #: ../../source/how-to-run-simulations.rst:108 msgid "Have the same Python environment in all nodes." -msgstr "" +msgstr "모든 노드에서 동일한 Python 환경을 유지합니다." #: ../../source/how-to-run-simulations.rst:109 msgid "Have a copy of your code (e.g. your entire repo) in all nodes." -msgstr "" +msgstr "모든 노드에 코드 사본(예: 전체 레포지토리)을 보관하세요." #: ../../source/how-to-run-simulations.rst:110 msgid "" -"Have a copy of your dataset in all nodes (more about this in " -":ref:`simulation considerations `)" +"Have a copy of your dataset in all nodes (more about this in :ref:" +"`simulation considerations `)" msgstr "" +"모든 노드에 데이터 세트의 사본을 보유하세요(자세한 내용은 :ref:`simulation " +"considerations `에서 확인하세요)" #: ../../source/how-to-run-simulations.rst:111 msgid "" -"Pass :code:`ray_init_args={\"address\"=\"auto\"}` to `start_simulation " -"`_ so the " -":code:`VirtualClientEngine` attaches to a running Ray instance." +"Pass :code:`ray_init_args={\"address\"=\"auto\"}` to `start_simulation `_ so the :code:" +"`VirtualClientEngine` attaches to a running Ray instance." msgstr "" +":code:`ray_init_args={\"address\"=\"auto\"}`를 `start_simulation `_에 전달하여 :code:" +"`VirtualClientEngine`이 실행 중인 Ray 인스턴스에 연결되도록 합니다." #: ../../source/how-to-run-simulations.rst:112 msgid "" -"Start Ray on you head node: on the terminal type :code:`ray start " -"--head`. This command will print a few lines, one of which indicates how " -"to attach other nodes to the head node." +"Start Ray on you head node: on the terminal type :code:`ray start --head`. " +"This command will print a few lines, one of which indicates how to attach " +"other nodes to the head node." msgstr "" +"헤드 노드에서 Ray 시작: 터미널에서 :code:`ray start --head`를 입력합니다. 이 " +"명령은 몇 줄을 출력하며, 그 중 하나는 다른 노드를 헤드 노드에 연결하는 방법" +"을 나타냅니다." #: ../../source/how-to-run-simulations.rst:113 msgid "" -"Attach other nodes to the head node: copy the command shown after " -"starting the head and execute it on terminal of a new node: for example " -":code:`ray start --address='192.168.1.132:6379'`" +"Attach other nodes to the head node: copy the command shown after starting " +"the head and execute it on terminal of a new node: for example :code:`ray " +"start --address='192.168.1.132:6379'`" msgstr "" +"헤드 노드에 다른 노드 연결: 헤드를 시작한 후 표시된 명령어을 복사하여 새 노드" +"의 터미널에서 실행합니다: 예: :code:`ray start --" +"address='192.168.1.132:6379'`" #: ../../source/how-to-run-simulations.rst:115 msgid "" "With all the above done, you can run your code from the head node as you " "would if the simulation was running on a single node." msgstr "" +"위의 모든 작업이 완료되면 단일 노드에서 시뮬레이션을 실행할 때와 마찬가지로 " +"헤드 노드에서 코드를 실행할 수 있습니다." #: ../../source/how-to-run-simulations.rst:117 msgid "" -"Once your simulation is finished, if you'd like to dismantle your cluster" -" you simply need to run the command :code:`ray stop` in each node's " -"terminal (including the head node)." +"Once your simulation is finished, if you'd like to dismantle your cluster " +"you simply need to run the command :code:`ray stop` in each node's terminal " +"(including the head node)." msgstr "" +"시뮬레이션이 완료되면 클러스터를 해체하려면 각 노드(헤드 노드 포함)의 터미널" +"에서 :code:`ray stop` 명령을 실행하기만 하면 됩니다." #: ../../source/how-to-run-simulations.rst:120 msgid "Multi-node simulation good-to-know" -msgstr "" +msgstr "멀티 노드 시뮬레이션에 대해 알아두면 좋은 사항" #: ../../source/how-to-run-simulations.rst:122 msgid "" "Here we list a few interesting functionality when running multi-node FL " "simulations:" msgstr "" +"여기에서는 멀티 노드 FL 시뮬레이션을 실행할 때 흥미로운 몇 가지 기능을 나열합" +"니다:" #: ../../source/how-to-run-simulations.rst:124 msgid "" -"User :code:`ray status` to check all nodes connected to your head node as" -" well as the total resources available to the " -":code:`VirtualClientEngine`." +"User :code:`ray status` to check all nodes connected to your head node as " +"well as the total resources available to the :code:`VirtualClientEngine`." msgstr "" +"사용자는 :code:`ray status`를 통해 헤드 노드에 연결된 모든 노드와 :code:" +"`VirtualClientEngine`에 사용 가능한 총 리소스를 확인할 수 있습니다." #: ../../source/how-to-run-simulations.rst:126 msgid "" -"When attaching a new node to the head, all its resources (i.e. all CPUs, " -"all GPUs) will be visible by the head node. This means that the " -":code:`VirtualClientEngine` can schedule as many `virtual` clients as " -"that node can possible run. In some settings you might want to exclude " -"certain resources from the simulation. You can do this by appending " -"`--num-cpus=` and/or `--num-" -"gpus=` in any :code:`ray start` command (including " -"when starting the head)" -msgstr "" +"When attaching a new node to the head, all its resources (i.e. all CPUs, all " +"GPUs) will be visible by the head node. This means that the :code:" +"`VirtualClientEngine` can schedule as many `virtual` clients as that node " +"can possible run. In some settings you might want to exclude certain " +"resources from the simulation. You can do this by appending `--num-" +"cpus=` and/or `--num-gpus=` in any :" +"code:`ray start` command (including when starting the head)" +msgstr "" +"새 노드를 헤드에 연결하면 해당 노드의 모든 리소스(즉, 모든 CPU, 모든 GPU)가 " +"헤드 노드에 표시됩니다. 즉, :code:`VirtualClientEngine`은 해당 노드가 실행할 " +"수 있는 만큼의 `가상` 클라이언트를 예약할 수 있습니다. 일부 설정에서는 시뮬레" +"이션에서 특정 리소스를 제외하고 싶을 수 있습니다. 모든 :code:`ray start` 명령" +"(헤드 시작 시 포함)에 `--num-cpus=` 및/또는 `--num-" +"gpus=`를 추가하여 이 작업을 수행하면 됩니다" #: ../../source/how-to-run-simulations.rst:132 msgid "Considerations for simulations" -msgstr "" +msgstr "시뮬레이션 시 고려 사항" #: ../../source/how-to-run-simulations.rst:135 msgid "" -"We are actively working on these fronts so to make it trivial to run any " -"FL workload with Flower simulation." +"We are actively working on these fronts so to make it trivial to run any FL " +"workload with Flower simulation." msgstr "" +"Flower 시뮬레이션으로 모든 FL 워크로드를 간편하게 실행할 수 있도록 이러한 측" +"면에서 적극적으로 노력하고 있습니다." #: ../../source/how-to-run-simulations.rst:138 msgid "" -"The current VCE allows you to run Federated Learning workloads in " -"simulation mode whether you are prototyping simple scenarios on your " -"personal laptop or you want to train a complex FL pipeline across " -"multiple high-performance GPU nodes. While we add more capabilities to " -"the VCE, the points below highlight some of the considerations to keep in" -" mind when designing your FL pipeline with Flower. We also highlight a " -"couple of current limitations in our implementation." +"The current VCE allows you to run Federated Learning workloads in simulation " +"mode whether you are prototyping simple scenarios on your personal laptop or " +"you want to train a complex FL pipeline across multiple high-performance GPU " +"nodes. While we add more capabilities to the VCE, the points below highlight " +"some of the considerations to keep in mind when designing your FL pipeline " +"with Flower. We also highlight a couple of current limitations in our " +"implementation." msgstr "" +"현재 VCE를 사용하면 개인 노트북에서 간단한 시나리오를 프로토타이핑하든, 여러 " +"고성능 GPU 노드에서 복잡한 FL 파이프라인을 훈련하든 상관없이 시뮬레이션 모드" +"에서 Federated 학습 워크로드를 실행할 수 있습니다. VCE에 더 많은 기능을 추가" +"하는 동안, 아래에서는 Flower로 FL 파이프라인을 설계할 때 염두에 두어야 할 몇 " +"가지 사항을 강조합니다. 또한 현재 구현에서 몇 가지 제한 사항을 강조합니다." #: ../../source/how-to-run-simulations.rst:141 msgid "GPU resources" -msgstr "" +msgstr "GPU 리소스" #: ../../source/how-to-run-simulations.rst:143 msgid "" -"The VCE assigns a share of GPU memory to a client that specifies the key " -":code:`num_gpus` in :code:`client_resources`. This being said, Ray (used " +"The VCE assigns a share of GPU memory to a client that specifies the key :" +"code:`num_gpus` in :code:`client_resources`. This being said, Ray (used " "internally by the VCE) is by default:" msgstr "" +"VCE는 :code:`client_resources`에서 :code:`num_gpus` 키를 지정하는 클라이언트" +"에 GPU 메모리 공유를 할당합니다. 즉, (VCE에서 내부적으로 사용하는) Ray가 기본" +"적으로 사용됩니다:" #: ../../source/how-to-run-simulations.rst:146 msgid "" -"not aware of the total VRAM available on the GPUs. This means that if you" -" set :code:`num_gpus=0.5` and you have two GPUs in your system with " -"different (e.g. 32GB and 8GB) VRAM amounts, they both would run 2 clients" -" concurrently." +"not aware of the total VRAM available on the GPUs. This means that if you " +"set :code:`num_gpus=0.5` and you have two GPUs in your system with different " +"(e.g. 32GB and 8GB) VRAM amounts, they both would run 2 clients concurrently." msgstr "" +"GPU에서 사용 가능한 총 VRAM을 인식하지 못합니다. 즉, 시스템에 서로 다른(예: " +"32GB와 8GB) VRAM 용량을 가진 두 개의 GPU가 있고 :code:`num_gpus=0.5`를 설정하" +"면 둘 다 동시에 2개의 클라이언트를 실행하게 됩니다." #: ../../source/how-to-run-simulations.rst:147 msgid "" "not aware of other unrelated (i.e. not created by the VCE) workloads are " "running on the GPU. Two takeaways from this are:" msgstr "" +"관련 없는(즉, VCE에 의해 생성되지 않은) 다른 워크로드가 GPU에서 실행되고 있는" +"지 알지 못합니다. 여기서 두 가지 시사점을 얻을 수 있습니다:" #: ../../source/how-to-run-simulations.rst:149 msgid "" @@ -5466,178 +6794,237 @@ msgid "" "aggregation (by instance when making use of the `evaluate method `_)" msgstr "" +"집계 후 '글로벌 모델'을 평가하려면 Flower 서버에 GPU가 필요할 수 있습니다" +"(예: `evaluate method `_를 사용할 때)" #: ../../source/how-to-run-simulations.rst:150 msgid "" "If you want to run several independent Flower simulations on the same " -"machine you need to mask-out your GPUs with " -":code:`CUDA_VISIBLE_DEVICES=\"\"` when launching your " -"experiment." +"machine you need to mask-out your GPUs with :code:" +"`CUDA_VISIBLE_DEVICES=\"\"` when launching your experiment." msgstr "" +"동일한 머신에서 여러 개의 독립적인 Flower 시뮬레이션을 실행하려면, 실험을 시" +"작할 때 :code:`CUDA_VISIBLE_DEVICES=\"\"`로 GPU를 마스킹해야 합니다." #: ../../source/how-to-run-simulations.rst:153 msgid "" -"In addition, the GPU resource limits passed to :code:`client_resources` " -"are not `enforced` (i.e. they can be exceeded) which can result in the " -"situation of client using more VRAM than the ratio specified when " -"starting the simulation." +"In addition, the GPU resource limits passed to :code:`client_resources` are " +"not `enforced` (i.e. they can be exceeded) which can result in the situation " +"of client using more VRAM than the ratio specified when starting the " +"simulation." msgstr "" +"또한 :code:`client_resources`에 전달된 GPU 리소스 제한이 '강제'되지 않아(즉, " +"초과할 수 있음) 클라이언트가 시뮬레이션을 시작할 때 지정된 비율보다 더 많은 " +"VRAM을 사용하는 상황이 발생할 수 있습니다." #: ../../source/how-to-run-simulations.rst:156 msgid "TensorFlow with GPUs" -msgstr "" +msgstr "GPU를 사용한 TensorFlow" #: ../../source/how-to-run-simulations.rst:158 msgid "" -"When `using a GPU with TensorFlow " -"`_ nearly your entire GPU memory of" -" all your GPUs visible to the process will be mapped. This is done by " -"TensorFlow for optimization purposes. However, in settings such as FL " -"simulations where we want to split the GPU into multiple `virtual` " -"clients, this is not a desirable mechanism. Luckily we can disable this " -"default behavior by `enabling memory growth " -"`_." -msgstr "" +"When `using a GPU with TensorFlow `_ " +"nearly your entire GPU memory of all your GPUs visible to the process will " +"be mapped. This is done by TensorFlow for optimization purposes. However, in " +"settings such as FL simulations where we want to split the GPU into multiple " +"`virtual` clients, this is not a desirable mechanism. Luckily we can disable " +"this default behavior by `enabling memory growth `_." +msgstr "" +"`TensorFlow와 함께 GPU를 사용 `_하면 프" +"로세스에 보이는 모든 GPU의 거의 전체 GPU 메모리가 매핑됩니다. 이는 최적화 목" +"적으로 TensorFlow에서 수행됩니다. 그러나 GPU를 여러 개의 '가상' 클라이언트로 " +"분할하려는 FL 시뮬레이션과 같은 설정에서는 이는 바람직한 메커니즘이 아닙니" +"다. 다행히도 '메모리 증가 활성화 `_'를 통해 이 기본 동작을 비활성화할 수 있습니" +"다." #: ../../source/how-to-run-simulations.rst:160 msgid "" -"This would need to be done in the main process (which is where the server" -" would run) and in each Actor created by the VCE. By means of " -":code:`actor_kwargs` we can pass the reserved key `\"on_actor_init_fn\"` " -"in order to specify a function to be executed upon actor initialization. " -"In this case, to enable GPU growth for TF workloads. It would look as " -"follows:" +"This would need to be done in the main process (which is where the server " +"would run) and in each Actor created by the VCE. By means of :code:" +"`actor_kwargs` we can pass the reserved key `\"on_actor_init_fn\"` in order " +"to specify a function to be executed upon actor initialization. In this " +"case, to enable GPU growth for TF workloads. It would look as follows:" msgstr "" +"이 작업은 메인 프로세스(서버가 실행되는 곳)와 VCE에서 생성한 각 액터에서 수행" +"해야 합니다. :code:`actor_kwargs`를 통해 예약 키 `\"on_actor_init_fn\"`을 전" +"달하여 액터 초기화 시 실행할 함수를 지정할 수 있습니다. 이 경우 TF 워크로드" +"에 대한 GPU 증가를 활성화합니다. 다음과 같이 보입니다:" #: ../../source/how-to-run-simulations.rst:179 msgid "" "This is precisely the mechanism used in `Tensorflow/Keras Simulation " -"`_ example." +"`_ " +"example." msgstr "" +"이것이 바로`Tensorflow/Keras Simulation `_ 예제에서 사용된 메커니즘입니다." #: ../../source/how-to-run-simulations.rst:183 msgid "Multi-node setups" -msgstr "" +msgstr "멀티 노드 설정" #: ../../source/how-to-run-simulations.rst:185 msgid "" -"The VCE does not currently offer a way to control on which node a " -"particular `virtual` client is executed. In other words, if more than a " -"single node have the resources needed by a client to run, then any of " -"those nodes could get the client workload scheduled onto. Later in the FL" -" process (i.e. in a different round) the same client could be executed by" -" a different node. Depending on how your clients access their datasets, " -"this might require either having a copy of all dataset partitions on all " -"nodes or a dataset serving mechanism (e.g. using nfs, a database) to " -"circumvent data duplication." -msgstr "" +"The VCE does not currently offer a way to control on which node a particular " +"`virtual` client is executed. In other words, if more than a single node " +"have the resources needed by a client to run, then any of those nodes could " +"get the client workload scheduled onto. Later in the FL process (i.e. in a " +"different round) the same client could be executed by a different node. " +"Depending on how your clients access their datasets, this might require " +"either having a copy of all dataset partitions on all nodes or a dataset " +"serving mechanism (e.g. using nfs, a database) to circumvent data " +"duplication." +msgstr "" +"VCE는 현재 특정 '가상' 클라이언트를 어느 노드에서 실행할지 제어하는 방법을 제" +"공하지 않습니다. 즉, 클라이언트가 실행하는 데 필요한 리소스가 하나 이상의 노" +"드에 있는 경우 해당 노드 중 어느 노드에나 클라이언트 워크로드가 예약될 수 있" +"습니다. FL 프로세스 후반부(즉, 다른 라운드에서)에는 동일한 클라이언트가 다른 " +"노드에서 실행될 수 있습니다. 클라이언트가 데이터 세트에 액세스하는 방식에 따" +"라 모든 노드에 모든 데이터 세트 파티션의 복사본을 보유하거나 데이터 중복을 피" +"하기 위해 데이터 세트 제공 메커니즘(예: nfs, 데이터베이스 사용)을 사용해야 " +"할 수 있습니다." #: ../../source/how-to-run-simulations.rst:187 msgid "" -"By definition virtual clients are `stateless` due to their ephemeral " -"nature. A client state can be implemented as part of the Flower client " -"class but users need to ensure this saved to persistent storage (e.g. a " -"database, disk) and that can be retrieve later by the same client " -"regardless on which node it is running from. This is related to the point" -" above also since, in some way, the client's dataset could be seen as a " -"type of `state`." +"By definition virtual clients are `stateless` due to their ephemeral nature. " +"A client state can be implemented as part of the Flower client class but " +"users need to ensure this saved to persistent storage (e.g. a database, " +"disk) and that can be retrieve later by the same client regardless on which " +"node it is running from. This is related to the point above also since, in " +"some way, the client's dataset could be seen as a type of `state`." msgstr "" +"정의상 가상 클라이언트는 임시적 특성으로 인해 '상태 없음'입니다. 클라이언트 " +"상태는 Flower 클라이언트 클래스의 일부로 구현할 수 있지만, 사용자는 이를 영" +"구 저장소(예: 데이터베이스, 디스크)에 저장하여 나중에 실행 중인 노드와 관계없" +"이 동일한 클라이언트가 검색할 수 있도록 해야 합니다. 이는 어떤 식으로든 클라" +"이언트의 데이터 세트가 일종의 '상태'로 볼 수 있기 때문에 위의 요점과도 관련" +"이 있습니다." #: ../../source/how-to-save-and-load-model-checkpoints.rst:2 msgid "Save and load model checkpoints" -msgstr "" +msgstr "모델 체크포인트 저장 및 로드" #: ../../source/how-to-save-and-load-model-checkpoints.rst:4 msgid "" -"Flower does not automatically save model updates on the server-side. This" -" how-to guide describes the steps to save (and load) model checkpoints in" -" Flower." +"Flower does not automatically save model updates on the server-side. This " +"how-to guide describes the steps to save (and load) model checkpoints in " +"Flower." msgstr "" +"Flower는 서버 측에서 모델 업데이트를 자동으로 저장하지 않습니다. 이 사용법 가" +"이드에서는 Flower에서 모델 체크포인트를 저장(및 로드)하는 단계에 대해 설명합" +"니다." #: ../../source/how-to-save-and-load-model-checkpoints.rst:8 msgid "Model checkpointing" -msgstr "" +msgstr "모델 체크포인트" #: ../../source/how-to-save-and-load-model-checkpoints.rst:10 msgid "" -"Model updates can be persisted on the server-side by customizing " -":code:`Strategy` methods. Implementing custom strategies is always an " -"option, but for many cases it may be more convenient to simply customize " -"an existing strategy. The following code example defines a new " -":code:`SaveModelStrategy` which customized the existing built-in " -":code:`FedAvg` strategy. In particular, it customizes " -":code:`aggregate_fit` by calling :code:`aggregate_fit` in the base class " -"(:code:`FedAvg`). It then continues to save returned (aggregated) weights" -" before it returns those aggregated weights to the caller (i.e., the " -"server):" -msgstr "" +"Model updates can be persisted on the server-side by customizing :code:" +"`Strategy` methods. Implementing custom strategies is always an option, but " +"for many cases it may be more convenient to simply customize an existing " +"strategy. The following code example defines a new :code:`SaveModelStrategy` " +"which customized the existing built-in :code:`FedAvg` strategy. In " +"particular, it customizes :code:`aggregate_fit` by calling :code:" +"`aggregate_fit` in the base class (:code:`FedAvg`). It then continues to " +"save returned (aggregated) weights before it returns those aggregated " +"weights to the caller (i.e., the server):" +msgstr "" +":code:`Strategy` 메소드를 사용자 지정하여 서버 측에서 모델 업데이트를 지속할 " +"수 있습니다. 사용자 지정 전략을 구현하는 것은 항상 옵션이지만 대부분의 경우 " +"기존 전략을 간단히 사용자 지정하는 것이 더 편리할 수 있습니다. 다음 코드 예시" +"는 기존의 기본 제공 :code:`FedAvg` 전략을 사용자 지정한 새로운 :code:" +"`SaveModelStrategy`를 정의합니다. 특히, 기본 클래스(:code:`FedAvg`)에서 :" +"code:`aggregate_fit`을 호출하여 :code:`aggregate_fit`을 사용자 지정합니다. 그" +"런 다음 호출자(즉, 서버)에게 집계된 가중치를 반환하기 전에 반환된(집계된) 가" +"중치를 계속 저장합니다:" #: ../../source/how-to-save-and-load-model-checkpoints.rst:47 msgid "Save and load PyTorch checkpoints" -msgstr "" +msgstr "파이토치 체크포인트 저장 및 로드" #: ../../source/how-to-save-and-load-model-checkpoints.rst:49 msgid "" -"Similar to the previous example but with a few extra steps, we'll show " -"how to store a PyTorch checkpoint we'll use the ``torch.save`` function. " -"Firstly, ``aggregate_fit`` returns a ``Parameters`` object that has to be" -" transformed into a list of NumPy ``ndarray``'s, then those are " -"transformed into the PyTorch ``state_dict`` following the ``OrderedDict``" -" class structure." +"Similar to the previous example but with a few extra steps, we'll show how " +"to store a PyTorch checkpoint we'll use the ``torch.save`` function. " +"Firstly, ``aggregate_fit`` returns a ``Parameters`` object that has to be " +"transformed into a list of NumPy ``ndarray``'s, then those are transformed " +"into the PyTorch ``state_dict`` following the ``OrderedDict`` class " +"structure." msgstr "" +"이전 예제와 비슷하지만 몇 가지 단계가 추가되어 ``torch.save`` 함수를 사용하" +"여 파이토치 체크포인트를 저장하는 방법을 보여드리겠습니다. 먼저, " +"``aggregate_fit``은 ``Parameters`` 객체를 반환하는데, 이 객체는 NumPy " +"``ndarray``의 목록으로 변환되어야 하며, ``OrderedDict`` 클래스 구조에 따라 파" +"이토치 ``state_dict``로 변환됩니다." #: ../../source/how-to-save-and-load-model-checkpoints.rst:85 msgid "" -"To load your progress, you simply append the following lines to your " -"code. Note that this will iterate over all saved checkpoints and load the" -" latest one:" +"To load your progress, you simply append the following lines to your code. " +"Note that this will iterate over all saved checkpoints and load the latest " +"one:" msgstr "" +"진행 상황을 로드하려면 코드에 다음 줄을 추가하기만 하면 됩니다. 이렇게 하면 " +"저장된 모든 체크포인트를 반복하고 최신 체크포인트를 로드합니다:" #: ../../source/how-to-save-and-load-model-checkpoints.rst:97 msgid "" -"Return/use this object of type ``Parameters`` wherever necessary, such as" -" in the ``initial_parameters`` when defining a ``Strategy``." +"Return/use this object of type ``Parameters`` wherever necessary, such as in " +"the ``initial_parameters`` when defining a ``Strategy``." msgstr "" +"``전략``을 정의할 때 ``초기_파라미터``와 같이 필요한 경우 ``파라미터`` 유형" +"의 이 객체를 반환/사용합니다." #: ../../source/how-to-upgrade-to-flower-1.0.rst:2 msgid "Upgrade to Flower 1.0" -msgstr "" +msgstr "Flower 1.0으로 업그레이드" #: ../../source/how-to-upgrade-to-flower-1.0.rst:4 msgid "" -"Flower 1.0 is here. Along with new features, Flower 1.0 provides a stable" -" foundation for future growth. Compared to Flower 0.19 (and other 0.x " -"series releases), there are a few breaking changes that make it necessary" -" to change the code of existing 0.x-series projects." +"Flower 1.0 is here. Along with new features, Flower 1.0 provides a stable " +"foundation for future growth. Compared to Flower 0.19 (and other 0.x series " +"releases), there are a few breaking changes that make it necessary to change " +"the code of existing 0.x-series projects." msgstr "" +"Flower 1.0이 출시되었습니다. 새로운 기능과 함께 Flower 1.0은 향후 성장을 위" +"한 안정적인 기반을 제공합니다. Flower 0.19(및 다른 0.x 시리즈 릴리스)와 비교" +"했을 때 기존 0.x 시리즈 프로젝트의 코드를 변경해야 하는 몇 가지 획기적인 변" +"경 사항이 있습니다." #: ../../source/how-to-upgrade-to-flower-1.0.rst:8 -#: ../../source/how-to-upgrade-to-flower-next.rst:42 +#: ../../source/how-to-upgrade-to-flower-next.rst:43 msgid "Install update" -msgstr "" +msgstr "업데이트 설치" #: ../../source/how-to-upgrade-to-flower-1.0.rst:10 msgid "" -"Here's how to update an existing installation to Flower 1.0 using either " -"pip or Poetry:" +"Here's how to update an existing installation to Flower 1.0 using either pip " +"or Poetry:" msgstr "" +"다음은 pip 또는 Poetry를 사용하여 기존 설치를 Flower 1.0으로 업데이트하는 방" +"법입니다:" #: ../../source/how-to-upgrade-to-flower-1.0.rst:12 msgid "pip: add ``-U`` when installing." -msgstr "" +msgstr "pip: 설치할 때 ``-U``를 추가합니다." #: ../../source/how-to-upgrade-to-flower-1.0.rst:14 msgid "" "``python -m pip install -U flwr`` (when using ``start_server`` and " "``start_client``)" msgstr "" +"``python -m pip install -U flwr``(``start_server`` 및 ``start_client``를 사용" +"하는 경우)" #: ../../source/how-to-upgrade-to-flower-1.0.rst:15 msgid "" "``python -m pip install -U flwr[simulation]`` (when using " "``start_simulation``)" msgstr "" +"``python -m pip install -U flwr[simulation]``(``start_simulation`` 사용 시)" #: ../../source/how-to-upgrade-to-flower-1.0.rst:17 msgid "" @@ -5645,140 +7032,170 @@ msgid "" "reinstall (don't forget to delete ``poetry.lock`` via ``rm poetry.lock`` " "before running ``poetry install``)." msgstr "" +"Poetry: ``pyproject.toml``에서 ``flwr`` dependency을 업데이트한 다음 다시 설" +"치하세요(``poetry 설치``를 실행하기 전에 ``rm poetry.lock``을 통해 ``poetry." +"lock``을 삭제하는 것을 잊지 마세요)." #: ../../source/how-to-upgrade-to-flower-1.0.rst:19 -msgid "``flwr = \"^1.0.0\"`` (when using ``start_server`` and ``start_client``)" -msgstr "" +msgid "" +"``flwr = \"^1.0.0\"`` (when using ``start_server`` and ``start_client``)" +msgstr "``flwr = \"^1.0.0\"``(``start_server`` 및 ``start_client`` 사용 시)" #: ../../source/how-to-upgrade-to-flower-1.0.rst:20 msgid "" -"``flwr = { version = \"^1.0.0\", extras = [\"simulation\"] }`` (when " -"using ``start_simulation``)" +"``flwr = { version = \"^1.0.0\", extras = [\"simulation\"] }`` (when using " +"``start_simulation``)" msgstr "" +"``flwr = { version = \"^1.0.0\", extras = [\"simulation\"] }`` " +"(``start_simulation`` 사용 시)" #: ../../source/how-to-upgrade-to-flower-1.0.rst:24 -#: ../../source/how-to-upgrade-to-flower-next.rst:99 +#: ../../source/how-to-upgrade-to-flower-next.rst:100 msgid "Required changes" -msgstr "" +msgstr "필수 변경 사항" #: ../../source/how-to-upgrade-to-flower-1.0.rst:26 msgid "The following breaking changes require manual updates." -msgstr "" +msgstr "다음과 같은 주요 변경 사항에는 수동 업데이트가 필요합니다." #: ../../source/how-to-upgrade-to-flower-1.0.rst:29 msgid "General" -msgstr "" +msgstr "일반" #: ../../source/how-to-upgrade-to-flower-1.0.rst:31 msgid "" "Pass all arguments as keyword arguments (not as positional arguments). " "Here's an example:" -msgstr "" +msgstr "모든 전달인자를 위치 전달인자가 아닌 키워드 전달인자로 전달합니다. 다음은 " +"예시입니다:" #: ../../source/how-to-upgrade-to-flower-1.0.rst:33 msgid "" "Flower 0.19 (positional arguments): ``start_client(\"127.0.0.1:8080\", " "FlowerClient())``" msgstr "" +"Flower 0.19 (위치 전달인자): ``start_client(\"127.0.0.1:8080\", " +"FlowerClient())``" #: ../../source/how-to-upgrade-to-flower-1.0.rst:34 msgid "" "Flower 1.0 (keyword arguments): " -"``start_client(server_address=\"127.0.0.1:8080\", " -"client=FlowerClient())``" +"``start_client(server_address=\"127.0.0.1:8080\", client=FlowerClient())``" msgstr "" +"Flower 1.0 (키워드 전달인자): ``start_client(server_address=\"127.0.0.1:" +"8080\", client=FlowerClient())``" #: ../../source/how-to-upgrade-to-flower-1.0.rst:37 #: ../../source/ref-api/flwr.client.Client.rst:2 msgid "Client" -msgstr "" +msgstr "클라이언트" #: ../../source/how-to-upgrade-to-flower-1.0.rst:39 msgid "" "Subclasses of ``NumPyClient``: change ``def get_parameters(self):``` to " "``def get_parameters(self, config):``" msgstr "" +"``NumPyClient``의 서브클래스: ``def get_parameters(self):``를 ``def " +"get_parameters(self, config):``로 변경합니다" #: ../../source/how-to-upgrade-to-flower-1.0.rst:40 msgid "" "Subclasses of ``Client``: change ``def get_parameters(self):``` to ``def " "get_parameters(self, ins: GetParametersIns):``" msgstr "" +"``클라이언트``의 서브클래스: ``def get_parameters(self):``를 ``def " +"get_parameters(self, ins: GetParametersIns):``로 변경합니다" #: ../../source/how-to-upgrade-to-flower-1.0.rst:43 msgid "Strategies / ``start_server`` / ``start_simulation``" -msgstr "" +msgstr "전략 / ``start_server`` / ``start_simulation``" #: ../../source/how-to-upgrade-to-flower-1.0.rst:45 msgid "" "Pass ``ServerConfig`` (instead of a dictionary) to ``start_server`` and " "``start_simulation``. Here's an example:" msgstr "" +"Dictionary 대신 ``ServerConfig``를 ``start_server`` 및 ``start_simulation``" +"에 전달합니다. 다음은 예제입니다:" #: ../../source/how-to-upgrade-to-flower-1.0.rst:47 msgid "" "Flower 0.19: ``start_server(..., config={\"num_rounds\": 3, " "\"round_timeout\": 600.0}, ...)``" msgstr "" +"Flower 0.19: ``start_server(..., config={\"num_rounds\": 3, " +"\"round_timeout\": 600.0}, ...)``" #: ../../source/how-to-upgrade-to-flower-1.0.rst:48 msgid "" -"Flower 1.0: ``start_server(..., " -"config=flwr.server.ServerConfig(num_rounds=3, round_timeout=600.0), " -"...)``" +"Flower 1.0: ``start_server(..., config=flwr.server." +"ServerConfig(num_rounds=3, round_timeout=600.0), ...)``" msgstr "" +"Flower 1.0: ``start_server(..., config=flwr.server." +"ServerConfig(num_rounds=3, round_timeout=600.0), ...)``" #: ../../source/how-to-upgrade-to-flower-1.0.rst:50 msgid "" "Replace ``num_rounds=1`` in ``start_simulation`` with the new " "``config=ServerConfig(...)`` (see previous item)" msgstr "" +"``start_simulation``의 ``num_rounds=1``을 새로운 ``config=ServerConfig(...)``" +"로 바꿉니다(이전 항목 참조)" #: ../../source/how-to-upgrade-to-flower-1.0.rst:51 msgid "" "Remove ``force_final_distributed_eval`` parameter from calls to " -"``start_server``. Distributed evaluation on all clients can be enabled by" -" configuring the strategy to sample all clients for evaluation after the " -"last round of training." +"``start_server``. Distributed evaluation on all clients can be enabled by " +"configuring the strategy to sample all clients for evaluation after the last " +"round of training." msgstr "" +"'start_server`` 호출에서 ``force_final_distributed_eval`` 매개변수를 제거합니" +"다. 모든 클라이언트에 대한 분산 평가는 마지막 훈련 라운드 후 평가를 위해 모" +"든 클라이언트를 샘플링하도록 전략을 구성하여 활성화할 수 있습니다." #: ../../source/how-to-upgrade-to-flower-1.0.rst:52 msgid "Rename parameter/ndarray conversion functions:" -msgstr "" +msgstr "매개변수/ndarray 변환 함수의 이름을 바꿉니다:" #: ../../source/how-to-upgrade-to-flower-1.0.rst:54 msgid "``parameters_to_weights`` --> ``parameters_to_ndarrays``" -msgstr "" +msgstr "``parameters_to_weights`` --> ``parameters_to_ndarrays``" #: ../../source/how-to-upgrade-to-flower-1.0.rst:55 msgid "``weights_to_parameters`` --> ``ndarrays_to_parameters``" -msgstr "" +msgstr "``weights_to_parameters`` --> ``ndarrays_to_parameters``" #: ../../source/how-to-upgrade-to-flower-1.0.rst:57 msgid "" -"Strategy initialization: if the strategy relies on the default values for" -" ``fraction_fit`` and ``fraction_evaluate``, set ``fraction_fit`` and " +"Strategy initialization: if the strategy relies on the default values for " +"``fraction_fit`` and ``fraction_evaluate``, set ``fraction_fit`` and " "``fraction_evaluate`` manually to ``0.1``. Projects that do not manually " "create a strategy (by calling ``start_server`` or ``start_simulation`` " -"without passing a strategy instance) should now manually initialize " -"FedAvg with ``fraction_fit`` and ``fraction_evaluate`` set to ``0.1``." +"without passing a strategy instance) should now manually initialize FedAvg " +"with ``fraction_fit`` and ``fraction_evaluate`` set to ``0.1``." msgstr "" +"전략 초기화: 전략이 ``fraction_fit`` 및 ``fraction_evaluate``의 기본값에 의존" +"하는 경우 ``fraction_fit`` 및 ``fraction_evaluate``를 ``0.1``로 수동 설정합니" +"다. 전략을 수동으로 생성하지 않는 프로젝트(전략 인스턴스를 전달하지 않고 " +"``start_server`` 또는 ``start_simulation``을 호출하여)는 이제 " +"``fraction_fit`` 및 ``fraction_evaluate``를 ``0.1``로 설정하여 FedAvg를 수동" +"으로 초기화해야 합니다." #: ../../source/how-to-upgrade-to-flower-1.0.rst:58 msgid "Rename built-in strategy parameters (e.g., ``FedAvg``):" -msgstr "" +msgstr "기본 제공 전략 매개변수의 이름을 바꿉니다(예: ``FedAvg``):" #: ../../source/how-to-upgrade-to-flower-1.0.rst:60 msgid "``fraction_eval`` --> ``fraction_evaluate``" -msgstr "" +msgstr "``fraction_eval`` --> ``fraction_evaluate``" #: ../../source/how-to-upgrade-to-flower-1.0.rst:61 msgid "``min_eval_clients`` --> ``min_evaluate_clients``" -msgstr "" +msgstr "``min_eval_clients`` --> ``min_evaluate_clients``" #: ../../source/how-to-upgrade-to-flower-1.0.rst:62 msgid "``eval_fn`` --> ``evaluate_fn``" -msgstr "" +msgstr "``eval_fn`` --> ``evaluate_fn``" #: ../../source/how-to-upgrade-to-flower-1.0.rst:64 msgid "" @@ -5786,72 +7203,90 @@ msgid "" "functions, for example, ``configure_fit``, ``aggregate_fit``, " "``configure_evaluate``, ``aggregate_evaluate``, and ``evaluate_fn``." msgstr "" +"``rnd``의 이름을 ``server_round``로 바꿉니다. 이는 여러 메서드 및 함수(예: " +"``configure_fit``, ``aggregate_fit``, ``configure_evaluate``, " +"``aggregate_evaluate`` 및 ``evaluate_fn``)에 영향을 미칩니다." #: ../../source/how-to-upgrade-to-flower-1.0.rst:65 msgid "Add ``server_round`` and ``config`` to ``evaluate_fn``:" -msgstr "" +msgstr "``server_round`` 및 ``config``를 ``evaluate_fn``에 추가합니다:" #: ../../source/how-to-upgrade-to-flower-1.0.rst:67 msgid "" -"Flower 0.19: ``def evaluate(parameters: NDArrays) -> " -"Optional[Tuple[float, Dict[str, Scalar]]]:``" +"Flower 0.19: ``def evaluate(parameters: NDArrays) -> Optional[Tuple[float, " +"Dict[str, Scalar]]]:``" msgstr "" +"Flower 0.19: ``def evaluate(parameters: NDArrays) -> Optional[Tuple[float, " +"Dict[str, Scalar]]]:``" #: ../../source/how-to-upgrade-to-flower-1.0.rst:68 msgid "" -"Flower 1.0: ``def evaluate(server_round: int, parameters: NDArrays, " -"config: Dict[str, Scalar]) -> Optional[Tuple[float, Dict[str, " -"Scalar]]]:``" +"Flower 1.0: ``def evaluate(server_round: int, parameters: NDArrays, config: " +"Dict[str, Scalar]) -> Optional[Tuple[float, Dict[str, Scalar]]]:``" msgstr "" +"Flower 1.0: ``def evaluate(server_round: int, parameters: NDArrays, config: " +"Dict[str, Scalar]) -> Optional[Tuple[float, Dict[str, Scalar]]]:``" #: ../../source/how-to-upgrade-to-flower-1.0.rst:71 msgid "Custom strategies" -msgstr "" +msgstr "사용자 정의 전략" #: ../../source/how-to-upgrade-to-flower-1.0.rst:73 msgid "" -"The type of parameter ``failures`` has changed from " -"``List[BaseException]`` to ``List[Union[Tuple[ClientProxy, FitRes], " -"BaseException]]`` (in ``aggregate_fit``) and " -"``List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]]`` (in " -"``aggregate_evaluate``)" +"The type of parameter ``failures`` has changed from ``List[BaseException]`` " +"to ``List[Union[Tuple[ClientProxy, FitRes], BaseException]]`` (in " +"``aggregate_fit``) and ``List[Union[Tuple[ClientProxy, EvaluateRes], " +"BaseException]]`` (in ``aggregate_evaluate``)" msgstr "" +"매개변수 ``failures``의 유형이 ``List[BaseException]``에서 " +"``List[Union[Tuple[ClientProxy], FitRes], BaseException]]``(``aggregate_fit``" +"에서) 및 ``List[Union[Tuple[ClientProxy], EvaluateRes], " +"BaseException]]``(``aggregate_evaluate``)로 변경되었습니다" #: ../../source/how-to-upgrade-to-flower-1.0.rst:74 msgid "" "The ``Strategy`` method ``evaluate`` now receives the current round of " "federated learning/evaluation as the first parameter:" msgstr "" +"이제 ``Strategy`` 메서드 ``evaluate``는 현재 federated 학습/평가 라운드를 첫 " +"번째 파라미터로 받습니다:" #: ../../source/how-to-upgrade-to-flower-1.0.rst:76 msgid "" "Flower 0.19: ``def evaluate(self, parameters: Parameters) -> " "Optional[Tuple[float, Dict[str, Scalar]]]:``" msgstr "" +"Flower 0.19: ``def evaluate(self, parameters: Parameters) -> " +"Optional[Tuple[float, Dict[str, Scalar]]]:``" #: ../../source/how-to-upgrade-to-flower-1.0.rst:77 msgid "" -"Flower 1.0: ``def evaluate(self, server_round: int, parameters: " -"Parameters) -> Optional[Tuple[float, Dict[str, Scalar]]]:``" +"Flower 1.0: ``def evaluate(self, server_round: int, parameters: Parameters) -" +"> Optional[Tuple[float, Dict[str, Scalar]]]:``" msgstr "" +"Flower 1.0: ``def evaluate(self, server_round: int, parameters: Parameters) -" +"> Optional[Tuple[float, Dict[str, Scalar]]]:``" #: ../../source/how-to-upgrade-to-flower-1.0.rst:80 msgid "Optional improvements" -msgstr "" +msgstr "선택적 개선 사항" #: ../../source/how-to-upgrade-to-flower-1.0.rst:82 msgid "" "Along with the necessary changes above, there are a number of potential " "improvements that just became possible:" msgstr "" +"위의 필수 변경 사항과 함께 방금 가능한 여러 가지 잠재적 개선 사항이 있습니다:" #: ../../source/how-to-upgrade-to-flower-1.0.rst:84 msgid "" "Remove \"placeholder\" methods from subclasses of ``Client`` or " -"``NumPyClient``. If you, for example, use server-side evaluation, then " -"empty placeholder implementations of ``evaluate`` are no longer " -"necessary." +"``NumPyClient``. If you, for example, use server-side evaluation, then empty " +"placeholder implementations of ``evaluate`` are no longer necessary." msgstr "" +"``Client`` 또는 ``NumPyClient``의 서브 클래스에서 \"placeholder\" 메서드를 제" +"거합니다. 예를 들어 서버 측 평가를 사용하는 경우 ``evaluate``의 빈 자리 표시" +"자 구현은 더 이상 필요하지 않습니다." #: ../../source/how-to-upgrade-to-flower-1.0.rst:85 msgid "" @@ -5859,514 +7294,640 @@ msgid "" "``start_simulation(..., config=flwr.server.ServerConfig(num_rounds=3, " "round_timeout=600.0), ...)``" msgstr "" +"``start_simulation``을 통해 라운드 타임아웃을 구성합니다: " +"``start_simulation(..., config=flwr.server.ServerConfig(num_rounds=3, " +"round_timeout=600.0), ...)``" #: ../../source/how-to-upgrade-to-flower-1.0.rst:89 -#: ../../source/how-to-upgrade-to-flower-next.rst:316 +#: ../../source/how-to-upgrade-to-flower-next.rst:317 msgid "Further help" -msgstr "" +msgstr "추가 도움말" #: ../../source/how-to-upgrade-to-flower-1.0.rst:91 msgid "" -"Most official `Flower code examples " -"`_ are already updated" -" to Flower 1.0, they can serve as a reference for using the Flower 1.0 " -"API. If there are further questions, `join the Flower Slack " -"`_ and use the channel ``#questions``." +"Most official `Flower code examples `_ are already updated to Flower 1.0, they can serve as a " +"reference for using the Flower 1.0 API. If there are further questions, " +"`join the Flower Slack `_ and use the channel " +"``#questions``." msgstr "" +"대부분의 공식 ``Flower code 예제 `_는 이미 Flower 1.0으로 업데이트되어 있으며, Flower 1.0 API를 사용" +"하기 위한 참고 자료로 사용할 수 있습니다. 더 궁금한 점이 있다면 ``플라워 슬" +"랙 `_에 가입하여 ``#questions`` 채널을 이용하" +"세요." #: ../../source/how-to-upgrade-to-flower-next.rst:2 msgid "Upgrade to Flower Next" -msgstr "" +msgstr "Flower Next 업그레이드" #: ../../source/how-to-upgrade-to-flower-next.rst:4 msgid "" -"Welcome to the migration guide for updating Flower to Flower Next! " -"Whether you're a seasoned user or just getting started, this guide will " -"help you smoothly transition your existing setup to take advantage of the" -" latest features and improvements in Flower Next, starting from version " -"1.8." +"Welcome to the migration guide for updating Flower to Flower Next! Whether " +"you're a seasoned user or just getting started, this guide will help you " +"smoothly transition your existing setup to take advantage of the latest " +"features and improvements in Flower Next, starting from version 1.8." msgstr "" +"Flower에서 Flower Next로의 업데이트를 위한 이동 가이드에 오신 것을 환영합니" +"다! 이 가이드는 숙련된 사용자든 이제 막 시작한 사용자든 상관없이 기존 설정을 " +"원활하게 전환하여 버전 1.8부터 Flower Next의 최신 기능 및 개선 사항을 활용할 " +"수 있도록 도와드립니다." #: ../../source/how-to-upgrade-to-flower-next.rst:9 msgid "" "This guide shows how to reuse pre-``1.8`` Flower code with minimum code " -"changes by using the *compatibility layer* in Flower Next. In another " -"guide, we will show how to run Flower Next end-to-end with pure Flower " -"Next APIs." +"changes by using the *compatibility layer* in Flower Next. In another guide, " +"we will show how to run Flower Next end-to-end with pure Flower Next APIs." msgstr "" +"이 가이드에서는 Flower Next의 *호환성 레이어*를 사용하여 최소한의 코드 변경으" +"로 ``1.8`` 이전의 Flower 코드를 재사용하는 방법을 보여줍니다. 다른 가이드에서" +"는 순수한 Flower Next API로 Flower Next를 end-to-end로 실행하는 방법을 보여드" +"리겠습니다." #: ../../source/how-to-upgrade-to-flower-next.rst:13 msgid "Let's dive in!" -msgstr "" +msgstr "자세히 알아봅시다!" -#: ../../source/how-to-upgrade-to-flower-next.rst:47 +#: ../../source/how-to-upgrade-to-flower-next.rst:48 msgid "" -"Here's how to update an existing installation of Flower to Flower Next " -"with ``pip``:" +"Here's how to update an existing installation of Flower to Flower Next with " +"``pip``:" msgstr "" +"기존에 설치된 Flower to Flower Next를 ``pip``으로 업데이트하는 방법은 다음과 " +"같습니다:" -#: ../../source/how-to-upgrade-to-flower-next.rst:53 +#: ../../source/how-to-upgrade-to-flower-next.rst:54 msgid "or if you need Flower Next with simulation:" -msgstr "" +msgstr "또는 시뮬레이션이 포함된 Flower Next가 필요한 경우:" -#: ../../source/how-to-upgrade-to-flower-next.rst:60 +#: ../../source/how-to-upgrade-to-flower-next.rst:61 msgid "" -"Ensure you set the following version constraint in your " -"``requirements.txt``" -msgstr "" +"Ensure you set the following version constraint in your ``requirements.txt``" +msgstr "``requirements.txt``에서 다음 버전 제약 조건을 설정했는지 확인하세요" -#: ../../source/how-to-upgrade-to-flower-next.rst:70 +#: ../../source/how-to-upgrade-to-flower-next.rst:71 msgid "or ``pyproject.toml``:" -msgstr "" +msgstr "또는 ``pyproject.toml``:" -#: ../../source/how-to-upgrade-to-flower-next.rst:81 +#: ../../source/how-to-upgrade-to-flower-next.rst:82 msgid "Using Poetry" -msgstr "" +msgstr "Poetry 사용" -#: ../../source/how-to-upgrade-to-flower-next.rst:83 +#: ../../source/how-to-upgrade-to-flower-next.rst:84 msgid "" "Update the ``flwr`` dependency in ``pyproject.toml`` and then reinstall " "(don't forget to delete ``poetry.lock`` via ``rm poetry.lock`` before " "running ``poetry install``)." msgstr "" +"``pyproject.toml``에서 ``flwr`` 의존성를 업데이트한 다음 다시 설치하세요(``" +"poetry install``을 실행하기 전에 ``rm poetry.lock``을 통해 ``poetry.lock``을 " +"삭제하는 것을 잊지 마세요)." -#: ../../source/how-to-upgrade-to-flower-next.rst:85 +#: ../../source/how-to-upgrade-to-flower-next.rst:86 msgid "" -"Ensure you set the following version constraint in your " -"``pyproject.toml``:" -msgstr "" +"Ensure you set the following version constraint in your ``pyproject.toml``:" +msgstr "``pyproject.toml``에 다음 버전 제약 조건을 설정했는지 확인하세요:" -#: ../../source/how-to-upgrade-to-flower-next.rst:101 +#: ../../source/how-to-upgrade-to-flower-next.rst:102 msgid "" "In Flower Next, the *infrastructure* and *application layers* have been " -"decoupled. Instead of starting a client in code via ``start_client()``, " -"you create a |clientapp_link|_ and start it via the command line. Instead" -" of starting a server in code via ``start_server()``, you create a " -"|serverapp_link|_ and start it via the command line. The long-running " +"decoupled. Instead of starting a client in code via ``start_client()``, you " +"create a |clientapp_link|_ and start it via the command line. Instead of " +"starting a server in code via ``start_server()``, you create a |" +"serverapp_link|_ and start it via the command line. The long-running " "components of server and client are called SuperLink and SuperNode. The " -"following non-breaking changes that require manual updates and allow you " -"to run your project both in the traditional way and in the Flower Next " -"way:" +"following non-breaking changes that require manual updates and allow you to " +"run your project both in the traditional way and in the Flower Next way:" msgstr "" +"Flower Next에서는 *infrastructure*와 *application layers*가 분리되었습니다. " +"코드에서 ``start_client()``를 통해 클라이언트를 시작하는 대신, 명령줄을 통해 " +"|clientapp_link|_를 생성하여 시작합니다. 코드에서 ``start_server()``를 통해 " +"서버를 시작하는 대신 |serverapp_link|_를 생성하고 명령줄을 통해 서버를 시작합" +"니다. 서버와 클라이언트의 장기 실행 컴포넌트를 SuperLink와 SuperNode라고 합니" +"다. 수동 업데이트가 필요하지 않고 기존 방식과 Flower Next 방식 모두에서 프로" +"젝트를 실행할 수 있는 non-breaking 변경 사항은 다음과 같습니다:" -#: ../../source/how-to-upgrade-to-flower-next.rst:108 +#: ../../source/how-to-upgrade-to-flower-next.rst:109 msgid "|clientapp_link|_" -msgstr "" +msgstr "|clientapp_link|_" -#: ../../source/how-to-upgrade-to-flower-next.rst:109 +#: ../../source/how-to-upgrade-to-flower-next.rst:110 msgid "" -"Wrap your existing client with |clientapp_link|_ instead of launching it " -"via |startclient_link|_. Here's an example:" +"Wrap your existing client with |clientapp_link|_ instead of launching it via " +"|startclient_link|_. Here's an example:" msgstr "" +"|clientapp_link|_를 통해 실행하는 대신 기존 클라이언트를 |clientapp_link|_로 " +"래핑하세요. 다음은 예시입니다:" -#: ../../source/how-to-upgrade-to-flower-next.rst:131 +#: ../../source/how-to-upgrade-to-flower-next.rst:132 msgid "|serverapp_link|_" -msgstr "" +msgstr "|serverapp_link|_" -#: ../../source/how-to-upgrade-to-flower-next.rst:132 +#: ../../source/how-to-upgrade-to-flower-next.rst:133 msgid "" -"Wrap your existing strategy with |serverapp_link|_ instead of starting " -"the server via |startserver_link|_. Here's an example:" +"Wrap your existing strategy with |serverapp_link|_ instead of starting the " +"server via |startserver_link|_. Here's an example:" msgstr "" +"서버를 시작하려면 |startserver_link|_를 통해 서버를 시작하는 대신 기존 전략" +"을 |serverapp_link|_로 래핑하세요. 다음은 예시입니다:" -#: ../../source/how-to-upgrade-to-flower-next.rst:153 +#: ../../source/how-to-upgrade-to-flower-next.rst:154 msgid "Deployment" -msgstr "" +msgstr "배포" -#: ../../source/how-to-upgrade-to-flower-next.rst:154 +#: ../../source/how-to-upgrade-to-flower-next.rst:155 msgid "" -"Run the ``SuperLink`` using |flowernext_superlink_link|_ before running, " -"in sequence, |flowernext_clientapp_link|_ (2x) and " -"|flowernext_serverapp_link|_. There is no need to execute `client.py` and" -" `server.py` as Python scripts." +"Run the ``SuperLink`` using |flowernext_superlink_link|_ before running, in " +"sequence, |flowernext_clientapp_link|_ (2x) and |flowernext_serverapp_link|" +"_. There is no need to execute `client.py` and `server.py` as Python scripts." msgstr "" +"실행하기 전에 |flowernext_superlink_link|_를 사용하여 ``SuperLink``를 실행한 " +"후 |flowernext_clientapp_link|_(2회) 및 |flowernext_serverapp_link|_를 " +"순서대로 실행합니다. 'client.py'와 'server.py'를 Python 스크립트로 실행할 " +"필요는 없습니다." -#: ../../source/how-to-upgrade-to-flower-next.rst:157 +#: ../../source/how-to-upgrade-to-flower-next.rst:158 msgid "" -"Here's an example to start the server without HTTPS (only for " -"prototyping):" +"Here's an example to start the server without HTTPS (only for prototyping):" msgstr "" +"다음은 HTTPS 없이 서버를 시작하는 예제입니다(프로토타이핑용으로만 사용):" -#: ../../source/how-to-upgrade-to-flower-next.rst:173 +#: ../../source/how-to-upgrade-to-flower-next.rst:174 msgid "" -"Here's another example to start with HTTPS. Use the ``--certificates`` " -"command line argument to pass paths to (CA certificate, server " -"certificate, and server private key)." +"Here's another example to start with HTTPS. Use the ``--ssl-ca-certfile``, " +"``--ssl-certfile``, and ``--ssl-keyfile`` command line options to pass paths " +"to (CA certificate, server certificate, and server private key)." msgstr "" +"다음은 HTTPS로 시작하는 또 다른 예제입니다. '`--ssl-ca-certfile``, '`--ssl-" +"certfile``, '`--ssl-keyfile`` 명령줄 옵션을 사용하여 (CA 인증서, 서버 인증서 " +"및 서버 개인 키)의 경로를 전달합니다." -#: ../../source/how-to-upgrade-to-flower-next.rst:200 +#: ../../source/how-to-upgrade-to-flower-next.rst:201 msgid "Simulation in CLI" -msgstr "" +msgstr "CLI 시뮬레이션" -#: ../../source/how-to-upgrade-to-flower-next.rst:201 +#: ../../source/how-to-upgrade-to-flower-next.rst:202 msgid "" -"Wrap your existing client and strategy with |clientapp_link|_ and " -"|serverapp_link|_, respectively. There is no need to use |startsim_link|_" -" anymore. Here's an example:" +"Wrap your existing client and strategy with |clientapp_link|_ and |" +"serverapp_link|_, respectively. There is no need to use |startsim_link|_ " +"anymore. Here's an example:" msgstr "" +"기존 클라이언트와 전략을 각각 |clientapp_link|_와 |serverapp_link|_로 래핑하" +"세요. 더 이상 |startsim_link|_를 사용할 필요가 없습니다. 다음은 예시입니다:" -#: ../../source/how-to-upgrade-to-flower-next.rst:231 +#: ../../source/how-to-upgrade-to-flower-next.rst:232 msgid "" -"Run |runsimcli_link|_ in CLI and point to the ``server_app`` / " -"``client_app`` object in the code instead of executing the Python script." -" Here's an example (assuming the ``server_app`` and ``client_app`` " -"objects are in a ``sim.py`` module):" +"Run |flower_simulation_link|_ in CLI and point to the ``server_app`` / " +"``client_app`` object in the code instead of executing the Python script. " +"Here's an example (assuming the ``server_app`` and ``client_app`` objects " +"are in a ``sim.py`` module):" msgstr "" +"CLI에서 |flower_simulation_link|_를 실행하고 Python 스크립트를 실행하는 대신 " +"코드에서 ``server_app`` / ``client_app`` 개체를 가리키세요. 다음은 예제입니다" +"(``server_app`` 및 ``client_app`` 객체가 ``sim.py`` 모듈에 있다고 가정):" -#: ../../source/how-to-upgrade-to-flower-next.rst:248 +#: ../../source/how-to-upgrade-to-flower-next.rst:249 msgid "" "Set default resources for each |clientapp_link|_ using the ``--backend-" -"config`` command line argument instead of setting the " -"``client_resources`` argument in |startsim_link|_. Here's an example:" +"config`` command line argument instead of setting the ``client_resources`` " +"argument in |startsim_link|_. Here's an example:" msgstr "" +"|startsim_link|_에서 ``client_resources`` 인수를 설정하는 대신 ``--backend-" +"config`` 명령줄 인수를 사용하여 각 |clientapp_link|_에 대한 기본 리소스를 설" +"정하세요. 다음은 예시입니다:" -#: ../../source/how-to-upgrade-to-flower-next.rst:274 +#: ../../source/how-to-upgrade-to-flower-next.rst:275 msgid "Simulation in a Notebook" -msgstr "" +msgstr "Notebook에서 시뮬레이션" -#: ../../source/how-to-upgrade-to-flower-next.rst:275 +#: ../../source/how-to-upgrade-to-flower-next.rst:276 msgid "" -"Run |runsim_link|_ in your notebook instead of |startsim_link|_. Here's " -"an example:" +"Run |runsim_link|_ in your notebook instead of |startsim_link|_. Here's an " +"example:" msgstr "" +"notebook에서 |startsim_link|_ 대신 |runsim_link|_를 실행하세요. 다음은 예시입" +"니다:" -#: ../../source/how-to-upgrade-to-flower-next.rst:318 +#: ../../source/how-to-upgrade-to-flower-next.rst:319 msgid "" -"Some official `Flower code examples `_ " -"are already updated to Flower Next so they can serve as a reference for " -"using the Flower Next API. If there are further questions, `join the " -"Flower Slack `_ and use the channel " -"``#questions``. You can also `participate in Flower Discuss " -"`_ where you can find us answering questions," -" or share and learn from others about migrating to Flower Next." +"Some official `Flower code examples `_ are " +"already updated to Flower Next so they can serve as a reference for using " +"the Flower Next API. If there are further questions, `join the Flower Slack " +"`_ and use the channel ``#questions``. You " +"can also `participate in Flower Discuss `_ where " +"you can find us answering questions, or share and learn from others about " +"migrating to Flower Next." msgstr "" +"일부 공식 ``Flower 코드 예제 `_는 이미 플라" +"워 넥스트에 업데이트되어 있으므로 플라워 넥스트 API를 사용하는 데 참고할 수 " +"있습니다. 더 궁금한 점이 있다면 ``플라워 슬랙 `_에 가입하고 ``#questions`` 채널을 이용하세요. 또한, ``Flower Discuss " +"`_에 참여하여 질문에 대한 답변을 확인하거나 다른 " +"사람들과 Flower Next로의 이동에 대해 공유하고 배울 수 있습니다." -#: ../../source/how-to-upgrade-to-flower-next.rst:324 +#: ../../source/how-to-upgrade-to-flower-next.rst:325 msgid "Important" -msgstr "" +msgstr "중요" -#: ../../source/how-to-upgrade-to-flower-next.rst:327 +#: ../../source/how-to-upgrade-to-flower-next.rst:328 msgid "" "As we continuously enhance Flower Next at a rapid pace, we'll be " "periodically updating this guide. Please feel free to share any feedback " "with us!" msgstr "" +"Flower Next는 빠른 속도로 지속적으로 개선되고 있으므로 이 가이드는 주기적으" +"로 업데이트될 예정입니다. 피드백이 있으면 언제든지 공유해 주세요!" -#: ../../source/how-to-upgrade-to-flower-next.rst:333 +#: ../../source/how-to-upgrade-to-flower-next.rst:334 msgid "Happy migrating! 🚀" -msgstr "" +msgstr "행복한 마이그레이션! 🚀" #: ../../source/how-to-use-built-in-mods.rst:2 msgid "Use Built-in Mods" -msgstr "" +msgstr "기본 제공 모드 사용" #: ../../source/how-to-use-built-in-mods.rst:4 msgid "" -"**Note: This tutorial covers experimental features. The functionality and" -" interfaces may change in future versions.**" +"**Note: This tutorial covers experimental features. The functionality and " +"interfaces may change in future versions.**" msgstr "" +"**참고: 이 튜토리얼은 실험적인 기능을 다룹니다. 기능 및 인터페이스는 향후 버" +"전에서 변경될 수 있습니다.**" #: ../../source/how-to-use-built-in-mods.rst:6 msgid "" -"In this tutorial, we will learn how to utilize built-in mods to augment " -"the behavior of a ``ClientApp``. Mods (sometimes also called Modifiers) " -"allow us to perform operations before and after a task is processed in " -"the ``ClientApp``." +"In this tutorial, we will learn how to utilize built-in mods to augment the " +"behavior of a ``ClientApp``. Mods (sometimes also called Modifiers) allow us " +"to perform operations before and after a task is processed in the " +"``ClientApp``." msgstr "" +"이 튜토리얼에서는 내장 모드를 활용하여 ``ClientApp``의 동작을 보강하는 방법" +"을 배우겠습니다. Mods(Modifiers라고도 함)를 사용하면 ``ClientApp``에서 작업" +"이 처리되기 전과 후에 작업을 수행할 수 있습니다." #: ../../source/how-to-use-built-in-mods.rst:9 msgid "What are Mods?" -msgstr "" +msgstr "Mods란 무엇인가요?" #: ../../source/how-to-use-built-in-mods.rst:11 msgid "" -"A Mod is a callable that wraps around a ``ClientApp``. It can manipulate " -"or inspect the incoming ``Message`` and the resulting outgoing " -"``Message``. The signature for a ``Mod`` is as follows:" +"A Mod is a callable that wraps around a ``ClientApp``. It can manipulate or " +"inspect the incoming ``Message`` and the resulting outgoing ``Message``. The " +"signature for a ``Mod`` is as follows:" msgstr "" +"Mod는 ``ClientApp``을 감싸는 콜러블입니다. 들어오는 ``Message``와 그 결과로 " +"나가는 ``Message``를 조작하거나 검사할 수 있습니다. ``Mod``의 시그니처는 다음" +"과 같습니다:" #: ../../source/how-to-use-built-in-mods.rst:18 msgid "A typical mod function might look something like this:" -msgstr "" +msgstr "일반적인 mod 함수는 다음과 같은 모습일 수 있습니다:" #: ../../source/how-to-use-built-in-mods.rst:31 msgid "Using Mods" -msgstr "" +msgstr "Mods 사용" #: ../../source/how-to-use-built-in-mods.rst:33 msgid "To use mods in your ``ClientApp``, you can follow these steps:" -msgstr "" +msgstr "``ClientApp``에서 mods를 사용하려면 다음 단계를 따르세요:" #: ../../source/how-to-use-built-in-mods.rst:36 msgid "1. Import the required mods" -msgstr "" +msgstr "1. 필요한 mods를 가져옵니다" #: ../../source/how-to-use-built-in-mods.rst:38 msgid "First, import the built-in mod you intend to use:" -msgstr "" +msgstr "먼저 사용하려는 기본 제공 mod를 가져옵니다:" #: ../../source/how-to-use-built-in-mods.rst:46 msgid "2. Define your client function" -msgstr "" +msgstr "2. 클라이언트 기능 정의" #: ../../source/how-to-use-built-in-mods.rst:48 msgid "" "Define your client function (``client_fn``) that will be wrapped by the " "mod(s):" -msgstr "" +msgstr "mod(s)로 래핑할 클라이언트 함수('``client_fn``)를 정의합니다:" #: ../../source/how-to-use-built-in-mods.rst:57 msgid "3. Create the ``ClientApp`` with mods" -msgstr "" +msgstr "3. mods로 ``ClientApp``을 생성합니다" #: ../../source/how-to-use-built-in-mods.rst:59 msgid "" "Create your ``ClientApp`` and pass the mods as a list to the ``mods`` " "argument. The order in which you provide the mods matters:" msgstr "" +"``ClientApp``을 생성하고 mods를 ``mods`` argument에 목록으로 전달합니다. mods" +"를 제공하는 순서가 중요합니다:" #: ../../source/how-to-use-built-in-mods.rst:72 msgid "Order of execution" -msgstr "" +msgstr "실행 순서" #: ../../source/how-to-use-built-in-mods.rst:74 msgid "" "When the ``ClientApp`` runs, the mods are executed in the order they are " "provided in the list:" -msgstr "" +msgstr "``ClientApp``이 실행되면 목록에 제공된 순서대로 모드가 실행됩니다:" #: ../../source/how-to-use-built-in-mods.rst:76 msgid "``example_mod_1`` (outermost mod)" -msgstr "" +msgstr "``example_mod_1``(가장 바깥쪽 mod)" #: ../../source/how-to-use-built-in-mods.rst:77 msgid "``example_mod_2`` (next mod)" -msgstr "" +msgstr "``example_mod_2`` (다음 mod)" #: ../../source/how-to-use-built-in-mods.rst:78 msgid "" "Message handler (core function that handles the incoming ``Message`` and " "returns the outgoing ``Message``)" msgstr "" +"Message handler(들어오는 ``Message``를 처리하고 나가는 ``Message``를 반환하" +"는 핵심 함수)" #: ../../source/how-to-use-built-in-mods.rst:79 msgid "``example_mod_2`` (on the way back)" -msgstr "" +msgstr "``example_mod_2``(돌아가는 방법)" #: ../../source/how-to-use-built-in-mods.rst:80 msgid "``example_mod_1`` (outermost mod on the way back)" -msgstr "" +msgstr "``example_mod_1``(돌아가는 방법에 가장 바깥쪽 모드)" #: ../../source/how-to-use-built-in-mods.rst:82 msgid "" -"Each mod has a chance to inspect and modify the incoming ``Message`` " -"before passing it to the next mod, and likewise with the outgoing " -"``Message`` before returning it up the stack." +"Each mod has a chance to inspect and modify the incoming ``Message`` before " +"passing it to the next mod, and likewise with the outgoing ``Message`` " +"before returning it up the stack." msgstr "" +"각 mod는 다음 mod로 전달하기 전에 들어오는 ``Message``를 검사하고 수정할 기회" +"가 있으며, 스택 위로 반환하기 전에 나가는 ``Message``도 마찬가지로 검사하고 " +"수정할 수 있습니다." #: ../../source/how-to-use-built-in-mods.rst:87 msgid "" "By following this guide, you have learned how to effectively use mods to " -"enhance your ``ClientApp``'s functionality. Remember that the order of " -"mods is crucial and affects how the input and output are processed." +"enhance your ``ClientApp``'s functionality. Remember that the order of mods " +"is crucial and affects how the input and output are processed." msgstr "" +"이 가이드를 따라 mods를 효과적으로 사용하여 ``ClientApp``의 기능을 향상시키" +"는 방법을 배웠습니다. mods 순서는 매우 중요하며 입력과 출력이 처리되는 방식" +"에 영향을 미친다는 점을 기억하세요." #: ../../source/how-to-use-built-in-mods.rst:89 msgid "Enjoy building a more robust and flexible ``ClientApp`` with mods!" -msgstr "" +msgstr "Mods를 통해 더욱 강력하고 유연한 ``ClientApp``을 구축해 보세요!" #: ../../source/how-to-use-differential-privacy.rst:2 msgid "Use Differential Privacy" -msgstr "" +msgstr "차분 개인정보 보호 사용" #: ../../source/how-to-use-differential-privacy.rst:3 msgid "" -"This guide explains how you can utilize differential privacy in the " -"Flower framework. If you are not yet familiar with differential privacy, " -"you can refer to :doc:`explanation-differential-privacy`." +"This guide explains how you can utilize differential privacy in the Flower " +"framework. If you are not yet familiar with differential privacy, you can " +"refer to :doc:`explanation-differential-privacy`." msgstr "" +"이 가이드에서는 Flower 프레임워크에서 차분 개인정보 보호 기능을 활용하는 방법" +"을 설명합니다. 차분 개인정보 보호에 대해 아직 익숙하지 않은 경우 :doc:" +"`explanation-differential-privacy`를 참조하세요." #: ../../source/how-to-use-differential-privacy.rst:7 msgid "" "Differential Privacy in Flower is in a preview phase. If you plan to use " -"these features in a production environment with sensitive data, feel free" -" contact us to discuss your requirements and to receive guidance on how " -"to best use these features." +"these features in a production environment with sensitive data, feel free " +"contact us to discuss your requirements and to receive guidance on how to " +"best use these features." msgstr "" +"Flower의 차분 개인정보 보호는 현재 프리뷰 단계에 있습니다. 민감한 데이터가 있" +"는 프로덕션 환경에서 이러한 기능을 사용할 계획이라면 언제든지 문의하여 요구 " +"사항을 논의하고 이러한 기능을 가장 잘 사용하는 방법에 대한 안내를 받으세요." #: ../../source/how-to-use-differential-privacy.rst:12 msgid "" -"This approach consists of two seprate phases: clipping of the updates and" -" adding noise to the aggregated model. For the clipping phase, Flower " -"framework has made it possible to decide whether to perform clipping on " -"the server side or the client side." +"This approach consists of two seprate phases: clipping of the updates and " +"adding noise to the aggregated model. For the clipping phase, Flower " +"framework has made it possible to decide whether to perform clipping on the " +"server side or the client side." msgstr "" +"이 접근 방식은 업데이트 클리핑과 집계된 모델에 노이즈 추가라는 두 가지 단계" +"로 구성됩니다. 클리핑 단계의 경우, Flower 프레임워크는 클리핑을 서버 측에서 " +"수행할지 클라이언트 측에서 수행할지 결정할 수 있도록 했습니다." #: ../../source/how-to-use-differential-privacy.rst:15 msgid "" "**Server-side Clipping**: This approach has the advantage of the server " "enforcing uniform clipping across all clients' updates and reducing the " "communication overhead for clipping values. However, it also has the " -"disadvantage of increasing the computational load on the server due to " -"the need to perform the clipping operation for all clients." +"disadvantage of increasing the computational load on the server due to the " +"need to perform the clipping operation for all clients." msgstr "" +"**Server-side Clipping**: 이 방식은 서버가 모든 클라이언트의 업데이트에 대해 " +"균일한 클리핑을 적용하고 클리핑 값에 대한 통신 오버헤드를 줄일 수 있다는 장점" +"이 있습니다. 하지만 모든 클라이언트에 대해 클리핑 작업을 수행해야 하기 때문" +"에 서버의 계산 부하가 증가한다는 단점도 있습니다." #: ../../source/how-to-use-differential-privacy.rst:16 msgid "" -"**Client-side Clipping**: This approach has the advantage of reducing the" -" computational overhead on the server. However, it also has the " -"disadvantage of lacking centralized control, as the server has less " -"control over the clipping process." +"**Client-side Clipping**: This approach has the advantage of reducing the " +"computational overhead on the server. However, it also has the disadvantage " +"of lacking centralized control, as the server has less control over the " +"clipping process." msgstr "" +"**Client-side Clipping**: 이 방식은 서버의 계산 오버헤드를 줄일 수 있다는 장" +"점이 있습니다. 하지만 서버가 클리핑 프로세스에 대한 통제력이 떨어지기 때문에 " +"centralized 제어가 부족하다는 단점도 있습니다." #: ../../source/how-to-use-differential-privacy.rst:21 msgid "Server-side Clipping" -msgstr "" +msgstr "서버 측 클리핑" #: ../../source/how-to-use-differential-privacy.rst:22 msgid "" "For central DP with server-side clipping, there are two :code:`Strategy` " "classes that act as wrappers around the actual :code:`Strategy` instance " -"(for example, :code:`FedAvg`). The two wrapper classes are " -":code:`DifferentialPrivacyServerSideFixedClipping` and " -":code:`DifferentialPrivacyServerSideAdaptiveClipping` for fixed and " -"adaptive clipping." +"(for example, :code:`FedAvg`). The two wrapper classes are :code:" +"`DifferentialPrivacyServerSideFixedClipping` and :code:" +"`DifferentialPrivacyServerSideAdaptiveClipping` for fixed and adaptive " +"clipping." msgstr "" +"서버 측 클리핑이 있는 중앙 DP의 경우, 실제 :code:`Strategy` 인스턴스를 감싸" +"는 래퍼 역할을 하는 두 개의 :code:`Strategy` 클래스가 있습니다(예: :code:" +"`FedAvg`). 두 개의 래퍼 클래스는 고정 및 적응형 클리핑을 위한 :code:" +"`DifferentialPrivacyServerSideFixedClipping`과 :code:" +"`DifferentialPrivacyServerSideAdaptiveClipping`입니다." #: ../../source/how-to-use-differential-privacy.rst:-1 msgid "server side clipping" -msgstr "" +msgstr "서버 측 클리핑" #: ../../source/how-to-use-differential-privacy.rst:31 msgid "" -"The code sample below enables the :code:`FedAvg` strategy to use server-" -"side fixed clipping using the " -":code:`DifferentialPrivacyServerSideFixedClipping` wrapper class. The " -"same approach can be used with " -":code:`DifferentialPrivacyServerSideAdaptiveClipping` by adjusting the " +"The code sample below enables the :code:`FedAvg` strategy to use server-side " +"fixed clipping using the :code:`DifferentialPrivacyServerSideFixedClipping` " +"wrapper class. The same approach can be used with :code:" +"`DifferentialPrivacyServerSideAdaptiveClipping` by adjusting the " "corresponding input parameters." msgstr "" +"아래 코드 샘플은 :code:`FedAvg` 전략이 :code:" +"`DifferentialPrivacyServerSideFixedClipping` 래퍼 클래스를 사용하여 서버 측 " +"고정 클리핑을 사용할 수 있도록 합니다. 해당 입력 매개변수를 조정하여 :code:" +"`DifferentialPrivacyServerSideAdaptiveClipping`과 동일한 접근 방식을 사용할 " +"수 있습니다." #: ../../source/how-to-use-differential-privacy.rst:52 msgid "Client-side Clipping" -msgstr "" +msgstr "클라이언트 측 클리핑" #: ../../source/how-to-use-differential-privacy.rst:53 msgid "" "For central DP with client-side clipping, the server sends the clipping " -"value to selected clients on each round. Clients can use existing Flower " -":code:`Mods` to perform the clipping. Two mods are available for fixed " -"and adaptive client-side clipping: :code:`fixedclipping_mod` and " -":code:`adaptiveclipping_mod` with corresponding server-side wrappers " -":code:`DifferentialPrivacyClientSideFixedClipping` and " -":code:`DifferentialPrivacyClientSideAdaptiveClipping`." -msgstr "" +"value to selected clients on each round. Clients can use existing Flower :" +"code:`Mods` to perform the clipping. Two mods are available for fixed and " +"adaptive client-side clipping: :code:`fixedclipping_mod` and :code:" +"`adaptiveclipping_mod` with corresponding server-side wrappers :code:" +"`DifferentialPrivacyClientSideFixedClipping` and :code:" +"`DifferentialPrivacyClientSideAdaptiveClipping`." +msgstr "" +"클라이언트 측 클리핑이 있는 중앙 DP의 경우 서버는 각 라운드마다 선택한 클라이" +"언트에 클리핑 값을 보냅니다. 클라이언트는 기존 Flower :code:`Mods`를 사용하" +"여 클리핑을 수행할 수 있습니다. 고정 및 적응형 클라이언트 측 클리핑에는 두 가" +"지 모드를 사용할 수 있습니다: :code:`fixedclipping_mod` 및 :code:" +"`adaptiveclipping_mod`와 해당 서버 측 래퍼 :code:" +"`DifferentialPrivacyClientSideFixedClipping` 및 :code:" +"`DifferentialPrivacyClientSideAdaptiveClipping`이 있습니다." #: ../../source/how-to-use-differential-privacy.rst:-1 msgid "client side clipping" -msgstr "" +msgstr "클라이언트 측 클리핑" #: ../../source/how-to-use-differential-privacy.rst:63 msgid "" "The code sample below enables the :code:`FedAvg` strategy to use " -"differential privacy with client-side fixed clipping using both the " -":code:`DifferentialPrivacyClientSideFixedClipping` wrapper class and, on " -"the client, :code:`fixedclipping_mod`:" +"differential privacy with client-side fixed clipping using both the :code:" +"`DifferentialPrivacyClientSideFixedClipping` wrapper class and, on the " +"client, :code:`fixedclipping_mod`:" msgstr "" +"아래 코드 샘플은 :code:`FedAvg` 전략이 클라이언트 측 고정 클리핑과 함께 차분 " +"프라이버시를 사용할 수 있도록 :code:" +"`DifferentialPrivacyClientSideFixedClipping` 래퍼 클래스와 클라이언트에서 :" +"code:`fixedclipping_mod`를 모두 사용하도록 합니다:" #: ../../source/how-to-use-differential-privacy.rst:80 msgid "" -"In addition to the server-side strategy wrapper, the :code:`ClientApp` " -"needs to configure the matching :code:`fixedclipping_mod` to perform the " -"client-side clipping:" +"In addition to the server-side strategy wrapper, the :code:`ClientApp` needs " +"to configure the matching :code:`fixedclipping_mod` to perform the client-" +"side clipping:" msgstr "" +"서버 측 전략 래퍼 외에도 클라이언트 측 클리핑을 수행하려면 :code:`ClientApp`" +"이 일치하는 :code:`fixedclipping_mod`를 구성해야 합니다:" #: ../../source/how-to-use-differential-privacy.rst:97 msgid "" -"To utilize local differential privacy (DP) and add noise to the client " -"model parameters before transmitting them to the server in Flower, you " -"can use the `LocalDpMod`. The following hyperparameters need to be set: " -"clipping norm value, sensitivity, epsilon, and delta." +"To utilize local differential privacy (DP) and add noise to the client model " +"parameters before transmitting them to the server in Flower, you can use the " +"`LocalDpMod`. The following hyperparameters need to be set: clipping norm " +"value, sensitivity, epsilon, and delta." msgstr "" +"로컬 차분 프라이버시(DP)를 활용하고 클라이언트 모델 파라미터를 서버로 전송하" +"기 전에 노이즈를 추가하려면 `LocalDpMod`를 사용하면 됩니다. 클리핑 노멀 값, " +"감도, 엡실론, 델타 등의 하이퍼파라미터를 설정해야 합니다." #: ../../source/how-to-use-differential-privacy.rst:-1 msgid "local DP mod" -msgstr "" +msgstr "로컬 DP mod" #: ../../source/how-to-use-differential-privacy.rst:104 msgid "Below is a code example that shows how to use :code:`LocalDpMod`:" -msgstr "" +msgstr "다음은 :code:`LocalDpMod`를 사용하는 방법을 보여주는 코드 예시입니다:" #: ../../source/how-to-use-differential-privacy.rst:122 msgid "" -"Please note that the order of mods, especially those that modify " -"parameters, is important when using multiple modifiers. Typically, " -"differential privacy (DP) modifiers should be the last to operate on " -"parameters." +"Please note that the order of mods, especially those that modify parameters, " +"is important when using multiple modifiers. Typically, differential privacy " +"(DP) modifiers should be the last to operate on parameters." msgstr "" +"여러 개의 수정자를 사용할 때는 수정자, 특히 매개변수를 수정하는 수정자의 순서" +"가 중요하다는 점에 유의하세요. 일반적으로 차분 프라이버시(DP) 수정자는 매개변" +"수에서 가장 마지막에 작동해야 합니다." #: ../../source/how-to-use-differential-privacy.rst:125 msgid "Local Training using Privacy Engines" -msgstr "" +msgstr "Privacy Engines을 사용한 로컬 훈련" #: ../../source/how-to-use-differential-privacy.rst:126 msgid "" -"For ensuring data instance-level privacy during local model training on " -"the client side, consider leveraging privacy engines such as Opacus and " -"TensorFlow Privacy. For examples of using Flower with these engines, " -"please refer to the Flower examples directory (`Opacus " -"`_, `Tensorflow" -" Privacy `_)." +"For ensuring data instance-level privacy during local model training on the " +"client side, consider leveraging privacy engines such as Opacus and " +"TensorFlow Privacy. For examples of using Flower with these engines, please " +"refer to the Flower examples directory (`Opacus `_, `Tensorflow Privacy `_)." msgstr "" +"클라이언트 측에서 로컬 모델을 훈련하는 동안 데이터 인스턴스 수준의 개인 정보 " +"보호를 보장하려면 Opacus 및 TensorFlow Privacy와 같은 개인 정보 보호 엔진을 " +"활용하는 것을 고려하세요. 이러한 엔진과 함께 Flower를 사용하는 예제는 Flower " +"examples directory (`Opacus `_, `Tensorflow Privacy `_)를 참조하세요." #: ../../source/how-to-use-strategies.rst:2 msgid "Use strategies" -msgstr "" +msgstr "전략 사용하기" #: ../../source/how-to-use-strategies.rst:4 msgid "" -"Flower allows full customization of the learning process through the " -":code:`Strategy` abstraction. A number of built-in strategies are " -"provided in the core framework." +"Flower allows full customization of the learning process through the :code:" +"`Strategy` abstraction. A number of built-in strategies are provided in the " +"core framework." msgstr "" +"Flower는 :code:`Strategy` abstraction를 통해 학습 과정을 완전히 사용자 정의" +"할 수 있습니다. 핵심 프레임워크에는 여러 가지 기본 제공 전략이 제공됩니다." #: ../../source/how-to-use-strategies.rst:6 msgid "" -"There are three ways to customize the way Flower orchestrates the " -"learning process on the server side:" +"There are three ways to customize the way Flower orchestrates the learning " +"process on the server side:" msgstr "" +"서버 측에서 Flower가 학습 과정을 조율하는 방식을 사용자 지정하는 방법에는 세 " +"가지가 있습니다:" #: ../../source/how-to-use-strategies.rst:8 msgid "Use an existing strategy, for example, :code:`FedAvg`" -msgstr "" +msgstr "기존 전략(예: :code:`FedAvg`)을 사용합니다" #: ../../source/how-to-use-strategies.rst:9 #: ../../source/how-to-use-strategies.rst:40 msgid "Customize an existing strategy with callback functions" -msgstr "" +msgstr "콜백 함수로 기존 전략 사용자 지정" #: ../../source/how-to-use-strategies.rst:10 #: ../../source/how-to-use-strategies.rst:87 msgid "Implement a novel strategy" -msgstr "" +msgstr "새로운 전략 구현" #: ../../source/how-to-use-strategies.rst:14 msgid "Use an existing strategy" -msgstr "" +msgstr "기존 전략 사용" #: ../../source/how-to-use-strategies.rst:16 msgid "" -"Flower comes with a number of popular federated learning strategies " -"built-in. A built-in strategy can be instantiated as follows:" +"Flower comes with a number of popular federated learning strategies built-" +"in. A built-in strategy can be instantiated as follows:" msgstr "" +"Flower에는 여러 가지 인기 있는 연합 학습 전략이 기본으로 제공됩니다. 기본 " +"제공 전략은 다음과 같이 인스턴스화할 수 있습니다:" #: ../../source/how-to-use-strategies.rst:25 msgid "" -"This creates a strategy with all parameters left at their default values " -"and passes it to the :code:`start_server` function. It is usually " -"recommended to adjust a few parameters during instantiation:" +"This creates a strategy with all parameters left at their default values and " +"passes it to the :code:`start_server` function. It is usually recommended to " +"adjust a few parameters during instantiation:" msgstr "" +"이렇게 하면 모든 매개변수가 기본값으로 유지된 전략이 생성되어 :code:" +"`start_server` 함수에 전달됩니다. 일반적으로 인스턴스화 중에 몇 가지 매개변수" +"를 조정하는 것이 좋습니다:" #: ../../source/how-to-use-strategies.rst:42 msgid "" @@ -6374,118 +7935,138 @@ msgid "" "Callback functions allow strategies to call user-provided code during " "execution." msgstr "" +"기존 전략은 동작을 사용자 지정하는 여러 가지 방법을 제공합니다. 콜백 함수를 " +"사용하면 전략이 실행 중에 사용자가 제공한 코드를 호출할 수 있습니다." #: ../../source/how-to-use-strategies.rst:45 msgid "Configuring client fit and client evaluate" -msgstr "" +msgstr "클라이언트 적합성 및 클라이언트 평가 구성" #: ../../source/how-to-use-strategies.rst:47 msgid "" "The server can pass new configuration values to the client each round by " -"providing a function to :code:`on_fit_config_fn`. The provided function " -"will be called by the strategy and must return a dictionary of " -"configuration key values pairs that will be sent to the client. It must " -"return a dictionary of arbitrary configuration values :code:`client.fit`" -" and :code:`client.evaluate` functions during each round of federated " -"learning." -msgstr "" +"providing a function to :code:`on_fit_config_fn`. The provided function will " +"be called by the strategy and must return a dictionary of configuration key " +"values pairs that will be sent to the client. It must return a dictionary of " +"arbitrary configuration values :code:`client.fit` and :code:`client." +"evaluate` functions during each round of federated learning." +msgstr "" +"서버는 매 라운드마다 새로운 설정 값을 클라이언트에 전달하기 위해 " +":code:`on_fit_config_fn`에 함수를 제공할 수 있습니다. 제공된 함수는 전략에 " +"의해 호출되며 클라이언트에 전송될 구성 키 값 쌍의 dictionary를 반환해야 " +"합니다. 연합 학습의 각 라운드 동안 임의의 구성 값 dictionary인 :code:`client." +"fit` 및 :code:`client.evaluate` 함수를 반환해야 합니다." #: ../../source/how-to-use-strategies.rst:75 msgid "" "The :code:`on_fit_config_fn` can be used to pass arbitrary configuration " "values from server to client, and poetentially change these values each " -"round, for example, to adjust the learning rate. The client will receive " -"the dictionary returned by the :code:`on_fit_config_fn` in its own " -":code:`client.fit()` function." +"round, for example, to adjust the learning rate. The client will receive the " +"dictionary returned by the :code:`on_fit_config_fn` in its own :code:`client." +"fit()` function." msgstr "" +":code:`on_fit_config_fn`은 서버에서 클라이언트로 임의의 구성 값을 전달하고, " +"예를 들어 학습 속도를 조정하기 위해 매 라운드마다 이 값을 잠재적으로 변경하" +"는 데 사용할 수 있습니다. 클라이언트는 자체 :code:`client.fit()` 함수에서 :" +"code:`on_fit_config_fn`이 반환한 dictionary를 받습니다." #: ../../source/how-to-use-strategies.rst:78 msgid "" -"Similar to :code:`on_fit_config_fn`, there is also " -":code:`on_evaluate_config_fn` to customize the configuration sent to " -":code:`client.evaluate()`" +"Similar to :code:`on_fit_config_fn`, there is also :code:" +"`on_evaluate_config_fn` to customize the configuration sent to :code:`client." +"evaluate()`" msgstr "" +":code:`on_fit_config_fn`과 유사하게, :code:`client.evaluate()`로 전송되는 구" +"성을 사용자 지정하는 :code:`on_evaluate_config_fn`도 있습니다" #: ../../source/how-to-use-strategies.rst:81 msgid "Configuring server-side evaluation" -msgstr "" +msgstr "서버 측 평가 구성" #: ../../source/how-to-use-strategies.rst:83 msgid "" -"Server-side evaluation can be enabled by passing an evaluation function " -"to :code:`evaluate_fn`." +"Server-side evaluation can be enabled by passing an evaluation function to :" +"code:`evaluate_fn`." msgstr "" +"서버 측 평가는 :code:`evaluate_fn`에 평가 함수를 전달하여 활성화할 수 있습니" +"다." #: ../../source/how-to-use-strategies.rst:89 msgid "" -"Writing a fully custom strategy is a bit more involved, but it provides " -"the most flexibility. Read the `Implementing Strategies `_ guide to learn more." +"Writing a fully custom strategy is a bit more involved, but it provides the " +"most flexibility. Read the `Implementing Strategies `_ guide to learn more." msgstr "" +"완전한 사용자 지정 전략을 작성하는 것은 조금 더 복잡하지만 유연성이 가장 뛰어" +"납니다. 자세한 내용은 `Implementing Strategies `_ 가이드를 참조하세요." #: ../../source/index.rst:34 msgid "Tutorial" -msgstr "" +msgstr "튜토리얼" #: ../../source/index.rst:44 msgid "Quickstart tutorials" -msgstr "" +msgstr "빠른 시작 튜토리얼" #: ../../source/index.rst:74 ../../source/index.rst:78 msgid "How-to guides" -msgstr "" +msgstr "사용 방법 가이드" #: ../../source/index.rst:99 msgid "Legacy example guides" -msgstr "" +msgstr "레거시 예제 가이드" #: ../../source/index.rst:108 ../../source/index.rst:112 msgid "Explanations" -msgstr "" +msgstr "설명" #: None:-1 msgid "API reference" -msgstr "" +msgstr "API 참조" #: ../../source/index.rst:137 msgid "Reference docs" -msgstr "" +msgstr "참조 문서" #: ../../source/index.rst:153 msgid "Contributor tutorials" -msgstr "" +msgstr "기여자 튜토리얼" #: ../../source/index.rst:160 msgid "Contributor how-to guides" -msgstr "" +msgstr "기여자 사용법 가이드" -#: ../../source/index.rst:173 +#: ../../source/index.rst:172 msgid "Contributor explanations" -msgstr "" +msgstr "기여자 설명" -#: ../../source/index.rst:179 +#: ../../source/index.rst:178 msgid "Contributor references" -msgstr "" +msgstr "기여자 참조" #: ../../source/index.rst:-1 msgid "" "Check out the documentation of the main Flower Framework enabling easy " "Python development for Federated Learning." -msgstr "" +msgstr "연합 학습을 위한 Python 개발을 쉽게 할 수 있는 주요 Flower 프레임워크의 " +"설명서를 확인하세요." #: ../../source/index.rst:2 msgid "Flower Framework Documentation" -msgstr "" +msgstr "플라워 프레임워크 문서" #: ../../source/index.rst:7 msgid "" "Welcome to Flower's documentation. `Flower `_ is a " "friendly federated learning framework." msgstr "" +"Flower 문서에 오신 것을 환영합니다. Flower `_는 편한 연합 " +"학습 프레임워크입니다." #: ../../source/index.rst:11 msgid "Join the Flower Community" -msgstr "" +msgstr "Flower 커뮤니티 가입하기" #: ../../source/index.rst:13 msgid "" @@ -6493,252 +8074,285 @@ msgid "" "researchers, engineers, students, professionals, academics, and other " "enthusiasts." msgstr "" +"Flower 커뮤니티는 연구원, 엔지니어, 학생, 전문가, 학자 및 기타 애호가들로 구" +"성된 편한 그룹으로 빠르게 성장하고 있습니다." #: ../../source/index.rst:15 msgid "Join us on Slack" -msgstr "" +msgstr "Slack에 가입하세요" #: ../../source/index.rst:23 msgid "Flower Framework" -msgstr "" +msgstr "Flower 프레임워크" #: ../../source/index.rst:25 msgid "" "The user guide is targeted at researchers and developers who want to use " "Flower to bring existing machine learning workloads into a federated " -"setting. One of Flower's design goals was to make this simple. Read on to" -" learn more." +"setting. One of Flower's design goals was to make this simple. Read on to " +"learn more." msgstr "" +"이 사용자 가이드는 Flower를 사용해 기존 머신 러닝 워크로드를 연합된 환경으로 " +"가져오고자 하는 연구자와 개발자를 대상으로 합니다. Flower의 설계 목표 중 하나" +"는 이를 간단하게 만드는 것이었습니다. 자세히 알아보려면 계속 읽어보세요." #: ../../source/index.rst:30 msgid "Tutorials" -msgstr "" +msgstr "튜토리얼" #: ../../source/index.rst:32 msgid "" -"A learning-oriented series of federated learning tutorials, the best " -"place to start." -msgstr "" +"A learning-oriented series of federated learning tutorials, the best place " +"to start." +msgstr "학습 중심의 연합 학습 튜토리얼 시리즈로, 시작하기에 가장 좋은 곳입니다." #: ../../source/index.rst:61 msgid "" -"QUICKSTART TUTORIALS: :doc:`PyTorch ` | " -":doc:`TensorFlow ` | :doc:`🤗 Transformers" -" ` | :doc:`JAX ` | :doc:`Pandas ` | :doc:`fastai " -"` | :doc:`PyTorch Lightning ` | :doc:`scikit-learn ` | :doc:`XGBoost ` | " -":doc:`Android ` | :doc:`iOS `" -msgstr "" +"QUICKSTART TUTORIALS: :doc:`PyTorch ` | :doc:" +"`TensorFlow ` | :doc:`🤗 Transformers " +"` | :doc:`JAX ` | :" +"doc:`Pandas ` | :doc:`fastai ` | :doc:`PyTorch Lightning ` | :doc:`scikit-learn ` | :doc:" +"`XGBoost ` | :doc:`Android ` | :doc:`iOS `" +msgstr "" +"QUICKSTART TUTORIALS: :doc:`PyTorch ` | :doc:" +"`TensorFlow ` | :doc:`🤗 Transformers " +"` | :doc:`JAX ` | :" +"doc:`Pandas ` | :doc:`fastai ` | :doc:`PyTorch Lightning ` | :doc:`scikit-learn ` | :doc:" +"`XGBoost ` | :doc:`Android ` | :doc:`iOS `" #: ../../source/index.rst:63 msgid "We also made video tutorials for PyTorch:" -msgstr "" +msgstr "파이토치용 동영상 튜토리얼도 만들었습니다:" #: ../../source/index.rst:68 msgid "And TensorFlow:" -msgstr "" +msgstr "그리고 TensorFlow도:" #: ../../source/index.rst:76 msgid "" -"Problem-oriented how-to guides show step-by-step how to achieve a " -"specific goal." +"Problem-oriented how-to guides show step-by-step how to achieve a specific " +"goal." msgstr "" +"문제 중심의 방법 가이드는 특정 목표를 달성하는 방법을 단계별로 보여줍니다." #: ../../source/index.rst:110 msgid "" "Understanding-oriented concept guides explain and discuss key topics and " "underlying ideas behind Flower and collaborative AI." msgstr "" +"이해 중심의 개념 가이드에서는 Flower와 협업 AI의 주요 주제와 기본 아이디어를 " +"설명하고 토론합니다." #: ../../source/index.rst:120 msgid "References" -msgstr "" +msgstr "참조" #: ../../source/index.rst:122 msgid "Information-oriented API reference and other reference material." -msgstr "" +msgstr "정보 지향 API 참조 및 기타 참고 자료." #: ../../source/index.rst:131::1 msgid ":py:obj:`flwr `\\" -msgstr "" +msgstr ":py:obj:`flwr `\\" #: ../../source/index.rst:131::1 flwr:1 of msgid "Flower main package." -msgstr "" +msgstr "Flower 메인 패키지." #: ../../source/index.rst:148 msgid "Contributor docs" -msgstr "" +msgstr "기여자 문서" #: ../../source/index.rst:150 msgid "" -"The Flower community welcomes contributions. The following docs are " -"intended to help along the way." +"The Flower community welcomes contributions. The following docs are intended " +"to help along the way." msgstr "" +"Flower 커뮤니티는 여러분의 기여를 환영합니다. 다음 문서는 그 과정에서 도움을 " +"드리기 위한 문서입니다." #: ../../source/ref-api-cli.rst:2 msgid "Flower CLI reference" -msgstr "" +msgstr "Flower CLI 참조" #: ../../source/ref-api-cli.rst:7 msgid "flower-simulation" -msgstr "" +msgstr "flower 시뮬레이션" #: ../../source/ref-api-cli.rst:17 msgid "flower-superlink" -msgstr "" +msgstr "flower 초연결" #: ../../source/ref-api-cli.rst:27 msgid "flower-client-app" -msgstr "" +msgstr "flower 클라이언트 앱" #: ../../source/ref-api-cli.rst:37 msgid "flower-server-app" -msgstr "" +msgstr "flower 서버 프로그램" #: ../../source/ref-api/flwr.rst:2 msgid "flwr" -msgstr "" +msgstr "flwr" -#: ../../source/ref-api/flwr.rst:25 ../../source/ref-api/flwr.server.rst:51 +#: ../../source/ref-api/flwr.client.rst:45 ../../source/ref-api/flwr.rst:25 +#: ../../source/ref-api/flwr.server.rst:49 msgid "Modules" -msgstr "" +msgstr "Modules" #: ../../source/ref-api/flwr.rst:35::1 msgid ":py:obj:`flwr.client `\\" -msgstr "" +msgstr ":py:obj:`flwr.client `\\" #: ../../source/ref-api/flwr.rst:35::1 flwr.client:1 of msgid "Flower client." -msgstr "" +msgstr "Flower 클라이언트." #: ../../source/ref-api/flwr.rst:35::1 msgid ":py:obj:`flwr.common `\\" -msgstr "" +msgstr ":py:obj:`flwr.common `\\" #: ../../source/ref-api/flwr.rst:35::1 flwr.common:1 of msgid "Common components shared between server and client." -msgstr "" +msgstr "서버와 클라이언트 간에 공유되는 공통 구성 요소입니다." #: ../../source/ref-api/flwr.rst:35::1 msgid ":py:obj:`flwr.server `\\" -msgstr "" +msgstr ":py:obj:`flwr.server `\\" #: ../../source/ref-api/flwr.rst:35::1 -#: ../../source/ref-api/flwr.server.rst:40::1 flwr.server:1 +#: ../../source/ref-api/flwr.server.rst:38::1 flwr.server:1 #: flwr.server.server.Server:1 of msgid "Flower server." -msgstr "" +msgstr "Flower 서버." #: ../../source/ref-api/flwr.rst:35::1 msgid ":py:obj:`flwr.simulation `\\" -msgstr "" +msgstr ":py:obj:`flwr.simulation `\\" #: ../../source/ref-api/flwr.rst:35::1 flwr.simulation:1 of msgid "Flower simulation." -msgstr "" +msgstr "Flower 시뮬레이션." #: ../../source/ref-api/flwr.client.rst:2 msgid "client" -msgstr "" +msgstr "클라이언트" +#: ../../source/ref-api/flwr.client.mod.rst:13 #: ../../source/ref-api/flwr.client.rst:13 #: ../../source/ref-api/flwr.common.rst:13 #: ../../source/ref-api/flwr.server.rst:13 #: ../../source/ref-api/flwr.simulation.rst:13 msgid "Functions" -msgstr "" +msgstr "함수" #: ../../source/ref-api/flwr.client.rst:25::1 msgid ":py:obj:`run_client_app `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`run_client_app `\\ \\(\\)" #: ../../source/ref-api/flwr.client.rst:25::1 #: flwr.client.supernode.app.run_client_app:1 of msgid "Run Flower client app." -msgstr "" +msgstr "Flower 클라이언트 앱을 실행합니다." #: ../../source/ref-api/flwr.client.rst:25::1 msgid ":py:obj:`run_supernode `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`run_supernode `\\ \\(\\)" #: ../../source/ref-api/flwr.client.rst:25::1 #: flwr.client.supernode.app.run_supernode:1 of msgid "Run Flower SuperNode." -msgstr "" +msgstr "Flower SuperNode를 실행합니다." #: ../../source/ref-api/flwr.client.rst:25::1 msgid "" ":py:obj:`start_client `\\ \\(\\*\\, " "server\\_address\\[\\, client\\_fn\\, ...\\]\\)" msgstr "" +":py:obj:`start_client `\\ \\(\\*\\, " +"server\\_address\\[\\, client\\_fn\\, ...\\]\\)" #: ../../source/ref-api/flwr.client.rst:25::1 #: flwr.client.app.start_client:1 of msgid "Start a Flower client node which connects to a Flower server." -msgstr "" +msgstr "Flower 서버에 연결되는 Flower 클라이언트 노드를 시작합니다." #: ../../source/ref-api/flwr.client.rst:25::1 msgid "" -":py:obj:`start_numpy_client `\\ \\(\\*\\," -" server\\_address\\, client\\)" +":py:obj:`start_numpy_client `\\ \\(\\*\\, " +"server\\_address\\, client\\)" msgstr "" +":py:obj:`start_numpy_client `\\ \\(\\*\\, " +"server\\_address\\, client\\)" #: ../../source/ref-api/flwr.client.rst:25::1 #: flwr.client.app.start_numpy_client:1 of msgid "Start a Flower NumPyClient which connects to a gRPC server." -msgstr "" +msgstr "gRPC 서버에 연결되는 Flower NumPyClient를 시작합니다." +#: ../../source/ref-api/flwr.client.mod.rst:30 #: ../../source/ref-api/flwr.client.rst:27 #: ../../source/ref-api/flwr.common.rst:32 -#: ../../source/ref-api/flwr.server.rst:28 +#: ../../source/ref-api/flwr.server.rst:26 #: ../../source/ref-api/flwr.server.strategy.rst:17 #: ../../source/ref-api/flwr.server.workflow.rst:17 msgid "Classes" -msgstr "" +msgstr "클래스" #: ../../source/ref-api/flwr.client.rst:34::1 msgid ":py:obj:`Client `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`Client `\\ \\(\\)" #: ../../source/ref-api/flwr.client.rst:34::1 #: flwr.client.client.Client:1 of msgid "Abstract base class for Flower clients." -msgstr "" +msgstr "Flower 클라이언트를 위한 추상 베이스 클래스입니다." #: ../../source/ref-api/flwr.client.rst:34::1 msgid "" -":py:obj:`ClientApp `\\ \\(\\[client\\_fn\\, " -"mods\\]\\)" +":py:obj:`ClientApp `\\ \\(\\[client\\_fn\\, mods\\]\\)" msgstr "" +":py:obj:`ClientApp `\\ \\(\\[client\\_fn\\, mods\\]\\)" #: ../../source/ref-api/flwr.client.rst:34::1 #: flwr.client.client_app.ClientApp:1 of msgid "Flower ClientApp." -msgstr "" +msgstr "Flower ClientApp." #: ../../source/ref-api/flwr.client.rst:34::1 msgid ":py:obj:`NumPyClient `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`NumPyClient `\\ \\(\\)" #: ../../source/ref-api/flwr.client.rst:34::1 #: flwr.client.numpy_client.NumPyClient:1 of msgid "Abstract base class for Flower clients using NumPy." -msgstr "" +msgstr "NumPy를 사용하는 Flower 클라이언트를 위한 추상 베이스 클래스입니다." + +#: ../../source/ref-api/flwr.client.rst:52::1 +msgid ":py:obj:`flwr.client.mod `\\" +msgstr ":py:obj:`flwr.client.mod `\\" + +#: ../../source/ref-api/flwr.client.rst:52::1 flwr.client.mod:1 of +msgid "Flower Built-in Mods." +msgstr "Flower 내장 모드." #: flwr.client.client.Client:1 flwr.client.numpy_client.NumPyClient:1 #: flwr.server.client_manager.ClientManager:1 #: flwr.server.driver.driver.Driver:1 flwr.server.strategy.strategy.Strategy:1 #: of msgid "Bases: :py:class:`~abc.ABC`" -msgstr "" +msgstr "Bases: :py:class:`~abc.ABC`" #: ../../source/ref-api/flwr.client.Client.rst:15 #: ../../source/ref-api/flwr.client.ClientApp.rst:15 #: ../../source/ref-api/flwr.client.NumPyClient.rst:15 +#: ../../source/ref-api/flwr.client.mod.LocalDpMod.rst:15 #: ../../source/ref-api/flwr.common.Array.rst:15 #: ../../source/ref-api/flwr.common.ClientMessage.rst:15 #: ../../source/ref-api/flwr.common.ConfigsRecord.rst:15 @@ -6801,78 +8415,82 @@ msgstr "" #: ../../source/ref-api/flwr.server.workflow.SecAggPlusWorkflow.rst:15 #: ../../source/ref-api/flwr.server.workflow.SecAggWorkflow.rst:15 msgid "Methods" -msgstr "" +msgstr "메소드" #: ../../source/ref-api/flwr.client.Client.rst:44::1 msgid ":py:obj:`evaluate `\\ \\(ins\\)" -msgstr "" +msgstr ":py:obj:`evaluate `\\ \\(ins\\)" #: ../../source/ref-api/flwr.client.Client.rst:44::1 #: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 #: flwr.client.client.Client.evaluate:1 #: flwr.client.numpy_client.NumPyClient.evaluate:1 of msgid "Evaluate the provided parameters using the locally held dataset." -msgstr "" +msgstr "로컬로 보유한 데이터 세트를 사용하여 제공된 매개변수를 평가합니다." #: ../../source/ref-api/flwr.client.Client.rst:44::1 msgid ":py:obj:`fit `\\ \\(ins\\)" -msgstr "" +msgstr ":py:obj:`fit `\\ \\(ins\\)" #: ../../source/ref-api/flwr.client.Client.rst:44::1 #: flwr.client.client.Client.fit:1 of msgid "Refine the provided parameters using the locally held dataset." -msgstr "" +msgstr "로컬로 보유한 데이터 세트를 사용하여 제공된 매개변수를 구체화합니다." #: ../../source/ref-api/flwr.client.Client.rst:44::1 msgid ":py:obj:`get_context `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`get_context `\\ \\(\\)" #: ../../source/ref-api/flwr.client.Client.rst:44::1 #: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 #: flwr.client.client.Client.get_context:1 #: flwr.client.numpy_client.NumPyClient.get_context:1 of msgid "Get the run context from this client." -msgstr "" +msgstr "이 클라이언트에서 실행 컨텍스트를 가져옵니다." #: ../../source/ref-api/flwr.client.Client.rst:44::1 -msgid ":py:obj:`get_parameters `\\ \\(ins\\)" +msgid "" +":py:obj:`get_parameters `\\ \\(ins\\)" msgstr "" +":py:obj:`get_parameters `\\ \\(ins\\)" #: ../../source/ref-api/flwr.client.Client.rst:44::1 #: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 #: flwr.client.client.Client.get_parameters:1 #: flwr.client.numpy_client.NumPyClient.get_parameters:1 of msgid "Return the current local model parameters." -msgstr "" +msgstr "현재 로컬 모델 파라미터를 반환합니다." #: ../../source/ref-api/flwr.client.Client.rst:44::1 -msgid ":py:obj:`get_properties `\\ \\(ins\\)" +msgid "" +":py:obj:`get_properties `\\ \\(ins\\)" msgstr "" +":py:obj:`get_properties `\\ \\(ins\\)" #: ../../source/ref-api/flwr.client.Client.rst:44::1 #: flwr.client.client.Client.get_properties:1 of msgid "Return set of client's properties." -msgstr "" +msgstr "클라이언트의 속성 집합을 반환합니다." #: ../../source/ref-api/flwr.client.Client.rst:44::1 msgid ":py:obj:`set_context `\\ \\(context\\)" -msgstr "" +msgstr ":py:obj:`set_context `\\ \\(context\\)" #: ../../source/ref-api/flwr.client.Client.rst:44::1 #: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 #: flwr.client.client.Client.set_context:1 #: flwr.client.numpy_client.NumPyClient.set_context:1 of msgid "Apply a run context to this client." -msgstr "" +msgstr "이 클라이언트에 실행 컨텍스트를 적용합니다." #: ../../source/ref-api/flwr.client.Client.rst:44::1 msgid ":py:obj:`to_client `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`to_client `\\ \\(\\)" #: ../../source/ref-api/flwr.client.Client.rst:44::1 #: flwr.client.client.Client.to_client:1 of msgid "Return client (itself)." -msgstr "" +msgstr "클라이언트(자체)를 반환합니다." #: ../../source/ref-api/flwr.client.Client.rst:46 #: ../../source/ref-api/flwr.client.NumPyClient.rst:46 @@ -6903,17 +8521,18 @@ msgstr "" #: ../../source/ref-api/flwr.server.LegacyContext.rst:25 #: ../../source/ref-api/flwr.server.ServerConfig.rst:25 msgid "Attributes" -msgstr "" +msgstr "속성" #: flwr.client.client.Client.evaluate:1::1 of msgid ":py:obj:`context `\\" -msgstr "" +msgstr ":py:obj:`context `\\" #: ../../source/ref-api/flwr.common.Parameters.rst:2 #: flwr.client.app.start_client flwr.client.app.start_numpy_client #: flwr.client.client.Client.evaluate flwr.client.client.Client.fit #: flwr.client.client.Client.get_parameters #: flwr.client.client.Client.get_properties +#: flwr.client.mod.localdp_mod.LocalDpMod #: flwr.client.numpy_client.NumPyClient.evaluate #: flwr.client.numpy_client.NumPyClient.fit #: flwr.client.numpy_client.NumPyClient.get_parameters @@ -6956,14 +8575,16 @@ msgstr "" #: flwr.simulation.app.start_simulation #: flwr.simulation.run_simulation.run_simulation of msgid "Parameters" -msgstr "" +msgstr "매개변수" #: flwr.client.client.Client.evaluate:3 of msgid "" -"The evaluation instructions containing (global) model parameters received" -" from the server and a dictionary of configuration values used to " -"customize the local evaluation process." +"The evaluation instructions containing (global) model parameters received " +"from the server and a dictionary of configuration values used to customize " +"the local evaluation process." msgstr "" +"서버에서 받은 (전역) 모델 파라미터와 로컬 평가 프로세스를 사용자 지정하는 데 " +"사용되는 구성 값 사전이 포함된 평가 지침입니다." #: flwr.client.client.Client.evaluate flwr.client.client.Client.fit #: flwr.client.client.Client.get_parameters @@ -6992,13 +8613,15 @@ msgstr "" #: flwr.server.strategy.strategy.Strategy.initialize_parameters #: flwr.simulation.app.start_simulation of msgid "Returns" -msgstr "" +msgstr "반환" #: flwr.client.client.Client.evaluate:8 of msgid "" "The evaluation result containing the loss on the local dataset and other " "details such as the number of local data examples used for evaluation." msgstr "" +"로컬 데이터 세트의 손실 및 평가에 사용된 로컬 데이터 예제 수와 같은 기타 세" +"부 정보가 포함된 평가 결과입니다." #: flwr.client.client.Client.evaluate flwr.client.client.Client.fit #: flwr.client.client.Client.get_parameters @@ -7025,49 +8648,55 @@ msgstr "" #: flwr.server.strategy.strategy.Strategy.initialize_parameters #: flwr.simulation.app.start_simulation of msgid "Return type" -msgstr "" +msgstr "반환 타입" #: flwr.client.client.Client.fit:3 of msgid "" -"The training instructions containing (global) model parameters received " -"from the server and a dictionary of configuration values used to " -"customize the local training process." +"The training instructions containing (global) model parameters received from " +"the server and a dictionary of configuration values used to customize the " +"local training process." msgstr "" +"서버에서 받은 (전역) 모델 파라미터와 로컬 학습 프로세스를 사용자 지정하는 데 " +"사용되는 구성 값 사전이 포함된 학습 지침입니다." #: flwr.client.client.Client.fit:8 of msgid "" -"The training result containing updated parameters and other details such " -"as the number of local training examples used for training." +"The training result containing updated parameters and other details such as " +"the number of local training examples used for training." msgstr "" +"업데이트된 매개변수와 훈련에 사용된 로컬 훈련 예제 수와 같은 기타 세부 정보" +"가 포함된 훈련 결과입니다." #: flwr.client.client.Client.get_parameters:3 of msgid "" "The get parameters instructions received from the server containing a " "dictionary of configuration values." msgstr "" +"구성 값 dictionary이 포함된 서버에서 받은 매개변수 가져오기 명령어입니다." #: flwr.client.client.Client.get_parameters:7 of msgid "The current local model parameters." -msgstr "" +msgstr "현재 로컬 모델 파라미터입니다." #: flwr.client.client.Client.get_properties:3 of msgid "" "The get properties instructions received from the server containing a " "dictionary of configuration values." -msgstr "" +msgstr "구성 값 dictionary이 포함된 서버로부터 받은 속성 가져오기 명령입니다." #: flwr.client.client.Client.get_properties:7 of msgid "The current client properties." -msgstr "" +msgstr "현재 클라이언트 속성입니다." #: ../../source/ref-api/flwr.client.ClientApp.rst:2 msgid "ClientApp" -msgstr "" +msgstr "클라이언트앱" -#: flwr.client.client_app.ClientApp:1 flwr.common.constant.MessageType:1 -#: flwr.common.constant.MessageTypeLegacy:1 flwr.common.context.Context:1 -#: flwr.common.message.Error:1 flwr.common.message.Message:1 -#: flwr.common.message.Metadata:1 flwr.common.record.parametersrecord.Array:1 +#: flwr.client.client_app.ClientApp:1 flwr.client.mod.localdp_mod.LocalDpMod:1 +#: flwr.common.constant.MessageType:1 flwr.common.constant.MessageTypeLegacy:1 +#: flwr.common.context.Context:1 flwr.common.message.Error:1 +#: flwr.common.message.Message:1 flwr.common.message.Metadata:1 +#: flwr.common.record.parametersrecord.Array:1 #: flwr.common.record.recordset.RecordSet:1 flwr.common.typing.ClientMessage:1 #: flwr.common.typing.DisconnectRes:1 flwr.common.typing.EvaluateIns:1 #: flwr.common.typing.EvaluateRes:1 flwr.common.typing.FitIns:1 @@ -7082,13 +8711,14 @@ msgstr "" #: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:1 #: of msgid "Bases: :py:class:`object`" -msgstr "" +msgstr "Bases: :py:class:`object`" #: flwr.client.app.start_client:41 flwr.client.app.start_numpy_client:36 #: flwr.client.client_app.ClientApp:4 #: flwr.client.client_app.ClientApp.evaluate:4 #: flwr.client.client_app.ClientApp.query:4 -#: flwr.client.client_app.ClientApp.train:4 flwr.server.app.start_server:41 +#: flwr.client.client_app.ClientApp.train:4 +#: flwr.client.mod.localdp_mod.LocalDpMod:22 flwr.server.app.start_server:41 #: flwr.server.server_app.ServerApp:4 flwr.server.server_app.ServerApp.main:4 #: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:29 #: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:22 @@ -7096,112 +8726,127 @@ msgstr "" #: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:14 #: of msgid "Examples" -msgstr "" +msgstr "예시" #: flwr.client.client_app.ClientApp:5 of msgid "" "Assuming a typical `Client` implementation named `FlowerClient`, you can " "wrap it in a `ClientApp` as follows:" msgstr "" +"일반적인 `Client` 구현의 이름이 `FlowerClient`라고 가정하면, 다음과 같이 " +"`ClientApp`으로 래핑할 수 있습니다:" #: flwr.client.client_app.ClientApp:16 of msgid "" -"If the above code is in a Python module called `client`, it can be " -"started as follows:" +"If the above code is in a Python module called `client`, it can be started " +"as follows:" msgstr "" +"위의 코드가 'client'라는 Python 모듈에 있는 경우 다음과 같이 시작할 수 있습니" +"다:" #: flwr.client.client_app.ClientApp:21 of msgid "" -"In this `client:app` example, `client` refers to the Python module " -"`client.py` in which the previous code lives in and `app` refers to the " -"global attribute `app` that points to an object of type `ClientApp`." +"In this `client:app` example, `client` refers to the Python module `client." +"py` in which the previous code lives in and `app` refers to the global " +"attribute `app` that points to an object of type `ClientApp`." msgstr "" +"이 `client:app` 예제에서 `client`는 이전 코드가 있는 Python 모듈 `client.py`" +"를 가리키고 `app`는 `ClientApp` 유형의 객체를 가리키는 전역 속성 `app`을 가리" +"킵니다." #: flwr.client.client_app.ClientApp.evaluate:1::1 of msgid ":py:obj:`evaluate `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`evaluate `\\ \\(\\)" #: flwr.client.client_app.ClientApp.evaluate:1 #: flwr.client.client_app.ClientApp.evaluate:1::1 of msgid "Return a decorator that registers the evaluate fn with the client app." -msgstr "" +msgstr "클라이언트 앱에 평가함수를 등록하는 데코레이터를 반환합니다." #: flwr.client.client_app.ClientApp.evaluate:1::1 of msgid ":py:obj:`query `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`query `\\ \\(\\)" #: flwr.client.client_app.ClientApp.evaluate:1::1 #: flwr.client.client_app.ClientApp.query:1 of msgid "Return a decorator that registers the query fn with the client app." -msgstr "" +msgstr "클라이언트 앱에 query fn을 등록하는 데코레이터를 반환합니다." #: flwr.client.client_app.ClientApp.evaluate:1::1 of msgid ":py:obj:`train `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`train `\\ \\(\\)" #: flwr.client.client_app.ClientApp.evaluate:1::1 #: flwr.client.client_app.ClientApp.train:1 of msgid "Return a decorator that registers the train fn with the client app." -msgstr "" +msgstr "클라이언트 앱에 train fn을 등록하는 데코레이터를 반환합니다." #: ../../source/ref-api/flwr.client.NumPyClient.rst:2 msgid "NumPyClient" -msgstr "" +msgstr "NumPyClient" #: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 msgid "" ":py:obj:`evaluate `\\ \\(parameters\\, " "config\\)" msgstr "" +":py:obj:`evaluate `\\ \\(parameters\\, " +"config\\)" #: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 -msgid ":py:obj:`fit `\\ \\(parameters\\, config\\)" +msgid "" +":py:obj:`fit `\\ \\(parameters\\, config\\)" msgstr "" +":py:obj:`fit `\\ \\(parameters\\, config\\)" #: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 #: flwr.client.numpy_client.NumPyClient.fit:1 of msgid "Train the provided parameters using the locally held dataset." -msgstr "" +msgstr "로컬로 보유한 데이터 세트를 사용하여 제공된 파라미터를 학습합니다." #: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 msgid ":py:obj:`get_context `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`get_context `\\ \\(\\)" #: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 msgid "" ":py:obj:`get_parameters `\\ " "\\(config\\)" msgstr "" +":py:obj:`get_parameters `\\ " +"\\(config\\)" #: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 msgid "" ":py:obj:`get_properties `\\ " "\\(config\\)" msgstr "" +":py:obj:`get_properties `\\ " +"\\(config\\)" #: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 #: flwr.client.numpy_client.NumPyClient.get_properties:1 of msgid "Return a client's set of properties." -msgstr "" +msgstr "클라이언트의 속성 집합을 반환합니다." #: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 msgid "" -":py:obj:`set_context `\\ " -"\\(context\\)" +":py:obj:`set_context `\\ \\(context\\)" msgstr "" +":py:obj:`set_context `\\ \\(context\\)" #: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 msgid ":py:obj:`to_client `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`to_client `\\ \\(\\)" #: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 #: flwr.client.numpy_client.NumPyClient.to_client:1 of msgid "Convert to object to Client type and return it." -msgstr "" +msgstr "객체를 클라이언트 유형으로 변환하고 반환합니다." #: flwr.client.numpy_client.NumPyClient.evaluate:1::1 of msgid ":py:obj:`context `\\" -msgstr "" +msgstr ":py:obj:`context `\\" #: flwr.client.numpy_client.NumPyClient.evaluate:3 #: flwr.client.numpy_client.NumPyClient.fit:3 @@ -7211,279 +8856,593 @@ msgstr "" #: flwr.server.strategy.strategy.Strategy.configure_fit:5 #: flwr.server.strategy.strategy.Strategy.evaluate:8 of msgid "The current (global) model parameters." -msgstr "" +msgstr "현재(전역) 모델 매개변수입니다." #: flwr.client.numpy_client.NumPyClient.evaluate:5 of msgid "" -"Configuration parameters which allow the server to influence evaluation " -"on the client. It can be used to communicate arbitrary values from the " -"server to the client, for example, to influence the number of examples " -"used for evaluation." +"Configuration parameters which allow the server to influence evaluation on " +"the client. It can be used to communicate arbitrary values from the server " +"to the client, for example, to influence the number of examples used for " +"evaluation." msgstr "" +"서버가 클라이언트의 평가에 영향을 줄 수 있는 구성 매개변수입니다. 예를 들어 " +"평가에 사용되는 예제 수에 영향을 주기 위해 서버에서 클라이언트로 임의의 값을 " +"전달하는 데 사용할 수 있습니다." #: flwr.client.numpy_client.NumPyClient.evaluate:11 of msgid "" "* **loss** (*float*) -- The evaluation loss of the model on the local " "dataset. * **num_examples** (*int*) -- The number of examples used for " "evaluation. * **metrics** (*Dict[str, Scalar]*) -- A dictionary mapping " -"arbitrary string keys to values of type bool, bytes, float, int, or " -"str. It can be used to communicate arbitrary values back to the server." +"arbitrary string keys to values of type bool, bytes, float, int, or str. " +"It can be used to communicate arbitrary values back to the server." msgstr "" +"* **loss** (*float*) - 로컬 데이터 세트에서 모델의 평가 손실입니다. * " +"**num_examples** (*int*) -- 평가에 사용된 예제 수입니다. * **metrics** " +"(*Dict[str, Scalar]*) -- 임의의 문자열 키를 부울, 바이트, float, int 또는 " +"str 유형의 값에 매핑하는 dictionary입니다. 임의의 값을 서버에 다시 전달하는 " +"데 사용할 수 있습니다." #: flwr.client.numpy_client.NumPyClient.evaluate:11 of msgid "" -"**loss** (*float*) -- The evaluation loss of the model on the local " -"dataset." -msgstr "" +"**loss** (*float*) -- The evaluation loss of the model on the local dataset." +msgstr "**loss** (*float*) -- 로컬 데이터 세트에서 모델의 평가 손실입니다." #: flwr.client.numpy_client.NumPyClient.evaluate:12 of msgid "**num_examples** (*int*) -- The number of examples used for evaluation." -msgstr "" +msgstr "**num_examples** (*int*) - 평가에 사용된 예제 수입니다." #: flwr.client.numpy_client.NumPyClient.evaluate:13 #: flwr.client.numpy_client.NumPyClient.fit:13 of msgid "" -"**metrics** (*Dict[str, Scalar]*) -- A dictionary mapping arbitrary " -"string keys to values of type bool, bytes, float, int, or str. It can be " -"used to communicate arbitrary values back to the server." +"**metrics** (*Dict[str, Scalar]*) -- A dictionary mapping arbitrary string " +"keys to values of type bool, bytes, float, int, or str. It can be used to " +"communicate arbitrary values back to the server." msgstr "" +"**metrics** (*Dict[str, Scalar]*) - 임의의 문자열 키를 bool, bytes, float, " +"int 또는 str 타입의 값에 매핑하는 dictionary입니다. 임의의 값을 서버에 다시 " +"전달하는 데 사용할 수 있습니다." #: flwr.client.numpy_client.NumPyClient.evaluate:19 of msgid "" -"The previous return type format (int, float, float) and the extended " -"format (int, float, float, Dict[str, Scalar]) have been deprecated and " -"removed since Flower 0.19." +"The previous return type format (int, float, float) and the extended format " +"(int, float, float, Dict[str, Scalar]) have been deprecated and removed " +"since Flower 0.19." +msgstr "" +"이전 반환 유형 형식(int, float, float)과 확장 형식(int, float, float, " +"Dict[str, Scalar])은 Flower 0.19부터 더 이상 사용되지 않으며 제거되었습니다." + +#: flwr.client.numpy_client.NumPyClient.fit:5 of +msgid "" +"Configuration parameters which allow the server to influence training on the " +"client. It can be used to communicate arbitrary values from the server to " +"the client, for example, to set the number of (local) training epochs." +msgstr "" +"서버가 클라이언트의 훈련에 영향을 줄 수 있는 구성 매개변수입니다. 예를 들어 " +"(로컬) 트레이닝 에포크 수를 설정하는 등 서버에서 클라이언트로 임의의 값을 전" +"달하는 데 사용할 수 있습니다." + +#: flwr.client.numpy_client.NumPyClient.fit:11 of +msgid "" +"* **parameters** (*NDArrays*) -- The locally updated model parameters. * " +"**num_examples** (*int*) -- The number of examples used for training. * " +"**metrics** (*Dict[str, Scalar]*) -- A dictionary mapping arbitrary string " +"keys to values of type bool, bytes, float, int, or str. It can be used to " +"communicate arbitrary values back to the server." +msgstr "" +"* **parameters** (*NDArrays*) - 로컬로 업데이트된 모델 파라미터입니다. * " +"**num_examples** (*int*) -- 학습에 사용된 예제 수입니다. * **metrics** " +"(*Dict[str, Scalar]*) - 임의의 문자열 키를 bool, bytes, float, int,또는 str " +"타입의 값에 매핑하는 dictionary입니다. 임의의 값을 서버에 다시 전달하는 데 사" +"용할 수 있습니다." + +#: flwr.client.numpy_client.NumPyClient.fit:11 of +msgid "**parameters** (*NDArrays*) -- The locally updated model parameters." +msgstr "**parameters** (*NDArrays*) - 로컬로 업데이트된 모델 파라미터입니다." + +#: flwr.client.numpy_client.NumPyClient.fit:12 of +msgid "**num_examples** (*int*) -- The number of examples used for training." +msgstr "**num_examples** (*int*) - 트레이닝에 사용된 예제 수입니다." + +#: flwr.client.numpy_client.NumPyClient.get_parameters:3 of +msgid "" +"Configuration parameters requested by the server. This can be used to tell " +"the client which parameters are needed along with some Scalar attributes." +msgstr "" +"서버에서 요청한 구성 매개변수입니다. 이는 일부 스칼라 속성과 함께 어떤 매개변" +"수가 필요한지 클라이언트에게 알려주는 데 사용할 수 있습니다." + +#: flwr.client.numpy_client.NumPyClient.get_parameters:8 of +msgid "" +"**parameters** -- The local model parameters as a list of NumPy ndarrays." +msgstr "**parameters** -- 로컬 모델 파라미터를 NumPy 배열 목록으로 표시합니다." + +#: flwr.client.numpy_client.NumPyClient.get_properties:3 of +msgid "" +"Configuration parameters requested by the server. This can be used to tell " +"the client which properties are needed along with some Scalar attributes." +msgstr "" +"서버에서 요청하는 구성 매개변수입니다. 이는 일부 스칼라 속성과 함께 어떤 속성" +"이 필요한지 클라이언트에게 알려주는 데 사용할 수 있습니다." + +#: flwr.client.numpy_client.NumPyClient.get_properties:8 of +msgid "" +"**properties** -- A dictionary mapping arbitrary string keys to values of " +"type bool, bytes, float, int, or str. It can be used to communicate " +"arbitrary property values back to the server." +msgstr "" +"**properties** -- 임의의 문자열 키를 bool, bytes, float, int 또는 str 타입의 " +"값에 매핑하는 dictionary입니다. 임의의 속성 값을 서버에 다시 전달하는 데 사용" +"할 수 있습니다." + +#: ../../source/ref-api/flwr.client.mod.rst:2 +msgid "mod" +msgstr "mod" + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +msgid "" +":py:obj:`adaptiveclipping_mod `\\ " +"\\(msg\\, ctxt\\, call\\_next\\)" +msgstr "" +":py:obj:`adaptiveclipping_mod `\\ " +"\\(msg\\, ctxt\\, call\\_next\\)" + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#: flwr.client.mod.centraldp_mods.adaptiveclipping_mod:1 of +msgid "Client-side adaptive clipping modifier." +msgstr "클라이언트 측 적응형 클리핑 수정자." + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +msgid "" +":py:obj:`fixedclipping_mod `\\ \\(msg\\, " +"ctxt\\, call\\_next\\)" +msgstr "" +":py:obj:`fixedclipping_mod `\\ \\(msg\\, " +"ctxt\\, call\\_next\\)" + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#: flwr.client.mod.centraldp_mods.fixedclipping_mod:1 of +msgid "Client-side fixed clipping modifier." +msgstr "클라이언트 측 고정 클리핑 수정자." + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +msgid ":py:obj:`make_ffn `\\ \\(ffn\\, mods\\)" +msgstr ":py:obj:`make_ffn `\\ \\(ffn\\, mods\\)" + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#: flwr.client.mod.utils.make_ffn:1 of +msgid "." +msgstr "." + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +msgid "" +":py:obj:`secagg_mod `\\ \\(msg\\, ctxt\\, " +"call\\_next\\)" +msgstr "" +":py:obj:`secagg_mod `\\ \\(msg\\, ctxt\\, " +"call\\_next\\)" + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#: flwr.client.mod.secure_aggregation.secagg_mod.secagg_mod:1 of +msgid "" +"Handle incoming message and return results, following the SecAgg protocol." +msgstr "SecAgg 프로토콜에 따라 수신 메시지를 처리하고 결과를 반환합니다." + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +msgid "" +":py:obj:`secaggplus_mod `\\ \\(msg\\, " +"ctxt\\, call\\_next\\)" +msgstr "" +":py:obj:`secaggplus_mod `\\ \\(msg\\, " +"ctxt\\, call\\_next\\)" + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#: flwr.client.mod.secure_aggregation.secaggplus_mod.secaggplus_mod:1 of +msgid "" +"Handle incoming message and return results, following the SecAgg+ protocol." +msgstr "SecAgg+ 프로토콜에 따라 수신 메시지를 처리하고 결과를 반환합니다." + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +msgid "" +":py:obj:`message_size_mod `\\ \\(msg\\, " +"ctxt\\, call\\_next\\)" +msgstr "" +":py:obj:`message_size_mod `\\ \\(msg\\, " +"ctxt\\, call\\_next\\)" + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#: flwr.client.mod.comms_mods.message_size_mod:1 of +msgid "Message size mod." +msgstr "메시지 크기 수정." + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +msgid "" +":py:obj:`parameters_size_mod `\\ " +"\\(msg\\, ctxt\\, call\\_next\\)" +msgstr "" +":py:obj:`parameters_size_mod `\\ " +"\\(msg\\, ctxt\\, call\\_next\\)" + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#: flwr.client.mod.comms_mods.parameters_size_mod:1 of +msgid "Parameters size mod." +msgstr "매개변수 크기 mod." + +#: ../../source/ref-api/flwr.client.mod.rst:35::1 +msgid "" +":py:obj:`LocalDpMod `\\ \\(clipping\\_norm\\, " +"sensitivity\\, ...\\)" +msgstr "" +":py:obj:`LocalDpMod `\\ \\(clipping\\_norm\\, " +"sensitivity\\, ...\\)" + +#: ../../source/ref-api/flwr.client.mod.rst:35::1 +#: flwr.client.mod.localdp_mod.LocalDpMod:1 of +msgid "Modifier for local differential privacy." +msgstr "로컬 차분 프라이버시를 위한 수정자." + +#: ../../source/ref-api/flwr.client.mod.LocalDpMod.rst:2 +msgid "LocalDpMod" +msgstr "LocalDpMod" + +#: flwr.client.mod.localdp_mod.LocalDpMod:3 of +msgid "" +"This mod clips the client model updates and adds noise to the params before " +"sending them to the server." +msgstr "" +"이 모드는 클라이언트 모델 업데이트를 클립하고 서버로 보내기 전에 파라미터에 " +"노이즈를 추가합니다." + +#: flwr.client.mod.centraldp_mods.adaptiveclipping_mod:12 +#: flwr.client.mod.centraldp_mods.fixedclipping_mod:10 +#: flwr.client.mod.localdp_mod.LocalDpMod:6 of +msgid "It operates on messages of type `MessageType.TRAIN`." +msgstr "이 함수는 `MessageType.TRAIN` 유형의 메시지에 대해 작동합니다." + +#: flwr.client.mod.localdp_mod.LocalDpMod:8 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:15 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:8 +#: of +msgid "The value of the clipping norm." +msgstr "클리핑 기준값입니다." + +#: flwr.client.mod.localdp_mod.LocalDpMod:10 of +msgid "The sensitivity of the client model." +msgstr "클라이언트 모델의 민감도입니다." + +#: flwr.client.mod.localdp_mod.LocalDpMod:12 of +msgid "" +"The privacy budget. Smaller value of epsilon indicates a higher level of " +"privacy protection." +msgstr "" +"개인정보 보호 예산. 엡실론 값이 작을수록 개인정보 보호 수준이 높음을 나타냅니" +"다." + +#: flwr.client.mod.localdp_mod.LocalDpMod:15 of +msgid "" +"The failure probability. The probability that the privacy mechanism fails to " +"provide the desired level of privacy. A smaller value of delta indicates a " +"stricter privacy guarantee." +msgstr "" +"실패 확률입니다. 프라이버시 메커니즘이 원하는 수준의 프라이버시를 제공하지 못" +"할 확률입니다. 델타 값이 작을수록 프라이버시가 더 엄격하게 보장된다는 의미입" +"니다." + +#: flwr.client.mod.localdp_mod.LocalDpMod:23 of +msgid "" +"Create an instance of the local DP mod and add it to the client-side mods:" +msgstr "로컬 DP 모드의 인스턴스를 생성하고 클라이언트 측 모드에 추가합니다:" + +#: ../../source/ref-api/flwr.client.mod.adaptiveclipping_mod.rst:2 +msgid "adaptiveclipping\\_mod" +msgstr "adaptiveclipping\\_mod" + +#: flwr.client.mod.centraldp_mods.adaptiveclipping_mod:3 of +msgid "" +"This mod needs to be used with the " +"DifferentialPrivacyClientSideAdaptiveClipping server-side strategy wrapper." msgstr "" +"이 모드는 서버 측 전략 래퍼인 차분 프라이버시 클라이언트 측 적응형 클리핑과 " +"함께 사용해야 합니다." -#: flwr.client.numpy_client.NumPyClient.fit:5 of +#: flwr.client.mod.centraldp_mods.adaptiveclipping_mod:6 +#: flwr.client.mod.centraldp_mods.fixedclipping_mod:6 of +msgid "The wrapper sends the clipping_norm value to the client." +msgstr "래퍼는 클라이언트에 clipping_norm 값을 전송합니다." + +#: flwr.client.mod.centraldp_mods.adaptiveclipping_mod:8 +#: flwr.client.mod.centraldp_mods.fixedclipping_mod:8 of msgid "" -"Configuration parameters which allow the server to influence training on " -"the client. It can be used to communicate arbitrary values from the " -"server to the client, for example, to set the number of (local) training " -"epochs." -msgstr "" +"This mod clips the client model updates before sending them to the server." +msgstr "이 모드는 클라이언트 모델 업데이트를 서버로 보내기 전에 클립합니다." -#: flwr.client.numpy_client.NumPyClient.fit:11 of +#: flwr.client.mod.centraldp_mods.adaptiveclipping_mod:10 of msgid "" -"* **parameters** (*NDArrays*) -- The locally updated model parameters. * " -"**num_examples** (*int*) -- The number of examples used for training. * " -"**metrics** (*Dict[str, Scalar]*) -- A dictionary mapping arbitrary " -"string keys to values of type bool, bytes, float, int, or str. It can " -"be used to communicate arbitrary values back to the server." -msgstr "" +"It also sends KEY_NORM_BIT to the server for computing the new clipping " +"value." +msgstr "또한 새 클리핑 값을 계산하기 위해 서버로 KEY_NORM_BIT을 전송합니다." -#: flwr.client.numpy_client.NumPyClient.fit:11 of -msgid "**parameters** (*NDArrays*) -- The locally updated model parameters." -msgstr "" +#: flwr.client.mod.centraldp_mods.adaptiveclipping_mod:15 +#: flwr.client.mod.centraldp_mods.fixedclipping_mod:13 +#: flwr.server.driver.driver.Driver.send_and_receive:18 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:53 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:60 +#: of +msgid "Notes" +msgstr "참고" -#: flwr.client.numpy_client.NumPyClient.fit:12 of -msgid "**num_examples** (*int*) -- The number of examples used for training." -msgstr "" +#: flwr.client.mod.centraldp_mods.adaptiveclipping_mod:16 +#: flwr.client.mod.centraldp_mods.fixedclipping_mod:14 of +msgid "Consider the order of mods when using multiple." +msgstr "여러 개를 사용할 때는 모드의 순서를 고려하세요." -#: flwr.client.numpy_client.NumPyClient.get_parameters:3 of +#: flwr.client.mod.centraldp_mods.adaptiveclipping_mod:18 of msgid "" -"Configuration parameters requested by the server. This can be used to " -"tell the client which parameters are needed along with some Scalar " -"attributes." +"Typically, adaptiveclipping_mod should be the last to operate on params." msgstr "" +"일반적으로 adaptiveclipping_mod는 매개변수에서 가장 마지막으로 작동해야 합니" +"다." -#: flwr.client.numpy_client.NumPyClient.get_parameters:8 of -msgid "**parameters** -- The local model parameters as a list of NumPy ndarrays." -msgstr "" +#: ../../source/ref-api/flwr.client.mod.fixedclipping_mod.rst:2 +msgid "fixedclipping\\_mod" +msgstr "fixedclipping\\_mod" -#: flwr.client.numpy_client.NumPyClient.get_properties:3 of +#: flwr.client.mod.centraldp_mods.fixedclipping_mod:3 of msgid "" -"Configuration parameters requested by the server. This can be used to " -"tell the client which properties are needed along with some Scalar " -"attributes." +"This mod needs to be used with the " +"DifferentialPrivacyClientSideFixedClipping server-side strategy wrapper." msgstr "" +"이 모드는 서버 측 전략 래퍼인 DifferentialPrivacyClientSideFixedClipping과 함" +"께 사용해야 합니다." -#: flwr.client.numpy_client.NumPyClient.get_properties:8 of +#: flwr.client.mod.centraldp_mods.fixedclipping_mod:16 of +msgid "Typically, fixedclipping_mod should be the last to operate on params." +msgstr "" +"일반적으로 fixedclipping_mod는 매개변수에서 가장 마지막으로 작동해야 합니다." + +#: ../../source/ref-api/flwr.client.mod.make_ffn.rst:2 +msgid "make\\_ffn" +msgstr "make\\_ffn" + +#: ../../source/ref-api/flwr.client.mod.message_size_mod.rst:2 +msgid "message\\_size\\_mod" +msgstr "message\\_size\\_mod" + +#: flwr.client.mod.comms_mods.message_size_mod:3 of +msgid "This mod logs the size in bytes of the message being transmited." +msgstr "이 모드는 전송되는 메시지의 크기를 바이트 단위로 기록합니다." + +#: ../../source/ref-api/flwr.client.mod.parameters_size_mod.rst:2 +msgid "parameters\\_size\\_mod" +msgstr "parameters\\_size\\_mod" + +#: flwr.client.mod.comms_mods.parameters_size_mod:3 of msgid "" -"**properties** -- A dictionary mapping arbitrary string keys to values of" -" type bool, bytes, float, int, or str. It can be used to communicate " -"arbitrary property values back to the server." +"This mod logs the number of parameters transmitted in the message as well as " +"their size in bytes." msgstr "" +"이 모드는 메시지에서 전송된 매개변수의 수와 그 크기를 바이트 단위로 기록합니" +"다." + +#: ../../source/ref-api/flwr.client.mod.secagg_mod.rst:2 +msgid "secagg\\_mod" +msgstr "secagg\\_mod" + +#: ../../source/ref-api/flwr.client.mod.secaggplus_mod.rst:2 +msgid "secaggplus\\_mod" +msgstr "secaggplus\\_mod" #: ../../source/ref-api/flwr.client.run_client_app.rst:2 msgid "run\\_client\\_app" -msgstr "" +msgstr "run\\_client\\_app" #: ../../source/ref-api/flwr.client.run_supernode.rst:2 msgid "run\\_supernode" -msgstr "" +msgstr "run\\_supernode" #: ../../source/ref-api/flwr.client.start_client.rst:2 msgid "start\\_client" -msgstr "" +msgstr "start\\_client" #: flwr.client.app.start_client:3 flwr.client.app.start_numpy_client:9 of msgid "" "The IPv4 or IPv6 address of the server. If the Flower server runs on the " -"same machine on port 8080, then `server_address` would be " -"`\"[::]:8080\"`." +"same machine on port 8080, then `server_address` would be `\"[::]:8080\"`." msgstr "" +"서버의 IPv4 또는 IPv6 주소입니다. Flower 서버가 포트 8080의 동일한 컴퓨터에" +"서 실행되는 경우 `서버_주소`는 `\"[::]:8080\"`이 됩니다." #: flwr.client.app.start_client:7 of msgid "A callable that instantiates a Client. (default: None)" -msgstr "" +msgstr "클라이언트를 인스턴스화하는 호출 가능 항목입니다. (기본값: None)" #: flwr.client.app.start_client:9 of msgid "" -"An implementation of the abstract base class `flwr.client.Client` " -"(default: None)" -msgstr "" +"An implementation of the abstract base class `flwr.client.Client` (default: " +"None)" +msgstr "추상 베이스 클래스 `flwr.client.Client`의 구현(기본값: None)" #: flwr.client.app.start_client:12 flwr.client.app.start_numpy_client:15 of msgid "" -"The maximum length of gRPC messages that can be exchanged with the Flower" -" server. The default should be sufficient for most models. Users who " -"train very large models might need to increase this value. Note that the " -"Flower server needs to be started with the same value (see " -"`flwr.server.start_server`), otherwise it will not know about the " -"increased limit and block larger messages." +"The maximum length of gRPC messages that can be exchanged with the Flower " +"server. The default should be sufficient for most models. Users who train " +"very large models might need to increase this value. Note that the Flower " +"server needs to be started with the same value (see `flwr.server." +"start_server`), otherwise it will not know about the increased limit and " +"block larger messages." msgstr "" +"Flower 서버와 교환할 수 있는 gRPC 메시지의 최대 길이입니다. 기본값은 대부분" +"의 모델에 충분합니다. 매우 큰 모델을 훈련하는 사용자는 이 값을 늘려야 할 수" +"도 있습니다. Flower 서버는 동일한 값으로 시작해야 하며(`flwr.server." +"start_server` 참조), 그렇지 않으면 증가된 제한을 알지 못해 더 큰 메시지를 차" +"단합니다." #: flwr.client.app.start_client:19 flwr.client.app.start_numpy_client:22 of msgid "" "The PEM-encoded root certificates as a byte string or a path string. If " -"provided, a secure connection using the certificates will be established " -"to an SSL-enabled Flower server." +"provided, a secure connection using the certificates will be established to " +"an SSL-enabled Flower server." msgstr "" +"바이트 문자열 또는 경로 문자열로 PEM 인코딩된 루트 인증서. 제공하면 인증서를 " +"사용하여 SSL이 활성화된 Flower 서버에 보안 연결이 설정됩니다." #: flwr.client.app.start_client:23 flwr.client.app.start_numpy_client:26 of msgid "" -"Starts an insecure gRPC connection when True. Enables HTTPS connection " -"when False, using system certificates if `root_certificates` is None." +"Starts an insecure gRPC connection when True. Enables HTTPS connection when " +"False, using system certificates if `root_certificates` is None." msgstr "" +"True일 경우 안전하지 않은 gRPC 연결을 시작합니다. root_certificates`가 None" +"인 경우 시스템 인증서를 사용하여 False일 때 HTTPS 연결을 활성화합니다." #: flwr.client.app.start_client:26 flwr.client.app.start_numpy_client:29 of msgid "" "Configure the transport layer. Allowed values: - 'grpc-bidi': gRPC, " -"bidirectional streaming - 'grpc-rere': gRPC, request-response " -"(experimental) - 'rest': HTTP (experimental)" +"bidirectional streaming - 'grpc-rere': gRPC, request-response (experimental) " +"- 'rest': HTTP (experimental)" msgstr "" +"전송 계층을 구성합니다. 허용되는 값입니다: - 'grpc-bidi': gRPC, 양방향 스트리" +"밍 - 'grpc-rere': gRPC, 요청-응답(실험적) - 'rest': HTTP(실험적)" #: flwr.client.app.start_client:31 of msgid "" "The maximum number of times the client will try to connect to the server " -"before giving up in case of a connection error. If set to None, there is " -"no limit to the number of tries." +"before giving up in case of a connection error. If set to None, there is no " +"limit to the number of tries." msgstr "" +"연결 오류 발생 시 클라이언트가 서버 연결을 포기하기 전에 시도하는 최대 횟수입" +"니다. None으로 설정하면 시도 횟수에 제한이 없습니다." #: flwr.client.app.start_client:35 of msgid "" -"The maximum duration before the client stops trying to connect to the " -"server in case of connection error. If set to None, there is no limit to " -"the total time." +"The maximum duration before the client stops trying to connect to the server " +"in case of connection error. If set to None, there is no limit to the total " +"time." msgstr "" +"연결 오류 발생 시 클라이언트가 서버에 대한 연결을 시도하지 않는 최대 기간입니" +"다. None으로 설정하면 총 시간에는 제한이 없습니다." #: flwr.client.app.start_client:42 flwr.client.app.start_numpy_client:37 of msgid "Starting a gRPC client with an insecure server connection:" -msgstr "" +msgstr "안전하지 않은 서버 연결로 gRPC 클라이언트 시작하기:" #: flwr.client.app.start_client:49 flwr.client.app.start_numpy_client:44 of msgid "Starting an SSL-enabled gRPC client using system certificates:" -msgstr "" +msgstr "시스템 인증서를 사용하여 SSL 사용 gRPC 클라이언트를 시작합니다:" #: flwr.client.app.start_client:60 flwr.client.app.start_numpy_client:52 of msgid "Starting an SSL-enabled gRPC client using provided certificates:" -msgstr "" +msgstr "제공된 인증서를 사용하여 SSL 지원 gRPC 클라이언트를 시작합니다:" #: ../../source/ref-api/flwr.client.start_numpy_client.rst:2 msgid "start\\_numpy\\_client" -msgstr "" +msgstr "start\\_numpy\\_client" #: flwr.client.app.start_numpy_client:5 of msgid "" -"This function is deprecated since 1.7.0. Use " -":code:`flwr.client.start_client` instead and first convert your " -":code:`NumPyClient` to type :code:`flwr.client.Client` by executing its " -":code:`to_client()` method." +"This function is deprecated since 1.7.0. Use :code:`flwr.client." +"start_client` instead and first convert your :code:`NumPyClient` to type :" +"code:`flwr.client.Client` by executing its :code:`to_client()` method." msgstr "" +"이 함수는 1.7.0부터 더 이상 사용되지 않습니다. 대신 :code:`flwr.client." +"start_client`를 사용하고 먼저 :code:`to_client()` 메서드를 실행하여 :code:" +"`NumPyClient`를 :code:`flwr.client.Client` 유형으로 변환합니다." #: flwr.client.app.start_numpy_client:13 of msgid "An implementation of the abstract base class `flwr.client.NumPyClient`." -msgstr "" +msgstr "추상 베이스 클래스 `flwr.client.NumPyClient`의 구현입니다." #: ../../source/ref-api/flwr.common.rst:2 msgid "common" -msgstr "" +msgstr "공통" #: ../../source/ref-api/flwr.common.rst:30::1 -msgid ":py:obj:`array_from_numpy `\\ \\(ndarray\\)" +msgid "" +":py:obj:`array_from_numpy `\\ \\(ndarray\\)" msgstr "" +":py:obj:`array_from_numpy `\\ \\(ndarray\\)" #: ../../source/ref-api/flwr.common.rst:30::1 #: flwr.common.record.conversion_utils.array_from_numpy:1 of msgid "Create Array from NumPy ndarray." -msgstr "" +msgstr "NumPy에서 배열을 만듭니다." #: ../../source/ref-api/flwr.common.rst:30::1 -msgid ":py:obj:`bytes_to_ndarray `\\ \\(tensor\\)" +msgid "" +":py:obj:`bytes_to_ndarray `\\ \\(tensor\\)" msgstr "" +":py:obj:`bytes_to_ndarray `\\ \\(tensor\\)" #: ../../source/ref-api/flwr.common.rst:30::1 #: flwr.common.parameter.bytes_to_ndarray:1 of msgid "Deserialize NumPy ndarray from bytes." -msgstr "" +msgstr "바이트에서 NumPy를 역직렬화합니다." #: ../../source/ref-api/flwr.common.rst:30::1 msgid "" ":py:obj:`configure `\\ \\(identifier\\[\\, " "filename\\, host\\]\\)" msgstr "" +":py:obj:`configure `\\ \\(identifier\\[\\, " +"filename\\, host\\]\\)" #: ../../source/ref-api/flwr.common.rst:30::1 #: flwr.common.logger.configure:1 of msgid "Configure logging to file and/or remote log server." -msgstr "" +msgstr "파일 및/또는 원격 로그 서버에 로깅을 구성합니다." #: ../../source/ref-api/flwr.common.rst:30::1 msgid "" ":py:obj:`event `\\ \\(event\\_type\\[\\, " "event\\_details\\]\\)" msgstr "" +":py:obj:`event `\\ \\(event\\_type\\[\\, " +"event\\_details\\]\\)" #: ../../source/ref-api/flwr.common.rst:30::1 #: flwr.common.telemetry.event:1 of msgid "Submit create_event to ThreadPoolExecutor to avoid blocking." -msgstr "" +msgstr "차단을 피하기 위해 create_event를 ThreadPoolExecutor에 제출합니다." #: ../../source/ref-api/flwr.common.rst:30::1 msgid "" ":py:obj:`log `\\ \\(level\\, msg\\, \\*args\\, " "\\*\\*kwargs\\)" msgstr "" +":py:obj:`log `\\ \\(level\\, msg\\, \\*args\\, " +"\\*\\*kwargs\\)" #: ../../source/ref-api/flwr.common.rst:30::1 logging.Logger.log:1 #: of msgid "Log 'msg % args' with the integer severity 'level'." -msgstr "" +msgstr "정수 심각도 'level'과 함께 'msg % args'를 기록합니다." #: ../../source/ref-api/flwr.common.rst:30::1 -msgid ":py:obj:`ndarray_to_bytes `\\ \\(ndarray\\)" +msgid "" +":py:obj:`ndarray_to_bytes `\\ \\(ndarray\\)" msgstr "" +":py:obj:`ndarray_to_bytes `\\ \\(ndarray\\)" #: ../../source/ref-api/flwr.common.rst:30::1 #: flwr.common.parameter.ndarray_to_bytes:1 of msgid "Serialize NumPy ndarray to bytes." -msgstr "" +msgstr "NumPy와 배열을 바이트열로 직렬화합니다." #: ../../source/ref-api/flwr.common.rst:30::1 msgid ":py:obj:`now `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`now `\\ \\(\\)" #: ../../source/ref-api/flwr.common.rst:30::1 #: flwr.common.date.now:1 of msgid "Construct a datetime from time.time() with time zone set to UTC." -msgstr "" +msgstr "표준 시간대를 UTC로 설정하여 time.time()에서 날짜 시간을 생성합니다." #: ../../source/ref-api/flwr.common.rst:30::1 msgid "" ":py:obj:`ndarrays_to_parameters `\\ " "\\(ndarrays\\)" msgstr "" +":py:obj:`ndarrays_to_parameters `\\ " +"\\(ndarrays\\)" #: ../../source/ref-api/flwr.common.rst:30::1 #: flwr.common.parameter.ndarrays_to_parameters:1 @@ -7491,1321 +9450,1485 @@ msgstr "" #: flwr.server.strategy.fedavg_android.FedAvgAndroid.ndarrays_to_parameters:1 #: of msgid "Convert NumPy ndarrays to parameters object." -msgstr "" +msgstr "NumPy 배열을 매개변수 객체로 변환합니다." #: ../../source/ref-api/flwr.common.rst:30::1 msgid "" ":py:obj:`parameters_to_ndarrays `\\ " "\\(parameters\\)" msgstr "" +":py:obj:`parameters_to_ndarrays `\\ " +"\\(parameters\\)" #: ../../source/ref-api/flwr.common.rst:30::1 #: flwr.common.parameter.parameters_to_ndarrays:1 of msgid "Convert parameters object to NumPy ndarrays." -msgstr "" +msgstr "매개변수 객체를 NumPy 배열로 변환합니다." #: ../../source/ref-api/flwr.common.rst:64::1 msgid "" -":py:obj:`Array `\\ \\(dtype\\, shape\\, stype\\, " -"data\\)" +":py:obj:`Array `\\ \\(dtype\\, shape\\, stype\\, data\\)" msgstr "" +":py:obj:`Array `\\ \\(dtype\\, shape\\, stype\\, data\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.record.parametersrecord.Array:1 of msgid "Array type." -msgstr "" +msgstr "배열 유형." #: ../../source/ref-api/flwr.common.rst:64::1 msgid "" ":py:obj:`ClientMessage `\\ " "\\(\\[get\\_properties\\_res\\, ...\\]\\)" msgstr "" +":py:obj:`ClientMessage `\\ " +"\\(\\[get\\_properties\\_res\\, ...\\]\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.typing.ClientMessage:1 of msgid "ClientMessage is a container used to hold one result message." msgstr "" +"ClientMessage는 하나의 결과 메시지를 저장하는 데 사용되는 컨테이너입니다." #: ../../source/ref-api/flwr.common.rst:64::1 msgid ":py:obj:`Code `\\ \\(value\\)" -msgstr "" +msgstr ":py:obj:`Code `\\ \\(value\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.typing.Code:1 of msgid "Client status codes." -msgstr "" +msgstr "클라이언트 상태 코드." #: ../../source/ref-api/flwr.common.rst:64::1 msgid "" ":py:obj:`ConfigsRecord `\\ " "\\(\\[configs\\_dict\\, keep\\_input\\]\\)" msgstr "" +":py:obj:`ConfigsRecord `\\ " +"\\(\\[configs\\_dict\\, keep\\_input\\]\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.record.configsrecord.ConfigsRecord:1 of msgid "Configs record." -msgstr "" +msgstr "레코드를 설정합니다." #: ../../source/ref-api/flwr.common.rst:64::1 msgid ":py:obj:`Context `\\ \\(state\\)" -msgstr "" +msgstr ":py:obj:`Context `\\ \\(state\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.context.Context:1 of msgid "State of your run." -msgstr "" +msgstr "실행 상태." #: ../../source/ref-api/flwr.common.rst:64::1 msgid ":py:obj:`DisconnectRes `\\ \\(reason\\)" -msgstr "" +msgstr ":py:obj:`DisconnectRes `\\ \\(reason\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.typing.DisconnectRes:1 of msgid "DisconnectRes message from client to server." -msgstr "" +msgstr "클라이언트에서 서버로 연결 해제 메시지를 보냅니다." #: ../../source/ref-api/flwr.common.rst:64::1 msgid "" -":py:obj:`EvaluateIns `\\ \\(parameters\\, " -"config\\)" +":py:obj:`EvaluateIns `\\ \\(parameters\\, config\\)" msgstr "" +":py:obj:`EvaluateIns `\\ \\(parameters\\, config\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.typing.EvaluateIns:1 of msgid "Evaluate instructions for a client." -msgstr "" +msgstr "클라이언트에 대한 지침을 평가합니다." #: ../../source/ref-api/flwr.common.rst:64::1 msgid "" ":py:obj:`EvaluateRes `\\ \\(status\\, loss\\, " "num\\_examples\\, metrics\\)" msgstr "" +":py:obj:`EvaluateRes `\\ \\(status\\, loss\\, " +"num\\_examples\\, metrics\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.typing.EvaluateRes:1 of msgid "Evaluate response from a client." -msgstr "" +msgstr "클라이언트의 응답을 평가합니다." #: ../../source/ref-api/flwr.common.rst:64::1 msgid ":py:obj:`EventType `\\ \\(value\\)" -msgstr "" +msgstr ":py:obj:`EventType `\\ \\(value\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.telemetry.EventType:1 of msgid "Types of telemetry events." -msgstr "" +msgstr "원격 분석 이벤트의 유형." #: ../../source/ref-api/flwr.common.rst:64::1 msgid ":py:obj:`FitIns `\\ \\(parameters\\, config\\)" -msgstr "" +msgstr ":py:obj:`FitIns `\\ \\(parameters\\, config\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.typing.FitIns:1 of msgid "Fit instructions for a client." -msgstr "" +msgstr "고객을 위한 맞춤 지침." #: ../../source/ref-api/flwr.common.rst:64::1 msgid "" ":py:obj:`FitRes `\\ \\(status\\, parameters\\, " "num\\_examples\\, metrics\\)" msgstr "" +":py:obj:`FitRes `\\ \\(status\\, parameters\\, " +"num\\_examples\\, metrics\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.typing.FitRes:1 of msgid "Fit response from a client." -msgstr "" +msgstr "클라이언트의 적합성 응답." #: ../../source/ref-api/flwr.common.rst:64::1 msgid ":py:obj:`Error `\\ \\(code\\[\\, reason\\]\\)" -msgstr "" +msgstr ":py:obj:`Error `\\ \\(code\\[\\, reason\\]\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.message.Error:1 of msgid "A dataclass that stores information about an error that occurred." -msgstr "" +msgstr "발생한 오류에 대한 정보를 저장하는 데이터 클래스입니다." #: ../../source/ref-api/flwr.common.rst:64::1 -msgid ":py:obj:`GetParametersIns `\\ \\(config\\)" +msgid "" +":py:obj:`GetParametersIns `\\ \\(config\\)" msgstr "" +":py:obj:`GetParametersIns `\\ \\(config\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.typing.GetParametersIns:1 of msgid "Parameters request for a client." -msgstr "" +msgstr "클라이언트에 대한 매개변수 요청입니다." #: ../../source/ref-api/flwr.common.rst:64::1 msgid "" ":py:obj:`GetParametersRes `\\ \\(status\\, " "parameters\\)" msgstr "" +":py:obj:`GetParametersRes `\\ \\(status\\, " +"parameters\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.typing.GetParametersRes:1 of msgid "Response when asked to return parameters." -msgstr "" +msgstr "매개변수 반환 요청 시 응답합니다." #: ../../source/ref-api/flwr.common.rst:64::1 -msgid ":py:obj:`GetPropertiesIns `\\ \\(config\\)" +msgid "" +":py:obj:`GetPropertiesIns `\\ \\(config\\)" msgstr "" +":py:obj:`GetPropertiesIns `\\ \\(config\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.typing.GetPropertiesIns:1 of msgid "Properties request for a client." -msgstr "" +msgstr "클라이언트에 대한 속성 요청." #: ../../source/ref-api/flwr.common.rst:64::1 msgid "" ":py:obj:`GetPropertiesRes `\\ \\(status\\, " "properties\\)" msgstr "" +":py:obj:`GetPropertiesRes `\\ \\(status\\, " +"properties\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.typing.GetPropertiesRes:1 of msgid "Properties response from a client." -msgstr "" +msgstr "클라이언트의 속성 응답을 확인합니다." #: ../../source/ref-api/flwr.common.rst:64::1 msgid "" ":py:obj:`Message `\\ \\(metadata\\[\\, content\\, " "error\\]\\)" msgstr "" +":py:obj:`Message `\\ \\(metadata\\[\\, content\\, " +"error\\]\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.message.Message:1 of msgid "State of your application from the viewpoint of the entity using it." -msgstr "" +msgstr "애플리케이션을 사용하는 엔티티의 관점에서 애플리케이션의 상태입니다." #: ../../source/ref-api/flwr.common.rst:64::1 msgid ":py:obj:`MessageType `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`MessageType `\\ \\(\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.constant.MessageType:1 of msgid "Message type." -msgstr "" +msgstr "메시지 타입." #: ../../source/ref-api/flwr.common.rst:64::1 msgid ":py:obj:`MessageTypeLegacy `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`MessageTypeLegacy `\\ \\(\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.constant.MessageTypeLegacy:1 of msgid "Legacy message type." -msgstr "" +msgstr "레거시 메시지 타입." #: ../../source/ref-api/flwr.common.rst:64::1 msgid "" -":py:obj:`Metadata `\\ \\(run\\_id\\, " -"message\\_id\\, src\\_node\\_id\\, ...\\)" +":py:obj:`Metadata `\\ \\(run\\_id\\, message\\_id\\, " +"src\\_node\\_id\\, ...\\)" msgstr "" +":py:obj:`Metadata `\\ \\(run\\_id\\, message\\_id\\, " +"src\\_node\\_id\\, ...\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.message.Metadata:1 of msgid "A dataclass holding metadata associated with the current message." -msgstr "" +msgstr "현재 메시지와 관련된 메타데이터를 보유한 데이터 클래스입니다." #: ../../source/ref-api/flwr.common.rst:64::1 msgid "" ":py:obj:`MetricsRecord `\\ " "\\(\\[metrics\\_dict\\, keep\\_input\\]\\)" msgstr "" +":py:obj:`MetricsRecord `\\ " +"\\(\\[metrics\\_dict\\, keep\\_input\\]\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.record.metricsrecord.MetricsRecord:1 of msgid "Metrics record." -msgstr "" +msgstr "메트릭 기록." #: ../../source/ref-api/flwr.common.rst:64::1 msgid ":py:obj:`NDArray `\\" -msgstr "" +msgstr ":py:obj:`NDArray `\\" #: ../../source/ref-api/flwr.common.rst:64::1 msgid "" -"alias of :py:class:`~numpy.ndarray`\\ [:py:obj:`~typing.Any`, " -":py:class:`~numpy.dtype`\\ [:py:obj:`~typing.Any`]]" +"alias of :py:class:`~numpy.ndarray`\\ [:py:obj:`~typing.Any`, :py:class:" +"`~numpy.dtype`\\ [:py:obj:`~typing.Any`]]" msgstr "" +"alias of :py:class:`~numpy.ndarray`\\ [:py:obj:`~typing.Any`, :py:class:" +"`~numpy.dtype`\\ [:py:obj:`~typing.Any`]]" #: ../../source/ref-api/flwr.common.rst:64::1 msgid "" ":py:obj:`Parameters `\\ \\(tensors\\, " "tensor\\_type\\)" msgstr "" +":py:obj:`Parameters `\\ \\(tensors\\, " +"tensor\\_type\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.typing.Parameters:1 of msgid "Model parameters." -msgstr "" +msgstr "모델 매개변수." #: ../../source/ref-api/flwr.common.rst:64::1 msgid "" ":py:obj:`ParametersRecord `\\ " "\\(\\[array\\_dict\\, keep\\_input\\]\\)" msgstr "" +":py:obj:`ParametersRecord `\\ " +"\\(\\[array\\_dict\\, keep\\_input\\]\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.record.parametersrecord.ParametersRecord:1 of msgid "Parameters record." -msgstr "" +msgstr "매개변수 기록." #: ../../source/ref-api/flwr.common.rst:64::1 msgid ":py:obj:`ReconnectIns `\\ \\(seconds\\)" -msgstr "" +msgstr ":py:obj:`ReconnectIns `\\ \\(seconds\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.typing.ReconnectIns:1 of msgid "ReconnectIns message from server to client." -msgstr "" +msgstr "서버에서 클라이언트로 메시지를 다시 연결합니다." #: ../../source/ref-api/flwr.common.rst:64::1 msgid "" ":py:obj:`RecordSet `\\ " "\\(\\[parameters\\_records\\, ...\\]\\)" msgstr "" +":py:obj:`RecordSet `\\ " +"\\(\\[parameters\\_records\\, ...\\]\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.record.recordset.RecordSet:1 of msgid "RecordSet stores groups of parameters, metrics and configs." -msgstr "" +msgstr "RecordSet은 매개변수, 메트릭 및 설정 그룹을 저장합니다." #: ../../source/ref-api/flwr.common.rst:64::1 msgid "" ":py:obj:`ServerMessage `\\ " "\\(\\[get\\_properties\\_ins\\, ...\\]\\)" msgstr "" +":py:obj:`ServerMessage `\\ " +"\\(\\[get\\_properties\\_ins\\, ...\\]\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.typing.ServerMessage:1 of msgid "ServerMessage is a container used to hold one instruction message." msgstr "" +"ServerMessage는 하나의 instruction 메시지를 저장하는 데 사용되는 컨테이너입니" +"다." #: ../../source/ref-api/flwr.common.rst:64::1 msgid ":py:obj:`Status `\\ \\(code\\, message\\)" -msgstr "" +msgstr ":py:obj:`Status `\\ \\(code\\, message\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.typing.Status:1 of msgid "Client status." -msgstr "" +msgstr "클라이언트 상태." #: ../../source/ref-api/flwr.common.Array.rst:2 msgid "Array" -msgstr "" +msgstr "배열" #: flwr.common.record.parametersrecord.Array:3 of msgid "" "A dataclass containing serialized data from an array-like or tensor-like " "object along with some metadata about it." msgstr "" +"배열형 또는 텐서형 객체의 직렬화된 데이터와 그에 대한 일부 메타데이터를 포함" +"하는 데이터 클래스입니다." #: flwr.common.record.parametersrecord.Array:6 of msgid "" -"A string representing the data type of the serialised object (e.g. " -"`np.float32`)" -msgstr "" +"A string representing the data type of the serialised object (e.g. `np." +"float32`)" +msgstr "직렬화된 객체의 데이터 유형을 나타내는 문자열(예: `np.float32`)" #: flwr.common.record.parametersrecord.Array:8 of msgid "" -"A list representing the shape of the unserialized array-like object. This" -" is used to deserialize the data (depending on the serialization method) " -"or simply as a metadata field." +"A list representing the shape of the unserialized array-like object. This is " +"used to deserialize the data (depending on the serialization method) or " +"simply as a metadata field." msgstr "" +"직렬화되지 않은 배열과 같은 객체의 모양을 나타내는 목록입니다. 직렬화 방법에 " +"따라 데이터를 역직렬화하는 데 사용되거나 단순히 메타데이터 필드로 사용됩니다." #: flwr.common.record.parametersrecord.Array:12 of msgid "" -"A string indicating the type of serialisation mechanism used to generate " -"the bytes in `data` from an array-like or tensor-like object." +"A string indicating the type of serialisation mechanism used to generate the " +"bytes in `data` from an array-like or tensor-like object." msgstr "" +"배열형 또는 텐서형 객체에서 `데이터`의 바이트를 생성하는 데 사용되는 직렬화 " +"메커니즘의 유형을 나타내는 문자열입니다." #: flwr.common.record.parametersrecord.Array:15 of msgid "A buffer of bytes containing the data." -msgstr "" +msgstr "데이터를 포함하는 바이트 버퍼입니다." #: ../../source/ref-api/flwr.common.Array.rst:26::1 msgid ":py:obj:`numpy `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`numpy `\\ \\(\\)" #: ../../source/ref-api/flwr.common.Array.rst:26::1 #: flwr.common.record.parametersrecord.Array.numpy:1 of msgid "Return the array as a NumPy array." -msgstr "" +msgstr "배열을 NumPy 배열로 반환합니다." #: flwr.common.record.parametersrecord.Array.numpy:1::1 of msgid ":py:obj:`dtype `\\" -msgstr "" +msgstr ":py:obj:`dtype `\\" #: flwr.common.record.parametersrecord.Array.numpy:1::1 of msgid ":py:obj:`shape `\\" -msgstr "" +msgstr ":py:obj:`shape `\\" #: flwr.common.record.parametersrecord.Array.numpy:1::1 of msgid ":py:obj:`stype `\\" -msgstr "" +msgstr ":py:obj:`stype `\\" #: flwr.common.record.parametersrecord.Array.numpy:1::1 of msgid ":py:obj:`data `\\" -msgstr "" +msgstr ":py:obj:`data `\\" #: ../../source/ref-api/flwr.common.ClientMessage.rst:2 msgid "ClientMessage" -msgstr "" +msgstr "클라이언트 메시지" #: ../../source/ref-api/flwr.common.ClientMessage.rst:31::1 msgid ":py:obj:`evaluate_res `\\" -msgstr "" +msgstr ":py:obj:`evaluate_res `\\" #: ../../source/ref-api/flwr.common.ClientMessage.rst:31::1 msgid ":py:obj:`fit_res `\\" -msgstr "" +msgstr ":py:obj:`fit_res `\\" #: ../../source/ref-api/flwr.common.ClientMessage.rst:31::1 msgid "" -":py:obj:`get_parameters_res " -"`\\" +":py:obj:`get_parameters_res `\\" msgstr "" +":py:obj:`get_parameters_res `\\" #: ../../source/ref-api/flwr.common.ClientMessage.rst:31::1 msgid "" -":py:obj:`get_properties_res " -"`\\" +":py:obj:`get_properties_res `\\" msgstr "" +":py:obj:`get_properties_res `\\" #: ../../source/ref-api/flwr.common.Code.rst:2 msgid "Code" -msgstr "" +msgstr "코드" #: flwr.common.typing.Code:1 of msgid "Bases: :py:class:`~enum.Enum`" -msgstr "" +msgstr "Bases: :py:class:`~enum.Enum`" #: ../../source/ref-api/flwr.common.Code.rst:26::1 msgid ":py:obj:`OK `\\" -msgstr "" +msgstr ":py:obj:`OK `\\" #: ../../source/ref-api/flwr.common.Code.rst:26::1 msgid "" -":py:obj:`GET_PROPERTIES_NOT_IMPLEMENTED " -"`\\" +":py:obj:`GET_PROPERTIES_NOT_IMPLEMENTED `\\" msgstr "" +":py:obj:`GET_PROPERTIES_NOT_IMPLEMENTED `\\" #: ../../source/ref-api/flwr.common.Code.rst:26::1 msgid "" -":py:obj:`GET_PARAMETERS_NOT_IMPLEMENTED " -"`\\" +":py:obj:`GET_PARAMETERS_NOT_IMPLEMENTED `\\" msgstr "" +":py:obj:`GET_PARAMETERS_NOT_IMPLEMENTED `\\" #: ../../source/ref-api/flwr.common.Code.rst:26::1 msgid ":py:obj:`FIT_NOT_IMPLEMENTED `\\" -msgstr "" +msgstr ":py:obj:`FIT_NOT_IMPLEMENTED `\\" #: ../../source/ref-api/flwr.common.Code.rst:26::1 msgid "" -":py:obj:`EVALUATE_NOT_IMPLEMENTED " -"`\\" +":py:obj:`EVALUATE_NOT_IMPLEMENTED `\\" msgstr "" +":py:obj:`EVALUATE_NOT_IMPLEMENTED `\\" #: ../../source/ref-api/flwr.common.ConfigsRecord.rst:2 msgid "ConfigsRecord" -msgstr "" +msgstr "컨피그 레코드" #: flwr.common.record.configsrecord.ConfigsRecord:1 of msgid "" -"Bases: :py:class:`~flwr.common.record.typeddict.TypedDict`\\ " -"[:py:class:`str`, :py:class:`int` | :py:class:`float` | :py:class:`str` |" -" :py:class:`bytes` | :py:class:`bool` | :py:class:`~typing.List`\\ " -"[:py:class:`int`] | :py:class:`~typing.List`\\ [:py:class:`float`] | " -":py:class:`~typing.List`\\ [:py:class:`str`] | :py:class:`~typing.List`\\" -" [:py:class:`bytes`] | :py:class:`~typing.List`\\ [:py:class:`bool`]]" +"Bases: :py:class:`~flwr.common.record.typeddict.TypedDict`\\ [:py:class:" +"`str`, :py:class:`int` | :py:class:`float` | :py:class:`str` | :py:class:" +"`bytes` | :py:class:`bool` | :py:class:`~typing.List`\\ [:py:class:`int`] | :" +"py:class:`~typing.List`\\ [:py:class:`float`] | :py:class:`~typing.List`\\ [:" +"py:class:`str`] | :py:class:`~typing.List`\\ [:py:class:`bytes`] | :py:class:" +"`~typing.List`\\ [:py:class:`bool`]]" msgstr "" +"Bases: :py:class:`~flwr.common.record.typeddict.TypedDict`\\ [:py:class:`str`" +", :py:class:`int` | :py:class:`float` | :py:class:`str` | :py:class:`bytes` |" +" :py:class:`bool` | :py:class:`~typing.List`\\ [:py:class:`int`] | " +":py:class:`~typing.List`\\ [:py:class:`float`] | :py:class:`~typing.List`\\ " +"[:py:class:`str`] | :py:class:`~typing.List`\\ [:py:class:`bytes`] | " +":py:class:`~typing.List`\\ [:py:class:`bool`]]" #: flwr.common.record.typeddict.TypedDict.clear:1::1 of msgid ":py:obj:`clear `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`clear `\\ \\(\\)" #: flwr.common.record.typeddict.TypedDict.clear:1 #: flwr.common.record.typeddict.TypedDict.clear:1::1 of msgid "Remove all items from R." -msgstr "" +msgstr "R에서 모든 항목을 제거합니다." #: flwr.common.record.typeddict.TypedDict.clear:1::1 of msgid ":py:obj:`count_bytes `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`count_bytes `\\ \\(\\)" #: flwr.common.record.configsrecord.ConfigsRecord.count_bytes:1 #: flwr.common.record.metricsrecord.MetricsRecord.count_bytes:1 #: flwr.common.record.parametersrecord.ParametersRecord.count_bytes:1 #: flwr.common.record.typeddict.TypedDict.clear:1::1 of msgid "Return number of Bytes stored in this object." -msgstr "" +msgstr "이 객체에 저장된 바이트 수를 반환합니다." #: flwr.common.record.typeddict.TypedDict.clear:1::1 of msgid ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" -msgstr "" +msgstr ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" #: flwr.common.record.typeddict.TypedDict.clear:1::1 #: flwr.common.record.typeddict.TypedDict.get:1 of msgid "d defaults to None." -msgstr "" +msgstr "d는 기본값이 None입니다." #: flwr.common.record.typeddict.TypedDict.clear:1::1 of msgid ":py:obj:`items `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`items `\\ \\(\\)" #: flwr.common.record.typeddict.TypedDict.clear:1::1 of msgid ":py:obj:`keys `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`keys `\\ \\(\\)" #: flwr.common.record.typeddict.TypedDict.clear:1::1 of msgid ":py:obj:`pop `\\ \\(k\\[\\,d\\]\\)" -msgstr "" +msgstr ":py:obj:`pop `\\ \\(k\\[\\,d\\]\\)" #: flwr.common.record.typeddict.TypedDict.clear:1::1 #: flwr.common.record.typeddict.TypedDict.pop:1 of -msgid "If key is not found, d is returned if given, otherwise KeyError is raised." -msgstr "" +msgid "" +"If key is not found, d is returned if given, otherwise KeyError is raised." +msgstr "키를 찾을 수 없으면 주어진 경우 d가 반환되고, 그렇지 않으면 KeyError가 " +"발생합니다." #: flwr.common.record.typeddict.TypedDict.clear:1::1 of msgid "" ":py:obj:`update `\\ \\(\\[E\\, " "\\]\\*\\*F\\)" msgstr "" +":py:obj:`update `\\ \\(\\[E\\, \\]\\*\\*F\\" +")" #: flwr.common.record.typeddict.TypedDict.clear:1::1 #: flwr.common.record.typeddict.TypedDict.update:1 of msgid "Update R from dict/iterable E and F." -msgstr "" +msgstr "dict/iterable E 및 F에서 R을 업데이트합니다." #: flwr.common.record.typeddict.TypedDict.clear:1::1 of msgid ":py:obj:`values `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`values `\\ \\(\\)" #: flwr.common.record.configsrecord.ConfigsRecord.count_bytes:3 of msgid "This function counts booleans as occupying 1 Byte." -msgstr "" +msgstr "이 함수는 booleans을 1바이트를 차지하는 것으로 계산합니다." #: ../../source/ref-api/flwr.common.Context.rst:2 msgid "Context" -msgstr "" +msgstr "컨텍스트" #: flwr.common.context.Context:3 of msgid "" -"Holds records added by the entity in a given run and that will stay " -"local. This means that the data it holds will never leave the system it's" -" running from. This can be used as an intermediate storage or scratchpad " -"when executing mods. It can also be used as a memory to access at " -"different points during the lifecycle of this entity (e.g. across " -"multiple rounds)" +"Holds records added by the entity in a given run and that will stay local. " +"This means that the data it holds will never leave the system it's running " +"from. This can be used as an intermediate storage or scratchpad when " +"executing mods. It can also be used as a memory to access at different " +"points during the lifecycle of this entity (e.g. across multiple rounds)" msgstr "" +"특정 실행에서 엔티티가 추가한 레코드를 보유하며 로컬에 유지됩니다. 즉, " +"저장된 데이터는 실행 중인 시스템을 벗어나지 않습니다. 모드를 실행할 때 중간 " +"저장소나 스크래치 패드로 사용할 수 있습니다. 또한 이 엔티티의 수명 주기 동안 " +"다른 시점에서 액세스하기 위한 메모리로도 사용할 수 있습니다(예: 여러 " +"라운드에 걸쳐)" #: ../../source/ref-api/flwr.common.Context.rst:28::1 msgid ":py:obj:`state `\\" -msgstr "" +msgstr ":py:obj:`state `\\" #: ../../source/ref-api/flwr.common.DisconnectRes.rst:2 msgid "DisconnectRes" -msgstr "" +msgstr "연결 해제" #: ../../source/ref-api/flwr.common.DisconnectRes.rst:28::1 msgid ":py:obj:`reason `\\" -msgstr "" +msgstr ":py:obj:`reason `\\" #: ../../source/ref-api/flwr.common.Error.rst:2 msgid "Error" -msgstr "" +msgstr "오류" #: flwr.common.message.Error:3 of msgid "An identifier for the error." -msgstr "" +msgstr "오류 식별자입니다." #: flwr.common.message.Error:5 of msgid "A reason for why the error arose (e.g. an exception stack-trace)" -msgstr "" +msgstr "오류가 발생한 이유(예: 예외 스택 추적)" #: flwr.common.Error.code:1::1 of msgid ":py:obj:`code `\\" -msgstr "" +msgstr ":py:obj:`code `\\" #: flwr.common.Error.code:1 flwr.common.Error.code:1::1 of msgid "Error code." -msgstr "" +msgstr "오류 코드." #: flwr.common.Error.code:1::1 of msgid ":py:obj:`reason `\\" -msgstr "" +msgstr ":py:obj:`reason `\\" #: flwr.common.Error.code:1::1 flwr.common.Error.reason:1 of msgid "Reason reported about the error." -msgstr "" +msgstr "오류에 대해 보고된 사유입니다." #: ../../source/ref-api/flwr.common.EvaluateIns.rst:2 msgid "EvaluateIns" -msgstr "" +msgstr "평가" #: ../../source/ref-api/flwr.common.EvaluateIns.rst:29::1 msgid ":py:obj:`parameters `\\" -msgstr "" +msgstr ":py:obj:`parameters `\\" #: ../../source/ref-api/flwr.common.EvaluateIns.rst:29::1 msgid ":py:obj:`config `\\" -msgstr "" +msgstr ":py:obj:`config `\\" #: ../../source/ref-api/flwr.common.EvaluateRes.rst:2 msgid "EvaluateRes" -msgstr "" +msgstr "EvaluateRes" #: ../../source/ref-api/flwr.common.EvaluateRes.rst:31::1 msgid ":py:obj:`status `\\" -msgstr "" +msgstr ":py:obj:`status `\\" #: ../../source/ref-api/flwr.common.EvaluateRes.rst:31::1 msgid ":py:obj:`loss `\\" -msgstr "" +msgstr ":py:obj:`loss `\\" #: ../../source/ref-api/flwr.common.EvaluateRes.rst:31::1 msgid ":py:obj:`num_examples `\\" -msgstr "" +msgstr ":py:obj:`num_examples `\\" #: ../../source/ref-api/flwr.common.EvaluateRes.rst:31::1 msgid ":py:obj:`metrics `\\" -msgstr "" +msgstr ":py:obj:`metrics `\\" #: ../../source/ref-api/flwr.common.EventType.rst:2 msgid "EventType" -msgstr "" +msgstr "이벤트 타입" #: flwr.common.telemetry.EventType:1 of msgid "Bases: :py:class:`str`, :py:class:`~enum.Enum`" -msgstr "" +msgstr "Bases: :py:class:`str`, :py:class:`~enum.Enum`" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" ":py:obj:`encode `\\ \\(\\[encoding\\, " "errors\\]\\)" msgstr "" +":py:obj:`encode `\\ \\(\\[encoding\\, errors\\]" +"\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 #: flwr.common.EventType.encode:1 of msgid "Encode the string using the codec registered for encoding." -msgstr "" +msgstr "인코딩용으로 등록된 코덱을 사용하여 문자열을 인코딩합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" ":py:obj:`replace `\\ \\(old\\, new\\[\\, " "count\\]\\)" msgstr "" +":py:obj:`replace `\\ \\(old\\, new\\[\\, " +"count\\]\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 #: flwr.common.EventType.replace:1 of msgid "Return a copy with all occurrences of substring old replaced by new." -msgstr "" +msgstr "이전 하위 문자열이 모두 새 하위 문자열로 바뀐 사본을 반환합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" -":py:obj:`split `\\ \\(\\[sep\\, " -"maxsplit\\]\\)" +":py:obj:`split `\\ \\(\\[sep\\, maxsplit\\]\\)" msgstr "" +":py:obj:`split `\\ \\(\\[sep\\, maxsplit\\]\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 #: flwr.common.EventType.rsplit:1 flwr.common.EventType.split:1 of msgid "" -"Return a list of the substrings in the string, using sep as the separator" -" string." -msgstr "" +"Return a list of the substrings in the string, using sep as the separator " +"string." +msgstr "sep를 구분 문자열로 사용하여 문자열의 하위 문자열 목록을 반환합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" -":py:obj:`rsplit `\\ \\(\\[sep\\, " -"maxsplit\\]\\)" +":py:obj:`rsplit `\\ \\(\\[sep\\, maxsplit\\]\\)" msgstr "" +":py:obj:`rsplit `\\ \\(\\[sep\\, maxsplit\\]\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid ":py:obj:`join `\\ \\(iterable\\, \\/\\)" -msgstr "" +msgstr ":py:obj:`join `\\ \\(iterable\\, \\/\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 #: flwr.common.EventType.join:1 of msgid "Concatenate any number of strings." -msgstr "" +msgstr "원하는 수의 문자열을 연결합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid ":py:obj:`capitalize `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`capitalize `\\ \\(\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 #: flwr.common.EventType.capitalize:1 of msgid "Return a capitalized version of the string." -msgstr "" +msgstr "대문자로 된 문자열을 반환합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid ":py:obj:`casefold `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`casefold `\\ \\(\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 #: flwr.common.EventType.casefold:1 of msgid "Return a version of the string suitable for caseless comparisons." -msgstr "" +msgstr "대소문자 구분 없는 비교에 적합한 문자열을 반환합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid ":py:obj:`title `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`title `\\ \\(\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 #: flwr.common.EventType.title:1 of msgid "Return a version of the string where each word is titlecased." -msgstr "" +msgstr "각 단어의 제목이 대소문자로 구분된 문자열을 반환합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" ":py:obj:`center `\\ \\(width\\[\\, " "fillchar\\]\\)" msgstr "" +":py:obj:`center `\\ \\(width\\[\\, fillchar\\]" +"\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 #: flwr.common.EventType.center:1 of msgid "Return a centered string of length width." -msgstr "" +msgstr "길이 너비의 가운데 문자열을 반환합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" ":py:obj:`count `\\ \\(sub\\[\\, start\\[\\, " "end\\]\\]\\)" msgstr "" +":py:obj:`count `\\ \\(sub\\[\\, start\\[\\, " +"end\\]\\]\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" -"Return the number of non-overlapping occurrences of substring sub in " -"string S[start:end]." -msgstr "" +"Return the number of non-overlapping occurrences of substring sub in string " +"S[start:end]." +msgstr "문자열 S[start:end]에서 하위 문자열 sub이 겹치지 않는 횟수를 반환합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" -":py:obj:`expandtabs `\\ " -"\\(\\[tabsize\\]\\)" +":py:obj:`expandtabs `\\ \\(\\[tabsize\\]\\)" msgstr "" +":py:obj:`expandtabs `\\ \\(\\[tabsize\\]\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 #: flwr.common.EventType.expandtabs:1 of msgid "Return a copy where all tab characters are expanded using spaces." -msgstr "" +msgstr "모든 탭 문자가 공백을 사용하여 확장된 사본을 반환합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" ":py:obj:`find `\\ \\(sub\\[\\, start\\[\\, " "end\\]\\]\\)" msgstr "" +":py:obj:`find `\\ \\(sub\\[\\, start\\[\\, end\\]" +"\\]\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" -"Return the lowest index in S where substring sub is found, such that sub " -"is contained within S[start:end]." -msgstr "" +"Return the lowest index in S where substring sub is found, such that sub is " +"contained within S[start:end]." +msgstr "하위 문자열 sub이 발견되는 S에서 하위가 S[start:end] 내에 포함되는 가장 낮은 " +"인덱스를 반환합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 -msgid ":py:obj:`partition `\\ \\(sep\\, \\/\\)" +msgid "" +":py:obj:`partition `\\ \\(sep\\, \\/\\)" msgstr "" +":py:obj:`partition `\\ \\(sep\\, \\/\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 #: flwr.common.EventType.partition:1 flwr.common.EventType.rpartition:1 of msgid "Partition the string into three parts using the given separator." -msgstr "" +msgstr "지정된 구분 기호를 사용하여 문자열을 세 부분으로 분할합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" ":py:obj:`index `\\ \\(sub\\[\\, start\\[\\, " "end\\]\\]\\)" msgstr "" +":py:obj:`index `\\ \\(sub\\[\\, start\\[\\, " +"end\\]\\]\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" -":py:obj:`ljust `\\ \\(width\\[\\, " -"fillchar\\]\\)" +":py:obj:`ljust `\\ \\(width\\[\\, fillchar\\]\\)" msgstr "" +":py:obj:`ljust `\\ \\(width\\[\\, fillchar\\]\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 #: flwr.common.EventType.ljust:1 of msgid "Return a left-justified string of length width." -msgstr "" +msgstr "왼쪽으로 정렬된 길이의 문자열을 반환합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid ":py:obj:`lower `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`lower `\\ \\(\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 #: flwr.common.EventType.lower:1 of msgid "Return a copy of the string converted to lowercase." -msgstr "" +msgstr "소문자로 변환된 문자열 사본을 반환합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid ":py:obj:`lstrip `\\ \\(\\[chars\\]\\)" -msgstr "" +msgstr ":py:obj:`lstrip `\\ \\(\\[chars\\]\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 #: flwr.common.EventType.lstrip:1 of msgid "Return a copy of the string with leading whitespace removed." -msgstr "" +msgstr "선행 공백이 제거된 문자열의 복사본을 반환합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" ":py:obj:`rfind `\\ \\(sub\\[\\, start\\[\\, " "end\\]\\]\\)" msgstr "" +":py:obj:`rfind `\\ \\(sub\\[\\, start\\[\\, " +"end\\]\\]\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" -"Return the highest index in S where substring sub is found, such that sub" -" is contained within S[start:end]." -msgstr "" +"Return the highest index in S where substring sub is found, such that sub is " +"contained within S[start:end]." +msgstr "부분 문자열 sub이 발견되는 곳에서 sub이 S[start:end] 내에 포함되도록 S에서 " +"가장 높은 인덱스를 반환합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" -":py:obj:`rindex `\\ \\(sub\\[\\, " -"start\\[\\, end\\]\\]\\)" +":py:obj:`rindex `\\ \\(sub\\[\\, start\\[\\, " +"end\\]\\]\\)" msgstr "" +":py:obj:`rindex `\\ \\(sub\\[\\, start\\[\\, " +"end\\]\\]\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" -":py:obj:`rjust `\\ \\(width\\[\\, " -"fillchar\\]\\)" +":py:obj:`rjust `\\ \\(width\\[\\, fillchar\\]\\)" msgstr "" +":py:obj:`rjust `\\ \\(width\\[\\, fillchar\\]\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 #: flwr.common.EventType.rjust:1 of msgid "Return a right-justified string of length width." -msgstr "" +msgstr "길이 너비의 오른쪽 정렬된 문자열을 반환합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid ":py:obj:`rstrip `\\ \\(\\[chars\\]\\)" -msgstr "" +msgstr ":py:obj:`rstrip `\\ \\(\\[chars\\]\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 #: flwr.common.EventType.rstrip:1 of msgid "Return a copy of the string with trailing whitespace removed." -msgstr "" +msgstr "후행 공백이 제거된 문자열의 복사본을 반환합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 -msgid ":py:obj:`rpartition `\\ \\(sep\\, \\/\\)" +msgid "" +":py:obj:`rpartition `\\ \\(sep\\, \\/\\)" msgstr "" +":py:obj:`rpartition `\\ \\(sep\\, \\/\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" ":py:obj:`splitlines `\\ " "\\(\\[keepends\\]\\)" msgstr "" +":py:obj:`splitlines `\\ \\(\\[keepends\\]\\" +")" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 #: flwr.common.EventType.splitlines:1 of msgid "Return a list of the lines in the string, breaking at line boundaries." -msgstr "" +msgstr "문자열의 줄 목록을 줄 경계에서 구분하여 반환합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid ":py:obj:`strip `\\ \\(\\[chars\\]\\)" -msgstr "" +msgstr ":py:obj:`strip `\\ \\(\\[chars\\]\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 #: flwr.common.EventType.strip:1 of -msgid "Return a copy of the string with leading and trailing whitespace removed." -msgstr "" +msgid "" +"Return a copy of the string with leading and trailing whitespace removed." +msgstr "선행 및 후행 공백이 제거된 문자열 사본을 반환합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid ":py:obj:`swapcase `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`swapcase `\\ \\(\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 #: flwr.common.EventType.swapcase:1 of msgid "" "Convert uppercase characters to lowercase and lowercase characters to " "uppercase." -msgstr "" +msgstr "대문자를 소문자로, 소문자를 대문자로 변환합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 -msgid ":py:obj:`translate `\\ \\(table\\, \\/\\)" +msgid "" +":py:obj:`translate `\\ \\(table\\, \\/\\)" msgstr "" +":py:obj:`translate `\\ \\(table\\, \\/\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 #: flwr.common.EventType.translate:1 of msgid "Replace each character in the string using the given translation table." -msgstr "" +msgstr "주어진 번역 테이블을 사용하여 문자열의 각 문자를 바꿉니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid ":py:obj:`upper `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`upper `\\ \\(\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 #: flwr.common.EventType.upper:1 of msgid "Return a copy of the string converted to uppercase." -msgstr "" +msgstr "Return a copy of the string converted to uppercase." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" -":py:obj:`startswith `\\ \\(prefix\\[\\," -" start\\[\\, end\\]\\]\\)" +":py:obj:`startswith `\\ \\(prefix\\[\\, " +"start\\[\\, end\\]\\]\\)" msgstr "" +":py:obj:`startswith `\\ \\(prefix\\[\\, " +"start\\[\\, end\\]\\]\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "Return True if S starts with the specified prefix, False otherwise." -msgstr "" +msgstr "S가 지정된 접두사로 시작하면 True를 반환하고, 그렇지 않으면 False를 " +"반환합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" ":py:obj:`endswith `\\ \\(suffix\\[\\, " "start\\[\\, end\\]\\]\\)" msgstr "" +":py:obj:`endswith `\\ \\(suffix\\[\\, start\\" +"[\\, end\\]\\]\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "Return True if S ends with the specified suffix, False otherwise." -msgstr "" +msgstr "S가 지정된 접미사로 끝나면 True를 반환하고 그렇지 않으면 False을 반환합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" -":py:obj:`removeprefix `\\ " -"\\(prefix\\, \\/\\)" +":py:obj:`removeprefix `\\ \\(prefix\\, " +"\\/\\)" msgstr "" +":py:obj:`removeprefix `\\ \\(prefix\\, \\" +"/\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 #: flwr.common.EventType.removeprefix:1 of msgid "Return a str with the given prefix string removed if present." -msgstr "" +msgstr "주어진 접두사 문자열이 있는 경우 제거된 문자열을 반환합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" -":py:obj:`removesuffix `\\ " -"\\(suffix\\, \\/\\)" +":py:obj:`removesuffix `\\ \\(suffix\\, " +"\\/\\)" msgstr "" +":py:obj:`removesuffix `\\ \\(suffix\\, \\" +"/\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 #: flwr.common.EventType.removesuffix:1 of msgid "Return a str with the given suffix string removed if present." -msgstr "" +msgstr "주어진 접미사 문자열이 있는 경우 제거된 문자열을 반환합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid ":py:obj:`isascii `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`isascii `\\ \\(\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 #: flwr.common.EventType.isascii:1 of msgid "Return True if all characters in the string are ASCII, False otherwise." -msgstr "" +msgstr "문자열의 모든 문자가 ASCII인 경우 True를 반환하고, 그렇지 않으면 False를 " +"반환합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid ":py:obj:`islower `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`islower `\\ \\(\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 #: flwr.common.EventType.islower:1 of msgid "Return True if the string is a lowercase string, False otherwise." -msgstr "" +msgstr "문자열이 소문자 문자열이면 True를 반환하고, 그렇지 않으면 False를 반환합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid ":py:obj:`isupper `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`isupper `\\ \\(\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 #: flwr.common.EventType.isupper:1 of msgid "Return True if the string is an uppercase string, False otherwise." -msgstr "" +msgstr "문자열이 대문자 문자열이면 True를 반환하고, 그렇지 않으면 False를 반환합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid ":py:obj:`istitle `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`istitle `\\ \\(\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 #: flwr.common.EventType.istitle:1 of msgid "Return True if the string is a title-cased string, False otherwise." -msgstr "" +msgstr "문자열이 제목 대/소문자가 구분된 문자열이면 True를 반환하고, 그렇지 않으면 " +"False를 반환합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid ":py:obj:`isspace `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`isspace `\\ \\(\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 #: flwr.common.EventType.isspace:1 of msgid "Return True if the string is a whitespace string, False otherwise." -msgstr "" +msgstr "문자열이 공백 문자열이면 True를 반환하고, 그렇지 않으면 False를 반환합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid ":py:obj:`isdecimal `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`isdecimal `\\ \\(\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 #: flwr.common.EventType.isdecimal:1 of msgid "Return True if the string is a decimal string, False otherwise." -msgstr "" +msgstr "문자열이 10진수 문자열이면 True를 반환하고, 그렇지 않으면 False를 반환합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid ":py:obj:`isdigit `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`isdigit `\\ \\(\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 #: flwr.common.EventType.isdigit:1 of msgid "Return True if the string is a digit string, False otherwise." -msgstr "" +msgstr "문자열이 숫자 문자열이면 True를 반환하고, 그렇지 않으면 False를 반환합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid ":py:obj:`isnumeric `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`isnumeric `\\ \\(\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 #: flwr.common.EventType.isnumeric:1 of msgid "Return True if the string is a numeric string, False otherwise." -msgstr "" +msgstr "문자열이 숫자 문자열이면 True를 반환하고, 그렇지 않으면 False를 반환합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid ":py:obj:`isalpha `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`isalpha `\\ \\(\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 #: flwr.common.EventType.isalpha:1 of msgid "Return True if the string is an alphabetic string, False otherwise." -msgstr "" +msgstr "문자열이 알파벳 문자열이면 True를 반환하고, 그렇지 않으면 False를 반환합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid ":py:obj:`isalnum `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`isalnum `\\ \\(\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 #: flwr.common.EventType.isalnum:1 of msgid "Return True if the string is an alpha-numeric string, False otherwise." -msgstr "" +msgstr "문자열이 영-숫자 문자열이면 True를 반환하고, 그렇지 않으면 False를 " +"반환합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid ":py:obj:`isidentifier `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`isidentifier `\\ \\(\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 #: flwr.common.EventType.isidentifier:1 of -msgid "Return True if the string is a valid Python identifier, False otherwise." -msgstr "" +msgid "" +"Return True if the string is a valid Python identifier, False otherwise." +msgstr "문자열이 유효한 파이썬 식별자인 경우 True를 반환하고, 그렇지 않으면 False를 " +"반환합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid ":py:obj:`isprintable `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`isprintable `\\ \\(\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 #: flwr.common.EventType.isprintable:1 of msgid "Return True if the string is printable, False otherwise." -msgstr "" +msgstr "문자열을 인쇄할 수 있으면 True를 반환하고, 그렇지 않으면 False를 반환합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid ":py:obj:`zfill `\\ \\(width\\, \\/\\)" -msgstr "" +msgstr ":py:obj:`zfill `\\ \\(width\\, \\/\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 #: flwr.common.EventType.zfill:1 of msgid "" -"Pad a numeric string with zeros on the left, to fill a field of the given" -" width." -msgstr "" +"Pad a numeric string with zeros on the left, to fill a field of the given " +"width." +msgstr "숫자 문자열을 왼쪽에 0으로 채워서 지정된 너비의 필드를 채웁니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" ":py:obj:`format `\\ \\(\\*args\\, " "\\*\\*kwargs\\)" msgstr "" +":py:obj:`format `\\ \\(\\*args\\, \\*\\*" +"kwargs\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 -msgid "Return a formatted version of S, using substitutions from args and kwargs." -msgstr "" +msgid "" +"Return a formatted version of S, using substitutions from args and kwargs." +msgstr "args와 kwarg의 치환을 사용하여 형식이 지정된 S를 반환합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid ":py:obj:`format_map `\\ \\(mapping\\)" -msgstr "" +msgstr ":py:obj:`format_map `\\ \\(mapping\\)" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "Return a formatted version of S, using substitutions from mapping." -msgstr "" +msgstr "매핑의 치환을 사용하여 형식이 지정된 S를 반환합니다." #: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid ":py:obj:`maketrans `\\" -msgstr "" +msgstr ":py:obj:`maketrans `\\" #: ../../source/ref-api/flwr.common.EventType.rst:163::1 #: flwr.common.EventType.maketrans:1 of msgid "Return a translation table usable for str.translate()." -msgstr "" +msgstr "str.translate()에 사용할 수 있는 번역 테이블을 반환합니다." #: flwr.common.EventType.capitalize:1::1 of msgid ":py:obj:`PING `\\" -msgstr "" +msgstr ":py:obj:`PING `\\" #: flwr.common.EventType.capitalize:1::1 of -msgid ":py:obj:`START_CLIENT_ENTER `\\" +msgid "" +":py:obj:`START_CLIENT_ENTER `\\" msgstr "" +":py:obj:`START_CLIENT_ENTER `\\" #: flwr.common.EventType.capitalize:1::1 of -msgid ":py:obj:`START_CLIENT_LEAVE `\\" +msgid "" +":py:obj:`START_CLIENT_LEAVE `\\" msgstr "" +":py:obj:`START_CLIENT_LEAVE `\\" #: flwr.common.EventType.capitalize:1::1 of -msgid ":py:obj:`START_SERVER_ENTER `\\" +msgid "" +":py:obj:`START_SERVER_ENTER `\\" msgstr "" +":py:obj:`START_SERVER_ENTER `\\" #: flwr.common.EventType.capitalize:1::1 of -msgid ":py:obj:`START_SERVER_LEAVE `\\" +msgid "" +":py:obj:`START_SERVER_LEAVE `\\" msgstr "" +":py:obj:`START_SERVER_LEAVE `\\" #: flwr.common.EventType.capitalize:1::1 of msgid "" -":py:obj:`RUN_DRIVER_API_ENTER " -"`\\" +":py:obj:`RUN_DRIVER_API_ENTER `\\" msgstr "" +":py:obj:`RUN_DRIVER_API_ENTER `\\" #: flwr.common.EventType.capitalize:1::1 of msgid "" -":py:obj:`RUN_DRIVER_API_LEAVE " -"`\\" +":py:obj:`RUN_DRIVER_API_LEAVE `\\" msgstr "" +":py:obj:`RUN_DRIVER_API_LEAVE `\\" #: flwr.common.EventType.capitalize:1::1 of msgid "" -":py:obj:`RUN_FLEET_API_ENTER " -"`\\" +":py:obj:`RUN_FLEET_API_ENTER `\\" msgstr "" +":py:obj:`RUN_FLEET_API_ENTER `\\" #: flwr.common.EventType.capitalize:1::1 of msgid "" -":py:obj:`RUN_FLEET_API_LEAVE " -"`\\" +":py:obj:`RUN_FLEET_API_LEAVE `\\" msgstr "" +":py:obj:`RUN_FLEET_API_LEAVE `\\" #: flwr.common.EventType.capitalize:1::1 of msgid "" -":py:obj:`RUN_SUPERLINK_ENTER " -"`\\" +":py:obj:`RUN_SUPERLINK_ENTER `\\" msgstr "" +":py:obj:`RUN_SUPERLINK_ENTER `\\" #: flwr.common.EventType.capitalize:1::1 of msgid "" -":py:obj:`RUN_SUPERLINK_LEAVE " -"`\\" +":py:obj:`RUN_SUPERLINK_LEAVE `\\" msgstr "" +":py:obj:`RUN_SUPERLINK_LEAVE `\\" #: flwr.common.EventType.capitalize:1::1 of msgid "" -":py:obj:`START_SIMULATION_ENTER " -"`\\" +":py:obj:`START_SIMULATION_ENTER `\\" msgstr "" +":py:obj:`START_SIMULATION_ENTER `\\" #: flwr.common.EventType.capitalize:1::1 of msgid "" -":py:obj:`START_SIMULATION_LEAVE " -"`\\" +":py:obj:`START_SIMULATION_LEAVE `\\" msgstr "" +":py:obj:`START_SIMULATION_LEAVE `\\" #: flwr.common.EventType.capitalize:1::1 of msgid ":py:obj:`DRIVER_CONNECT `\\" -msgstr "" +msgstr ":py:obj:`DRIVER_CONNECT `\\" #: flwr.common.EventType.capitalize:1::1 of msgid ":py:obj:`DRIVER_DISCONNECT `\\" +msgstr ":py:obj:`DRIVER_DISCONNECT `\\" + +#: flwr.common.EventType.capitalize:1::1 of +msgid "" +":py:obj:`START_DRIVER_ENTER `\\" msgstr "" +":py:obj:`START_DRIVER_ENTER `\\" #: flwr.common.EventType.capitalize:1::1 of -msgid ":py:obj:`START_DRIVER_ENTER `\\" +msgid "" +":py:obj:`START_DRIVER_LEAVE `\\" +msgstr "" +":py:obj:`START_DRIVER_LEAVE `\\" + +#: flwr.common.EventType.capitalize:1::1 of +msgid "" +":py:obj:`RUN_CLIENT_APP_ENTER `\\" msgstr "" +":py:obj:`RUN_CLIENT_APP_ENTER `\\" #: flwr.common.EventType.capitalize:1::1 of -msgid ":py:obj:`START_DRIVER_LEAVE `\\" +msgid "" +":py:obj:`RUN_CLIENT_APP_LEAVE `\\" msgstr "" +":py:obj:`RUN_CLIENT_APP_LEAVE `\\" #: flwr.common.EventType.capitalize:1::1 of msgid "" -":py:obj:`RUN_CLIENT_APP_ENTER " -"`\\" +":py:obj:`RUN_SERVER_APP_ENTER `\\" msgstr "" +":py:obj:`RUN_SERVER_APP_ENTER `\\" #: flwr.common.EventType.capitalize:1::1 of msgid "" -":py:obj:`RUN_CLIENT_APP_LEAVE " -"`\\" +":py:obj:`RUN_SERVER_APP_LEAVE `\\" msgstr "" +":py:obj:`RUN_SERVER_APP_LEAVE `\\" #: flwr.common.EventType.capitalize:1::1 of msgid "" -":py:obj:`RUN_SERVER_APP_ENTER " -"`\\" +":py:obj:`RUN_SUPERNODE_ENTER `\\" msgstr "" +":py:obj:`RUN_SUPERNODE_ENTER `\\" #: flwr.common.EventType.capitalize:1::1 of msgid "" -":py:obj:`RUN_SERVER_APP_LEAVE " -"`\\" +":py:obj:`RUN_SUPERNODE_LEAVE `\\" msgstr "" +":py:obj:`RUN_SUPERNODE_LEAVE `\\" #: flwr.common.EventType.capitalize:1::1 of msgid "" -":py:obj:`RUN_SUPERNODE_ENTER " -"`\\" +":py:obj:`RUN_SUPEREXEC_ENTER `\\" msgstr "" +":py:obj:`RUN_SUPEREXEC_ENTER `\\" #: flwr.common.EventType.capitalize:1::1 of msgid "" -":py:obj:`RUN_SUPERNODE_LEAVE " -"`\\" +":py:obj:`RUN_SUPEREXEC_LEAVE `\\" msgstr "" +":py:obj:`RUN_SUPEREXEC_LEAVE `\\" #: flwr.common.EventType.capitalize:3 of msgid "" "More specifically, make the first character have upper case and the rest " "lower case." -msgstr "" +msgstr "보다 구체적으로, 첫 번째 문자는 대문자로, 나머지는 소문자로 만듭니다." #: flwr.common.EventType.center:3 flwr.common.EventType.ljust:3 #: flwr.common.EventType.rjust:3 of -msgid "Padding is done using the specified fill character (default is a space)." -msgstr "" +msgid "" +"Padding is done using the specified fill character (default is a space)." +msgstr "패딩은 지정된 채우기 문자를 사용하여 수행됩니다(기본값은 공백)." #: flwr.common.EventType.count:1 of msgid "" -"Return the number of non-overlapping occurrences of substring sub in " -"string S[start:end]. Optional arguments start and end are interpreted as" -" in slice notation." +"Return the number of non-overlapping occurrences of substring sub in string " +"S[start:end]. Optional arguments start and end are interpreted as in slice " +"notation." msgstr "" +"문자열 S[start:end]에서 부분 문자열 sub의 겹치지 않는 횟수를 반환합니다. " +"선택적 인자 start와 end는 슬라이스 표기법과 같이 해석됩니다." #: flwr.common.EventType.encode:3 of msgid "encoding" -msgstr "" +msgstr "인코딩" #: flwr.common.EventType.encode:4 of msgid "The encoding in which to encode the string." -msgstr "" +msgstr "문자열을 인코딩합니다." #: flwr.common.EventType.encode:9 of msgid "errors" -msgstr "" +msgstr "오류" #: flwr.common.EventType.encode:6 of msgid "" "The error handling scheme to use for encoding errors. The default is " "'strict' meaning that encoding errors raise a UnicodeEncodeError. Other " -"possible values are 'ignore', 'replace' and 'xmlcharrefreplace' as well " -"as any other name registered with codecs.register_error that can handle " +"possible values are 'ignore', 'replace' and 'xmlcharrefreplace' as well as " +"any other name registered with codecs.register_error that can handle " "UnicodeEncodeErrors." msgstr "" +"인코딩 오류에 사용할 오류 처리 방식입니다. 기본값은 'strict'로, 인코딩 " +"오류가 발생하면 UnicodeEncodeError를 발생시킵니다. 다른 가능한 값으로는 " +"'ignore', 'replace', 'xmlcharrefreplace', 그리고 UnicodeEncodeError를 처리할 " +"수 있는 codecs.register_error에 등록된 다른 이름도 사용할 수 있습니다." #: flwr.common.EventType.endswith:1 of msgid "" "Return True if S ends with the specified suffix, False otherwise. With " -"optional start, test S beginning at that position. With optional end, " -"stop comparing S at that position. suffix can also be a tuple of strings " -"to try." +"optional start, test S beginning at that position. With optional end, stop " +"comparing S at that position. suffix can also be a tuple of strings to try." msgstr "" +"S가 지정된 접미사로 끝나면 True를 반환하고, 그렇지 않으면 False를 " +"반환합니다. 시작 옵션을 사용하면 해당 위치부터 S를 테스트합니다. end 옵션을 " +"사용하면 해당 위치에서 S 비교를 중지합니다. 접미사는 시도할 문자열의 튜플일 " +"수도 있습니다." #: flwr.common.EventType.expandtabs:3 of msgid "If tabsize is not given, a tab size of 8 characters is assumed." -msgstr "" +msgstr "탭 크기를 지정하지 않으면 크기가 8로 지정됩니다." #: flwr.common.EventType.find:1 flwr.common.EventType.index:1 of msgid "" -"Return the lowest index in S where substring sub is found, such that sub " -"is contained within S[start:end]. Optional arguments start and end are " +"Return the lowest index in S where substring sub is found, such that sub is " +"contained within S[start:end]. Optional arguments start and end are " "interpreted as in slice notation." msgstr "" +"부분 문자열 sub가 발견되는 곳의 가장 낮은 인덱스를 반환하며, sub는 " +"S[start:end] 내에 포함되어야 합니다. 선택적 인자 start와 end는 슬라이스 " +"표기법과 같이 해석됩니다." #: flwr.common.EventType.find:5 flwr.common.EventType.rfind:5 of msgid "Return -1 on failure." -msgstr "" +msgstr "실패 시 -1을 반환합니다." #: flwr.common.EventType.format:1 of msgid "" -"Return a formatted version of S, using substitutions from args and " -"kwargs. The substitutions are identified by braces ('{' and '}')." -msgstr "" +"Return a formatted version of S, using substitutions from args and kwargs. " +"The substitutions are identified by braces ('{' and '}')." +msgstr "args와 kwargs의 치환을 사용하여 형식이 지정된 S를 반환합니다. 치환은 " +"중괄호('{' 및 '}')로 식별됩니다." #: flwr.common.EventType.format_map:1 of msgid "" "Return a formatted version of S, using substitutions from mapping. The " "substitutions are identified by braces ('{' and '}')." -msgstr "" +msgstr "매핑의 치환을 사용하여 형식이 지정된 S를 반환합니다. 치환은 중괄호('{' 및 " +"'}')로 식별됩니다." #: flwr.common.EventType.index:5 flwr.common.EventType.rindex:5 of msgid "Raises ValueError when the substring is not found." -msgstr "" +msgstr "부분 문자열을 찾을 수 없을 때 ValueError를 발생시킵니다." #: flwr.common.EventType.isalnum:3 of msgid "" -"A string is alpha-numeric if all characters in the string are alpha-" -"numeric and there is at least one character in the string." -msgstr "" +"A string is alpha-numeric if all characters in the string are alpha-numeric " +"and there is at least one character in the string." +msgstr "문자열의 모든 문자가 영숫자이고 문자열에 하나 이상의 문자가 있는 경우 " +"문자열은 영-숫자입니다." #: flwr.common.EventType.isalpha:3 of msgid "" -"A string is alphabetic if all characters in the string are alphabetic and" -" there is at least one character in the string." -msgstr "" +"A string is alphabetic if all characters in the string are alphabetic and " +"there is at least one character in the string." +msgstr "문자열의 모든 문자가 알파벳이고 문자열에 하나 이상의 문자가 있는 경우 " +"문자열은 알파벳입니다." #: flwr.common.EventType.isascii:3 of msgid "" -"ASCII characters have code points in the range U+0000-U+007F. Empty " -"string is ASCII too." -msgstr "" +"ASCII characters have code points in the range U+0000-U+007F. Empty string " +"is ASCII too." +msgstr "ASCII 문자는 U+0000-U+007F 범위의 코드 포인트가 있습니다. 빈 문자열도 " +"ASCII입니다." #: flwr.common.EventType.isdecimal:3 of msgid "" -"A string is a decimal string if all characters in the string are decimal " -"and there is at least one character in the string." -msgstr "" +"A string is a decimal string if all characters in the string are decimal and " +"there is at least one character in the string." +msgstr "문자열의 모든 문자가 10진수이고 문자열에 하나 이상의 문자가 있는 경우 " +"문자열은 10진수 문자열입니다." #: flwr.common.EventType.isdigit:3 of msgid "" -"A string is a digit string if all characters in the string are digits and" -" there is at least one character in the string." -msgstr "" +"A string is a digit string if all characters in the string are digits and " +"there is at least one character in the string." +msgstr "문자열의 모든 문자가 숫자이고 문자열에 하나 이상의 문자가 있는 경우 문자열은 " +"숫자 문자열입니다." #: flwr.common.EventType.isidentifier:3 of msgid "" -"Call keyword.iskeyword(s) to test whether string s is a reserved " -"identifier, such as \"def\" or \"class\"." +"Call keyword.iskeyword(s) to test whether string s is a reserved identifier, " +"such as \"def\" or \"class\"." msgstr "" +"keyword.iskeyword(s)를 호출하여 문자열 s가 \"def\" 또는 \"class\"와 같은 " +"예약 식별자인지 테스트합니다." #: flwr.common.EventType.islower:3 of msgid "" -"A string is lowercase if all cased characters in the string are lowercase" -" and there is at least one cased character in the string." -msgstr "" +"A string is lowercase if all cased characters in the string are lowercase " +"and there is at least one cased character in the string." +msgstr "문자열이 모두 소문자이고 문자열에 문자가 하나 이상 있는 경우 문자열은 " +"소문자입니다." #: flwr.common.EventType.isnumeric:3 of msgid "" -"A string is numeric if all characters in the string are numeric and there" -" is at least one character in the string." -msgstr "" +"A string is numeric if all characters in the string are numeric and there is " +"at least one character in the string." +msgstr "문자열의 모든 문자가 숫자이고 문자열에 하나 이상의 문자가 있는 경우 문자열은 " +"숫자입니다." #: flwr.common.EventType.isprintable:3 of msgid "" -"A string is printable if all of its characters are considered printable " -"in repr() or if it is empty." -msgstr "" +"A string is printable if all of its characters are considered printable in " +"repr() or if it is empty." +msgstr "문자열은 repr()에서 모든 문자가 인쇄 가능한 것으로 간주되거나 비어 있는 경우 " +"인쇄할 수 있습니다." #: flwr.common.EventType.isspace:3 of msgid "" -"A string is whitespace if all characters in the string are whitespace and" -" there is at least one character in the string." -msgstr "" +"A string is whitespace if all characters in the string are whitespace and " +"there is at least one character in the string." +msgstr "문자열의 모든 문자가 공백이고 문자열에 하나 이상의 문자가 있는 경우 문자열은 " +"공백입니다." #: flwr.common.EventType.istitle:3 of msgid "" -"In a title-cased string, upper- and title-case characters may only follow" -" uncased characters and lowercase characters only cased ones." -msgstr "" +"In a title-cased string, upper- and title-case characters may only follow " +"uncased characters and lowercase characters only cased ones." +msgstr "제목 대/소문자 문자열에서 대문자와 제목 대문자는 대소문자만, 소문자는 " +"대문자만 뒤에 올 수 있습니다." #: flwr.common.EventType.isupper:3 of msgid "" -"A string is uppercase if all cased characters in the string are uppercase" -" and there is at least one cased character in the string." -msgstr "" +"A string is uppercase if all cased characters in the string are uppercase " +"and there is at least one cased character in the string." +msgstr "문자열의 모든 문자가 대문자이고 문자열에 문자가 하나 이상 있는 경우 문자열은 " +"대문자입니다." #: flwr.common.EventType.join:3 of msgid "" -"The string whose method is called is inserted in between each given " -"string. The result is returned as a new string." -msgstr "" +"The string whose method is called is inserted in between each given string. " +"The result is returned as a new string." +msgstr "메서드가 호출되는 문자열은 주어진 각 문자열 사이에 삽입됩니다. 결과는 새 " +"문자열로 반환됩니다." #: flwr.common.EventType.join:6 of msgid "Example: '.'.join(['ab', 'pq', 'rs']) -> 'ab.pq.rs'" -msgstr "" +msgstr "Example: '.'.join(['ab', 'pq', 'rs']) -> 'ab.pq.rs'" #: flwr.common.EventType.lstrip:3 flwr.common.EventType.rstrip:3 #: flwr.common.EventType.strip:3 of msgid "If chars is given and not None, remove characters in chars instead." -msgstr "" +msgstr "None이 아닌 문자가 지정되면 대신 문자열에서 문자를 제거합니다." #: flwr.common.EventType.maketrans:3 of msgid "" @@ -8813,10 +10936,15 @@ msgid "" "ordinals (integers) or characters to Unicode ordinals, strings or None. " "Character keys will be then converted to ordinals. If there are two " "arguments, they must be strings of equal length, and in the resulting " -"dictionary, each character in x will be mapped to the character at the " -"same position in y. If there is a third argument, it must be a string, " -"whose characters will be mapped to None in the result." +"dictionary, each character in x will be mapped to the character at the same " +"position in y. If there is a third argument, it must be a string, whose " +"characters will be mapped to None in the result." msgstr "" +"argument이 하나만 있는 경우, 유니코드 서수(정수) 또는 문자를 유니코드 서수, " +"문자열 또는 None에 매핑하는 dictionary이어야 합니다. 그러면 문자 키가 서수로 " +"변환됩니다. 인수가 두 개이면 길이가 같은 문자열이어야 하며, 결과 " +"dictionary에서 x의 각 문자는 y의 같은 위치에 있는 문자에 매핑됩니다. 세 번째 " +"인수가 있으면 문자열이어야 하며, 그 문자는 결과에서 None에 매핑됩니다." #: flwr.common.EventType.partition:3 of msgid "" @@ -8824,18 +10952,23 @@ msgid "" "found, returns a 3-tuple containing the part before the separator, the " "separator itself, and the part after it." msgstr "" +"문자열에서 구분 기호를 검색합니다. 구분 기호가 발견되면 구분 기호 앞 부분, " +"구분 기호 자체, 구분 기호 뒤 부분을 포함하는 3-tuple을 반환합니다." #: flwr.common.EventType.partition:7 of msgid "" "If the separator is not found, returns a 3-tuple containing the original " "string and two empty strings." -msgstr "" +msgstr "구분 기호를 찾을 수 없으면 원래 문자열과 빈 문자열 2개를 포함하는 3-튜플을 " +"반환합니다." #: flwr.common.EventType.removeprefix:3 of msgid "" -"If the string starts with the prefix string, return string[len(prefix):]." -" Otherwise, return a copy of the original string." +"If the string starts with the prefix string, return string[len(prefix):]. " +"Otherwise, return a copy of the original string." msgstr "" +"문자열이 접두사 문자열로 시작하면 문자열[len(prefix):]을 반환합니다. 그렇지 " +"않으면 원본 문자열의 복사본을 반환합니다." #: flwr.common.EventType.removesuffix:3 of msgid "" @@ -8843,667 +10976,727 @@ msgid "" "return string[:-len(suffix)]. Otherwise, return a copy of the original " "string." msgstr "" +"문자열이 접미사 문자열로 끝나고 해당 접미사가 비어 있지 않으면 " +"문자열[:-len(suffix)]을 반환합니다. 그렇지 않으면 원본 문자열의 복사본을 " +"반환합니다." #: flwr.common.EventType.replace:5 of msgid "count" -msgstr "" +msgstr "카운트" #: flwr.common.EventType.replace:4 of msgid "" "Maximum number of occurrences to replace. -1 (the default value) means " "replace all occurrences." -msgstr "" +msgstr "대체할 최대 발생 횟수입니다. -1(기본값)은 모든 항목을 교체한다는 의미입니다." #: flwr.common.EventType.replace:7 of msgid "" -"If the optional argument count is given, only the first count occurrences" -" are replaced." -msgstr "" +"If the optional argument count is given, only the first count occurrences " +"are replaced." +msgstr "선택적 argument 개수를 지정하면 첫 번째 개수만 바뀝니다." #: flwr.common.EventType.rfind:1 flwr.common.EventType.rindex:1 of msgid "" -"Return the highest index in S where substring sub is found, such that sub" -" is contained within S[start:end]. Optional arguments start and end are " +"Return the highest index in S where substring sub is found, such that sub is " +"contained within S[start:end]. Optional arguments start and end are " "interpreted as in slice notation." msgstr "" +"부분 문자열 sub가 발견되는 곳의 가장 높은 인덱스를 반환하며, sub는 " +"S[start:end] 내에 포함되어야 합니다. 선택적 인자 start와 end는 슬라이스 " +"표기법과 같이 해석됩니다." #: flwr.common.EventType.rpartition:3 of msgid "" -"This will search for the separator in the string, starting at the end. If" -" the separator is found, returns a 3-tuple containing the part before the" -" separator, the separator itself, and the part after it." +"This will search for the separator in the string, starting at the end. If " +"the separator is found, returns a 3-tuple containing the part before the " +"separator, the separator itself, and the part after it." msgstr "" +"그러면 문자열에서 끝 부분부터 시작하여 구분 기호를 검색합니다. 구분 기호가 " +"발견되면 구분 기호 앞 부분, 구분 기호 자체, 구분 기호 뒤 부분을 포함하는 3-" +"tuple을 반환합니다." #: flwr.common.EventType.rpartition:7 of msgid "" "If the separator is not found, returns a 3-tuple containing two empty " "strings and the original string." -msgstr "" +msgstr "구분 기호를 찾을 수 없는 경우 빈 문자열 2개와 원래 문자열을 포함하는 3-" +"tuple을 반환합니다." #: flwr.common.EventType.rsplit:7 flwr.common.EventType.split:7 of msgid "sep" -msgstr "" +msgstr "sep" #: flwr.common.EventType.rsplit:4 flwr.common.EventType.split:4 of msgid "The separator used to split the string." -msgstr "" +msgstr "문자열을 분할하는 데 사용되는 구분 기호입니다." #: flwr.common.EventType.rsplit:6 flwr.common.EventType.split:6 of msgid "" -"When set to None (the default value), will split on any whitespace " -"character (including \\\\n \\\\r \\\\t \\\\f and spaces) and will discard" -" empty strings from the result." +"When set to None (the default value), will split on any whitespace character " +"(including \\\\n \\\\r \\\\t \\\\f and spaces) and will discard empty " +"strings from the result." msgstr "" +"None(기본값)으로 설정하면 모든 공백 문자(\\\\n" +" \\\\r \\\\t \\\\f 및 공백 포함)를 분할하고 결과에서 빈 문자열을 삭제합니다." #: flwr.common.EventType.rsplit:11 flwr.common.EventType.split:11 of msgid "maxsplit" -msgstr "" +msgstr "maxsplit" #: flwr.common.EventType.rsplit:10 flwr.common.EventType.split:10 of msgid "" -"Maximum number of splits (starting from the left). -1 (the default value)" -" means no limit." -msgstr "" +"Maximum number of splits (starting from the left). -1 (the default value) " +"means no limit." +msgstr "최대 분할 횟수(왼쪽부터 시작). -1(기본값)은 제한이 없음을 의미합니다." #: flwr.common.EventType.rsplit:13 of msgid "Splitting starts at the end of the string and works to the front." -msgstr "" +msgstr "분할은 문자열 끝에서 시작하여 앞쪽으로 진행됩니다." #: flwr.common.EventType.split:13 of msgid "" "Note, str.split() is mainly useful for data that has been intentionally " -"delimited. With natural text that includes punctuation, consider using " -"the regular expression module." +"delimited. With natural text that includes punctuation, consider using the " +"regular expression module." msgstr "" +"참고로 str.split()은 주로 의도적으로 구분된 데이터에 유용합니다. 구두점이 " +"포함된 자연 텍스트의 경우 정규식 모듈을 사용하는 것이 좋습니다." #: flwr.common.EventType.splitlines:3 of msgid "" -"Line breaks are not included in the resulting list unless keepends is " -"given and true." -msgstr "" +"Line breaks are not included in the resulting list unless keepends is given " +"and true." +msgstr "줄 바꿈은 keepends가 주어지고 참이 아니면 결과 목록에 포함되지 않습니다." #: flwr.common.EventType.startswith:1 of msgid "" "Return True if S starts with the specified prefix, False otherwise. With " -"optional start, test S beginning at that position. With optional end, " -"stop comparing S at that position. prefix can also be a tuple of strings " -"to try." +"optional start, test S beginning at that position. With optional end, stop " +"comparing S at that position. prefix can also be a tuple of strings to try." msgstr "" +"S가 지정된 접두사로 시작하면 True를 반환하고, 그렇지 않으면 False를 " +"반환합니다. 시작 옵션을 사용하면 해당 위치에서 시작되는 S를 테스트합니다. " +"선택적 end를 사용하면 해당 위치에서 S 비교를 중지합니다. 접두사는 시도할 " +"문자열의 튜플일 수도 있습니다." #: flwr.common.EventType.title:3 of msgid "" -"More specifically, words start with uppercased characters and all " -"remaining cased characters have lower case." -msgstr "" +"More specifically, words start with uppercased characters and all remaining " +"cased characters have lower case." +msgstr "보다 구체적으로, 단어는 대문자로 시작하고 나머지 모든 대소문자는 소문자로 " +"표기합니다." #: flwr.common.EventType.translate:5 of msgid "table" -msgstr "" +msgstr "table" #: flwr.common.EventType.translate:4 of msgid "" -"Translation table, which must be a mapping of Unicode ordinals to Unicode" -" ordinals, strings, or None." -msgstr "" +"Translation table, which must be a mapping of Unicode ordinals to Unicode " +"ordinals, strings, or None." +msgstr "유니코드 서수를 유니코드 서수, 문자열 또는 없음으로 매핑하는 번역 " +"테이블이어야 합니다." #: flwr.common.EventType.translate:7 of msgid "" "The table must implement lookup/indexing via __getitem__, for instance a " -"dictionary or list. If this operation raises LookupError, the character " -"is left untouched. Characters mapped to None are deleted." +"dictionary or list. If this operation raises LookupError, the character is " +"left untouched. Characters mapped to None are deleted." msgstr "" +"테이블은 사전이나 목록과 같이 __getitem__을 통해 조회/색인을 구현해야 " +"합니다. 이 작업에서 LookupError가 발생하면 문자는 그대로 유지됩니다. " +"없음으로 매핑된 문자는 삭제됩니다." #: flwr.common.EventType.zfill:3 of msgid "The string is never truncated." -msgstr "" +msgstr "문자열은 잘리지 않습니다." #: ../../source/ref-api/flwr.common.FitIns.rst:2 msgid "FitIns" -msgstr "" +msgstr "FitIns" #: ../../source/ref-api/flwr.common.FitIns.rst:29::1 msgid ":py:obj:`parameters `\\" -msgstr "" +msgstr ":py:obj:`parameters `\\" #: ../../source/ref-api/flwr.common.FitIns.rst:29::1 msgid ":py:obj:`config `\\" -msgstr "" +msgstr ":py:obj:`config `\\" #: ../../source/ref-api/flwr.common.FitRes.rst:2 msgid "FitRes" -msgstr "" +msgstr "FitRes" #: ../../source/ref-api/flwr.common.FitRes.rst:31::1 msgid ":py:obj:`status `\\" -msgstr "" +msgstr ":py:obj:`status `\\" #: ../../source/ref-api/flwr.common.FitRes.rst:31::1 msgid ":py:obj:`parameters `\\" -msgstr "" +msgstr ":py:obj:`parameters `\\" #: ../../source/ref-api/flwr.common.FitRes.rst:31::1 msgid ":py:obj:`num_examples `\\" -msgstr "" +msgstr ":py:obj:`num_examples `\\" #: ../../source/ref-api/flwr.common.FitRes.rst:31::1 msgid ":py:obj:`metrics `\\" -msgstr "" +msgstr ":py:obj:`metrics `\\" #: ../../source/ref-api/flwr.common.GetParametersIns.rst:2 msgid "GetParametersIns" -msgstr "" +msgstr "GetParametersIns" #: ../../source/ref-api/flwr.common.GetParametersIns.rst:28::1 msgid ":py:obj:`config `\\" -msgstr "" +msgstr ":py:obj:`config `\\" #: ../../source/ref-api/flwr.common.GetParametersRes.rst:2 msgid "GetParametersRes" -msgstr "" +msgstr "GetParametersRes" #: ../../source/ref-api/flwr.common.GetParametersRes.rst:29::1 msgid ":py:obj:`status `\\" -msgstr "" +msgstr ":py:obj:`status `\\" #: ../../source/ref-api/flwr.common.GetParametersRes.rst:29::1 msgid ":py:obj:`parameters `\\" -msgstr "" +msgstr ":py:obj:`parameters `\\" #: ../../source/ref-api/flwr.common.GetPropertiesIns.rst:2 msgid "GetPropertiesIns" -msgstr "" +msgstr "GetPropertiesIns" #: ../../source/ref-api/flwr.common.GetPropertiesIns.rst:28::1 msgid ":py:obj:`config `\\" -msgstr "" +msgstr ":py:obj:`config `\\" #: ../../source/ref-api/flwr.common.GetPropertiesRes.rst:2 msgid "GetPropertiesRes" -msgstr "" +msgstr "GetPropertiesRes" #: ../../source/ref-api/flwr.common.GetPropertiesRes.rst:29::1 msgid ":py:obj:`status `\\" -msgstr "" +msgstr ":py:obj:`status `\\" #: ../../source/ref-api/flwr.common.GetPropertiesRes.rst:29::1 msgid ":py:obj:`properties `\\" -msgstr "" +msgstr ":py:obj:`properties `\\" #: ../../source/ref-api/flwr.common.Message.rst:2 msgid "Message" -msgstr "" +msgstr "Message" #: flwr.common.Message.content:1::1 flwr.common.Message.metadata:1 #: flwr.common.message.Message:3 of msgid "A dataclass including information about the message to be executed." -msgstr "" +msgstr "실행할 메시지에 대한 정보를 포함한 데이터 클래스입니다." #: flwr.common.message.Message:5 of msgid "" -"Holds records either sent by another entity (e.g. sent by the server-side" -" logic to a client, or vice-versa) or that will be sent to it." -msgstr "" +"Holds records either sent by another entity (e.g. sent by the server-side " +"logic to a client, or vice-versa) or that will be sent to it." +msgstr "다른 엔터티(예: 서버 측 로직이 클라이언트로 전송하거나 그 반대로 전송하는 등)" +"가 전송했거나 전송할 레코드를 보유합니다." #: flwr.common.message.Message:8 of msgid "" -"A dataclass that captures information about an error that took place when" -" processing another message." -msgstr "" +"A dataclass that captures information about an error that took place when " +"processing another message." +msgstr "다른 메시지를 처리할 때 발생한 오류에 대한 정보를 캡처하는 데이터 " +"클래스입니다." #: ../../source/ref-api/flwr.common.Message.rst:35::1 msgid "" ":py:obj:`create_error_reply `\\ " "\\(error\\[\\, ttl\\]\\)" msgstr "" +":py:obj:`create_error_reply `\\ \\(" +"error\\[\\, ttl\\]\\)" #: ../../source/ref-api/flwr.common.Message.rst:35::1 #: flwr.common.message.Message.create_error_reply:1 of msgid "Construct a reply message indicating an error happened." -msgstr "" +msgstr "오류가 발생했음을 나타내는 답장 메시지를 작성합니다." #: ../../source/ref-api/flwr.common.Message.rst:35::1 msgid "" -":py:obj:`create_reply `\\ " -"\\(content\\[\\, ttl\\]\\)" +":py:obj:`create_reply `\\ \\(content\\[\\, " +"ttl\\]\\)" msgstr "" +":py:obj:`create_reply `\\ \\(content\\[\\, " +"ttl\\]\\)" #: ../../source/ref-api/flwr.common.Message.rst:35::1 #: flwr.common.message.Message.create_reply:1 of msgid "Create a reply to this message with specified content and TTL." -msgstr "" +msgstr "지정된 콘텐츠와 TTL을 사용하여 이 메시지에 대한 답글을 작성합니다." #: ../../source/ref-api/flwr.common.Message.rst:35::1 msgid ":py:obj:`has_content `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`has_content `\\ \\(\\)" #: ../../source/ref-api/flwr.common.Message.rst:35::1 #: flwr.common.message.Message.has_content:1 of msgid "Return True if message has content, else False." -msgstr "" +msgstr "메시지에 콘텐츠가 있으면 True을 반환하고, 그렇지 않으면 False을 반환합니다." #: ../../source/ref-api/flwr.common.Message.rst:35::1 msgid ":py:obj:`has_error `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`has_error `\\ \\(\\)" #: ../../source/ref-api/flwr.common.Message.rst:35::1 #: flwr.common.message.Message.has_error:1 of msgid "Return True if message has an error, else False." -msgstr "" +msgstr "메시지에 오류가 있으면 True을 반환하고, 그렇지 않으면 False을 반환합니다." #: flwr.common.Message.content:1::1 of msgid ":py:obj:`content `\\" -msgstr "" +msgstr ":py:obj:`content `\\" #: flwr.common.Message.content:1 flwr.common.Message.content:1::1 #: of msgid "The content of this message." -msgstr "" +msgstr "이 메시지의 내용입니다." #: flwr.common.Message.content:1::1 of msgid ":py:obj:`error `\\" -msgstr "" +msgstr ":py:obj:`error `\\" #: flwr.common.Message.content:1::1 flwr.common.Message.error:1 of msgid "Error captured by this message." -msgstr "" +msgstr "이 메시지가 캡처한 오류입니다." #: flwr.common.Message.content:1::1 of msgid ":py:obj:`metadata `\\" -msgstr "" +msgstr ":py:obj:`metadata `\\" #: flwr.common.message.Message.create_error_reply:3 of msgid "The error that was encountered." -msgstr "" +msgstr "오류가 발생했습니다." #: flwr.common.message.Message.create_error_reply:5 #: flwr.common.message.Message.create_reply:9 of msgid "" -"Time-to-live for this message in seconds. If unset, it will be set based " -"on the remaining time for the received message before it expires. This " -"follows the equation: ttl = msg.meta.ttl - (reply.meta.created_at - " -"msg.meta.created_at)" +"Time-to-live for this message in seconds. If unset, it will be set based on " +"the remaining time for the received message before it expires. This follows " +"the equation: ttl = msg.meta.ttl - (reply.meta.created_at - msg.meta." +"created_at)" msgstr "" +"이 메시지의 남은 시간(초)입니다. 설정하지 않으면 수신된 메시지가 만료되기 " +"전까지 남은 시간을 기준으로 설정됩니다. 이는 다음과 같은 공식을 따릅니다: " +"ttl = msg.meta.ttl - (reply.meta.created_at - msg.meta.created_at)" #: flwr.common.message.Message.create_error_reply:5 #: flwr.common.message.Message.create_reply:9 of msgid "" -"Time-to-live for this message in seconds. If unset, it will be set based " -"on the remaining time for the received message before it expires. This " -"follows the equation:" +"Time-to-live for this message in seconds. If unset, it will be set based on " +"the remaining time for the received message before it expires. This follows " +"the equation:" msgstr "" +"이 메시지의 남은 시간(초)입니다. 설정하지 않으면 수신된 메시지가 만료되기 " +"전까지 남은 시간을 기준으로 설정됩니다. 이는 다음 공식을 따릅니다:" #: flwr.common.message.Message.create_error_reply:9 #: flwr.common.message.Message.create_reply:13 of msgid "ttl = msg.meta.ttl - (reply.meta.created_at - msg.meta.created_at)" -msgstr "" +msgstr "ttl = msg.meta.ttl - (reply.meta.created_at - msg.meta.created_at)" #: flwr.common.message.Message.create_reply:3 of msgid "" -"The method generates a new `Message` as a reply to this message. It " -"inherits 'run_id', 'src_node_id', 'dst_node_id', and 'message_type' from " -"this message and sets 'reply_to_message' to the ID of this message." +"The method generates a new `Message` as a reply to this message. It inherits " +"'run_id', 'src_node_id', 'dst_node_id', and 'message_type' from this message " +"and sets 'reply_to_message' to the ID of this message." msgstr "" +"이 메서드는 이 메시지에 대한 응답으로 새로운 '메시지'를 생성합니다. 이 " +"메시지에서 'run_id', 'src_node_id', 'dst_node_id', 'message_type'을 상속하고 " +"'reply_to_message'를 이 메시지의 ID로 설정합니다." #: flwr.common.message.Message.create_reply:7 of msgid "The content for the reply message." -msgstr "" +msgstr "답장 메시지의 콘텐츠입니다." #: flwr.common.message.Message.create_reply:16 of msgid "A new `Message` instance representing the reply." -msgstr "" +msgstr "답장을 나타내는 새로운 `메시지` 인스턴스입니다." #: ../../source/ref-api/flwr.common.MessageType.rst:2 msgid "MessageType" -msgstr "" +msgstr "MessageType" #: ../../source/ref-api/flwr.common.MessageType.rst:30::1 msgid ":py:obj:`EVALUATE `\\" -msgstr "" +msgstr ":py:obj:`EVALUATE `\\" #: ../../source/ref-api/flwr.common.MessageType.rst:30::1 msgid ":py:obj:`QUERY `\\" -msgstr "" +msgstr ":py:obj:`QUERY `\\" #: ../../source/ref-api/flwr.common.MessageType.rst:30::1 msgid ":py:obj:`TRAIN `\\" -msgstr "" +msgstr ":py:obj:`TRAIN `\\" #: ../../source/ref-api/flwr.common.MessageTypeLegacy.rst:2 msgid "MessageTypeLegacy" -msgstr "" +msgstr "MessageTypeLegacy" #: ../../source/ref-api/flwr.common.MessageTypeLegacy.rst:29::1 -msgid ":py:obj:`GET_PARAMETERS `\\" +msgid "" +":py:obj:`GET_PARAMETERS `\\" msgstr "" +":py:obj:`GET_PARAMETERS `\\" #: ../../source/ref-api/flwr.common.MessageTypeLegacy.rst:29::1 -msgid ":py:obj:`GET_PROPERTIES `\\" +msgid "" +":py:obj:`GET_PROPERTIES `\\" msgstr "" +":py:obj:`GET_PROPERTIES `\\" #: flwr.common.Metadata.created_at:1::1 #: flwr.common.Metadata.run_id:1 flwr.common.message.Metadata:3 of msgid "An identifier for the current run." -msgstr "" +msgstr "현재 실행에 대한 식별자입니다." #: flwr.common.Metadata.created_at:1::1 #: flwr.common.Metadata.message_id:1 flwr.common.message.Metadata:5 of msgid "An identifier for the current message." -msgstr "" +msgstr "현재 메시지의 식별자입니다." #: flwr.common.Metadata.created_at:1::1 #: flwr.common.Metadata.src_node_id:1 flwr.common.message.Metadata:7 of msgid "An identifier for the node sending this message." -msgstr "" +msgstr "이 메시지를 보내는 노드의 식별자입니다." #: flwr.common.Metadata.created_at:1::1 #: flwr.common.Metadata.dst_node_id:1 flwr.common.message.Metadata:9 of msgid "An identifier for the node receiving this message." -msgstr "" +msgstr "이 메시지를 수신하는 노드의 식별자입니다." #: flwr.common.Metadata.created_at:1::1 #: flwr.common.Metadata.reply_to_message:1 flwr.common.message.Metadata:11 of msgid "An identifier for the message this message replies to." -msgstr "" +msgstr "이 메시지가 회신하는 메시지의 식별자입니다." #: flwr.common.message.Metadata:13 of msgid "" -"An identifier for grouping messages. In some settings, this is used as " -"the FL round." -msgstr "" +"An identifier for grouping messages. In some settings, this is used as the " +"FL round." +msgstr "메시지를 그룹화하기 위한 식별자입니다. 일부 설정에서는 FL 라운드로 " +"사용됩니다." #: flwr.common.message.Metadata:16 of msgid "Time-to-live for this message in seconds." -msgstr "" +msgstr "이 메시지의 유효 시간(초)입니다." #: flwr.common.Metadata.created_at:1::1 #: flwr.common.Metadata.message_type:1 flwr.common.message.Metadata:18 of msgid "A string that encodes the action to be executed on the receiving end." -msgstr "" +msgstr "수신 측에서 실행할 작업을 인코딩하는 문자열입니다." #: flwr.common.message.Metadata:21 of msgid "" -"An identifier that can be used when loading a particular data partition " -"for a ClientApp. Making use of this identifier is more relevant when " -"conducting simulations." +"An identifier that can be used when loading a particular data partition for " +"a ClientApp. Making use of this identifier is more relevant when conducting " +"simulations." msgstr "" +"클라이언트 앱의 특정 데이터 파티션을 로드할 때 사용할 수 있는 식별자입니다. " +"시뮬레이션을 수행할 때 이 식별자를 사용하는 것이 더 적절합니다." #: flwr.common.Metadata.created_at:1::1 of msgid ":py:obj:`created_at `\\" -msgstr "" +msgstr ":py:obj:`created_at `\\" #: flwr.common.Metadata.created_at:1 #: flwr.common.Metadata.created_at:1::1 of msgid "Unix timestamp when the message was created." -msgstr "" +msgstr "메시지가 생성된 때의 Unix timestamp입니다." #: flwr.common.Metadata.created_at:1::1 of msgid ":py:obj:`dst_node_id `\\" -msgstr "" +msgstr ":py:obj:`dst_node_id `\\" #: flwr.common.Metadata.created_at:1::1 of msgid ":py:obj:`group_id `\\" -msgstr "" +msgstr ":py:obj:`group_id `\\" #: flwr.common.Metadata.created_at:1::1 #: flwr.common.Metadata.group_id:1 of msgid "An identifier for grouping messages." -msgstr "" +msgstr "메시지를 그룹화하기 위한 식별자입니다." #: flwr.common.Metadata.created_at:1::1 of msgid ":py:obj:`message_id `\\" -msgstr "" +msgstr ":py:obj:`message_id `\\" #: flwr.common.Metadata.created_at:1::1 of msgid ":py:obj:`message_type `\\" -msgstr "" +msgstr ":py:obj:`message_type `\\" #: flwr.common.Metadata.created_at:1::1 of msgid ":py:obj:`partition_id `\\" -msgstr "" +msgstr ":py:obj:`partition_id `\\" #: flwr.common.Metadata.created_at:1::1 #: flwr.common.Metadata.partition_id:1 of msgid "An identifier telling which data partition a ClientApp should use." -msgstr "" +msgstr "클라이언트앱이 사용해야 하는 데이터 파티션을 알려주는 식별자입니다." #: flwr.common.Metadata.created_at:1::1 of msgid ":py:obj:`reply_to_message `\\" -msgstr "" +msgstr ":py:obj:`reply_to_message `\\" #: flwr.common.Metadata.created_at:1::1 of msgid ":py:obj:`run_id `\\" -msgstr "" +msgstr ":py:obj:`run_id `\\" #: flwr.common.Metadata.created_at:1::1 of msgid ":py:obj:`src_node_id `\\" -msgstr "" +msgstr ":py:obj:`src_node_id `\\" #: flwr.common.Metadata.created_at:1::1 of msgid ":py:obj:`ttl `\\" -msgstr "" +msgstr ":py:obj:`ttl `\\" #: flwr.common.Metadata.created_at:1::1 flwr.common.Metadata.ttl:1 #: of msgid "Time-to-live for this message." -msgstr "" +msgstr "이 메시지를 기다리는 시간입니다." #: ../../source/ref-api/flwr.common.MetricsRecord.rst:2 msgid "MetricsRecord" -msgstr "" +msgstr "MetricsRecord" #: flwr.common.record.metricsrecord.MetricsRecord:1 of msgid "" -"Bases: :py:class:`~flwr.common.record.typeddict.TypedDict`\\ " -"[:py:class:`str`, :py:class:`int` | :py:class:`float` | " -":py:class:`~typing.List`\\ [:py:class:`int`] | :py:class:`~typing.List`\\" -" [:py:class:`float`]]" +"Bases: :py:class:`~flwr.common.record.typeddict.TypedDict`\\ [:py:class:" +"`str`, :py:class:`int` | :py:class:`float` | :py:class:`~typing.List`\\ [:py:" +"class:`int`] | :py:class:`~typing.List`\\ [:py:class:`float`]]" msgstr "" +"Bases: :py:class:`~flwr.common.record.typeddict.TypedDict`\\ [:py:class:`str`" +", :py:class:`int` | :py:class:`float` | :py:class:`~typing.List`\\ " +"[:py:class:`int`] | :py:class:`~typing.List`\\ [:py:class:`float`]]" #: flwr.common.record.typeddict.TypedDict.clear:1::1 of msgid ":py:obj:`clear `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`clear `\\ \\(\\)" #: flwr.common.record.typeddict.TypedDict.clear:1::1 of msgid ":py:obj:`count_bytes `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`count_bytes `\\ \\(\\)" #: flwr.common.record.typeddict.TypedDict.clear:1::1 of msgid ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" -msgstr "" +msgstr ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" #: flwr.common.record.typeddict.TypedDict.clear:1::1 of msgid ":py:obj:`items `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`items `\\ \\(\\)" #: flwr.common.record.typeddict.TypedDict.clear:1::1 of msgid ":py:obj:`keys `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`keys `\\ \\(\\)" #: flwr.common.record.typeddict.TypedDict.clear:1::1 of msgid ":py:obj:`pop `\\ \\(k\\[\\,d\\]\\)" -msgstr "" +msgstr ":py:obj:`pop `\\ \\(k\\[\\,d\\]\\)" #: flwr.common.record.typeddict.TypedDict.clear:1::1 of msgid "" ":py:obj:`update `\\ \\(\\[E\\, " "\\]\\*\\*F\\)" msgstr "" +":py:obj:`update `\\ \\(\\[E\\, \\]\\*\\*F\\" +")" #: flwr.common.record.typeddict.TypedDict.clear:1::1 of msgid ":py:obj:`values `\\ \\(\\)" msgstr "" +":py:obj:`update `\\ \\(\\[E\\, \\]\\*\\*F\\" +")" #: ../../source/ref-api/flwr.common.NDArray.rst:2 msgid "NDArray" -msgstr "" +msgstr "NDArray" #: ../../source/ref-api/flwr.common.Parameters.rst:29::1 msgid ":py:obj:`tensors `\\" -msgstr "" +msgstr ":py:obj:`tensors `\\" #: ../../source/ref-api/flwr.common.Parameters.rst:29::1 msgid ":py:obj:`tensor_type `\\" -msgstr "" +msgstr ":py:obj:`tensor_type `\\" #: ../../source/ref-api/flwr.common.ParametersRecord.rst:2 msgid "ParametersRecord" -msgstr "" +msgstr "ParametersRecord" #: flwr.common.record.parametersrecord.ParametersRecord:1 of msgid "" -"Bases: :py:class:`~flwr.common.record.typeddict.TypedDict`\\ " -"[:py:class:`str`, :py:class:`~flwr.common.record.parametersrecord.Array`]" +"Bases: :py:class:`~flwr.common.record.typeddict.TypedDict`\\ [:py:class:" +"`str`, :py:class:`~flwr.common.record.parametersrecord.Array`]" msgstr "" +"Bases: :py:class:`~flwr.common.record.typeddict.TypedDict`\\ [:py:class:`str`" +", :py:class:`~flwr.common.record.parametersrecord.Array`]" #: flwr.common.record.parametersrecord.ParametersRecord:3 of msgid "" -"A dataclass storing named Arrays in order. This means that it holds " -"entries as an OrderedDict[str, Array]. ParametersRecord objects can be " -"viewed as an equivalent to PyTorch's state_dict, but holding serialised " -"tensors instead." +"A dataclass storing named Arrays in order. This means that it holds entries " +"as an OrderedDict[str, Array]. ParametersRecord objects can be viewed as an " +"equivalent to PyTorch's state_dict, but holding serialised tensors instead." msgstr "" +"Arrays라는 이름의 데이터 클래스를 순서대로 저장합니다. 즉, OrderedDict[str, " +"Array]로 항목을 보유합니다. ParametersRecord 객체는 파이토치의 state_dict와 " +"동등한 것으로 볼 수 있지만, 대신 직렬화된 텐서를 보유합니다." #: flwr.common.record.typeddict.TypedDict.clear:1::1 of msgid ":py:obj:`clear `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`clear `\\ \\(\\)" #: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`count_bytes `\\ \\(\\)" +msgid "" +":py:obj:`count_bytes `\\ \\(\\)" msgstr "" +":py:obj:`count_bytes `\\ \\(\\)" #: flwr.common.record.typeddict.TypedDict.clear:1::1 of msgid ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" -msgstr "" +msgstr ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" #: flwr.common.record.typeddict.TypedDict.clear:1::1 of msgid ":py:obj:`items `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`items `\\ \\(\\)" #: flwr.common.record.typeddict.TypedDict.clear:1::1 of msgid ":py:obj:`keys `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`keys `\\ \\(\\)" #: flwr.common.record.typeddict.TypedDict.clear:1::1 of msgid ":py:obj:`pop `\\ \\(k\\[\\,d\\]\\)" -msgstr "" +msgstr ":py:obj:`pop `\\ \\(k\\[\\,d\\]\\)" #: flwr.common.record.typeddict.TypedDict.clear:1::1 of msgid "" ":py:obj:`update `\\ \\(\\[E\\, " "\\]\\*\\*F\\)" msgstr "" +":py:obj:`update `\\ \\(\\[E\\, \\]\\*\\*" +"F\\)" #: flwr.common.record.typeddict.TypedDict.clear:1::1 of msgid ":py:obj:`values `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`values `\\ \\(\\)" #: flwr.common.record.parametersrecord.ParametersRecord.count_bytes:3 of msgid "" -"Note that a small amount of Bytes might also be included in this counting" -" that correspond to metadata of the serialized object (e.g. of NumPy " -"array) needed for deseralization." -msgstr "" +"Note that a small amount of Bytes might also be included in this counting " +"that correspond to metadata of the serialized object (e.g. of NumPy array) " +"needed for deseralization." +msgstr "역직렬화에 필요한 직렬화된 객체의 메타데이터(예: NumPy 배열)에 해당하는 " +"소량의 바이트도 이 카운팅에 포함될 수 있습니다." #: ../../source/ref-api/flwr.common.ReconnectIns.rst:2 msgid "ReconnectIns" -msgstr "" +msgstr "ReconnectIns" #: ../../source/ref-api/flwr.common.ReconnectIns.rst:28::1 msgid ":py:obj:`seconds `\\" -msgstr "" +msgstr ":py:obj:`seconds `\\" #: ../../source/ref-api/flwr.common.RecordSet.rst:2 msgid "RecordSet" -msgstr "" +msgstr "RecordSet" #: flwr.common.RecordSet.configs_records:1::1 of msgid ":py:obj:`configs_records `\\" -msgstr "" +msgstr ":py:obj:`configs_records `\\" #: flwr.common.RecordSet.configs_records:1 #: flwr.common.RecordSet.configs_records:1::1 of msgid "Dictionary holding ConfigsRecord instances." -msgstr "" +msgstr "Dictionary holding ConfigsRecord instances." #: flwr.common.RecordSet.configs_records:1::1 of msgid ":py:obj:`metrics_records `\\" -msgstr "" +msgstr ":py:obj:`metrics_records `\\" #: flwr.common.RecordSet.configs_records:1::1 #: flwr.common.RecordSet.metrics_records:1 of msgid "Dictionary holding MetricsRecord instances." -msgstr "" +msgstr "Dictionary holding MetricsRecord instances." #: flwr.common.RecordSet.configs_records:1::1 of -msgid ":py:obj:`parameters_records `\\" +msgid "" +":py:obj:`parameters_records `\\" msgstr "" +":py:obj:`parameters_records `\\" #: flwr.common.RecordSet.configs_records:1::1 #: flwr.common.RecordSet.parameters_records:1 of msgid "Dictionary holding ParametersRecord instances." -msgstr "" +msgstr "Dictionary holding ParametersRecord instances." #: ../../source/ref-api/flwr.common.ServerMessage.rst:2 msgid "ServerMessage" -msgstr "" +msgstr "ServerMessage" #: ../../source/ref-api/flwr.common.ServerMessage.rst:31::1 msgid ":py:obj:`evaluate_ins `\\" -msgstr "" +msgstr ":py:obj:`evaluate_ins `\\" #: ../../source/ref-api/flwr.common.ServerMessage.rst:31::1 msgid ":py:obj:`fit_ins `\\" -msgstr "" +msgstr ":py:obj:`fit_ins `\\" #: ../../source/ref-api/flwr.common.ServerMessage.rst:31::1 msgid "" -":py:obj:`get_parameters_ins " -"`\\" +":py:obj:`get_parameters_ins `\\" msgstr "" +":py:obj:`get_parameters_ins `\\" #: ../../source/ref-api/flwr.common.ServerMessage.rst:31::1 msgid "" -":py:obj:`get_properties_ins " -"`\\" +":py:obj:`get_properties_ins `\\" msgstr "" +":py:obj:`get_properties_ins `\\" #: ../../source/ref-api/flwr.common.Status.rst:2 msgid "Status" -msgstr "" +msgstr "Status" #: ../../source/ref-api/flwr.common.Status.rst:29::1 msgid ":py:obj:`code `\\" -msgstr "" +msgstr ":py:obj:`code `\\" #: ../../source/ref-api/flwr.common.Status.rst:29::1 msgid ":py:obj:`message `\\" -msgstr "" +msgstr ":py:obj:`message `\\" #: ../../source/ref-api/flwr.common.array_from_numpy.rst:2 msgid "array\\_from\\_numpy" -msgstr "" +msgstr "array\\_from\\_numpy" #: ../../source/ref-api/flwr.common.bytes_to_ndarray.rst:2 msgid "bytes\\_to\\_ndarray" -msgstr "" +msgstr "bytes\\_to\\_ndarray" #: ../../source/ref-api/flwr.common.configure.rst:2 msgid "configure" -msgstr "" +msgstr "구성" #: ../../source/ref-api/flwr.common.event.rst:2 msgid "event" -msgstr "" +msgstr "이벤트" #: ../../source/ref-api/flwr.common.log.rst:2 msgid "log" -msgstr "" +msgstr "로그" #: logging.Logger.log:3 of msgid "" -"To pass exception information, use the keyword argument exc_info with a " -"true value, e.g." -msgstr "" +"To pass exception information, use the keyword argument exc_info with a true " +"value, e.g." +msgstr "예외 정보를 전달하려면 키워드 argument exc_info를 참 값과 함께 사용합니다." #: logging.Logger.log:6 of #, python-format @@ -9530,142 +11723,125 @@ msgstr "" msgid "server" msgstr "" -#: ../../source/ref-api/flwr.server.rst:26::1 -msgid ":py:obj:`run_driver_api `\\ \\(\\)" -msgstr "" - -#: ../../source/ref-api/flwr.server.rst:26::1 -#: flwr.server.app.run_driver_api:1 of -msgid "Run Flower server (Driver API)." -msgstr "" - -#: ../../source/ref-api/flwr.server.rst:26::1 -msgid ":py:obj:`run_fleet_api `\\ \\(\\)" -msgstr "" - -#: ../../source/ref-api/flwr.server.rst:26::1 -#: flwr.server.app.run_fleet_api:1 of -msgid "Run Flower server (Fleet API)." -msgstr "" - -#: ../../source/ref-api/flwr.server.rst:26::1 +#: ../../source/ref-api/flwr.server.rst:24::1 msgid ":py:obj:`run_server_app `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.server.rst:26::1 +#: ../../source/ref-api/flwr.server.rst:24::1 #: flwr.server.run_serverapp.run_server_app:1 of msgid "Run Flower server app." msgstr "" -#: ../../source/ref-api/flwr.server.rst:26::1 +#: ../../source/ref-api/flwr.server.rst:24::1 msgid ":py:obj:`run_superlink `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.server.rst:26::1 +#: ../../source/ref-api/flwr.server.rst:24::1 #: flwr.server.app.run_superlink:1 of msgid "Run Flower SuperLink (Driver API and Fleet API)." msgstr "" -#: ../../source/ref-api/flwr.server.rst:26::1 +#: ../../source/ref-api/flwr.server.rst:24::1 msgid "" ":py:obj:`start_server `\\ \\(\\*\\[\\, " "server\\_address\\, server\\, ...\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.server.rst:26::1 +#: ../../source/ref-api/flwr.server.rst:24::1 #: flwr.server.app.start_server:1 of msgid "Start a Flower server using the gRPC transport layer." msgstr "" -#: ../../source/ref-api/flwr.server.rst:40::1 +#: ../../source/ref-api/flwr.server.rst:38::1 msgid ":py:obj:`ClientManager `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.server.rst:40::1 +#: ../../source/ref-api/flwr.server.rst:38::1 #: flwr.server.client_manager.ClientManager:1 of msgid "Abstract base class for managing Flower clients." msgstr "" -#: ../../source/ref-api/flwr.server.rst:40::1 +#: ../../source/ref-api/flwr.server.rst:38::1 msgid ":py:obj:`Driver `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.server.rst:40::1 +#: ../../source/ref-api/flwr.server.rst:38::1 #: flwr.server.driver.driver.Driver:1 of msgid "Abstract base Driver class for the Driver API." msgstr "" -#: ../../source/ref-api/flwr.server.rst:40::1 +#: ../../source/ref-api/flwr.server.rst:38::1 msgid ":py:obj:`History `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.server.rst:40::1 +#: ../../source/ref-api/flwr.server.rst:38::1 #: flwr.server.history.History:1 of msgid "History class for training and/or evaluation metrics collection." msgstr "" -#: ../../source/ref-api/flwr.server.rst:40::1 +#: ../../source/ref-api/flwr.server.rst:38::1 msgid "" ":py:obj:`LegacyContext `\\ \\(state\\[\\, " "config\\, strategy\\, ...\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.server.rst:40::1 +#: ../../source/ref-api/flwr.server.rst:38::1 #: flwr.server.compat.legacy_context.LegacyContext:1 of msgid "Legacy Context." msgstr "" -#: ../../source/ref-api/flwr.server.rst:40::1 +#: ../../source/ref-api/flwr.server.rst:38::1 msgid "" ":py:obj:`Server `\\ \\(\\*\\, client\\_manager\\[\\, " "strategy\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.server.rst:40::1 +#: ../../source/ref-api/flwr.server.rst:38::1 msgid "" ":py:obj:`ServerApp `\\ \\(\\[server\\, config\\, " "strategy\\, ...\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.server.rst:40::1 +#: ../../source/ref-api/flwr.server.rst:38::1 #: flwr.server.server_app.ServerApp:1 of msgid "Flower ServerApp." msgstr "" -#: ../../source/ref-api/flwr.server.rst:40::1 +#: ../../source/ref-api/flwr.server.rst:38::1 msgid "" -":py:obj:`ServerConfig `\\ \\(\\[num\\_rounds\\," -" round\\_timeout\\]\\)" +":py:obj:`ServerConfig `\\ \\(\\[num\\_rounds\\, " +"round\\_timeout\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.server.rst:40::1 +#: ../../source/ref-api/flwr.server.rst:38::1 #: flwr.server.server_config.ServerConfig:1 of msgid "Flower server config." msgstr "" -#: ../../source/ref-api/flwr.server.rst:40::1 -msgid ":py:obj:`SimpleClientManager `\\ \\(\\)" +#: ../../source/ref-api/flwr.server.rst:38::1 +msgid "" +":py:obj:`SimpleClientManager `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.server.rst:40::1 +#: ../../source/ref-api/flwr.server.rst:38::1 #: flwr.server.client_manager.SimpleClientManager:1 of msgid "Provides a pool of available clients." msgstr "" -#: ../../source/ref-api/flwr.server.rst:59::1 +#: ../../source/ref-api/flwr.server.rst:57::1 msgid ":py:obj:`flwr.server.strategy `\\" msgstr "" -#: ../../source/ref-api/flwr.server.rst:59::1 +#: ../../source/ref-api/flwr.server.rst:57::1 #: flwr.server.strategy:1 of msgid "Contains the strategy abstraction and different implementations." msgstr "" -#: ../../source/ref-api/flwr.server.rst:59::1 +#: ../../source/ref-api/flwr.server.rst:57::1 msgid ":py:obj:`flwr.server.workflow `\\" msgstr "" -#: ../../source/ref-api/flwr.server.rst:59::1 +#: ../../source/ref-api/flwr.server.rst:57::1 #: flwr.server.workflow:1 of msgid "Workflows." msgstr "" @@ -9686,7 +11862,8 @@ msgid "Return all available clients." msgstr "" #: flwr.server.client_manager.ClientManager.all:1::1 of -msgid ":py:obj:`num_available `\\ \\(\\)" +msgid "" +":py:obj:`num_available `\\ \\(\\)" msgstr "" #: flwr.server.client_manager.ClientManager.all:1::1 @@ -9709,8 +11886,8 @@ msgstr "" #: flwr.server.client_manager.ClientManager.all:1::1 of msgid "" -":py:obj:`sample `\\ " -"\\(num\\_clients\\[\\, min\\_num\\_clients\\, criterion\\]\\)" +":py:obj:`sample `\\ \\(num\\_clients\\[\\, " +"min\\_num\\_clients\\, criterion\\]\\)" msgstr "" #: flwr.server.client_manager.ClientManager.all:1::1 @@ -9721,7 +11898,8 @@ msgid "Sample a number of Flower ClientProxy instances." msgstr "" #: flwr.server.client_manager.ClientManager.all:1::1 of -msgid ":py:obj:`unregister `\\ \\(client\\)" +msgid "" +":py:obj:`unregister `\\ \\(client\\)" msgstr "" #: flwr.server.client_manager.ClientManager.all:1::1 @@ -9753,8 +11931,7 @@ msgstr "" #: flwr.server.client_manager.SimpleClientManager.register:6 of msgid "" "**success** -- Indicating if registration was successful. False if " -"ClientProxy is already registered or can not be registered for any " -"reason." +"ClientProxy is already registered or can not be registered for any reason." msgstr "" #: flwr.server.client_manager.ClientManager.unregister:3 @@ -9768,8 +11945,8 @@ msgstr "" #: flwr.server.driver.driver.Driver.create_message:1::1 of msgid "" -":py:obj:`create_message `\\ " -"\\(content\\, message\\_type\\, ...\\[\\, ttl\\]\\)" +":py:obj:`create_message `\\ \\(content\\, " +"message\\_type\\, ...\\[\\, ttl\\]\\)" msgstr "" #: flwr.server.driver.driver.Driver.create_message:1 @@ -9799,8 +11976,7 @@ msgstr "" #: flwr.server.driver.driver.Driver.create_message:1::1 of msgid "" -":py:obj:`push_messages `\\ " -"\\(messages\\)" +":py:obj:`push_messages `\\ \\(messages\\)" msgstr "" #: flwr.server.driver.driver.Driver.create_message:1::1 @@ -9821,20 +11997,20 @@ msgstr "" #: flwr.server.driver.driver.Driver.create_message:3 of msgid "" -"This method constructs a new `Message` with given content and metadata. " -"The `run_id` and `src_node_id` will be set automatically." +"This method constructs a new `Message` with given content and metadata. The " +"`run_id` and `src_node_id` will be set automatically." msgstr "" #: flwr.server.driver.driver.Driver.create_message:6 of msgid "" -"The content for the new message. This holds records that are to be sent " -"to the destination node." +"The content for the new message. This holds records that are to be sent to " +"the destination node." msgstr "" #: flwr.server.driver.driver.Driver.create_message:9 of msgid "" -"The type of the message, defining the action to be executed on the " -"receiving end." +"The type of the message, defining the action to be executed on the receiving " +"end." msgstr "" #: flwr.server.driver.driver.Driver.create_message:12 of @@ -9843,17 +12019,16 @@ msgstr "" #: flwr.server.driver.driver.Driver.create_message:14 of msgid "" -"The ID of the group to which this message is associated. In some " -"settings, this is used as the FL round." +"The ID of the group to which this message is associated. In some settings, " +"this is used as the FL round." msgstr "" #: flwr.server.driver.driver.Driver.create_message:17 of msgid "" -"Time-to-live for the round trip of this message, i.e., the time from " -"sending this message to receiving a reply. It specifies in seconds the " -"duration for which the message and its potential reply are considered " -"valid. If unset, the default TTL (i.e., `common.DEFAULT_TTL`) will be " -"used." +"Time-to-live for the round trip of this message, i.e., the time from sending " +"this message to receiving a reply. It specifies in seconds the duration for " +"which the message and its potential reply are considered valid. If unset, " +"the default TTL (i.e., `common.DEFAULT_TTL`) will be used." msgstr "" #: flwr.server.driver.driver.Driver.create_message:23 of @@ -9864,12 +12039,13 @@ msgstr "" #: flwr.server.driver.driver.Driver.pull_messages:3 of msgid "" -"This method is used to collect messages from the SuperLink that " -"correspond to a set of given message IDs." +"This method is used to collect messages from the SuperLink that correspond " +"to a set of given message IDs." msgstr "" #: flwr.server.driver.driver.Driver.pull_messages:6 of -msgid "An iterable of message IDs for which reply messages are to be retrieved." +msgid "" +"An iterable of message IDs for which reply messages are to be retrieved." msgstr "" #: flwr.server.driver.driver.Driver.pull_messages:9 of @@ -9878,8 +12054,8 @@ msgstr "" #: flwr.server.driver.driver.Driver.push_messages:3 of msgid "" -"This method takes an iterable of messages and sends each message to the " -"node specified in `dst_node_id`." +"This method takes an iterable of messages and sends each message to the node " +"specified in `dst_node_id`." msgstr "" #: flwr.server.driver.driver.Driver.push_messages:6 @@ -9889,41 +12065,35 @@ msgstr "" #: flwr.server.driver.driver.Driver.push_messages:9 of msgid "" -"**message_ids** -- An iterable of IDs for the messages that were sent, " -"which can be used to pull replies." +"**message_ids** -- An iterable of IDs for the messages that were sent, which " +"can be used to pull replies." msgstr "" #: flwr.server.driver.driver.Driver.send_and_receive:3 of msgid "" -"This method sends a list of messages to their destination node IDs and " -"then waits for the replies. It continues to pull replies until either all" -" replies are received or the specified timeout duration is exceeded." +"This method sends a list of messages to their destination node IDs and then " +"waits for the replies. It continues to pull replies until either all replies " +"are received or the specified timeout duration is exceeded." msgstr "" #: flwr.server.driver.driver.Driver.send_and_receive:9 of msgid "" "The timeout duration in seconds. If specified, the method will wait for " -"replies for this duration. If `None`, there is no time limit and the " -"method will wait until replies for all messages are received." +"replies for this duration. If `None`, there is no time limit and the method " +"will wait until replies for all messages are received." msgstr "" #: flwr.server.driver.driver.Driver.send_and_receive:14 of -msgid "**replies** -- An iterable of reply messages received from the SuperLink." -msgstr "" - -#: flwr.server.driver.driver.Driver.send_and_receive:18 -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:53 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:60 -#: of -msgid "Notes" +msgid "" +"**replies** -- An iterable of reply messages received from the SuperLink." msgstr "" #: flwr.server.driver.driver.Driver.send_and_receive:19 of msgid "" -"This method uses `push_messages` to send the messages and `pull_messages`" -" to collect the replies. If `timeout` is set, the method may not return " -"replies for all sent messages. A message remains valid until its TTL, " -"which is not affected by `timeout`." +"This method uses `push_messages` to send the messages and `pull_messages` to " +"collect the replies. If `timeout` is set, the method may not return replies " +"for all sent messages. A message remains valid until its TTL, which is not " +"affected by `timeout`." msgstr "" #: ../../source/ref-api/flwr.server.History.rst:2 @@ -9932,9 +12102,8 @@ msgstr "" #: flwr.server.history.History.add_loss_centralized:1::1 of msgid "" -":py:obj:`add_loss_centralized " -"`\\ \\(server\\_round\\, " -"loss\\)" +":py:obj:`add_loss_centralized `\\ " +"\\(server\\_round\\, loss\\)" msgstr "" #: flwr.server.history.History.add_loss_centralized:1 @@ -9944,9 +12113,8 @@ msgstr "" #: flwr.server.history.History.add_loss_centralized:1::1 of msgid "" -":py:obj:`add_loss_distributed " -"`\\ \\(server\\_round\\, " -"loss\\)" +":py:obj:`add_loss_distributed `\\ " +"\\(server\\_round\\, loss\\)" msgstr "" #: flwr.server.history.History.add_loss_centralized:1::1 @@ -9956,9 +12124,8 @@ msgstr "" #: flwr.server.history.History.add_loss_centralized:1::1 of msgid "" -":py:obj:`add_metrics_centralized " -"`\\ \\(server\\_round\\, " -"metrics\\)" +":py:obj:`add_metrics_centralized `\\ \\(server\\_round\\, metrics\\)" msgstr "" #: flwr.server.history.History.add_loss_centralized:1::1 @@ -9968,9 +12135,8 @@ msgstr "" #: flwr.server.history.History.add_loss_centralized:1::1 of msgid "" -":py:obj:`add_metrics_distributed " -"`\\ \\(server\\_round\\, " -"metrics\\)" +":py:obj:`add_metrics_distributed `\\ \\(server\\_round\\, metrics\\)" msgstr "" #: flwr.server.history.History.add_loss_centralized:1::1 @@ -9980,9 +12146,8 @@ msgstr "" #: flwr.server.history.History.add_loss_centralized:1::1 of msgid "" -":py:obj:`add_metrics_distributed_fit " -"`\\ \\(server\\_round\\," -" ...\\)" +":py:obj:`add_metrics_distributed_fit `\\ \\(server\\_round\\, ...\\)" msgstr "" #: flwr.server.history.History.add_loss_centralized:1::1 @@ -10033,8 +12198,8 @@ msgstr "" #: flwr.server.server.Server.client_manager:1::1 of msgid "" -":py:obj:`disconnect_all_clients " -"`\\ \\(timeout\\)" +":py:obj:`disconnect_all_clients `\\ \\(timeout\\)" msgstr "" #: flwr.server.server.Server.client_manager:1::1 @@ -10064,8 +12229,8 @@ msgstr "" #: flwr.server.server.Server.client_manager:1::1 of msgid "" -":py:obj:`fit_round `\\ \\(server\\_round\\," -" timeout\\)" +":py:obj:`fit_round `\\ \\(server\\_round\\, " +"timeout\\)" msgstr "" #: flwr.server.server.Server.client_manager:1::1 @@ -10085,7 +12250,8 @@ msgid "Set the max_workers used by ThreadPoolExecutor." msgstr "" #: flwr.server.server.Server.client_manager:1::1 of -msgid ":py:obj:`set_strategy `\\ \\(strategy\\)" +msgid "" +":py:obj:`set_strategy `\\ \\(strategy\\)" msgstr "" #: flwr.server.server.Server.client_manager:1::1 @@ -10120,8 +12286,8 @@ msgstr "" #: flwr.server.server_config.ServerConfig:3 of msgid "" -"All attributes have default values which allows users to configure just " -"the ones they care about." +"All attributes have default values which allows users to configure just the " +"ones they care about." msgstr "" #: ../../source/ref-api/flwr.server.ServerConfig.rst:29::1 @@ -10146,14 +12312,13 @@ msgstr "" #: flwr.server.client_manager.SimpleClientManager.all:1::1 of msgid "" -":py:obj:`num_available `\\" -" \\(\\)" +":py:obj:`num_available `\\ " +"\\(\\)" msgstr "" #: flwr.server.client_manager.SimpleClientManager.all:1::1 of msgid "" -":py:obj:`register `\\ " -"\\(client\\)" +":py:obj:`register `\\ \\(client\\)" msgstr "" #: flwr.server.client_manager.SimpleClientManager.all:1::1 of @@ -10176,8 +12341,8 @@ msgstr "" #: flwr.server.client_manager.SimpleClientManager.wait_for:3 of msgid "" -"Blocks until the requested number of clients is available or until a " -"timeout is reached. Current timeout default: 1 day." +"Blocks until the requested number of clients is available or until a timeout " +"is reached. Current timeout default: 1 day." msgstr "" #: flwr.server.client_manager.SimpleClientManager.wait_for:6 of @@ -10208,10 +12373,6 @@ msgstr "" msgid "run\\_superlink" msgstr "" -#: ../../source/ref-api/flwr.server.start_driver.rst:2 -msgid "start\\_driver" -msgstr "" - #: ../../source/ref-api/flwr.server.start_server.rst:2 msgid "start\\_server" msgstr "" @@ -10222,8 +12383,8 @@ msgstr "" #: flwr.server.app.start_server:5 of msgid "" -"A server implementation, either `flwr.server.Server` or a subclass " -"thereof. If no instance is provided, then `start_server` will create one." +"A server implementation, either `flwr.server.Server` or a subclass thereof. " +"If no instance is provided, then `start_server` will create one." msgstr "" #: flwr.server.app.start_server:9 flwr.simulation.app.start_simulation:28 of @@ -10234,41 +12395,41 @@ msgstr "" #: flwr.server.app.start_server:12 of msgid "" -"An implementation of the abstract base class " -"`flwr.server.strategy.Strategy`. If no strategy is provided, then " -"`start_server` will use `flwr.server.strategy.FedAvg`." +"An implementation of the abstract base class `flwr.server.strategy." +"Strategy`. If no strategy is provided, then `start_server` will use `flwr." +"server.strategy.FedAvg`." msgstr "" #: flwr.server.app.start_server:16 of msgid "" -"An implementation of the abstract base class `flwr.server.ClientManager`." -" If no implementation is provided, then `start_server` will use " -"`flwr.server.client_manager.SimpleClientManager`." +"An implementation of the abstract base class `flwr.server.ClientManager`. If " +"no implementation is provided, then `start_server` will use `flwr.server." +"client_manager.SimpleClientManager`." msgstr "" #: flwr.server.app.start_server:21 of msgid "" -"The maximum length of gRPC messages that can be exchanged with the Flower" -" clients. The default should be sufficient for most models. Users who " -"train very large models might need to increase this value. Note that the " -"Flower clients need to be started with the same value (see " -"`flwr.client.start_client`), otherwise clients will not know about the " -"increased limit and block larger messages." +"The maximum length of gRPC messages that can be exchanged with the Flower " +"clients. The default should be sufficient for most models. Users who train " +"very large models might need to increase this value. Note that the Flower " +"clients need to be started with the same value (see `flwr.client." +"start_client`), otherwise clients will not know about the increased limit " +"and block larger messages." msgstr "" #: flwr.server.app.start_server:28 of msgid "" -"Tuple containing root certificate, server certificate, and private key to" -" start a secure SSL-enabled server. The tuple is expected to have three " -"bytes elements in the following order: * CA certificate. * " -"server certificate. * server private key." +"Tuple containing root certificate, server certificate, and private key to " +"start a secure SSL-enabled server. The tuple is expected to have three bytes " +"elements in the following order: * CA certificate. * server " +"certificate. * server private key." msgstr "" #: flwr.server.app.start_server:28 of msgid "" -"Tuple containing root certificate, server certificate, and private key to" -" start a secure SSL-enabled server. The tuple is expected to have three " -"bytes elements in the following order:" +"Tuple containing root certificate, server certificate, and private key to " +"start a secure SSL-enabled server. The tuple is expected to have three bytes " +"elements in the following order:" msgstr "" #: flwr.server.app.start_server:32 of @@ -10301,8 +12462,8 @@ msgstr "" #: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`Bulyan `\\ \\(\\*\\, " -"fraction\\_fit\\, fraction\\_evaluate\\, ...\\)" +":py:obj:`Bulyan `\\ \\(\\*\\, fraction\\_fit\\, " +"fraction\\_evaluate\\, ...\\)" msgstr "" #: ../../source/ref-api/flwr.server.strategy.rst:45::1 @@ -10334,9 +12495,8 @@ msgstr "" #: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`DifferentialPrivacyClientSideAdaptiveClipping " -"`\\ " -"\\(...\\)" +":py:obj:`DifferentialPrivacyClientSideAdaptiveClipping `\\ \\(...\\)" msgstr "" #: ../../source/ref-api/flwr.server.strategy.rst:45::1 @@ -10347,9 +12507,8 @@ msgstr "" #: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`DifferentialPrivacyServerSideAdaptiveClipping " -"`\\ " -"\\(...\\)" +":py:obj:`DifferentialPrivacyServerSideAdaptiveClipping `\\ \\(...\\)" msgstr "" #: ../../source/ref-api/flwr.server.strategy.rst:45::1 @@ -10360,9 +12519,8 @@ msgstr "" #: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`DifferentialPrivacyClientSideFixedClipping " -"`\\ " -"\\(...\\)" +":py:obj:`DifferentialPrivacyClientSideFixedClipping `\\ \\(...\\)" msgstr "" #: ../../source/ref-api/flwr.server.strategy.rst:45::1 @@ -10373,9 +12531,8 @@ msgstr "" #: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`DifferentialPrivacyServerSideFixedClipping " -"`\\ " -"\\(...\\)" +":py:obj:`DifferentialPrivacyServerSideFixedClipping `\\ \\(...\\)" msgstr "" #: ../../source/ref-api/flwr.server.strategy.rst:45::1 @@ -10420,8 +12577,8 @@ msgstr "" #: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`FedAvgAndroid `\\ " -"\\(\\*\\[\\, fraction\\_fit\\, ...\\]\\)" +":py:obj:`FedAvgAndroid `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, ...\\]\\)" msgstr "" #: ../../source/ref-api/flwr.server.strategy.rst:45::1 @@ -10470,8 +12627,8 @@ msgstr "" #: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`FedTrimmedAvg `\\ " -"\\(\\*\\[\\, fraction\\_fit\\, ...\\]\\)" +":py:obj:`FedTrimmedAvg `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, ...\\]\\)" msgstr "" #: ../../source/ref-api/flwr.server.strategy.rst:45::1 @@ -10525,9 +12682,8 @@ msgstr "" #: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`FaultTolerantFedAvg " -"`\\ \\(\\*\\[\\, " -"fraction\\_fit\\, ...\\]\\)" +":py:obj:`FaultTolerantFedAvg `\\ " +"\\(\\*\\[\\, fraction\\_fit\\, ...\\]\\)" msgstr "" #: ../../source/ref-api/flwr.server.strategy.rst:45::1 @@ -10537,8 +12693,8 @@ msgstr "" #: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`Krum `\\ \\(\\*\\[\\, " -"fraction\\_fit\\, fraction\\_evaluate\\, ...\\]\\)" +":py:obj:`Krum `\\ \\(\\*\\[\\, fraction\\_fit\\, " +"fraction\\_evaluate\\, ...\\]\\)" msgstr "" #: ../../source/ref-api/flwr.server.strategy.rst:45::1 @@ -10548,8 +12704,8 @@ msgstr "" #: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`QFedAvg `\\ \\(\\*\\[\\, " -"q\\_param\\, qffl\\_learning\\_rate\\, ...\\]\\)" +":py:obj:`QFedAvg `\\ \\(\\*\\[\\, q\\_param\\, " +"qffl\\_learning\\_rate\\, ...\\]\\)" msgstr "" #: ../../source/ref-api/flwr.server.strategy.rst:45::1 @@ -10717,8 +12873,8 @@ msgstr "" #: flwr.server.strategy.bulyan.Bulyan:27 of msgid "" -"Byzantine resilient aggregation rule that is used as the first step of " -"the Bulyan (e.g., Krum)" +"Byzantine resilient aggregation rule that is used as the first step of the " +"Bulyan (e.g., Krum)" msgstr "" #: flwr.server.strategy.bulyan.Bulyan:29 of @@ -10727,9 +12883,8 @@ msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`aggregate_evaluate " -"`\\ \\(server\\_round\\, " -"results\\, ...\\)" +":py:obj:`aggregate_evaluate `\\ \\(server\\_round\\, results\\, ...\\)" msgstr "" #: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1 @@ -10756,9 +12911,8 @@ msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`configure_evaluate " -"`\\ \\(server\\_round\\, " -"parameters\\, ...\\)" +":py:obj:`configure_evaluate `\\ \\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 @@ -10837,9 +12991,8 @@ msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +":py:obj:`initialize_parameters `\\ \\(client\\_manager\\)" msgstr "" #: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 @@ -10856,9 +13009,8 @@ msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`num_evaluation_clients `\\ \\(num\\_available\\_clients\\)" msgstr "" #: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 @@ -10875,8 +13027,8 @@ msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`num_fit_clients `\\" -" \\(num\\_available\\_clients\\)" +":py:obj:`num_fit_clients `\\ " +"\\(num\\_available\\_clients\\)" msgstr "" #: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 @@ -10907,9 +13059,8 @@ msgstr "" #: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" +":py:obj:`aggregate_evaluate `\\ \\(server\\_round\\, results\\, ...\\)" msgstr "" #: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1 @@ -10929,9 +13080,8 @@ msgstr "" #: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`aggregate_fit " -"`\\ " -"\\(server\\_round\\, results\\, failures\\)" +":py:obj:`aggregate_fit `\\ \\(server\\_round\\, results\\, failures\\)" msgstr "" #: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive.aggregate_fit:1 @@ -10943,9 +13093,8 @@ msgstr "" #: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`configure_evaluate `\\ \\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 @@ -10956,9 +13105,8 @@ msgstr "" #: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`configure_fit " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`configure_fit `\\ \\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 @@ -10978,15 +13126,15 @@ msgstr "" #: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.evaluate:1 #: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 #: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.evaluate:1 of -msgid "Evaluate model parameters using an evaluation function from the strategy." +msgid "" +"Evaluate model parameters using an evaluation function from the strategy." msgstr "" #: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +":py:obj:`initialize_parameters `\\ \\(client\\_manager\\)" msgstr "" #: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 @@ -11025,9 +13173,9 @@ msgstr "" msgid "" "**evaluate_configuration** -- A list of tuples. Each tuple in the list " "identifies a `ClientProxy` and the `EvaluateIns` for this particular " -"`ClientProxy`. If a particular `ClientProxy` is not included in this " -"list, it means that this `ClientProxy` will not participate in the next " -"round of federated evaluation." +"`ClientProxy`. If a particular `ClientProxy` is not included in this list, " +"it means that this `ClientProxy` will not participate in the next round of " +"federated evaluation." msgstr "" #: ../../source/ref-api/flwr.server.strategy.DPFedAvgFixed.rst:2 @@ -11047,16 +13195,14 @@ msgstr "" #: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" +":py:obj:`aggregate_evaluate `\\ \\(server\\_round\\, results\\, ...\\)" msgstr "" #: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`aggregate_fit " -"`\\ " +":py:obj:`aggregate_fit `\\ " "\\(server\\_round\\, results\\, failures\\)" msgstr "" @@ -11068,24 +13214,21 @@ msgstr "" #: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`configure_evaluate `\\ \\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`configure_fit " -"`\\ " +":py:obj:`configure_fit `\\ " "\\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 #: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:1 of msgid "" -"Configure the next round of training incorporating Differential Privacy " -"(DP)." +"Configure the next round of training incorporating Differential Privacy (DP)." msgstr "" #: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 @@ -11098,25 +13241,23 @@ msgstr "" #: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +":py:obj:`initialize_parameters `\\ \\(client\\_manager\\)" msgstr "" #: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:3 of msgid "" -"Configuration of the next training round includes information related to " -"DP, such as clip norm and noise stddev." +"Configuration of the next training round includes information related to DP, " +"such as clip norm and noise stddev." msgstr "" #: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:13 #: flwr.server.strategy.strategy.Strategy.configure_fit:10 of msgid "" -"**fit_configuration** -- A list of tuples. Each tuple in the list " -"identifies a `ClientProxy` and the `FitIns` for this particular " -"`ClientProxy`. If a particular `ClientProxy` is not included in this " -"list, it means that this `ClientProxy` will not participate in the next " -"round of federated learning." +"**fit_configuration** -- A list of tuples. Each tuple in the list identifies " +"a `ClientProxy` and the `FitIns` for this particular `ClientProxy`. If a " +"particular `ClientProxy` is not included in this list, it means that this " +"`ClientProxy` will not participate in the next round of federated learning." msgstr "" #: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyClientSideAdaptiveClipping.rst:2 @@ -11133,9 +13274,8 @@ msgstr "" msgid "" "In comparison to `DifferentialPrivacyServerSideAdaptiveClipping`, which " "performs clipping on the server-side, " -"`DifferentialPrivacyClientSideAdaptiveClipping` expects clipping to " -"happen on the client-side, usually by using the built-in " -"`adaptiveclipping_mod`." +"`DifferentialPrivacyClientSideAdaptiveClipping` expects clipping to happen " +"on the client-side, usually by using the built-in `adaptiveclipping_mod`." msgstr "" #: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:10 @@ -11171,22 +13311,23 @@ msgstr "" #: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:19 #: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:12 #: of -msgid "The desired quantile of updates which should be clipped. Defaults to 0.5." +msgid "" +"The desired quantile of updates which should be clipped. Defaults to 0.5." msgstr "" #: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:21 #: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:14 #: of msgid "" -"The learning rate for the clipping norm adaptation. Defaults to 0.2. " -"Andrew et al. recommends to set to 0.2." +"The learning rate for the clipping norm adaptation. Defaults to 0.2. Andrew " +"et al. recommends to set to 0.2." msgstr "" #: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:24 #: of msgid "" -"The stddev of the noise added to the count of updates currently below the" -" estimate. Andrew et al. recommends to set to `expected_num_records/20`" +"The stddev of the noise added to the count of updates currently below the " +"estimate. Andrew et al. recommends to set to `expected_num_records/20`" msgstr "" #: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:30 @@ -11200,8 +13341,8 @@ msgstr "" #: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:34 #: of msgid "" -"Wrap the strategy with the " -"`DifferentialPrivacyClientSideAdaptiveClipping` wrapper:" +"Wrap the strategy with the `DifferentialPrivacyClientSideAdaptiveClipping` " +"wrapper:" msgstr "" #: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:40 @@ -11212,17 +13353,17 @@ msgstr "" #: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`aggregate_evaluate " -"`\\" -" \\(server\\_round\\, results\\, ...\\)" +":py:obj:`aggregate_evaluate `\\ " +"\\(server\\_round\\, results\\, ...\\)" msgstr "" #: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`aggregate_fit " -"`\\" -" \\(server\\_round\\, results\\, failures\\)" +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" #: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 @@ -11236,33 +13377,33 @@ msgstr "" #: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`configure_evaluate " -"`\\" -" \\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`configure_evaluate `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`configure_fit " -"`\\" -" \\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`evaluate " -"`\\" -" \\(server\\_round\\, parameters\\)" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" #: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`initialize_parameters " -"`\\" -" \\(client\\_manager\\)" +":py:obj:`initialize_parameters `\\ " +"\\(client\\_manager\\)" msgstr "" #: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyClientSideFixedClipping.rst:2 @@ -11279,22 +13420,16 @@ msgstr "" msgid "" "In comparison to `DifferentialPrivacyServerSideFixedClipping`, which " "performs clipping on the server-side, " -"`DifferentialPrivacyClientSideFixedClipping` expects clipping to happen " -"on the client-side, usually by using the built-in `fixedclipping_mod`." +"`DifferentialPrivacyClientSideFixedClipping` expects clipping to happen on " +"the client-side, usually by using the built-in `fixedclipping_mod`." msgstr "" #: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:12 #: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:5 #: of msgid "" -"The noise multiplier for the Gaussian mechanism for model updates. A " -"value of 1.0 or higher is recommended for strong privacy." -msgstr "" - -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:15 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:8 -#: of -msgid "The value of the clipping norm." +"The noise multiplier for the Gaussian mechanism for model updates. A value " +"of 1.0 or higher is recommended for strong privacy." msgstr "" #: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:26 @@ -11312,17 +13447,17 @@ msgstr "" #: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`aggregate_evaluate " -"`\\" -" \\(server\\_round\\, results\\, ...\\)" +":py:obj:`aggregate_evaluate `\\ " +"\\(server\\_round\\, results\\, ...\\)" msgstr "" #: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`aggregate_fit " -"`\\" -" \\(server\\_round\\, results\\, failures\\)" +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" #: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 @@ -11334,33 +13469,33 @@ msgstr "" #: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`configure_evaluate " -"`\\" -" \\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`configure_evaluate `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`configure_fit " -"`\\" -" \\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`evaluate " -"`\\" -" \\(server\\_round\\, parameters\\)" +":py:obj:`evaluate `\\ \\(server\\_round\\, " +"parameters\\)" msgstr "" #: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`initialize_parameters " -"`\\" -" \\(client\\_manager\\)" +":py:obj:`initialize_parameters `\\ " +"\\(client\\_manager\\)" msgstr "" #: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyServerSideAdaptiveClipping.rst:2 @@ -11370,9 +13505,8 @@ msgstr "" #: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:17 #: of msgid "" -"The standard deviation of the noise added to the count of updates below " -"the estimate. Andrew et al. recommends to set to " -"`expected_num_records/20`" +"The standard deviation of the noise added to the count of updates below the " +"estimate. Andrew et al. recommends to set to `expected_num_records/20`" msgstr "" #: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:27 @@ -11385,49 +13519,49 @@ msgstr "" #: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`aggregate_evaluate " -"`\\" -" \\(server\\_round\\, results\\, ...\\)" +":py:obj:`aggregate_evaluate `\\ " +"\\(server\\_round\\, results\\, ...\\)" msgstr "" #: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`aggregate_fit " -"`\\" -" \\(server\\_round\\, results\\, failures\\)" +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" #: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`configure_evaluate " -"`\\" -" \\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`configure_evaluate `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`configure_fit " -"`\\" -" \\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`evaluate " -"`\\" -" \\(server\\_round\\, parameters\\)" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" #: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`initialize_parameters " -"`\\" -" \\(client\\_manager\\)" +":py:obj:`initialize_parameters `\\ " +"\\(client\\_manager\\)" msgstr "" #: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyServerSideFixedClipping.rst:2 @@ -11437,24 +13571,23 @@ msgstr "" #: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:19 #: of msgid "" -"Wrap the strategy with the DifferentialPrivacyServerSideFixedClipping " -"wrapper" +"Wrap the strategy with the DifferentialPrivacyServerSideFixedClipping wrapper" msgstr "" #: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`aggregate_evaluate " -"`\\" -" \\(server\\_round\\, results\\, ...\\)" +":py:obj:`aggregate_evaluate `\\ " +"\\(server\\_round\\, results\\, ...\\)" msgstr "" #: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`aggregate_fit " -"`\\" -" \\(server\\_round\\, results\\, failures\\)" +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" #: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 @@ -11466,33 +13599,33 @@ msgstr "" #: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`configure_evaluate " -"`\\" -" \\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`configure_evaluate `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`configure_fit " -"`\\" -" \\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`evaluate " -"`\\" -" \\(server\\_round\\, parameters\\)" +":py:obj:`evaluate `\\ \\(server\\_round\\, " +"parameters\\)" msgstr "" #: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`initialize_parameters " -"`\\" -" \\(client\\_manager\\)" +":py:obj:`initialize_parameters `\\ " +"\\(client\\_manager\\)" msgstr "" #: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_fit:3 @@ -11507,17 +13640,15 @@ msgstr "" #: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" +":py:obj:`aggregate_evaluate `\\ \\(server\\_round\\, results\\, ...\\)" msgstr "" #: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`aggregate_fit " -"`\\ " -"\\(server\\_round\\, results\\, failures\\)" +":py:obj:`aggregate_fit `\\ \\(server\\_round\\, results\\, failures\\)" msgstr "" #: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 @@ -11539,17 +13670,15 @@ msgstr "" #: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`configure_evaluate `\\ \\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`configure_fit " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`configure_fit `\\ \\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 @@ -11562,29 +13691,26 @@ msgstr "" #: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +":py:obj:`initialize_parameters `\\ \\(client\\_manager\\)" msgstr "" #: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`num_evaluation_clients `\\ \\(num\\_available\\_clients\\)" msgstr "" #: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`num_fit_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`num_fit_clients `\\ \\(num\\_available\\_clients\\)" msgstr "" #: ../../source/ref-api/flwr.server.strategy.FedAdagrad.rst:2 -#: ../../source/ref-changelog.md:905 +#: ../../source/ref-changelog.md:997 msgid "FedAdagrad" msgstr "" @@ -11633,28 +13759,26 @@ msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" +":py:obj:`aggregate_evaluate `\\ \\(server\\_round\\, results\\, ...\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`aggregate_fit `\\" -" \\(server\\_round\\, results\\, failures\\)" +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`configure_evaluate `\\ \\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`configure_fit `\\" -" \\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of @@ -11665,23 +13789,20 @@ msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +":py:obj:`initialize_parameters `\\ \\(client\\_manager\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`num_evaluation_clients `\\ \\(num\\_available\\_clients\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`num_fit_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`num_fit_clients `\\ \\(num\\_available\\_clients\\)" msgstr "" #: ../../source/ref-api/flwr.server.strategy.FedAdam.rst:2 @@ -11700,9 +13821,8 @@ msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`aggregate_evaluate " -"`\\ \\(server\\_round\\," -" results\\, ...\\)" +":py:obj:`aggregate_evaluate `\\ \\(server\\_round\\, results\\, ...\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of @@ -11713,9 +13833,8 @@ msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`configure_evaluate " -"`\\ \\(server\\_round\\," -" parameters\\, ...\\)" +":py:obj:`configure_evaluate `\\ \\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of @@ -11732,22 +13851,19 @@ msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +":py:obj:`initialize_parameters `\\ \\(client\\_manager\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`num_evaluation_clients `\\ \\(num\\_available\\_clients\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`num_fit_clients " -"`\\ " +":py:obj:`num_fit_clients `\\ " "\\(num\\_available\\_clients\\)" msgstr "" @@ -11764,17 +13880,16 @@ msgstr "" #: of msgid "" "Fraction of clients used during training. In case `min_fit_clients` is " -"larger than `fraction_fit * available_clients`, `min_fit_clients` will " -"still be sampled. Defaults to 1.0." +"larger than `fraction_fit * available_clients`, `min_fit_clients` will still " +"be sampled. Defaults to 1.0." msgstr "" #: flwr.server.strategy.fedavg.FedAvg:9 flwr.server.strategy.fedprox.FedProx:41 #: of msgid "" -"Fraction of clients used during validation. In case " -"`min_evaluate_clients` is larger than `fraction_evaluate * " -"available_clients`, `min_evaluate_clients` will still be sampled. " -"Defaults to 1.0." +"Fraction of clients used during validation. In case `min_evaluate_clients` " +"is larger than `fraction_evaluate * available_clients`, " +"`min_evaluate_clients` will still be sampled. Defaults to 1.0." msgstr "" #: flwr.server.strategy.fedavg.FedAvg:33 of @@ -11783,9 +13898,8 @@ msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`aggregate_evaluate " -"`\\ \\(server\\_round\\, " -"results\\, ...\\)" +":py:obj:`aggregate_evaluate `\\ \\(server\\_round\\, results\\, ...\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of @@ -11796,9 +13910,8 @@ msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`configure_evaluate " -"`\\ \\(server\\_round\\, " -"parameters\\, ...\\)" +":py:obj:`configure_evaluate `\\ \\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of @@ -11815,22 +13928,20 @@ msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +":py:obj:`initialize_parameters `\\ \\(client\\_manager\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`num_evaluation_clients `\\ \\(num\\_available\\_clients\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`num_fit_clients `\\" -" \\(num\\_available\\_clients\\)" +":py:obj:`num_fit_clients `\\ " +"\\(num\\_available\\_clients\\)" msgstr "" #: ../../source/ref-api/flwr.server.strategy.FedAvgAndroid.rst:2 @@ -11840,24 +13951,22 @@ msgstr "" #: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" +":py:obj:`aggregate_evaluate `\\ \\(server\\_round\\, results\\, ...\\)" msgstr "" #: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`aggregate_fit " -"`\\ " +":py:obj:`aggregate_fit `\\ " "\\(server\\_round\\, results\\, failures\\)" msgstr "" #: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`bytes_to_ndarray " -"`\\ \\(tensor\\)" +":py:obj:`bytes_to_ndarray `\\ \\(tensor\\)" msgstr "" #: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 @@ -11868,16 +13977,14 @@ msgstr "" #: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`configure_evaluate `\\ \\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`configure_fit " -"`\\ " +":py:obj:`configure_fit `\\ " "\\(server\\_round\\, parameters\\, ...\\)" msgstr "" @@ -11891,16 +13998,15 @@ msgstr "" #: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +":py:obj:`initialize_parameters `\\ \\(client\\_manager\\)" msgstr "" #: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`ndarray_to_bytes " -"`\\ \\(ndarray\\)" +":py:obj:`ndarray_to_bytes `\\ \\(ndarray\\)" msgstr "" #: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 @@ -11911,33 +14017,29 @@ msgstr "" #: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`ndarrays_to_parameters " -"`\\ " -"\\(ndarrays\\)" +":py:obj:`ndarrays_to_parameters `\\ \\(ndarrays\\)" msgstr "" #: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`num_evaluation_clients `\\ \\(num\\_available\\_clients\\)" msgstr "" #: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`num_fit_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`num_fit_clients `\\ \\(num\\_available\\_clients\\)" msgstr "" #: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`parameters_to_ndarrays " -"`\\ " -"\\(parameters\\)" +":py:obj:`parameters_to_ndarrays `\\ \\(parameters\\)" msgstr "" #: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 @@ -11956,8 +14058,7 @@ msgstr "" #: flwr.server.strategy.fedavgm.FedAvgM:25 of msgid "" -"Server-side learning rate used in server-side optimization. Defaults to " -"1.0." +"Server-side learning rate used in server-side optimization. Defaults to 1.0." msgstr "" #: flwr.server.strategy.fedavgm.FedAvgM:28 of @@ -11966,9 +14067,8 @@ msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`aggregate_evaluate " -"`\\ \\(server\\_round\\," -" results\\, ...\\)" +":py:obj:`aggregate_evaluate `\\ \\(server\\_round\\, results\\, ...\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of @@ -11979,9 +14079,8 @@ msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`configure_evaluate " -"`\\ \\(server\\_round\\," -" parameters\\, ...\\)" +":py:obj:`configure_evaluate `\\ \\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of @@ -11998,22 +14097,19 @@ msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +":py:obj:`initialize_parameters `\\ \\(client\\_manager\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`num_evaluation_clients `\\ \\(num\\_available\\_clients\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`num_fit_clients " -"`\\ " +":py:obj:`num_fit_clients `\\ " "\\(num\\_available\\_clients\\)" msgstr "" @@ -12023,9 +14119,8 @@ msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" +":py:obj:`aggregate_evaluate `\\ \\(server\\_round\\, results\\, ...\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of @@ -12041,9 +14136,8 @@ msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`configure_evaluate `\\ \\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of @@ -12060,22 +14154,19 @@ msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +":py:obj:`initialize_parameters `\\ \\(client\\_manager\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`num_evaluation_clients `\\ \\(num\\_available\\_clients\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`num_fit_clients " -"`\\ " +":py:obj:`num_fit_clients `\\ " "\\(num\\_available\\_clients\\)" msgstr "" @@ -12093,9 +14184,8 @@ msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`aggregate_evaluate " -"`\\ \\(server\\_round\\, " -"results\\, ...\\)" +":py:obj:`aggregate_evaluate `\\ \\(server\\_round\\, results\\, ...\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of @@ -12106,9 +14196,8 @@ msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`configure_evaluate " -"`\\ \\(server\\_round\\, " -"parameters\\, ...\\)" +":py:obj:`configure_evaluate `\\ \\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of @@ -12125,22 +14214,20 @@ msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +":py:obj:`initialize_parameters `\\ \\(client\\_manager\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`num_evaluation_clients `\\ \\(num\\_available\\_clients\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`num_fit_clients `\\" -" \\(num\\_available\\_clients\\)" +":py:obj:`num_fit_clients `\\ " +"\\(num\\_available\\_clients\\)" msgstr "" #: ../../source/ref-api/flwr.server.strategy.FedProx.rst:2 @@ -12153,9 +14240,9 @@ msgstr "" #: flwr.server.strategy.fedprox.FedProx:5 of msgid "" -"The strategy in itself will not be different than FedAvg, the client " -"needs to be adjusted. A proximal term needs to be added to the loss " -"function during the training:" +"The strategy in itself will not be different than FedAvg, the client needs " +"to be adjusted. A proximal term needs to be added to the loss function " +"during the training:" msgstr "" #: flwr.server.strategy.fedprox.FedProx:9 of @@ -12188,15 +14275,14 @@ msgstr "" msgid "" "The weight of the proximal term used in the optimization. 0.0 makes this " "strategy equivalent to FedAvg, and the higher the coefficient, the more " -"regularization will be used (that is, the client parameters will need to " -"be closer to the server parameters during training)." +"regularization will be used (that is, the client parameters will need to be " +"closer to the server parameters during training)." msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`aggregate_evaluate " -"`\\ \\(server\\_round\\," -" results\\, ...\\)" +":py:obj:`aggregate_evaluate `\\ \\(server\\_round\\, results\\, ...\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of @@ -12207,9 +14293,8 @@ msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`configure_evaluate " -"`\\ \\(server\\_round\\," -" parameters\\, ...\\)" +":py:obj:`configure_evaluate `\\ \\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of @@ -12226,22 +14311,19 @@ msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +":py:obj:`initialize_parameters `\\ \\(client\\_manager\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`num_evaluation_clients `\\ \\(num\\_available\\_clients\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`num_fit_clients " -"`\\ " +":py:obj:`num_fit_clients `\\ " "\\(num\\_available\\_clients\\)" msgstr "" @@ -12263,15 +14345,13 @@ msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" +":py:obj:`aggregate_evaluate `\\ \\(server\\_round\\, results\\, ...\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`aggregate_fit " -"`\\ " +":py:obj:`aggregate_fit `\\ " "\\(server\\_round\\, results\\, failures\\)" msgstr "" @@ -12282,15 +14362,13 @@ msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`configure_evaluate `\\ \\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`configure_fit " -"`\\ " +":py:obj:`configure_fit `\\ " "\\(server\\_round\\, parameters\\, ...\\)" msgstr "" @@ -12302,23 +14380,20 @@ msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +":py:obj:`initialize_parameters `\\ \\(client\\_manager\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`num_evaluation_clients `\\ \\(num\\_available\\_clients\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`num_fit_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`num_fit_clients `\\ \\(num\\_available\\_clients\\)" msgstr "" #: ../../source/ref-api/flwr.server.strategy.FedXgbBagging.rst:2 @@ -12328,9 +14403,8 @@ msgstr "" #: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" +":py:obj:`aggregate_evaluate `\\ \\(server\\_round\\, results\\, ...\\)" msgstr "" #: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1 @@ -12344,8 +14418,7 @@ msgstr "" #: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`aggregate_fit " -"`\\ " +":py:obj:`aggregate_fit `\\ " "\\(server\\_round\\, results\\, failures\\)" msgstr "" @@ -12359,16 +14432,14 @@ msgstr "" #: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`configure_evaluate `\\ \\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`configure_fit " -"`\\ " +":py:obj:`configure_fit `\\ " "\\(server\\_round\\, parameters\\, ...\\)" msgstr "" @@ -12382,25 +14453,22 @@ msgstr "" #: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +":py:obj:`initialize_parameters `\\ \\(client\\_manager\\)" msgstr "" #: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`num_evaluation_clients `\\ \\(num\\_available\\_clients\\)" msgstr "" #: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`num_fit_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`num_fit_clients `\\ \\(num\\_available\\_clients\\)" msgstr "" #: ../../source/ref-api/flwr.server.strategy.FedXgbCyclic.rst:2 @@ -12410,33 +14478,29 @@ msgstr "" #: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" +":py:obj:`aggregate_evaluate `\\ \\(server\\_round\\, results\\, ...\\)" msgstr "" #: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`aggregate_fit " -"`\\ \\(server\\_round\\," -" results\\, failures\\)" +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" #: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`configure_evaluate `\\ \\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`configure_fit " -"`\\ \\(server\\_round\\," -" parameters\\, ...\\)" +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 @@ -12449,25 +14513,22 @@ msgstr "" #: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +":py:obj:`initialize_parameters `\\ \\(client\\_manager\\)" msgstr "" #: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`num_evaluation_clients `\\ \\(num\\_available\\_clients\\)" msgstr "" #: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`num_fit_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`num_fit_clients `\\ \\(num\\_available\\_clients\\)" msgstr "" #: ../../source/ref-api/flwr.server.strategy.FedXgbNnAvg.rst:2 @@ -12477,36 +14538,31 @@ msgstr "" #: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg:5 of msgid "" "This strategy is deprecated, but a copy of it is available in Flower " -"Baselines: " -"https://github.com/adap/flower/tree/main/baselines/hfedxgboost." +"Baselines: https://github.com/adap/flower/tree/main/baselines/hfedxgboost." msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" +":py:obj:`aggregate_evaluate `\\ \\(server\\_round\\, results\\, ...\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`aggregate_fit " -"`\\ \\(server\\_round\\, " -"results\\, failures\\)" +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`configure_evaluate `\\ \\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`configure_fit " -"`\\ \\(server\\_round\\, " -"parameters\\, ...\\)" +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of @@ -12517,23 +14573,20 @@ msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +":py:obj:`initialize_parameters `\\ \\(client\\_manager\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`num_evaluation_clients `\\ \\(num\\_available\\_clients\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`num_fit_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`num_fit_clients `\\ \\(num\\_available\\_clients\\)" msgstr "" #: ../../source/ref-api/flwr.server.strategy.FedYogi.rst:2 @@ -12554,9 +14607,8 @@ msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`aggregate_evaluate " -"`\\ \\(server\\_round\\," -" results\\, ...\\)" +":py:obj:`aggregate_evaluate `\\ \\(server\\_round\\, results\\, ...\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of @@ -12567,9 +14619,8 @@ msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`configure_evaluate " -"`\\ \\(server\\_round\\," -" parameters\\, ...\\)" +":py:obj:`configure_evaluate `\\ \\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of @@ -12586,22 +14637,19 @@ msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +":py:obj:`initialize_parameters `\\ \\(client\\_manager\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`num_evaluation_clients `\\ \\(num\\_available\\_clients\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`num_fit_clients " -"`\\ " +":py:obj:`num_fit_clients `\\ " "\\(num\\_available\\_clients\\)" msgstr "" @@ -12615,15 +14663,14 @@ msgstr "" #: flwr.server.strategy.krum.Krum:17 of msgid "" -"Number of clients to keep before averaging (MultiKrum). Defaults to 0, in" -" that case classical Krum is applied." +"Number of clients to keep before averaging (MultiKrum). Defaults to 0, in " +"that case classical Krum is applied." msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`aggregate_evaluate " -"`\\ \\(server\\_round\\, " -"results\\, ...\\)" +":py:obj:`aggregate_evaluate `\\ \\(server\\_round\\, results\\, ...\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of @@ -12639,9 +14686,8 @@ msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`configure_evaluate " -"`\\ \\(server\\_round\\, " -"parameters\\, ...\\)" +":py:obj:`configure_evaluate `\\ \\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of @@ -12658,16 +14704,14 @@ msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +":py:obj:`initialize_parameters `\\ \\(client\\_manager\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`num_evaluation_clients `\\ \\(num\\_available\\_clients\\)" msgstr "" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of @@ -12682,9 +14726,8 @@ msgstr "" #: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`aggregate_evaluate " -"`\\ \\(server\\_round\\," -" results\\, ...\\)" +":py:obj:`aggregate_evaluate `\\ \\(server\\_round\\, results\\, ...\\)" msgstr "" #: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of @@ -12695,9 +14738,8 @@ msgstr "" #: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`configure_evaluate " -"`\\ \\(server\\_round\\," -" parameters\\, ...\\)" +":py:obj:`configure_evaluate `\\ \\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of @@ -12714,22 +14756,19 @@ msgstr "" #: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +":py:obj:`initialize_parameters `\\ \\(client\\_manager\\)" msgstr "" #: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`num_evaluation_clients `\\ \\(num\\_available\\_clients\\)" msgstr "" #: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`num_fit_clients " -"`\\ " +":py:obj:`num_fit_clients `\\ " "\\(num\\_available\\_clients\\)" msgstr "" @@ -12740,9 +14779,8 @@ msgstr "" #: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" +":py:obj:`aggregate_evaluate `\\ \\(server\\_round\\, results\\, ...\\)" msgstr "" #: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1 @@ -12766,9 +14804,8 @@ msgstr "" #: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`configure_evaluate `\\ \\(server\\_round\\, parameters\\, ...\\)" msgstr "" #: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 @@ -12793,9 +14830,8 @@ msgstr "" #: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +":py:obj:`initialize_parameters `\\ \\(client\\_manager\\)" msgstr "" #: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 @@ -12805,17 +14841,18 @@ msgstr "" #: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:5 of msgid "" -"Successful updates from the previously selected and configured clients. " -"Each pair of `(ClientProxy, FitRes` constitutes a successful update from " -"one of the previously selected clients. Not that not all previously " -"selected clients are necessarily included in this list: a client might " -"drop out and not submit a result. For each client that did not submit an " -"update, there should be an `Exception` in `failures`." +"Successful updates from the previously selected and configured clients. Each " +"pair of `(ClientProxy, FitRes` constitutes a successful update from one of " +"the previously selected clients. Not that not all previously selected " +"clients are necessarily included in this list: a client might drop out and " +"not submit a result. For each client that did not submit an update, there " +"should be an `Exception` in `failures`." msgstr "" #: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:13 #: flwr.server.strategy.strategy.Strategy.aggregate_fit:13 of -msgid "Exceptions that occurred while the server was waiting for client updates." +msgid "" +"Exceptions that occurred while the server was waiting for client updates." msgstr "" #: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:16 of @@ -12826,23 +14863,22 @@ msgstr "" #: flwr.server.strategy.strategy.Strategy.aggregate_fit:5 of msgid "" -"Successful updates from the previously selected and configured clients. " -"Each pair of `(ClientProxy, FitRes)` constitutes a successful update from" -" one of the previously selected clients. Not that not all previously " -"selected clients are necessarily included in this list: a client might " -"drop out and not submit a result. For each client that did not submit an " -"update, there should be an `Exception` in `failures`." +"Successful updates from the previously selected and configured clients. Each " +"pair of `(ClientProxy, FitRes)` constitutes a successful update from one of " +"the previously selected clients. Not that not all previously selected " +"clients are necessarily included in this list: a client might drop out and " +"not submit a result. For each client that did not submit an update, there " +"should be an `Exception` in `failures`." msgstr "" #: flwr.server.strategy.strategy.Strategy.aggregate_fit:17 of msgid "" "**parameters** -- If parameters are returned, then the server will treat " -"these as the new global model parameters (i.e., it will replace the " -"previous parameters with the ones returned from this method). If `None` " -"is returned (e.g., because there were only failures and no viable " -"results) then the server will no update the previous model parameters, " -"the updates received in this round are discarded, and the global model " -"parameters remain the same." +"these as the new global model parameters (i.e., it will replace the previous " +"parameters with the ones returned from this method). If `None` is returned " +"(e.g., because there were only failures and no viable results) then the " +"server will no update the previous model parameters, the updates received in " +"this round are discarded, and the global model parameters remain the same." msgstr "" #: flwr.server.strategy.strategy.Strategy.evaluate:3 of @@ -12853,9 +14889,8 @@ msgstr "" #: flwr.server.strategy.strategy.Strategy.evaluate:11 of msgid "" -"**evaluation_result** -- The evaluation result, usually a Tuple " -"containing loss and a dictionary containing task-specific metrics (e.g., " -"accuracy)." +"**evaluation_result** -- The evaluation result, usually a Tuple containing " +"loss and a dictionary containing task-specific metrics (e.g., accuracy)." msgstr "" #: flwr.server.strategy.strategy.Strategy.initialize_parameters:6 of @@ -12913,17 +14948,17 @@ msgstr "" #: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:3 #: of msgid "" -"The SecAgg+ protocol ensures the secure summation of integer vectors " -"owned by multiple parties, without accessing any individual integer " -"vector. This workflow allows the server to compute the weighted average " -"of model parameters across all clients, ensuring individual contributions" -" remain private. This is achieved by clients sending both, a weighting " -"factor and a weighted version of the locally updated parameters, both of " -"which are masked for privacy. Specifically, each client uploads \"[w, w *" -" params]\" with masks, where weighting factor 'w' is the number of " -"examples ('num_examples') and 'params' represents the model parameters " -"('parameters') from the client's `FitRes`. The server then aggregates " -"these contributions to compute the weighted average of model parameters." +"The SecAgg+ protocol ensures the secure summation of integer vectors owned " +"by multiple parties, without accessing any individual integer vector. This " +"workflow allows the server to compute the weighted average of model " +"parameters across all clients, ensuring individual contributions remain " +"private. This is achieved by clients sending both, a weighting factor and a " +"weighted version of the locally updated parameters, both of which are masked " +"for privacy. Specifically, each client uploads \"[w, w * params]\" with " +"masks, where weighting factor 'w' is the number of examples ('num_examples') " +"and 'params' represents the model parameters ('parameters') from the " +"client's `FitRes`. The server then aggregates these contributions to compute " +"the weighted average of model parameters." msgstr "" #: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:14 @@ -12960,39 +14995,39 @@ msgstr "" #: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:22 #: of msgid "" -"Only the aggregated model parameters are exposed and passed to " -"`Strategy.aggregate_fit`, ensuring individual data privacy." +"Only the aggregated model parameters are exposed and passed to `Strategy." +"aggregate_fit`, ensuring individual data privacy." msgstr "" #: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:25 #: of msgid "" -"The number of shares into which each client's private key is split under " -"the SecAgg+ protocol. If specified as a float, it represents the " -"proportion of all selected clients, and the number of shares will be set " -"dynamically in the run time. A private key can be reconstructed from " -"these shares, allowing for the secure aggregation of model updates. Each " -"client sends one share to each of its neighbors while retaining one." +"The number of shares into which each client's private key is split under the " +"SecAgg+ protocol. If specified as a float, it represents the proportion of " +"all selected clients, and the number of shares will be set dynamically in " +"the run time. A private key can be reconstructed from these shares, allowing " +"for the secure aggregation of model updates. Each client sends one share to " +"each of its neighbors while retaining one." msgstr "" #: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:25 #: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:32 #: of msgid "" -"The minimum number of shares required to reconstruct a client's private " -"key, or, if specified as a float, it represents the proportion of the " -"total number of shares needed for reconstruction. This threshold ensures " -"privacy by allowing for the recovery of contributions from dropped " -"clients during aggregation, without compromising individual client data." +"The minimum number of shares required to reconstruct a client's private key, " +"or, if specified as a float, it represents the proportion of the total " +"number of shares needed for reconstruction. This threshold ensures privacy " +"by allowing for the recovery of contributions from dropped clients during " +"aggregation, without compromising individual client data." msgstr "" #: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:31 #: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:38 #: of msgid "" -"The maximum value of the weight that can be assigned to any single " -"client's update during the weighted average calculation on the server " -"side, e.g., in the FedAvg algorithm." +"The maximum value of the weight that can be assigned to any single client's " +"update during the weighted average calculation on the server side, e.g., in " +"the FedAvg algorithm." msgstr "" #: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:35 @@ -13000,8 +15035,8 @@ msgstr "" #: of msgid "" "The range within which model parameters are clipped before quantization. " -"This parameter ensures each model parameter is bounded within " -"[-clipping_range, clipping_range], facilitating quantization." +"This parameter ensures each model parameter is bounded within [-" +"clipping_range, clipping_range], facilitating quantization." msgstr "" #: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:39 @@ -13019,31 +15054,32 @@ msgstr "" #: of msgid "" "The range of values from which random mask entries are uniformly sampled " -"([0, modulus_range-1]). `modulus_range` must be less than 4294967296. " -"Please use 2**n values for `modulus_range` to prevent overflow issues." +"([0, modulus_range-1]). `modulus_range` must be less than 4294967296. Please " +"use 2**n values for `modulus_range` to prevent overflow issues." msgstr "" #: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:47 #: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:54 #: of msgid "" -"The timeout duration in seconds. If specified, the workflow will wait for" -" replies for this duration each time. If `None`, there is no time limit " -"and the workflow will wait until replies for all messages are received." +"The timeout duration in seconds. If specified, the workflow will wait for " +"replies for this duration each time. If `None`, there is no time limit and " +"the workflow will wait until replies for all messages are received." msgstr "" #: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:61 #: of msgid "" "Generally, higher `num_shares` means more robust to dropouts while " -"increasing the computational costs; higher `reconstruction_threshold` " -"means better privacy guarantees but less tolerance to dropouts." +"increasing the computational costs; higher `reconstruction_threshold` means " +"better privacy guarantees but less tolerance to dropouts." msgstr "" #: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:58 #: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:64 #: of -msgid "Too large `max_weight` may compromise the precision of the quantization." +msgid "" +"Too large `max_weight` may compromise the precision of the quantization." msgstr "" #: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:59 @@ -13056,35 +15092,34 @@ msgstr "" #: of msgid "" "When `num_shares` is a float, it is interpreted as the proportion of all " -"selected clients, and hence the number of shares will be determined in " -"the runtime. This allows for dynamic adjustment based on the total number" -" of participating clients." +"selected clients, and hence the number of shares will be determined in the " +"runtime. This allows for dynamic adjustment based on the total number of " +"participating clients." msgstr "" #: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:69 #: of msgid "" -"Similarly, when `reconstruction_threshold` is a float, it is interpreted " -"as the proportion of the number of shares needed for the reconstruction " -"of a private key. This feature enables flexibility in setting the " -"security threshold relative to the number of distributed shares." +"Similarly, when `reconstruction_threshold` is a float, it is interpreted as " +"the proportion of the number of shares needed for the reconstruction of a " +"private key. This feature enables flexibility in setting the security " +"threshold relative to the number of distributed shares." msgstr "" #: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:73 #: of msgid "" -"`num_shares`, `reconstruction_threshold`, and the quantization parameters" -" (`clipping_range`, `quantization_range`, `modulus_range`) play critical " -"roles in balancing privacy, robustness, and efficiency within the SecAgg+" -" protocol." +"`num_shares`, `reconstruction_threshold`, and the quantization parameters " +"(`clipping_range`, `quantization_range`, `modulus_range`) play critical " +"roles in balancing privacy, robustness, and efficiency within the SecAgg+ " +"protocol." msgstr "" #: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 #: of msgid "" -":py:obj:`collect_masked_vectors_stage " -"`\\" -" \\(driver\\, ...\\)" +":py:obj:`collect_masked_vectors_stage `\\ \\(driver\\, ...\\)" msgstr "" #: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1 @@ -13096,9 +15131,8 @@ msgstr "" #: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 #: of msgid "" -":py:obj:`setup_stage " -"`\\ \\(driver\\, " -"context\\, state\\)" +":py:obj:`setup_stage `\\ \\(driver\\, context\\, state\\)" msgstr "" #: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 @@ -13110,9 +15144,8 @@ msgstr "" #: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 #: of msgid "" -":py:obj:`share_keys_stage " -"`\\ " -"\\(driver\\, context\\, state\\)" +":py:obj:`share_keys_stage `\\ \\(driver\\, context\\, state\\)" msgstr "" #: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 @@ -13124,9 +15157,8 @@ msgstr "" #: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 #: of msgid "" -":py:obj:`unmask_stage " -"`\\ \\(driver\\, " -"context\\, state\\)" +":py:obj:`unmask_stage `\\ \\(driver\\, context\\, state\\)" msgstr "" #: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 @@ -13141,51 +15173,50 @@ msgstr "" #: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:1 of msgid "" -"Bases: " -":py:class:`~flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow`" +"Bases: :py:class:`~flwr.server.workflow.secure_aggregation." +"secaggplus_workflow.SecAggPlusWorkflow`" msgstr "" #: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:3 of msgid "" -"The SecAgg protocol ensures the secure summation of integer vectors owned" -" by multiple parties, without accessing any individual integer vector. " -"This workflow allows the server to compute the weighted average of model " +"The SecAgg protocol ensures the secure summation of integer vectors owned by " +"multiple parties, without accessing any individual integer vector. This " +"workflow allows the server to compute the weighted average of model " "parameters across all clients, ensuring individual contributions remain " -"private. This is achieved by clients sending both, a weighting factor and" -" a weighted version of the locally updated parameters, both of which are " -"masked for privacy. Specifically, each client uploads \"[w, w * params]\"" -" with masks, where weighting factor 'w' is the number of examples " -"('num_examples') and 'params' represents the model parameters " -"('parameters') from the client's `FitRes`. The server then aggregates " -"these contributions to compute the weighted average of model parameters." +"private. This is achieved by clients sending both, a weighting factor and a " +"weighted version of the locally updated parameters, both of which are masked " +"for privacy. Specifically, each client uploads \"[w, w * params]\" with " +"masks, where weighting factor 'w' is the number of examples ('num_examples') " +"and 'params' represents the model parameters ('parameters') from the " +"client's `FitRes`. The server then aggregates these contributions to compute " +"the weighted average of model parameters." msgstr "" #: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:14 of msgid "" -"The protocol involves four main stages: - 'setup': Send SecAgg " -"configuration to clients and collect their public keys. - 'share keys': " -"Broadcast public keys among clients and collect encrypted secret" +"The protocol involves four main stages: - 'setup': Send SecAgg configuration " +"to clients and collect their public keys. - 'share keys': Broadcast public " +"keys among clients and collect encrypted secret" msgstr "" #: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:54 of msgid "" -"Each client's private key is split into N shares under the SecAgg " -"protocol, where N is the number of selected clients." +"Each client's private key is split into N shares under the SecAgg protocol, " +"where N is the number of selected clients." msgstr "" #: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:56 of msgid "" -"Generally, higher `reconstruction_threshold` means better privacy " -"guarantees but less tolerance to dropouts." +"Generally, higher `reconstruction_threshold` means better privacy guarantees " +"but less tolerance to dropouts." msgstr "" #: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:60 of msgid "" "When `reconstruction_threshold` is a float, it is interpreted as the " "proportion of the number of all selected clients needed for the " -"reconstruction of a private key. This feature enables flexibility in " -"setting the security threshold relative to the number of selected " -"clients." +"reconstruction of a private key. This feature enables flexibility in setting " +"the security threshold relative to the number of selected clients." msgstr "" #: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:64 of @@ -13199,32 +15230,29 @@ msgstr "" #: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 #: of msgid "" -":py:obj:`collect_masked_vectors_stage " -"`\\ " -"\\(driver\\, ...\\)" +":py:obj:`collect_masked_vectors_stage `\\ \\(driver\\, ...\\)" msgstr "" #: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 #: of msgid "" -":py:obj:`setup_stage `\\" -" \\(driver\\, context\\, state\\)" +":py:obj:`setup_stage `\\ " +"\\(driver\\, context\\, state\\)" msgstr "" #: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 #: of msgid "" -":py:obj:`share_keys_stage " -"`\\ \\(driver\\, " -"context\\, state\\)" +":py:obj:`share_keys_stage `\\ \\(driver\\, context\\, state\\)" msgstr "" #: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 #: of msgid "" -":py:obj:`unmask_stage " -"`\\ \\(driver\\, " -"context\\, state\\)" +":py:obj:`unmask_stage `\\ " +"\\(driver\\, context\\, state\\)" msgstr "" #: ../../source/ref-api/flwr.simulation.rst:2 @@ -13233,8 +15261,8 @@ msgstr "" #: ../../source/ref-api/flwr.simulation.rst:18::1 msgid "" -":py:obj:`start_simulation `\\ \\(\\*\\," -" client\\_fn\\[\\, ...\\]\\)" +":py:obj:`start_simulation `\\ \\(\\*\\, " +"client\\_fn\\[\\, ...\\]\\)" msgstr "" #: ../../source/ref-api/flwr.simulation.rst:18::1 @@ -13265,15 +15293,14 @@ msgstr "" #: flwr.simulation.run_simulation.run_simulation:6 of msgid "" -"The `ClientApp` to be executed by each of the SuperNodes. It will receive" -" messages sent by the `ServerApp`." +"The `ClientApp` to be executed by each of the SuperNodes. It will receive " +"messages sent by the `ServerApp`." msgstr "" #: flwr.simulation.run_simulation.run_simulation:9 of msgid "" -"Number of nodes that run a ClientApp. They can be sampled by a Driver in " -"the ServerApp and receive a Message describing what the ClientApp should " -"perform." +"Number of nodes that run a ClientApp. They can be sampled by a Driver in the " +"ServerApp and receive a Message describing what the ClientApp should perform." msgstr "" #: flwr.simulation.run_simulation.run_simulation:13 of @@ -13282,30 +15309,26 @@ msgstr "" #: flwr.simulation.run_simulation.run_simulation:15 of msgid "" -"'A dictionary, e.g {\"\": , \"\": } to " -"configure a backend. Values supported in are those included by " -"`flwr.common.typing.ConfigsRecordValues`." +"'A dictionary, e.g {\"\": , \"\": } to configure a " +"backend. Values supported in are those included by `flwr.common." +"typing.ConfigsRecordValues`." msgstr "" #: flwr.simulation.run_simulation.run_simulation:19 of msgid "" -"A boolean to indicate whether to enable GPU growth on the main thread. " -"This is desirable if you make use of a TensorFlow model on your " -"`ServerApp` while having your `ClientApp` running on the same GPU. " -"Without enabling this, you might encounter an out-of-memory error because" -" TensorFlow, by default, allocates all GPU memory. Read more about how " -"`tf.config.experimental.set_memory_growth()` works in the TensorFlow " -"documentation: https://www.tensorflow.org/api/stable." +"A boolean to indicate whether to enable GPU growth on the main thread. This " +"is desirable if you make use of a TensorFlow model on your `ServerApp` while " +"having your `ClientApp` running on the same GPU. Without enabling this, you " +"might encounter an out-of-memory error because TensorFlow, by default, " +"allocates all GPU memory. Read more about how `tf.config.experimental." +"set_memory_growth()` works in the TensorFlow documentation: https://www." +"tensorflow.org/api/stable." msgstr "" #: flwr.simulation.run_simulation.run_simulation:26 of msgid "" -"When diabled, only INFO, WARNING and ERROR log messages will be shown. If" -" enabled, DEBUG-level logs will be displayed." -msgstr "" - -#: ../../source/ref-api/flwr.simulation.run_simulation_from_cli.rst:2 -msgid "run\\_simulation\\_from\\_cli" +"When diabled, only INFO, WARNING and ERROR log messages will be shown. If " +"enabled, DEBUG-level logs will be displayed." msgstr "" #: ../../source/ref-api/flwr.simulation.start_simulation.rst:2 @@ -13314,15 +15337,15 @@ msgstr "" #: flwr.simulation.app.start_simulation:3 of msgid "" -"A function creating client instances. The function must take a single " -"`str` argument called `cid`. It should return a single client instance of" -" type Client. Note that the created client instances are ephemeral and " -"will often be destroyed after a single method invocation. Since client " -"instances are not long-lived, they should not attempt to carry state over" -" method invocations. Any state required by the instance (model, dataset, " +"A function creating client instances. The function must take a single `str` " +"argument called `cid`. It should return a single client instance of type " +"Client. Note that the created client instances are ephemeral and will often " +"be destroyed after a single method invocation. Since client instances are " +"not long-lived, they should not attempt to carry state over method " +"invocations. Any state required by the instance (model, dataset, " "hyperparameters, ...) should be (re-)created in either the call to " -"`client_fn` or the call to any of the client methods (e.g., load " -"evaluation data in the `evaluate` method itself)." +"`client_fn` or the call to any of the client methods (e.g., load evaluation " +"data in the `evaluate` method itself)." msgstr "" #: flwr.simulation.app.start_simulation:13 of @@ -13333,16 +15356,16 @@ msgstr "" #: flwr.simulation.app.start_simulation:16 of msgid "" -"List `client_id`s for each client. This is only required if `num_clients`" -" is not set. Setting both `num_clients` and `clients_ids` with " +"List `client_id`s for each client. This is only required if `num_clients` is " +"not set. Setting both `num_clients` and `clients_ids` with " "`len(clients_ids)` not equal to `num_clients` generates an error." msgstr "" #: flwr.simulation.app.start_simulation:20 of msgid "" -"CPU and GPU resources for a single client. Supported keys are `num_cpus` " -"and `num_gpus`. To understand the GPU utilization caused by `num_gpus`, " -"as well as using custom resources, please consult the Ray documentation." +"CPU and GPU resources for a single client. Supported keys are `num_cpus` and " +"`num_gpus`. To understand the GPU utilization caused by `num_gpus`, as well " +"as using custom resources, please consult the Ray documentation." msgstr "" #: flwr.simulation.app.start_simulation:25 of @@ -13353,16 +15376,16 @@ msgstr "" #: flwr.simulation.app.start_simulation:31 of msgid "" -"An implementation of the abstract base class `flwr.server.Strategy`. If " -"no strategy is provided, then `start_server` will use " -"`flwr.server.strategy.FedAvg`." +"An implementation of the abstract base class `flwr.server.Strategy`. If no " +"strategy is provided, then `start_server` will use `flwr.server.strategy." +"FedAvg`." msgstr "" #: flwr.simulation.app.start_simulation:35 of msgid "" -"An implementation of the abstract base class `flwr.server.ClientManager`." -" If no implementation is provided, then `start_simulation` will use " -"`flwr.server.client_manager.SimpleClientManager`." +"An implementation of the abstract base class `flwr.server.ClientManager`. If " +"no implementation is provided, then `start_simulation` will use `flwr.server." +"client_manager.SimpleClientManager`." msgstr "" #: flwr.simulation.app.start_simulation:39 of @@ -13371,8 +15394,7 @@ msgid "" "ray_init_args is None (the default), Ray will be initialized with the " "following default args: { \"ignore_reinit_error\": True, " "\"include_dashboard\": False } An empty dictionary can be used " -"(ray_init_args={}) to prevent any arguments from being passed to " -"ray.init." +"(ray_init_args={}) to prevent any arguments from being passed to ray.init." msgstr "" #: flwr.simulation.app.start_simulation:39 of @@ -13388,14 +15410,13 @@ msgstr "" #: flwr.simulation.app.start_simulation:45 of msgid "" -"An empty dictionary can be used (ray_init_args={}) to prevent any " -"arguments from being passed to ray.init." +"An empty dictionary can be used (ray_init_args={}) to prevent any arguments " +"from being passed to ray.init." msgstr "" #: flwr.simulation.app.start_simulation:48 of msgid "" -"Set to True to prevent `ray.shutdown()` in case " -"`ray.is_initialized()=True`." +"Set to True to prevent `ray.shutdown()` in case `ray.is_initialized()=True`." msgstr "" #: flwr.simulation.app.start_simulation:50 of @@ -13407,19 +15428,19 @@ msgstr "" #: flwr.simulation.app.start_simulation:54 of msgid "" -"If you want to create your own Actor classes, you might need to pass some" -" input argument. You can use this dictionary for such purpose." +"If you want to create your own Actor classes, you might need to pass some " +"input argument. You can use this dictionary for such purpose." msgstr "" #: flwr.simulation.app.start_simulation:57 of msgid "" -"(default: \"DEFAULT\") Optional string (\"DEFAULT\" or \"SPREAD\") for " -"the VCE to choose in which node the actor is placed. If you are an " -"advanced user needed more control you can use lower-level scheduling " -"strategies to pin actors to specific compute nodes (e.g. via " -"NodeAffinitySchedulingStrategy). Please note this is an advanced feature." -" For all details, please refer to the Ray documentation: " -"https://docs.ray.io/en/latest/ray-core/scheduling/index.html" +"(default: \"DEFAULT\") Optional string (\"DEFAULT\" or \"SPREAD\") for the " +"VCE to choose in which node the actor is placed. If you are an advanced user " +"needed more control you can use lower-level scheduling strategies to pin " +"actors to specific compute nodes (e.g. via NodeAffinitySchedulingStrategy). " +"Please note this is an advanced feature. For all details, please refer to " +"the Ray documentation: https://docs.ray.io/en/latest/ray-core/scheduling/" +"index.html" msgstr "" #: flwr.simulation.app.start_simulation:66 of @@ -13431,1160 +15452,1431 @@ msgid "Changelog" msgstr "" #: ../../source/ref-changelog.md:3 -msgid "Unreleased" -msgstr "" - -#: ../../source/ref-changelog.md:5 ../../source/ref-changelog.md:19 -#: ../../source/ref-changelog.md:83 ../../source/ref-changelog.md:176 -#: ../../source/ref-changelog.md:276 ../../source/ref-changelog.md:360 -#: ../../source/ref-changelog.md:424 ../../source/ref-changelog.md:482 -#: ../../source/ref-changelog.md:551 ../../source/ref-changelog.md:680 -#: ../../source/ref-changelog.md:722 ../../source/ref-changelog.md:789 -#: ../../source/ref-changelog.md:855 ../../source/ref-changelog.md:900 -#: ../../source/ref-changelog.md:939 ../../source/ref-changelog.md:972 -#: ../../source/ref-changelog.md:1022 -msgid "What's new?" +msgid "v1.9.0 (2024-06-10)" msgstr "" -#: ../../source/ref-changelog.md:7 ../../source/ref-changelog.md:71 -#: ../../source/ref-changelog.md:146 ../../source/ref-changelog.md:258 -#: ../../source/ref-changelog.md:348 ../../source/ref-changelog.md:412 -#: ../../source/ref-changelog.md:470 ../../source/ref-changelog.md:539 -#: ../../source/ref-changelog.md:601 ../../source/ref-changelog.md:620 -#: ../../source/ref-changelog.md:776 ../../source/ref-changelog.md:847 -#: ../../source/ref-changelog.md:884 ../../source/ref-changelog.md:927 -msgid "Incompatible changes" +#: ../../source/ref-changelog.md:5 ../../source/ref-changelog.md:105 +#: ../../source/ref-changelog.md:169 ../../source/ref-changelog.md:262 +#: ../../source/ref-changelog.md:362 ../../source/ref-changelog.md:446 +#: ../../source/ref-changelog.md:510 ../../source/ref-changelog.md:568 +#: ../../source/ref-changelog.md:637 ../../source/ref-changelog.md:706 +msgid "Thanks to our contributors" msgstr "" -#: ../../source/ref-changelog.md:9 ../../source/ref-changelog.md:73 -#: ../../source/ref-changelog.md:350 ../../source/ref-changelog.md:414 -#: ../../source/ref-changelog.md:472 ../../source/ref-changelog.md:541 -#: ../../source/ref-changelog.md:603 -msgid "None" +#: ../../source/ref-changelog.md:7 ../../source/ref-changelog.md:107 +#: ../../source/ref-changelog.md:171 ../../source/ref-changelog.md:264 +#: ../../source/ref-changelog.md:364 ../../source/ref-changelog.md:448 +#: ../../source/ref-changelog.md:512 ../../source/ref-changelog.md:570 +msgid "" +"We would like to give our special thanks to all the contributors who made " +"the new version of Flower possible (in `git shortlog` order):" msgstr "" -#: ../../source/ref-changelog.md:11 -msgid "v1.8.0 (2024-04-03)" +#: ../../source/ref-changelog.md:9 +msgid "" +"`Adam Narozniak`, `Charles Beauville`, `Chong Shen Ng`, `Daniel J. Beutel`, " +"`Daniel Nata Nugraha`, `Heng Pan`, `Javier`, `Mahdi Beitollahi`, `Robert " +"Steiner`, `Taner Topal`, `Yan Gao`, `bapic`, `mohammadnaseri` " msgstr "" -#: ../../source/ref-changelog.md:13 ../../source/ref-changelog.md:77 -#: ../../source/ref-changelog.md:170 ../../source/ref-changelog.md:270 -#: ../../source/ref-changelog.md:354 ../../source/ref-changelog.md:418 -#: ../../source/ref-changelog.md:476 ../../source/ref-changelog.md:545 -#: ../../source/ref-changelog.md:614 -msgid "Thanks to our contributors" +#: ../../source/ref-changelog.md:11 ../../source/ref-changelog.md:111 +#: ../../source/ref-changelog.md:175 ../../source/ref-changelog.md:268 +#: ../../source/ref-changelog.md:368 ../../source/ref-changelog.md:452 +#: ../../source/ref-changelog.md:516 ../../source/ref-changelog.md:574 +#: ../../source/ref-changelog.md:643 ../../source/ref-changelog.md:772 +#: ../../source/ref-changelog.md:814 ../../source/ref-changelog.md:881 +#: ../../source/ref-changelog.md:947 ../../source/ref-changelog.md:992 +#: ../../source/ref-changelog.md:1031 ../../source/ref-changelog.md:1064 +#: ../../source/ref-changelog.md:1114 +msgid "What's new?" msgstr "" -#: ../../source/ref-changelog.md:15 ../../source/ref-changelog.md:79 -#: ../../source/ref-changelog.md:172 ../../source/ref-changelog.md:272 -#: ../../source/ref-changelog.md:356 ../../source/ref-changelog.md:420 -#: ../../source/ref-changelog.md:478 +#: ../../source/ref-changelog.md:13 msgid "" -"We would like to give our special thanks to all the contributors who made" -" the new version of Flower possible (in `git shortlog` order):" +"**Introduce built-in authentication (preview)** ([#2946](https://github.com/" +"adap/flower/pull/2946), [#3388](https://github.com/adap/flower/pull/3388), " +"[#2948](https://github.com/adap/flower/pull/2948), [#2917](https://github." +"com/adap/flower/pull/2917), [#3386](https://github.com/adap/flower/" +"pull/3386), [#3308](https://github.com/adap/flower/pull/3308), [#3001]" +"(https://github.com/adap/flower/pull/3001), [#3409](https://github.com/adap/" +"flower/pull/3409), [#2999](https://github.com/adap/flower/pull/2999), [#2979]" +"(https://github.com/adap/flower/pull/2979), [#3389](https://github.com/adap/" +"flower/pull/3389), [#3503](https://github.com/adap/flower/pull/3503), [#3366]" +"(https://github.com/adap/flower/pull/3366), [#3357](https://github.com/adap/" +"flower/pull/3357))" +msgstr "" + +#: ../../source/ref-changelog.md:15 +msgid "" +"Flower 1.9 introduces the first build-in version of client node " +"authentication. In previous releases, users often wrote glue code to connect " +"Flower to external authentication systems. With this release, the SuperLink " +"can authenticate SuperNodes using a built-in authentication system. A new " +"[how-to guide](https://flower.ai/docs/framework/how-to-authenticate-" +"supernodes.html) and a new [code example](https://github.com/adap/flower/" +"tree/main/examples/flower-authentication) help you to get started." msgstr "" #: ../../source/ref-changelog.md:17 msgid "" -"`Adam Narozniak`, `Charles Beauville`, `Daniel J. Beutel`, `Daniel Nata " -"Nugraha`, `Danny`, `Gustavo Bertoli`, `Heng Pan`, `Ikko Eltociear " -"Ashimine`, `Jack Cook`, `Javier`, `Raj Parekh`, `Robert Steiner`, " -"`Sebastian van der Voort`, `Taner Topal`, `Yan Gao`, `mohammadnaseri`, " -"`tabdar-khan` " +"This is the first preview release of the Flower-native authentication " +"system. Many additional features are on the roadmap for upcoming Flower " +"releases - stay tuned." +msgstr "" + +#: ../../source/ref-changelog.md:19 +msgid "" +"**Introduce end-to-end Docker support** ([#3483](https://github.com/adap/" +"flower/pull/3483), [#3266](https://github.com/adap/flower/pull/3266), [#3390]" +"(https://github.com/adap/flower/pull/3390), [#3283](https://github.com/adap/" +"flower/pull/3283), [#3285](https://github.com/adap/flower/pull/3285), [#3391]" +"(https://github.com/adap/flower/pull/3391), [#3403](https://github.com/adap/" +"flower/pull/3403), [#3458](https://github.com/adap/flower/pull/3458), [#3533]" +"(https://github.com/adap/flower/pull/3533), [#3453](https://github.com/adap/" +"flower/pull/3453), [#3486](https://github.com/adap/flower/pull/3486), [#3290]" +"(https://github.com/adap/flower/pull/3290))" msgstr "" #: ../../source/ref-changelog.md:21 msgid "" -"**Introduce Flower Next high-level API (stable)** " -"([#3002](https://github.com/adap/flower/pull/3002), " -"[#2934](https://github.com/adap/flower/pull/2934), " -"[#2958](https://github.com/adap/flower/pull/2958), " -"[#3173](https://github.com/adap/flower/pull/3173), " -"[#3174](https://github.com/adap/flower/pull/3174), " -"[#2923](https://github.com/adap/flower/pull/2923), " -"[#2691](https://github.com/adap/flower/pull/2691), " -"[#3079](https://github.com/adap/flower/pull/3079), " -"[#2961](https://github.com/adap/flower/pull/2961), " -"[#2924](https://github.com/adap/flower/pull/2924), " -"[#3166](https://github.com/adap/flower/pull/3166), " -"[#3031](https://github.com/adap/flower/pull/3031), " -"[#3057](https://github.com/adap/flower/pull/3057), " -"[#3000](https://github.com/adap/flower/pull/3000), " -"[#3113](https://github.com/adap/flower/pull/3113), " -"[#2957](https://github.com/adap/flower/pull/2957), " -"[#3183](https://github.com/adap/flower/pull/3183), " -"[#3180](https://github.com/adap/flower/pull/3180), " -"[#3035](https://github.com/adap/flower/pull/3035), " -"[#3189](https://github.com/adap/flower/pull/3189), " -"[#3185](https://github.com/adap/flower/pull/3185), " -"[#3190](https://github.com/adap/flower/pull/3190), " -"[#3191](https://github.com/adap/flower/pull/3191), " -"[#3195](https://github.com/adap/flower/pull/3195), " -"[#3197](https://github.com/adap/flower/pull/3197))" +"Full Flower Next Docker support is here! With the release of Flower 1.9, " +"Flower provides stable Docker images for the Flower SuperLink, the Flower " +"SuperNode, and the Flower `ServerApp`. This set of images enables you to run " +"all Flower components in Docker. Check out the new [how-to guide](https://" +"flower.ai/docs/framework/how-to-run-flower-using-docker.html) to get stated." msgstr "" #: ../../source/ref-changelog.md:23 msgid "" -"The Flower Next high-level API is stable! Flower Next is the future of " -"Flower - all new features (like Flower Mods) will be built on top of it. " -"You can start to migrate your existing projects to Flower Next by using " -"`ServerApp` and `ClientApp` (check out `quickstart-pytorch` or " -"`quickstart-tensorflow`, a detailed migration guide will follow shortly)." -" Flower Next allows you to run multiple projects concurrently (we call " -"this multi-run) and execute the same project in either simulation " -"environments or deployment environments without having to change a single" -" line of code. The best part? It's fully compatible with existing Flower " -"projects that use `Strategy`, `NumPyClient` & co." +"**Re-architect Flower Next simulation engine** ([#3307](https://github.com/" +"adap/flower/pull/3307), [#3355](https://github.com/adap/flower/pull/3355), " +"[#3272](https://github.com/adap/flower/pull/3272), [#3273](https://github." +"com/adap/flower/pull/3273), [#3417](https://github.com/adap/flower/" +"pull/3417), [#3281](https://github.com/adap/flower/pull/3281), [#3343]" +"(https://github.com/adap/flower/pull/3343), [#3326](https://github.com/adap/" +"flower/pull/3326))" msgstr "" #: ../../source/ref-changelog.md:25 msgid "" -"**Introduce Flower Next low-level API (preview)** " -"([#3062](https://github.com/adap/flower/pull/3062), " -"[#3034](https://github.com/adap/flower/pull/3034), " -"[#3069](https://github.com/adap/flower/pull/3069))" +"Flower Next simulations now use a new in-memory `Driver` that improves the " +"reliability of simulations, especially in notebook environments. This is a " +"significant step towards a complete overhaul of the Flower Next simulation " +"architecture." msgstr "" #: ../../source/ref-changelog.md:27 msgid "" -"In addition to the Flower Next *high-level* API that uses `Strategy`, " -"`NumPyClient` & co, Flower 1.8 also comes with a preview version of the " -"new Flower Next *low-level* API. The low-level API allows for granular " -"control of every aspect of the learning process by sending/receiving " -"individual messages to/from client nodes. The new `ServerApp` supports " -"registering a custom `main` function that allows writing custom training " -"loops for methods like async FL, cyclic training, or federated analytics." -" The new `ClientApp` supports registering `train`, `evaluate` and `query`" -" functions that can access the raw message received from the `ServerApp`." -" New abstractions like `RecordSet`, `Message` and `Context` further " -"enable sending multiple models, multiple sets of config values and " -"metrics, stateful computations on the client node and implementations of " -"custom SMPC protocols, to name just a few." +"**Upgrade simulation engine** ([#3354](https://github.com/adap/flower/" +"pull/3354), [#3378](https://github.com/adap/flower/pull/3378), [#3262]" +"(https://github.com/adap/flower/pull/3262), [#3435](https://github.com/adap/" +"flower/pull/3435), [#3501](https://github.com/adap/flower/pull/3501), [#3482]" +"(https://github.com/adap/flower/pull/3482), [#3494](https://github.com/adap/" +"flower/pull/3494))" msgstr "" #: ../../source/ref-changelog.md:29 msgid "" -"**Introduce Flower Mods (preview)** " -"([#3054](https://github.com/adap/flower/pull/3054), " -"[#2911](https://github.com/adap/flower/pull/2911), " -"[#3083](https://github.com/adap/flower/pull/3083))" +"The Flower Next simulation engine comes with improved and configurable " +"logging. The Ray-based simulation backend in Flower 1.9 was updated to use " +"Ray 2.10." msgstr "" #: ../../source/ref-changelog.md:31 msgid "" -"Flower Modifiers (we call them Mods) can intercept messages and analyze, " -"edit or handle them directly. Mods can be used to develop pluggable " -"modules that work across different projects. Flower 1.8 already includes " -"mods to log the size of a message, the number of parameters sent over the" -" network, differential privacy with fixed clipping and adaptive clipping," -" local differential privacy and secure aggregation protocols SecAgg and " -"SecAgg+. The Flower Mods API is released as a preview, but researchers " -"can already use it to experiment with arbirtrary SMPC protocols." +"**Introduce FedPFT baseline** ([#3268](https://github.com/adap/flower/" +"pull/3268))" msgstr "" #: ../../source/ref-changelog.md:33 msgid "" -"**Fine-tune LLMs with LLM FlowerTune** " -"([#3029](https://github.com/adap/flower/pull/3029), " -"[#3089](https://github.com/adap/flower/pull/3089), " -"[#3092](https://github.com/adap/flower/pull/3092), " -"[#3100](https://github.com/adap/flower/pull/3100), " -"[#3114](https://github.com/adap/flower/pull/3114), " -"[#3162](https://github.com/adap/flower/pull/3162), " -"[#3172](https://github.com/adap/flower/pull/3172))" +"FedPFT allows you to perform one-shot Federated Learning by leveraging " +"widely available foundational models, dramatically reducing communication " +"costs while delivering high performing models. This is work led by Mahdi " +"Beitollahi from Huawei Noah's Ark Lab (Montreal, Canada). Read all the " +"details in their paper: \"Parametric Feature Transfer: One-shot Federated " +"Learning with Foundation Models\" ([arxiv](https://arxiv.org/abs/2402.01862))" msgstr "" #: ../../source/ref-changelog.md:35 msgid "" -"We are introducing LLM FlowerTune, an introductory example that " -"demonstrates federated LLM fine-tuning of pre-trained Llama2 models on " -"the Alpaca-GPT4 dataset. The example is built to be easily adapted to use" -" different models and/or datasets. Read our blog post [LLM FlowerTune: " -"Federated LLM Fine-tuning with Flower](https://flower.ai/blog/2024-03-14" -"-llm-flowertune-federated-llm-finetuning-with-flower/) for more details." +"**Launch additional** `flwr new` **templates for Apple MLX, Hugging Face " +"Transformers, scikit-learn and TensorFlow** ([#3291](https://github.com/adap/" +"flower/pull/3291), [#3139](https://github.com/adap/flower/pull/3139), [#3284]" +"(https://github.com/adap/flower/pull/3284), [#3251](https://github.com/adap/" +"flower/pull/3251), [#3376](https://github.com/adap/flower/pull/3376), [#3287]" +"(https://github.com/adap/flower/pull/3287))" msgstr "" #: ../../source/ref-changelog.md:37 msgid "" -"**Introduce built-in Differential Privacy (preview)** " -"([#2798](https://github.com/adap/flower/pull/2798), " -"[#2959](https://github.com/adap/flower/pull/2959), " -"[#3038](https://github.com/adap/flower/pull/3038), " -"[#3147](https://github.com/adap/flower/pull/3147), " -"[#2909](https://github.com/adap/flower/pull/2909), " -"[#2893](https://github.com/adap/flower/pull/2893), " -"[#2892](https://github.com/adap/flower/pull/2892), " -"[#3039](https://github.com/adap/flower/pull/3039), " -"[#3074](https://github.com/adap/flower/pull/3074))" +"The `flwr` CLI's `flwr new` command is starting to become everone's favorite " +"way of creating new Flower projects. This release introduces additional " +"`flwr new` templates for Apple MLX, Hugging Face Transformers, scikit-learn " +"and TensorFlow. In addition to that, existing templates also received " +"updates." msgstr "" #: ../../source/ref-changelog.md:39 msgid "" -"Built-in Differential Privacy is here! Flower supports both central and " -"local differential privacy (DP). Central DP can be configured with either" -" fixed or adaptive clipping. The clipping can happen either on the " -"server-side or the client-side. Local DP does both clipping and noising " -"on the client-side. A new documentation page [explains Differential " -"Privacy approaches](https://flower.ai/docs/framework/explanation-" -"differential-privacy.html) and a new how-to guide describes [how to use " -"the new Differential Privacy components](https://flower.ai/docs/framework" -"/how-to-use-differential-privacy.html) in Flower." +"**Refine** `RecordSet` **API** ([#3209](https://github.com/adap/flower/" +"pull/3209), [#3331](https://github.com/adap/flower/pull/3331), [#3334]" +"(https://github.com/adap/flower/pull/3334), [#3335](https://github.com/adap/" +"flower/pull/3335), [#3375](https://github.com/adap/flower/pull/3375), [#3368]" +"(https://github.com/adap/flower/pull/3368))" msgstr "" #: ../../source/ref-changelog.md:41 msgid "" -"**Introduce built-in Secure Aggregation (preview)** " -"([#3120](https://github.com/adap/flower/pull/3120), " -"[#3110](https://github.com/adap/flower/pull/3110), " -"[#3108](https://github.com/adap/flower/pull/3108))" +"`RecordSet` is part of the Flower Next low-level API preview release. In " +"Flower 1.9, `RecordSet` received a number of usability improvements that " +"make it easier to build `RecordSet`-based `ServerApp`s and `ClientApp`s." msgstr "" #: ../../source/ref-changelog.md:43 msgid "" -"Built-in Secure Aggregation is here! Flower now supports different secure" -" aggregation protocols out-of-the-box. The best part? You can add secure " -"aggregation to your Flower projects with only a few lines of code. In " -"this initial release, we inlcude support for SecAgg and SecAgg+, but more" -" protocols will be implemented shortly. We'll also add detailed docs that" -" explain secure aggregation and how to use it in Flower. You can already " -"check out the new code example that shows how to use Flower to easily " -"combine Federated Learning, Differential Privacy and Secure Aggregation " -"in the same project." +"**Beautify logging** ([#3379](https://github.com/adap/flower/pull/3379), " +"[#3430](https://github.com/adap/flower/pull/3430), [#3461](https://github." +"com/adap/flower/pull/3461), [#3360](https://github.com/adap/flower/" +"pull/3360), [#3433](https://github.com/adap/flower/pull/3433))" msgstr "" #: ../../source/ref-changelog.md:45 msgid "" -"**Introduce** `flwr` **CLI (preview)** " -"([#2942](https://github.com/adap/flower/pull/2942), " -"[#3055](https://github.com/adap/flower/pull/3055), " -"[#3111](https://github.com/adap/flower/pull/3111), " -"[#3130](https://github.com/adap/flower/pull/3130), " -"[#3136](https://github.com/adap/flower/pull/3136), " -"[#3094](https://github.com/adap/flower/pull/3094), " -"[#3059](https://github.com/adap/flower/pull/3059), " -"[#3049](https://github.com/adap/flower/pull/3049), " -"[#3142](https://github.com/adap/flower/pull/3142))" +"Logs received a substantial update. Not only are logs now much nicer to look " +"at, but they are also more configurable." msgstr "" #: ../../source/ref-changelog.md:47 msgid "" -"A new `flwr` CLI command allows creating new Flower projects (`flwr new`)" -" and then running them using the Simulation Engine (`flwr run`)." +"**Improve reliability** ([#3564](https://github.com/adap/flower/pull/3564), " +"[#3561](https://github.com/adap/flower/pull/3561), [#3566](https://github." +"com/adap/flower/pull/3566), [#3462](https://github.com/adap/flower/" +"pull/3462), [#3225](https://github.com/adap/flower/pull/3225), [#3514]" +"(https://github.com/adap/flower/pull/3514), [#3535](https://github.com/adap/" +"flower/pull/3535), [#3372](https://github.com/adap/flower/pull/3372))" msgstr "" #: ../../source/ref-changelog.md:49 msgid "" -"**Introduce Flower Next Simulation Engine** " -"([#3024](https://github.com/adap/flower/pull/3024), " -"[#3061](https://github.com/adap/flower/pull/3061), " -"[#2997](https://github.com/adap/flower/pull/2997), " -"[#2783](https://github.com/adap/flower/pull/2783), " -"[#3184](https://github.com/adap/flower/pull/3184), " -"[#3075](https://github.com/adap/flower/pull/3075), " -"[#3047](https://github.com/adap/flower/pull/3047), " -"[#2998](https://github.com/adap/flower/pull/2998), " -"[#3009](https://github.com/adap/flower/pull/3009), " -"[#3008](https://github.com/adap/flower/pull/3008))" +"Flower 1.9 includes reliability improvements across many parts of the " +"system. One example is a much improved SuperNode shutdown procedure." msgstr "" #: ../../source/ref-changelog.md:51 msgid "" -"The Flower Simulation Engine can now run Flower Next projects. For " -"notebook environments, there's also a new `run_simulation` function that " -"can run `ServerApp` and `ClientApp`." +"**Update Swift and C++ SDKs** ([#3321](https://github.com/adap/flower/" +"pull/3321), [#2763](https://github.com/adap/flower/pull/2763))" msgstr "" #: ../../source/ref-changelog.md:53 msgid "" -"**Handle SuperNode connection errors** " -"([#2969](https://github.com/adap/flower/pull/2969))" +"In the C++ SDK, communication-related code is now separate from main client " +"logic. A new abstract class `Communicator` has been introduced alongside a " +"gRPC implementation of it." msgstr "" #: ../../source/ref-changelog.md:55 msgid "" -"A SuperNode will now try to reconnect indefinitely to the SuperLink in " -"case of connection errors. The arguments `--max-retries` and `--max-wait-" -"time` can now be passed to the `flower-client-app` command. `--max-" -"retries` will define the number of tentatives the client should make " -"before it gives up trying to reconnect to the SuperLink, and, `--max-" -"wait-time` defines the time before the SuperNode gives up trying to " -"reconnect to the SuperLink." +"**Improve testing, tooling and CI/CD infrastructure** ([#3294](https://" +"github.com/adap/flower/pull/3294), [#3282](https://github.com/adap/flower/" +"pull/3282), [#3311](https://github.com/adap/flower/pull/3311), [#2878]" +"(https://github.com/adap/flower/pull/2878), [#3333](https://github.com/adap/" +"flower/pull/3333), [#3255](https://github.com/adap/flower/pull/3255), [#3349]" +"(https://github.com/adap/flower/pull/3349), [#3400](https://github.com/adap/" +"flower/pull/3400), [#3401](https://github.com/adap/flower/pull/3401), [#3399]" +"(https://github.com/adap/flower/pull/3399), [#3346](https://github.com/adap/" +"flower/pull/3346), [#3398](https://github.com/adap/flower/pull/3398), [#3397]" +"(https://github.com/adap/flower/pull/3397), [#3347](https://github.com/adap/" +"flower/pull/3347), [#3502](https://github.com/adap/flower/pull/3502), [#3387]" +"(https://github.com/adap/flower/pull/3387), [#3542](https://github.com/adap/" +"flower/pull/3542), [#3396](https://github.com/adap/flower/pull/3396), [#3496]" +"(https://github.com/adap/flower/pull/3496), [#3465](https://github.com/adap/" +"flower/pull/3465), [#3473](https://github.com/adap/flower/pull/3473), [#3484]" +"(https://github.com/adap/flower/pull/3484), [#3521](https://github.com/adap/" +"flower/pull/3521), [#3363](https://github.com/adap/flower/pull/3363), [#3497]" +"(https://github.com/adap/flower/pull/3497), [#3464](https://github.com/adap/" +"flower/pull/3464), [#3495](https://github.com/adap/flower/pull/3495), [#3478]" +"(https://github.com/adap/flower/pull/3478), [#3271](https://github.com/adap/" +"flower/pull/3271))" msgstr "" #: ../../source/ref-changelog.md:57 msgid "" -"**General updates to Flower Baselines** " -"([#2904](https://github.com/adap/flower/pull/2904), " -"[#2482](https://github.com/adap/flower/pull/2482), " -"[#2985](https://github.com/adap/flower/pull/2985), " -"[#2968](https://github.com/adap/flower/pull/2968))" +"As always, the Flower tooling, testing, and CI/CD infrastructure has " +"received many updates." msgstr "" #: ../../source/ref-changelog.md:59 msgid "" -"There's a new [FedStar](https://flower.ai/docs/baselines/fedstar.html) " -"baseline. Several other baselined have been updated as well." +"**Improve documentation** ([#3530](https://github.com/adap/flower/" +"pull/3530), [#3539](https://github.com/adap/flower/pull/3539), [#3425]" +"(https://github.com/adap/flower/pull/3425), [#3520](https://github.com/adap/" +"flower/pull/3520), [#3286](https://github.com/adap/flower/pull/3286), [#3516]" +"(https://github.com/adap/flower/pull/3516), [#3523](https://github.com/adap/" +"flower/pull/3523), [#3545](https://github.com/adap/flower/pull/3545), [#3498]" +"(https://github.com/adap/flower/pull/3498), [#3439](https://github.com/adap/" +"flower/pull/3439), [#3440](https://github.com/adap/flower/pull/3440), [#3382]" +"(https://github.com/adap/flower/pull/3382), [#3559](https://github.com/adap/" +"flower/pull/3559), [#3432](https://github.com/adap/flower/pull/3432), [#3278]" +"(https://github.com/adap/flower/pull/3278), [#3371](https://github.com/adap/" +"flower/pull/3371), [#3519](https://github.com/adap/flower/pull/3519), [#3267]" +"(https://github.com/adap/flower/pull/3267), [#3204](https://github.com/adap/" +"flower/pull/3204), [#3274](https://github.com/adap/flower/pull/3274))" msgstr "" #: ../../source/ref-changelog.md:61 msgid "" -"**Improve documentation and translations** " -"([#3050](https://github.com/adap/flower/pull/3050), " -"[#3044](https://github.com/adap/flower/pull/3044), " -"[#3043](https://github.com/adap/flower/pull/3043), " -"[#2986](https://github.com/adap/flower/pull/2986), " -"[#3041](https://github.com/adap/flower/pull/3041), " -"[#3046](https://github.com/adap/flower/pull/3046), " -"[#3042](https://github.com/adap/flower/pull/3042), " -"[#2978](https://github.com/adap/flower/pull/2978), " -"[#2952](https://github.com/adap/flower/pull/2952), " -"[#3167](https://github.com/adap/flower/pull/3167), " -"[#2953](https://github.com/adap/flower/pull/2953), " -"[#3045](https://github.com/adap/flower/pull/3045), " -"[#2654](https://github.com/adap/flower/pull/2654), " -"[#3082](https://github.com/adap/flower/pull/3082), " -"[#2990](https://github.com/adap/flower/pull/2990), " -"[#2989](https://github.com/adap/flower/pull/2989))" +"As always, the Flower documentation has received many updates. Notable new " +"pages include:" msgstr "" #: ../../source/ref-changelog.md:63 msgid "" -"As usual, we merged many smaller and larger improvements to the " -"documentation. A special thank you goes to [Sebastian van der " -"Voort](https://github.com/svdvoort) for landing a big documentation PR!" +"[How-to upgrate to Flower Next (Flower Next migration guide)](https://flower." +"ai/docs/framework/how-to-upgrade-to-flower-next.html)" msgstr "" #: ../../source/ref-changelog.md:65 msgid "" -"**General updates to Flower Examples** " -"([3134](https://github.com/adap/flower/pull/3134), " -"[2996](https://github.com/adap/flower/pull/2996), " -"[2930](https://github.com/adap/flower/pull/2930), " -"[2967](https://github.com/adap/flower/pull/2967), " -"[2467](https://github.com/adap/flower/pull/2467), " -"[2910](https://github.com/adap/flower/pull/2910), " -"[#2918](https://github.com/adap/flower/pull/2918), " -"[#2773](https://github.com/adap/flower/pull/2773), " -"[#3063](https://github.com/adap/flower/pull/3063), " -"[#3116](https://github.com/adap/flower/pull/3116), " -"[#3117](https://github.com/adap/flower/pull/3117))" +"[How-to run Flower using Docker](https://flower.ai/docs/framework/how-to-run-" +"flower-using-docker.html)" msgstr "" #: ../../source/ref-changelog.md:67 msgid "" -"Two new examples show federated training of a Vision Transformer (ViT) " -"and federated learning in a medical context using the popular MONAI " -"library. `quickstart-pytorch` and `quickstart-tensorflow` demonstrate the" -" new Flower Next `ServerApp` and `ClientApp`. Many other examples " -"received considerable updates as well." +"[Flower Mods reference](https://flower.ai/docs/framework/ref-api/flwr.client." +"mod.html#module-flwr.client.mod)" +msgstr "" + +#: ../../source/ref-changelog.md:69 +msgid "" +"**General updates to Flower Examples** ([#3205](https://github.com/adap/" +"flower/pull/3205), [#3226](https://github.com/adap/flower/pull/3226), [#3211]" +"(https://github.com/adap/flower/pull/3211), [#3252](https://github.com/adap/" +"flower/pull/3252), [#3427](https://github.com/adap/flower/pull/3427), [#3410]" +"(https://github.com/adap/flower/pull/3410), [#3426](https://github.com/adap/" +"flower/pull/3426), [#3228](https://github.com/adap/flower/pull/3228), [#3342]" +"(https://github.com/adap/flower/pull/3342), [#3200](https://github.com/adap/" +"flower/pull/3200), [#3202](https://github.com/adap/flower/pull/3202), [#3394]" +"(https://github.com/adap/flower/pull/3394), [#3488](https://github.com/adap/" +"flower/pull/3488), [#3329](https://github.com/adap/flower/pull/3329), [#3526]" +"(https://github.com/adap/flower/pull/3526), [#3392](https://github.com/adap/" +"flower/pull/3392), [#3474](https://github.com/adap/flower/pull/3474), [#3269]" +"(https://github.com/adap/flower/pull/3269))" +msgstr "" + +#: ../../source/ref-changelog.md:71 +msgid "As always, Flower code examples have received many updates." +msgstr "" + +#: ../../source/ref-changelog.md:73 +msgid "" +"**General improvements** ([#3532](https://github.com/adap/flower/pull/3532), " +"[#3318](https://github.com/adap/flower/pull/3318), [#3565](https://github." +"com/adap/flower/pull/3565), [#3296](https://github.com/adap/flower/" +"pull/3296), [#3305](https://github.com/adap/flower/pull/3305), [#3246]" +"(https://github.com/adap/flower/pull/3246), [#3224](https://github.com/adap/" +"flower/pull/3224), [#3475](https://github.com/adap/flower/pull/3475), [#3297]" +"(https://github.com/adap/flower/pull/3297), [#3317](https://github.com/adap/" +"flower/pull/3317), [#3429](https://github.com/adap/flower/pull/3429), [#3196]" +"(https://github.com/adap/flower/pull/3196), [#3534](https://github.com/adap/" +"flower/pull/3534), [#3240](https://github.com/adap/flower/pull/3240), [#3365]" +"(https://github.com/adap/flower/pull/3365), [#3407](https://github.com/adap/" +"flower/pull/3407), [#3563](https://github.com/adap/flower/pull/3563), [#3344]" +"(https://github.com/adap/flower/pull/3344), [#3330](https://github.com/adap/" +"flower/pull/3330), [#3436](https://github.com/adap/flower/pull/3436), [#3300]" +"(https://github.com/adap/flower/pull/3300), [#3327](https://github.com/adap/" +"flower/pull/3327), [#3254](https://github.com/adap/flower/pull/3254), [#3253]" +"(https://github.com/adap/flower/pull/3253), [#3419](https://github.com/adap/" +"flower/pull/3419), [#3289](https://github.com/adap/flower/pull/3289), [#3208]" +"(https://github.com/adap/flower/pull/3208), [#3245](https://github.com/adap/" +"flower/pull/3245), [#3319](https://github.com/adap/flower/pull/3319), [#3203]" +"(https://github.com/adap/flower/pull/3203), [#3423](https://github.com/adap/" +"flower/pull/3423), [#3352](https://github.com/adap/flower/pull/3352), [#3292]" +"(https://github.com/adap/flower/pull/3292), [#3261](https://github.com/adap/" +"flower/pull/3261))" +msgstr "" + +#: ../../source/ref-changelog.md:75 ../../source/ref-changelog.md:1058 +msgid "Deprecations" +msgstr "" + +#: ../../source/ref-changelog.md:77 +msgid "**Deprecate Python 3.8 support**" +msgstr "" + +#: ../../source/ref-changelog.md:79 +msgid "" +"Python 3.8 will stop receiving security fixes in [October 2024](https://" +"devguide.python.org/versions/). Support for Python 3.8 is now deprecated and " +"will be removed in an upcoming release." +msgstr "" + +#: ../../source/ref-changelog.md:81 +msgid "" +"**Deprecate (experimental)** `flower-driver-api` **and** `flower-fleet-api` " +"([#3416](https://github.com/adap/flower/pull/3416), [#3420](https://github." +"com/adap/flower/pull/3420))" +msgstr "" + +#: ../../source/ref-changelog.md:83 +msgid "" +"Flower 1.9 deprecates the two (experimental) commands `flower-driver-api` " +"and `flower-fleet-api`. Both commands will be removed in an upcoming " +"release. Use `flower-superlink` instead." +msgstr "" + +#: ../../source/ref-changelog.md:85 +msgid "" +"**Deprecate** `--server` **in favor of** `--superlink` ([#3518](https://" +"github.com/adap/flower/pull/3518))" +msgstr "" + +#: ../../source/ref-changelog.md:87 +msgid "" +"The commands `flower-server-app` and `flower-client-app` should use `--" +"superlink` instead of the now deprecated `--server`. Support for `--server` " +"will be removed in a future release." +msgstr "" + +#: ../../source/ref-changelog.md:89 ../../source/ref-changelog.md:163 +#: ../../source/ref-changelog.md:238 ../../source/ref-changelog.md:350 +#: ../../source/ref-changelog.md:440 ../../source/ref-changelog.md:504 +#: ../../source/ref-changelog.md:562 ../../source/ref-changelog.md:631 +#: ../../source/ref-changelog.md:693 ../../source/ref-changelog.md:712 +#: ../../source/ref-changelog.md:868 ../../source/ref-changelog.md:939 +#: ../../source/ref-changelog.md:976 ../../source/ref-changelog.md:1019 +msgid "Incompatible changes" +msgstr "" + +#: ../../source/ref-changelog.md:91 +msgid "" +"**Replace** `flower-superlink` **CLI option** `--certificates` **with** `--" +"ssl-ca-certfile` **,** `--ssl-certfile` **and** `--ssl-keyfile` ([#3512]" +"(https://github.com/adap/flower/pull/3512), [#3408](https://github.com/adap/" +"flower/pull/3408))" +msgstr "" + +#: ../../source/ref-changelog.md:93 +msgid "" +"SSL-related `flower-superlink` CLI arguments were restructured in an " +"incompatible way. Instead of passing a single `--certificates` flag with " +"three values, you now need to pass three flags (`--ssl-ca-certfile`, `--ssl-" +"certfile` and `--ssl-keyfile`) with one value each. Check out the [SSL " +"connections](https://flower.ai/docs/framework/how-to-enable-ssl-connections." +"html) documentation page for details." +msgstr "" + +#: ../../source/ref-changelog.md:95 +msgid "" +"**Remove SuperLink** `--vce` **option** ([#3513](https://github.com/adap/" +"flower/pull/3513))" +msgstr "" + +#: ../../source/ref-changelog.md:97 +msgid "" +"Instead of separately starting a SuperLink and a `ServerApp` for simulation, " +"simulations must now be started using the single `flower-simulation` command." +msgstr "" + +#: ../../source/ref-changelog.md:99 +msgid "" +"**Merge** `--grpc-rere` **and** `--rest` **SuperLink options** ([#3527]" +"(https://github.com/adap/flower/pull/3527))" +msgstr "" + +#: ../../source/ref-changelog.md:101 +msgid "" +"To simplify the usage of `flower-superlink`, previously separate sets of CLI " +"options for gRPC and REST were merged into one unified set of options. " +"Consult the [Flower CLI reference documentation](https://flower.ai/docs/" +"framework/ref-api-cli.html) for details." +msgstr "" + +#: ../../source/ref-changelog.md:103 +msgid "v1.8.0 (2024-04-03)" +msgstr "" + +#: ../../source/ref-changelog.md:109 +msgid "" +"`Adam Narozniak`, `Charles Beauville`, `Daniel J. Beutel`, `Daniel Nata " +"Nugraha`, `Danny`, `Gustavo Bertoli`, `Heng Pan`, `Ikko Eltociear Ashimine`, " +"`Jack Cook`, `Javier`, `Raj Parekh`, `Robert Steiner`, `Sebastian van der " +"Voort`, `Taner Topal`, `Yan Gao`, `mohammadnaseri`, `tabdar-khan` " +msgstr "" + +#: ../../source/ref-changelog.md:113 +msgid "" +"**Introduce Flower Next high-level API (stable)** ([#3002](https://github." +"com/adap/flower/pull/3002), [#2934](https://github.com/adap/flower/" +"pull/2934), [#2958](https://github.com/adap/flower/pull/2958), [#3173]" +"(https://github.com/adap/flower/pull/3173), [#3174](https://github.com/adap/" +"flower/pull/3174), [#2923](https://github.com/adap/flower/pull/2923), [#2691]" +"(https://github.com/adap/flower/pull/2691), [#3079](https://github.com/adap/" +"flower/pull/3079), [#2961](https://github.com/adap/flower/pull/2961), [#2924]" +"(https://github.com/adap/flower/pull/2924), [#3166](https://github.com/adap/" +"flower/pull/3166), [#3031](https://github.com/adap/flower/pull/3031), [#3057]" +"(https://github.com/adap/flower/pull/3057), [#3000](https://github.com/adap/" +"flower/pull/3000), [#3113](https://github.com/adap/flower/pull/3113), [#2957]" +"(https://github.com/adap/flower/pull/2957), [#3183](https://github.com/adap/" +"flower/pull/3183), [#3180](https://github.com/adap/flower/pull/3180), [#3035]" +"(https://github.com/adap/flower/pull/3035), [#3189](https://github.com/adap/" +"flower/pull/3189), [#3185](https://github.com/adap/flower/pull/3185), [#3190]" +"(https://github.com/adap/flower/pull/3190), [#3191](https://github.com/adap/" +"flower/pull/3191), [#3195](https://github.com/adap/flower/pull/3195), [#3197]" +"(https://github.com/adap/flower/pull/3197))" +msgstr "" + +#: ../../source/ref-changelog.md:115 +msgid "" +"The Flower Next high-level API is stable! Flower Next is the future of " +"Flower - all new features (like Flower Mods) will be built on top of it. You " +"can start to migrate your existing projects to Flower Next by using " +"`ServerApp` and `ClientApp` (check out `quickstart-pytorch` or `quickstart-" +"tensorflow`, a detailed migration guide will follow shortly). Flower Next " +"allows you to run multiple projects concurrently (we call this multi-run) " +"and execute the same project in either simulation environments or deployment " +"environments without having to change a single line of code. The best part? " +"It's fully compatible with existing Flower projects that use `Strategy`, " +"`NumPyClient` & co." +msgstr "" + +#: ../../source/ref-changelog.md:117 +msgid "" +"**Introduce Flower Next low-level API (preview)** ([#3062](https://github." +"com/adap/flower/pull/3062), [#3034](https://github.com/adap/flower/" +"pull/3034), [#3069](https://github.com/adap/flower/pull/3069))" +msgstr "" + +#: ../../source/ref-changelog.md:119 +msgid "" +"In addition to the Flower Next *high-level* API that uses `Strategy`, " +"`NumPyClient` & co, Flower 1.8 also comes with a preview version of the new " +"Flower Next *low-level* API. The low-level API allows for granular control " +"of every aspect of the learning process by sending/receiving individual " +"messages to/from client nodes. The new `ServerApp` supports registering a " +"custom `main` function that allows writing custom training loops for methods " +"like async FL, cyclic training, or federated analytics. The new `ClientApp` " +"supports registering `train`, `evaluate` and `query` functions that can " +"access the raw message received from the `ServerApp`. New abstractions like " +"`RecordSet`, `Message` and `Context` further enable sending multiple models, " +"multiple sets of config values and metrics, stateful computations on the " +"client node and implementations of custom SMPC protocols, to name just a few." +msgstr "" + +#: ../../source/ref-changelog.md:121 +msgid "" +"**Introduce Flower Mods (preview)** ([#3054](https://github.com/adap/flower/" +"pull/3054), [#2911](https://github.com/adap/flower/pull/2911), [#3083]" +"(https://github.com/adap/flower/pull/3083))" +msgstr "" + +#: ../../source/ref-changelog.md:123 +msgid "" +"Flower Modifiers (we call them Mods) can intercept messages and analyze, " +"edit or handle them directly. Mods can be used to develop pluggable modules " +"that work across different projects. Flower 1.8 already includes mods to log " +"the size of a message, the number of parameters sent over the network, " +"differential privacy with fixed clipping and adaptive clipping, local " +"differential privacy and secure aggregation protocols SecAgg and SecAgg+. " +"The Flower Mods API is released as a preview, but researchers can already " +"use it to experiment with arbirtrary SMPC protocols." +msgstr "" + +#: ../../source/ref-changelog.md:125 +msgid "" +"**Fine-tune LLMs with LLM FlowerTune** ([#3029](https://github.com/adap/" +"flower/pull/3029), [#3089](https://github.com/adap/flower/pull/3089), [#3092]" +"(https://github.com/adap/flower/pull/3092), [#3100](https://github.com/adap/" +"flower/pull/3100), [#3114](https://github.com/adap/flower/pull/3114), [#3162]" +"(https://github.com/adap/flower/pull/3162), [#3172](https://github.com/adap/" +"flower/pull/3172))" +msgstr "" + +#: ../../source/ref-changelog.md:127 +msgid "" +"We are introducing LLM FlowerTune, an introductory example that demonstrates " +"federated LLM fine-tuning of pre-trained Llama2 models on the Alpaca-GPT4 " +"dataset. The example is built to be easily adapted to use different models " +"and/or datasets. Read our blog post [LLM FlowerTune: Federated LLM Fine-" +"tuning with Flower](https://flower.ai/blog/2024-03-14-llm-flowertune-" +"federated-llm-finetuning-with-flower/) for more details." +msgstr "" + +#: ../../source/ref-changelog.md:129 +msgid "" +"**Introduce built-in Differential Privacy (preview)** ([#2798](https://" +"github.com/adap/flower/pull/2798), [#2959](https://github.com/adap/flower/" +"pull/2959), [#3038](https://github.com/adap/flower/pull/3038), [#3147]" +"(https://github.com/adap/flower/pull/3147), [#2909](https://github.com/adap/" +"flower/pull/2909), [#2893](https://github.com/adap/flower/pull/2893), [#2892]" +"(https://github.com/adap/flower/pull/2892), [#3039](https://github.com/adap/" +"flower/pull/3039), [#3074](https://github.com/adap/flower/pull/3074))" +msgstr "" + +#: ../../source/ref-changelog.md:131 +msgid "" +"Built-in Differential Privacy is here! Flower supports both central and " +"local differential privacy (DP). Central DP can be configured with either " +"fixed or adaptive clipping. The clipping can happen either on the server-" +"side or the client-side. Local DP does both clipping and noising on the " +"client-side. A new documentation page [explains Differential Privacy " +"approaches](https://flower.ai/docs/framework/explanation-differential-" +"privacy.html) and a new how-to guide describes [how to use the new " +"Differential Privacy components](https://flower.ai/docs/framework/how-to-use-" +"differential-privacy.html) in Flower." +msgstr "" + +#: ../../source/ref-changelog.md:133 +msgid "" +"**Introduce built-in Secure Aggregation (preview)** ([#3120](https://github." +"com/adap/flower/pull/3120), [#3110](https://github.com/adap/flower/" +"pull/3110), [#3108](https://github.com/adap/flower/pull/3108))" +msgstr "" + +#: ../../source/ref-changelog.md:135 +msgid "" +"Built-in Secure Aggregation is here! Flower now supports different secure " +"aggregation protocols out-of-the-box. The best part? You can add secure " +"aggregation to your Flower projects with only a few lines of code. In this " +"initial release, we inlcude support for SecAgg and SecAgg+, but more " +"protocols will be implemented shortly. We'll also add detailed docs that " +"explain secure aggregation and how to use it in Flower. You can already " +"check out the new code example that shows how to use Flower to easily " +"combine Federated Learning, Differential Privacy and Secure Aggregation in " +"the same project." +msgstr "" + +#: ../../source/ref-changelog.md:137 +msgid "" +"**Introduce** `flwr` **CLI (preview)** ([#2942](https://github.com/adap/" +"flower/pull/2942), [#3055](https://github.com/adap/flower/pull/3055), [#3111]" +"(https://github.com/adap/flower/pull/3111), [#3130](https://github.com/adap/" +"flower/pull/3130), [#3136](https://github.com/adap/flower/pull/3136), [#3094]" +"(https://github.com/adap/flower/pull/3094), [#3059](https://github.com/adap/" +"flower/pull/3059), [#3049](https://github.com/adap/flower/pull/3049), [#3142]" +"(https://github.com/adap/flower/pull/3142))" +msgstr "" + +#: ../../source/ref-changelog.md:139 +msgid "" +"A new `flwr` CLI command allows creating new Flower projects (`flwr new`) " +"and then running them using the Simulation Engine (`flwr run`)." +msgstr "" + +#: ../../source/ref-changelog.md:141 +msgid "" +"**Introduce Flower Next Simulation Engine** ([#3024](https://github.com/adap/" +"flower/pull/3024), [#3061](https://github.com/adap/flower/pull/3061), [#2997]" +"(https://github.com/adap/flower/pull/2997), [#2783](https://github.com/adap/" +"flower/pull/2783), [#3184](https://github.com/adap/flower/pull/3184), [#3075]" +"(https://github.com/adap/flower/pull/3075), [#3047](https://github.com/adap/" +"flower/pull/3047), [#2998](https://github.com/adap/flower/pull/2998), [#3009]" +"(https://github.com/adap/flower/pull/3009), [#3008](https://github.com/adap/" +"flower/pull/3008))" +msgstr "" + +#: ../../source/ref-changelog.md:143 +msgid "" +"The Flower Simulation Engine can now run Flower Next projects. For notebook " +"environments, there's also a new `run_simulation` function that can run " +"`ServerApp` and `ClientApp`." +msgstr "" + +#: ../../source/ref-changelog.md:145 +msgid "" +"**Handle SuperNode connection errors** ([#2969](https://github.com/adap/" +"flower/pull/2969))" +msgstr "" + +#: ../../source/ref-changelog.md:147 +msgid "" +"A SuperNode will now try to reconnect indefinitely to the SuperLink in case " +"of connection errors. The arguments `--max-retries` and `--max-wait-time` " +"can now be passed to the `flower-client-app` command. `--max-retries` will " +"define the number of tentatives the client should make before it gives up " +"trying to reconnect to the SuperLink, and, `--max-wait-time` defines the " +"time before the SuperNode gives up trying to reconnect to the SuperLink." +msgstr "" + +#: ../../source/ref-changelog.md:149 +msgid "" +"**General updates to Flower Baselines** ([#2904](https://github.com/adap/" +"flower/pull/2904), [#2482](https://github.com/adap/flower/pull/2482), [#2985]" +"(https://github.com/adap/flower/pull/2985), [#2968](https://github.com/adap/" +"flower/pull/2968))" +msgstr "" + +#: ../../source/ref-changelog.md:151 +msgid "" +"There's a new [FedStar](https://flower.ai/docs/baselines/fedstar.html) " +"baseline. Several other baselined have been updated as well." +msgstr "" + +#: ../../source/ref-changelog.md:153 +msgid "" +"**Improve documentation and translations** ([#3050](https://github.com/adap/" +"flower/pull/3050), [#3044](https://github.com/adap/flower/pull/3044), [#3043]" +"(https://github.com/adap/flower/pull/3043), [#2986](https://github.com/adap/" +"flower/pull/2986), [#3041](https://github.com/adap/flower/pull/3041), [#3046]" +"(https://github.com/adap/flower/pull/3046), [#3042](https://github.com/adap/" +"flower/pull/3042), [#2978](https://github.com/adap/flower/pull/2978), [#2952]" +"(https://github.com/adap/flower/pull/2952), [#3167](https://github.com/adap/" +"flower/pull/3167), [#2953](https://github.com/adap/flower/pull/2953), [#3045]" +"(https://github.com/adap/flower/pull/3045), [#2654](https://github.com/adap/" +"flower/pull/2654), [#3082](https://github.com/adap/flower/pull/3082), [#2990]" +"(https://github.com/adap/flower/pull/2990), [#2989](https://github.com/adap/" +"flower/pull/2989))" +msgstr "" + +#: ../../source/ref-changelog.md:155 +msgid "" +"As usual, we merged many smaller and larger improvements to the " +"documentation. A special thank you goes to [Sebastian van der Voort](https://" +"github.com/svdvoort) for landing a big documentation PR!" +msgstr "" + +#: ../../source/ref-changelog.md:157 +msgid "" +"**General updates to Flower Examples** ([3134](https://github.com/adap/" +"flower/pull/3134), [2996](https://github.com/adap/flower/pull/2996), [2930]" +"(https://github.com/adap/flower/pull/2930), [2967](https://github.com/adap/" +"flower/pull/2967), [2467](https://github.com/adap/flower/pull/2467), [2910]" +"(https://github.com/adap/flower/pull/2910), [#2918](https://github.com/adap/" +"flower/pull/2918), [#2773](https://github.com/adap/flower/pull/2773), [#3063]" +"(https://github.com/adap/flower/pull/3063), [#3116](https://github.com/adap/" +"flower/pull/3116), [#3117](https://github.com/adap/flower/pull/3117))" +msgstr "" + +#: ../../source/ref-changelog.md:159 +msgid "" +"Two new examples show federated training of a Vision Transformer (ViT) and " +"federated learning in a medical context using the popular MONAI library. " +"`quickstart-pytorch` and `quickstart-tensorflow` demonstrate the new Flower " +"Next `ServerApp` and `ClientApp`. Many other examples received considerable " +"updates as well." +msgstr "" + +#: ../../source/ref-changelog.md:161 +msgid "" +"**General improvements** ([#3171](https://github.com/adap/flower/pull/3171), " +"[3099](https://github.com/adap/flower/pull/3099), [3003](https://github.com/" +"adap/flower/pull/3003), [3145](https://github.com/adap/flower/pull/3145), " +"[3017](https://github.com/adap/flower/pull/3017), [3085](https://github.com/" +"adap/flower/pull/3085), [3012](https://github.com/adap/flower/pull/3012), " +"[3119](https://github.com/adap/flower/pull/3119), [2991](https://github.com/" +"adap/flower/pull/2991), [2970](https://github.com/adap/flower/pull/2970), " +"[2980](https://github.com/adap/flower/pull/2980), [3086](https://github.com/" +"adap/flower/pull/3086), [2932](https://github.com/adap/flower/pull/2932), " +"[2928](https://github.com/adap/flower/pull/2928), [2941](https://github.com/" +"adap/flower/pull/2941), [2933](https://github.com/adap/flower/pull/2933), " +"[3181](https://github.com/adap/flower/pull/3181), [2973](https://github.com/" +"adap/flower/pull/2973), [2992](https://github.com/adap/flower/pull/2992), " +"[2915](https://github.com/adap/flower/pull/2915), [3040](https://github.com/" +"adap/flower/pull/3040), [3022](https://github.com/adap/flower/pull/3022), " +"[3032](https://github.com/adap/flower/pull/3032), [2902](https://github.com/" +"adap/flower/pull/2902), [2931](https://github.com/adap/flower/pull/2931), " +"[3005](https://github.com/adap/flower/pull/3005), [3132](https://github.com/" +"adap/flower/pull/3132), [3115](https://github.com/adap/flower/pull/3115), " +"[2944](https://github.com/adap/flower/pull/2944), [3064](https://github.com/" +"adap/flower/pull/3064), [3106](https://github.com/adap/flower/pull/3106), " +"[2974](https://github.com/adap/flower/pull/2974), [3178](https://github.com/" +"adap/flower/pull/3178), [2993](https://github.com/adap/flower/pull/2993), " +"[3186](https://github.com/adap/flower/pull/3186), [3091](https://github.com/" +"adap/flower/pull/3091), [3125](https://github.com/adap/flower/pull/3125), " +"[3093](https://github.com/adap/flower/pull/3093), [3013](https://github.com/" +"adap/flower/pull/3013), [3033](https://github.com/adap/flower/pull/3033), " +"[3133](https://github.com/adap/flower/pull/3133), [3068](https://github.com/" +"adap/flower/pull/3068), [2916](https://github.com/adap/flower/pull/2916), " +"[2975](https://github.com/adap/flower/pull/2975), [2984](https://github.com/" +"adap/flower/pull/2984), [2846](https://github.com/adap/flower/pull/2846), " +"[3077](https://github.com/adap/flower/pull/3077), [3143](https://github.com/" +"adap/flower/pull/3143), [2921](https://github.com/adap/flower/pull/2921), " +"[3101](https://github.com/adap/flower/pull/3101), [2927](https://github.com/" +"adap/flower/pull/2927), [2995](https://github.com/adap/flower/pull/2995), " +"[2972](https://github.com/adap/flower/pull/2972), [2912](https://github.com/" +"adap/flower/pull/2912), [3065](https://github.com/adap/flower/pull/3065), " +"[3028](https://github.com/adap/flower/pull/3028), [2922](https://github.com/" +"adap/flower/pull/2922), [2982](https://github.com/adap/flower/pull/2982), " +"[2914](https://github.com/adap/flower/pull/2914), [3179](https://github.com/" +"adap/flower/pull/3179), [3080](https://github.com/adap/flower/pull/3080), " +"[2994](https://github.com/adap/flower/pull/2994), [3187](https://github.com/" +"adap/flower/pull/3187), [2926](https://github.com/adap/flower/pull/2926), " +"[3018](https://github.com/adap/flower/pull/3018), [3144](https://github.com/" +"adap/flower/pull/3144), [3011](https://github.com/adap/flower/pull/3011), " +"[#3152](https://github.com/adap/flower/pull/3152), [#2836](https://github." +"com/adap/flower/pull/2836), [#2929](https://github.com/adap/flower/" +"pull/2929), [#2943](https://github.com/adap/flower/pull/2943), [#2955]" +"(https://github.com/adap/flower/pull/2955), [#2954](https://github.com/adap/" +"flower/pull/2954))" +msgstr "" + +#: ../../source/ref-changelog.md:165 ../../source/ref-changelog.md:442 +#: ../../source/ref-changelog.md:506 ../../source/ref-changelog.md:564 +#: ../../source/ref-changelog.md:633 ../../source/ref-changelog.md:695 +msgid "None" msgstr "" -#: ../../source/ref-changelog.md:69 -msgid "" -"**General improvements** " -"([#3171](https://github.com/adap/flower/pull/3171), " -"[3099](https://github.com/adap/flower/pull/3099), " -"[3003](https://github.com/adap/flower/pull/3003), " -"[3145](https://github.com/adap/flower/pull/3145), " -"[3017](https://github.com/adap/flower/pull/3017), " -"[3085](https://github.com/adap/flower/pull/3085), " -"[3012](https://github.com/adap/flower/pull/3012), " -"[3119](https://github.com/adap/flower/pull/3119), " -"[2991](https://github.com/adap/flower/pull/2991), " -"[2970](https://github.com/adap/flower/pull/2970), " -"[2980](https://github.com/adap/flower/pull/2980), " -"[3086](https://github.com/adap/flower/pull/3086), " -"[2932](https://github.com/adap/flower/pull/2932), " -"[2928](https://github.com/adap/flower/pull/2928), " -"[2941](https://github.com/adap/flower/pull/2941), " -"[2933](https://github.com/adap/flower/pull/2933), " -"[3181](https://github.com/adap/flower/pull/3181), " -"[2973](https://github.com/adap/flower/pull/2973), " -"[2992](https://github.com/adap/flower/pull/2992), " -"[2915](https://github.com/adap/flower/pull/2915), " -"[3040](https://github.com/adap/flower/pull/3040), " -"[3022](https://github.com/adap/flower/pull/3022), " -"[3032](https://github.com/adap/flower/pull/3032), " -"[2902](https://github.com/adap/flower/pull/2902), " -"[2931](https://github.com/adap/flower/pull/2931), " -"[3005](https://github.com/adap/flower/pull/3005), " -"[3132](https://github.com/adap/flower/pull/3132), " -"[3115](https://github.com/adap/flower/pull/3115), " -"[2944](https://github.com/adap/flower/pull/2944), " -"[3064](https://github.com/adap/flower/pull/3064), " -"[3106](https://github.com/adap/flower/pull/3106), " -"[2974](https://github.com/adap/flower/pull/2974), " -"[3178](https://github.com/adap/flower/pull/3178), " -"[2993](https://github.com/adap/flower/pull/2993), " -"[3186](https://github.com/adap/flower/pull/3186), " -"[3091](https://github.com/adap/flower/pull/3091), " -"[3125](https://github.com/adap/flower/pull/3125), " -"[3093](https://github.com/adap/flower/pull/3093), " -"[3013](https://github.com/adap/flower/pull/3013), " -"[3033](https://github.com/adap/flower/pull/3033), " -"[3133](https://github.com/adap/flower/pull/3133), " -"[3068](https://github.com/adap/flower/pull/3068), " -"[2916](https://github.com/adap/flower/pull/2916), " -"[2975](https://github.com/adap/flower/pull/2975), " -"[2984](https://github.com/adap/flower/pull/2984), " -"[2846](https://github.com/adap/flower/pull/2846), " -"[3077](https://github.com/adap/flower/pull/3077), " -"[3143](https://github.com/adap/flower/pull/3143), " -"[2921](https://github.com/adap/flower/pull/2921), " -"[3101](https://github.com/adap/flower/pull/3101), " -"[2927](https://github.com/adap/flower/pull/2927), " -"[2995](https://github.com/adap/flower/pull/2995), " -"[2972](https://github.com/adap/flower/pull/2972), " -"[2912](https://github.com/adap/flower/pull/2912), " -"[3065](https://github.com/adap/flower/pull/3065), " -"[3028](https://github.com/adap/flower/pull/3028), " -"[2922](https://github.com/adap/flower/pull/2922), " -"[2982](https://github.com/adap/flower/pull/2982), " -"[2914](https://github.com/adap/flower/pull/2914), " -"[3179](https://github.com/adap/flower/pull/3179), " -"[3080](https://github.com/adap/flower/pull/3080), " -"[2994](https://github.com/adap/flower/pull/2994), " -"[3187](https://github.com/adap/flower/pull/3187), " -"[2926](https://github.com/adap/flower/pull/2926), " -"[3018](https://github.com/adap/flower/pull/3018), " -"[3144](https://github.com/adap/flower/pull/3144), " -"[3011](https://github.com/adap/flower/pull/3011), " -"[#3152](https://github.com/adap/flower/pull/3152), " -"[#2836](https://github.com/adap/flower/pull/2836), " -"[#2929](https://github.com/adap/flower/pull/2929), " -"[#2943](https://github.com/adap/flower/pull/2943), " -"[#2955](https://github.com/adap/flower/pull/2955), " -"[#2954](https://github.com/adap/flower/pull/2954))" -msgstr "" - -#: ../../source/ref-changelog.md:75 +#: ../../source/ref-changelog.md:167 msgid "v1.7.0 (2024-02-05)" msgstr "" -#: ../../source/ref-changelog.md:81 +#: ../../source/ref-changelog.md:173 msgid "" -"`Aasheesh Singh`, `Adam Narozniak`, `Aml Hassan Esmil`, `Charles " -"Beauville`, `Daniel J. Beutel`, `Daniel Nata Nugraha`, `Edoardo " -"Gabrielli`, `Gustavo Bertoli`, `HelinLin`, `Heng Pan`, `Javier`, `M S " -"Chaitanya Kumar`, `Mohammad Naseri`, `Nikos Vlachakis`, `Pritam Neog`, " -"`Robert Kuska`, `Robert Steiner`, `Taner Topal`, `Yahia Salaheldin " -"Shaaban`, `Yan Gao`, `Yasar Abbas` " +"`Aasheesh Singh`, `Adam Narozniak`, `Aml Hassan Esmil`, `Charles Beauville`, " +"`Daniel J. Beutel`, `Daniel Nata Nugraha`, `Edoardo Gabrielli`, `Gustavo " +"Bertoli`, `HelinLin`, `Heng Pan`, `Javier`, `M S Chaitanya Kumar`, `Mohammad " +"Naseri`, `Nikos Vlachakis`, `Pritam Neog`, `Robert Kuska`, `Robert Steiner`, " +"`Taner Topal`, `Yahia Salaheldin Shaaban`, `Yan Gao`, `Yasar Abbas` " msgstr "" -#: ../../source/ref-changelog.md:85 +#: ../../source/ref-changelog.md:177 msgid "" -"**Introduce stateful clients (experimental)** " -"([#2770](https://github.com/adap/flower/pull/2770), " -"[#2686](https://github.com/adap/flower/pull/2686), " -"[#2696](https://github.com/adap/flower/pull/2696), " -"[#2643](https://github.com/adap/flower/pull/2643), " -"[#2769](https://github.com/adap/flower/pull/2769))" +"**Introduce stateful clients (experimental)** ([#2770](https://github.com/" +"adap/flower/pull/2770), [#2686](https://github.com/adap/flower/pull/2686), " +"[#2696](https://github.com/adap/flower/pull/2696), [#2643](https://github." +"com/adap/flower/pull/2643), [#2769](https://github.com/adap/flower/" +"pull/2769))" msgstr "" -#: ../../source/ref-changelog.md:87 +#: ../../source/ref-changelog.md:179 msgid "" "Subclasses of `Client` and `NumPyClient` can now store local state that " "remains on the client. Let's start with the highlight first: this new " -"feature is compatible with both simulated clients (via " -"`start_simulation`) and networked clients (via `start_client`). It's also" -" the first preview of new abstractions like `Context` and `RecordSet`. " -"Clients can access state of type `RecordSet` via `state: RecordSet = " -"self.context.state`. Changes to this `RecordSet` are preserved across " -"different rounds of execution to enable stateful computations in a " -"unified way across simulation and deployment." +"feature is compatible with both simulated clients (via `start_simulation`) " +"and networked clients (via `start_client`). It's also the first preview of " +"new abstractions like `Context` and `RecordSet`. Clients can access state of " +"type `RecordSet` via `state: RecordSet = self.context.state`. Changes to " +"this `RecordSet` are preserved across different rounds of execution to " +"enable stateful computations in a unified way across simulation and " +"deployment." msgstr "" -#: ../../source/ref-changelog.md:89 +#: ../../source/ref-changelog.md:181 msgid "" -"**Improve performance** " -"([#2293](https://github.com/adap/flower/pull/2293))" +"**Improve performance** ([#2293](https://github.com/adap/flower/pull/2293))" msgstr "" -#: ../../source/ref-changelog.md:91 +#: ../../source/ref-changelog.md:183 msgid "" -"Flower is faster than ever. All `FedAvg`-derived strategies now use in-" -"place aggregation to reduce memory consumption. The Flower client " -"serialization/deserialization has been rewritten from the ground up, " -"which results in significant speedups, especially when the client-side " -"training time is short." +"Flower is faster than ever. All `FedAvg`-derived strategies now use in-place " +"aggregation to reduce memory consumption. The Flower client serialization/" +"deserialization has been rewritten from the ground up, which results in " +"significant speedups, especially when the client-side training time is short." msgstr "" -#: ../../source/ref-changelog.md:93 +#: ../../source/ref-changelog.md:185 msgid "" -"**Support Federated Learning with Apple MLX and Flower** " -"([#2693](https://github.com/adap/flower/pull/2693))" +"**Support Federated Learning with Apple MLX and Flower** ([#2693](https://" +"github.com/adap/flower/pull/2693))" msgstr "" -#: ../../source/ref-changelog.md:95 +#: ../../source/ref-changelog.md:187 msgid "" -"Flower has official support for federated learning using [Apple " -"MLX](https://ml-explore.github.io/mlx) via the new `quickstart-mlx` code " -"example." +"Flower has official support for federated learning using [Apple MLX](https://" +"ml-explore.github.io/mlx) via the new `quickstart-mlx` code example." msgstr "" -#: ../../source/ref-changelog.md:97 +#: ../../source/ref-changelog.md:189 msgid "" -"**Introduce new XGBoost cyclic strategy** " -"([#2666](https://github.com/adap/flower/pull/2666), " -"[#2668](https://github.com/adap/flower/pull/2668))" +"**Introduce new XGBoost cyclic strategy** ([#2666](https://github.com/adap/" +"flower/pull/2666), [#2668](https://github.com/adap/flower/pull/2668))" msgstr "" -#: ../../source/ref-changelog.md:99 +#: ../../source/ref-changelog.md:191 msgid "" -"A new strategy called `FedXgbCyclic` supports a client-by-client style of" -" training (often called cyclic). The `xgboost-comprehensive` code example" -" shows how to use it in a full project. In addition to that, `xgboost-" -"comprehensive` now also supports simulation mode. With this, Flower " -"offers best-in-class XGBoost support." +"A new strategy called `FedXgbCyclic` supports a client-by-client style of " +"training (often called cyclic). The `xgboost-comprehensive` code example " +"shows how to use it in a full project. In addition to that, `xgboost-" +"comprehensive` now also supports simulation mode. With this, Flower offers " +"best-in-class XGBoost support." msgstr "" -#: ../../source/ref-changelog.md:101 +#: ../../source/ref-changelog.md:193 msgid "" -"**Support Python 3.11** " -"([#2394](https://github.com/adap/flower/pull/2394))" +"**Support Python 3.11** ([#2394](https://github.com/adap/flower/pull/2394))" msgstr "" -#: ../../source/ref-changelog.md:103 +#: ../../source/ref-changelog.md:195 msgid "" -"Framework tests now run on Python 3.8, 3.9, 3.10, and 3.11. This will " -"ensure better support for users using more recent Python versions." +"Framework tests now run on Python 3.8, 3.9, 3.10, and 3.11. This will ensure " +"better support for users using more recent Python versions." msgstr "" -#: ../../source/ref-changelog.md:105 +#: ../../source/ref-changelog.md:197 msgid "" -"**Update gRPC and ProtoBuf dependencies** " -"([#2814](https://github.com/adap/flower/pull/2814))" +"**Update gRPC and ProtoBuf dependencies** ([#2814](https://github.com/adap/" +"flower/pull/2814))" msgstr "" -#: ../../source/ref-changelog.md:107 +#: ../../source/ref-changelog.md:199 msgid "" "The `grpcio` and `protobuf` dependencies were updated to their latest " "versions for improved security and performance." msgstr "" -#: ../../source/ref-changelog.md:109 +#: ../../source/ref-changelog.md:201 msgid "" -"**Introduce Docker image for Flower server** " -"([#2700](https://github.com/adap/flower/pull/2700), " -"[#2688](https://github.com/adap/flower/pull/2688), " -"[#2705](https://github.com/adap/flower/pull/2705), " -"[#2695](https://github.com/adap/flower/pull/2695), " -"[#2747](https://github.com/adap/flower/pull/2747), " -"[#2746](https://github.com/adap/flower/pull/2746), " -"[#2680](https://github.com/adap/flower/pull/2680), " -"[#2682](https://github.com/adap/flower/pull/2682), " -"[#2701](https://github.com/adap/flower/pull/2701))" +"**Introduce Docker image for Flower server** ([#2700](https://github.com/" +"adap/flower/pull/2700), [#2688](https://github.com/adap/flower/pull/2688), " +"[#2705](https://github.com/adap/flower/pull/2705), [#2695](https://github." +"com/adap/flower/pull/2695), [#2747](https://github.com/adap/flower/" +"pull/2747), [#2746](https://github.com/adap/flower/pull/2746), [#2680]" +"(https://github.com/adap/flower/pull/2680), [#2682](https://github.com/adap/" +"flower/pull/2682), [#2701](https://github.com/adap/flower/pull/2701))" msgstr "" -#: ../../source/ref-changelog.md:111 +#: ../../source/ref-changelog.md:203 msgid "" -"The Flower server can now be run using an official Docker image. A new " -"how-to guide explains [how to run Flower using " -"Docker](https://flower.ai/docs/framework/how-to-run-flower-using-" -"docker.html). An official Flower client Docker image will follow." +"The Flower server can now be run using an official Docker image. A new how-" +"to guide explains [how to run Flower using Docker](https://flower.ai/docs/" +"framework/how-to-run-flower-using-docker.html). An official Flower client " +"Docker image will follow." msgstr "" -#: ../../source/ref-changelog.md:113 +#: ../../source/ref-changelog.md:205 msgid "" -"**Introduce** `flower-via-docker-compose` **example** " -"([#2626](https://github.com/adap/flower/pull/2626))" +"**Introduce** `flower-via-docker-compose` **example** ([#2626](https://" +"github.com/adap/flower/pull/2626))" msgstr "" -#: ../../source/ref-changelog.md:115 +#: ../../source/ref-changelog.md:207 msgid "" -"**Introduce** `quickstart-sklearn-tabular` **example** " -"([#2719](https://github.com/adap/flower/pull/2719))" +"**Introduce** `quickstart-sklearn-tabular` **example** ([#2719](https://" +"github.com/adap/flower/pull/2719))" msgstr "" -#: ../../source/ref-changelog.md:117 +#: ../../source/ref-changelog.md:209 msgid "" -"**Introduce** `custom-metrics` **example** " -"([#1958](https://github.com/adap/flower/pull/1958))" +"**Introduce** `custom-metrics` **example** ([#1958](https://github.com/adap/" +"flower/pull/1958))" msgstr "" -#: ../../source/ref-changelog.md:119 +#: ../../source/ref-changelog.md:211 msgid "" -"**Update code examples to use Flower Datasets** " -"([#2450](https://github.com/adap/flower/pull/2450), " -"[#2456](https://github.com/adap/flower/pull/2456), " -"[#2318](https://github.com/adap/flower/pull/2318), " -"[#2712](https://github.com/adap/flower/pull/2712))" +"**Update code examples to use Flower Datasets** ([#2450](https://github.com/" +"adap/flower/pull/2450), [#2456](https://github.com/adap/flower/pull/2456), " +"[#2318](https://github.com/adap/flower/pull/2318), [#2712](https://github." +"com/adap/flower/pull/2712))" msgstr "" -#: ../../source/ref-changelog.md:121 +#: ../../source/ref-changelog.md:213 msgid "" -"Several code examples were updated to use [Flower " -"Datasets](https://flower.ai/docs/datasets/)." +"Several code examples were updated to use [Flower Datasets](https://flower." +"ai/docs/datasets/)." msgstr "" -#: ../../source/ref-changelog.md:123 +#: ../../source/ref-changelog.md:215 msgid "" -"**General updates to Flower Examples** " -"([#2381](https://github.com/adap/flower/pull/2381), " -"[#2805](https://github.com/adap/flower/pull/2805), " -"[#2782](https://github.com/adap/flower/pull/2782), " -"[#2806](https://github.com/adap/flower/pull/2806), " -"[#2829](https://github.com/adap/flower/pull/2829), " -"[#2825](https://github.com/adap/flower/pull/2825), " -"[#2816](https://github.com/adap/flower/pull/2816), " -"[#2726](https://github.com/adap/flower/pull/2726), " -"[#2659](https://github.com/adap/flower/pull/2659), " -"[#2655](https://github.com/adap/flower/pull/2655))" +"**General updates to Flower Examples** ([#2381](https://github.com/adap/" +"flower/pull/2381), [#2805](https://github.com/adap/flower/pull/2805), [#2782]" +"(https://github.com/adap/flower/pull/2782), [#2806](https://github.com/adap/" +"flower/pull/2806), [#2829](https://github.com/adap/flower/pull/2829), [#2825]" +"(https://github.com/adap/flower/pull/2825), [#2816](https://github.com/adap/" +"flower/pull/2816), [#2726](https://github.com/adap/flower/pull/2726), [#2659]" +"(https://github.com/adap/flower/pull/2659), [#2655](https://github.com/adap/" +"flower/pull/2655))" msgstr "" -#: ../../source/ref-changelog.md:125 +#: ../../source/ref-changelog.md:217 msgid "Many Flower code examples received substantial updates." msgstr "" -#: ../../source/ref-changelog.md:127 ../../source/ref-changelog.md:220 +#: ../../source/ref-changelog.md:219 ../../source/ref-changelog.md:312 msgid "**Update Flower Baselines**" msgstr "" -#: ../../source/ref-changelog.md:129 +#: ../../source/ref-changelog.md:221 msgid "" -"HFedXGBoost ([#2226](https://github.com/adap/flower/pull/2226), " -"[#2771](https://github.com/adap/flower/pull/2771))" +"HFedXGBoost ([#2226](https://github.com/adap/flower/pull/2226), [#2771]" +"(https://github.com/adap/flower/pull/2771))" msgstr "" -#: ../../source/ref-changelog.md:130 +#: ../../source/ref-changelog.md:222 msgid "FedVSSL ([#2412](https://github.com/adap/flower/pull/2412))" msgstr "" -#: ../../source/ref-changelog.md:131 +#: ../../source/ref-changelog.md:223 msgid "FedNova ([#2179](https://github.com/adap/flower/pull/2179))" msgstr "" -#: ../../source/ref-changelog.md:132 +#: ../../source/ref-changelog.md:224 msgid "HeteroFL ([#2439](https://github.com/adap/flower/pull/2439))" msgstr "" -#: ../../source/ref-changelog.md:133 +#: ../../source/ref-changelog.md:225 msgid "FedAvgM ([#2246](https://github.com/adap/flower/pull/2246))" msgstr "" -#: ../../source/ref-changelog.md:134 +#: ../../source/ref-changelog.md:226 msgid "FedPara ([#2722](https://github.com/adap/flower/pull/2722))" msgstr "" -#: ../../source/ref-changelog.md:136 -msgid "" -"**Improve documentation** " -"([#2674](https://github.com/adap/flower/pull/2674), " -"[#2480](https://github.com/adap/flower/pull/2480), " -"[#2826](https://github.com/adap/flower/pull/2826), " -"[#2727](https://github.com/adap/flower/pull/2727), " -"[#2761](https://github.com/adap/flower/pull/2761), " -"[#2900](https://github.com/adap/flower/pull/2900))" -msgstr "" - -#: ../../source/ref-changelog.md:138 -msgid "" -"**Improved testing and development infrastructure** " -"([#2797](https://github.com/adap/flower/pull/2797), " -"[#2676](https://github.com/adap/flower/pull/2676), " -"[#2644](https://github.com/adap/flower/pull/2644), " -"[#2656](https://github.com/adap/flower/pull/2656), " -"[#2848](https://github.com/adap/flower/pull/2848), " -"[#2675](https://github.com/adap/flower/pull/2675), " -"[#2735](https://github.com/adap/flower/pull/2735), " -"[#2767](https://github.com/adap/flower/pull/2767), " -"[#2732](https://github.com/adap/flower/pull/2732), " -"[#2744](https://github.com/adap/flower/pull/2744), " -"[#2681](https://github.com/adap/flower/pull/2681), " -"[#2699](https://github.com/adap/flower/pull/2699), " -"[#2745](https://github.com/adap/flower/pull/2745), " -"[#2734](https://github.com/adap/flower/pull/2734), " -"[#2731](https://github.com/adap/flower/pull/2731), " -"[#2652](https://github.com/adap/flower/pull/2652), " -"[#2720](https://github.com/adap/flower/pull/2720), " -"[#2721](https://github.com/adap/flower/pull/2721), " -"[#2717](https://github.com/adap/flower/pull/2717), " -"[#2864](https://github.com/adap/flower/pull/2864), " -"[#2694](https://github.com/adap/flower/pull/2694), " -"[#2709](https://github.com/adap/flower/pull/2709), " -"[#2658](https://github.com/adap/flower/pull/2658), " -"[#2796](https://github.com/adap/flower/pull/2796), " -"[#2692](https://github.com/adap/flower/pull/2692), " -"[#2657](https://github.com/adap/flower/pull/2657), " -"[#2813](https://github.com/adap/flower/pull/2813), " -"[#2661](https://github.com/adap/flower/pull/2661), " -"[#2398](https://github.com/adap/flower/pull/2398))" -msgstr "" - -#: ../../source/ref-changelog.md:140 -msgid "" -"The Flower testing and development infrastructure has received " -"substantial updates. This makes Flower 1.7 the most tested release ever." -msgstr "" - -#: ../../source/ref-changelog.md:142 -msgid "" -"**Update dependencies** " -"([#2753](https://github.com/adap/flower/pull/2753), " -"[#2651](https://github.com/adap/flower/pull/2651), " -"[#2739](https://github.com/adap/flower/pull/2739), " -"[#2837](https://github.com/adap/flower/pull/2837), " -"[#2788](https://github.com/adap/flower/pull/2788), " -"[#2811](https://github.com/adap/flower/pull/2811), " -"[#2774](https://github.com/adap/flower/pull/2774), " -"[#2790](https://github.com/adap/flower/pull/2790), " -"[#2751](https://github.com/adap/flower/pull/2751), " -"[#2850](https://github.com/adap/flower/pull/2850), " -"[#2812](https://github.com/adap/flower/pull/2812), " -"[#2872](https://github.com/adap/flower/pull/2872), " -"[#2736](https://github.com/adap/flower/pull/2736), " -"[#2756](https://github.com/adap/flower/pull/2756), " -"[#2857](https://github.com/adap/flower/pull/2857), " -"[#2757](https://github.com/adap/flower/pull/2757), " -"[#2810](https://github.com/adap/flower/pull/2810), " -"[#2740](https://github.com/adap/flower/pull/2740), " -"[#2789](https://github.com/adap/flower/pull/2789))" -msgstr "" - -#: ../../source/ref-changelog.md:144 -msgid "" -"**General improvements** " -"([#2803](https://github.com/adap/flower/pull/2803), " -"[#2847](https://github.com/adap/flower/pull/2847), " -"[#2877](https://github.com/adap/flower/pull/2877), " -"[#2690](https://github.com/adap/flower/pull/2690), " -"[#2889](https://github.com/adap/flower/pull/2889), " -"[#2874](https://github.com/adap/flower/pull/2874), " -"[#2819](https://github.com/adap/flower/pull/2819), " -"[#2689](https://github.com/adap/flower/pull/2689), " -"[#2457](https://github.com/adap/flower/pull/2457), " -"[#2870](https://github.com/adap/flower/pull/2870), " -"[#2669](https://github.com/adap/flower/pull/2669), " -"[#2876](https://github.com/adap/flower/pull/2876), " -"[#2885](https://github.com/adap/flower/pull/2885), " -"[#2858](https://github.com/adap/flower/pull/2858), " -"[#2867](https://github.com/adap/flower/pull/2867), " -"[#2351](https://github.com/adap/flower/pull/2351), " -"[#2886](https://github.com/adap/flower/pull/2886), " -"[#2860](https://github.com/adap/flower/pull/2860), " -"[#2828](https://github.com/adap/flower/pull/2828), " -"[#2869](https://github.com/adap/flower/pull/2869), " -"[#2875](https://github.com/adap/flower/pull/2875), " -"[#2733](https://github.com/adap/flower/pull/2733), " -"[#2488](https://github.com/adap/flower/pull/2488), " -"[#2646](https://github.com/adap/flower/pull/2646), " -"[#2879](https://github.com/adap/flower/pull/2879), " -"[#2821](https://github.com/adap/flower/pull/2821), " -"[#2855](https://github.com/adap/flower/pull/2855), " -"[#2800](https://github.com/adap/flower/pull/2800), " -"[#2807](https://github.com/adap/flower/pull/2807), " -"[#2801](https://github.com/adap/flower/pull/2801), " -"[#2804](https://github.com/adap/flower/pull/2804), " -"[#2851](https://github.com/adap/flower/pull/2851), " -"[#2787](https://github.com/adap/flower/pull/2787), " -"[#2852](https://github.com/adap/flower/pull/2852), " -"[#2672](https://github.com/adap/flower/pull/2672), " -"[#2759](https://github.com/adap/flower/pull/2759))" -msgstr "" - -#: ../../source/ref-changelog.md:148 -msgid "" -"**Deprecate** `start_numpy_client` " -"([#2563](https://github.com/adap/flower/pull/2563), " -"[#2718](https://github.com/adap/flower/pull/2718))" -msgstr "" - -#: ../../source/ref-changelog.md:150 +#: ../../source/ref-changelog.md:228 +msgid "" +"**Improve documentation** ([#2674](https://github.com/adap/flower/" +"pull/2674), [#2480](https://github.com/adap/flower/pull/2480), [#2826]" +"(https://github.com/adap/flower/pull/2826), [#2727](https://github.com/adap/" +"flower/pull/2727), [#2761](https://github.com/adap/flower/pull/2761), [#2900]" +"(https://github.com/adap/flower/pull/2900))" +msgstr "" + +#: ../../source/ref-changelog.md:230 +msgid "" +"**Improved testing and development infrastructure** ([#2797](https://github." +"com/adap/flower/pull/2797), [#2676](https://github.com/adap/flower/" +"pull/2676), [#2644](https://github.com/adap/flower/pull/2644), [#2656]" +"(https://github.com/adap/flower/pull/2656), [#2848](https://github.com/adap/" +"flower/pull/2848), [#2675](https://github.com/adap/flower/pull/2675), [#2735]" +"(https://github.com/adap/flower/pull/2735), [#2767](https://github.com/adap/" +"flower/pull/2767), [#2732](https://github.com/adap/flower/pull/2732), [#2744]" +"(https://github.com/adap/flower/pull/2744), [#2681](https://github.com/adap/" +"flower/pull/2681), [#2699](https://github.com/adap/flower/pull/2699), [#2745]" +"(https://github.com/adap/flower/pull/2745), [#2734](https://github.com/adap/" +"flower/pull/2734), [#2731](https://github.com/adap/flower/pull/2731), [#2652]" +"(https://github.com/adap/flower/pull/2652), [#2720](https://github.com/adap/" +"flower/pull/2720), [#2721](https://github.com/adap/flower/pull/2721), [#2717]" +"(https://github.com/adap/flower/pull/2717), [#2864](https://github.com/adap/" +"flower/pull/2864), [#2694](https://github.com/adap/flower/pull/2694), [#2709]" +"(https://github.com/adap/flower/pull/2709), [#2658](https://github.com/adap/" +"flower/pull/2658), [#2796](https://github.com/adap/flower/pull/2796), [#2692]" +"(https://github.com/adap/flower/pull/2692), [#2657](https://github.com/adap/" +"flower/pull/2657), [#2813](https://github.com/adap/flower/pull/2813), [#2661]" +"(https://github.com/adap/flower/pull/2661), [#2398](https://github.com/adap/" +"flower/pull/2398))" +msgstr "" + +#: ../../source/ref-changelog.md:232 +msgid "" +"The Flower testing and development infrastructure has received substantial " +"updates. This makes Flower 1.7 the most tested release ever." +msgstr "" + +#: ../../source/ref-changelog.md:234 +msgid "" +"**Update dependencies** ([#2753](https://github.com/adap/flower/pull/2753), " +"[#2651](https://github.com/adap/flower/pull/2651), [#2739](https://github." +"com/adap/flower/pull/2739), [#2837](https://github.com/adap/flower/" +"pull/2837), [#2788](https://github.com/adap/flower/pull/2788), [#2811]" +"(https://github.com/adap/flower/pull/2811), [#2774](https://github.com/adap/" +"flower/pull/2774), [#2790](https://github.com/adap/flower/pull/2790), [#2751]" +"(https://github.com/adap/flower/pull/2751), [#2850](https://github.com/adap/" +"flower/pull/2850), [#2812](https://github.com/adap/flower/pull/2812), [#2872]" +"(https://github.com/adap/flower/pull/2872), [#2736](https://github.com/adap/" +"flower/pull/2736), [#2756](https://github.com/adap/flower/pull/2756), [#2857]" +"(https://github.com/adap/flower/pull/2857), [#2757](https://github.com/adap/" +"flower/pull/2757), [#2810](https://github.com/adap/flower/pull/2810), [#2740]" +"(https://github.com/adap/flower/pull/2740), [#2789](https://github.com/adap/" +"flower/pull/2789))" +msgstr "" + +#: ../../source/ref-changelog.md:236 +msgid "" +"**General improvements** ([#2803](https://github.com/adap/flower/pull/2803), " +"[#2847](https://github.com/adap/flower/pull/2847), [#2877](https://github." +"com/adap/flower/pull/2877), [#2690](https://github.com/adap/flower/" +"pull/2690), [#2889](https://github.com/adap/flower/pull/2889), [#2874]" +"(https://github.com/adap/flower/pull/2874), [#2819](https://github.com/adap/" +"flower/pull/2819), [#2689](https://github.com/adap/flower/pull/2689), [#2457]" +"(https://github.com/adap/flower/pull/2457), [#2870](https://github.com/adap/" +"flower/pull/2870), [#2669](https://github.com/adap/flower/pull/2669), [#2876]" +"(https://github.com/adap/flower/pull/2876), [#2885](https://github.com/adap/" +"flower/pull/2885), [#2858](https://github.com/adap/flower/pull/2858), [#2867]" +"(https://github.com/adap/flower/pull/2867), [#2351](https://github.com/adap/" +"flower/pull/2351), [#2886](https://github.com/adap/flower/pull/2886), [#2860]" +"(https://github.com/adap/flower/pull/2860), [#2828](https://github.com/adap/" +"flower/pull/2828), [#2869](https://github.com/adap/flower/pull/2869), [#2875]" +"(https://github.com/adap/flower/pull/2875), [#2733](https://github.com/adap/" +"flower/pull/2733), [#2488](https://github.com/adap/flower/pull/2488), [#2646]" +"(https://github.com/adap/flower/pull/2646), [#2879](https://github.com/adap/" +"flower/pull/2879), [#2821](https://github.com/adap/flower/pull/2821), [#2855]" +"(https://github.com/adap/flower/pull/2855), [#2800](https://github.com/adap/" +"flower/pull/2800), [#2807](https://github.com/adap/flower/pull/2807), [#2801]" +"(https://github.com/adap/flower/pull/2801), [#2804](https://github.com/adap/" +"flower/pull/2804), [#2851](https://github.com/adap/flower/pull/2851), [#2787]" +"(https://github.com/adap/flower/pull/2787), [#2852](https://github.com/adap/" +"flower/pull/2852), [#2672](https://github.com/adap/flower/pull/2672), [#2759]" +"(https://github.com/adap/flower/pull/2759))" +msgstr "" + +#: ../../source/ref-changelog.md:240 +msgid "" +"**Deprecate** `start_numpy_client` ([#2563](https://github.com/adap/flower/" +"pull/2563), [#2718](https://github.com/adap/flower/pull/2718))" +msgstr "" + +#: ../../source/ref-changelog.md:242 msgid "" "Until now, clients of type `NumPyClient` needed to be started via " -"`start_numpy_client`. In our efforts to consolidate framework APIs, we " -"have introduced changes, and now all client types should start via " -"`start_client`. To continue using `NumPyClient` clients, you simply need " -"to first call the `.to_client()` method and then pass returned `Client` " -"object to `start_client`. The examples and the documentation have been " -"updated accordingly." +"`start_numpy_client`. In our efforts to consolidate framework APIs, we have " +"introduced changes, and now all client types should start via " +"`start_client`. To continue using `NumPyClient` clients, you simply need to " +"first call the `.to_client()` method and then pass returned `Client` object " +"to `start_client`. The examples and the documentation have been updated " +"accordingly." msgstr "" -#: ../../source/ref-changelog.md:152 +#: ../../source/ref-changelog.md:244 msgid "" -"**Deprecate legacy DP wrappers** " -"([#2749](https://github.com/adap/flower/pull/2749))" +"**Deprecate legacy DP wrappers** ([#2749](https://github.com/adap/flower/" +"pull/2749))" msgstr "" -#: ../../source/ref-changelog.md:154 +#: ../../source/ref-changelog.md:246 msgid "" -"Legacy DP wrapper classes are deprecated, but still functional. This is " -"in preparation for an all-new pluggable version of differential privacy " -"support in Flower." +"Legacy DP wrapper classes are deprecated, but still functional. This is in " +"preparation for an all-new pluggable version of differential privacy support " +"in Flower." msgstr "" -#: ../../source/ref-changelog.md:156 +#: ../../source/ref-changelog.md:248 msgid "" "**Make optional arg** `--callable` **in** `flower-client` **a required " "positional arg** ([#2673](https://github.com/adap/flower/pull/2673))" msgstr "" -#: ../../source/ref-changelog.md:158 +#: ../../source/ref-changelog.md:250 msgid "" -"**Rename** `certificates` **to** `root_certificates` **in** `Driver` " -"([#2890](https://github.com/adap/flower/pull/2890))" +"**Rename** `certificates` **to** `root_certificates` **in** `Driver` ([#2890]" +"(https://github.com/adap/flower/pull/2890))" msgstr "" -#: ../../source/ref-changelog.md:160 +#: ../../source/ref-changelog.md:252 msgid "" -"**Drop experimental** `Task` **fields** " -"([#2866](https://github.com/adap/flower/pull/2866), " -"[#2865](https://github.com/adap/flower/pull/2865))" +"**Drop experimental** `Task` **fields** ([#2866](https://github.com/adap/" +"flower/pull/2866), [#2865](https://github.com/adap/flower/pull/2865))" msgstr "" -#: ../../source/ref-changelog.md:162 +#: ../../source/ref-changelog.md:254 msgid "" "Experimental fields `sa`, `legacy_server_message` and " -"`legacy_client_message` were removed from `Task` message. The removed " -"fields are superseded by the new `RecordSet` abstraction." +"`legacy_client_message` were removed from `Task` message. The removed fields " +"are superseded by the new `RecordSet` abstraction." msgstr "" -#: ../../source/ref-changelog.md:164 +#: ../../source/ref-changelog.md:256 msgid "" -"**Retire MXNet examples** " -"([#2724](https://github.com/adap/flower/pull/2724))" +"**Retire MXNet examples** ([#2724](https://github.com/adap/flower/pull/2724))" msgstr "" -#: ../../source/ref-changelog.md:166 +#: ../../source/ref-changelog.md:258 msgid "" "The development of the MXNet fremework has ended and the project is now " "[archived on GitHub](https://github.com/apache/mxnet). Existing MXNet " "examples won't receive updates." msgstr "" -#: ../../source/ref-changelog.md:168 +#: ../../source/ref-changelog.md:260 msgid "v1.6.0 (2023-11-28)" msgstr "" -#: ../../source/ref-changelog.md:174 +#: ../../source/ref-changelog.md:266 msgid "" "`Aashish Kolluri`, `Adam Narozniak`, `Alessio Mora`, `Barathwaja S`, " "`Charles Beauville`, `Daniel J. Beutel`, `Daniel Nata Nugraha`, `Gabriel " -"Mota`, `Heng Pan`, `Ivan Agarský`, `JS.KIM`, `Javier`, `Marius Schlegel`," -" `Navin Chandra`, `Nic Lane`, `Peterpan828`, `Qinbin Li`, `Shaz-hash`, " -"`Steve Laskaridis`, `Taner Topal`, `William Lindskog`, `Yan Gao`, " -"`cnxdeveloper`, `k3nfalt` " +"Mota`, `Heng Pan`, `Ivan Agarský`, `JS.KIM`, `Javier`, `Marius Schlegel`, " +"`Navin Chandra`, `Nic Lane`, `Peterpan828`, `Qinbin Li`, `Shaz-hash`, `Steve " +"Laskaridis`, `Taner Topal`, `William Lindskog`, `Yan Gao`, `cnxdeveloper`, " +"`k3nfalt` " msgstr "" -#: ../../source/ref-changelog.md:178 +#: ../../source/ref-changelog.md:270 msgid "" -"**Add experimental support for Python 3.12** " -"([#2565](https://github.com/adap/flower/pull/2565))" +"**Add experimental support for Python 3.12** ([#2565](https://github.com/" +"adap/flower/pull/2565))" msgstr "" -#: ../../source/ref-changelog.md:180 +#: ../../source/ref-changelog.md:272 msgid "" -"**Add new XGBoost examples** " -"([#2612](https://github.com/adap/flower/pull/2612), " -"[#2554](https://github.com/adap/flower/pull/2554), " -"[#2617](https://github.com/adap/flower/pull/2617), " -"[#2618](https://github.com/adap/flower/pull/2618), " -"[#2619](https://github.com/adap/flower/pull/2619), " -"[#2567](https://github.com/adap/flower/pull/2567))" +"**Add new XGBoost examples** ([#2612](https://github.com/adap/flower/" +"pull/2612), [#2554](https://github.com/adap/flower/pull/2554), [#2617]" +"(https://github.com/adap/flower/pull/2617), [#2618](https://github.com/adap/" +"flower/pull/2618), [#2619](https://github.com/adap/flower/pull/2619), [#2567]" +"(https://github.com/adap/flower/pull/2567))" msgstr "" -#: ../../source/ref-changelog.md:182 +#: ../../source/ref-changelog.md:274 msgid "" -"We have added a new `xgboost-quickstart` example alongside a new " -"`xgboost-comprehensive` example that goes more in-depth." +"We have added a new `xgboost-quickstart` example alongside a new `xgboost-" +"comprehensive` example that goes more in-depth." msgstr "" -#: ../../source/ref-changelog.md:184 +#: ../../source/ref-changelog.md:276 msgid "" -"**Add Vertical FL example** " -"([#2598](https://github.com/adap/flower/pull/2598))" +"**Add Vertical FL example** ([#2598](https://github.com/adap/flower/" +"pull/2598))" msgstr "" -#: ../../source/ref-changelog.md:186 +#: ../../source/ref-changelog.md:278 msgid "" -"We had many questions about Vertical Federated Learning using Flower, so " -"we decided to add an simple example for it on the [Titanic " -"dataset](https://www.kaggle.com/competitions/titanic/data) alongside a " -"tutorial (in the README)." +"We had many questions about Vertical Federated Learning using Flower, so we " +"decided to add an simple example for it on the [Titanic dataset](https://www." +"kaggle.com/competitions/titanic/data) alongside a tutorial (in the README)." msgstr "" -#: ../../source/ref-changelog.md:188 +#: ../../source/ref-changelog.md:280 msgid "" -"**Support custom** `ClientManager` **in** `start_driver()` " -"([#2292](https://github.com/adap/flower/pull/2292))" +"**Support custom** `ClientManager` **in** `start_driver()` ([#2292](https://" +"github.com/adap/flower/pull/2292))" msgstr "" -#: ../../source/ref-changelog.md:190 +#: ../../source/ref-changelog.md:282 msgid "" -"**Update REST API to support create and delete nodes** " -"([#2283](https://github.com/adap/flower/pull/2283))" +"**Update REST API to support create and delete nodes** ([#2283](https://" +"github.com/adap/flower/pull/2283))" msgstr "" -#: ../../source/ref-changelog.md:192 +#: ../../source/ref-changelog.md:284 msgid "" -"**Update the Android SDK** " -"([#2187](https://github.com/adap/flower/pull/2187))" +"**Update the Android SDK** ([#2187](https://github.com/adap/flower/" +"pull/2187))" msgstr "" -#: ../../source/ref-changelog.md:194 +#: ../../source/ref-changelog.md:286 msgid "Add gRPC request-response capability to the Android SDK." msgstr "" -#: ../../source/ref-changelog.md:196 +#: ../../source/ref-changelog.md:288 msgid "" -"**Update the C++ SDK** " -"([#2537](https://github.com/adap/flower/pull/2537), " -"[#2528](https://github.com/adap/flower/pull/2528), " -"[#2523](https://github.com/adap/flower/pull/2523), " -"[#2522](https://github.com/adap/flower/pull/2522))" +"**Update the C++ SDK** ([#2537](https://github.com/adap/flower/pull/2537), " +"[#2528](https://github.com/adap/flower/pull/2528), [#2523](https://github." +"com/adap/flower/pull/2523), [#2522](https://github.com/adap/flower/" +"pull/2522))" msgstr "" -#: ../../source/ref-changelog.md:198 +#: ../../source/ref-changelog.md:290 msgid "Add gRPC request-response capability to the C++ SDK." msgstr "" -#: ../../source/ref-changelog.md:200 +#: ../../source/ref-changelog.md:292 msgid "" -"**Make HTTPS the new default** " -"([#2591](https://github.com/adap/flower/pull/2591), " -"[#2636](https://github.com/adap/flower/pull/2636))" +"**Make HTTPS the new default** ([#2591](https://github.com/adap/flower/" +"pull/2591), [#2636](https://github.com/adap/flower/pull/2636))" msgstr "" -#: ../../source/ref-changelog.md:202 +#: ../../source/ref-changelog.md:294 msgid "" "Flower is moving to HTTPS by default. The new `flower-server` requires " -"passing `--certificates`, but users can enable `--insecure` to use HTTP " -"for prototyping. The same applies to `flower-client`, which can either " -"use user-provided credentials or gRPC-bundled certificates to connect to " -"an HTTPS-enabled server or requires opt-out via passing `--insecure` to " -"enable insecure HTTP connections." +"passing `--certificates`, but users can enable `--insecure` to use HTTP for " +"prototyping. The same applies to `flower-client`, which can either use user-" +"provided credentials or gRPC-bundled certificates to connect to an HTTPS-" +"enabled server or requires opt-out via passing `--insecure` to enable " +"insecure HTTP connections." msgstr "" -#: ../../source/ref-changelog.md:204 +#: ../../source/ref-changelog.md:296 msgid "" -"For backward compatibility, `start_client()` and `start_numpy_client()` " -"will still start in insecure mode by default. In a future release, " -"insecure connections will require user opt-in by passing `insecure=True`." +"For backward compatibility, `start_client()` and `start_numpy_client()` will " +"still start in insecure mode by default. In a future release, insecure " +"connections will require user opt-in by passing `insecure=True`." msgstr "" -#: ../../source/ref-changelog.md:206 +#: ../../source/ref-changelog.md:298 msgid "" "**Unify client API** ([#2303](https://github.com/adap/flower/pull/2303), " -"[#2390](https://github.com/adap/flower/pull/2390), " -"[#2493](https://github.com/adap/flower/pull/2493))" +"[#2390](https://github.com/adap/flower/pull/2390), [#2493](https://github." +"com/adap/flower/pull/2493))" msgstr "" -#: ../../source/ref-changelog.md:208 +#: ../../source/ref-changelog.md:300 msgid "" -"Using the `client_fn`, Flower clients can interchangeably run as " -"standalone processes (i.e. via `start_client`) or in simulation (i.e. via" -" `start_simulation`) without requiring changes to how the client class is" -" defined and instantiated. The `to_client()` function is introduced to " +"Using the `client_fn`, Flower clients can interchangeably run as standalone " +"processes (i.e. via `start_client`) or in simulation (i.e. via " +"`start_simulation`) without requiring changes to how the client class is " +"defined and instantiated. The `to_client()` function is introduced to " "convert a `NumPyClient` to a `Client`." msgstr "" -#: ../../source/ref-changelog.md:210 +#: ../../source/ref-changelog.md:302 msgid "" -"**Add new** `Bulyan` **strategy** " -"([#1817](https://github.com/adap/flower/pull/1817), " -"[#1891](https://github.com/adap/flower/pull/1891))" +"**Add new** `Bulyan` **strategy** ([#1817](https://github.com/adap/flower/" +"pull/1817), [#1891](https://github.com/adap/flower/pull/1891))" msgstr "" -#: ../../source/ref-changelog.md:212 +#: ../../source/ref-changelog.md:304 msgid "" -"The new `Bulyan` strategy implements Bulyan by [El Mhamdi et al., " -"2018](https://arxiv.org/abs/1802.07927)" +"The new `Bulyan` strategy implements Bulyan by [El Mhamdi et al., 2018]" +"(https://arxiv.org/abs/1802.07927)" msgstr "" -#: ../../source/ref-changelog.md:214 +#: ../../source/ref-changelog.md:306 msgid "" -"**Add new** `XGB Bagging` **strategy** " -"([#2611](https://github.com/adap/flower/pull/2611))" +"**Add new** `XGB Bagging` **strategy** ([#2611](https://github.com/adap/" +"flower/pull/2611))" msgstr "" -#: ../../source/ref-changelog.md:216 ../../source/ref-changelog.md:218 +#: ../../source/ref-changelog.md:308 ../../source/ref-changelog.md:310 msgid "" -"**Introduce `WorkloadState`** " -"([#2564](https://github.com/adap/flower/pull/2564), " -"[#2632](https://github.com/adap/flower/pull/2632))" +"**Introduce `WorkloadState`** ([#2564](https://github.com/adap/flower/" +"pull/2564), [#2632](https://github.com/adap/flower/pull/2632))" msgstr "" -#: ../../source/ref-changelog.md:222 +#: ../../source/ref-changelog.md:314 msgid "" -"FedProx ([#2210](https://github.com/adap/flower/pull/2210), " -"[#2286](https://github.com/adap/flower/pull/2286), " -"[#2509](https://github.com/adap/flower/pull/2509))" +"FedProx ([#2210](https://github.com/adap/flower/pull/2210), [#2286](https://" +"github.com/adap/flower/pull/2286), [#2509](https://github.com/adap/flower/" +"pull/2509))" msgstr "" -#: ../../source/ref-changelog.md:224 +#: ../../source/ref-changelog.md:316 msgid "" -"Baselines Docs ([#2290](https://github.com/adap/flower/pull/2290), " -"[#2400](https://github.com/adap/flower/pull/2400))" +"Baselines Docs ([#2290](https://github.com/adap/flower/pull/2290), [#2400]" +"(https://github.com/adap/flower/pull/2400))" msgstr "" -#: ../../source/ref-changelog.md:226 +#: ../../source/ref-changelog.md:318 msgid "" -"FedMLB ([#2340](https://github.com/adap/flower/pull/2340), " -"[#2507](https://github.com/adap/flower/pull/2507))" +"FedMLB ([#2340](https://github.com/adap/flower/pull/2340), [#2507](https://" +"github.com/adap/flower/pull/2507))" msgstr "" -#: ../../source/ref-changelog.md:228 +#: ../../source/ref-changelog.md:320 msgid "" -"TAMUNA ([#2254](https://github.com/adap/flower/pull/2254), " -"[#2508](https://github.com/adap/flower/pull/2508))" +"TAMUNA ([#2254](https://github.com/adap/flower/pull/2254), [#2508](https://" +"github.com/adap/flower/pull/2508))" msgstr "" -#: ../../source/ref-changelog.md:230 +#: ../../source/ref-changelog.md:322 msgid "FedMeta [#2438](https://github.com/adap/flower/pull/2438)" msgstr "" -#: ../../source/ref-changelog.md:232 +#: ../../source/ref-changelog.md:324 msgid "FjORD [#2431](https://github.com/adap/flower/pull/2431)" msgstr "" -#: ../../source/ref-changelog.md:234 +#: ../../source/ref-changelog.md:326 msgid "MOON [#2421](https://github.com/adap/flower/pull/2421)" msgstr "" -#: ../../source/ref-changelog.md:236 +#: ../../source/ref-changelog.md:328 msgid "DepthFL [#2295](https://github.com/adap/flower/pull/2295)" msgstr "" -#: ../../source/ref-changelog.md:238 +#: ../../source/ref-changelog.md:330 msgid "FedPer [#2266](https://github.com/adap/flower/pull/2266)" msgstr "" -#: ../../source/ref-changelog.md:240 +#: ../../source/ref-changelog.md:332 msgid "FedWav2vec [#2551](https://github.com/adap/flower/pull/2551)" msgstr "" -#: ../../source/ref-changelog.md:242 +#: ../../source/ref-changelog.md:334 msgid "niid-Bench [#2428](https://github.com/adap/flower/pull/2428)" msgstr "" -#: ../../source/ref-changelog.md:244 +#: ../../source/ref-changelog.md:336 msgid "" -"FedBN ([#2608](https://github.com/adap/flower/pull/2608), " -"[#2615](https://github.com/adap/flower/pull/2615))" +"FedBN ([#2608](https://github.com/adap/flower/pull/2608), [#2615](https://" +"github.com/adap/flower/pull/2615))" msgstr "" -#: ../../source/ref-changelog.md:246 +#: ../../source/ref-changelog.md:338 msgid "" -"**General updates to Flower Examples** " -"([#2384](https://github.com/adap/flower/pull/2384), " -"[#2425](https://github.com/adap/flower/pull/2425), " -"[#2526](https://github.com/adap/flower/pull/2526), " -"[#2302](https://github.com/adap/flower/pull/2302), " -"[#2545](https://github.com/adap/flower/pull/2545))" +"**General updates to Flower Examples** ([#2384](https://github.com/adap/" +"flower/pull/2384), [#2425](https://github.com/adap/flower/pull/2425), [#2526]" +"(https://github.com/adap/flower/pull/2526), [#2302](https://github.com/adap/" +"flower/pull/2302), [#2545](https://github.com/adap/flower/pull/2545))" msgstr "" -#: ../../source/ref-changelog.md:248 +#: ../../source/ref-changelog.md:340 msgid "" -"**General updates to Flower Baselines** " -"([#2301](https://github.com/adap/flower/pull/2301), " -"[#2305](https://github.com/adap/flower/pull/2305), " -"[#2307](https://github.com/adap/flower/pull/2307), " -"[#2327](https://github.com/adap/flower/pull/2327), " -"[#2435](https://github.com/adap/flower/pull/2435), " -"[#2462](https://github.com/adap/flower/pull/2462), " -"[#2463](https://github.com/adap/flower/pull/2463), " -"[#2461](https://github.com/adap/flower/pull/2461), " -"[#2469](https://github.com/adap/flower/pull/2469), " -"[#2466](https://github.com/adap/flower/pull/2466), " -"[#2471](https://github.com/adap/flower/pull/2471), " -"[#2472](https://github.com/adap/flower/pull/2472), " -"[#2470](https://github.com/adap/flower/pull/2470))" +"**General updates to Flower Baselines** ([#2301](https://github.com/adap/" +"flower/pull/2301), [#2305](https://github.com/adap/flower/pull/2305), [#2307]" +"(https://github.com/adap/flower/pull/2307), [#2327](https://github.com/adap/" +"flower/pull/2327), [#2435](https://github.com/adap/flower/pull/2435), [#2462]" +"(https://github.com/adap/flower/pull/2462), [#2463](https://github.com/adap/" +"flower/pull/2463), [#2461](https://github.com/adap/flower/pull/2461), [#2469]" +"(https://github.com/adap/flower/pull/2469), [#2466](https://github.com/adap/" +"flower/pull/2466), [#2471](https://github.com/adap/flower/pull/2471), [#2472]" +"(https://github.com/adap/flower/pull/2472), [#2470](https://github.com/adap/" +"flower/pull/2470))" msgstr "" -#: ../../source/ref-changelog.md:250 +#: ../../source/ref-changelog.md:342 msgid "" -"**General updates to the simulation engine** " -"([#2331](https://github.com/adap/flower/pull/2331), " -"[#2447](https://github.com/adap/flower/pull/2447), " -"[#2448](https://github.com/adap/flower/pull/2448), " -"[#2294](https://github.com/adap/flower/pull/2294))" +"**General updates to the simulation engine** ([#2331](https://github.com/" +"adap/flower/pull/2331), [#2447](https://github.com/adap/flower/pull/2447), " +"[#2448](https://github.com/adap/flower/pull/2448), [#2294](https://github." +"com/adap/flower/pull/2294))" msgstr "" -#: ../../source/ref-changelog.md:252 +#: ../../source/ref-changelog.md:344 msgid "" -"**General updates to Flower SDKs** " -"([#2288](https://github.com/adap/flower/pull/2288), " -"[#2429](https://github.com/adap/flower/pull/2429), " -"[#2555](https://github.com/adap/flower/pull/2555), " -"[#2543](https://github.com/adap/flower/pull/2543), " -"[#2544](https://github.com/adap/flower/pull/2544), " -"[#2597](https://github.com/adap/flower/pull/2597), " -"[#2623](https://github.com/adap/flower/pull/2623))" +"**General updates to Flower SDKs** ([#2288](https://github.com/adap/flower/" +"pull/2288), [#2429](https://github.com/adap/flower/pull/2429), [#2555]" +"(https://github.com/adap/flower/pull/2555), [#2543](https://github.com/adap/" +"flower/pull/2543), [#2544](https://github.com/adap/flower/pull/2544), [#2597]" +"(https://github.com/adap/flower/pull/2597), [#2623](https://github.com/adap/" +"flower/pull/2623))" msgstr "" -#: ../../source/ref-changelog.md:254 +#: ../../source/ref-changelog.md:346 msgid "" -"**General improvements** " -"([#2309](https://github.com/adap/flower/pull/2309), " -"[#2310](https://github.com/adap/flower/pull/2310), " -"[#2313](https://github.com/adap/flower/pull/2313), " -"[#2316](https://github.com/adap/flower/pull/2316), " -"[#2317](https://github.com/adap/flower/pull/2317), " -"[#2349](https://github.com/adap/flower/pull/2349), " -"[#2360](https://github.com/adap/flower/pull/2360), " -"[#2402](https://github.com/adap/flower/pull/2402), " -"[#2446](https://github.com/adap/flower/pull/2446), " -"[#2561](https://github.com/adap/flower/pull/2561), " -"[#2273](https://github.com/adap/flower/pull/2273), " -"[#2267](https://github.com/adap/flower/pull/2267), " -"[#2274](https://github.com/adap/flower/pull/2274), " -"[#2275](https://github.com/adap/flower/pull/2275), " -"[#2432](https://github.com/adap/flower/pull/2432), " -"[#2251](https://github.com/adap/flower/pull/2251), " -"[#2321](https://github.com/adap/flower/pull/2321), " -"[#1936](https://github.com/adap/flower/pull/1936), " -"[#2408](https://github.com/adap/flower/pull/2408), " -"[#2413](https://github.com/adap/flower/pull/2413), " -"[#2401](https://github.com/adap/flower/pull/2401), " -"[#2531](https://github.com/adap/flower/pull/2531), " -"[#2534](https://github.com/adap/flower/pull/2534), " -"[#2535](https://github.com/adap/flower/pull/2535), " -"[#2521](https://github.com/adap/flower/pull/2521), " -"[#2553](https://github.com/adap/flower/pull/2553), " -"[#2596](https://github.com/adap/flower/pull/2596))" -msgstr "" - -#: ../../source/ref-changelog.md:256 ../../source/ref-changelog.md:346 -#: ../../source/ref-changelog.md:410 ../../source/ref-changelog.md:464 -#: ../../source/ref-changelog.md:531 -msgid "Flower received many improvements under the hood, too many to list here." +"**General improvements** ([#2309](https://github.com/adap/flower/pull/2309), " +"[#2310](https://github.com/adap/flower/pull/2310), [#2313](https://github." +"com/adap/flower/pull/2313), [#2316](https://github.com/adap/flower/" +"pull/2316), [#2317](https://github.com/adap/flower/pull/2317), [#2349]" +"(https://github.com/adap/flower/pull/2349), [#2360](https://github.com/adap/" +"flower/pull/2360), [#2402](https://github.com/adap/flower/pull/2402), [#2446]" +"(https://github.com/adap/flower/pull/2446), [#2561](https://github.com/adap/" +"flower/pull/2561), [#2273](https://github.com/adap/flower/pull/2273), [#2267]" +"(https://github.com/adap/flower/pull/2267), [#2274](https://github.com/adap/" +"flower/pull/2274), [#2275](https://github.com/adap/flower/pull/2275), [#2432]" +"(https://github.com/adap/flower/pull/2432), [#2251](https://github.com/adap/" +"flower/pull/2251), [#2321](https://github.com/adap/flower/pull/2321), [#1936]" +"(https://github.com/adap/flower/pull/1936), [#2408](https://github.com/adap/" +"flower/pull/2408), [#2413](https://github.com/adap/flower/pull/2413), [#2401]" +"(https://github.com/adap/flower/pull/2401), [#2531](https://github.com/adap/" +"flower/pull/2531), [#2534](https://github.com/adap/flower/pull/2534), [#2535]" +"(https://github.com/adap/flower/pull/2535), [#2521](https://github.com/adap/" +"flower/pull/2521), [#2553](https://github.com/adap/flower/pull/2553), [#2596]" +"(https://github.com/adap/flower/pull/2596))" msgstr "" -#: ../../source/ref-changelog.md:260 +#: ../../source/ref-changelog.md:348 ../../source/ref-changelog.md:438 +#: ../../source/ref-changelog.md:502 ../../source/ref-changelog.md:556 +#: ../../source/ref-changelog.md:623 +msgid "" +"Flower received many improvements under the hood, too many to list here." +msgstr "" + +#: ../../source/ref-changelog.md:352 msgid "" -"**Remove support for Python 3.7** " -"([#2280](https://github.com/adap/flower/pull/2280), " -"[#2299](https://github.com/adap/flower/pull/2299), " -"[#2304](https://github.com/adap/flower/pull/2304), " -"[#2306](https://github.com/adap/flower/pull/2306), " -"[#2355](https://github.com/adap/flower/pull/2355), " -"[#2356](https://github.com/adap/flower/pull/2356))" +"**Remove support for Python 3.7** ([#2280](https://github.com/adap/flower/" +"pull/2280), [#2299](https://github.com/adap/flower/pull/2299), [#2304]" +"(https://github.com/adap/flower/pull/2304), [#2306](https://github.com/adap/" +"flower/pull/2306), [#2355](https://github.com/adap/flower/pull/2355), [#2356]" +"(https://github.com/adap/flower/pull/2356))" msgstr "" -#: ../../source/ref-changelog.md:262 +#: ../../source/ref-changelog.md:354 msgid "" -"Python 3.7 support was deprecated in Flower 1.5, and this release removes" -" support. Flower now requires Python 3.8." +"Python 3.7 support was deprecated in Flower 1.5, and this release removes " +"support. Flower now requires Python 3.8." msgstr "" -#: ../../source/ref-changelog.md:264 +#: ../../source/ref-changelog.md:356 msgid "" -"**Remove experimental argument** `rest` **from** `start_client` " -"([#2324](https://github.com/adap/flower/pull/2324))" +"**Remove experimental argument** `rest` **from** `start_client` ([#2324]" +"(https://github.com/adap/flower/pull/2324))" msgstr "" -#: ../../source/ref-changelog.md:266 +#: ../../source/ref-changelog.md:358 msgid "" -"The (still experimental) argument `rest` was removed from `start_client` " -"and `start_numpy_client`. Use `transport=\"rest\"` to opt into the " -"experimental REST API instead." +"The (still experimental) argument `rest` was removed from `start_client` and " +"`start_numpy_client`. Use `transport=\"rest\"` to opt into the experimental " +"REST API instead." msgstr "" -#: ../../source/ref-changelog.md:268 +#: ../../source/ref-changelog.md:360 msgid "v1.5.0 (2023-08-31)" msgstr "" -#: ../../source/ref-changelog.md:274 +#: ../../source/ref-changelog.md:366 msgid "" "`Adam Narozniak`, `Anass Anhari`, `Charles Beauville`, `Dana-Farber`, " "`Daniel J. Beutel`, `Daniel Nata Nugraha`, `Edoardo Gabrielli`, `Gustavo " @@ -14593,1979 +16885,1854 @@ msgid "" "TOKEN_v1.5.0-->" msgstr "" -#: ../../source/ref-changelog.md:278 +#: ../../source/ref-changelog.md:370 msgid "" -"**Introduce new simulation engine** " -"([#1969](https://github.com/adap/flower/pull/1969), " -"[#2221](https://github.com/adap/flower/pull/2221), " -"[#2248](https://github.com/adap/flower/pull/2248))" +"**Introduce new simulation engine** ([#1969](https://github.com/adap/flower/" +"pull/1969), [#2221](https://github.com/adap/flower/pull/2221), [#2248]" +"(https://github.com/adap/flower/pull/2248))" msgstr "" -#: ../../source/ref-changelog.md:280 +#: ../../source/ref-changelog.md:372 msgid "" "The new simulation engine has been rewritten from the ground up, yet it " -"remains fully backwards compatible. It offers much improved stability and" -" memory handling, especially when working with GPUs. Simulations " -"transparently adapt to different settings to scale simulation in CPU-" -"only, CPU+GPU, multi-GPU, or multi-node multi-GPU environments." +"remains fully backwards compatible. It offers much improved stability and " +"memory handling, especially when working with GPUs. Simulations " +"transparently adapt to different settings to scale simulation in CPU-only, " +"CPU+GPU, multi-GPU, or multi-node multi-GPU environments." msgstr "" -#: ../../source/ref-changelog.md:282 +#: ../../source/ref-changelog.md:374 msgid "" -"Comprehensive documentation includes a new [how-to run " -"simulations](https://flower.ai/docs/framework/how-to-run-" -"simulations.html) guide, new [simulation-" +"Comprehensive documentation includes a new [how-to run simulations](https://" +"flower.ai/docs/framework/how-to-run-simulations.html) guide, new [simulation-" "pytorch](https://flower.ai/docs/examples/simulation-pytorch.html) and " "[simulation-tensorflow](https://flower.ai/docs/examples/simulation-" -"tensorflow.html) notebooks, and a new [YouTube tutorial " -"series](https://www.youtube.com/watch?v=cRebUIGB5RU&list=PLNG4feLHqCWlnj8a_E1A_n5zr2-8pafTB)." +"tensorflow.html) notebooks, and a new [YouTube tutorial series](https://www." +"youtube.com/watch?v=cRebUIGB5RU&list=PLNG4feLHqCWlnj8a_E1A_n5zr2-8pafTB)." msgstr "" -#: ../../source/ref-changelog.md:284 +#: ../../source/ref-changelog.md:376 msgid "" -"**Restructure Flower Docs** " -"([#1824](https://github.com/adap/flower/pull/1824), " -"[#1865](https://github.com/adap/flower/pull/1865), " -"[#1884](https://github.com/adap/flower/pull/1884), " -"[#1887](https://github.com/adap/flower/pull/1887), " -"[#1919](https://github.com/adap/flower/pull/1919), " -"[#1922](https://github.com/adap/flower/pull/1922), " -"[#1920](https://github.com/adap/flower/pull/1920), " -"[#1923](https://github.com/adap/flower/pull/1923), " -"[#1924](https://github.com/adap/flower/pull/1924), " -"[#1962](https://github.com/adap/flower/pull/1962), " -"[#2006](https://github.com/adap/flower/pull/2006), " -"[#2133](https://github.com/adap/flower/pull/2133), " -"[#2203](https://github.com/adap/flower/pull/2203), " -"[#2215](https://github.com/adap/flower/pull/2215), " -"[#2122](https://github.com/adap/flower/pull/2122), " -"[#2223](https://github.com/adap/flower/pull/2223), " -"[#2219](https://github.com/adap/flower/pull/2219), " -"[#2232](https://github.com/adap/flower/pull/2232), " -"[#2233](https://github.com/adap/flower/pull/2233), " -"[#2234](https://github.com/adap/flower/pull/2234), " -"[#2235](https://github.com/adap/flower/pull/2235), " -"[#2237](https://github.com/adap/flower/pull/2237), " -"[#2238](https://github.com/adap/flower/pull/2238), " -"[#2242](https://github.com/adap/flower/pull/2242), " -"[#2231](https://github.com/adap/flower/pull/2231), " -"[#2243](https://github.com/adap/flower/pull/2243), " -"[#2227](https://github.com/adap/flower/pull/2227))" +"**Restructure Flower Docs** ([#1824](https://github.com/adap/flower/" +"pull/1824), [#1865](https://github.com/adap/flower/pull/1865), [#1884]" +"(https://github.com/adap/flower/pull/1884), [#1887](https://github.com/adap/" +"flower/pull/1887), [#1919](https://github.com/adap/flower/pull/1919), [#1922]" +"(https://github.com/adap/flower/pull/1922), [#1920](https://github.com/adap/" +"flower/pull/1920), [#1923](https://github.com/adap/flower/pull/1923), [#1924]" +"(https://github.com/adap/flower/pull/1924), [#1962](https://github.com/adap/" +"flower/pull/1962), [#2006](https://github.com/adap/flower/pull/2006), [#2133]" +"(https://github.com/adap/flower/pull/2133), [#2203](https://github.com/adap/" +"flower/pull/2203), [#2215](https://github.com/adap/flower/pull/2215), [#2122]" +"(https://github.com/adap/flower/pull/2122), [#2223](https://github.com/adap/" +"flower/pull/2223), [#2219](https://github.com/adap/flower/pull/2219), [#2232]" +"(https://github.com/adap/flower/pull/2232), [#2233](https://github.com/adap/" +"flower/pull/2233), [#2234](https://github.com/adap/flower/pull/2234), [#2235]" +"(https://github.com/adap/flower/pull/2235), [#2237](https://github.com/adap/" +"flower/pull/2237), [#2238](https://github.com/adap/flower/pull/2238), [#2242]" +"(https://github.com/adap/flower/pull/2242), [#2231](https://github.com/adap/" +"flower/pull/2231), [#2243](https://github.com/adap/flower/pull/2243), [#2227]" +"(https://github.com/adap/flower/pull/2227))" msgstr "" -#: ../../source/ref-changelog.md:286 +#: ../../source/ref-changelog.md:378 msgid "" -"Much effort went into a completely restructured Flower docs experience. " -"The documentation on [flower.ai/docs](https://flower.ai/docs) is now " -"divided into Flower Framework, Flower Baselines, Flower Android SDK, " -"Flower iOS SDK, and code example projects." +"Much effort went into a completely restructured Flower docs experience. The " +"documentation on [flower.ai/docs](https://flower.ai/docs) is now divided " +"into Flower Framework, Flower Baselines, Flower Android SDK, Flower iOS SDK, " +"and code example projects." msgstr "" -#: ../../source/ref-changelog.md:288 +#: ../../source/ref-changelog.md:380 msgid "" -"**Introduce Flower Swift SDK** " -"([#1858](https://github.com/adap/flower/pull/1858), " -"[#1897](https://github.com/adap/flower/pull/1897))" +"**Introduce Flower Swift SDK** ([#1858](https://github.com/adap/flower/" +"pull/1858), [#1897](https://github.com/adap/flower/pull/1897))" msgstr "" -#: ../../source/ref-changelog.md:290 +#: ../../source/ref-changelog.md:382 msgid "" -"This is the first preview release of the Flower Swift SDK. Flower support" -" on iOS is improving, and alongside the Swift SDK and code example, there" -" is now also an iOS quickstart tutorial." +"This is the first preview release of the Flower Swift SDK. Flower support on " +"iOS is improving, and alongside the Swift SDK and code example, there is now " +"also an iOS quickstart tutorial." msgstr "" -#: ../../source/ref-changelog.md:292 +#: ../../source/ref-changelog.md:384 msgid "" -"**Introduce Flower Android SDK** " -"([#2131](https://github.com/adap/flower/pull/2131))" +"**Introduce Flower Android SDK** ([#2131](https://github.com/adap/flower/" +"pull/2131))" msgstr "" -#: ../../source/ref-changelog.md:294 +#: ../../source/ref-changelog.md:386 msgid "" -"This is the first preview release of the Flower Kotlin SDK. Flower " -"support on Android is improving, and alongside the Kotlin SDK and code " -"example, there is now also an Android quickstart tutorial." +"This is the first preview release of the Flower Kotlin SDK. Flower support " +"on Android is improving, and alongside the Kotlin SDK and code example, " +"there is now also an Android quickstart tutorial." msgstr "" -#: ../../source/ref-changelog.md:296 +#: ../../source/ref-changelog.md:388 msgid "" -"**Introduce new end-to-end testing infrastructure** " -"([#1842](https://github.com/adap/flower/pull/1842), " -"[#2071](https://github.com/adap/flower/pull/2071), " -"[#2072](https://github.com/adap/flower/pull/2072), " -"[#2068](https://github.com/adap/flower/pull/2068), " -"[#2067](https://github.com/adap/flower/pull/2067), " -"[#2069](https://github.com/adap/flower/pull/2069), " -"[#2073](https://github.com/adap/flower/pull/2073), " -"[#2070](https://github.com/adap/flower/pull/2070), " -"[#2074](https://github.com/adap/flower/pull/2074), " -"[#2082](https://github.com/adap/flower/pull/2082), " -"[#2084](https://github.com/adap/flower/pull/2084), " -"[#2093](https://github.com/adap/flower/pull/2093), " -"[#2109](https://github.com/adap/flower/pull/2109), " -"[#2095](https://github.com/adap/flower/pull/2095), " -"[#2140](https://github.com/adap/flower/pull/2140), " -"[#2137](https://github.com/adap/flower/pull/2137), " -"[#2165](https://github.com/adap/flower/pull/2165))" +"**Introduce new end-to-end testing infrastructure** ([#1842](https://github." +"com/adap/flower/pull/1842), [#2071](https://github.com/adap/flower/" +"pull/2071), [#2072](https://github.com/adap/flower/pull/2072), [#2068]" +"(https://github.com/adap/flower/pull/2068), [#2067](https://github.com/adap/" +"flower/pull/2067), [#2069](https://github.com/adap/flower/pull/2069), [#2073]" +"(https://github.com/adap/flower/pull/2073), [#2070](https://github.com/adap/" +"flower/pull/2070), [#2074](https://github.com/adap/flower/pull/2074), [#2082]" +"(https://github.com/adap/flower/pull/2082), [#2084](https://github.com/adap/" +"flower/pull/2084), [#2093](https://github.com/adap/flower/pull/2093), [#2109]" +"(https://github.com/adap/flower/pull/2109), [#2095](https://github.com/adap/" +"flower/pull/2095), [#2140](https://github.com/adap/flower/pull/2140), [#2137]" +"(https://github.com/adap/flower/pull/2137), [#2165](https://github.com/adap/" +"flower/pull/2165))" msgstr "" -#: ../../source/ref-changelog.md:298 +#: ../../source/ref-changelog.md:390 msgid "" -"A new testing infrastructure ensures that new changes stay compatible " -"with existing framework integrations or strategies." +"A new testing infrastructure ensures that new changes stay compatible with " +"existing framework integrations or strategies." msgstr "" -#: ../../source/ref-changelog.md:300 +#: ../../source/ref-changelog.md:392 msgid "**Deprecate Python 3.7**" msgstr "" -#: ../../source/ref-changelog.md:302 +#: ../../source/ref-changelog.md:394 msgid "" -"Since Python 3.7 reached its end of life (EOL) on 2023-06-27, support for" -" Python 3.7 is now deprecated and will be removed in an upcoming release." +"Since Python 3.7 reached its end of life (EOL) on 2023-06-27, support for " +"Python 3.7 is now deprecated and will be removed in an upcoming release." msgstr "" -#: ../../source/ref-changelog.md:304 +#: ../../source/ref-changelog.md:396 msgid "" -"**Add new** `FedTrimmedAvg` **strategy** " -"([#1769](https://github.com/adap/flower/pull/1769), " -"[#1853](https://github.com/adap/flower/pull/1853))" +"**Add new** `FedTrimmedAvg` **strategy** ([#1769](https://github.com/adap/" +"flower/pull/1769), [#1853](https://github.com/adap/flower/pull/1853))" msgstr "" -#: ../../source/ref-changelog.md:306 +#: ../../source/ref-changelog.md:398 msgid "" -"The new `FedTrimmedAvg` strategy implements Trimmed Mean by [Dong Yin, " -"2018](https://arxiv.org/abs/1803.01498)." +"The new `FedTrimmedAvg` strategy implements Trimmed Mean by [Dong Yin, 2018]" +"(https://arxiv.org/abs/1803.01498)." msgstr "" -#: ../../source/ref-changelog.md:308 +#: ../../source/ref-changelog.md:400 msgid "" -"**Introduce start_driver** " -"([#1697](https://github.com/adap/flower/pull/1697))" +"**Introduce start_driver** ([#1697](https://github.com/adap/flower/" +"pull/1697))" msgstr "" -#: ../../source/ref-changelog.md:310 +#: ../../source/ref-changelog.md:402 msgid "" -"In addition to `start_server` and using the raw Driver API, there is a " -"new `start_driver` function that allows for running `start_server` " -"scripts as a Flower driver with only a single-line code change. Check out" -" the `mt-pytorch` code example to see a working example using " -"`start_driver`." +"In addition to `start_server` and using the raw Driver API, there is a new " +"`start_driver` function that allows for running `start_server` scripts as a " +"Flower driver with only a single-line code change. Check out the `mt-" +"pytorch` code example to see a working example using `start_driver`." msgstr "" -#: ../../source/ref-changelog.md:312 +#: ../../source/ref-changelog.md:404 msgid "" -"**Add parameter aggregation to** `mt-pytorch` **code example** " -"([#1785](https://github.com/adap/flower/pull/1785))" +"**Add parameter aggregation to** `mt-pytorch` **code example** ([#1785]" +"(https://github.com/adap/flower/pull/1785))" msgstr "" -#: ../../source/ref-changelog.md:314 +#: ../../source/ref-changelog.md:406 msgid "" -"The `mt-pytorch` example shows how to aggregate parameters when writing a" -" driver script. The included `driver.py` and `server.py` have been " -"aligned to demonstrate both the low-level way and the high-level way of " -"building server-side logic." +"The `mt-pytorch` example shows how to aggregate parameters when writing a " +"driver script. The included `driver.py` and `server.py` have been aligned to " +"demonstrate both the low-level way and the high-level way of building server-" +"side logic." msgstr "" -#: ../../source/ref-changelog.md:316 +#: ../../source/ref-changelog.md:408 msgid "" -"**Migrate experimental REST API to Starlette** " -"([2171](https://github.com/adap/flower/pull/2171))" +"**Migrate experimental REST API to Starlette** ([2171](https://github.com/" +"adap/flower/pull/2171))" msgstr "" -#: ../../source/ref-changelog.md:318 +#: ../../source/ref-changelog.md:410 msgid "" -"The (experimental) REST API used to be implemented in " -"[FastAPI](https://fastapi.tiangolo.com/), but it has now been migrated to" -" use [Starlette](https://www.starlette.io/) directly." +"The (experimental) REST API used to be implemented in [FastAPI](https://" +"fastapi.tiangolo.com/), but it has now been migrated to use [Starlette]" +"(https://www.starlette.io/) directly." msgstr "" -#: ../../source/ref-changelog.md:320 +#: ../../source/ref-changelog.md:412 msgid "" -"Please note: The REST request-response API is still experimental and will" -" likely change significantly over time." +"Please note: The REST request-response API is still experimental and will " +"likely change significantly over time." msgstr "" -#: ../../source/ref-changelog.md:322 +#: ../../source/ref-changelog.md:414 msgid "" -"**Introduce experimental gRPC request-response API** " -"([#1867](https://github.com/adap/flower/pull/1867), " -"[#1901](https://github.com/adap/flower/pull/1901))" +"**Introduce experimental gRPC request-response API** ([#1867](https://github." +"com/adap/flower/pull/1867), [#1901](https://github.com/adap/flower/" +"pull/1901))" msgstr "" -#: ../../source/ref-changelog.md:324 +#: ../../source/ref-changelog.md:416 msgid "" -"In addition to the existing gRPC API (based on bidirectional streaming) " -"and the experimental REST API, there is now a new gRPC API that uses a " -"request-response model to communicate with client nodes." +"In addition to the existing gRPC API (based on bidirectional streaming) and " +"the experimental REST API, there is now a new gRPC API that uses a request-" +"response model to communicate with client nodes." msgstr "" -#: ../../source/ref-changelog.md:326 +#: ../../source/ref-changelog.md:418 msgid "" -"Please note: The gRPC request-response API is still experimental and will" -" likely change significantly over time." +"Please note: The gRPC request-response API is still experimental and will " +"likely change significantly over time." msgstr "" -#: ../../source/ref-changelog.md:328 +#: ../../source/ref-changelog.md:420 msgid "" "**Replace the experimental** `start_client(rest=True)` **with the new** " -"`start_client(transport=\"rest\")` " -"([#1880](https://github.com/adap/flower/pull/1880))" +"`start_client(transport=\"rest\")` ([#1880](https://github.com/adap/flower/" +"pull/1880))" msgstr "" -#: ../../source/ref-changelog.md:330 +#: ../../source/ref-changelog.md:422 msgid "" -"The (experimental) `start_client` argument `rest` was deprecated in " -"favour of a new argument `transport`. `start_client(transport=\"rest\")` " -"will yield the same behaviour as `start_client(rest=True)` did before. " -"All code should migrate to the new argument `transport`. The deprecated " -"argument `rest` will be removed in a future release." +"The (experimental) `start_client` argument `rest` was deprecated in favour " +"of a new argument `transport`. `start_client(transport=\"rest\")` will yield " +"the same behaviour as `start_client(rest=True)` did before. All code should " +"migrate to the new argument `transport`. The deprecated argument `rest` will " +"be removed in a future release." msgstr "" -#: ../../source/ref-changelog.md:332 +#: ../../source/ref-changelog.md:424 msgid "" -"**Add a new gRPC option** " -"([#2197](https://github.com/adap/flower/pull/2197))" +"**Add a new gRPC option** ([#2197](https://github.com/adap/flower/pull/2197))" msgstr "" -#: ../../source/ref-changelog.md:334 +#: ../../source/ref-changelog.md:426 msgid "" -"We now start a gRPC server with the `grpc.keepalive_permit_without_calls`" -" option set to 0 by default. This prevents the clients from sending " -"keepalive pings when there is no outstanding stream." +"We now start a gRPC server with the `grpc.keepalive_permit_without_calls` " +"option set to 0 by default. This prevents the clients from sending keepalive " +"pings when there is no outstanding stream." msgstr "" -#: ../../source/ref-changelog.md:336 +#: ../../source/ref-changelog.md:428 msgid "" -"**Improve example notebooks** " -"([#2005](https://github.com/adap/flower/pull/2005))" +"**Improve example notebooks** ([#2005](https://github.com/adap/flower/" +"pull/2005))" msgstr "" -#: ../../source/ref-changelog.md:338 +#: ../../source/ref-changelog.md:430 msgid "There's a new 30min Federated Learning PyTorch tutorial!" msgstr "" -#: ../../source/ref-changelog.md:340 +#: ../../source/ref-changelog.md:432 msgid "" "**Example updates** ([#1772](https://github.com/adap/flower/pull/1772), " -"[#1873](https://github.com/adap/flower/pull/1873), " -"[#1981](https://github.com/adap/flower/pull/1981), " -"[#1988](https://github.com/adap/flower/pull/1988), " -"[#1984](https://github.com/adap/flower/pull/1984), " -"[#1982](https://github.com/adap/flower/pull/1982), " -"[#2112](https://github.com/adap/flower/pull/2112), " -"[#2144](https://github.com/adap/flower/pull/2144), " -"[#2174](https://github.com/adap/flower/pull/2174), " -"[#2225](https://github.com/adap/flower/pull/2225), " -"[#2183](https://github.com/adap/flower/pull/2183))" +"[#1873](https://github.com/adap/flower/pull/1873), [#1981](https://github." +"com/adap/flower/pull/1981), [#1988](https://github.com/adap/flower/" +"pull/1988), [#1984](https://github.com/adap/flower/pull/1984), [#1982]" +"(https://github.com/adap/flower/pull/1982), [#2112](https://github.com/adap/" +"flower/pull/2112), [#2144](https://github.com/adap/flower/pull/2144), [#2174]" +"(https://github.com/adap/flower/pull/2174), [#2225](https://github.com/adap/" +"flower/pull/2225), [#2183](https://github.com/adap/flower/pull/2183))" msgstr "" -#: ../../source/ref-changelog.md:342 +#: ../../source/ref-changelog.md:434 msgid "" "Many examples have received significant updates, including simplified " "advanced-tensorflow and advanced-pytorch examples, improved macOS " -"compatibility of TensorFlow examples, and code examples for simulation. A" -" major upgrade is that all code examples now have a `requirements.txt` " -"(in addition to `pyproject.toml`)." +"compatibility of TensorFlow examples, and code examples for simulation. A " +"major upgrade is that all code examples now have a `requirements.txt` (in " +"addition to `pyproject.toml`)." msgstr "" -#: ../../source/ref-changelog.md:344 +#: ../../source/ref-changelog.md:436 msgid "" -"**General improvements** " -"([#1872](https://github.com/adap/flower/pull/1872), " -"[#1866](https://github.com/adap/flower/pull/1866), " -"[#1884](https://github.com/adap/flower/pull/1884), " -"[#1837](https://github.com/adap/flower/pull/1837), " -"[#1477](https://github.com/adap/flower/pull/1477), " -"[#2171](https://github.com/adap/flower/pull/2171))" +"**General improvements** ([#1872](https://github.com/adap/flower/pull/1872), " +"[#1866](https://github.com/adap/flower/pull/1866), [#1884](https://github." +"com/adap/flower/pull/1884), [#1837](https://github.com/adap/flower/" +"pull/1837), [#1477](https://github.com/adap/flower/pull/1477), [#2171]" +"(https://github.com/adap/flower/pull/2171))" msgstr "" -#: ../../source/ref-changelog.md:352 +#: ../../source/ref-changelog.md:444 msgid "v1.4.0 (2023-04-21)" msgstr "" -#: ../../source/ref-changelog.md:358 +#: ../../source/ref-changelog.md:450 msgid "" "`Adam Narozniak`, `Alexander Viala Bellander`, `Charles Beauville`, " -"`Chenyang Ma (Danny)`, `Daniel J. Beutel`, `Edoardo`, `Gautam Jajoo`, " -"`Iacob-Alexandru-Andrei`, `JDRanpariya`, `Jean Charle Yaacoub`, `Kunal " -"Sarkhel`, `L. Jiang`, `Lennart Behme`, `Max Kapsecker`, `Michał`, `Nic " -"Lane`, `Nikolaos Episkopos`, `Ragy`, `Saurav Maheshkar`, `Semo Yang`, " -"`Steve Laskaridis`, `Steven Hé (Sīchàng)`, `Taner Topal`" +"`Chenyang Ma (Danny)`, `Daniel J. Beutel`, `Edoardo`, `Gautam Jajoo`, `Iacob-" +"Alexandru-Andrei`, `JDRanpariya`, `Jean Charle Yaacoub`, `Kunal Sarkhel`, " +"`L. Jiang`, `Lennart Behme`, `Max Kapsecker`, `Michał`, `Nic Lane`, " +"`Nikolaos Episkopos`, `Ragy`, `Saurav Maheshkar`, `Semo Yang`, `Steve " +"Laskaridis`, `Steven Hé (Sīchàng)`, `Taner Topal`" msgstr "" -#: ../../source/ref-changelog.md:362 +#: ../../source/ref-changelog.md:454 msgid "" -"**Introduce support for XGBoost (**`FedXgbNnAvg` **strategy and " -"example)** ([#1694](https://github.com/adap/flower/pull/1694), " -"[#1709](https://github.com/adap/flower/pull/1709), " -"[#1715](https://github.com/adap/flower/pull/1715), " -"[#1717](https://github.com/adap/flower/pull/1717), " -"[#1763](https://github.com/adap/flower/pull/1763), " -"[#1795](https://github.com/adap/flower/pull/1795))" +"**Introduce support for XGBoost (**`FedXgbNnAvg` **strategy and example)** " +"([#1694](https://github.com/adap/flower/pull/1694), [#1709](https://github." +"com/adap/flower/pull/1709), [#1715](https://github.com/adap/flower/" +"pull/1715), [#1717](https://github.com/adap/flower/pull/1717), [#1763]" +"(https://github.com/adap/flower/pull/1763), [#1795](https://github.com/adap/" +"flower/pull/1795))" msgstr "" -#: ../../source/ref-changelog.md:364 +#: ../../source/ref-changelog.md:456 msgid "" "XGBoost is a tree-based ensemble machine learning algorithm that uses " -"gradient boosting to improve model accuracy. We added a new `FedXgbNnAvg`" -" " -"[strategy](https://github.com/adap/flower/tree/main/src/py/flwr/server/strategy/fedxgb_nn_avg.py)," -" and a [code example](https://github.com/adap/flower/tree/main/examples" -"/xgboost-quickstart) that demonstrates the usage of this new strategy in " -"an XGBoost project." +"gradient boosting to improve model accuracy. We added a new `FedXgbNnAvg` " +"[strategy](https://github.com/adap/flower/tree/main/src/py/flwr/server/" +"strategy/fedxgb_nn_avg.py), and a [code example](https://github.com/adap/" +"flower/tree/main/examples/xgboost-quickstart) that demonstrates the usage of " +"this new strategy in an XGBoost project." msgstr "" -#: ../../source/ref-changelog.md:366 +#: ../../source/ref-changelog.md:458 msgid "" -"**Introduce iOS SDK (preview)** " -"([#1621](https://github.com/adap/flower/pull/1621), " -"[#1764](https://github.com/adap/flower/pull/1764))" +"**Introduce iOS SDK (preview)** ([#1621](https://github.com/adap/flower/" +"pull/1621), [#1764](https://github.com/adap/flower/pull/1764))" msgstr "" -#: ../../source/ref-changelog.md:368 +#: ../../source/ref-changelog.md:460 msgid "" -"This is a major update for anyone wanting to implement Federated Learning" -" on iOS mobile devices. We now have a swift iOS SDK present under " -"[src/swift/flwr](https://github.com/adap/flower/tree/main/src/swift/flwr)" -" that will facilitate greatly the app creating process. To showcase its " -"use, the [iOS " +"This is a major update for anyone wanting to implement Federated Learning on " +"iOS mobile devices. We now have a swift iOS SDK present under [src/swift/" +"flwr](https://github.com/adap/flower/tree/main/src/swift/flwr) that will " +"facilitate greatly the app creating process. To showcase its use, the [iOS " "example](https://github.com/adap/flower/tree/main/examples/ios) has also " "been updated!" msgstr "" -#: ../../source/ref-changelog.md:370 +#: ../../source/ref-changelog.md:462 msgid "" -"**Introduce new \"What is Federated Learning?\" tutorial** " -"([#1657](https://github.com/adap/flower/pull/1657), " -"[#1721](https://github.com/adap/flower/pull/1721))" +"**Introduce new \"What is Federated Learning?\" tutorial** ([#1657](https://" +"github.com/adap/flower/pull/1657), [#1721](https://github.com/adap/flower/" +"pull/1721))" msgstr "" -#: ../../source/ref-changelog.md:372 +#: ../../source/ref-changelog.md:464 msgid "" -"A new [entry-level tutorial](https://flower.ai/docs/framework/tutorial-" -"what-is-federated-learning.html) in our documentation explains the basics" -" of Fedetated Learning. It enables anyone who's unfamiliar with Federated" -" Learning to start their journey with Flower. Forward it to anyone who's " +"A new [entry-level tutorial](https://flower.ai/docs/framework/tutorial-what-" +"is-federated-learning.html) in our documentation explains the basics of " +"Fedetated Learning. It enables anyone who's unfamiliar with Federated " +"Learning to start their journey with Flower. Forward it to anyone who's " "interested in Federated Learning!" msgstr "" -#: ../../source/ref-changelog.md:374 +#: ../../source/ref-changelog.md:466 msgid "" -"**Introduce new Flower Baseline: FedProx MNIST** " -"([#1513](https://github.com/adap/flower/pull/1513), " -"[#1680](https://github.com/adap/flower/pull/1680), " -"[#1681](https://github.com/adap/flower/pull/1681), " -"[#1679](https://github.com/adap/flower/pull/1679))" +"**Introduce new Flower Baseline: FedProx MNIST** ([#1513](https://github.com/" +"adap/flower/pull/1513), [#1680](https://github.com/adap/flower/pull/1680), " +"[#1681](https://github.com/adap/flower/pull/1681), [#1679](https://github." +"com/adap/flower/pull/1679))" msgstr "" -#: ../../source/ref-changelog.md:376 +#: ../../source/ref-changelog.md:468 msgid "" -"This new baseline replicates the MNIST+CNN task from the paper [Federated" -" Optimization in Heterogeneous Networks (Li et al., " -"2018)](https://arxiv.org/abs/1812.06127). It uses the `FedProx` strategy," -" which aims at making convergence more robust in heterogeneous settings." +"This new baseline replicates the MNIST+CNN task from the paper [Federated " +"Optimization in Heterogeneous Networks (Li et al., 2018)](https://arxiv.org/" +"abs/1812.06127). It uses the `FedProx` strategy, which aims at making " +"convergence more robust in heterogeneous settings." msgstr "" -#: ../../source/ref-changelog.md:378 +#: ../../source/ref-changelog.md:470 msgid "" -"**Introduce new Flower Baseline: FedAvg FEMNIST** " -"([#1655](https://github.com/adap/flower/pull/1655))" +"**Introduce new Flower Baseline: FedAvg FEMNIST** ([#1655](https://github." +"com/adap/flower/pull/1655))" msgstr "" -#: ../../source/ref-changelog.md:380 +#: ../../source/ref-changelog.md:472 msgid "" -"This new baseline replicates an experiment evaluating the performance of " -"the FedAvg algorithm on the FEMNIST dataset from the paper [LEAF: A " -"Benchmark for Federated Settings (Caldas et al., " -"2018)](https://arxiv.org/abs/1812.01097)." +"This new baseline replicates an experiment evaluating the performance of the " +"FedAvg algorithm on the FEMNIST dataset from the paper [LEAF: A Benchmark " +"for Federated Settings (Caldas et al., 2018)](https://arxiv.org/" +"abs/1812.01097)." msgstr "" -#: ../../source/ref-changelog.md:382 +#: ../../source/ref-changelog.md:474 msgid "" -"**Introduce (experimental) REST API** " -"([#1594](https://github.com/adap/flower/pull/1594), " -"[#1690](https://github.com/adap/flower/pull/1690), " -"[#1695](https://github.com/adap/flower/pull/1695), " -"[#1712](https://github.com/adap/flower/pull/1712), " -"[#1802](https://github.com/adap/flower/pull/1802), " -"[#1770](https://github.com/adap/flower/pull/1770), " -"[#1733](https://github.com/adap/flower/pull/1733))" +"**Introduce (experimental) REST API** ([#1594](https://github.com/adap/" +"flower/pull/1594), [#1690](https://github.com/adap/flower/pull/1690), [#1695]" +"(https://github.com/adap/flower/pull/1695), [#1712](https://github.com/adap/" +"flower/pull/1712), [#1802](https://github.com/adap/flower/pull/1802), [#1770]" +"(https://github.com/adap/flower/pull/1770), [#1733](https://github.com/adap/" +"flower/pull/1733))" msgstr "" -#: ../../source/ref-changelog.md:384 +#: ../../source/ref-changelog.md:476 msgid "" "A new REST API has been introduced as an alternative to the gRPC-based " "communication stack. In this initial version, the REST API only supports " "anonymous clients." msgstr "" -#: ../../source/ref-changelog.md:386 +#: ../../source/ref-changelog.md:478 msgid "" "Please note: The REST API is still experimental and will likely change " "significantly over time." msgstr "" -#: ../../source/ref-changelog.md:388 -msgid "" -"**Improve the (experimental) Driver API** " -"([#1663](https://github.com/adap/flower/pull/1663), " -"[#1666](https://github.com/adap/flower/pull/1666), " -"[#1667](https://github.com/adap/flower/pull/1667), " -"[#1664](https://github.com/adap/flower/pull/1664), " -"[#1675](https://github.com/adap/flower/pull/1675), " -"[#1676](https://github.com/adap/flower/pull/1676), " -"[#1693](https://github.com/adap/flower/pull/1693), " -"[#1662](https://github.com/adap/flower/pull/1662), " -"[#1794](https://github.com/adap/flower/pull/1794))" -msgstr "" - -#: ../../source/ref-changelog.md:390 +#: ../../source/ref-changelog.md:480 msgid "" -"The Driver API is still an experimental feature, but this release " -"introduces some major upgrades. One of the main improvements is the " -"introduction of an SQLite database to store server state on disk (instead" -" of in-memory). Another improvement is that tasks (instructions or " -"results) that have been delivered will now be deleted. This greatly " -"improves the memory efficiency of a long-running Flower server." +"**Improve the (experimental) Driver API** ([#1663](https://github.com/adap/" +"flower/pull/1663), [#1666](https://github.com/adap/flower/pull/1666), [#1667]" +"(https://github.com/adap/flower/pull/1667), [#1664](https://github.com/adap/" +"flower/pull/1664), [#1675](https://github.com/adap/flower/pull/1675), [#1676]" +"(https://github.com/adap/flower/pull/1676), [#1693](https://github.com/adap/" +"flower/pull/1693), [#1662](https://github.com/adap/flower/pull/1662), [#1794]" +"(https://github.com/adap/flower/pull/1794))" msgstr "" -#: ../../source/ref-changelog.md:392 +#: ../../source/ref-changelog.md:482 msgid "" -"**Fix spilling issues related to Ray during simulations** " -"([#1698](https://github.com/adap/flower/pull/1698))" +"The Driver API is still an experimental feature, but this release introduces " +"some major upgrades. One of the main improvements is the introduction of an " +"SQLite database to store server state on disk (instead of in-memory). " +"Another improvement is that tasks (instructions or results) that have been " +"delivered will now be deleted. This greatly improves the memory efficiency " +"of a long-running Flower server." msgstr "" -#: ../../source/ref-changelog.md:394 +#: ../../source/ref-changelog.md:484 msgid "" -"While running long simulations, `ray` was sometimes spilling huge amounts" -" of data that would make the training unable to continue. This is now " -"fixed! 🎉" +"**Fix spilling issues related to Ray during simulations** ([#1698](https://" +"github.com/adap/flower/pull/1698))" msgstr "" -#: ../../source/ref-changelog.md:396 +#: ../../source/ref-changelog.md:486 msgid "" -"**Add new example using** `TabNet` **and Flower** " -"([#1725](https://github.com/adap/flower/pull/1725))" +"While running long simulations, `ray` was sometimes spilling huge amounts of " +"data that would make the training unable to continue. This is now fixed! 🎉" msgstr "" -#: ../../source/ref-changelog.md:398 +#: ../../source/ref-changelog.md:488 msgid "" -"TabNet is a powerful and flexible framework for training machine learning" -" models on tabular data. We now have a federated example using Flower: " -"[quickstart-tabnet](https://github.com/adap/flower/tree/main/examples" -"/quickstart-tabnet)." +"**Add new example using** `TabNet` **and Flower** ([#1725](https://github." +"com/adap/flower/pull/1725))" msgstr "" -#: ../../source/ref-changelog.md:400 +#: ../../source/ref-changelog.md:490 msgid "" -"**Add new how-to guide for monitoring simulations** " -"([#1649](https://github.com/adap/flower/pull/1649))" +"TabNet is a powerful and flexible framework for training machine learning " +"models on tabular data. We now have a federated example using Flower: " +"[quickstart-tabnet](https://github.com/adap/flower/tree/main/examples/" +"quickstart-tabnet)." msgstr "" -#: ../../source/ref-changelog.md:402 +#: ../../source/ref-changelog.md:492 msgid "" -"We now have a documentation guide to help users monitor their performance" -" during simulations." +"**Add new how-to guide for monitoring simulations** ([#1649](https://github." +"com/adap/flower/pull/1649))" msgstr "" -#: ../../source/ref-changelog.md:404 +#: ../../source/ref-changelog.md:494 msgid "" -"**Add training metrics to** `History` **object during simulations** " -"([#1696](https://github.com/adap/flower/pull/1696))" +"We now have a documentation guide to help users monitor their performance " +"during simulations." msgstr "" -#: ../../source/ref-changelog.md:406 +#: ../../source/ref-changelog.md:496 msgid "" -"The `fit_metrics_aggregation_fn` can be used to aggregate training " -"metrics, but previous releases did not save the results in the `History` " -"object. This is now the case!" +"**Add training metrics to** `History` **object during simulations** ([#1696]" +"(https://github.com/adap/flower/pull/1696))" msgstr "" -#: ../../source/ref-changelog.md:408 +#: ../../source/ref-changelog.md:498 msgid "" -"**General improvements** " -"([#1659](https://github.com/adap/flower/pull/1659), " -"[#1646](https://github.com/adap/flower/pull/1646), " -"[#1647](https://github.com/adap/flower/pull/1647), " -"[#1471](https://github.com/adap/flower/pull/1471), " -"[#1648](https://github.com/adap/flower/pull/1648), " -"[#1651](https://github.com/adap/flower/pull/1651), " -"[#1652](https://github.com/adap/flower/pull/1652), " -"[#1653](https://github.com/adap/flower/pull/1653), " -"[#1659](https://github.com/adap/flower/pull/1659), " -"[#1665](https://github.com/adap/flower/pull/1665), " -"[#1670](https://github.com/adap/flower/pull/1670), " -"[#1672](https://github.com/adap/flower/pull/1672), " -"[#1677](https://github.com/adap/flower/pull/1677), " -"[#1684](https://github.com/adap/flower/pull/1684), " -"[#1683](https://github.com/adap/flower/pull/1683), " -"[#1686](https://github.com/adap/flower/pull/1686), " -"[#1682](https://github.com/adap/flower/pull/1682), " -"[#1685](https://github.com/adap/flower/pull/1685), " -"[#1692](https://github.com/adap/flower/pull/1692), " -"[#1705](https://github.com/adap/flower/pull/1705), " -"[#1708](https://github.com/adap/flower/pull/1708), " -"[#1711](https://github.com/adap/flower/pull/1711), " -"[#1713](https://github.com/adap/flower/pull/1713), " -"[#1714](https://github.com/adap/flower/pull/1714), " -"[#1718](https://github.com/adap/flower/pull/1718), " -"[#1716](https://github.com/adap/flower/pull/1716), " -"[#1723](https://github.com/adap/flower/pull/1723), " -"[#1735](https://github.com/adap/flower/pull/1735), " -"[#1678](https://github.com/adap/flower/pull/1678), " -"[#1750](https://github.com/adap/flower/pull/1750), " -"[#1753](https://github.com/adap/flower/pull/1753), " -"[#1736](https://github.com/adap/flower/pull/1736), " -"[#1766](https://github.com/adap/flower/pull/1766), " -"[#1760](https://github.com/adap/flower/pull/1760), " -"[#1775](https://github.com/adap/flower/pull/1775), " -"[#1776](https://github.com/adap/flower/pull/1776), " -"[#1777](https://github.com/adap/flower/pull/1777), " -"[#1779](https://github.com/adap/flower/pull/1779), " -"[#1784](https://github.com/adap/flower/pull/1784), " -"[#1773](https://github.com/adap/flower/pull/1773), " -"[#1755](https://github.com/adap/flower/pull/1755), " -"[#1789](https://github.com/adap/flower/pull/1789), " -"[#1788](https://github.com/adap/flower/pull/1788), " -"[#1798](https://github.com/adap/flower/pull/1798), " -"[#1799](https://github.com/adap/flower/pull/1799), " -"[#1739](https://github.com/adap/flower/pull/1739), " -"[#1800](https://github.com/adap/flower/pull/1800), " -"[#1804](https://github.com/adap/flower/pull/1804), " -"[#1805](https://github.com/adap/flower/pull/1805))" -msgstr "" - -#: ../../source/ref-changelog.md:416 +"The `fit_metrics_aggregation_fn` can be used to aggregate training metrics, " +"but previous releases did not save the results in the `History` object. This " +"is now the case!" +msgstr "" + +#: ../../source/ref-changelog.md:500 +msgid "" +"**General improvements** ([#1659](https://github.com/adap/flower/pull/1659), " +"[#1646](https://github.com/adap/flower/pull/1646), [#1647](https://github." +"com/adap/flower/pull/1647), [#1471](https://github.com/adap/flower/" +"pull/1471), [#1648](https://github.com/adap/flower/pull/1648), [#1651]" +"(https://github.com/adap/flower/pull/1651), [#1652](https://github.com/adap/" +"flower/pull/1652), [#1653](https://github.com/adap/flower/pull/1653), [#1659]" +"(https://github.com/adap/flower/pull/1659), [#1665](https://github.com/adap/" +"flower/pull/1665), [#1670](https://github.com/adap/flower/pull/1670), [#1672]" +"(https://github.com/adap/flower/pull/1672), [#1677](https://github.com/adap/" +"flower/pull/1677), [#1684](https://github.com/adap/flower/pull/1684), [#1683]" +"(https://github.com/adap/flower/pull/1683), [#1686](https://github.com/adap/" +"flower/pull/1686), [#1682](https://github.com/adap/flower/pull/1682), [#1685]" +"(https://github.com/adap/flower/pull/1685), [#1692](https://github.com/adap/" +"flower/pull/1692), [#1705](https://github.com/adap/flower/pull/1705), [#1708]" +"(https://github.com/adap/flower/pull/1708), [#1711](https://github.com/adap/" +"flower/pull/1711), [#1713](https://github.com/adap/flower/pull/1713), [#1714]" +"(https://github.com/adap/flower/pull/1714), [#1718](https://github.com/adap/" +"flower/pull/1718), [#1716](https://github.com/adap/flower/pull/1716), [#1723]" +"(https://github.com/adap/flower/pull/1723), [#1735](https://github.com/adap/" +"flower/pull/1735), [#1678](https://github.com/adap/flower/pull/1678), [#1750]" +"(https://github.com/adap/flower/pull/1750), [#1753](https://github.com/adap/" +"flower/pull/1753), [#1736](https://github.com/adap/flower/pull/1736), [#1766]" +"(https://github.com/adap/flower/pull/1766), [#1760](https://github.com/adap/" +"flower/pull/1760), [#1775](https://github.com/adap/flower/pull/1775), [#1776]" +"(https://github.com/adap/flower/pull/1776), [#1777](https://github.com/adap/" +"flower/pull/1777), [#1779](https://github.com/adap/flower/pull/1779), [#1784]" +"(https://github.com/adap/flower/pull/1784), [#1773](https://github.com/adap/" +"flower/pull/1773), [#1755](https://github.com/adap/flower/pull/1755), [#1789]" +"(https://github.com/adap/flower/pull/1789), [#1788](https://github.com/adap/" +"flower/pull/1788), [#1798](https://github.com/adap/flower/pull/1798), [#1799]" +"(https://github.com/adap/flower/pull/1799), [#1739](https://github.com/adap/" +"flower/pull/1739), [#1800](https://github.com/adap/flower/pull/1800), [#1804]" +"(https://github.com/adap/flower/pull/1804), [#1805](https://github.com/adap/" +"flower/pull/1805))" +msgstr "" + +#: ../../source/ref-changelog.md:508 msgid "v1.3.0 (2023-02-06)" msgstr "" -#: ../../source/ref-changelog.md:422 +#: ../../source/ref-changelog.md:514 msgid "" -"`Adam Narozniak`, `Alexander Viala Bellander`, `Charles Beauville`, " -"`Daniel J. Beutel`, `JDRanpariya`, `Lennart Behme`, `Taner Topal`" +"`Adam Narozniak`, `Alexander Viala Bellander`, `Charles Beauville`, `Daniel " +"J. Beutel`, `JDRanpariya`, `Lennart Behme`, `Taner Topal`" msgstr "" -#: ../../source/ref-changelog.md:426 +#: ../../source/ref-changelog.md:518 msgid "" "**Add support for** `workload_id` **and** `group_id` **in Driver API** " "([#1595](https://github.com/adap/flower/pull/1595))" msgstr "" -#: ../../source/ref-changelog.md:428 +#: ../../source/ref-changelog.md:520 msgid "" -"The (experimental) Driver API now supports a `workload_id` that can be " -"used to identify which workload a task belongs to. It also supports a new" -" `group_id` that can be used, for example, to indicate the current " -"training round. Both the `workload_id` and `group_id` enable client nodes" -" to decide whether they want to handle a task or not." +"The (experimental) Driver API now supports a `workload_id` that can be used " +"to identify which workload a task belongs to. It also supports a new " +"`group_id` that can be used, for example, to indicate the current training " +"round. Both the `workload_id` and `group_id` enable client nodes to decide " +"whether they want to handle a task or not." msgstr "" -#: ../../source/ref-changelog.md:430 +#: ../../source/ref-changelog.md:522 msgid "" -"**Make Driver API and Fleet API address configurable** " -"([#1637](https://github.com/adap/flower/pull/1637))" +"**Make Driver API and Fleet API address configurable** ([#1637](https://" +"github.com/adap/flower/pull/1637))" msgstr "" -#: ../../source/ref-changelog.md:432 +#: ../../source/ref-changelog.md:524 msgid "" -"The (experimental) long-running Flower server (Driver API and Fleet API) " -"can now configure the server address of both Driver API (via `--driver-" -"api-address`) and Fleet API (via `--fleet-api-address`) when starting:" +"The (experimental) long-running Flower server (Driver API and Fleet API) can " +"now configure the server address of both Driver API (via `--driver-api-" +"address`) and Fleet API (via `--fleet-api-address`) when starting:" msgstr "" -#: ../../source/ref-changelog.md:434 +#: ../../source/ref-changelog.md:526 msgid "" "`flower-server --driver-api-address \"0.0.0.0:8081\" --fleet-api-address " "\"0.0.0.0:8086\"`" msgstr "" -#: ../../source/ref-changelog.md:436 +#: ../../source/ref-changelog.md:528 msgid "Both IPv4 and IPv6 addresses are supported." msgstr "" -#: ../../source/ref-changelog.md:438 +#: ../../source/ref-changelog.md:530 msgid "" -"**Add new example of Federated Learning using fastai and Flower** " -"([#1598](https://github.com/adap/flower/pull/1598))" +"**Add new example of Federated Learning using fastai and Flower** ([#1598]" +"(https://github.com/adap/flower/pull/1598))" msgstr "" -#: ../../source/ref-changelog.md:440 +#: ../../source/ref-changelog.md:532 msgid "" "A new code example (`quickstart-fastai`) demonstrates federated learning " "with [fastai](https://www.fast.ai/) and Flower. You can find it here: " -"[quickstart-fastai](https://github.com/adap/flower/tree/main/examples" -"/quickstart-fastai)." +"[quickstart-fastai](https://github.com/adap/flower/tree/main/examples/" +"quickstart-fastai)." msgstr "" -#: ../../source/ref-changelog.md:442 +#: ../../source/ref-changelog.md:534 msgid "" -"**Make Android example compatible with** `flwr >= 1.0.0` **and the latest" -" versions of Android** " -"([#1603](https://github.com/adap/flower/pull/1603))" +"**Make Android example compatible with** `flwr >= 1.0.0` **and the latest " +"versions of Android** ([#1603](https://github.com/adap/flower/pull/1603))" msgstr "" -#: ../../source/ref-changelog.md:444 +#: ../../source/ref-changelog.md:536 msgid "" -"The Android code example has received a substantial update: the project " -"is compatible with Flower 1.0 (and later), the UI received a full " -"refresh, and the project is updated to be compatible with newer Android " -"tooling." +"The Android code example has received a substantial update: the project is " +"compatible with Flower 1.0 (and later), the UI received a full refresh, and " +"the project is updated to be compatible with newer Android tooling." msgstr "" -#: ../../source/ref-changelog.md:446 +#: ../../source/ref-changelog.md:538 msgid "" -"**Add new `FedProx` strategy** " -"([#1619](https://github.com/adap/flower/pull/1619))" +"**Add new `FedProx` strategy** ([#1619](https://github.com/adap/flower/" +"pull/1619))" msgstr "" -#: ../../source/ref-changelog.md:448 +#: ../../source/ref-changelog.md:540 msgid "" -"This " -"[strategy](https://github.com/adap/flower/blob/main/src/py/flwr/server/strategy/fedprox.py)" -" is almost identical to " -"[`FedAvg`](https://github.com/adap/flower/blob/main/src/py/flwr/server/strategy/fedavg.py)," -" but helps users replicate what is described in this " -"[paper](https://arxiv.org/abs/1812.06127). It essentially adds a " -"parameter called `proximal_mu` to regularize the local models with " -"respect to the global models." +"This [strategy](https://github.com/adap/flower/blob/main/src/py/flwr/server/" +"strategy/fedprox.py) is almost identical to [`FedAvg`](https://github.com/" +"adap/flower/blob/main/src/py/flwr/server/strategy/fedavg.py), but helps " +"users replicate what is described in this [paper](https://arxiv.org/" +"abs/1812.06127). It essentially adds a parameter called `proximal_mu` to " +"regularize the local models with respect to the global models." msgstr "" -#: ../../source/ref-changelog.md:450 +#: ../../source/ref-changelog.md:542 msgid "" -"**Add new metrics to telemetry events** " -"([#1640](https://github.com/adap/flower/pull/1640))" +"**Add new metrics to telemetry events** ([#1640](https://github.com/adap/" +"flower/pull/1640))" msgstr "" -#: ../../source/ref-changelog.md:452 +#: ../../source/ref-changelog.md:544 msgid "" "An updated event structure allows, for example, the clustering of events " "within the same workload." msgstr "" -#: ../../source/ref-changelog.md:454 +#: ../../source/ref-changelog.md:546 msgid "" -"**Add new custom strategy tutorial section** " -"[#1623](https://github.com/adap/flower/pull/1623)" +"**Add new custom strategy tutorial section** [#1623](https://github.com/adap/" +"flower/pull/1623)" msgstr "" -#: ../../source/ref-changelog.md:456 +#: ../../source/ref-changelog.md:548 msgid "" -"The Flower tutorial now has a new section that covers implementing a " -"custom strategy from scratch: [Open in " -"Colab](https://colab.research.google.com/github/adap/flower/blob/main/doc/source" -"/tutorial-build-a-strategy-from-scratch-pytorch.ipynb)" +"The Flower tutorial now has a new section that covers implementing a custom " +"strategy from scratch: [Open in Colab](https://colab.research.google.com/" +"github/adap/flower/blob/main/doc/source/tutorial-build-a-strategy-from-" +"scratch-pytorch.ipynb)" msgstr "" -#: ../../source/ref-changelog.md:458 +#: ../../source/ref-changelog.md:550 msgid "" -"**Add new custom serialization tutorial section** " -"([#1622](https://github.com/adap/flower/pull/1622))" +"**Add new custom serialization tutorial section** ([#1622](https://github." +"com/adap/flower/pull/1622))" msgstr "" -#: ../../source/ref-changelog.md:460 +#: ../../source/ref-changelog.md:552 msgid "" -"The Flower tutorial now has a new section that covers custom " -"serialization: [Open in " -"Colab](https://colab.research.google.com/github/adap/flower/blob/main/doc/source" -"/tutorial-customize-the-client-pytorch.ipynb)" +"The Flower tutorial now has a new section that covers custom serialization: " +"[Open in Colab](https://colab.research.google.com/github/adap/flower/blob/" +"main/doc/source/tutorial-customize-the-client-pytorch.ipynb)" msgstr "" -#: ../../source/ref-changelog.md:462 +#: ../../source/ref-changelog.md:554 msgid "" -"**General improvements** " -"([#1638](https://github.com/adap/flower/pull/1638), " -"[#1634](https://github.com/adap/flower/pull/1634), " -"[#1636](https://github.com/adap/flower/pull/1636), " -"[#1635](https://github.com/adap/flower/pull/1635), " -"[#1633](https://github.com/adap/flower/pull/1633), " -"[#1632](https://github.com/adap/flower/pull/1632), " -"[#1631](https://github.com/adap/flower/pull/1631), " -"[#1630](https://github.com/adap/flower/pull/1630), " -"[#1627](https://github.com/adap/flower/pull/1627), " -"[#1593](https://github.com/adap/flower/pull/1593), " -"[#1616](https://github.com/adap/flower/pull/1616), " -"[#1615](https://github.com/adap/flower/pull/1615), " -"[#1607](https://github.com/adap/flower/pull/1607), " -"[#1609](https://github.com/adap/flower/pull/1609), " -"[#1608](https://github.com/adap/flower/pull/1608), " -"[#1603](https://github.com/adap/flower/pull/1603), " -"[#1590](https://github.com/adap/flower/pull/1590), " -"[#1580](https://github.com/adap/flower/pull/1580), " -"[#1599](https://github.com/adap/flower/pull/1599), " -"[#1600](https://github.com/adap/flower/pull/1600), " -"[#1601](https://github.com/adap/flower/pull/1601), " -"[#1597](https://github.com/adap/flower/pull/1597), " -"[#1595](https://github.com/adap/flower/pull/1595), " -"[#1591](https://github.com/adap/flower/pull/1591), " -"[#1588](https://github.com/adap/flower/pull/1588), " -"[#1589](https://github.com/adap/flower/pull/1589), " -"[#1587](https://github.com/adap/flower/pull/1587), " -"[#1573](https://github.com/adap/flower/pull/1573), " -"[#1581](https://github.com/adap/flower/pull/1581), " -"[#1578](https://github.com/adap/flower/pull/1578), " -"[#1574](https://github.com/adap/flower/pull/1574), " -"[#1572](https://github.com/adap/flower/pull/1572), " -"[#1586](https://github.com/adap/flower/pull/1586))" +"**General improvements** ([#1638](https://github.com/adap/flower/pull/1638), " +"[#1634](https://github.com/adap/flower/pull/1634), [#1636](https://github." +"com/adap/flower/pull/1636), [#1635](https://github.com/adap/flower/" +"pull/1635), [#1633](https://github.com/adap/flower/pull/1633), [#1632]" +"(https://github.com/adap/flower/pull/1632), [#1631](https://github.com/adap/" +"flower/pull/1631), [#1630](https://github.com/adap/flower/pull/1630), [#1627]" +"(https://github.com/adap/flower/pull/1627), [#1593](https://github.com/adap/" +"flower/pull/1593), [#1616](https://github.com/adap/flower/pull/1616), [#1615]" +"(https://github.com/adap/flower/pull/1615), [#1607](https://github.com/adap/" +"flower/pull/1607), [#1609](https://github.com/adap/flower/pull/1609), [#1608]" +"(https://github.com/adap/flower/pull/1608), [#1603](https://github.com/adap/" +"flower/pull/1603), [#1590](https://github.com/adap/flower/pull/1590), [#1580]" +"(https://github.com/adap/flower/pull/1580), [#1599](https://github.com/adap/" +"flower/pull/1599), [#1600](https://github.com/adap/flower/pull/1600), [#1601]" +"(https://github.com/adap/flower/pull/1601), [#1597](https://github.com/adap/" +"flower/pull/1597), [#1595](https://github.com/adap/flower/pull/1595), [#1591]" +"(https://github.com/adap/flower/pull/1591), [#1588](https://github.com/adap/" +"flower/pull/1588), [#1589](https://github.com/adap/flower/pull/1589), [#1587]" +"(https://github.com/adap/flower/pull/1587), [#1573](https://github.com/adap/" +"flower/pull/1573), [#1581](https://github.com/adap/flower/pull/1581), [#1578]" +"(https://github.com/adap/flower/pull/1578), [#1574](https://github.com/adap/" +"flower/pull/1574), [#1572](https://github.com/adap/flower/pull/1572), [#1586]" +"(https://github.com/adap/flower/pull/1586))" msgstr "" -#: ../../source/ref-changelog.md:466 +#: ../../source/ref-changelog.md:558 msgid "" -"**Updated documentation** " -"([#1629](https://github.com/adap/flower/pull/1629), " -"[#1628](https://github.com/adap/flower/pull/1628), " -"[#1620](https://github.com/adap/flower/pull/1620), " -"[#1618](https://github.com/adap/flower/pull/1618), " -"[#1617](https://github.com/adap/flower/pull/1617), " -"[#1613](https://github.com/adap/flower/pull/1613), " -"[#1614](https://github.com/adap/flower/pull/1614))" +"**Updated documentation** ([#1629](https://github.com/adap/flower/" +"pull/1629), [#1628](https://github.com/adap/flower/pull/1628), [#1620]" +"(https://github.com/adap/flower/pull/1620), [#1618](https://github.com/adap/" +"flower/pull/1618), [#1617](https://github.com/adap/flower/pull/1617), [#1613]" +"(https://github.com/adap/flower/pull/1613), [#1614](https://github.com/adap/" +"flower/pull/1614))" msgstr "" -#: ../../source/ref-changelog.md:468 ../../source/ref-changelog.md:535 +#: ../../source/ref-changelog.md:560 ../../source/ref-changelog.md:627 msgid "" -"As usual, the documentation has improved quite a bit. It is another step " -"in our effort to make the Flower documentation the best documentation of " -"any project. Stay tuned and as always, feel free to provide feedback!" +"As usual, the documentation has improved quite a bit. It is another step in " +"our effort to make the Flower documentation the best documentation of any " +"project. Stay tuned and as always, feel free to provide feedback!" msgstr "" -#: ../../source/ref-changelog.md:474 +#: ../../source/ref-changelog.md:566 msgid "v1.2.0 (2023-01-13)" msgstr "" -#: ../../source/ref-changelog.md:480 +#: ../../source/ref-changelog.md:572 msgid "" -"`Adam Narozniak`, `Charles Beauville`, `Daniel J. Beutel`, `Edoardo`, `L." -" Jiang`, `Ragy`, `Taner Topal`, `dannymcy`" +"`Adam Narozniak`, `Charles Beauville`, `Daniel J. Beutel`, `Edoardo`, `L. " +"Jiang`, `Ragy`, `Taner Topal`, `dannymcy`" msgstr "" -#: ../../source/ref-changelog.md:484 +#: ../../source/ref-changelog.md:576 msgid "" -"**Introduce new Flower Baseline: FedAvg MNIST** " -"([#1497](https://github.com/adap/flower/pull/1497), " -"[#1552](https://github.com/adap/flower/pull/1552))" +"**Introduce new Flower Baseline: FedAvg MNIST** ([#1497](https://github.com/" +"adap/flower/pull/1497), [#1552](https://github.com/adap/flower/pull/1552))" msgstr "" -#: ../../source/ref-changelog.md:486 +#: ../../source/ref-changelog.md:578 msgid "" "Over the coming weeks, we will be releasing a number of new reference " "implementations useful especially to FL newcomers. They will typically " "revisit well known papers from the literature, and be suitable for " "integration in your own application or for experimentation, in order to " -"deepen your knowledge of FL in general. Today's release is the first in " -"this series. [Read more.](https://flower.ai/blog/2023-01-12-fl-starter-" -"pack-fedavg-mnist-cnn/)" +"deepen your knowledge of FL in general. Today's release is the first in this " +"series. [Read more.](https://flower.ai/blog/2023-01-12-fl-starter-pack-" +"fedavg-mnist-cnn/)" msgstr "" -#: ../../source/ref-changelog.md:488 +#: ../../source/ref-changelog.md:580 msgid "" -"**Improve GPU support in simulations** " -"([#1555](https://github.com/adap/flower/pull/1555))" +"**Improve GPU support in simulations** ([#1555](https://github.com/adap/" +"flower/pull/1555))" msgstr "" -#: ../../source/ref-changelog.md:490 +#: ../../source/ref-changelog.md:582 msgid "" -"The Ray-based Virtual Client Engine (`start_simulation`) has been updated" -" to improve GPU support. The update includes some of the hard-earned " -"lessons from scaling simulations in GPU cluster environments. New " -"defaults make running GPU-based simulations substantially more robust." +"The Ray-based Virtual Client Engine (`start_simulation`) has been updated to " +"improve GPU support. The update includes some of the hard-earned lessons " +"from scaling simulations in GPU cluster environments. New defaults make " +"running GPU-based simulations substantially more robust." msgstr "" -#: ../../source/ref-changelog.md:492 +#: ../../source/ref-changelog.md:584 msgid "" -"**Improve GPU support in Jupyter Notebook tutorials** " -"([#1527](https://github.com/adap/flower/pull/1527), " -"[#1558](https://github.com/adap/flower/pull/1558))" +"**Improve GPU support in Jupyter Notebook tutorials** ([#1527](https://" +"github.com/adap/flower/pull/1527), [#1558](https://github.com/adap/flower/" +"pull/1558))" msgstr "" -#: ../../source/ref-changelog.md:494 +#: ../../source/ref-changelog.md:586 msgid "" -"Some users reported that Jupyter Notebooks have not always been easy to " -"use on GPU instances. We listened and made improvements to all of our " -"Jupyter notebooks! Check out the updated notebooks here:" +"Some users reported that Jupyter Notebooks have not always been easy to use " +"on GPU instances. We listened and made improvements to all of our Jupyter " +"notebooks! Check out the updated notebooks here:" msgstr "" -#: ../../source/ref-changelog.md:496 +#: ../../source/ref-changelog.md:588 msgid "" -"[An Introduction to Federated Learning](https://flower.ai/docs/framework" -"/tutorial-get-started-with-flower-pytorch.html)" +"[An Introduction to Federated Learning](https://flower.ai/docs/framework/" +"tutorial-get-started-with-flower-pytorch.html)" msgstr "" -#: ../../source/ref-changelog.md:497 +#: ../../source/ref-changelog.md:589 msgid "" -"[Strategies in Federated Learning](https://flower.ai/docs/framework" -"/tutorial-use-a-federated-learning-strategy-pytorch.html)" +"[Strategies in Federated Learning](https://flower.ai/docs/framework/tutorial-" +"use-a-federated-learning-strategy-pytorch.html)" msgstr "" -#: ../../source/ref-changelog.md:498 +#: ../../source/ref-changelog.md:590 msgid "" -"[Building a Strategy](https://flower.ai/docs/framework/tutorial-build-a" -"-strategy-from-scratch-pytorch.html)" +"[Building a Strategy](https://flower.ai/docs/framework/tutorial-build-a-" +"strategy-from-scratch-pytorch.html)" msgstr "" -#: ../../source/ref-changelog.md:499 +#: ../../source/ref-changelog.md:591 msgid "" -"[Client and NumPyClient](https://flower.ai/docs/framework/tutorial-" -"customize-the-client-pytorch.html)" +"[Client and NumPyClient](https://flower.ai/docs/framework/tutorial-customize-" +"the-client-pytorch.html)" msgstr "" -#: ../../source/ref-changelog.md:501 +#: ../../source/ref-changelog.md:593 msgid "" -"**Introduce optional telemetry** " -"([#1533](https://github.com/adap/flower/pull/1533), " -"[#1544](https://github.com/adap/flower/pull/1544), " -"[#1584](https://github.com/adap/flower/pull/1584))" +"**Introduce optional telemetry** ([#1533](https://github.com/adap/flower/" +"pull/1533), [#1544](https://github.com/adap/flower/pull/1544), [#1584]" +"(https://github.com/adap/flower/pull/1584))" msgstr "" -#: ../../source/ref-changelog.md:503 +#: ../../source/ref-changelog.md:595 msgid "" -"After a [request for " -"feedback](https://github.com/adap/flower/issues/1534) from the community," -" the Flower open-source project introduces optional collection of " -"*anonymous* usage metrics to make well-informed decisions to improve " -"Flower. Doing this enables the Flower team to understand how Flower is " -"used and what challenges users might face." +"After a [request for feedback](https://github.com/adap/flower/issues/1534) " +"from the community, the Flower open-source project introduces optional " +"collection of *anonymous* usage metrics to make well-informed decisions to " +"improve Flower. Doing this enables the Flower team to understand how Flower " +"is used and what challenges users might face." msgstr "" -#: ../../source/ref-changelog.md:505 +#: ../../source/ref-changelog.md:597 msgid "" -"**Flower is a friendly framework for collaborative AI and data science.**" -" Staying true to this statement, Flower makes it easy to disable " -"telemetry for users who do not want to share anonymous usage metrics. " -"[Read more.](https://flower.ai/docs/telemetry.html)." +"**Flower is a friendly framework for collaborative AI and data science.** " +"Staying true to this statement, Flower makes it easy to disable telemetry " +"for users who do not want to share anonymous usage metrics. [Read more.]" +"(https://flower.ai/docs/telemetry.html)." msgstr "" -#: ../../source/ref-changelog.md:507 +#: ../../source/ref-changelog.md:599 msgid "" -"**Introduce (experimental) Driver API** " -"([#1520](https://github.com/adap/flower/pull/1520), " -"[#1525](https://github.com/adap/flower/pull/1525), " -"[#1545](https://github.com/adap/flower/pull/1545), " -"[#1546](https://github.com/adap/flower/pull/1546), " -"[#1550](https://github.com/adap/flower/pull/1550), " -"[#1551](https://github.com/adap/flower/pull/1551), " -"[#1567](https://github.com/adap/flower/pull/1567))" +"**Introduce (experimental) Driver API** ([#1520](https://github.com/adap/" +"flower/pull/1520), [#1525](https://github.com/adap/flower/pull/1525), [#1545]" +"(https://github.com/adap/flower/pull/1545), [#1546](https://github.com/adap/" +"flower/pull/1546), [#1550](https://github.com/adap/flower/pull/1550), [#1551]" +"(https://github.com/adap/flower/pull/1551), [#1567](https://github.com/adap/" +"flower/pull/1567))" msgstr "" -#: ../../source/ref-changelog.md:509 +#: ../../source/ref-changelog.md:601 msgid "" "Flower now has a new (experimental) Driver API which will enable fully " "programmable, async, and multi-tenant Federated Learning and Federated " -"Analytics applications. Phew, that's a lot! Going forward, the Driver API" -" will be the abstraction that many upcoming features will be built on - " -"and you can start building those things now, too." +"Analytics applications. Phew, that's a lot! Going forward, the Driver API " +"will be the abstraction that many upcoming features will be built on - and " +"you can start building those things now, too." msgstr "" -#: ../../source/ref-changelog.md:511 +#: ../../source/ref-changelog.md:603 msgid "" -"The Driver API also enables a new execution mode in which the server runs" -" indefinitely. Multiple individual workloads can run concurrently and " -"start and stop their execution independent of the server. This is " -"especially useful for users who want to deploy Flower in production." +"The Driver API also enables a new execution mode in which the server runs " +"indefinitely. Multiple individual workloads can run concurrently and start " +"and stop their execution independent of the server. This is especially " +"useful for users who want to deploy Flower in production." msgstr "" -#: ../../source/ref-changelog.md:513 +#: ../../source/ref-changelog.md:605 msgid "" -"To learn more, check out the `mt-pytorch` code example. We look forward " -"to you feedback!" +"To learn more, check out the `mt-pytorch` code example. We look forward to " +"you feedback!" msgstr "" -#: ../../source/ref-changelog.md:515 +#: ../../source/ref-changelog.md:607 msgid "" -"Please note: *The Driver API is still experimental and will likely change" -" significantly over time.*" +"Please note: *The Driver API is still experimental and will likely change " +"significantly over time.*" msgstr "" -#: ../../source/ref-changelog.md:517 +#: ../../source/ref-changelog.md:609 msgid "" -"**Add new Federated Analytics with Pandas example** " -"([#1469](https://github.com/adap/flower/pull/1469), " -"[#1535](https://github.com/adap/flower/pull/1535))" +"**Add new Federated Analytics with Pandas example** ([#1469](https://github." +"com/adap/flower/pull/1469), [#1535](https://github.com/adap/flower/" +"pull/1535))" msgstr "" -#: ../../source/ref-changelog.md:519 +#: ../../source/ref-changelog.md:611 msgid "" -"A new code example (`quickstart-pandas`) demonstrates federated analytics" -" with Pandas and Flower. You can find it here: [quickstart-" -"pandas](https://github.com/adap/flower/tree/main/examples/quickstart-" -"pandas)." +"A new code example (`quickstart-pandas`) demonstrates federated analytics " +"with Pandas and Flower. You can find it here: [quickstart-pandas](https://" +"github.com/adap/flower/tree/main/examples/quickstart-pandas)." msgstr "" -#: ../../source/ref-changelog.md:521 +#: ../../source/ref-changelog.md:613 msgid "" -"**Add new strategies: Krum and MultiKrum** " -"([#1481](https://github.com/adap/flower/pull/1481))" +"**Add new strategies: Krum and MultiKrum** ([#1481](https://github.com/adap/" +"flower/pull/1481))" msgstr "" -#: ../../source/ref-changelog.md:523 +#: ../../source/ref-changelog.md:615 msgid "" "Edoardo, a computer science student at the Sapienza University of Rome, " -"contributed a new `Krum` strategy that enables users to easily use Krum " -"and MultiKrum in their workloads." +"contributed a new `Krum` strategy that enables users to easily use Krum and " +"MultiKrum in their workloads." msgstr "" -#: ../../source/ref-changelog.md:525 +#: ../../source/ref-changelog.md:617 msgid "" -"**Update C++ example to be compatible with Flower v1.2.0** " -"([#1495](https://github.com/adap/flower/pull/1495))" +"**Update C++ example to be compatible with Flower v1.2.0** ([#1495](https://" +"github.com/adap/flower/pull/1495))" msgstr "" -#: ../../source/ref-changelog.md:527 +#: ../../source/ref-changelog.md:619 msgid "" -"The C++ code example has received a substantial update to make it " -"compatible with the latest version of Flower." +"The C++ code example has received a substantial update to make it compatible " +"with the latest version of Flower." msgstr "" -#: ../../source/ref-changelog.md:529 +#: ../../source/ref-changelog.md:621 msgid "" -"**General improvements** " -"([#1491](https://github.com/adap/flower/pull/1491), " -"[#1504](https://github.com/adap/flower/pull/1504), " -"[#1506](https://github.com/adap/flower/pull/1506), " -"[#1514](https://github.com/adap/flower/pull/1514), " -"[#1522](https://github.com/adap/flower/pull/1522), " -"[#1523](https://github.com/adap/flower/pull/1523), " -"[#1526](https://github.com/adap/flower/pull/1526), " -"[#1528](https://github.com/adap/flower/pull/1528), " -"[#1547](https://github.com/adap/flower/pull/1547), " -"[#1549](https://github.com/adap/flower/pull/1549), " -"[#1560](https://github.com/adap/flower/pull/1560), " -"[#1564](https://github.com/adap/flower/pull/1564), " -"[#1566](https://github.com/adap/flower/pull/1566))" +"**General improvements** ([#1491](https://github.com/adap/flower/pull/1491), " +"[#1504](https://github.com/adap/flower/pull/1504), [#1506](https://github." +"com/adap/flower/pull/1506), [#1514](https://github.com/adap/flower/" +"pull/1514), [#1522](https://github.com/adap/flower/pull/1522), [#1523]" +"(https://github.com/adap/flower/pull/1523), [#1526](https://github.com/adap/" +"flower/pull/1526), [#1528](https://github.com/adap/flower/pull/1528), [#1547]" +"(https://github.com/adap/flower/pull/1547), [#1549](https://github.com/adap/" +"flower/pull/1549), [#1560](https://github.com/adap/flower/pull/1560), [#1564]" +"(https://github.com/adap/flower/pull/1564), [#1566](https://github.com/adap/" +"flower/pull/1566))" msgstr "" -#: ../../source/ref-changelog.md:533 +#: ../../source/ref-changelog.md:625 msgid "" -"**Updated documentation** " -"([#1494](https://github.com/adap/flower/pull/1494), " -"[#1496](https://github.com/adap/flower/pull/1496), " -"[#1500](https://github.com/adap/flower/pull/1500), " -"[#1503](https://github.com/adap/flower/pull/1503), " -"[#1505](https://github.com/adap/flower/pull/1505), " -"[#1524](https://github.com/adap/flower/pull/1524), " -"[#1518](https://github.com/adap/flower/pull/1518), " -"[#1519](https://github.com/adap/flower/pull/1519), " -"[#1515](https://github.com/adap/flower/pull/1515))" +"**Updated documentation** ([#1494](https://github.com/adap/flower/" +"pull/1494), [#1496](https://github.com/adap/flower/pull/1496), [#1500]" +"(https://github.com/adap/flower/pull/1500), [#1503](https://github.com/adap/" +"flower/pull/1503), [#1505](https://github.com/adap/flower/pull/1505), [#1524]" +"(https://github.com/adap/flower/pull/1524), [#1518](https://github.com/adap/" +"flower/pull/1518), [#1519](https://github.com/adap/flower/pull/1519), [#1515]" +"(https://github.com/adap/flower/pull/1515))" msgstr "" -#: ../../source/ref-changelog.md:537 +#: ../../source/ref-changelog.md:629 msgid "" -"One highlight is the new [first time contributor " -"guide](https://flower.ai/docs/first-time-contributors.html): if you've " -"never contributed on GitHub before, this is the perfect place to start!" +"One highlight is the new [first time contributor guide](https://flower.ai/" +"docs/first-time-contributors.html): if you've never contributed on GitHub " +"before, this is the perfect place to start!" msgstr "" -#: ../../source/ref-changelog.md:543 +#: ../../source/ref-changelog.md:635 msgid "v1.1.0 (2022-10-31)" msgstr "" -#: ../../source/ref-changelog.md:547 +#: ../../source/ref-changelog.md:639 msgid "" "We would like to give our **special thanks** to all the contributors who " "made the new version of Flower possible (in `git shortlog` order):" msgstr "" -#: ../../source/ref-changelog.md:549 +#: ../../source/ref-changelog.md:641 msgid "" "`Akis Linardos`, `Christopher S`, `Daniel J. Beutel`, `George`, `Jan " "Schlicht`, `Mohammad Fares`, `Pedro Porto Buarque de Gusmão`, `Philipp " -"Wiesner`, `Rob Luke`, `Taner Topal`, `VasundharaAgarwal`, " -"`danielnugraha`, `edogab33`" +"Wiesner`, `Rob Luke`, `Taner Topal`, `VasundharaAgarwal`, `danielnugraha`, " +"`edogab33`" msgstr "" -#: ../../source/ref-changelog.md:553 +#: ../../source/ref-changelog.md:645 msgid "" -"**Introduce Differential Privacy wrappers (preview)** " -"([#1357](https://github.com/adap/flower/pull/1357), " -"[#1460](https://github.com/adap/flower/pull/1460))" +"**Introduce Differential Privacy wrappers (preview)** ([#1357](https://" +"github.com/adap/flower/pull/1357), [#1460](https://github.com/adap/flower/" +"pull/1460))" msgstr "" -#: ../../source/ref-changelog.md:555 +#: ../../source/ref-changelog.md:647 msgid "" -"The first (experimental) preview of pluggable Differential Privacy " -"wrappers enables easy configuration and usage of differential privacy " -"(DP). The pluggable DP wrappers enable framework-agnostic **and** " -"strategy-agnostic usage of both client-side DP and server-side DP. Head " -"over to the Flower docs, a new explainer goes into more detail." +"The first (experimental) preview of pluggable Differential Privacy wrappers " +"enables easy configuration and usage of differential privacy (DP). The " +"pluggable DP wrappers enable framework-agnostic **and** strategy-agnostic " +"usage of both client-side DP and server-side DP. Head over to the Flower " +"docs, a new explainer goes into more detail." msgstr "" -#: ../../source/ref-changelog.md:557 +#: ../../source/ref-changelog.md:649 msgid "" -"**New iOS CoreML code example** " -"([#1289](https://github.com/adap/flower/pull/1289))" +"**New iOS CoreML code example** ([#1289](https://github.com/adap/flower/" +"pull/1289))" msgstr "" -#: ../../source/ref-changelog.md:559 +#: ../../source/ref-changelog.md:651 msgid "" -"Flower goes iOS! A massive new code example shows how Flower clients can " -"be built for iOS. The code example contains both Flower iOS SDK " -"components that can be used for many tasks, and one task example running " -"on CoreML." +"Flower goes iOS! A massive new code example shows how Flower clients can be " +"built for iOS. The code example contains both Flower iOS SDK components that " +"can be used for many tasks, and one task example running on CoreML." msgstr "" -#: ../../source/ref-changelog.md:561 +#: ../../source/ref-changelog.md:653 msgid "" -"**New FedMedian strategy** " -"([#1461](https://github.com/adap/flower/pull/1461))" +"**New FedMedian strategy** ([#1461](https://github.com/adap/flower/" +"pull/1461))" msgstr "" -#: ../../source/ref-changelog.md:563 +#: ../../source/ref-changelog.md:655 msgid "" -"The new `FedMedian` strategy implements Federated Median (FedMedian) by " -"[Yin et al., 2018](https://arxiv.org/pdf/1803.01498v1.pdf)." +"The new `FedMedian` strategy implements Federated Median (FedMedian) by [Yin " +"et al., 2018](https://arxiv.org/pdf/1803.01498v1.pdf)." msgstr "" -#: ../../source/ref-changelog.md:565 +#: ../../source/ref-changelog.md:657 msgid "" -"**Log** `Client` **exceptions in Virtual Client Engine** " -"([#1493](https://github.com/adap/flower/pull/1493))" +"**Log** `Client` **exceptions in Virtual Client Engine** ([#1493](https://" +"github.com/adap/flower/pull/1493))" msgstr "" -#: ../../source/ref-changelog.md:567 +#: ../../source/ref-changelog.md:659 msgid "" -"All `Client` exceptions happening in the VCE are now logged by default " -"and not just exposed to the configured `Strategy` (via the `failures` " -"argument)." +"All `Client` exceptions happening in the VCE are now logged by default and " +"not just exposed to the configured `Strategy` (via the `failures` argument)." msgstr "" -#: ../../source/ref-changelog.md:569 +#: ../../source/ref-changelog.md:661 msgid "" -"**Improve Virtual Client Engine internals** " -"([#1401](https://github.com/adap/flower/pull/1401), " -"[#1453](https://github.com/adap/flower/pull/1453))" +"**Improve Virtual Client Engine internals** ([#1401](https://github.com/adap/" +"flower/pull/1401), [#1453](https://github.com/adap/flower/pull/1453))" msgstr "" -#: ../../source/ref-changelog.md:571 +#: ../../source/ref-changelog.md:663 msgid "" -"Some internals of the Virtual Client Engine have been revamped. The VCE " -"now uses Ray 2.0 under the hood, the value type of the `client_resources`" -" dictionary changed to `float` to allow fractions of resources to be " +"Some internals of the Virtual Client Engine have been revamped. The VCE now " +"uses Ray 2.0 under the hood, the value type of the `client_resources` " +"dictionary changed to `float` to allow fractions of resources to be " "allocated." msgstr "" -#: ../../source/ref-changelog.md:573 +#: ../../source/ref-changelog.md:665 msgid "" -"**Support optional** `Client`**/**`NumPyClient` **methods in Virtual " -"Client Engine**" +"**Support optional** `Client`**/**`NumPyClient` **methods in Virtual Client " +"Engine**" msgstr "" -#: ../../source/ref-changelog.md:575 +#: ../../source/ref-changelog.md:667 msgid "" -"The Virtual Client Engine now has full support for optional `Client` (and" -" `NumPyClient`) methods." +"The Virtual Client Engine now has full support for optional `Client` (and " +"`NumPyClient`) methods." msgstr "" -#: ../../source/ref-changelog.md:577 +#: ../../source/ref-changelog.md:669 msgid "" -"**Provide type information to packages using** `flwr` " -"([#1377](https://github.com/adap/flower/pull/1377))" +"**Provide type information to packages using** `flwr` ([#1377](https://" +"github.com/adap/flower/pull/1377))" msgstr "" -#: ../../source/ref-changelog.md:579 +#: ../../source/ref-changelog.md:671 msgid "" -"The package `flwr` is now bundled with a `py.typed` file indicating that " -"the package is typed. This enables typing support for projects or " -"packages that use `flwr` by enabling them to improve their code using " -"static type checkers like `mypy`." +"The package `flwr` is now bundled with a `py.typed` file indicating that the " +"package is typed. This enables typing support for projects or packages that " +"use `flwr` by enabling them to improve their code using static type checkers " +"like `mypy`." msgstr "" -#: ../../source/ref-changelog.md:581 +#: ../../source/ref-changelog.md:673 msgid "" -"**Updated code example** " -"([#1344](https://github.com/adap/flower/pull/1344), " +"**Updated code example** ([#1344](https://github.com/adap/flower/pull/1344), " "[#1347](https://github.com/adap/flower/pull/1347))" msgstr "" -#: ../../source/ref-changelog.md:583 +#: ../../source/ref-changelog.md:675 msgid "" "The code examples covering scikit-learn and PyTorch Lightning have been " "updated to work with the latest version of Flower." msgstr "" -#: ../../source/ref-changelog.md:585 +#: ../../source/ref-changelog.md:677 msgid "" -"**Updated documentation** " -"([#1355](https://github.com/adap/flower/pull/1355), " -"[#1558](https://github.com/adap/flower/pull/1558), " -"[#1379](https://github.com/adap/flower/pull/1379), " -"[#1380](https://github.com/adap/flower/pull/1380), " -"[#1381](https://github.com/adap/flower/pull/1381), " -"[#1332](https://github.com/adap/flower/pull/1332), " -"[#1391](https://github.com/adap/flower/pull/1391), " -"[#1403](https://github.com/adap/flower/pull/1403), " -"[#1364](https://github.com/adap/flower/pull/1364), " -"[#1409](https://github.com/adap/flower/pull/1409), " -"[#1419](https://github.com/adap/flower/pull/1419), " -"[#1444](https://github.com/adap/flower/pull/1444), " -"[#1448](https://github.com/adap/flower/pull/1448), " -"[#1417](https://github.com/adap/flower/pull/1417), " -"[#1449](https://github.com/adap/flower/pull/1449), " -"[#1465](https://github.com/adap/flower/pull/1465), " -"[#1467](https://github.com/adap/flower/pull/1467))" +"**Updated documentation** ([#1355](https://github.com/adap/flower/" +"pull/1355), [#1558](https://github.com/adap/flower/pull/1558), [#1379]" +"(https://github.com/adap/flower/pull/1379), [#1380](https://github.com/adap/" +"flower/pull/1380), [#1381](https://github.com/adap/flower/pull/1381), [#1332]" +"(https://github.com/adap/flower/pull/1332), [#1391](https://github.com/adap/" +"flower/pull/1391), [#1403](https://github.com/adap/flower/pull/1403), [#1364]" +"(https://github.com/adap/flower/pull/1364), [#1409](https://github.com/adap/" +"flower/pull/1409), [#1419](https://github.com/adap/flower/pull/1419), [#1444]" +"(https://github.com/adap/flower/pull/1444), [#1448](https://github.com/adap/" +"flower/pull/1448), [#1417](https://github.com/adap/flower/pull/1417), [#1449]" +"(https://github.com/adap/flower/pull/1449), [#1465](https://github.com/adap/" +"flower/pull/1465), [#1467](https://github.com/adap/flower/pull/1467))" msgstr "" -#: ../../source/ref-changelog.md:587 +#: ../../source/ref-changelog.md:679 msgid "" "There have been so many documentation updates that it doesn't even make " "sense to list them individually." msgstr "" -#: ../../source/ref-changelog.md:589 +#: ../../source/ref-changelog.md:681 msgid "" -"**Restructured documentation** " -"([#1387](https://github.com/adap/flower/pull/1387))" +"**Restructured documentation** ([#1387](https://github.com/adap/flower/" +"pull/1387))" msgstr "" -#: ../../source/ref-changelog.md:591 +#: ../../source/ref-changelog.md:683 msgid "" -"The documentation has been restructured to make it easier to navigate. " -"This is just the first step in a larger effort to make the Flower " -"documentation the best documentation of any project ever. Stay tuned!" +"The documentation has been restructured to make it easier to navigate. This " +"is just the first step in a larger effort to make the Flower documentation " +"the best documentation of any project ever. Stay tuned!" msgstr "" -#: ../../source/ref-changelog.md:593 +#: ../../source/ref-changelog.md:685 msgid "" -"**Open in Colab button** " -"([#1389](https://github.com/adap/flower/pull/1389))" +"**Open in Colab button** ([#1389](https://github.com/adap/flower/pull/1389))" msgstr "" -#: ../../source/ref-changelog.md:595 +#: ../../source/ref-changelog.md:687 msgid "" -"The four parts of the Flower Federated Learning Tutorial now come with a " -"new `Open in Colab` button. No need to install anything on your local " -"machine, you can now use and learn about Flower in your browser, it's " -"only a single click away." +"The four parts of the Flower Federated Learning Tutorial now come with a new " +"`Open in Colab` button. No need to install anything on your local machine, " +"you can now use and learn about Flower in your browser, it's only a single " +"click away." msgstr "" -#: ../../source/ref-changelog.md:597 +#: ../../source/ref-changelog.md:689 msgid "" -"**Improved tutorial** ([#1468](https://github.com/adap/flower/pull/1468)," -" [#1470](https://github.com/adap/flower/pull/1470), " -"[#1472](https://github.com/adap/flower/pull/1472), " -"[#1473](https://github.com/adap/flower/pull/1473), " -"[#1474](https://github.com/adap/flower/pull/1474), " -"[#1475](https://github.com/adap/flower/pull/1475))" +"**Improved tutorial** ([#1468](https://github.com/adap/flower/pull/1468), " +"[#1470](https://github.com/adap/flower/pull/1470), [#1472](https://github." +"com/adap/flower/pull/1472), [#1473](https://github.com/adap/flower/" +"pull/1473), [#1474](https://github.com/adap/flower/pull/1474), [#1475]" +"(https://github.com/adap/flower/pull/1475))" msgstr "" -#: ../../source/ref-changelog.md:599 +#: ../../source/ref-changelog.md:691 msgid "" "The Flower Federated Learning Tutorial has two brand-new parts covering " "custom strategies (still WIP) and the distinction between `Client` and " -"`NumPyClient`. The existing parts one and two have also been improved " -"(many small changes and fixes)." +"`NumPyClient`. The existing parts one and two have also been improved (many " +"small changes and fixes)." msgstr "" -#: ../../source/ref-changelog.md:605 +#: ../../source/ref-changelog.md:697 msgid "v1.0.0 (2022-07-28)" msgstr "" -#: ../../source/ref-changelog.md:607 +#: ../../source/ref-changelog.md:699 msgid "Highlights" msgstr "" -#: ../../source/ref-changelog.md:609 +#: ../../source/ref-changelog.md:701 msgid "Stable **Virtual Client Engine** (accessible via `start_simulation`)" msgstr "" -#: ../../source/ref-changelog.md:610 +#: ../../source/ref-changelog.md:702 msgid "All `Client`/`NumPyClient` methods are now optional" msgstr "" -#: ../../source/ref-changelog.md:611 +#: ../../source/ref-changelog.md:703 msgid "Configurable `get_parameters`" msgstr "" -#: ../../source/ref-changelog.md:612 +#: ../../source/ref-changelog.md:704 msgid "" -"Tons of small API cleanups resulting in a more coherent developer " -"experience" +"Tons of small API cleanups resulting in a more coherent developer experience" msgstr "" -#: ../../source/ref-changelog.md:616 +#: ../../source/ref-changelog.md:708 msgid "" "We would like to give our **special thanks** to all the contributors who " -"made Flower 1.0 possible (in reverse [GitHub " -"Contributors](https://github.com/adap/flower/graphs/contributors) order):" -msgstr "" - -#: ../../source/ref-changelog.md:618 -msgid "" -"[@rtaiello](https://github.com/rtaiello), " -"[@g-pichler](https://github.com/g-pichler), [@rob-" -"luke](https://github.com/rob-luke), [@andreea-zaharia](https://github.com" -"/andreea-zaharia), [@kinshukdua](https://github.com/kinshukdua), " -"[@nfnt](https://github.com/nfnt), " -"[@tatiana-s](https://github.com/tatiana-s), " -"[@TParcollet](https://github.com/TParcollet), " -"[@vballoli](https://github.com/vballoli), " -"[@negedng](https://github.com/negedng), " -"[@RISHIKESHAVAN](https://github.com/RISHIKESHAVAN), " -"[@hei411](https://github.com/hei411), " -"[@SebastianSpeitel](https://github.com/SebastianSpeitel), " -"[@AmitChaulwar](https://github.com/AmitChaulwar), " -"[@Rubiel1](https://github.com/Rubiel1), [@FANTOME-PAN](https://github.com" -"/FANTOME-PAN), [@Rono-BC](https://github.com/Rono-BC), " -"[@lbhm](https://github.com/lbhm), " -"[@sishtiaq](https://github.com/sishtiaq), " -"[@remde](https://github.com/remde), [@Jueun-Park](https://github.com" -"/Jueun-Park), [@architjen](https://github.com/architjen), " -"[@PratikGarai](https://github.com/PratikGarai), " -"[@mrinaald](https://github.com/mrinaald), " -"[@zliel](https://github.com/zliel), " -"[@MeiruiJiang](https://github.com/MeiruiJiang), " -"[@sancarlim](https://github.com/sancarlim), " -"[@gubertoli](https://github.com/gubertoli), " -"[@Vingt100](https://github.com/Vingt100), " -"[@MakGulati](https://github.com/MakGulati), " -"[@cozek](https://github.com/cozek), " -"[@jafermarq](https://github.com/jafermarq), " -"[@sisco0](https://github.com/sisco0), " -"[@akhilmathurs](https://github.com/akhilmathurs), " -"[@CanTuerk](https://github.com/CanTuerk), " -"[@mariaboerner1987](https://github.com/mariaboerner1987), " -"[@pedropgusmao](https://github.com/pedropgusmao), " -"[@tanertopal](https://github.com/tanertopal), " -"[@danieljanes](https://github.com/danieljanes)." -msgstr "" - -#: ../../source/ref-changelog.md:622 -msgid "" -"**All arguments must be passed as keyword arguments** " -"([#1338](https://github.com/adap/flower/pull/1338))" -msgstr "" - -#: ../../source/ref-changelog.md:624 -msgid "" -"Pass all arguments as keyword arguments, positional arguments are not " -"longer supported. Code that uses positional arguments (e.g., " -"`start_client(\"127.0.0.1:8080\", FlowerClient())`) must add the keyword " -"for each positional argument (e.g., " -"`start_client(server_address=\"127.0.0.1:8080\", " -"client=FlowerClient())`)." -msgstr "" - -#: ../../source/ref-changelog.md:626 +"made Flower 1.0 possible (in reverse [GitHub Contributors](https://github." +"com/adap/flower/graphs/contributors) order):" +msgstr "" + +#: ../../source/ref-changelog.md:710 +msgid "" +"[@rtaiello](https://github.com/rtaiello), [@g-pichler](https://github.com/g-" +"pichler), [@rob-luke](https://github.com/rob-luke), [@andreea-zaharia]" +"(https://github.com/andreea-zaharia), [@kinshukdua](https://github.com/" +"kinshukdua), [@nfnt](https://github.com/nfnt), [@tatiana-s](https://github." +"com/tatiana-s), [@TParcollet](https://github.com/TParcollet), [@vballoli]" +"(https://github.com/vballoli), [@negedng](https://github.com/negedng), " +"[@RISHIKESHAVAN](https://github.com/RISHIKESHAVAN), [@hei411](https://github." +"com/hei411), [@SebastianSpeitel](https://github.com/SebastianSpeitel), " +"[@AmitChaulwar](https://github.com/AmitChaulwar), [@Rubiel1](https://github." +"com/Rubiel1), [@FANTOME-PAN](https://github.com/FANTOME-PAN), [@Rono-BC]" +"(https://github.com/Rono-BC), [@lbhm](https://github.com/lbhm), [@sishtiaq]" +"(https://github.com/sishtiaq), [@remde](https://github.com/remde), [@Jueun-" +"Park](https://github.com/Jueun-Park), [@architjen](https://github.com/" +"architjen), [@PratikGarai](https://github.com/PratikGarai), [@mrinaald]" +"(https://github.com/mrinaald), [@zliel](https://github.com/zliel), " +"[@MeiruiJiang](https://github.com/MeiruiJiang), [@sancarlim](https://github." +"com/sancarlim), [@gubertoli](https://github.com/gubertoli), [@Vingt100]" +"(https://github.com/Vingt100), [@MakGulati](https://github.com/MakGulati), " +"[@cozek](https://github.com/cozek), [@jafermarq](https://github.com/" +"jafermarq), [@sisco0](https://github.com/sisco0), [@akhilmathurs](https://" +"github.com/akhilmathurs), [@CanTuerk](https://github.com/CanTuerk), " +"[@mariaboerner1987](https://github.com/mariaboerner1987), [@pedropgusmao]" +"(https://github.com/pedropgusmao), [@tanertopal](https://github.com/" +"tanertopal), [@danieljanes](https://github.com/danieljanes)." +msgstr "" + +#: ../../source/ref-changelog.md:714 +msgid "" +"**All arguments must be passed as keyword arguments** ([#1338](https://" +"github.com/adap/flower/pull/1338))" +msgstr "" + +#: ../../source/ref-changelog.md:716 +msgid "" +"Pass all arguments as keyword arguments, positional arguments are not longer " +"supported. Code that uses positional arguments (e.g., " +"`start_client(\"127.0.0.1:8080\", FlowerClient())`) must add the keyword for " +"each positional argument (e.g., " +"`start_client(server_address=\"127.0.0.1:8080\", client=FlowerClient())`)." +msgstr "" + +#: ../../source/ref-changelog.md:718 msgid "" "**Introduce configuration object** `ServerConfig` **in** `start_server` " -"**and** `start_simulation` " -"([#1317](https://github.com/adap/flower/pull/1317))" +"**and** `start_simulation` ([#1317](https://github.com/adap/flower/" +"pull/1317))" msgstr "" -#: ../../source/ref-changelog.md:628 +#: ../../source/ref-changelog.md:720 msgid "" -"Instead of a config dictionary `{\"num_rounds\": 3, \"round_timeout\": " -"600.0}`, `start_server` and `start_simulation` now expect a configuration" -" object of type `flwr.server.ServerConfig`. `ServerConfig` takes the same" -" arguments that as the previous config dict, but it makes writing type-" -"safe code easier and the default parameters values more transparent." +"Instead of a config dictionary `{\"num_rounds\": 3, \"round_timeout\": 600.0}" +"`, `start_server` and `start_simulation` now expect a configuration object " +"of type `flwr.server.ServerConfig`. `ServerConfig` takes the same arguments " +"that as the previous config dict, but it makes writing type-safe code easier " +"and the default parameters values more transparent." msgstr "" -#: ../../source/ref-changelog.md:630 +#: ../../source/ref-changelog.md:722 msgid "" -"**Rename built-in strategy parameters for clarity** " -"([#1334](https://github.com/adap/flower/pull/1334))" +"**Rename built-in strategy parameters for clarity** ([#1334](https://github." +"com/adap/flower/pull/1334))" msgstr "" -#: ../../source/ref-changelog.md:632 +#: ../../source/ref-changelog.md:724 msgid "" "The following built-in strategy parameters were renamed to improve " "readability and consistency with other API's:" msgstr "" -#: ../../source/ref-changelog.md:634 +#: ../../source/ref-changelog.md:726 msgid "`fraction_eval` --> `fraction_evaluate`" msgstr "" -#: ../../source/ref-changelog.md:635 +#: ../../source/ref-changelog.md:727 msgid "`min_eval_clients` --> `min_evaluate_clients`" msgstr "" -#: ../../source/ref-changelog.md:636 +#: ../../source/ref-changelog.md:728 msgid "`eval_fn` --> `evaluate_fn`" msgstr "" -#: ../../source/ref-changelog.md:638 +#: ../../source/ref-changelog.md:730 msgid "" -"**Update default arguments of built-in strategies** " -"([#1278](https://github.com/adap/flower/pull/1278))" +"**Update default arguments of built-in strategies** ([#1278](https://github." +"com/adap/flower/pull/1278))" msgstr "" -#: ../../source/ref-changelog.md:640 +#: ../../source/ref-changelog.md:732 msgid "" "All built-in strategies now use `fraction_fit=1.0` and " -"`fraction_evaluate=1.0`, which means they select *all* currently " -"available clients for training and evaluation. Projects that relied on " -"the previous default values can get the previous behaviour by " -"initializing the strategy in the following way:" +"`fraction_evaluate=1.0`, which means they select *all* currently available " +"clients for training and evaluation. Projects that relied on the previous " +"default values can get the previous behaviour by initializing the strategy " +"in the following way:" msgstr "" -#: ../../source/ref-changelog.md:642 +#: ../../source/ref-changelog.md:734 msgid "`strategy = FedAvg(fraction_fit=0.1, fraction_evaluate=0.1)`" msgstr "" -#: ../../source/ref-changelog.md:644 +#: ../../source/ref-changelog.md:736 msgid "" -"**Add** `server_round` **to** `Strategy.evaluate` " -"([#1334](https://github.com/adap/flower/pull/1334))" +"**Add** `server_round` **to** `Strategy.evaluate` ([#1334](https://github." +"com/adap/flower/pull/1334))" msgstr "" -#: ../../source/ref-changelog.md:646 +#: ../../source/ref-changelog.md:738 msgid "" -"The `Strategy` method `evaluate` now receives the current round of " -"federated learning/evaluation as the first parameter." +"The `Strategy` method `evaluate` now receives the current round of federated " +"learning/evaluation as the first parameter." msgstr "" -#: ../../source/ref-changelog.md:648 +#: ../../source/ref-changelog.md:740 msgid "" "**Add** `server_round` **and** `config` **parameters to** `evaluate_fn` " "([#1334](https://github.com/adap/flower/pull/1334))" msgstr "" -#: ../../source/ref-changelog.md:650 +#: ../../source/ref-changelog.md:742 msgid "" "The `evaluate_fn` passed to built-in strategies like `FedAvg` now takes " "three parameters: (1) The current round of federated learning/evaluation " -"(`server_round`), (2) the model parameters to evaluate (`parameters`), " -"and (3) a config dictionary (`config`)." +"(`server_round`), (2) the model parameters to evaluate (`parameters`), and " +"(3) a config dictionary (`config`)." msgstr "" -#: ../../source/ref-changelog.md:652 +#: ../../source/ref-changelog.md:744 msgid "" -"**Rename** `rnd` **to** `server_round` " -"([#1321](https://github.com/adap/flower/pull/1321))" +"**Rename** `rnd` **to** `server_round` ([#1321](https://github.com/adap/" +"flower/pull/1321))" msgstr "" -#: ../../source/ref-changelog.md:654 +#: ../../source/ref-changelog.md:746 msgid "" "Several Flower methods and functions (`evaluate_fn`, `configure_fit`, " "`aggregate_fit`, `configure_evaluate`, `aggregate_evaluate`) receive the " -"current round of federated learning/evaluation as their first parameter. " -"To improve reaability and avoid confusion with *random*, this parameter " -"has been renamed from `rnd` to `server_round`." +"current round of federated learning/evaluation as their first parameter. To " +"improve reaability and avoid confusion with *random*, this parameter has " +"been renamed from `rnd` to `server_round`." msgstr "" -#: ../../source/ref-changelog.md:656 +#: ../../source/ref-changelog.md:748 msgid "" -"**Move** `flwr.dataset` **to** `flwr_baselines` " -"([#1273](https://github.com/adap/flower/pull/1273))" +"**Move** `flwr.dataset` **to** `flwr_baselines` ([#1273](https://github.com/" +"adap/flower/pull/1273))" msgstr "" -#: ../../source/ref-changelog.md:658 -msgid "The experimental package `flwr.dataset` was migrated to Flower Baselines." +#: ../../source/ref-changelog.md:750 +msgid "" +"The experimental package `flwr.dataset` was migrated to Flower Baselines." msgstr "" -#: ../../source/ref-changelog.md:660 +#: ../../source/ref-changelog.md:752 msgid "" -"**Remove experimental strategies** " -"([#1280](https://github.com/adap/flower/pull/1280))" +"**Remove experimental strategies** ([#1280](https://github.com/adap/flower/" +"pull/1280))" msgstr "" -#: ../../source/ref-changelog.md:662 +#: ../../source/ref-changelog.md:754 msgid "" "Remove unmaintained experimental strategies (`FastAndSlow`, `FedFSv0`, " "`FedFSv1`)." msgstr "" -#: ../../source/ref-changelog.md:664 +#: ../../source/ref-changelog.md:756 msgid "" -"**Rename** `Weights` **to** `NDArrays` " -"([#1258](https://github.com/adap/flower/pull/1258), " -"[#1259](https://github.com/adap/flower/pull/1259))" +"**Rename** `Weights` **to** `NDArrays` ([#1258](https://github.com/adap/" +"flower/pull/1258), [#1259](https://github.com/adap/flower/pull/1259))" msgstr "" -#: ../../source/ref-changelog.md:666 +#: ../../source/ref-changelog.md:758 msgid "" "`flwr.common.Weights` was renamed to `flwr.common.NDArrays` to better " "capture what this type is all about." msgstr "" -#: ../../source/ref-changelog.md:668 +#: ../../source/ref-changelog.md:760 msgid "" -"**Remove antiquated** `force_final_distributed_eval` **from** " -"`start_server` ([#1258](https://github.com/adap/flower/pull/1258), " -"[#1259](https://github.com/adap/flower/pull/1259))" +"**Remove antiquated** `force_final_distributed_eval` **from** `start_server` " +"([#1258](https://github.com/adap/flower/pull/1258), [#1259](https://github." +"com/adap/flower/pull/1259))" msgstr "" -#: ../../source/ref-changelog.md:670 +#: ../../source/ref-changelog.md:762 msgid "" -"The `start_server` parameter `force_final_distributed_eval` has long been" -" a historic artefact, in this release it is finally gone for good." +"The `start_server` parameter `force_final_distributed_eval` has long been a " +"historic artefact, in this release it is finally gone for good." msgstr "" -#: ../../source/ref-changelog.md:672 +#: ../../source/ref-changelog.md:764 msgid "" -"**Make** `get_parameters` **configurable** " -"([#1242](https://github.com/adap/flower/pull/1242))" +"**Make** `get_parameters` **configurable** ([#1242](https://github.com/adap/" +"flower/pull/1242))" msgstr "" -#: ../../source/ref-changelog.md:674 +#: ../../source/ref-changelog.md:766 msgid "" "The `get_parameters` method now accepts a configuration dictionary, just " "like `get_properties`, `fit`, and `evaluate`." msgstr "" -#: ../../source/ref-changelog.md:676 +#: ../../source/ref-changelog.md:768 msgid "" "**Replace** `num_rounds` **in** `start_simulation` **with new** `config` " "**parameter** ([#1281](https://github.com/adap/flower/pull/1281))" msgstr "" -#: ../../source/ref-changelog.md:678 +#: ../../source/ref-changelog.md:770 msgid "" "The `start_simulation` function now accepts a configuration dictionary " -"`config` instead of the `num_rounds` integer. This improves the " -"consistency between `start_simulation` and `start_server` and makes " -"transitioning between the two easier." +"`config` instead of the `num_rounds` integer. This improves the consistency " +"between `start_simulation` and `start_server` and makes transitioning " +"between the two easier." msgstr "" -#: ../../source/ref-changelog.md:682 +#: ../../source/ref-changelog.md:774 msgid "" -"**Support Python 3.10** " -"([#1320](https://github.com/adap/flower/pull/1320))" +"**Support Python 3.10** ([#1320](https://github.com/adap/flower/pull/1320))" msgstr "" -#: ../../source/ref-changelog.md:684 +#: ../../source/ref-changelog.md:776 msgid "" -"The previous Flower release introduced experimental support for Python " -"3.10, this release declares Python 3.10 support as stable." +"The previous Flower release introduced experimental support for Python 3.10, " +"this release declares Python 3.10 support as stable." msgstr "" -#: ../../source/ref-changelog.md:686 +#: ../../source/ref-changelog.md:778 msgid "" -"**Make all** `Client` **and** `NumPyClient` **methods optional** " -"([#1260](https://github.com/adap/flower/pull/1260), " -"[#1277](https://github.com/adap/flower/pull/1277))" +"**Make all** `Client` **and** `NumPyClient` **methods optional** ([#1260]" +"(https://github.com/adap/flower/pull/1260), [#1277](https://github.com/adap/" +"flower/pull/1277))" msgstr "" -#: ../../source/ref-changelog.md:688 +#: ../../source/ref-changelog.md:780 msgid "" "The `Client`/`NumPyClient` methods `get_properties`, `get_parameters`, " -"`fit`, and `evaluate` are all optional. This enables writing clients that" -" implement, for example, only `fit`, but no other method. No need to " +"`fit`, and `evaluate` are all optional. This enables writing clients that " +"implement, for example, only `fit`, but no other method. No need to " "implement `evaluate` when using centralized evaluation!" msgstr "" -#: ../../source/ref-changelog.md:690 +#: ../../source/ref-changelog.md:782 msgid "" -"**Enable passing a** `Server` **instance to** `start_simulation` " -"([#1281](https://github.com/adap/flower/pull/1281))" +"**Enable passing a** `Server` **instance to** `start_simulation` ([#1281]" +"(https://github.com/adap/flower/pull/1281))" msgstr "" -#: ../../source/ref-changelog.md:692 +#: ../../source/ref-changelog.md:784 msgid "" -"Similar to `start_server`, `start_simulation` now accepts a full `Server`" -" instance. This enables users to heavily customize the execution of " -"eperiments and opens the door to running, for example, async FL using the" -" Virtual Client Engine." +"Similar to `start_server`, `start_simulation` now accepts a full `Server` " +"instance. This enables users to heavily customize the execution of " +"eperiments and opens the door to running, for example, async FL using the " +"Virtual Client Engine." msgstr "" -#: ../../source/ref-changelog.md:694 +#: ../../source/ref-changelog.md:786 msgid "" -"**Update code examples** " -"([#1291](https://github.com/adap/flower/pull/1291), " -"[#1286](https://github.com/adap/flower/pull/1286), " -"[#1282](https://github.com/adap/flower/pull/1282))" +"**Update code examples** ([#1291](https://github.com/adap/flower/pull/1291), " +"[#1286](https://github.com/adap/flower/pull/1286), [#1282](https://github." +"com/adap/flower/pull/1282))" msgstr "" -#: ../../source/ref-changelog.md:696 +#: ../../source/ref-changelog.md:788 msgid "" -"Many code examples received small or even large maintenance updates, " -"among them are" +"Many code examples received small or even large maintenance updates, among " +"them are" msgstr "" -#: ../../source/ref-changelog.md:698 +#: ../../source/ref-changelog.md:790 msgid "`scikit-learn`" msgstr "" -#: ../../source/ref-changelog.md:699 +#: ../../source/ref-changelog.md:791 msgid "`simulation_pytorch`" msgstr "" -#: ../../source/ref-changelog.md:700 +#: ../../source/ref-changelog.md:792 msgid "`quickstart_pytorch`" msgstr "" -#: ../../source/ref-changelog.md:701 +#: ../../source/ref-changelog.md:793 msgid "`quickstart_simulation`" msgstr "" -#: ../../source/ref-changelog.md:702 +#: ../../source/ref-changelog.md:794 msgid "`quickstart_tensorflow`" msgstr "" -#: ../../source/ref-changelog.md:703 +#: ../../source/ref-changelog.md:795 msgid "`advanced_tensorflow`" msgstr "" -#: ../../source/ref-changelog.md:705 +#: ../../source/ref-changelog.md:797 msgid "" -"**Remove the obsolete simulation example** " -"([#1328](https://github.com/adap/flower/pull/1328))" +"**Remove the obsolete simulation example** ([#1328](https://github.com/adap/" +"flower/pull/1328))" msgstr "" -#: ../../source/ref-changelog.md:707 +#: ../../source/ref-changelog.md:799 msgid "" "Removes the obsolete `simulation` example and renames " "`quickstart_simulation` to `simulation_tensorflow` so it fits withs the " "naming of `simulation_pytorch`" msgstr "" -#: ../../source/ref-changelog.md:709 +#: ../../source/ref-changelog.md:801 msgid "" -"**Update documentation** " -"([#1223](https://github.com/adap/flower/pull/1223), " -"[#1209](https://github.com/adap/flower/pull/1209), " -"[#1251](https://github.com/adap/flower/pull/1251), " -"[#1257](https://github.com/adap/flower/pull/1257), " -"[#1267](https://github.com/adap/flower/pull/1267), " -"[#1268](https://github.com/adap/flower/pull/1268), " -"[#1300](https://github.com/adap/flower/pull/1300), " -"[#1304](https://github.com/adap/flower/pull/1304), " -"[#1305](https://github.com/adap/flower/pull/1305), " -"[#1307](https://github.com/adap/flower/pull/1307))" +"**Update documentation** ([#1223](https://github.com/adap/flower/pull/1223), " +"[#1209](https://github.com/adap/flower/pull/1209), [#1251](https://github." +"com/adap/flower/pull/1251), [#1257](https://github.com/adap/flower/" +"pull/1257), [#1267](https://github.com/adap/flower/pull/1267), [#1268]" +"(https://github.com/adap/flower/pull/1268), [#1300](https://github.com/adap/" +"flower/pull/1300), [#1304](https://github.com/adap/flower/pull/1304), [#1305]" +"(https://github.com/adap/flower/pull/1305), [#1307](https://github.com/adap/" +"flower/pull/1307))" msgstr "" -#: ../../source/ref-changelog.md:711 +#: ../../source/ref-changelog.md:803 msgid "" "One substantial documentation update fixes multiple smaller rendering " "issues, makes titles more succinct to improve navigation, removes a " -"deprecated library, updates documentation dependencies, includes the " -"`flwr.common` module in the API reference, includes support for markdown-" -"based documentation, migrates the changelog from `.rst` to `.md`, and " -"fixes a number of smaller details!" +"deprecated library, updates documentation dependencies, includes the `flwr." +"common` module in the API reference, includes support for markdown-based " +"documentation, migrates the changelog from `.rst` to `.md`, and fixes a " +"number of smaller details!" msgstr "" -#: ../../source/ref-changelog.md:713 ../../source/ref-changelog.md:768 -#: ../../source/ref-changelog.md:837 ../../source/ref-changelog.md:876 +#: ../../source/ref-changelog.md:805 ../../source/ref-changelog.md:860 +#: ../../source/ref-changelog.md:929 ../../source/ref-changelog.md:968 msgid "**Minor updates**" msgstr "" -#: ../../source/ref-changelog.md:715 +#: ../../source/ref-changelog.md:807 msgid "" -"Add round number to fit and evaluate log messages " -"([#1266](https://github.com/adap/flower/pull/1266))" +"Add round number to fit and evaluate log messages ([#1266](https://github." +"com/adap/flower/pull/1266))" msgstr "" -#: ../../source/ref-changelog.md:716 +#: ../../source/ref-changelog.md:808 msgid "" -"Add secure gRPC connection to the `advanced_tensorflow` code example " -"([#847](https://github.com/adap/flower/pull/847))" +"Add secure gRPC connection to the `advanced_tensorflow` code example ([#847]" +"(https://github.com/adap/flower/pull/847))" msgstr "" -#: ../../source/ref-changelog.md:717 +#: ../../source/ref-changelog.md:809 msgid "" -"Update developer tooling " -"([#1231](https://github.com/adap/flower/pull/1231), " -"[#1276](https://github.com/adap/flower/pull/1276), " -"[#1301](https://github.com/adap/flower/pull/1301), " -"[#1310](https://github.com/adap/flower/pull/1310))" +"Update developer tooling ([#1231](https://github.com/adap/flower/pull/1231), " +"[#1276](https://github.com/adap/flower/pull/1276), [#1301](https://github." +"com/adap/flower/pull/1301), [#1310](https://github.com/adap/flower/" +"pull/1310))" msgstr "" -#: ../../source/ref-changelog.md:718 +#: ../../source/ref-changelog.md:810 msgid "" -"Rename ProtoBuf messages to improve consistency " -"([#1214](https://github.com/adap/flower/pull/1214), " -"[#1258](https://github.com/adap/flower/pull/1258), " +"Rename ProtoBuf messages to improve consistency ([#1214](https://github.com/" +"adap/flower/pull/1214), [#1258](https://github.com/adap/flower/pull/1258), " "[#1259](https://github.com/adap/flower/pull/1259))" msgstr "" -#: ../../source/ref-changelog.md:720 +#: ../../source/ref-changelog.md:812 msgid "v0.19.0 (2022-05-18)" msgstr "" -#: ../../source/ref-changelog.md:724 +#: ../../source/ref-changelog.md:816 msgid "" -"**Flower Baselines (preview): FedOpt, FedBN, FedAvgM** " -"([#919](https://github.com/adap/flower/pull/919), " -"[#1127](https://github.com/adap/flower/pull/1127), " -"[#914](https://github.com/adap/flower/pull/914))" +"**Flower Baselines (preview): FedOpt, FedBN, FedAvgM** ([#919](https://" +"github.com/adap/flower/pull/919), [#1127](https://github.com/adap/flower/" +"pull/1127), [#914](https://github.com/adap/flower/pull/914))" msgstr "" -#: ../../source/ref-changelog.md:726 +#: ../../source/ref-changelog.md:818 msgid "" "The first preview release of Flower Baselines has arrived! We're " "kickstarting Flower Baselines with implementations of FedOpt (FedYogi, " -"FedAdam, FedAdagrad), FedBN, and FedAvgM. Check the documentation on how " -"to use [Flower Baselines](https://flower.ai/docs/using-baselines.html). " -"With this first preview release we're also inviting the community to " -"[contribute their own baselines](https://flower.ai/docs/baselines/how-to-" -"contribute-baselines.html)." +"FedAdam, FedAdagrad), FedBN, and FedAvgM. Check the documentation on how to " +"use [Flower Baselines](https://flower.ai/docs/using-baselines.html). With " +"this first preview release we're also inviting the community to [contribute " +"their own baselines](https://flower.ai/docs/baselines/how-to-contribute-" +"baselines.html)." msgstr "" -#: ../../source/ref-changelog.md:728 +#: ../../source/ref-changelog.md:820 msgid "" -"**C++ client SDK (preview) and code example** " -"([#1111](https://github.com/adap/flower/pull/1111))" +"**C++ client SDK (preview) and code example** ([#1111](https://github.com/" +"adap/flower/pull/1111))" msgstr "" -#: ../../source/ref-changelog.md:730 +#: ../../source/ref-changelog.md:822 msgid "" -"Preview support for Flower clients written in C++. The C++ preview " -"includes a Flower client SDK and a quickstart code example that " -"demonstrates a simple C++ client using the SDK." +"Preview support for Flower clients written in C++. The C++ preview includes " +"a Flower client SDK and a quickstart code example that demonstrates a simple " +"C++ client using the SDK." msgstr "" -#: ../../source/ref-changelog.md:732 +#: ../../source/ref-changelog.md:824 msgid "" -"**Add experimental support for Python 3.10 and Python 3.11** " -"([#1135](https://github.com/adap/flower/pull/1135))" +"**Add experimental support for Python 3.10 and Python 3.11** ([#1135]" +"(https://github.com/adap/flower/pull/1135))" msgstr "" -#: ../../source/ref-changelog.md:734 +#: ../../source/ref-changelog.md:826 msgid "" -"Python 3.10 is the latest stable release of Python and Python 3.11 is due" -" to be released in October. This Flower release adds experimental support" -" for both Python versions." +"Python 3.10 is the latest stable release of Python and Python 3.11 is due to " +"be released in October. This Flower release adds experimental support for " +"both Python versions." msgstr "" -#: ../../source/ref-changelog.md:736 +#: ../../source/ref-changelog.md:828 msgid "" -"**Aggregate custom metrics through user-provided functions** " -"([#1144](https://github.com/adap/flower/pull/1144))" +"**Aggregate custom metrics through user-provided functions** ([#1144]" +"(https://github.com/adap/flower/pull/1144))" msgstr "" -#: ../../source/ref-changelog.md:738 +#: ../../source/ref-changelog.md:830 msgid "" -"Custom metrics (e.g., `accuracy`) can now be aggregated without having to" -" customize the strategy. Built-in strategies support two new arguments, " +"Custom metrics (e.g., `accuracy`) can now be aggregated without having to " +"customize the strategy. Built-in strategies support two new arguments, " "`fit_metrics_aggregation_fn` and `evaluate_metrics_aggregation_fn`, that " "allow passing custom metric aggregation functions." msgstr "" -#: ../../source/ref-changelog.md:740 +#: ../../source/ref-changelog.md:832 msgid "" -"**User-configurable round timeout** " -"([#1162](https://github.com/adap/flower/pull/1162))" +"**User-configurable round timeout** ([#1162](https://github.com/adap/flower/" +"pull/1162))" msgstr "" -#: ../../source/ref-changelog.md:742 +#: ../../source/ref-changelog.md:834 msgid "" "A new configuration value allows the round timeout to be set for " -"`start_server` and `start_simulation`. If the `config` dictionary " -"contains a `round_timeout` key (with a `float` value in seconds), the " -"server will wait *at least* `round_timeout` seconds before it closes the " -"connection." +"`start_server` and `start_simulation`. If the `config` dictionary contains a " +"`round_timeout` key (with a `float` value in seconds), the server will wait " +"*at least* `round_timeout` seconds before it closes the connection." msgstr "" -#: ../../source/ref-changelog.md:744 +#: ../../source/ref-changelog.md:836 msgid "" -"**Enable both federated evaluation and centralized evaluation to be used " -"at the same time in all built-in strategies** " -"([#1091](https://github.com/adap/flower/pull/1091))" +"**Enable both federated evaluation and centralized evaluation to be used at " +"the same time in all built-in strategies** ([#1091](https://github.com/adap/" +"flower/pull/1091))" msgstr "" -#: ../../source/ref-changelog.md:746 +#: ../../source/ref-changelog.md:838 msgid "" -"Built-in strategies can now perform both federated evaluation (i.e., " -"client-side) and centralized evaluation (i.e., server-side) in the same " -"round. Federated evaluation can be disabled by setting `fraction_eval` to" -" `0.0`." +"Built-in strategies can now perform both federated evaluation (i.e., client-" +"side) and centralized evaluation (i.e., server-side) in the same round. " +"Federated evaluation can be disabled by setting `fraction_eval` to `0.0`." msgstr "" -#: ../../source/ref-changelog.md:748 +#: ../../source/ref-changelog.md:840 msgid "" -"**Two new Jupyter Notebook tutorials** " -"([#1141](https://github.com/adap/flower/pull/1141))" +"**Two new Jupyter Notebook tutorials** ([#1141](https://github.com/adap/" +"flower/pull/1141))" msgstr "" -#: ../../source/ref-changelog.md:750 +#: ../../source/ref-changelog.md:842 msgid "" -"Two Jupyter Notebook tutorials (compatible with Google Colab) explain " -"basic and intermediate Flower features:" +"Two Jupyter Notebook tutorials (compatible with Google Colab) explain basic " +"and intermediate Flower features:" msgstr "" -#: ../../source/ref-changelog.md:752 +#: ../../source/ref-changelog.md:844 msgid "" -"*An Introduction to Federated Learning*: [Open in " -"Colab](https://colab.research.google.com/github/adap/flower/blob/main/tutorials/Flower-1" -"-Intro-to-FL-PyTorch.ipynb)" +"*An Introduction to Federated Learning*: [Open in Colab](https://colab." +"research.google.com/github/adap/flower/blob/main/tutorials/Flower-1-Intro-to-" +"FL-PyTorch.ipynb)" msgstr "" -#: ../../source/ref-changelog.md:754 +#: ../../source/ref-changelog.md:846 msgid "" -"*Using Strategies in Federated Learning*: [Open in " -"Colab](https://colab.research.google.com/github/adap/flower/blob/main/tutorials/Flower-2" -"-Strategies-in-FL-PyTorch.ipynb)" +"*Using Strategies in Federated Learning*: [Open in Colab](https://colab." +"research.google.com/github/adap/flower/blob/main/tutorials/Flower-2-" +"Strategies-in-FL-PyTorch.ipynb)" msgstr "" -#: ../../source/ref-changelog.md:756 +#: ../../source/ref-changelog.md:848 msgid "" -"**New FedAvgM strategy (Federated Averaging with Server Momentum)** " -"([#1076](https://github.com/adap/flower/pull/1076))" +"**New FedAvgM strategy (Federated Averaging with Server Momentum)** ([#1076]" +"(https://github.com/adap/flower/pull/1076))" msgstr "" -#: ../../source/ref-changelog.md:758 +#: ../../source/ref-changelog.md:850 msgid "" "The new `FedAvgM` strategy implements Federated Averaging with Server " "Momentum \\[Hsu et al., 2019\\]." msgstr "" -#: ../../source/ref-changelog.md:760 +#: ../../source/ref-changelog.md:852 msgid "" -"**New advanced PyTorch code example** " -"([#1007](https://github.com/adap/flower/pull/1007))" +"**New advanced PyTorch code example** ([#1007](https://github.com/adap/" +"flower/pull/1007))" msgstr "" -#: ../../source/ref-changelog.md:762 +#: ../../source/ref-changelog.md:854 msgid "" "A new code example (`advanced_pytorch`) demonstrates advanced Flower " "concepts with PyTorch." msgstr "" -#: ../../source/ref-changelog.md:764 +#: ../../source/ref-changelog.md:856 msgid "" -"**New JAX code example** " -"([#906](https://github.com/adap/flower/pull/906), " +"**New JAX code example** ([#906](https://github.com/adap/flower/pull/906), " "[#1143](https://github.com/adap/flower/pull/1143))" msgstr "" -#: ../../source/ref-changelog.md:766 +#: ../../source/ref-changelog.md:858 msgid "" "A new code example (`jax_from_centralized_to_federated`) shows federated " "learning with JAX and Flower." msgstr "" -#: ../../source/ref-changelog.md:770 +#: ../../source/ref-changelog.md:862 msgid "" "New option to keep Ray running if Ray was already initialized in " "`start_simulation` ([#1177](https://github.com/adap/flower/pull/1177))" msgstr "" -#: ../../source/ref-changelog.md:771 +#: ../../source/ref-changelog.md:863 msgid "" "Add support for custom `ClientManager` as a `start_simulation` parameter " "([#1171](https://github.com/adap/flower/pull/1171))" msgstr "" -#: ../../source/ref-changelog.md:772 +#: ../../source/ref-changelog.md:864 msgid "" -"New documentation for [implementing " -"strategies](https://flower.ai/docs/framework/how-to-implement-" -"strategies.html) ([#1097](https://github.com/adap/flower/pull/1097), " -"[#1175](https://github.com/adap/flower/pull/1175))" +"New documentation for [implementing strategies](https://flower.ai/docs/" +"framework/how-to-implement-strategies.html) ([#1097](https://github.com/adap/" +"flower/pull/1097), [#1175](https://github.com/adap/flower/pull/1175))" msgstr "" -#: ../../source/ref-changelog.md:773 +#: ../../source/ref-changelog.md:865 msgid "" -"New mobile-friendly documentation theme " -"([#1174](https://github.com/adap/flower/pull/1174))" +"New mobile-friendly documentation theme ([#1174](https://github.com/adap/" +"flower/pull/1174))" msgstr "" -#: ../../source/ref-changelog.md:774 +#: ../../source/ref-changelog.md:866 msgid "" "Limit version range for (optional) `ray` dependency to include only " -"compatible releases (`>=1.9.2,<1.12.0`) " -"([#1205](https://github.com/adap/flower/pull/1205))" +"compatible releases (`>=1.9.2,<1.12.0`) ([#1205](https://github.com/adap/" +"flower/pull/1205))" msgstr "" -#: ../../source/ref-changelog.md:778 +#: ../../source/ref-changelog.md:870 msgid "" -"**Remove deprecated support for Python 3.6** " -"([#871](https://github.com/adap/flower/pull/871))" +"**Remove deprecated support for Python 3.6** ([#871](https://github.com/adap/" +"flower/pull/871))" msgstr "" -#: ../../source/ref-changelog.md:779 +#: ../../source/ref-changelog.md:871 msgid "" -"**Remove deprecated KerasClient** " -"([#857](https://github.com/adap/flower/pull/857))" +"**Remove deprecated KerasClient** ([#857](https://github.com/adap/flower/" +"pull/857))" msgstr "" -#: ../../source/ref-changelog.md:780 +#: ../../source/ref-changelog.md:872 msgid "" -"**Remove deprecated no-op extra installs** " -"([#973](https://github.com/adap/flower/pull/973))" +"**Remove deprecated no-op extra installs** ([#973](https://github.com/adap/" +"flower/pull/973))" msgstr "" -#: ../../source/ref-changelog.md:781 +#: ../../source/ref-changelog.md:873 msgid "" "**Remove deprecated proto fields from** `FitRes` **and** `EvaluateRes` " "([#869](https://github.com/adap/flower/pull/869))" msgstr "" -#: ../../source/ref-changelog.md:782 +#: ../../source/ref-changelog.md:874 msgid "" -"**Remove deprecated QffedAvg strategy (replaced by QFedAvg)** " -"([#1107](https://github.com/adap/flower/pull/1107))" +"**Remove deprecated QffedAvg strategy (replaced by QFedAvg)** ([#1107]" +"(https://github.com/adap/flower/pull/1107))" msgstr "" -#: ../../source/ref-changelog.md:783 +#: ../../source/ref-changelog.md:875 msgid "" -"**Remove deprecated DefaultStrategy strategy** " -"([#1142](https://github.com/adap/flower/pull/1142))" +"**Remove deprecated DefaultStrategy strategy** ([#1142](https://github.com/" +"adap/flower/pull/1142))" msgstr "" -#: ../../source/ref-changelog.md:784 +#: ../../source/ref-changelog.md:876 msgid "" -"**Remove deprecated support for eval_fn accuracy return value** " -"([#1142](https://github.com/adap/flower/pull/1142))" +"**Remove deprecated support for eval_fn accuracy return value** ([#1142]" +"(https://github.com/adap/flower/pull/1142))" msgstr "" -#: ../../source/ref-changelog.md:785 +#: ../../source/ref-changelog.md:877 msgid "" "**Remove deprecated support for passing initial parameters as NumPy " "ndarrays** ([#1142](https://github.com/adap/flower/pull/1142))" msgstr "" -#: ../../source/ref-changelog.md:787 +#: ../../source/ref-changelog.md:879 msgid "v0.18.0 (2022-02-28)" msgstr "" -#: ../../source/ref-changelog.md:791 +#: ../../source/ref-changelog.md:883 msgid "" "**Improved Virtual Client Engine compatibility with Jupyter Notebook / " -"Google Colab** ([#866](https://github.com/adap/flower/pull/866), " -"[#872](https://github.com/adap/flower/pull/872), " -"[#833](https://github.com/adap/flower/pull/833), " -"[#1036](https://github.com/adap/flower/pull/1036))" +"Google Colab** ([#866](https://github.com/adap/flower/pull/866), [#872]" +"(https://github.com/adap/flower/pull/872), [#833](https://github.com/adap/" +"flower/pull/833), [#1036](https://github.com/adap/flower/pull/1036))" msgstr "" -#: ../../source/ref-changelog.md:793 +#: ../../source/ref-changelog.md:885 msgid "" -"Simulations (using the Virtual Client Engine through `start_simulation`) " -"now work more smoothly on Jupyter Notebooks (incl. Google Colab) after " +"Simulations (using the Virtual Client Engine through `start_simulation`) now " +"work more smoothly on Jupyter Notebooks (incl. Google Colab) after " "installing Flower with the `simulation` extra (`pip install " "flwr[simulation]`)." msgstr "" -#: ../../source/ref-changelog.md:795 +#: ../../source/ref-changelog.md:887 msgid "" -"**New Jupyter Notebook code example** " -"([#833](https://github.com/adap/flower/pull/833))" +"**New Jupyter Notebook code example** ([#833](https://github.com/adap/flower/" +"pull/833))" msgstr "" -#: ../../source/ref-changelog.md:797 +#: ../../source/ref-changelog.md:889 msgid "" -"A new code example (`quickstart_simulation`) demonstrates Flower " -"simulations using the Virtual Client Engine through Jupyter Notebook " -"(incl. Google Colab)." +"A new code example (`quickstart_simulation`) demonstrates Flower simulations " +"using the Virtual Client Engine through Jupyter Notebook (incl. Google " +"Colab)." msgstr "" -#: ../../source/ref-changelog.md:799 +#: ../../source/ref-changelog.md:891 msgid "" -"**Client properties (feature preview)** " -"([#795](https://github.com/adap/flower/pull/795))" +"**Client properties (feature preview)** ([#795](https://github.com/adap/" +"flower/pull/795))" msgstr "" -#: ../../source/ref-changelog.md:801 +#: ../../source/ref-changelog.md:893 msgid "" -"Clients can implement a new method `get_properties` to enable server-side" -" strategies to query client properties." +"Clients can implement a new method `get_properties` to enable server-side " +"strategies to query client properties." msgstr "" -#: ../../source/ref-changelog.md:803 +#: ../../source/ref-changelog.md:895 msgid "" -"**Experimental Android support with TFLite** " -"([#865](https://github.com/adap/flower/pull/865))" +"**Experimental Android support with TFLite** ([#865](https://github.com/adap/" +"flower/pull/865))" msgstr "" -#: ../../source/ref-changelog.md:805 +#: ../../source/ref-changelog.md:897 msgid "" "Android support has finally arrived in `main`! Flower is both client-" "agnostic and framework-agnostic by design. One can integrate arbitrary " -"client platforms and with this release, using Flower on Android has " -"become a lot easier." +"client platforms and with this release, using Flower on Android has become a " +"lot easier." msgstr "" -#: ../../source/ref-changelog.md:807 +#: ../../source/ref-changelog.md:899 msgid "" -"The example uses TFLite on the client side, along with a new " -"`FedAvgAndroid` strategy. The Android client and `FedAvgAndroid` are " -"still experimental, but they are a first step towards a fully-fledged " -"Android SDK and a unified `FedAvg` implementation that integrated the new" -" functionality from `FedAvgAndroid`." +"The example uses TFLite on the client side, along with a new `FedAvgAndroid` " +"strategy. The Android client and `FedAvgAndroid` are still experimental, but " +"they are a first step towards a fully-fledged Android SDK and a unified " +"`FedAvg` implementation that integrated the new functionality from " +"`FedAvgAndroid`." msgstr "" -#: ../../source/ref-changelog.md:809 +#: ../../source/ref-changelog.md:901 msgid "" -"**Make gRPC keepalive time user-configurable and decrease default " -"keepalive time** ([#1069](https://github.com/adap/flower/pull/1069))" +"**Make gRPC keepalive time user-configurable and decrease default keepalive " +"time** ([#1069](https://github.com/adap/flower/pull/1069))" msgstr "" -#: ../../source/ref-changelog.md:811 +#: ../../source/ref-changelog.md:903 msgid "" "The default gRPC keepalive time has been reduced to increase the " -"compatibility of Flower with more cloud environments (for example, " -"Microsoft Azure). Users can configure the keepalive time to customize the" -" gRPC stack based on specific requirements." +"compatibility of Flower with more cloud environments (for example, Microsoft " +"Azure). Users can configure the keepalive time to customize the gRPC stack " +"based on specific requirements." msgstr "" -#: ../../source/ref-changelog.md:813 +#: ../../source/ref-changelog.md:905 msgid "" -"**New differential privacy example using Opacus and PyTorch** " -"([#805](https://github.com/adap/flower/pull/805))" +"**New differential privacy example using Opacus and PyTorch** ([#805]" +"(https://github.com/adap/flower/pull/805))" msgstr "" -#: ../../source/ref-changelog.md:815 +#: ../../source/ref-changelog.md:907 msgid "" -"A new code example (`opacus`) demonstrates differentially-private " -"federated learning with Opacus, PyTorch, and Flower." +"A new code example (`opacus`) demonstrates differentially-private federated " +"learning with Opacus, PyTorch, and Flower." msgstr "" -#: ../../source/ref-changelog.md:817 +#: ../../source/ref-changelog.md:909 msgid "" -"**New Hugging Face Transformers code example** " -"([#863](https://github.com/adap/flower/pull/863))" +"**New Hugging Face Transformers code example** ([#863](https://github.com/" +"adap/flower/pull/863))" msgstr "" -#: ../../source/ref-changelog.md:819 +#: ../../source/ref-changelog.md:911 msgid "" -"A new code example (`quickstart_huggingface`) demonstrates usage of " -"Hugging Face Transformers with Flower." +"A new code example (`quickstart_huggingface`) demonstrates usage of Hugging " +"Face Transformers with Flower." msgstr "" -#: ../../source/ref-changelog.md:821 +#: ../../source/ref-changelog.md:913 msgid "" -"**New MLCube code example** " -"([#779](https://github.com/adap/flower/pull/779), " -"[#1034](https://github.com/adap/flower/pull/1034), " -"[#1065](https://github.com/adap/flower/pull/1065), " -"[#1090](https://github.com/adap/flower/pull/1090))" +"**New MLCube code example** ([#779](https://github.com/adap/flower/" +"pull/779), [#1034](https://github.com/adap/flower/pull/1034), [#1065]" +"(https://github.com/adap/flower/pull/1065), [#1090](https://github.com/adap/" +"flower/pull/1090))" msgstr "" -#: ../../source/ref-changelog.md:823 +#: ../../source/ref-changelog.md:915 msgid "" -"A new code example (`quickstart_mlcube`) demonstrates usage of MLCube " -"with Flower." +"A new code example (`quickstart_mlcube`) demonstrates usage of MLCube with " +"Flower." msgstr "" -#: ../../source/ref-changelog.md:825 +#: ../../source/ref-changelog.md:917 msgid "" -"**SSL-enabled server and client** " -"([#842](https://github.com/adap/flower/pull/842), " -"[#844](https://github.com/adap/flower/pull/844), " -"[#845](https://github.com/adap/flower/pull/845), " -"[#847](https://github.com/adap/flower/pull/847), " -"[#993](https://github.com/adap/flower/pull/993), " -"[#994](https://github.com/adap/flower/pull/994))" +"**SSL-enabled server and client** ([#842](https://github.com/adap/flower/" +"pull/842), [#844](https://github.com/adap/flower/pull/844), [#845](https://" +"github.com/adap/flower/pull/845), [#847](https://github.com/adap/flower/" +"pull/847), [#993](https://github.com/adap/flower/pull/993), [#994](https://" +"github.com/adap/flower/pull/994))" msgstr "" -#: ../../source/ref-changelog.md:827 +#: ../../source/ref-changelog.md:919 msgid "" -"SSL enables secure encrypted connections between clients and servers. " -"This release open-sources the Flower secure gRPC implementation to make " -"encrypted communication channels accessible to all Flower users." +"SSL enables secure encrypted connections between clients and servers. This " +"release open-sources the Flower secure gRPC implementation to make encrypted " +"communication channels accessible to all Flower users." msgstr "" -#: ../../source/ref-changelog.md:829 +#: ../../source/ref-changelog.md:921 msgid "" -"**Updated** `FedAdam` **and** `FedYogi` **strategies** " -"([#885](https://github.com/adap/flower/pull/885), " -"[#895](https://github.com/adap/flower/pull/895))" +"**Updated** `FedAdam` **and** `FedYogi` **strategies** ([#885](https://" +"github.com/adap/flower/pull/885), [#895](https://github.com/adap/flower/" +"pull/895))" msgstr "" -#: ../../source/ref-changelog.md:831 +#: ../../source/ref-changelog.md:923 msgid "" -"`FedAdam` and `FedAdam` match the latest version of the Adaptive " -"Federated Optimization paper." +"`FedAdam` and `FedAdam` match the latest version of the Adaptive Federated " +"Optimization paper." msgstr "" -#: ../../source/ref-changelog.md:833 +#: ../../source/ref-changelog.md:925 msgid "" -"**Initialize** `start_simulation` **with a list of client IDs** " -"([#860](https://github.com/adap/flower/pull/860))" +"**Initialize** `start_simulation` **with a list of client IDs** ([#860]" +"(https://github.com/adap/flower/pull/860))" msgstr "" -#: ../../source/ref-changelog.md:835 +#: ../../source/ref-changelog.md:927 msgid "" "`start_simulation` can now be called with a list of client IDs " "(`clients_ids`, type: `List[str]`). Those IDs will be passed to the " @@ -16574,232 +18741,227 @@ msgid "" "identifiers." msgstr "" -#: ../../source/ref-changelog.md:839 +#: ../../source/ref-changelog.md:931 msgid "" -"Update `num_examples` calculation in PyTorch code examples in " -"([#909](https://github.com/adap/flower/pull/909))" +"Update `num_examples` calculation in PyTorch code examples in ([#909]" +"(https://github.com/adap/flower/pull/909))" msgstr "" -#: ../../source/ref-changelog.md:840 +#: ../../source/ref-changelog.md:932 msgid "" -"Expose Flower version through `flwr.__version__` " -"([#952](https://github.com/adap/flower/pull/952))" +"Expose Flower version through `flwr.__version__` ([#952](https://github.com/" +"adap/flower/pull/952))" msgstr "" -#: ../../source/ref-changelog.md:841 +#: ../../source/ref-changelog.md:933 msgid "" -"`start_server` in `app.py` now returns a `History` object containing " -"metrics from training ([#974](https://github.com/adap/flower/pull/974))" +"`start_server` in `app.py` now returns a `History` object containing metrics " +"from training ([#974](https://github.com/adap/flower/pull/974))" msgstr "" -#: ../../source/ref-changelog.md:842 +#: ../../source/ref-changelog.md:934 msgid "" -"Make `max_workers` (used by `ThreadPoolExecutor`) configurable " -"([#978](https://github.com/adap/flower/pull/978))" +"Make `max_workers` (used by `ThreadPoolExecutor`) configurable ([#978]" +"(https://github.com/adap/flower/pull/978))" msgstr "" -#: ../../source/ref-changelog.md:843 +#: ../../source/ref-changelog.md:935 msgid "" -"Increase sleep time after server start to three seconds in all code " -"examples ([#1086](https://github.com/adap/flower/pull/1086))" +"Increase sleep time after server start to three seconds in all code examples " +"([#1086](https://github.com/adap/flower/pull/1086))" msgstr "" -#: ../../source/ref-changelog.md:844 +#: ../../source/ref-changelog.md:936 msgid "" -"Added a new FAQ section to the documentation " -"([#948](https://github.com/adap/flower/pull/948))" +"Added a new FAQ section to the documentation ([#948](https://github.com/adap/" +"flower/pull/948))" msgstr "" -#: ../../source/ref-changelog.md:845 +#: ../../source/ref-changelog.md:937 msgid "" "And many more under-the-hood changes, library updates, documentation " "changes, and tooling improvements!" msgstr "" -#: ../../source/ref-changelog.md:849 +#: ../../source/ref-changelog.md:941 msgid "" "**Removed** `flwr_example` **and** `flwr_experimental` **from release " "build** ([#869](https://github.com/adap/flower/pull/869))" msgstr "" -#: ../../source/ref-changelog.md:851 +#: ../../source/ref-changelog.md:943 msgid "" "The packages `flwr_example` and `flwr_experimental` have been deprecated " "since Flower 0.12.0 and they are not longer included in Flower release " "builds. The associated extras (`baseline`, `examples-pytorch`, `examples-" -"tensorflow`, `http-logger`, `ops`) are now no-op and will be removed in " -"an upcoming release." +"tensorflow`, `http-logger`, `ops`) are now no-op and will be removed in an " +"upcoming release." msgstr "" -#: ../../source/ref-changelog.md:853 +#: ../../source/ref-changelog.md:945 msgid "v0.17.0 (2021-09-24)" msgstr "" -#: ../../source/ref-changelog.md:857 +#: ../../source/ref-changelog.md:949 msgid "" -"**Experimental virtual client engine** " -"([#781](https://github.com/adap/flower/pull/781) " -"[#790](https://github.com/adap/flower/pull/790) " -"[#791](https://github.com/adap/flower/pull/791))" +"**Experimental virtual client engine** ([#781](https://github.com/adap/" +"flower/pull/781) [#790](https://github.com/adap/flower/pull/790) [#791]" +"(https://github.com/adap/flower/pull/791))" msgstr "" -#: ../../source/ref-changelog.md:859 +#: ../../source/ref-changelog.md:951 msgid "" -"One of Flower's goals is to enable research at scale. This release " -"enables a first (experimental) peek at a major new feature, codenamed the" -" virtual client engine. Virtual clients enable simulations that scale to " -"a (very) large number of clients on a single machine or compute cluster. " -"The easiest way to test the new functionality is to look at the two new " -"code examples called `quickstart_simulation` and `simulation_pytorch`." +"One of Flower's goals is to enable research at scale. This release enables a " +"first (experimental) peek at a major new feature, codenamed the virtual " +"client engine. Virtual clients enable simulations that scale to a (very) " +"large number of clients on a single machine or compute cluster. The easiest " +"way to test the new functionality is to look at the two new code examples " +"called `quickstart_simulation` and `simulation_pytorch`." msgstr "" -#: ../../source/ref-changelog.md:861 +#: ../../source/ref-changelog.md:953 msgid "" -"The feature is still experimental, so there's no stability guarantee for " -"the API. It's also not quite ready for prime time and comes with a few " -"known caveats. However, those who are curious are encouraged to try it " -"out and share their thoughts." +"The feature is still experimental, so there's no stability guarantee for the " +"API. It's also not quite ready for prime time and comes with a few known " +"caveats. However, those who are curious are encouraged to try it out and " +"share their thoughts." msgstr "" -#: ../../source/ref-changelog.md:863 +#: ../../source/ref-changelog.md:955 msgid "" -"**New built-in strategies** " -"([#828](https://github.com/adap/flower/pull/828) " +"**New built-in strategies** ([#828](https://github.com/adap/flower/pull/828) " "[#822](https://github.com/adap/flower/pull/822))" msgstr "" -#: ../../source/ref-changelog.md:865 +#: ../../source/ref-changelog.md:957 msgid "" "FedYogi - Federated learning strategy using Yogi on server-side. " "Implementation based on https://arxiv.org/abs/2003.00295" msgstr "" -#: ../../source/ref-changelog.md:866 +#: ../../source/ref-changelog.md:958 msgid "" "FedAdam - Federated learning strategy using Adam on server-side. " "Implementation based on https://arxiv.org/abs/2003.00295" msgstr "" -#: ../../source/ref-changelog.md:868 +#: ../../source/ref-changelog.md:960 msgid "" -"**New PyTorch Lightning code example** " -"([#617](https://github.com/adap/flower/pull/617))" +"**New PyTorch Lightning code example** ([#617](https://github.com/adap/" +"flower/pull/617))" msgstr "" -#: ../../source/ref-changelog.md:870 +#: ../../source/ref-changelog.md:962 msgid "" -"**New Variational Auto-Encoder code example** " -"([#752](https://github.com/adap/flower/pull/752))" +"**New Variational Auto-Encoder code example** ([#752](https://github.com/" +"adap/flower/pull/752))" msgstr "" -#: ../../source/ref-changelog.md:872 +#: ../../source/ref-changelog.md:964 msgid "" -"**New scikit-learn code example** " -"([#748](https://github.com/adap/flower/pull/748))" +"**New scikit-learn code example** ([#748](https://github.com/adap/flower/" +"pull/748))" msgstr "" -#: ../../source/ref-changelog.md:874 +#: ../../source/ref-changelog.md:966 msgid "" -"**New experimental TensorBoard strategy** " -"([#789](https://github.com/adap/flower/pull/789))" +"**New experimental TensorBoard strategy** ([#789](https://github.com/adap/" +"flower/pull/789))" msgstr "" -#: ../../source/ref-changelog.md:878 +#: ../../source/ref-changelog.md:970 msgid "" -"Improved advanced TensorFlow code example " -"([#769](https://github.com/adap/flower/pull/769))" +"Improved advanced TensorFlow code example ([#769](https://github.com/adap/" +"flower/pull/769))" msgstr "" -#: ../../source/ref-changelog.md:879 +#: ../../source/ref-changelog.md:971 msgid "" -"Warning when `min_available_clients` is misconfigured " -"([#830](https://github.com/adap/flower/pull/830))" +"Warning when `min_available_clients` is misconfigured ([#830](https://github." +"com/adap/flower/pull/830))" msgstr "" -#: ../../source/ref-changelog.md:880 +#: ../../source/ref-changelog.md:972 msgid "" -"Improved gRPC server docs " -"([#841](https://github.com/adap/flower/pull/841))" +"Improved gRPC server docs ([#841](https://github.com/adap/flower/pull/841))" msgstr "" -#: ../../source/ref-changelog.md:881 +#: ../../source/ref-changelog.md:973 msgid "" -"Improved error message in `NumPyClient` " -"([#851](https://github.com/adap/flower/pull/851))" +"Improved error message in `NumPyClient` ([#851](https://github.com/adap/" +"flower/pull/851))" msgstr "" -#: ../../source/ref-changelog.md:882 +#: ../../source/ref-changelog.md:974 msgid "" -"Improved PyTorch quickstart code example " -"([#852](https://github.com/adap/flower/pull/852))" +"Improved PyTorch quickstart code example ([#852](https://github.com/adap/" +"flower/pull/852))" msgstr "" -#: ../../source/ref-changelog.md:886 +#: ../../source/ref-changelog.md:978 msgid "" -"**Disabled final distributed evaluation** " -"([#800](https://github.com/adap/flower/pull/800))" +"**Disabled final distributed evaluation** ([#800](https://github.com/adap/" +"flower/pull/800))" msgstr "" -#: ../../source/ref-changelog.md:888 +#: ../../source/ref-changelog.md:980 msgid "" -"Prior behaviour was to perform a final round of distributed evaluation on" -" all connected clients, which is often not required (e.g., when using " -"server-side evaluation). The prior behaviour can be enabled by passing " +"Prior behaviour was to perform a final round of distributed evaluation on " +"all connected clients, which is often not required (e.g., when using server-" +"side evaluation). The prior behaviour can be enabled by passing " "`force_final_distributed_eval=True` to `start_server`." msgstr "" -#: ../../source/ref-changelog.md:890 +#: ../../source/ref-changelog.md:982 msgid "" -"**Renamed q-FedAvg strategy** " -"([#802](https://github.com/adap/flower/pull/802))" +"**Renamed q-FedAvg strategy** ([#802](https://github.com/adap/flower/" +"pull/802))" msgstr "" -#: ../../source/ref-changelog.md:892 +#: ../../source/ref-changelog.md:984 msgid "" -"The strategy named `QffedAvg` was renamed to `QFedAvg` to better reflect " -"the notation given in the original paper (q-FFL is the optimization " -"objective, q-FedAvg is the proposed solver). Note the original (now " -"deprecated) `QffedAvg` class is still available for compatibility reasons" -" (it will be removed in a future release)." +"The strategy named `QffedAvg` was renamed to `QFedAvg` to better reflect the " +"notation given in the original paper (q-FFL is the optimization objective, q-" +"FedAvg is the proposed solver). Note the original (now deprecated) " +"`QffedAvg` class is still available for compatibility reasons (it will be " +"removed in a future release)." msgstr "" -#: ../../source/ref-changelog.md:894 +#: ../../source/ref-changelog.md:986 msgid "" "**Deprecated and renamed code example** `simulation_pytorch` **to** " -"`simulation_pytorch_legacy` " -"([#791](https://github.com/adap/flower/pull/791))" +"`simulation_pytorch_legacy` ([#791](https://github.com/adap/flower/pull/791))" msgstr "" -#: ../../source/ref-changelog.md:896 +#: ../../source/ref-changelog.md:988 msgid "" -"This example has been replaced by a new example. The new example is based" -" on the experimental virtual client engine, which will become the new " -"default way of doing most types of large-scale simulations in Flower. The" -" existing example was kept for reference purposes, but it might be " -"removed in the future." +"This example has been replaced by a new example. The new example is based on " +"the experimental virtual client engine, which will become the new default " +"way of doing most types of large-scale simulations in Flower. The existing " +"example was kept for reference purposes, but it might be removed in the " +"future." msgstr "" -#: ../../source/ref-changelog.md:898 +#: ../../source/ref-changelog.md:990 msgid "v0.16.0 (2021-05-11)" msgstr "" -#: ../../source/ref-changelog.md:902 +#: ../../source/ref-changelog.md:994 msgid "" -"**New built-in strategies** " -"([#549](https://github.com/adap/flower/pull/549))" +"**New built-in strategies** ([#549](https://github.com/adap/flower/pull/549))" msgstr "" -#: ../../source/ref-changelog.md:904 +#: ../../source/ref-changelog.md:996 msgid "(abstract) FedOpt" msgstr "" -#: ../../source/ref-changelog.md:907 +#: ../../source/ref-changelog.md:999 msgid "" -"**Custom metrics for server and strategies** " -"([#717](https://github.com/adap/flower/pull/717))" +"**Custom metrics for server and strategies** ([#717](https://github.com/adap/" +"flower/pull/717))" msgstr "" -#: ../../source/ref-changelog.md:909 +#: ../../source/ref-changelog.md:1001 msgid "" "The Flower server is now fully task-agnostic, all remaining instances of " "task-specific metrics (such as `accuracy`) have been replaced by custom " @@ -16808,125 +18970,119 @@ msgid "" "release, custom metrics replace task-specific metrics on the server." msgstr "" -#: ../../source/ref-changelog.md:911 +#: ../../source/ref-changelog.md:1003 msgid "" -"Custom metric dictionaries are now used in two user-facing APIs: they are" -" returned from Strategy methods `aggregate_fit`/`aggregate_evaluate` and " -"they enable evaluation functions passed to built-in strategies (via " -"`eval_fn`) to return more than two evaluation metrics. Strategies can " -"even return *aggregated* metrics dictionaries for the server to keep " -"track of." +"Custom metric dictionaries are now used in two user-facing APIs: they are " +"returned from Strategy methods `aggregate_fit`/`aggregate_evaluate` and they " +"enable evaluation functions passed to built-in strategies (via `eval_fn`) to " +"return more than two evaluation metrics. Strategies can even return " +"*aggregated* metrics dictionaries for the server to keep track of." msgstr "" -#: ../../source/ref-changelog.md:913 +#: ../../source/ref-changelog.md:1005 msgid "" "Strategy implementations should migrate their `aggregate_fit` and " "`aggregate_evaluate` methods to the new return type (e.g., by simply " -"returning an empty `{}`), server-side evaluation functions should migrate" -" from `return loss, accuracy` to `return loss, {\"accuracy\": accuracy}`." +"returning an empty `{}`), server-side evaluation functions should migrate " +"from `return loss, accuracy` to `return loss, {\"accuracy\": accuracy}`." msgstr "" -#: ../../source/ref-changelog.md:915 +#: ../../source/ref-changelog.md:1007 msgid "" "Flower 0.15-style return types are deprecated (but still supported), " "compatibility will be removed in a future release." msgstr "" -#: ../../source/ref-changelog.md:917 +#: ../../source/ref-changelog.md:1009 msgid "" -"**Migration warnings for deprecated functionality** " -"([#690](https://github.com/adap/flower/pull/690))" +"**Migration warnings for deprecated functionality** ([#690](https://github." +"com/adap/flower/pull/690))" msgstr "" -#: ../../source/ref-changelog.md:919 +#: ../../source/ref-changelog.md:1011 msgid "" "Earlier versions of Flower were often migrated to new APIs, while " -"maintaining compatibility with legacy APIs. This release introduces " -"detailed warning messages if usage of deprecated APIs is detected. The " -"new warning messages often provide details on how to migrate to more " -"recent APIs, thus easing the transition from one release to another." +"maintaining compatibility with legacy APIs. This release introduces detailed " +"warning messages if usage of deprecated APIs is detected. The new warning " +"messages often provide details on how to migrate to more recent APIs, thus " +"easing the transition from one release to another." msgstr "" -#: ../../source/ref-changelog.md:921 +#: ../../source/ref-changelog.md:1013 msgid "" -"Improved docs and docstrings " -"([#691](https://github.com/adap/flower/pull/691) " -"[#692](https://github.com/adap/flower/pull/692) " -"[#713](https://github.com/adap/flower/pull/713))" +"Improved docs and docstrings ([#691](https://github.com/adap/flower/" +"pull/691) [#692](https://github.com/adap/flower/pull/692) [#713](https://" +"github.com/adap/flower/pull/713))" msgstr "" -#: ../../source/ref-changelog.md:923 +#: ../../source/ref-changelog.md:1015 msgid "MXNet example and documentation" msgstr "" -#: ../../source/ref-changelog.md:925 +#: ../../source/ref-changelog.md:1017 msgid "" "FedBN implementation in example PyTorch: From Centralized To Federated " -"([#696](https://github.com/adap/flower/pull/696) " -"[#702](https://github.com/adap/flower/pull/702) " -"[#705](https://github.com/adap/flower/pull/705))" +"([#696](https://github.com/adap/flower/pull/696) [#702](https://github.com/" +"adap/flower/pull/702) [#705](https://github.com/adap/flower/pull/705))" msgstr "" -#: ../../source/ref-changelog.md:929 +#: ../../source/ref-changelog.md:1021 msgid "" -"**Serialization-agnostic server** " -"([#721](https://github.com/adap/flower/pull/721))" +"**Serialization-agnostic server** ([#721](https://github.com/adap/flower/" +"pull/721))" msgstr "" -#: ../../source/ref-changelog.md:931 +#: ../../source/ref-changelog.md:1023 msgid "" -"The Flower server is now fully serialization-agnostic. Prior usage of " -"class `Weights` (which represents parameters as deserialized NumPy " -"ndarrays) was replaced by class `Parameters` (e.g., in `Strategy`). " -"`Parameters` objects are fully serialization-agnostic and represents " -"parameters as byte arrays, the `tensor_type` attributes indicates how " -"these byte arrays should be interpreted (e.g., for " -"serialization/deserialization)." +"The Flower server is now fully serialization-agnostic. Prior usage of class " +"`Weights` (which represents parameters as deserialized NumPy ndarrays) was " +"replaced by class `Parameters` (e.g., in `Strategy`). `Parameters` objects " +"are fully serialization-agnostic and represents parameters as byte arrays, " +"the `tensor_type` attributes indicates how these byte arrays should be " +"interpreted (e.g., for serialization/deserialization)." msgstr "" -#: ../../source/ref-changelog.md:933 +#: ../../source/ref-changelog.md:1025 msgid "" -"Built-in strategies implement this approach by handling serialization and" -" deserialization to/from `Weights` internally. Custom/3rd-party Strategy " +"Built-in strategies implement this approach by handling serialization and " +"deserialization to/from `Weights` internally. Custom/3rd-party Strategy " "implementations should update to the slightly changed Strategy method " -"definitions. Strategy authors can consult PR " -"[#721](https://github.com/adap/flower/pull/721) to see how strategies can" -" easily migrate to the new format." +"definitions. Strategy authors can consult PR [#721](https://github.com/adap/" +"flower/pull/721) to see how strategies can easily migrate to the new format." msgstr "" -#: ../../source/ref-changelog.md:935 +#: ../../source/ref-changelog.md:1027 msgid "" -"Deprecated `flwr.server.Server.evaluate`, use " -"`flwr.server.Server.evaluate_round` instead " -"([#717](https://github.com/adap/flower/pull/717))" +"Deprecated `flwr.server.Server.evaluate`, use `flwr.server.Server." +"evaluate_round` instead ([#717](https://github.com/adap/flower/pull/717))" msgstr "" -#: ../../source/ref-changelog.md:937 +#: ../../source/ref-changelog.md:1029 msgid "v0.15.0 (2021-03-12)" msgstr "" -#: ../../source/ref-changelog.md:941 +#: ../../source/ref-changelog.md:1033 msgid "" -"**Server-side parameter initialization** " -"([#658](https://github.com/adap/flower/pull/658))" +"**Server-side parameter initialization** ([#658](https://github.com/adap/" +"flower/pull/658))" msgstr "" -#: ../../source/ref-changelog.md:943 +#: ../../source/ref-changelog.md:1035 msgid "" "Model parameters can now be initialized on the server-side. Server-side " "parameter initialization works via a new `Strategy` method called " "`initialize_parameters`." msgstr "" -#: ../../source/ref-changelog.md:945 +#: ../../source/ref-changelog.md:1037 msgid "" "Built-in strategies support a new constructor argument called " -"`initial_parameters` to set the initial parameters. Built-in strategies " -"will provide these initial parameters to the server on startup and then " -"delete them to free the memory afterwards." +"`initial_parameters` to set the initial parameters. Built-in strategies will " +"provide these initial parameters to the server on startup and then delete " +"them to free the memory afterwards." msgstr "" -#: ../../source/ref-changelog.md:964 +#: ../../source/ref-changelog.md:1056 msgid "" "If no initial parameters are provided to the strategy, the server will " "continue to use the current behaviour (namely, it will ask one of the " @@ -16934,225 +19090,216 @@ msgid "" "parameters)." msgstr "" -#: ../../source/ref-changelog.md:966 -msgid "Deprecations" -msgstr "" - -#: ../../source/ref-changelog.md:968 +#: ../../source/ref-changelog.md:1060 msgid "" -"Deprecate `flwr.server.strategy.DefaultStrategy` (migrate to " -"`flwr.server.strategy.FedAvg`, which is equivalent)" +"Deprecate `flwr.server.strategy.DefaultStrategy` (migrate to `flwr.server." +"strategy.FedAvg`, which is equivalent)" msgstr "" -#: ../../source/ref-changelog.md:970 +#: ../../source/ref-changelog.md:1062 msgid "v0.14.0 (2021-02-18)" msgstr "" -#: ../../source/ref-changelog.md:974 +#: ../../source/ref-changelog.md:1066 msgid "" "**Generalized** `Client.fit` **and** `Client.evaluate` **return values** " -"([#610](https://github.com/adap/flower/pull/610) " -"[#572](https://github.com/adap/flower/pull/572) " -"[#633](https://github.com/adap/flower/pull/633))" +"([#610](https://github.com/adap/flower/pull/610) [#572](https://github.com/" +"adap/flower/pull/572) [#633](https://github.com/adap/flower/pull/633))" msgstr "" -#: ../../source/ref-changelog.md:976 +#: ../../source/ref-changelog.md:1068 msgid "" -"Clients can now return an additional dictionary mapping `str` keys to " -"values of the following types: `bool`, `bytes`, `float`, `int`, `str`. " -"This means one can return almost arbitrary values from `fit`/`evaluate` " -"and make use of them on the server side!" +"Clients can now return an additional dictionary mapping `str` keys to values " +"of the following types: `bool`, `bytes`, `float`, `int`, `str`. This means " +"one can return almost arbitrary values from `fit`/`evaluate` and make use of " +"them on the server side!" msgstr "" -#: ../../source/ref-changelog.md:978 +#: ../../source/ref-changelog.md:1070 msgid "" -"This improvement also allowed for more consistent return types between " -"`fit` and `evaluate`: `evaluate` should now return a tuple `(float, int, " -"dict)` representing the loss, number of examples, and a dictionary " -"holding arbitrary problem-specific values like accuracy." +"This improvement also allowed for more consistent return types between `fit` " +"and `evaluate`: `evaluate` should now return a tuple `(float, int, dict)` " +"representing the loss, number of examples, and a dictionary holding " +"arbitrary problem-specific values like accuracy." msgstr "" -#: ../../source/ref-changelog.md:980 +#: ../../source/ref-changelog.md:1072 msgid "" -"In case you wondered: this feature is compatible with existing projects, " -"the additional dictionary return value is optional. New code should " -"however migrate to the new return types to be compatible with upcoming " -"Flower releases (`fit`: `List[np.ndarray], int, Dict[str, Scalar]`, " -"`evaluate`: `float, int, Dict[str, Scalar]`). See the example below for " -"details." +"In case you wondered: this feature is compatible with existing projects, the " +"additional dictionary return value is optional. New code should however " +"migrate to the new return types to be compatible with upcoming Flower " +"releases (`fit`: `List[np.ndarray], int, Dict[str, Scalar]`, `evaluate`: " +"`float, int, Dict[str, Scalar]`). See the example below for details." msgstr "" -#: ../../source/ref-changelog.md:982 +#: ../../source/ref-changelog.md:1074 msgid "" "*Code example:* note the additional dictionary return values in both " "`FlwrClient.fit` and `FlwrClient.evaluate`:" msgstr "" -#: ../../source/ref-changelog.md:997 +#: ../../source/ref-changelog.md:1089 msgid "" -"**Generalized** `config` **argument in** `Client.fit` **and** " -"`Client.evaluate` ([#595](https://github.com/adap/flower/pull/595))" +"**Generalized** `config` **argument in** `Client.fit` **and** `Client." +"evaluate` ([#595](https://github.com/adap/flower/pull/595))" msgstr "" -#: ../../source/ref-changelog.md:999 +#: ../../source/ref-changelog.md:1091 msgid "" -"The `config` argument used to be of type `Dict[str, str]`, which means " -"that dictionary values were expected to be strings. The new release " -"generalizes this to enable values of the following types: `bool`, " -"`bytes`, `float`, `int`, `str`." +"The `config` argument used to be of type `Dict[str, str]`, which means that " +"dictionary values were expected to be strings. The new release generalizes " +"this to enable values of the following types: `bool`, `bytes`, `float`, " +"`int`, `str`." msgstr "" -#: ../../source/ref-changelog.md:1001 +#: ../../source/ref-changelog.md:1093 msgid "" "This means one can now pass almost arbitrary values to `fit`/`evaluate` " -"using the `config` dictionary. Yay, no more `str(epochs)` on the server-" -"side and `int(config[\"epochs\"])` on the client side!" +"using the `config` dictionary. Yay, no more `str(epochs)` on the server-side " +"and `int(config[\"epochs\"])` on the client side!" msgstr "" -#: ../../source/ref-changelog.md:1003 +#: ../../source/ref-changelog.md:1095 msgid "" "*Code example:* note that the `config` dictionary now contains non-`str` " "values in both `Client.fit` and `Client.evaluate`:" msgstr "" -#: ../../source/ref-changelog.md:1020 +#: ../../source/ref-changelog.md:1112 msgid "v0.13.0 (2021-01-08)" msgstr "" -#: ../../source/ref-changelog.md:1024 +#: ../../source/ref-changelog.md:1116 msgid "" -"New example: PyTorch From Centralized To Federated " -"([#549](https://github.com/adap/flower/pull/549))" +"New example: PyTorch From Centralized To Federated ([#549](https://github." +"com/adap/flower/pull/549))" msgstr "" -#: ../../source/ref-changelog.md:1025 +#: ../../source/ref-changelog.md:1117 msgid "Improved documentation" msgstr "" -#: ../../source/ref-changelog.md:1026 -msgid "New documentation theme ([#551](https://github.com/adap/flower/pull/551))" +#: ../../source/ref-changelog.md:1118 +msgid "" +"New documentation theme ([#551](https://github.com/adap/flower/pull/551))" msgstr "" -#: ../../source/ref-changelog.md:1027 +#: ../../source/ref-changelog.md:1119 msgid "New API reference ([#554](https://github.com/adap/flower/pull/554))" msgstr "" -#: ../../source/ref-changelog.md:1028 +#: ../../source/ref-changelog.md:1120 msgid "" -"Updated examples documentation " -"([#549](https://github.com/adap/flower/pull/549))" +"Updated examples documentation ([#549](https://github.com/adap/flower/" +"pull/549))" msgstr "" -#: ../../source/ref-changelog.md:1029 +#: ../../source/ref-changelog.md:1121 msgid "" -"Removed obsolete documentation " -"([#548](https://github.com/adap/flower/pull/548))" +"Removed obsolete documentation ([#548](https://github.com/adap/flower/" +"pull/548))" msgstr "" -#: ../../source/ref-changelog.md:1031 +#: ../../source/ref-changelog.md:1123 msgid "Bugfix:" msgstr "" -#: ../../source/ref-changelog.md:1033 +#: ../../source/ref-changelog.md:1125 msgid "" -"`Server.fit` does not disconnect clients when finished, disconnecting the" -" clients is now handled in `flwr.server.start_server` " -"([#553](https://github.com/adap/flower/pull/553) " -"[#540](https://github.com/adap/flower/issues/540))." +"`Server.fit` does not disconnect clients when finished, disconnecting the " +"clients is now handled in `flwr.server.start_server` ([#553](https://github." +"com/adap/flower/pull/553) [#540](https://github.com/adap/flower/issues/540))." msgstr "" -#: ../../source/ref-changelog.md:1035 +#: ../../source/ref-changelog.md:1127 msgid "v0.12.0 (2020-12-07)" msgstr "" -#: ../../source/ref-changelog.md:1037 ../../source/ref-changelog.md:1053 +#: ../../source/ref-changelog.md:1129 ../../source/ref-changelog.md:1145 msgid "Important changes:" msgstr "" -#: ../../source/ref-changelog.md:1039 +#: ../../source/ref-changelog.md:1131 msgid "" -"Added an example for embedded devices " -"([#507](https://github.com/adap/flower/pull/507))" +"Added an example for embedded devices ([#507](https://github.com/adap/flower/" +"pull/507))" msgstr "" -#: ../../source/ref-changelog.md:1040 +#: ../../source/ref-changelog.md:1132 msgid "" -"Added a new NumPyClient (in addition to the existing KerasClient) " -"([#504](https://github.com/adap/flower/pull/504) " -"[#508](https://github.com/adap/flower/pull/508))" +"Added a new NumPyClient (in addition to the existing KerasClient) ([#504]" +"(https://github.com/adap/flower/pull/504) [#508](https://github.com/adap/" +"flower/pull/508))" msgstr "" -#: ../../source/ref-changelog.md:1041 +#: ../../source/ref-changelog.md:1133 msgid "" -"Deprecated `flwr_example` package and started to migrate examples into " -"the top-level `examples` directory " -"([#494](https://github.com/adap/flower/pull/494) " -"[#512](https://github.com/adap/flower/pull/512))" +"Deprecated `flwr_example` package and started to migrate examples into the " +"top-level `examples` directory ([#494](https://github.com/adap/flower/" +"pull/494) [#512](https://github.com/adap/flower/pull/512))" msgstr "" -#: ../../source/ref-changelog.md:1043 +#: ../../source/ref-changelog.md:1135 msgid "v0.11.0 (2020-11-30)" msgstr "" -#: ../../source/ref-changelog.md:1045 +#: ../../source/ref-changelog.md:1137 msgid "Incompatible changes:" msgstr "" -#: ../../source/ref-changelog.md:1047 +#: ../../source/ref-changelog.md:1139 msgid "" -"Renamed strategy methods " -"([#486](https://github.com/adap/flower/pull/486)) to unify the naming of " -"Flower's public APIs. Other public methods/functions (e.g., every method " -"in `Client`, but also `Strategy.evaluate`) do not use the `on_` prefix, " -"which is why we're removing it from the four methods in Strategy. To " -"migrate rename the following `Strategy` methods accordingly:" +"Renamed strategy methods ([#486](https://github.com/adap/flower/pull/486)) " +"to unify the naming of Flower's public APIs. Other public methods/functions " +"(e.g., every method in `Client`, but also `Strategy.evaluate`) do not use " +"the `on_` prefix, which is why we're removing it from the four methods in " +"Strategy. To migrate rename the following `Strategy` methods accordingly:" msgstr "" -#: ../../source/ref-changelog.md:1048 +#: ../../source/ref-changelog.md:1140 msgid "`on_configure_evaluate` => `configure_evaluate`" msgstr "" -#: ../../source/ref-changelog.md:1049 +#: ../../source/ref-changelog.md:1141 msgid "`on_aggregate_evaluate` => `aggregate_evaluate`" msgstr "" -#: ../../source/ref-changelog.md:1050 +#: ../../source/ref-changelog.md:1142 msgid "`on_configure_fit` => `configure_fit`" msgstr "" -#: ../../source/ref-changelog.md:1051 +#: ../../source/ref-changelog.md:1143 msgid "`on_aggregate_fit` => `aggregate_fit`" msgstr "" -#: ../../source/ref-changelog.md:1055 +#: ../../source/ref-changelog.md:1147 msgid "" -"Deprecated `DefaultStrategy` " -"([#479](https://github.com/adap/flower/pull/479)). To migrate use " -"`FedAvg` instead." +"Deprecated `DefaultStrategy` ([#479](https://github.com/adap/flower/" +"pull/479)). To migrate use `FedAvg` instead." msgstr "" -#: ../../source/ref-changelog.md:1056 +#: ../../source/ref-changelog.md:1148 msgid "" -"Simplified examples and baselines " -"([#484](https://github.com/adap/flower/pull/484))." +"Simplified examples and baselines ([#484](https://github.com/adap/flower/" +"pull/484))." msgstr "" -#: ../../source/ref-changelog.md:1057 +#: ../../source/ref-changelog.md:1149 msgid "" -"Removed presently unused `on_conclude_round` from strategy interface " -"([#483](https://github.com/adap/flower/pull/483))." +"Removed presently unused `on_conclude_round` from strategy interface ([#483]" +"(https://github.com/adap/flower/pull/483))." msgstr "" -#: ../../source/ref-changelog.md:1058 +#: ../../source/ref-changelog.md:1150 msgid "" -"Set minimal Python version to 3.6.1 instead of 3.6.9 " -"([#471](https://github.com/adap/flower/pull/471))." +"Set minimal Python version to 3.6.1 instead of 3.6.9 ([#471](https://github." +"com/adap/flower/pull/471))." msgstr "" -#: ../../source/ref-changelog.md:1059 +#: ../../source/ref-changelog.md:1151 msgid "" -"Improved `Strategy` docstrings " -"([#470](https://github.com/adap/flower/pull/470))." +"Improved `Strategy` docstrings ([#470](https://github.com/adap/flower/" +"pull/470))." msgstr "" #: ../../source/ref-example-projects.rst:2 @@ -17161,11 +19308,11 @@ msgstr "" #: ../../source/ref-example-projects.rst:4 msgid "" -"Flower comes with a number of usage examples. The examples demonstrate " -"how Flower can be used to federate different kinds of existing machine " -"learning pipelines, usually leveraging popular machine learning " -"frameworks such as `PyTorch `_ or `TensorFlow " -"`_." +"Flower comes with a number of usage examples. The examples demonstrate how " +"Flower can be used to federate different kinds of existing machine learning " +"pipelines, usually leveraging popular machine learning frameworks such as " +"`PyTorch `_ or `TensorFlow `_." msgstr "" #: ../../source/ref-example-projects.rst:10 @@ -17176,25 +19323,25 @@ msgstr "" #: ../../source/ref-example-projects.rst:14 msgid "" -"The TensorFlow/Keras quickstart example shows CIFAR-10 image " -"classification with MobileNetV2:" +"The TensorFlow/Keras quickstart example shows CIFAR-10 image classification " +"with MobileNetV2:" msgstr "" #: ../../source/ref-example-projects.rst:17 msgid "" -"`Quickstart TensorFlow (Code) " -"`_" +"`Quickstart TensorFlow (Code) `_" msgstr "" #: ../../source/ref-example-projects.rst:18 -msgid ":doc:`Quickstart TensorFlow (Tutorial) `" +msgid "" +":doc:`Quickstart TensorFlow (Tutorial) `" msgstr "" #: ../../source/ref-example-projects.rst:19 msgid "" -"`Quickstart TensorFlow (Blog Post) `_" +"`Quickstart TensorFlow (Blog Post) `_" msgstr "" #: ../../source/ref-example-projects.rst:23 @@ -17204,14 +19351,14 @@ msgstr "" #: ../../source/ref-example-projects.rst:25 msgid "" -"The PyTorch quickstart example shows CIFAR-10 image classification with a" -" simple Convolutional Neural Network:" +"The PyTorch quickstart example shows CIFAR-10 image classification with a " +"simple Convolutional Neural Network:" msgstr "" #: ../../source/ref-example-projects.rst:28 msgid "" -"`Quickstart PyTorch (Code) " -"`_" +"`Quickstart PyTorch (Code) `_" msgstr "" #: ../../source/ref-example-projects.rst:29 @@ -17230,9 +19377,8 @@ msgstr "" #: ../../source/ref-example-projects.rst:37 msgid "" -"`PyTorch: From Centralized To Federated (Code) " -"`_" +"`PyTorch: From Centralized To Federated (Code) `_" msgstr "" #: ../../source/ref-example-projects.rst:38 @@ -17253,14 +19399,15 @@ msgstr "" #: ../../source/ref-example-projects.rst:46 msgid "" -"`Federated Learning on Raspberry Pi and Nvidia Jetson (Code) " -"`_" +"`Federated Learning on Raspberry Pi and Nvidia Jetson (Code) `_" msgstr "" #: ../../source/ref-example-projects.rst:47 msgid "" -"`Federated Learning on Raspberry Pi and Nvidia Jetson (Blog Post) " -"`_" +"`Federated Learning on Raspberry Pi and Nvidia Jetson (Blog Post) `_" msgstr "" #: ../../source/ref-faq.rst:4 @@ -17275,22 +19422,20 @@ msgstr "" #: ../../source/ref-faq.rst:8 msgid "" -"Yes, it can! Flower even comes with a few under-the-hood optimizations to" -" make it work even better on Colab. Here's a quickstart example:" +"Yes, it can! Flower even comes with a few under-the-hood optimizations to " +"make it work even better on Colab. Here's a quickstart example:" msgstr "" #: ../../source/ref-faq.rst:10 msgid "" -"`Flower simulation PyTorch " -"`_" +"`Flower simulation PyTorch `_" msgstr "" #: ../../source/ref-faq.rst:11 msgid "" -"`Flower simulation TensorFlow/Keras " -"`_" +"`Flower simulation TensorFlow/Keras `_" msgstr "" #: ../../source/ref-faq.rst @@ -17300,26 +19445,28 @@ msgstr "" #: ../../source/ref-faq.rst:15 msgid "" "Find the `blog post about federated learning on embedded device here " -"`_" -" and the corresponding `GitHub code example " -"`_." +"`_ " +"and the corresponding `GitHub code example `_." msgstr "" #: ../../source/ref-faq.rst -msgid ":fa:`eye,mr-1` Does Flower support federated learning on Android devices?" +msgid "" +":fa:`eye,mr-1` Does Flower support federated learning on Android devices?" msgstr "" #: ../../source/ref-faq.rst:19 msgid "" -"Yes, it does. Please take a look at our `blog post " -"`_ or check out the code examples:" +"Yes, it does. Please take a look at our `blog post `_ or " +"check out the code examples:" msgstr "" #: ../../source/ref-faq.rst:21 msgid "" -"`Android Kotlin example `_" +"`Android Kotlin example `_" msgstr "" #: ../../source/ref-faq.rst:22 @@ -17338,33 +19485,33 @@ msgstr "" #: ../../source/ref-faq.rst:28 msgid "" -"`Flower meets Nevermined GitHub Repository `_." +"`Flower meets Nevermined GitHub Repository `_." msgstr "" #: ../../source/ref-faq.rst:29 msgid "" -"`Flower meets Nevermined YouTube video " -"`_." +"`Flower meets Nevermined YouTube video `_." msgstr "" #: ../../source/ref-faq.rst:30 msgid "" -"`Flower meets KOSMoS `_." +"`Flower meets KOSMoS `_." msgstr "" #: ../../source/ref-faq.rst:31 msgid "" "`Flower meets Talan blog post `_ ." +"learning-same-mask-different-faces-imen-ayari/?" +"trackingId=971oIlxLQ9%2BA9RB0IQ73XQ%3D%3D>`_ ." msgstr "" #: ../../source/ref-faq.rst:32 msgid "" -"`Flower meets Talan GitHub Repository " -"`_ ." +"`Flower meets Talan GitHub Repository `_ ." msgstr "" #: ../../source/ref-telemetry.md:1 @@ -17373,17 +19520,16 @@ msgstr "" #: ../../source/ref-telemetry.md:3 msgid "" -"The Flower open-source project collects **anonymous** usage metrics to " -"make well-informed decisions to improve Flower. Doing this enables the " -"Flower team to understand how Flower is used and what challenges users " -"might face." +"The Flower open-source project collects **anonymous** usage metrics to make " +"well-informed decisions to improve Flower. Doing this enables the Flower " +"team to understand how Flower is used and what challenges users might face." msgstr "" #: ../../source/ref-telemetry.md:5 msgid "" -"**Flower is a friendly framework for collaborative AI and data science.**" -" Staying true to this statement, Flower makes it easy to disable " -"telemetry for users that do not want to share anonymous usage metrics." +"**Flower is a friendly framework for collaborative AI and data science.** " +"Staying true to this statement, Flower makes it easy to disable telemetry " +"for users that do not want to share anonymous usage metrics." msgstr "" #: ../../source/ref-telemetry.md:7 @@ -17391,35 +19537,34 @@ msgid "Principles" msgstr "" #: ../../source/ref-telemetry.md:9 -msgid "We follow strong principles guarding anonymous usage metrics collection:" +msgid "" +"We follow strong principles guarding anonymous usage metrics collection:" msgstr "" #: ../../source/ref-telemetry.md:11 msgid "" -"**Optional:** You will always be able to disable telemetry; read on to " -"learn “[How to opt-out](#how-to-opt-out)”." +"**Optional:** You will always be able to disable telemetry; read on to learn " +"“[How to opt-out](#how-to-opt-out)”." msgstr "" #: ../../source/ref-telemetry.md:12 msgid "" -"**Anonymous:** The reported usage metrics are anonymous and do not " -"contain any personally identifiable information (PII). See “[Collected " -"metrics](#collected-metrics)” to understand what metrics are being " -"reported." +"**Anonymous:** The reported usage metrics are anonymous and do not contain " +"any personally identifiable information (PII). See “[Collected metrics]" +"(#collected-metrics)” to understand what metrics are being reported." msgstr "" #: ../../source/ref-telemetry.md:13 msgid "" "**Transparent:** You can easily inspect what anonymous metrics are being " -"reported; see the section “[How to inspect what is being reported](#how-" -"to-inspect-what-is-being-reported)”" +"reported; see the section “[How to inspect what is being reported](#how-to-" +"inspect-what-is-being-reported)”" msgstr "" #: ../../source/ref-telemetry.md:14 msgid "" -"**Open for feedback:** You can always reach out to us if you have " -"feedback; see the section “[How to contact us](#how-to-contact-us)” for " -"details." +"**Open for feedback:** You can always reach out to us if you have feedback; " +"see the section “[How to contact us](#how-to-contact-us)” for details." msgstr "" #: ../../source/ref-telemetry.md:16 @@ -17436,9 +19581,9 @@ msgstr "" #: ../../source/ref-telemetry.md:24 msgid "" -"Alternatively, you can export `FLWR_TELEMETRY_ENABLED=0` in, for example," -" `.bashrc` (or whatever configuration file applies to your environment) " -"to disable Flower telemetry permanently." +"Alternatively, you can export `FLWR_TELEMETRY_ENABLED=0` in, for example, `." +"bashrc` (or whatever configuration file applies to your environment) to " +"disable Flower telemetry permanently." msgstr "" #: ../../source/ref-telemetry.md:26 @@ -17451,10 +19596,10 @@ msgstr "" #: ../../source/ref-telemetry.md:30 msgid "" -"**Flower version.** Understand which versions of Flower are currently " -"being used. This helps us to decide whether we should invest effort into " -"releasing a patch version for an older version of Flower or instead use " -"the bandwidth to build new features." +"**Flower version.** Understand which versions of Flower are currently being " +"used. This helps us to decide whether we should invest effort into releasing " +"a patch version for an older version of Flower or instead use the bandwidth " +"to build new features." msgstr "" #: ../../source/ref-telemetry.md:32 @@ -17473,15 +19618,15 @@ msgstr "" #: ../../source/ref-telemetry.md:36 msgid "" -"**Hardware properties.** Understanding the hardware environment that " -"Flower is being used in helps to decide whether we should, for example, " -"put more effort into supporting low-resource environments." +"**Hardware properties.** Understanding the hardware environment that Flower " +"is being used in helps to decide whether we should, for example, put more " +"effort into supporting low-resource environments." msgstr "" #: ../../source/ref-telemetry.md:38 msgid "" -"**Execution mode.** Knowing what execution mode Flower starts in enables " -"us to understand how heavily certain features are being used and better " +"**Execution mode.** Knowing what execution mode Flower starts in enables us " +"to understand how heavily certain features are being used and better " "prioritize based on that." msgstr "" @@ -17489,37 +19634,34 @@ msgstr "" msgid "" "**Cluster.** Flower telemetry assigns a random in-memory cluster ID each " "time a Flower workload starts. This allows us to understand which device " -"types not only start Flower workloads but also successfully complete " -"them." +"types not only start Flower workloads but also successfully complete them." msgstr "" #: ../../source/ref-telemetry.md:42 msgid "" -"**Source.** Flower telemetry tries to store a random source ID in " -"`~/.flwr/source` the first time a telemetry event is generated. The " -"source ID is important to identify whether an issue is recurring or " -"whether an issue is triggered by multiple clusters running concurrently " -"(which often happens in simulation). For example, if a device runs " -"multiple workloads at the same time, and this results in an issue, then, " -"in order to reproduce the issue, multiple workloads must be started at " -"the same time." +"**Source.** Flower telemetry tries to store a random source ID in `~/.flwr/" +"source` the first time a telemetry event is generated. The source ID is " +"important to identify whether an issue is recurring or whether an issue is " +"triggered by multiple clusters running concurrently (which often happens in " +"simulation). For example, if a device runs multiple workloads at the same " +"time, and this results in an issue, then, in order to reproduce the issue, " +"multiple workloads must be started at the same time." msgstr "" #: ../../source/ref-telemetry.md:44 msgid "" -"You may delete the source ID at any time. If you wish for all events " -"logged under a specific source ID to be deleted, you can send a deletion " -"request mentioning the source ID to `telemetry@flower.ai`. All events " -"related to that source ID will then be permanently deleted." +"You may delete the source ID at any time. If you wish for all events logged " +"under a specific source ID to be deleted, you can send a deletion request " +"mentioning the source ID to `telemetry@flower.ai`. All events related to " +"that source ID will then be permanently deleted." msgstr "" #: ../../source/ref-telemetry.md:46 msgid "" -"We will not collect any personally identifiable information. If you think" -" any of the metrics collected could be misused in any way, please [get in" -" touch with us](#how-to-contact-us). We will update this page to reflect " -"any changes to the metrics collected and publish changes in the " -"changelog." +"We will not collect any personally identifiable information. If you think " +"any of the metrics collected could be misused in any way, please [get in " +"touch with us](#how-to-contact-us). We will update this page to reflect any " +"changes to the metrics collected and publish changes in the changelog." msgstr "" #: ../../source/ref-telemetry.md:48 @@ -17536,17 +19678,17 @@ msgstr "" #: ../../source/ref-telemetry.md:52 msgid "" "We wanted to make it very easy for you to inspect what anonymous usage " -"metrics are reported. You can view all the reported telemetry information" -" by setting the environment variable `FLWR_TELEMETRY_LOGGING=1`. Logging " -"is disabled by default. You may use logging independently from " +"metrics are reported. You can view all the reported telemetry information by " +"setting the environment variable `FLWR_TELEMETRY_LOGGING=1`. Logging is " +"disabled by default. You may use logging independently from " "`FLWR_TELEMETRY_ENABLED` so that you can inspect the telemetry feature " "without sending any metrics." msgstr "" #: ../../source/ref-telemetry.md:58 msgid "" -"The inspect Flower telemetry without sending any anonymous usage metrics," -" use both environment variables:" +"The inspect Flower telemetry without sending any anonymous usage metrics, " +"use both environment variables:" msgstr "" #: ../../source/ref-telemetry.md:64 @@ -17563,8 +19705,8 @@ msgstr "" #: ../../source/tutorial-quickstart-android.rst:-1 msgid "" -"Read this Federated Learning quickstart tutorial for creating an Android " -"app using Flower." +"Read this Federated Learning quickstart tutorial for creating an Android app " +"using Flower." msgstr "" #: ../../source/tutorial-quickstart-android.rst:5 @@ -17573,21 +19715,19 @@ msgstr "" #: ../../source/tutorial-quickstart-android.rst:10 msgid "" -"Let's build a federated learning system using TFLite and Flower on " -"Android!" +"Let's build a federated learning system using TFLite and Flower on Android!" msgstr "" #: ../../source/tutorial-quickstart-android.rst:12 msgid "" -"Please refer to the `full code example " -"`_ to learn " -"more." +"Please refer to the `full code example `_ to learn more." msgstr "" #: ../../source/tutorial-quickstart-fastai.rst:-1 msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with FastAI to train a vision model on CIFAR-10." +"Check out this Federated Learning quickstart tutorial for using Flower with " +"FastAI to train a vision model on CIFAR-10." msgstr "" #: ../../source/tutorial-quickstart-fastai.rst:5 @@ -17600,15 +19740,14 @@ msgstr "" #: ../../source/tutorial-quickstart-fastai.rst:12 msgid "" -"Please refer to the `full code example " -"`_ " -"to learn more." +"Please refer to the `full code example `_ to learn more." msgstr "" #: ../../source/tutorial-quickstart-huggingface.rst:-1 msgid "" -"Check out this Federating Learning quickstart tutorial for using Flower " -"with HuggingFace Transformers in order to fine-tune an LLM." +"Check out this Federating Learning quickstart tutorial for using Flower with " +"HuggingFace Transformers in order to fine-tune an LLM." msgstr "" #: ../../source/tutorial-quickstart-huggingface.rst:5 @@ -17617,17 +19756,17 @@ msgstr "" #: ../../source/tutorial-quickstart-huggingface.rst:10 msgid "" -"Let's build a federated learning system using Hugging Face Transformers " -"and Flower!" +"Let's build a federated learning system using Hugging Face Transformers and " +"Flower!" msgstr "" #: ../../source/tutorial-quickstart-huggingface.rst:12 msgid "" -"We will leverage Hugging Face to federate the training of language models" -" over multiple clients using Flower. More specifically, we will fine-tune" -" a pre-trained Transformer model (distilBERT) for sequence classification" -" over a dataset of IMDB ratings. The end goal is to detect if a movie " -"rating is positive or negative." +"We will leverage Hugging Face to federate the training of language models " +"over multiple clients using Flower. More specifically, we will fine-tune a " +"pre-trained Transformer model (distilBERT) for sequence classification over " +"a dataset of IMDB ratings. The end goal is to detect if a movie rating is " +"positive or negative." msgstr "" #: ../../source/tutorial-quickstart-huggingface.rst:18 @@ -17637,9 +19776,8 @@ msgstr "" #: ../../source/tutorial-quickstart-huggingface.rst:20 msgid "" "To follow along this tutorial you will need to install the following " -"packages: :code:`datasets`, :code:`evaluate`, :code:`flwr`, " -":code:`torch`, and :code:`transformers`. This can be done using " -":code:`pip`:" +"packages: :code:`datasets`, :code:`evaluate`, :code:`flwr`, :code:`torch`, " +"and :code:`transformers`. This can be done using :code:`pip`:" msgstr "" #: ../../source/tutorial-quickstart-huggingface.rst:30 @@ -17663,9 +19801,9 @@ msgstr "" #: ../../source/tutorial-quickstart-huggingface.rst:83 msgid "" -"Once we have a way of creating our trainloader and testloader, we can " -"take care of the training and testing. This is very similar to any " -":code:`PyTorch` training or testing loop:" +"Once we have a way of creating our trainloader and testloader, we can take " +"care of the training and testing. This is very similar to any :code:" +"`PyTorch` training or testing loop:" msgstr "" #: ../../source/tutorial-quickstart-huggingface.rst:121 @@ -17674,8 +19812,8 @@ msgstr "" #: ../../source/tutorial-quickstart-huggingface.rst:123 msgid "" -"To create the model itself, we will just load the pre-trained distillBERT" -" model using Hugging Face’s :code:`AutoModelForSequenceClassification` :" +"To create the model itself, we will just load the pre-trained distillBERT " +"model using Hugging Face’s :code:`AutoModelForSequenceClassification` :" msgstr "" #: ../../source/tutorial-quickstart-huggingface.rst:136 @@ -17689,18 +19827,17 @@ msgstr "" #: ../../source/tutorial-quickstart-huggingface.rst:141 msgid "" "To federate our example to multiple clients, we first need to write our " -"Flower client class (inheriting from :code:`flwr.client.NumPyClient`). " -"This is very easy, as our model is a standard :code:`PyTorch` model:" +"Flower client class (inheriting from :code:`flwr.client.NumPyClient`). This " +"is very easy, as our model is a standard :code:`PyTorch` model:" msgstr "" #: ../../source/tutorial-quickstart-huggingface.rst:169 msgid "" "The :code:`get_parameters` function lets the server get the client's " -"parameters. Inversely, the :code:`set_parameters` function allows the " -"server to send its parameters to the client. Finally, the :code:`fit` " -"function trains the model locally for the client, and the " -":code:`evaluate` function tests the model locally and returns the " -"relevant metrics." +"parameters. Inversely, the :code:`set_parameters` function allows the server " +"to send its parameters to the client. Finally, the :code:`fit` function " +"trains the model locally for the client, and the :code:`evaluate` function " +"tests the model locally and returns the relevant metrics." msgstr "" #: ../../source/tutorial-quickstart-huggingface.rst:175 @@ -17709,19 +19846,19 @@ msgstr "" #: ../../source/tutorial-quickstart-huggingface.rst:177 msgid "" -"Now that we have a way to instantiate clients, we need to create our " -"server in order to aggregate the results. Using Flower, this can be done " -"very easily by first choosing a strategy (here, we are using " -":code:`FedAvg`, which will define the global weights as the average of " -"all the clients' weights at each round) and then using the " -":code:`flwr.server.start_server` function:" +"Now that we have a way to instantiate clients, we need to create our server " +"in order to aggregate the results. Using Flower, this can be done very " +"easily by first choosing a strategy (here, we are using :code:`FedAvg`, " +"which will define the global weights as the average of all the clients' " +"weights at each round) and then using the :code:`flwr.server.start_server` " +"function:" msgstr "" #: ../../source/tutorial-quickstart-huggingface.rst:205 msgid "" -"The :code:`weighted_average` function is there to provide a way to " -"aggregate the metrics distributed amongst the clients (basically this " -"allows us to display a nice average accuracy and loss for every round)." +"The :code:`weighted_average` function is there to provide a way to aggregate " +"the metrics distributed amongst the clients (basically this allows us to " +"display a nice average accuracy and loss for every round)." msgstr "" #: ../../source/tutorial-quickstart-huggingface.rst:209 @@ -17740,22 +19877,22 @@ msgstr "" #: ../../source/tutorial-quickstart-huggingface.rst:223 msgid "" -"If you want to check out everything put together, you should check out " -"the `full code example `_ ." +"If you want to check out everything put together, you should check out the " +"`full code example `_ ." msgstr "" #: ../../source/tutorial-quickstart-huggingface.rst:226 msgid "" -"Of course, this is a very basic example, and a lot can be added or " -"modified, it was just to showcase how simply we could federate a Hugging " -"Face workflow using Flower." +"Of course, this is a very basic example, and a lot can be added or modified, " +"it was just to showcase how simply we could federate a Hugging Face workflow " +"using Flower." msgstr "" #: ../../source/tutorial-quickstart-huggingface.rst:229 msgid "" -"Note that in this example we used :code:`PyTorch`, but we could have very" -" well used :code:`TensorFlow`." +"Note that in this example we used :code:`PyTorch`, but we could have very " +"well used :code:`TensorFlow`." msgstr "" #: ../../source/tutorial-quickstart-ios.rst:-1 @@ -17770,38 +19907,38 @@ msgstr "" #: ../../source/tutorial-quickstart-ios.rst:10 msgid "" -"In this tutorial we will learn how to train a Neural Network on MNIST " -"using Flower and CoreML on iOS devices." +"In this tutorial we will learn how to train a Neural Network on MNIST using " +"Flower and CoreML on iOS devices." msgstr "" #: ../../source/tutorial-quickstart-ios.rst:12 msgid "" "First of all, for running the Flower Python server, it is recommended to " -"create a virtual environment and run everything within a :doc:`virtualenv" -" `. For the Flower client " +"create a virtual environment and run everything within a :doc:`virtualenv " +"`. For the Flower client " "implementation in iOS, it is recommended to use Xcode as our IDE." msgstr "" #: ../../source/tutorial-quickstart-ios.rst:15 msgid "" -"Our example consists of one Python *server* and two iPhone *clients* that" -" all have the same model." +"Our example consists of one Python *server* and two iPhone *clients* that " +"all have the same model." msgstr "" #: ../../source/tutorial-quickstart-ios.rst:17 msgid "" -"*Clients* are responsible for generating individual weight updates for " -"the model based on their local datasets. These updates are then sent to " -"the *server* which will aggregate them to produce a better model. " -"Finally, the *server* sends this improved version of the model back to " -"each *client*. A complete cycle of weight updates is called a *round*." +"*Clients* are responsible for generating individual weight updates for the " +"model based on their local datasets. These updates are then sent to the " +"*server* which will aggregate them to produce a better model. Finally, the " +"*server* sends this improved version of the model back to each *client*. A " +"complete cycle of weight updates is called a *round*." msgstr "" #: ../../source/tutorial-quickstart-ios.rst:21 msgid "" "Now that we have a rough idea of what is going on, let's get started to " -"setup our Flower server environment. We first need to install Flower. You" -" can do this by using pip:" +"setup our Flower server environment. We first need to install Flower. You " +"can do this by using pip:" msgstr "" #: ../../source/tutorial-quickstart-ios.rst:27 @@ -17819,21 +19956,20 @@ msgstr "" #: ../../source/tutorial-quickstart-ios.rst:36 msgid "" "Now that we have all our dependencies installed, let's run a simple " -"distributed training using CoreML as our local training pipeline and " -"MNIST as our dataset. For simplicity reasons we will use the complete " -"Flower client with CoreML, that has been implemented and stored inside " -"the Swift SDK. The client implementation can be seen below:" +"distributed training using CoreML as our local training pipeline and MNIST " +"as our dataset. For simplicity reasons we will use the complete Flower " +"client with CoreML, that has been implemented and stored inside the Swift " +"SDK. The client implementation can be seen below:" msgstr "" #: ../../source/tutorial-quickstart-ios.rst:72 msgid "" -"Let's create a new application project in Xcode and add :code:`flwr` as a" -" dependency in your project. For our application, we will store the logic" -" of our app in :code:`FLiOSModel.swift` and the UI elements in " -":code:`ContentView.swift`. We will focus more on :code:`FLiOSModel.swift`" -" in this quickstart. Please refer to the `full code example " -"`_ to learn more " -"about the app." +"Let's create a new application project in Xcode and add :code:`flwr` as a " +"dependency in your project. For our application, we will store the logic of " +"our app in :code:`FLiOSModel.swift` and the UI elements in :code:" +"`ContentView.swift`. We will focus more on :code:`FLiOSModel.swift` in this " +"quickstart. Please refer to the `full code example `_ to learn more about the app." msgstr "" #: ../../source/tutorial-quickstart-ios.rst:75 @@ -17843,22 +19979,21 @@ msgstr "" #: ../../source/tutorial-quickstart-ios.rst:83 msgid "" "Then add the mlmodel to the project simply by drag-and-drop, the mlmodel " -"will be bundled inside the application during deployment to your iOS " -"device. We need to pass the url to access mlmodel and run CoreML machine " -"learning processes, it can be retrieved by calling the function " -":code:`Bundle.main.url`. For the MNIST dataset, we need to preprocess it " -"into :code:`MLBatchProvider` object. The preprocessing is done inside " -":code:`DataLoader.swift`." +"will be bundled inside the application during deployment to your iOS device. " +"We need to pass the url to access mlmodel and run CoreML machine learning " +"processes, it can be retrieved by calling the function :code:`Bundle.main." +"url`. For the MNIST dataset, we need to preprocess it into :code:" +"`MLBatchProvider` object. The preprocessing is done inside :code:`DataLoader." +"swift`." msgstr "" #: ../../source/tutorial-quickstart-ios.rst:99 msgid "" -"Since CoreML does not allow the model parameters to be seen before " -"training, and accessing the model parameters during or after the training" -" can only be done by specifying the layer name, we need to know this " -"information beforehand, through looking at the model specification, which" -" are written as proto files. The implementation can be seen in " -":code:`MLModelInspect`." +"Since CoreML does not allow the model parameters to be seen before training, " +"and accessing the model parameters during or after the training can only be " +"done by specifying the layer name, we need to know this information " +"beforehand, through looking at the model specification, which are written as " +"proto files. The implementation can be seen in :code:`MLModelInspect`." msgstr "" #: ../../source/tutorial-quickstart-ios.rst:102 @@ -17869,18 +20004,18 @@ msgstr "" #: ../../source/tutorial-quickstart-ios.rst:117 msgid "" -"Then start the Flower gRPC client and start communicating to the server " -"by passing our Flower client to the function :code:`startFlwrGRPC`." +"Then start the Flower gRPC client and start communicating to the server by " +"passing our Flower client to the function :code:`startFlwrGRPC`." msgstr "" #: ../../source/tutorial-quickstart-ios.rst:124 msgid "" -"That's it for the client. We only have to implement :code:`Client` or " -"call the provided :code:`MLFlwrClient` and call :code:`startFlwrGRPC()`. " -"The attribute :code:`hostname` and :code:`port` tells the client which " -"server to connect to. This can be done by entering the hostname and port " -"in the application before clicking the start button to start the " -"federated learning process." +"That's it for the client. We only have to implement :code:`Client` or call " +"the provided :code:`MLFlwrClient` and call :code:`startFlwrGRPC()`. The " +"attribute :code:`hostname` and :code:`port` tells the client which server to " +"connect to. This can be done by entering the hostname and port in the " +"application before clicking the start button to start the federated learning " +"process." msgstr "" #: ../../source/tutorial-quickstart-ios.rst:129 @@ -17896,8 +20031,8 @@ msgstr "" #: ../../source/tutorial-quickstart-tensorflow.rst:100 msgid "" "For simple workloads we can start a Flower server and leave all the " -"configuration possibilities at their default values. In a file named " -":code:`server.py`, import Flower and start the server:" +"configuration possibilities at their default values. In a file named :code:" +"`server.py`, import Flower and start the server:" msgstr "" #: ../../source/tutorial-quickstart-ios.rst:142 @@ -17913,32 +20048,31 @@ msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:525 msgid "" "With both client and server ready, we can now run everything and see " -"federated learning in action. FL systems usually have a server and " -"multiple clients. We therefore have to start the server first:" +"federated learning in action. FL systems usually have a server and multiple " +"clients. We therefore have to start the server first:" msgstr "" #: ../../source/tutorial-quickstart-ios.rst:152 msgid "" -"Once the server is running we can start the clients in different " -"terminals. Build and run the client through your Xcode, one through Xcode" -" Simulator and the other by deploying it to your iPhone. To see more " -"about how to deploy your app to iPhone or Simulator visit `here " -"`_." +"Once the server is running we can start the clients in different terminals. " +"Build and run the client through your Xcode, one through Xcode Simulator and " +"the other by deploying it to your iPhone. To see more about how to deploy " +"your app to iPhone or Simulator visit `here `_." msgstr "" #: ../../source/tutorial-quickstart-ios.rst:156 msgid "" "Congratulations! You've successfully built and run your first federated " -"learning system in your ios device. The full `source code " -"`_ for this " -"example can be found in :code:`examples/ios`." +"learning system in your ios device. The full `source code `_ for this example can be found in :" +"code:`examples/ios`." msgstr "" #: ../../source/tutorial-quickstart-jax.rst:-1 msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with Jax to train a linear regression model on a scikit-learn dataset." +"Check out this Federated Learning quickstart tutorial for using Flower with " +"Jax to train a linear regression model on a scikit-learn dataset." msgstr "" #: ../../source/tutorial-quickstart-jax.rst:5 @@ -17947,8 +20081,8 @@ msgstr "" #: ../../source/tutorial-quickstart-pandas.rst:-1 msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with Pandas to perform Federated Analytics." +"Check out this Federated Learning quickstart tutorial for using Flower with " +"Pandas to perform Federated Analytics." msgstr "" #: ../../source/tutorial-quickstart-pandas.rst:5 @@ -17961,45 +20095,44 @@ msgstr "" #: ../../source/tutorial-quickstart-pandas.rst:12 msgid "" -"Please refer to the `full code example " -"`_ " -"to learn more." +"Please refer to the `full code example `_ to learn more." msgstr "" #: ../../source/tutorial-quickstart-pytorch.rst:-1 msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with PyTorch to train a CNN model on MNIST." +"Check out this Federated Learning quickstart tutorial for using Flower with " +"PyTorch to train a CNN model on MNIST." msgstr "" #: ../../source/tutorial-quickstart-pytorch.rst:13 msgid "" -"In this tutorial we will learn how to train a Convolutional Neural " -"Network on CIFAR10 using Flower and PyTorch." +"In this tutorial we will learn how to train a Convolutional Neural Network " +"on CIFAR10 using Flower and PyTorch." msgstr "" #: ../../source/tutorial-quickstart-pytorch.rst:15 #: ../../source/tutorial-quickstart-xgboost.rst:39 msgid "" "First of all, it is recommended to create a virtual environment and run " -"everything within a :doc:`virtualenv `." +"everything within a :doc:`virtualenv `." msgstr "" #: ../../source/tutorial-quickstart-pytorch.rst:17 #: ../../source/tutorial-quickstart-scikitlearn.rst:14 msgid "" -"Our example consists of one *server* and two *clients* all having the " -"same model." +"Our example consists of one *server* and two *clients* all having the same " +"model." msgstr "" #: ../../source/tutorial-quickstart-pytorch.rst:19 msgid "" -"*Clients* are responsible for generating individual weight-updates for " -"the model based on their local datasets. These updates are then sent to " -"the *server* which will aggregate them to produce a better model. " -"Finally, the *server* sends this improved version of the model back to " -"each *client*. A complete cycle of weight updates is called a *round*." +"*Clients* are responsible for generating individual weight-updates for the " +"model based on their local datasets. These updates are then sent to the " +"*server* which will aggregate them to produce a better model. Finally, the " +"*server* sends this improved version of the model back to each *client*. A " +"complete cycle of weight updates is called a *round*." msgstr "" #: ../../source/tutorial-quickstart-pytorch.rst:23 @@ -18010,16 +20143,15 @@ msgstr "" #: ../../source/tutorial-quickstart-pytorch.rst:29 msgid "" -"Since we want to use PyTorch to solve a computer vision task, let's go " -"ahead and install PyTorch and the **torchvision** library:" +"Since we want to use PyTorch to solve a computer vision task, let's go ahead " +"and install PyTorch and the **torchvision** library:" msgstr "" #: ../../source/tutorial-quickstart-pytorch.rst:39 msgid "" "Now that we have all our dependencies installed, let's run a simple " -"distributed training with two clients and one server. Our training " -"procedure and network architecture are based on PyTorch's `Deep Learning " -"with PyTorch " +"distributed training with two clients and one server. Our training procedure " +"and network architecture are based on PyTorch's `Deep Learning with PyTorch " "`_." msgstr "" @@ -18036,33 +20168,33 @@ msgstr "" #: ../../source/tutorial-quickstart-pytorch.rst:62 msgid "" "We use PyTorch to load CIFAR10, a popular colored image classification " -"dataset for machine learning. The PyTorch :code:`DataLoader()` downloads " -"the training and test data that are then normalized." +"dataset for machine learning. The PyTorch :code:`DataLoader()` downloads the " +"training and test data that are then normalized." msgstr "" #: ../../source/tutorial-quickstart-pytorch.rst:78 msgid "" -"Define the loss and optimizer with PyTorch. The training of the dataset " -"is done by looping over the dataset, measure the corresponding loss and " +"Define the loss and optimizer with PyTorch. The training of the dataset is " +"done by looping over the dataset, measure the corresponding loss and " "optimize it." msgstr "" #: ../../source/tutorial-quickstart-pytorch.rst:94 msgid "" -"Define then the validation of the machine learning network. We loop over" -" the test set and measure the loss and accuracy of the test set." +"Define then the validation of the machine learning network. We loop over " +"the test set and measure the loss and accuracy of the test set." msgstr "" #: ../../source/tutorial-quickstart-pytorch.rst:113 msgid "" -"After defining the training and testing of a PyTorch machine learning " -"model, we use the functions for the Flower clients." +"After defining the training and testing of a PyTorch machine learning model, " +"we use the functions for the Flower clients." msgstr "" #: ../../source/tutorial-quickstart-pytorch.rst:115 msgid "" -"The Flower clients will use a simple CNN adapted from 'PyTorch: A 60 " -"Minute Blitz':" +"The Flower clients will use a simple CNN adapted from 'PyTorch: A 60 Minute " +"Blitz':" msgstr "" #: ../../source/tutorial-quickstart-pytorch.rst:142 @@ -18074,20 +20206,19 @@ msgstr "" #: ../../source/tutorial-quickstart-pytorch.rst:144 #: ../../source/tutorial-quickstart-tensorflow.rst:54 msgid "" -"The Flower server interacts with clients through an interface called " -":code:`Client`. When the server selects a particular client for training," -" it sends training instructions over the network. The client receives " -"those instructions and calls one of the :code:`Client` methods to run " -"your code (i.e., to train the neural network we defined earlier)." +"The Flower server interacts with clients through an interface called :code:" +"`Client`. When the server selects a particular client for training, it sends " +"training instructions over the network. The client receives those " +"instructions and calls one of the :code:`Client` methods to run your code (i." +"e., to train the neural network we defined earlier)." msgstr "" #: ../../source/tutorial-quickstart-pytorch.rst:150 msgid "" -"Flower provides a convenience class called :code:`NumPyClient` which " -"makes it easier to implement the :code:`Client` interface when your " -"workload uses PyTorch. Implementing :code:`NumPyClient` usually means " -"defining the following methods (:code:`set_parameters` is optional " -"though):" +"Flower provides a convenience class called :code:`NumPyClient` which makes " +"it easier to implement the :code:`Client` interface when your workload uses " +"PyTorch. Implementing :code:`NumPyClient` usually means defining the " +"following methods (:code:`set_parameters` is optional though):" msgstr "" #: ../../source/tutorial-quickstart-pytorch.rst:156 @@ -18103,8 +20234,7 @@ msgstr "" #: ../../source/tutorial-quickstart-pytorch.rst:158 #: ../../source/tutorial-quickstart-scikitlearn.rst:121 msgid "" -"update the local model weights with the parameters received from the " -"server" +"update the local model weights with the parameters received from the server" msgstr "" #: ../../source/tutorial-quickstart-pytorch.rst:160 @@ -18134,22 +20264,22 @@ msgstr "" #: ../../source/tutorial-quickstart-pytorch.rst:189 #: ../../source/tutorial-quickstart-tensorflow.rst:82 msgid "" -"We can now create an instance of our class :code:`CifarClient` and add " -"one line to actually run this client:" +"We can now create an instance of our class :code:`CifarClient` and add one " +"line to actually run this client:" msgstr "" #: ../../source/tutorial-quickstart-pytorch.rst:196 #: ../../source/tutorial-quickstart-tensorflow.rst:90 msgid "" -"That's it for the client. We only have to implement :code:`Client` or " -":code:`NumPyClient` and call :code:`fl.client.start_client()`. If you " -"implement a client of type :code:`NumPyClient` you'll need to first call " -"its :code:`to_client()` method. The string :code:`\"[::]:8080\"` tells " -"the client which server to connect to. In our case we can run the server " -"and the client on the same machine, therefore we use " -":code:`\"[::]:8080\"`. If we run a truly federated workload with the " -"server and clients running on different machines, all that needs to " -"change is the :code:`server_address` we point the client at." +"That's it for the client. We only have to implement :code:`Client` or :code:" +"`NumPyClient` and call :code:`fl.client.start_client()`. If you implement a " +"client of type :code:`NumPyClient` you'll need to first call its :code:" +"`to_client()` method. The string :code:`\"[::]:8080\"` tells the client " +"which server to connect to. In our case we can run the server and the client " +"on the same machine, therefore we use :code:`\"[::]:8080\"`. If we run a " +"truly federated workload with the server and clients running on different " +"machines, all that needs to change is the :code:`server_address` we point " +"the client at." msgstr "" #: ../../source/tutorial-quickstart-pytorch.rst:226 @@ -18157,8 +20287,8 @@ msgstr "" #: ../../source/tutorial-quickstart-tensorflow.rst:122 #: ../../source/tutorial-quickstart-xgboost.rst:533 msgid "" -"Once the server is running we can start the clients in different " -"terminals. Open a new terminal and start the first client:" +"Once the server is running we can start the clients in different terminals. " +"Open a new terminal and start the first client:" msgstr "" #: ../../source/tutorial-quickstart-pytorch.rst:233 @@ -18172,24 +20302,22 @@ msgstr "" #: ../../source/tutorial-quickstart-scikitlearn.rst:252 #: ../../source/tutorial-quickstart-xgboost.rst:546 msgid "" -"Each client will have its own dataset. You should now see how the " -"training does in the very first terminal (the one that started the " -"server):" +"Each client will have its own dataset. You should now see how the training " +"does in the very first terminal (the one that started the server):" msgstr "" #: ../../source/tutorial-quickstart-pytorch.rst:271 msgid "" "Congratulations! You've successfully built and run your first federated " -"learning system. The full `source code " -"`_ for this example can be found in :code:`examples" -"/quickstart-pytorch`." +"learning system. The full `source code `_ for this example can be found " +"in :code:`examples/quickstart-pytorch`." msgstr "" #: ../../source/tutorial-quickstart-pytorch-lightning.rst:-1 msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with PyTorch Lightning to train an Auto Encoder model on MNIST." +"Check out this Federated Learning quickstart tutorial for using Flower with " +"PyTorch Lightning to train an Auto Encoder model on MNIST." msgstr "" #: ../../source/tutorial-quickstart-pytorch-lightning.rst:5 @@ -18198,21 +20326,20 @@ msgstr "" #: ../../source/tutorial-quickstart-pytorch-lightning.rst:10 msgid "" -"Let's build a horizontal federated learning system using PyTorch " -"Lightning and Flower!" +"Let's build a horizontal federated learning system using PyTorch Lightning " +"and Flower!" msgstr "" #: ../../source/tutorial-quickstart-pytorch-lightning.rst:12 msgid "" -"Please refer to the `full code example " -"`_ to learn more." +"Please refer to the `full code example `_ to learn more." msgstr "" #: ../../source/tutorial-quickstart-scikitlearn.rst:-1 msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with scikit-learn to train a linear regression model." +"Check out this Federated Learning quickstart tutorial for using Flower with " +"scikit-learn to train a linear regression model." msgstr "" #: ../../source/tutorial-quickstart-scikitlearn.rst:5 @@ -18221,24 +20348,23 @@ msgstr "" #: ../../source/tutorial-quickstart-scikitlearn.rst:10 msgid "" -"In this tutorial, we will learn how to train a :code:`Logistic " -"Regression` model on MNIST using Flower and scikit-learn." +"In this tutorial, we will learn how to train a :code:`Logistic Regression` " +"model on MNIST using Flower and scikit-learn." msgstr "" #: ../../source/tutorial-quickstart-scikitlearn.rst:12 msgid "" -"It is recommended to create a virtual environment and run everything " -"within this :doc:`virtualenv `." +"It is recommended to create a virtual environment and run everything within " +"this :doc:`virtualenv `." msgstr "" #: ../../source/tutorial-quickstart-scikitlearn.rst:16 msgid "" -"*Clients* are responsible for generating individual model parameter " -"updates for the model based on their local datasets. These updates are " -"then sent to the *server* which will aggregate them to produce an updated" -" global model. Finally, the *server* sends this improved version of the " -"model back to each *client*. A complete cycle of parameters updates is " -"called a *round*." +"*Clients* are responsible for generating individual model parameter updates " +"for the model based on their local datasets. These updates are then sent to " +"the *server* which will aggregate them to produce an updated global model. " +"Finally, the *server* sends this improved version of the model back to each " +"*client*. A complete cycle of parameters updates is called a *round*." msgstr "" #: ../../source/tutorial-quickstart-scikitlearn.rst:20 @@ -18259,10 +20385,10 @@ msgstr "" msgid "" "Now that we have all our dependencies installed, let's run a simple " "distributed training with two clients and one server. However, before " -"setting up the client and server, we will define all functionalities that" -" we need for our federated learning setup within :code:`utils.py`. The " -":code:`utils.py` contains different functions defining all the machine " -"learning basics:" +"setting up the client and server, we will define all functionalities that we " +"need for our federated learning setup within :code:`utils.py`. The :code:" +"`utils.py` contains different functions defining all the machine learning " +"basics:" msgstr "" #: ../../source/tutorial-quickstart-scikitlearn.rst:45 @@ -18291,46 +20417,44 @@ msgstr "" #: ../../source/tutorial-quickstart-scikitlearn.rst:52 msgid "" -"Please check out :code:`utils.py` `here " -"`_ for more details. The pre-defined functions are used in" -" the :code:`client.py` and imported. The :code:`client.py` also requires " -"to import several packages such as Flower and scikit-learn:" +"Please check out :code:`utils.py` `here `_ for more details. The pre-" +"defined functions are used in the :code:`client.py` and imported. The :code:" +"`client.py` also requires to import several packages such as Flower and " +"scikit-learn:" msgstr "" #: ../../source/tutorial-quickstart-scikitlearn.rst:67 msgid "" -"Prior to local training, we need to load the MNIST dataset, a popular " -"image classification dataset of handwritten digits for machine learning, " -"and partition the dataset for FL. This can be conveniently achieved using" -" `Flower Datasets `_. The " -":code:`FederatedDataset.load_partition()` method loads the partitioned " -"training set for each partition ID defined in the :code:`--partition-id` " -"argument." +"Prior to local training, we need to load the MNIST dataset, a popular image " +"classification dataset of handwritten digits for machine learning, and " +"partition the dataset for FL. This can be conveniently achieved using " +"`Flower Datasets `_. The :code:" +"`FederatedDataset.load_partition()` method loads the partitioned training " +"set for each partition ID defined in the :code:`--partition-id` argument." msgstr "" #: ../../source/tutorial-quickstart-scikitlearn.rst:95 msgid "" -"Next, the logistic regression model is defined and initialized with " -":code:`utils.set_initial_params()`." +"Next, the logistic regression model is defined and initialized with :code:" +"`utils.set_initial_params()`." msgstr "" #: ../../source/tutorial-quickstart-scikitlearn.rst:107 msgid "" -"The Flower server interacts with clients through an interface called " -":code:`Client`. When the server selects a particular client for training," -" it sends training instructions over the network. The client receives " -"those instructions and calls one of the :code:`Client` methods to run " -"your code (i.e., to fit the logistic regression we defined earlier)." +"The Flower server interacts with clients through an interface called :code:" +"`Client`. When the server selects a particular client for training, it sends " +"training instructions over the network. The client receives those " +"instructions and calls one of the :code:`Client` methods to run your code (i." +"e., to fit the logistic regression we defined earlier)." msgstr "" #: ../../source/tutorial-quickstart-scikitlearn.rst:113 msgid "" -"Flower provides a convenience class called :code:`NumPyClient` which " -"makes it easier to implement the :code:`Client` interface when your " -"workload uses scikit-learn. Implementing :code:`NumPyClient` usually " -"means defining the following methods (:code:`set_parameters` is optional " -"though):" +"Flower provides a convenience class called :code:`NumPyClient` which makes " +"it easier to implement the :code:`Client` interface when your workload uses " +"scikit-learn. Implementing :code:`NumPyClient` usually means defining the " +"following methods (:code:`set_parameters` is optional though):" msgstr "" #: ../../source/tutorial-quickstart-scikitlearn.rst:122 @@ -18343,28 +20467,28 @@ msgstr "" #: ../../source/tutorial-quickstart-scikitlearn.rst:153 msgid "" -"We can now create an instance of our class :code:`MnistClient` and add " -"one line to actually run this client:" +"We can now create an instance of our class :code:`MnistClient` and add one " +"line to actually run this client:" msgstr "" #: ../../source/tutorial-quickstart-scikitlearn.rst:160 msgid "" -"That's it for the client. We only have to implement :code:`Client` or " -":code:`NumPyClient` and call :code:`fl.client.start_client()`. If you " -"implement a client of type :code:`NumPyClient` you'll need to first call " -"its :code:`to_client()` method. The string :code:`\"0.0.0.0:8080\"` tells" -" the client which server to connect to. In our case we can run the server" -" and the client on the same machine, therefore we use " -":code:`\"0.0.0.0:8080\"`. If we run a truly federated workload with the " -"server and clients running on different machines, all that needs to " -"change is the :code:`server_address` we pass to the client." +"That's it for the client. We only have to implement :code:`Client` or :code:" +"`NumPyClient` and call :code:`fl.client.start_client()`. If you implement a " +"client of type :code:`NumPyClient` you'll need to first call its :code:" +"`to_client()` method. The string :code:`\"0.0.0.0:8080\"` tells the client " +"which server to connect to. In our case we can run the server and the client " +"on the same machine, therefore we use :code:`\"0.0.0.0:8080\"`. If we run a " +"truly federated workload with the server and clients running on different " +"machines, all that needs to change is the :code:`server_address` we pass to " +"the client." msgstr "" #: ../../source/tutorial-quickstart-scikitlearn.rst:169 msgid "" "The following Flower server is a little bit more advanced and returns an " -"evaluation function for the server-side evaluation. First, we import " -"again all required libraries such as Flower and scikit-learn." +"evaluation function for the server-side evaluation. First, we import again " +"all required libraries such as Flower and scikit-learn." msgstr "" #: ../../source/tutorial-quickstart-scikitlearn.rst:172 @@ -18373,46 +20497,44 @@ msgstr "" #: ../../source/tutorial-quickstart-scikitlearn.rst:185 msgid "" -"The number of federated learning rounds is set in :code:`fit_round()` and" -" the evaluation is defined in :code:`get_evaluate_fn()`. The evaluation " +"The number of federated learning rounds is set in :code:`fit_round()` and " +"the evaluation is defined in :code:`get_evaluate_fn()`. The evaluation " "function is called after each federated learning round and gives you " -"information about loss and accuracy. Note that we also make use of Flower" -" Datasets here to load the test split of the MNIST dataset for server-" -"side evaluation." +"information about loss and accuracy. Note that we also make use of Flower " +"Datasets here to load the test split of the MNIST dataset for server-side " +"evaluation." msgstr "" #: ../../source/tutorial-quickstart-scikitlearn.rst:213 msgid "" -"The :code:`main` contains the server-side parameter initialization " -":code:`utils.set_initial_params()` as well as the aggregation strategy " -":code:`fl.server.strategy:FedAvg()`. The strategy is the default one, " -"federated averaging (or FedAvg), with two clients and evaluation after " -"each federated learning round. The server can be started with the command" -" :code:`fl.server.start_server(server_address=\"0.0.0.0:8080\", " -"strategy=strategy, config=fl.server.ServerConfig(num_rounds=3))`." +"The :code:`main` contains the server-side parameter initialization :code:" +"`utils.set_initial_params()` as well as the aggregation strategy :code:`fl." +"server.strategy:FedAvg()`. The strategy is the default one, federated " +"averaging (or FedAvg), with two clients and evaluation after each federated " +"learning round. The server can be started with the command :code:`fl.server." +"start_server(server_address=\"0.0.0.0:8080\", strategy=strategy, config=fl." +"server.ServerConfig(num_rounds=3))`." msgstr "" #: ../../source/tutorial-quickstart-scikitlearn.rst:232 msgid "" "With both client and server ready, we can now run everything and see " "federated learning in action. Federated learning systems usually have a " -"server and multiple clients. We, therefore, have to start the server " -"first:" +"server and multiple clients. We, therefore, have to start the server first:" msgstr "" #: ../../source/tutorial-quickstart-scikitlearn.rst:286 msgid "" "Congratulations! You've successfully built and run your first federated " -"learning system. The full `source code " -"`_ for this example can be found in :code:`examples/sklearn-logreg-" -"mnist`." +"learning system. The full `source code `_ for this example can be found in :code:" +"`examples/sklearn-logreg-mnist`." msgstr "" #: ../../source/tutorial-quickstart-tensorflow.rst:-1 msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with TensorFlow to train a MobilNetV2 model on CIFAR-10." +"Check out this Federated Learning quickstart tutorial for using Flower with " +"TensorFlow to train a MobilNetV2 model on CIFAR-10." msgstr "" #: ../../source/tutorial-quickstart-tensorflow.rst:5 @@ -18429,8 +20551,8 @@ msgstr "" #: ../../source/tutorial-quickstart-tensorflow.rst:21 msgid "" -"Since we want to use the Keras API of TensorFlow (TF), we have to install" -" TF as well:" +"Since we want to use the Keras API of TensorFlow (TF), we have to install TF " +"as well:" msgstr "" #: ../../source/tutorial-quickstart-tensorflow.rst:31 @@ -18439,25 +20561,24 @@ msgstr "" #: ../../source/tutorial-quickstart-tensorflow.rst:38 msgid "" -"We use the Keras utilities of TF to load CIFAR10, a popular colored image" -" classification dataset for machine learning. The call to " -":code:`tf.keras.datasets.cifar10.load_data()` downloads CIFAR10, caches " -"it locally, and then returns the entire training and test set as NumPy " -"ndarrays." +"We use the Keras utilities of TF to load CIFAR10, a popular colored image " +"classification dataset for machine learning. The call to :code:`tf.keras." +"datasets.cifar10.load_data()` downloads CIFAR10, caches it locally, and then " +"returns the entire training and test set as NumPy ndarrays." msgstr "" #: ../../source/tutorial-quickstart-tensorflow.rst:47 msgid "" -"Next, we need a model. For the purpose of this tutorial, we use " -"MobilNetV2 with 10 output classes:" +"Next, we need a model. For the purpose of this tutorial, we use MobilNetV2 " +"with 10 output classes:" msgstr "" #: ../../source/tutorial-quickstart-tensorflow.rst:60 msgid "" -"Flower provides a convenience class called :code:`NumPyClient` which " -"makes it easier to implement the :code:`Client` interface when your " -"workload uses Keras. The :code:`NumPyClient` interface defines three " -"methods which can be implemented in the following way:" +"Flower provides a convenience class called :code:`NumPyClient` which makes " +"it easier to implement the :code:`Client` interface when your workload uses " +"Keras. The :code:`NumPyClient` interface defines three methods which can be " +"implemented in the following way:" msgstr "" #: ../../source/tutorial-quickstart-tensorflow.rst:135 @@ -18466,23 +20587,22 @@ msgstr "" #: ../../source/tutorial-quickstart-tensorflow.rst:137 msgid "" -"You should now see how the training does in the very first terminal (the " -"one that started the server):" +"You should now see how the training does in the very first terminal (the one " +"that started the server):" msgstr "" #: ../../source/tutorial-quickstart-tensorflow.rst:169 msgid "" "Congratulations! You've successfully built and run your first federated " -"learning system. The full `source code " -"`_ for this can be found in :code:`examples" -"/quickstart-tensorflow/client.py`." +"learning system. The full `source code `_ for this can be found in :" +"code:`examples/quickstart-tensorflow/client.py`." msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:-1 msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with XGBoost to train classification models on trees." +"Check out this Federated Learning quickstart tutorial for using Flower with " +"XGBoost to train classification models on trees." msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:5 @@ -18496,18 +20616,17 @@ msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:16 msgid "" "EXtreme Gradient Boosting (**XGBoost**) is a robust and efficient " -"implementation of gradient-boosted decision tree (**GBDT**), that " -"maximises the computational boundaries for boosted tree methods. It's " -"primarily designed to enhance both the performance and computational " -"speed of machine learning models. In XGBoost, trees are constructed " -"concurrently, unlike the sequential approach taken by GBDT." +"implementation of gradient-boosted decision tree (**GBDT**), that maximises " +"the computational boundaries for boosted tree methods. It's primarily " +"designed to enhance both the performance and computational speed of machine " +"learning models. In XGBoost, trees are constructed concurrently, unlike the " +"sequential approach taken by GBDT." msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:20 msgid "" "Often, for tabular data on medium-sized datasets with fewer than 10k " -"training examples, XGBoost surpasses the results of deep learning " -"techniques." +"training examples, XGBoost surpasses the results of deep learning techniques." msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:23 @@ -18517,30 +20636,30 @@ msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:25 msgid "" "Indeed, as the demand for data privacy and decentralized learning grows, " -"there's an increasing requirement to implement federated XGBoost systems " -"for specialised applications, like survival analysis and financial fraud " +"there's an increasing requirement to implement federated XGBoost systems for " +"specialised applications, like survival analysis and financial fraud " "detection." msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:27 msgid "" -"Federated learning ensures that raw data remains on the local device, " -"making it an attractive approach for sensitive domains where data " -"security and privacy are paramount. Given the robustness and efficiency " -"of XGBoost, combining it with federated learning offers a promising " -"solution for these specific challenges." +"Federated learning ensures that raw data remains on the local device, making " +"it an attractive approach for sensitive domains where data security and " +"privacy are paramount. Given the robustness and efficiency of XGBoost, " +"combining it with federated learning offers a promising solution for these " +"specific challenges." msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:30 msgid "" "In this tutorial we will learn how to train a federated XGBoost model on " "HIGGS dataset using Flower and :code:`xgboost` package. We use a simple " -"example (`full code xgboost-quickstart " -"`_)" -" with two *clients* and one *server* to demonstrate how federated XGBoost" -" works, and then we dive into a more complex example (`full code xgboost-" -"comprehensive `_) to run various experiments." +"example (`full code xgboost-quickstart `_) with two *clients* and one *server* to " +"demonstrate how federated XGBoost works, and then we dive into a more " +"complex example (`full code xgboost-comprehensive `_) to run various " +"experiments." msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:37 @@ -18561,16 +20680,16 @@ msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:57 msgid "" -"*Clients* are responsible for generating individual weight-updates for " -"the model based on their local datasets. Now that we have all our " -"dependencies installed, let's run a simple distributed training with two " -"clients and one server." +"*Clients* are responsible for generating individual weight-updates for the " +"model based on their local datasets. Now that we have all our dependencies " +"installed, let's run a simple distributed training with two clients and one " +"server." msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:60 msgid "" -"In a file called :code:`client.py`, import xgboost, Flower, Flower " -"Datasets and other related functions:" +"In a file called :code:`client.py`, import xgboost, Flower, Flower Datasets " +"and other related functions:" msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:87 @@ -18579,15 +20698,15 @@ msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:89 msgid "" -"Prior to local training, we require loading the HIGGS dataset from Flower" -" Datasets and conduct data partitioning for FL:" +"Prior to local training, we require loading the HIGGS dataset from Flower " +"Datasets and conduct data partitioning for FL:" msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:102 msgid "" "In this example, we split the dataset into two partitions with uniform " -"distribution (:code:`IidPartitioner(num_partitions=2)`). Then, we load " -"the partition for the given client based on :code:`node_id`:" +"distribution (:code:`IidPartitioner(num_partitions=2)`). Then, we load the " +"partition for the given client based on :code:`node_id`:" msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:121 @@ -18598,8 +20717,8 @@ msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:134 msgid "" -"The functions of :code:`train_test_split` and " -":code:`transform_dataset_to_dmatrix` are defined as below:" +"The functions of :code:`train_test_split` and :code:" +"`transform_dataset_to_dmatrix` are defined as below:" msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:158 @@ -18608,10 +20727,10 @@ msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:174 msgid "" -"The :code:`num_local_round` represents the number of iterations for local" -" tree boost. We use CPU for the training in default. One can shift it to " -"GPU by setting :code:`tree_method` to :code:`gpu_hist`. We use AUC as " -"evaluation metric." +"The :code:`num_local_round` represents the number of iterations for local " +"tree boost. We use CPU for the training in default. One can shift it to GPU " +"by setting :code:`tree_method` to :code:`gpu_hist`. We use AUC as evaluation " +"metric." msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:181 @@ -18620,86 +20739,85 @@ msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:183 msgid "" -"After loading the dataset we define the Flower client. We follow the " -"general rule to define :code:`XgbClient` class inherited from " -":code:`fl.client.Client`." +"After loading the dataset we define the Flower client. We follow the general " +"rule to define :code:`XgbClient` class inherited from :code:`fl.client." +"Client`." msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:193 msgid "" "The :code:`self.bst` is used to keep the Booster objects that remain " "consistent across rounds, allowing them to store predictions from trees " -"integrated in earlier rounds and maintain other essential data structures" -" for training." +"integrated in earlier rounds and maintain other essential data structures " +"for training." msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:196 msgid "" -"Then, we override :code:`get_parameters`, :code:`fit` and " -":code:`evaluate` methods insides :code:`XgbClient` class as follows." +"Then, we override :code:`get_parameters`, :code:`fit` and :code:`evaluate` " +"methods insides :code:`XgbClient` class as follows." msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:210 msgid "" "Unlike neural network training, XGBoost trees are not started from a " -"specified random weights. In this case, we do not use " -":code:`get_parameters` and :code:`set_parameters` to initialise model " -"parameters for XGBoost. As a result, let's return an empty tensor in " -":code:`get_parameters` when it is called by the server at the first " -"round." +"specified random weights. In this case, we do not use :code:`get_parameters` " +"and :code:`set_parameters` to initialise model parameters for XGBoost. As a " +"result, let's return an empty tensor in :code:`get_parameters` when it is " +"called by the server at the first round." msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:251 msgid "" -"In :code:`fit`, at the first round, we call :code:`xgb.train()` to build " -"up the first set of trees. the returned Booster object and config are " -"stored in :code:`self.bst` and :code:`self.config`, respectively. From " -"the second round, we load the global model sent from server to " -":code:`self.bst`, and then update model weights on local training data " -"with function :code:`local_boost` as follows:" +"In :code:`fit`, at the first round, we call :code:`xgb.train()` to build up " +"the first set of trees. the returned Booster object and config are stored " +"in :code:`self.bst` and :code:`self.config`, respectively. From the second " +"round, we load the global model sent from server to :code:`self.bst`, and " +"then update model weights on local training data with function :code:" +"`local_boost` as follows:" msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:269 msgid "" -"Given :code:`num_local_round`, we update trees by calling " -":code:`self.bst.update` method. After training, the last " -":code:`N=num_local_round` trees will be extracted to send to the server." +"Given :code:`num_local_round`, we update trees by calling :code:`self.bst." +"update` method. After training, the last :code:`N=num_local_round` trees " +"will be extracted to send to the server." msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:291 msgid "" -"In :code:`evaluate`, we call :code:`self.bst.eval_set` function to " -"conduct evaluation on valid set. The AUC value will be returned." +"In :code:`evaluate`, we call :code:`self.bst.eval_set` function to conduct " +"evaluation on valid set. The AUC value will be returned." msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:294 msgid "" -"Now, we can create an instance of our class :code:`XgbClient` and add one" -" line to actually run this client:" +"Now, we can create an instance of our class :code:`XgbClient` and add one " +"line to actually run this client:" msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:300 msgid "" -"That's it for the client. We only have to implement :code:`Client`and " -"call :code:`fl.client.start_client()`. The string :code:`\"[::]:8080\"` " -"tells the client which server to connect to. In our case we can run the " -"server and the client on the same machine, therefore we use " -":code:`\"[::]:8080\"`. If we run a truly federated workload with the " -"server and clients running on different machines, all that needs to " -"change is the :code:`server_address` we point the client at." +"That's it for the client. We only have to implement :code:`Client`and call :" +"code:`fl.client.start_client()`. The string :code:`\"[::]:8080\"` tells the " +"client which server to connect to. In our case we can run the server and the " +"client on the same machine, therefore we use :code:`\"[::]:8080\"`. If we " +"run a truly federated workload with the server and clients running on " +"different machines, all that needs to change is the :code:`server_address` " +"we point the client at." msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:311 msgid "" "These updates are then sent to the *server* which will aggregate them to " -"produce a better model. Finally, the *server* sends this improved version" -" of the model back to each *client* to finish a complete FL round." +"produce a better model. Finally, the *server* sends this improved version of " +"the model back to each *client* to finish a complete FL round." msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:314 msgid "" -"In a file named :code:`server.py`, import Flower and FedXgbBagging from " -":code:`flwr.server.strategy`." +"In a file named :code:`server.py`, import Flower and FedXgbBagging from :" +"code:`flwr.server.strategy`." msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:316 @@ -18708,9 +20826,9 @@ msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:339 msgid "" -"We use two clients for this example. An " -":code:`evaluate_metrics_aggregation` function is defined to collect and " -"wighted average the AUC values from clients." +"We use two clients for this example. An :code:`evaluate_metrics_aggregation` " +"function is defined to collect and wighted average the AUC values from " +"clients." msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:342 @@ -18723,16 +20841,16 @@ msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:356 msgid "" -"You must be curious about how bagging aggregation works. Let's look into " -"the details." +"You must be curious about how bagging aggregation works. Let's look into the " +"details." msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:358 msgid "" -"In file :code:`flwr.server.strategy.fedxgb_bagging.py`, we define " -":code:`FedXgbBagging` inherited from :code:`flwr.server.strategy.FedAvg`." -" Then, we override the :code:`aggregate_fit`, :code:`aggregate_evaluate` " -"and :code:`evaluate` methods as follows:" +"In file :code:`flwr.server.strategy.fedxgb_bagging.py`, we define :code:" +"`FedXgbBagging` inherited from :code:`flwr.server.strategy.FedAvg`. Then, we " +"override the :code:`aggregate_fit`, :code:`aggregate_evaluate` and :code:" +"`evaluate` methods as follows:" msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:454 @@ -18744,10 +20862,10 @@ msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:513 msgid "" "In this function, we first fetch the number of trees and the number of " -"parallel trees for the current and previous model by calling " -":code:`_get_tree_nums`. Then, the fetched information will be aggregated." -" After that, the trees (containing model weights) are aggregated to " -"generate a new tree model." +"parallel trees for the current and previous model by calling :code:" +"`_get_tree_nums`. Then, the fetched information will be aggregated. After " +"that, the trees (containing model weights) are aggregated to generate a new " +"tree model." msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:518 @@ -18763,16 +20881,16 @@ msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:585 msgid "" "Congratulations! You've successfully built and run your first federated " -"XGBoost system. The AUC values can be checked in " -":code:`metrics_distributed`. One can see that the average AUC increases " -"over FL rounds." +"XGBoost system. The AUC values can be checked in :code:" +"`metrics_distributed`. One can see that the average AUC increases over FL " +"rounds." msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:590 msgid "" -"The full `source code `_ for this example can be found in :code:`examples" -"/xgboost-quickstart`." +"The full `source code `_ for this example can be found in :code:`examples/" +"xgboost-quickstart`." msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:594 @@ -18781,15 +20899,15 @@ msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:596 msgid "" -"Now that you have known how federated XGBoost work with Flower, it's time" -" to run some more comprehensive experiments by customising the " -"experimental settings. In the xgboost-comprehensive example (`full code " -"`_), we provide more options to define various experimental" -" setups, including aggregation strategies, data partitioning and " -"centralised/distributed evaluation. We also support :doc:`Flower " -"simulation ` making it easy to simulate large " -"client cohorts in a resource-aware manner. Let's take a look!" +"Now that you have known how federated XGBoost work with Flower, it's time to " +"run some more comprehensive experiments by customising the experimental " +"settings. In the xgboost-comprehensive example (`full code `_), we provide " +"more options to define various experimental setups, including aggregation " +"strategies, data partitioning and centralised/distributed evaluation. We " +"also support :doc:`Flower simulation ` making it " +"easy to simulate large client cohorts in a resource-aware manner. Let's take " +"a look!" msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:603 @@ -18798,41 +20916,40 @@ msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:605 msgid "" -"In addition to bagging aggregation, we offer a cyclic training scheme, " -"which performs FL in a client-by-client fashion. Instead of aggregating " -"multiple clients, there is only one single client participating in the " -"training per round in the cyclic training scenario. The trained local " -"XGBoost trees will be passed to the next client as an initialised model " -"for next round's boosting." +"In addition to bagging aggregation, we offer a cyclic training scheme, which " +"performs FL in a client-by-client fashion. Instead of aggregating multiple " +"clients, there is only one single client participating in the training per " +"round in the cyclic training scenario. The trained local XGBoost trees will " +"be passed to the next client as an initialised model for next round's " +"boosting." msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:609 msgid "" -"To do this, we first customise a :code:`ClientManager` in " -":code:`server_utils.py`:" +"To do this, we first customise a :code:`ClientManager` in :code:" +"`server_utils.py`:" msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:649 msgid "" -"The customised :code:`ClientManager` samples all available clients in " -"each FL round based on the order of connection to the server. Then, we " -"define a new strategy :code:`FedXgbCyclic` in " -":code:`flwr.server.strategy.fedxgb_cyclic.py`, in order to sequentially " -"select only one client in given round and pass the received model to next" -" client." +"The customised :code:`ClientManager` samples all available clients in each " +"FL round based on the order of connection to the server. Then, we define a " +"new strategy :code:`FedXgbCyclic` in :code:`flwr.server.strategy." +"fedxgb_cyclic.py`, in order to sequentially select only one client in given " +"round and pass the received model to next client." msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:690 msgid "" "Unlike the original :code:`FedAvg`, we don't perform aggregation here. " -"Instead, we just make a copy of the received client model as global model" -" by overriding :code:`aggregate_fit`." +"Instead, we just make a copy of the received client model as global model by " +"overriding :code:`aggregate_fit`." msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:693 msgid "" -"Also, the customised :code:`configure_fit` and :code:`configure_evaluate`" -" methods ensure the clients to be sequentially selected given FL round:" +"Also, the customised :code:`configure_fit` and :code:`configure_evaluate` " +"methods ensure the clients to be sequentially selected given FL round:" msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:757 @@ -18841,11 +20958,11 @@ msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:759 msgid "" -"In :code:`dataset.py`, we have a function :code:`instantiate_partitioner`" -" to instantiate the data partitioner based on the given " -":code:`num_partitions` and :code:`partitioner_type`. Currently, we " -"provide four supported partitioner type to simulate the uniformity/non-" -"uniformity in data quantity (uniform, linear, square, exponential)." +"In :code:`dataset.py`, we have a function :code:`instantiate_partitioner` to " +"instantiate the data partitioner based on the given :code:`num_partitions` " +"and :code:`partitioner_type`. Currently, we provide four supported " +"partitioner type to simulate the uniformity/non-uniformity in data quantity " +"(uniform, linear, square, exponential)." msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:790 @@ -18854,23 +20971,23 @@ msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:792 msgid "" -"To facilitate centralised evaluation, we define a function in " -":code:`server_utils.py`:" +"To facilitate centralised evaluation, we define a function in :code:" +"`server_utils.py`:" msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:824 msgid "" -"This function returns a evaluation function which instantiates a " -":code:`Booster` object and loads the global model weights to it. The " -"evaluation is conducted by calling :code:`eval_set()` method, and the " -"tested AUC value is reported." +"This function returns a evaluation function which instantiates a :code:" +"`Booster` object and loads the global model weights to it. The evaluation is " +"conducted by calling :code:`eval_set()` method, and the tested AUC value is " +"reported." msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:827 msgid "" -"As for distributed evaluation on the clients, it's same as the quick-" -"start example by overriding the :code:`evaluate()` method insides the " -":code:`XgbClient` class in :code:`client_utils.py`." +"As for distributed evaluation on the clients, it's same as the quick-start " +"example by overriding the :code:`evaluate()` method insides the :code:" +"`XgbClient` class in :code:`client_utils.py`." msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:831 @@ -18880,21 +20997,21 @@ msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:832 msgid "" "We also provide an example code (:code:`sim.py`) to use the simulation " -"capabilities of Flower to simulate federated XGBoost training on either a" -" single machine or a cluster of machines." +"capabilities of Flower to simulate federated XGBoost training on either a " +"single machine or a cluster of machines." msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:866 msgid "" -"After importing all required packages, we define a :code:`main()` " -"function to perform the simulation process:" +"After importing all required packages, we define a :code:`main()` function " +"to perform the simulation process:" msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:921 msgid "" "We first load the dataset and perform data partitioning, and the pre-" -"processed data is stored in a :code:`list`. After the simulation begins, " -"the clients won't need to pre-process their partitions again." +"processed data is stored in a :code:`list`. After the simulation begins, the " +"clients won't need to pre-process their partitions again." msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:924 @@ -18903,8 +21020,8 @@ msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:975 msgid "" -"After that, we start the simulation by calling " -":code:`fl.simulation.start_simulation`:" +"After that, we start the simulation by calling :code:`fl.simulation." +"start_simulation`:" msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:995 @@ -18919,18 +21036,18 @@ msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:1040 msgid "" -"In :code:`utils.py`, we define the arguments parsers for clients, server " -"and simulation, allowing users to specify different experimental " -"settings. Let's first see the sever side:" +"In :code:`utils.py`, we define the arguments parsers for clients, server and " +"simulation, allowing users to specify different experimental settings. Let's " +"first see the sever side:" msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:1086 msgid "" "This allows user to specify training strategies / the number of total " -"clients / FL rounds / participating clients / clients for evaluation, and" -" evaluation fashion. Note that with :code:`--centralised-eval`, the sever" -" will do centralised evaluation and all functionalities for client " -"evaluation will be disabled." +"clients / FL rounds / participating clients / clients for evaluation, and " +"evaluation fashion. Note that with :code:`--centralised-eval`, the sever " +"will do centralised evaluation and all functionalities for client evaluation " +"will be disabled." msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:1090 @@ -18939,11 +21056,10 @@ msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:1144 msgid "" -"This defines various options for client data partitioning. Besides, " -"clients also have an option to conduct evaluation on centralised test set" -" by setting :code:`--centralised-eval`, as well as an option to perform " -"scaled learning rate based on the number of clients by setting :code" -":`--scaled-lr`." +"This defines various options for client data partitioning. Besides, clients " +"also have an option to conduct evaluation on centralised test set by " +"setting :code:`--centralised-eval`, as well as an option to perform scaled " +"learning rate based on the number of clients by setting :code:`--scaled-lr`." msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:1148 @@ -18960,9 +21076,9 @@ msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:1231 msgid "" -"To run a centralised evaluated experiment with bagging strategy on 5 " -"clients with exponential distribution for 50 rounds, we first start the " -"server as below:" +"To run a centralised evaluated experiment with bagging strategy on 5 clients " +"with exponential distribution for 50 rounds, we first start the server as " +"below:" msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:1238 @@ -18975,9 +21091,9 @@ msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:1250 msgid "" -"The full `code `_ for this comprehensive example can be found in" -" :code:`examples/xgboost-comprehensive`." +"The full `code `_ for this comprehensive example can be found in :code:" +"`examples/xgboost-comprehensive`." msgstr "" #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:9 @@ -18988,19 +21104,18 @@ msgstr "" msgid "" "Welcome to the third part of the Flower federated learning tutorial. In " "previous parts of this tutorial, we introduced federated learning with " -"PyTorch and Flower (`part 1 `__) and we learned how strategies " -"can be used to customize the execution on both the server and the clients" -" (`part 2 `__)." +"PyTorch and Flower (`part 1 `__) and we learned how strategies can be " +"used to customize the execution on both the server and the clients (`part 2 " +"`__)." msgstr "" #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:13 msgid "" -"In this notebook, we'll continue to customize the federated learning " -"system we built previously by creating a custom version of FedAvg (again," -" using `Flower `__ and `PyTorch " -"`__)." +"In this notebook, we'll continue to customize the federated learning system " +"we built previously by creating a custom version of FedAvg (again, using " +"`Flower `__ and `PyTorch `__)." msgstr "" #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:15 @@ -19008,11 +21123,11 @@ msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:15 #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:15 msgid "" -"`Star Flower on GitHub `__ ⭐️ and join " -"the Flower community on Slack to connect, ask questions, and get help: " -"`Join Slack `__ 🌼 We'd love to hear from " -"you in the ``#introductions`` channel! And if anything is unclear, head " -"over to the ``#questions`` channel." +"`Star Flower on GitHub `__ ⭐️ and join the " +"Flower community on Slack to connect, ask questions, and get help: `Join " +"Slack `__ 🌼 We'd love to hear from you in the " +"``#introductions`` channel! And if anything is unclear, head over to the " +"``#questions`` channel." msgstr "" #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:17 @@ -19058,14 +21173,14 @@ msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:102 #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:101 msgid "" -"It is possible to switch to a runtime that has GPU acceleration enabled " -"(on Google Colab: ``Runtime > Change runtime type > Hardware acclerator: " -"GPU > Save``). Note, however, that Google Colab is not always able to " -"offer GPU acceleration. If you see an error related to GPU availability " -"in one of the following sections, consider switching back to CPU-based " -"execution by setting ``DEVICE = torch.device(\"cpu\")``. If the runtime " -"has GPU acceleration enabled, you should see the output ``Training on " -"cuda``, otherwise it'll say ``Training on cpu``." +"It is possible to switch to a runtime that has GPU acceleration enabled (on " +"Google Colab: ``Runtime > Change runtime type > Hardware acclerator: GPU > " +"Save``). Note, however, that Google Colab is not always able to offer GPU " +"acceleration. If you see an error related to GPU availability in one of the " +"following sections, consider switching back to CPU-based execution by " +"setting ``DEVICE = torch.device(\"cpu\")``. If the runtime has GPU " +"acceleration enabled, you should see the output ``Training on cuda``, " +"otherwise it'll say ``Training on cpu``." msgstr "" #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:114 @@ -19077,11 +21192,11 @@ msgstr "" #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:116 #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:116 msgid "" -"Let's now load the CIFAR-10 training and test set, partition them into " -"ten smaller datasets (each split into training and validation set), and " -"wrap everything in their own ``DataLoader``. We introduce a new parameter" -" ``num_clients`` which allows us to call ``load_datasets`` with different" -" numbers of clients." +"Let's now load the CIFAR-10 training and test set, partition them into ten " +"smaller datasets (each split into training and validation set), and wrap " +"everything in their own ``DataLoader``. We introduce a new parameter " +"``num_clients`` which allows us to call ``load_datasets`` with different " +"numbers of clients." msgstr "" #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:167 @@ -19094,8 +21209,8 @@ msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:170 #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:169 msgid "" -"Let's continue with the usual model definition (including " -"``set_parameters`` and ``get_parameters``), training and test functions:" +"Let's continue with the usual model definition (including ``set_parameters`` " +"and ``get_parameters``), training and test functions:" msgstr "" #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:258 @@ -19106,10 +21221,10 @@ msgstr "" #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:260 #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:260 msgid "" -"To implement the Flower client, we (again) create a subclass of " -"``flwr.client.NumPyClient`` and implement the three methods " -"``get_parameters``, ``fit``, and ``evaluate``. Here, we also pass the " -"``cid`` to the client and use it log additional details:" +"To implement the Flower client, we (again) create a subclass of ``flwr." +"client.NumPyClient`` and implement the three methods ``get_parameters``, " +"``fit``, and ``evaluate``. Here, we also pass the ``cid`` to the client and " +"use it log additional details:" msgstr "" #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:308 @@ -19122,11 +21237,11 @@ msgstr "" #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:341 msgid "" -"Let’s overwrite the ``configure_fit`` method such that it passes a higher" -" learning rate (potentially also other hyperparameters) to the optimizer " -"of a fraction of the clients. We will keep the sampling of the clients as" -" it is in ``FedAvg`` and then change the configuration dictionary (one of" -" the ``FitIns`` attributes)." +"Let’s overwrite the ``configure_fit`` method such that it passes a higher " +"learning rate (potentially also other hyperparameters) to the optimizer of a " +"fraction of the clients. We will keep the sampling of the clients as it is " +"in ``FedAvg`` and then change the configuration dictionary (one of the " +"``FitIns`` attributes)." msgstr "" #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:507 @@ -19143,13 +21258,13 @@ msgstr "" #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:536 msgid "" -"In this notebook, we’ve seen how to implement a custom strategy. A custom" -" strategy enables granular control over client node configuration, result" -" aggregation, and more. To define a custom strategy, you only have to " -"overwrite the abstract methods of the (abstract) base class ``Strategy``." -" To make custom strategies even more powerful, you can pass custom " -"functions to the constructor of your new class (``__init__``) and then " -"call these functions whenever needed." +"In this notebook, we’ve seen how to implement a custom strategy. A custom " +"strategy enables granular control over client node configuration, result " +"aggregation, and more. To define a custom strategy, you only have to " +"overwrite the abstract methods of the (abstract) base class ``Strategy``. To " +"make custom strategies even more powerful, you can pass custom functions to " +"the constructor of your new class (``__init__``) and then call these " +"functions whenever needed." msgstr "" #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:550 @@ -19158,8 +21273,8 @@ msgstr "" #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:715 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:369 msgid "" -"Before you continue, make sure to join the Flower community on Slack: " -"`Join Slack `__" +"Before you continue, make sure to join the Flower community on Slack: `Join " +"Slack `__" msgstr "" #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:552 @@ -19168,16 +21283,15 @@ msgstr "" #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:717 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:371 msgid "" -"There's a dedicated ``#questions`` channel if you need help, but we'd " -"also love to hear who you are in ``#introductions``!" +"There's a dedicated ``#questions`` channel if you need help, but we'd also " +"love to hear who you are in ``#introductions``!" msgstr "" #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:554 msgid "" -"The `Flower Federated Learning Tutorial - Part 4 " -"`__ introduces ``Client``, the flexible API underlying " -"``NumPyClient``." +"The `Flower Federated Learning Tutorial - Part 4 `__ introduces " +"``Client``, the flexible API underlying ``NumPyClient``." msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:9 @@ -19186,26 +21300,26 @@ msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:11 msgid "" -"Welcome to the fourth part of the Flower federated learning tutorial. In " -"the previous parts of this tutorial, we introduced federated learning " -"with PyTorch and Flower (`part 1 `__), we learned how " -"strategies can be used to customize the execution on both the server and " -"the clients (`part 2 `__), and we built our own " -"custom strategy from scratch (`part 3 `__)." +"Welcome to the fourth part of the Flower federated learning tutorial. In the " +"previous parts of this tutorial, we introduced federated learning with " +"PyTorch and Flower (`part 1 `__), we learned how strategies can be used " +"to customize the execution on both the server and the clients (`part 2 " +"`__), and we built our own custom strategy from scratch (`part " +"3 `__)." msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:14 msgid "" -"In this notebook, we revisit ``NumPyClient`` and introduce a new " -"baseclass for building clients, simply named ``Client``. In previous " -"parts of this tutorial, we've based our client on ``NumPyClient``, a " -"convenience class which makes it easy to work with machine learning " -"libraries that have good NumPy interoperability. With ``Client``, we gain" -" a lot of flexibility that we didn't have before, but we'll also have to " -"do a few things the we didn't have to do before." +"In this notebook, we revisit ``NumPyClient`` and introduce a new baseclass " +"for building clients, simply named ``Client``. In previous parts of this " +"tutorial, we've based our client on ``NumPyClient``, a convenience class " +"which makes it easy to work with machine learning libraries that have good " +"NumPy interoperability. With ``Client``, we gain a lot of flexibility that " +"we didn't have before, but we'll also have to do a few things the we didn't " +"have to do before." msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:18 @@ -19221,9 +21335,9 @@ msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:117 msgid "" -"Let's now load the CIFAR-10 training and test set, partition them into " -"ten smaller datasets (each split into training and validation set), and " -"wrap everything in their own ``DataLoader``." +"Let's now load the CIFAR-10 training and test set, partition them into ten " +"smaller datasets (each split into training and validation set), and wrap " +"everything in their own ``DataLoader``." msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:259 @@ -19232,10 +21346,10 @@ msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:261 msgid "" -"So far, we've implemented our client by subclassing " -"``flwr.client.NumPyClient``. The three methods we implemented are " -"``get_parameters``, ``fit``, and ``evaluate``. Finally, we wrap the " -"creation of instances of this class in a function called ``client_fn``:" +"So far, we've implemented our client by subclassing ``flwr.client." +"NumPyClient``. The three methods we implemented are ``get_parameters``, " +"``fit``, and ``evaluate``. Finally, we wrap the creation of instances of " +"this class in a function called ``client_fn``:" msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:309 @@ -19257,25 +21371,24 @@ msgid "" "Let's dive a little bit deeper and discuss how Flower executes this " "simulation. Whenever a client is selected to do some work, " "``start_simulation`` calls the function ``numpyclient_fn`` to create an " -"instance of our ``FlowerNumPyClient`` (along with loading the model and " -"the data)." +"instance of our ``FlowerNumPyClient`` (along with loading the model and the " +"data)." msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:343 msgid "" "But here's the perhaps surprising part: Flower doesn't actually use the " -"``FlowerNumPyClient`` object directly. Instead, it wraps the object to " -"makes it look like a subclass of ``flwr.client.Client``, not " -"``flwr.client.NumPyClient``. In fact, the Flower core framework doesn't " -"know how to handle ``NumPyClient``'s, it only knows how to handle " -"``Client``'s. ``NumPyClient`` is just a convenience abstraction built on " -"top of ``Client``." +"``FlowerNumPyClient`` object directly. Instead, it wraps the object to makes " +"it look like a subclass of ``flwr.client.Client``, not ``flwr.client." +"NumPyClient``. In fact, the Flower core framework doesn't know how to handle " +"``NumPyClient``'s, it only knows how to handle ``Client``'s. ``NumPyClient`` " +"is just a convenience abstraction built on top of ``Client``." msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:345 msgid "" -"Instead of building on top of ``NumPyClient``, we can directly build on " -"top of ``Client``." +"Instead of building on top of ``NumPyClient``, we can directly build on top " +"of ``Client``." msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:357 @@ -19284,14 +21397,13 @@ msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:359 msgid "" -"Let's try to do the same thing using ``Client`` instead of " -"``NumPyClient``." +"Let's try to do the same thing using ``Client`` instead of ``NumPyClient``." msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:465 msgid "" -"Before we discuss the code in more detail, let's try to run it! Gotta " -"make sure our new ``Client``-based client works, right?" +"Before we discuss the code in more detail, let's try to run it! Gotta make " +"sure our new ``Client``-based client works, right?" msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:490 @@ -19302,40 +21414,40 @@ msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:492 msgid "" -"First of all, it's more code. But why? The difference comes from the fact" -" that ``Client`` expects us to take care of parameter serialization and " -"deserialization. For Flower to be able to send parameters over the " -"network, it eventually needs to turn these parameters into ``bytes``. " -"Turning parameters (e.g., NumPy ``ndarray``'s) into raw bytes is called " +"First of all, it's more code. But why? The difference comes from the fact " +"that ``Client`` expects us to take care of parameter serialization and " +"deserialization. For Flower to be able to send parameters over the network, " +"it eventually needs to turn these parameters into ``bytes``. Turning " +"parameters (e.g., NumPy ``ndarray``'s) into raw bytes is called " "serialization. Turning raw bytes into something more useful (like NumPy " -"``ndarray``'s) is called deserialization. Flower needs to do both: it " -"needs to serialize parameters on the server-side and send them to the " -"client, the client needs to deserialize them to use them for local " -"training, and then serialize the updated parameters again to send them " -"back to the server, which (finally!) deserializes them again in order to " -"aggregate them with the updates received from other clients." +"``ndarray``'s) is called deserialization. Flower needs to do both: it needs " +"to serialize parameters on the server-side and send them to the client, the " +"client needs to deserialize them to use them for local training, and then " +"serialize the updated parameters again to send them back to the server, " +"which (finally!) deserializes them again in order to aggregate them with the " +"updates received from other clients." msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:495 msgid "" "The only *real* difference between Client and NumPyClient is that " -"NumPyClient takes care of serialization and deserialization for you. It " -"can do so because it expects you to return parameters as NumPy ndarray's," -" and it knows how to handle these. This makes working with machine " -"learning libraries that have good NumPy support (most of them) a breeze." +"NumPyClient takes care of serialization and deserialization for you. It can " +"do so because it expects you to return parameters as NumPy ndarray's, and it " +"knows how to handle these. This makes working with machine learning " +"libraries that have good NumPy support (most of them) a breeze." msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:497 msgid "" -"In terms of API, there's one major difference: all methods in Client take" -" exactly one argument (e.g., ``FitIns`` in ``Client.fit``) and return " -"exactly one value (e.g., ``FitRes`` in ``Client.fit``). The methods in " +"In terms of API, there's one major difference: all methods in Client take " +"exactly one argument (e.g., ``FitIns`` in ``Client.fit``) and return exactly " +"one value (e.g., ``FitRes`` in ``Client.fit``). The methods in " "``NumPyClient`` on the other hand have multiple arguments (e.g., " -"``parameters`` and ``config`` in ``NumPyClient.fit``) and multiple return" -" values (e.g., ``parameters``, ``num_example``, and ``metrics`` in " -"``NumPyClient.fit``) if there are multiple things to handle. These " -"``*Ins`` and ``*Res`` objects in ``Client`` wrap all the individual " -"values you're used to from ``NumPyClient``." +"``parameters`` and ``config`` in ``NumPyClient.fit``) and multiple return " +"values (e.g., ``parameters``, ``num_example``, and ``metrics`` in " +"``NumPyClient.fit``) if there are multiple things to handle. These ``*Ins`` " +"and ``*Res`` objects in ``Client`` wrap all the individual values you're " +"used to from ``NumPyClient``." msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:510 @@ -19352,17 +21464,16 @@ msgstr "" msgid "" "But first what is serialization? Serialization is just the process of " "converting an object into raw bytes, and equally as important, " -"deserialization is the process of converting raw bytes back into an " -"object. This is very useful for network communication. Indeed, without " +"deserialization is the process of converting raw bytes back into an object. " +"This is very useful for network communication. Indeed, without " "serialization, you could not just a Python object through the internet." msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:516 msgid "" -"Federated Learning relies heavily on internet communication for training " -"by sending Python objects back and forth between the clients and the " -"server. This means that serialization is an essential part of Federated " -"Learning." +"Federated Learning relies heavily on internet communication for training by " +"sending Python objects back and forth between the clients and the server. " +"This means that serialization is an essential part of Federated Learning." msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:518 @@ -19382,15 +21493,15 @@ msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:523 msgid "" -"This is where the real serialization/deserialization will happen, " -"especially in ``ndarray_to_sparse_bytes`` for serialization and " +"This is where the real serialization/deserialization will happen, especially " +"in ``ndarray_to_sparse_bytes`` for serialization and " "``sparse_bytes_to_ndarray`` for deserialization." msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:525 msgid "" -"Note that we imported the ``scipy.sparse`` library in order to convert " -"our arrays." +"Note that we imported the ``scipy.sparse`` library in order to convert our " +"arrays." msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:613 @@ -19399,30 +21510,28 @@ msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:615 msgid "" -"To be able to serialize our ``ndarray``\\ s into sparse parameters, we " -"will just have to call our custom functions in our " -"``flwr.client.Client``." +"To be able to serialize our ``ndarray``\\ s into sparse parameters, we will " +"just have to call our custom functions in our ``flwr.client.Client``." msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:617 msgid "" "Indeed, in ``get_parameters`` we need to serialize the parameters we got " -"from our network using our custom ``ndarrays_to_sparse_parameters`` " -"defined above." +"from our network using our custom ``ndarrays_to_sparse_parameters`` defined " +"above." msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:619 msgid "" "In ``fit``, we first need to deserialize the parameters coming from the " -"server using our custom ``sparse_parameters_to_ndarrays`` and then we " -"need to serialize our local results with " -"``ndarrays_to_sparse_parameters``." +"server using our custom ``sparse_parameters_to_ndarrays`` and then we need " +"to serialize our local results with ``ndarrays_to_sparse_parameters``." msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:621 msgid "" -"In ``evaluate``, we will only need to deserialize the global parameters " -"with our custom function." +"In ``evaluate``, we will only need to deserialize the global parameters with " +"our custom function." msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:725 @@ -19431,11 +21540,10 @@ msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:727 msgid "" -"For this example, we will just use ``FedAvg`` as a strategy. To change " -"the serialization and deserialization here, we only need to reimplement " -"the ``evaluate`` and ``aggregate_fit`` functions of ``FedAvg``. The other" -" functions of the strategy will be inherited from the super class " -"``FedAvg``." +"For this example, we will just use ``FedAvg`` as a strategy. To change the " +"serialization and deserialization here, we only need to reimplement the " +"``evaluate`` and ``aggregate_fit`` functions of ``FedAvg``. The other " +"functions of the strategy will be inherited from the super class ``FedAvg``." msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:729 @@ -19460,19 +21568,19 @@ msgstr "" msgid "" "In this part of the tutorial, we've seen how we can build clients by " "subclassing either ``NumPyClient`` or ``Client``. ``NumPyClient`` is a " -"convenience abstraction that makes it easier to work with machine " -"learning libraries that have good NumPy interoperability. ``Client`` is a" -" more flexible abstraction that allows us to do things that are not " -"possible in ``NumPyClient``. In order to do so, it requires us to handle " -"parameter serialization and deserialization ourselves." +"convenience abstraction that makes it easier to work with machine learning " +"libraries that have good NumPy interoperability. ``Client`` is a more " +"flexible abstraction that allows us to do things that are not possible in " +"``NumPyClient``. In order to do so, it requires us to handle parameter " +"serialization and deserialization ourselves." msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:952 msgid "" -"This is the final part of the Flower tutorial (for now!), " -"congratulations! You're now well equipped to understand the rest of the " -"documentation. There are many topics we didn't cover in the tutorial, we " -"recommend the following resources:" +"This is the final part of the Flower tutorial (for now!), congratulations! " +"You're now well equipped to understand the rest of the documentation. There " +"are many topics we didn't cover in the tutorial, we recommend the following " +"resources:" msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:954 @@ -19481,20 +21589,20 @@ msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:955 msgid "" -"`Check out Flower Code Examples " -"`__" +"`Check out Flower Code Examples `__" msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:956 msgid "" -"`Use Flower Baselines for your research " -"`__" +"`Use Flower Baselines for your research `__" msgstr "" #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:957 msgid "" -"`Watch Flower Summit 2023 videos `__" +"`Watch Flower Summit 2023 videos `__" msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:9 @@ -19509,10 +21617,9 @@ msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:13 msgid "" "In this notebook, we'll build a federated learning system using Flower, " -"`Flower Datasets `__ and PyTorch. In " -"part 1, we use PyTorch for the model training pipeline and data loading. " -"In part 2, we continue to federate the PyTorch-based pipeline using " -"Flower." +"`Flower Datasets `__ and PyTorch. In part " +"1, we use PyTorch for the model training pipeline and data loading. In part " +"2, we continue to federate the PyTorch-based pipeline using Flower." msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:17 @@ -19529,20 +21636,19 @@ msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:45 msgid "" "Next, we install the necessary packages for PyTorch (``torch`` and " -"``torchvision``), Flower Datasets (``flwr-datasets``) and Flower " -"(``flwr``):" +"``torchvision``), Flower Datasets (``flwr-datasets``) and Flower (``flwr``):" msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:105 msgid "" -"It is possible to switch to a runtime that has GPU acceleration enabled " -"(on Google Colab: ``Runtime > Change runtime type > Hardware accelerator:" -" GPU > Save``). Note, however, that Google Colab is not always able to " -"offer GPU acceleration. If you see an error related to GPU availability " -"in one of the following sections, consider switching back to CPU-based " -"execution by setting ``DEVICE = torch.device(\"cpu\")``. If the runtime " -"has GPU acceleration enabled, you should see the output ``Training on " -"cuda``, otherwise it'll say ``Training on cpu``." +"It is possible to switch to a runtime that has GPU acceleration enabled (on " +"Google Colab: ``Runtime > Change runtime type > Hardware accelerator: GPU > " +"Save``). Note, however, that Google Colab is not always able to offer GPU " +"acceleration. If you see an error related to GPU availability in one of the " +"following sections, consider switching back to CPU-based execution by " +"setting ``DEVICE = torch.device(\"cpu\")``. If the runtime has GPU " +"acceleration enabled, you should see the output ``Training on cuda``, " +"otherwise it'll say ``Training on cpu``." msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:118 @@ -19551,51 +21657,50 @@ msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:120 msgid "" -"Federated learning can be applied to many different types of tasks across" -" different domains. In this tutorial, we introduce federated learning by " -"training a simple convolutional neural network (CNN) on the popular " -"CIFAR-10 dataset. CIFAR-10 can be used to train image classifiers that " -"distinguish between images from ten different classes: 'airplane', " -"'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', and " -"'truck'." +"Federated learning can be applied to many different types of tasks across " +"different domains. In this tutorial, we introduce federated learning by " +"training a simple convolutional neural network (CNN) on the popular CIFAR-10 " +"dataset. CIFAR-10 can be used to train image classifiers that distinguish " +"between images from ten different classes: 'airplane', 'automobile', 'bird', " +"'cat', 'deer', 'dog', 'frog', 'horse', 'ship', and 'truck'." msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:131 msgid "" "We simulate having multiple datasets from multiple organizations (also " -"called the \"cross-silo\" setting in federated learning) by splitting the" -" original CIFAR-10 dataset into multiple partitions. Each partition will " -"represent the data from a single organization. We're doing this purely " -"for experimentation purposes, in the real world there's no need for data " -"splitting because each organization already has their own data (so the " -"data is naturally partitioned)." +"called the \"cross-silo\" setting in federated learning) by splitting the " +"original CIFAR-10 dataset into multiple partitions. Each partition will " +"represent the data from a single organization. We're doing this purely for " +"experimentation purposes, in the real world there's no need for data " +"splitting because each organization already has their own data (so the data " +"is naturally partitioned)." msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:133 msgid "" -"Each organization will act as a client in the federated learning system. " -"So having ten organizations participate in a federation means having ten " +"Each organization will act as a client in the federated learning system. So " +"having ten organizations participate in a federation means having ten " "clients connected to the federated learning server." msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:144 msgid "" "Let's now create the Federated Dataset abstraction that from ``flwr-" -"datasets`` that partitions the CIFAR-10. We will create small training " -"and test set for each edge device and wrap each of them into a PyTorch " +"datasets`` that partitions the CIFAR-10. We will create small training and " +"test set for each edge device and wrap each of them into a PyTorch " "``DataLoader``:" msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:198 msgid "" "We now have a list of ten training sets and ten validation sets " -"(``trainloaders`` and ``valloaders``) representing the data of ten " -"different organizations. Each ``trainloader``/``valloader`` pair contains" -" 4500 training examples and 500 validation examples. There's also a " -"single ``testloader`` (we did not split the test set). Again, this is " -"only necessary for building research or educational systems, actual " -"federated learning systems have their data naturally distributed across " -"multiple partitions." +"(``trainloaders`` and ``valloaders``) representing the data of ten different " +"organizations. Each ``trainloader``/``valloader`` pair contains 4000 " +"training examples and 1000 validation examples. There's also a single " +"``testloader`` (we did not split the test set). Again, this is only " +"necessary for building research or educational systems, actual federated " +"learning systems have their data naturally distributed across multiple " +"partitions." msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:201 @@ -19609,8 +21714,8 @@ msgid "" "The output above shows a random batch of images from the first " "``trainloader`` in our list of ten ``trainloaders``. It also prints the " "labels associated with each image (i.e., one of the ten possible labels " -"we've seen above). If you run the cell again, you should see another " -"batch of images." +"we've seen above). If you run the cell again, you should see another batch " +"of images." msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:252 @@ -19623,8 +21728,8 @@ msgid "" "network. This introduction assumes basic familiarity with PyTorch, so it " "doesn't cover the PyTorch-related aspects in full detail. If you want to " "dive deeper into PyTorch, we recommend `DEEP LEARNING WITH PYTORCH: A 60 " -"MINUTE BLITZ " -"`__." +"MINUTE BLITZ `__." msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:275 @@ -19633,9 +21738,9 @@ msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:277 msgid "" -"We use the simple CNN described in the `PyTorch tutorial " -"`__:" +"We use the simple CNN described in the `PyTorch tutorial `__:" msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:314 @@ -19649,20 +21754,19 @@ msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:376 msgid "" "We now have all the basic building blocks we need: a dataset, a model, a " -"training function, and a test function. Let's put them together to train " -"the model on the dataset of one of our organizations " -"(``trainloaders[0]``). This simulates the reality of most machine " -"learning projects today: each organization has their own data and trains " -"models only on this internal data:" +"training function, and a test function. Let's put them together to train the " +"model on the dataset of one of our organizations (``trainloaders[0]``). This " +"simulates the reality of most machine learning projects today: each " +"organization has their own data and trains models only on this internal data:" msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:406 msgid "" -"Training the simple CNN on our CIFAR-10 split for 5 epochs should result " -"in a test set accuracy of about 41%, which is not good, but at the same " -"time, it doesn't really matter for the purposes of this tutorial. The " -"intent was just to show a simplistic centralized training pipeline that " -"sets the stage for what comes next - federated learning!" +"Training the simple CNN on our CIFAR-10 split for 5 epochs should result in " +"a test set accuracy of about 41%, which is not good, but at the same time, " +"it doesn't really matter for the purposes of this tutorial. The intent was " +"just to show a simplistic centralized training pipeline that sets the stage " +"for what comes next - federated learning!" msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:418 @@ -19671,11 +21775,11 @@ msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:420 msgid "" -"Step 1 demonstrated a simple centralized training pipeline. All data was " -"in one place (i.e., a single ``trainloader`` and a single ``valloader``)." -" Next, we'll simulate a situation where we have multiple datasets in " -"multiple organizations and where we train a model over these " -"organizations using federated learning." +"Step 1 demonstrated a simple centralized training pipeline. All data was in " +"one place (i.e., a single ``trainloader`` and a single ``valloader``). Next, " +"we'll simulate a situation where we have multiple datasets in multiple " +"organizations and where we train a model over these organizations using " +"federated learning." msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:432 @@ -19684,30 +21788,29 @@ msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:434 msgid "" -"In federated learning, the server sends the global model parameters to " -"the client, and the client updates the local model with the parameters " -"received from the server. It then trains the model on the local data " -"(which changes the model parameters locally) and sends the " -"updated/changed model parameters back to the server (or, alternatively, " -"it sends just the gradients back to the server, not the full model " -"parameters)." +"In federated learning, the server sends the global model parameters to the " +"client, and the client updates the local model with the parameters received " +"from the server. It then trains the model on the local data (which changes " +"the model parameters locally) and sends the updated/changed model parameters " +"back to the server (or, alternatively, it sends just the gradients back to " +"the server, not the full model parameters)." msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:436 msgid "" "We need two helper functions to update the local model with parameters " -"received from the server and to get the updated model parameters from the" -" local model: ``set_parameters`` and ``get_parameters``. The following " -"two functions do just that for the PyTorch model above." +"received from the server and to get the updated model parameters from the " +"local model: ``set_parameters`` and ``get_parameters``. The following two " +"functions do just that for the PyTorch model above." msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:438 msgid "" -"The details of how this works are not really important here (feel free to" -" consult the PyTorch documentation if you want to learn more). In " -"essence, we use ``state_dict`` to access PyTorch model parameter tensors." -" The parameter tensors are then converted to/from a list of NumPy " -"ndarray's (which Flower knows how to serialize/deserialize):" +"The details of how this works are not really important here (feel free to " +"consult the PyTorch documentation if you want to learn more). In essence, we " +"use ``state_dict`` to access PyTorch model parameter tensors. The parameter " +"tensors are then converted to/from a list of NumPy ndarray's (which Flower " +"knows how to serialize/deserialize):" msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:466 @@ -19716,19 +21819,18 @@ msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:468 msgid "" -"With that out of the way, let's move on to the interesting part. " -"Federated learning systems consist of a server and multiple clients. In " -"Flower, we create clients by implementing subclasses of " -"``flwr.client.Client`` or ``flwr.client.NumPyClient``. We use " -"``NumPyClient`` in this tutorial because it is easier to implement and " -"requires us to write less boilerplate." +"With that out of the way, let's move on to the interesting part. Federated " +"learning systems consist of a server and multiple clients. In Flower, we " +"create clients by implementing subclasses of ``flwr.client.Client`` or " +"``flwr.client.NumPyClient``. We use ``NumPyClient`` in this tutorial because " +"it is easier to implement and requires us to write less boilerplate." msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:470 msgid "" -"To implement the Flower client, we create a subclass of " -"``flwr.client.NumPyClient`` and implement the three methods " -"``get_parameters``, ``fit``, and ``evaluate``:" +"To implement the Flower client, we create a subclass of ``flwr.client." +"NumPyClient`` and implement the three methods ``get_parameters``, ``fit``, " +"and ``evaluate``:" msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:472 @@ -19738,15 +21840,14 @@ msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:473 msgid "" "``fit``: Receive model parameters from the server, train the model " -"parameters on the local data, and return the (updated) model parameters " -"to the server" +"parameters on the local data, and return the (updated) model parameters to " +"the server" msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:474 msgid "" -"``evaluate``: Receive model parameters from the server, evaluate the " -"model parameters on the local data, and return the evaluation result to " -"the server" +"``evaluate``: Receive model parameters from the server, evaluate the model " +"parameters on the local data, and return the evaluation result to the server" msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:476 @@ -19759,16 +21860,15 @@ msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:513 msgid "" "Our class ``FlowerClient`` defines how local training/evaluation will be " -"performed and allows Flower to call the local training/evaluation through" -" ``fit`` and ``evaluate``. Each instance of ``FlowerClient`` represents a" -" *single client* in our federated learning system. Federated learning " -"systems have multiple clients (otherwise, there's not much to federate), " -"so each client will be represented by its own instance of " -"``FlowerClient``. If we have, for example, three clients in our workload," -" then we'd have three instances of ``FlowerClient``. Flower calls " -"``FlowerClient.fit`` on the respective instance when the server selects a" -" particular client for training (and ``FlowerClient.evaluate`` for " -"evaluation)." +"performed and allows Flower to call the local training/evaluation through " +"``fit`` and ``evaluate``. Each instance of ``FlowerClient`` represents a " +"*single client* in our federated learning system. Federated learning systems " +"have multiple clients (otherwise, there's not much to federate), so each " +"client will be represented by its own instance of ``FlowerClient``. If we " +"have, for example, three clients in our workload, then we'd have three " +"instances of ``FlowerClient``. Flower calls ``FlowerClient.fit`` on the " +"respective instance when the server selects a particular client for training " +"(and ``FlowerClient.evaluate`` for evaluation)." msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:517 @@ -19777,13 +21877,13 @@ msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:519 msgid "" -"In this notebook, we want to simulate a federated learning system with 10" -" clients on a single machine. This means that the server and all 10 " -"clients will live on a single machine and share resources such as CPU, " -"GPU, and memory. Having 10 clients would mean having 10 instances of " -"``FlowerClient`` in memory. Doing this on a single machine can quickly " -"exhaust the available memory resources, even if only a subset of these " -"clients participates in a single round of federated learning." +"In this notebook, we want to simulate a federated learning system with 10 " +"clients on a single machine. This means that the server and all 10 clients " +"will live on a single machine and share resources such as CPU, GPU, and " +"memory. Having 10 clients would mean having 10 instances of ``FlowerClient`` " +"in memory. Doing this on a single machine can quickly exhaust the available " +"memory resources, even if only a subset of these clients participates in a " +"single round of federated learning." msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:521 @@ -19792,14 +21892,14 @@ msgid "" "multiple machines, Flower, therefore, provides special simulation " "capabilities that create ``FlowerClient`` instances only when they are " "actually necessary for training or evaluation. To enable the Flower " -"framework to create clients when necessary, we need to implement a " -"function called ``client_fn`` that creates a ``FlowerClient`` instance on" -" demand. Flower calls ``client_fn`` whenever it needs an instance of one " -"particular client to call ``fit`` or ``evaluate`` (those instances are " -"usually discarded after use, so they should not keep any local state). " -"Clients are identified by a client ID, or short ``cid``. The ``cid`` can " -"be used, for example, to load different local data partitions for " -"different clients, as can be seen below:" +"framework to create clients when necessary, we need to implement a function " +"called ``client_fn`` that creates a ``FlowerClient`` instance on demand. " +"Flower calls ``client_fn`` whenever it needs an instance of one particular " +"client to call ``fit`` or ``evaluate`` (those instances are usually " +"discarded after use, so they should not keep any local state). Clients are " +"identified by a client ID, or short ``cid``. The ``cid`` can be used, for " +"example, to load different local data partitions for different clients, as " +"can be seen below:" msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:556 @@ -19808,31 +21908,31 @@ msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:558 msgid "" -"We now have the class ``FlowerClient`` which defines client-side " -"training/evaluation and ``client_fn`` which allows Flower to create " -"``FlowerClient`` instances whenever it needs to call ``fit`` or " -"``evaluate`` on one particular client. The last step is to start the " -"actual simulation using ``flwr.simulation.start_simulation``." +"We now have the class ``FlowerClient`` which defines client-side training/" +"evaluation and ``client_fn`` which allows Flower to create ``FlowerClient`` " +"instances whenever it needs to call ``fit`` or ``evaluate`` on one " +"particular client. The last step is to start the actual simulation using " +"``flwr.simulation.start_simulation``." msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:560 msgid "" "The function ``start_simulation`` accepts a number of arguments, amongst " -"them the ``client_fn`` used to create ``FlowerClient`` instances, the " -"number of clients to simulate (``num_clients``), the number of federated " -"learning rounds (``num_rounds``), and the strategy. The strategy " -"encapsulates the federated learning approach/algorithm, for example, " -"*Federated Averaging* (FedAvg)." +"them the ``client_fn`` used to create ``FlowerClient`` instances, the number " +"of clients to simulate (``num_clients``), the number of federated learning " +"rounds (``num_rounds``), and the strategy. The strategy encapsulates the " +"federated learning approach/algorithm, for example, *Federated Averaging* " +"(FedAvg)." msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:562 msgid "" "Flower has a number of built-in strategies, but we can also use our own " -"strategy implementations to customize nearly all aspects of the federated" -" learning approach. For this example, we use the built-in ``FedAvg`` " -"implementation and customize it using a few basic parameters. The last " -"step is the actual call to ``start_simulation`` which - you guessed it - " -"starts the simulation:" +"strategy implementations to customize nearly all aspects of the federated " +"learning approach. For this example, we use the built-in ``FedAvg`` " +"implementation and customize it using a few basic parameters. The last step " +"is the actual call to ``start_simulation`` which - you guessed it - starts " +"the simulation:" msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:608 @@ -19846,20 +21946,20 @@ msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:612 #, python-format msgid "" -"When we call ``start_simulation``, we tell Flower that there are 10 " -"clients (``num_clients=10``). Flower then goes ahead an asks the " -"``FedAvg`` strategy to select clients. ``FedAvg`` knows that it should " -"select 100% of the available clients (``fraction_fit=1.0``), so it goes " -"ahead and selects 10 random clients (i.e., 100% of 10)." +"When we call ``start_simulation``, we tell Flower that there are 10 clients " +"(``num_clients=10``). Flower then goes ahead an asks the ``FedAvg`` strategy " +"to select clients. ``FedAvg`` knows that it should select 100% of the " +"available clients (``fraction_fit=1.0``), so it goes ahead and selects 10 " +"random clients (i.e., 100% of 10)." msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:614 msgid "" -"Flower then asks the selected 10 clients to train the model. When the " -"server receives the model parameter updates from the clients, it hands " -"those updates over to the strategy (*FedAvg*) for aggregation. The " -"strategy aggregates those updates and returns the new global model, which" -" then gets used in the next round of federated learning." +"Flower then asks the selected 10 clients to train the model. When the server " +"receives the model parameter updates from the clients, it hands those " +"updates over to the strategy (*FedAvg*) for aggregation. The strategy " +"aggregates those updates and returns the new global model, which then gets " +"used in the next round of federated learning." msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:626 @@ -19868,28 +21968,27 @@ msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:628 msgid "" -"You may have noticed that all metrics except for ``losses_distributed`` " -"are empty. Where did the ``{\"accuracy\": float(accuracy)}`` go?" +"You may have noticed that all metrics except for ``losses_distributed`` are " +"empty. Where did the ``{\"accuracy\": float(accuracy)}`` go?" msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:630 msgid "" -"Flower can automatically aggregate losses returned by individual clients," -" but it cannot do the same for metrics in the generic metrics dictionary " -"(the one with the ``accuracy`` key). Metrics dictionaries can contain " -"very different kinds of metrics and even key/value pairs that are not " -"metrics at all, so the framework does not (and can not) know how to " -"handle these automatically." +"Flower can automatically aggregate losses returned by individual clients, " +"but it cannot do the same for metrics in the generic metrics dictionary (the " +"one with the ``accuracy`` key). Metrics dictionaries can contain very " +"different kinds of metrics and even key/value pairs that are not metrics at " +"all, so the framework does not (and can not) know how to handle these " +"automatically." msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:632 msgid "" -"As users, we need to tell the framework how to handle/aggregate these " -"custom metrics, and we do so by passing metric aggregation functions to " -"the strategy. The strategy will then call these functions whenever it " -"receives fit or evaluate metrics from clients. The two possible functions" -" are ``fit_metrics_aggregation_fn`` and " -"``evaluate_metrics_aggregation_fn``." +"As users, we need to tell the framework how to handle/aggregate these custom " +"metrics, and we do so by passing metric aggregation functions to the " +"strategy. The strategy will then call these functions whenever it receives " +"fit or evaluate metrics from clients. The two possible functions are " +"``fit_metrics_aggregation_fn`` and ``evaluate_metrics_aggregation_fn``." msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:634 @@ -19907,17 +22006,17 @@ msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:697 msgid "" "We now have a full system that performs federated training and federated " -"evaluation. It uses the ``weighted_average`` function to aggregate custom" -" evaluation metrics and calculates a single ``accuracy`` metric across " -"all clients on the server side." +"evaluation. It uses the ``weighted_average`` function to aggregate custom " +"evaluation metrics and calculates a single ``accuracy`` metric across all " +"clients on the server side." msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:699 msgid "" "The other two categories of metrics (``losses_centralized`` and " "``metrics_centralized``) are still empty because they only apply when " -"centralized evaluation is being used. Part two of the Flower tutorial " -"will cover centralized evaluation." +"centralized evaluation is being used. Part two of the Flower tutorial will " +"cover centralized evaluation." msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:711 @@ -19927,28 +22026,28 @@ msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:713 msgid "" -"Congratulations, you just trained a convolutional neural network, " -"federated over 10 clients! With that, you understand the basics of " -"federated learning with Flower. The same approach you've seen can be used" -" with other machine learning frameworks (not just PyTorch) and tasks (not" -" just CIFAR-10 images classification), for example NLP with Hugging Face " -"Transformers or speech with SpeechBrain." +"Congratulations, you just trained a convolutional neural network, federated " +"over 10 clients! With that, you understand the basics of federated learning " +"with Flower. The same approach you've seen can be used with other machine " +"learning frameworks (not just PyTorch) and tasks (not just CIFAR-10 images " +"classification), for example NLP with Hugging Face Transformers or speech " +"with SpeechBrain." msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:715 msgid "" -"In the next notebook, we're going to cover some more advanced concepts. " -"Want to customize your strategy? Initialize parameters on the server " -"side? Or evaluate the aggregated model on the server side? We'll cover " -"all this and more in the next tutorial." +"In the next notebook, we're going to cover some more advanced concepts. Want " +"to customize your strategy? Initialize parameters on the server side? Or " +"evaluate the aggregated model on the server side? We'll cover all this and " +"more in the next tutorial." msgstr "" #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:733 msgid "" -"The `Flower Federated Learning Tutorial - Part 2 " -"`__ goes into more depth about strategies and all " -"the advanced things you can build with them." +"The `Flower Federated Learning Tutorial - Part 2 `__ goes " +"into more depth about strategies and all the advanced things you can build " +"with them." msgstr "" #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:9 @@ -19958,16 +22057,16 @@ msgstr "" #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:11 msgid "" "Welcome to the next part of the federated learning tutorial. In previous " -"parts of this tutorial, we introduced federated learning with PyTorch and" -" Flower (`part 1 `__)." +"parts of this tutorial, we introduced federated learning with PyTorch and " +"Flower (`part 1 `__)." msgstr "" #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:13 msgid "" -"In this notebook, we'll begin to customize the federated learning system " -"we built in the introductory notebook (again, using `Flower " -"`__ and `PyTorch `__)." +"In this notebook, we'll begin to customize the federated learning system we " +"built in the introductory notebook (again, using `Flower `__ and `PyTorch `__)." msgstr "" #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:17 @@ -19981,8 +22080,8 @@ msgstr "" #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:311 msgid "" "So far, everything should look familiar if you've worked through the " -"introductory notebook. With that, we're ready to introduce a number of " -"new features." +"introductory notebook. With that, we're ready to introduce a number of new " +"features." msgstr "" #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:323 @@ -19991,16 +22090,16 @@ msgstr "" #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:325 msgid "" -"Flower, by default, initializes the global model by asking one random " -"client for the initial parameters. In many cases, we want more control " -"over parameter initialization though. Flower therefore allows you to " -"directly pass the initial parameters to the Strategy:" +"Flower, by default, initializes the global model by asking one random client " +"for the initial parameters. In many cases, we want more control over " +"parameter initialization though. Flower therefore allows you to directly " +"pass the initial parameters to the Strategy:" msgstr "" #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:370 msgid "" -"Passing ``initial_parameters`` to the ``FedAvg`` strategy prevents Flower" -" from asking one of the clients for the initial parameters. If we look " +"Passing ``initial_parameters`` to the ``FedAvg`` strategy prevents Flower " +"from asking one of the clients for the initial parameters. If we look " "closely, we can see that the logs do not show any calls to the " "``FlowerClient.get_parameters`` method." msgstr "" @@ -20011,17 +22110,17 @@ msgstr "" #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:384 msgid "" -"We've seen the function ``start_simulation`` before. It accepts a number " -"of arguments, amongst them the ``client_fn`` used to create " -"``FlowerClient`` instances, the number of clients to simulate " -"``num_clients``, the number of rounds ``num_rounds``, and the strategy." +"We've seen the function ``start_simulation`` before. It accepts a number of " +"arguments, amongst them the ``client_fn`` used to create ``FlowerClient`` " +"instances, the number of clients to simulate ``num_clients``, the number of " +"rounds ``num_rounds``, and the strategy." msgstr "" #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:386 msgid "" "The strategy encapsulates the federated learning approach/algorithm, for " -"example, ``FedAvg`` or ``FedAdagrad``. Let's try to use a different " -"strategy this time:" +"example, ``FedAvg`` or ``FedAdagrad``. Let's try to use a different strategy " +"this time:" msgstr "" #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:424 @@ -20030,9 +22129,9 @@ msgstr "" #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:426 msgid "" -"Flower can evaluate the aggregated model on the server-side or on the " -"client-side. Client-side and server-side evaluation are similar in some " -"ways, but different in others." +"Flower can evaluate the aggregated model on the server-side or on the client-" +"side. Client-side and server-side evaluation are similar in some ways, but " +"different in others." msgstr "" #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:428 @@ -20040,33 +22139,33 @@ msgid "" "**Centralized Evaluation** (or *server-side evaluation*) is conceptually " "simple: it works the same way that evaluation in centralized machine " "learning does. If there is a server-side dataset that can be used for " -"evaluation purposes, then that's great. We can evaluate the newly " -"aggregated model after each round of training without having to send the " -"model to clients. We're also fortunate in the sense that our entire " -"evaluation dataset is available at all times." +"evaluation purposes, then that's great. We can evaluate the newly aggregated " +"model after each round of training without having to send the model to " +"clients. We're also fortunate in the sense that our entire evaluation " +"dataset is available at all times." msgstr "" #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:430 msgid "" -"**Federated Evaluation** (or *client-side evaluation*) is more complex, " -"but also more powerful: it doesn't require a centralized dataset and " -"allows us to evaluate models over a larger set of data, which often " -"yields more realistic evaluation results. In fact, many scenarios require" -" us to use **Federated Evaluation** if we want to get representative " -"evaluation results at all. But this power comes at a cost: once we start " -"to evaluate on the client side, we should be aware that our evaluation " -"dataset can change over consecutive rounds of learning if those clients " -"are not always available. Moreover, the dataset held by each client can " -"also change over consecutive rounds. This can lead to evaluation results " -"that are not stable, so even if we would not change the model, we'd see " -"our evaluation results fluctuate over consecutive rounds." +"**Federated Evaluation** (or *client-side evaluation*) is more complex, but " +"also more powerful: it doesn't require a centralized dataset and allows us " +"to evaluate models over a larger set of data, which often yields more " +"realistic evaluation results. In fact, many scenarios require us to use " +"**Federated Evaluation** if we want to get representative evaluation results " +"at all. But this power comes at a cost: once we start to evaluate on the " +"client side, we should be aware that our evaluation dataset can change over " +"consecutive rounds of learning if those clients are not always available. " +"Moreover, the dataset held by each client can also change over consecutive " +"rounds. This can lead to evaluation results that are not stable, so even if " +"we would not change the model, we'd see our evaluation results fluctuate " +"over consecutive rounds." msgstr "" #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:433 msgid "" "We've seen how federated evaluation works on the client side (i.e., by " -"implementing the ``evaluate`` method in ``FlowerClient``). Now let's see " -"how we can evaluate aggregated model parameters on the server-side:" +"implementing the ``evaluate`` method in ``FlowerClient``). Now let's see how " +"we can evaluate aggregated model parameters on the server-side:" msgstr "" #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:490 @@ -20075,50 +22174,48 @@ msgstr "" #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:492 msgid "" -"In some situations, we want to configure client-side execution (training," -" evaluation) from the server-side. One example for that is the server " -"asking the clients to train for a certain number of local epochs. Flower " -"provides a way to send configuration values from the server to the " -"clients using a dictionary. Let's look at an example where the clients " -"receive values from the server through the ``config`` parameter in " -"``fit`` (``config`` is also available in ``evaluate``). The ``fit`` " -"method receives the configuration dictionary through the ``config`` " -"parameter and can then read values from this dictionary. In this example," -" it reads ``server_round`` and ``local_epochs`` and uses those values to " -"improve the logging and configure the number of local training epochs:" +"In some situations, we want to configure client-side execution (training, " +"evaluation) from the server-side. One example for that is the server asking " +"the clients to train for a certain number of local epochs. Flower provides a " +"way to send configuration values from the server to the clients using a " +"dictionary. Let's look at an example where the clients receive values from " +"the server through the ``config`` parameter in ``fit`` (``config`` is also " +"available in ``evaluate``). The ``fit`` method receives the configuration " +"dictionary through the ``config`` parameter and can then read values from " +"this dictionary. In this example, it reads ``server_round`` and " +"``local_epochs`` and uses those values to improve the logging and configure " +"the number of local training epochs:" msgstr "" #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:546 msgid "" -"So how can we send this config dictionary from server to clients? The " -"built-in Flower Strategies provide way to do this, and it works similarly" -" to the way server-side evaluation works. We provide a function to the " -"strategy, and the strategy calls this function for every round of " -"federated learning:" +"So how can we send this config dictionary from server to clients? The built-" +"in Flower Strategies provide way to do this, and it works similarly to the " +"way server-side evaluation works. We provide a function to the strategy, and " +"the strategy calls this function for every round of federated learning:" msgstr "" #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:576 msgid "" -"Next, we'll just pass this function to the FedAvg strategy before " -"starting the simulation:" +"Next, we'll just pass this function to the FedAvg strategy before starting " +"the simulation:" msgstr "" #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:613 msgid "" -"As we can see, the client logs now include the current round of federated" -" learning (which they read from the ``config`` dictionary). We can also " -"configure local training to run for one epoch during the first and second" -" round of federated learning, and then for two epochs during the third " -"round." +"As we can see, the client logs now include the current round of federated " +"learning (which they read from the ``config`` dictionary). We can also " +"configure local training to run for one epoch during the first and second " +"round of federated learning, and then for two epochs during the third round." msgstr "" #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:615 msgid "" "Clients can also return arbitrary values to the server. To do so, they " -"return a dictionary from ``fit`` and/or ``evaluate``. We have seen and " -"used this concept throughout this notebook without mentioning it " -"explicitly: our ``FlowerClient`` returns a dictionary containing a custom" -" key/value pair as the third return value in ``evaluate``." +"return a dictionary from ``fit`` and/or ``evaluate``. We have seen and used " +"this concept throughout this notebook without mentioning it explicitly: our " +"``FlowerClient`` returns a dictionary containing a custom key/value pair as " +"the third return value in ``evaluate``." msgstr "" #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:627 @@ -20135,14 +22232,13 @@ msgstr "" #, python-format msgid "" "We now have 1000 partitions, each holding 45 training and 5 validation " -"examples. Given that the number of training examples on each client is " -"quite small, we should probably train the model a bit longer, so we " -"configure the clients to perform 3 local training epochs. We should also " -"adjust the fraction of clients selected for training during each round " -"(we don't want all 1000 clients participating in every round), so we " -"adjust ``fraction_fit`` to ``0.05``, which means that only 5% of " -"available clients (so 50 clients) will be selected for training each " -"round:" +"examples. Given that the number of training examples on each client is quite " +"small, we should probably train the model a bit longer, so we configure the " +"clients to perform 3 local training epochs. We should also adjust the " +"fraction of clients selected for training during each round (we don't want " +"all 1000 clients participating in every round), so we adjust " +"``fraction_fit`` to ``0.05``, which means that only 5% of available clients " +"(so 50 clients) will be selected for training each round:" msgstr "" #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:699 @@ -20155,94 +22251,111 @@ msgstr "" #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:701 msgid "" -"In the later sections, we've seen how we can communicate arbitrary values" -" between server and clients to fully customize client-side execution. " -"With that capability, we built a large-scale Federated Learning " -"simulation using the Flower Virtual Client Engine and ran an experiment " -"involving 1000 clients in the same workload - all in a Jupyter Notebook!" +"In the later sections, we've seen how we can communicate arbitrary values " +"between server and clients to fully customize client-side execution. With " +"that capability, we built a large-scale Federated Learning simulation using " +"the Flower Virtual Client Engine and ran an experiment involving 1000 " +"clients in the same workload - all in a Jupyter Notebook!" msgstr "" #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:719 msgid "" -"The `Flower Federated Learning Tutorial - Part 3 " -"`__ shows how to build a fully custom ``Strategy`` from " -"scratch." +"The `Flower Federated Learning Tutorial - Part 3 `__ shows how " +"to build a fully custom ``Strategy`` from scratch." msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:9 msgid "What is Federated Learning?" -msgstr "" +msgstr "연합 학습이란 무엇입니까?" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:13 msgid "" "In this tutorial, you will learn what federated learning is, build your " "first system in Flower, and gradually extend it. If you work through all " -"parts of the tutorial, you will be able to build advanced federated " -"learning systems that approach the current state of the art in the field." +"parts of the tutorial, you will be able to build advanced federated learning " +"systems that approach the current state of the art in the field." msgstr "" +"이 튜토리얼에서 연합 학습이 무엇인지 배우고 Flower로 첫 번째 시스템을 " +"구축하고 점진적으로 확장해 나갈 것입니다. 본 튜토리얼의 모든 부분을 완성할 " +"수 있다면, 당신은 고급 연방 학습 시스템을 구축하여 그 분야의 현재 기술 " +"수준에 접근할 수 있을 것입니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:15 msgid "" -"🧑‍🏫 This tutorial starts at zero and expects no familiarity with " -"federated learning. Only a basic understanding of data science and Python" -" programming is assumed." +"🧑‍🏫 This tutorial starts at zero and expects no familiarity with federated " +"learning. Only a basic understanding of data science and Python programming " +"is assumed." msgstr "" +"🧑‍🏫이 튜토리얼은 제로베이부터 시작되며 연방 학습에 상세히 아는 필요가 " +"없습니다. 데이터 과학과 파이썬 프로그래밍에 대한 기본적인 이해만 가정합니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:17 msgid "" -"`Star Flower on GitHub `__ ⭐️ and join " -"the open-source Flower community on Slack to connect, ask questions, and " -"get help: `Join Slack `__ 🌼 We'd love to " -"hear from you in the ``#introductions`` channel! And if anything is " -"unclear, head over to the ``#questions`` channel." +"`Star Flower on GitHub `__ ⭐️ and join the " +"open-source Flower community on Slack to connect, ask questions, and get " +"help: `Join Slack `__ 🌼 We'd love to hear " +"from you in the ``#introductions`` channel! And if anything is unclear, head " +"over to the ``#questions`` channel." msgstr "" +"`Star Flower on GitHub `__ ⭐️ Slack의 " +"오픈소스 Flower 커뮤니티에 가입하여 소통하고 질문하고 도움을 받을 수 " +"있습니다: `Slack 가입`__ 🌼 ``#introductions``" +"채널에서 당신의 목소리를 듣고 싶습니다! 궁금한 점이 있으시면``#questions`` " +"채널로 방문해 주시기 바랍니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:31 msgid "Classic machine learning" -msgstr "" +msgstr "클래식 머신러닝" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:33 msgid "" -"Before we begin to discuss federated learning, let us quickly recap how " -"most machine learning works today." -msgstr "" +"Before we begin to discuss federated learning, let us quickly recap how most " +"machine learning works today." +msgstr "연방 학습에 대해 논의하기 전에 현재 대부분의 머신러닝이 어떻게 작동하는지 " +"간략히 요약하겠습니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:35 msgid "" -"In machine learning, we have a model, and we have data. The model could " -"be a neural network (as depicted here), or something else, like classical" -" linear regression." +"In machine learning, we have a model, and we have data. The model could be a " +"neural network (as depicted here), or something else, like classical linear " +"regression." msgstr "" +"기계 학습에서 우리는 모델과 데이터를 가지고 있습니다.모델은 신경망((그림과 " +"같이))일 수도 있고 고전적인 선형 회귀와 같은 다른 것일 수도 있습니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:41 -msgid "|191c6b8b5e1d46f99de4872746afa8af|" -msgstr "" +msgid "|93b02017c78049bbbd5ae456dcb2c91b|" +msgstr "|93b02017c78049bbbd5ae456dcb2c91b|" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:109 msgid "Model and data" -msgstr "" +msgstr "모델과 데이터" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:47 msgid "" -"We train the model using the data to perform a useful task. A task could " -"be to detect objects in images, transcribe an audio recording, or play a " -"game like Go." +"We train the model using the data to perform a useful task. A task could be " +"to detect objects in images, transcribe an audio recording, or play a game " +"like Go." msgstr "" +"우리는 유용한 작업을 수행하기 위해 데이터를 사용하여 모델을 훈련합니다. " +"작업은 이미지 속 물체를 감지하거나 음성 녹음을 기록하거나 바둑과 같은 게임을 " +"하는 것일 수 있습니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:53 -msgid "|21b83f3feb024a049617190555a13549|" -msgstr "" +msgid "|01471150fd5144c080a176b43e92a3ff|" +msgstr "|01471150fd5144c080a176b43e92a3ff|" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:111 msgid "Train model using data" -msgstr "" +msgstr "데이터를 이용한 모델 훈련" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:59 msgid "" -"Now, in practice, the training data we work with doesn't originate on the" -" machine we train the model on. It gets created somewhere else." -msgstr "" +"Now, in practice, the training data we work with doesn't originate on the " +"machine we train the model on. It gets created somewhere else." +msgstr "실제로 우리가 사용하는 훈련 데이터는 모델을 훈련시키는 기계에서 비롯된 것이 " +"아닙니다. 그 데이터는 다른 곳에서 만들어졌습니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:61 msgid "" @@ -20250,46 +22363,54 @@ msgid "" "collecting sensor data, a laptop receiving input via the keyboard, or a " "smart speaker listening to someone trying to sing a song." msgstr "" +"스마트폰에서 사용자와 앱의 상호 작용, 센서 데이터를 수집하는 자동차, " +"키보드를 통해 입력을 받는 노트북 또는 누군가 노래를 부르리는 것을 듣는 " +"스마트 스피커에서 비롯됩니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:67 -msgid "|0dd15b4df7e3422f88aaf74cb401bfa7|" -msgstr "" +msgid "|9bc21c7dbd17444a8f070c60786e3484|" +msgstr "|9bc21c7dbd17444a8f070c60786e3484|" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:113 msgid "Data on a phone" -msgstr "" +msgstr "핸드푼에 있는 데이터" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:73 msgid "" "What's also important to mention, this \"somewhere else\" is usually not " -"just one place, it's many places. It could be several devices all running" -" the same app. But it could also be several organizations, all generating" -" data for the same task." +"just one place, it's many places. It could be several devices all running " +"the same app. But it could also be several organizations, all generating " +"data for the same task." msgstr "" +"또한 중요한 것은 이 \"다른 곳\"이 보통 한 곳만 아니라 여러 곳이라는 " +"것입니다. 같은 앱을 실행하는 여러 기기일 수도 있습니다. 하지만 여러 조직이 " +"모두 같은 작업을 위해 데이터를 생성하는 것일 수도 있습니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:79 -msgid "|60e16f6be7354ca793444e01aa7adf25|" -msgstr "" +msgid "|3047bbce54b34099ae559963d0420d79|" +msgstr "|3047bbce54b34099ae559963d0420d79|" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:115 msgid "Data is on many devices" -msgstr "" +msgstr "데이터가 여러 장치에 있습니다" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:85 msgid "" -"So to use machine learning, or any kind of data analysis, the approach " -"that has been used in the past was to collect all data on a central " -"server. This server can be somewhere in a data center, or somewhere in " -"the cloud." +"So to use machine learning, or any kind of data analysis, the approach that " +"has been used in the past was to collect all data on a central server. This " +"server can be somewhere in a data center, or somewhere in the cloud." msgstr "" +"따라서 머신러닝이나 어떤 종류의 데이터 분석을 이용하려면 과거에는 중앙 " +"서버에서 모든 데이터를 수집하는 방법이 사용되었습니다.이 서버는 데이터 센터 " +"어딘가에 있을 수도 있고 클라우드 어딘가에 있을 수도 있습니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:91 -msgid "|a7032acbd65948a8beef8bccbbb9b83a|" -msgstr "" +msgid "|e9f8ce948593444fb838d2f354c7ec5d|" +msgstr "|e9f8ce948593444fb838d2f354c7ec5d|" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:117 msgid "Central data collection" -msgstr "" +msgstr "중앙 데이터 수집" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:97 msgid "" @@ -20297,56 +22418,68 @@ msgid "" "learning algorithms to train our model on the data. This is the machine " "learning approach that we've basically always relied on." msgstr "" +"모든 데이터가 한 곳에 모이면, 우리는 궁극적으로 머신러닝 알고리즘을 사용하여 " +"데이터에서 모델을 훈련시킬 수 있습니다.이것이 바로 우리가 기본적으로 " +"의지해왔던 머신러닝 방법입니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:103 -msgid "|dd0e05706e584ee29e07cd39e6af5498|" -msgstr "" +msgid "|c24c1478b30e4f74839208628a842d1e|" +msgstr "|c24c1478b30e4f74839208628a842d1e|" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:119 msgid "Central model training" -msgstr "" +msgstr "중앙 데이터 훈련" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:130 msgid "Challenges of classical machine learning" -msgstr "" +msgstr "클래식 머신러닝이 만난 도전" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:132 msgid "" -"The classic machine learning approach we've just seen can be used in some" -" cases. Great examples include categorizing holiday photos, or analyzing " -"web traffic. Cases, where all the data is naturally available on a " -"centralized server." +"The classic machine learning approach we've just seen can be used in some " +"cases. Great examples include categorizing holiday photos, or analyzing web " +"traffic. Cases, where all the data is naturally available on a centralized " +"server." msgstr "" +"우리가 방금 본 클래식 머신러닝 접근 방식은 경우에 따라 사용될 수 있습니다. " +"좋은 예로는 휴일 사진을 분류하거나 웹 트래픽을 분석하는 것이 있습니다. " +"이러한 사례에서 모든 데이터는 자연스럽게 중앙 서버에서 사용할 수 있습니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:138 -msgid "|2a2031018a1c4f81a69ea16df4947bd0|" -msgstr "" +msgid "|1b3613d7a58847b59e1d3180802dbc09|" +msgstr "|1b3613d7a58847b59e1d3180802dbc09|" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:173 msgid "Centralized possible" -msgstr "" +msgstr "집중화 가능" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:144 msgid "" -"But the approach can not be used in many other cases. Cases, where the " -"data is not available on a centralized server, or cases where the data " -"available on one server is not enough to train a good model." +"But the approach can not be used in many other cases. Cases, where the data " +"is not available on a centralized server, or cases where the data available " +"on one server is not enough to train a good model." msgstr "" +"그러나 이 방법은 다른 많은 경우에 적용되지 않습니다.예를 들어, 중앙 집중식 " +"서버에 데이터가 없거나 서버의 데이터가 좋은 모델을 훈련하기에 충분하지 " +"않습니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:150 -msgid "|5e841497933340d3b5c2efbf37e3e6a6|" -msgstr "" +msgid "|9980b5213db547d0b8024a50992b9e3f|" +msgstr "|9980b5213db547d0b8024a50992b9e3f|" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:175 msgid "Centralized impossible" -msgstr "" +msgstr "집중화 가능" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:156 msgid "" -"There are many reasons why the classic centralized machine learning " -"approach does not work for a large number of highly important real-world " -"use cases. Those reasons include:" +"There are many reasons why the classic centralized machine learning approach " +"does not work for a large number of highly important real-world use cases. " +"Those reasons include:" msgstr "" +"클래식 중앙 집중식 머신러닝 방법이 현실 세계에서 매우 중요한 수많은 사용 " +"사례를 충족시킬 수 없는 이유가 있습니다.이유는 다음과 같은 여러 가지가 " +"있습니다:" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:158 msgid "" @@ -20355,32 +22488,39 @@ msgid "" "(Russia), CDPR (China), PDPB (India), PIPA (Korea), APPI (Japan), PDP " "(Indonesia), PDPA (Singapore), APP (Australia), and other regulations " "protect sensitive data from being moved. In fact, those regulations " -"sometimes even prevent single organizations from combining their own " -"users' data for artificial intelligence training because those users live" -" in different parts of the world, and their data is governed by different" -" data protection regulations." -msgstr "" +"sometimes even prevent single organizations from combining their own users' " +"data for artificial intelligence training because those users live in " +"different parts of the world, and their data is governed by different data " +"protection regulations." +msgstr "" +"**규정**: GDPR (유럽), CCPA (캘리포니아), PIPEDA (캐나다), LGPD (브라질), " +"PDPL (아르헨티나), KVKK (터키), POPI (남아프리카공화국), FSS (러시아), CDPR " +"(중국), PDPB (인도), PIPA (한국), APPI (일본), PDP (인도네시아), PDPA " +"(싱가포르), APP (호주)등의 법규로 민감한 데이터가 이동하지 않도록 보호하고 " +"있습니 다. 실제 로이러한 규정은 사용자가 세계의 다른 지역에 살고 데이터가 " +"다른 데이터 보호 규정에 의해 통제되기 때문에 단일 조직이 자체 사용자 " +"데이터를 인공 지능 교육에 사용하는 것을 방지하기도 합니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:160 msgid "" -"**User preference**: In addition to regulation, there are use cases where" -" users just expect that no data leaves their device, ever. If you type " -"your passwords and credit card info into the digital keyboard of your " -"phone, you don't expect those passwords to end up on the server of the " -"company that developed that keyboard, do you? In fact, that use case was " -"the reason federated learning was invented in the first place." +"**User preference**: In addition to regulation, there are use cases where " +"users just expect that no data leaves their device, ever. If you type your " +"passwords and credit card info into the digital keyboard of your phone, you " +"don't expect those passwords to end up on the server of the company that " +"developed that keyboard, do you? In fact, that use case was the reason " +"federated learning was invented in the first place." msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:161 msgid "" -"**Data volume**: Some sensors, like cameras, produce such a high data " -"volume that it is neither feasible nor economic to collect all the data " -"(due to, for example, bandwidth or communication efficiency). Think about" -" a national rail service with hundreds of train stations across the " -"country. If each of these train stations is outfitted with a number of " -"security cameras, the volume of raw on-device data they produce requires " -"incredibly powerful and exceedingly expensive infrastructure to process " -"and store. And most of the data isn't even useful." +"**Data volume**: Some sensors, like cameras, produce such a high data volume " +"that it is neither feasible nor economic to collect all the data (due to, " +"for example, bandwidth or communication efficiency). Think about a national " +"rail service with hundreds of train stations across the country. If each of " +"these train stations is outfitted with a number of security cameras, the " +"volume of raw on-device data they produce requires incredibly powerful and " +"exceedingly expensive infrastructure to process and store. And most of the " +"data isn't even useful." msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:164 @@ -20395,8 +22535,7 @@ msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:167 msgid "" -"Financial information from different organizations to detect financial " -"fraud" +"Financial information from different organizations to detect financial fraud" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:168 @@ -20409,13 +22548,13 @@ msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:171 msgid "" -"The popularity of privacy-enhancing systems like the `Brave " -"`__ browser or the `Signal `__ " -"messenger shows that users care about privacy. In fact, they choose the " -"privacy-enhancing version over other alternatives, if such an alternative" -" exists. But what can we do to apply machine learning and data science to" -" these cases to utilize private data? After all, these are all areas that" -" would benefit significantly from recent advances in AI." +"The popularity of privacy-enhancing systems like the `Brave `__ browser or the `Signal `__ messenger shows " +"that users care about privacy. In fact, they choose the privacy-enhancing " +"version over other alternatives, if such an alternative exists. But what can " +"we do to apply machine learning and data science to these cases to utilize " +"private data? After all, these are all areas that would benefit " +"significantly from recent advances in AI." msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:186 @@ -20425,9 +22564,8 @@ msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:188 msgid "" "Federated learning simply reverses this approach. It enables machine " -"learning on distributed data by moving the training to the data, instead " -"of moving the data to the training. Here's the single-sentence " -"explanation:" +"learning on distributed data by moving the training to the data, instead of " +"moving the data to the training. Here's the single-sentence explanation:" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:190 @@ -20440,22 +22578,22 @@ msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:193 msgid "" -"By doing so, it enables us to use machine learning (and other data " -"science approaches) in areas where it wasn't possible before. We can now " -"train excellent medical AI models by enabling different hospitals to work" -" together. We can solve financial fraud by training AI models on the data" -" of different financial institutions. We can build novel privacy-" -"enhancing applications (such as secure messaging) that have better built-" -"in AI than their non-privacy-enhancing alternatives. And those are just a" -" few of the examples that come to mind. As we deploy federated learning, " -"we discover more and more areas that can suddenly be reinvented because " -"they now have access to vast amounts of previously inaccessible data." +"By doing so, it enables us to use machine learning (and other data science " +"approaches) in areas where it wasn't possible before. We can now train " +"excellent medical AI models by enabling different hospitals to work " +"together. We can solve financial fraud by training AI models on the data of " +"different financial institutions. We can build novel privacy-enhancing " +"applications (such as secure messaging) that have better built-in AI than " +"their non-privacy-enhancing alternatives. And those are just a few of the " +"examples that come to mind. As we deploy federated learning, we discover " +"more and more areas that can suddenly be reinvented because they now have " +"access to vast amounts of previously inaccessible data." msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:196 msgid "" -"So how does federated learning work, exactly? Let's start with an " -"intuitive explanation." +"So how does federated learning work, exactly? Let's start with an intuitive " +"explanation." msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:199 @@ -20468,13 +22606,13 @@ msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:204 msgid "" -"We start by initializing the model on the server. This is exactly the " -"same in classic centralized learning: we initialize the model parameters," -" either randomly or from a previously saved checkpoint." +"We start by initializing the model on the server. This is exactly the same " +"in classic centralized learning: we initialize the model parameters, either " +"randomly or from a previously saved checkpoint." msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:210 -msgid "|19687aecbc3a485da999b66fe2051005|" +msgid "|c7afb4c92d154bfaa5e8cb9a150e17f1|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:307 @@ -20483,22 +22621,22 @@ msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:217 msgid "" -"Step 1: Send model to a number of connected organizations/devices (client" -" nodes)" +"Step 1: Send model to a number of connected organizations/devices (client " +"nodes)" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:219 msgid "" "Next, we send the parameters of the global model to the connected client " "nodes (think: edge devices like smartphones or servers belonging to " -"organizations). This is to ensure that each participating node starts " -"their local training using the same model parameters. We often use only a" -" few of the connected nodes instead of all nodes. The reason for this is " -"that selecting more and more client nodes has diminishing returns." +"organizations). This is to ensure that each participating node starts their " +"local training using the same model parameters. We often use only a few of " +"the connected nodes instead of all nodes. The reason for this is that " +"selecting more and more client nodes has diminishing returns." msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:225 -msgid "|32ef0bbade4d4500b7be97cf62405661|" +msgid "|032eb6fed6924ac387b9f13854919196|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:309 @@ -20507,22 +22645,22 @@ msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:232 msgid "" -"Step 2: Train model locally on the data of each organization/device " -"(client node)" +"Step 2: Train model locally on the data of each organization/device (client " +"node)" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:234 msgid "" -"Now that all (selected) client nodes have the latest version of the " -"global model parameters, they start the local training. They use their " -"own local dataset to train their own local model. They don't train the " -"model until full convergence, but they only train for a little while. " -"This could be as little as one epoch on the local data, or even just a " -"few steps (mini-batches)." +"Now that all (selected) client nodes have the latest version of the global " +"model parameters, they start the local training. They use their own local " +"dataset to train their own local model. They don't train the model until " +"full convergence, but they only train for a little while. This could be as " +"little as one epoch on the local data, or even just a few steps (mini-" +"batches)." msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:240 -msgid "|9d57ed324b304a698263f5a983a56a6b|" +msgid "|fbf225add7fd4df5a9bf25a95597d954|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:311 @@ -20535,17 +22673,16 @@ msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:249 msgid "" -"After local training, each client node has a slightly different version " -"of the model parameters they originally received. The parameters are all " +"After local training, each client node has a slightly different version of " +"the model parameters they originally received. The parameters are all " "different because each client node has different examples in its local " -"dataset. The client nodes then send those model updates back to the " -"server. The model updates they send can either be the full model " -"parameters or just the gradients that were accumulated during local " -"training." +"dataset. The client nodes then send those model updates back to the server. " +"The model updates they send can either be the full model parameters or just " +"the gradients that were accumulated during local training." msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:255 -msgid "|d41510e6781c4bf18c234c6bfb8d4937|" +msgid "|7efbe3d29d8349b89594e8947e910525|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:313 @@ -20559,31 +22696,30 @@ msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:264 msgid "" "The server receives model updates from the selected client nodes. If it " -"selected 100 client nodes, it now has 100 slightly different versions of " -"the original global model, each trained on the local data of one client. " -"But didn't we want to have one model that contains the learnings from the" -" data of all 100 client nodes?" +"selected 100 client nodes, it now has 100 slightly different versions of the " +"original global model, each trained on the local data of one client. But " +"didn't we want to have one model that contains the learnings from the data " +"of all 100 client nodes?" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:266 msgid "" -"In order to get one single model, we have to combine all the model " -"updates we received from the client nodes. This process is called " -"*aggregation*, and there are many different ways to do it. The most basic" -" way to do it is called *Federated Averaging* (`McMahan et al., 2016 " -"`__), often abbreviated as *FedAvg*. " -"*FedAvg* takes the 100 model updates and, as the name suggests, averages " -"them. To be more precise, it takes the *weighted average* of the model " -"updates, weighted by the number of examples each client used for " -"training. The weighting is important to make sure that each data example " -"has the same \"influence\" on the resulting global model. If one client " -"has 10 examples, and another client has 100 examples, then - without " -"weighting - each of the 10 examples would influence the global model ten " -"times as much as each of the 100 examples." +"In order to get one single model, we have to combine all the model updates " +"we received from the client nodes. This process is called *aggregation*, and " +"there are many different ways to do it. The most basic way to do it is " +"called *Federated Averaging* (`McMahan et al., 2016 `__), often abbreviated as *FedAvg*. *FedAvg* takes the 100 " +"model updates and, as the name suggests, averages them. To be more precise, " +"it takes the *weighted average* of the model updates, weighted by the number " +"of examples each client used for training. The weighting is important to " +"make sure that each data example has the same \"influence\" on the resulting " +"global model. If one client has 10 examples, and another client has 100 " +"examples, then - without weighting - each of the 10 examples would influence " +"the global model ten times as much as each of the 100 examples." msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:273 -msgid "|a0198a7ebbfb4b9289e7312711cbc967|" +msgid "|329fb3c04c744eda83bb51fa444c2266|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:315 @@ -20597,41 +22733,39 @@ msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:282 msgid "" "Steps 1 to 4 are what we call a single round of federated learning. The " -"global model parameters get sent to the participating client nodes (step " -"1), the client nodes train on their local data (step 2), they send their " -"updated models to the server (step 3), and the server then aggregates the" -" model updates to get a new version of the global model (step 4)." +"global model parameters get sent to the participating client nodes (step 1), " +"the client nodes train on their local data (step 2), they send their updated " +"models to the server (step 3), and the server then aggregates the model " +"updates to get a new version of the global model (step 4)." msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:284 msgid "" -"During a single round, each client node that participates in that " -"iteration only trains for a little while. This means that after the " -"aggregation step (step 4), we have a model that has been trained on all " -"the data of all participating client nodes, but only for a little while. " -"We then have to repeat this training process over and over again to " -"eventually arrive at a fully trained model that performs well across the " -"data of all client nodes." +"During a single round, each client node that participates in that iteration " +"only trains for a little while. This means that after the aggregation step " +"(step 4), we have a model that has been trained on all the data of all " +"participating client nodes, but only for a little while. We then have to " +"repeat this training process over and over again to eventually arrive at a " +"fully trained model that performs well across the data of all client nodes." msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:289 msgid "" "Congratulations, you now understand the basics of federated learning. " -"There's a lot more to discuss, of course, but that was federated learning" -" in a nutshell. In later parts of this tutorial, we will go into more " -"detail. Interesting questions include: How can we select the best client " -"nodes that should participate in the next round? What's the best way to " -"aggregate model updates? How can we handle failing client nodes " -"(stragglers)?" +"There's a lot more to discuss, of course, but that was federated learning in " +"a nutshell. In later parts of this tutorial, we will go into more detail. " +"Interesting questions include: How can we select the best client nodes that " +"should participate in the next round? What's the best way to aggregate model " +"updates? How can we handle failing client nodes (stragglers)?" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:294 msgid "" -"Just like we can train a model on the decentralized data of different " -"client nodes, we can also evaluate the model on that data to receive " -"valuable metrics. This is called federated evaluation, sometimes " -"abbreviated as FE. In fact, federated evaluation is an integral part of " -"most federated learning systems." +"Just like we can train a model on the decentralized data of different client " +"nodes, we can also evaluate the model on that data to receive valuable " +"metrics. This is called federated evaluation, sometimes abbreviated as FE. " +"In fact, federated evaluation is an integral part of most federated learning " +"systems." msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:297 @@ -20640,25 +22774,24 @@ msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:299 msgid "" -"In many cases, machine learning isn't necessary to derive value from " -"data. Data analysis can yield valuable insights, but again, there's often" -" not enough data to get a clear answer. What's the average age at which " -"people develop a certain type of health condition? Federated analytics " -"enables such queries over multiple client nodes. It is usually used in " -"conjunction with other privacy-enhancing technologies like secure " -"aggregation to prevent the server from seeing the results submitted by " -"individual client nodes." +"In many cases, machine learning isn't necessary to derive value from data. " +"Data analysis can yield valuable insights, but again, there's often not " +"enough data to get a clear answer. What's the average age at which people " +"develop a certain type of health condition? Federated analytics enables such " +"queries over multiple client nodes. It is usually used in conjunction with " +"other privacy-enhancing technologies like secure aggregation to prevent the " +"server from seeing the results submitted by individual client nodes." msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:305 msgid "" "Differential privacy (DP) is often mentioned in the context of Federated " -"Learning. It is a privacy-preserving method used when analyzing and " -"sharing statistical data, ensuring the privacy of individual " -"participants. DP achieves this by adding statistical noise to the model " -"updates, ensuring any individual participants’ information cannot be " -"distinguished or re-identified. This technique can be considered an " -"optimization that provides a quantifiable privacy protection measure." +"Learning. It is a privacy-preserving method used when analyzing and sharing " +"statistical data, ensuring the privacy of individual participants. DP " +"achieves this by adding statistical noise to the model updates, ensuring any " +"individual participants’ information cannot be distinguished or re-" +"identified. This technique can be considered an optimization that provides a " +"quantifiable privacy protection measure." msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:326 @@ -20667,42 +22800,83 @@ msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:328 msgid "" -"Federated learning, federated evaluation, and federated analytics require" -" infrastructure to move machine learning models back and forth, train and" -" evaluate them on local data, and then aggregate the updated models. " -"Flower provides the infrastructure to do exactly that in an easy, " -"scalable, and secure way. In short, Flower presents a unified approach to" -" federated learning, analytics, and evaluation. It allows the user to " -"federate any workload, any ML framework, and any programming language." +"Federated learning, federated evaluation, and federated analytics require " +"infrastructure to move machine learning models back and forth, train and " +"evaluate them on local data, and then aggregate the updated models. Flower " +"provides the infrastructure to do exactly that in an easy, scalable, and " +"secure way. In short, Flower presents a unified approach to federated " +"learning, analytics, and evaluation. It allows the user to federate any " +"workload, any ML framework, and any programming language." msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:334 -msgid "|2c13f726c8c843fc8aae997bf906125b|" +msgid "|c00bf2750bc24d229737a0fe1395f0fc|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:340 msgid "" -"Flower federated learning server and client nodes (car, scooter, personal" -" computer, roomba, and phone)" +"Flower federated learning server and client nodes (car, scooter, personal " +"computer, roomba, and phone)" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:353 msgid "" -"Congratulations, you just learned the basics of federated learning and " -"how it relates to the classic (centralized) machine learning!" +"Congratulations, you just learned the basics of federated learning and how " +"it relates to the classic (centralized) machine learning!" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:355 msgid "" -"In the next part of this tutorial, we are going to build a first " -"federated learning system with Flower." +"In the next part of this tutorial, we are going to build a first federated " +"learning system with Flower." msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:373 msgid "" -"The `Flower Federated Learning Tutorial - Part 1 " -"`__ shows how to build a simple federated learning system " -"with PyTorch and Flower." -msgstr "" - +"The `Flower Federated Learning Tutorial - Part 1 `__ shows how to " +"build a simple federated learning system with PyTorch and Flower." +msgstr "" + +#~ msgid "" +#~ "Currently, Flower provides two images, a ``base`` image and a " +#~ "``superlink`` image. The base image, as the name suggests, contains basic " +#~ "dependencies that the SuperLink needs. This includes system dependencies, " +#~ "Python and Python tools. The SuperLink image is based on the base image, " +#~ "but it additionally installs the SuperLink using ``pip``." +#~ msgstr "" +#~ "현재, Flower는 \"base\" 이미지 그리고 \"superlink\" 이미지를 제공합니다. " +#~ "base 이미지는 이름에서 알 수 있듯이 SuperLink가 필요로 하는 기본 " +#~ "dependencies를 포함하고 있습니다. 여기에는 시스템 dependencies, Python 및 " +#~ "Python 도구가 포함됩니다. SuperLink 이미지는 base 이미지를 기반으로 하지" +#~ "만 \"pip\"을 사용하여 SuperLink를 추가로 설치합니다." + +#~ msgid "``3.11``" +#~ msgstr "``3.11``" + +#~ msgid "Defaults to ``22.04``." +#~ msgstr "``22.04``이 기본값." + +#~ msgid "Defaults to ``flwr/base``." +#~ msgstr "``flwr/base``이 기본값." + +#~ msgid "The Python version of the base image." +#~ msgstr "base 이미지의 Python 버전." + +#~ msgid "Defaults to ``py3.11``." +#~ msgstr "``py3.11``이 기본값." + +#~ msgid "Defaults to ``ubuntu22.04``." +#~ msgstr "``ubuntu22.04``이 기본값." + +#~ msgid "Defaults to ``flwr``." +#~ msgstr "``flwr``이 기본값." + +#~ msgid "" +#~ "The name of image is ``flwr_superlink`` and the tag ``0.1.0``. Remember " +#~ "that the build arguments as well as the name and tag can be adapted to " +#~ "your needs. These values serve as examples only." +#~ msgstr "" +#~ "이미지의 이름은 ``flwr_superlink``이고 태그는 ``0.1.0``입니다. 필요에 따" +#~ "라 빌드 argument들 뿐만 아니라 이름과 태그도 정할 수 있습니다. 이 값들은 " +#~ "예시일 뿐입니다." diff --git a/doc/locales/ko/LC_MESSAGES/sphinx.po b/doc/locales/ko/LC_MESSAGES/sphinx.po index bcfa940edeac..fbb6c75085a1 100644 --- a/doc/locales/ko/LC_MESSAGES/sphinx.po +++ b/doc/locales/ko/LC_MESSAGES/sphinx.po @@ -8,7 +8,7 @@ msgid "" msgstr "" "Project-Id-Version: Flower main\n" "Report-Msgid-Bugs-To: \n" -"POT-Creation-Date: 2024-05-13 09:48+0200\n" +"POT-Creation-Date: 2024-05-28 11:47+0200\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: ko\n" @@ -17,7 +17,7 @@ msgstr "" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" -"Generated-By: Babel 2.14.0\n" +"Generated-By: Babel 2.15.0\n" #: ../../source/_templates/base.html:18 msgid "About these documents" @@ -28,6 +28,7 @@ msgid "Index" msgstr "" #: ../../source/_templates/base.html:24 +#: ../../source/_templates/sidebar/search.html:2 msgid "Search" msgstr "" @@ -35,3 +36,7 @@ msgstr "" msgid "Copyright" msgstr "" +#: ../../source/_templates/sidebar/search.html:2 +msgid "Type / to search" +msgstr "" + diff --git a/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po b/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po index 4e117619f9b5..49bb01908421 100644 --- a/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po +++ b/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po @@ -3,279 +3,353 @@ # This file is distributed under the same license as the Flower package. # FIRST AUTHOR , 2023. # -#, fuzzy msgid "" msgstr "" "Project-Id-Version: Flower main\n" "Report-Msgid-Bugs-To: \n" -"POT-Creation-Date: 2024-03-15 14:23+0000\n" -"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" -"Last-Translator: FULL NAME \n" +"POT-Creation-Date: 2024-06-17 16:09+0200\n" +"PO-Revision-Date: 2024-05-25 11:09+0000\n" +"Last-Translator: Gustavo Bertoli \n" "Language: pt_BR\n" -"Language-Team: pt_BR \n" -"Plural-Forms: nplurals=2; plural=(n > 1);\n" +"Language-Team: Portuguese (Brazil) \n" +"Plural-Forms: nplurals=2; plural=n > 1;\n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" -"Generated-By: Babel 2.14.0\n" +"Generated-By: Babel 2.15.0\n" #: ../../source/contributor-explanation-architecture.rst:2 msgid "Flower Architecture" -msgstr "" +msgstr "Arquitetura do Flower" #: ../../source/contributor-explanation-architecture.rst:5 msgid "Edge Client Engine" -msgstr "" +msgstr "Engine do Edge Client" #: ../../source/contributor-explanation-architecture.rst:7 msgid "" "`Flower `_ core framework architecture with Edge " "Client Engine" msgstr "" +"`Flower `_ arquitetura principal do framework com " +"Engine do Edge Client" #: ../../source/contributor-explanation-architecture.rst:13 msgid "Virtual Client Engine" -msgstr "" +msgstr "Engine do Virtual Client" #: ../../source/contributor-explanation-architecture.rst:15 msgid "" "`Flower `_ core framework architecture with Virtual " "Client Engine" msgstr "" +"`Flower `_ arquitetura principal do framework com " +"Engine do Virtual Client" #: ../../source/contributor-explanation-architecture.rst:21 msgid "Virtual Client Engine and Edge Client Engine in the same workload" msgstr "" +"Engine do Virtual Client e do Edge Client no mesma carga de trabalho " +"(workload)" #: ../../source/contributor-explanation-architecture.rst:23 msgid "" "`Flower `_ core framework architecture with both " "Virtual Client Engine and Edge Client Engine" msgstr "" +"`Flower `_ arquitetura principal do framework com " +"ambas engines do Virtual Client e do Edge Client" #: ../../source/contributor-how-to-build-docker-images.rst:2 msgid "How to build Docker Flower images locally" -msgstr "" +msgstr "Como construir imagens Docker do Flower localmente" #: ../../source/contributor-how-to-build-docker-images.rst:4 +#, fuzzy msgid "" "Flower provides pre-made docker images on `Docker Hub " -"`_ that include all necessary " -"dependencies for running the server. You can also build your own custom " -"docker images from scratch with a different version of Python or Ubuntu " -"if that is what you need. In this guide, we will explain what images " -"exist and how to build them locally." -msgstr "" - -#: ../../source/contributor-how-to-build-docker-images.rst:9 +"`_ that include all necessary dependencies" +" for running the SuperLink, SuperNode or ServerApp. You can also build " +"your own custom docker images from scratch with a different version of " +"Python or Linux distribution (Ubuntu/Alpine) if that is what you need. In" +" this guide, we will explain what images exist and how to build them " +"locally." +msgstr "" +"Flower disponibiliza imagens docker em `Docker Hub " +"`_ que incluem todas as " +"dependências necesárias para executar o servidor. Você pode também " +"compilar suas próprias imagens docker customizadas do zero com uma versão" +" diferente do Python ou do Ubuntu se isso for o que você precisa. Neste " +"guia, explicaremos quais imagens existem e como compilar localmente." + +#: ../../source/contributor-how-to-build-docker-images.rst:10 msgid "" "Before we can start, we need to meet a few prerequisites in our local " "development environment." msgstr "" +"Antes de começarmos, precisamos encontrar alguns pré-requisitos em nosso " +"ambiente de desenvolvimento local." -#: ../../source/contributor-how-to-build-docker-images.rst:11 +#: ../../source/contributor-how-to-build-docker-images.rst:12 msgid "Clone the flower repository." -msgstr "" +msgstr "Clone o repositório do flower." -#: ../../source/contributor-how-to-build-docker-images.rst:17 +#: ../../source/contributor-how-to-build-docker-images.rst:18 +#: ../../source/how-to-run-flower-using-docker.rst:165 msgid "Verify the Docker daemon is running." -msgstr "" +msgstr "Verifique que o serviço Docker está rodando." -#: ../../source/contributor-how-to-build-docker-images.rst:19 +#: ../../source/contributor-how-to-build-docker-images.rst:20 +#: ../../source/how-to-run-flower-using-docker.rst:167 msgid "" "Please follow the first section on :doc:`Run Flower using Docker ` which covers this step in more detail." msgstr "" +"Por favor, siga a primeira seção em :doc:`Execute o Flower usando Docker " +"` que cobre este passo em mais detalhes." -#: ../../source/contributor-how-to-build-docker-images.rst:23 -msgid "" -"Currently, Flower provides two images, a base image and a server image. " -"There will also be a client image soon. The base image, as the name " -"suggests, contains basic dependencies that both the server and the client" -" need. This includes system dependencies, Python and Python tools. The " -"server image is based on the base image, but it additionally installs the" -" Flower server using ``pip``." -msgstr "" - -#: ../../source/contributor-how-to-build-docker-images.rst:28 +#: ../../source/contributor-how-to-build-docker-images.rst:25 msgid "" "The build instructions that assemble the images are located in the " "respective Dockerfiles. You can find them in the subdirectories of " "``src/docker``." msgstr "" +"As instruções de compilação que montam as imagens estão localizadas nos " +"respectivos Dockerfiles. Você pode encontrá-los nos subdiretórios " +"``src/docker```." -#: ../../source/contributor-how-to-build-docker-images.rst:31 +#: ../../source/contributor-how-to-build-docker-images.rst:28 +#, fuzzy msgid "" -"Both, base and server image are configured via build arguments. Through " -"build arguments, we can make our build more flexible. For example, in the" -" base image, we can specify the version of Python to install using the " -"``PYTHON_VERSION`` build argument. Some of the build arguments have " +"Flower Docker images are configured via build arguments. Through build " +"arguments, we can make the creation of images more flexible. For example," +" in the base image, we can specify the version of Python to install using" +" the ``PYTHON_VERSION`` build argument. Some of the build arguments have " "default values, others must be specified when building the image. All " "available build arguments for each image are listed in one of the tables " "below." msgstr "" +"Ambas, imagens base e do servidor são configuradas através dos argumentos" +" de compilação. Através dos argumentos de compilação, podemos tornar " +"nossa compilação mais flexível. Por exemplo, na imagem base, podemos " +"especificar a versão do Python para instalar usando o argumento de " +"compilação `PYTHON_VERSION`. Alguns dos argumentos de compilação têm " +"valores padrão, outros devem ser especificados ao compilar a imagem. " +"Todos os argumentos de compilação disponíveis para cada imagem estão " +"listados em uma das tabelas abaixo." -#: ../../source/contributor-how-to-build-docker-images.rst:38 +#: ../../source/contributor-how-to-build-docker-images.rst:35 msgid "Building the base image" -msgstr "" +msgstr "Construindo a imagem base" -#: ../../source/contributor-how-to-build-docker-images.rst:44 -#: ../../source/contributor-how-to-build-docker-images.rst:86 +#: ../../source/contributor-how-to-build-docker-images.rst:41 +#: ../../source/contributor-how-to-build-docker-images.rst:98 msgid "Build argument" -msgstr "" +msgstr "Argumento de compilação" -#: ../../source/contributor-how-to-build-docker-images.rst:45 -#: ../../source/contributor-how-to-build-docker-images.rst:87 +#: ../../source/contributor-how-to-build-docker-images.rst:42 +#: ../../source/contributor-how-to-build-docker-images.rst:99 msgid "Description" -msgstr "" +msgstr "Descrição" -#: ../../source/contributor-how-to-build-docker-images.rst:46 -#: ../../source/contributor-how-to-build-docker-images.rst:88 +#: ../../source/contributor-how-to-build-docker-images.rst:43 +#: ../../source/contributor-how-to-build-docker-images.rst:100 msgid "Required" +msgstr "Necessário" + +#: ../../source/contributor-how-to-build-docker-images.rst:44 +#: ../../source/contributor-how-to-build-docker-images.rst:101 +msgid "Example" +msgstr "Exemplo" + +#: ../../source/contributor-how-to-build-docker-images.rst:45 +msgid "``DISTRO``" msgstr "" +#: ../../source/contributor-how-to-build-docker-images.rst:46 +#, fuzzy +msgid "The Linux distribution to use as the base image." +msgstr "O nome do repositório da imagem base." + #: ../../source/contributor-how-to-build-docker-images.rst:47 -#: ../../source/contributor-how-to-build-docker-images.rst:89 -msgid "Example" +#: ../../source/contributor-how-to-build-docker-images.rst:51 +#: ../../source/contributor-how-to-build-docker-images.rst:55 +#: ../../source/contributor-how-to-build-docker-images.rst:71 +#: ../../source/contributor-how-to-build-docker-images.rst:104 +msgid "No" msgstr "" #: ../../source/contributor-how-to-build-docker-images.rst:48 -msgid "``PYTHON_VERSION``" -msgstr "" +#, fuzzy +msgid "``ubuntu``" +msgstr "``UBUNTU_VERSION``" #: ../../source/contributor-how-to-build-docker-images.rst:49 -msgid "Version of ``python`` to be installed." -msgstr "" +#, fuzzy +msgid "``DISTRO_VERSION``" +msgstr "``PIP_VERSION``" #: ../../source/contributor-how-to-build-docker-images.rst:50 -#: ../../source/contributor-how-to-build-docker-images.rst:54 -#: ../../source/contributor-how-to-build-docker-images.rst:58 -#: ../../source/contributor-how-to-build-docker-images.rst:100 -msgid "Yes" -msgstr "" - -#: ../../source/contributor-how-to-build-docker-images.rst:51 -msgid "``3.11``" +msgid "Version of the Linux distribution." msgstr "" #: ../../source/contributor-how-to-build-docker-images.rst:52 -msgid "``PIP_VERSION``" -msgstr "" +#, fuzzy +msgid "``22.04``" +msgstr "``23.0.1``" #: ../../source/contributor-how-to-build-docker-images.rst:53 -msgid "Version of ``pip`` to be installed." -msgstr "" +msgid "``PYTHON_VERSION``" +msgstr "``PYTHON_VERSION``" -#: ../../source/contributor-how-to-build-docker-images.rst:55 -msgid "``23.0.1``" -msgstr "" +#: ../../source/contributor-how-to-build-docker-images.rst:54 +msgid "Version of ``python`` to be installed." +msgstr "Versão do ``python`` a ser instalada." #: ../../source/contributor-how-to-build-docker-images.rst:56 -msgid "``SETUPTOOLS_VERSION``" +msgid "``3.11`` or ``3.11.1``" msgstr "" #: ../../source/contributor-how-to-build-docker-images.rst:57 -msgid "Version of ``setuptools`` to be installed." -msgstr "" +msgid "``PIP_VERSION``" +msgstr "``PIP_VERSION``" + +#: ../../source/contributor-how-to-build-docker-images.rst:58 +msgid "Version of ``pip`` to be installed." +msgstr "Versão do ``pip`` a ser instalada." #: ../../source/contributor-how-to-build-docker-images.rst:59 -msgid "``69.0.2``" -msgstr "" +#: ../../source/contributor-how-to-build-docker-images.rst:63 +#: ../../source/contributor-how-to-build-docker-images.rst:67 +#: ../../source/contributor-how-to-build-docker-images.rst:108 +msgid "Yes" +msgstr "Sim" #: ../../source/contributor-how-to-build-docker-images.rst:60 -msgid "``UBUNTU_VERSION``" -msgstr "" +msgid "``23.0.1``" +msgstr "``23.0.1``" #: ../../source/contributor-how-to-build-docker-images.rst:61 -msgid "Version of the official Ubuntu Docker image." -msgstr "" +msgid "``SETUPTOOLS_VERSION``" +msgstr "``SETUPTOOLS_VERSION``" #: ../../source/contributor-how-to-build-docker-images.rst:62 -msgid "Defaults to ``22.04``." -msgstr "" +msgid "Version of ``setuptools`` to be installed." +msgstr "Versão do ``setuptools`` a ser instalada." + +#: ../../source/contributor-how-to-build-docker-images.rst:64 +msgid "``69.0.2``" +msgstr "``69.0.2``" #: ../../source/contributor-how-to-build-docker-images.rst:65 +msgid "``FLWR_VERSION``" +msgstr "``FLWR_VERSION``" + +#: ../../source/contributor-how-to-build-docker-images.rst:66 +msgid "Version of Flower to be installed." +msgstr "Versão do Flower a ser instalada." + +#: ../../source/contributor-how-to-build-docker-images.rst:68 +#, fuzzy +msgid "``1.8.0``" +msgstr "``1.7.0``" + +#: ../../source/contributor-how-to-build-docker-images.rst:69 +#, fuzzy +msgid "``FLWR_PACKAGE``" +msgstr "``FLWR_VERSION``" + +#: ../../source/contributor-how-to-build-docker-images.rst:70 +#, fuzzy +msgid "The Flower package to be installed." +msgstr "Versão do Flower a ser instalada." + +#: ../../source/contributor-how-to-build-docker-images.rst:72 +msgid "``flwr`` or ``flwr-nightly``" +msgstr "" + +#: ../../source/contributor-how-to-build-docker-images.rst:75 +#, fuzzy msgid "" -"The following example creates a base image with Python 3.11.0, pip 23.0.1" -" and setuptools 69.0.2:" +"The following example creates a base Ubuntu/Alpine image with Python " +"3.11.0, pip 23.0.1, setuptools 69.0.2 and Flower 1.8.0:" msgstr "" +"O exemplo seguinte cria uma imagem base com Python 3.11.0, pip 23.0.1 e " +"setuptools 69.0.2:" -#: ../../source/contributor-how-to-build-docker-images.rst:76 +#: ../../source/contributor-how-to-build-docker-images.rst:88 msgid "" "The name of image is ``flwr_base`` and the tag ``0.1.0``. Remember that " "the build arguments as well as the name and tag can be adapted to your " "needs. These values serve as examples only." msgstr "" +"O nome da imagem é ``flwr_base`` com a tag ``0.1.0``. Lembre-se que os " +"argumentos de construção assim como o nome e a tag podem ser adaptados de" +" acordo com suas necessidades. Estes valores servem apenas como exemplo." -#: ../../source/contributor-how-to-build-docker-images.rst:80 -msgid "Building the server image" -msgstr "" +#: ../../source/contributor-how-to-build-docker-images.rst:92 +#, fuzzy +msgid "Building the SuperLink/SuperNode or ServerApp image" +msgstr "Construindo a imagem do servidor" -#: ../../source/contributor-how-to-build-docker-images.rst:90 +#: ../../source/contributor-how-to-build-docker-images.rst:102 msgid "``BASE_REPOSITORY``" -msgstr "" +msgstr "``BASE_REPOSITORY``" -#: ../../source/contributor-how-to-build-docker-images.rst:91 +#: ../../source/contributor-how-to-build-docker-images.rst:103 msgid "The repository name of the base image." -msgstr "" - -#: ../../source/contributor-how-to-build-docker-images.rst:92 -msgid "Defaults to ``flwr/server``." -msgstr "" - -#: ../../source/contributor-how-to-build-docker-images.rst:94 -msgid "``BASE_IMAGE_TAG``" -msgstr "" - -#: ../../source/contributor-how-to-build-docker-images.rst:95 -msgid "The image tag of the base image." -msgstr "" - -#: ../../source/contributor-how-to-build-docker-images.rst:96 -msgid "Defaults to ``py3.11-ubuntu22.04``." -msgstr "" +msgstr "O nome do repositório da imagem base." -#: ../../source/contributor-how-to-build-docker-images.rst:98 -msgid "``FLWR_VERSION``" -msgstr "" +#: ../../source/contributor-how-to-build-docker-images.rst:105 +#, fuzzy +msgid "``flwr/base``" +msgstr "``FLWR_VERSION``" -#: ../../source/contributor-how-to-build-docker-images.rst:99 -msgid "Version of Flower to be installed." -msgstr "" +#: ../../source/contributor-how-to-build-docker-images.rst:106 +#, fuzzy +msgid "``BASE_IMAGE``" +msgstr "``BASE_REPOSITORY``" -#: ../../source/contributor-how-to-build-docker-images.rst:101 -msgid "``1.7.0``" -msgstr "" +#: ../../source/contributor-how-to-build-docker-images.rst:107 +#, fuzzy +msgid "The Tag of the Flower base image." +msgstr "O nome do repositório da imagem base." -#: ../../source/contributor-how-to-build-docker-images.rst:103 -msgid "" -"The following example creates a server image with the official Flower " -"base image py3.11-ubuntu22.04 and Flower 1.7.0:" +#: ../../source/contributor-how-to-build-docker-images.rst:109 +msgid "``1.8.0-py3.10-ubuntu22.04``" msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:114 +#: ../../source/contributor-how-to-build-docker-images.rst:111 +#, fuzzy msgid "" -"The name of image is ``flwr_server`` and the tag ``0.1.0``. Remember that" -" the build arguments as well as the name and tag can be adapted to your " -"needs. These values serve as examples only." +"The following example creates a SuperLink/SuperNode or ServerApp image " +"with the official Flower base image:" msgstr "" +"O exemplo a seguir cria uma imagem de servidor com a imagem base oficial " +"do Flower py3.11-ubuntu22.04 e Flower 1.7.0:" -#: ../../source/contributor-how-to-build-docker-images.rst:117 +#: ../../source/contributor-how-to-build-docker-images.rst:122 +#, fuzzy msgid "" "If you want to use your own base image instead of the official Flower " -"base image, all you need to do is set the ``BASE_REPOSITORY`` and " -"``BASE_IMAGE_TAG`` build arguments. The value of ``BASE_REPOSITORY`` must" -" match the name of your image and the value of ``BASE_IMAGE_TAG`` must " -"match the tag of your image." +"base image, all you need to do is set the ``BASE_REPOSITORY`` build " +"argument." msgstr "" +"Se você quiser usar sua própria imagem base ao invés da imagem oficial " +"base do Flower, tudo que você precisa fazer é definir os argumentos " +"``BASE_REPOSITORY`` e ``BASE_IMAGE_TAG`` como parte do comando de " +"compilação. O valor de ``BASE_REPOSITORY`` deve corresponder ao nome da " +"sua imagem e o valor de ``BASE_IMAGE_TAG`` deve corresponder à tag da sua" +" imagem." -#: ../../source/contributor-how-to-build-docker-images.rst:131 +#: ../../source/contributor-how-to-build-docker-images.rst:133 msgid "After creating the image, we can test whether the image is working:" -msgstr "" +msgstr "Depois de criar a imagem, podemos testar se a imagem está funcionando:" #: ../../source/contributor-how-to-contribute-translations.rst:2 msgid "Contribute translations" -msgstr "" +msgstr "Contribua com traduções" #: ../../source/contributor-how-to-contribute-translations.rst:4 msgid "" @@ -288,17 +362,31 @@ msgid "" "also be a great opportunity for those wanting to become open source " "contributors with little prerequisites." msgstr "" +"Desde o `Flower 1.5 `_ nós introduzimos traduções para " +"nossas páginas de documentação, mas, como você pode ter notado, as " +"traduções são muitas vezes imperfeitas. Se você fala línguas diferentes " +"do inglês, você pode ser capaz de nos ajudar neste esforço para tornar o " +"aprendizado federado acessível a tantas pessoas quanto possível, " +"contribuindo para essas traduções! Isso também pode ser uma grande " +"oportunidade para aqueles que querem se tornar contribuintes de código " +"aberto com poucos pré-requisitos." #: ../../source/contributor-how-to-contribute-translations.rst:13 +#, fuzzy msgid "" "Our translation project is publicly available over on `Weblate " "`_, this " "where most of the work will happen." msgstr "" +"Nosso projeto de tradução está disponível publicamente em `Weblate " +"`_, onde a " +"maioria do trabalho acontecerá." #: ../../source/contributor-how-to-contribute-translations.rst:18 +#, fuzzy msgid "Contribute to existing languages" -msgstr "" +msgstr "Contribuir para as línguas existentes" #: ../../source/contributor-how-to-contribute-translations.rst:23 msgid "" @@ -308,6 +396,11 @@ msgid "" " profile settings can be found `here " "`_." msgstr "" +"A primeira coisa que você precisa fazer para contribuir é criar uma conta" +" Weblate gratuita nesta `página " +"`_. Mais informações sobre" +" as configurações de perfil podem ser encontradas `aqui " +"`_." #: ../../source/contributor-how-to-contribute-translations.rst:29 msgid "" @@ -316,12 +409,18 @@ msgid "" "docs/framework/>`_. Here, you should see the different existing languages" " that can be found on the website." msgstr "" +"Uma vez que você esteja conectado ao Weblate, você pode navegar até o " +"projeto `Flower Framework `_. Aqui, você deve ver os diferentes idiomas existentes " +"que podem ser encontrados no site." #: ../../source/contributor-how-to-contribute-translations.rst:34 msgid "" "Once you have selected the language you want to contribute to, you should" " see a similar interface to this:" msgstr "" +"Uma vez que você tenha selecionado o idioma que deseja contribuir, você " +"deve ver uma interface semelhante a esta:" #: ../../source/contributor-how-to-contribute-translations.rst:39 msgid "" @@ -330,10 +429,15 @@ msgid "" "will automatically bring you to the translation interface for " "untranslated strings." msgstr "" +"A opção mais direta aqui é clicar no botão ``Translate`` no canto " +"superior direito (na seção ``Translation status``). Isso te levará " +"automaticamente para a interface de tradução de strings ainda não " +"traduzidas." #: ../../source/contributor-how-to-contribute-translations.rst:43 +#, fuzzy msgid "This is what the interface looks like:" -msgstr "" +msgstr "É assim que a interface se parece:" #: ../../source/contributor-how-to-contribute-translations.rst:47 msgid "" @@ -344,6 +448,13 @@ msgid "" "your translation to suggestions for other users to view), or ``Skip`` (to" " go to the next untranslated string without saving anything)." msgstr "" +"Você insire sua tradução na caixa de texto no topo e depois, uma vez que " +"você está satisfeito com ela, você pressiona ``Save and continue`` (para " +"salvar a tradução e ir para a próxima string não traduzida), ``Save and " +"stay`` (para salvar a tradução e ficar na mesma página), ``Suggest`` " +"(para adicionar sua tradução para sugestões para outros usuários verem), " +"ou ``Skip`` (para ir para a próxima string não traduzida sem salvar nada " +"na atual)." #: ../../source/contributor-how-to-contribute-translations.rst:54 msgid "" @@ -353,6 +464,12 @@ msgid "" "translations in ``Other languages``, and the ``History`` of translations " "for this string." msgstr "" +"Para ajudar com as traduções, você pode ver na parte inferior o ``Nearby " +"strings`` (strings próximas), o ``Comments`` (comentários de outros " +"contribuidores), o ``Automatic suggestions`` (sugestões atuomáticas de " +"sistemas de tradução automática), as traduções em ``Other languages`` " +"(outras línguas), e o ``History`` (histórico) de traduções para esta " +"string." #: ../../source/contributor-how-to-contribute-translations.rst:59 msgid "" @@ -360,6 +477,9 @@ msgid "" "click the link under ``Source string location`` in order to view the " "source of the doc file containing the string." msgstr "" +"À direita, sob a seção ``String information``, você também pode clicar no" +" link sob ``Source string location`` para visualizar a fonte do arquivo " +"doc que contém a string." #: ../../source/contributor-how-to-contribute-translations.rst:63 msgid "" @@ -367,10 +487,14 @@ msgid "" "this `in-depth guide " "`_." msgstr "" +"Para obter mais informações sobre como traduzir usando o Weblate, você " +"pode conferir este `guia detalhado " +"`_." #: ../../source/contributor-how-to-contribute-translations.rst:67 +#, fuzzy msgid "Add new languages" -msgstr "" +msgstr "Adicionar novos idiomas" #: ../../source/contributor-how-to-contribute-translations.rst:69 msgid "" @@ -378,119 +502,10 @@ msgid "" "either on `Slack `_, or by opening an issue" " on our `GitHub repo `_." msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:2 -msgid "Creating New Messages" -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:4 -msgid "" -"This is a simple guide for creating a new type of message between the " -"server and clients in Flower." -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:6 -msgid "" -"Let's suppose we have the following example functions in " -":code:`server.py` and :code:`numpy_client.py`..." -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:8 -msgid "Server's side:" -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:17 -msgid "Client's side:" -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:26 -msgid "" -"Let's now see what we need to implement in order to get this simple " -"function between the server and client to work!" -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:30 -msgid "Message Types for Protocol Buffers" -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:32 -msgid "" -"The first thing we need to do is to define a message type for the RPC " -"system in :code:`transport.proto`. Note that we have to do it for both " -"the request and response messages. For more details on the syntax of " -"proto3, please see the `official documentation `_." -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:35 -msgid "Within the :code:`ServerMessage` block:" -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:52 -msgid "Within the ClientMessage block:" -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:70 -msgid "" -"Make sure to also add a field of the newly created message type in " -":code:`oneof msg`." -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:72 -msgid "Once that is done, we will compile the file with:" -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:78 -msgid "If it compiles successfully, you should see the following message:" -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:87 -msgid "Serialization and Deserialization Functions" -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:89 -msgid "" -"Our next step is to add functions to serialize and deserialize Python " -"datatypes to or from our defined RPC message types. You should add these " -"functions in :code:`serde.py`." -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:91 -msgid "The four functions:" -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:112 -msgid "Sending the Message from the Server" -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:114 -msgid "" -"Now write the request function in your Client Proxy class (e.g., " -":code:`grpc_client_proxy.py`) using the serde functions you just created:" -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:128 -msgid "Receiving the Message by the Client" -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:130 -msgid "" -"Last step! Modify the code in :code:`message_handler.py` to check the " -"field of your message and call the :code:`example_response` function. " -"Remember to use the serde functions!" -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:132 -msgid "Within the handle function:" -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:139 -msgid "And add a new function:" -msgstr "" - -#: ../../source/contributor-how-to-create-new-messages.rst:149 -msgid "Hopefully, when you run your program you will get the intended result!" -msgstr "" +"Se você quiser adicionar uma nova língua, primeiro você terá que entrar " +"em contato conosco, no `Slack `_, ou " +"abrindo uma issue no nosso `repositório GitHub " +"`_." #: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:2 msgid "Develop in VSCode Dev Containers" @@ -706,14 +721,14 @@ msgstr "" #: ../../source/contributor-how-to-install-development-versions.rst:51 msgid "" -"Open the notebook ``doc/source/tutorial-get-started-with-flower-" +"Open the notebook ``doc/source/tutorial-series-get-started-with-flower-" "pytorch.ipynb``:" msgstr "" #: ../../source/contributor-how-to-install-development-versions.rst:53 msgid "" "https://colab.research.google.com/github/adap/flower/blob/main/doc/source" -"/tutorial-get-started-with-flower-pytorch.ipynb" +"/tutorial-series-get-started-with-flower-pytorch.ipynb" msgstr "" #: ../../source/contributor-how-to-install-development-versions.rst:55 @@ -725,7 +740,7 @@ msgstr "" #: ../../source/contributor-how-to-install-development-versions.rst:57 msgid "" "https://colab.research.google.com/github/adap/flower/blob/branch-" -"name/doc/source/tutorial-get-started-with-flower-pytorch.ipynb" +"name/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb" msgstr "" #: ../../source/contributor-how-to-install-development-versions.rst:59 @@ -800,96 +815,145 @@ msgstr "" msgid "Check the draft release on GitHub, and if everything is good, publish it." msgstr "" +#: ../../source/contributor-how-to-release-flower.rst:15 +#, fuzzy +msgid "Trigger the CI for building the Docker images." +msgstr "Versão da imagem Docker oficial do Ubuntu." + #: ../../source/contributor-how-to-release-flower.rst:17 +msgid "" +"To trigger the workflow, a collaborator must create a " +"``workflow_dispatch`` event in the GitHub CI. This can be done either " +"through the UI or via the GitHub CLI. The event requires only one input, " +"the Flower version, to be released." +msgstr "" + +#: ../../source/contributor-how-to-release-flower.rst:21 +msgid "**Via the UI**" +msgstr "" + +#: ../../source/contributor-how-to-release-flower.rst:23 +msgid "" +"Go to the ``Build docker images`` workflow `page " +"`_." +msgstr "" + +#: ../../source/contributor-how-to-release-flower.rst:24 +msgid "" +"Click on the ``Run workflow`` button and type the new version of Flower " +"in the ``Version of Flower`` input field." +msgstr "" + +#: ../../source/contributor-how-to-release-flower.rst:25 +msgid "Click on the **green** ``Run workflow`` button." +msgstr "" + +#: ../../source/contributor-how-to-release-flower.rst:29 +msgid "**Via the GitHub CI**" +msgstr "" + +#: ../../source/contributor-how-to-release-flower.rst:31 +msgid "" +"Make sure you are logged in via ``gh auth login`` and that the current " +"working directory is the root of the Flower repository." +msgstr "" + +#: ../../source/contributor-how-to-release-flower.rst:32 +msgid "" +"Trigger the workflow via ``gh workflow run docker-images.yml -f flwr-" +"version=``." +msgstr "" + +#: ../../source/contributor-how-to-release-flower.rst:35 msgid "After the release" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:19 +#: ../../source/contributor-how-to-release-flower.rst:37 msgid "Create a pull request which contains the following changes:" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:21 +#: ../../source/contributor-how-to-release-flower.rst:39 msgid "Increase the minor version in ``pyproject.toml`` by one." msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:22 +#: ../../source/contributor-how-to-release-flower.rst:40 msgid "Update all files which contain the current version number if necessary." msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:23 +#: ../../source/contributor-how-to-release-flower.rst:41 msgid "Add a new ``Unreleased`` section in ``changelog.md``." msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:25 +#: ../../source/contributor-how-to-release-flower.rst:43 msgid "" "Merge the pull request on the same day (i.e., before a new nightly " "release gets published to PyPI)." msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:28 +#: ../../source/contributor-how-to-release-flower.rst:46 msgid "Publishing a pre-release" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:31 +#: ../../source/contributor-how-to-release-flower.rst:49 msgid "Pre-release naming" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:33 +#: ../../source/contributor-how-to-release-flower.rst:51 msgid "" "PyPI supports pre-releases (alpha, beta, release candidate). Pre-releases" " MUST use one of the following naming patterns:" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:35 +#: ../../source/contributor-how-to-release-flower.rst:53 msgid "Alpha: ``MAJOR.MINOR.PATCHaN``" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:36 +#: ../../source/contributor-how-to-release-flower.rst:54 msgid "Beta: ``MAJOR.MINOR.PATCHbN``" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:37 +#: ../../source/contributor-how-to-release-flower.rst:55 msgid "Release candidate (RC): ``MAJOR.MINOR.PATCHrcN``" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:39 +#: ../../source/contributor-how-to-release-flower.rst:57 msgid "Examples include:" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:41 +#: ../../source/contributor-how-to-release-flower.rst:59 msgid "``1.0.0a0``" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:42 +#: ../../source/contributor-how-to-release-flower.rst:60 msgid "``1.0.0b0``" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:43 +#: ../../source/contributor-how-to-release-flower.rst:61 msgid "``1.0.0rc0``" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:44 +#: ../../source/contributor-how-to-release-flower.rst:62 msgid "``1.0.0rc1``" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:46 +#: ../../source/contributor-how-to-release-flower.rst:64 msgid "" "This is in line with PEP-440 and the recommendations from the Python " "Packaging Authority (PyPA):" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:49 +#: ../../source/contributor-how-to-release-flower.rst:67 msgid "`PEP-440 `_" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:50 +#: ../../source/contributor-how-to-release-flower.rst:68 msgid "" "`PyPA Choosing a versioning scheme " "`_" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:52 +#: ../../source/contributor-how-to-release-flower.rst:70 msgid "" "Note that the approach defined by PyPA is not compatible with SemVer " "2.0.0 spec, for details consult the `Semantic Versioning Specification " @@ -897,26 +961,26 @@ msgid "" "11 on precedence)." msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:55 +#: ../../source/contributor-how-to-release-flower.rst:73 msgid "Pre-release classification" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:57 +#: ../../source/contributor-how-to-release-flower.rst:75 msgid "Should the next pre-release be called alpha, beta, or release candidate?" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:59 +#: ../../source/contributor-how-to-release-flower.rst:77 msgid "" "RC: feature complete, no known issues (apart from issues that are " "classified as \"won't fix\" for the next stable release) - if no issues " "surface this will become the next stable release" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:60 +#: ../../source/contributor-how-to-release-flower.rst:78 msgid "Beta: feature complete, allowed to have known issues" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:61 +#: ../../source/contributor-how-to-release-flower.rst:79 msgid "Alpha: not feature complete, allowed to have known issues" msgstr "" @@ -945,10 +1009,18 @@ msgid "" msgstr "" #: ../../source/contributor-how-to-set-up-a-virtual-env.rst:14 -msgid "Virutualenv with Pyenv/Virtualenv" +msgid "" +"Due to a known incompatibility with `ray " +"`_, we currently recommend utilizing at " +"most `Python 3.11 `_ for running Flower " +"simulations." +msgstr "" + +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:19 +msgid "Virtualenv with Pyenv/Virtualenv" msgstr "" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:16 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:21 msgid "" "One of the recommended virtual environment is `pyenv " "`_/`virtualenv `_ for details." msgstr "" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:18 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:23 msgid "" "Once Pyenv is set up, you can use it to install `Python Version 3.10 " "`_ or above:" msgstr "" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:24 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:29 msgid "Create the virtualenv with:" msgstr "" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:31 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:36 msgid "Activate the virtualenv by running the following command:" msgstr "" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:39 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:44 msgid "Virtualenv with Poetry" msgstr "" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:41 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:46 msgid "" "The Flower examples are based on `Poetry `_ to manage dependencies. After installing Poetry you " "simply create a virtual environment with:" msgstr "" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:47 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:52 msgid "" "If you open a new terminal you can activate the previously created " "virtual environment with the following command:" msgstr "" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:55 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:60 msgid "Virtualenv with Anaconda" msgstr "" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:57 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:62 msgid "" "If you prefer to use Anaconda for your virtual environment then install " "and setup the `conda `_." @@ -1029,7 +1101,7 @@ msgid "" msgstr "" #: ../../source/contributor-how-to-write-documentation.rst:10 -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:142 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:169 msgid "" "Note that, in order to build the documentation locally (with ``poetry run" " make html``, like described below), `Pandoc " @@ -1469,7 +1541,7 @@ msgstr "" msgid "Creating and merging a pull request (PR)" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:205 +#: ../../source/contributor-tutorial-contribute-on-github.rst:206 msgid "**Create the PR**" msgstr "" @@ -1502,178 +1574,180 @@ msgstr "" #: ../../source/contributor-tutorial-contribute-on-github.rst:193 msgid "" +"The title should be changed to adhere to the :ref:`pr_title_format` " +"guidelines, otherwise it won't be possible to merge the PR. So in this " +"case, a correct title might be ``docs(framework:skip) Fix typos``." +msgstr "" + +#: ../../source/contributor-tutorial-contribute-on-github.rst:196 +msgid "" "The input box in the middle is there for you to describe what your PR " "does and to link it to existing issues. We have placed comments (that " "won't be rendered once the PR is opened) to guide you through the " "process." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:196 -msgid "" -"It is important to follow the instructions described in comments. For " -"instance, in order to not break how our changelog system works, you " -"should read the information above the ``Changelog entry`` section " -"carefully. You can also checkout some examples and details in the " -":ref:`changelogentry` appendix." +#: ../../source/contributor-tutorial-contribute-on-github.rst:199 +msgid "It is important to follow the instructions described in comments." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:200 +#: ../../source/contributor-tutorial-contribute-on-github.rst:201 msgid "" "At the bottom you will find the button to open the PR. This will notify " "reviewers that a new PR has been opened and that they should look over it" " to merge or to request changes." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:203 +#: ../../source/contributor-tutorial-contribute-on-github.rst:204 msgid "" "If your PR is not yet ready for review, and you don't want to notify " "anyone, you have the option to create a draft pull request:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:208 +#: ../../source/contributor-tutorial-contribute-on-github.rst:209 msgid "**Making new changes**" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:208 +#: ../../source/contributor-tutorial-contribute-on-github.rst:209 msgid "" "Once the PR has been opened (as draft or not), you can still push new " "commits to it the same way we did before, by making changes to the branch" " associated with the PR." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:230 +#: ../../source/contributor-tutorial-contribute-on-github.rst:231 msgid "**Review the PR**" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:211 +#: ../../source/contributor-tutorial-contribute-on-github.rst:212 msgid "" "Once the PR has been opened or once the draft PR has been marked as " "ready, a review from code owners will be automatically requested:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:215 +#: ../../source/contributor-tutorial-contribute-on-github.rst:216 msgid "" "Code owners will then look into the code, ask questions, request changes " "or validate the PR." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:217 +#: ../../source/contributor-tutorial-contribute-on-github.rst:218 msgid "Merging will be blocked if there are ongoing requested changes." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:221 +#: ../../source/contributor-tutorial-contribute-on-github.rst:222 msgid "" "To resolve them, just push the necessary changes to the branch associated" " with the PR:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:225 +#: ../../source/contributor-tutorial-contribute-on-github.rst:226 msgid "And resolve the conversation:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:229 +#: ../../source/contributor-tutorial-contribute-on-github.rst:230 msgid "" "Once all the conversations have been resolved, you can re-request a " "review." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:250 +#: ../../source/contributor-tutorial-contribute-on-github.rst:251 msgid "**Once the PR is merged**" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:233 +#: ../../source/contributor-tutorial-contribute-on-github.rst:234 msgid "" "If all the automatic tests have passed and reviewers have no more changes" " to request, they can approve the PR and merge it." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:237 +#: ../../source/contributor-tutorial-contribute-on-github.rst:238 msgid "" "Once it is merged, you can delete the branch on GitHub (a button should " "appear to do so) and also delete it locally by doing:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:244 +#: ../../source/contributor-tutorial-contribute-on-github.rst:245 msgid "Then you should update your forked repository by doing:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:253 +#: ../../source/contributor-tutorial-contribute-on-github.rst:254 msgid "Example of first contribution" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:256 +#: ../../source/contributor-tutorial-contribute-on-github.rst:257 msgid "Problem" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:258 +#: ../../source/contributor-tutorial-contribute-on-github.rst:259 msgid "" "For our documentation, we've started to use the `Diàtaxis framework " "`_." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:260 +#: ../../source/contributor-tutorial-contribute-on-github.rst:261 msgid "" "Our \"How to\" guides should have titles that continue the sentence \"How" " to …\", for example, \"How to upgrade to Flower 1.0\"." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:262 +#: ../../source/contributor-tutorial-contribute-on-github.rst:263 msgid "" "Most of our guides do not follow this new format yet, and changing their " "title is (unfortunately) more involved than one might think." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:264 +#: ../../source/contributor-tutorial-contribute-on-github.rst:265 msgid "" "This issue is about changing the title of a doc from present continuous " "to present simple." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:266 +#: ../../source/contributor-tutorial-contribute-on-github.rst:267 msgid "" "Let's take the example of \"Saving Progress\" which we changed to \"Save " "Progress\". Does this pass our check?" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:268 +#: ../../source/contributor-tutorial-contribute-on-github.rst:269 msgid "Before: \"How to saving progress\" ❌" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:270 +#: ../../source/contributor-tutorial-contribute-on-github.rst:271 msgid "After: \"How to save progress\" ✅" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:273 +#: ../../source/contributor-tutorial-contribute-on-github.rst:274 msgid "Solution" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:275 +#: ../../source/contributor-tutorial-contribute-on-github.rst:276 msgid "" "This is a tiny change, but it'll allow us to test your end-to-end setup. " "After cloning and setting up the Flower repo, here's what you should do:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:277 +#: ../../source/contributor-tutorial-contribute-on-github.rst:278 msgid "Find the source file in ``doc/source``" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:278 +#: ../../source/contributor-tutorial-contribute-on-github.rst:279 msgid "" "Make the change in the ``.rst`` file (beware, the dashes under the title " "should be the same length as the title itself)" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:279 +#: ../../source/contributor-tutorial-contribute-on-github.rst:280 msgid "" "Build the docs and `check the result `_" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:282 +#: ../../source/contributor-tutorial-contribute-on-github.rst:283 msgid "Rename file" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:284 +#: ../../source/contributor-tutorial-contribute-on-github.rst:285 msgid "" "You might have noticed that the file name still reflects the old wording." " If we just change the file, then we break all existing links to it - it " @@ -1681,142 +1755,68 @@ msgid "" "engine ranking." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:287 +#: ../../source/contributor-tutorial-contribute-on-github.rst:288 msgid "Here's how to change the file name:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:289 +#: ../../source/contributor-tutorial-contribute-on-github.rst:290 msgid "Change the file name to ``save-progress.rst``" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:290 +#: ../../source/contributor-tutorial-contribute-on-github.rst:291 msgid "Add a redirect rule to ``doc/source/conf.py``" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:292 +#: ../../source/contributor-tutorial-contribute-on-github.rst:293 msgid "" "This will cause a redirect from ``saving-progress.html`` to ``save-" "progress.html``, old links will continue to work." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:295 +#: ../../source/contributor-tutorial-contribute-on-github.rst:296 msgid "Apply changes in the index file" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:297 +#: ../../source/contributor-tutorial-contribute-on-github.rst:298 msgid "" "For the lateral navigation bar to work properly, it is very important to " "update the ``index.rst`` file as well. This is where we define the whole " "arborescence of the navbar." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:300 +#: ../../source/contributor-tutorial-contribute-on-github.rst:301 msgid "Find and modify the file name in ``index.rst``" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:303 +#: ../../source/contributor-tutorial-contribute-on-github.rst:304 msgid "Open PR" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:305 +#: ../../source/contributor-tutorial-contribute-on-github.rst:306 msgid "" "Commit the changes (commit messages are always imperative: \"Do " "something\", in this case \"Change …\")" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:306 -msgid "Push the changes to your fork" -msgstr "" - #: ../../source/contributor-tutorial-contribute-on-github.rst:307 -msgid "Open a PR (as shown above)" +msgid "Push the changes to your fork" msgstr "" #: ../../source/contributor-tutorial-contribute-on-github.rst:308 -msgid "Wait for it to be approved!" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:309 -msgid "Congrats! 🥳 You're now officially a Flower contributor!" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:313 -msgid "How to write a good PR title" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:315 -msgid "" -"A well-crafted PR title helps team members quickly understand the purpose" -" and scope of the changes being proposed. Here's a guide to help you " -"write a good GitHub PR title:" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:317 -msgid "" -"1. Be Clear and Concise: Provide a clear summary of the changes in a " -"concise manner. 1. Use Actionable Verbs: Start with verbs like \"Add,\" " -"\"Update,\" or \"Fix\" to indicate the purpose. 1. Include Relevant " -"Information: Mention the affected feature or module for context. 1. Keep " -"it Short: Avoid lengthy titles for easy readability. 1. Use Proper " -"Capitalization and Punctuation: Follow grammar rules for clarity." -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:323 -msgid "" -"Let's start with a few examples for titles that should be avoided because" -" they do not provide meaningful information:" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:325 -msgid "Implement Algorithm" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:326 -msgid "Database" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:327 -msgid "Add my_new_file.py to codebase" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:328 -msgid "Improve code in module" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:329 -msgid "Change SomeModule" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:331 msgid "" -"Here are a few positive examples which provide helpful information " -"without repeating how they do it, as that is already visible in the " -"\"Files changed\" section of the PR:" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:333 -msgid "Update docs banner to mention Flower Summit 2023" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:334 -msgid "Remove unnecessary XGBoost dependency" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:335 -msgid "Remove redundant attributes in strategies subclassing FedAvg" +"Open a PR (as shown above) with title ``docs(framework) Update how-to " +"guide title``" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:336 -msgid "Add CI job to deploy the staging system when the ``main`` branch changes" +#: ../../source/contributor-tutorial-contribute-on-github.rst:309 +msgid "Wait for it to be approved!" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:337 -msgid "" -"Add new amazing library which will be used to improve the simulation " -"engine" +#: ../../source/contributor-tutorial-contribute-on-github.rst:310 +msgid "Congrats! 🥳 You're now officially a Flower contributor!" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:341 +#: ../../source/contributor-tutorial-contribute-on-github.rst:314 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:548 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:946 #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:727 @@ -1825,153 +1825,92 @@ msgstr "" msgid "Next steps" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:343 +#: ../../source/contributor-tutorial-contribute-on-github.rst:316 msgid "" "Once you have made your first PR, and want to contribute more, be sure to" " check out the following :" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:345 +#: ../../source/contributor-tutorial-contribute-on-github.rst:318 msgid "" ":doc:`Good first contributions `, where you should particularly look into the " ":code:`baselines` contributions." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:349 +#: ../../source/contributor-tutorial-contribute-on-github.rst:322 #: ../../source/fed/0000-20200102-fed-template.md:60 msgid "Appendix" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:354 -msgid "Changelog entry" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:356 -msgid "" -"When opening a new PR, inside its description, there should be a " -"``Changelog entry`` header." -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:358 -msgid "" -"Above this header you should see the following comment that explains how " -"to write your changelog entry:" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:360 -msgid "" -"Inside the following 'Changelog entry' section, you should put the " -"description of your changes that will be added to the changelog alongside" -" your PR title." +#: ../../source/contributor-tutorial-contribute-on-github.rst:327 +msgid "PR title format" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:363 -msgid "" -"If the section is completely empty (without any token) or non-existent, " -"the changelog will just contain the title of the PR for the changelog " -"entry, without any description." +#: ../../source/contributor-tutorial-contribute-on-github.rst:329 +msgid "We enforce the following PR title format:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:366 +#: ../../source/contributor-tutorial-contribute-on-github.rst:335 msgid "" -"If the section contains some text other than tokens, it will use it to " -"add a description to the change." +"(or ``(:skip) `` to ignore the PR in the " +"changelog)" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:368 +#: ../../source/contributor-tutorial-contribute-on-github.rst:337 msgid "" -"If the section contains one of the following tokens it will ignore any " -"other text and put the PR under the corresponding section of the " -"changelog:" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:370 -msgid " is for classifying a PR as a general improvement." -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:372 -msgid " is to not add the PR to the changelog" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:374 -msgid " is to add a general baselines change to the PR" +"Where ```` needs to be in ``{ci, fix, feat, docs, refactor, " +"break}``, ```` should be in ``{framework, baselines, datasets, " +"examples, or '*' when modifying multiple projects which requires the " +"':skip' flag to be used}``, and ```` starts with a capitalised " +"verb in the imperative mood." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:376 -msgid " is to add a general examples change to the PR" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:378 -msgid " is to add a general sdk change to the PR" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:380 -msgid " is to add a general simulations change to the PR" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:382 -msgid "Note that only one token should be used." -msgstr "" +#: ../../source/contributor-tutorial-contribute-on-github.rst:341 +#, fuzzy +msgid "Valid examples:" +msgstr "Exemplo" -#: ../../source/contributor-tutorial-contribute-on-github.rst:384 -msgid "" -"Its content must have a specific format. We will break down what each " -"possibility does:" +#: ../../source/contributor-tutorial-contribute-on-github.rst:343 +msgid "``feat(framework) Add flwr build CLI command``" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:386 -msgid "" -"If the ``### Changelog entry`` section contains nothing or doesn't exist," -" the following text will be added to the changelog::" +#: ../../source/contributor-tutorial-contribute-on-github.rst:344 +msgid "``refactor(examples:skip) Improve quickstart-pytorch logging``" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:390 -msgid "" -"If the ``### Changelog entry`` section contains a description (and no " -"token), the following text will be added to the changelog::" +#: ../../source/contributor-tutorial-contribute-on-github.rst:345 +msgid "``ci(*:skip) Enforce PR title format``" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:396 -msgid "" -"If the ``### Changelog entry`` section contains ````, nothing will " -"change in the changelog." +#: ../../source/contributor-tutorial-contribute-on-github.rst:347 +msgid "Invalid examples:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:398 -msgid "" -"If the ``### Changelog entry`` section contains ````, the " -"following text will be added to the changelog::" +#: ../../source/contributor-tutorial-contribute-on-github.rst:349 +msgid "``feat(framework): Add flwr build CLI command`` (extra ``:``)" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:402 +#: ../../source/contributor-tutorial-contribute-on-github.rst:350 msgid "" -"If the ``### Changelog entry`` section contains ````, the " -"following text will be added to the changelog::" +"``feat(*) Add flwr build CLI command`` (missing ``skip`` flag along with " +"``*``)" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:406 -msgid "" -"If the ``### Changelog entry`` section contains ````, the " -"following text will be added to the changelog::" +#: ../../source/contributor-tutorial-contribute-on-github.rst:351 +msgid "``feat(skip) Add flwr build CLI command`` (missing ````)" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:410 -msgid "" -"If the ``### Changelog entry`` section contains ````, the following " -"text will be added to the changelog::" +#: ../../source/contributor-tutorial-contribute-on-github.rst:352 +msgid "``feat(framework) add flwr build CLI command`` (non capitalised verb)" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:414 -msgid "" -"If the ``### Changelog entry`` section contains ````, the " -"following text will be added to the changelog::" +#: ../../source/contributor-tutorial-contribute-on-github.rst:353 +msgid "``feat(framework) Add flwr build CLI command.`` (dot at the end)" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:418 -msgid "" -"Note that only one token must be provided, otherwise, only the first " -"action (in the order listed above), will be performed." +#: ../../source/contributor-tutorial-contribute-on-github.rst:354 +msgid "``Add flwr build CLI command.`` (missing ``()``)" msgstr "" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:2 @@ -1979,6 +1918,7 @@ msgid "Get started as a contributor" msgstr "" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:5 +#: ../../source/how-to-run-flower-using-docker.rst:153 msgid "Prerequisites" msgstr "" @@ -2111,51 +2051,96 @@ msgid "Run Linters and Tests" msgstr "" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:106 -msgid "Run Github Actions (CI) locally" +msgid "Add a pre-commit hook" msgstr "" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:108 msgid "" +"Developers may integrate a pre-commit hook into their workflow utilizing " +"the `pre-commit `_ library. The pre-" +"commit hook is configured to execute two primary operations: " +"``./dev/format.sh`` and ``./dev/test.sh`` scripts." +msgstr "" + +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:110 +msgid "There are multiple ways developers can use this:" +msgstr "" + +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:112 +msgid "Install the pre-commit hook to your local git directory by simply running:" +msgstr "" + +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:118 +msgid "" +"Each ``git commit`` will trigger the execution of formatting and " +"linting/test scripts." +msgstr "" + +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:119 +msgid "" +"If in a hurry, bypass the hook using ``--no-verify`` with the ``git " +"commit`` command. ::" +msgstr "" + +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:124 +msgid "" +"For developers who prefer not to install the hook permanently, it is " +"possible to execute a one-time check prior to committing changes by using" +" the following command:" +msgstr "" + +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:130 +msgid "" +"This executes the formatting and linting checks/tests on all the files " +"without modifying the default behavior of ``git commit``." +msgstr "" + +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:133 +msgid "Run Github Actions (CI) locally" +msgstr "" + +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:135 +msgid "" "Developers could run the full set of Github Actions workflows under their" " local environment by using `Act `_. " "Please refer to the installation instructions under the linked repository" " and run the next command under Flower main cloned repository folder::" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:115 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:142 msgid "" "The Flower default workflow would run by setting up the required Docker " "machines underneath." msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:120 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:147 msgid "Build Release" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:122 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:149 msgid "" "Flower uses Poetry to build releases. The necessary command is wrapped in" " a simple script::" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:127 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:154 msgid "" "The resulting :code:`.whl` and :code:`.tar.gz` releases will be stored in" " the :code:`/dist` subdirectory." msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:132 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:159 msgid "Build Documentation" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:134 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:161 msgid "" "Flower's documentation uses `Sphinx `_. " "There's no convenience script to re-build the documentation yet, but it's" " pretty easy::" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:140 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:167 msgid "This will generate HTML documentation in ``doc/build/html``." msgstr "" @@ -2249,7 +2234,6 @@ msgstr "" #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:94 #: ../../source/example-jax-from-centralized-to-federated.rst:277 -#: ../../source/example-mxnet-walk-through.rst:356 #: ../../source/example-pytorch-from-centralized-to-federated.rst:310 #: ../../source/tutorial-quickstart-jax.rst:283 msgid "Next Steps" @@ -2392,7 +2376,6 @@ msgid "" msgstr "" #: ../../source/example-jax-from-centralized-to-federated.rst:123 -#: ../../source/example-mxnet-walk-through.rst:204 #: ../../source/example-pytorch-from-centralized-to-federated.rst:181 #: ../../source/tutorial-quickstart-jax.rst:129 msgid "" @@ -2403,7 +2386,6 @@ msgid "" msgstr "" #: ../../source/example-jax-from-centralized-to-federated.rst:133 -#: ../../source/example-mxnet-walk-through.rst:214 #: ../../source/example-pytorch-from-centralized-to-federated.rst:191 #: ../../source/tutorial-quickstart-jax.rst:139 msgid "We can already start the *server*:" @@ -2434,13 +2416,11 @@ msgid "" msgstr "" #: ../../source/example-jax-from-centralized-to-federated.rst:161 -#: ../../source/example-mxnet-walk-through.rst:242 #: ../../source/tutorial-quickstart-jax.rst:167 msgid ":code:`set_parameters (optional)`" msgstr "" #: ../../source/example-jax-from-centralized-to-federated.rst:160 -#: ../../source/example-mxnet-walk-through.rst:241 #: ../../source/example-pytorch-from-centralized-to-federated.rst:219 #: ../../source/tutorial-quickstart-jax.rst:166 msgid "" @@ -2454,7 +2434,6 @@ msgid "transform parameters to NumPy :code:`ndarray`'s" msgstr "" #: ../../source/example-jax-from-centralized-to-federated.rst:162 -#: ../../source/example-mxnet-walk-through.rst:243 #: ../../source/example-pytorch-from-centralized-to-federated.rst:220 #: ../../source/tutorial-quickstart-jax.rst:168 msgid "" @@ -2463,17 +2442,14 @@ msgid "" msgstr "" #: ../../source/example-jax-from-centralized-to-federated.rst:163 -#: ../../source/example-mxnet-walk-through.rst:244 #: ../../source/example-pytorch-from-centralized-to-federated.rst:221 #: ../../source/tutorial-quickstart-jax.rst:169 -#: ../../source/tutorial-quickstart-mxnet.rst:171 #: ../../source/tutorial-quickstart-pytorch.rst:155 -#: ../../source/tutorial-quickstart-scikitlearn.rst:108 +#: ../../source/tutorial-quickstart-scikitlearn.rst:118 msgid ":code:`get_parameters`" msgstr "" #: ../../source/example-jax-from-centralized-to-federated.rst:164 -#: ../../source/example-mxnet-walk-through.rst:245 #: ../../source/example-pytorch-from-centralized-to-federated.rst:222 #: ../../source/tutorial-quickstart-jax.rst:170 msgid "" @@ -2482,19 +2458,15 @@ msgid "" msgstr "" #: ../../source/example-jax-from-centralized-to-federated.rst:167 -#: ../../source/example-mxnet-walk-through.rst:248 #: ../../source/example-pytorch-from-centralized-to-federated.rst:225 #: ../../source/tutorial-quickstart-jax.rst:173 -#: ../../source/tutorial-quickstart-mxnet.rst:177 #: ../../source/tutorial-quickstart-pytorch.rst:161 -#: ../../source/tutorial-quickstart-scikitlearn.rst:115 +#: ../../source/tutorial-quickstart-scikitlearn.rst:125 msgid ":code:`fit`" msgstr "" #: ../../source/example-jax-from-centralized-to-federated.rst:166 #: ../../source/example-jax-from-centralized-to-federated.rst:170 -#: ../../source/example-mxnet-walk-through.rst:247 -#: ../../source/example-mxnet-walk-through.rst:251 #: ../../source/example-pytorch-from-centralized-to-federated.rst:224 #: ../../source/example-pytorch-from-centralized-to-federated.rst:228 #: ../../source/tutorial-quickstart-jax.rst:172 @@ -2505,7 +2477,6 @@ msgid "" msgstr "" #: ../../source/example-jax-from-centralized-to-federated.rst:167 -#: ../../source/example-mxnet-walk-through.rst:248 #: ../../source/example-pytorch-from-centralized-to-federated.rst:225 #: ../../source/tutorial-quickstart-jax.rst:173 msgid "train the model on the local training set" @@ -2517,17 +2488,14 @@ msgid "get the updated local model parameters and return them to the server" msgstr "" #: ../../source/example-jax-from-centralized-to-federated.rst:172 -#: ../../source/example-mxnet-walk-through.rst:253 #: ../../source/example-pytorch-from-centralized-to-federated.rst:230 #: ../../source/tutorial-quickstart-jax.rst:178 -#: ../../source/tutorial-quickstart-mxnet.rst:180 #: ../../source/tutorial-quickstart-pytorch.rst:164 -#: ../../source/tutorial-quickstart-scikitlearn.rst:118 +#: ../../source/tutorial-quickstart-scikitlearn.rst:128 msgid ":code:`evaluate`" msgstr "" #: ../../source/example-jax-from-centralized-to-federated.rst:171 -#: ../../source/example-mxnet-walk-through.rst:252 #: ../../source/example-pytorch-from-centralized-to-federated.rst:229 #: ../../source/tutorial-quickstart-jax.rst:177 msgid "evaluate the updated model on the local test set" @@ -2564,7 +2532,6 @@ msgid "Having defined the federation process, we can run it." msgstr "" #: ../../source/example-jax-from-centralized-to-federated.rst:268 -#: ../../source/example-mxnet-walk-through.rst:347 #: ../../source/example-pytorch-from-centralized-to-federated.rst:301 #: ../../source/tutorial-quickstart-jax.rst:274 msgid "And that's it. You can now open two additional terminal windows and run" @@ -2595,203 +2562,6 @@ msgid "" "clients?" msgstr "" -#: ../../source/example-mxnet-walk-through.rst:2 -msgid "Example: MXNet - Run MXNet Federated" -msgstr "" - -#: ../../source/example-mxnet-walk-through.rst:4 -msgid "" -"This tutorial will show you how to use Flower to build a federated " -"version of an existing MXNet workload. We are using MXNet to train a " -"Sequential model on the MNIST dataset. We will structure the example " -"similar to our `PyTorch - From Centralized To Federated " -"`_ walkthrough. MXNet and PyTorch are very " -"similar and a very good comparison between MXNet and PyTorch is given " -"`here `_. First, we build a centralized " -"training approach based on the `Handwritten Digit Recognition " -"`_" -" tutorial. Then, we build upon the centralized training code to run the " -"training in a federated fashion." -msgstr "" - -#: ../../source/example-mxnet-walk-through.rst:10 -msgid "" -"Before we start setting up our MXNet example, we install the " -":code:`mxnet` and :code:`flwr` packages:" -msgstr "" - -#: ../../source/example-mxnet-walk-through.rst:19 -msgid "MNIST Training with MXNet" -msgstr "" - -#: ../../source/example-mxnet-walk-through.rst:21 -msgid "" -"We begin with a brief description of the centralized training code based " -"on a :code:`Sequential` model. If you want a more in-depth explanation of" -" what's going on then have a look at the official `MXNet tutorial " -"`_." -msgstr "" - -#: ../../source/example-mxnet-walk-through.rst:24 -msgid "" -"Let's create a new file called:code:`mxnet_mnist.py` with all the " -"components required for a traditional (centralized) MNIST training. " -"First, the MXNet package :code:`mxnet` needs to be imported. You can see " -"that we do not yet import the :code:`flwr` package for federated " -"learning. This will be done later." -msgstr "" - -#: ../../source/example-mxnet-walk-through.rst:42 -msgid "The :code:`load_data()` function loads the MNIST training and test sets." -msgstr "" - -#: ../../source/example-mxnet-walk-through.rst:57 -msgid "" -"As already mentioned, we will use the MNIST dataset for this machine " -"learning workload. The model architecture (a very simple " -":code:`Sequential` model) is defined in :code:`model()`." -msgstr "" - -#: ../../source/example-mxnet-walk-through.rst:70 -msgid "" -"We now need to define the training (function :code:`train()`) which loops" -" over the training set and measures the loss for each batch of training " -"examples." -msgstr "" - -#: ../../source/example-mxnet-walk-through.rst:123 -msgid "" -"The evaluation of the model is defined in function :code:`test()`. The " -"function loops over all test samples and measures the loss and accuracy " -"of the model based on the test dataset." -msgstr "" - -#: ../../source/example-mxnet-walk-through.rst:158 -msgid "" -"Having defined the data loading, model architecture, training, and " -"evaluation we can put everything together and train our model on MNIST. " -"Note that the GPU/CPU device for the training and testing is defined " -"within the :code:`ctx` (context)." -msgstr "" - -#: ../../source/example-mxnet-walk-through.rst:184 -msgid "You can now run your (centralized) MXNet machine learning workload:" -msgstr "" - -#: ../../source/example-mxnet-walk-through.rst:190 -msgid "" -"So far this should all look fairly familiar if you've used MXNet (or even" -" PyTorch) before. Let's take the next step and use what we've built to " -"create a simple federated learning system consisting of one server and " -"two clients." -msgstr "" - -#: ../../source/example-mxnet-walk-through.rst:194 -msgid "MXNet meets Flower" -msgstr "" - -#: ../../source/example-mxnet-walk-through.rst:196 -msgid "" -"So far, it was not easily possible to use MXNet workloads for federated " -"learning because federated learning is not supported in MXNet. Since " -"Flower is fully agnostic towards the underlying machine learning " -"framework, it can be used to federated arbitrary machine learning " -"workloads. This section will show you how Flower can be used to federate " -"our centralized MXNet workload." -msgstr "" - -#: ../../source/example-mxnet-walk-through.rst:198 -msgid "" -"The concept to federate an existing workload is always the same and easy " -"to understand. We have to start a *server* and then use the code in " -":code:`mxnet_mnist.py` for the *clients* that are connected to the " -"*server*. The *server* sends model parameters to the clients. The " -"*clients* run the training and update the parameters. The updated " -"parameters are sent back to the *server* which averages all received " -"parameter updates. This describes one round of the federated learning " -"process and we repeat this for multiple rounds." -msgstr "" - -#: ../../source/example-mxnet-walk-through.rst:220 -msgid "" -"Finally, we will define our *client* logic in :code:`client.py` and build" -" upon the previously defined MXNet training in :code:`mxnet_mnist.py`. " -"Our *client* needs to import :code:`flwr`, but also :code:`mxnet` to " -"update the parameters on our MXNet model:" -msgstr "" - -#: ../../source/example-mxnet-walk-through.rst:235 -msgid "" -"Implementing a Flower *client* basically means implementing a subclass of" -" either :code:`flwr.client.Client` or :code:`flwr.client.NumPyClient`. " -"Our implementation will be based on :code:`flwr.client.NumPyClient` and " -"we'll call it :code:`MNISTClient`. :code:`NumPyClient` is slightly easier" -" to implement than :code:`Client` if you use a framework with good NumPy " -"interoperability (like PyTorch or MXNet) because it avoids some of the " -"boilerplate that would otherwise be necessary. :code:`MNISTClient` needs " -"to implement four methods, two methods for getting/setting model " -"parameters, one method for training the model, and one method for testing" -" the model:" -msgstr "" - -#: ../../source/example-mxnet-walk-through.rst:242 -msgid "transform MXNet :code:`NDArray`'s to NumPy :code:`ndarray`'s" -msgstr "" - -#: ../../source/example-mxnet-walk-through.rst:249 -#: ../../source/example-pytorch-from-centralized-to-federated.rst:226 -msgid "get the updated local model weights and return them to the server" -msgstr "" - -#: ../../source/example-mxnet-walk-through.rst:253 -#: ../../source/example-pytorch-from-centralized-to-federated.rst:230 -msgid "return the local loss and accuracy to the server" -msgstr "" - -#: ../../source/example-mxnet-walk-through.rst:255 -msgid "" -"The challenging part is to transform the MXNet parameters from " -":code:`NDArray` to :code:`NumPy Arrays` to make it readable for Flower." -msgstr "" - -#: ../../source/example-mxnet-walk-through.rst:257 -msgid "" -"The two :code:`NumPyClient` methods :code:`fit` and :code:`evaluate` make" -" use of the functions :code:`train()` and :code:`test()` previously " -"defined in :code:`mxnet_mnist.py`. So what we really do here is we tell " -"Flower through our :code:`NumPyClient` subclass which of our already " -"defined functions to call for training and evaluation. We included type " -"annotations to give you a better understanding of the data types that get" -" passed around." -msgstr "" - -#: ../../source/example-mxnet-walk-through.rst:319 -msgid "" -"Having defined data loading, model architecture, training, and evaluation" -" we can put everything together and train our :code:`Sequential` model on" -" MNIST." -msgstr "" - -#: ../../source/example-mxnet-walk-through.rst:353 -msgid "" -"in each window (make sure that the server is still running before you do " -"so) and see your MXNet project run federated learning across two clients." -" Congratulations!" -msgstr "" - -#: ../../source/example-mxnet-walk-through.rst:358 -msgid "" -"The full source code for this example: `MXNet: From Centralized To " -"Federated (Code) `_. Our example is of course " -"somewhat over-simplified because both clients load the exact same " -"dataset, which isn't realistic. You're now prepared to explore this topic" -" further. How about using a CNN or using a different dataset? How about " -"adding more clients?" -msgstr "" - #: ../../source/example-pytorch-from-centralized-to-federated.rst:2 msgid "Example: PyTorch - From Centralized To Federated" msgstr "" @@ -2921,6 +2691,14 @@ msgstr "" msgid ":code:`set_parameters`" msgstr "" +#: ../../source/example-pytorch-from-centralized-to-federated.rst:226 +msgid "get the updated local model weights and return them to the server" +msgstr "" + +#: ../../source/example-pytorch-from-centralized-to-federated.rst:230 +msgid "return the local loss and accuracy to the server" +msgstr "" + #: ../../source/example-pytorch-from-centralized-to-federated.rst:232 msgid "" "The two :code:`NumPyClient` methods :code:`fit` and :code:`evaluate` make" @@ -3863,35 +3641,191 @@ msgid "" "provided in these dictionaries:" msgstr "" -#: ../../source/how-to-configure-clients.rst:2 -msgid "Configure clients" +#: ../../source/how-to-authenticate-supernodes.rst:2 +msgid "Authenticate SuperNodes" msgstr "" -#: ../../source/how-to-configure-clients.rst:4 +#: ../../source/how-to-authenticate-supernodes.rst:4 msgid "" -"Along with model parameters, Flower can send configuration values to " -"clients. Configuration values can be used for various purposes. They are," -" for example, a popular way to control client-side hyperparameters from " -"the server." +"Flower has built-in support for authenticated SuperNodes that you can use" +" to verify the identities of each SuperNode connecting to a SuperLink. " +"Flower node authentication works similar to how GitHub SSH authentication" +" works:" msgstr "" -#: ../../source/how-to-configure-clients.rst:7 -msgid "Configuration values" +#: ../../source/how-to-authenticate-supernodes.rst:7 +msgid "SuperLink (server) stores a list of known (client) node public keys" msgstr "" -#: ../../source/how-to-configure-clients.rst:9 +#: ../../source/how-to-authenticate-supernodes.rst:8 msgid "" -"Configuration values are represented as a dictionary with ``str`` keys " -"and values of type ``bool``, ``bytes``, ``double`` (64-bit precision " -"float), ``int``, or ``str`` (or equivalent types in different languages)." -" Here is an example of a configuration dictionary in Python:" +"Using ECDH, both SuperNode and SuperLink independently derive a shared " +"secret" msgstr "" -#: ../../source/how-to-configure-clients.rst:20 +#: ../../source/how-to-authenticate-supernodes.rst:9 msgid "" -"Flower serializes these configuration dictionaries (or *config dict* for " -"short) to their ProtoBuf representation, transports them to the client " -"using gRPC, and then deserializes them back to Python dictionaries." +"Shared secret is used to compute the HMAC value of the message sent from " +"SuperNode to SuperLink as a token" +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:10 +msgid "SuperLink verifies the token" +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:12 +msgid "" +"We recommend you to check out the complete `code example " +"`_ demonstrating federated learning with Flower in an " +"authenticated setting." +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:15 +msgid "" +"This guide covers a preview feature that might change in future versions " +"of Flower." +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:18 +msgid "" +"For increased security, node authentication can only be used when " +"encrypted connections (SSL/TLS) are enabled." +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:21 +msgid "Enable node authentication in :code:`SuperLink`" +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:23 +msgid "" +"To enable node authentication, first you need to configure SSL/TLS " +"connections to secure the SuperLink<>SuperNode communication. You can " +"find the complete guide `here `_. After configuring secure connections, you" +" can enable client authentication in a long-running Flower " +":code:`SuperLink`. Use the following terminal command to start a Flower " +":code:`SuperNode` that has both secure connections and node " +"authentication enabled:" +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:38 +msgid "Let's break down the authentication flags:" +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:40 +msgid "" +"The first flag :code:`--auth-list-public-keys` expects a path to a CSV " +"file storing all known node public keys. You need to store all known node" +" public keys that are allowed to participate in a federation in one CSV " +"file (:code:`.csv`)." +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:42 +msgid "" +"A valid CSV file storing known node public keys should list the keys in " +"OpenSSH format, separated by commas and without any comments. For an " +"example, refer to our code sample, which contains a CSV file with two " +"known node public keys." +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:44 +msgid "" +"The second and third flags :code:`--auth-superlink-private-key` and :code" +":`--auth-superlink-public-key` expect paths to the server's private and " +"public keys. For development purposes, you can generate a private and " +"public key pair using :code:`ssh-keygen -t ecdsa -b 384`." +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:47 +msgid "" +"In Flower 1.9, there is no support for dynamically removing, editing, or " +"adding known node public keys to the SuperLink. To change the set of " +"known nodes, you need to shut the server down, edit the CSV file, and " +"start the server again. Support for dynamically changing the set of known" +" nodes is on the roadmap to be released in Flower 1.10 (ETA: June)." +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:53 +msgid "Enable node authentication in :code:`SuperNode`" +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:55 +msgid "" +"Similar to the long-running Flower server (:code:`SuperLink`), you can " +"easily enable node authentication in the long-running Flower client " +"(:code:`SuperNode`). Use the following terminal command to start an " +"authenticated :code:`SuperNode`:" +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:66 +msgid "" +"The :code:`--auth-supernode-private-key` flag expects a path to the " +"node's private key file and the :code:`--auth-supernode-public-key` flag " +"expects a path to the node's public key file. For development purposes, " +"you can generate a private and public key pair using :code:`ssh-keygen -t" +" ecdsa -b 384`." +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:70 +msgid "Security notice" +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:72 +msgid "" +"The system's security relies on the credentials of the SuperLink and each" +" SuperNode. Therefore, it is imperative to safeguard and safely store the" +" credentials to avoid security risks such as Public Key Infrastructure " +"(PKI) impersonation attacks. The node authentication mechanism also " +"involves human interaction, so please ensure that all of the " +"communication is done in a secure manner, using trusted communication " +"methods." +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:77 +#: ../../source/how-to-enable-ssl-connections.rst:68 +#: ../../source/how-to-use-built-in-mods.rst:85 +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:287 +msgid "Conclusion" +msgstr "" + +#: ../../source/how-to-authenticate-supernodes.rst:79 +msgid "" +"You should now have learned how to start a long-running Flower server " +"(:code:`SuperLink`) and client (:code:`SuperNode`) with node " +"authentication enabled. You should also know the significance of the " +"private key and store it safely to minimize security risks." +msgstr "" + +#: ../../source/how-to-configure-clients.rst:2 +msgid "Configure clients" +msgstr "" + +#: ../../source/how-to-configure-clients.rst:4 +msgid "" +"Along with model parameters, Flower can send configuration values to " +"clients. Configuration values can be used for various purposes. They are," +" for example, a popular way to control client-side hyperparameters from " +"the server." +msgstr "" + +#: ../../source/how-to-configure-clients.rst:7 +msgid "Configuration values" +msgstr "" + +#: ../../source/how-to-configure-clients.rst:9 +msgid "" +"Configuration values are represented as a dictionary with ``str`` keys " +"and values of type ``bool``, ``bytes``, ``double`` (64-bit precision " +"float), ``int``, or ``str`` (or equivalent types in different languages)." +" Here is an example of a configuration dictionary in Python:" +msgstr "" + +#: ../../source/how-to-configure-clients.rst:20 +msgid "" +"Flower serializes these configuration dictionaries (or *config dict* for " +"short) to their ProtoBuf representation, transports them to the client " +"using gRPC, and then deserializes them back to Python dictionaries." msgstr "" #: ../../source/how-to-configure-clients.rst:24 @@ -4071,8 +4005,9 @@ msgstr "" #: ../../source/how-to-enable-ssl-connections.rst:4 msgid "" -"This guide describes how to a SSL-enabled secure Flower server can be " -"started and how a Flower client can establish a secure connections to it." +"This guide describes how to a SSL-enabled secure Flower server " +"(:code:`SuperLink`) can be started and how a Flower client " +"(:code:`SuperNode`) can establish a secure connections to it." msgstr "" #: ../../source/how-to-enable-ssl-connections.rst:7 @@ -4084,10 +4019,10 @@ msgstr "" #: ../../source/how-to-enable-ssl-connections.rst:10 msgid "" -"The code example comes with a README.md file which will explain how to " -"start it. Although it is already SSL-enabled, it might be less " -"descriptive on how. Stick to this guide for a deeper introduction to the " -"topic." +"The code example comes with a :code:`README.md` file which explains how " +"to start it. Although it is already SSL-enabled, it might be less " +"descriptive on how it does so. Stick to this guide for a deeper " +"introduction to the topic." msgstr "" #: ../../source/how-to-enable-ssl-connections.rst:16 @@ -4100,100 +4035,81 @@ msgid "" "server and client. For the purpose of this guide we are going to generate" " self-signed certificates. As this can become quite complex we are going " "to ask you to run the script in :code:`examples/advanced-" -"tensorflow/certificates/generate.sh`" -msgstr "" - -#: ../../source/how-to-enable-ssl-connections.rst:23 -msgid "with the following command sequence:" +"tensorflow/certificates/generate.sh` with the following command sequence:" msgstr "" -#: ../../source/how-to-enable-ssl-connections.rst:30 +#: ../../source/how-to-enable-ssl-connections.rst:29 msgid "" "This will generate the certificates in :code:`examples/advanced-" "tensorflow/.cache/certificates`." msgstr "" -#: ../../source/how-to-enable-ssl-connections.rst:32 +#: ../../source/how-to-enable-ssl-connections.rst:31 msgid "" -"The approach how the SSL certificates are generated in this example can " -"serve as an inspiration and starting point but should not be taken as " -"complete for production environments. Please refer to other sources " -"regarding the issue of correctly generating certificates for production " -"environments." +"The approach for generating SSL certificates in the context of this " +"example can serve as an inspiration and starting point, but it should not" +" be used as a reference for production environments. Please refer to " +"other sources regarding the issue of correctly generating certificates " +"for production environments. For non-critical prototyping or research " +"projects, it might be sufficient to use the self-signed certificates " +"generated using the scripts mentioned in this guide." msgstr "" -#: ../../source/how-to-enable-ssl-connections.rst:36 -msgid "" -"In case you are a researcher you might be just fine using the self-signed" -" certificates generated using the scripts which are part of this guide." +#: ../../source/how-to-enable-ssl-connections.rst:39 +msgid "Server (SuperLink)" msgstr "" #: ../../source/how-to-enable-ssl-connections.rst:41 -#: ../../source/ref-api/flwr.server.Server.rst:2 -msgid "Server" -msgstr "" - -#: ../../source/how-to-enable-ssl-connections.rst:43 msgid "" -"We are now going to show how to write a sever which uses the previously " -"generated scripts." +"Use the following terminal command to start a sever (SuperLink) that uses" +" the previously generated certificates:" msgstr "" -#: ../../source/how-to-enable-ssl-connections.rst:61 +#: ../../source/how-to-enable-ssl-connections.rst:50 msgid "" "When providing certificates, the server expects a tuple of three " -"certificates. :code:`Path` can be used to easily read the contents of " -"those files into byte strings, which is the data type " -":code:`start_server` expects." +"certificates paths: CA certificate, server certificate and server private" +" key." msgstr "" -#: ../../source/how-to-enable-ssl-connections.rst:65 -#: ../../source/how-to-upgrade-to-flower-1.0.rst:37 -#: ../../source/ref-api/flwr.client.Client.rst:2 -msgid "Client" +#: ../../source/how-to-enable-ssl-connections.rst:54 +msgid "Client (SuperNode)" msgstr "" -#: ../../source/how-to-enable-ssl-connections.rst:67 +#: ../../source/how-to-enable-ssl-connections.rst:56 msgid "" -"We are now going to show how to write a client which uses the previously " -"generated scripts:" +"Use the following terminal command to start a client (SuperNode) that " +"uses the previously generated certificates:" msgstr "" -#: ../../source/how-to-enable-ssl-connections.rst:84 +#: ../../source/how-to-enable-ssl-connections.rst:64 msgid "" -"When setting :code:`root_certificates`, the client expects the PEM-" -"encoded root certificates as a byte string. We are again using " -":code:`Path` to simplify reading those as byte strings." -msgstr "" - -#: ../../source/how-to-enable-ssl-connections.rst:89 -#: ../../source/how-to-use-built-in-mods.rst:85 -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:287 -msgid "Conclusion" +"When setting :code:`root_certificates`, the client expects a file path to" +" PEM-encoded root certificates." msgstr "" -#: ../../source/how-to-enable-ssl-connections.rst:91 +#: ../../source/how-to-enable-ssl-connections.rst:70 msgid "" "You should now have learned how to generate self-signed certificates " -"using the given script, start a SSL-enabled server, and have a client " +"using the given script, start an SSL-enabled server and have a client " "establish a secure connection to it." msgstr "" -#: ../../source/how-to-enable-ssl-connections.rst:96 +#: ../../source/how-to-enable-ssl-connections.rst:75 msgid "Additional resources" msgstr "" -#: ../../source/how-to-enable-ssl-connections.rst:98 +#: ../../source/how-to-enable-ssl-connections.rst:77 msgid "" "These additional sources might be relevant if you would like to dive " "deeper into the topic of certificates:" msgstr "" -#: ../../source/how-to-enable-ssl-connections.rst:100 +#: ../../source/how-to-enable-ssl-connections.rst:79 msgid "`Let's Encrypt `_" msgstr "" -#: ../../source/how-to-enable-ssl-connections.rst:101 +#: ../../source/how-to-enable-ssl-connections.rst:80 msgid "`certbot `_" msgstr "" @@ -4465,6 +4381,7 @@ msgid "Install stable release" msgstr "" #: ../../source/how-to-install-flower.rst:15 +#: ../../source/how-to-upgrade-to-flower-next.rst:46 msgid "Using pip" msgstr "" @@ -4802,14 +4719,15 @@ msgstr "" msgid "" "The simplest way to get started with Flower is by using the pre-made " "Docker images, which you can find on `Docker Hub " -"`_." +"`__. Supported architectures include " +"``amd64`` and ``arm64v8``." msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:7 +#: ../../source/how-to-run-flower-using-docker.rst:8 msgid "Before you start, make sure that the Docker daemon is running:" msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:14 +#: ../../source/how-to-run-flower-using-docker.rst:15 msgid "" "If you do not see the version of Docker but instead get an error saying " "that the command was not found, you will need to install Docker first. " @@ -4817,7 +4735,7 @@ msgid "" "docker/>`_." msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:20 +#: ../../source/how-to-run-flower-using-docker.rst:21 msgid "" "On Linux, Docker commands require ``sudo`` privilege. If you want to " "avoid using ``sudo``, you can follow the `Post-installation steps " @@ -4825,16528 +4743,20166 @@ msgid "" "official Docker website." msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:25 -msgid "Flower server" +#: ../../source/how-to-run-flower-using-docker.rst:27 +msgid "" +"To ensure optimal performance and compatibility, the SuperLink, SuperNode" +" and ServerApp image must have the same version when running together. " +"This guarantees seamless integration and avoids potential conflicts or " +"issues that may arise from using different versions." +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:32 +msgid "Flower SuperLink" msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:28 +#: ../../source/how-to-run-flower-using-docker.rst:35 msgid "Quickstart" msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:30 +#: ../../source/how-to-run-flower-using-docker.rst:37 msgid "If you're looking to try out Flower, you can use the following command:" msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:37 +#: ../../source/how-to-run-flower-using-docker.rst:43 msgid "" -"The command will pull the Docker image with the tag " -"``1.7.0-py3.11-ubuntu22.04`` from Docker Hub. The tag contains the " -"information which Flower, Python and Ubuntu is used. In this case, it " -"uses Flower 1.7.0, Python 3.11 and Ubuntu 22.04. The ``--rm`` flag tells " -"Docker to remove the container after it exits." +"The command pulls the Docker image with the tag ``1.8.0`` from Docker " +"Hub. The tag specifies the Flower version. In this case, Flower 1.8.0. " +"The ``--rm`` flag tells Docker to remove the container after it exits." msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:44 +#: ../../source/how-to-run-flower-using-docker.rst:49 msgid "" -"By default, the Flower server keeps state in-memory. When using the " +"By default, the Flower SuperLink keeps state in-memory. When using the " "Docker flag ``--rm``, the state is not persisted between container " "starts. We will show below how to save the state in a file on your host " "system." msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:48 +#: ../../source/how-to-run-flower-using-docker.rst:53 msgid "" "The ``-p :`` flag tells Docker to map the ports " "``9091``/``9092`` of the host to ``9091``/``9092`` of the container, " "allowing you to access the Driver API on ``http://localhost:9091`` and " "the Fleet API on ``http://localhost:9092``. Lastly, any flag that comes " -"after the tag is passed to the Flower server. Here, we are passing the " -"flag ``--insecure``." +"after the tag is passed to the Flower SuperLink. Here, we are passing the" +" flag ``--insecure``." msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:55 +#: ../../source/how-to-run-flower-using-docker.rst:60 +#: ../../source/how-to-run-flower-using-docker.rst:259 +#: ../../source/how-to-run-flower-using-docker.rst:376 msgid "" "The ``--insecure`` flag enables insecure communication (using HTTP, not " "HTTPS) and should only be used for testing purposes. We strongly " "recommend enabling `SSL `_ when " +"flower-using-docker.html#enabling-ssl-for-secure-connections>`__ when " "deploying to a production environment." msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:60 +#: ../../source/how-to-run-flower-using-docker.rst:65 msgid "" -"You can use ``--help`` to view all available flags that the server " +"You can use ``--help`` to view all available flags that the SuperLink " "supports:" msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:67 +#: ../../source/how-to-run-flower-using-docker.rst:72 msgid "Mounting a volume to store the state on the host system" msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:69 +#: ../../source/how-to-run-flower-using-docker.rst:74 msgid "" -"If you want to persist the state of the server on your host system, all " -"you need to do is specify a path where you want to save the file on your " -"host system and a name for the database file. In the example below, we " -"tell Docker via the flag ``-v`` to mount the user's home directory " -"(``~/`` on your host) into the ``/app/`` directory of the container. " +"If you want to persist the state of the SuperLink on your host system, " +"all you need to do is specify a directory where you want to save the file" +" on your host system and a name for the database file. By default, the " +"SuperLink container runs with a non-root user called ``app`` with the " +"user ID ``49999``. It is recommended to create new directory and change " +"the user ID of the directory to ``49999`` to ensure the mounted directory" +" has the proper permissions. If you later want to delete the directory, " +"you can change the user ID back to the current user ID by running ``sudo " +"chown -R $USER:$(id -gn) state``." +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:82 +msgid "" +"In the example below, we create a new directory, change the user ID and " +"tell Docker via the flag ``--volume`` to mount the local ``state`` " +"directory into the ``/app/state`` directory of the container. " "Furthermore, we use the flag ``--database`` to specify the name of the " "database file." msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:82 +#: ../../source/how-to-run-flower-using-docker.rst:95 msgid "" -"As soon as the server starts, the file ``state.db`` is created in the " -"user's home directory on your host system. If the file already exists, " -"the server tries to restore the state from the file. To start the server " -"with an empty database, simply remove the ``state.db`` file." +"As soon as the SuperLink starts, the file ``state.db`` is created in the " +"``state`` directory on your host system. If the file already exists, the " +"SuperLink tries to restore the state from the file. To start the " +"SuperLink with an empty database, simply remove the ``state.db`` file." msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:87 +#: ../../source/how-to-run-flower-using-docker.rst:100 +#: ../../source/how-to-run-flower-using-docker.rst:281 +#: ../../source/how-to-run-flower-using-docker.rst:397 msgid "Enabling SSL for secure connections" msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:89 +#: ../../source/how-to-run-flower-using-docker.rst:102 msgid "" -"To enable SSL, you will need a CA certificate, a server certificate and a" -" server private key." +"To enable SSL, you will need a PEM-encoded root certificate, a PEM-" +"encoded private key and a PEM-encoded certificate chain." msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:92 +#: ../../source/how-to-run-flower-using-docker.rst:106 msgid "" "For testing purposes, you can generate your own self-signed certificates." " The `Enable SSL connections `_ page contains a section that " -"will guide you through the process." +"enable-ssl-connections.html#certificates>`__ page contains a section that" +" will guide you through the process." msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:96 +#: ../../source/how-to-run-flower-using-docker.rst:110 msgid "" "Assuming all files we need are in the local ``certificates`` directory, " -"we can use the flag ``-v`` to mount the local directory into the " -"``/app/`` directory of the container. This allows the server to access " -"the files within the container. Finally, we pass the names of the " -"certificates to the server with the ``--certificates`` flag." +"we can use the flag ``--volume`` to mount the local directory into the " +"``/app/certificates/`` directory of the container. This allows the " +"SuperLink to access the files within the container. The ``ro`` stands for" +" ``read-only``. Docker volumes default to ``read-write``; that option " +"tells Docker to make the volume ``read-only`` instead. Finally, we pass " +"the names of the certificates and key file to the SuperLink with the " +"``--ssl-ca-certfile``, ``--ssl-certfile`` and ``--ssl-keyfile`` flag." msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:108 -msgid "Using a different Flower or Python version" +#: ../../source/how-to-run-flower-using-docker.rst:128 +msgid "" +"Because Flower containers, by default, run with a non-root user ``app``, " +"the mounted files and directories must have the proper permissions for " +"the user ID ``49999``. For example, to change the user ID of all files in" +" the ``certificates/`` directory, you can run ``sudo chown -R 49999:49999" +" certificates/*``." msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:110 -msgid "" -"If you want to use a different version of Flower or Python, you can do so" -" by changing the tag. All versions we provide are available on `Docker " -"Hub `_." +#: ../../source/how-to-run-flower-using-docker.rst:134 +msgid "Flower SuperNode" msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:114 -msgid "Pinning a Docker image to a specific version" +#: ../../source/how-to-run-flower-using-docker.rst:136 +msgid "" +"The SuperNode Docker image comes with a pre-installed version of Flower " +"and serves as a base for building your own SuperNode image." msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:116 +#: ../../source/how-to-run-flower-using-docker.rst:141 msgid "" -"It may happen that we update the images behind the tags. Such updates " -"usually include security updates of system dependencies that should not " -"change the functionality of Flower. However, if you want to ensure that " -"you always use the same image, you can specify the hash of the image " -"instead of the tag." +"The SuperNode Docker image currently works only with the 1.9.0-nightly " +"release. A stable version will be available when Flower 1.9.0 (stable) " +"gets released (ETA: May). A SuperNode nightly image must be paired with " +"the corresponding SuperLink and ServerApp nightly images released on the " +"same day. To ensure the versions are in sync, using the concrete tag, " +"e.g., ``1.9.0.dev20240501`` instead of ``nightly`` is recommended." msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:121 +#: ../../source/how-to-run-flower-using-docker.rst:147 msgid "" -"The following command returns the current image hash referenced by the " -"``server:1.7.0-py3.11-ubuntu22.04`` tag:" +"We will use the ``quickstart-pytorch`` example, which you can find in the" +" Flower repository, to illustrate how you can dockerize your ClientApp." msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:128 -msgid "Next, we can pin the hash when running a new server container:" +#: ../../source/how-to-run-flower-using-docker.rst:155 +#, fuzzy +msgid "" +"Before we can start, we need to meet a few prerequisites in our local " +"development environment. You can skip the first part if you want to run " +"your ClientApp instead of the ``quickstart-pytorch`` example." msgstr "" +"Antes de começarmos, precisamos encontrar alguns pré-requisitos em nosso " +"ambiente de desenvolvimento local." -#: ../../source/how-to-run-flower-using-docker.rst:137 -msgid "Setting environment variables" +#: ../../source/how-to-run-flower-using-docker.rst:159 +#, fuzzy +msgid "Clone the Flower repository." +msgstr "Clone o repositório do flower." + +#: ../../source/how-to-run-flower-using-docker.rst:173 +msgid "Creating a SuperNode Dockerfile" msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:139 -msgid "" -"To set a variable inside a Docker container, you can use the ``-e " -"=`` flag." +#: ../../source/how-to-run-flower-using-docker.rst:175 +#: ../../source/how-to-run-flower-using-docker.rst:311 +msgid "Let's assume the following project layout:" msgstr "" -#: ../../source/how-to-run-simulations.rst:2 -msgid "Run simulations" +#: ../../source/how-to-run-flower-using-docker.rst:184 +msgid "" +"First, we need to create a ``requirements.txt`` file in the directory " +"where the ``ClientApp`` code is located. In the file, we list all the " +"dependencies that the ClientApp requires." msgstr "" -#: ../../source/how-to-run-simulations.rst:8 +#: ../../source/how-to-run-flower-using-docker.rst:196 msgid "" -"Simulating Federated Learning workloads is useful for a multitude of use-" -"cases: you might want to run your workload on a large cohort of clients " -"but without having to source, configure and mange a large number of " -"physical devices; you might want to run your FL workloads as fast as " -"possible on the compute systems you have access to without having to go " -"through a complex setup process; you might want to validate your " -"algorithm on different scenarios at varying levels of data and system " -"heterogeneity, client availability, privacy budgets, etc. These are among" -" some of the use-cases where simulating FL workloads makes sense. Flower " -"can accommodate these scenarios by means of its `VirtualClientEngine " -"`_ or " -"VCE." +"Note that `flwr `__ is already installed " +"in the ``flwr/supernode`` base image, so you only need to include other " +"package dependencies in your ``requirements.txt``, such as ``torch``, " +"``tensorflow``, etc." msgstr "" -#: ../../source/how-to-run-simulations.rst:10 +#: ../../source/how-to-run-flower-using-docker.rst:200 msgid "" -"The :code:`VirtualClientEngine` schedules, launches and manages `virtual`" -" clients. These clients are identical to `non-virtual` clients (i.e. the " -"ones you launch via the command `flwr.client.start_client `_) in the sense that they can be configure by " -"creating a class inheriting, for example, from `flwr.client.NumPyClient " -"`_ and therefore behave in an " -"identical way. In addition to that, clients managed by the " -":code:`VirtualClientEngine` are:" +"Next, we create a Dockerfile. If you use the ``quickstart-pytorch`` " +"example, create a new file called ``Dockerfile.supernode`` in ``examples" +"/quickstart-pytorch``." msgstr "" -#: ../../source/how-to-run-simulations.rst:12 +#: ../../source/how-to-run-flower-using-docker.rst:203 msgid "" -"resource-aware: this means that each client gets assigned a portion of " -"the compute and memory on your system. You as a user can control this at " -"the beginning of the simulation and allows you to control the degree of " -"parallelism of your Flower FL simulation. The fewer the resources per " -"client, the more clients can run concurrently on the same hardware." +"The ``Dockerfile.supernode`` contains the instructions that assemble the " +"SuperNode image." msgstr "" -#: ../../source/how-to-run-simulations.rst:13 +#: ../../source/how-to-run-flower-using-docker.rst:217 msgid "" -"self-managed: this means that you as a user do not need to launch clients" -" manually, instead this gets delegated to :code:`VirtualClientEngine`'s " -"internals." +"In the first two lines, we instruct Docker to use the SuperNode image " +"tagged ``nightly`` as a base image and set our working directory to " +"``/app``. The following instructions will now be executed in the ``/app``" +" directory. Next, we install the ClientApp dependencies by copying the " +"``requirements.txt`` file into the image and run ``pip install``. In the " +"last two lines, we copy the ``client.py`` module into the image and set " +"the entry point to ``flower-client-app`` with the argument " +"``client:app``. The argument is the object reference of the ClientApp " +"(``:``) that will be run inside the ClientApp." msgstr "" -#: ../../source/how-to-run-simulations.rst:14 +#: ../../source/how-to-run-flower-using-docker.rst:226 +#, fuzzy +msgid "Building the SuperNode Docker image" +msgstr "Construindo a imagem do servidor" + +#: ../../source/how-to-run-flower-using-docker.rst:228 msgid "" -"ephemeral: this means that a client is only materialized when it is " -"required in the FL process (e.g. to do `fit() `_). The object is destroyed afterwards," -" releasing the resources it was assigned and allowing in this way other " -"clients to participate." +"Next, we build the SuperNode Docker image by running the following " +"command in the directory where Dockerfile and ClientApp code are located." msgstr "" -#: ../../source/how-to-run-simulations.rst:16 +#: ../../source/how-to-run-flower-using-docker.rst:235 msgid "" -"The :code:`VirtualClientEngine` implements `virtual` clients using `Ray " -"`_, an open-source framework for scalable Python " -"workloads. In particular, Flower's :code:`VirtualClientEngine` makes use " -"of `Actors `_ to " -"spawn `virtual` clients and run their workload." +"We gave the image the name ``flwr_supernode``, and the tag ``0.0.1``. " +"Remember that the here chosen values only serve as an example. You can " +"change them to your needs." msgstr "" -#: ../../source/how-to-run-simulations.rst:20 -msgid "Launch your Flower simulation" +#: ../../source/how-to-run-flower-using-docker.rst:240 +#, fuzzy +msgid "Running the SuperNode Docker image" +msgstr "Construindo a imagem do servidor" + +#: ../../source/how-to-run-flower-using-docker.rst:242 +msgid "Now that we have built the SuperNode image, we can finally run it." msgstr "" -#: ../../source/how-to-run-simulations.rst:22 -msgid "" -"Running Flower simulations still require you to define your client class," -" a strategy, and utility functions to download and load (and potentially " -"partition) your dataset. With that out of the way, launching your " -"simulation is done with `start_simulation `_ and a minimal example looks" -" as follows:" +#: ../../source/how-to-run-flower-using-docker.rst:250 +#: ../../source/how-to-run-flower-using-docker.rst:367 +msgid "Let's break down each part of this command:" msgstr "" -#: ../../source/how-to-run-simulations.rst:44 -msgid "VirtualClientEngine resources" +#: ../../source/how-to-run-flower-using-docker.rst:252 +#: ../../source/how-to-run-flower-using-docker.rst:369 +msgid "``docker run``: This is the command to run a new Docker container." msgstr "" -#: ../../source/how-to-run-simulations.rst:45 +#: ../../source/how-to-run-flower-using-docker.rst:253 +#: ../../source/how-to-run-flower-using-docker.rst:370 msgid "" -"By default the VCE has access to all system resources (i.e. all CPUs, all" -" GPUs, etc) since that is also the default behavior when starting Ray. " -"However, in some settings you might want to limit how many of your system" -" resources are used for simulation. You can do this via the " -":code:`ray_init_args` input argument to :code:`start_simulation` which " -"the VCE internally passes to Ray's :code:`ray.init` command. For a " -"complete list of settings you can configure check the `ray.init " -"`_" -" documentation. Do not set :code:`ray_init_args` if you want the VCE to " -"use all your system's CPUs and GPUs." +"``--rm``: This option specifies that the container should be " +"automatically removed when it stops." msgstr "" -#: ../../source/how-to-run-simulations.rst:62 -msgid "Assigning client resources" +#: ../../source/how-to-run-flower-using-docker.rst:254 +msgid "``flwr_supernode:0.0.1``: The name the tag of the Docker image to use." msgstr "" -#: ../../source/how-to-run-simulations.rst:63 -msgid "" -"By default the :code:`VirtualClientEngine` assigns a single CPU core (and" -" nothing else) to each virtual client. This means that if your system has" -" 10 cores, that many virtual clients can be concurrently running." +#: ../../source/how-to-run-flower-using-docker.rst:255 +#: ../../source/how-to-run-flower-using-docker.rst:372 +msgid "``--insecure``: This option enables insecure communication." msgstr "" -#: ../../source/how-to-run-simulations.rst:65 +#: ../../source/how-to-run-flower-using-docker.rst msgid "" -"More often than not, you would probably like to adjust the resources your" -" clients get assigned based on the complexity (i.e. compute and memory " -"footprint) of your FL workload. You can do so when starting your " -"simulation by setting the argument `client_resources` to " -"`start_simulation `_." -" Two keys are internally used by Ray to schedule and spawn workloads (in " -"our case Flower clients):" +"``--superlink 192.168.1.100:9092``: This option specifies the address of " +"the SuperLinks Fleet" msgstr "" -#: ../../source/how-to-run-simulations.rst:67 -msgid ":code:`num_cpus` indicates the number of CPU cores a client would get." +#: ../../source/how-to-run-flower-using-docker.rst +msgid "API to connect to. Remember to update it with your SuperLink IP." msgstr "" -#: ../../source/how-to-run-simulations.rst:68 +#: ../../source/how-to-run-flower-using-docker.rst:269 msgid "" -":code:`num_gpus` indicates the **ratio** of GPU memory a client gets " -"assigned." +"To test running Flower locally, you can create a `bridge network " +"`__, use the ``--network`` argument and pass the " +"name of the Docker network to run your SuperNodes." msgstr "" -#: ../../source/how-to-run-simulations.rst:70 -msgid "Let's see a few examples:" +#: ../../source/how-to-run-flower-using-docker.rst:273 +msgid "" +"Any argument that comes after the tag is passed to the Flower SuperNode " +"binary. To see all available flags that the SuperNode supports, run:" msgstr "" -#: ../../source/how-to-run-simulations.rst:89 +#: ../../source/how-to-run-flower-using-docker.rst:283 msgid "" -"While the :code:`client_resources` can be used to control the degree of " -"concurrency in your FL simulation, this does not stop you from running " -"dozens, hundreds or even thousands of clients in the same round and " -"having orders of magnitude more `dormant` (i.e. not participating in a " -"round) clients. Let's say you want to have 100 clients per round but your" -" system can only accommodate 8 clients concurrently. The " -":code:`VirtualClientEngine` will schedule 100 jobs to run (each " -"simulating a client sampled by the strategy) and then will execute them " -"in a resource-aware manner in batches of 8." +"To enable SSL, we will need to mount a PEM-encoded root certificate into " +"your SuperNode container." msgstr "" -#: ../../source/how-to-run-simulations.rst:91 +#: ../../source/how-to-run-flower-using-docker.rst:285 msgid "" -"To understand all the intricate details on how resources are used to " -"schedule FL clients and how to define custom resources, please take a " -"look at the `Ray documentation `_." +"Assuming the certificate already exists locally, we can use the flag " +"``--volume`` to mount the local certificate into the container's " +"``/app/`` directory. This allows the SuperNode to access the certificate " +"within the container. Use the ``--root-certificates`` flag when starting " +"the container." msgstr "" -#: ../../source/how-to-run-simulations.rst:94 -msgid "Simulation examples" +#: ../../source/how-to-run-flower-using-docker.rst:297 +msgid "Flower ServerApp" msgstr "" -#: ../../source/how-to-run-simulations.rst:96 +#: ../../source/how-to-run-flower-using-docker.rst:299 msgid "" -"A few ready-to-run complete examples for Flower simulation in " -"Tensorflow/Keras and PyTorch are provided in the `Flower repository " -"`_. You can run them on Google Colab too:" +"The procedure for building and running a ServerApp image is almost " +"identical to the SuperNode image." msgstr "" -#: ../../source/how-to-run-simulations.rst:98 +#: ../../source/how-to-run-flower-using-docker.rst:301 msgid "" -"`Tensorflow/Keras Simulation " -"`_: 100 clients collaboratively train a MLP model on MNIST." +"Similar to the SuperNode image, the ServerApp Docker image comes with a " +"pre-installed version of Flower and serves as a base for building your " +"own ServerApp image." msgstr "" -#: ../../source/how-to-run-simulations.rst:99 +#: ../../source/how-to-run-flower-using-docker.rst:304 msgid "" -"`PyTorch Simulation `_: 100 clients collaboratively train a CNN model on " -"MNIST." +"We will use the same ``quickstart-pytorch`` example as we do in the " +"Flower SuperNode section. If you have not already done so, please follow " +"the `SuperNode Prerequisites`_ before proceeding." msgstr "" -#: ../../source/how-to-run-simulations.rst:104 -msgid "Multi-node Flower simulations" +#: ../../source/how-to-run-flower-using-docker.rst:309 +msgid "Creating a ServerApp Dockerfile" msgstr "" -#: ../../source/how-to-run-simulations.rst:106 +#: ../../source/how-to-run-flower-using-docker.rst:320 msgid "" -"Flower's :code:`VirtualClientEngine` allows you to run FL simulations " -"across multiple compute nodes. Before starting your multi-node simulation" -" ensure that you:" -msgstr "" - -#: ../../source/how-to-run-simulations.rst:108 -msgid "Have the same Python environment in all nodes." -msgstr "" - -#: ../../source/how-to-run-simulations.rst:109 -msgid "Have a copy of your code (e.g. your entire repo) in all nodes." +"First, we need to create a Dockerfile in the directory where the " +"``ServerApp`` code is located. If you use the ``quickstart-pytorch`` " +"example, create a new file called ``Dockerfile.serverapp`` in ``examples" +"/quickstart-pytorch``." msgstr "" -#: ../../source/how-to-run-simulations.rst:110 +#: ../../source/how-to-run-flower-using-docker.rst:324 msgid "" -"Have a copy of your dataset in all nodes (more about this in " -":ref:`simulation considerations `)" +"The ``Dockerfile.serverapp`` contains the instructions that assemble the " +"ServerApp image." msgstr "" -#: ../../source/how-to-run-simulations.rst:111 +#: ../../source/how-to-run-flower-using-docker.rst:335 msgid "" -"Pass :code:`ray_init_args={\"address\"=\"auto\"}` to `start_simulation " -"`_ so the " -":code:`VirtualClientEngine` attaches to a running Ray instance." +"In the first two lines, we instruct Docker to use the ServerApp image " +"tagged ``1.8.0`` as a base image and set our working directory to " +"``/app``. The following instructions will now be executed in the ``/app``" +" directory. In the last two lines, we copy the ``server.py`` module into " +"the image and set the entry point to ``flower-server-app`` with the " +"argument ``server:app``. The argument is the object reference of the " +"ServerApp (``:``) that will be run inside the " +"ServerApp container." msgstr "" -#: ../../source/how-to-run-simulations.rst:112 -msgid "" -"Start Ray on you head node: on the terminal type :code:`ray start " -"--head`. This command will print a few lines, one of which indicates how " -"to attach other nodes to the head node." -msgstr "" +#: ../../source/how-to-run-flower-using-docker.rst:343 +#, fuzzy +msgid "Building the ServerApp Docker image" +msgstr "Construindo a imagem do servidor" -#: ../../source/how-to-run-simulations.rst:113 +#: ../../source/how-to-run-flower-using-docker.rst:345 msgid "" -"Attach other nodes to the head node: copy the command shown after " -"starting the head and execute it on terminal of a new node: for example " -":code:`ray start --address='192.168.1.132:6379'`" +"Next, we build the ServerApp Docker image by running the following " +"command in the directory where Dockerfile and ServerApp code are located." msgstr "" -#: ../../source/how-to-run-simulations.rst:115 +#: ../../source/how-to-run-flower-using-docker.rst:352 msgid "" -"With all the above done, you can run your code from the head node as you " -"would if the simulation was running on a single node." +"We gave the image the name ``flwr_serverapp``, and the tag ``0.0.1``. " +"Remember that the here chosen values only serve as an example. You can " +"change them to your needs." msgstr "" -#: ../../source/how-to-run-simulations.rst:117 -msgid "" -"Once your simulation is finished, if you'd like to dismantle your cluster" -" you simply need to run the command :code:`ray stop` in each node's " -"terminal (including the head node)." +#: ../../source/how-to-run-flower-using-docker.rst:357 +#, fuzzy +msgid "Running the ServerApp Docker image" +msgstr "Construindo a imagem do servidor" + +#: ../../source/how-to-run-flower-using-docker.rst:359 +msgid "Now that we have built the ServerApp image, we can finally run it." msgstr "" -#: ../../source/how-to-run-simulations.rst:120 -msgid "Multi-node simulation good-to-know" +#: ../../source/how-to-run-flower-using-docker.rst:371 +msgid "``flwr_serverapp:0.0.1``: The name the tag of the Docker image to use." msgstr "" -#: ../../source/how-to-run-simulations.rst:122 +#: ../../source/how-to-run-flower-using-docker.rst msgid "" -"Here we list a few interesting functionality when running multi-node FL " -"simulations:" +"``--superlink 192.168.1.100:9091``: This option specifies the address of " +"the SuperLinks Driver" msgstr "" -#: ../../source/how-to-run-simulations.rst:124 +#: ../../source/how-to-run-flower-using-docker.rst:385 msgid "" -"User :code:`ray status` to check all nodes connected to your head node as" -" well as the total resources available to the " -":code:`VirtualClientEngine`." +"To test running Flower locally, you can create a `bridge network " +"`__, use the ``--network`` argument and pass the " +"name of the Docker network to run your ServerApps." msgstr "" -#: ../../source/how-to-run-simulations.rst:126 +#: ../../source/how-to-run-flower-using-docker.rst:389 msgid "" -"When attaching a new node to the head, all its resources (i.e. all CPUs, " -"all GPUs) will be visible by the head node. This means that the " -":code:`VirtualClientEngine` can schedule as many `virtual` clients as " -"that node can possible run. In some settings you might want to exclude " -"certain resources from the simulation. You can do this by appending " -"`--num-cpus=` and/or `--num-" -"gpus=` in any :code:`ray start` command (including " -"when starting the head)" +"Any argument that comes after the tag is passed to the Flower ServerApp " +"binary. To see all available flags that the ServerApp supports, run:" msgstr "" -#: ../../source/how-to-run-simulations.rst:132 -msgid "Considerations for simulations" +#: ../../source/how-to-run-flower-using-docker.rst:399 +msgid "" +"To enable SSL, we will need to mount a PEM-encoded root certificate into " +"your ServerApp container." msgstr "" -#: ../../source/how-to-run-simulations.rst:135 +#: ../../source/how-to-run-flower-using-docker.rst:401 msgid "" -"We are actively working on these fronts so to make it trivial to run any " -"FL workload with Flower simulation." +"Assuming the certificate already exists locally, we can use the flag " +"``--volume`` to mount the local certificate into the container's " +"``/app/`` directory. This allows the ServerApp to access the certificate " +"within the container. Use the ``--root-certificates`` flags when starting" +" the container." msgstr "" -#: ../../source/how-to-run-simulations.rst:138 -msgid "" -"The current VCE allows you to run Federated Learning workloads in " -"simulation mode whether you are prototyping simple scenarios on your " -"personal laptop or you want to train a complex FL pipeline across " -"multiple high-performance GPU nodes. While we add more capabilities to " -"the VCE, the points below highlight some of the considerations to keep in" -" mind when designing your FL pipeline with Flower. We also highlight a " -"couple of current limitations in our implementation." +#: ../../source/how-to-run-flower-using-docker.rst:412 +msgid "Advanced Docker options" msgstr "" -#: ../../source/how-to-run-simulations.rst:141 -msgid "GPU resources" +#: ../../source/how-to-run-flower-using-docker.rst:415 +msgid "Run with root user privileges" msgstr "" -#: ../../source/how-to-run-simulations.rst:143 +#: ../../source/how-to-run-flower-using-docker.rst:417 msgid "" -"The VCE assigns a share of GPU memory to a client that specifies the key " -":code:`num_gpus` in :code:`client_resources`. This being said, Ray (used " -"internally by the VCE) is by default:" +"Flower Docker images, by default, run with a non-root user " +"(username/groupname: ``app``, UID/GID: ``49999``). Using root user is not" +" recommended unless it is necessary for specific tasks during the build " +"process. Always make sure to run the container as a non-root user in " +"production to maintain security best practices." msgstr "" -#: ../../source/how-to-run-simulations.rst:146 -msgid "" -"not aware of the total VRAM available on the GPUs. This means that if you" -" set :code:`num_gpus=0.5` and you have two GPUs in your system with " -"different (e.g. 32GB and 8GB) VRAM amounts, they both would run 2 clients" -" concurrently." +#: ../../source/how-to-run-flower-using-docker.rst:422 +msgid "**Run a container with root user privileges**" msgstr "" -#: ../../source/how-to-run-simulations.rst:147 +#: ../../source/how-to-run-flower-using-docker.rst:424 msgid "" -"not aware of other unrelated (i.e. not created by the VCE) workloads are " -"running on the GPU. Two takeaways from this are:" +"Run the Docker image with the ``-u`` flag and specify ``root`` as the " +"username:" msgstr "" -#: ../../source/how-to-run-simulations.rst:149 -msgid "" -"Your Flower server might need a GPU to evaluate the `global model` after " -"aggregation (by instance when making use of the `evaluate method `_)" +#: ../../source/how-to-run-flower-using-docker.rst:430 +msgid "This command will run the Docker container with root user privileges." msgstr "" -#: ../../source/how-to-run-simulations.rst:150 -msgid "" -"If you want to run several independent Flower simulations on the same " -"machine you need to mask-out your GPUs with " -":code:`CUDA_VISIBLE_DEVICES=\"\"` when launching your " -"experiment." +#: ../../source/how-to-run-flower-using-docker.rst:432 +msgid "**Run the build process with root user privileges**" msgstr "" -#: ../../source/how-to-run-simulations.rst:153 +#: ../../source/how-to-run-flower-using-docker.rst:434 msgid "" -"In addition, the GPU resource limits passed to :code:`client_resources` " -"are not `enforced` (i.e. they can be exceeded) which can result in the " -"situation of client using more VRAM than the ratio specified when " -"starting the simulation." +"If you want to switch to the root user during the build process of the " +"Docker image to install missing system dependencies, you can use the " +"``USER root`` directive within your Dockerfile." msgstr "" -#: ../../source/how-to-run-simulations.rst:156 -msgid "TensorFlow with GPUs" +#: ../../source/how-to-run-flower-using-docker.rst:454 +msgid "Using a different Flower version" msgstr "" -#: ../../source/how-to-run-simulations.rst:158 +#: ../../source/how-to-run-flower-using-docker.rst:456 msgid "" -"When `using a GPU with TensorFlow " -"`_ nearly your entire GPU memory of" -" all your GPUs visible to the process will be mapped. This is done by " -"TensorFlow for optimization purposes. However, in settings such as FL " -"simulations where we want to split the GPU into multiple `virtual` " -"clients, this is not a desirable mechanism. Luckily we can disable this " -"default behavior by `enabling memory growth " -"`_." +"If you want to use a different version of Flower, for example Flower " +"nightly, you can do so by changing the tag. All available versions are on" +" `Docker Hub `__." msgstr "" -#: ../../source/how-to-run-simulations.rst:160 +#: ../../source/how-to-run-flower-using-docker.rst:460 +msgid "Pinning a Docker image to a specific version" +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:462 msgid "" -"This would need to be done in the main process (which is where the server" -" would run) and in each Actor created by the VCE. By means of " -":code:`actor_kwargs` we can pass the reserved key `\"on_actor_init_fn\"` " -"in order to specify a function to be executed upon actor initialization. " -"In this case, to enable GPU growth for TF workloads. It would look as " -"follows:" +"It may happen that we update the images behind the tags. Such updates " +"usually include security updates of system dependencies that should not " +"change the functionality of Flower. However, if you want to ensure that " +"you always use the same image, you can specify the hash of the image " +"instead of the tag." msgstr "" -#: ../../source/how-to-run-simulations.rst:179 +#: ../../source/how-to-run-flower-using-docker.rst:467 msgid "" -"This is precisely the mechanism used in `Tensorflow/Keras Simulation " -"`_ example." +"The following command returns the current image hash referenced by the " +"``superlink:1.8.0`` tag:" msgstr "" -#: ../../source/how-to-run-simulations.rst:183 -msgid "Multi-node setups" +#: ../../source/how-to-run-flower-using-docker.rst:474 +msgid "Next, we can pin the hash when running a new SuperLink container:" msgstr "" -#: ../../source/how-to-run-simulations.rst:185 -msgid "" -"The VCE does not currently offer a way to control on which node a " -"particular `virtual` client is executed. In other words, if more than a " -"single node have the resources needed by a client to run, then any of " -"those nodes could get the client workload scheduled onto. Later in the FL" -" process (i.e. in a different round) the same client could be executed by" -" a different node. Depending on how your clients access their datasets, " -"this might require either having a copy of all dataset partitions on all " -"nodes or a dataset serving mechanism (e.g. using nfs, a database) to " -"circumvent data duplication." +#: ../../source/how-to-run-flower-using-docker.rst:483 +msgid "Setting environment variables" msgstr "" -#: ../../source/how-to-run-simulations.rst:187 +#: ../../source/how-to-run-flower-using-docker.rst:485 msgid "" -"By definition virtual clients are `stateless` due to their ephemeral " -"nature. A client state can be implemented as part of the Flower client " -"class but users need to ensure this saved to persistent storage (e.g. a " -"database, disk) and that can be retrieve later by the same client " -"regardless on which node it is running from. This is related to the point" -" above also since, in some way, the client's dataset could be seen as a " -"type of `state`." +"To set a variable inside a Docker container, you can use the ``-e " +"=`` flag." msgstr "" -#: ../../source/how-to-save-and-load-model-checkpoints.rst:2 -msgid "Save and load model checkpoints" +#: ../../source/how-to-run-simulations.rst:2 +msgid "Run simulations" msgstr "" -#: ../../source/how-to-save-and-load-model-checkpoints.rst:4 +#: ../../source/how-to-run-simulations.rst:8 msgid "" -"Flower does not automatically save model updates on the server-side. This" -" how-to guide describes the steps to save (and load) model checkpoints in" -" Flower." -msgstr "" - -#: ../../source/how-to-save-and-load-model-checkpoints.rst:8 -msgid "Model checkpointing" +"Simulating Federated Learning workloads is useful for a multitude of use-" +"cases: you might want to run your workload on a large cohort of clients " +"but without having to source, configure and mange a large number of " +"physical devices; you might want to run your FL workloads as fast as " +"possible on the compute systems you have access to without having to go " +"through a complex setup process; you might want to validate your " +"algorithm on different scenarios at varying levels of data and system " +"heterogeneity, client availability, privacy budgets, etc. These are among" +" some of the use-cases where simulating FL workloads makes sense. Flower " +"can accommodate these scenarios by means of its `VirtualClientEngine " +"`_ or " +"VCE." msgstr "" -#: ../../source/how-to-save-and-load-model-checkpoints.rst:10 +#: ../../source/how-to-run-simulations.rst:10 msgid "" -"Model updates can be persisted on the server-side by customizing " -":code:`Strategy` methods. Implementing custom strategies is always an " -"option, but for many cases it may be more convenient to simply customize " -"an existing strategy. The following code example defines a new " -":code:`SaveModelStrategy` which customized the existing built-in " -":code:`FedAvg` strategy. In particular, it customizes " -":code:`aggregate_fit` by calling :code:`aggregate_fit` in the base class " -"(:code:`FedAvg`). It then continues to save returned (aggregated) weights" -" before it returns those aggregated weights to the caller (i.e., the " -"server):" +"The :code:`VirtualClientEngine` schedules, launches and manages `virtual`" +" clients. These clients are identical to `non-virtual` clients (i.e. the " +"ones you launch via the command `flwr.client.start_client `_) in the sense that they can be configure by " +"creating a class inheriting, for example, from `flwr.client.NumPyClient " +"`_ and therefore behave in an " +"identical way. In addition to that, clients managed by the " +":code:`VirtualClientEngine` are:" msgstr "" -#: ../../source/how-to-save-and-load-model-checkpoints.rst:47 -msgid "Save and load PyTorch checkpoints" +#: ../../source/how-to-run-simulations.rst:12 +msgid "" +"resource-aware: this means that each client gets assigned a portion of " +"the compute and memory on your system. You as a user can control this at " +"the beginning of the simulation and allows you to control the degree of " +"parallelism of your Flower FL simulation. The fewer the resources per " +"client, the more clients can run concurrently on the same hardware." msgstr "" -#: ../../source/how-to-save-and-load-model-checkpoints.rst:49 +#: ../../source/how-to-run-simulations.rst:13 msgid "" -"Similar to the previous example but with a few extra steps, we'll show " -"how to store a PyTorch checkpoint we'll use the ``torch.save`` function. " -"Firstly, ``aggregate_fit`` returns a ``Parameters`` object that has to be" -" transformed into a list of NumPy ``ndarray``'s, then those are " -"transformed into the PyTorch ``state_dict`` following the ``OrderedDict``" -" class structure." +"self-managed: this means that you as a user do not need to launch clients" +" manually, instead this gets delegated to :code:`VirtualClientEngine`'s " +"internals." msgstr "" -#: ../../source/how-to-save-and-load-model-checkpoints.rst:85 +#: ../../source/how-to-run-simulations.rst:14 msgid "" -"To load your progress, you simply append the following lines to your " -"code. Note that this will iterate over all saved checkpoints and load the" -" latest one:" +"ephemeral: this means that a client is only materialized when it is " +"required in the FL process (e.g. to do `fit() `_). The object is destroyed afterwards," +" releasing the resources it was assigned and allowing in this way other " +"clients to participate." msgstr "" -#: ../../source/how-to-save-and-load-model-checkpoints.rst:97 +#: ../../source/how-to-run-simulations.rst:16 msgid "" -"Return/use this object of type ``Parameters`` wherever necessary, such as" -" in the ``initial_parameters`` when defining a ``Strategy``." +"The :code:`VirtualClientEngine` implements `virtual` clients using `Ray " +"`_, an open-source framework for scalable Python " +"workloads. In particular, Flower's :code:`VirtualClientEngine` makes use " +"of `Actors `_ to " +"spawn `virtual` clients and run their workload." msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:2 -msgid "Upgrade to Flower 1.0" +#: ../../source/how-to-run-simulations.rst:20 +msgid "Launch your Flower simulation" msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:4 +#: ../../source/how-to-run-simulations.rst:22 msgid "" -"Flower 1.0 is here. Along with new features, Flower 1.0 provides a stable" -" foundation for future growth. Compared to Flower 0.19 (and other 0.x " -"series releases), there are a few breaking changes that make it necessary" -" to change the code of existing 0.x-series projects." +"Running Flower simulations still require you to define your client class," +" a strategy, and utility functions to download and load (and potentially " +"partition) your dataset. With that out of the way, launching your " +"simulation is done with `start_simulation `_ and a minimal example looks" +" as follows:" msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:8 -msgid "Install update" +#: ../../source/how-to-run-simulations.rst:44 +msgid "VirtualClientEngine resources" msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:10 +#: ../../source/how-to-run-simulations.rst:45 msgid "" -"Here's how to update an existing installation to Flower 1.0 using either " -"pip or Poetry:" +"By default the VCE has access to all system resources (i.e. all CPUs, all" +" GPUs, etc) since that is also the default behavior when starting Ray. " +"However, in some settings you might want to limit how many of your system" +" resources are used for simulation. You can do this via the " +":code:`ray_init_args` input argument to :code:`start_simulation` which " +"the VCE internally passes to Ray's :code:`ray.init` command. For a " +"complete list of settings you can configure check the `ray.init " +"`_" +" documentation. Do not set :code:`ray_init_args` if you want the VCE to " +"use all your system's CPUs and GPUs." msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:12 -msgid "pip: add ``-U`` when installing." +#: ../../source/how-to-run-simulations.rst:62 +msgid "Assigning client resources" msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:14 +#: ../../source/how-to-run-simulations.rst:63 msgid "" -"``python -m pip install -U flwr`` (when using ``start_server`` and " -"``start_client``)" +"By default the :code:`VirtualClientEngine` assigns a single CPU core (and" +" nothing else) to each virtual client. This means that if your system has" +" 10 cores, that many virtual clients can be concurrently running." msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:15 +#: ../../source/how-to-run-simulations.rst:65 msgid "" -"``python -m pip install -U flwr[simulation]`` (when using " -"``start_simulation``)" +"More often than not, you would probably like to adjust the resources your" +" clients get assigned based on the complexity (i.e. compute and memory " +"footprint) of your FL workload. You can do so when starting your " +"simulation by setting the argument `client_resources` to " +"`start_simulation `_." +" Two keys are internally used by Ray to schedule and spawn workloads (in " +"our case Flower clients):" msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:17 -msgid "" -"Poetry: update the ``flwr`` dependency in ``pyproject.toml`` and then " -"reinstall (don't forget to delete ``poetry.lock`` via ``rm poetry.lock`` " -"before running ``poetry install``)." +#: ../../source/how-to-run-simulations.rst:67 +msgid ":code:`num_cpus` indicates the number of CPU cores a client would get." msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:19 -msgid "``flwr = \"^1.0.0\"`` (when using ``start_server`` and ``start_client``)" +#: ../../source/how-to-run-simulations.rst:68 +msgid "" +":code:`num_gpus` indicates the **ratio** of GPU memory a client gets " +"assigned." msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:20 -msgid "" -"``flwr = { version = \"^1.0.0\", extras = [\"simulation\"] }`` (when " -"using ``start_simulation``)" +#: ../../source/how-to-run-simulations.rst:70 +msgid "Let's see a few examples:" msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:24 -msgid "Required changes" +#: ../../source/how-to-run-simulations.rst:89 +msgid "" +"While the :code:`client_resources` can be used to control the degree of " +"concurrency in your FL simulation, this does not stop you from running " +"dozens, hundreds or even thousands of clients in the same round and " +"having orders of magnitude more `dormant` (i.e. not participating in a " +"round) clients. Let's say you want to have 100 clients per round but your" +" system can only accommodate 8 clients concurrently. The " +":code:`VirtualClientEngine` will schedule 100 jobs to run (each " +"simulating a client sampled by the strategy) and then will execute them " +"in a resource-aware manner in batches of 8." msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:26 -msgid "The following breaking changes require manual updates." +#: ../../source/how-to-run-simulations.rst:91 +msgid "" +"To understand all the intricate details on how resources are used to " +"schedule FL clients and how to define custom resources, please take a " +"look at the `Ray documentation `_." msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:29 -msgid "General" +#: ../../source/how-to-run-simulations.rst:94 +msgid "Simulation examples" msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:31 +#: ../../source/how-to-run-simulations.rst:96 msgid "" -"Pass all arguments as keyword arguments (not as positional arguments). " -"Here's an example:" +"A few ready-to-run complete examples for Flower simulation in " +"Tensorflow/Keras and PyTorch are provided in the `Flower repository " +"`_. You can run them on Google Colab too:" msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:33 +#: ../../source/how-to-run-simulations.rst:98 msgid "" -"Flower 0.19 (positional arguments): ``start_client(\"127.0.0.1:8080\", " -"FlowerClient())``" +"`Tensorflow/Keras Simulation " +"`_: 100 clients collaboratively train a MLP model on MNIST." msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:34 +#: ../../source/how-to-run-simulations.rst:99 msgid "" -"Flower 1.0 (keyword arguments): " -"``start_client(server_address=\"127.0.0.1:8080\", " -"client=FlowerClient())``" +"`PyTorch Simulation `_: 100 clients collaboratively train a CNN model on " +"MNIST." msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:39 -msgid "" -"Subclasses of ``NumPyClient``: change ``def get_parameters(self):``` to " -"``def get_parameters(self, config):``" +#: ../../source/how-to-run-simulations.rst:104 +msgid "Multi-node Flower simulations" msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:40 +#: ../../source/how-to-run-simulations.rst:106 msgid "" -"Subclasses of ``Client``: change ``def get_parameters(self):``` to ``def " -"get_parameters(self, ins: GetParametersIns):``" +"Flower's :code:`VirtualClientEngine` allows you to run FL simulations " +"across multiple compute nodes. Before starting your multi-node simulation" +" ensure that you:" msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:43 -msgid "Strategies / ``start_server`` / ``start_simulation``" +#: ../../source/how-to-run-simulations.rst:108 +msgid "Have the same Python environment in all nodes." msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:45 -msgid "" -"Pass ``ServerConfig`` (instead of a dictionary) to ``start_server`` and " -"``start_simulation``. Here's an example:" +#: ../../source/how-to-run-simulations.rst:109 +msgid "Have a copy of your code (e.g. your entire repo) in all nodes." msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:47 +#: ../../source/how-to-run-simulations.rst:110 msgid "" -"Flower 0.19: ``start_server(..., config={\"num_rounds\": 3, " -"\"round_timeout\": 600.0}, ...)``" +"Have a copy of your dataset in all nodes (more about this in " +":ref:`simulation considerations `)" msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:48 +#: ../../source/how-to-run-simulations.rst:111 msgid "" -"Flower 1.0: ``start_server(..., " -"config=flwr.server.ServerConfig(num_rounds=3, round_timeout=600.0), " -"...)``" +"Pass :code:`ray_init_args={\"address\"=\"auto\"}` to `start_simulation " +"`_ so the " +":code:`VirtualClientEngine` attaches to a running Ray instance." msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:50 +#: ../../source/how-to-run-simulations.rst:112 msgid "" -"Replace ``num_rounds=1`` in ``start_simulation`` with the new " -"``config=ServerConfig(...)`` (see previous item)" +"Start Ray on you head node: on the terminal type :code:`ray start " +"--head`. This command will print a few lines, one of which indicates how " +"to attach other nodes to the head node." msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:51 +#: ../../source/how-to-run-simulations.rst:113 msgid "" -"Remove ``force_final_distributed_eval`` parameter from calls to " -"``start_server``. Distributed evaluation on all clients can be enabled by" -" configuring the strategy to sample all clients for evaluation after the " -"last round of training." +"Attach other nodes to the head node: copy the command shown after " +"starting the head and execute it on terminal of a new node: for example " +":code:`ray start --address='192.168.1.132:6379'`" msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:52 -msgid "Rename parameter/ndarray conversion functions:" +#: ../../source/how-to-run-simulations.rst:115 +msgid "" +"With all the above done, you can run your code from the head node as you " +"would if the simulation was running on a single node." msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:54 -msgid "``parameters_to_weights`` --> ``parameters_to_ndarrays``" +#: ../../source/how-to-run-simulations.rst:117 +msgid "" +"Once your simulation is finished, if you'd like to dismantle your cluster" +" you simply need to run the command :code:`ray stop` in each node's " +"terminal (including the head node)." msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:55 -msgid "``weights_to_parameters`` --> ``ndarrays_to_parameters``" +#: ../../source/how-to-run-simulations.rst:120 +msgid "Multi-node simulation good-to-know" msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:57 +#: ../../source/how-to-run-simulations.rst:122 msgid "" -"Strategy initialization: if the strategy relies on the default values for" -" ``fraction_fit`` and ``fraction_evaluate``, set ``fraction_fit`` and " -"``fraction_evaluate`` manually to ``0.1``. Projects that do not manually " -"create a strategy (by calling ``start_server`` or ``start_simulation`` " -"without passing a strategy instance) should now manually initialize " -"FedAvg with ``fraction_fit`` and ``fraction_evaluate`` set to ``0.1``." +"Here we list a few interesting functionality when running multi-node FL " +"simulations:" msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:58 -msgid "Rename built-in strategy parameters (e.g., ``FedAvg``):" +#: ../../source/how-to-run-simulations.rst:124 +msgid "" +"User :code:`ray status` to check all nodes connected to your head node as" +" well as the total resources available to the " +":code:`VirtualClientEngine`." msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:60 -msgid "``fraction_eval`` --> ``fraction_evaluate``" +#: ../../source/how-to-run-simulations.rst:126 +msgid "" +"When attaching a new node to the head, all its resources (i.e. all CPUs, " +"all GPUs) will be visible by the head node. This means that the " +":code:`VirtualClientEngine` can schedule as many `virtual` clients as " +"that node can possible run. In some settings you might want to exclude " +"certain resources from the simulation. You can do this by appending " +"`--num-cpus=` and/or `--num-" +"gpus=` in any :code:`ray start` command (including " +"when starting the head)" msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:61 -msgid "``min_eval_clients`` --> ``min_evaluate_clients``" +#: ../../source/how-to-run-simulations.rst:132 +msgid "Considerations for simulations" msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:62 -msgid "``eval_fn`` --> ``evaluate_fn``" +#: ../../source/how-to-run-simulations.rst:135 +msgid "" +"We are actively working on these fronts so to make it trivial to run any " +"FL workload with Flower simulation." msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:64 +#: ../../source/how-to-run-simulations.rst:138 msgid "" -"Rename ``rnd`` to ``server_round``. This impacts multiple methods and " -"functions, for example, ``configure_fit``, ``aggregate_fit``, " -"``configure_evaluate``, ``aggregate_evaluate``, and ``evaluate_fn``." +"The current VCE allows you to run Federated Learning workloads in " +"simulation mode whether you are prototyping simple scenarios on your " +"personal laptop or you want to train a complex FL pipeline across " +"multiple high-performance GPU nodes. While we add more capabilities to " +"the VCE, the points below highlight some of the considerations to keep in" +" mind when designing your FL pipeline with Flower. We also highlight a " +"couple of current limitations in our implementation." msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:65 -msgid "Add ``server_round`` and ``config`` to ``evaluate_fn``:" +#: ../../source/how-to-run-simulations.rst:141 +msgid "GPU resources" msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:67 +#: ../../source/how-to-run-simulations.rst:143 msgid "" -"Flower 0.19: ``def evaluate(parameters: NDArrays) -> " -"Optional[Tuple[float, Dict[str, Scalar]]]:``" +"The VCE assigns a share of GPU memory to a client that specifies the key " +":code:`num_gpus` in :code:`client_resources`. This being said, Ray (used " +"internally by the VCE) is by default:" msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:68 +#: ../../source/how-to-run-simulations.rst:146 msgid "" -"Flower 1.0: ``def evaluate(server_round: int, parameters: NDArrays, " -"config: Dict[str, Scalar]) -> Optional[Tuple[float, Dict[str, " -"Scalar]]]:``" +"not aware of the total VRAM available on the GPUs. This means that if you" +" set :code:`num_gpus=0.5` and you have two GPUs in your system with " +"different (e.g. 32GB and 8GB) VRAM amounts, they both would run 2 clients" +" concurrently." msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:71 -msgid "Custom strategies" -msgstr "" - -#: ../../source/how-to-upgrade-to-flower-1.0.rst:73 +#: ../../source/how-to-run-simulations.rst:147 msgid "" -"The type of parameter ``failures`` has changed from " -"``List[BaseException]`` to ``List[Union[Tuple[ClientProxy, FitRes], " -"BaseException]]`` (in ``aggregate_fit``) and " -"``List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]]`` (in " -"``aggregate_evaluate``)" +"not aware of other unrelated (i.e. not created by the VCE) workloads are " +"running on the GPU. Two takeaways from this are:" msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:74 +#: ../../source/how-to-run-simulations.rst:149 msgid "" -"The ``Strategy`` method ``evaluate`` now receives the current round of " -"federated learning/evaluation as the first parameter:" +"Your Flower server might need a GPU to evaluate the `global model` after " +"aggregation (by instance when making use of the `evaluate method `_)" msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:76 +#: ../../source/how-to-run-simulations.rst:150 msgid "" -"Flower 0.19: ``def evaluate(self, parameters: Parameters) -> " -"Optional[Tuple[float, Dict[str, Scalar]]]:``" +"If you want to run several independent Flower simulations on the same " +"machine you need to mask-out your GPUs with " +":code:`CUDA_VISIBLE_DEVICES=\"\"` when launching your " +"experiment." msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:77 +#: ../../source/how-to-run-simulations.rst:153 msgid "" -"Flower 1.0: ``def evaluate(self, server_round: int, parameters: " -"Parameters) -> Optional[Tuple[float, Dict[str, Scalar]]]:``" -msgstr "" - -#: ../../source/how-to-upgrade-to-flower-1.0.rst:80 -msgid "Optional improvements" +"In addition, the GPU resource limits passed to :code:`client_resources` " +"are not `enforced` (i.e. they can be exceeded) which can result in the " +"situation of client using more VRAM than the ratio specified when " +"starting the simulation." msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:82 -msgid "" -"Along with the necessary changes above, there are a number of potential " -"improvements that just became possible:" +#: ../../source/how-to-run-simulations.rst:156 +msgid "TensorFlow with GPUs" msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:84 +#: ../../source/how-to-run-simulations.rst:158 msgid "" -"Remove \"placeholder\" methods from subclasses of ``Client`` or " -"``NumPyClient``. If you, for example, use server-side evaluation, then " -"empty placeholder implementations of ``evaluate`` are no longer " -"necessary." +"When `using a GPU with TensorFlow " +"`_ nearly your entire GPU memory of" +" all your GPUs visible to the process will be mapped. This is done by " +"TensorFlow for optimization purposes. However, in settings such as FL " +"simulations where we want to split the GPU into multiple `virtual` " +"clients, this is not a desirable mechanism. Luckily we can disable this " +"default behavior by `enabling memory growth " +"`_." msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:85 +#: ../../source/how-to-run-simulations.rst:160 msgid "" -"Configure the round timeout via ``start_simulation``: " -"``start_simulation(..., config=flwr.server.ServerConfig(num_rounds=3, " -"round_timeout=600.0), ...)``" -msgstr "" - -#: ../../source/how-to-upgrade-to-flower-1.0.rst:89 -msgid "Further help" +"This would need to be done in the main process (which is where the server" +" would run) and in each Actor created by the VCE. By means of " +":code:`actor_kwargs` we can pass the reserved key `\"on_actor_init_fn\"` " +"in order to specify a function to be executed upon actor initialization. " +"In this case, to enable GPU growth for TF workloads. It would look as " +"follows:" msgstr "" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:91 +#: ../../source/how-to-run-simulations.rst:179 msgid "" -"Most official `Flower code examples " -"`_ are already updated" -" to Flower 1.0, they can serve as a reference for using the Flower 1.0 " -"API. If there are further questions, `join the Flower Slack " -"`_ and use the channel ``#questions``." +"This is precisely the mechanism used in `Tensorflow/Keras Simulation " +"`_ example." msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:2 -msgid "Use Built-in Mods" +#: ../../source/how-to-run-simulations.rst:183 +msgid "Multi-node setups" msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:4 +#: ../../source/how-to-run-simulations.rst:185 msgid "" -"**Note: This tutorial covers experimental features. The functionality and" -" interfaces may change in future versions.**" +"The VCE does not currently offer a way to control on which node a " +"particular `virtual` client is executed. In other words, if more than a " +"single node have the resources needed by a client to run, then any of " +"those nodes could get the client workload scheduled onto. Later in the FL" +" process (i.e. in a different round) the same client could be executed by" +" a different node. Depending on how your clients access their datasets, " +"this might require either having a copy of all dataset partitions on all " +"nodes or a dataset serving mechanism (e.g. using nfs, a database) to " +"circumvent data duplication." msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:6 +#: ../../source/how-to-run-simulations.rst:187 msgid "" -"In this tutorial, we will learn how to utilize built-in mods to augment " -"the behavior of a ``ClientApp``. Mods (sometimes also called Modifiers) " -"allow us to perform operations before and after a task is processed in " -"the ``ClientApp``." +"By definition virtual clients are `stateless` due to their ephemeral " +"nature. A client state can be implemented as part of the Flower client " +"class but users need to ensure this saved to persistent storage (e.g. a " +"database, disk) and that can be retrieve later by the same client " +"regardless on which node it is running from. This is related to the point" +" above also since, in some way, the client's dataset could be seen as a " +"type of `state`." msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:9 -msgid "What are Mods?" +#: ../../source/how-to-save-and-load-model-checkpoints.rst:2 +msgid "Save and load model checkpoints" msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:11 +#: ../../source/how-to-save-and-load-model-checkpoints.rst:4 msgid "" -"A Mod is a callable that wraps around a ``ClientApp``. It can manipulate " -"or inspect the incoming ``Message`` and the resulting outgoing " -"``Message``. The signature for a ``Mod`` is as follows:" -msgstr "" - -#: ../../source/how-to-use-built-in-mods.rst:18 -msgid "A typical mod function might look something like this:" +"Flower does not automatically save model updates on the server-side. This" +" how-to guide describes the steps to save (and load) model checkpoints in" +" Flower." msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:31 -msgid "Using Mods" +#: ../../source/how-to-save-and-load-model-checkpoints.rst:8 +msgid "Model checkpointing" msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:33 -msgid "To use mods in your ``ClientApp``, you can follow these steps:" +#: ../../source/how-to-save-and-load-model-checkpoints.rst:10 +msgid "" +"Model updates can be persisted on the server-side by customizing " +":code:`Strategy` methods. Implementing custom strategies is always an " +"option, but for many cases it may be more convenient to simply customize " +"an existing strategy. The following code example defines a new " +":code:`SaveModelStrategy` which customized the existing built-in " +":code:`FedAvg` strategy. In particular, it customizes " +":code:`aggregate_fit` by calling :code:`aggregate_fit` in the base class " +"(:code:`FedAvg`). It then continues to save returned (aggregated) weights" +" before it returns those aggregated weights to the caller (i.e., the " +"server):" msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:36 -msgid "1. Import the required mods" +#: ../../source/how-to-save-and-load-model-checkpoints.rst:47 +msgid "Save and load PyTorch checkpoints" msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:38 -msgid "First, import the built-in mod you intend to use:" +#: ../../source/how-to-save-and-load-model-checkpoints.rst:49 +msgid "" +"Similar to the previous example but with a few extra steps, we'll show " +"how to store a PyTorch checkpoint we'll use the ``torch.save`` function. " +"Firstly, ``aggregate_fit`` returns a ``Parameters`` object that has to be" +" transformed into a list of NumPy ``ndarray``'s, then those are " +"transformed into the PyTorch ``state_dict`` following the ``OrderedDict``" +" class structure." msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:46 -msgid "2. Define your client function" +#: ../../source/how-to-save-and-load-model-checkpoints.rst:85 +msgid "" +"To load your progress, you simply append the following lines to your " +"code. Note that this will iterate over all saved checkpoints and load the" +" latest one:" msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:48 +#: ../../source/how-to-save-and-load-model-checkpoints.rst:97 msgid "" -"Define your client function (``client_fn``) that will be wrapped by the " -"mod(s):" +"Return/use this object of type ``Parameters`` wherever necessary, such as" +" in the ``initial_parameters`` when defining a ``Strategy``." msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:57 -msgid "3. Create the ``ClientApp`` with mods" +#: ../../source/how-to-upgrade-to-flower-1.0.rst:2 +msgid "Upgrade to Flower 1.0" msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:59 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:4 msgid "" -"Create your ``ClientApp`` and pass the mods as a list to the ``mods`` " -"argument. The order in which you provide the mods matters:" +"Flower 1.0 is here. Along with new features, Flower 1.0 provides a stable" +" foundation for future growth. Compared to Flower 0.19 (and other 0.x " +"series releases), there are a few breaking changes that make it necessary" +" to change the code of existing 0.x-series projects." msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:72 -msgid "Order of execution" +#: ../../source/how-to-upgrade-to-flower-1.0.rst:8 +#: ../../source/how-to-upgrade-to-flower-next.rst:43 +msgid "Install update" msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:74 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:10 msgid "" -"When the ``ClientApp`` runs, the mods are executed in the order they are " -"provided in the list:" +"Here's how to update an existing installation to Flower 1.0 using either " +"pip or Poetry:" msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:76 -msgid "``example_mod_1`` (outermost mod)" +#: ../../source/how-to-upgrade-to-flower-1.0.rst:12 +msgid "pip: add ``-U`` when installing." msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:77 -msgid "``example_mod_2`` (next mod)" +#: ../../source/how-to-upgrade-to-flower-1.0.rst:14 +msgid "" +"``python -m pip install -U flwr`` (when using ``start_server`` and " +"``start_client``)" msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:78 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:15 msgid "" -"Message handler (core function that handles the incoming ``Message`` and " -"returns the outgoing ``Message``)" +"``python -m pip install -U flwr[simulation]`` (when using " +"``start_simulation``)" msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:79 -msgid "``example_mod_2`` (on the way back)" +#: ../../source/how-to-upgrade-to-flower-1.0.rst:17 +msgid "" +"Poetry: update the ``flwr`` dependency in ``pyproject.toml`` and then " +"reinstall (don't forget to delete ``poetry.lock`` via ``rm poetry.lock`` " +"before running ``poetry install``)." msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:80 -msgid "``example_mod_1`` (outermost mod on the way back)" +#: ../../source/how-to-upgrade-to-flower-1.0.rst:19 +msgid "``flwr = \"^1.0.0\"`` (when using ``start_server`` and ``start_client``)" msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:82 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:20 msgid "" -"Each mod has a chance to inspect and modify the incoming ``Message`` " -"before passing it to the next mod, and likewise with the outgoing " -"``Message`` before returning it up the stack." +"``flwr = { version = \"^1.0.0\", extras = [\"simulation\"] }`` (when " +"using ``start_simulation``)" msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:87 -msgid "" -"By following this guide, you have learned how to effectively use mods to " -"enhance your ``ClientApp``'s functionality. Remember that the order of " -"mods is crucial and affects how the input and output are processed." +#: ../../source/how-to-upgrade-to-flower-1.0.rst:24 +#: ../../source/how-to-upgrade-to-flower-next.rst:100 +msgid "Required changes" msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:89 -msgid "Enjoy building a more robust and flexible ``ClientApp`` with mods!" +#: ../../source/how-to-upgrade-to-flower-1.0.rst:26 +msgid "The following breaking changes require manual updates." msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:2 -msgid "Use Differential Privacy" +#: ../../source/how-to-upgrade-to-flower-1.0.rst:29 +msgid "General" msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:3 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:31 msgid "" -"This guide explains how you can utilize differential privacy in the " -"Flower framework. If you are not yet familiar with differential privacy, " -"you can refer to :doc:`explanation-differential-privacy`." +"Pass all arguments as keyword arguments (not as positional arguments). " +"Here's an example:" msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:7 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:33 msgid "" -"Differential Privacy in Flower is in a preview phase. If you plan to use " -"these features in a production environment with sensitive data, feel free" -" contact us to discuss your requirements and to receive guidance on how " -"to best use these features." +"Flower 0.19 (positional arguments): ``start_client(\"127.0.0.1:8080\", " +"FlowerClient())``" msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:12 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:34 msgid "" -"This approach consists of two seprate phases: clipping of the updates and" -" adding noise to the aggregated model. For the clipping phase, Flower " -"framework has made it possible to decide whether to perform clipping on " -"the server side or the client side." +"Flower 1.0 (keyword arguments): " +"``start_client(server_address=\"127.0.0.1:8080\", " +"client=FlowerClient())``" msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:15 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:37 +#: ../../source/ref-api/flwr.client.Client.rst:2 +msgid "Client" +msgstr "" + +#: ../../source/how-to-upgrade-to-flower-1.0.rst:39 msgid "" -"**Server-side Clipping**: This approach has the advantage of the server " -"enforcing uniform clipping across all clients' updates and reducing the " -"communication overhead for clipping values. However, it also has the " -"disadvantage of increasing the computational load on the server due to " -"the need to perform the clipping operation for all clients." +"Subclasses of ``NumPyClient``: change ``def get_parameters(self):``` to " +"``def get_parameters(self, config):``" msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:16 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:40 msgid "" -"**Client-side Clipping**: This approach has the advantage of reducing the" -" computational overhead on the server. However, it also has the " -"disadvantage of lacking centralized control, as the server has less " -"control over the clipping process." +"Subclasses of ``Client``: change ``def get_parameters(self):``` to ``def " +"get_parameters(self, ins: GetParametersIns):``" msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:21 -msgid "Server-side Clipping" +#: ../../source/how-to-upgrade-to-flower-1.0.rst:43 +msgid "Strategies / ``start_server`` / ``start_simulation``" msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:22 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:45 msgid "" -"For central DP with server-side clipping, there are two :code:`Strategy` " -"classes that act as wrappers around the actual :code:`Strategy` instance " -"(for example, :code:`FedAvg`). The two wrapper classes are " -":code:`DifferentialPrivacyServerSideFixedClipping` and " -":code:`DifferentialPrivacyServerSideAdaptiveClipping` for fixed and " -"adaptive clipping." +"Pass ``ServerConfig`` (instead of a dictionary) to ``start_server`` and " +"``start_simulation``. Here's an example:" msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:-1 -msgid "server side clipping" +#: ../../source/how-to-upgrade-to-flower-1.0.rst:47 +msgid "" +"Flower 0.19: ``start_server(..., config={\"num_rounds\": 3, " +"\"round_timeout\": 600.0}, ...)``" msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:31 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:48 msgid "" -"The code sample below enables the :code:`FedAvg` strategy to use server-" -"side fixed clipping using the " -":code:`DifferentialPrivacyServerSideFixedClipping` wrapper class. The " -"same approach can be used with " -":code:`DifferentialPrivacyServerSideAdaptiveClipping` by adjusting the " -"corresponding input parameters." +"Flower 1.0: ``start_server(..., " +"config=flwr.server.ServerConfig(num_rounds=3, round_timeout=600.0), " +"...)``" msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:52 -msgid "Client-side Clipping" +#: ../../source/how-to-upgrade-to-flower-1.0.rst:50 +msgid "" +"Replace ``num_rounds=1`` in ``start_simulation`` with the new " +"``config=ServerConfig(...)`` (see previous item)" msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:53 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:51 msgid "" -"For central DP with client-side clipping, the server sends the clipping " -"value to selected clients on each round. Clients can use existing Flower " -":code:`Mods` to perform the clipping. Two mods are available for fixed " -"and adaptive client-side clipping: :code:`fixedclipping_mod` and " -":code:`adaptiveclipping_mod` with corresponding server-side wrappers " -":code:`DifferentialPrivacyClientSideFixedClipping` and " -":code:`DifferentialPrivacyClientSideAdaptiveClipping`." +"Remove ``force_final_distributed_eval`` parameter from calls to " +"``start_server``. Distributed evaluation on all clients can be enabled by" +" configuring the strategy to sample all clients for evaluation after the " +"last round of training." msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:-1 -msgid "client side clipping" +#: ../../source/how-to-upgrade-to-flower-1.0.rst:52 +msgid "Rename parameter/ndarray conversion functions:" msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:63 -msgid "" -"The code sample below enables the :code:`FedAvg` strategy to use " -"differential privacy with client-side fixed clipping using both the " -":code:`DifferentialPrivacyClientSideFixedClipping` wrapper class and, on " -"the client, :code:`fixedclipping_mod`:" +#: ../../source/how-to-upgrade-to-flower-1.0.rst:54 +msgid "``parameters_to_weights`` --> ``parameters_to_ndarrays``" msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:80 -msgid "" -"In addition to the server-side strategy wrapper, the :code:`ClientApp` " -"needs to configure the matching :code:`fixedclipping_mod` to perform the " -"client-side clipping:" +#: ../../source/how-to-upgrade-to-flower-1.0.rst:55 +msgid "``weights_to_parameters`` --> ``ndarrays_to_parameters``" msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:97 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:57 msgid "" -"To utilize local differential privacy (DP) and add noise to the client " -"model parameters before transmitting them to the server in Flower, you " -"can use the `LocalDpMod`. The following hyperparameters need to be set: " -"clipping norm value, sensitivity, epsilon, and delta." +"Strategy initialization: if the strategy relies on the default values for" +" ``fraction_fit`` and ``fraction_evaluate``, set ``fraction_fit`` and " +"``fraction_evaluate`` manually to ``0.1``. Projects that do not manually " +"create a strategy (by calling ``start_server`` or ``start_simulation`` " +"without passing a strategy instance) should now manually initialize " +"FedAvg with ``fraction_fit`` and ``fraction_evaluate`` set to ``0.1``." msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:-1 -msgid "local DP mod" +#: ../../source/how-to-upgrade-to-flower-1.0.rst:58 +msgid "Rename built-in strategy parameters (e.g., ``FedAvg``):" msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:104 -msgid "Below is a code example that shows how to use :code:`LocalDpMod`:" +#: ../../source/how-to-upgrade-to-flower-1.0.rst:60 +msgid "``fraction_eval`` --> ``fraction_evaluate``" msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:122 -msgid "" -"Please note that the order of mods, especially those that modify " -"parameters, is important when using multiple modifiers. Typically, " -"differential privacy (DP) modifiers should be the last to operate on " -"parameters." +#: ../../source/how-to-upgrade-to-flower-1.0.rst:61 +msgid "``min_eval_clients`` --> ``min_evaluate_clients``" msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:125 -msgid "Local Training using Privacy Engines" +#: ../../source/how-to-upgrade-to-flower-1.0.rst:62 +msgid "``eval_fn`` --> ``evaluate_fn``" msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:126 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:64 msgid "" -"For ensuring data instance-level privacy during local model training on " -"the client side, consider leveraging privacy engines such as Opacus and " -"TensorFlow Privacy. For examples of using Flower with these engines, " -"please refer to the Flower examples directory (`Opacus " -"`_, `Tensorflow" -" Privacy `_)." +"Rename ``rnd`` to ``server_round``. This impacts multiple methods and " +"functions, for example, ``configure_fit``, ``aggregate_fit``, " +"``configure_evaluate``, ``aggregate_evaluate``, and ``evaluate_fn``." msgstr "" -#: ../../source/how-to-use-strategies.rst:2 -msgid "Use strategies" +#: ../../source/how-to-upgrade-to-flower-1.0.rst:65 +msgid "Add ``server_round`` and ``config`` to ``evaluate_fn``:" msgstr "" -#: ../../source/how-to-use-strategies.rst:4 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:67 msgid "" -"Flower allows full customization of the learning process through the " -":code:`Strategy` abstraction. A number of built-in strategies are " -"provided in the core framework." +"Flower 0.19: ``def evaluate(parameters: NDArrays) -> " +"Optional[Tuple[float, Dict[str, Scalar]]]:``" msgstr "" -#: ../../source/how-to-use-strategies.rst:6 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:68 msgid "" -"There are three ways to customize the way Flower orchestrates the " -"learning process on the server side:" -msgstr "" - -#: ../../source/how-to-use-strategies.rst:8 -msgid "Use an existing strategy, for example, :code:`FedAvg`" -msgstr "" - -#: ../../source/how-to-use-strategies.rst:9 -#: ../../source/how-to-use-strategies.rst:40 -msgid "Customize an existing strategy with callback functions" +"Flower 1.0: ``def evaluate(server_round: int, parameters: NDArrays, " +"config: Dict[str, Scalar]) -> Optional[Tuple[float, Dict[str, " +"Scalar]]]:``" msgstr "" -#: ../../source/how-to-use-strategies.rst:10 -#: ../../source/how-to-use-strategies.rst:87 -msgid "Implement a novel strategy" +#: ../../source/how-to-upgrade-to-flower-1.0.rst:71 +msgid "Custom strategies" msgstr "" -#: ../../source/how-to-use-strategies.rst:14 -msgid "Use an existing strategy" +#: ../../source/how-to-upgrade-to-flower-1.0.rst:73 +msgid "" +"The type of parameter ``failures`` has changed from " +"``List[BaseException]`` to ``List[Union[Tuple[ClientProxy, FitRes], " +"BaseException]]`` (in ``aggregate_fit``) and " +"``List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]]`` (in " +"``aggregate_evaluate``)" msgstr "" -#: ../../source/how-to-use-strategies.rst:16 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:74 msgid "" -"Flower comes with a number of popular federated learning strategies " -"built-in. A built-in strategy can be instantiated as follows:" +"The ``Strategy`` method ``evaluate`` now receives the current round of " +"federated learning/evaluation as the first parameter:" msgstr "" -#: ../../source/how-to-use-strategies.rst:25 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:76 msgid "" -"This creates a strategy with all parameters left at their default values " -"and passes it to the :code:`start_server` function. It is usually " -"recommended to adjust a few parameters during instantiation:" +"Flower 0.19: ``def evaluate(self, parameters: Parameters) -> " +"Optional[Tuple[float, Dict[str, Scalar]]]:``" msgstr "" -#: ../../source/how-to-use-strategies.rst:42 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:77 msgid "" -"Existing strategies provide several ways to customize their behaviour. " -"Callback functions allow strategies to call user-provided code during " -"execution." +"Flower 1.0: ``def evaluate(self, server_round: int, parameters: " +"Parameters) -> Optional[Tuple[float, Dict[str, Scalar]]]:``" msgstr "" -#: ../../source/how-to-use-strategies.rst:45 -msgid "Configuring client fit and client evaluate" +#: ../../source/how-to-upgrade-to-flower-1.0.rst:80 +msgid "Optional improvements" msgstr "" -#: ../../source/how-to-use-strategies.rst:47 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:82 msgid "" -"The server can pass new configuration values to the client each round by " -"providing a function to :code:`on_fit_config_fn`. The provided function " -"will be called by the strategy and must return a dictionary of " -"configuration key values pairs that will be sent to the client. It must " -"return a dictionary of arbitrary configuration values :code:`client.fit`" -" and :code:`client.evaluate` functions during each round of federated " -"learning." +"Along with the necessary changes above, there are a number of potential " +"improvements that just became possible:" msgstr "" -#: ../../source/how-to-use-strategies.rst:75 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:84 msgid "" -"The :code:`on_fit_config_fn` can be used to pass arbitrary configuration " -"values from server to client, and poetentially change these values each " -"round, for example, to adjust the learning rate. The client will receive " -"the dictionary returned by the :code:`on_fit_config_fn` in its own " -":code:`client.fit()` function." +"Remove \"placeholder\" methods from subclasses of ``Client`` or " +"``NumPyClient``. If you, for example, use server-side evaluation, then " +"empty placeholder implementations of ``evaluate`` are no longer " +"necessary." msgstr "" -#: ../../source/how-to-use-strategies.rst:78 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:85 msgid "" -"Similar to :code:`on_fit_config_fn`, there is also " -":code:`on_evaluate_config_fn` to customize the configuration sent to " -":code:`client.evaluate()`" +"Configure the round timeout via ``start_simulation``: " +"``start_simulation(..., config=flwr.server.ServerConfig(num_rounds=3, " +"round_timeout=600.0), ...)``" msgstr "" -#: ../../source/how-to-use-strategies.rst:81 -msgid "Configuring server-side evaluation" +#: ../../source/how-to-upgrade-to-flower-1.0.rst:89 +#: ../../source/how-to-upgrade-to-flower-next.rst:317 +msgid "Further help" msgstr "" -#: ../../source/how-to-use-strategies.rst:83 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:91 msgid "" -"Server-side evaluation can be enabled by passing an evaluation function " -"to :code:`evaluate_fn`." +"Most official `Flower code examples " +"`_ are already updated" +" to Flower 1.0, they can serve as a reference for using the Flower 1.0 " +"API. If there are further questions, `join the Flower Slack " +"`_ and use the channel ``#questions``." msgstr "" -#: ../../source/how-to-use-strategies.rst:89 +#: ../../source/how-to-upgrade-to-flower-next.rst:2 +msgid "Upgrade to Flower Next" +msgstr "" + +#: ../../source/how-to-upgrade-to-flower-next.rst:4 msgid "" -"Writing a fully custom strategy is a bit more involved, but it provides " -"the most flexibility. Read the `Implementing Strategies `_ guide to learn more." +"Welcome to the migration guide for updating Flower to Flower Next! " +"Whether you're a seasoned user or just getting started, this guide will " +"help you smoothly transition your existing setup to take advantage of the" +" latest features and improvements in Flower Next, starting from version " +"1.8." msgstr "" -#: ../../source/index.rst:34 -msgid "Tutorial" +#: ../../source/how-to-upgrade-to-flower-next.rst:9 +msgid "" +"This guide shows how to reuse pre-``1.8`` Flower code with minimum code " +"changes by using the *compatibility layer* in Flower Next. In another " +"guide, we will show how to run Flower Next end-to-end with pure Flower " +"Next APIs." msgstr "" -#: ../../source/index.rst:44 -msgid "Quickstart tutorials" +#: ../../source/how-to-upgrade-to-flower-next.rst:13 +msgid "Let's dive in!" msgstr "" -#: ../../source/index.rst:75 ../../source/index.rst:79 -msgid "How-to guides" +#: ../../source/how-to-upgrade-to-flower-next.rst:48 +msgid "" +"Here's how to update an existing installation of Flower to Flower Next " +"with ``pip``:" msgstr "" -#: ../../source/index.rst:98 -msgid "Legacy example guides" +#: ../../source/how-to-upgrade-to-flower-next.rst:54 +msgid "or if you need Flower Next with simulation:" msgstr "" -#: ../../source/index.rst:109 ../../source/index.rst:113 -msgid "Explanations" +#: ../../source/how-to-upgrade-to-flower-next.rst:61 +msgid "" +"Ensure you set the following version constraint in your " +"``requirements.txt``" msgstr "" -#: None:-1 -msgid "API reference" +#: ../../source/how-to-upgrade-to-flower-next.rst:71 +msgid "or ``pyproject.toml``:" msgstr "" -#: ../../source/index.rst:138 -msgid "Reference docs" +#: ../../source/how-to-upgrade-to-flower-next.rst:82 +msgid "Using Poetry" msgstr "" -#: ../../source/index.rst:154 -msgid "Contributor tutorials" +#: ../../source/how-to-upgrade-to-flower-next.rst:84 +msgid "" +"Update the ``flwr`` dependency in ``pyproject.toml`` and then reinstall " +"(don't forget to delete ``poetry.lock`` via ``rm poetry.lock`` before " +"running ``poetry install``)." msgstr "" -#: ../../source/index.rst:161 -msgid "Contributor how-to guides" +#: ../../source/how-to-upgrade-to-flower-next.rst:86 +msgid "" +"Ensure you set the following version constraint in your " +"``pyproject.toml``:" msgstr "" -#: ../../source/index.rst:174 -msgid "Contributor explanations" +#: ../../source/how-to-upgrade-to-flower-next.rst:102 +msgid "" +"In Flower Next, the *infrastructure* and *application layers* have been " +"decoupled. Instead of starting a client in code via ``start_client()``, " +"you create a |clientapp_link|_ and start it via the command line. Instead" +" of starting a server in code via ``start_server()``, you create a " +"|serverapp_link|_ and start it via the command line. The long-running " +"components of server and client are called SuperLink and SuperNode. The " +"following non-breaking changes that require manual updates and allow you " +"to run your project both in the traditional way and in the Flower Next " +"way:" msgstr "" -#: ../../source/index.rst:180 -msgid "Contributor references" +#: ../../source/how-to-upgrade-to-flower-next.rst:109 +msgid "|clientapp_link|_" msgstr "" -#: ../../source/index.rst:-1 +#: ../../source/how-to-upgrade-to-flower-next.rst:110 msgid "" -"Check out the documentation of the main Flower Framework enabling easy " -"Python development for Federated Learning." +"Wrap your existing client with |clientapp_link|_ instead of launching it " +"via |startclient_link|_. Here's an example:" msgstr "" -#: ../../source/index.rst:2 -msgid "Flower Framework Documentation" +#: ../../source/how-to-upgrade-to-flower-next.rst:132 +msgid "|serverapp_link|_" msgstr "" -#: ../../source/index.rst:7 +#: ../../source/how-to-upgrade-to-flower-next.rst:133 msgid "" -"Welcome to Flower's documentation. `Flower `_ is a " -"friendly federated learning framework." +"Wrap your existing strategy with |serverapp_link|_ instead of starting " +"the server via |startserver_link|_. Here's an example:" msgstr "" -#: ../../source/index.rst:11 -msgid "Join the Flower Community" +#: ../../source/how-to-upgrade-to-flower-next.rst:154 +msgid "Deployment" msgstr "" -#: ../../source/index.rst:13 +#: ../../source/how-to-upgrade-to-flower-next.rst:155 msgid "" -"The Flower Community is growing quickly - we're a friendly group of " -"researchers, engineers, students, professionals, academics, and other " -"enthusiasts." -msgstr "" - -#: ../../source/index.rst:15 -msgid "Join us on Slack" +"Run the ``SuperLink`` using |flowernext_superlink_link|_ before running, " +"in sequence, |flowernext_clientapp_link|_ (2x) and " +"|flowernext_serverapp_link|_. There is no need to execute `client.py` and" +" `server.py` as Python scripts." msgstr "" -#: ../../source/index.rst:23 -msgid "Flower Framework" +#: ../../source/how-to-upgrade-to-flower-next.rst:158 +msgid "" +"Here's an example to start the server without HTTPS (only for " +"prototyping):" msgstr "" -#: ../../source/index.rst:25 +#: ../../source/how-to-upgrade-to-flower-next.rst:174 msgid "" -"The user guide is targeted at researchers and developers who want to use " -"Flower to bring existing machine learning workloads into a federated " -"setting. One of Flower's design goals was to make this simple. Read on to" -" learn more." +"Here's another example to start with HTTPS. Use the ``--ssl-ca-" +"certfile``, ``--ssl-certfile``, and ``--ssl-keyfile`` command line " +"options to pass paths to (CA certificate, server certificate, and server " +"private key)." msgstr "" -#: ../../source/index.rst:30 -msgid "Tutorials" +#: ../../source/how-to-upgrade-to-flower-next.rst:201 +msgid "Simulation in CLI" msgstr "" -#: ../../source/index.rst:32 +#: ../../source/how-to-upgrade-to-flower-next.rst:202 msgid "" -"A learning-oriented series of federated learning tutorials, the best " -"place to start." +"Wrap your existing client and strategy with |clientapp_link|_ and " +"|serverapp_link|_, respectively. There is no need to use |startsim_link|_" +" anymore. Here's an example:" msgstr "" -#: ../../source/index.rst:62 +#: ../../source/how-to-upgrade-to-flower-next.rst:232 msgid "" -"QUICKSTART TUTORIALS: :doc:`PyTorch ` | " -":doc:`TensorFlow ` | :doc:`🤗 Transformers" -" ` | :doc:`JAX ` | :doc:`Pandas ` | :doc:`fastai " -"` | :doc:`PyTorch Lightning ` | :doc:`MXNet `" -" | :doc:`scikit-learn ` | :doc:`XGBoost " -"` | :doc:`Android ` | :doc:`iOS `" +"Run |flower_simulation_link|_ in CLI and point to the ``server_app`` / " +"``client_app`` object in the code instead of executing the Python script." +" Here's an example (assuming the ``server_app`` and ``client_app`` " +"objects are in a ``sim.py`` module):" msgstr "" -#: ../../source/index.rst:64 -msgid "We also made video tutorials for PyTorch:" +#: ../../source/how-to-upgrade-to-flower-next.rst:249 +msgid "" +"Set default resources for each |clientapp_link|_ using the ``--backend-" +"config`` command line argument instead of setting the " +"``client_resources`` argument in |startsim_link|_. Here's an example:" msgstr "" -#: ../../source/index.rst:69 -msgid "And TensorFlow:" +#: ../../source/how-to-upgrade-to-flower-next.rst:275 +msgid "Simulation in a Notebook" msgstr "" -#: ../../source/index.rst:77 +#: ../../source/how-to-upgrade-to-flower-next.rst:276 msgid "" -"Problem-oriented how-to guides show step-by-step how to achieve a " -"specific goal." +"Run |runsim_link|_ in your notebook instead of |startsim_link|_. Here's " +"an example:" msgstr "" -#: ../../source/index.rst:111 +#: ../../source/how-to-upgrade-to-flower-next.rst:319 msgid "" -"Understanding-oriented concept guides explain and discuss key topics and " -"underlying ideas behind Flower and collaborative AI." -msgstr "" - -#: ../../source/index.rst:121 -msgid "References" +"Some official `Flower code examples `_ " +"are already updated to Flower Next so they can serve as a reference for " +"using the Flower Next API. If there are further questions, `join the " +"Flower Slack `_ and use the channel " +"``#questions``. You can also `participate in Flower Discuss " +"`_ where you can find us answering questions," +" or share and learn from others about migrating to Flower Next." msgstr "" -#: ../../source/index.rst:123 -msgid "Information-oriented API reference and other reference material." +#: ../../source/how-to-upgrade-to-flower-next.rst:325 +msgid "Important" msgstr "" -#: ../../source/index.rst:132::1 -msgid ":py:obj:`flwr `\\" +#: ../../source/how-to-upgrade-to-flower-next.rst:328 +msgid "" +"As we continuously enhance Flower Next at a rapid pace, we'll be " +"periodically updating this guide. Please feel free to share any feedback " +"with us!" msgstr "" -#: ../../source/index.rst:132::1 flwr:1 of -msgid "Flower main package." +#: ../../source/how-to-upgrade-to-flower-next.rst:334 +msgid "Happy migrating! 🚀" msgstr "" -#: ../../source/index.rst:149 -msgid "Contributor docs" +#: ../../source/how-to-use-built-in-mods.rst:2 +msgid "Use Built-in Mods" msgstr "" -#: ../../source/index.rst:151 +#: ../../source/how-to-use-built-in-mods.rst:4 msgid "" -"The Flower community welcomes contributions. The following docs are " -"intended to help along the way." +"**Note: This tutorial covers experimental features. The functionality and" +" interfaces may change in future versions.**" msgstr "" -#: ../../source/ref-api-cli.rst:2 -msgid "Flower CLI reference" +#: ../../source/how-to-use-built-in-mods.rst:6 +msgid "" +"In this tutorial, we will learn how to utilize built-in mods to augment " +"the behavior of a ``ClientApp``. Mods (sometimes also called Modifiers) " +"allow us to perform operations before and after a task is processed in " +"the ``ClientApp``." msgstr "" -#: ../../source/ref-api-cli.rst:7 -msgid "flower-superlink" +#: ../../source/how-to-use-built-in-mods.rst:9 +msgid "What are Mods?" msgstr "" -#: ../../source/ref-api-cli.rst:17 -msgid "flower-driver-api" +#: ../../source/how-to-use-built-in-mods.rst:11 +msgid "" +"A Mod is a callable that wraps around a ``ClientApp``. It can manipulate " +"or inspect the incoming ``Message`` and the resulting outgoing " +"``Message``. The signature for a ``Mod`` is as follows:" msgstr "" -#: ../../source/ref-api-cli.rst:27 -msgid "flower-fleet-api" +#: ../../source/how-to-use-built-in-mods.rst:18 +msgid "A typical mod function might look something like this:" msgstr "" -#: ../../source/ref-api-cli.rst:37 -msgid "flower-client-app" +#: ../../source/how-to-use-built-in-mods.rst:31 +msgid "Using Mods" msgstr "" -#: ../../source/ref-api-cli.rst:47 -msgid "flower-server-app" +#: ../../source/how-to-use-built-in-mods.rst:33 +msgid "To use mods in your ``ClientApp``, you can follow these steps:" msgstr "" -#: ../../source/ref-api/flwr.rst:2 -msgid "flwr" +#: ../../source/how-to-use-built-in-mods.rst:36 +msgid "1. Import the required mods" msgstr "" -#: ../../source/ref-api/flwr.rst:25 ../../source/ref-api/flwr.server.rst:52 -msgid "Modules" +#: ../../source/how-to-use-built-in-mods.rst:38 +msgid "First, import the built-in mod you intend to use:" msgstr "" -#: ../../source/ref-api/flwr.rst:35::1 -msgid ":py:obj:`flwr.client `\\" +#: ../../source/how-to-use-built-in-mods.rst:46 +msgid "2. Define your client function" msgstr "" -#: ../../source/ref-api/flwr.rst:35::1 flwr.client:1 of -msgid "Flower client." +#: ../../source/how-to-use-built-in-mods.rst:48 +msgid "" +"Define your client function (``client_fn``) that will be wrapped by the " +"mod(s):" msgstr "" -#: ../../source/ref-api/flwr.rst:35::1 -msgid ":py:obj:`flwr.common `\\" +#: ../../source/how-to-use-built-in-mods.rst:57 +msgid "3. Create the ``ClientApp`` with mods" msgstr "" -#: ../../source/ref-api/flwr.rst:35::1 flwr.common:1 of -msgid "Common components shared between server and client." +#: ../../source/how-to-use-built-in-mods.rst:59 +msgid "" +"Create your ``ClientApp`` and pass the mods as a list to the ``mods`` " +"argument. The order in which you provide the mods matters:" msgstr "" -#: ../../source/ref-api/flwr.rst:35::1 -msgid ":py:obj:`flwr.server `\\" +#: ../../source/how-to-use-built-in-mods.rst:72 +msgid "Order of execution" msgstr "" -#: ../../source/ref-api/flwr.rst:35::1 -#: ../../source/ref-api/flwr.server.rst:41::1 flwr.server:1 -#: flwr.server.server.Server:1 of -msgid "Flower server." +#: ../../source/how-to-use-built-in-mods.rst:74 +msgid "" +"When the ``ClientApp`` runs, the mods are executed in the order they are " +"provided in the list:" msgstr "" -#: ../../source/ref-api/flwr.rst:35::1 -msgid ":py:obj:`flwr.simulation `\\" +#: ../../source/how-to-use-built-in-mods.rst:76 +msgid "``example_mod_1`` (outermost mod)" msgstr "" -#: ../../source/ref-api/flwr.rst:35::1 flwr.simulation:1 of -msgid "Flower simulation." +#: ../../source/how-to-use-built-in-mods.rst:77 +msgid "``example_mod_2`` (next mod)" msgstr "" -#: ../../source/ref-api/flwr.client.rst:2 -msgid "client" +#: ../../source/how-to-use-built-in-mods.rst:78 +msgid "" +"Message handler (core function that handles the incoming ``Message`` and " +"returns the outgoing ``Message``)" msgstr "" -#: ../../source/ref-api/flwr.client.rst:13 -#: ../../source/ref-api/flwr.common.rst:13 -#: ../../source/ref-api/flwr.server.rst:13 -#: ../../source/ref-api/flwr.simulation.rst:13 -msgid "Functions" +#: ../../source/how-to-use-built-in-mods.rst:79 +msgid "``example_mod_2`` (on the way back)" msgstr "" -#: ../../source/ref-api/flwr.client.rst:24::1 -msgid ":py:obj:`run_client_app `\\ \\(\\)" +#: ../../source/how-to-use-built-in-mods.rst:80 +msgid "``example_mod_1`` (outermost mod on the way back)" msgstr "" -#: ../../source/ref-api/flwr.client.rst:24::1 -#: flwr.client.app.run_client_app:1 of -msgid "Run Flower client app." +#: ../../source/how-to-use-built-in-mods.rst:82 +msgid "" +"Each mod has a chance to inspect and modify the incoming ``Message`` " +"before passing it to the next mod, and likewise with the outgoing " +"``Message`` before returning it up the stack." msgstr "" -#: ../../source/ref-api/flwr.client.rst:24::1 +#: ../../source/how-to-use-built-in-mods.rst:87 msgid "" -":py:obj:`start_client `\\ \\(\\*\\, " -"server\\_address\\[\\, client\\_fn\\, ...\\]\\)" +"By following this guide, you have learned how to effectively use mods to " +"enhance your ``ClientApp``'s functionality. Remember that the order of " +"mods is crucial and affects how the input and output are processed." msgstr "" -#: ../../source/ref-api/flwr.client.rst:24::1 -#: flwr.client.app.start_client:1 of -msgid "Start a Flower client node which connects to a Flower server." +#: ../../source/how-to-use-built-in-mods.rst:89 +msgid "Enjoy building a more robust and flexible ``ClientApp`` with mods!" msgstr "" -#: ../../source/ref-api/flwr.client.rst:24::1 -msgid "" -":py:obj:`start_numpy_client `\\ \\(\\*\\," -" server\\_address\\, client\\)" +#: ../../source/how-to-use-differential-privacy.rst:2 +msgid "Use Differential Privacy" msgstr "" -#: ../../source/ref-api/flwr.client.rst:24::1 -#: flwr.client.app.start_numpy_client:1 of -msgid "Start a Flower NumPyClient which connects to a gRPC server." +#: ../../source/how-to-use-differential-privacy.rst:3 +msgid "" +"This guide explains how you can utilize differential privacy in the " +"Flower framework. If you are not yet familiar with differential privacy, " +"you can refer to :doc:`explanation-differential-privacy`." msgstr "" -#: ../../source/ref-api/flwr.client.rst:26 -#: ../../source/ref-api/flwr.common.rst:32 -#: ../../source/ref-api/flwr.server.rst:29 -#: ../../source/ref-api/flwr.server.strategy.rst:17 -#: ../../source/ref-api/flwr.server.workflow.rst:17 -msgid "Classes" +#: ../../source/how-to-use-differential-privacy.rst:7 +msgid "" +"Differential Privacy in Flower is in a preview phase. If you plan to use " +"these features in a production environment with sensitive data, feel free" +" contact us to discuss your requirements and to receive guidance on how " +"to best use these features." msgstr "" -#: ../../source/ref-api/flwr.client.rst:33::1 -msgid ":py:obj:`Client `\\ \\(\\)" +#: ../../source/how-to-use-differential-privacy.rst:12 +msgid "" +"This approach consists of two seprate phases: clipping of the updates and" +" adding noise to the aggregated model. For the clipping phase, Flower " +"framework has made it possible to decide whether to perform clipping on " +"the server side or the client side." msgstr "" -#: ../../source/ref-api/flwr.client.rst:33::1 -#: flwr.client.client.Client:1 of -msgid "Abstract base class for Flower clients." +#: ../../source/how-to-use-differential-privacy.rst:15 +msgid "" +"**Server-side Clipping**: This approach has the advantage of the server " +"enforcing uniform clipping across all clients' updates and reducing the " +"communication overhead for clipping values. However, it also has the " +"disadvantage of increasing the computational load on the server due to " +"the need to perform the clipping operation for all clients." msgstr "" -#: ../../source/ref-api/flwr.client.rst:33::1 +#: ../../source/how-to-use-differential-privacy.rst:16 msgid "" -":py:obj:`ClientApp `\\ \\(\\[client\\_fn\\, " -"mods\\]\\)" +"**Client-side Clipping**: This approach has the advantage of reducing the" +" computational overhead on the server. However, it also has the " +"disadvantage of lacking centralized control, as the server has less " +"control over the clipping process." msgstr "" -#: ../../source/ref-api/flwr.client.rst:33::1 -#: flwr.client.client_app.ClientApp:1 of -msgid "Flower ClientApp." +#: ../../source/how-to-use-differential-privacy.rst:21 +msgid "Server-side Clipping" msgstr "" -#: ../../source/ref-api/flwr.client.rst:33::1 -msgid ":py:obj:`NumPyClient `\\ \\(\\)" +#: ../../source/how-to-use-differential-privacy.rst:22 +msgid "" +"For central DP with server-side clipping, there are two :code:`Strategy` " +"classes that act as wrappers around the actual :code:`Strategy` instance " +"(for example, :code:`FedAvg`). The two wrapper classes are " +":code:`DifferentialPrivacyServerSideFixedClipping` and " +":code:`DifferentialPrivacyServerSideAdaptiveClipping` for fixed and " +"adaptive clipping." msgstr "" -#: ../../source/ref-api/flwr.client.rst:33::1 -#: flwr.client.numpy_client.NumPyClient:1 of -msgid "Abstract base class for Flower clients using NumPy." +#: ../../source/how-to-use-differential-privacy.rst:-1 +msgid "server side clipping" msgstr "" -#: flwr.client.client.Client:1 flwr.client.numpy_client.NumPyClient:1 -#: flwr.server.client_manager.ClientManager:1 -#: flwr.server.strategy.strategy.Strategy:1 of -msgid "Bases: :py:class:`~abc.ABC`" +#: ../../source/how-to-use-differential-privacy.rst:31 +msgid "" +"The code sample below enables the :code:`FedAvg` strategy to use server-" +"side fixed clipping using the " +":code:`DifferentialPrivacyServerSideFixedClipping` wrapper class. The " +"same approach can be used with " +":code:`DifferentialPrivacyServerSideAdaptiveClipping` by adjusting the " +"corresponding input parameters." msgstr "" -#: ../../source/ref-api/flwr.client.Client.rst:15 -#: ../../source/ref-api/flwr.client.ClientApp.rst:15 -#: ../../source/ref-api/flwr.client.NumPyClient.rst:15 -#: ../../source/ref-api/flwr.common.Array.rst:15 -#: ../../source/ref-api/flwr.common.ClientMessage.rst:15 -#: ../../source/ref-api/flwr.common.ConfigsRecord.rst:15 -#: ../../source/ref-api/flwr.common.Context.rst:15 -#: ../../source/ref-api/flwr.common.DisconnectRes.rst:15 -#: ../../source/ref-api/flwr.common.Error.rst:15 -#: ../../source/ref-api/flwr.common.EvaluateIns.rst:15 -#: ../../source/ref-api/flwr.common.EvaluateRes.rst:15 -#: ../../source/ref-api/flwr.common.FitIns.rst:15 -#: ../../source/ref-api/flwr.common.FitRes.rst:15 -#: ../../source/ref-api/flwr.common.GetParametersIns.rst:15 -#: ../../source/ref-api/flwr.common.GetParametersRes.rst:15 -#: ../../source/ref-api/flwr.common.GetPropertiesIns.rst:15 -#: ../../source/ref-api/flwr.common.GetPropertiesRes.rst:15 -#: ../../source/ref-api/flwr.common.Message.rst:15 -#: ../../source/ref-api/flwr.common.MessageType.rst:15 -#: ../../source/ref-api/flwr.common.MessageTypeLegacy.rst:15 -#: ../../source/ref-api/flwr.common.Metadata.rst:15 -#: ../../source/ref-api/flwr.common.MetricsRecord.rst:15 -#: ../../source/ref-api/flwr.common.Parameters.rst:15 -#: ../../source/ref-api/flwr.common.ParametersRecord.rst:15 -#: ../../source/ref-api/flwr.common.ReconnectIns.rst:15 -#: ../../source/ref-api/flwr.common.RecordSet.rst:15 -#: ../../source/ref-api/flwr.common.ServerMessage.rst:15 -#: ../../source/ref-api/flwr.common.Status.rst:15 -#: ../../source/ref-api/flwr.server.ClientManager.rst:15 -#: ../../source/ref-api/flwr.server.Driver.rst:15 -#: ../../source/ref-api/flwr.server.History.rst:15 -#: ../../source/ref-api/flwr.server.LegacyContext.rst:15 -#: ../../source/ref-api/flwr.server.Server.rst:15 -#: ../../source/ref-api/flwr.server.ServerApp.rst:15 -#: ../../source/ref-api/flwr.server.ServerConfig.rst:15 -#: ../../source/ref-api/flwr.server.SimpleClientManager.rst:15 -#: ../../source/ref-api/flwr.server.strategy.Bulyan.rst:15 -#: ../../source/ref-api/flwr.server.strategy.DPFedAvgAdaptive.rst:15 -#: ../../source/ref-api/flwr.server.strategy.DPFedAvgFixed.rst:15 -#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyClientSideAdaptiveClipping.rst:15 -#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyClientSideFixedClipping.rst:15 -#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyServerSideAdaptiveClipping.rst:15 -#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyServerSideFixedClipping.rst:15 -#: ../../source/ref-api/flwr.server.strategy.FaultTolerantFedAvg.rst:15 -#: ../../source/ref-api/flwr.server.strategy.FedAdagrad.rst:15 -#: ../../source/ref-api/flwr.server.strategy.FedAdam.rst:15 -#: ../../source/ref-api/flwr.server.strategy.FedAvg.rst:15 -#: ../../source/ref-api/flwr.server.strategy.FedAvgAndroid.rst:15 -#: ../../source/ref-api/flwr.server.strategy.FedAvgM.rst:15 -#: ../../source/ref-api/flwr.server.strategy.FedMedian.rst:15 -#: ../../source/ref-api/flwr.server.strategy.FedOpt.rst:15 -#: ../../source/ref-api/flwr.server.strategy.FedProx.rst:15 -#: ../../source/ref-api/flwr.server.strategy.FedTrimmedAvg.rst:15 -#: ../../source/ref-api/flwr.server.strategy.FedXgbBagging.rst:15 -#: ../../source/ref-api/flwr.server.strategy.FedXgbCyclic.rst:15 -#: ../../source/ref-api/flwr.server.strategy.FedXgbNnAvg.rst:15 -#: ../../source/ref-api/flwr.server.strategy.FedYogi.rst:15 -#: ../../source/ref-api/flwr.server.strategy.Krum.rst:15 -#: ../../source/ref-api/flwr.server.strategy.QFedAvg.rst:15 -#: ../../source/ref-api/flwr.server.strategy.Strategy.rst:15 -#: ../../source/ref-api/flwr.server.workflow.DefaultWorkflow.rst:15 -#: ../../source/ref-api/flwr.server.workflow.SecAggPlusWorkflow.rst:15 -#: ../../source/ref-api/flwr.server.workflow.SecAggWorkflow.rst:15 -msgid "Methods" +#: ../../source/how-to-use-differential-privacy.rst:52 +msgid "Client-side Clipping" msgstr "" -#: ../../source/ref-api/flwr.client.Client.rst:44::1 -msgid ":py:obj:`evaluate `\\ \\(ins\\)" +#: ../../source/how-to-use-differential-privacy.rst:53 +msgid "" +"For central DP with client-side clipping, the server sends the clipping " +"value to selected clients on each round. Clients can use existing Flower " +":code:`Mods` to perform the clipping. Two mods are available for fixed " +"and adaptive client-side clipping: :code:`fixedclipping_mod` and " +":code:`adaptiveclipping_mod` with corresponding server-side wrappers " +":code:`DifferentialPrivacyClientSideFixedClipping` and " +":code:`DifferentialPrivacyClientSideAdaptiveClipping`." msgstr "" -#: ../../source/ref-api/flwr.client.Client.rst:44::1 -#: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 -#: flwr.client.client.Client.evaluate:1 -#: flwr.client.numpy_client.NumPyClient.evaluate:1 of -msgid "Evaluate the provided parameters using the locally held dataset." +#: ../../source/how-to-use-differential-privacy.rst:-1 +msgid "client side clipping" msgstr "" -#: ../../source/ref-api/flwr.client.Client.rst:44::1 -msgid ":py:obj:`fit `\\ \\(ins\\)" +#: ../../source/how-to-use-differential-privacy.rst:63 +msgid "" +"The code sample below enables the :code:`FedAvg` strategy to use " +"differential privacy with client-side fixed clipping using both the " +":code:`DifferentialPrivacyClientSideFixedClipping` wrapper class and, on " +"the client, :code:`fixedclipping_mod`:" msgstr "" -#: ../../source/ref-api/flwr.client.Client.rst:44::1 -#: flwr.client.client.Client.fit:1 of -msgid "Refine the provided parameters using the locally held dataset." +#: ../../source/how-to-use-differential-privacy.rst:80 +msgid "" +"In addition to the server-side strategy wrapper, the :code:`ClientApp` " +"needs to configure the matching :code:`fixedclipping_mod` to perform the " +"client-side clipping:" msgstr "" -#: ../../source/ref-api/flwr.client.Client.rst:44::1 -msgid ":py:obj:`get_context `\\ \\(\\)" +#: ../../source/how-to-use-differential-privacy.rst:97 +msgid "" +"To utilize local differential privacy (DP) and add noise to the client " +"model parameters before transmitting them to the server in Flower, you " +"can use the `LocalDpMod`. The following hyperparameters need to be set: " +"clipping norm value, sensitivity, epsilon, and delta." msgstr "" -#: ../../source/ref-api/flwr.client.Client.rst:44::1 -#: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 -#: flwr.client.client.Client.get_context:1 -#: flwr.client.numpy_client.NumPyClient.get_context:1 of -msgid "Get the run context from this client." +#: ../../source/how-to-use-differential-privacy.rst:-1 +msgid "local DP mod" msgstr "" -#: ../../source/ref-api/flwr.client.Client.rst:44::1 -msgid ":py:obj:`get_parameters `\\ \\(ins\\)" +#: ../../source/how-to-use-differential-privacy.rst:104 +msgid "Below is a code example that shows how to use :code:`LocalDpMod`:" msgstr "" -#: ../../source/ref-api/flwr.client.Client.rst:44::1 -#: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 -#: flwr.client.client.Client.get_parameters:1 -#: flwr.client.numpy_client.NumPyClient.get_parameters:1 of -msgid "Return the current local model parameters." +#: ../../source/how-to-use-differential-privacy.rst:122 +msgid "" +"Please note that the order of mods, especially those that modify " +"parameters, is important when using multiple modifiers. Typically, " +"differential privacy (DP) modifiers should be the last to operate on " +"parameters." msgstr "" -#: ../../source/ref-api/flwr.client.Client.rst:44::1 -msgid ":py:obj:`get_properties `\\ \\(ins\\)" +#: ../../source/how-to-use-differential-privacy.rst:125 +msgid "Local Training using Privacy Engines" msgstr "" -#: ../../source/ref-api/flwr.client.Client.rst:44::1 -#: flwr.client.client.Client.get_properties:1 of -msgid "Return set of client's properties." +#: ../../source/how-to-use-differential-privacy.rst:126 +msgid "" +"For ensuring data instance-level privacy during local model training on " +"the client side, consider leveraging privacy engines such as Opacus and " +"TensorFlow Privacy. For examples of using Flower with these engines, " +"please refer to the Flower examples directory (`Opacus " +"`_, `Tensorflow" +" Privacy `_)." msgstr "" -#: ../../source/ref-api/flwr.client.Client.rst:44::1 -msgid ":py:obj:`set_context `\\ \\(context\\)" +#: ../../source/how-to-use-strategies.rst:2 +msgid "Use strategies" msgstr "" -#: ../../source/ref-api/flwr.client.Client.rst:44::1 -#: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 -#: flwr.client.client.Client.set_context:1 -#: flwr.client.numpy_client.NumPyClient.set_context:1 of -msgid "Apply a run context to this client." +#: ../../source/how-to-use-strategies.rst:4 +msgid "" +"Flower allows full customization of the learning process through the " +":code:`Strategy` abstraction. A number of built-in strategies are " +"provided in the core framework." msgstr "" -#: ../../source/ref-api/flwr.client.Client.rst:44::1 -msgid ":py:obj:`to_client `\\ \\(\\)" +#: ../../source/how-to-use-strategies.rst:6 +msgid "" +"There are three ways to customize the way Flower orchestrates the " +"learning process on the server side:" msgstr "" -#: ../../source/ref-api/flwr.client.Client.rst:44::1 -#: flwr.client.client.Client.to_client:1 of -msgid "Return client (itself)." +#: ../../source/how-to-use-strategies.rst:8 +msgid "Use an existing strategy, for example, :code:`FedAvg`" msgstr "" -#: ../../source/ref-api/flwr.client.Client.rst:46 -#: ../../source/ref-api/flwr.client.NumPyClient.rst:46 -#: ../../source/ref-api/flwr.common.Array.rst:28 -#: ../../source/ref-api/flwr.common.ClientMessage.rst:25 -#: ../../source/ref-api/flwr.common.Code.rst:19 -#: ../../source/ref-api/flwr.common.Context.rst:25 -#: ../../source/ref-api/flwr.common.DisconnectRes.rst:25 -#: ../../source/ref-api/flwr.common.Error.rst:25 -#: ../../source/ref-api/flwr.common.EvaluateIns.rst:25 -#: ../../source/ref-api/flwr.common.EvaluateRes.rst:25 -#: ../../source/ref-api/flwr.common.EventType.rst:19 -#: ../../source/ref-api/flwr.common.FitIns.rst:25 -#: ../../source/ref-api/flwr.common.FitRes.rst:25 -#: ../../source/ref-api/flwr.common.GetParametersIns.rst:25 -#: ../../source/ref-api/flwr.common.GetParametersRes.rst:25 -#: ../../source/ref-api/flwr.common.GetPropertiesIns.rst:25 -#: ../../source/ref-api/flwr.common.GetPropertiesRes.rst:25 -#: ../../source/ref-api/flwr.common.Message.rst:37 -#: ../../source/ref-api/flwr.common.MessageType.rst:25 -#: ../../source/ref-api/flwr.common.MessageTypeLegacy.rst:25 -#: ../../source/ref-api/flwr.common.Metadata.rst:25 -#: ../../source/ref-api/flwr.common.Parameters.rst:25 -#: ../../source/ref-api/flwr.common.ReconnectIns.rst:25 -#: ../../source/ref-api/flwr.common.RecordSet.rst:25 -#: ../../source/ref-api/flwr.common.ServerMessage.rst:25 -#: ../../source/ref-api/flwr.common.Status.rst:25 -#: ../../source/ref-api/flwr.server.LegacyContext.rst:25 -#: ../../source/ref-api/flwr.server.ServerConfig.rst:25 -msgid "Attributes" +#: ../../source/how-to-use-strategies.rst:9 +#: ../../source/how-to-use-strategies.rst:40 +msgid "Customize an existing strategy with callback functions" msgstr "" -#: flwr.client.client.Client.evaluate:1::1 of -msgid ":py:obj:`context `\\" +#: ../../source/how-to-use-strategies.rst:10 +#: ../../source/how-to-use-strategies.rst:87 +msgid "Implement a novel strategy" msgstr "" -#: ../../source/ref-api/flwr.common.Parameters.rst:2 -#: flwr.client.app.start_client flwr.client.app.start_numpy_client -#: flwr.client.client.Client.evaluate flwr.client.client.Client.fit -#: flwr.client.client.Client.get_parameters -#: flwr.client.client.Client.get_properties -#: flwr.client.numpy_client.NumPyClient.evaluate -#: flwr.client.numpy_client.NumPyClient.fit -#: flwr.client.numpy_client.NumPyClient.get_parameters -#: flwr.client.numpy_client.NumPyClient.get_properties -#: flwr.common.context.Context flwr.common.message.Error -#: flwr.common.message.Message flwr.common.message.Message.create_error_reply -#: flwr.common.message.Message.create_reply flwr.common.message.Metadata -#: flwr.common.record.parametersrecord.Array flwr.server.app.start_server -#: flwr.server.client_manager.ClientManager.register -#: flwr.server.client_manager.ClientManager.unregister -#: flwr.server.client_manager.SimpleClientManager.register -#: flwr.server.client_manager.SimpleClientManager.unregister -#: flwr.server.client_manager.SimpleClientManager.wait_for -#: flwr.server.compat.app.start_driver flwr.server.driver.driver.Driver -#: flwr.server.driver.driver.Driver.create_message -#: flwr.server.driver.driver.Driver.pull_messages -#: flwr.server.driver.driver.Driver.push_messages -#: flwr.server.driver.driver.Driver.send_and_receive -#: flwr.server.strategy.bulyan.Bulyan -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit -#: flwr.server.strategy.fedadagrad.FedAdagrad -#: flwr.server.strategy.fedadam.FedAdam flwr.server.strategy.fedavg.FedAvg -#: flwr.server.strategy.fedavg_android.FedAvgAndroid -#: flwr.server.strategy.fedavgm.FedAvgM flwr.server.strategy.fedopt.FedOpt -#: flwr.server.strategy.fedprox.FedProx -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg -#: flwr.server.strategy.fedyogi.FedYogi flwr.server.strategy.krum.Krum -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate -#: flwr.server.strategy.strategy.Strategy.aggregate_fit -#: flwr.server.strategy.strategy.Strategy.configure_evaluate -#: flwr.server.strategy.strategy.Strategy.configure_fit -#: flwr.server.strategy.strategy.Strategy.evaluate -#: flwr.server.strategy.strategy.Strategy.initialize_parameters -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow -#: flwr.simulation.app.start_simulation -#: flwr.simulation.run_simulation.run_simulation of -msgid "Parameters" +#: ../../source/how-to-use-strategies.rst:14 +msgid "Use an existing strategy" msgstr "" -#: flwr.client.client.Client.evaluate:3 of +#: ../../source/how-to-use-strategies.rst:16 msgid "" -"The evaluation instructions containing (global) model parameters received" -" from the server and a dictionary of configuration values used to " -"customize the local evaluation process." +"Flower comes with a number of popular federated learning strategies " +"built-in. A built-in strategy can be instantiated as follows:" msgstr "" -#: flwr.client.client.Client.evaluate flwr.client.client.Client.fit -#: flwr.client.client.Client.get_parameters -#: flwr.client.client.Client.get_properties -#: flwr.client.numpy_client.NumPyClient.evaluate -#: flwr.client.numpy_client.NumPyClient.fit -#: flwr.client.numpy_client.NumPyClient.get_parameters -#: flwr.client.numpy_client.NumPyClient.get_properties -#: flwr.common.message.Message.create_reply flwr.server.app.start_server -#: flwr.server.client_manager.ClientManager.num_available -#: flwr.server.client_manager.ClientManager.register -#: flwr.server.client_manager.SimpleClientManager.num_available -#: flwr.server.client_manager.SimpleClientManager.register -#: flwr.server.client_manager.SimpleClientManager.wait_for -#: flwr.server.compat.app.start_driver -#: flwr.server.driver.driver.Driver.create_message -#: flwr.server.driver.driver.Driver.pull_messages -#: flwr.server.driver.driver.Driver.push_messages -#: flwr.server.driver.driver.Driver.send_and_receive -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate -#: flwr.server.strategy.strategy.Strategy.aggregate_fit -#: flwr.server.strategy.strategy.Strategy.configure_evaluate -#: flwr.server.strategy.strategy.Strategy.configure_fit -#: flwr.server.strategy.strategy.Strategy.evaluate -#: flwr.server.strategy.strategy.Strategy.initialize_parameters -#: flwr.simulation.app.start_simulation of -msgid "Returns" +#: ../../source/how-to-use-strategies.rst:25 +msgid "" +"This creates a strategy with all parameters left at their default values " +"and passes it to the :code:`start_server` function. It is usually " +"recommended to adjust a few parameters during instantiation:" msgstr "" -#: flwr.client.client.Client.evaluate:8 of +#: ../../source/how-to-use-strategies.rst:42 msgid "" -"The evaluation result containing the loss on the local dataset and other " -"details such as the number of local data examples used for evaluation." +"Existing strategies provide several ways to customize their behaviour. " +"Callback functions allow strategies to call user-provided code during " +"execution." msgstr "" -#: flwr.client.client.Client.evaluate flwr.client.client.Client.fit -#: flwr.client.client.Client.get_parameters -#: flwr.client.client.Client.get_properties -#: flwr.client.numpy_client.NumPyClient.get_parameters -#: flwr.client.numpy_client.NumPyClient.get_properties -#: flwr.common.message.Message.create_reply flwr.server.app.start_server -#: flwr.server.client_manager.ClientManager.num_available -#: flwr.server.client_manager.ClientManager.register -#: flwr.server.client_manager.SimpleClientManager.num_available -#: flwr.server.client_manager.SimpleClientManager.register -#: flwr.server.client_manager.SimpleClientManager.wait_for -#: flwr.server.compat.app.start_driver -#: flwr.server.driver.driver.Driver.create_message -#: flwr.server.driver.driver.Driver.pull_messages -#: flwr.server.driver.driver.Driver.push_messages -#: flwr.server.driver.driver.Driver.send_and_receive -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate -#: flwr.server.strategy.strategy.Strategy.aggregate_fit -#: flwr.server.strategy.strategy.Strategy.configure_evaluate -#: flwr.server.strategy.strategy.Strategy.configure_fit -#: flwr.server.strategy.strategy.Strategy.evaluate -#: flwr.server.strategy.strategy.Strategy.initialize_parameters -#: flwr.simulation.app.start_simulation of -msgid "Return type" +#: ../../source/how-to-use-strategies.rst:45 +msgid "Configuring client fit and client evaluate" msgstr "" -#: flwr.client.client.Client.fit:3 of +#: ../../source/how-to-use-strategies.rst:47 msgid "" -"The training instructions containing (global) model parameters received " -"from the server and a dictionary of configuration values used to " -"customize the local training process." +"The server can pass new configuration values to the client each round by " +"providing a function to :code:`on_fit_config_fn`. The provided function " +"will be called by the strategy and must return a dictionary of " +"configuration key values pairs that will be sent to the client. It must " +"return a dictionary of arbitrary configuration values :code:`client.fit`" +" and :code:`client.evaluate` functions during each round of federated " +"learning." msgstr "" -#: flwr.client.client.Client.fit:8 of +#: ../../source/how-to-use-strategies.rst:75 msgid "" -"The training result containing updated parameters and other details such " -"as the number of local training examples used for training." +"The :code:`on_fit_config_fn` can be used to pass arbitrary configuration " +"values from server to client, and poetentially change these values each " +"round, for example, to adjust the learning rate. The client will receive " +"the dictionary returned by the :code:`on_fit_config_fn` in its own " +":code:`client.fit()` function." msgstr "" -#: flwr.client.client.Client.get_parameters:3 of +#: ../../source/how-to-use-strategies.rst:78 msgid "" -"The get parameters instructions received from the server containing a " -"dictionary of configuration values." +"Similar to :code:`on_fit_config_fn`, there is also " +":code:`on_evaluate_config_fn` to customize the configuration sent to " +":code:`client.evaluate()`" msgstr "" -#: flwr.client.client.Client.get_parameters:7 of -msgid "The current local model parameters." +#: ../../source/how-to-use-strategies.rst:81 +msgid "Configuring server-side evaluation" msgstr "" -#: flwr.client.client.Client.get_properties:3 of +#: ../../source/how-to-use-strategies.rst:83 msgid "" -"The get properties instructions received from the server containing a " -"dictionary of configuration values." +"Server-side evaluation can be enabled by passing an evaluation function " +"to :code:`evaluate_fn`." msgstr "" -#: flwr.client.client.Client.get_properties:7 of -msgid "The current client properties." +#: ../../source/how-to-use-strategies.rst:89 +msgid "" +"Writing a fully custom strategy is a bit more involved, but it provides " +"the most flexibility. Read the `Implementing Strategies `_ guide to learn more." msgstr "" -#: ../../source/ref-api/flwr.client.ClientApp.rst:2 -msgid "ClientApp" +#: ../../source/index.rst:34 +msgid "Tutorial" msgstr "" -#: flwr.client.client_app.ClientApp:1 flwr.common.constant.MessageType:1 -#: flwr.common.constant.MessageTypeLegacy:1 flwr.common.context.Context:1 -#: flwr.common.message.Error:1 flwr.common.message.Message:1 -#: flwr.common.message.Metadata:1 flwr.common.record.parametersrecord.Array:1 -#: flwr.common.record.recordset.RecordSet:1 flwr.common.typing.ClientMessage:1 -#: flwr.common.typing.DisconnectRes:1 flwr.common.typing.EvaluateIns:1 -#: flwr.common.typing.EvaluateRes:1 flwr.common.typing.FitIns:1 -#: flwr.common.typing.FitRes:1 flwr.common.typing.GetParametersIns:1 -#: flwr.common.typing.GetParametersRes:1 flwr.common.typing.GetPropertiesIns:1 -#: flwr.common.typing.GetPropertiesRes:1 flwr.common.typing.Parameters:1 -#: flwr.common.typing.ReconnectIns:1 flwr.common.typing.ServerMessage:1 -#: flwr.common.typing.Status:1 flwr.server.driver.driver.Driver:1 -#: flwr.server.history.History:1 flwr.server.server.Server:1 -#: flwr.server.server_app.ServerApp:1 flwr.server.server_config.ServerConfig:1 -#: flwr.server.workflow.default_workflows.DefaultWorkflow:1 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:1 -#: of -msgid "Bases: :py:class:`object`" +#: ../../source/index.rst:44 +msgid "Quickstart tutorials" msgstr "" -#: flwr.client.app.start_client:41 flwr.client.app.start_numpy_client:36 -#: flwr.client.client_app.ClientApp:4 -#: flwr.client.client_app.ClientApp.evaluate:4 -#: flwr.client.client_app.ClientApp.query:4 -#: flwr.client.client_app.ClientApp.train:4 flwr.server.app.start_server:41 -#: flwr.server.compat.app.start_driver:32 flwr.server.server_app.ServerApp:4 -#: flwr.server.server_app.ServerApp.main:4 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:29 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:22 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:21 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:14 -#: of -msgid "Examples" +#: ../../source/index.rst:74 ../../source/index.rst:78 +msgid "How-to guides" msgstr "" -#: flwr.client.client_app.ClientApp:5 of -msgid "" -"Assuming a typical `Client` implementation named `FlowerClient`, you can " -"wrap it in a `ClientApp` as follows:" +#: ../../source/index.rst:99 +msgid "Legacy example guides" msgstr "" -#: flwr.client.client_app.ClientApp:16 of -msgid "" -"If the above code is in a Python module called `client`, it can be " -"started as follows:" +#: ../../source/index.rst:108 ../../source/index.rst:112 +msgid "Explanations" msgstr "" -#: flwr.client.client_app.ClientApp:21 of -msgid "" -"In this `client:app` example, `client` refers to the Python module " -"`client.py` in which the previous code lives in and `app` refers to the " -"global attribute `app` that points to an object of type `ClientApp`." +#: None:-1 +msgid "API reference" msgstr "" -#: flwr.client.client_app.ClientApp.evaluate:1::1 of -msgid ":py:obj:`evaluate `\\ \\(\\)" +#: ../../source/index.rst:137 +msgid "Reference docs" msgstr "" -#: flwr.client.client_app.ClientApp.evaluate:1 -#: flwr.client.client_app.ClientApp.evaluate:1::1 of -msgid "Return a decorator that registers the evaluate fn with the client app." +#: ../../source/index.rst:153 +msgid "Contributor tutorials" msgstr "" -#: flwr.client.client_app.ClientApp.evaluate:1::1 of -msgid ":py:obj:`query `\\ \\(\\)" +#: ../../source/index.rst:160 +msgid "Contributor how-to guides" msgstr "" -#: flwr.client.client_app.ClientApp.evaluate:1::1 -#: flwr.client.client_app.ClientApp.query:1 of -msgid "Return a decorator that registers the query fn with the client app." +#: ../../source/index.rst:172 +msgid "Contributor explanations" msgstr "" -#: flwr.client.client_app.ClientApp.evaluate:1::1 of -msgid ":py:obj:`train `\\ \\(\\)" +#: ../../source/index.rst:178 +msgid "Contributor references" msgstr "" -#: flwr.client.client_app.ClientApp.evaluate:1::1 -#: flwr.client.client_app.ClientApp.train:1 of -msgid "Return a decorator that registers the train fn with the client app." +#: ../../source/index.rst:-1 +msgid "" +"Check out the documentation of the main Flower Framework enabling easy " +"Python development for Federated Learning." msgstr "" -#: ../../source/ref-api/flwr.client.NumPyClient.rst:2 -msgid "NumPyClient" +#: ../../source/index.rst:2 +msgid "Flower Framework Documentation" msgstr "" -#: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 +#: ../../source/index.rst:7 msgid "" -":py:obj:`evaluate `\\ \\(parameters\\, " -"config\\)" +"Welcome to Flower's documentation. `Flower `_ is a " +"friendly federated learning framework." msgstr "" -#: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 -msgid ":py:obj:`fit `\\ \\(parameters\\, config\\)" +#: ../../source/index.rst:11 +msgid "Join the Flower Community" msgstr "" -#: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 -#: flwr.client.numpy_client.NumPyClient.fit:1 of -msgid "Train the provided parameters using the locally held dataset." +#: ../../source/index.rst:13 +msgid "" +"The Flower Community is growing quickly - we're a friendly group of " +"researchers, engineers, students, professionals, academics, and other " +"enthusiasts." msgstr "" -#: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 -msgid ":py:obj:`get_context `\\ \\(\\)" +#: ../../source/index.rst:15 +msgid "Join us on Slack" msgstr "" -#: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 -msgid "" -":py:obj:`get_parameters `\\ " -"\\(config\\)" +#: ../../source/index.rst:23 +msgid "Flower Framework" msgstr "" -#: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 +#: ../../source/index.rst:25 msgid "" -":py:obj:`get_properties `\\ " -"\\(config\\)" +"The user guide is targeted at researchers and developers who want to use " +"Flower to bring existing machine learning workloads into a federated " +"setting. One of Flower's design goals was to make this simple. Read on to" +" learn more." msgstr "" -#: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 -#: flwr.client.numpy_client.NumPyClient.get_properties:1 of -msgid "Return a client's set of properties." +#: ../../source/index.rst:30 +msgid "Tutorials" msgstr "" -#: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 +#: ../../source/index.rst:32 msgid "" -":py:obj:`set_context `\\ " -"\\(context\\)" +"A learning-oriented series of federated learning tutorials, the best " +"place to start." msgstr "" -#: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 -msgid ":py:obj:`to_client `\\ \\(\\)" +#: ../../source/index.rst:61 +msgid "" +"QUICKSTART TUTORIALS: :doc:`PyTorch ` | " +":doc:`TensorFlow ` | :doc:`🤗 Transformers" +" ` | :doc:`JAX ` | :doc:`Pandas ` | :doc:`fastai " +"` | :doc:`PyTorch Lightning ` | :doc:`scikit-learn ` | :doc:`XGBoost ` | " +":doc:`Android ` | :doc:`iOS `" msgstr "" -#: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 -#: flwr.client.numpy_client.NumPyClient.to_client:1 of -msgid "Convert to object to Client type and return it." -msgstr "" - -#: flwr.client.numpy_client.NumPyClient.evaluate:1::1 of -msgid ":py:obj:`context `\\" +#: ../../source/index.rst:63 +msgid "We also made video tutorials for PyTorch:" msgstr "" -#: flwr.client.numpy_client.NumPyClient.evaluate:3 -#: flwr.client.numpy_client.NumPyClient.fit:3 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:5 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:8 -#: flwr.server.strategy.strategy.Strategy.configure_evaluate:5 -#: flwr.server.strategy.strategy.Strategy.configure_fit:5 -#: flwr.server.strategy.strategy.Strategy.evaluate:8 of -msgid "The current (global) model parameters." +#: ../../source/index.rst:68 +msgid "And TensorFlow:" msgstr "" -#: flwr.client.numpy_client.NumPyClient.evaluate:5 of +#: ../../source/index.rst:76 msgid "" -"Configuration parameters which allow the server to influence evaluation " -"on the client. It can be used to communicate arbitrary values from the " -"server to the client, for example, to influence the number of examples " -"used for evaluation." +"Problem-oriented how-to guides show step-by-step how to achieve a " +"specific goal." msgstr "" -#: flwr.client.numpy_client.NumPyClient.evaluate:11 of +#: ../../source/index.rst:110 msgid "" -"* **loss** (*float*) -- The evaluation loss of the model on the local " -"dataset. * **num_examples** (*int*) -- The number of examples used for " -"evaluation. * **metrics** (*Dict[str, Scalar]*) -- A dictionary mapping " -"arbitrary string keys to values of type bool, bytes, float, int, or " -"str. It can be used to communicate arbitrary values back to the server." +"Understanding-oriented concept guides explain and discuss key topics and " +"underlying ideas behind Flower and collaborative AI." msgstr "" -#: flwr.client.numpy_client.NumPyClient.evaluate:11 of -msgid "" -"**loss** (*float*) -- The evaluation loss of the model on the local " -"dataset." +#: ../../source/index.rst:120 +msgid "References" msgstr "" -#: flwr.client.numpy_client.NumPyClient.evaluate:12 of -msgid "**num_examples** (*int*) -- The number of examples used for evaluation." +#: ../../source/index.rst:122 +msgid "Information-oriented API reference and other reference material." msgstr "" -#: flwr.client.numpy_client.NumPyClient.evaluate:13 -#: flwr.client.numpy_client.NumPyClient.fit:13 of -msgid "" -"**metrics** (*Dict[str, Scalar]*) -- A dictionary mapping arbitrary " -"string keys to values of type bool, bytes, float, int, or str. It can be " -"used to communicate arbitrary values back to the server." +#: ../../source/index.rst:131::1 +msgid ":py:obj:`flwr `\\" msgstr "" -#: flwr.client.numpy_client.NumPyClient.evaluate:19 of -msgid "" -"The previous return type format (int, float, float) and the extended " -"format (int, float, float, Dict[str, Scalar]) have been deprecated and " -"removed since Flower 0.19." +#: ../../source/index.rst:131::1 flwr:1 of +msgid "Flower main package." msgstr "" -#: flwr.client.numpy_client.NumPyClient.fit:5 of -msgid "" -"Configuration parameters which allow the server to influence training on " -"the client. It can be used to communicate arbitrary values from the " -"server to the client, for example, to set the number of (local) training " -"epochs." +#: ../../source/index.rst:148 +msgid "Contributor docs" msgstr "" -#: flwr.client.numpy_client.NumPyClient.fit:11 of +#: ../../source/index.rst:150 msgid "" -"* **parameters** (*NDArrays*) -- The locally updated model parameters. * " -"**num_examples** (*int*) -- The number of examples used for training. * " -"**metrics** (*Dict[str, Scalar]*) -- A dictionary mapping arbitrary " -"string keys to values of type bool, bytes, float, int, or str. It can " -"be used to communicate arbitrary values back to the server." +"The Flower community welcomes contributions. The following docs are " +"intended to help along the way." msgstr "" -#: flwr.client.numpy_client.NumPyClient.fit:11 of -msgid "**parameters** (*NDArrays*) -- The locally updated model parameters." +#: ../../source/ref-api-cli.rst:2 +msgid "Flower CLI reference" msgstr "" -#: flwr.client.numpy_client.NumPyClient.fit:12 of -msgid "**num_examples** (*int*) -- The number of examples used for training." +#: ../../source/ref-api-cli.rst:7 +msgid "flower-simulation" msgstr "" -#: flwr.client.numpy_client.NumPyClient.get_parameters:3 of -msgid "" -"Configuration parameters requested by the server. This can be used to " -"tell the client which parameters are needed along with some Scalar " -"attributes." +#: ../../source/ref-api-cli.rst:17 +msgid "flower-superlink" msgstr "" -#: flwr.client.numpy_client.NumPyClient.get_parameters:8 of -msgid "**parameters** -- The local model parameters as a list of NumPy ndarrays." +#: ../../source/ref-api-cli.rst:27 +msgid "flower-client-app" msgstr "" -#: flwr.client.numpy_client.NumPyClient.get_properties:3 of -msgid "" -"Configuration parameters requested by the server. This can be used to " -"tell the client which properties are needed along with some Scalar " -"attributes." +#: ../../source/ref-api-cli.rst:37 +msgid "flower-server-app" msgstr "" -#: flwr.client.numpy_client.NumPyClient.get_properties:8 of -msgid "" -"**properties** -- A dictionary mapping arbitrary string keys to values of" -" type bool, bytes, float, int, or str. It can be used to communicate " -"arbitrary property values back to the server." +#: ../../source/ref-api/flwr.rst:2 +msgid "flwr" msgstr "" -#: ../../source/ref-api/flwr.client.run_client_app.rst:2 -msgid "run\\_client\\_app" +#: ../../source/ref-api/flwr.client.rst:45 ../../source/ref-api/flwr.rst:25 +#: ../../source/ref-api/flwr.server.rst:49 +msgid "Modules" msgstr "" -#: ../../source/ref-api/flwr.client.start_client.rst:2 -msgid "start\\_client" +#: ../../source/ref-api/flwr.rst:35::1 +msgid ":py:obj:`flwr.client `\\" msgstr "" -#: flwr.client.app.start_client:3 flwr.client.app.start_numpy_client:9 of -msgid "" -"The IPv4 or IPv6 address of the server. If the Flower server runs on the " -"same machine on port 8080, then `server_address` would be " -"`\"[::]:8080\"`." +#: ../../source/ref-api/flwr.rst:35::1 flwr.client:1 of +msgid "Flower client." msgstr "" -#: flwr.client.app.start_client:7 of -msgid "A callable that instantiates a Client. (default: None)" +#: ../../source/ref-api/flwr.rst:35::1 +msgid ":py:obj:`flwr.common `\\" msgstr "" -#: flwr.client.app.start_client:9 of -msgid "" -"An implementation of the abstract base class `flwr.client.Client` " -"(default: None)" +#: ../../source/ref-api/flwr.rst:35::1 flwr.common:1 of +msgid "Common components shared between server and client." msgstr "" -#: flwr.client.app.start_client:12 flwr.client.app.start_numpy_client:15 of -msgid "" -"The maximum length of gRPC messages that can be exchanged with the Flower" -" server. The default should be sufficient for most models. Users who " -"train very large models might need to increase this value. Note that the " -"Flower server needs to be started with the same value (see " -"`flwr.server.start_server`), otherwise it will not know about the " -"increased limit and block larger messages." +#: ../../source/ref-api/flwr.rst:35::1 +msgid ":py:obj:`flwr.server `\\" msgstr "" -#: flwr.client.app.start_client:19 flwr.client.app.start_numpy_client:22 -#: flwr.server.compat.app.start_driver:21 of -msgid "" -"The PEM-encoded root certificates as a byte string or a path string. If " -"provided, a secure connection using the certificates will be established " -"to an SSL-enabled Flower server." +#: ../../source/ref-api/flwr.rst:35::1 +#: ../../source/ref-api/flwr.server.rst:38::1 flwr.server:1 +#: flwr.server.server.Server:1 of +msgid "Flower server." msgstr "" -#: flwr.client.app.start_client:23 flwr.client.app.start_numpy_client:26 of -msgid "" -"Starts an insecure gRPC connection when True. Enables HTTPS connection " -"when False, using system certificates if `root_certificates` is None." +#: ../../source/ref-api/flwr.rst:35::1 +msgid ":py:obj:`flwr.simulation `\\" msgstr "" -#: flwr.client.app.start_client:26 flwr.client.app.start_numpy_client:29 of -msgid "" -"Configure the transport layer. Allowed values: - 'grpc-bidi': gRPC, " -"bidirectional streaming - 'grpc-rere': gRPC, request-response " -"(experimental) - 'rest': HTTP (experimental)" +#: ../../source/ref-api/flwr.rst:35::1 flwr.simulation:1 of +msgid "Flower simulation." msgstr "" -#: flwr.client.app.start_client:31 of -msgid "" -"The maximum number of times the client will try to connect to the server " -"before giving up in case of a connection error. If set to None, there is " -"no limit to the number of tries." +#: ../../source/ref-api/flwr.client.rst:2 +msgid "client" msgstr "" -#: flwr.client.app.start_client:35 of -msgid "" -"The maximum duration before the client stops trying to connect to the " -"server in case of connection error. If set to None, there is no limit to " -"the total time." +#: ../../source/ref-api/flwr.client.mod.rst:13 +#: ../../source/ref-api/flwr.client.rst:13 +#: ../../source/ref-api/flwr.common.rst:13 +#: ../../source/ref-api/flwr.server.rst:13 +#: ../../source/ref-api/flwr.simulation.rst:13 +msgid "Functions" msgstr "" -#: flwr.client.app.start_client:42 flwr.client.app.start_numpy_client:37 of -msgid "Starting a gRPC client with an insecure server connection:" +#: ../../source/ref-api/flwr.client.rst:25::1 +msgid ":py:obj:`run_client_app `\\ \\(\\)" msgstr "" -#: flwr.client.app.start_client:49 flwr.client.app.start_numpy_client:44 of -msgid "Starting an SSL-enabled gRPC client using system certificates:" +#: ../../source/ref-api/flwr.client.rst:25::1 +#: flwr.client.supernode.app.run_client_app:1 of +msgid "Run Flower client app." msgstr "" -#: flwr.client.app.start_client:60 flwr.client.app.start_numpy_client:52 of -msgid "Starting an SSL-enabled gRPC client using provided certificates:" +#: ../../source/ref-api/flwr.client.rst:25::1 +msgid ":py:obj:`run_supernode `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.client.start_numpy_client.rst:2 -msgid "start\\_numpy\\_client" +#: ../../source/ref-api/flwr.client.rst:25::1 +#: flwr.client.supernode.app.run_supernode:1 of +msgid "Run Flower SuperNode." msgstr "" -#: flwr.client.app.start_numpy_client:5 of +#: ../../source/ref-api/flwr.client.rst:25::1 msgid "" -"This function is deprecated since 1.7.0. Use " -":code:`flwr.client.start_client` instead and first convert your " -":code:`NumPyClient` to type :code:`flwr.client.Client` by executing its " -":code:`to_client()` method." +":py:obj:`start_client `\\ \\(\\*\\, " +"server\\_address\\[\\, client\\_fn\\, ...\\]\\)" msgstr "" -#: flwr.client.app.start_numpy_client:13 of -msgid "An implementation of the abstract base class `flwr.client.NumPyClient`." +#: ../../source/ref-api/flwr.client.rst:25::1 +#: flwr.client.app.start_client:1 of +msgid "Start a Flower client node which connects to a Flower server." msgstr "" -#: ../../source/ref-api/flwr.common.rst:2 -msgid "common" +#: ../../source/ref-api/flwr.client.rst:25::1 +msgid "" +":py:obj:`start_numpy_client `\\ \\(\\*\\," +" server\\_address\\, client\\)" msgstr "" -#: ../../source/ref-api/flwr.common.rst:30::1 -msgid ":py:obj:`array_from_numpy `\\ \\(ndarray\\)" +#: ../../source/ref-api/flwr.client.rst:25::1 +#: flwr.client.app.start_numpy_client:1 of +msgid "Start a Flower NumPyClient which connects to a gRPC server." msgstr "" -#: ../../source/ref-api/flwr.common.rst:30::1 -#: flwr.common.record.conversion_utils.array_from_numpy:1 of -msgid "Create Array from NumPy ndarray." +#: ../../source/ref-api/flwr.client.mod.rst:30 +#: ../../source/ref-api/flwr.client.rst:27 +#: ../../source/ref-api/flwr.common.rst:32 +#: ../../source/ref-api/flwr.server.rst:26 +#: ../../source/ref-api/flwr.server.strategy.rst:17 +#: ../../source/ref-api/flwr.server.workflow.rst:17 +msgid "Classes" msgstr "" -#: ../../source/ref-api/flwr.common.rst:30::1 -msgid ":py:obj:`bytes_to_ndarray `\\ \\(tensor\\)" +#: ../../source/ref-api/flwr.client.rst:34::1 +msgid ":py:obj:`Client `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.common.rst:30::1 -#: flwr.common.parameter.bytes_to_ndarray:1 of -msgid "Deserialize NumPy ndarray from bytes." +#: ../../source/ref-api/flwr.client.rst:34::1 +#: flwr.client.client.Client:1 of +msgid "Abstract base class for Flower clients." msgstr "" -#: ../../source/ref-api/flwr.common.rst:30::1 +#: ../../source/ref-api/flwr.client.rst:34::1 msgid "" -":py:obj:`configure `\\ \\(identifier\\[\\, " -"filename\\, host\\]\\)" -msgstr "" - -#: ../../source/ref-api/flwr.common.rst:30::1 -#: flwr.common.logger.configure:1 of -msgid "Configure logging to file and/or remote log server." +":py:obj:`ClientApp `\\ \\(\\[client\\_fn\\, " +"mods\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.common.rst:30::1 -msgid "" -":py:obj:`event `\\ \\(event\\_type\\[\\, " -"event\\_details\\]\\)" +#: ../../source/ref-api/flwr.client.rst:34::1 +#: flwr.client.client_app.ClientApp:1 of +msgid "Flower ClientApp." msgstr "" -#: ../../source/ref-api/flwr.common.rst:30::1 -#: flwr.common.telemetry.event:1 of -msgid "Submit create_event to ThreadPoolExecutor to avoid blocking." +#: ../../source/ref-api/flwr.client.rst:34::1 +msgid ":py:obj:`NumPyClient `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.common.rst:30::1 -msgid "" -":py:obj:`log `\\ \\(level\\, msg\\, \\*args\\, " -"\\*\\*kwargs\\)" +#: ../../source/ref-api/flwr.client.rst:34::1 +#: flwr.client.numpy_client.NumPyClient:1 of +msgid "Abstract base class for Flower clients using NumPy." msgstr "" -#: ../../source/ref-api/flwr.common.rst:30::1 logging.Logger.log:1 -#: of -msgid "Log 'msg % args' with the integer severity 'level'." +#: ../../source/ref-api/flwr.client.rst:52::1 +msgid ":py:obj:`flwr.client.mod `\\" msgstr "" -#: ../../source/ref-api/flwr.common.rst:30::1 -msgid ":py:obj:`ndarray_to_bytes `\\ \\(ndarray\\)" +#: ../../source/ref-api/flwr.client.rst:52::1 flwr.client.mod:1 of +msgid "Flower Built-in Mods." msgstr "" -#: ../../source/ref-api/flwr.common.rst:30::1 -#: flwr.common.parameter.ndarray_to_bytes:1 of -msgid "Serialize NumPy ndarray to bytes." +#: flwr.client.client.Client:1 flwr.client.numpy_client.NumPyClient:1 +#: flwr.server.client_manager.ClientManager:1 +#: flwr.server.driver.driver.Driver:1 flwr.server.strategy.strategy.Strategy:1 +#: of +msgid "Bases: :py:class:`~abc.ABC`" msgstr "" -#: ../../source/ref-api/flwr.common.rst:30::1 -msgid ":py:obj:`now `\\ \\(\\)" -msgstr "" - -#: ../../source/ref-api/flwr.common.rst:30::1 -#: flwr.common.date.now:1 of -msgid "Construct a datetime from time.time() with time zone set to UTC." +#: ../../source/ref-api/flwr.client.Client.rst:15 +#: ../../source/ref-api/flwr.client.ClientApp.rst:15 +#: ../../source/ref-api/flwr.client.NumPyClient.rst:15 +#: ../../source/ref-api/flwr.client.mod.LocalDpMod.rst:15 +#: ../../source/ref-api/flwr.common.Array.rst:15 +#: ../../source/ref-api/flwr.common.ClientMessage.rst:15 +#: ../../source/ref-api/flwr.common.ConfigsRecord.rst:15 +#: ../../source/ref-api/flwr.common.Context.rst:15 +#: ../../source/ref-api/flwr.common.DisconnectRes.rst:15 +#: ../../source/ref-api/flwr.common.Error.rst:15 +#: ../../source/ref-api/flwr.common.EvaluateIns.rst:15 +#: ../../source/ref-api/flwr.common.EvaluateRes.rst:15 +#: ../../source/ref-api/flwr.common.EventType.rst:15 +#: ../../source/ref-api/flwr.common.FitIns.rst:15 +#: ../../source/ref-api/flwr.common.FitRes.rst:15 +#: ../../source/ref-api/flwr.common.GetParametersIns.rst:15 +#: ../../source/ref-api/flwr.common.GetParametersRes.rst:15 +#: ../../source/ref-api/flwr.common.GetPropertiesIns.rst:15 +#: ../../source/ref-api/flwr.common.GetPropertiesRes.rst:15 +#: ../../source/ref-api/flwr.common.Message.rst:15 +#: ../../source/ref-api/flwr.common.MessageType.rst:15 +#: ../../source/ref-api/flwr.common.MessageTypeLegacy.rst:15 +#: ../../source/ref-api/flwr.common.Metadata.rst:15 +#: ../../source/ref-api/flwr.common.MetricsRecord.rst:15 +#: ../../source/ref-api/flwr.common.Parameters.rst:15 +#: ../../source/ref-api/flwr.common.ParametersRecord.rst:15 +#: ../../source/ref-api/flwr.common.ReconnectIns.rst:15 +#: ../../source/ref-api/flwr.common.RecordSet.rst:15 +#: ../../source/ref-api/flwr.common.ServerMessage.rst:15 +#: ../../source/ref-api/flwr.common.Status.rst:15 +#: ../../source/ref-api/flwr.server.ClientManager.rst:15 +#: ../../source/ref-api/flwr.server.Driver.rst:15 +#: ../../source/ref-api/flwr.server.History.rst:15 +#: ../../source/ref-api/flwr.server.LegacyContext.rst:15 +#: ../../source/ref-api/flwr.server.Server.rst:15 +#: ../../source/ref-api/flwr.server.ServerApp.rst:15 +#: ../../source/ref-api/flwr.server.ServerConfig.rst:15 +#: ../../source/ref-api/flwr.server.SimpleClientManager.rst:15 +#: ../../source/ref-api/flwr.server.strategy.Bulyan.rst:15 +#: ../../source/ref-api/flwr.server.strategy.DPFedAvgAdaptive.rst:15 +#: ../../source/ref-api/flwr.server.strategy.DPFedAvgFixed.rst:15 +#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyClientSideAdaptiveClipping.rst:15 +#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyClientSideFixedClipping.rst:15 +#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyServerSideAdaptiveClipping.rst:15 +#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyServerSideFixedClipping.rst:15 +#: ../../source/ref-api/flwr.server.strategy.FaultTolerantFedAvg.rst:15 +#: ../../source/ref-api/flwr.server.strategy.FedAdagrad.rst:15 +#: ../../source/ref-api/flwr.server.strategy.FedAdam.rst:15 +#: ../../source/ref-api/flwr.server.strategy.FedAvg.rst:15 +#: ../../source/ref-api/flwr.server.strategy.FedAvgAndroid.rst:15 +#: ../../source/ref-api/flwr.server.strategy.FedAvgM.rst:15 +#: ../../source/ref-api/flwr.server.strategy.FedMedian.rst:15 +#: ../../source/ref-api/flwr.server.strategy.FedOpt.rst:15 +#: ../../source/ref-api/flwr.server.strategy.FedProx.rst:15 +#: ../../source/ref-api/flwr.server.strategy.FedTrimmedAvg.rst:15 +#: ../../source/ref-api/flwr.server.strategy.FedXgbBagging.rst:15 +#: ../../source/ref-api/flwr.server.strategy.FedXgbCyclic.rst:15 +#: ../../source/ref-api/flwr.server.strategy.FedXgbNnAvg.rst:15 +#: ../../source/ref-api/flwr.server.strategy.FedYogi.rst:15 +#: ../../source/ref-api/flwr.server.strategy.Krum.rst:15 +#: ../../source/ref-api/flwr.server.strategy.QFedAvg.rst:15 +#: ../../source/ref-api/flwr.server.strategy.Strategy.rst:15 +#: ../../source/ref-api/flwr.server.workflow.DefaultWorkflow.rst:15 +#: ../../source/ref-api/flwr.server.workflow.SecAggPlusWorkflow.rst:15 +#: ../../source/ref-api/flwr.server.workflow.SecAggWorkflow.rst:15 +msgid "Methods" msgstr "" -#: ../../source/ref-api/flwr.common.rst:30::1 -msgid "" -":py:obj:`ndarrays_to_parameters `\\ " -"\\(ndarrays\\)" +#: ../../source/ref-api/flwr.client.Client.rst:44::1 +msgid ":py:obj:`evaluate `\\ \\(ins\\)" msgstr "" -#: ../../source/ref-api/flwr.common.rst:30::1 -#: flwr.common.parameter.ndarrays_to_parameters:1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.ndarrays_to_parameters:1 -#: of -msgid "Convert NumPy ndarrays to parameters object." +#: ../../source/ref-api/flwr.client.Client.rst:44::1 +#: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 +#: flwr.client.client.Client.evaluate:1 +#: flwr.client.numpy_client.NumPyClient.evaluate:1 of +msgid "Evaluate the provided parameters using the locally held dataset." msgstr "" -#: ../../source/ref-api/flwr.common.rst:30::1 -msgid "" -":py:obj:`parameters_to_ndarrays `\\ " -"\\(parameters\\)" +#: ../../source/ref-api/flwr.client.Client.rst:44::1 +msgid ":py:obj:`fit `\\ \\(ins\\)" msgstr "" -#: ../../source/ref-api/flwr.common.rst:30::1 -#: flwr.common.parameter.parameters_to_ndarrays:1 of -msgid "Convert parameters object to NumPy ndarrays." +#: ../../source/ref-api/flwr.client.Client.rst:44::1 +#: flwr.client.client.Client.fit:1 of +msgid "Refine the provided parameters using the locally held dataset." msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -msgid "" -":py:obj:`Array `\\ \\(dtype\\, shape\\, stype\\, " -"data\\)" +#: ../../source/ref-api/flwr.client.Client.rst:44::1 +msgid ":py:obj:`get_context `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -#: flwr.common.record.parametersrecord.Array:1 of -msgid "Array type." +#: ../../source/ref-api/flwr.client.Client.rst:44::1 +#: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 +#: flwr.client.client.Client.get_context:1 +#: flwr.client.numpy_client.NumPyClient.get_context:1 of +msgid "Get the run context from this client." msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -msgid "" -":py:obj:`ClientMessage `\\ " -"\\(\\[get\\_properties\\_res\\, ...\\]\\)" +#: ../../source/ref-api/flwr.client.Client.rst:44::1 +msgid ":py:obj:`get_parameters `\\ \\(ins\\)" msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -#: flwr.common.typing.ClientMessage:1 of -msgid "ClientMessage is a container used to hold one result message." +#: ../../source/ref-api/flwr.client.Client.rst:44::1 +#: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 +#: flwr.client.client.Client.get_parameters:1 +#: flwr.client.numpy_client.NumPyClient.get_parameters:1 of +msgid "Return the current local model parameters." msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -msgid ":py:obj:`Code `\\ \\(value\\)" +#: ../../source/ref-api/flwr.client.Client.rst:44::1 +msgid ":py:obj:`get_properties `\\ \\(ins\\)" msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -#: flwr.common.typing.Code:1 of -msgid "Client status codes." +#: ../../source/ref-api/flwr.client.Client.rst:44::1 +#: flwr.client.client.Client.get_properties:1 of +msgid "Return set of client's properties." msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -msgid "" -":py:obj:`ConfigsRecord `\\ " -"\\(\\[configs\\_dict\\, keep\\_input\\]\\)" +#: ../../source/ref-api/flwr.client.Client.rst:44::1 +msgid ":py:obj:`set_context `\\ \\(context\\)" msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -#: flwr.common.record.configsrecord.ConfigsRecord:1 of -msgid "Configs record." +#: ../../source/ref-api/flwr.client.Client.rst:44::1 +#: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 +#: flwr.client.client.Client.set_context:1 +#: flwr.client.numpy_client.NumPyClient.set_context:1 of +msgid "Apply a run context to this client." msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -msgid ":py:obj:`Context `\\ \\(state\\)" +#: ../../source/ref-api/flwr.client.Client.rst:44::1 +msgid ":py:obj:`to_client `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -#: flwr.common.context.Context:1 of -msgid "State of your run." +#: ../../source/ref-api/flwr.client.Client.rst:44::1 +#: flwr.client.client.Client.to_client:1 of +msgid "Return client (itself)." msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -msgid ":py:obj:`DisconnectRes `\\ \\(reason\\)" +#: ../../source/ref-api/flwr.client.Client.rst:46 +#: ../../source/ref-api/flwr.client.NumPyClient.rst:46 +#: ../../source/ref-api/flwr.common.Array.rst:28 +#: ../../source/ref-api/flwr.common.ClientMessage.rst:25 +#: ../../source/ref-api/flwr.common.Code.rst:19 +#: ../../source/ref-api/flwr.common.Context.rst:25 +#: ../../source/ref-api/flwr.common.DisconnectRes.rst:25 +#: ../../source/ref-api/flwr.common.Error.rst:25 +#: ../../source/ref-api/flwr.common.EvaluateIns.rst:25 +#: ../../source/ref-api/flwr.common.EvaluateRes.rst:25 +#: ../../source/ref-api/flwr.common.EventType.rst:165 +#: ../../source/ref-api/flwr.common.FitIns.rst:25 +#: ../../source/ref-api/flwr.common.FitRes.rst:25 +#: ../../source/ref-api/flwr.common.GetParametersIns.rst:25 +#: ../../source/ref-api/flwr.common.GetParametersRes.rst:25 +#: ../../source/ref-api/flwr.common.GetPropertiesIns.rst:25 +#: ../../source/ref-api/flwr.common.GetPropertiesRes.rst:25 +#: ../../source/ref-api/flwr.common.Message.rst:37 +#: ../../source/ref-api/flwr.common.MessageType.rst:25 +#: ../../source/ref-api/flwr.common.MessageTypeLegacy.rst:25 +#: ../../source/ref-api/flwr.common.Metadata.rst:25 +#: ../../source/ref-api/flwr.common.Parameters.rst:25 +#: ../../source/ref-api/flwr.common.ReconnectIns.rst:25 +#: ../../source/ref-api/flwr.common.RecordSet.rst:25 +#: ../../source/ref-api/flwr.common.ServerMessage.rst:25 +#: ../../source/ref-api/flwr.common.Status.rst:25 +#: ../../source/ref-api/flwr.server.LegacyContext.rst:25 +#: ../../source/ref-api/flwr.server.ServerConfig.rst:25 +msgid "Attributes" msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -#: flwr.common.typing.DisconnectRes:1 of -msgid "DisconnectRes message from client to server." +#: flwr.client.client.Client.evaluate:1::1 of +msgid ":py:obj:`context `\\" msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -msgid "" -":py:obj:`EvaluateIns `\\ \\(parameters\\, " -"config\\)" +#: ../../source/ref-api/flwr.common.Parameters.rst:2 +#: flwr.client.app.start_client flwr.client.app.start_numpy_client +#: flwr.client.client.Client.evaluate flwr.client.client.Client.fit +#: flwr.client.client.Client.get_parameters +#: flwr.client.client.Client.get_properties +#: flwr.client.mod.localdp_mod.LocalDpMod +#: flwr.client.numpy_client.NumPyClient.evaluate +#: flwr.client.numpy_client.NumPyClient.fit +#: flwr.client.numpy_client.NumPyClient.get_parameters +#: flwr.client.numpy_client.NumPyClient.get_properties +#: flwr.common.context.Context flwr.common.message.Error +#: flwr.common.message.Message flwr.common.message.Message.create_error_reply +#: flwr.common.message.Message.create_reply flwr.common.message.Metadata +#: flwr.common.record.parametersrecord.Array flwr.server.app.start_server +#: flwr.server.client_manager.ClientManager.register +#: flwr.server.client_manager.ClientManager.unregister +#: flwr.server.client_manager.SimpleClientManager.register +#: flwr.server.client_manager.SimpleClientManager.unregister +#: flwr.server.client_manager.SimpleClientManager.wait_for +#: flwr.server.driver.driver.Driver.create_message +#: flwr.server.driver.driver.Driver.pull_messages +#: flwr.server.driver.driver.Driver.push_messages +#: flwr.server.driver.driver.Driver.send_and_receive +#: flwr.server.strategy.bulyan.Bulyan +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit +#: flwr.server.strategy.fedadagrad.FedAdagrad +#: flwr.server.strategy.fedadam.FedAdam flwr.server.strategy.fedavg.FedAvg +#: flwr.server.strategy.fedavg_android.FedAvgAndroid +#: flwr.server.strategy.fedavgm.FedAvgM flwr.server.strategy.fedopt.FedOpt +#: flwr.server.strategy.fedprox.FedProx +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg +#: flwr.server.strategy.fedyogi.FedYogi flwr.server.strategy.krum.Krum +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate +#: flwr.server.strategy.strategy.Strategy.aggregate_fit +#: flwr.server.strategy.strategy.Strategy.configure_evaluate +#: flwr.server.strategy.strategy.Strategy.configure_fit +#: flwr.server.strategy.strategy.Strategy.evaluate +#: flwr.server.strategy.strategy.Strategy.initialize_parameters +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow +#: flwr.simulation.app.start_simulation +#: flwr.simulation.run_simulation.run_simulation of +msgid "Parameters" msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -#: flwr.common.typing.EvaluateIns:1 of -msgid "Evaluate instructions for a client." +#: flwr.client.client.Client.evaluate:3 of +msgid "" +"The evaluation instructions containing (global) model parameters received" +" from the server and a dictionary of configuration values used to " +"customize the local evaluation process." msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -msgid "" -":py:obj:`EvaluateRes `\\ \\(status\\, loss\\, " -"num\\_examples\\, metrics\\)" +#: flwr.client.client.Client.evaluate flwr.client.client.Client.fit +#: flwr.client.client.Client.get_parameters +#: flwr.client.client.Client.get_properties +#: flwr.client.numpy_client.NumPyClient.evaluate +#: flwr.client.numpy_client.NumPyClient.fit +#: flwr.client.numpy_client.NumPyClient.get_parameters +#: flwr.client.numpy_client.NumPyClient.get_properties +#: flwr.common.message.Message.create_reply flwr.server.app.start_server +#: flwr.server.client_manager.ClientManager.num_available +#: flwr.server.client_manager.ClientManager.register +#: flwr.server.client_manager.SimpleClientManager.num_available +#: flwr.server.client_manager.SimpleClientManager.register +#: flwr.server.client_manager.SimpleClientManager.wait_for +#: flwr.server.driver.driver.Driver.create_message +#: flwr.server.driver.driver.Driver.pull_messages +#: flwr.server.driver.driver.Driver.push_messages +#: flwr.server.driver.driver.Driver.send_and_receive +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate +#: flwr.server.strategy.strategy.Strategy.aggregate_fit +#: flwr.server.strategy.strategy.Strategy.configure_evaluate +#: flwr.server.strategy.strategy.Strategy.configure_fit +#: flwr.server.strategy.strategy.Strategy.evaluate +#: flwr.server.strategy.strategy.Strategy.initialize_parameters +#: flwr.simulation.app.start_simulation of +msgid "Returns" msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -#: flwr.common.typing.EvaluateRes:1 of -msgid "Evaluate response from a client." +#: flwr.client.client.Client.evaluate:8 of +msgid "" +"The evaluation result containing the loss on the local dataset and other " +"details such as the number of local data examples used for evaluation." msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -msgid ":py:obj:`EventType `\\ \\(value\\)" +#: flwr.client.client.Client.evaluate flwr.client.client.Client.fit +#: flwr.client.client.Client.get_parameters +#: flwr.client.client.Client.get_properties +#: flwr.client.numpy_client.NumPyClient.get_parameters +#: flwr.client.numpy_client.NumPyClient.get_properties +#: flwr.common.message.Message.create_reply flwr.server.app.start_server +#: flwr.server.client_manager.ClientManager.num_available +#: flwr.server.client_manager.ClientManager.register +#: flwr.server.client_manager.SimpleClientManager.num_available +#: flwr.server.client_manager.SimpleClientManager.register +#: flwr.server.client_manager.SimpleClientManager.wait_for +#: flwr.server.driver.driver.Driver.create_message +#: flwr.server.driver.driver.Driver.pull_messages +#: flwr.server.driver.driver.Driver.push_messages +#: flwr.server.driver.driver.Driver.send_and_receive +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate +#: flwr.server.strategy.strategy.Strategy.aggregate_fit +#: flwr.server.strategy.strategy.Strategy.configure_evaluate +#: flwr.server.strategy.strategy.Strategy.configure_fit +#: flwr.server.strategy.strategy.Strategy.evaluate +#: flwr.server.strategy.strategy.Strategy.initialize_parameters +#: flwr.simulation.app.start_simulation of +msgid "Return type" msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -#: flwr.common.telemetry.EventType:1 of -msgid "Types of telemetry events." +#: flwr.client.client.Client.fit:3 of +msgid "" +"The training instructions containing (global) model parameters received " +"from the server and a dictionary of configuration values used to " +"customize the local training process." msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -msgid ":py:obj:`FitIns `\\ \\(parameters\\, config\\)" +#: flwr.client.client.Client.fit:8 of +msgid "" +"The training result containing updated parameters and other details such " +"as the number of local training examples used for training." msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -#: flwr.common.typing.FitIns:1 of -msgid "Fit instructions for a client." +#: flwr.client.client.Client.get_parameters:3 of +msgid "" +"The get parameters instructions received from the server containing a " +"dictionary of configuration values." msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -msgid "" -":py:obj:`FitRes `\\ \\(status\\, parameters\\, " -"num\\_examples\\, metrics\\)" +#: flwr.client.client.Client.get_parameters:7 of +msgid "The current local model parameters." msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -#: flwr.common.typing.FitRes:1 of -msgid "Fit response from a client." +#: flwr.client.client.Client.get_properties:3 of +msgid "" +"The get properties instructions received from the server containing a " +"dictionary of configuration values." msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -msgid ":py:obj:`Error `\\ \\(code\\[\\, reason\\]\\)" +#: flwr.client.client.Client.get_properties:7 of +msgid "The current client properties." msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -#: flwr.common.message.Error:1 of -msgid "A dataclass that stores information about an error that occurred." +#: ../../source/ref-api/flwr.client.ClientApp.rst:2 +msgid "ClientApp" msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -msgid ":py:obj:`GetParametersIns `\\ \\(config\\)" +#: flwr.client.client_app.ClientApp:1 flwr.client.mod.localdp_mod.LocalDpMod:1 +#: flwr.common.constant.MessageType:1 flwr.common.constant.MessageTypeLegacy:1 +#: flwr.common.context.Context:1 flwr.common.message.Error:1 +#: flwr.common.message.Message:1 flwr.common.message.Metadata:1 +#: flwr.common.record.parametersrecord.Array:1 +#: flwr.common.record.recordset.RecordSet:1 flwr.common.typing.ClientMessage:1 +#: flwr.common.typing.DisconnectRes:1 flwr.common.typing.EvaluateIns:1 +#: flwr.common.typing.EvaluateRes:1 flwr.common.typing.FitIns:1 +#: flwr.common.typing.FitRes:1 flwr.common.typing.GetParametersIns:1 +#: flwr.common.typing.GetParametersRes:1 flwr.common.typing.GetPropertiesIns:1 +#: flwr.common.typing.GetPropertiesRes:1 flwr.common.typing.Parameters:1 +#: flwr.common.typing.ReconnectIns:1 flwr.common.typing.ServerMessage:1 +#: flwr.common.typing.Status:1 flwr.server.history.History:1 +#: flwr.server.server.Server:1 flwr.server.server_app.ServerApp:1 +#: flwr.server.server_config.ServerConfig:1 +#: flwr.server.workflow.default_workflows.DefaultWorkflow:1 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:1 +#: of +msgid "Bases: :py:class:`object`" msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -#: flwr.common.typing.GetParametersIns:1 of -msgid "Parameters request for a client." +#: flwr.client.app.start_client:41 flwr.client.app.start_numpy_client:36 +#: flwr.client.client_app.ClientApp:4 +#: flwr.client.client_app.ClientApp.evaluate:4 +#: flwr.client.client_app.ClientApp.query:4 +#: flwr.client.client_app.ClientApp.train:4 +#: flwr.client.mod.localdp_mod.LocalDpMod:22 flwr.server.app.start_server:41 +#: flwr.server.server_app.ServerApp:4 flwr.server.server_app.ServerApp.main:4 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:29 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:22 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:21 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:14 +#: of +msgid "Examples" msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 +#: flwr.client.client_app.ClientApp:5 of msgid "" -":py:obj:`GetParametersRes `\\ \\(status\\, " -"parameters\\)" +"Assuming a typical `Client` implementation named `FlowerClient`, you can " +"wrap it in a `ClientApp` as follows:" msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -#: flwr.common.typing.GetParametersRes:1 of -msgid "Response when asked to return parameters." +#: flwr.client.client_app.ClientApp:16 of +msgid "" +"If the above code is in a Python module called `client`, it can be " +"started as follows:" msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -msgid ":py:obj:`GetPropertiesIns `\\ \\(config\\)" +#: flwr.client.client_app.ClientApp:21 of +msgid "" +"In this `client:app` example, `client` refers to the Python module " +"`client.py` in which the previous code lives in and `app` refers to the " +"global attribute `app` that points to an object of type `ClientApp`." msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -#: flwr.common.typing.GetPropertiesIns:1 of -msgid "Properties request for a client." +#: flwr.client.client_app.ClientApp.evaluate:1::1 of +msgid ":py:obj:`evaluate `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -msgid "" -":py:obj:`GetPropertiesRes `\\ \\(status\\, " -"properties\\)" +#: flwr.client.client_app.ClientApp.evaluate:1 +#: flwr.client.client_app.ClientApp.evaluate:1::1 of +msgid "Return a decorator that registers the evaluate fn with the client app." msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -#: flwr.common.typing.GetPropertiesRes:1 of -msgid "Properties response from a client." +#: flwr.client.client_app.ClientApp.evaluate:1::1 of +msgid ":py:obj:`query `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -msgid "" -":py:obj:`Message `\\ \\(metadata\\[\\, content\\, " -"error\\]\\)" +#: flwr.client.client_app.ClientApp.evaluate:1::1 +#: flwr.client.client_app.ClientApp.query:1 of +msgid "Return a decorator that registers the query fn with the client app." msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -#: flwr.common.message.Message:1 of -msgid "State of your application from the viewpoint of the entity using it." +#: flwr.client.client_app.ClientApp.evaluate:1::1 of +msgid ":py:obj:`train `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -msgid ":py:obj:`MessageType `\\ \\(\\)" +#: flwr.client.client_app.ClientApp.evaluate:1::1 +#: flwr.client.client_app.ClientApp.train:1 of +msgid "Return a decorator that registers the train fn with the client app." msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -#: flwr.common.constant.MessageType:1 of -msgid "Message type." +#: ../../source/ref-api/flwr.client.NumPyClient.rst:2 +msgid "NumPyClient" msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -msgid ":py:obj:`MessageTypeLegacy `\\ \\(\\)" +#: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 +msgid "" +":py:obj:`evaluate `\\ \\(parameters\\, " +"config\\)" msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -#: flwr.common.constant.MessageTypeLegacy:1 of -msgid "Legacy message type." +#: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 +msgid ":py:obj:`fit `\\ \\(parameters\\, config\\)" msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -msgid "" -":py:obj:`Metadata `\\ \\(run\\_id\\, " -"message\\_id\\, src\\_node\\_id\\, ...\\)" +#: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 +#: flwr.client.numpy_client.NumPyClient.fit:1 of +msgid "Train the provided parameters using the locally held dataset." msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -#: flwr.common.message.Metadata:1 of -msgid "A dataclass holding metadata associated with the current message." +#: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 +msgid ":py:obj:`get_context `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 +#: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 msgid "" -":py:obj:`MetricsRecord `\\ " -"\\(\\[metrics\\_dict\\, keep\\_input\\]\\)" +":py:obj:`get_parameters `\\ " +"\\(config\\)" msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -#: flwr.common.record.metricsrecord.MetricsRecord:1 of -msgid "Metrics record." +#: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 +msgid "" +":py:obj:`get_properties `\\ " +"\\(config\\)" msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -msgid ":py:obj:`NDArray `\\" +#: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 +#: flwr.client.numpy_client.NumPyClient.get_properties:1 of +msgid "Return a client's set of properties." msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 +#: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 msgid "" -"alias of :py:class:`~numpy.ndarray`\\ [:py:obj:`~typing.Any`, " -":py:class:`~numpy.dtype`\\ [:py:obj:`~typing.Any`]]" +":py:obj:`set_context `\\ " +"\\(context\\)" msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -msgid "" -":py:obj:`Parameters `\\ \\(tensors\\, " -"tensor\\_type\\)" +#: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 +msgid ":py:obj:`to_client `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -#: flwr.common.typing.Parameters:1 of -msgid "Model parameters." +#: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 +#: flwr.client.numpy_client.NumPyClient.to_client:1 of +msgid "Convert to object to Client type and return it." msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -msgid "" -":py:obj:`ParametersRecord `\\ " -"\\(\\[array\\_dict\\, keep\\_input\\]\\)" +#: flwr.client.numpy_client.NumPyClient.evaluate:1::1 of +msgid ":py:obj:`context `\\" msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -#: flwr.common.record.parametersrecord.ParametersRecord:1 of -msgid "Parameters record." +#: flwr.client.numpy_client.NumPyClient.evaluate:3 +#: flwr.client.numpy_client.NumPyClient.fit:3 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:5 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:8 +#: flwr.server.strategy.strategy.Strategy.configure_evaluate:5 +#: flwr.server.strategy.strategy.Strategy.configure_fit:5 +#: flwr.server.strategy.strategy.Strategy.evaluate:8 of +msgid "The current (global) model parameters." msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -msgid ":py:obj:`ReconnectIns `\\ \\(seconds\\)" +#: flwr.client.numpy_client.NumPyClient.evaluate:5 of +msgid "" +"Configuration parameters which allow the server to influence evaluation " +"on the client. It can be used to communicate arbitrary values from the " +"server to the client, for example, to influence the number of examples " +"used for evaluation." msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -#: flwr.common.typing.ReconnectIns:1 of -msgid "ReconnectIns message from server to client." +#: flwr.client.numpy_client.NumPyClient.evaluate:11 of +msgid "" +"* **loss** (*float*) -- The evaluation loss of the model on the local " +"dataset. * **num_examples** (*int*) -- The number of examples used for " +"evaluation. * **metrics** (*Dict[str, Scalar]*) -- A dictionary mapping " +"arbitrary string keys to values of type bool, bytes, float, int, or " +"str. It can be used to communicate arbitrary values back to the server." msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 +#: flwr.client.numpy_client.NumPyClient.evaluate:11 of msgid "" -":py:obj:`RecordSet `\\ " -"\\(\\[parameters\\_records\\, ...\\]\\)" +"**loss** (*float*) -- The evaluation loss of the model on the local " +"dataset." msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -#: flwr.common.record.recordset.RecordSet:1 of -msgid "RecordSet stores groups of parameters, metrics and configs." +#: flwr.client.numpy_client.NumPyClient.evaluate:12 of +msgid "**num_examples** (*int*) -- The number of examples used for evaluation." msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 +#: flwr.client.numpy_client.NumPyClient.evaluate:13 +#: flwr.client.numpy_client.NumPyClient.fit:13 of msgid "" -":py:obj:`ServerMessage `\\ " -"\\(\\[get\\_properties\\_ins\\, ...\\]\\)" +"**metrics** (*Dict[str, Scalar]*) -- A dictionary mapping arbitrary " +"string keys to values of type bool, bytes, float, int, or str. It can be " +"used to communicate arbitrary values back to the server." msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -#: flwr.common.typing.ServerMessage:1 of -msgid "ServerMessage is a container used to hold one instruction message." +#: flwr.client.numpy_client.NumPyClient.evaluate:19 of +msgid "" +"The previous return type format (int, float, float) and the extended " +"format (int, float, float, Dict[str, Scalar]) have been deprecated and " +"removed since Flower 0.19." msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -msgid ":py:obj:`Status `\\ \\(code\\, message\\)" +#: flwr.client.numpy_client.NumPyClient.fit:5 of +msgid "" +"Configuration parameters which allow the server to influence training on " +"the client. It can be used to communicate arbitrary values from the " +"server to the client, for example, to set the number of (local) training " +"epochs." msgstr "" -#: ../../source/ref-api/flwr.common.rst:64::1 -#: flwr.common.typing.Status:1 of -msgid "Client status." +#: flwr.client.numpy_client.NumPyClient.fit:11 of +msgid "" +"* **parameters** (*NDArrays*) -- The locally updated model parameters. * " +"**num_examples** (*int*) -- The number of examples used for training. * " +"**metrics** (*Dict[str, Scalar]*) -- A dictionary mapping arbitrary " +"string keys to values of type bool, bytes, float, int, or str. It can " +"be used to communicate arbitrary values back to the server." msgstr "" -#: ../../source/ref-api/flwr.common.Array.rst:2 -msgid "Array" +#: flwr.client.numpy_client.NumPyClient.fit:11 of +msgid "**parameters** (*NDArrays*) -- The locally updated model parameters." msgstr "" -#: flwr.common.record.parametersrecord.Array:3 of -msgid "" -"A dataclass containing serialized data from an array-like or tensor-like " -"object along with some metadata about it." +#: flwr.client.numpy_client.NumPyClient.fit:12 of +msgid "**num_examples** (*int*) -- The number of examples used for training." msgstr "" -#: flwr.common.record.parametersrecord.Array:6 of +#: flwr.client.numpy_client.NumPyClient.get_parameters:3 of msgid "" -"A string representing the data type of the serialised object (e.g. " -"`np.float32`)" +"Configuration parameters requested by the server. This can be used to " +"tell the client which parameters are needed along with some Scalar " +"attributes." msgstr "" -#: flwr.common.record.parametersrecord.Array:8 of -msgid "" -"A list representing the shape of the unserialized array-like object. This" -" is used to deserialize the data (depending on the serialization method) " -"or simply as a metadata field." +#: flwr.client.numpy_client.NumPyClient.get_parameters:8 of +msgid "**parameters** -- The local model parameters as a list of NumPy ndarrays." msgstr "" -#: flwr.common.record.parametersrecord.Array:12 of +#: flwr.client.numpy_client.NumPyClient.get_properties:3 of msgid "" -"A string indicating the type of serialisation mechanism used to generate " -"the bytes in `data` from an array-like or tensor-like object." +"Configuration parameters requested by the server. This can be used to " +"tell the client which properties are needed along with some Scalar " +"attributes." msgstr "" -#: flwr.common.record.parametersrecord.Array:15 of -msgid "A buffer of bytes containing the data." +#: flwr.client.numpy_client.NumPyClient.get_properties:8 of +msgid "" +"**properties** -- A dictionary mapping arbitrary string keys to values of" +" type bool, bytes, float, int, or str. It can be used to communicate " +"arbitrary property values back to the server." msgstr "" -#: ../../source/ref-api/flwr.common.Array.rst:26::1 -msgid ":py:obj:`numpy `\\ \\(\\)" +#: ../../source/ref-api/flwr.client.mod.rst:2 +msgid "mod" msgstr "" -#: ../../source/ref-api/flwr.common.Array.rst:26::1 -#: flwr.common.record.parametersrecord.Array.numpy:1 of -msgid "Return the array as a NumPy array." +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +msgid "" +":py:obj:`adaptiveclipping_mod `\\ " +"\\(msg\\, ctxt\\, call\\_next\\)" msgstr "" -#: flwr.common.record.parametersrecord.Array.numpy:1::1 of -msgid ":py:obj:`dtype `\\" +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#: flwr.client.mod.centraldp_mods.adaptiveclipping_mod:1 of +msgid "Client-side adaptive clipping modifier." msgstr "" -#: flwr.common.record.parametersrecord.Array.numpy:1::1 of -msgid ":py:obj:`shape `\\" +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +msgid "" +":py:obj:`fixedclipping_mod `\\ " +"\\(msg\\, ctxt\\, call\\_next\\)" msgstr "" -#: flwr.common.record.parametersrecord.Array.numpy:1::1 of -msgid ":py:obj:`stype `\\" +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#: flwr.client.mod.centraldp_mods.fixedclipping_mod:1 of +msgid "Client-side fixed clipping modifier." msgstr "" -#: flwr.common.record.parametersrecord.Array.numpy:1::1 of -msgid ":py:obj:`data `\\" +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +msgid ":py:obj:`make_ffn `\\ \\(ffn\\, mods\\)" msgstr "" -#: ../../source/ref-api/flwr.common.ClientMessage.rst:2 -msgid "ClientMessage" +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#: flwr.client.mod.utils.make_ffn:1 of +msgid "." msgstr "" -#: ../../source/ref-api/flwr.common.ClientMessage.rst:31::1 -msgid ":py:obj:`evaluate_res `\\" +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +msgid "" +":py:obj:`secagg_mod `\\ \\(msg\\, ctxt\\, " +"call\\_next\\)" msgstr "" -#: ../../source/ref-api/flwr.common.ClientMessage.rst:31::1 -msgid ":py:obj:`fit_res `\\" +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#: flwr.client.mod.secure_aggregation.secagg_mod.secagg_mod:1 of +msgid "Handle incoming message and return results, following the SecAgg protocol." msgstr "" -#: ../../source/ref-api/flwr.common.ClientMessage.rst:31::1 +#: ../../source/ref-api/flwr.client.mod.rst:28::1 msgid "" -":py:obj:`get_parameters_res " -"`\\" +":py:obj:`secaggplus_mod `\\ \\(msg\\, " +"ctxt\\, call\\_next\\)" msgstr "" -#: ../../source/ref-api/flwr.common.ClientMessage.rst:31::1 +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#: flwr.client.mod.secure_aggregation.secaggplus_mod.secaggplus_mod:1 of msgid "" -":py:obj:`get_properties_res " -"`\\" -msgstr "" - -#: ../../source/ref-api/flwr.common.Code.rst:2 -msgid "Code" +"Handle incoming message and return results, following the SecAgg+ " +"protocol." msgstr "" -#: flwr.common.typing.Code:1 of -msgid "Bases: :py:class:`~enum.Enum`" +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +msgid "" +":py:obj:`message_size_mod `\\ \\(msg\\," +" ctxt\\, call\\_next\\)" msgstr "" -#: ../../source/ref-api/flwr.common.Code.rst:26::1 -msgid ":py:obj:`OK `\\" +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#: flwr.client.mod.comms_mods.message_size_mod:1 of +msgid "Message size mod." msgstr "" -#: ../../source/ref-api/flwr.common.Code.rst:26::1 +#: ../../source/ref-api/flwr.client.mod.rst:28::1 msgid "" -":py:obj:`GET_PROPERTIES_NOT_IMPLEMENTED " -"`\\" +":py:obj:`parameters_size_mod `\\ " +"\\(msg\\, ctxt\\, call\\_next\\)" msgstr "" -#: ../../source/ref-api/flwr.common.Code.rst:26::1 -msgid "" -":py:obj:`GET_PARAMETERS_NOT_IMPLEMENTED " -"`\\" +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#: flwr.client.mod.comms_mods.parameters_size_mod:1 of +msgid "Parameters size mod." msgstr "" -#: ../../source/ref-api/flwr.common.Code.rst:26::1 -msgid ":py:obj:`FIT_NOT_IMPLEMENTED `\\" +#: ../../source/ref-api/flwr.client.mod.rst:35::1 +msgid "" +":py:obj:`LocalDpMod `\\ \\(clipping\\_norm\\," +" sensitivity\\, ...\\)" msgstr "" -#: ../../source/ref-api/flwr.common.Code.rst:26::1 -msgid "" -":py:obj:`EVALUATE_NOT_IMPLEMENTED " -"`\\" +#: ../../source/ref-api/flwr.client.mod.rst:35::1 +#: flwr.client.mod.localdp_mod.LocalDpMod:1 of +msgid "Modifier for local differential privacy." msgstr "" -#: ../../source/ref-api/flwr.common.ConfigsRecord.rst:2 -msgid "ConfigsRecord" +#: ../../source/ref-api/flwr.client.mod.LocalDpMod.rst:2 +msgid "LocalDpMod" msgstr "" -#: flwr.common.record.configsrecord.ConfigsRecord:1 of +#: flwr.client.mod.localdp_mod.LocalDpMod:3 of msgid "" -"Bases: :py:class:`~flwr.common.record.typeddict.TypedDict`\\ " -"[:py:class:`str`, :py:obj:`~typing.Union`\\ [:py:class:`int`, " -":py:class:`float`, :py:class:`str`, :py:class:`bytes`, :py:class:`bool`, " -":py:class:`~typing.List`\\ [:py:class:`int`], :py:class:`~typing.List`\\ " -"[:py:class:`float`], :py:class:`~typing.List`\\ [:py:class:`str`], " -":py:class:`~typing.List`\\ [:py:class:`bytes`], " -":py:class:`~typing.List`\\ [:py:class:`bool`]]]" +"This mod clips the client model updates and adds noise to the params " +"before sending them to the server." msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`clear `\\ \\(\\)" +#: flwr.client.mod.centraldp_mods.adaptiveclipping_mod:12 +#: flwr.client.mod.centraldp_mods.fixedclipping_mod:10 +#: flwr.client.mod.localdp_mod.LocalDpMod:6 of +msgid "It operates on messages of type `MessageType.TRAIN`." msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1 -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid "Remove all items from R." +#: flwr.client.mod.localdp_mod.LocalDpMod:8 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:15 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:8 +#: of +msgid "The value of the clipping norm." msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`count_bytes `\\ \\(\\)" +#: flwr.client.mod.localdp_mod.LocalDpMod:10 of +msgid "The sensitivity of the client model." msgstr "" -#: flwr.common.record.configsrecord.ConfigsRecord.count_bytes:1 -#: flwr.common.record.metricsrecord.MetricsRecord.count_bytes:1 -#: flwr.common.record.parametersrecord.ParametersRecord.count_bytes:1 -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid "Return number of Bytes stored in this object." +#: flwr.client.mod.localdp_mod.LocalDpMod:12 of +msgid "" +"The privacy budget. Smaller value of epsilon indicates a higher level of " +"privacy protection." msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" +#: flwr.client.mod.localdp_mod.LocalDpMod:15 of +msgid "" +"The failure probability. The probability that the privacy mechanism fails" +" to provide the desired level of privacy. A smaller value of delta " +"indicates a stricter privacy guarantee." msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 -#: flwr.common.record.typeddict.TypedDict.get:1 of -msgid "d defaults to None." +#: flwr.client.mod.localdp_mod.LocalDpMod:23 of +msgid "Create an instance of the local DP mod and add it to the client-side mods:" msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`items `\\ \\(\\)" +#: ../../source/ref-api/flwr.client.mod.adaptiveclipping_mod.rst:2 +msgid "adaptiveclipping\\_mod" msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`keys `\\ \\(\\)" +#: flwr.client.mod.centraldp_mods.adaptiveclipping_mod:3 of +msgid "" +"This mod needs to be used with the " +"DifferentialPrivacyClientSideAdaptiveClipping server-side strategy " +"wrapper." msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`pop `\\ \\(k\\[\\,d\\]\\)" +#: flwr.client.mod.centraldp_mods.adaptiveclipping_mod:6 +#: flwr.client.mod.centraldp_mods.fixedclipping_mod:6 of +msgid "The wrapper sends the clipping_norm value to the client." msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 -#: flwr.common.record.typeddict.TypedDict.pop:1 of -msgid "If key is not found, d is returned if given, otherwise KeyError is raised." +#: flwr.client.mod.centraldp_mods.adaptiveclipping_mod:8 +#: flwr.client.mod.centraldp_mods.fixedclipping_mod:8 of +msgid "This mod clips the client model updates before sending them to the server." msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +#: flwr.client.mod.centraldp_mods.adaptiveclipping_mod:10 of msgid "" -":py:obj:`update `\\ \\(\\[E\\, " -"\\]\\*\\*F\\)" +"It also sends KEY_NORM_BIT to the server for computing the new clipping " +"value." msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 -#: flwr.common.record.typeddict.TypedDict.update:1 of -msgid "Update R from dict/iterable E and F." +#: flwr.client.mod.centraldp_mods.adaptiveclipping_mod:15 +#: flwr.client.mod.centraldp_mods.fixedclipping_mod:13 +#: flwr.server.driver.driver.Driver.send_and_receive:18 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:53 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:60 +#: of +msgid "Notes" msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`values `\\ \\(\\)" +#: flwr.client.mod.centraldp_mods.adaptiveclipping_mod:16 +#: flwr.client.mod.centraldp_mods.fixedclipping_mod:14 of +msgid "Consider the order of mods when using multiple." msgstr "" -#: flwr.common.record.configsrecord.ConfigsRecord.count_bytes:3 of -msgid "This function counts booleans as occupying 1 Byte." +#: flwr.client.mod.centraldp_mods.adaptiveclipping_mod:18 of +msgid "Typically, adaptiveclipping_mod should be the last to operate on params." msgstr "" -#: ../../source/ref-api/flwr.common.Context.rst:2 -msgid "Context" +#: ../../source/ref-api/flwr.client.mod.fixedclipping_mod.rst:2 +msgid "fixedclipping\\_mod" msgstr "" -#: flwr.common.context.Context:3 of +#: flwr.client.mod.centraldp_mods.fixedclipping_mod:3 of msgid "" -"Holds records added by the entity in a given run and that will stay " -"local. This means that the data it holds will never leave the system it's" -" running from. This can be used as an intermediate storage or scratchpad " -"when executing mods. It can also be used as a memory to access at " -"different points during the lifecycle of this entity (e.g. across " -"multiple rounds)" +"This mod needs to be used with the " +"DifferentialPrivacyClientSideFixedClipping server-side strategy wrapper." msgstr "" -#: ../../source/ref-api/flwr.common.Context.rst:28::1 -msgid ":py:obj:`state `\\" -msgstr "" - -#: ../../source/ref-api/flwr.common.DisconnectRes.rst:2 -msgid "DisconnectRes" +#: flwr.client.mod.centraldp_mods.fixedclipping_mod:16 of +msgid "Typically, fixedclipping_mod should be the last to operate on params." msgstr "" -#: ../../source/ref-api/flwr.common.DisconnectRes.rst:28::1 -msgid ":py:obj:`reason `\\" +#: ../../source/ref-api/flwr.client.mod.make_ffn.rst:2 +msgid "make\\_ffn" msgstr "" -#: ../../source/ref-api/flwr.common.Error.rst:2 -msgid "Error" +#: ../../source/ref-api/flwr.client.mod.message_size_mod.rst:2 +msgid "message\\_size\\_mod" msgstr "" -#: flwr.common.message.Error:3 of -msgid "An identifier for the error." +#: flwr.client.mod.comms_mods.message_size_mod:3 of +msgid "This mod logs the size in bytes of the message being transmited." msgstr "" -#: flwr.common.message.Error:5 of -msgid "A reason for why the error arose (e.g. an exception stack-trace)" +#: ../../source/ref-api/flwr.client.mod.parameters_size_mod.rst:2 +msgid "parameters\\_size\\_mod" msgstr "" -#: flwr.common.Error.code:1::1 of -msgid ":py:obj:`code `\\" +#: flwr.client.mod.comms_mods.parameters_size_mod:3 of +msgid "" +"This mod logs the number of parameters transmitted in the message as well" +" as their size in bytes." msgstr "" -#: flwr.common.Error.code:1 flwr.common.Error.code:1::1 of -msgid "Error code." +#: ../../source/ref-api/flwr.client.mod.secagg_mod.rst:2 +msgid "secagg\\_mod" msgstr "" -#: flwr.common.Error.code:1::1 of -msgid ":py:obj:`reason `\\" +#: ../../source/ref-api/flwr.client.mod.secaggplus_mod.rst:2 +msgid "secaggplus\\_mod" msgstr "" -#: flwr.common.Error.code:1::1 flwr.common.Error.reason:1 of -msgid "Reason reported about the error." +#: ../../source/ref-api/flwr.client.run_client_app.rst:2 +msgid "run\\_client\\_app" msgstr "" -#: ../../source/ref-api/flwr.common.EvaluateIns.rst:2 -msgid "EvaluateIns" +#: ../../source/ref-api/flwr.client.run_supernode.rst:2 +msgid "run\\_supernode" msgstr "" -#: ../../source/ref-api/flwr.common.EvaluateIns.rst:29::1 -msgid ":py:obj:`parameters `\\" +#: ../../source/ref-api/flwr.client.start_client.rst:2 +msgid "start\\_client" msgstr "" -#: ../../source/ref-api/flwr.common.EvaluateIns.rst:29::1 -msgid ":py:obj:`config `\\" +#: flwr.client.app.start_client:3 flwr.client.app.start_numpy_client:9 of +msgid "" +"The IPv4 or IPv6 address of the server. If the Flower server runs on the " +"same machine on port 8080, then `server_address` would be " +"`\"[::]:8080\"`." msgstr "" -#: ../../source/ref-api/flwr.common.EvaluateRes.rst:2 -msgid "EvaluateRes" +#: flwr.client.app.start_client:7 of +msgid "A callable that instantiates a Client. (default: None)" msgstr "" -#: ../../source/ref-api/flwr.common.EvaluateRes.rst:31::1 -msgid ":py:obj:`status `\\" +#: flwr.client.app.start_client:9 of +msgid "" +"An implementation of the abstract base class `flwr.client.Client` " +"(default: None)" msgstr "" -#: ../../source/ref-api/flwr.common.EvaluateRes.rst:31::1 -msgid ":py:obj:`loss `\\" +#: flwr.client.app.start_client:12 flwr.client.app.start_numpy_client:15 of +msgid "" +"The maximum length of gRPC messages that can be exchanged with the Flower" +" server. The default should be sufficient for most models. Users who " +"train very large models might need to increase this value. Note that the " +"Flower server needs to be started with the same value (see " +"`flwr.server.start_server`), otherwise it will not know about the " +"increased limit and block larger messages." msgstr "" -#: ../../source/ref-api/flwr.common.EvaluateRes.rst:31::1 -msgid ":py:obj:`num_examples `\\" +#: flwr.client.app.start_client:19 flwr.client.app.start_numpy_client:22 of +msgid "" +"The PEM-encoded root certificates as a byte string or a path string. If " +"provided, a secure connection using the certificates will be established " +"to an SSL-enabled Flower server." msgstr "" -#: ../../source/ref-api/flwr.common.EvaluateRes.rst:31::1 -msgid ":py:obj:`metrics `\\" +#: flwr.client.app.start_client:23 flwr.client.app.start_numpy_client:26 of +msgid "" +"Starts an insecure gRPC connection when True. Enables HTTPS connection " +"when False, using system certificates if `root_certificates` is None." msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:2 -msgid "EventType" +#: flwr.client.app.start_client:26 flwr.client.app.start_numpy_client:29 of +msgid "" +"Configure the transport layer. Allowed values: - 'grpc-bidi': gRPC, " +"bidirectional streaming - 'grpc-rere': gRPC, request-response " +"(experimental) - 'rest': HTTP (experimental)" msgstr "" -#: flwr.common.telemetry.EventType:1 of -msgid "Bases: :py:class:`str`, :py:class:`~enum.Enum`" +#: flwr.client.app.start_client:31 of +msgid "" +"The maximum number of times the client will try to connect to the server " +"before giving up in case of a connection error. If set to None, there is " +"no limit to the number of tries." msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid ":py:obj:`PING `\\" +#: flwr.client.app.start_client:35 of +msgid "" +"The maximum duration before the client stops trying to connect to the " +"server in case of connection error. If set to None, there is no limit to " +"the total time." msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid ":py:obj:`START_CLIENT_ENTER `\\" +#: flwr.client.app.start_client:42 flwr.client.app.start_numpy_client:37 of +msgid "Starting a gRPC client with an insecure server connection:" msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid ":py:obj:`START_CLIENT_LEAVE `\\" +#: flwr.client.app.start_client:49 flwr.client.app.start_numpy_client:44 of +msgid "Starting an SSL-enabled gRPC client using system certificates:" msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid ":py:obj:`START_SERVER_ENTER `\\" +#: flwr.client.app.start_client:60 flwr.client.app.start_numpy_client:52 of +msgid "Starting an SSL-enabled gRPC client using provided certificates:" msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid ":py:obj:`START_SERVER_LEAVE `\\" +#: ../../source/ref-api/flwr.client.start_numpy_client.rst:2 +msgid "start\\_numpy\\_client" msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 +#: flwr.client.app.start_numpy_client:5 of msgid "" -":py:obj:`RUN_DRIVER_API_ENTER " -"`\\" +"This function is deprecated since 1.7.0. Use " +":code:`flwr.client.start_client` instead and first convert your " +":code:`NumPyClient` to type :code:`flwr.client.Client` by executing its " +":code:`to_client()` method." msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid "" -":py:obj:`RUN_DRIVER_API_LEAVE " -"`\\" +#: flwr.client.app.start_numpy_client:13 of +msgid "An implementation of the abstract base class `flwr.client.NumPyClient`." msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid "" -":py:obj:`RUN_FLEET_API_ENTER " -"`\\" +#: ../../source/ref-api/flwr.common.rst:2 +msgid "common" msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid "" -":py:obj:`RUN_FLEET_API_LEAVE " -"`\\" +#: ../../source/ref-api/flwr.common.rst:30::1 +msgid ":py:obj:`array_from_numpy `\\ \\(ndarray\\)" msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid "" -":py:obj:`RUN_SUPERLINK_ENTER " -"`\\" +#: ../../source/ref-api/flwr.common.rst:30::1 +#: flwr.common.record.conversion_utils.array_from_numpy:1 of +msgid "Create Array from NumPy ndarray." msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid "" -":py:obj:`RUN_SUPERLINK_LEAVE " -"`\\" +#: ../../source/ref-api/flwr.common.rst:30::1 +msgid ":py:obj:`bytes_to_ndarray `\\ \\(tensor\\)" msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid "" -":py:obj:`START_SIMULATION_ENTER " -"`\\" +#: ../../source/ref-api/flwr.common.rst:30::1 +#: flwr.common.parameter.bytes_to_ndarray:1 of +msgid "Deserialize NumPy ndarray from bytes." msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 +#: ../../source/ref-api/flwr.common.rst:30::1 msgid "" -":py:obj:`START_SIMULATION_LEAVE " -"`\\" +":py:obj:`configure `\\ \\(identifier\\[\\, " +"filename\\, host\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid ":py:obj:`DRIVER_CONNECT `\\" +#: ../../source/ref-api/flwr.common.rst:30::1 +#: flwr.common.logger.configure:1 of +msgid "Configure logging to file and/or remote log server." msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid ":py:obj:`DRIVER_DISCONNECT `\\" +#: ../../source/ref-api/flwr.common.rst:30::1 +msgid "" +":py:obj:`event `\\ \\(event\\_type\\[\\, " +"event\\_details\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid ":py:obj:`START_DRIVER_ENTER `\\" +#: ../../source/ref-api/flwr.common.rst:30::1 +#: flwr.common.telemetry.event:1 of +msgid "Submit create_event to ThreadPoolExecutor to avoid blocking." msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid ":py:obj:`START_DRIVER_LEAVE `\\" +#: ../../source/ref-api/flwr.common.rst:30::1 +msgid "" +":py:obj:`log `\\ \\(level\\, msg\\, \\*args\\, " +"\\*\\*kwargs\\)" msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid "" -":py:obj:`RUN_CLIENT_APP_ENTER " -"`\\" +#: ../../source/ref-api/flwr.common.rst:30::1 logging.Logger.log:1 +#: of +msgid "Log 'msg % args' with the integer severity 'level'." msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid "" -":py:obj:`RUN_CLIENT_APP_LEAVE " -"`\\" +#: ../../source/ref-api/flwr.common.rst:30::1 +msgid ":py:obj:`ndarray_to_bytes `\\ \\(ndarray\\)" msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid "" -":py:obj:`RUN_SERVER_APP_ENTER " -"`\\" +#: ../../source/ref-api/flwr.common.rst:30::1 +#: flwr.common.parameter.ndarray_to_bytes:1 of +msgid "Serialize NumPy ndarray to bytes." msgstr "" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid "" -":py:obj:`RUN_SERVER_APP_LEAVE " -"`\\" +#: ../../source/ref-api/flwr.common.rst:30::1 +msgid ":py:obj:`now `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.common.FitIns.rst:2 -msgid "FitIns" +#: ../../source/ref-api/flwr.common.rst:30::1 +#: flwr.common.date.now:1 of +msgid "Construct a datetime from time.time() with time zone set to UTC." msgstr "" -#: ../../source/ref-api/flwr.common.FitIns.rst:29::1 -msgid ":py:obj:`parameters `\\" +#: ../../source/ref-api/flwr.common.rst:30::1 +msgid "" +":py:obj:`ndarrays_to_parameters `\\ " +"\\(ndarrays\\)" msgstr "" -#: ../../source/ref-api/flwr.common.FitIns.rst:29::1 -msgid ":py:obj:`config `\\" +#: ../../source/ref-api/flwr.common.rst:30::1 +#: flwr.common.parameter.ndarrays_to_parameters:1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.ndarrays_to_parameters:1 +#: of +msgid "Convert NumPy ndarrays to parameters object." msgstr "" -#: ../../source/ref-api/flwr.common.FitRes.rst:2 -msgid "FitRes" +#: ../../source/ref-api/flwr.common.rst:30::1 +msgid "" +":py:obj:`parameters_to_ndarrays `\\ " +"\\(parameters\\)" msgstr "" -#: ../../source/ref-api/flwr.common.FitRes.rst:31::1 -msgid ":py:obj:`status `\\" +#: ../../source/ref-api/flwr.common.rst:30::1 +#: flwr.common.parameter.parameters_to_ndarrays:1 of +msgid "Convert parameters object to NumPy ndarrays." msgstr "" -#: ../../source/ref-api/flwr.common.FitRes.rst:31::1 -msgid ":py:obj:`parameters `\\" +#: ../../source/ref-api/flwr.common.rst:64::1 +msgid "" +":py:obj:`Array `\\ \\(dtype\\, shape\\, stype\\, " +"data\\)" msgstr "" -#: ../../source/ref-api/flwr.common.FitRes.rst:31::1 -msgid ":py:obj:`num_examples `\\" +#: ../../source/ref-api/flwr.common.rst:64::1 +#: flwr.common.record.parametersrecord.Array:1 of +msgid "Array type." msgstr "" -#: ../../source/ref-api/flwr.common.FitRes.rst:31::1 -msgid ":py:obj:`metrics `\\" +#: ../../source/ref-api/flwr.common.rst:64::1 +msgid "" +":py:obj:`ClientMessage `\\ " +"\\(\\[get\\_properties\\_res\\, ...\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.common.GetParametersIns.rst:2 -msgid "GetParametersIns" +#: ../../source/ref-api/flwr.common.rst:64::1 +#: flwr.common.typing.ClientMessage:1 of +msgid "ClientMessage is a container used to hold one result message." msgstr "" -#: ../../source/ref-api/flwr.common.GetParametersIns.rst:28::1 -msgid ":py:obj:`config `\\" +#: ../../source/ref-api/flwr.common.rst:64::1 +msgid ":py:obj:`Code `\\ \\(value\\)" msgstr "" -#: ../../source/ref-api/flwr.common.GetParametersRes.rst:2 -msgid "GetParametersRes" +#: ../../source/ref-api/flwr.common.rst:64::1 +#: flwr.common.typing.Code:1 of +msgid "Client status codes." msgstr "" -#: ../../source/ref-api/flwr.common.GetParametersRes.rst:29::1 -msgid ":py:obj:`status `\\" +#: ../../source/ref-api/flwr.common.rst:64::1 +msgid "" +":py:obj:`ConfigsRecord `\\ " +"\\(\\[configs\\_dict\\, keep\\_input\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.common.GetParametersRes.rst:29::1 -msgid ":py:obj:`parameters `\\" +#: ../../source/ref-api/flwr.common.rst:64::1 +#: flwr.common.record.configsrecord.ConfigsRecord:1 of +msgid "Configs record." msgstr "" -#: ../../source/ref-api/flwr.common.GetPropertiesIns.rst:2 -msgid "GetPropertiesIns" +#: ../../source/ref-api/flwr.common.rst:64::1 +msgid ":py:obj:`Context `\\ \\(state\\)" msgstr "" -#: ../../source/ref-api/flwr.common.GetPropertiesIns.rst:28::1 -msgid ":py:obj:`config `\\" +#: ../../source/ref-api/flwr.common.rst:64::1 +#: flwr.common.context.Context:1 of +msgid "State of your run." msgstr "" -#: ../../source/ref-api/flwr.common.GetPropertiesRes.rst:2 -msgid "GetPropertiesRes" +#: ../../source/ref-api/flwr.common.rst:64::1 +msgid ":py:obj:`DisconnectRes `\\ \\(reason\\)" msgstr "" -#: ../../source/ref-api/flwr.common.GetPropertiesRes.rst:29::1 -msgid ":py:obj:`status `\\" +#: ../../source/ref-api/flwr.common.rst:64::1 +#: flwr.common.typing.DisconnectRes:1 of +msgid "DisconnectRes message from client to server." msgstr "" -#: ../../source/ref-api/flwr.common.GetPropertiesRes.rst:29::1 -msgid ":py:obj:`properties `\\" +#: ../../source/ref-api/flwr.common.rst:64::1 +msgid "" +":py:obj:`EvaluateIns `\\ \\(parameters\\, " +"config\\)" msgstr "" -#: ../../source/ref-api/flwr.common.Message.rst:2 -msgid "Message" +#: ../../source/ref-api/flwr.common.rst:64::1 +#: flwr.common.typing.EvaluateIns:1 of +msgid "Evaluate instructions for a client." msgstr "" -#: flwr.common.Message.content:1::1 flwr.common.Message.metadata:1 -#: flwr.common.message.Message:3 of -msgid "A dataclass including information about the message to be executed." -msgstr "" - -#: flwr.common.message.Message:5 of -msgid "" -"Holds records either sent by another entity (e.g. sent by the server-side" -" logic to a client, or vice-versa) or that will be sent to it." -msgstr "" - -#: flwr.common.message.Message:8 of +#: ../../source/ref-api/flwr.common.rst:64::1 msgid "" -"A dataclass that captures information about an error that took place when" -" processing another message." +":py:obj:`EvaluateRes `\\ \\(status\\, loss\\, " +"num\\_examples\\, metrics\\)" msgstr "" -#: ../../source/ref-api/flwr.common.Message.rst:35::1 -msgid "" -":py:obj:`create_error_reply `\\ " -"\\(error\\, ttl\\)" +#: ../../source/ref-api/flwr.common.rst:64::1 +#: flwr.common.typing.EvaluateRes:1 of +msgid "Evaluate response from a client." msgstr "" -#: ../../source/ref-api/flwr.common.Message.rst:35::1 -#: flwr.common.message.Message.create_error_reply:1 of -msgid "Construct a reply message indicating an error happened." +#: ../../source/ref-api/flwr.common.rst:64::1 +msgid ":py:obj:`EventType `\\ \\(value\\)" msgstr "" -#: ../../source/ref-api/flwr.common.Message.rst:35::1 -msgid "" -":py:obj:`create_reply `\\ \\(content\\," -" ttl\\)" +#: ../../source/ref-api/flwr.common.rst:64::1 +#: flwr.common.telemetry.EventType:1 of +msgid "Types of telemetry events." msgstr "" -#: ../../source/ref-api/flwr.common.Message.rst:35::1 -#: flwr.common.message.Message.create_reply:1 of -msgid "Create a reply to this message with specified content and TTL." +#: ../../source/ref-api/flwr.common.rst:64::1 +msgid ":py:obj:`FitIns `\\ \\(parameters\\, config\\)" msgstr "" -#: ../../source/ref-api/flwr.common.Message.rst:35::1 -msgid ":py:obj:`has_content `\\ \\(\\)" +#: ../../source/ref-api/flwr.common.rst:64::1 +#: flwr.common.typing.FitIns:1 of +msgid "Fit instructions for a client." msgstr "" -#: ../../source/ref-api/flwr.common.Message.rst:35::1 -#: flwr.common.message.Message.has_content:1 of -msgid "Return True if message has content, else False." +#: ../../source/ref-api/flwr.common.rst:64::1 +msgid "" +":py:obj:`FitRes `\\ \\(status\\, parameters\\, " +"num\\_examples\\, metrics\\)" msgstr "" -#: ../../source/ref-api/flwr.common.Message.rst:35::1 -msgid ":py:obj:`has_error `\\ \\(\\)" +#: ../../source/ref-api/flwr.common.rst:64::1 +#: flwr.common.typing.FitRes:1 of +msgid "Fit response from a client." msgstr "" -#: ../../source/ref-api/flwr.common.Message.rst:35::1 -#: flwr.common.message.Message.has_error:1 of -msgid "Return True if message has an error, else False." +#: ../../source/ref-api/flwr.common.rst:64::1 +msgid ":py:obj:`Error `\\ \\(code\\[\\, reason\\]\\)" msgstr "" -#: flwr.common.Message.content:1::1 of -msgid ":py:obj:`content `\\" +#: ../../source/ref-api/flwr.common.rst:64::1 +#: flwr.common.message.Error:1 of +msgid "A dataclass that stores information about an error that occurred." msgstr "" -#: flwr.common.Message.content:1 flwr.common.Message.content:1::1 -#: of -msgid "The content of this message." +#: ../../source/ref-api/flwr.common.rst:64::1 +msgid ":py:obj:`GetParametersIns `\\ \\(config\\)" msgstr "" -#: flwr.common.Message.content:1::1 of -msgid ":py:obj:`error `\\" +#: ../../source/ref-api/flwr.common.rst:64::1 +#: flwr.common.typing.GetParametersIns:1 of +msgid "Parameters request for a client." msgstr "" -#: flwr.common.Message.content:1::1 flwr.common.Message.error:1 of -msgid "Error captured by this message." +#: ../../source/ref-api/flwr.common.rst:64::1 +msgid "" +":py:obj:`GetParametersRes `\\ \\(status\\, " +"parameters\\)" msgstr "" -#: flwr.common.Message.content:1::1 of -msgid ":py:obj:`metadata `\\" +#: ../../source/ref-api/flwr.common.rst:64::1 +#: flwr.common.typing.GetParametersRes:1 of +msgid "Response when asked to return parameters." msgstr "" -#: flwr.common.message.Message.create_error_reply:3 of -msgid "The error that was encountered." +#: ../../source/ref-api/flwr.common.rst:64::1 +msgid ":py:obj:`GetPropertiesIns `\\ \\(config\\)" msgstr "" -#: flwr.common.Metadata.dst_node_id:1::1 -#: flwr.common.Metadata.ttl:1 flwr.common.message.Message.create_error_reply:5 -#: flwr.common.message.Message.create_reply:9 flwr.common.message.Metadata:16 -#: of -msgid "Time-to-live for this message." +#: ../../source/ref-api/flwr.common.rst:64::1 +#: flwr.common.typing.GetPropertiesIns:1 of +msgid "Properties request for a client." msgstr "" -#: flwr.common.message.Message.create_reply:3 of +#: ../../source/ref-api/flwr.common.rst:64::1 msgid "" -"The method generates a new `Message` as a reply to this message. It " -"inherits 'run_id', 'src_node_id', 'dst_node_id', and 'message_type' from " -"this message and sets 'reply_to_message' to the ID of this message." -msgstr "" - -#: flwr.common.message.Message.create_reply:7 of -msgid "The content for the reply message." +":py:obj:`GetPropertiesRes `\\ \\(status\\, " +"properties\\)" msgstr "" -#: flwr.common.message.Message.create_reply:12 of -msgid "A new `Message` instance representing the reply." +#: ../../source/ref-api/flwr.common.rst:64::1 +#: flwr.common.typing.GetPropertiesRes:1 of +msgid "Properties response from a client." msgstr "" -#: ../../source/ref-api/flwr.common.MessageType.rst:2 -msgid "MessageType" +#: ../../source/ref-api/flwr.common.rst:64::1 +msgid "" +":py:obj:`Message `\\ \\(metadata\\[\\, content\\, " +"error\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.common.MessageType.rst:30::1 -msgid ":py:obj:`EVALUATE `\\" +#: ../../source/ref-api/flwr.common.rst:64::1 +#: flwr.common.message.Message:1 of +msgid "State of your application from the viewpoint of the entity using it." msgstr "" -#: ../../source/ref-api/flwr.common.MessageType.rst:30::1 -msgid ":py:obj:`QUERY `\\" +#: ../../source/ref-api/flwr.common.rst:64::1 +msgid ":py:obj:`MessageType `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.common.MessageType.rst:30::1 -msgid ":py:obj:`TRAIN `\\" +#: ../../source/ref-api/flwr.common.rst:64::1 +#: flwr.common.constant.MessageType:1 of +msgid "Message type." msgstr "" -#: ../../source/ref-api/flwr.common.MessageTypeLegacy.rst:2 -msgid "MessageTypeLegacy" +#: ../../source/ref-api/flwr.common.rst:64::1 +msgid ":py:obj:`MessageTypeLegacy `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.common.MessageTypeLegacy.rst:29::1 -msgid ":py:obj:`GET_PARAMETERS `\\" +#: ../../source/ref-api/flwr.common.rst:64::1 +#: flwr.common.constant.MessageTypeLegacy:1 of +msgid "Legacy message type." msgstr "" -#: ../../source/ref-api/flwr.common.MessageTypeLegacy.rst:29::1 -msgid ":py:obj:`GET_PROPERTIES `\\" +#: ../../source/ref-api/flwr.common.rst:64::1 +msgid "" +":py:obj:`Metadata `\\ \\(run\\_id\\, " +"message\\_id\\, src\\_node\\_id\\, ...\\)" msgstr "" -#: flwr.common.Metadata.dst_node_id:1::1 -#: flwr.common.Metadata.run_id:1 flwr.common.message.Metadata:3 of -msgid "An identifier for the current run." +#: ../../source/ref-api/flwr.common.rst:64::1 +#: flwr.common.message.Metadata:1 of +msgid "A dataclass holding metadata associated with the current message." msgstr "" -#: flwr.common.Metadata.dst_node_id:1::1 -#: flwr.common.Metadata.message_id:1 flwr.common.message.Metadata:5 of -msgid "An identifier for the current message." +#: ../../source/ref-api/flwr.common.rst:64::1 +msgid "" +":py:obj:`MetricsRecord `\\ " +"\\(\\[metrics\\_dict\\, keep\\_input\\]\\)" msgstr "" -#: flwr.common.Metadata.dst_node_id:1::1 -#: flwr.common.Metadata.src_node_id:1 flwr.common.message.Metadata:7 of -msgid "An identifier for the node sending this message." +#: ../../source/ref-api/flwr.common.rst:64::1 +#: flwr.common.record.metricsrecord.MetricsRecord:1 of +msgid "Metrics record." msgstr "" -#: flwr.common.Metadata.dst_node_id:1 -#: flwr.common.Metadata.dst_node_id:1::1 -#: flwr.common.message.Metadata:9 of -msgid "An identifier for the node receiving this message." +#: ../../source/ref-api/flwr.common.rst:64::1 +msgid ":py:obj:`NDArray `\\" msgstr "" -#: flwr.common.Metadata.dst_node_id:1::1 -#: flwr.common.Metadata.reply_to_message:1 flwr.common.message.Metadata:11 of -msgid "An identifier for the message this message replies to." +#: ../../source/ref-api/flwr.common.rst:64::1 +msgid "" +"alias of :py:class:`~numpy.ndarray`\\ [:py:obj:`~typing.Any`, " +":py:class:`~numpy.dtype`\\ [:py:obj:`~typing.Any`]]" msgstr "" -#: flwr.common.message.Metadata:13 of +#: ../../source/ref-api/flwr.common.rst:64::1 msgid "" -"An identifier for grouping messages. In some settings, this is used as " -"the FL round." +":py:obj:`Parameters `\\ \\(tensors\\, " +"tensor\\_type\\)" msgstr "" -#: flwr.common.Metadata.dst_node_id:1::1 -#: flwr.common.Metadata.message_type:1 flwr.common.message.Metadata:18 of -msgid "A string that encodes the action to be executed on the receiving end." +#: ../../source/ref-api/flwr.common.rst:64::1 +#: flwr.common.typing.Parameters:1 of +msgid "Model parameters." msgstr "" -#: flwr.common.message.Metadata:21 of +#: ../../source/ref-api/flwr.common.rst:64::1 msgid "" -"An identifier that can be used when loading a particular data partition " -"for a ClientApp. Making use of this identifier is more relevant when " -"conducting simulations." +":py:obj:`ParametersRecord `\\ " +"\\(\\[array\\_dict\\, keep\\_input\\]\\)" msgstr "" -#: flwr.common.Metadata.dst_node_id:1::1 of -msgid ":py:obj:`dst_node_id `\\" +#: ../../source/ref-api/flwr.common.rst:64::1 +#: flwr.common.record.parametersrecord.ParametersRecord:1 of +msgid "Parameters record." msgstr "" -#: flwr.common.Metadata.dst_node_id:1::1 of -msgid ":py:obj:`group_id `\\" +#: ../../source/ref-api/flwr.common.rst:64::1 +msgid ":py:obj:`ReconnectIns `\\ \\(seconds\\)" msgstr "" -#: flwr.common.Metadata.dst_node_id:1::1 -#: flwr.common.Metadata.group_id:1 of -msgid "An identifier for grouping messages." +#: ../../source/ref-api/flwr.common.rst:64::1 +#: flwr.common.typing.ReconnectIns:1 of +msgid "ReconnectIns message from server to client." msgstr "" -#: flwr.common.Metadata.dst_node_id:1::1 of -msgid ":py:obj:`message_id `\\" +#: ../../source/ref-api/flwr.common.rst:64::1 +msgid "" +":py:obj:`RecordSet `\\ " +"\\(\\[parameters\\_records\\, ...\\]\\)" msgstr "" -#: flwr.common.Metadata.dst_node_id:1::1 of -msgid ":py:obj:`message_type `\\" +#: ../../source/ref-api/flwr.common.rst:64::1 +#: flwr.common.record.recordset.RecordSet:1 of +msgid "RecordSet stores groups of parameters, metrics and configs." msgstr "" -#: flwr.common.Metadata.dst_node_id:1::1 of -msgid ":py:obj:`partition_id `\\" +#: ../../source/ref-api/flwr.common.rst:64::1 +msgid "" +":py:obj:`ServerMessage `\\ " +"\\(\\[get\\_properties\\_ins\\, ...\\]\\)" msgstr "" -#: flwr.common.Metadata.dst_node_id:1::1 -#: flwr.common.Metadata.partition_id:1 of -msgid "An identifier telling which data partition a ClientApp should use." +#: ../../source/ref-api/flwr.common.rst:64::1 +#: flwr.common.typing.ServerMessage:1 of +msgid "ServerMessage is a container used to hold one instruction message." msgstr "" -#: flwr.common.Metadata.dst_node_id:1::1 of -msgid ":py:obj:`reply_to_message `\\" +#: ../../source/ref-api/flwr.common.rst:64::1 +msgid ":py:obj:`Status `\\ \\(code\\, message\\)" msgstr "" -#: flwr.common.Metadata.dst_node_id:1::1 of -msgid ":py:obj:`run_id `\\" +#: ../../source/ref-api/flwr.common.rst:64::1 +#: flwr.common.typing.Status:1 of +msgid "Client status." msgstr "" -#: flwr.common.Metadata.dst_node_id:1::1 of -msgid ":py:obj:`src_node_id `\\" +#: ../../source/ref-api/flwr.common.Array.rst:2 +msgid "Array" msgstr "" -#: flwr.common.Metadata.dst_node_id:1::1 of -msgid ":py:obj:`ttl `\\" +#: flwr.common.record.parametersrecord.Array:3 of +msgid "" +"A dataclass containing serialized data from an array-like or tensor-like " +"object along with some metadata about it." msgstr "" -#: ../../source/ref-api/flwr.common.MetricsRecord.rst:2 -msgid "MetricsRecord" +#: flwr.common.record.parametersrecord.Array:6 of +msgid "" +"A string representing the data type of the serialised object (e.g. " +"`np.float32`)" msgstr "" -#: flwr.common.record.metricsrecord.MetricsRecord:1 of +#: flwr.common.record.parametersrecord.Array:8 of msgid "" -"Bases: :py:class:`~flwr.common.record.typeddict.TypedDict`\\ " -"[:py:class:`str`, :py:obj:`~typing.Union`\\ [:py:class:`int`, " -":py:class:`float`, :py:class:`~typing.List`\\ [:py:class:`int`], " -":py:class:`~typing.List`\\ [:py:class:`float`]]]" +"A list representing the shape of the unserialized array-like object. This" +" is used to deserialize the data (depending on the serialization method) " +"or simply as a metadata field." msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`clear `\\ \\(\\)" +#: flwr.common.record.parametersrecord.Array:12 of +msgid "" +"A string indicating the type of serialisation mechanism used to generate " +"the bytes in `data` from an array-like or tensor-like object." msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`count_bytes `\\ \\(\\)" +#: flwr.common.record.parametersrecord.Array:15 of +msgid "A buffer of bytes containing the data." msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" -msgstr "" - -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`items `\\ \\(\\)" +#: ../../source/ref-api/flwr.common.Array.rst:26::1 +msgid ":py:obj:`numpy `\\ \\(\\)" msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`keys `\\ \\(\\)" +#: ../../source/ref-api/flwr.common.Array.rst:26::1 +#: flwr.common.record.parametersrecord.Array.numpy:1 of +msgid "Return the array as a NumPy array." msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`pop `\\ \\(k\\[\\,d\\]\\)" +#: flwr.common.record.parametersrecord.Array.numpy:1::1 of +msgid ":py:obj:`dtype `\\" msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid "" -":py:obj:`update `\\ \\(\\[E\\, " -"\\]\\*\\*F\\)" +#: flwr.common.record.parametersrecord.Array.numpy:1::1 of +msgid ":py:obj:`shape `\\" msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`values `\\ \\(\\)" +#: flwr.common.record.parametersrecord.Array.numpy:1::1 of +msgid ":py:obj:`stype `\\" msgstr "" -#: ../../source/ref-api/flwr.common.NDArray.rst:2 -msgid "NDArray" +#: flwr.common.record.parametersrecord.Array.numpy:1::1 of +msgid ":py:obj:`data `\\" msgstr "" -#: ../../source/ref-api/flwr.common.Parameters.rst:29::1 -msgid ":py:obj:`tensors `\\" +#: ../../source/ref-api/flwr.common.ClientMessage.rst:2 +msgid "ClientMessage" msgstr "" -#: ../../source/ref-api/flwr.common.Parameters.rst:29::1 -msgid ":py:obj:`tensor_type `\\" +#: ../../source/ref-api/flwr.common.ClientMessage.rst:31::1 +msgid ":py:obj:`evaluate_res `\\" msgstr "" -#: ../../source/ref-api/flwr.common.ParametersRecord.rst:2 -msgid "ParametersRecord" +#: ../../source/ref-api/flwr.common.ClientMessage.rst:31::1 +msgid ":py:obj:`fit_res `\\" msgstr "" -#: flwr.common.record.parametersrecord.ParametersRecord:1 of +#: ../../source/ref-api/flwr.common.ClientMessage.rst:31::1 msgid "" -"Bases: :py:class:`~flwr.common.record.typeddict.TypedDict`\\ " -"[:py:class:`str`, :py:class:`~flwr.common.record.parametersrecord.Array`]" +":py:obj:`get_parameters_res " +"`\\" msgstr "" -#: flwr.common.record.parametersrecord.ParametersRecord:3 of +#: ../../source/ref-api/flwr.common.ClientMessage.rst:31::1 msgid "" -"A dataclass storing named Arrays in order. This means that it holds " -"entries as an OrderedDict[str, Array]. ParametersRecord objects can be " -"viewed as an equivalent to PyTorch's state_dict, but holding serialised " -"tensors instead." +":py:obj:`get_properties_res " +"`\\" msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`clear `\\ \\(\\)" +#: ../../source/ref-api/flwr.common.Code.rst:2 +msgid "Code" msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`count_bytes `\\ \\(\\)" +#: flwr.common.typing.Code:1 of +msgid "Bases: :py:class:`~enum.Enum`" msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" +#: ../../source/ref-api/flwr.common.Code.rst:26::1 +msgid ":py:obj:`OK `\\" msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`items `\\ \\(\\)" +#: ../../source/ref-api/flwr.common.Code.rst:26::1 +msgid "" +":py:obj:`GET_PROPERTIES_NOT_IMPLEMENTED " +"`\\" msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`keys `\\ \\(\\)" +#: ../../source/ref-api/flwr.common.Code.rst:26::1 +msgid "" +":py:obj:`GET_PARAMETERS_NOT_IMPLEMENTED " +"`\\" msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`pop `\\ \\(k\\[\\,d\\]\\)" +#: ../../source/ref-api/flwr.common.Code.rst:26::1 +msgid ":py:obj:`FIT_NOT_IMPLEMENTED `\\" msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +#: ../../source/ref-api/flwr.common.Code.rst:26::1 msgid "" -":py:obj:`update `\\ \\(\\[E\\, " -"\\]\\*\\*F\\)" +":py:obj:`EVALUATE_NOT_IMPLEMENTED " +"`\\" msgstr "" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`values `\\ \\(\\)" +#: ../../source/ref-api/flwr.common.ConfigsRecord.rst:2 +msgid "ConfigsRecord" msgstr "" -#: flwr.common.record.parametersrecord.ParametersRecord.count_bytes:3 of +#: flwr.common.record.configsrecord.ConfigsRecord:1 of msgid "" -"Note that a small amount of Bytes might also be included in this counting" -" that correspond to metadata of the serialized object (e.g. of NumPy " -"array) needed for deseralization." +"Bases: :py:class:`~flwr.common.record.typeddict.TypedDict`\\ " +"[:py:class:`str`, :py:class:`int` | :py:class:`float` | :py:class:`str` |" +" :py:class:`bytes` | :py:class:`bool` | :py:class:`~typing.List`\\ " +"[:py:class:`int`] | :py:class:`~typing.List`\\ [:py:class:`float`] | " +":py:class:`~typing.List`\\ [:py:class:`str`] | :py:class:`~typing.List`\\" +" [:py:class:`bytes`] | :py:class:`~typing.List`\\ [:py:class:`bool`]]" msgstr "" -#: ../../source/ref-api/flwr.common.ReconnectIns.rst:2 -msgid "ReconnectIns" +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid ":py:obj:`clear `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.common.ReconnectIns.rst:28::1 -msgid ":py:obj:`seconds `\\" +#: flwr.common.record.typeddict.TypedDict.clear:1 +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid "Remove all items from R." msgstr "" -#: ../../source/ref-api/flwr.common.RecordSet.rst:2 -msgid "RecordSet" +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid ":py:obj:`count_bytes `\\ \\(\\)" msgstr "" -#: flwr.common.RecordSet.configs_records:1::1 of -msgid ":py:obj:`configs_records `\\" +#: flwr.common.record.configsrecord.ConfigsRecord.count_bytes:1 +#: flwr.common.record.metricsrecord.MetricsRecord.count_bytes:1 +#: flwr.common.record.parametersrecord.ParametersRecord.count_bytes:1 +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid "Return number of Bytes stored in this object." msgstr "" -#: flwr.common.RecordSet.configs_records:1 -#: flwr.common.RecordSet.configs_records:1::1 of -msgid "Dictionary holding ConfigsRecord instances." +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" msgstr "" -#: flwr.common.RecordSet.configs_records:1::1 of -msgid ":py:obj:`metrics_records `\\" +#: flwr.common.record.typeddict.TypedDict.clear:1::1 +#: flwr.common.record.typeddict.TypedDict.get:1 of +msgid "d defaults to None." msgstr "" -#: flwr.common.RecordSet.configs_records:1::1 -#: flwr.common.RecordSet.metrics_records:1 of -msgid "Dictionary holding MetricsRecord instances." +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid ":py:obj:`items `\\ \\(\\)" msgstr "" -#: flwr.common.RecordSet.configs_records:1::1 of -msgid ":py:obj:`parameters_records `\\" +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid ":py:obj:`keys `\\ \\(\\)" msgstr "" -#: flwr.common.RecordSet.configs_records:1::1 -#: flwr.common.RecordSet.parameters_records:1 of -msgid "Dictionary holding ParametersRecord instances." +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid ":py:obj:`pop `\\ \\(k\\[\\,d\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.common.ServerMessage.rst:2 -msgid "ServerMessage" +#: flwr.common.record.typeddict.TypedDict.clear:1::1 +#: flwr.common.record.typeddict.TypedDict.pop:1 of +msgid "If key is not found, d is returned if given, otherwise KeyError is raised." msgstr "" -#: ../../source/ref-api/flwr.common.ServerMessage.rst:31::1 -msgid ":py:obj:`evaluate_ins `\\" +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid "" +":py:obj:`update `\\ \\(\\[E\\, " +"\\]\\*\\*F\\)" msgstr "" -#: ../../source/ref-api/flwr.common.ServerMessage.rst:31::1 -msgid ":py:obj:`fit_ins `\\" +#: flwr.common.record.typeddict.TypedDict.clear:1::1 +#: flwr.common.record.typeddict.TypedDict.update:1 of +msgid "Update R from dict/iterable E and F." msgstr "" -#: ../../source/ref-api/flwr.common.ServerMessage.rst:31::1 -msgid "" -":py:obj:`get_parameters_ins " -"`\\" +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid ":py:obj:`values `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.common.ServerMessage.rst:31::1 -msgid "" -":py:obj:`get_properties_ins " -"`\\" +#: flwr.common.record.configsrecord.ConfigsRecord.count_bytes:3 of +msgid "This function counts booleans as occupying 1 Byte." msgstr "" -#: ../../source/ref-api/flwr.common.Status.rst:2 -msgid "Status" +#: ../../source/ref-api/flwr.common.Context.rst:2 +msgid "Context" msgstr "" -#: ../../source/ref-api/flwr.common.Status.rst:29::1 -msgid ":py:obj:`code `\\" +#: flwr.common.context.Context:3 of +msgid "" +"Holds records added by the entity in a given run and that will stay " +"local. This means that the data it holds will never leave the system it's" +" running from. This can be used as an intermediate storage or scratchpad " +"when executing mods. It can also be used as a memory to access at " +"different points during the lifecycle of this entity (e.g. across " +"multiple rounds)" msgstr "" -#: ../../source/ref-api/flwr.common.Status.rst:29::1 -msgid ":py:obj:`message `\\" +#: ../../source/ref-api/flwr.common.Context.rst:28::1 +msgid ":py:obj:`state `\\" msgstr "" -#: ../../source/ref-api/flwr.common.array_from_numpy.rst:2 -msgid "array\\_from\\_numpy" +#: ../../source/ref-api/flwr.common.DisconnectRes.rst:2 +msgid "DisconnectRes" msgstr "" -#: ../../source/ref-api/flwr.common.bytes_to_ndarray.rst:2 -msgid "bytes\\_to\\_ndarray" +#: ../../source/ref-api/flwr.common.DisconnectRes.rst:28::1 +msgid ":py:obj:`reason `\\" msgstr "" -#: ../../source/ref-api/flwr.common.configure.rst:2 -msgid "configure" +#: ../../source/ref-api/flwr.common.Error.rst:2 +msgid "Error" msgstr "" -#: ../../source/ref-api/flwr.common.event.rst:2 -msgid "event" +#: flwr.common.message.Error:3 of +msgid "An identifier for the error." msgstr "" -#: ../../source/ref-api/flwr.common.log.rst:2 -msgid "log" +#: flwr.common.message.Error:5 of +msgid "A reason for why the error arose (e.g. an exception stack-trace)" msgstr "" -#: logging.Logger.log:3 of -msgid "" -"To pass exception information, use the keyword argument exc_info with a " -"true value, e.g." +#: flwr.common.Error.code:1::1 of +msgid ":py:obj:`code `\\" msgstr "" -#: logging.Logger.log:6 of -#, python-format -msgid "logger.log(level, \"We have a %s\", \"mysterious problem\", exc_info=1)" +#: flwr.common.Error.code:1 flwr.common.Error.code:1::1 of +msgid "Error code." msgstr "" -#: ../../source/ref-api/flwr.common.ndarray_to_bytes.rst:2 -msgid "ndarray\\_to\\_bytes" +#: flwr.common.Error.code:1::1 of +msgid ":py:obj:`reason `\\" msgstr "" -#: ../../source/ref-api/flwr.common.ndarrays_to_parameters.rst:2 -msgid "ndarrays\\_to\\_parameters" +#: flwr.common.Error.code:1::1 flwr.common.Error.reason:1 of +msgid "Reason reported about the error." msgstr "" -#: ../../source/ref-api/flwr.common.now.rst:2 -msgid "now" +#: ../../source/ref-api/flwr.common.EvaluateIns.rst:2 +msgid "EvaluateIns" msgstr "" -#: ../../source/ref-api/flwr.common.parameters_to_ndarrays.rst:2 -msgid "parameters\\_to\\_ndarrays" +#: ../../source/ref-api/flwr.common.EvaluateIns.rst:29::1 +msgid ":py:obj:`parameters `\\" msgstr "" -#: ../../source/ref-api/flwr.server.rst:2 -msgid "server" +#: ../../source/ref-api/flwr.common.EvaluateIns.rst:29::1 +msgid ":py:obj:`config `\\" msgstr "" -#: ../../source/ref-api/flwr.server.rst:27::1 -msgid ":py:obj:`run_driver_api `\\ \\(\\)" +#: ../../source/ref-api/flwr.common.EvaluateRes.rst:2 +msgid "EvaluateRes" msgstr "" -#: ../../source/ref-api/flwr.server.rst:27::1 -#: flwr.server.app.run_driver_api:1 of -msgid "Run Flower server (Driver API)." +#: ../../source/ref-api/flwr.common.EvaluateRes.rst:31::1 +msgid ":py:obj:`status `\\" msgstr "" -#: ../../source/ref-api/flwr.server.rst:27::1 -msgid ":py:obj:`run_fleet_api `\\ \\(\\)" +#: ../../source/ref-api/flwr.common.EvaluateRes.rst:31::1 +msgid ":py:obj:`loss `\\" msgstr "" -#: ../../source/ref-api/flwr.server.rst:27::1 -#: flwr.server.app.run_fleet_api:1 of -msgid "Run Flower server (Fleet API)." +#: ../../source/ref-api/flwr.common.EvaluateRes.rst:31::1 +msgid ":py:obj:`num_examples `\\" msgstr "" -#: ../../source/ref-api/flwr.server.rst:27::1 -msgid ":py:obj:`run_server_app `\\ \\(\\)" +#: ../../source/ref-api/flwr.common.EvaluateRes.rst:31::1 +msgid ":py:obj:`metrics `\\" msgstr "" -#: ../../source/ref-api/flwr.server.rst:27::1 -#: flwr.server.run_serverapp.run_server_app:1 of -msgid "Run Flower server app." +#: ../../source/ref-api/flwr.common.EventType.rst:2 +msgid "EventType" msgstr "" -#: ../../source/ref-api/flwr.server.rst:27::1 -msgid ":py:obj:`run_superlink `\\ \\(\\)" -msgstr "" - -#: ../../source/ref-api/flwr.server.rst:27::1 -#: flwr.server.app.run_superlink:1 of -msgid "Run Flower server (Driver API and Fleet API)." +#: flwr.common.telemetry.EventType:1 of +msgid "Bases: :py:class:`str`, :py:class:`~enum.Enum`" msgstr "" -#: ../../source/ref-api/flwr.server.rst:27::1 +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" -":py:obj:`start_driver `\\ \\(\\*\\[\\, " -"server\\_address\\, server\\, ...\\]\\)" +":py:obj:`encode `\\ \\(\\[encoding\\, " +"errors\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.server.rst:27::1 -#: flwr.server.compat.app.start_driver:1 of -msgid "Start a Flower Driver API server." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.encode:1 of +msgid "Encode the string using the codec registered for encoding." msgstr "" -#: ../../source/ref-api/flwr.server.rst:27::1 +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" -":py:obj:`start_server `\\ \\(\\*\\[\\, " -"server\\_address\\, server\\, ...\\]\\)" +":py:obj:`replace `\\ \\(old\\, new\\[\\, " +"count\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.server.rst:27::1 -#: flwr.server.app.start_server:1 of -msgid "Start a Flower server using the gRPC transport layer." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.replace:1 of +msgid "Return a copy with all occurrences of substring old replaced by new." msgstr "" -#: ../../source/ref-api/flwr.server.rst:41::1 -msgid ":py:obj:`ClientManager `\\ \\(\\)" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid "" +":py:obj:`split `\\ \\(\\[sep\\, " +"maxsplit\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.server.rst:41::1 -#: flwr.server.client_manager.ClientManager:1 of -msgid "Abstract base class for managing Flower clients." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.rsplit:1 flwr.common.EventType.split:1 of +msgid "" +"Return a list of the substrings in the string, using sep as the separator" +" string." msgstr "" -#: ../../source/ref-api/flwr.server.rst:41::1 +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" -":py:obj:`Driver `\\ " -"\\(\\[driver\\_service\\_address\\, ...\\]\\)" +":py:obj:`rsplit `\\ \\(\\[sep\\, " +"maxsplit\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.server.rst:41::1 -#: flwr.server.driver.driver.Driver:1 of -msgid "`Driver` class provides an interface to the Driver API." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid ":py:obj:`join `\\ \\(iterable\\, \\/\\)" msgstr "" -#: ../../source/ref-api/flwr.server.rst:41::1 -msgid ":py:obj:`History `\\ \\(\\)" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.join:1 of +msgid "Concatenate any number of strings." msgstr "" -#: ../../source/ref-api/flwr.server.rst:41::1 -#: flwr.server.history.History:1 of -msgid "History class for training and/or evaluation metrics collection." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid ":py:obj:`capitalize `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.server.rst:41::1 -msgid "" -":py:obj:`LegacyContext `\\ \\(state\\[\\, " -"config\\, strategy\\, ...\\]\\)" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.capitalize:1 of +msgid "Return a capitalized version of the string." msgstr "" -#: ../../source/ref-api/flwr.server.rst:41::1 -#: flwr.server.compat.legacy_context.LegacyContext:1 of -msgid "Legacy Context." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid ":py:obj:`casefold `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.server.rst:41::1 -msgid "" -":py:obj:`Server `\\ \\(\\*\\, client\\_manager\\[\\, " -"strategy\\]\\)" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.casefold:1 of +msgid "Return a version of the string suitable for caseless comparisons." msgstr "" -#: ../../source/ref-api/flwr.server.rst:41::1 -msgid "" -":py:obj:`ServerApp `\\ \\(\\[server\\, config\\, " -"strategy\\, ...\\]\\)" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid ":py:obj:`title `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.server.rst:41::1 -#: flwr.server.server_app.ServerApp:1 of -msgid "Flower ServerApp." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.title:1 of +msgid "Return a version of the string where each word is titlecased." msgstr "" -#: ../../source/ref-api/flwr.server.rst:41::1 +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" -":py:obj:`ServerConfig `\\ \\(\\[num\\_rounds\\," -" round\\_timeout\\]\\)" +":py:obj:`center `\\ \\(width\\[\\, " +"fillchar\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.server.rst:41::1 -#: flwr.server.server_config.ServerConfig:1 of -msgid "Flower server config." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.center:1 of +msgid "Return a centered string of length width." msgstr "" -#: ../../source/ref-api/flwr.server.rst:41::1 -msgid ":py:obj:`SimpleClientManager `\\ \\(\\)" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid "" +":py:obj:`count `\\ \\(sub\\[\\, start\\[\\, " +"end\\]\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.server.rst:41::1 -#: flwr.server.client_manager.SimpleClientManager:1 of -msgid "Provides a pool of available clients." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid "" +"Return the number of non-overlapping occurrences of substring sub in " +"string S[start:end]." msgstr "" -#: ../../source/ref-api/flwr.server.rst:60::1 -msgid ":py:obj:`flwr.server.strategy `\\" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid "" +":py:obj:`expandtabs `\\ " +"\\(\\[tabsize\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.server.rst:60::1 -#: flwr.server.strategy:1 of -msgid "Contains the strategy abstraction and different implementations." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.expandtabs:1 of +msgid "Return a copy where all tab characters are expanded using spaces." msgstr "" -#: ../../source/ref-api/flwr.server.rst:60::1 -msgid ":py:obj:`flwr.server.workflow `\\" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid "" +":py:obj:`find `\\ \\(sub\\[\\, start\\[\\, " +"end\\]\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.server.rst:60::1 -#: flwr.server.workflow:1 of -msgid "Workflows." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid "" +"Return the lowest index in S where substring sub is found, such that sub " +"is contained within S[start:end]." msgstr "" -#: ../../source/ref-api/flwr.server.ClientManager.rst:2 -msgid "ClientManager" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid ":py:obj:`partition `\\ \\(sep\\, \\/\\)" msgstr "" -#: flwr.server.client_manager.ClientManager.all:1::1 of -msgid ":py:obj:`all `\\ \\(\\)" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.partition:1 flwr.common.EventType.rpartition:1 of +msgid "Partition the string into three parts using the given separator." msgstr "" -#: flwr.server.client_manager.ClientManager.all:1 -#: flwr.server.client_manager.ClientManager.all:1::1 -#: flwr.server.client_manager.SimpleClientManager.all:1 -#: flwr.server.client_manager.SimpleClientManager.all:1::1 of -msgid "Return all available clients." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid "" +":py:obj:`index `\\ \\(sub\\[\\, start\\[\\, " +"end\\]\\]\\)" msgstr "" -#: flwr.server.client_manager.ClientManager.all:1::1 of -msgid ":py:obj:`num_available `\\ \\(\\)" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid "" +":py:obj:`ljust `\\ \\(width\\[\\, " +"fillchar\\]\\)" msgstr "" -#: flwr.server.client_manager.ClientManager.all:1::1 -#: flwr.server.client_manager.ClientManager.num_available:1 -#: flwr.server.client_manager.SimpleClientManager.all:1::1 -#: flwr.server.client_manager.SimpleClientManager.num_available:1 of -msgid "Return the number of available clients." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.ljust:1 of +msgid "Return a left-justified string of length width." msgstr "" -#: flwr.server.client_manager.ClientManager.all:1::1 of -msgid ":py:obj:`register `\\ \\(client\\)" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid ":py:obj:`lower `\\ \\(\\)" msgstr "" -#: flwr.server.client_manager.ClientManager.all:1::1 -#: flwr.server.client_manager.ClientManager.register:1 -#: flwr.server.client_manager.SimpleClientManager.all:1::1 -#: flwr.server.client_manager.SimpleClientManager.register:1 of -msgid "Register Flower ClientProxy instance." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.lower:1 of +msgid "Return a copy of the string converted to lowercase." msgstr "" -#: flwr.server.client_manager.ClientManager.all:1::1 of -msgid "" -":py:obj:`sample `\\ " -"\\(num\\_clients\\[\\, min\\_num\\_clients\\, criterion\\]\\)" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid ":py:obj:`lstrip `\\ \\(\\[chars\\]\\)" msgstr "" -#: flwr.server.client_manager.ClientManager.all:1::1 -#: flwr.server.client_manager.ClientManager.sample:1 -#: flwr.server.client_manager.SimpleClientManager.all:1::1 -#: flwr.server.client_manager.SimpleClientManager.sample:1 of -msgid "Sample a number of Flower ClientProxy instances." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.lstrip:1 of +msgid "Return a copy of the string with leading whitespace removed." msgstr "" -#: flwr.server.client_manager.ClientManager.all:1::1 of -msgid ":py:obj:`unregister `\\ \\(client\\)" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid "" +":py:obj:`rfind `\\ \\(sub\\[\\, start\\[\\, " +"end\\]\\]\\)" msgstr "" -#: flwr.server.client_manager.ClientManager.all:1::1 -#: flwr.server.client_manager.ClientManager.unregister:1 -#: flwr.server.client_manager.SimpleClientManager.all:1::1 -#: flwr.server.client_manager.SimpleClientManager.unregister:1 of -msgid "Unregister Flower ClientProxy instance." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid "" +"Return the highest index in S where substring sub is found, such that sub" +" is contained within S[start:end]." msgstr "" -#: flwr.server.client_manager.ClientManager.all:1::1 of +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" -":py:obj:`wait_for `\\ " -"\\(num\\_clients\\, timeout\\)" +":py:obj:`rindex `\\ \\(sub\\[\\, " +"start\\[\\, end\\]\\]\\)" msgstr "" -#: flwr.server.client_manager.ClientManager.all:1::1 -#: flwr.server.client_manager.ClientManager.wait_for:1 -#: flwr.server.client_manager.SimpleClientManager.all:1::1 -#: flwr.server.client_manager.SimpleClientManager.wait_for:1 of -msgid "Wait until at least `num_clients` are available." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid "" +":py:obj:`rjust `\\ \\(width\\[\\, " +"fillchar\\]\\)" msgstr "" -#: flwr.server.client_manager.ClientManager.num_available:3 -#: flwr.server.client_manager.SimpleClientManager.num_available:3 of -msgid "**num_available** -- The number of currently available clients." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.rjust:1 of +msgid "Return a right-justified string of length width." msgstr "" -#: flwr.server.client_manager.ClientManager.register:6 -#: flwr.server.client_manager.SimpleClientManager.register:6 of -msgid "" -"**success** -- Indicating if registration was successful. False if " -"ClientProxy is already registered or can not be registered for any " -"reason." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid ":py:obj:`rstrip `\\ \\(\\[chars\\]\\)" msgstr "" -#: flwr.server.client_manager.ClientManager.unregister:3 -#: flwr.server.client_manager.SimpleClientManager.unregister:3 of -msgid "This method is idempotent." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.rstrip:1 of +msgid "Return a copy of the string with trailing whitespace removed." msgstr "" -#: ../../source/ref-api/flwr.server.Driver.rst:2 -msgid "Driver" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid ":py:obj:`rpartition `\\ \\(sep\\, \\/\\)" msgstr "" -#: flwr.server.driver.driver.Driver:3 of +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" -"The IPv4 or IPv6 address of the Driver API server. Defaults to " -"`\"[::]:9091\"`." +":py:obj:`splitlines `\\ " +"\\(\\[keepends\\]\\)" msgstr "" -#: flwr.server.app.start_server:28 flwr.server.driver.driver.Driver:6 of -msgid "" -"Tuple containing root certificate, server certificate, and private key to" -" start a secure SSL-enabled server. The tuple is expected to have three " -"bytes elements in the following order: * CA certificate. * " -"server certificate. * server private key." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.splitlines:1 of +msgid "Return a list of the lines in the string, breaking at line boundaries." msgstr "" -#: flwr.server.app.start_server:28 flwr.server.driver.driver.Driver:6 of -msgid "" -"Tuple containing root certificate, server certificate, and private key to" -" start a secure SSL-enabled server. The tuple is expected to have three " -"bytes elements in the following order:" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid ":py:obj:`strip `\\ \\(\\[chars\\]\\)" msgstr "" -#: flwr.server.app.start_server:32 flwr.server.driver.driver.Driver:10 of -msgid "CA certificate." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.strip:1 of +msgid "Return a copy of the string with leading and trailing whitespace removed." msgstr "" -#: flwr.server.app.start_server:33 flwr.server.driver.driver.Driver:11 of -msgid "server certificate." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid ":py:obj:`swapcase `\\ \\(\\)" msgstr "" -#: flwr.server.app.start_server:34 flwr.server.driver.driver.Driver:12 of -msgid "server private key." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.swapcase:1 of +msgid "" +"Convert uppercase characters to lowercase and lowercase characters to " +"uppercase." msgstr "" -#: flwr.server.driver.driver.Driver.close:1::1 of -msgid ":py:obj:`close `\\ \\(\\)" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid ":py:obj:`translate `\\ \\(table\\, \\/\\)" msgstr "" -#: flwr.server.driver.driver.Driver.close:1 -#: flwr.server.driver.driver.Driver.close:1::1 of -msgid "Disconnect from the SuperLink if connected." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.translate:1 of +msgid "Replace each character in the string using the given translation table." msgstr "" -#: flwr.server.driver.driver.Driver.close:1::1 of -msgid "" -":py:obj:`create_message `\\ " -"\\(content\\, message\\_type\\, ...\\)" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid ":py:obj:`upper `\\ \\(\\)" msgstr "" -#: flwr.server.driver.driver.Driver.close:1::1 -#: flwr.server.driver.driver.Driver.create_message:1 of -msgid "Create a new message with specified parameters." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.upper:1 of +msgid "Return a copy of the string converted to uppercase." msgstr "" -#: flwr.server.driver.driver.Driver.close:1::1 of -msgid ":py:obj:`get_node_ids `\\ \\(\\)" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid "" +":py:obj:`startswith `\\ \\(prefix\\[\\," +" start\\[\\, end\\]\\]\\)" msgstr "" -#: flwr.server.driver.driver.Driver.close:1::1 -#: flwr.server.driver.driver.Driver.get_node_ids:1 of -msgid "Get node IDs." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid "Return True if S starts with the specified prefix, False otherwise." msgstr "" -#: flwr.server.driver.driver.Driver.close:1::1 of +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" -":py:obj:`pull_messages `\\ " -"\\(message\\_ids\\)" +":py:obj:`endswith `\\ \\(suffix\\[\\, " +"start\\[\\, end\\]\\]\\)" msgstr "" -#: flwr.server.driver.driver.Driver.close:1::1 -#: flwr.server.driver.driver.Driver.pull_messages:1 of -msgid "Pull messages based on message IDs." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid "Return True if S ends with the specified suffix, False otherwise." msgstr "" -#: flwr.server.driver.driver.Driver.close:1::1 of +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" -":py:obj:`push_messages `\\ " -"\\(messages\\)" +":py:obj:`removeprefix `\\ " +"\\(prefix\\, \\/\\)" msgstr "" -#: flwr.server.driver.driver.Driver.close:1::1 -#: flwr.server.driver.driver.Driver.push_messages:1 of -msgid "Push messages to specified node IDs." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.removeprefix:1 of +msgid "Return a str with the given prefix string removed if present." msgstr "" -#: flwr.server.driver.driver.Driver.close:1::1 of +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 msgid "" -":py:obj:`send_and_receive `\\ " -"\\(messages\\, \\*\\[\\, timeout\\]\\)" +":py:obj:`removesuffix `\\ " +"\\(suffix\\, \\/\\)" msgstr "" -#: flwr.server.driver.driver.Driver.close:1::1 -#: flwr.server.driver.driver.Driver.send_and_receive:1 of -msgid "Push messages to specified node IDs and pull the reply messages." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.removesuffix:1 of +msgid "Return a str with the given suffix string removed if present." msgstr "" -#: flwr.server.driver.driver.Driver.create_message:3 of -msgid "" -"This method constructs a new `Message` with given content and metadata. " -"The `run_id` and `src_node_id` will be set automatically." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid ":py:obj:`isascii `\\ \\(\\)" msgstr "" -#: flwr.server.driver.driver.Driver.create_message:6 of -msgid "" -"The content for the new message. This holds records that are to be sent " -"to the destination node." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.isascii:1 of +msgid "Return True if all characters in the string are ASCII, False otherwise." msgstr "" -#: flwr.server.driver.driver.Driver.create_message:9 of -msgid "" -"The type of the message, defining the action to be executed on the " -"receiving end." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid ":py:obj:`islower `\\ \\(\\)" msgstr "" -#: flwr.server.driver.driver.Driver.create_message:12 of -msgid "The ID of the destination node to which the message is being sent." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.islower:1 of +msgid "Return True if the string is a lowercase string, False otherwise." msgstr "" -#: flwr.server.driver.driver.Driver.create_message:14 of -msgid "" -"The ID of the group to which this message is associated. In some " -"settings, this is used as the FL round." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid ":py:obj:`isupper `\\ \\(\\)" msgstr "" -#: flwr.server.driver.driver.Driver.create_message:17 of -msgid "" -"Time-to-live for the round trip of this message, i.e., the time from " -"sending this message to receiving a reply. It specifies the duration for " -"which the message and its potential reply are considered valid." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.isupper:1 of +msgid "Return True if the string is an uppercase string, False otherwise." msgstr "" -#: flwr.server.driver.driver.Driver.create_message:22 of -msgid "" -"**message** -- A new `Message` instance with the specified content and " -"metadata." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid ":py:obj:`istitle `\\ \\(\\)" msgstr "" -#: flwr.server.driver.driver.Driver.pull_messages:3 of -msgid "" -"This method is used to collect messages from the SuperLink that " -"correspond to a set of given message IDs." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.istitle:1 of +msgid "Return True if the string is a title-cased string, False otherwise." msgstr "" -#: flwr.server.driver.driver.Driver.pull_messages:6 of -msgid "An iterable of message IDs for which reply messages are to be retrieved." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid ":py:obj:`isspace `\\ \\(\\)" msgstr "" -#: flwr.server.driver.driver.Driver.pull_messages:9 of -msgid "**messages** -- An iterable of messages received." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.isspace:1 of +msgid "Return True if the string is a whitespace string, False otherwise." msgstr "" -#: flwr.server.driver.driver.Driver.push_messages:3 of -msgid "" -"This method takes an iterable of messages and sends each message to the " -"node specified in `dst_node_id`." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid ":py:obj:`isdecimal `\\ \\(\\)" msgstr "" -#: flwr.server.driver.driver.Driver.push_messages:6 -#: flwr.server.driver.driver.Driver.send_and_receive:7 of -msgid "An iterable of messages to be sent." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.isdecimal:1 of +msgid "Return True if the string is a decimal string, False otherwise." msgstr "" -#: flwr.server.driver.driver.Driver.push_messages:9 of -msgid "" -"**message_ids** -- An iterable of IDs for the messages that were sent, " -"which can be used to pull replies." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid ":py:obj:`isdigit `\\ \\(\\)" msgstr "" -#: flwr.server.driver.driver.Driver.send_and_receive:3 of -msgid "" -"This method sends a list of messages to their destination node IDs and " -"then waits for the replies. It continues to pull replies until either all" -" replies are received or the specified timeout duration is exceeded." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.isdigit:1 of +msgid "Return True if the string is a digit string, False otherwise." msgstr "" -#: flwr.server.driver.driver.Driver.send_and_receive:9 of -msgid "" -"The timeout duration in seconds. If specified, the method will wait for " -"replies for this duration. If `None`, there is no time limit and the " -"method will wait until replies for all messages are received." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid ":py:obj:`isnumeric `\\ \\(\\)" msgstr "" -#: flwr.server.driver.driver.Driver.send_and_receive:14 of -msgid "**replies** -- An iterable of reply messages received from the SuperLink." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.isnumeric:1 of +msgid "Return True if the string is a numeric string, False otherwise." msgstr "" -#: flwr.server.driver.driver.Driver.send_and_receive:18 -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:53 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:60 -#: of -msgid "Notes" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid ":py:obj:`isalpha `\\ \\(\\)" msgstr "" -#: flwr.server.driver.driver.Driver.send_and_receive:19 of -msgid "" -"This method uses `push_messages` to send the messages and `pull_messages`" -" to collect the replies. If `timeout` is set, the method may not return " -"replies for all sent messages. A message remains valid until its TTL, " -"which is not affected by `timeout`." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.isalpha:1 of +msgid "Return True if the string is an alphabetic string, False otherwise." msgstr "" -#: ../../source/ref-api/flwr.server.History.rst:2 -msgid "History" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid ":py:obj:`isalnum `\\ \\(\\)" msgstr "" -#: flwr.server.history.History.add_loss_centralized:1::1 of -msgid "" -":py:obj:`add_loss_centralized " -"`\\ \\(server\\_round\\, " -"loss\\)" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.isalnum:1 of +msgid "Return True if the string is an alpha-numeric string, False otherwise." msgstr "" -#: flwr.server.history.History.add_loss_centralized:1 -#: flwr.server.history.History.add_loss_centralized:1::1 of -msgid "Add one loss entry (from centralized evaluation)." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid ":py:obj:`isidentifier `\\ \\(\\)" msgstr "" -#: flwr.server.history.History.add_loss_centralized:1::1 of -msgid "" -":py:obj:`add_loss_distributed " -"`\\ \\(server\\_round\\, " -"loss\\)" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.isidentifier:1 of +msgid "Return True if the string is a valid Python identifier, False otherwise." msgstr "" -#: flwr.server.history.History.add_loss_centralized:1::1 -#: flwr.server.history.History.add_loss_distributed:1 of -msgid "Add one loss entry (from distributed evaluation)." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid ":py:obj:`isprintable `\\ \\(\\)" msgstr "" -#: flwr.server.history.History.add_loss_centralized:1::1 of -msgid "" -":py:obj:`add_metrics_centralized " -"`\\ \\(server\\_round\\, " -"metrics\\)" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.isprintable:1 of +msgid "Return True if the string is printable, False otherwise." msgstr "" -#: flwr.server.history.History.add_loss_centralized:1::1 -#: flwr.server.history.History.add_metrics_centralized:1 of -msgid "Add metrics entries (from centralized evaluation)." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid ":py:obj:`zfill `\\ \\(width\\, \\/\\)" msgstr "" -#: flwr.server.history.History.add_loss_centralized:1::1 of +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.zfill:1 of msgid "" -":py:obj:`add_metrics_distributed " -"`\\ \\(server\\_round\\, " -"metrics\\)" +"Pad a numeric string with zeros on the left, to fill a field of the given" +" width." msgstr "" -#: flwr.server.history.History.add_loss_centralized:1::1 -#: flwr.server.history.History.add_metrics_distributed:1 of -msgid "Add metrics entries (from distributed evaluation)." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid "" +":py:obj:`format `\\ \\(\\*args\\, " +"\\*\\*kwargs\\)" msgstr "" -#: flwr.server.history.History.add_loss_centralized:1::1 of -msgid "" -":py:obj:`add_metrics_distributed_fit " -"`\\ \\(server\\_round\\," -" ...\\)" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid "Return a formatted version of S, using substitutions from args and kwargs." msgstr "" -#: flwr.server.history.History.add_loss_centralized:1::1 -#: flwr.server.history.History.add_metrics_distributed_fit:1 of -msgid "Add metrics entries (from distributed fit)." +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid ":py:obj:`format_map `\\ \\(mapping\\)" msgstr "" -#: ../../source/ref-api/flwr.server.LegacyContext.rst:2 -msgid "LegacyContext" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid "Return a formatted version of S, using substitutions from mapping." msgstr "" -#: flwr.server.compat.legacy_context.LegacyContext:1 of -msgid "Bases: :py:class:`~flwr.common.context.Context`" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +msgid ":py:obj:`maketrans `\\" msgstr "" -#: ../../source/ref-api/flwr.server.LegacyContext.rst:32::1 -msgid ":py:obj:`config `\\" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.maketrans:1 of +msgid "Return a translation table usable for str.translate()." msgstr "" -#: ../../source/ref-api/flwr.server.LegacyContext.rst:32::1 -msgid ":py:obj:`strategy `\\" +#: flwr.common.EventType.capitalize:1::1 of +msgid ":py:obj:`PING `\\" msgstr "" -#: ../../source/ref-api/flwr.server.LegacyContext.rst:32::1 -msgid ":py:obj:`client_manager `\\" +#: flwr.common.EventType.capitalize:1::1 of +msgid ":py:obj:`START_CLIENT_ENTER `\\" msgstr "" -#: ../../source/ref-api/flwr.server.LegacyContext.rst:32::1 -msgid ":py:obj:`history `\\" +#: flwr.common.EventType.capitalize:1::1 of +msgid ":py:obj:`START_CLIENT_LEAVE `\\" msgstr "" -#: ../../source/ref-api/flwr.server.LegacyContext.rst:32::1 -msgid ":py:obj:`state `\\" +#: flwr.common.EventType.capitalize:1::1 of +msgid ":py:obj:`START_SERVER_ENTER `\\" msgstr "" -#: flwr.server.server.Server.client_manager:1::1 of -msgid ":py:obj:`client_manager `\\ \\(\\)" +#: flwr.common.EventType.capitalize:1::1 of +msgid ":py:obj:`START_SERVER_LEAVE `\\" msgstr "" -#: flwr.server.server.Server.client_manager:1 -#: flwr.server.server.Server.client_manager:1::1 of -msgid "Return ClientManager." +#: flwr.common.EventType.capitalize:1::1 of +msgid "" +":py:obj:`RUN_DRIVER_API_ENTER " +"`\\" msgstr "" -#: flwr.server.server.Server.client_manager:1::1 of +#: flwr.common.EventType.capitalize:1::1 of msgid "" -":py:obj:`disconnect_all_clients " -"`\\ \\(timeout\\)" +":py:obj:`RUN_DRIVER_API_LEAVE " +"`\\" msgstr "" -#: flwr.server.server.Server.client_manager:1::1 -#: flwr.server.server.Server.disconnect_all_clients:1 of -msgid "Send shutdown signal to all clients." +#: flwr.common.EventType.capitalize:1::1 of +msgid "" +":py:obj:`RUN_FLEET_API_ENTER " +"`\\" msgstr "" -#: flwr.server.server.Server.client_manager:1::1 of +#: flwr.common.EventType.capitalize:1::1 of msgid "" -":py:obj:`evaluate_round `\\ " -"\\(server\\_round\\, timeout\\)" +":py:obj:`RUN_FLEET_API_LEAVE " +"`\\" msgstr "" -#: flwr.server.server.Server.client_manager:1::1 -#: flwr.server.server.Server.evaluate_round:1 of -msgid "Validate current global model on a number of clients." +#: flwr.common.EventType.capitalize:1::1 of +msgid "" +":py:obj:`RUN_SUPERLINK_ENTER " +"`\\" msgstr "" -#: flwr.server.server.Server.client_manager:1::1 of -msgid ":py:obj:`fit `\\ \\(num\\_rounds\\, timeout\\)" +#: flwr.common.EventType.capitalize:1::1 of +msgid "" +":py:obj:`RUN_SUPERLINK_LEAVE " +"`\\" msgstr "" -#: flwr.server.server.Server.client_manager:1::1 -#: flwr.server.server.Server.fit:1 of -msgid "Run federated averaging for a number of rounds." +#: flwr.common.EventType.capitalize:1::1 of +msgid "" +":py:obj:`START_SIMULATION_ENTER " +"`\\" msgstr "" -#: flwr.server.server.Server.client_manager:1::1 of +#: flwr.common.EventType.capitalize:1::1 of msgid "" -":py:obj:`fit_round `\\ \\(server\\_round\\," -" timeout\\)" +":py:obj:`START_SIMULATION_LEAVE " +"`\\" msgstr "" -#: flwr.server.server.Server.client_manager:1::1 -#: flwr.server.server.Server.fit_round:1 of -msgid "Perform a single round of federated averaging." +#: flwr.common.EventType.capitalize:1::1 of +msgid ":py:obj:`DRIVER_CONNECT `\\" msgstr "" -#: flwr.server.server.Server.client_manager:1::1 of -msgid "" -":py:obj:`set_max_workers `\\ " -"\\(max\\_workers\\)" +#: flwr.common.EventType.capitalize:1::1 of +msgid ":py:obj:`DRIVER_DISCONNECT `\\" msgstr "" -#: flwr.server.server.Server.client_manager:1::1 -#: flwr.server.server.Server.set_max_workers:1 of -msgid "Set the max_workers used by ThreadPoolExecutor." +#: flwr.common.EventType.capitalize:1::1 of +msgid ":py:obj:`START_DRIVER_ENTER `\\" msgstr "" -#: flwr.server.server.Server.client_manager:1::1 of -msgid ":py:obj:`set_strategy `\\ \\(strategy\\)" +#: flwr.common.EventType.capitalize:1::1 of +msgid ":py:obj:`START_DRIVER_LEAVE `\\" msgstr "" -#: flwr.server.server.Server.client_manager:1::1 -#: flwr.server.server.Server.set_strategy:1 of -msgid "Replace server strategy." +#: flwr.common.EventType.capitalize:1::1 of +msgid "" +":py:obj:`RUN_CLIENT_APP_ENTER " +"`\\" msgstr "" -#: ../../source/ref-api/flwr.server.ServerApp.rst:2 -msgid "ServerApp" +#: flwr.common.EventType.capitalize:1::1 of +msgid "" +":py:obj:`RUN_CLIENT_APP_LEAVE " +"`\\" msgstr "" -#: flwr.server.server_app.ServerApp:5 of -msgid "Use the `ServerApp` with an existing `Strategy`:" +#: flwr.common.EventType.capitalize:1::1 of +msgid "" +":py:obj:`RUN_SERVER_APP_ENTER " +"`\\" msgstr "" -#: flwr.server.server_app.ServerApp:15 of -msgid "Use the `ServerApp` with a custom main function:" +#: flwr.common.EventType.capitalize:1::1 of +msgid "" +":py:obj:`RUN_SERVER_APP_LEAVE " +"`\\" msgstr "" -#: flwr.server.server_app.ServerApp.main:1::1 of -msgid ":py:obj:`main `\\ \\(\\)" +#: flwr.common.EventType.capitalize:1::1 of +msgid "" +":py:obj:`RUN_SUPERNODE_ENTER " +"`\\" msgstr "" -#: flwr.server.server_app.ServerApp.main:1 -#: flwr.server.server_app.ServerApp.main:1::1 of -msgid "Return a decorator that registers the main fn with the server app." +#: flwr.common.EventType.capitalize:1::1 of +msgid "" +":py:obj:`RUN_SUPERNODE_LEAVE " +"`\\" msgstr "" -#: ../../source/ref-api/flwr.server.ServerConfig.rst:2 -msgid "ServerConfig" +#: flwr.common.EventType.capitalize:1::1 of +msgid "" +":py:obj:`RUN_SUPEREXEC_ENTER " +"`\\" msgstr "" -#: flwr.server.server_config.ServerConfig:3 of +#: flwr.common.EventType.capitalize:1::1 of msgid "" -"All attributes have default values which allows users to configure just " -"the ones they care about." +":py:obj:`RUN_SUPEREXEC_LEAVE " +"`\\" msgstr "" -#: ../../source/ref-api/flwr.server.ServerConfig.rst:29::1 -msgid ":py:obj:`num_rounds `\\" +#: flwr.common.EventType.capitalize:3 of +msgid "" +"More specifically, make the first character have upper case and the rest " +"lower case." msgstr "" -#: ../../source/ref-api/flwr.server.ServerConfig.rst:29::1 -msgid ":py:obj:`round_timeout `\\" +#: flwr.common.EventType.center:3 flwr.common.EventType.ljust:3 +#: flwr.common.EventType.rjust:3 of +msgid "Padding is done using the specified fill character (default is a space)." msgstr "" -#: ../../source/ref-api/flwr.server.SimpleClientManager.rst:2 -msgid "SimpleClientManager" +#: flwr.common.EventType.count:1 of +msgid "" +"Return the number of non-overlapping occurrences of substring sub in " +"string S[start:end]. Optional arguments start and end are interpreted as" +" in slice notation." msgstr "" -#: flwr.server.client_manager.SimpleClientManager:1 of -msgid "Bases: :py:class:`~flwr.server.client_manager.ClientManager`" +#: flwr.common.EventType.encode:3 of +msgid "encoding" msgstr "" -#: flwr.server.client_manager.SimpleClientManager.all:1::1 of -msgid ":py:obj:`all `\\ \\(\\)" +#: flwr.common.EventType.encode:4 of +msgid "The encoding in which to encode the string." msgstr "" -#: flwr.server.client_manager.SimpleClientManager.all:1::1 of -msgid "" -":py:obj:`num_available `\\" -" \\(\\)" +#: flwr.common.EventType.encode:9 of +msgid "errors" msgstr "" -#: flwr.server.client_manager.SimpleClientManager.all:1::1 of +#: flwr.common.EventType.encode:6 of msgid "" -":py:obj:`register `\\ " -"\\(client\\)" +"The error handling scheme to use for encoding errors. The default is " +"'strict' meaning that encoding errors raise a UnicodeEncodeError. Other " +"possible values are 'ignore', 'replace' and 'xmlcharrefreplace' as well " +"as any other name registered with codecs.register_error that can handle " +"UnicodeEncodeErrors." msgstr "" -#: flwr.server.client_manager.SimpleClientManager.all:1::1 of +#: flwr.common.EventType.endswith:1 of msgid "" -":py:obj:`sample `\\ " -"\\(num\\_clients\\[\\, min\\_num\\_clients\\, criterion\\]\\)" +"Return True if S ends with the specified suffix, False otherwise. With " +"optional start, test S beginning at that position. With optional end, " +"stop comparing S at that position. suffix can also be a tuple of strings " +"to try." msgstr "" -#: flwr.server.client_manager.SimpleClientManager.all:1::1 of -msgid "" -":py:obj:`unregister `\\ " -"\\(client\\)" +#: flwr.common.EventType.expandtabs:3 of +msgid "If tabsize is not given, a tab size of 8 characters is assumed." msgstr "" -#: flwr.server.client_manager.SimpleClientManager.all:1::1 of +#: flwr.common.EventType.find:1 flwr.common.EventType.index:1 of msgid "" -":py:obj:`wait_for `\\ " -"\\(num\\_clients\\[\\, timeout\\]\\)" +"Return the lowest index in S where substring sub is found, such that sub " +"is contained within S[start:end]. Optional arguments start and end are " +"interpreted as in slice notation." msgstr "" -#: flwr.server.client_manager.SimpleClientManager.wait_for:3 of -msgid "" -"Blocks until the requested number of clients is available or until a " -"timeout is reached. Current timeout default: 1 day." +#: flwr.common.EventType.find:5 flwr.common.EventType.rfind:5 of +msgid "Return -1 on failure." msgstr "" -#: flwr.server.client_manager.SimpleClientManager.wait_for:6 of -msgid "The number of clients to wait for." +#: flwr.common.EventType.format:1 of +msgid "" +"Return a formatted version of S, using substitutions from args and " +"kwargs. The substitutions are identified by braces ('{' and '}')." msgstr "" -#: flwr.server.client_manager.SimpleClientManager.wait_for:8 of -msgid "The time in seconds to wait for, defaults to 86400 (24h)." +#: flwr.common.EventType.format_map:1 of +msgid "" +"Return a formatted version of S, using substitutions from mapping. The " +"substitutions are identified by braces ('{' and '}')." msgstr "" -#: flwr.server.client_manager.SimpleClientManager.wait_for:11 of -msgid "**success**" +#: flwr.common.EventType.index:5 flwr.common.EventType.rindex:5 of +msgid "Raises ValueError when the substring is not found." msgstr "" -#: ../../source/ref-api/flwr.server.run_driver_api.rst:2 -msgid "run\\_driver\\_api" +#: flwr.common.EventType.isalnum:3 of +msgid "" +"A string is alpha-numeric if all characters in the string are alpha-" +"numeric and there is at least one character in the string." msgstr "" -#: ../../source/ref-api/flwr.server.run_fleet_api.rst:2 -msgid "run\\_fleet\\_api" +#: flwr.common.EventType.isalpha:3 of +msgid "" +"A string is alphabetic if all characters in the string are alphabetic and" +" there is at least one character in the string." msgstr "" -#: ../../source/ref-api/flwr.server.run_server_app.rst:2 -msgid "run\\_server\\_app" +#: flwr.common.EventType.isascii:3 of +msgid "" +"ASCII characters have code points in the range U+0000-U+007F. Empty " +"string is ASCII too." msgstr "" -#: ../../source/ref-api/flwr.server.run_superlink.rst:2 -msgid "run\\_superlink" +#: flwr.common.EventType.isdecimal:3 of +msgid "" +"A string is a decimal string if all characters in the string are decimal " +"and there is at least one character in the string." msgstr "" -#: ../../source/ref-api/flwr.server.start_driver.rst:2 -msgid "start\\_driver" +#: flwr.common.EventType.isdigit:3 of +msgid "" +"A string is a digit string if all characters in the string are digits and" +" there is at least one character in the string." msgstr "" -#: flwr.server.compat.app.start_driver:3 of +#: flwr.common.EventType.isidentifier:3 of msgid "" -"The IPv4 or IPv6 address of the Driver API server. Defaults to " -"`\"[::]:8080\"`." +"Call keyword.iskeyword(s) to test whether string s is a reserved " +"identifier, such as \"def\" or \"class\"." msgstr "" -#: flwr.server.compat.app.start_driver:6 of +#: flwr.common.EventType.islower:3 of msgid "" -"A server implementation, either `flwr.server.Server` or a subclass " -"thereof. If no instance is provided, then `start_driver` will create one." +"A string is lowercase if all cased characters in the string are lowercase" +" and there is at least one cased character in the string." msgstr "" -#: flwr.server.app.start_server:9 flwr.server.compat.app.start_driver:10 -#: flwr.simulation.app.start_simulation:28 of +#: flwr.common.EventType.isnumeric:3 of msgid "" -"Currently supported values are `num_rounds` (int, default: 1) and " -"`round_timeout` in seconds (float, default: None)." +"A string is numeric if all characters in the string are numeric and there" +" is at least one character in the string." msgstr "" -#: flwr.server.app.start_server:12 flwr.server.compat.app.start_driver:13 of +#: flwr.common.EventType.isprintable:3 of msgid "" -"An implementation of the abstract base class " -"`flwr.server.strategy.Strategy`. If no strategy is provided, then " -"`start_server` will use `flwr.server.strategy.FedAvg`." +"A string is printable if all of its characters are considered printable " +"in repr() or if it is empty." msgstr "" -#: flwr.server.compat.app.start_driver:17 of +#: flwr.common.EventType.isspace:3 of msgid "" -"An implementation of the class `flwr.server.ClientManager`. If no " -"implementation is provided, then `start_driver` will use " -"`flwr.server.SimpleClientManager`." +"A string is whitespace if all characters in the string are whitespace and" +" there is at least one character in the string." msgstr "" -#: flwr.server.compat.app.start_driver:25 of -msgid "The Driver object to use." +#: flwr.common.EventType.istitle:3 of +msgid "" +"In a title-cased string, upper- and title-case characters may only follow" +" uncased characters and lowercase characters only cased ones." msgstr "" -#: flwr.server.app.start_server:37 flwr.server.compat.app.start_driver:28 of -msgid "**hist** -- Object containing training and evaluation metrics." +#: flwr.common.EventType.isupper:3 of +msgid "" +"A string is uppercase if all cased characters in the string are uppercase" +" and there is at least one cased character in the string." msgstr "" -#: flwr.server.compat.app.start_driver:33 of -msgid "Starting a driver that connects to an insecure server:" +#: flwr.common.EventType.join:3 of +msgid "" +"The string whose method is called is inserted in between each given " +"string. The result is returned as a new string." msgstr "" -#: flwr.server.compat.app.start_driver:37 of -msgid "Starting a driver that connects to an SSL-enabled server:" +#: flwr.common.EventType.join:6 of +msgid "Example: '.'.join(['ab', 'pq', 'rs']) -> 'ab.pq.rs'" msgstr "" -#: ../../source/ref-api/flwr.server.start_server.rst:2 -msgid "start\\_server" +#: flwr.common.EventType.lstrip:3 flwr.common.EventType.rstrip:3 +#: flwr.common.EventType.strip:3 of +msgid "If chars is given and not None, remove characters in chars instead." msgstr "" -#: flwr.server.app.start_server:3 of -msgid "The IPv4 or IPv6 address of the server. Defaults to `\"[::]:8080\"`." +#: flwr.common.EventType.maketrans:3 of +msgid "" +"If there is only one argument, it must be a dictionary mapping Unicode " +"ordinals (integers) or characters to Unicode ordinals, strings or None. " +"Character keys will be then converted to ordinals. If there are two " +"arguments, they must be strings of equal length, and in the resulting " +"dictionary, each character in x will be mapped to the character at the " +"same position in y. If there is a third argument, it must be a string, " +"whose characters will be mapped to None in the result." msgstr "" -#: flwr.server.app.start_server:5 of +#: flwr.common.EventType.partition:3 of msgid "" -"A server implementation, either `flwr.server.Server` or a subclass " -"thereof. If no instance is provided, then `start_server` will create one." +"This will search for the separator in the string. If the separator is " +"found, returns a 3-tuple containing the part before the separator, the " +"separator itself, and the part after it." msgstr "" -#: flwr.server.app.start_server:16 of +#: flwr.common.EventType.partition:7 of msgid "" -"An implementation of the abstract base class `flwr.server.ClientManager`." -" If no implementation is provided, then `start_server` will use " -"`flwr.server.client_manager.SimpleClientManager`." +"If the separator is not found, returns a 3-tuple containing the original " +"string and two empty strings." msgstr "" -#: flwr.server.app.start_server:21 of +#: flwr.common.EventType.removeprefix:3 of msgid "" -"The maximum length of gRPC messages that can be exchanged with the Flower" -" clients. The default should be sufficient for most models. Users who " -"train very large models might need to increase this value. Note that the " -"Flower clients need to be started with the same value (see " -"`flwr.client.start_client`), otherwise clients will not know about the " -"increased limit and block larger messages." +"If the string starts with the prefix string, return string[len(prefix):]." +" Otherwise, return a copy of the original string." msgstr "" -#: flwr.server.app.start_server:42 of -msgid "Starting an insecure server:" +#: flwr.common.EventType.removesuffix:3 of +msgid "" +"If the string ends with the suffix string and that suffix is not empty, " +"return string[:-len(suffix)]. Otherwise, return a copy of the original " +"string." msgstr "" -#: flwr.server.app.start_server:46 of -msgid "Starting an SSL-enabled server:" +#: flwr.common.EventType.replace:5 of +msgid "count" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:2 -msgid "strategy" +#: flwr.common.EventType.replace:4 of +msgid "" +"Maximum number of occurrences to replace. -1 (the default value) means " +"replace all occurrences." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.common.EventType.replace:7 of msgid "" -":py:obj:`Bulyan `\\ \\(\\*\\, " -"fraction\\_fit\\, fraction\\_evaluate\\, ...\\)" +"If the optional argument count is given, only the first count occurrences" +" are replaced." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.bulyan.Bulyan:1 of -msgid "Bulyan strategy." +#: flwr.common.EventType.rfind:1 flwr.common.EventType.rindex:1 of +msgid "" +"Return the highest index in S where substring sub is found, such that sub" +" is contained within S[start:end]. Optional arguments start and end are " +"interpreted as in slice notation." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.common.EventType.rpartition:3 of msgid "" -":py:obj:`DPFedAvgAdaptive `\\ " -"\\(strategy\\, num\\_sampled\\_clients\\)" +"This will search for the separator in the string, starting at the end. If" +" the separator is found, returns a 3-tuple containing the part before the" +" separator, the separator itself, and the part after it." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive:1 of -msgid "Wrapper for configuring a Strategy for DP with Adaptive Clipping." +#: flwr.common.EventType.rpartition:7 of +msgid "" +"If the separator is not found, returns a 3-tuple containing two empty " +"strings and the original string." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -msgid "" -":py:obj:`DPFedAvgFixed `\\ " -"\\(strategy\\, num\\_sampled\\_clients\\, ...\\)" +#: flwr.common.EventType.rsplit:7 flwr.common.EventType.split:7 of +msgid "sep" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed:1 of -msgid "Wrapper for configuring a Strategy for DP with Fixed Clipping." +#: flwr.common.EventType.rsplit:4 flwr.common.EventType.split:4 of +msgid "The separator used to split the string." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.common.EventType.rsplit:6 flwr.common.EventType.split:6 of msgid "" -":py:obj:`DifferentialPrivacyClientSideAdaptiveClipping " -"`\\ " -"\\(...\\)" +"When set to None (the default value), will split on any whitespace " +"character (including \\\\n \\\\r \\\\t \\\\f and spaces) and will discard" +" empty strings from the result." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:1 -#: of -msgid "Strategy wrapper for central DP with client-side adaptive clipping." +#: flwr.common.EventType.rsplit:11 flwr.common.EventType.split:11 of +msgid "maxsplit" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.common.EventType.rsplit:10 flwr.common.EventType.split:10 of msgid "" -":py:obj:`DifferentialPrivacyServerSideAdaptiveClipping " -"`\\ " -"\\(...\\)" +"Maximum number of splits (starting from the left). -1 (the default value)" +" means no limit." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:1 -#: of -msgid "Strategy wrapper for central DP with server-side adaptive clipping." +#: flwr.common.EventType.rsplit:13 of +msgid "Splitting starts at the end of the string and works to the front." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.common.EventType.split:13 of msgid "" -":py:obj:`DifferentialPrivacyClientSideFixedClipping " -"`\\ " -"\\(...\\)" +"Note, str.split() is mainly useful for data that has been intentionally " +"delimited. With natural text that includes punctuation, consider using " +"the regular expression module." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:1 -#: of -msgid "Strategy wrapper for central DP with client-side fixed clipping." +#: flwr.common.EventType.splitlines:3 of +msgid "" +"Line breaks are not included in the resulting list unless keepends is " +"given and true." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.common.EventType.startswith:1 of msgid "" -":py:obj:`DifferentialPrivacyServerSideFixedClipping " -"`\\ " -"\\(...\\)" +"Return True if S starts with the specified prefix, False otherwise. With " +"optional start, test S beginning at that position. With optional end, " +"stop comparing S at that position. prefix can also be a tuple of strings " +"to try." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:1 -#: of -msgid "Strategy wrapper for central DP with server-side fixed clipping." +#: flwr.common.EventType.title:3 of +msgid "" +"More specifically, words start with uppercased characters and all " +"remaining cased characters have lower case." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -msgid "" -":py:obj:`FedAdagrad `\\ \\(\\*\\[\\, " -"fraction\\_fit\\, ...\\]\\)" +#: flwr.common.EventType.translate:5 of +msgid "table" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fedadagrad.FedAdagrad:1 of -msgid "FedAdagrad strategy - Adaptive Federated Optimization using Adagrad." +#: flwr.common.EventType.translate:4 of +msgid "" +"Translation table, which must be a mapping of Unicode ordinals to Unicode" +" ordinals, strings, or None." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.common.EventType.translate:7 of msgid "" -":py:obj:`FedAdam `\\ \\(\\*\\[\\, " -"fraction\\_fit\\, ...\\]\\)" +"The table must implement lookup/indexing via __getitem__, for instance a " +"dictionary or list. If this operation raises LookupError, the character " +"is left untouched. Characters mapped to None are deleted." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fedadam.FedAdam:1 of -msgid "FedAdam - Adaptive Federated Optimization using Adam." +#: flwr.common.EventType.zfill:3 of +msgid "The string is never truncated." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -msgid "" -":py:obj:`FedAvg `\\ \\(\\*\\[\\, " -"fraction\\_fit\\, fraction\\_evaluate\\, ...\\]\\)" +#: ../../source/ref-api/flwr.common.FitIns.rst:2 +msgid "FitIns" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fedavg.FedAvg:1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:1 of -msgid "Federated Averaging strategy." +#: ../../source/ref-api/flwr.common.FitIns.rst:29::1 +msgid ":py:obj:`parameters `\\" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -msgid "" -":py:obj:`FedAvgAndroid `\\ " -"\\(\\*\\[\\, fraction\\_fit\\, ...\\]\\)" +#: ../../source/ref-api/flwr.common.FitIns.rst:29::1 +msgid ":py:obj:`config `\\" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -msgid "" -":py:obj:`FedAvgM `\\ \\(\\*\\[\\, " -"fraction\\_fit\\, ...\\]\\)" +#: ../../source/ref-api/flwr.common.FitRes.rst:2 +msgid "FitRes" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fedavgm.FedAvgM:1 of -msgid "Federated Averaging with Momentum strategy." +#: ../../source/ref-api/flwr.common.FitRes.rst:31::1 +msgid ":py:obj:`status `\\" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -msgid "" -":py:obj:`FedMedian `\\ \\(\\*\\[\\, " -"fraction\\_fit\\, ...\\]\\)" +#: ../../source/ref-api/flwr.common.FitRes.rst:31::1 +msgid ":py:obj:`parameters `\\" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fedmedian.FedMedian:1 of -msgid "Configurable FedMedian strategy implementation." +#: ../../source/ref-api/flwr.common.FitRes.rst:31::1 +msgid ":py:obj:`num_examples `\\" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -msgid "" -":py:obj:`FedOpt `\\ \\(\\*\\[\\, " -"fraction\\_fit\\, fraction\\_evaluate\\, ...\\]\\)" +#: ../../source/ref-api/flwr.common.FitRes.rst:31::1 +msgid ":py:obj:`metrics `\\" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fedopt.FedOpt:1 of -msgid "Federated Optim strategy." +#: ../../source/ref-api/flwr.common.GetParametersIns.rst:2 +msgid "GetParametersIns" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -msgid "" -":py:obj:`FedProx `\\ \\(\\*\\[\\, " -"fraction\\_fit\\, ...\\]\\)" +#: ../../source/ref-api/flwr.common.GetParametersIns.rst:28::1 +msgid ":py:obj:`config `\\" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fedprox.FedProx:1 of -msgid "Federated Optimization strategy." +#: ../../source/ref-api/flwr.common.GetParametersRes.rst:2 +msgid "GetParametersRes" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -msgid "" -":py:obj:`FedTrimmedAvg `\\ " -"\\(\\*\\[\\, fraction\\_fit\\, ...\\]\\)" +#: ../../source/ref-api/flwr.common.GetParametersRes.rst:29::1 +msgid ":py:obj:`status `\\" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:1 of -msgid "Federated Averaging with Trimmed Mean [Dong Yin, et al., 2021]." +#: ../../source/ref-api/flwr.common.GetParametersRes.rst:29::1 +msgid ":py:obj:`parameters `\\" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -msgid "" -":py:obj:`FedXgbBagging `\\ " -"\\(\\[evaluate\\_function\\]\\)" +#: ../../source/ref-api/flwr.common.GetPropertiesIns.rst:2 +msgid "GetPropertiesIns" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging:1 of -msgid "Configurable FedXgbBagging strategy implementation." +#: ../../source/ref-api/flwr.common.GetPropertiesIns.rst:28::1 +msgid ":py:obj:`config `\\" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -msgid "" -":py:obj:`FedXgbCyclic `\\ " -"\\(\\*\\*kwargs\\)" +#: ../../source/ref-api/flwr.common.GetPropertiesRes.rst:2 +msgid "GetPropertiesRes" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic:1 of -msgid "Configurable FedXgbCyclic strategy implementation." +#: ../../source/ref-api/flwr.common.GetPropertiesRes.rst:29::1 +msgid ":py:obj:`status `\\" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -msgid "" -":py:obj:`FedXgbNnAvg `\\ \\(\\*args\\, " -"\\*\\*kwargs\\)" +#: ../../source/ref-api/flwr.common.GetPropertiesRes.rst:29::1 +msgid ":py:obj:`properties `\\" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg:1 of -msgid "Configurable FedXgbNnAvg strategy implementation." +#: ../../source/ref-api/flwr.common.Message.rst:2 +msgid "Message" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.common.Message.content:1::1 flwr.common.Message.metadata:1 +#: flwr.common.message.Message:3 of +msgid "A dataclass including information about the message to be executed." +msgstr "" + +#: flwr.common.message.Message:5 of msgid "" -":py:obj:`FedYogi `\\ \\(\\*\\[\\, " -"fraction\\_fit\\, ...\\]\\)" +"Holds records either sent by another entity (e.g. sent by the server-side" +" logic to a client, or vice-versa) or that will be sent to it." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fedyogi.FedYogi:1 of -msgid "FedYogi [Reddi et al., 2020] strategy." +#: flwr.common.message.Message:8 of +msgid "" +"A dataclass that captures information about an error that took place when" +" processing another message." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: ../../source/ref-api/flwr.common.Message.rst:35::1 msgid "" -":py:obj:`FaultTolerantFedAvg " -"`\\ \\(\\*\\[\\, " -"fraction\\_fit\\, ...\\]\\)" +":py:obj:`create_error_reply `\\ " +"\\(error\\[\\, ttl\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg:1 of -msgid "Configurable fault-tolerant FedAvg strategy implementation." +#: ../../source/ref-api/flwr.common.Message.rst:35::1 +#: flwr.common.message.Message.create_error_reply:1 of +msgid "Construct a reply message indicating an error happened." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: ../../source/ref-api/flwr.common.Message.rst:35::1 msgid "" -":py:obj:`Krum `\\ \\(\\*\\[\\, " -"fraction\\_fit\\, fraction\\_evaluate\\, ...\\]\\)" +":py:obj:`create_reply `\\ " +"\\(content\\[\\, ttl\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.krum.Krum:1 of -msgid "Krum [Blanchard et al., 2017] strategy." +#: ../../source/ref-api/flwr.common.Message.rst:35::1 +#: flwr.common.message.Message.create_reply:1 of +msgid "Create a reply to this message with specified content and TTL." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -msgid "" -":py:obj:`QFedAvg `\\ \\(\\*\\[\\, " -"q\\_param\\, qffl\\_learning\\_rate\\, ...\\]\\)" +#: ../../source/ref-api/flwr.common.Message.rst:35::1 +msgid ":py:obj:`has_content `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.qfedavg.QFedAvg:1 of -msgid "Configurable QFedAvg strategy implementation." +#: ../../source/ref-api/flwr.common.Message.rst:35::1 +#: flwr.common.message.Message.has_content:1 of +msgid "Return True if message has content, else False." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -msgid ":py:obj:`Strategy `\\ \\(\\)" +#: ../../source/ref-api/flwr.common.Message.rst:35::1 +msgid ":py:obj:`has_error `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.strategy.Strategy:1 of -msgid "Abstract base class for server strategy implementations." +#: ../../source/ref-api/flwr.common.Message.rst:35::1 +#: flwr.common.message.Message.has_error:1 of +msgid "Return True if message has an error, else False." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.Bulyan.rst:2 -msgid "Bulyan" +#: flwr.common.Message.content:1::1 of +msgid ":py:obj:`content `\\" msgstr "" -#: flwr.server.strategy.bulyan.Bulyan:1 -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg:1 -#: flwr.server.strategy.fedavgm.FedAvgM:1 -#: flwr.server.strategy.fedmedian.FedMedian:1 -#: flwr.server.strategy.fedopt.FedOpt:1 flwr.server.strategy.fedprox.FedProx:1 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:1 -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging:1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic:1 -#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg:1 -#: flwr.server.strategy.krum.Krum:1 flwr.server.strategy.qfedavg.QFedAvg:1 of -msgid "Bases: :py:class:`~flwr.server.strategy.fedavg.FedAvg`" +#: flwr.common.Message.content:1 flwr.common.Message.content:1::1 +#: of +msgid "The content of this message." msgstr "" -#: flwr.server.strategy.bulyan.Bulyan:3 of -msgid "Implementation based on https://arxiv.org/abs/1802.07927." +#: flwr.common.Message.content:1::1 of +msgid ":py:obj:`error `\\" msgstr "" -#: flwr.server.strategy.bulyan.Bulyan:5 -#: flwr.server.strategy.fedadagrad.FedAdagrad:5 -#: flwr.server.strategy.fedadam.FedAdam:5 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:5 -#: flwr.server.strategy.fedavgm.FedAvgM:5 flwr.server.strategy.fedopt.FedOpt:5 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:5 -#: flwr.server.strategy.fedyogi.FedYogi:5 flwr.server.strategy.krum.Krum:5 of -msgid "Fraction of clients used during training. Defaults to 1.0." +#: flwr.common.Message.content:1::1 flwr.common.Message.error:1 of +msgid "Error captured by this message." msgstr "" -#: flwr.server.strategy.bulyan.Bulyan:7 -#: flwr.server.strategy.fedadagrad.FedAdagrad:7 -#: flwr.server.strategy.fedadam.FedAdam:7 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:7 -#: flwr.server.strategy.fedavgm.FedAvgM:7 flwr.server.strategy.fedopt.FedOpt:7 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:7 -#: flwr.server.strategy.fedyogi.FedYogi:7 flwr.server.strategy.krum.Krum:7 of -msgid "Fraction of clients used during validation. Defaults to 1.0." +#: flwr.common.Message.content:1::1 of +msgid ":py:obj:`metadata `\\" msgstr "" -#: flwr.server.strategy.bulyan.Bulyan:9 -#: flwr.server.strategy.fedadagrad.FedAdagrad:9 -#: flwr.server.strategy.fedadam.FedAdam:9 flwr.server.strategy.fedavg.FedAvg:13 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:9 -#: flwr.server.strategy.fedavgm.FedAvgM:9 flwr.server.strategy.fedopt.FedOpt:9 -#: flwr.server.strategy.fedprox.FedProx:45 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:9 -#: flwr.server.strategy.fedyogi.FedYogi:9 flwr.server.strategy.krum.Krum:9 of -msgid "Minimum number of clients used during training. Defaults to 2." +#: flwr.common.message.Message.create_error_reply:3 of +msgid "The error that was encountered." msgstr "" -#: flwr.server.strategy.bulyan.Bulyan:11 -#: flwr.server.strategy.fedadagrad.FedAdagrad:11 -#: flwr.server.strategy.fedadam.FedAdam:11 -#: flwr.server.strategy.fedavg.FedAvg:15 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:11 -#: flwr.server.strategy.fedavgm.FedAvgM:11 -#: flwr.server.strategy.fedopt.FedOpt:11 -#: flwr.server.strategy.fedprox.FedProx:47 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:11 -#: flwr.server.strategy.fedyogi.FedYogi:11 flwr.server.strategy.krum.Krum:11 of -msgid "Minimum number of clients used during validation. Defaults to 2." +#: flwr.common.message.Message.create_error_reply:5 +#: flwr.common.message.Message.create_reply:9 of +msgid "" +"Time-to-live for this message in seconds. If unset, it will be set based " +"on the remaining time for the received message before it expires. This " +"follows the equation: ttl = msg.meta.ttl - (reply.meta.created_at - " +"msg.meta.created_at)" msgstr "" -#: flwr.server.strategy.bulyan.Bulyan:13 -#: flwr.server.strategy.fedadagrad.FedAdagrad:13 -#: flwr.server.strategy.fedadam.FedAdam:13 -#: flwr.server.strategy.fedavg.FedAvg:17 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:13 -#: flwr.server.strategy.fedavgm.FedAvgM:13 -#: flwr.server.strategy.fedopt.FedOpt:13 -#: flwr.server.strategy.fedprox.FedProx:49 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:13 -#: flwr.server.strategy.fedyogi.FedYogi:13 flwr.server.strategy.krum.Krum:13 of -msgid "Minimum number of total clients in the system. Defaults to 2." +#: flwr.common.message.Message.create_error_reply:5 +#: flwr.common.message.Message.create_reply:9 of +msgid "" +"Time-to-live for this message in seconds. If unset, it will be set based " +"on the remaining time for the received message before it expires. This " +"follows the equation:" msgstr "" -#: flwr.server.strategy.bulyan.Bulyan:15 flwr.server.strategy.krum.Krum:15 of -msgid "Number of malicious clients in the system. Defaults to 0." +#: flwr.common.message.Message.create_error_reply:9 +#: flwr.common.message.Message.create_reply:13 of +msgid "ttl = msg.meta.ttl - (reply.meta.created_at - msg.meta.created_at)" msgstr "" -#: flwr.server.strategy.bulyan.Bulyan:17 -#: flwr.server.strategy.fedadagrad.FedAdagrad:15 -#: flwr.server.strategy.fedadam.FedAdam:15 -#: flwr.server.strategy.fedavg.FedAvg:19 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:15 -#: flwr.server.strategy.fedavgm.FedAvgM:15 -#: flwr.server.strategy.fedopt.FedOpt:15 -#: flwr.server.strategy.fedprox.FedProx:51 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:15 -#: flwr.server.strategy.fedyogi.FedYogi:17 -#: flwr.server.strategy.fedyogi.FedYogi:18 -#: flwr.server.strategy.fedyogi.FedYogi:19 flwr.server.strategy.krum.Krum:20 of -msgid "Optional function used for validation. Defaults to None." +#: flwr.common.message.Message.create_reply:3 of +msgid "" +"The method generates a new `Message` as a reply to this message. It " +"inherits 'run_id', 'src_node_id', 'dst_node_id', and 'message_type' from " +"this message and sets 'reply_to_message' to the ID of this message." msgstr "" -#: flwr.server.strategy.bulyan.Bulyan:19 -#: flwr.server.strategy.fedadagrad.FedAdagrad:17 -#: flwr.server.strategy.fedadam.FedAdam:17 -#: flwr.server.strategy.fedavg.FedAvg:21 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:17 -#: flwr.server.strategy.fedavgm.FedAvgM:17 -#: flwr.server.strategy.fedopt.FedOpt:17 -#: flwr.server.strategy.fedprox.FedProx:53 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:17 -#: flwr.server.strategy.fedyogi.FedYogi:20 flwr.server.strategy.krum.Krum:22 of -msgid "Function used to configure training. Defaults to None." +#: flwr.common.message.Message.create_reply:7 of +msgid "The content for the reply message." msgstr "" -#: flwr.server.strategy.bulyan.Bulyan:21 -#: flwr.server.strategy.fedadagrad.FedAdagrad:19 -#: flwr.server.strategy.fedadam.FedAdam:19 -#: flwr.server.strategy.fedavg.FedAvg:23 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:19 -#: flwr.server.strategy.fedavgm.FedAvgM:19 -#: flwr.server.strategy.fedopt.FedOpt:19 -#: flwr.server.strategy.fedprox.FedProx:55 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:19 -#: flwr.server.strategy.fedyogi.FedYogi:22 flwr.server.strategy.krum.Krum:24 of -msgid "Function used to configure validation. Defaults to None." +#: flwr.common.message.Message.create_reply:16 of +msgid "A new `Message` instance representing the reply." msgstr "" -#: flwr.server.strategy.bulyan.Bulyan:23 -#: flwr.server.strategy.fedadagrad.FedAdagrad:25 -#: flwr.server.strategy.fedadam.FedAdam:21 -#: flwr.server.strategy.fedavg.FedAvg:25 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:21 -#: flwr.server.strategy.fedavgm.FedAvgM:21 -#: flwr.server.strategy.fedopt.FedOpt:21 -#: flwr.server.strategy.fedprox.FedProx:57 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:21 -#: flwr.server.strategy.fedyogi.FedYogi:24 flwr.server.strategy.krum.Krum:26 of -msgid "Whether or not accept rounds containing failures. Defaults to True." +#: ../../source/ref-api/flwr.common.MessageType.rst:2 +msgid "MessageType" msgstr "" -#: flwr.server.strategy.bulyan.Bulyan:25 -#: flwr.server.strategy.fedadagrad.FedAdagrad:27 -#: flwr.server.strategy.fedadam.FedAdam:23 -#: flwr.server.strategy.fedavg.FedAvg:27 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:24 -#: flwr.server.strategy.fedavgm.FedAvgM:23 -#: flwr.server.strategy.fedopt.FedOpt:23 -#: flwr.server.strategy.fedprox.FedProx:59 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:23 -#: flwr.server.strategy.fedyogi.FedYogi:26 flwr.server.strategy.krum.Krum:28 of -msgid "Initial global model parameters." +#: ../../source/ref-api/flwr.common.MessageType.rst:30::1 +msgid ":py:obj:`EVALUATE `\\" msgstr "" -#: flwr.server.strategy.bulyan.Bulyan:27 of -msgid "" -"Byzantine resilient aggregation rule that is used as the first step of " -"the Bulyan (e.g., Krum)" +#: ../../source/ref-api/flwr.common.MessageType.rst:30::1 +msgid ":py:obj:`QUERY `\\" msgstr "" -#: flwr.server.strategy.bulyan.Bulyan:29 of -msgid "arguments to the first_aggregation rule" +#: ../../source/ref-api/flwr.common.MessageType.rst:30::1 +msgid ":py:obj:`TRAIN `\\" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`aggregate_evaluate " -"`\\ \\(server\\_round\\, " -"results\\, ...\\)" +#: ../../source/ref-api/flwr.common.MessageTypeLegacy.rst:2 +msgid "MessageTypeLegacy" msgstr "" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1 -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1 -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1 -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of -msgid "Aggregate evaluation losses using weighted average." +#: ../../source/ref-api/flwr.common.MessageTypeLegacy.rst:29::1 +msgid ":py:obj:`GET_PARAMETERS `\\" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`aggregate_fit `\\ " -"\\(server\\_round\\, results\\, failures\\)" +#: ../../source/ref-api/flwr.common.MessageTypeLegacy.rst:29::1 +msgid ":py:obj:`GET_PROPERTIES `\\" msgstr "" -#: flwr.server.strategy.bulyan.Bulyan.aggregate_fit:1 -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "Aggregate fit results using Bulyan." +#: flwr.common.Metadata.created_at:1::1 +#: flwr.common.Metadata.run_id:1 flwr.common.message.Metadata:3 of +msgid "An identifier for the current run." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`configure_evaluate " -"`\\ \\(server\\_round\\, " -"parameters\\, ...\\)" +#: flwr.common.Metadata.created_at:1::1 +#: flwr.common.Metadata.message_id:1 flwr.common.message.Metadata:5 of +msgid "An identifier for the current message." msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.configure_evaluate:1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.configure_evaluate:1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.configure_evaluate:1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.configure_evaluate:1 -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.configure_evaluate:1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.configure_evaluate:1 -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.configure_evaluate:1 -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.qfedavg.QFedAvg.configure_evaluate:1 -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 -#: flwr.server.strategy.strategy.Strategy.configure_evaluate:1 of -msgid "Configure the next round of evaluation." +#: flwr.common.Metadata.created_at:1::1 +#: flwr.common.Metadata.src_node_id:1 flwr.common.message.Metadata:7 of +msgid "An identifier for the node sending this message." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`configure_fit `\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +#: flwr.common.Metadata.created_at:1::1 +#: flwr.common.Metadata.dst_node_id:1 flwr.common.message.Metadata:9 of +msgid "An identifier for the node receiving this message." msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.configure_fit:1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.configure_fit:1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.configure_fit:1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.configure_fit:1 -#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive.configure_fit:1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.configure_fit:1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.configure_fit:1 -#: flwr.server.strategy.fedprox.FedProx.configure_fit:1 -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.configure_fit:1 -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.qfedavg.QFedAvg.configure_fit:1 -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 -#: flwr.server.strategy.strategy.Strategy.configure_fit:1 of -msgid "Configure the next round of training." +#: flwr.common.Metadata.created_at:1::1 +#: flwr.common.Metadata.reply_to_message:1 flwr.common.message.Metadata:11 of +msgid "An identifier for the message this message replies to." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.common.message.Metadata:13 of msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" +"An identifier for grouping messages. In some settings, this is used as " +"the FL round." msgstr "" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.evaluate:1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.evaluate:1 -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.evaluate:1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg.evaluate:1 -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of -msgid "Evaluate model parameters using an evaluation function." +#: flwr.common.message.Metadata:16 of +msgid "Time-to-live for this message in seconds." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +#: flwr.common.Metadata.created_at:1::1 +#: flwr.common.Metadata.message_type:1 flwr.common.message.Metadata:18 of +msgid "A string that encodes the action to be executed on the receiving end." msgstr "" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.initialize_parameters:1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.initialize_parameters:1 -#: flwr.server.strategy.fedavgm.FedAvgM.initialize_parameters:1 -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of -msgid "Initialize global model parameters." +#: flwr.common.message.Metadata:21 of +msgid "" +"An identifier that can be used when loading a particular data partition " +"for a ClientApp. Making use of this identifier is more relevant when " +"conducting simulations." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +#: flwr.common.Metadata.created_at:1::1 of +msgid ":py:obj:`created_at `\\" msgstr "" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.num_evaluation_clients:1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.num_evaluation_clients:1 -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.qfedavg.QFedAvg.num_evaluation_clients:1 of -msgid "Use a fraction of available clients for evaluation." +#: flwr.common.Metadata.created_at:1 +#: flwr.common.Metadata.created_at:1::1 of +msgid "Unix timestamp when the message was created." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`num_fit_clients `\\" -" \\(num\\_available\\_clients\\)" +#: flwr.common.Metadata.created_at:1::1 of +msgid ":py:obj:`dst_node_id `\\" msgstr "" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.num_fit_clients:1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.num_fit_clients:1 -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.qfedavg.QFedAvg.num_fit_clients:1 of -msgid "Return the sample size and the required number of available clients." +#: flwr.common.Metadata.created_at:1::1 of +msgid ":py:obj:`group_id `\\" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.DPFedAvgAdaptive.rst:2 -msgid "DPFedAvgAdaptive" +#: flwr.common.Metadata.created_at:1::1 +#: flwr.common.Metadata.group_id:1 of +msgid "An identifier for grouping messages." msgstr "" -#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive:1 of -msgid "Bases: :py:class:`~flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed`" -msgstr "" - -#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive:3 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed:3 of -msgid "This class is deprecated and will be removed in a future release." +#: flwr.common.Metadata.created_at:1::1 of +msgid ":py:obj:`message_id `\\" msgstr "" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" +#: flwr.common.Metadata.created_at:1::1 of +msgid ":py:obj:`message_type `\\" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of -msgid "Aggregate evaluation losses using the given strategy." +#: flwr.common.Metadata.created_at:1::1 of +msgid ":py:obj:`partition_id `\\" msgstr "" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`aggregate_fit " -"`\\ " -"\\(server\\_round\\, results\\, failures\\)" +#: flwr.common.Metadata.created_at:1::1 +#: flwr.common.Metadata.partition_id:1 of +msgid "An identifier telling which data partition a ClientApp should use." msgstr "" -#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive.aggregate_fit:1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of -msgid "Aggregate training results as in DPFedAvgFixed and update clip norms." +#: flwr.common.Metadata.created_at:1::1 of +msgid ":py:obj:`reply_to_message `\\" msgstr "" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +#: flwr.common.Metadata.created_at:1::1 of +msgid ":py:obj:`run_id `\\" msgstr "" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:1 of -msgid "Configure the next round of evaluation using the specified strategy." +#: flwr.common.Metadata.created_at:1::1 of +msgid ":py:obj:`src_node_id `\\" msgstr "" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`configure_fit " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +#: flwr.common.Metadata.created_at:1::1 of +msgid ":py:obj:`ttl `\\" msgstr "" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: flwr.common.Metadata.created_at:1::1 flwr.common.Metadata.ttl:1 #: of -msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" +msgid "Time-to-live for this message." msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.evaluate:1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.evaluate:1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.evaluate:1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.evaluate:1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.evaluate:1 of -msgid "Evaluate model parameters using an evaluation function from the strategy." +#: ../../source/ref-api/flwr.common.MetricsRecord.rst:2 +msgid "MetricsRecord" msgstr "" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of +#: flwr.common.record.metricsrecord.MetricsRecord:1 of msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +"Bases: :py:class:`~flwr.common.record.typeddict.TypedDict`\\ " +"[:py:class:`str`, :py:class:`int` | :py:class:`float` | " +":py:class:`~typing.List`\\ [:py:class:`int`] | :py:class:`~typing.List`\\" +" [:py:class:`float`]]" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.initialize_parameters:1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.initialize_parameters:1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.initialize_parameters:1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.initialize_parameters:1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.initialize_parameters:1 of -msgid "Initialize global model parameters using given strategy." +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid ":py:obj:`clear `\\ \\(\\)" msgstr "" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:3 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:6 -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:3 -#: flwr.server.strategy.strategy.Strategy.aggregate_fit:3 -#: flwr.server.strategy.strategy.Strategy.configure_evaluate:3 -#: flwr.server.strategy.strategy.Strategy.configure_fit:3 -#: flwr.server.strategy.strategy.Strategy.evaluate:6 of -msgid "The current round of federated learning." +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid ":py:obj:`count_bytes `\\ \\(\\)" msgstr "" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:7 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:10 -#: flwr.server.strategy.strategy.Strategy.configure_evaluate:7 -#: flwr.server.strategy.strategy.Strategy.configure_fit:7 -#: flwr.server.strategy.strategy.Strategy.initialize_parameters:3 of -msgid "The client manager which holds all currently connected clients." +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" msgstr "" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:10 -#: flwr.server.strategy.strategy.Strategy.configure_evaluate:10 of -msgid "" -"**evaluate_configuration** -- A list of tuples. Each tuple in the list " -"identifies a `ClientProxy` and the `EvaluateIns` for this particular " -"`ClientProxy`. If a particular `ClientProxy` is not included in this " -"list, it means that this `ClientProxy` will not participate in the next " -"round of federated evaluation." +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid ":py:obj:`items `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.DPFedAvgFixed.rst:2 -msgid "DPFedAvgFixed" +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid ":py:obj:`keys `\\ \\(\\)" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed:1 -#: flwr.server.strategy.fedavg.FedAvg:1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:1 of -msgid "Bases: :py:class:`~flwr.server.strategy.strategy.Strategy`" +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid ":py:obj:`pop `\\ \\(k\\[\\,d\\]\\)" msgstr "" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of msgid "" -":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" +":py:obj:`update `\\ \\(\\[E\\, " +"\\]\\*\\*F\\)" msgstr "" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`aggregate_fit " -"`\\ " -"\\(server\\_round\\, results\\, failures\\)" +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid ":py:obj:`values `\\ \\(\\)" msgstr "" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_fit:1 of -msgid "Aggregate training results using unweighted aggregation." +#: ../../source/ref-api/flwr.common.NDArray.rst:2 +msgid "NDArray" msgstr "" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +#: ../../source/ref-api/flwr.common.Parameters.rst:29::1 +msgid ":py:obj:`tensors `\\" msgstr "" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`configure_fit " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +#: ../../source/ref-api/flwr.common.Parameters.rst:29::1 +msgid ":py:obj:`tensor_type `\\" msgstr "" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:1 of -msgid "" -"Configure the next round of training incorporating Differential Privacy " -"(DP)." +#: ../../source/ref-api/flwr.common.ParametersRecord.rst:2 +msgid "ParametersRecord" msgstr "" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of +#: flwr.common.record.parametersrecord.ParametersRecord:1 of msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" +"Bases: :py:class:`~flwr.common.record.typeddict.TypedDict`\\ " +"[:py:class:`str`, :py:class:`~flwr.common.record.parametersrecord.Array`]" msgstr "" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of +#: flwr.common.record.parametersrecord.ParametersRecord:3 of msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +"A dataclass storing named Arrays in order. This means that it holds " +"entries as an OrderedDict[str, Array]. ParametersRecord objects can be " +"viewed as an equivalent to PyTorch's state_dict, but holding serialised " +"tensors instead." msgstr "" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:3 of -msgid "" -"Configuration of the next training round includes information related to " -"DP, such as clip norm and noise stddev." +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid ":py:obj:`clear `\\ \\(\\)" msgstr "" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:13 -#: flwr.server.strategy.strategy.Strategy.configure_fit:10 of -msgid "" -"**fit_configuration** -- A list of tuples. Each tuple in the list " -"identifies a `ClientProxy` and the `FitIns` for this particular " -"`ClientProxy`. If a particular `ClientProxy` is not included in this " -"list, it means that this `ClientProxy` will not participate in the next " -"round of federated learning." +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid ":py:obj:`count_bytes `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyClientSideAdaptiveClipping.rst:2 -msgid "DifferentialPrivacyClientSideAdaptiveClipping" +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:3 -#: of -msgid "Use `adaptiveclipping_mod` modifier at the client side." +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid ":py:obj:`items `\\ \\(\\)" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:5 -#: of -msgid "" -"In comparison to `DifferentialPrivacyServerSideAdaptiveClipping`, which " -"performs clipping on the server-side, " -"`DifferentialPrivacyClientSideAdaptiveClipping` expects clipping to " -"happen on the client-side, usually by using the built-in " -"`adaptiveclipping_mod`." +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid ":py:obj:`keys `\\ \\(\\)" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:10 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:3 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:10 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:3 -#: of -msgid "The strategy to which DP functionalities will be added by this wrapper." +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid ":py:obj:`pop `\\ \\(k\\[\\,d\\]\\)" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:12 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:5 -#: of -msgid "The noise multiplier for the Gaussian mechanism for model updates." +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid "" +":py:obj:`update `\\ \\(\\[E\\, " +"\\]\\*\\*F\\)" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:14 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:7 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:17 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:10 -#: of -msgid "The number of clients that are sampled on each round." +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +msgid ":py:obj:`values `\\ \\(\\)" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:16 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:9 -#: of +#: flwr.common.record.parametersrecord.ParametersRecord.count_bytes:3 of msgid "" -"The initial value of clipping norm. Defaults to 0.1. Andrew et al. " -"recommends to set to 0.1." +"Note that a small amount of Bytes might also be included in this counting" +" that correspond to metadata of the serialized object (e.g. of NumPy " +"array) needed for deseralization." msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:19 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:12 -#: of -msgid "The desired quantile of updates which should be clipped. Defaults to 0.5." +#: ../../source/ref-api/flwr.common.ReconnectIns.rst:2 +msgid "ReconnectIns" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:21 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:14 -#: of -msgid "" -"The learning rate for the clipping norm adaptation. Defaults to 0.2. " -"Andrew et al. recommends to set to 0.2." +#: ../../source/ref-api/flwr.common.ReconnectIns.rst:28::1 +msgid ":py:obj:`seconds `\\" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:24 -#: of -msgid "" -"The stddev of the noise added to the count of updates currently below the" -" estimate. Andrew et al. recommends to set to `expected_num_records/20`" +#: ../../source/ref-api/flwr.common.RecordSet.rst:2 +msgid "RecordSet" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:30 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:23 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:22 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:15 -#: of -msgid "Create a strategy:" +#: flwr.common.RecordSet.configs_records:1::1 of +msgid ":py:obj:`configs_records `\\" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:34 -#: of -msgid "" -"Wrap the strategy with the " -"`DifferentialPrivacyClientSideAdaptiveClipping` wrapper:" +#: flwr.common.RecordSet.configs_records:1 +#: flwr.common.RecordSet.configs_records:1::1 of +msgid "Dictionary holding ConfigsRecord instances." msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:40 -#: of -msgid "On the client, add the `adaptiveclipping_mod` to the client-side mods:" +#: flwr.common.RecordSet.configs_records:1::1 of +msgid ":py:obj:`metrics_records `\\" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`aggregate_evaluate " -"`\\" -" \\(server\\_round\\, results\\, ...\\)" +#: flwr.common.RecordSet.configs_records:1::1 +#: flwr.common.RecordSet.metrics_records:1 of +msgid "Dictionary holding MetricsRecord instances." msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`aggregate_fit " -"`\\" -" \\(server\\_round\\, results\\, failures\\)" +#: flwr.common.RecordSet.configs_records:1::1 of +msgid ":py:obj:`parameters_records `\\" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_fit:1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_fit:1 -#: of -msgid "Aggregate training results and update clip norms." +#: flwr.common.RecordSet.configs_records:1::1 +#: flwr.common.RecordSet.parameters_records:1 of +msgid "Dictionary holding ParametersRecord instances." msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`configure_evaluate " -"`\\" -" \\(server\\_round\\, parameters\\, ...\\)" +#: ../../source/ref-api/flwr.common.ServerMessage.rst:2 +msgid "ServerMessage" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`configure_fit " -"`\\" -" \\(server\\_round\\, parameters\\, ...\\)" +#: ../../source/ref-api/flwr.common.ServerMessage.rst:31::1 +msgid ":py:obj:`evaluate_ins `\\" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.common.ServerMessage.rst:31::1 +msgid ":py:obj:`fit_ins `\\" +msgstr "" + +#: ../../source/ref-api/flwr.common.ServerMessage.rst:31::1 msgid "" -":py:obj:`evaluate " -"`\\" -" \\(server\\_round\\, parameters\\)" +":py:obj:`get_parameters_ins " +"`\\" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.common.ServerMessage.rst:31::1 msgid "" -":py:obj:`initialize_parameters " -"`\\" -" \\(client\\_manager\\)" +":py:obj:`get_properties_ins " +"`\\" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyClientSideFixedClipping.rst:2 -msgid "DifferentialPrivacyClientSideFixedClipping" +#: ../../source/ref-api/flwr.common.Status.rst:2 +msgid "Status" msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:3 -#: of -msgid "Use `fixedclipping_mod` modifier at the client side." +#: ../../source/ref-api/flwr.common.Status.rst:29::1 +msgid ":py:obj:`code `\\" msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:5 -#: of -msgid "" -"In comparison to `DifferentialPrivacyServerSideFixedClipping`, which " -"performs clipping on the server-side, " -"`DifferentialPrivacyClientSideFixedClipping` expects clipping to happen " -"on the client-side, usually by using the built-in `fixedclipping_mod`." +#: ../../source/ref-api/flwr.common.Status.rst:29::1 +msgid ":py:obj:`message `\\" msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:12 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:5 -#: of -msgid "" -"The noise multiplier for the Gaussian mechanism for model updates. A " -"value of 1.0 or higher is recommended for strong privacy." +#: ../../source/ref-api/flwr.common.array_from_numpy.rst:2 +msgid "array\\_from\\_numpy" msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:15 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:8 -#: of -msgid "The value of the clipping norm." +#: ../../source/ref-api/flwr.common.bytes_to_ndarray.rst:2 +msgid "bytes\\_to\\_ndarray" msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:26 -#: of -msgid "" -"Wrap the strategy with the `DifferentialPrivacyClientSideFixedClipping` " -"wrapper:" +#: ../../source/ref-api/flwr.common.configure.rst:2 +msgid "configure" msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:32 -#: of -msgid "On the client, add the `fixedclipping_mod` to the client-side mods:" +#: ../../source/ref-api/flwr.common.event.rst:2 +msgid "event" msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`aggregate_evaluate " -"`\\" -" \\(server\\_round\\, results\\, ...\\)" +#: ../../source/ref-api/flwr.common.log.rst:2 +msgid "log" msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 -#: of +#: logging.Logger.log:3 of msgid "" -":py:obj:`aggregate_fit " -"`\\" -" \\(server\\_round\\, results\\, failures\\)" +"To pass exception information, use the keyword argument exc_info with a " +"true value, e.g." msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_fit:1 -#: of -msgid "Add noise to the aggregated parameters." +#: logging.Logger.log:6 of +#, python-format +msgid "logger.log(level, \"We have a %s\", \"mysterious problem\", exc_info=1)" msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`configure_evaluate " -"`\\" -" \\(server\\_round\\, parameters\\, ...\\)" +#: ../../source/ref-api/flwr.common.ndarray_to_bytes.rst:2 +msgid "ndarray\\_to\\_bytes" msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`configure_fit " -"`\\" -" \\(server\\_round\\, parameters\\, ...\\)" +#: ../../source/ref-api/flwr.common.ndarrays_to_parameters.rst:2 +msgid "ndarrays\\_to\\_parameters" msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`evaluate " -"`\\" -" \\(server\\_round\\, parameters\\)" +#: ../../source/ref-api/flwr.common.now.rst:2 +msgid "now" msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`initialize_parameters " -"`\\" -" \\(client\\_manager\\)" +#: ../../source/ref-api/flwr.common.parameters_to_ndarrays.rst:2 +msgid "parameters\\_to\\_ndarrays" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyServerSideAdaptiveClipping.rst:2 -msgid "DifferentialPrivacyServerSideAdaptiveClipping" +#: ../../source/ref-api/flwr.server.rst:2 +msgid "server" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:17 -#: of -msgid "" -"The standard deviation of the noise added to the count of updates below " -"the estimate. Andrew et al. recommends to set to " -"`expected_num_records/20`" +#: ../../source/ref-api/flwr.server.rst:24::1 +msgid ":py:obj:`run_server_app `\\ \\(\\)" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:27 -#: of -msgid "" -"Wrap the strategy with the DifferentialPrivacyServerSideAdaptiveClipping " -"wrapper" +#: ../../source/ref-api/flwr.server.rst:24::1 +#: flwr.server.run_serverapp.run_server_app:1 of +msgid "Run Flower server app." msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`aggregate_evaluate " -"`\\" -" \\(server\\_round\\, results\\, ...\\)" +#: ../../source/ref-api/flwr.server.rst:24::1 +msgid ":py:obj:`run_superlink `\\ \\(\\)" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`aggregate_fit " -"`\\" -" \\(server\\_round\\, results\\, failures\\)" +#: ../../source/ref-api/flwr.server.rst:24::1 +#: flwr.server.app.run_superlink:1 of +msgid "Run Flower SuperLink (Driver API and Fleet API)." msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.rst:24::1 msgid "" -":py:obj:`configure_evaluate " -"`\\" -" \\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`start_server `\\ \\(\\*\\[\\, " +"server\\_address\\, server\\, ...\\]\\)" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`configure_fit " -"`\\" -" \\(server\\_round\\, parameters\\, ...\\)" +#: ../../source/ref-api/flwr.server.rst:24::1 +#: flwr.server.app.start_server:1 of +msgid "Start a Flower server using the gRPC transport layer." msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`evaluate " -"`\\" -" \\(server\\_round\\, parameters\\)" +#: ../../source/ref-api/flwr.server.rst:38::1 +msgid ":py:obj:`ClientManager `\\ \\(\\)" msgstr "" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`initialize_parameters " -"`\\" -" \\(client\\_manager\\)" +#: ../../source/ref-api/flwr.server.rst:38::1 +#: flwr.server.client_manager.ClientManager:1 of +msgid "Abstract base class for managing Flower clients." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyServerSideFixedClipping.rst:2 -msgid "DifferentialPrivacyServerSideFixedClipping" +#: ../../source/ref-api/flwr.server.rst:38::1 +msgid ":py:obj:`Driver `\\ \\(\\)" msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:19 -#: of -msgid "" -"Wrap the strategy with the DifferentialPrivacyServerSideFixedClipping " -"wrapper" +#: ../../source/ref-api/flwr.server.rst:38::1 +#: flwr.server.driver.driver.Driver:1 of +msgid "Abstract base Driver class for the Driver API." msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`aggregate_evaluate " -"`\\" -" \\(server\\_round\\, results\\, ...\\)" +#: ../../source/ref-api/flwr.server.rst:38::1 +msgid ":py:obj:`History `\\ \\(\\)" msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.rst:38::1 +#: flwr.server.history.History:1 of +msgid "History class for training and/or evaluation metrics collection." +msgstr "" + +#: ../../source/ref-api/flwr.server.rst:38::1 msgid "" -":py:obj:`aggregate_fit " -"`\\" -" \\(server\\_round\\, results\\, failures\\)" +":py:obj:`LegacyContext `\\ \\(state\\[\\, " +"config\\, strategy\\, ...\\]\\)" msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_fit:1 -#: of -msgid "Compute the updates, clip, and pass them for aggregation." +#: ../../source/ref-api/flwr.server.rst:38::1 +#: flwr.server.compat.legacy_context.LegacyContext:1 of +msgid "Legacy Context." msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.rst:38::1 msgid "" -":py:obj:`configure_evaluate " -"`\\" -" \\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`Server `\\ \\(\\*\\, client\\_manager\\[\\, " +"strategy\\]\\)" msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.rst:38::1 msgid "" -":py:obj:`configure_fit " -"`\\" -" \\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`ServerApp `\\ \\(\\[server\\, config\\, " +"strategy\\, ...\\]\\)" msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`evaluate " -"`\\" -" \\(server\\_round\\, parameters\\)" +#: ../../source/ref-api/flwr.server.rst:38::1 +#: flwr.server.server_app.ServerApp:1 of +msgid "Flower ServerApp." msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.rst:38::1 msgid "" -":py:obj:`initialize_parameters " -"`\\" -" \\(client\\_manager\\)" +":py:obj:`ServerConfig `\\ \\(\\[num\\_rounds\\," +" round\\_timeout\\]\\)" msgstr "" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_fit:3 -#: of -msgid "Afterward, add noise to the aggregated parameters." +#: ../../source/ref-api/flwr.server.rst:38::1 +#: flwr.server.server_config.ServerConfig:1 of +msgid "Flower server config." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.FaultTolerantFedAvg.rst:2 -msgid "FaultTolerantFedAvg" +#: ../../source/ref-api/flwr.server.rst:38::1 +msgid ":py:obj:`SimpleClientManager `\\ \\(\\)" msgstr "" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" +#: ../../source/ref-api/flwr.server.rst:38::1 +#: flwr.server.client_manager.SimpleClientManager:1 of +msgid "Provides a pool of available clients." msgstr "" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`aggregate_fit " -"`\\ " -"\\(server\\_round\\, results\\, failures\\)" +#: ../../source/ref-api/flwr.server.rst:57::1 +msgid ":py:obj:`flwr.server.strategy `\\" msgstr "" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_fit:1 -#: flwr.server.strategy.fedadagrad.FedAdagrad.aggregate_fit:1 -#: flwr.server.strategy.fedadam.FedAdam.aggregate_fit:1 -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.aggregate_fit:1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_fit:1 -#: flwr.server.strategy.fedavgm.FedAvgM.aggregate_fit:1 -#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg.aggregate_fit:1 -#: flwr.server.strategy.fedyogi.FedYogi.aggregate_fit:1 -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_fit:1 of -msgid "Aggregate fit results using weighted average." +#: ../../source/ref-api/flwr.server.rst:57::1 +#: flwr.server.strategy:1 of +msgid "Contains the strategy abstraction and different implementations." msgstr "" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +#: ../../source/ref-api/flwr.server.rst:57::1 +msgid ":py:obj:`flwr.server.workflow `\\" msgstr "" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`configure_fit " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +#: ../../source/ref-api/flwr.server.rst:57::1 +#: flwr.server.workflow:1 of +msgid "Workflows." msgstr "" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" +#: ../../source/ref-api/flwr.server.ClientManager.rst:2 +msgid "ClientManager" msgstr "" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +#: flwr.server.client_manager.ClientManager.all:1::1 of +msgid ":py:obj:`all `\\ \\(\\)" msgstr "" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +#: flwr.server.client_manager.ClientManager.all:1 +#: flwr.server.client_manager.ClientManager.all:1::1 +#: flwr.server.client_manager.SimpleClientManager.all:1 +#: flwr.server.client_manager.SimpleClientManager.all:1::1 of +msgid "Return all available clients." msgstr "" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`num_fit_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +#: flwr.server.client_manager.ClientManager.all:1::1 of +msgid ":py:obj:`num_available `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.FedAdagrad.rst:2 -#: ../../source/ref-changelog.md:839 -msgid "FedAdagrad" +#: flwr.server.client_manager.ClientManager.all:1::1 +#: flwr.server.client_manager.ClientManager.num_available:1 +#: flwr.server.client_manager.SimpleClientManager.all:1::1 +#: flwr.server.client_manager.SimpleClientManager.num_available:1 of +msgid "Return the number of available clients." msgstr "" -#: flwr.server.strategy.fedadagrad.FedAdagrad:1 -#: flwr.server.strategy.fedadam.FedAdam:1 -#: flwr.server.strategy.fedyogi.FedYogi:1 of -msgid "Bases: :py:class:`~flwr.server.strategy.fedopt.FedOpt`" +#: flwr.server.client_manager.ClientManager.all:1::1 of +msgid ":py:obj:`register `\\ \\(client\\)" msgstr "" -#: flwr.server.strategy.fedadagrad.FedAdagrad:3 -#: flwr.server.strategy.fedadam.FedAdam:3 flwr.server.strategy.fedopt.FedOpt:3 -#: flwr.server.strategy.fedyogi.FedYogi:3 of -msgid "Implementation based on https://arxiv.org/abs/2003.00295v5" +#: flwr.server.client_manager.ClientManager.all:1::1 +#: flwr.server.client_manager.ClientManager.register:1 +#: flwr.server.client_manager.SimpleClientManager.all:1::1 +#: flwr.server.client_manager.SimpleClientManager.register:1 of +msgid "Register Flower ClientProxy instance." msgstr "" -#: flwr.server.strategy.fedadagrad.FedAdagrad:21 -#: flwr.server.strategy.fedadagrad.FedAdagrad:23 -#: flwr.server.strategy.fedadam.FedAdam:25 -#: flwr.server.strategy.fedadam.FedAdam:27 -#: flwr.server.strategy.fedavg.FedAvg:29 flwr.server.strategy.fedavg.FedAvg:31 -#: flwr.server.strategy.fedopt.FedOpt:25 flwr.server.strategy.fedopt.FedOpt:27 -#: flwr.server.strategy.fedprox.FedProx:61 -#: flwr.server.strategy.fedprox.FedProx:63 -#: flwr.server.strategy.fedyogi.FedYogi:28 -#: flwr.server.strategy.fedyogi.FedYogi:30 of -msgid "Metrics aggregation function, optional." +#: flwr.server.client_manager.ClientManager.all:1::1 of +msgid "" +":py:obj:`sample `\\ " +"\\(num\\_clients\\[\\, min\\_num\\_clients\\, criterion\\]\\)" msgstr "" -#: flwr.server.strategy.fedadagrad.FedAdagrad:29 -#: flwr.server.strategy.fedadam.FedAdam:29 -#: flwr.server.strategy.fedopt.FedOpt:29 of -msgid "Server-side learning rate. Defaults to 1e-1." +#: flwr.server.client_manager.ClientManager.all:1::1 +#: flwr.server.client_manager.ClientManager.sample:1 +#: flwr.server.client_manager.SimpleClientManager.all:1::1 +#: flwr.server.client_manager.SimpleClientManager.sample:1 of +msgid "Sample a number of Flower ClientProxy instances." msgstr "" -#: flwr.server.strategy.fedadagrad.FedAdagrad:31 -#: flwr.server.strategy.fedadam.FedAdam:31 -#: flwr.server.strategy.fedopt.FedOpt:31 of -msgid "Client-side learning rate. Defaults to 1e-1." +#: flwr.server.client_manager.ClientManager.all:1::1 of +msgid ":py:obj:`unregister `\\ \\(client\\)" msgstr "" -#: flwr.server.strategy.fedadagrad.FedAdagrad:33 -#: flwr.server.strategy.fedadam.FedAdam:37 -#: flwr.server.strategy.fedopt.FedOpt:37 of -msgid "Controls the algorithm's degree of adaptability. Defaults to 1e-9." +#: flwr.server.client_manager.ClientManager.all:1::1 +#: flwr.server.client_manager.ClientManager.unregister:1 +#: flwr.server.client_manager.SimpleClientManager.all:1::1 +#: flwr.server.client_manager.SimpleClientManager.unregister:1 of +msgid "Unregister Flower ClientProxy instance." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.client_manager.ClientManager.all:1::1 of msgid "" -":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" +":py:obj:`wait_for `\\ " +"\\(num\\_clients\\, timeout\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`aggregate_fit `\\" -" \\(server\\_round\\, results\\, failures\\)" +#: flwr.server.client_manager.ClientManager.all:1::1 +#: flwr.server.client_manager.ClientManager.wait_for:1 +#: flwr.server.client_manager.SimpleClientManager.all:1::1 +#: flwr.server.client_manager.SimpleClientManager.wait_for:1 of +msgid "Wait until at least `num_clients` are available." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +#: flwr.server.client_manager.ClientManager.num_available:3 +#: flwr.server.client_manager.SimpleClientManager.num_available:3 of +msgid "**num_available** -- The number of currently available clients." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`configure_fit `\\" -" \\(server\\_round\\, parameters\\, ...\\)" -msgstr "" - -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.client_manager.ClientManager.register:6 +#: flwr.server.client_manager.SimpleClientManager.register:6 of msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" +"**success** -- Indicating if registration was successful. False if " +"ClientProxy is already registered or can not be registered for any " +"reason." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +#: flwr.server.client_manager.ClientManager.unregister:3 +#: flwr.server.client_manager.SimpleClientManager.unregister:3 of +msgid "This method is idempotent." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +#: ../../source/ref-api/flwr.server.Driver.rst:2 +msgid "Driver" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.driver.driver.Driver.create_message:1::1 of msgid "" -":py:obj:`num_fit_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`create_message `\\ " +"\\(content\\, message\\_type\\, ...\\[\\, ttl\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.FedAdam.rst:2 -msgid "FedAdam" +#: flwr.server.driver.driver.Driver.create_message:1 +#: flwr.server.driver.driver.Driver.create_message:1::1 of +msgid "Create a new message with specified parameters." msgstr "" -#: flwr.server.strategy.fedadam.FedAdam:33 -#: flwr.server.strategy.fedyogi.FedYogi:36 of -msgid "Momentum parameter. Defaults to 0.9." +#: flwr.server.driver.driver.Driver.create_message:1::1 of +msgid ":py:obj:`get_node_ids `\\ \\(\\)" msgstr "" -#: flwr.server.strategy.fedadam.FedAdam:35 -#: flwr.server.strategy.fedyogi.FedYogi:38 of -msgid "Second moment parameter. Defaults to 0.99." +#: flwr.server.driver.driver.Driver.create_message:1::1 +#: flwr.server.driver.driver.Driver.get_node_ids:1 of +msgid "Get node IDs." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.driver.driver.Driver.create_message:1::1 of msgid "" -":py:obj:`aggregate_evaluate " -"`\\ \\(server\\_round\\," -" results\\, ...\\)" +":py:obj:`pull_messages `\\ " +"\\(message\\_ids\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`aggregate_fit `\\ " -"\\(server\\_round\\, results\\, failures\\)" +#: flwr.server.driver.driver.Driver.create_message:1::1 +#: flwr.server.driver.driver.Driver.pull_messages:1 of +msgid "Pull messages based on message IDs." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.driver.driver.Driver.create_message:1::1 of msgid "" -":py:obj:`configure_evaluate " -"`\\ \\(server\\_round\\," -" parameters\\, ...\\)" +":py:obj:`push_messages `\\ " +"\\(messages\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`configure_fit `\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +#: flwr.server.driver.driver.Driver.create_message:1::1 +#: flwr.server.driver.driver.Driver.push_messages:1 of +msgid "Push messages to specified node IDs." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.driver.driver.Driver.create_message:1::1 of msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" +":py:obj:`send_and_receive `\\ " +"\\(messages\\, \\*\\[\\, timeout\\]\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +#: flwr.server.driver.driver.Driver.create_message:1::1 +#: flwr.server.driver.driver.Driver.send_and_receive:1 of +msgid "Push messages to specified node IDs and pull the reply messages." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.driver.driver.Driver.create_message:3 of msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +"This method constructs a new `Message` with given content and metadata. " +"The `run_id` and `src_node_id` will be set automatically." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.driver.driver.Driver.create_message:6 of msgid "" -":py:obj:`num_fit_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +"The content for the new message. This holds records that are to be sent " +"to the destination node." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.FedAvg.rst:2 -msgid "FedAvg" +#: flwr.server.driver.driver.Driver.create_message:9 of +msgid "" +"The type of the message, defining the action to be executed on the " +"receiving end." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg:3 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:3 of -msgid "Implementation based on https://arxiv.org/abs/1602.05629" +#: flwr.server.driver.driver.Driver.create_message:12 of +msgid "The ID of the destination node to which the message is being sent." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg:5 flwr.server.strategy.fedprox.FedProx:37 -#: of +#: flwr.server.driver.driver.Driver.create_message:14 of msgid "" -"Fraction of clients used during training. In case `min_fit_clients` is " -"larger than `fraction_fit * available_clients`, `min_fit_clients` will " -"still be sampled. Defaults to 1.0." +"The ID of the group to which this message is associated. In some " +"settings, this is used as the FL round." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg:9 flwr.server.strategy.fedprox.FedProx:41 -#: of +#: flwr.server.driver.driver.Driver.create_message:17 of msgid "" -"Fraction of clients used during validation. In case " -"`min_evaluate_clients` is larger than `fraction_evaluate * " -"available_clients`, `min_evaluate_clients` will still be sampled. " -"Defaults to 1.0." +"Time-to-live for the round trip of this message, i.e., the time from " +"sending this message to receiving a reply. It specifies in seconds the " +"duration for which the message and its potential reply are considered " +"valid. If unset, the default TTL (i.e., `common.DEFAULT_TTL`) will be " +"used." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg:33 of -msgid "Enable (True) or disable (False) in-place aggregation of model updates." +#: flwr.server.driver.driver.Driver.create_message:23 of +msgid "" +"**message** -- A new `Message` instance with the specified content and " +"metadata." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.driver.driver.Driver.pull_messages:3 of msgid "" -":py:obj:`aggregate_evaluate " -"`\\ \\(server\\_round\\, " -"results\\, ...\\)" +"This method is used to collect messages from the SuperLink that " +"correspond to a set of given message IDs." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`aggregate_fit `\\ " -"\\(server\\_round\\, results\\, failures\\)" +#: flwr.server.driver.driver.Driver.pull_messages:6 of +msgid "An iterable of message IDs for which reply messages are to be retrieved." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`configure_evaluate " -"`\\ \\(server\\_round\\, " -"parameters\\, ...\\)" +#: flwr.server.driver.driver.Driver.pull_messages:9 of +msgid "**messages** -- An iterable of messages received." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.driver.driver.Driver.push_messages:3 of msgid "" -":py:obj:`configure_fit `\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +"This method takes an iterable of messages and sends each message to the " +"node specified in `dst_node_id`." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" +#: flwr.server.driver.driver.Driver.push_messages:6 +#: flwr.server.driver.driver.Driver.send_and_receive:7 of +msgid "An iterable of messages to be sent." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.driver.driver.Driver.push_messages:9 of msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +"**message_ids** -- An iterable of IDs for the messages that were sent, " +"which can be used to pull replies." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.driver.driver.Driver.send_and_receive:3 of msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +"This method sends a list of messages to their destination node IDs and " +"then waits for the replies. It continues to pull replies until either all" +" replies are received or the specified timeout duration is exceeded." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.driver.driver.Driver.send_and_receive:9 of msgid "" -":py:obj:`num_fit_clients `\\" -" \\(num\\_available\\_clients\\)" +"The timeout duration in seconds. If specified, the method will wait for " +"replies for this duration. If `None`, there is no time limit and the " +"method will wait until replies for all messages are received." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.FedAvgAndroid.rst:2 -msgid "FedAvgAndroid" +#: flwr.server.driver.driver.Driver.send_and_receive:14 of +msgid "**replies** -- An iterable of reply messages received from the SuperLink." msgstr "" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: of +#: flwr.server.driver.driver.Driver.send_and_receive:19 of msgid "" -":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" +"This method uses `push_messages` to send the messages and `pull_messages`" +" to collect the replies. If `timeout` is set, the method may not return " +"replies for all sent messages. A message remains valid until its TTL, " +"which is not affected by `timeout`." msgstr "" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`aggregate_fit " -"`\\ " -"\\(server\\_round\\, results\\, failures\\)" +#: ../../source/ref-api/flwr.server.History.rst:2 +msgid "History" msgstr "" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: of +#: flwr.server.history.History.add_loss_centralized:1::1 of msgid "" -":py:obj:`bytes_to_ndarray " -"`\\ \\(tensor\\)" +":py:obj:`add_loss_centralized " +"`\\ \\(server\\_round\\, " +"loss\\)" msgstr "" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.bytes_to_ndarray:1 of -msgid "Deserialize NumPy array from bytes." +#: flwr.server.history.History.add_loss_centralized:1 +#: flwr.server.history.History.add_loss_centralized:1::1 of +msgid "Add one loss entry (from centralized evaluation)." msgstr "" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: of +#: flwr.server.history.History.add_loss_centralized:1::1 of msgid "" -":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`add_loss_distributed " +"`\\ \\(server\\_round\\, " +"loss\\)" msgstr "" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`configure_fit " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +#: flwr.server.history.History.add_loss_centralized:1::1 +#: flwr.server.history.History.add_loss_distributed:1 of +msgid "Add one loss entry (from distributed evaluation)." msgstr "" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: of +#: flwr.server.history.History.add_loss_centralized:1::1 of msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" +":py:obj:`add_metrics_centralized " +"`\\ \\(server\\_round\\, " +"metrics\\)" msgstr "" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +#: flwr.server.history.History.add_loss_centralized:1::1 +#: flwr.server.history.History.add_metrics_centralized:1 of +msgid "Add metrics entries (from centralized evaluation)." msgstr "" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: of +#: flwr.server.history.History.add_loss_centralized:1::1 of msgid "" -":py:obj:`ndarray_to_bytes " -"`\\ \\(ndarray\\)" -msgstr "" - -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.ndarray_to_bytes:1 of -msgid "Serialize NumPy array to bytes." +":py:obj:`add_metrics_distributed " +"`\\ \\(server\\_round\\, " +"metrics\\)" msgstr "" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`ndarrays_to_parameters " -"`\\ " -"\\(ndarrays\\)" +#: flwr.server.history.History.add_loss_centralized:1::1 +#: flwr.server.history.History.add_metrics_distributed:1 of +msgid "Add metrics entries (from distributed evaluation)." msgstr "" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: of +#: flwr.server.history.History.add_loss_centralized:1::1 of msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`add_metrics_distributed_fit " +"`\\ \\(server\\_round\\," +" ...\\)" msgstr "" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`num_fit_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +#: flwr.server.history.History.add_loss_centralized:1::1 +#: flwr.server.history.History.add_metrics_distributed_fit:1 of +msgid "Add metrics entries (from distributed fit)." msgstr "" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`parameters_to_ndarrays " -"`\\ " -"\\(parameters\\)" +#: ../../source/ref-api/flwr.server.LegacyContext.rst:2 +msgid "LegacyContext" msgstr "" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.parameters_to_ndarrays:1 -#: of -msgid "Convert parameters object to NumPy weights." +#: flwr.server.compat.legacy_context.LegacyContext:1 of +msgid "Bases: :py:class:`~flwr.common.context.Context`" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.FedAvgM.rst:2 -msgid "FedAvgM" +#: ../../source/ref-api/flwr.server.LegacyContext.rst:32::1 +msgid ":py:obj:`config `\\" msgstr "" -#: flwr.server.strategy.fedavgm.FedAvgM:3 of -msgid "Implementation based on https://arxiv.org/abs/1909.06335" +#: ../../source/ref-api/flwr.server.LegacyContext.rst:32::1 +msgid ":py:obj:`strategy `\\" msgstr "" -#: flwr.server.strategy.fedavgm.FedAvgM:25 of -msgid "" -"Server-side learning rate used in server-side optimization. Defaults to " -"1.0." +#: ../../source/ref-api/flwr.server.LegacyContext.rst:32::1 +msgid ":py:obj:`client_manager `\\" msgstr "" -#: flwr.server.strategy.fedavgm.FedAvgM:28 of -msgid "Server-side momentum factor used for FedAvgM. Defaults to 0.0." +#: ../../source/ref-api/flwr.server.LegacyContext.rst:32::1 +msgid ":py:obj:`history `\\" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`aggregate_evaluate " -"`\\ \\(server\\_round\\," -" results\\, ...\\)" +#: ../../source/ref-api/flwr.server.LegacyContext.rst:32::1 +msgid ":py:obj:`state `\\" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`aggregate_fit `\\ " -"\\(server\\_round\\, results\\, failures\\)" +#: ../../source/ref-api/flwr.server.Server.rst:2 +msgid "Server" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`configure_evaluate " -"`\\ \\(server\\_round\\," -" parameters\\, ...\\)" +#: flwr.server.server.Server.client_manager:1::1 of +msgid ":py:obj:`client_manager `\\ \\(\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`configure_fit `\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +#: flwr.server.server.Server.client_manager:1 +#: flwr.server.server.Server.client_manager:1::1 of +msgid "Return ClientManager." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.server.Server.client_manager:1::1 of msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" +":py:obj:`disconnect_all_clients " +"`\\ \\(timeout\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +#: flwr.server.server.Server.client_manager:1::1 +#: flwr.server.server.Server.disconnect_all_clients:1 of +msgid "Send shutdown signal to all clients." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.server.Server.client_manager:1::1 of msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`evaluate_round `\\ " +"\\(server\\_round\\, timeout\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`num_fit_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +#: flwr.server.server.Server.client_manager:1::1 +#: flwr.server.server.Server.evaluate_round:1 of +msgid "Validate current global model on a number of clients." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.FedMedian.rst:2 -msgid "FedMedian" +#: flwr.server.server.Server.client_manager:1::1 of +msgid ":py:obj:`fit `\\ \\(num\\_rounds\\, timeout\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" +#: flwr.server.server.Server.client_manager:1::1 +#: flwr.server.server.Server.fit:1 of +msgid "Run federated averaging for a number of rounds." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.server.Server.client_manager:1::1 of msgid "" -":py:obj:`aggregate_fit `\\ " -"\\(server\\_round\\, results\\, failures\\)" +":py:obj:`fit_round `\\ \\(server\\_round\\," +" timeout\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedmedian.FedMedian.aggregate_fit:1 of -msgid "Aggregate fit results using median." +#: flwr.server.server.Server.client_manager:1::1 +#: flwr.server.server.Server.fit_round:1 of +msgid "Perform a single round of federated averaging." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.server.Server.client_manager:1::1 of msgid "" -":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`set_max_workers `\\ " +"\\(max\\_workers\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`configure_fit `\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +#: flwr.server.server.Server.client_manager:1::1 +#: flwr.server.server.Server.set_max_workers:1 of +msgid "Set the max_workers used by ThreadPoolExecutor." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" +#: flwr.server.server.Server.client_manager:1::1 of +msgid ":py:obj:`set_strategy `\\ \\(strategy\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +#: flwr.server.server.Server.client_manager:1::1 +#: flwr.server.server.Server.set_strategy:1 of +msgid "Replace server strategy." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +#: ../../source/ref-api/flwr.server.ServerApp.rst:2 +msgid "ServerApp" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`num_fit_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +#: flwr.server.server_app.ServerApp:5 of +msgid "Use the `ServerApp` with an existing `Strategy`:" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.FedOpt.rst:2 -msgid "FedOpt" +#: flwr.server.server_app.ServerApp:15 of +msgid "Use the `ServerApp` with a custom main function:" msgstr "" -#: flwr.server.strategy.fedopt.FedOpt:33 of -msgid "Momentum parameter. Defaults to 0.0." +#: flwr.server.server_app.ServerApp.main:1::1 of +msgid ":py:obj:`main `\\ \\(\\)" msgstr "" -#: flwr.server.strategy.fedopt.FedOpt:35 of -msgid "Second moment parameter. Defaults to 0.0." +#: flwr.server.server_app.ServerApp.main:1 +#: flwr.server.server_app.ServerApp.main:1::1 of +msgid "Return a decorator that registers the main fn with the server app." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`aggregate_evaluate " -"`\\ \\(server\\_round\\, " -"results\\, ...\\)" +#: ../../source/ref-api/flwr.server.ServerConfig.rst:2 +msgid "ServerConfig" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.server_config.ServerConfig:3 of msgid "" -":py:obj:`aggregate_fit `\\ " -"\\(server\\_round\\, results\\, failures\\)" +"All attributes have default values which allows users to configure just " +"the ones they care about." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`configure_evaluate " -"`\\ \\(server\\_round\\, " -"parameters\\, ...\\)" +#: ../../source/ref-api/flwr.server.ServerConfig.rst:29::1 +msgid ":py:obj:`num_rounds `\\" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`configure_fit `\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +#: ../../source/ref-api/flwr.server.ServerConfig.rst:29::1 +msgid ":py:obj:`round_timeout `\\" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" +#: ../../source/ref-api/flwr.server.SimpleClientManager.rst:2 +msgid "SimpleClientManager" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +#: flwr.server.client_manager.SimpleClientManager:1 of +msgid "Bases: :py:class:`~flwr.server.client_manager.ClientManager`" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +#: flwr.server.client_manager.SimpleClientManager.all:1::1 of +msgid ":py:obj:`all `\\ \\(\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.client_manager.SimpleClientManager.all:1::1 of msgid "" -":py:obj:`num_fit_clients `\\" -" \\(num\\_available\\_clients\\)" +":py:obj:`num_available `\\" +" \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.FedProx.rst:2 -msgid "FedProx" +#: flwr.server.client_manager.SimpleClientManager.all:1::1 of +msgid "" +":py:obj:`register `\\ " +"\\(client\\)" msgstr "" -#: flwr.server.strategy.fedprox.FedProx:3 of -msgid "Implementation based on https://arxiv.org/abs/1812.06127" +#: flwr.server.client_manager.SimpleClientManager.all:1::1 of +msgid "" +":py:obj:`sample `\\ " +"\\(num\\_clients\\[\\, min\\_num\\_clients\\, criterion\\]\\)" msgstr "" -#: flwr.server.strategy.fedprox.FedProx:5 of +#: flwr.server.client_manager.SimpleClientManager.all:1::1 of msgid "" -"The strategy in itself will not be different than FedAvg, the client " -"needs to be adjusted. A proximal term needs to be added to the loss " -"function during the training:" +":py:obj:`unregister `\\ " +"\\(client\\)" msgstr "" -#: flwr.server.strategy.fedprox.FedProx:9 of +#: flwr.server.client_manager.SimpleClientManager.all:1::1 of msgid "" -"\\\\frac{\\\\mu}{2} || w - w^t ||^2\n" -"\n" +":py:obj:`wait_for `\\ " +"\\(num\\_clients\\[\\, timeout\\]\\)" msgstr "" -#: flwr.server.strategy.fedprox.FedProx:12 of +#: flwr.server.client_manager.SimpleClientManager.wait_for:3 of msgid "" -"Where $w^t$ are the global parameters and $w$ are the local weights the " -"function will be optimized with." +"Blocks until the requested number of clients is available or until a " +"timeout is reached. Current timeout default: 1 day." msgstr "" -#: flwr.server.strategy.fedprox.FedProx:15 of -msgid "In PyTorch, for example, the loss would go from:" +#: flwr.server.client_manager.SimpleClientManager.wait_for:6 of +msgid "The number of clients to wait for." msgstr "" -#: flwr.server.strategy.fedprox.FedProx:21 of -msgid "To:" +#: flwr.server.client_manager.SimpleClientManager.wait_for:8 of +msgid "The time in seconds to wait for, defaults to 86400 (24h)." msgstr "" -#: flwr.server.strategy.fedprox.FedProx:30 of -msgid "" -"With `global_params` being a copy of the parameters before the training " -"takes place." +#: flwr.server.client_manager.SimpleClientManager.wait_for:11 of +msgid "**success**" msgstr "" -#: flwr.server.strategy.fedprox.FedProx:65 of -msgid "" -"The weight of the proximal term used in the optimization. 0.0 makes this " -"strategy equivalent to FedAvg, and the higher the coefficient, the more " -"regularization will be used (that is, the client parameters will need to " -"be closer to the server parameters during training)." +#: ../../source/ref-api/flwr.server.run_driver_api.rst:2 +msgid "run\\_driver\\_api" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`aggregate_evaluate " -"`\\ \\(server\\_round\\," -" results\\, ...\\)" +#: ../../source/ref-api/flwr.server.run_fleet_api.rst:2 +msgid "run\\_fleet\\_api" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`aggregate_fit `\\ " -"\\(server\\_round\\, results\\, failures\\)" +#: ../../source/ref-api/flwr.server.run_server_app.rst:2 +msgid "run\\_server\\_app" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: ../../source/ref-api/flwr.server.run_superlink.rst:2 +msgid "run\\_superlink" +msgstr "" + +#: ../../source/ref-api/flwr.server.start_server.rst:2 +msgid "start\\_server" +msgstr "" + +#: flwr.server.app.start_server:3 of +msgid "The IPv4 or IPv6 address of the server. Defaults to `\"[::]:8080\"`." +msgstr "" + +#: flwr.server.app.start_server:5 of msgid "" -":py:obj:`configure_evaluate " -"`\\ \\(server\\_round\\," -" parameters\\, ...\\)" +"A server implementation, either `flwr.server.Server` or a subclass " +"thereof. If no instance is provided, then `start_server` will create one." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.app.start_server:9 flwr.simulation.app.start_simulation:28 of msgid "" -":py:obj:`configure_fit `\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +"Currently supported values are `num_rounds` (int, default: 1) and " +"`round_timeout` in seconds (float, default: None)." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.app.start_server:12 of msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" +"An implementation of the abstract base class " +"`flwr.server.strategy.Strategy`. If no strategy is provided, then " +"`start_server` will use `flwr.server.strategy.FedAvg`." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.app.start_server:16 of msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +"An implementation of the abstract base class `flwr.server.ClientManager`." +" If no implementation is provided, then `start_server` will use " +"`flwr.server.client_manager.SimpleClientManager`." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.app.start_server:21 of msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +"The maximum length of gRPC messages that can be exchanged with the Flower" +" clients. The default should be sufficient for most models. Users who " +"train very large models might need to increase this value. Note that the " +"Flower clients need to be started with the same value (see " +"`flwr.client.start_client`), otherwise clients will not know about the " +"increased limit and block larger messages." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.app.start_server:28 of msgid "" -":py:obj:`num_fit_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +"Tuple containing root certificate, server certificate, and private key to" +" start a secure SSL-enabled server. The tuple is expected to have three " +"bytes elements in the following order: * CA certificate. * " +"server certificate. * server private key." msgstr "" -#: flwr.server.strategy.fedprox.FedProx.configure_fit:3 of -msgid "Sends the proximal factor mu to the clients" +#: flwr.server.app.start_server:28 of +msgid "" +"Tuple containing root certificate, server certificate, and private key to" +" start a secure SSL-enabled server. The tuple is expected to have three " +"bytes elements in the following order:" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.FedTrimmedAvg.rst:2 -msgid "FedTrimmedAvg" +#: flwr.server.app.start_server:32 of +msgid "CA certificate." msgstr "" -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:3 of -msgid "Implemented based on: https://arxiv.org/abs/1803.01498" +#: flwr.server.app.start_server:33 of +msgid "server certificate." msgstr "" -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:25 of -msgid "Fraction to cut off of both tails of the distribution. Defaults to 0.2." +#: flwr.server.app.start_server:34 of +msgid "server private key." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" +#: flwr.server.app.start_server:37 of +msgid "**hist** -- Object containing training and evaluation metrics." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`aggregate_fit " -"`\\ " -"\\(server\\_round\\, results\\, failures\\)" +#: flwr.server.app.start_server:42 of +msgid "Starting an insecure server:" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.aggregate_fit:1 of -msgid "Aggregate fit results using trimmed average." +#: flwr.server.app.start_server:46 of +msgid "Starting an SSL-enabled server:" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +#: ../../source/ref-api/flwr.server.strategy.rst:2 +msgid "strategy" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`configure_fit " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`Bulyan `\\ \\(\\*\\, " +"fraction\\_fit\\, fraction\\_evaluate\\, ...\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.bulyan.Bulyan:1 of +msgid "Bulyan strategy." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +":py:obj:`DPFedAvgAdaptive `\\ " +"\\(strategy\\, num\\_sampled\\_clients\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive:1 of +msgid "Wrapper for configuring a Strategy for DP with Adaptive Clipping." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`num_fit_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`DPFedAvgFixed `\\ " +"\\(strategy\\, num\\_sampled\\_clients\\, ...\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.FedXgbBagging.rst:2 -msgid "FedXgbBagging" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed:1 of +msgid "Wrapper for configuring a Strategy for DP with Fixed Clipping." msgstr "" -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" +":py:obj:`DifferentialPrivacyClientSideAdaptiveClipping " +"`\\ " +"\\(...\\)" msgstr "" -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1 -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:1 #: of -msgid "Aggregate evaluation metrics using average." +msgid "Strategy wrapper for central DP with client-side adaptive clipping." msgstr "" -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`aggregate_fit " -"`\\ " -"\\(server\\_round\\, results\\, failures\\)" +":py:obj:`DifferentialPrivacyServerSideAdaptiveClipping " +"`\\ " +"\\(...\\)" msgstr "" -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_fit:1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_fit:1 of -msgid "Aggregate fit results using bagging." +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:1 +#: of +msgid "Strategy wrapper for central DP with server-side adaptive clipping." msgstr "" -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`DifferentialPrivacyClientSideFixedClipping " +"`\\ " +"\\(...\\)" msgstr "" -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:1 #: of -msgid "" -":py:obj:`configure_fit " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +msgid "Strategy wrapper for central DP with client-side fixed clipping." msgstr "" -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" -msgstr "" - -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +":py:obj:`DifferentialPrivacyServerSideFixedClipping " +"`\\ " +"\\(...\\)" msgstr "" -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:1 #: of -msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +msgid "Strategy wrapper for central DP with server-side fixed clipping." msgstr "" -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`num_fit_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`FedAdagrad `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, ...\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.FedXgbCyclic.rst:2 -msgid "FedXgbCyclic" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fedadagrad.FedAdagrad:1 of +msgid "FedAdagrad strategy - Adaptive Federated Optimization using Adagrad." msgstr "" -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" +":py:obj:`FedAdam `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, ...\\]\\)" msgstr "" -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`aggregate_fit " -"`\\ \\(server\\_round\\," -" results\\, failures\\)" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fedadam.FedAdam:1 of +msgid "FedAdam - Adaptive Federated Optimization using Adam." msgstr "" -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`FedAvg `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, fraction\\_evaluate\\, ...\\]\\)" msgstr "" -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`configure_fit " -"`\\ \\(server\\_round\\," -" parameters\\, ...\\)" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fedavg.FedAvg:1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:1 of +msgid "Federated Averaging strategy." msgstr "" -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" +":py:obj:`FedAvgAndroid `\\ " +"\\(\\*\\[\\, fraction\\_fit\\, ...\\]\\)" msgstr "" -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +":py:obj:`FedAvgM `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, ...\\]\\)" msgstr "" -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fedavgm.FedAvgM:1 of +msgid "Federated Averaging with Momentum strategy." msgstr "" -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`num_fit_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`FedMedian `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, ...\\]\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.FedXgbNnAvg.rst:2 -msgid "FedXgbNnAvg" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fedmedian.FedMedian:1 of +msgid "Configurable FedMedian strategy implementation." msgstr "" -#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg:5 of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -"This strategy is deprecated, but a copy of it is available in Flower " -"Baselines: " -"https://github.com/adap/flower/tree/main/baselines/hfedxgboost." +":py:obj:`FedOpt `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, fraction\\_evaluate\\, ...\\]\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fedopt.FedOpt:1 of +msgid "Federated Optim strategy." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`aggregate_fit " -"`\\ \\(server\\_round\\, " -"results\\, failures\\)" +":py:obj:`FedProx `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, ...\\]\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fedprox.FedProx:1 of +msgid "Federated Optimization strategy." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`configure_fit " -"`\\ \\(server\\_round\\, " -"parameters\\, ...\\)" +":py:obj:`FedTrimmedAvg `\\ " +"\\(\\*\\[\\, fraction\\_fit\\, ...\\]\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:1 of +msgid "Federated Averaging with Trimmed Mean [Dong Yin, et al., 2021]." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +":py:obj:`FedXgbBagging `\\ " +"\\(\\[evaluate\\_function\\]\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging:1 of +msgid "Configurable FedXgbBagging strategy implementation." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`num_fit_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`FedXgbCyclic `\\ " +"\\(\\*\\*kwargs\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.FedYogi.rst:2 -msgid "FedYogi" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic:1 of +msgid "Configurable FedXgbCyclic strategy implementation." msgstr "" -#: flwr.server.strategy.fedyogi.FedYogi:32 of -msgid "Server-side learning rate. Defaults to 1e-2." +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +msgid "" +":py:obj:`FedXgbNnAvg `\\ \\(\\*args\\, " +"\\*\\*kwargs\\)" msgstr "" -#: flwr.server.strategy.fedyogi.FedYogi:34 of -msgid "Client-side learning rate. Defaults to 0.0316." +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg:1 of +msgid "Configurable FedXgbNnAvg strategy implementation." msgstr "" -#: flwr.server.strategy.fedyogi.FedYogi:40 of -msgid "Controls the algorithm's degree of adaptability. Defaults to 1e-3." +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +msgid "" +":py:obj:`FedYogi `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, ...\\]\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`aggregate_evaluate " -"`\\ \\(server\\_round\\," -" results\\, ...\\)" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fedyogi.FedYogi:1 of +msgid "FedYogi [Reddi et al., 2020] strategy." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`aggregate_fit `\\ " -"\\(server\\_round\\, results\\, failures\\)" +":py:obj:`FaultTolerantFedAvg " +"`\\ \\(\\*\\[\\, " +"fraction\\_fit\\, ...\\]\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`configure_evaluate " -"`\\ \\(server\\_round\\," -" parameters\\, ...\\)" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg:1 of +msgid "Configurable fault-tolerant FedAvg strategy implementation." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`configure_fit `\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`Krum `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, fraction\\_evaluate\\, ...\\]\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.krum.Krum:1 of +msgid "Krum [Blanchard et al., 2017] strategy." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +":py:obj:`QFedAvg `\\ \\(\\*\\[\\, " +"q\\_param\\, qffl\\_learning\\_rate\\, ...\\]\\)" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.qfedavg.QFedAvg:1 of +msgid "Configurable QFedAvg strategy implementation." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`num_fit_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +msgid ":py:obj:`Strategy `\\ \\(\\)" msgstr "" -#: ../../source/ref-api/flwr.server.strategy.Krum.rst:2 -msgid "Krum" -msgstr "" - -#: flwr.server.strategy.krum.Krum:3 of -msgid "Implementation based on https://arxiv.org/abs/1703.02757" -msgstr "" - -#: flwr.server.strategy.krum.Krum:17 of -msgid "" -"Number of clients to keep before averaging (MultiKrum). Defaults to 0, in" -" that case classical Krum is applied." +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.strategy.Strategy:1 of +msgid "Abstract base class for server strategy implementations." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`aggregate_evaluate " -"`\\ \\(server\\_round\\, " -"results\\, ...\\)" +#: ../../source/ref-api/flwr.server.strategy.Bulyan.rst:2 +msgid "Bulyan" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`aggregate_fit `\\ " -"\\(server\\_round\\, results\\, failures\\)" +#: flwr.server.strategy.bulyan.Bulyan:1 +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg:1 +#: flwr.server.strategy.fedavgm.FedAvgM:1 +#: flwr.server.strategy.fedmedian.FedMedian:1 +#: flwr.server.strategy.fedopt.FedOpt:1 flwr.server.strategy.fedprox.FedProx:1 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:1 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging:1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic:1 +#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg:1 +#: flwr.server.strategy.krum.Krum:1 flwr.server.strategy.qfedavg.QFedAvg:1 of +msgid "Bases: :py:class:`~flwr.server.strategy.fedavg.FedAvg`" msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.krum.Krum.aggregate_fit:1 of -msgid "Aggregate fit results using Krum." +#: flwr.server.strategy.bulyan.Bulyan:3 of +msgid "Implementation based on https://arxiv.org/abs/1802.07927." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`configure_evaluate " -"`\\ \\(server\\_round\\, " -"parameters\\, ...\\)" +#: flwr.server.strategy.bulyan.Bulyan:5 +#: flwr.server.strategy.fedadagrad.FedAdagrad:5 +#: flwr.server.strategy.fedadam.FedAdam:5 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:5 +#: flwr.server.strategy.fedavgm.FedAvgM:5 flwr.server.strategy.fedopt.FedOpt:5 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:5 +#: flwr.server.strategy.fedyogi.FedYogi:5 flwr.server.strategy.krum.Krum:5 of +msgid "Fraction of clients used during training. Defaults to 1.0." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`configure_fit `\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +#: flwr.server.strategy.bulyan.Bulyan:7 +#: flwr.server.strategy.fedadagrad.FedAdagrad:7 +#: flwr.server.strategy.fedadam.FedAdam:7 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:7 +#: flwr.server.strategy.fedavgm.FedAvgM:7 flwr.server.strategy.fedopt.FedOpt:7 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:7 +#: flwr.server.strategy.fedyogi.FedYogi:7 flwr.server.strategy.krum.Krum:7 of +msgid "Fraction of clients used during validation. Defaults to 1.0." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" +#: flwr.server.strategy.bulyan.Bulyan:9 +#: flwr.server.strategy.fedadagrad.FedAdagrad:9 +#: flwr.server.strategy.fedadam.FedAdam:9 flwr.server.strategy.fedavg.FedAvg:13 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:9 +#: flwr.server.strategy.fedavgm.FedAvgM:9 flwr.server.strategy.fedopt.FedOpt:9 +#: flwr.server.strategy.fedprox.FedProx:45 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:9 +#: flwr.server.strategy.fedyogi.FedYogi:9 flwr.server.strategy.krum.Krum:9 of +msgid "Minimum number of clients used during training. Defaults to 2." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +#: flwr.server.strategy.bulyan.Bulyan:11 +#: flwr.server.strategy.fedadagrad.FedAdagrad:11 +#: flwr.server.strategy.fedadam.FedAdam:11 +#: flwr.server.strategy.fedavg.FedAvg:15 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:11 +#: flwr.server.strategy.fedavgm.FedAvgM:11 +#: flwr.server.strategy.fedopt.FedOpt:11 +#: flwr.server.strategy.fedprox.FedProx:47 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:11 +#: flwr.server.strategy.fedyogi.FedYogi:11 flwr.server.strategy.krum.Krum:11 of +msgid "Minimum number of clients used during validation. Defaults to 2." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +#: flwr.server.strategy.bulyan.Bulyan:13 +#: flwr.server.strategy.fedadagrad.FedAdagrad:13 +#: flwr.server.strategy.fedadam.FedAdam:13 +#: flwr.server.strategy.fedavg.FedAvg:17 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:13 +#: flwr.server.strategy.fedavgm.FedAvgM:13 +#: flwr.server.strategy.fedopt.FedOpt:13 +#: flwr.server.strategy.fedprox.FedProx:49 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:13 +#: flwr.server.strategy.fedyogi.FedYogi:13 flwr.server.strategy.krum.Krum:13 of +msgid "Minimum number of total clients in the system. Defaults to 2." msgstr "" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`num_fit_clients `\\ " -"\\(num\\_available\\_clients\\)" +#: flwr.server.strategy.bulyan.Bulyan:15 flwr.server.strategy.krum.Krum:15 of +msgid "Number of malicious clients in the system. Defaults to 0." msgstr "" -#: ../../source/ref-api/flwr.server.strategy.QFedAvg.rst:2 -msgid "QFedAvg" +#: flwr.server.strategy.bulyan.Bulyan:17 +#: flwr.server.strategy.fedadagrad.FedAdagrad:15 +#: flwr.server.strategy.fedadam.FedAdam:15 +#: flwr.server.strategy.fedavg.FedAvg:19 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:15 +#: flwr.server.strategy.fedavgm.FedAvgM:15 +#: flwr.server.strategy.fedopt.FedOpt:15 +#: flwr.server.strategy.fedprox.FedProx:51 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:15 +#: flwr.server.strategy.fedyogi.FedYogi:17 +#: flwr.server.strategy.fedyogi.FedYogi:18 +#: flwr.server.strategy.fedyogi.FedYogi:19 flwr.server.strategy.krum.Krum:20 of +msgid "Optional function used for validation. Defaults to None." msgstr "" -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`aggregate_evaluate " -"`\\ \\(server\\_round\\," -" results\\, ...\\)" +#: flwr.server.strategy.bulyan.Bulyan:19 +#: flwr.server.strategy.fedadagrad.FedAdagrad:17 +#: flwr.server.strategy.fedadam.FedAdam:17 +#: flwr.server.strategy.fedavg.FedAvg:21 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:17 +#: flwr.server.strategy.fedavgm.FedAvgM:17 +#: flwr.server.strategy.fedopt.FedOpt:17 +#: flwr.server.strategy.fedprox.FedProx:53 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:17 +#: flwr.server.strategy.fedyogi.FedYogi:20 flwr.server.strategy.krum.Krum:22 of +msgid "Function used to configure training. Defaults to None." msgstr "" -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`aggregate_fit `\\ " -"\\(server\\_round\\, results\\, failures\\)" +#: flwr.server.strategy.bulyan.Bulyan:21 +#: flwr.server.strategy.fedadagrad.FedAdagrad:19 +#: flwr.server.strategy.fedadam.FedAdam:19 +#: flwr.server.strategy.fedavg.FedAvg:23 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:19 +#: flwr.server.strategy.fedavgm.FedAvgM:19 +#: flwr.server.strategy.fedopt.FedOpt:19 +#: flwr.server.strategy.fedprox.FedProx:55 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:19 +#: flwr.server.strategy.fedyogi.FedYogi:22 flwr.server.strategy.krum.Krum:24 of +msgid "Function used to configure validation. Defaults to None." msgstr "" -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`configure_evaluate " -"`\\ \\(server\\_round\\," -" parameters\\, ...\\)" +#: flwr.server.strategy.bulyan.Bulyan:23 +#: flwr.server.strategy.fedadagrad.FedAdagrad:25 +#: flwr.server.strategy.fedadam.FedAdam:21 +#: flwr.server.strategy.fedavg.FedAvg:25 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:21 +#: flwr.server.strategy.fedavgm.FedAvgM:21 +#: flwr.server.strategy.fedopt.FedOpt:21 +#: flwr.server.strategy.fedprox.FedProx:57 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:21 +#: flwr.server.strategy.fedyogi.FedYogi:24 flwr.server.strategy.krum.Krum:26 of +msgid "Whether or not accept rounds containing failures. Defaults to True." msgstr "" -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`configure_fit `\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +#: flwr.server.strategy.bulyan.Bulyan:25 +#: flwr.server.strategy.fedadagrad.FedAdagrad:27 +#: flwr.server.strategy.fedadam.FedAdam:23 +#: flwr.server.strategy.fedavg.FedAvg:27 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:24 +#: flwr.server.strategy.fedavgm.FedAvgM:23 +#: flwr.server.strategy.fedopt.FedOpt:23 +#: flwr.server.strategy.fedprox.FedProx:59 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:23 +#: flwr.server.strategy.fedyogi.FedYogi:26 flwr.server.strategy.krum.Krum:28 of +msgid "Initial global model parameters." msgstr "" -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.bulyan.Bulyan:27 of msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" +"Byzantine resilient aggregation rule that is used as the first step of " +"the Bulyan (e.g., Krum)" msgstr "" -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +#: flwr.server.strategy.bulyan.Bulyan:29 of +msgid "arguments to the first_aggregation rule" msgstr "" -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`aggregate_evaluate " +"`\\ \\(server\\_round\\, " +"results\\, ...\\)" msgstr "" +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1 +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1 #: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`num_fit_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" -msgstr "" - -#: ../../source/ref-api/flwr.server.strategy.Strategy.rst:2 -msgid "Strategy" +msgid "Aggregate evaluation losses using weighted average." msgstr "" -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1 -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 -#: of -msgid "Aggregate evaluation results." +#: flwr.server.strategy.bulyan.Bulyan.aggregate_fit:1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +msgid "Aggregate fit results using Bulyan." msgstr "" -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`aggregate_fit `\\ " -"\\(server\\_round\\, results\\, failures\\)" +":py:obj:`configure_evaluate " +"`\\ \\(server\\_round\\, " +"parameters\\, ...\\)" msgstr "" -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 -#: flwr.server.strategy.strategy.Strategy.aggregate_fit:1 of -msgid "Aggregate training results." +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.configure_evaluate:1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.configure_evaluate:1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.configure_evaluate:1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.configure_evaluate:1 +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.configure_evaluate:1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.configure_evaluate:1 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.configure_evaluate:1 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.qfedavg.QFedAvg.configure_evaluate:1 +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 +#: flwr.server.strategy.strategy.Strategy.configure_evaluate:1 of +msgid "Configure the next round of evaluation." msgstr "" -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`configure_evaluate " -"`\\ " +":py:obj:`configure_fit `\\ " "\\(server\\_round\\, parameters\\, ...\\)" msgstr "" +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.configure_fit:1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.configure_fit:1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.configure_fit:1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.configure_fit:1 +#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive.configure_fit:1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.configure_fit:1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.configure_fit:1 +#: flwr.server.strategy.fedprox.FedProx.configure_fit:1 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.configure_fit:1 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.qfedavg.QFedAvg.configure_fit:1 #: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`configure_fit `\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +#: flwr.server.strategy.strategy.Strategy.configure_fit:1 of +msgid "Configure the next round of training." msgstr "" -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -":py:obj:`evaluate `\\ " +":py:obj:`evaluate `\\ " "\\(server\\_round\\, parameters\\)" msgstr "" -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 -#: flwr.server.strategy.strategy.Strategy.evaluate:1 of -msgid "Evaluate the current model parameters." +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.evaluate:1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.evaluate:1 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.evaluate:1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg.evaluate:1 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of +msgid "Evaluate model parameters using an evaluation function." msgstr "" -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" ":py:obj:`initialize_parameters " -"`\\ " +"`\\ " "\\(client\\_manager\\)" msgstr "" -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 -#: flwr.server.strategy.strategy.Strategy.initialize_parameters:1 of -msgid "Initialize the (global) model parameters." +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.initialize_parameters:1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.initialize_parameters:1 +#: flwr.server.strategy.fedavgm.FedAvgM.initialize_parameters:1 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of +msgid "Initialize global model parameters." msgstr "" -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:5 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"Successful updates from the previously selected and configured clients. " -"Each pair of `(ClientProxy, FitRes` constitutes a successful update from " -"one of the previously selected clients. Not that not all previously " -"selected clients are necessarily included in this list: a client might " -"drop out and not submit a result. For each client that did not submit an " -"update, there should be an `Exception` in `failures`." +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:13 -#: flwr.server.strategy.strategy.Strategy.aggregate_fit:13 of -msgid "Exceptions that occurred while the server was waiting for client updates." +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.num_evaluation_clients:1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.num_evaluation_clients:1 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.qfedavg.QFedAvg.num_evaluation_clients:1 of +msgid "Use a fraction of available clients for evaluation." msgstr "" -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:16 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**aggregation_result** -- The aggregated evaluation result. Aggregation " -"typically uses some variant of a weighted average." +":py:obj:`num_fit_clients `\\" +" \\(num\\_available\\_clients\\)" msgstr "" -#: flwr.server.strategy.strategy.Strategy.aggregate_fit:5 of -msgid "" -"Successful updates from the previously selected and configured clients. " -"Each pair of `(ClientProxy, FitRes)` constitutes a successful update from" -" one of the previously selected clients. Not that not all previously " -"selected clients are necessarily included in this list: a client might " -"drop out and not submit a result. For each client that did not submit an " -"update, there should be an `Exception` in `failures`." +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.num_fit_clients:1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.num_fit_clients:1 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.qfedavg.QFedAvg.num_fit_clients:1 of +msgid "Return the sample size and the required number of available clients." msgstr "" -#: flwr.server.strategy.strategy.Strategy.aggregate_fit:17 of -msgid "" -"**parameters** -- If parameters are returned, then the server will treat " -"these as the new global model parameters (i.e., it will replace the " -"previous parameters with the ones returned from this method). If `None` " -"is returned (e.g., because there were only failures and no viable " -"results) then the server will no update the previous model parameters, " -"the updates received in this round are discarded, and the global model " -"parameters remain the same." +#: ../../source/ref-api/flwr.server.strategy.DPFedAvgAdaptive.rst:2 +msgid "DPFedAvgAdaptive" msgstr "" -#: flwr.server.strategy.strategy.Strategy.evaluate:3 of -msgid "" -"This function can be used to perform centralized (i.e., server-side) " -"evaluation of model parameters." +#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive:1 of +msgid "Bases: :py:class:`~flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed`" msgstr "" -#: flwr.server.strategy.strategy.Strategy.evaluate:11 of -msgid "" -"**evaluation_result** -- The evaluation result, usually a Tuple " -"containing loss and a dictionary containing task-specific metrics (e.g., " -"accuracy)." +#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive:3 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed:3 of +msgid "This class is deprecated and will be removed in a future release." msgstr "" -#: flwr.server.strategy.strategy.Strategy.initialize_parameters:6 of +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: of msgid "" -"**parameters** -- If parameters are returned, then the server will treat " -"these as the initial global model parameters." +":py:obj:`aggregate_evaluate " +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" msgstr "" -#: ../../source/ref-api/flwr.server.workflow.rst:2 -msgid "workflow" +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: of +msgid "Aggregate evaluation losses using the given strategy." msgstr "" -#: ../../source/ref-api/flwr.server.workflow.rst:24::1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: of msgid "" -":py:obj:`DefaultWorkflow `\\ " -"\\(\\[fit\\_workflow\\, ...\\]\\)" +":py:obj:`aggregate_fit " +"`\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" -#: ../../source/ref-api/flwr.server.workflow.rst:24::1 -#: flwr.server.workflow.default_workflows.DefaultWorkflow:1 of -msgid "Default workflow in Flower." +#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive.aggregate_fit:1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: of +msgid "Aggregate training results as in DPFedAvgFixed and update clip norms." msgstr "" -#: ../../source/ref-api/flwr.server.workflow.rst:24::1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: of msgid "" -":py:obj:`SecAggPlusWorkflow `\\ " -"\\(num\\_shares\\, ...\\[\\, ...\\]\\)" +":py:obj:`configure_evaluate " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: ../../source/ref-api/flwr.server.workflow.rst:24::1 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:1 -#: of -msgid "The workflow for the SecAgg+ protocol." +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:1 of +msgid "Configure the next round of evaluation using the specified strategy." msgstr "" -#: ../../source/ref-api/flwr.server.workflow.rst:24::1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: of msgid "" -":py:obj:`SecAggWorkflow `\\ " -"\\(reconstruction\\_threshold\\, \\*\\)" -msgstr "" - -#: ../../source/ref-api/flwr.server.workflow.rst:24::1 -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:1 of -msgid "The workflow for the SecAgg protocol." +":py:obj:`configure_fit " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: ../../source/ref-api/flwr.server.workflow.DefaultWorkflow.rst:2 -msgid "DefaultWorkflow" +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: of +msgid "" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" -#: ../../source/ref-api/flwr.server.workflow.SecAggPlusWorkflow.rst:2 -msgid "SecAggPlusWorkflow" +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.evaluate:1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.evaluate:1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.evaluate:1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.evaluate:1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.evaluate:1 of +msgid "Evaluate model parameters using an evaluation function from the strategy." msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:3 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 #: of msgid "" -"The SecAgg+ protocol ensures the secure summation of integer vectors " -"owned by multiple parties, without accessing any individual integer " -"vector. This workflow allows the server to compute the weighted average " -"of model parameters across all clients, ensuring individual contributions" -" remain private. This is achieved by clients sending both, a weighting " -"factor and a weighted version of the locally updated parameters, both of " -"which are masked for privacy. Specifically, each client uploads \"[w, w *" -" params]\" with masks, where weighting factor 'w' is the number of " -"examples ('num_examples') and 'params' represents the model parameters " -"('parameters') from the client's `FitRes`. The server then aggregates " -"these contributions to compute the weighted average of model parameters." +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:14 -#: of -msgid "" -"The protocol involves four main stages: - 'setup': Send SecAgg+ " -"configuration to clients and collect their public keys. - 'share keys': " -"Broadcast public keys among clients and collect encrypted secret" +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.initialize_parameters:1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.initialize_parameters:1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.initialize_parameters:1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.initialize_parameters:1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.initialize_parameters:1 of +msgid "Initialize global model parameters using given strategy." msgstr "" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:17 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:17 -#: of -msgid "key shares." +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:3 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:6 +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:3 +#: flwr.server.strategy.strategy.Strategy.aggregate_fit:3 +#: flwr.server.strategy.strategy.Strategy.configure_evaluate:3 +#: flwr.server.strategy.strategy.Strategy.configure_fit:3 +#: flwr.server.strategy.strategy.Strategy.evaluate:6 of +msgid "The current round of federated learning." msgstr "" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:18 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:18 -#: of -msgid "" -"'collect masked vectors': Forward encrypted secret key shares to target " -"clients and collect masked model parameters." +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:7 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:10 +#: flwr.server.strategy.strategy.Strategy.configure_evaluate:7 +#: flwr.server.strategy.strategy.Strategy.configure_fit:7 +#: flwr.server.strategy.strategy.Strategy.initialize_parameters:3 of +msgid "The client manager which holds all currently connected clients." msgstr "" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:20 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:20 -#: of +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:10 +#: flwr.server.strategy.strategy.Strategy.configure_evaluate:10 of msgid "" -"'unmask': Collect secret key shares to decrypt and aggregate the model " -"parameters." +"**evaluate_configuration** -- A list of tuples. Each tuple in the list " +"identifies a `ClientProxy` and the `EvaluateIns` for this particular " +"`ClientProxy`. If a particular `ClientProxy` is not included in this " +"list, it means that this `ClientProxy` will not participate in the next " +"round of federated evaluation." msgstr "" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:22 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:22 -#: of -msgid "" -"Only the aggregated model parameters are exposed and passed to " -"`Strategy.aggregate_fit`, ensuring individual data privacy." +#: ../../source/ref-api/flwr.server.strategy.DPFedAvgFixed.rst:2 +msgid "DPFedAvgFixed" msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:25 -#: of -msgid "" -"The number of shares into which each client's private key is split under " -"the SecAgg+ protocol. If specified as a float, it represents the " -"proportion of all selected clients, and the number of shares will be set " -"dynamically in the run time. A private key can be reconstructed from " -"these shares, allowing for the secure aggregation of model updates. Each " -"client sends one share to each of its neighbors while retaining one." +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed:1 +#: flwr.server.strategy.fedavg.FedAvg:1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:1 of +msgid "Bases: :py:class:`~flwr.server.strategy.strategy.Strategy`" msgstr "" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:25 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:32 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 #: of msgid "" -"The minimum number of shares required to reconstruct a client's private " -"key, or, if specified as a float, it represents the proportion of the " -"total number of shares needed for reconstruction. This threshold ensures " -"privacy by allowing for the recovery of contributions from dropped " -"clients during aggregation, without compromising individual client data." +":py:obj:`aggregate_evaluate " +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:31 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:38 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 #: of msgid "" -"The maximum value of the weight that can be assigned to any single " -"client's update during the weighted average calculation on the server " -"side, e.g., in the FedAvg algorithm." +":py:obj:`aggregate_fit " +"`\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:35 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:42 -#: of -msgid "" -"The range within which model parameters are clipped before quantization. " -"This parameter ensures each model parameter is bounded within " -"[-clipping_range, clipping_range], facilitating quantization." +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_fit:1 of +msgid "Aggregate training results using unweighted aggregation." msgstr "" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:39 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:46 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 #: of msgid "" -"The size of the range into which floating-point model parameters are " -"quantized, mapping each parameter to an integer in [0, " -"quantization_range-1]. This facilitates cryptographic operations on the " -"model updates." +":py:obj:`configure_evaluate " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:43 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:50 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 #: of msgid "" -"The range of values from which random mask entries are uniformly sampled " -"([0, modulus_range-1]). `modulus_range` must be less than 4294967296. " -"Please use 2**n values for `modulus_range` to prevent overflow issues." +":py:obj:`configure_fit " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:47 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:54 -#: of +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:1 of msgid "" -"The timeout duration in seconds. If specified, the workflow will wait for" -" replies for this duration each time. If `None`, there is no time limit " -"and the workflow will wait until replies for all messages are received." +"Configure the next round of training incorporating Differential Privacy " +"(DP)." msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:61 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 #: of msgid "" -"Generally, higher `num_shares` means more robust to dropouts while " -"increasing the computational costs; higher `reconstruction_threshold` " -"means better privacy guarantees but less tolerance to dropouts." +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:58 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:64 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 #: of -msgid "Too large `max_weight` may compromise the precision of the quantization." +msgid "" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:59 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:65 -#: of -msgid "`modulus_range` must be 2**n and larger than `quantization_range`." +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:3 of +msgid "" +"Configuration of the next training round includes information related to " +"DP, such as clip norm and noise stddev." msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:66 -#: of +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:13 +#: flwr.server.strategy.strategy.Strategy.configure_fit:10 of msgid "" -"When `num_shares` is a float, it is interpreted as the proportion of all " -"selected clients, and hence the number of shares will be determined in " -"the runtime. This allows for dynamic adjustment based on the total number" -" of participating clients." +"**fit_configuration** -- A list of tuples. Each tuple in the list " +"identifies a `ClientProxy` and the `FitIns` for this particular " +"`ClientProxy`. If a particular `ClientProxy` is not included in this " +"list, it means that this `ClientProxy` will not participate in the next " +"round of federated learning." msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:69 -#: of -msgid "" -"Similarly, when `reconstruction_threshold` is a float, it is interpreted " -"as the proportion of the number of shares needed for the reconstruction " -"of a private key. This feature enables flexibility in setting the " -"security threshold relative to the number of distributed shares." +#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyClientSideAdaptiveClipping.rst:2 +msgid "DifferentialPrivacyClientSideAdaptiveClipping" msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:73 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:3 #: of -msgid "" -"`num_shares`, `reconstruction_threshold`, and the quantization parameters" -" (`clipping_range`, `quantization_range`, `modulus_range`) play critical " -"roles in balancing privacy, robustness, and efficiency within the SecAgg+" -" protocol." +msgid "Use `adaptiveclipping_mod` modifier at the client side." msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:5 #: of msgid "" -":py:obj:`collect_masked_vectors_stage " -"`\\" -" \\(driver\\, ...\\)" +"In comparison to `DifferentialPrivacyServerSideAdaptiveClipping`, which " +"performs clipping on the server-side, " +"`DifferentialPrivacyClientSideAdaptiveClipping` expects clipping to " +"happen on the client-side, usually by using the built-in " +"`adaptiveclipping_mod`." msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:10 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:3 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:10 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:3 #: of -msgid "Execute the 'collect masked vectors' stage." +msgid "The strategy to which DP functionalities will be added by this wrapper." msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:12 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:5 #: of -msgid "" -":py:obj:`setup_stage " -"`\\ \\(driver\\, " -"context\\, state\\)" +msgid "The noise multiplier for the Gaussian mechanism for model updates." msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.setup_stage:1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:14 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:7 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:17 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:10 #: of -msgid "Execute the 'setup' stage." +msgid "The number of clients that are sampled on each round." msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:16 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:9 #: of msgid "" -":py:obj:`share_keys_stage " -"`\\ " -"\\(driver\\, context\\, state\\)" +"The initial value of clipping norm. Defaults to 0.1. Andrew et al. " +"recommends to set to 0.1." msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.share_keys_stage:1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:19 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:12 #: of -msgid "Execute the 'share keys' stage." +msgid "The desired quantile of updates which should be clipped. Defaults to 0.5." msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:21 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:14 #: of msgid "" -":py:obj:`unmask_stage " -"`\\ \\(driver\\, " -"context\\, state\\)" +"The learning rate for the clipping norm adaptation. Defaults to 0.2. " +"Andrew et al. recommends to set to 0.2." msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.unmask_stage:1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:24 #: of -msgid "Execute the 'unmask' stage." +msgid "" +"The stddev of the noise added to the count of updates currently below the" +" estimate. Andrew et al. recommends to set to `expected_num_records/20`" msgstr "" -#: ../../source/ref-api/flwr.server.workflow.SecAggWorkflow.rst:2 -msgid "SecAggWorkflow" +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:30 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:23 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:22 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:15 +#: of +msgid "Create a strategy:" msgstr "" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:1 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:34 +#: of msgid "" -"Bases: " -":py:class:`~flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow`" +"Wrap the strategy with the " +"`DifferentialPrivacyClientSideAdaptiveClipping` wrapper:" msgstr "" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:3 of -msgid "" -"The SecAgg protocol ensures the secure summation of integer vectors owned" -" by multiple parties, without accessing any individual integer vector. " -"This workflow allows the server to compute the weighted average of model " -"parameters across all clients, ensuring individual contributions remain " -"private. This is achieved by clients sending both, a weighting factor and" -" a weighted version of the locally updated parameters, both of which are " -"masked for privacy. Specifically, each client uploads \"[w, w * params]\"" -" with masks, where weighting factor 'w' is the number of examples " -"('num_examples') and 'params' represents the model parameters " -"('parameters') from the client's `FitRes`. The server then aggregates " -"these contributions to compute the weighted average of model parameters." +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:40 +#: of +msgid "On the client, add the `adaptiveclipping_mod` to the client-side mods:" msgstr "" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:14 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 +#: of msgid "" -"The protocol involves four main stages: - 'setup': Send SecAgg " -"configuration to clients and collect their public keys. - 'share keys': " -"Broadcast public keys among clients and collect encrypted secret" +":py:obj:`aggregate_evaluate " +"`\\" +" \\(server\\_round\\, results\\, ...\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:54 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 +#: of msgid "" -"Each client's private key is split into N shares under the SecAgg " -"protocol, where N is the number of selected clients." +":py:obj:`aggregate_fit " +"`\\" +" \\(server\\_round\\, results\\, failures\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:56 of -msgid "" -"Generally, higher `reconstruction_threshold` means better privacy " -"guarantees but less tolerance to dropouts." +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_fit:1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_fit:1 +#: of +msgid "Aggregate training results and update clip norms." msgstr "" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:60 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 +#: of msgid "" -"When `reconstruction_threshold` is a float, it is interpreted as the " -"proportion of the number of all selected clients needed for the " -"reconstruction of a private key. This feature enables flexibility in " -"setting the security threshold relative to the number of selected " -"clients." +":py:obj:`configure_evaluate " +"`\\" +" \\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:64 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 +#: of msgid "" -"`reconstruction_threshold`, and the quantization parameters " -"(`clipping_range`, `quantization_range`, `modulus_range`) play critical " -"roles in balancing privacy, robustness, and efficiency within the SecAgg " -"protocol." +":py:obj:`configure_fit " +"`\\" +" \\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`collect_masked_vectors_stage " -"`\\ " -"\\(driver\\, ...\\)" +":py:obj:`evaluate " +"`\\" +" \\(server\\_round\\, parameters\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 #: of msgid "" -":py:obj:`setup_stage `\\" -" \\(driver\\, context\\, state\\)" +":py:obj:`initialize_parameters " +"`\\" +" \\(client\\_manager\\)" msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyClientSideFixedClipping.rst:2 +msgid "DifferentialPrivacyClientSideFixedClipping" +msgstr "" + +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:3 #: of -msgid "" -":py:obj:`share_keys_stage " -"`\\ \\(driver\\, " -"context\\, state\\)" +msgid "Use `fixedclipping_mod` modifier at the client side." msgstr "" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:5 #: of msgid "" -":py:obj:`unmask_stage " -"`\\ \\(driver\\, " -"context\\, state\\)" +"In comparison to `DifferentialPrivacyServerSideFixedClipping`, which " +"performs clipping on the server-side, " +"`DifferentialPrivacyClientSideFixedClipping` expects clipping to happen " +"on the client-side, usually by using the built-in `fixedclipping_mod`." msgstr "" -#: ../../source/ref-api/flwr.simulation.rst:2 -msgid "simulation" +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:12 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:5 +#: of +msgid "" +"The noise multiplier for the Gaussian mechanism for model updates. A " +"value of 1.0 or higher is recommended for strong privacy." msgstr "" -#: ../../source/ref-api/flwr.simulation.rst:19::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:26 +#: of msgid "" -":py:obj:`start_simulation `\\ \\(\\*\\," -" client\\_fn\\[\\, ...\\]\\)" +"Wrap the strategy with the `DifferentialPrivacyClientSideFixedClipping` " +"wrapper:" msgstr "" -#: ../../source/ref-api/flwr.simulation.rst:19::1 -#: flwr.simulation.app.start_simulation:1 of -msgid "Start a Ray-based Flower simulation server." +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:32 +#: of +msgid "On the client, add the `fixedclipping_mod` to the client-side mods:" msgstr "" -#: ../../source/ref-api/flwr.simulation.rst:19::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: of msgid "" -":py:obj:`run_simulation_from_cli " -"`\\ \\(\\)" -msgstr "" - -#: ../../source/ref-api/flwr.simulation.rst:19::1 -#: flwr.simulation.run_simulation.run_simulation_from_cli:1 of -msgid "Run Simulation Engine from the CLI." +":py:obj:`aggregate_evaluate " +"`\\" +" \\(server\\_round\\, results\\, ...\\)" msgstr "" -#: ../../source/ref-api/flwr.simulation.rst:19::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: of msgid "" -":py:obj:`run_simulation `\\ " -"\\(server\\_app\\, client\\_app\\, ...\\)" +":py:obj:`aggregate_fit " +"`\\" +" \\(server\\_round\\, results\\, failures\\)" msgstr "" -#: ../../source/ref-api/flwr.simulation.rst:19::1 -#: flwr.simulation.run_simulation.run_simulation:1 of -msgid "Run a Flower App using the Simulation Engine." +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_fit:1 +#: of +msgid "Add noise to the aggregated parameters." msgstr "" -#: ../../source/ref-api/flwr.simulation.run_simulation.rst:2 -msgid "run\\_simulation" +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: of +msgid "" +":py:obj:`configure_evaluate " +"`\\" +" \\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.simulation.run_simulation.run_simulation:3 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: of msgid "" -"The `ServerApp` to be executed. It will send messages to different " -"`ClientApp` instances running on different (virtual) SuperNodes." +":py:obj:`configure_fit " +"`\\" +" \\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.simulation.run_simulation.run_simulation:6 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: of msgid "" -"The `ClientApp` to be executed by each of the SuperNodes. It will receive" -" messages sent by the `ServerApp`." +":py:obj:`evaluate " +"`\\" +" \\(server\\_round\\, parameters\\)" msgstr "" -#: flwr.simulation.run_simulation.run_simulation:9 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: of msgid "" -"Number of nodes that run a ClientApp. They can be sampled by a Driver in " -"the ServerApp and receive a Message describing what the ClientApp should " -"perform." +":py:obj:`initialize_parameters " +"`\\" +" \\(client\\_manager\\)" msgstr "" -#: flwr.simulation.run_simulation.run_simulation:13 of -msgid "A simulation backend that runs `ClientApp`s." +#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyServerSideAdaptiveClipping.rst:2 +msgid "DifferentialPrivacyServerSideAdaptiveClipping" msgstr "" -#: flwr.simulation.run_simulation.run_simulation:15 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:17 +#: of msgid "" -"'A dictionary, e.g {\"\": , \"\": } to " -"configure a backend. Values supported in are those included by " -"`flwr.common.typing.ConfigsRecordValues`." +"The standard deviation of the noise added to the count of updates below " +"the estimate. Andrew et al. recommends to set to " +"`expected_num_records/20`" msgstr "" -#: flwr.simulation.run_simulation.run_simulation:19 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:27 +#: of msgid "" -"A boolean to indicate whether to enable GPU growth on the main thread. " -"This is desirable if you make use of a TensorFlow model on your " -"`ServerApp` while having your `ClientApp` running on the same GPU. " -"Without enabling this, you might encounter an out-of-memory error because" -" TensorFlow, by default, allocates all GPU memory. Read more about how " -"`tf.config.experimental.set_memory_growth()` works in the TensorFlow " -"documentation: https://www.tensorflow.org/api/stable." +"Wrap the strategy with the DifferentialPrivacyServerSideAdaptiveClipping " +"wrapper" msgstr "" -#: flwr.simulation.run_simulation.run_simulation:26 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 +#: of msgid "" -"When diabled, only INFO, WARNING and ERROR log messages will be shown. If" -" enabled, DEBUG-level logs will be displayed." +":py:obj:`aggregate_evaluate " +"`\\" +" \\(server\\_round\\, results\\, ...\\)" msgstr "" -#: ../../source/ref-api/flwr.simulation.run_simulation_from_cli.rst:2 -msgid "run\\_simulation\\_from\\_cli" +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 +#: of +msgid "" +":py:obj:`aggregate_fit " +"`\\" +" \\(server\\_round\\, results\\, failures\\)" msgstr "" -#: ../../source/ref-api/flwr.simulation.start_simulation.rst:2 -msgid "start\\_simulation" +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 +#: of +msgid "" +":py:obj:`configure_evaluate " +"`\\" +" \\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.simulation.app.start_simulation:3 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 +#: of msgid "" -"A function creating client instances. The function must take a single " -"`str` argument called `cid`. It should return a single client instance of" -" type Client. Note that the created client instances are ephemeral and " -"will often be destroyed after a single method invocation. Since client " -"instances are not long-lived, they should not attempt to carry state over" -" method invocations. Any state required by the instance (model, dataset, " -"hyperparameters, ...) should be (re-)created in either the call to " -"`client_fn` or the call to any of the client methods (e.g., load " -"evaluation data in the `evaluate` method itself)." +":py:obj:`configure_fit " +"`\\" +" \\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.simulation.app.start_simulation:13 of -msgid "" -"The total number of clients in this simulation. This must be set if " -"`clients_ids` is not set and vice-versa." -msgstr "" - -#: flwr.simulation.app.start_simulation:16 of -msgid "" -"List `client_id`s for each client. This is only required if `num_clients`" -" is not set. Setting both `num_clients` and `clients_ids` with " -"`len(clients_ids)` not equal to `num_clients` generates an error." -msgstr "" - -#: flwr.simulation.app.start_simulation:20 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 +#: of msgid "" -"CPU and GPU resources for a single client. Supported keys are `num_cpus` " -"and `num_gpus`. To understand the GPU utilization caused by `num_gpus`, " -"as well as using custom resources, please consult the Ray documentation." +":py:obj:`evaluate " +"`\\" +" \\(server\\_round\\, parameters\\)" msgstr "" -#: flwr.simulation.app.start_simulation:25 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 +#: of msgid "" -"An implementation of the abstract base class `flwr.server.Server`. If no " -"instance is provided, then `start_server` will create one." +":py:obj:`initialize_parameters " +"`\\" +" \\(client\\_manager\\)" msgstr "" -#: flwr.simulation.app.start_simulation:31 of -msgid "" -"An implementation of the abstract base class `flwr.server.Strategy`. If " -"no strategy is provided, then `start_server` will use " -"`flwr.server.strategy.FedAvg`." +#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyServerSideFixedClipping.rst:2 +msgid "DifferentialPrivacyServerSideFixedClipping" msgstr "" -#: flwr.simulation.app.start_simulation:35 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:19 +#: of msgid "" -"An implementation of the abstract base class `flwr.server.ClientManager`." -" If no implementation is provided, then `start_simulation` will use " -"`flwr.server.client_manager.SimpleClientManager`." +"Wrap the strategy with the DifferentialPrivacyServerSideFixedClipping " +"wrapper" msgstr "" -#: flwr.simulation.app.start_simulation:39 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 +#: of msgid "" -"Optional dictionary containing arguments for the call to `ray.init`. If " -"ray_init_args is None (the default), Ray will be initialized with the " -"following default args: { \"ignore_reinit_error\": True, " -"\"include_dashboard\": False } An empty dictionary can be used " -"(ray_init_args={}) to prevent any arguments from being passed to " -"ray.init." +":py:obj:`aggregate_evaluate " +"`\\" +" \\(server\\_round\\, results\\, ...\\)" msgstr "" -#: flwr.simulation.app.start_simulation:39 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 +#: of msgid "" -"Optional dictionary containing arguments for the call to `ray.init`. If " -"ray_init_args is None (the default), Ray will be initialized with the " -"following default args:" -msgstr "" - -#: flwr.simulation.app.start_simulation:43 of -msgid "{ \"ignore_reinit_error\": True, \"include_dashboard\": False }" +":py:obj:`aggregate_fit " +"`\\" +" \\(server\\_round\\, results\\, failures\\)" msgstr "" -#: flwr.simulation.app.start_simulation:45 of -msgid "" -"An empty dictionary can be used (ray_init_args={}) to prevent any " -"arguments from being passed to ray.init." +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_fit:1 +#: of +msgid "Compute the updates, clip, and pass them for aggregation." msgstr "" -#: flwr.simulation.app.start_simulation:48 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 +#: of msgid "" -"Set to True to prevent `ray.shutdown()` in case " -"`ray.is_initialized()=True`." +":py:obj:`configure_evaluate " +"`\\" +" \\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.simulation.app.start_simulation:50 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 +#: of msgid "" -"Optionally specify the type of actor to use. The actor object, which " -"persists throughout the simulation, will be the process in charge of " -"executing a ClientApp wrapping input argument `client_fn`." +":py:obj:`configure_fit " +"`\\" +" \\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: flwr.simulation.app.start_simulation:54 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 +#: of msgid "" -"If you want to create your own Actor classes, you might need to pass some" -" input argument. You can use this dictionary for such purpose." +":py:obj:`evaluate " +"`\\" +" \\(server\\_round\\, parameters\\)" msgstr "" -#: flwr.simulation.app.start_simulation:57 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 +#: of msgid "" -"(default: \"DEFAULT\") Optional string (\"DEFAULT\" or \"SPREAD\") for " -"the VCE to choose in which node the actor is placed. If you are an " -"advanced user needed more control you can use lower-level scheduling " -"strategies to pin actors to specific compute nodes (e.g. via " -"NodeAffinitySchedulingStrategy). Please note this is an advanced feature." -" For all details, please refer to the Ray documentation: " -"https://docs.ray.io/en/latest/ray-core/scheduling/index.html" -msgstr "" - -#: flwr.simulation.app.start_simulation:66 of -msgid "**hist** -- Object containing metrics from training." -msgstr "" - -#: ../../source/ref-changelog.md:1 -msgid "Changelog" +":py:obj:`initialize_parameters " +"`\\" +" \\(client\\_manager\\)" msgstr "" -#: ../../source/ref-changelog.md:3 -msgid "Unreleased" +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_fit:3 +#: of +msgid "Afterward, add noise to the aggregated parameters." msgstr "" -#: ../../source/ref-changelog.md:5 ../../source/ref-changelog.md:17 -#: ../../source/ref-changelog.md:110 ../../source/ref-changelog.md:210 -#: ../../source/ref-changelog.md:294 ../../source/ref-changelog.md:358 -#: ../../source/ref-changelog.md:416 ../../source/ref-changelog.md:485 -#: ../../source/ref-changelog.md:614 ../../source/ref-changelog.md:656 -#: ../../source/ref-changelog.md:723 ../../source/ref-changelog.md:789 -#: ../../source/ref-changelog.md:834 ../../source/ref-changelog.md:873 -#: ../../source/ref-changelog.md:906 ../../source/ref-changelog.md:956 -msgid "What's new?" +#: ../../source/ref-api/flwr.server.strategy.FaultTolerantFedAvg.rst:2 +msgid "FaultTolerantFedAvg" msgstr "" -#: ../../source/ref-changelog.md:7 ../../source/ref-changelog.md:80 -#: ../../source/ref-changelog.md:192 ../../source/ref-changelog.md:282 -#: ../../source/ref-changelog.md:346 ../../source/ref-changelog.md:404 -#: ../../source/ref-changelog.md:473 ../../source/ref-changelog.md:535 -#: ../../source/ref-changelog.md:554 ../../source/ref-changelog.md:710 -#: ../../source/ref-changelog.md:781 ../../source/ref-changelog.md:818 -#: ../../source/ref-changelog.md:861 -msgid "Incompatible changes" +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: of +msgid "" +":py:obj:`aggregate_evaluate " +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:9 -msgid "v1.7.0 (2024-02-05)" +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: of +msgid "" +":py:obj:`aggregate_fit " +"`\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" -#: ../../source/ref-changelog.md:11 ../../source/ref-changelog.md:104 -#: ../../source/ref-changelog.md:204 ../../source/ref-changelog.md:288 -#: ../../source/ref-changelog.md:352 ../../source/ref-changelog.md:410 -#: ../../source/ref-changelog.md:479 ../../source/ref-changelog.md:548 -msgid "Thanks to our contributors" +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_fit:1 +#: flwr.server.strategy.fedadagrad.FedAdagrad.aggregate_fit:1 +#: flwr.server.strategy.fedadam.FedAdam.aggregate_fit:1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_fit:1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_fit:1 +#: flwr.server.strategy.fedavgm.FedAvgM.aggregate_fit:1 +#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg.aggregate_fit:1 +#: flwr.server.strategy.fedyogi.FedYogi.aggregate_fit:1 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_fit:1 of +msgid "Aggregate fit results using weighted average." msgstr "" -#: ../../source/ref-changelog.md:13 ../../source/ref-changelog.md:106 -#: ../../source/ref-changelog.md:206 ../../source/ref-changelog.md:290 -#: ../../source/ref-changelog.md:354 ../../source/ref-changelog.md:412 +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: of msgid "" -"We would like to give our special thanks to all the contributors who made" -" the new version of Flower possible (in `git shortlog` order):" +":py:obj:`configure_evaluate " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:15 +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: of msgid "" -"`Aasheesh Singh`, `Adam Narozniak`, `Aml Hassan Esmil`, `Charles " -"Beauville`, `Daniel J. Beutel`, `Daniel Nata Nugraha`, `Edoardo " -"Gabrielli`, `Gustavo Bertoli`, `HelinLin`, `Heng Pan`, `Javier`, `M S " -"Chaitanya Kumar`, `Mohammad Naseri`, `Nikos Vlachakis`, `Pritam Neog`, " -"`Robert Kuska`, `Robert Steiner`, `Taner Topal`, `Yahia Salaheldin " -"Shaaban`, `Yan Gao`, `Yasar Abbas` " +":py:obj:`configure_fit " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:19 +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: of msgid "" -"**Introduce stateful clients (experimental)** " -"([#2770](https://github.com/adap/flower/pull/2770), " -"[#2686](https://github.com/adap/flower/pull/2686), " -"[#2696](https://github.com/adap/flower/pull/2696), " -"[#2643](https://github.com/adap/flower/pull/2643), " -"[#2769](https://github.com/adap/flower/pull/2769))" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" -#: ../../source/ref-changelog.md:21 +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: of msgid "" -"Subclasses of `Client` and `NumPyClient` can now store local state that " -"remains on the client. Let's start with the highlight first: this new " -"feature is compatible with both simulated clients (via " -"`start_simulation`) and networked clients (via `start_client`). It's also" -" the first preview of new abstractions like `Context` and `RecordSet`. " -"Clients can access state of type `RecordSet` via `state: RecordSet = " -"self.context.state`. Changes to this `RecordSet` are preserved across " -"different rounds of execution to enable stateful computations in a " -"unified way across simulation and deployment." +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" -#: ../../source/ref-changelog.md:23 +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: of msgid "" -"**Improve performance** " -"([#2293](https://github.com/adap/flower/pull/2293))" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-changelog.md:25 +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: of msgid "" -"Flower is faster than ever. All `FedAvg`-derived strategies now use in-" -"place aggregation to reduce memory consumption. The Flower client " -"serialization/deserialization has been rewritten from the ground up, " -"which results in significant speedups, especially when the client-side " -"training time is short." +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-changelog.md:27 -msgid "" -"**Support Federated Learning with Apple MLX and Flower** " -"([#2693](https://github.com/adap/flower/pull/2693))" +#: ../../source/ref-api/flwr.server.strategy.FedAdagrad.rst:2 +#: ../../source/ref-changelog.md:997 +msgid "FedAdagrad" msgstr "" -#: ../../source/ref-changelog.md:29 -msgid "" -"Flower has official support for federated learning using [Apple " -"MLX](https://ml-explore.github.io/mlx) via the new `quickstart-mlx` code " -"example." +#: flwr.server.strategy.fedadagrad.FedAdagrad:1 +#: flwr.server.strategy.fedadam.FedAdam:1 +#: flwr.server.strategy.fedyogi.FedYogi:1 of +msgid "Bases: :py:class:`~flwr.server.strategy.fedopt.FedOpt`" msgstr "" -#: ../../source/ref-changelog.md:31 -msgid "" -"**Introduce new XGBoost cyclic strategy** " -"([#2666](https://github.com/adap/flower/pull/2666), " -"[#2668](https://github.com/adap/flower/pull/2668))" +#: flwr.server.strategy.fedadagrad.FedAdagrad:3 +#: flwr.server.strategy.fedadam.FedAdam:3 flwr.server.strategy.fedopt.FedOpt:3 +#: flwr.server.strategy.fedyogi.FedYogi:3 of +msgid "Implementation based on https://arxiv.org/abs/2003.00295v5" msgstr "" -#: ../../source/ref-changelog.md:33 -msgid "" -"A new strategy called `FedXgbCyclic` supports a client-by-client style of" -" training (often called cyclic). The `xgboost-comprehensive` code example" -" shows how to use it in a full project. In addition to that, `xgboost-" -"comprehensive` now also supports simulation mode. With this, Flower " -"offers best-in-class XGBoost support." -msgstr "" +#: flwr.server.strategy.fedadagrad.FedAdagrad:21 +#: flwr.server.strategy.fedadagrad.FedAdagrad:23 +#: flwr.server.strategy.fedadam.FedAdam:25 +#: flwr.server.strategy.fedadam.FedAdam:27 +#: flwr.server.strategy.fedavg.FedAvg:29 flwr.server.strategy.fedavg.FedAvg:31 +#: flwr.server.strategy.fedopt.FedOpt:25 flwr.server.strategy.fedopt.FedOpt:27 +#: flwr.server.strategy.fedprox.FedProx:61 +#: flwr.server.strategy.fedprox.FedProx:63 +#: flwr.server.strategy.fedyogi.FedYogi:28 +#: flwr.server.strategy.fedyogi.FedYogi:30 of +msgid "Metrics aggregation function, optional." +msgstr "" -#: ../../source/ref-changelog.md:35 +#: flwr.server.strategy.fedadagrad.FedAdagrad:29 +#: flwr.server.strategy.fedadam.FedAdam:29 +#: flwr.server.strategy.fedopt.FedOpt:29 of +msgid "Server-side learning rate. Defaults to 1e-1." +msgstr "" + +#: flwr.server.strategy.fedadagrad.FedAdagrad:31 +#: flwr.server.strategy.fedadam.FedAdam:31 +#: flwr.server.strategy.fedopt.FedOpt:31 of +msgid "Client-side learning rate. Defaults to 1e-1." +msgstr "" + +#: flwr.server.strategy.fedadagrad.FedAdagrad:33 +#: flwr.server.strategy.fedadam.FedAdam:37 +#: flwr.server.strategy.fedopt.FedOpt:37 of +msgid "Controls the algorithm's degree of adaptability. Defaults to 1e-9." +msgstr "" + +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**Support Python 3.11** " -"([#2394](https://github.com/adap/flower/pull/2394))" +":py:obj:`aggregate_evaluate " +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:37 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"Framework tests now run on Python 3.8, 3.9, 3.10, and 3.11. This will " -"ensure better support for users using more recent Python versions." +":py:obj:`aggregate_fit `\\" +" \\(server\\_round\\, results\\, failures\\)" msgstr "" -#: ../../source/ref-changelog.md:39 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**Update gRPC and ProtoBuf dependencies** " -"([#2814](https://github.com/adap/flower/pull/2814))" +":py:obj:`configure_evaluate " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:41 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"The `grpcio` and `protobuf` dependencies were updated to their latest " -"versions for improved security and performance." +":py:obj:`configure_fit `\\" +" \\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:43 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**Introduce Docker image for Flower server** " -"([#2700](https://github.com/adap/flower/pull/2700), " -"[#2688](https://github.com/adap/flower/pull/2688), " -"[#2705](https://github.com/adap/flower/pull/2705), " -"[#2695](https://github.com/adap/flower/pull/2695), " -"[#2747](https://github.com/adap/flower/pull/2747), " -"[#2746](https://github.com/adap/flower/pull/2746), " -"[#2680](https://github.com/adap/flower/pull/2680), " -"[#2682](https://github.com/adap/flower/pull/2682), " -"[#2701](https://github.com/adap/flower/pull/2701))" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" -#: ../../source/ref-changelog.md:45 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"The Flower server can now be run using an official Docker image. A new " -"how-to guide explains [how to run Flower using " -"Docker](https://flower.ai/docs/framework/how-to-run-flower-using-" -"docker.html). An official Flower client Docker image will follow." +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" -#: ../../source/ref-changelog.md:47 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**Introduce** `flower-via-docker-compose` **example** " -"([#2626](https://github.com/adap/flower/pull/2626))" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-changelog.md:49 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**Introduce** `quickstart-sklearn-tabular` **example** " -"([#2719](https://github.com/adap/flower/pull/2719))" +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-changelog.md:51 +#: ../../source/ref-api/flwr.server.strategy.FedAdam.rst:2 +msgid "FedAdam" +msgstr "" + +#: flwr.server.strategy.fedadam.FedAdam:33 +#: flwr.server.strategy.fedyogi.FedYogi:36 of +msgid "Momentum parameter. Defaults to 0.9." +msgstr "" + +#: flwr.server.strategy.fedadam.FedAdam:35 +#: flwr.server.strategy.fedyogi.FedYogi:38 of +msgid "Second moment parameter. Defaults to 0.99." +msgstr "" + +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**Introduce** `custom-metrics` **example** " -"([#1958](https://github.com/adap/flower/pull/1958))" +":py:obj:`aggregate_evaluate " +"`\\ \\(server\\_round\\," +" results\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:53 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**Update code examples to use Flower Datasets** " -"([#2450](https://github.com/adap/flower/pull/2450), " -"[#2456](https://github.com/adap/flower/pull/2456), " -"[#2318](https://github.com/adap/flower/pull/2318), " -"[#2712](https://github.com/adap/flower/pull/2712))" +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" -#: ../../source/ref-changelog.md:55 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"Several code examples were updated to use [Flower " -"Datasets](https://flower.ai/docs/datasets/)." +":py:obj:`configure_evaluate " +"`\\ \\(server\\_round\\," +" parameters\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:57 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**General updates to Flower Examples** " -"([#2381](https://github.com/adap/flower/pull/2381), " -"[#2805](https://github.com/adap/flower/pull/2805), " -"[#2782](https://github.com/adap/flower/pull/2782), " -"[#2806](https://github.com/adap/flower/pull/2806), " -"[#2829](https://github.com/adap/flower/pull/2829), " -"[#2825](https://github.com/adap/flower/pull/2825), " -"[#2816](https://github.com/adap/flower/pull/2816), " -"[#2726](https://github.com/adap/flower/pull/2726), " -"[#2659](https://github.com/adap/flower/pull/2659), " -"[#2655](https://github.com/adap/flower/pull/2655))" +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:59 -msgid "Many Flower code examples received substantial updates." +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +msgid "" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" -#: ../../source/ref-changelog.md:61 ../../source/ref-changelog.md:154 -msgid "**Update Flower Baselines**" +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +msgid "" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" -#: ../../source/ref-changelog.md:63 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"HFedXGBoost ([#2226](https://github.com/adap/flower/pull/2226), " -"[#2771](https://github.com/adap/flower/pull/2771))" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-changelog.md:64 -msgid "FedVSSL ([#2412](https://github.com/adap/flower/pull/2412))" +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +msgid "" +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-changelog.md:65 -msgid "FedNova ([#2179](https://github.com/adap/flower/pull/2179))" +#: ../../source/ref-api/flwr.server.strategy.FedAvg.rst:2 +msgid "FedAvg" msgstr "" -#: ../../source/ref-changelog.md:66 -msgid "HeteroFL ([#2439](https://github.com/adap/flower/pull/2439))" +#: flwr.server.strategy.fedavg.FedAvg:3 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:3 of +msgid "Implementation based on https://arxiv.org/abs/1602.05629" msgstr "" -#: ../../source/ref-changelog.md:67 -msgid "FedAvgM ([#2246](https://github.com/adap/flower/pull/2246))" +#: flwr.server.strategy.fedavg.FedAvg:5 flwr.server.strategy.fedprox.FedProx:37 +#: of +msgid "" +"Fraction of clients used during training. In case `min_fit_clients` is " +"larger than `fraction_fit * available_clients`, `min_fit_clients` will " +"still be sampled. Defaults to 1.0." msgstr "" -#: ../../source/ref-changelog.md:68 -msgid "FedPara ([#2722](https://github.com/adap/flower/pull/2722))" +#: flwr.server.strategy.fedavg.FedAvg:9 flwr.server.strategy.fedprox.FedProx:41 +#: of +msgid "" +"Fraction of clients used during validation. In case " +"`min_evaluate_clients` is larger than `fraction_evaluate * " +"available_clients`, `min_evaluate_clients` will still be sampled. " +"Defaults to 1.0." +msgstr "" + +#: flwr.server.strategy.fedavg.FedAvg:33 of +msgid "Enable (True) or disable (False) in-place aggregation of model updates." msgstr "" -#: ../../source/ref-changelog.md:70 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**Improve documentation** " -"([#2674](https://github.com/adap/flower/pull/2674), " -"[#2480](https://github.com/adap/flower/pull/2480), " -"[#2826](https://github.com/adap/flower/pull/2826), " -"[#2727](https://github.com/adap/flower/pull/2727), " -"[#2761](https://github.com/adap/flower/pull/2761), " -"[#2900](https://github.com/adap/flower/pull/2900))" +":py:obj:`aggregate_evaluate " +"`\\ \\(server\\_round\\, " +"results\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:72 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**Improved testing and development infrastructure** " -"([#2797](https://github.com/adap/flower/pull/2797), " -"[#2676](https://github.com/adap/flower/pull/2676), " -"[#2644](https://github.com/adap/flower/pull/2644), " -"[#2656](https://github.com/adap/flower/pull/2656), " -"[#2848](https://github.com/adap/flower/pull/2848), " -"[#2675](https://github.com/adap/flower/pull/2675), " -"[#2735](https://github.com/adap/flower/pull/2735), " -"[#2767](https://github.com/adap/flower/pull/2767), " -"[#2732](https://github.com/adap/flower/pull/2732), " -"[#2744](https://github.com/adap/flower/pull/2744), " -"[#2681](https://github.com/adap/flower/pull/2681), " -"[#2699](https://github.com/adap/flower/pull/2699), " -"[#2745](https://github.com/adap/flower/pull/2745), " -"[#2734](https://github.com/adap/flower/pull/2734), " -"[#2731](https://github.com/adap/flower/pull/2731), " -"[#2652](https://github.com/adap/flower/pull/2652), " -"[#2720](https://github.com/adap/flower/pull/2720), " -"[#2721](https://github.com/adap/flower/pull/2721), " -"[#2717](https://github.com/adap/flower/pull/2717), " -"[#2864](https://github.com/adap/flower/pull/2864), " -"[#2694](https://github.com/adap/flower/pull/2694), " -"[#2709](https://github.com/adap/flower/pull/2709), " -"[#2658](https://github.com/adap/flower/pull/2658), " -"[#2796](https://github.com/adap/flower/pull/2796), " -"[#2692](https://github.com/adap/flower/pull/2692), " -"[#2657](https://github.com/adap/flower/pull/2657), " -"[#2813](https://github.com/adap/flower/pull/2813), " -"[#2661](https://github.com/adap/flower/pull/2661), " -"[#2398](https://github.com/adap/flower/pull/2398))" +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" -#: ../../source/ref-changelog.md:74 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"The Flower testing and development infrastructure has received " -"substantial updates. This makes Flower 1.7 the most tested release ever." +":py:obj:`configure_evaluate " +"`\\ \\(server\\_round\\, " +"parameters\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:76 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**Update dependencies** " -"([#2753](https://github.com/adap/flower/pull/2753), " -"[#2651](https://github.com/adap/flower/pull/2651), " -"[#2739](https://github.com/adap/flower/pull/2739), " -"[#2837](https://github.com/adap/flower/pull/2837), " -"[#2788](https://github.com/adap/flower/pull/2788), " -"[#2811](https://github.com/adap/flower/pull/2811), " -"[#2774](https://github.com/adap/flower/pull/2774), " -"[#2790](https://github.com/adap/flower/pull/2790), " -"[#2751](https://github.com/adap/flower/pull/2751), " -"[#2850](https://github.com/adap/flower/pull/2850), " -"[#2812](https://github.com/adap/flower/pull/2812), " -"[#2872](https://github.com/adap/flower/pull/2872), " -"[#2736](https://github.com/adap/flower/pull/2736), " -"[#2756](https://github.com/adap/flower/pull/2756), " -"[#2857](https://github.com/adap/flower/pull/2857), " -"[#2757](https://github.com/adap/flower/pull/2757), " -"[#2810](https://github.com/adap/flower/pull/2810), " -"[#2740](https://github.com/adap/flower/pull/2740), " -"[#2789](https://github.com/adap/flower/pull/2789))" +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:78 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**General improvements** " -"([#2803](https://github.com/adap/flower/pull/2803), " -"[#2847](https://github.com/adap/flower/pull/2847), " -"[#2877](https://github.com/adap/flower/pull/2877), " -"[#2690](https://github.com/adap/flower/pull/2690), " -"[#2889](https://github.com/adap/flower/pull/2889), " -"[#2874](https://github.com/adap/flower/pull/2874), " -"[#2819](https://github.com/adap/flower/pull/2819), " -"[#2689](https://github.com/adap/flower/pull/2689), " -"[#2457](https://github.com/adap/flower/pull/2457), " -"[#2870](https://github.com/adap/flower/pull/2870), " -"[#2669](https://github.com/adap/flower/pull/2669), " -"[#2876](https://github.com/adap/flower/pull/2876), " -"[#2885](https://github.com/adap/flower/pull/2885), " -"[#2858](https://github.com/adap/flower/pull/2858), " -"[#2867](https://github.com/adap/flower/pull/2867), " -"[#2351](https://github.com/adap/flower/pull/2351), " -"[#2886](https://github.com/adap/flower/pull/2886), " -"[#2860](https://github.com/adap/flower/pull/2860), " -"[#2828](https://github.com/adap/flower/pull/2828), " -"[#2869](https://github.com/adap/flower/pull/2869), " -"[#2875](https://github.com/adap/flower/pull/2875), " -"[#2733](https://github.com/adap/flower/pull/2733), " -"[#2488](https://github.com/adap/flower/pull/2488), " -"[#2646](https://github.com/adap/flower/pull/2646), " -"[#2879](https://github.com/adap/flower/pull/2879), " -"[#2821](https://github.com/adap/flower/pull/2821), " -"[#2855](https://github.com/adap/flower/pull/2855), " -"[#2800](https://github.com/adap/flower/pull/2800), " -"[#2807](https://github.com/adap/flower/pull/2807), " -"[#2801](https://github.com/adap/flower/pull/2801), " -"[#2804](https://github.com/adap/flower/pull/2804), " -"[#2851](https://github.com/adap/flower/pull/2851), " -"[#2787](https://github.com/adap/flower/pull/2787), " -"[#2852](https://github.com/adap/flower/pull/2852), " -"[#2672](https://github.com/adap/flower/pull/2672), " -"[#2759](https://github.com/adap/flower/pull/2759))" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" -#: ../../source/ref-changelog.md:82 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**Deprecate** `start_numpy_client` " -"([#2563](https://github.com/adap/flower/pull/2563), " -"[#2718](https://github.com/adap/flower/pull/2718))" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" -#: ../../source/ref-changelog.md:84 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"Until now, clients of type `NumPyClient` needed to be started via " -"`start_numpy_client`. In our efforts to consolidate framework APIs, we " -"have introduced changes, and now all client types should start via " -"`start_client`. To continue using `NumPyClient` clients, you simply need " -"to first call the `.to_client()` method and then pass returned `Client` " -"object to `start_client`. The examples and the documentation have been " -"updated accordingly." +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-changelog.md:86 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**Deprecate legacy DP wrappers** " -"([#2749](https://github.com/adap/flower/pull/2749))" +":py:obj:`num_fit_clients `\\" +" \\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-changelog.md:88 -msgid "" -"Legacy DP wrapper classes are deprecated, but still functional. This is " -"in preparation for an all-new pluggable version of differential privacy " -"support in Flower." +#: ../../source/ref-api/flwr.server.strategy.FedAvgAndroid.rst:2 +msgid "FedAvgAndroid" msgstr "" -#: ../../source/ref-changelog.md:90 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: of msgid "" -"**Make optional arg** `--callable` **in** `flower-client` **a required " -"positional arg** ([#2673](https://github.com/adap/flower/pull/2673))" +":py:obj:`aggregate_evaluate " +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:92 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: of msgid "" -"**Rename** `certificates` **to** `root_certificates` **in** `Driver` " -"([#2890](https://github.com/adap/flower/pull/2890))" +":py:obj:`aggregate_fit " +"`\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" -#: ../../source/ref-changelog.md:94 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: of msgid "" -"**Drop experimental** `Task` **fields** " -"([#2866](https://github.com/adap/flower/pull/2866), " -"[#2865](https://github.com/adap/flower/pull/2865))" +":py:obj:`bytes_to_ndarray " +"`\\ \\(tensor\\)" msgstr "" -#: ../../source/ref-changelog.md:96 -msgid "" -"Experimental fields `sa`, `legacy_server_message` and " -"`legacy_client_message` were removed from `Task` message. The removed " -"fields are superseded by the new `RecordSet` abstraction." +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.bytes_to_ndarray:1 of +msgid "Deserialize NumPy array from bytes." msgstr "" -#: ../../source/ref-changelog.md:98 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: of msgid "" -"**Retire MXNet examples** " -"([#2724](https://github.com/adap/flower/pull/2724))" +":py:obj:`configure_evaluate " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:100 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: of msgid "" -"The development of the MXNet fremework has ended and the project is now " -"[archived on GitHub](https://github.com/apache/mxnet). Existing MXNet " -"examples won't receive updates." +":py:obj:`configure_fit " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:102 -msgid "v1.6.0 (2023-11-28)" +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: of +msgid "" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" -#: ../../source/ref-changelog.md:108 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: of msgid "" -"`Aashish Kolluri`, `Adam Narozniak`, `Alessio Mora`, `Barathwaja S`, " -"`Charles Beauville`, `Daniel J. Beutel`, `Daniel Nata Nugraha`, `Gabriel " -"Mota`, `Heng Pan`, `Ivan Agarský`, `JS.KIM`, `Javier`, `Marius Schlegel`," -" `Navin Chandra`, `Nic Lane`, `Peterpan828`, `Qinbin Li`, `Shaz-hash`, " -"`Steve Laskaridis`, `Taner Topal`, `William Lindskog`, `Yan Gao`, " -"`cnxdeveloper`, `k3nfalt` " +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" -#: ../../source/ref-changelog.md:112 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: of msgid "" -"**Add experimental support for Python 3.12** " -"([#2565](https://github.com/adap/flower/pull/2565))" +":py:obj:`ndarray_to_bytes " +"`\\ \\(ndarray\\)" msgstr "" -#: ../../source/ref-changelog.md:114 -msgid "" -"**Add new XGBoost examples** " -"([#2612](https://github.com/adap/flower/pull/2612), " -"[#2554](https://github.com/adap/flower/pull/2554), " -"[#2617](https://github.com/adap/flower/pull/2617), " -"[#2618](https://github.com/adap/flower/pull/2618), " -"[#2619](https://github.com/adap/flower/pull/2619), " -"[#2567](https://github.com/adap/flower/pull/2567))" +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.ndarray_to_bytes:1 of +msgid "Serialize NumPy array to bytes." msgstr "" -#: ../../source/ref-changelog.md:116 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: of msgid "" -"We have added a new `xgboost-quickstart` example alongside a new " -"`xgboost-comprehensive` example that goes more in-depth." +":py:obj:`ndarrays_to_parameters " +"`\\ " +"\\(ndarrays\\)" msgstr "" -#: ../../source/ref-changelog.md:118 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: of msgid "" -"**Add Vertical FL example** " -"([#2598](https://github.com/adap/flower/pull/2598))" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-changelog.md:120 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: of msgid "" -"We had many questions about Vertical Federated Learning using Flower, so " -"we decided to add an simple example for it on the [Titanic " -"dataset](https://www.kaggle.com/competitions/titanic/data) alongside a " -"tutorial (in the README)." +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-changelog.md:122 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: of msgid "" -"**Support custom** `ClientManager` **in** `start_driver()` " -"([#2292](https://github.com/adap/flower/pull/2292))" +":py:obj:`parameters_to_ndarrays " +"`\\ " +"\\(parameters\\)" msgstr "" -#: ../../source/ref-changelog.md:124 -msgid "" -"**Update REST API to support create and delete nodes** " -"([#2283](https://github.com/adap/flower/pull/2283))" +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.parameters_to_ndarrays:1 +#: of +msgid "Convert parameters object to NumPy weights." msgstr "" -#: ../../source/ref-changelog.md:126 -msgid "" -"**Update the Android SDK** " -"([#2187](https://github.com/adap/flower/pull/2187))" +#: ../../source/ref-api/flwr.server.strategy.FedAvgM.rst:2 +msgid "FedAvgM" msgstr "" -#: ../../source/ref-changelog.md:128 -msgid "Add gRPC request-response capability to the Android SDK." +#: flwr.server.strategy.fedavgm.FedAvgM:3 of +msgid "Implementation based on https://arxiv.org/abs/1909.06335" msgstr "" -#: ../../source/ref-changelog.md:130 +#: flwr.server.strategy.fedavgm.FedAvgM:25 of msgid "" -"**Update the C++ SDK** " -"([#2537](https://github.com/adap/flower/pull/2537), " -"[#2528](https://github.com/adap/flower/pull/2528), " -"[#2523](https://github.com/adap/flower/pull/2523), " -"[#2522](https://github.com/adap/flower/pull/2522))" +"Server-side learning rate used in server-side optimization. Defaults to " +"1.0." msgstr "" -#: ../../source/ref-changelog.md:132 -msgid "Add gRPC request-response capability to the C++ SDK." +#: flwr.server.strategy.fedavgm.FedAvgM:28 of +msgid "Server-side momentum factor used for FedAvgM. Defaults to 0.0." msgstr "" -#: ../../source/ref-changelog.md:134 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**Make HTTPS the new default** " -"([#2591](https://github.com/adap/flower/pull/2591), " -"[#2636](https://github.com/adap/flower/pull/2636))" +":py:obj:`aggregate_evaluate " +"`\\ \\(server\\_round\\," +" results\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:136 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"Flower is moving to HTTPS by default. The new `flower-server` requires " -"passing `--certificates`, but users can enable `--insecure` to use HTTP " -"for prototyping. The same applies to `flower-client`, which can either " -"use user-provided credentials or gRPC-bundled certificates to connect to " -"an HTTPS-enabled server or requires opt-out via passing `--insecure` to " -"enable insecure HTTP connections." +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" -#: ../../source/ref-changelog.md:138 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"For backward compatibility, `start_client()` and `start_numpy_client()` " -"will still start in insecure mode by default. In a future release, " -"insecure connections will require user opt-in by passing `insecure=True`." +":py:obj:`configure_evaluate " +"`\\ \\(server\\_round\\," +" parameters\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:140 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**Unify client API** ([#2303](https://github.com/adap/flower/pull/2303), " -"[#2390](https://github.com/adap/flower/pull/2390), " -"[#2493](https://github.com/adap/flower/pull/2493))" +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:142 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"Using the `client_fn`, Flower clients can interchangeably run as " -"standalone processes (i.e. via `start_client`) or in simulation (i.e. via" -" `start_simulation`) without requiring changes to how the client class is" -" defined and instantiated. The `to_client()` function is introduced to " -"convert a `NumPyClient` to a `Client`." +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" -#: ../../source/ref-changelog.md:144 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**Add new** `Bulyan` **strategy** " -"([#1817](https://github.com/adap/flower/pull/1817), " -"[#1891](https://github.com/adap/flower/pull/1891))" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" -#: ../../source/ref-changelog.md:146 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"The new `Bulyan` strategy implements Bulyan by [El Mhamdi et al., " -"2018](https://arxiv.org/abs/1802.07927)" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-changelog.md:148 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**Add new** `XGB Bagging` **strategy** " -"([#2611](https://github.com/adap/flower/pull/2611))" +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-changelog.md:150 ../../source/ref-changelog.md:152 -msgid "" -"**Introduce `WorkloadState`** " -"([#2564](https://github.com/adap/flower/pull/2564), " -"[#2632](https://github.com/adap/flower/pull/2632))" +#: ../../source/ref-api/flwr.server.strategy.FedMedian.rst:2 +msgid "FedMedian" msgstr "" -#: ../../source/ref-changelog.md:156 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"FedProx ([#2210](https://github.com/adap/flower/pull/2210), " -"[#2286](https://github.com/adap/flower/pull/2286), " -"[#2509](https://github.com/adap/flower/pull/2509))" +":py:obj:`aggregate_evaluate " +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:158 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"Baselines Docs ([#2290](https://github.com/adap/flower/pull/2290), " -"[#2400](https://github.com/adap/flower/pull/2400))" +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" +msgstr "" + +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedmedian.FedMedian.aggregate_fit:1 of +msgid "Aggregate fit results using median." msgstr "" -#: ../../source/ref-changelog.md:160 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"FedMLB ([#2340](https://github.com/adap/flower/pull/2340), " -"[#2507](https://github.com/adap/flower/pull/2507))" +":py:obj:`configure_evaluate " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:162 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"TAMUNA ([#2254](https://github.com/adap/flower/pull/2254), " -"[#2508](https://github.com/adap/flower/pull/2508))" +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:164 -msgid "FedMeta [#2438](https://github.com/adap/flower/pull/2438)" +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +msgid "" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" -#: ../../source/ref-changelog.md:166 -msgid "FjORD [#2431](https://github.com/adap/flower/pull/2431)" +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +msgid "" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" -#: ../../source/ref-changelog.md:168 -msgid "MOON [#2421](https://github.com/adap/flower/pull/2421)" +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +msgid "" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-changelog.md:170 -msgid "DepthFL [#2295](https://github.com/adap/flower/pull/2295)" +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +msgid "" +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-changelog.md:172 -msgid "FedPer [#2266](https://github.com/adap/flower/pull/2266)" +#: ../../source/ref-api/flwr.server.strategy.FedOpt.rst:2 +msgid "FedOpt" msgstr "" -#: ../../source/ref-changelog.md:174 -msgid "FedWav2vec [#2551](https://github.com/adap/flower/pull/2551)" +#: flwr.server.strategy.fedopt.FedOpt:33 of +msgid "Momentum parameter. Defaults to 0.0." msgstr "" -#: ../../source/ref-changelog.md:176 -msgid "niid-Bench [#2428](https://github.com/adap/flower/pull/2428)" +#: flwr.server.strategy.fedopt.FedOpt:35 of +msgid "Second moment parameter. Defaults to 0.0." msgstr "" -#: ../../source/ref-changelog.md:178 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"FedBN ([#2608](https://github.com/adap/flower/pull/2608), " -"[#2615](https://github.com/adap/flower/pull/2615))" +":py:obj:`aggregate_evaluate " +"`\\ \\(server\\_round\\, " +"results\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:180 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**General updates to Flower Examples** " -"([#2384](https://github.com/adap/flower/pull/2384), " -"[#2425](https://github.com/adap/flower/pull/2425), " -"[#2526](https://github.com/adap/flower/pull/2526), " -"[#2302](https://github.com/adap/flower/pull/2302), " -"[#2545](https://github.com/adap/flower/pull/2545))" +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" -#: ../../source/ref-changelog.md:182 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**General updates to Flower Baselines** " -"([#2301](https://github.com/adap/flower/pull/2301), " -"[#2305](https://github.com/adap/flower/pull/2305), " -"[#2307](https://github.com/adap/flower/pull/2307), " -"[#2327](https://github.com/adap/flower/pull/2327), " -"[#2435](https://github.com/adap/flower/pull/2435), " -"[#2462](https://github.com/adap/flower/pull/2462), " -"[#2463](https://github.com/adap/flower/pull/2463), " -"[#2461](https://github.com/adap/flower/pull/2461), " -"[#2469](https://github.com/adap/flower/pull/2469), " -"[#2466](https://github.com/adap/flower/pull/2466), " -"[#2471](https://github.com/adap/flower/pull/2471), " -"[#2472](https://github.com/adap/flower/pull/2472), " -"[#2470](https://github.com/adap/flower/pull/2470))" +":py:obj:`configure_evaluate " +"`\\ \\(server\\_round\\, " +"parameters\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:184 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**General updates to the simulation engine** " -"([#2331](https://github.com/adap/flower/pull/2331), " -"[#2447](https://github.com/adap/flower/pull/2447), " -"[#2448](https://github.com/adap/flower/pull/2448), " -"[#2294](https://github.com/adap/flower/pull/2294))" +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:186 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**General updates to Flower SDKs** " -"([#2288](https://github.com/adap/flower/pull/2288), " -"[#2429](https://github.com/adap/flower/pull/2429), " -"[#2555](https://github.com/adap/flower/pull/2555), " -"[#2543](https://github.com/adap/flower/pull/2543), " -"[#2544](https://github.com/adap/flower/pull/2544), " -"[#2597](https://github.com/adap/flower/pull/2597), " -"[#2623](https://github.com/adap/flower/pull/2623))" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" -#: ../../source/ref-changelog.md:188 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**General improvements** " -"([#2309](https://github.com/adap/flower/pull/2309), " -"[#2310](https://github.com/adap/flower/pull/2310), " -"[#2313](https://github.com/adap/flower/pull/2313), " -"[#2316](https://github.com/adap/flower/pull/2316), " -"[#2317](https://github.com/adap/flower/pull/2317), " -"[#2349](https://github.com/adap/flower/pull/2349), " -"[#2360](https://github.com/adap/flower/pull/2360), " -"[#2402](https://github.com/adap/flower/pull/2402), " -"[#2446](https://github.com/adap/flower/pull/2446), " -"[#2561](https://github.com/adap/flower/pull/2561), " -"[#2273](https://github.com/adap/flower/pull/2273), " -"[#2267](https://github.com/adap/flower/pull/2267), " -"[#2274](https://github.com/adap/flower/pull/2274), " -"[#2275](https://github.com/adap/flower/pull/2275), " -"[#2432](https://github.com/adap/flower/pull/2432), " -"[#2251](https://github.com/adap/flower/pull/2251), " -"[#2321](https://github.com/adap/flower/pull/2321), " -"[#1936](https://github.com/adap/flower/pull/1936), " -"[#2408](https://github.com/adap/flower/pull/2408), " -"[#2413](https://github.com/adap/flower/pull/2413), " -"[#2401](https://github.com/adap/flower/pull/2401), " -"[#2531](https://github.com/adap/flower/pull/2531), " -"[#2534](https://github.com/adap/flower/pull/2534), " -"[#2535](https://github.com/adap/flower/pull/2535), " -"[#2521](https://github.com/adap/flower/pull/2521), " -"[#2553](https://github.com/adap/flower/pull/2553), " -"[#2596](https://github.com/adap/flower/pull/2596))" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" -#: ../../source/ref-changelog.md:190 ../../source/ref-changelog.md:280 -#: ../../source/ref-changelog.md:344 ../../source/ref-changelog.md:398 -#: ../../source/ref-changelog.md:465 -msgid "Flower received many improvements under the hood, too many to list here." +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +msgid "" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-changelog.md:194 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**Remove support for Python 3.7** " -"([#2280](https://github.com/adap/flower/pull/2280), " -"[#2299](https://github.com/adap/flower/pull/2299), " -"[#2304](https://github.com/adap/flower/pull/2304), " -"[#2306](https://github.com/adap/flower/pull/2306), " -"[#2355](https://github.com/adap/flower/pull/2355), " -"[#2356](https://github.com/adap/flower/pull/2356))" +":py:obj:`num_fit_clients `\\" +" \\(num\\_available\\_clients\\)" +msgstr "" + +#: ../../source/ref-api/flwr.server.strategy.FedProx.rst:2 +msgid "FedProx" +msgstr "" + +#: flwr.server.strategy.fedprox.FedProx:3 of +msgid "Implementation based on https://arxiv.org/abs/1812.06127" msgstr "" -#: ../../source/ref-changelog.md:196 +#: flwr.server.strategy.fedprox.FedProx:5 of msgid "" -"Python 3.7 support was deprecated in Flower 1.5, and this release removes" -" support. Flower now requires Python 3.8." +"The strategy in itself will not be different than FedAvg, the client " +"needs to be adjusted. A proximal term needs to be added to the loss " +"function during the training:" msgstr "" -#: ../../source/ref-changelog.md:198 +#: flwr.server.strategy.fedprox.FedProx:9 of msgid "" -"**Remove experimental argument** `rest` **from** `start_client` " -"([#2324](https://github.com/adap/flower/pull/2324))" +"\\\\frac{\\\\mu}{2} || w - w^t ||^2\n" +"\n" msgstr "" -#: ../../source/ref-changelog.md:200 +#: flwr.server.strategy.fedprox.FedProx:12 of msgid "" -"The (still experimental) argument `rest` was removed from `start_client` " -"and `start_numpy_client`. Use `transport=\"rest\"` to opt into the " -"experimental REST API instead." +"Where $w^t$ are the global parameters and $w$ are the local weights the " +"function will be optimized with." msgstr "" -#: ../../source/ref-changelog.md:202 -msgid "v1.5.0 (2023-08-31)" +#: flwr.server.strategy.fedprox.FedProx:15 of +msgid "In PyTorch, for example, the loss would go from:" +msgstr "" + +#: flwr.server.strategy.fedprox.FedProx:21 of +msgid "To:" msgstr "" -#: ../../source/ref-changelog.md:208 +#: flwr.server.strategy.fedprox.FedProx:30 of msgid "" -"`Adam Narozniak`, `Anass Anhari`, `Charles Beauville`, `Dana-Farber`, " -"`Daniel J. Beutel`, `Daniel Nata Nugraha`, `Edoardo Gabrielli`, `Gustavo " -"Bertoli`, `Heng Pan`, `Javier`, `Mahdi`, `Steven Hé (Sīchàng)`, `Taner " -"Topal`, `achiverram28`, `danielnugraha`, `eunchung`, `ruthgal` " +"With `global_params` being a copy of the parameters before the training " +"takes place." msgstr "" -#: ../../source/ref-changelog.md:212 +#: flwr.server.strategy.fedprox.FedProx:65 of msgid "" -"**Introduce new simulation engine** " -"([#1969](https://github.com/adap/flower/pull/1969), " -"[#2221](https://github.com/adap/flower/pull/2221), " -"[#2248](https://github.com/adap/flower/pull/2248))" +"The weight of the proximal term used in the optimization. 0.0 makes this " +"strategy equivalent to FedAvg, and the higher the coefficient, the more " +"regularization will be used (that is, the client parameters will need to " +"be closer to the server parameters during training)." msgstr "" -#: ../../source/ref-changelog.md:214 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"The new simulation engine has been rewritten from the ground up, yet it " -"remains fully backwards compatible. It offers much improved stability and" -" memory handling, especially when working with GPUs. Simulations " -"transparently adapt to different settings to scale simulation in CPU-" -"only, CPU+GPU, multi-GPU, or multi-node multi-GPU environments." +":py:obj:`aggregate_evaluate " +"`\\ \\(server\\_round\\," +" results\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:216 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"Comprehensive documentation includes a new [how-to run " -"simulations](https://flower.ai/docs/framework/how-to-run-" -"simulations.html) guide, new [simulation-" -"pytorch](https://flower.ai/docs/examples/simulation-pytorch.html) and " -"[simulation-tensorflow](https://flower.ai/docs/examples/simulation-" -"tensorflow.html) notebooks, and a new [YouTube tutorial " -"series](https://www.youtube.com/watch?v=cRebUIGB5RU&list=PLNG4feLHqCWlnj8a_E1A_n5zr2-8pafTB)." +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" -#: ../../source/ref-changelog.md:218 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**Restructure Flower Docs** " -"([#1824](https://github.com/adap/flower/pull/1824), " -"[#1865](https://github.com/adap/flower/pull/1865), " -"[#1884](https://github.com/adap/flower/pull/1884), " -"[#1887](https://github.com/adap/flower/pull/1887), " -"[#1919](https://github.com/adap/flower/pull/1919), " -"[#1922](https://github.com/adap/flower/pull/1922), " -"[#1920](https://github.com/adap/flower/pull/1920), " -"[#1923](https://github.com/adap/flower/pull/1923), " -"[#1924](https://github.com/adap/flower/pull/1924), " -"[#1962](https://github.com/adap/flower/pull/1962), " -"[#2006](https://github.com/adap/flower/pull/2006), " -"[#2133](https://github.com/adap/flower/pull/2133), " -"[#2203](https://github.com/adap/flower/pull/2203), " -"[#2215](https://github.com/adap/flower/pull/2215), " -"[#2122](https://github.com/adap/flower/pull/2122), " -"[#2223](https://github.com/adap/flower/pull/2223), " -"[#2219](https://github.com/adap/flower/pull/2219), " -"[#2232](https://github.com/adap/flower/pull/2232), " -"[#2233](https://github.com/adap/flower/pull/2233), " -"[#2234](https://github.com/adap/flower/pull/2234), " -"[#2235](https://github.com/adap/flower/pull/2235), " -"[#2237](https://github.com/adap/flower/pull/2237), " -"[#2238](https://github.com/adap/flower/pull/2238), " -"[#2242](https://github.com/adap/flower/pull/2242), " -"[#2231](https://github.com/adap/flower/pull/2231), " -"[#2243](https://github.com/adap/flower/pull/2243), " -"[#2227](https://github.com/adap/flower/pull/2227))" +":py:obj:`configure_evaluate " +"`\\ \\(server\\_round\\," +" parameters\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:220 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"Much effort went into a completely restructured Flower docs experience. " -"The documentation on [flower.ai/docs](https://flower.ai/docs) is now " -"divided into Flower Framework, Flower Baselines, Flower Android SDK, " -"Flower iOS SDK, and code example projects." +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:222 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**Introduce Flower Swift SDK** " -"([#1858](https://github.com/adap/flower/pull/1858), " -"[#1897](https://github.com/adap/flower/pull/1897))" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" -#: ../../source/ref-changelog.md:224 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"This is the first preview release of the Flower Swift SDK. Flower support" -" on iOS is improving, and alongside the Swift SDK and code example, there" -" is now also an iOS quickstart tutorial." +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" -#: ../../source/ref-changelog.md:226 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**Introduce Flower Android SDK** " -"([#2131](https://github.com/adap/flower/pull/2131))" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-changelog.md:228 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"This is the first preview release of the Flower Kotlin SDK. Flower " -"support on Android is improving, and alongside the Kotlin SDK and code " -"example, there is now also an Android quickstart tutorial." +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-changelog.md:230 -msgid "" -"**Introduce new end-to-end testing infrastructure** " -"([#1842](https://github.com/adap/flower/pull/1842), " -"[#2071](https://github.com/adap/flower/pull/2071), " -"[#2072](https://github.com/adap/flower/pull/2072), " -"[#2068](https://github.com/adap/flower/pull/2068), " -"[#2067](https://github.com/adap/flower/pull/2067), " -"[#2069](https://github.com/adap/flower/pull/2069), " -"[#2073](https://github.com/adap/flower/pull/2073), " -"[#2070](https://github.com/adap/flower/pull/2070), " -"[#2074](https://github.com/adap/flower/pull/2074), " -"[#2082](https://github.com/adap/flower/pull/2082), " -"[#2084](https://github.com/adap/flower/pull/2084), " -"[#2093](https://github.com/adap/flower/pull/2093), " -"[#2109](https://github.com/adap/flower/pull/2109), " -"[#2095](https://github.com/adap/flower/pull/2095), " -"[#2140](https://github.com/adap/flower/pull/2140), " -"[#2137](https://github.com/adap/flower/pull/2137), " -"[#2165](https://github.com/adap/flower/pull/2165))" +#: flwr.server.strategy.fedprox.FedProx.configure_fit:3 of +msgid "Sends the proximal factor mu to the clients" msgstr "" -#: ../../source/ref-changelog.md:232 -msgid "" -"A new testing infrastructure ensures that new changes stay compatible " -"with existing framework integrations or strategies." +#: ../../source/ref-api/flwr.server.strategy.FedTrimmedAvg.rst:2 +msgid "FedTrimmedAvg" msgstr "" -#: ../../source/ref-changelog.md:234 -msgid "**Deprecate Python 3.7**" +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:3 of +msgid "Implemented based on: https://arxiv.org/abs/1803.01498" msgstr "" -#: ../../source/ref-changelog.md:236 -msgid "" -"Since Python 3.7 reached its end of life (EOL) on 2023-06-27, support for" -" Python 3.7 is now deprecated and will be removed in an upcoming release." +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:25 of +msgid "Fraction to cut off of both tails of the distribution. Defaults to 0.2." msgstr "" -#: ../../source/ref-changelog.md:238 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**Add new** `FedTrimmedAvg` **strategy** " -"([#1769](https://github.com/adap/flower/pull/1769), " -"[#1853](https://github.com/adap/flower/pull/1853))" +":py:obj:`aggregate_evaluate " +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:240 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"The new `FedTrimmedAvg` strategy implements Trimmed Mean by [Dong Yin, " -"2018](https://arxiv.org/abs/1803.01498)." +":py:obj:`aggregate_fit " +"`\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" -#: ../../source/ref-changelog.md:242 -msgid "" -"**Introduce start_driver** " -"([#1697](https://github.com/adap/flower/pull/1697))" +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.aggregate_fit:1 of +msgid "Aggregate fit results using trimmed average." msgstr "" -#: ../../source/ref-changelog.md:244 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"In addition to `start_server` and using the raw Driver API, there is a " -"new `start_driver` function that allows for running `start_server` " -"scripts as a Flower driver with only a single-line code change. Check out" -" the `mt-pytorch` code example to see a working example using " -"`start_driver`." +":py:obj:`configure_evaluate " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:246 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**Add parameter aggregation to** `mt-pytorch` **code example** " -"([#1785](https://github.com/adap/flower/pull/1785))" +":py:obj:`configure_fit " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:248 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"The `mt-pytorch` example shows how to aggregate parameters when writing a" -" driver script. The included `driver.py` and `server.py` have been " -"aligned to demonstrate both the low-level way and the high-level way of " -"building server-side logic." +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" -#: ../../source/ref-changelog.md:250 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**Migrate experimental REST API to Starlette** " -"([2171](https://github.com/adap/flower/pull/2171))" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" -#: ../../source/ref-changelog.md:252 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"The (experimental) REST API used to be implemented in " -"[FastAPI](https://fastapi.tiangolo.com/), but it has now been migrated to" -" use [Starlette](https://www.starlette.io/) directly." +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-changelog.md:254 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"Please note: The REST request-response API is still experimental and will" -" likely change significantly over time." +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-changelog.md:256 -msgid "" -"**Introduce experimental gRPC request-response API** " -"([#1867](https://github.com/adap/flower/pull/1867), " -"[#1901](https://github.com/adap/flower/pull/1901))" +#: ../../source/ref-api/flwr.server.strategy.FedXgbBagging.rst:2 +msgid "FedXgbBagging" msgstr "" -#: ../../source/ref-changelog.md:258 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 +#: of msgid "" -"In addition to the existing gRPC API (based on bidirectional streaming) " -"and the experimental REST API, there is now a new gRPC API that uses a " -"request-response model to communicate with client nodes." +":py:obj:`aggregate_evaluate " +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:260 -msgid "" -"Please note: The gRPC request-response API is still experimental and will" -" likely change significantly over time." +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 +#: of +msgid "Aggregate evaluation metrics using average." msgstr "" -#: ../../source/ref-changelog.md:262 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 +#: of msgid "" -"**Replace the experimental** `start_client(rest=True)` **with the new** " -"`start_client(transport=\"rest\")` " -"([#1880](https://github.com/adap/flower/pull/1880))" +":py:obj:`aggregate_fit " +"`\\ " +"\\(server\\_round\\, results\\, failures\\)" +msgstr "" + +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_fit:1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_fit:1 of +msgid "Aggregate fit results using bagging." msgstr "" -#: ../../source/ref-changelog.md:264 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 +#: of msgid "" -"The (experimental) `start_client` argument `rest` was deprecated in " -"favour of a new argument `transport`. `start_client(transport=\"rest\")` " -"will yield the same behaviour as `start_client(rest=True)` did before. " -"All code should migrate to the new argument `transport`. The deprecated " -"argument `rest` will be removed in a future release." +":py:obj:`configure_evaluate " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:266 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 +#: of msgid "" -"**Add a new gRPC option** " -"([#2197](https://github.com/adap/flower/pull/2197))" +":py:obj:`configure_fit " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:268 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 +#: of msgid "" -"We now start a gRPC server with the `grpc.keepalive_permit_without_calls`" -" option set to 0 by default. This prevents the clients from sending " -"keepalive pings when there is no outstanding stream." +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" -#: ../../source/ref-changelog.md:270 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 +#: of msgid "" -"**Improve example notebooks** " -"([#2005](https://github.com/adap/flower/pull/2005))" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" -#: ../../source/ref-changelog.md:272 -msgid "There's a new 30min Federated Learning PyTorch tutorial!" +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 +#: of +msgid "" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-changelog.md:274 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 +#: of msgid "" -"**Example updates** ([#1772](https://github.com/adap/flower/pull/1772), " -"[#1873](https://github.com/adap/flower/pull/1873), " -"[#1981](https://github.com/adap/flower/pull/1981), " -"[#1988](https://github.com/adap/flower/pull/1988), " -"[#1984](https://github.com/adap/flower/pull/1984), " -"[#1982](https://github.com/adap/flower/pull/1982), " -"[#2112](https://github.com/adap/flower/pull/2112), " -"[#2144](https://github.com/adap/flower/pull/2144), " -"[#2174](https://github.com/adap/flower/pull/2174), " -"[#2225](https://github.com/adap/flower/pull/2225), " -"[#2183](https://github.com/adap/flower/pull/2183))" +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-changelog.md:276 -msgid "" -"Many examples have received significant updates, including simplified " -"advanced-tensorflow and advanced-pytorch examples, improved macOS " -"compatibility of TensorFlow examples, and code examples for simulation. A" -" major upgrade is that all code examples now have a `requirements.txt` " -"(in addition to `pyproject.toml`)." +#: ../../source/ref-api/flwr.server.strategy.FedXgbCyclic.rst:2 +msgid "FedXgbCyclic" msgstr "" -#: ../../source/ref-changelog.md:278 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 +#: of msgid "" -"**General improvements** " -"([#1872](https://github.com/adap/flower/pull/1872), " -"[#1866](https://github.com/adap/flower/pull/1866), " -"[#1884](https://github.com/adap/flower/pull/1884), " -"[#1837](https://github.com/adap/flower/pull/1837), " -"[#1477](https://github.com/adap/flower/pull/1477), " -"[#2171](https://github.com/adap/flower/pull/2171))" -msgstr "" - -#: ../../source/ref-changelog.md:284 ../../source/ref-changelog.md:348 -#: ../../source/ref-changelog.md:406 ../../source/ref-changelog.md:475 -#: ../../source/ref-changelog.md:537 -msgid "None" +":py:obj:`aggregate_evaluate " +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:286 -msgid "v1.4.0 (2023-04-21)" +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 +#: of +msgid "" +":py:obj:`aggregate_fit " +"`\\ \\(server\\_round\\," +" results\\, failures\\)" msgstr "" -#: ../../source/ref-changelog.md:292 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 +#: of msgid "" -"`Adam Narozniak`, `Alexander Viala Bellander`, `Charles Beauville`, " -"`Chenyang Ma (Danny)`, `Daniel J. Beutel`, `Edoardo`, `Gautam Jajoo`, " -"`Iacob-Alexandru-Andrei`, `JDRanpariya`, `Jean Charle Yaacoub`, `Kunal " -"Sarkhel`, `L. Jiang`, `Lennart Behme`, `Max Kapsecker`, `Michał`, `Nic " -"Lane`, `Nikolaos Episkopos`, `Ragy`, `Saurav Maheshkar`, `Semo Yang`, " -"`Steve Laskaridis`, `Steven Hé (Sīchàng)`, `Taner Topal`" +":py:obj:`configure_evaluate " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:296 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 +#: of msgid "" -"**Introduce support for XGBoost (**`FedXgbNnAvg` **strategy and " -"example)** ([#1694](https://github.com/adap/flower/pull/1694), " -"[#1709](https://github.com/adap/flower/pull/1709), " -"[#1715](https://github.com/adap/flower/pull/1715), " -"[#1717](https://github.com/adap/flower/pull/1717), " -"[#1763](https://github.com/adap/flower/pull/1763), " -"[#1795](https://github.com/adap/flower/pull/1795))" +":py:obj:`configure_fit " +"`\\ \\(server\\_round\\," +" parameters\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:298 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 +#: of msgid "" -"XGBoost is a tree-based ensemble machine learning algorithm that uses " -"gradient boosting to improve model accuracy. We added a new `FedXgbNnAvg`" -" " -"[strategy](https://github.com/adap/flower/tree/main/src/py/flwr/server/strategy/fedxgb_nn_avg.py)," -" and a [code example](https://github.com/adap/flower/tree/main/examples" -"/xgboost-quickstart) that demonstrates the usage of this new strategy in " -"an XGBoost project." +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" -#: ../../source/ref-changelog.md:300 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 +#: of msgid "" -"**Introduce iOS SDK (preview)** " -"([#1621](https://github.com/adap/flower/pull/1621), " -"[#1764](https://github.com/adap/flower/pull/1764))" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" -#: ../../source/ref-changelog.md:302 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 +#: of msgid "" -"This is a major update for anyone wanting to implement Federated Learning" -" on iOS mobile devices. We now have a swift iOS SDK present under " -"[src/swift/flwr](https://github.com/adap/flower/tree/main/src/swift/flwr)" -" that will facilitate greatly the app creating process. To showcase its " -"use, the [iOS " -"example](https://github.com/adap/flower/tree/main/examples/ios) has also " -"been updated!" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-changelog.md:304 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 +#: of msgid "" -"**Introduce new \"What is Federated Learning?\" tutorial** " -"([#1657](https://github.com/adap/flower/pull/1657), " -"[#1721](https://github.com/adap/flower/pull/1721))" +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-changelog.md:306 -msgid "" -"A new [entry-level tutorial](https://flower.ai/docs/framework/tutorial-" -"what-is-federated-learning.html) in our documentation explains the basics" -" of Fedetated Learning. It enables anyone who's unfamiliar with Federated" -" Learning to start their journey with Flower. Forward it to anyone who's " -"interested in Federated Learning!" +#: ../../source/ref-api/flwr.server.strategy.FedXgbNnAvg.rst:2 +msgid "FedXgbNnAvg" msgstr "" -#: ../../source/ref-changelog.md:308 +#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg:5 of msgid "" -"**Introduce new Flower Baseline: FedProx MNIST** " -"([#1513](https://github.com/adap/flower/pull/1513), " -"[#1680](https://github.com/adap/flower/pull/1680), " -"[#1681](https://github.com/adap/flower/pull/1681), " -"[#1679](https://github.com/adap/flower/pull/1679))" +"This strategy is deprecated, but a copy of it is available in Flower " +"Baselines: " +"https://github.com/adap/flower/tree/main/baselines/hfedxgboost." msgstr "" -#: ../../source/ref-changelog.md:310 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"This new baseline replicates the MNIST+CNN task from the paper [Federated" -" Optimization in Heterogeneous Networks (Li et al., " -"2018)](https://arxiv.org/abs/1812.06127). It uses the `FedProx` strategy," -" which aims at making convergence more robust in heterogeneous settings." +":py:obj:`aggregate_evaluate " +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:312 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**Introduce new Flower Baseline: FedAvg FEMNIST** " -"([#1655](https://github.com/adap/flower/pull/1655))" +":py:obj:`aggregate_fit " +"`\\ \\(server\\_round\\, " +"results\\, failures\\)" msgstr "" -#: ../../source/ref-changelog.md:314 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"This new baseline replicates an experiment evaluating the performance of " -"the FedAvg algorithm on the FEMNIST dataset from the paper [LEAF: A " -"Benchmark for Federated Settings (Caldas et al., " -"2018)](https://arxiv.org/abs/1812.01097)." +":py:obj:`configure_evaluate " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:316 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**Introduce (experimental) REST API** " -"([#1594](https://github.com/adap/flower/pull/1594), " -"[#1690](https://github.com/adap/flower/pull/1690), " -"[#1695](https://github.com/adap/flower/pull/1695), " -"[#1712](https://github.com/adap/flower/pull/1712), " -"[#1802](https://github.com/adap/flower/pull/1802), " -"[#1770](https://github.com/adap/flower/pull/1770), " -"[#1733](https://github.com/adap/flower/pull/1733))" +":py:obj:`configure_fit " +"`\\ \\(server\\_round\\, " +"parameters\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:318 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"A new REST API has been introduced as an alternative to the gRPC-based " -"communication stack. In this initial version, the REST API only supports " -"anonymous clients." +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" -#: ../../source/ref-changelog.md:320 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"Please note: The REST API is still experimental and will likely change " -"significantly over time." +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" -#: ../../source/ref-changelog.md:322 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**Improve the (experimental) Driver API** " -"([#1663](https://github.com/adap/flower/pull/1663), " -"[#1666](https://github.com/adap/flower/pull/1666), " -"[#1667](https://github.com/adap/flower/pull/1667), " -"[#1664](https://github.com/adap/flower/pull/1664), " -"[#1675](https://github.com/adap/flower/pull/1675), " -"[#1676](https://github.com/adap/flower/pull/1676), " -"[#1693](https://github.com/adap/flower/pull/1693), " -"[#1662](https://github.com/adap/flower/pull/1662), " -"[#1794](https://github.com/adap/flower/pull/1794))" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-changelog.md:324 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"The Driver API is still an experimental feature, but this release " -"introduces some major upgrades. One of the main improvements is the " -"introduction of an SQLite database to store server state on disk (instead" -" of in-memory). Another improvement is that tasks (instructions or " -"results) that have been delivered will now be deleted. This greatly " -"improves the memory efficiency of a long-running Flower server." +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-changelog.md:326 -msgid "" -"**Fix spilling issues related to Ray during simulations** " -"([#1698](https://github.com/adap/flower/pull/1698))" +#: ../../source/ref-api/flwr.server.strategy.FedYogi.rst:2 +msgid "FedYogi" msgstr "" -#: ../../source/ref-changelog.md:328 -msgid "" -"While running long simulations, `ray` was sometimes spilling huge amounts" -" of data that would make the training unable to continue. This is now " -"fixed! 🎉" +#: flwr.server.strategy.fedyogi.FedYogi:32 of +msgid "Server-side learning rate. Defaults to 1e-2." msgstr "" -#: ../../source/ref-changelog.md:330 -msgid "" -"**Add new example using** `TabNet` **and Flower** " -"([#1725](https://github.com/adap/flower/pull/1725))" +#: flwr.server.strategy.fedyogi.FedYogi:34 of +msgid "Client-side learning rate. Defaults to 0.0316." msgstr "" -#: ../../source/ref-changelog.md:332 -msgid "" -"TabNet is a powerful and flexible framework for training machine learning" -" models on tabular data. We now have a federated example using Flower: " -"[quickstart-tabnet](https://github.com/adap/flower/tree/main/examples" -"/quickstart-tabnet)." +#: flwr.server.strategy.fedyogi.FedYogi:40 of +msgid "Controls the algorithm's degree of adaptability. Defaults to 1e-3." msgstr "" -#: ../../source/ref-changelog.md:334 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**Add new how-to guide for monitoring simulations** " -"([#1649](https://github.com/adap/flower/pull/1649))" +":py:obj:`aggregate_evaluate " +"`\\ \\(server\\_round\\," +" results\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:336 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"We now have a documentation guide to help users monitor their performance" -" during simulations." +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" -#: ../../source/ref-changelog.md:338 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**Add training metrics to** `History` **object during simulations** " -"([#1696](https://github.com/adap/flower/pull/1696))" +":py:obj:`configure_evaluate " +"`\\ \\(server\\_round\\," +" parameters\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:340 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"The `fit_metrics_aggregation_fn` can be used to aggregate training " -"metrics, but previous releases did not save the results in the `History` " -"object. This is now the case!" +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:342 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**General improvements** " -"([#1659](https://github.com/adap/flower/pull/1659), " -"[#1646](https://github.com/adap/flower/pull/1646), " -"[#1647](https://github.com/adap/flower/pull/1647), " -"[#1471](https://github.com/adap/flower/pull/1471), " -"[#1648](https://github.com/adap/flower/pull/1648), " -"[#1651](https://github.com/adap/flower/pull/1651), " -"[#1652](https://github.com/adap/flower/pull/1652), " -"[#1653](https://github.com/adap/flower/pull/1653), " -"[#1659](https://github.com/adap/flower/pull/1659), " -"[#1665](https://github.com/adap/flower/pull/1665), " -"[#1670](https://github.com/adap/flower/pull/1670), " -"[#1672](https://github.com/adap/flower/pull/1672), " -"[#1677](https://github.com/adap/flower/pull/1677), " -"[#1684](https://github.com/adap/flower/pull/1684), " -"[#1683](https://github.com/adap/flower/pull/1683), " -"[#1686](https://github.com/adap/flower/pull/1686), " -"[#1682](https://github.com/adap/flower/pull/1682), " -"[#1685](https://github.com/adap/flower/pull/1685), " -"[#1692](https://github.com/adap/flower/pull/1692), " -"[#1705](https://github.com/adap/flower/pull/1705), " -"[#1708](https://github.com/adap/flower/pull/1708), " -"[#1711](https://github.com/adap/flower/pull/1711), " -"[#1713](https://github.com/adap/flower/pull/1713), " -"[#1714](https://github.com/adap/flower/pull/1714), " -"[#1718](https://github.com/adap/flower/pull/1718), " -"[#1716](https://github.com/adap/flower/pull/1716), " -"[#1723](https://github.com/adap/flower/pull/1723), " -"[#1735](https://github.com/adap/flower/pull/1735), " -"[#1678](https://github.com/adap/flower/pull/1678), " -"[#1750](https://github.com/adap/flower/pull/1750), " -"[#1753](https://github.com/adap/flower/pull/1753), " -"[#1736](https://github.com/adap/flower/pull/1736), " -"[#1766](https://github.com/adap/flower/pull/1766), " -"[#1760](https://github.com/adap/flower/pull/1760), " -"[#1775](https://github.com/adap/flower/pull/1775), " -"[#1776](https://github.com/adap/flower/pull/1776), " -"[#1777](https://github.com/adap/flower/pull/1777), " -"[#1779](https://github.com/adap/flower/pull/1779), " -"[#1784](https://github.com/adap/flower/pull/1784), " -"[#1773](https://github.com/adap/flower/pull/1773), " -"[#1755](https://github.com/adap/flower/pull/1755), " -"[#1789](https://github.com/adap/flower/pull/1789), " -"[#1788](https://github.com/adap/flower/pull/1788), " -"[#1798](https://github.com/adap/flower/pull/1798), " -"[#1799](https://github.com/adap/flower/pull/1799), " -"[#1739](https://github.com/adap/flower/pull/1739), " -"[#1800](https://github.com/adap/flower/pull/1800), " -"[#1804](https://github.com/adap/flower/pull/1804), " -"[#1805](https://github.com/adap/flower/pull/1805))" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" -#: ../../source/ref-changelog.md:350 -msgid "v1.3.0 (2023-02-06)" +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +msgid "" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" -#: ../../source/ref-changelog.md:356 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"`Adam Narozniak`, `Alexander Viala Bellander`, `Charles Beauville`, " -"`Daniel J. Beutel`, `JDRanpariya`, `Lennart Behme`, `Taner Topal`" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-changelog.md:360 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**Add support for** `workload_id` **and** `group_id` **in Driver API** " -"([#1595](https://github.com/adap/flower/pull/1595))" +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-changelog.md:362 -msgid "" -"The (experimental) Driver API now supports a `workload_id` that can be " -"used to identify which workload a task belongs to. It also supports a new" -" `group_id` that can be used, for example, to indicate the current " -"training round. Both the `workload_id` and `group_id` enable client nodes" -" to decide whether they want to handle a task or not." +#: ../../source/ref-api/flwr.server.strategy.Krum.rst:2 +msgid "Krum" msgstr "" -#: ../../source/ref-changelog.md:364 +#: flwr.server.strategy.krum.Krum:3 of +msgid "Implementation based on https://arxiv.org/abs/1703.02757" +msgstr "" + +#: flwr.server.strategy.krum.Krum:17 of msgid "" -"**Make Driver API and Fleet API address configurable** " -"([#1637](https://github.com/adap/flower/pull/1637))" +"Number of clients to keep before averaging (MultiKrum). Defaults to 0, in" +" that case classical Krum is applied." msgstr "" -#: ../../source/ref-changelog.md:366 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"The (experimental) long-running Flower server (Driver API and Fleet API) " -"can now configure the server address of both Driver API (via `--driver-" -"api-address`) and Fleet API (via `--fleet-api-address`) when starting:" +":py:obj:`aggregate_evaluate " +"`\\ \\(server\\_round\\, " +"results\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:368 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"`flower-server --driver-api-address \"0.0.0.0:8081\" --fleet-api-address " -"\"0.0.0.0:8086\"`" +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" -#: ../../source/ref-changelog.md:370 -msgid "Both IPv4 and IPv6 addresses are supported." +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.krum.Krum.aggregate_fit:1 of +msgid "Aggregate fit results using Krum." msgstr "" -#: ../../source/ref-changelog.md:372 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**Add new example of Federated Learning using fastai and Flower** " -"([#1598](https://github.com/adap/flower/pull/1598))" +":py:obj:`configure_evaluate " +"`\\ \\(server\\_round\\, " +"parameters\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:374 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"A new code example (`quickstart-fastai`) demonstrates federated learning " -"with [fastai](https://www.fast.ai/) and Flower. You can find it here: " -"[quickstart-fastai](https://github.com/adap/flower/tree/main/examples" -"/quickstart-fastai)." +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:376 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**Make Android example compatible with** `flwr >= 1.0.0` **and the latest" -" versions of Android** " -"([#1603](https://github.com/adap/flower/pull/1603))" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" -#: ../../source/ref-changelog.md:378 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"The Android code example has received a substantial update: the project " -"is compatible with Flower 1.0 (and later), the UI received a full " -"refresh, and the project is updated to be compatible with newer Android " -"tooling." +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" -#: ../../source/ref-changelog.md:380 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"**Add new `FedProx` strategy** " -"([#1619](https://github.com/adap/flower/pull/1619))" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-changelog.md:382 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" -"This " -"[strategy](https://github.com/adap/flower/blob/main/src/py/flwr/server/strategy/fedprox.py)" -" is almost identical to " -"[`FedAvg`](https://github.com/adap/flower/blob/main/src/py/flwr/server/strategy/fedavg.py)," -" but helps users replicate what is described in this " -"[paper](https://arxiv.org/abs/1812.06127). It essentially adds a " -"parameter called `proximal_mu` to regularize the local models with " -"respect to the global models." +":py:obj:`num_fit_clients `\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-changelog.md:384 +#: ../../source/ref-api/flwr.server.strategy.QFedAvg.rst:2 +msgid "QFedAvg" +msgstr "" + +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of msgid "" -"**Add new metrics to telemetry events** " -"([#1640](https://github.com/adap/flower/pull/1640))" +":py:obj:`aggregate_evaluate " +"`\\ \\(server\\_round\\," +" results\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:386 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of msgid "" -"An updated event structure allows, for example, the clustering of events " -"within the same workload." +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" -#: ../../source/ref-changelog.md:388 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of msgid "" -"**Add new custom strategy tutorial section** " -"[#1623](https://github.com/adap/flower/pull/1623)" +":py:obj:`configure_evaluate " +"`\\ \\(server\\_round\\," +" parameters\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:390 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of msgid "" -"The Flower tutorial now has a new section that covers implementing a " -"custom strategy from scratch: [Open in " -"Colab](https://colab.research.google.com/github/adap/flower/blob/main/doc/source" -"/tutorial-build-a-strategy-from-scratch-pytorch.ipynb)" +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:392 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of msgid "" -"**Add new custom serialization tutorial section** " -"([#1622](https://github.com/adap/flower/pull/1622))" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" -#: ../../source/ref-changelog.md:394 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of msgid "" -"The Flower tutorial now has a new section that covers custom " -"serialization: [Open in " -"Colab](https://colab.research.google.com/github/adap/flower/blob/main/doc/source" -"/tutorial-customize-the-client-pytorch.ipynb)" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" -#: ../../source/ref-changelog.md:396 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of msgid "" -"**General improvements** " -"([#1638](https://github.com/adap/flower/pull/1638), " -"[#1634](https://github.com/adap/flower/pull/1634), " -"[#1636](https://github.com/adap/flower/pull/1636), " -"[#1635](https://github.com/adap/flower/pull/1635), " -"[#1633](https://github.com/adap/flower/pull/1633), " -"[#1632](https://github.com/adap/flower/pull/1632), " -"[#1631](https://github.com/adap/flower/pull/1631), " -"[#1630](https://github.com/adap/flower/pull/1630), " -"[#1627](https://github.com/adap/flower/pull/1627), " -"[#1593](https://github.com/adap/flower/pull/1593), " -"[#1616](https://github.com/adap/flower/pull/1616), " -"[#1615](https://github.com/adap/flower/pull/1615), " -"[#1607](https://github.com/adap/flower/pull/1607), " -"[#1609](https://github.com/adap/flower/pull/1609), " -"[#1608](https://github.com/adap/flower/pull/1608), " -"[#1603](https://github.com/adap/flower/pull/1603), " -"[#1590](https://github.com/adap/flower/pull/1590), " -"[#1580](https://github.com/adap/flower/pull/1580), " -"[#1599](https://github.com/adap/flower/pull/1599), " -"[#1600](https://github.com/adap/flower/pull/1600), " -"[#1601](https://github.com/adap/flower/pull/1601), " -"[#1597](https://github.com/adap/flower/pull/1597), " -"[#1595](https://github.com/adap/flower/pull/1595), " -"[#1591](https://github.com/adap/flower/pull/1591), " -"[#1588](https://github.com/adap/flower/pull/1588), " -"[#1589](https://github.com/adap/flower/pull/1589), " -"[#1587](https://github.com/adap/flower/pull/1587), " -"[#1573](https://github.com/adap/flower/pull/1573), " -"[#1581](https://github.com/adap/flower/pull/1581), " -"[#1578](https://github.com/adap/flower/pull/1578), " -"[#1574](https://github.com/adap/flower/pull/1574), " -"[#1572](https://github.com/adap/flower/pull/1572), " -"[#1586](https://github.com/adap/flower/pull/1586))" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-changelog.md:400 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of msgid "" -"**Updated documentation** " -"([#1629](https://github.com/adap/flower/pull/1629), " -"[#1628](https://github.com/adap/flower/pull/1628), " -"[#1620](https://github.com/adap/flower/pull/1620), " -"[#1618](https://github.com/adap/flower/pull/1618), " -"[#1617](https://github.com/adap/flower/pull/1617), " -"[#1613](https://github.com/adap/flower/pull/1613), " -"[#1614](https://github.com/adap/flower/pull/1614))" +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -#: ../../source/ref-changelog.md:402 ../../source/ref-changelog.md:469 +#: ../../source/ref-api/flwr.server.strategy.Strategy.rst:2 +msgid "Strategy" +msgstr "" + +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 +#: of msgid "" -"As usual, the documentation has improved quite a bit. It is another step " -"in our effort to make the Flower documentation the best documentation of " -"any project. Stay tuned and as always, feel free to provide feedback!" +":py:obj:`aggregate_evaluate " +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:408 -msgid "v1.2.0 (2023-01-13)" +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1 +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 +#: of +msgid "Aggregate evaluation results." msgstr "" -#: ../../source/ref-changelog.md:414 +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 +#: of msgid "" -"`Adam Narozniak`, `Charles Beauville`, `Daniel J. Beutel`, `Edoardo`, `L." -" Jiang`, `Ragy`, `Taner Topal`, `dannymcy`" +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" -#: ../../source/ref-changelog.md:418 -msgid "" -"**Introduce new Flower Baseline: FedAvg MNIST** " -"([#1497](https://github.com/adap/flower/pull/1497), " -"[#1552](https://github.com/adap/flower/pull/1552))" +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 +#: flwr.server.strategy.strategy.Strategy.aggregate_fit:1 of +msgid "Aggregate training results." msgstr "" -#: ../../source/ref-changelog.md:420 +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 +#: of msgid "" -"Over the coming weeks, we will be releasing a number of new reference " -"implementations useful especially to FL newcomers. They will typically " -"revisit well known papers from the literature, and be suitable for " -"integration in your own application or for experimentation, in order to " -"deepen your knowledge of FL in general. Today's release is the first in " -"this series. [Read more.](https://flower.ai/blog/2023-01-12-fl-starter-" -"pack-fedavg-mnist-cnn/)" +":py:obj:`configure_evaluate " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:422 +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 +#: of msgid "" -"**Improve GPU support in simulations** " -"([#1555](https://github.com/adap/flower/pull/1555))" +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:424 +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 +#: of msgid "" -"The Ray-based Virtual Client Engine (`start_simulation`) has been updated" -" to improve GPU support. The update includes some of the hard-earned " -"lessons from scaling simulations in GPU cluster environments. New " -"defaults make running GPU-based simulations substantially more robust." +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" -#: ../../source/ref-changelog.md:426 -msgid "" -"**Improve GPU support in Jupyter Notebook tutorials** " -"([#1527](https://github.com/adap/flower/pull/1527), " -"[#1558](https://github.com/adap/flower/pull/1558))" +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 +#: flwr.server.strategy.strategy.Strategy.evaluate:1 of +msgid "Evaluate the current model parameters." msgstr "" -#: ../../source/ref-changelog.md:428 +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 +#: of msgid "" -"Some users reported that Jupyter Notebooks have not always been easy to " -"use on GPU instances. We listened and made improvements to all of our " -"Jupyter notebooks! Check out the updated notebooks here:" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" -#: ../../source/ref-changelog.md:430 -msgid "" -"[An Introduction to Federated Learning](https://flower.ai/docs/framework" -"/tutorial-get-started-with-flower-pytorch.html)" +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 +#: flwr.server.strategy.strategy.Strategy.initialize_parameters:1 of +msgid "Initialize the (global) model parameters." msgstr "" -#: ../../source/ref-changelog.md:431 +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:5 of msgid "" -"[Strategies in Federated Learning](https://flower.ai/docs/framework" -"/tutorial-use-a-federated-learning-strategy-pytorch.html)" +"Successful updates from the previously selected and configured clients. " +"Each pair of `(ClientProxy, FitRes` constitutes a successful update from " +"one of the previously selected clients. Not that not all previously " +"selected clients are necessarily included in this list: a client might " +"drop out and not submit a result. For each client that did not submit an " +"update, there should be an `Exception` in `failures`." msgstr "" -#: ../../source/ref-changelog.md:432 -msgid "" -"[Building a Strategy](https://flower.ai/docs/framework/tutorial-build-a" -"-strategy-from-scratch-pytorch.html)" +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:13 +#: flwr.server.strategy.strategy.Strategy.aggregate_fit:13 of +msgid "Exceptions that occurred while the server was waiting for client updates." msgstr "" -#: ../../source/ref-changelog.md:433 +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:16 of msgid "" -"[Client and NumPyClient](https://flower.ai/docs/framework/tutorial-" -"customize-the-client-pytorch.html)" +"**aggregation_result** -- The aggregated evaluation result. Aggregation " +"typically uses some variant of a weighted average." msgstr "" -#: ../../source/ref-changelog.md:435 +#: flwr.server.strategy.strategy.Strategy.aggregate_fit:5 of msgid "" -"**Introduce optional telemetry** " -"([#1533](https://github.com/adap/flower/pull/1533), " -"[#1544](https://github.com/adap/flower/pull/1544), " -"[#1584](https://github.com/adap/flower/pull/1584))" +"Successful updates from the previously selected and configured clients. " +"Each pair of `(ClientProxy, FitRes)` constitutes a successful update from" +" one of the previously selected clients. Not that not all previously " +"selected clients are necessarily included in this list: a client might " +"drop out and not submit a result. For each client that did not submit an " +"update, there should be an `Exception` in `failures`." msgstr "" -#: ../../source/ref-changelog.md:437 +#: flwr.server.strategy.strategy.Strategy.aggregate_fit:17 of msgid "" -"After a [request for " -"feedback](https://github.com/adap/flower/issues/1534) from the community," -" the Flower open-source project introduces optional collection of " -"*anonymous* usage metrics to make well-informed decisions to improve " -"Flower. Doing this enables the Flower team to understand how Flower is " -"used and what challenges users might face." +"**parameters** -- If parameters are returned, then the server will treat " +"these as the new global model parameters (i.e., it will replace the " +"previous parameters with the ones returned from this method). If `None` " +"is returned (e.g., because there were only failures and no viable " +"results) then the server will no update the previous model parameters, " +"the updates received in this round are discarded, and the global model " +"parameters remain the same." msgstr "" -#: ../../source/ref-changelog.md:439 +#: flwr.server.strategy.strategy.Strategy.evaluate:3 of msgid "" -"**Flower is a friendly framework for collaborative AI and data science.**" -" Staying true to this statement, Flower makes it easy to disable " -"telemetry for users who do not want to share anonymous usage metrics. " -"[Read more.](https://flower.ai/docs/telemetry.html)." +"This function can be used to perform centralized (i.e., server-side) " +"evaluation of model parameters." msgstr "" -#: ../../source/ref-changelog.md:441 +#: flwr.server.strategy.strategy.Strategy.evaluate:11 of msgid "" -"**Introduce (experimental) Driver API** " -"([#1520](https://github.com/adap/flower/pull/1520), " -"[#1525](https://github.com/adap/flower/pull/1525), " -"[#1545](https://github.com/adap/flower/pull/1545), " -"[#1546](https://github.com/adap/flower/pull/1546), " -"[#1550](https://github.com/adap/flower/pull/1550), " -"[#1551](https://github.com/adap/flower/pull/1551), " -"[#1567](https://github.com/adap/flower/pull/1567))" +"**evaluation_result** -- The evaluation result, usually a Tuple " +"containing loss and a dictionary containing task-specific metrics (e.g., " +"accuracy)." msgstr "" -#: ../../source/ref-changelog.md:443 +#: flwr.server.strategy.strategy.Strategy.initialize_parameters:6 of msgid "" -"Flower now has a new (experimental) Driver API which will enable fully " -"programmable, async, and multi-tenant Federated Learning and Federated " -"Analytics applications. Phew, that's a lot! Going forward, the Driver API" -" will be the abstraction that many upcoming features will be built on - " -"and you can start building those things now, too." +"**parameters** -- If parameters are returned, then the server will treat " +"these as the initial global model parameters." msgstr "" -#: ../../source/ref-changelog.md:445 -msgid "" -"The Driver API also enables a new execution mode in which the server runs" -" indefinitely. Multiple individual workloads can run concurrently and " -"start and stop their execution independent of the server. This is " -"especially useful for users who want to deploy Flower in production." +#: ../../source/ref-api/flwr.server.workflow.rst:2 +msgid "workflow" msgstr "" -#: ../../source/ref-changelog.md:447 +#: ../../source/ref-api/flwr.server.workflow.rst:24::1 msgid "" -"To learn more, check out the `mt-pytorch` code example. We look forward " -"to you feedback!" +":py:obj:`DefaultWorkflow `\\ " +"\\(\\[fit\\_workflow\\, ...\\]\\)" msgstr "" -#: ../../source/ref-changelog.md:449 -msgid "" -"Please note: *The Driver API is still experimental and will likely change" -" significantly over time.*" +#: ../../source/ref-api/flwr.server.workflow.rst:24::1 +#: flwr.server.workflow.default_workflows.DefaultWorkflow:1 of +msgid "Default workflow in Flower." msgstr "" -#: ../../source/ref-changelog.md:451 +#: ../../source/ref-api/flwr.server.workflow.rst:24::1 msgid "" -"**Add new Federated Analytics with Pandas example** " -"([#1469](https://github.com/adap/flower/pull/1469), " -"[#1535](https://github.com/adap/flower/pull/1535))" +":py:obj:`SecAggPlusWorkflow `\\ " +"\\(num\\_shares\\, ...\\[\\, ...\\]\\)" msgstr "" -#: ../../source/ref-changelog.md:453 -msgid "" -"A new code example (`quickstart-pandas`) demonstrates federated analytics" -" with Pandas and Flower. You can find it here: [quickstart-" -"pandas](https://github.com/adap/flower/tree/main/examples/quickstart-" -"pandas)." +#: ../../source/ref-api/flwr.server.workflow.rst:24::1 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:1 +#: of +msgid "The workflow for the SecAgg+ protocol." msgstr "" -#: ../../source/ref-changelog.md:455 +#: ../../source/ref-api/flwr.server.workflow.rst:24::1 msgid "" -"**Add new strategies: Krum and MultiKrum** " -"([#1481](https://github.com/adap/flower/pull/1481))" +":py:obj:`SecAggWorkflow `\\ " +"\\(reconstruction\\_threshold\\, \\*\\)" msgstr "" -#: ../../source/ref-changelog.md:457 -msgid "" -"Edoardo, a computer science student at the Sapienza University of Rome, " -"contributed a new `Krum` strategy that enables users to easily use Krum " -"and MultiKrum in their workloads." +#: ../../source/ref-api/flwr.server.workflow.rst:24::1 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:1 of +msgid "The workflow for the SecAgg protocol." msgstr "" -#: ../../source/ref-changelog.md:459 -msgid "" -"**Update C++ example to be compatible with Flower v1.2.0** " -"([#1495](https://github.com/adap/flower/pull/1495))" +#: ../../source/ref-api/flwr.server.workflow.DefaultWorkflow.rst:2 +msgid "DefaultWorkflow" msgstr "" -#: ../../source/ref-changelog.md:461 -msgid "" -"The C++ code example has received a substantial update to make it " -"compatible with the latest version of Flower." +#: ../../source/ref-api/flwr.server.workflow.SecAggPlusWorkflow.rst:2 +msgid "SecAggPlusWorkflow" msgstr "" -#: ../../source/ref-changelog.md:463 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:3 +#: of msgid "" -"**General improvements** " -"([#1491](https://github.com/adap/flower/pull/1491), " -"[#1504](https://github.com/adap/flower/pull/1504), " -"[#1506](https://github.com/adap/flower/pull/1506), " -"[#1514](https://github.com/adap/flower/pull/1514), " -"[#1522](https://github.com/adap/flower/pull/1522), " -"[#1523](https://github.com/adap/flower/pull/1523), " -"[#1526](https://github.com/adap/flower/pull/1526), " -"[#1528](https://github.com/adap/flower/pull/1528), " -"[#1547](https://github.com/adap/flower/pull/1547), " -"[#1549](https://github.com/adap/flower/pull/1549), " -"[#1560](https://github.com/adap/flower/pull/1560), " -"[#1564](https://github.com/adap/flower/pull/1564), " -"[#1566](https://github.com/adap/flower/pull/1566))" +"The SecAgg+ protocol ensures the secure summation of integer vectors " +"owned by multiple parties, without accessing any individual integer " +"vector. This workflow allows the server to compute the weighted average " +"of model parameters across all clients, ensuring individual contributions" +" remain private. This is achieved by clients sending both, a weighting " +"factor and a weighted version of the locally updated parameters, both of " +"which are masked for privacy. Specifically, each client uploads \"[w, w *" +" params]\" with masks, where weighting factor 'w' is the number of " +"examples ('num_examples') and 'params' represents the model parameters " +"('parameters') from the client's `FitRes`. The server then aggregates " +"these contributions to compute the weighted average of model parameters." msgstr "" -#: ../../source/ref-changelog.md:467 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:14 +#: of msgid "" -"**Updated documentation** " -"([#1494](https://github.com/adap/flower/pull/1494), " -"[#1496](https://github.com/adap/flower/pull/1496), " -"[#1500](https://github.com/adap/flower/pull/1500), " -"[#1503](https://github.com/adap/flower/pull/1503), " -"[#1505](https://github.com/adap/flower/pull/1505), " -"[#1524](https://github.com/adap/flower/pull/1524), " -"[#1518](https://github.com/adap/flower/pull/1518), " -"[#1519](https://github.com/adap/flower/pull/1519), " -"[#1515](https://github.com/adap/flower/pull/1515))" -msgstr "" - -#: ../../source/ref-changelog.md:471 -msgid "" -"One highlight is the new [first time contributor " -"guide](https://flower.ai/docs/first-time-contributors.html): if you've " -"never contributed on GitHub before, this is the perfect place to start!" +"The protocol involves four main stages: - 'setup': Send SecAgg+ " +"configuration to clients and collect their public keys. - 'share keys': " +"Broadcast public keys among clients and collect encrypted secret" msgstr "" -#: ../../source/ref-changelog.md:477 -msgid "v1.1.0 (2022-10-31)" +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:17 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:17 +#: of +msgid "key shares." msgstr "" -#: ../../source/ref-changelog.md:481 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:18 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:18 +#: of msgid "" -"We would like to give our **special thanks** to all the contributors who " -"made the new version of Flower possible (in `git shortlog` order):" +"'collect masked vectors': Forward encrypted secret key shares to target " +"clients and collect masked model parameters." msgstr "" -#: ../../source/ref-changelog.md:483 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:20 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:20 +#: of msgid "" -"`Akis Linardos`, `Christopher S`, `Daniel J. Beutel`, `George`, `Jan " -"Schlicht`, `Mohammad Fares`, `Pedro Porto Buarque de Gusmão`, `Philipp " -"Wiesner`, `Rob Luke`, `Taner Topal`, `VasundharaAgarwal`, " -"`danielnugraha`, `edogab33`" +"'unmask': Collect secret key shares to decrypt and aggregate the model " +"parameters." msgstr "" -#: ../../source/ref-changelog.md:487 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:22 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:22 +#: of msgid "" -"**Introduce Differential Privacy wrappers (preview)** " -"([#1357](https://github.com/adap/flower/pull/1357), " -"[#1460](https://github.com/adap/flower/pull/1460))" +"Only the aggregated model parameters are exposed and passed to " +"`Strategy.aggregate_fit`, ensuring individual data privacy." msgstr "" -#: ../../source/ref-changelog.md:489 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:25 +#: of msgid "" -"The first (experimental) preview of pluggable Differential Privacy " -"wrappers enables easy configuration and usage of differential privacy " -"(DP). The pluggable DP wrappers enable framework-agnostic **and** " -"strategy-agnostic usage of both client-side DP and server-side DP. Head " -"over to the Flower docs, a new explainer goes into more detail." +"The number of shares into which each client's private key is split under " +"the SecAgg+ protocol. If specified as a float, it represents the " +"proportion of all selected clients, and the number of shares will be set " +"dynamically in the run time. A private key can be reconstructed from " +"these shares, allowing for the secure aggregation of model updates. Each " +"client sends one share to each of its neighbors while retaining one." msgstr "" -#: ../../source/ref-changelog.md:491 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:25 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:32 +#: of msgid "" -"**New iOS CoreML code example** " -"([#1289](https://github.com/adap/flower/pull/1289))" +"The minimum number of shares required to reconstruct a client's private " +"key, or, if specified as a float, it represents the proportion of the " +"total number of shares needed for reconstruction. This threshold ensures " +"privacy by allowing for the recovery of contributions from dropped " +"clients during aggregation, without compromising individual client data." msgstr "" -#: ../../source/ref-changelog.md:493 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:31 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:38 +#: of msgid "" -"Flower goes iOS! A massive new code example shows how Flower clients can " -"be built for iOS. The code example contains both Flower iOS SDK " -"components that can be used for many tasks, and one task example running " -"on CoreML." +"The maximum value of the weight that can be assigned to any single " +"client's update during the weighted average calculation on the server " +"side, e.g., in the FedAvg algorithm." msgstr "" -#: ../../source/ref-changelog.md:495 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:35 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:42 +#: of msgid "" -"**New FedMedian strategy** " -"([#1461](https://github.com/adap/flower/pull/1461))" +"The range within which model parameters are clipped before quantization. " +"This parameter ensures each model parameter is bounded within " +"[-clipping_range, clipping_range], facilitating quantization." msgstr "" -#: ../../source/ref-changelog.md:497 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:39 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:46 +#: of msgid "" -"The new `FedMedian` strategy implements Federated Median (FedMedian) by " -"[Yin et al., 2018](https://arxiv.org/pdf/1803.01498v1.pdf)." +"The size of the range into which floating-point model parameters are " +"quantized, mapping each parameter to an integer in [0, " +"quantization_range-1]. This facilitates cryptographic operations on the " +"model updates." msgstr "" -#: ../../source/ref-changelog.md:499 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:43 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:50 +#: of msgid "" -"**Log** `Client` **exceptions in Virtual Client Engine** " -"([#1493](https://github.com/adap/flower/pull/1493))" +"The range of values from which random mask entries are uniformly sampled " +"([0, modulus_range-1]). `modulus_range` must be less than 4294967296. " +"Please use 2**n values for `modulus_range` to prevent overflow issues." msgstr "" -#: ../../source/ref-changelog.md:501 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:47 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:54 +#: of msgid "" -"All `Client` exceptions happening in the VCE are now logged by default " -"and not just exposed to the configured `Strategy` (via the `failures` " -"argument)." +"The timeout duration in seconds. If specified, the workflow will wait for" +" replies for this duration each time. If `None`, there is no time limit " +"and the workflow will wait until replies for all messages are received." msgstr "" -#: ../../source/ref-changelog.md:503 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:61 +#: of msgid "" -"**Improve Virtual Client Engine internals** " -"([#1401](https://github.com/adap/flower/pull/1401), " -"[#1453](https://github.com/adap/flower/pull/1453))" +"Generally, higher `num_shares` means more robust to dropouts while " +"increasing the computational costs; higher `reconstruction_threshold` " +"means better privacy guarantees but less tolerance to dropouts." msgstr "" -#: ../../source/ref-changelog.md:505 -msgid "" -"Some internals of the Virtual Client Engine have been revamped. The VCE " -"now uses Ray 2.0 under the hood, the value type of the `client_resources`" -" dictionary changed to `float` to allow fractions of resources to be " -"allocated." +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:58 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:64 +#: of +msgid "Too large `max_weight` may compromise the precision of the quantization." msgstr "" -#: ../../source/ref-changelog.md:507 -msgid "" -"**Support optional** `Client`**/**`NumPyClient` **methods in Virtual " -"Client Engine**" +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:59 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:65 +#: of +msgid "`modulus_range` must be 2**n and larger than `quantization_range`." msgstr "" -#: ../../source/ref-changelog.md:509 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:66 +#: of msgid "" -"The Virtual Client Engine now has full support for optional `Client` (and" -" `NumPyClient`) methods." +"When `num_shares` is a float, it is interpreted as the proportion of all " +"selected clients, and hence the number of shares will be determined in " +"the runtime. This allows for dynamic adjustment based on the total number" +" of participating clients." msgstr "" -#: ../../source/ref-changelog.md:511 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:69 +#: of msgid "" -"**Provide type information to packages using** `flwr` " -"([#1377](https://github.com/adap/flower/pull/1377))" +"Similarly, when `reconstruction_threshold` is a float, it is interpreted " +"as the proportion of the number of shares needed for the reconstruction " +"of a private key. This feature enables flexibility in setting the " +"security threshold relative to the number of distributed shares." msgstr "" -#: ../../source/ref-changelog.md:513 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:73 +#: of msgid "" -"The package `flwr` is now bundled with a `py.typed` file indicating that " -"the package is typed. This enables typing support for projects or " -"packages that use `flwr` by enabling them to improve their code using " -"static type checkers like `mypy`." +"`num_shares`, `reconstruction_threshold`, and the quantization parameters" +" (`clipping_range`, `quantization_range`, `modulus_range`) play critical " +"roles in balancing privacy, robustness, and efficiency within the SecAgg+" +" protocol." msgstr "" -#: ../../source/ref-changelog.md:515 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: of msgid "" -"**Updated code example** " -"([#1344](https://github.com/adap/flower/pull/1344), " -"[#1347](https://github.com/adap/flower/pull/1347))" +":py:obj:`collect_masked_vectors_stage " +"`\\" +" \\(driver\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:517 -msgid "" -"The code examples covering scikit-learn and PyTorch Lightning have been " -"updated to work with the latest version of Flower." +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: of +msgid "Execute the 'collect masked vectors' stage." msgstr "" -#: ../../source/ref-changelog.md:519 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: of msgid "" -"**Updated documentation** " -"([#1355](https://github.com/adap/flower/pull/1355), " -"[#1558](https://github.com/adap/flower/pull/1558), " -"[#1379](https://github.com/adap/flower/pull/1379), " -"[#1380](https://github.com/adap/flower/pull/1380), " -"[#1381](https://github.com/adap/flower/pull/1381), " -"[#1332](https://github.com/adap/flower/pull/1332), " -"[#1391](https://github.com/adap/flower/pull/1391), " -"[#1403](https://github.com/adap/flower/pull/1403), " -"[#1364](https://github.com/adap/flower/pull/1364), " -"[#1409](https://github.com/adap/flower/pull/1409), " -"[#1419](https://github.com/adap/flower/pull/1419), " -"[#1444](https://github.com/adap/flower/pull/1444), " -"[#1448](https://github.com/adap/flower/pull/1448), " -"[#1417](https://github.com/adap/flower/pull/1417), " -"[#1449](https://github.com/adap/flower/pull/1449), " -"[#1465](https://github.com/adap/flower/pull/1465), " -"[#1467](https://github.com/adap/flower/pull/1467))" +":py:obj:`setup_stage " +"`\\ \\(driver\\, " +"context\\, state\\)" msgstr "" -#: ../../source/ref-changelog.md:521 -msgid "" -"There have been so many documentation updates that it doesn't even make " -"sense to list them individually." +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.setup_stage:1 +#: of +msgid "Execute the 'setup' stage." msgstr "" -#: ../../source/ref-changelog.md:523 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: of msgid "" -"**Restructured documentation** " -"([#1387](https://github.com/adap/flower/pull/1387))" +":py:obj:`share_keys_stage " +"`\\ " +"\\(driver\\, context\\, state\\)" msgstr "" -#: ../../source/ref-changelog.md:525 -msgid "" -"The documentation has been restructured to make it easier to navigate. " -"This is just the first step in a larger effort to make the Flower " -"documentation the best documentation of any project ever. Stay tuned!" +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.share_keys_stage:1 +#: of +msgid "Execute the 'share keys' stage." msgstr "" -#: ../../source/ref-changelog.md:527 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: of msgid "" -"**Open in Colab button** " -"([#1389](https://github.com/adap/flower/pull/1389))" +":py:obj:`unmask_stage " +"`\\ \\(driver\\, " +"context\\, state\\)" msgstr "" -#: ../../source/ref-changelog.md:529 -msgid "" -"The four parts of the Flower Federated Learning Tutorial now come with a " -"new `Open in Colab` button. No need to install anything on your local " -"machine, you can now use and learn about Flower in your browser, it's " -"only a single click away." +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.unmask_stage:1 +#: of +msgid "Execute the 'unmask' stage." msgstr "" -#: ../../source/ref-changelog.md:531 -msgid "" -"**Improved tutorial** ([#1468](https://github.com/adap/flower/pull/1468)," -" [#1470](https://github.com/adap/flower/pull/1470), " -"[#1472](https://github.com/adap/flower/pull/1472), " -"[#1473](https://github.com/adap/flower/pull/1473), " -"[#1474](https://github.com/adap/flower/pull/1474), " -"[#1475](https://github.com/adap/flower/pull/1475))" +#: ../../source/ref-api/flwr.server.workflow.SecAggWorkflow.rst:2 +msgid "SecAggWorkflow" msgstr "" -#: ../../source/ref-changelog.md:533 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:1 of msgid "" -"The Flower Federated Learning Tutorial has two brand-new parts covering " -"custom strategies (still WIP) and the distinction between `Client` and " -"`NumPyClient`. The existing parts one and two have also been improved " -"(many small changes and fixes)." -msgstr "" - -#: ../../source/ref-changelog.md:539 -msgid "v1.0.0 (2022-07-28)" +"Bases: " +":py:class:`~flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow`" msgstr "" -#: ../../source/ref-changelog.md:541 -msgid "Highlights" -msgstr "" - -#: ../../source/ref-changelog.md:543 -msgid "Stable **Virtual Client Engine** (accessible via `start_simulation`)" -msgstr "" - -#: ../../source/ref-changelog.md:544 -msgid "All `Client`/`NumPyClient` methods are now optional" -msgstr "" - -#: ../../source/ref-changelog.md:545 -msgid "Configurable `get_parameters`" +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:3 of +msgid "" +"The SecAgg protocol ensures the secure summation of integer vectors owned" +" by multiple parties, without accessing any individual integer vector. " +"This workflow allows the server to compute the weighted average of model " +"parameters across all clients, ensuring individual contributions remain " +"private. This is achieved by clients sending both, a weighting factor and" +" a weighted version of the locally updated parameters, both of which are " +"masked for privacy. Specifically, each client uploads \"[w, w * params]\"" +" with masks, where weighting factor 'w' is the number of examples " +"('num_examples') and 'params' represents the model parameters " +"('parameters') from the client's `FitRes`. The server then aggregates " +"these contributions to compute the weighted average of model parameters." msgstr "" -#: ../../source/ref-changelog.md:546 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:14 of msgid "" -"Tons of small API cleanups resulting in a more coherent developer " -"experience" +"The protocol involves four main stages: - 'setup': Send SecAgg " +"configuration to clients and collect their public keys. - 'share keys': " +"Broadcast public keys among clients and collect encrypted secret" msgstr "" -#: ../../source/ref-changelog.md:550 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:54 of msgid "" -"We would like to give our **special thanks** to all the contributors who " -"made Flower 1.0 possible (in reverse [GitHub " -"Contributors](https://github.com/adap/flower/graphs/contributors) order):" +"Each client's private key is split into N shares under the SecAgg " +"protocol, where N is the number of selected clients." msgstr "" -#: ../../source/ref-changelog.md:552 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:56 of msgid "" -"[@rtaiello](https://github.com/rtaiello), " -"[@g-pichler](https://github.com/g-pichler), [@rob-" -"luke](https://github.com/rob-luke), [@andreea-zaharia](https://github.com" -"/andreea-zaharia), [@kinshukdua](https://github.com/kinshukdua), " -"[@nfnt](https://github.com/nfnt), " -"[@tatiana-s](https://github.com/tatiana-s), " -"[@TParcollet](https://github.com/TParcollet), " -"[@vballoli](https://github.com/vballoli), " -"[@negedng](https://github.com/negedng), " -"[@RISHIKESHAVAN](https://github.com/RISHIKESHAVAN), " -"[@hei411](https://github.com/hei411), " -"[@SebastianSpeitel](https://github.com/SebastianSpeitel), " -"[@AmitChaulwar](https://github.com/AmitChaulwar), " -"[@Rubiel1](https://github.com/Rubiel1), [@FANTOME-PAN](https://github.com" -"/FANTOME-PAN), [@Rono-BC](https://github.com/Rono-BC), " -"[@lbhm](https://github.com/lbhm), " -"[@sishtiaq](https://github.com/sishtiaq), " -"[@remde](https://github.com/remde), [@Jueun-Park](https://github.com" -"/Jueun-Park), [@architjen](https://github.com/architjen), " -"[@PratikGarai](https://github.com/PratikGarai), " -"[@mrinaald](https://github.com/mrinaald), " -"[@zliel](https://github.com/zliel), " -"[@MeiruiJiang](https://github.com/MeiruiJiang), " -"[@sancarlim](https://github.com/sancarlim), " -"[@gubertoli](https://github.com/gubertoli), " -"[@Vingt100](https://github.com/Vingt100), " -"[@MakGulati](https://github.com/MakGulati), " -"[@cozek](https://github.com/cozek), " -"[@jafermarq](https://github.com/jafermarq), " -"[@sisco0](https://github.com/sisco0), " -"[@akhilmathurs](https://github.com/akhilmathurs), " -"[@CanTuerk](https://github.com/CanTuerk), " -"[@mariaboerner1987](https://github.com/mariaboerner1987), " -"[@pedropgusmao](https://github.com/pedropgusmao), " -"[@tanertopal](https://github.com/tanertopal), " -"[@danieljanes](https://github.com/danieljanes)." +"Generally, higher `reconstruction_threshold` means better privacy " +"guarantees but less tolerance to dropouts." msgstr "" -#: ../../source/ref-changelog.md:556 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:60 of msgid "" -"**All arguments must be passed as keyword arguments** " -"([#1338](https://github.com/adap/flower/pull/1338))" +"When `reconstruction_threshold` is a float, it is interpreted as the " +"proportion of the number of all selected clients needed for the " +"reconstruction of a private key. This feature enables flexibility in " +"setting the security threshold relative to the number of selected " +"clients." msgstr "" -#: ../../source/ref-changelog.md:558 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:64 of msgid "" -"Pass all arguments as keyword arguments, positional arguments are not " -"longer supported. Code that uses positional arguments (e.g., " -"`start_client(\"127.0.0.1:8080\", FlowerClient())`) must add the keyword " -"for each positional argument (e.g., " -"`start_client(server_address=\"127.0.0.1:8080\", " -"client=FlowerClient())`)." +"`reconstruction_threshold`, and the quantization parameters " +"(`clipping_range`, `quantization_range`, `modulus_range`) play critical " +"roles in balancing privacy, robustness, and efficiency within the SecAgg " +"protocol." msgstr "" -#: ../../source/ref-changelog.md:560 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: of msgid "" -"**Introduce configuration object** `ServerConfig` **in** `start_server` " -"**and** `start_simulation` " -"([#1317](https://github.com/adap/flower/pull/1317))" +":py:obj:`collect_masked_vectors_stage " +"`\\ " +"\\(driver\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:562 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: of msgid "" -"Instead of a config dictionary `{\"num_rounds\": 3, \"round_timeout\": " -"600.0}`, `start_server` and `start_simulation` now expect a configuration" -" object of type `flwr.server.ServerConfig`. `ServerConfig` takes the same" -" arguments that as the previous config dict, but it makes writing type-" -"safe code easier and the default parameters values more transparent." +":py:obj:`setup_stage `\\" +" \\(driver\\, context\\, state\\)" msgstr "" -#: ../../source/ref-changelog.md:564 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: of msgid "" -"**Rename built-in strategy parameters for clarity** " -"([#1334](https://github.com/adap/flower/pull/1334))" +":py:obj:`share_keys_stage " +"`\\ \\(driver\\, " +"context\\, state\\)" msgstr "" -#: ../../source/ref-changelog.md:566 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: of msgid "" -"The following built-in strategy parameters were renamed to improve " -"readability and consistency with other API's:" +":py:obj:`unmask_stage " +"`\\ \\(driver\\, " +"context\\, state\\)" msgstr "" -#: ../../source/ref-changelog.md:568 -msgid "`fraction_eval` --> `fraction_evaluate`" +#: ../../source/ref-api/flwr.simulation.rst:2 +msgid "simulation" msgstr "" -#: ../../source/ref-changelog.md:569 -msgid "`min_eval_clients` --> `min_evaluate_clients`" +#: ../../source/ref-api/flwr.simulation.rst:18::1 +msgid "" +":py:obj:`start_simulation `\\ \\(\\*\\," +" client\\_fn\\[\\, ...\\]\\)" msgstr "" -#: ../../source/ref-changelog.md:570 -msgid "`eval_fn` --> `evaluate_fn`" +#: ../../source/ref-api/flwr.simulation.rst:18::1 +#: flwr.simulation.app.start_simulation:1 of +msgid "Start a Ray-based Flower simulation server." msgstr "" -#: ../../source/ref-changelog.md:572 +#: ../../source/ref-api/flwr.simulation.rst:18::1 msgid "" -"**Update default arguments of built-in strategies** " -"([#1278](https://github.com/adap/flower/pull/1278))" +":py:obj:`run_simulation `\\ " +"\\(server\\_app\\, client\\_app\\, ...\\)" msgstr "" -#: ../../source/ref-changelog.md:574 -msgid "" -"All built-in strategies now use `fraction_fit=1.0` and " -"`fraction_evaluate=1.0`, which means they select *all* currently " -"available clients for training and evaluation. Projects that relied on " -"the previous default values can get the previous behaviour by " -"initializing the strategy in the following way:" +#: ../../source/ref-api/flwr.simulation.rst:18::1 +#: flwr.simulation.run_simulation.run_simulation:1 of +msgid "Run a Flower App using the Simulation Engine." msgstr "" -#: ../../source/ref-changelog.md:576 -msgid "`strategy = FedAvg(fraction_fit=0.1, fraction_evaluate=0.1)`" +#: ../../source/ref-api/flwr.simulation.run_simulation.rst:2 +msgid "run\\_simulation" msgstr "" -#: ../../source/ref-changelog.md:578 +#: flwr.simulation.run_simulation.run_simulation:3 of msgid "" -"**Add** `server_round` **to** `Strategy.evaluate` " -"([#1334](https://github.com/adap/flower/pull/1334))" +"The `ServerApp` to be executed. It will send messages to different " +"`ClientApp` instances running on different (virtual) SuperNodes." msgstr "" -#: ../../source/ref-changelog.md:580 +#: flwr.simulation.run_simulation.run_simulation:6 of msgid "" -"The `Strategy` method `evaluate` now receives the current round of " -"federated learning/evaluation as the first parameter." +"The `ClientApp` to be executed by each of the SuperNodes. It will receive" +" messages sent by the `ServerApp`." msgstr "" -#: ../../source/ref-changelog.md:582 +#: flwr.simulation.run_simulation.run_simulation:9 of msgid "" -"**Add** `server_round` **and** `config` **parameters to** `evaluate_fn` " -"([#1334](https://github.com/adap/flower/pull/1334))" +"Number of nodes that run a ClientApp. They can be sampled by a Driver in " +"the ServerApp and receive a Message describing what the ClientApp should " +"perform." msgstr "" -#: ../../source/ref-changelog.md:584 -msgid "" -"The `evaluate_fn` passed to built-in strategies like `FedAvg` now takes " -"three parameters: (1) The current round of federated learning/evaluation " -"(`server_round`), (2) the model parameters to evaluate (`parameters`), " -"and (3) a config dictionary (`config`)." +#: flwr.simulation.run_simulation.run_simulation:13 of +msgid "A simulation backend that runs `ClientApp`s." msgstr "" -#: ../../source/ref-changelog.md:586 +#: flwr.simulation.run_simulation.run_simulation:15 of msgid "" -"**Rename** `rnd` **to** `server_round` " -"([#1321](https://github.com/adap/flower/pull/1321))" +"'A dictionary, e.g {\"\": , \"\": } to " +"configure a backend. Values supported in are those included by " +"`flwr.common.typing.ConfigsRecordValues`." msgstr "" -#: ../../source/ref-changelog.md:588 +#: flwr.simulation.run_simulation.run_simulation:19 of msgid "" -"Several Flower methods and functions (`evaluate_fn`, `configure_fit`, " -"`aggregate_fit`, `configure_evaluate`, `aggregate_evaluate`) receive the " -"current round of federated learning/evaluation as their first parameter. " -"To improve reaability and avoid confusion with *random*, this parameter " -"has been renamed from `rnd` to `server_round`." +"A boolean to indicate whether to enable GPU growth on the main thread. " +"This is desirable if you make use of a TensorFlow model on your " +"`ServerApp` while having your `ClientApp` running on the same GPU. " +"Without enabling this, you might encounter an out-of-memory error because" +" TensorFlow, by default, allocates all GPU memory. Read more about how " +"`tf.config.experimental.set_memory_growth()` works in the TensorFlow " +"documentation: https://www.tensorflow.org/api/stable." msgstr "" -#: ../../source/ref-changelog.md:590 +#: flwr.simulation.run_simulation.run_simulation:26 of msgid "" -"**Move** `flwr.dataset` **to** `flwr_baselines` " -"([#1273](https://github.com/adap/flower/pull/1273))" +"When diabled, only INFO, WARNING and ERROR log messages will be shown. If" +" enabled, DEBUG-level logs will be displayed." msgstr "" -#: ../../source/ref-changelog.md:592 -msgid "The experimental package `flwr.dataset` was migrated to Flower Baselines." +#: ../../source/ref-api/flwr.simulation.start_simulation.rst:2 +msgid "start\\_simulation" msgstr "" -#: ../../source/ref-changelog.md:594 +#: flwr.simulation.app.start_simulation:3 of msgid "" -"**Remove experimental strategies** " -"([#1280](https://github.com/adap/flower/pull/1280))" +"A function creating client instances. The function must take a single " +"`str` argument called `cid`. It should return a single client instance of" +" type Client. Note that the created client instances are ephemeral and " +"will often be destroyed after a single method invocation. Since client " +"instances are not long-lived, they should not attempt to carry state over" +" method invocations. Any state required by the instance (model, dataset, " +"hyperparameters, ...) should be (re-)created in either the call to " +"`client_fn` or the call to any of the client methods (e.g., load " +"evaluation data in the `evaluate` method itself)." msgstr "" -#: ../../source/ref-changelog.md:596 +#: flwr.simulation.app.start_simulation:13 of msgid "" -"Remove unmaintained experimental strategies (`FastAndSlow`, `FedFSv0`, " -"`FedFSv1`)." +"The total number of clients in this simulation. This must be set if " +"`clients_ids` is not set and vice-versa." msgstr "" -#: ../../source/ref-changelog.md:598 +#: flwr.simulation.app.start_simulation:16 of msgid "" -"**Rename** `Weights` **to** `NDArrays` " -"([#1258](https://github.com/adap/flower/pull/1258), " -"[#1259](https://github.com/adap/flower/pull/1259))" +"List `client_id`s for each client. This is only required if `num_clients`" +" is not set. Setting both `num_clients` and `clients_ids` with " +"`len(clients_ids)` not equal to `num_clients` generates an error." msgstr "" -#: ../../source/ref-changelog.md:600 +#: flwr.simulation.app.start_simulation:20 of msgid "" -"`flwr.common.Weights` was renamed to `flwr.common.NDArrays` to better " -"capture what this type is all about." +"CPU and GPU resources for a single client. Supported keys are `num_cpus` " +"and `num_gpus`. To understand the GPU utilization caused by `num_gpus`, " +"as well as using custom resources, please consult the Ray documentation." msgstr "" -#: ../../source/ref-changelog.md:602 +#: flwr.simulation.app.start_simulation:25 of msgid "" -"**Remove antiquated** `force_final_distributed_eval` **from** " -"`start_server` ([#1258](https://github.com/adap/flower/pull/1258), " -"[#1259](https://github.com/adap/flower/pull/1259))" +"An implementation of the abstract base class `flwr.server.Server`. If no " +"instance is provided, then `start_server` will create one." msgstr "" -#: ../../source/ref-changelog.md:604 +#: flwr.simulation.app.start_simulation:31 of msgid "" -"The `start_server` parameter `force_final_distributed_eval` has long been" -" a historic artefact, in this release it is finally gone for good." +"An implementation of the abstract base class `flwr.server.Strategy`. If " +"no strategy is provided, then `start_server` will use " +"`flwr.server.strategy.FedAvg`." msgstr "" -#: ../../source/ref-changelog.md:606 +#: flwr.simulation.app.start_simulation:35 of msgid "" -"**Make** `get_parameters` **configurable** " -"([#1242](https://github.com/adap/flower/pull/1242))" +"An implementation of the abstract base class `flwr.server.ClientManager`." +" If no implementation is provided, then `start_simulation` will use " +"`flwr.server.client_manager.SimpleClientManager`." msgstr "" -#: ../../source/ref-changelog.md:608 +#: flwr.simulation.app.start_simulation:39 of msgid "" -"The `get_parameters` method now accepts a configuration dictionary, just " -"like `get_properties`, `fit`, and `evaluate`." +"Optional dictionary containing arguments for the call to `ray.init`. If " +"ray_init_args is None (the default), Ray will be initialized with the " +"following default args: { \"ignore_reinit_error\": True, " +"\"include_dashboard\": False } An empty dictionary can be used " +"(ray_init_args={}) to prevent any arguments from being passed to " +"ray.init." msgstr "" -#: ../../source/ref-changelog.md:610 +#: flwr.simulation.app.start_simulation:39 of msgid "" -"**Replace** `num_rounds` **in** `start_simulation` **with new** `config` " -"**parameter** ([#1281](https://github.com/adap/flower/pull/1281))" +"Optional dictionary containing arguments for the call to `ray.init`. If " +"ray_init_args is None (the default), Ray will be initialized with the " +"following default args:" msgstr "" -#: ../../source/ref-changelog.md:612 -msgid "" -"The `start_simulation` function now accepts a configuration dictionary " -"`config` instead of the `num_rounds` integer. This improves the " -"consistency between `start_simulation` and `start_server` and makes " -"transitioning between the two easier." +#: flwr.simulation.app.start_simulation:43 of +msgid "{ \"ignore_reinit_error\": True, \"include_dashboard\": False }" msgstr "" -#: ../../source/ref-changelog.md:616 +#: flwr.simulation.app.start_simulation:45 of msgid "" -"**Support Python 3.10** " -"([#1320](https://github.com/adap/flower/pull/1320))" +"An empty dictionary can be used (ray_init_args={}) to prevent any " +"arguments from being passed to ray.init." msgstr "" -#: ../../source/ref-changelog.md:618 +#: flwr.simulation.app.start_simulation:48 of msgid "" -"The previous Flower release introduced experimental support for Python " -"3.10, this release declares Python 3.10 support as stable." +"Set to True to prevent `ray.shutdown()` in case " +"`ray.is_initialized()=True`." msgstr "" -#: ../../source/ref-changelog.md:620 +#: flwr.simulation.app.start_simulation:50 of msgid "" -"**Make all** `Client` **and** `NumPyClient` **methods optional** " -"([#1260](https://github.com/adap/flower/pull/1260), " -"[#1277](https://github.com/adap/flower/pull/1277))" +"Optionally specify the type of actor to use. The actor object, which " +"persists throughout the simulation, will be the process in charge of " +"executing a ClientApp wrapping input argument `client_fn`." msgstr "" -#: ../../source/ref-changelog.md:622 +#: flwr.simulation.app.start_simulation:54 of msgid "" -"The `Client`/`NumPyClient` methods `get_properties`, `get_parameters`, " -"`fit`, and `evaluate` are all optional. This enables writing clients that" -" implement, for example, only `fit`, but no other method. No need to " -"implement `evaluate` when using centralized evaluation!" +"If you want to create your own Actor classes, you might need to pass some" +" input argument. You can use this dictionary for such purpose." msgstr "" -#: ../../source/ref-changelog.md:624 +#: flwr.simulation.app.start_simulation:57 of msgid "" -"**Enable passing a** `Server` **instance to** `start_simulation` " -"([#1281](https://github.com/adap/flower/pull/1281))" +"(default: \"DEFAULT\") Optional string (\"DEFAULT\" or \"SPREAD\") for " +"the VCE to choose in which node the actor is placed. If you are an " +"advanced user needed more control you can use lower-level scheduling " +"strategies to pin actors to specific compute nodes (e.g. via " +"NodeAffinitySchedulingStrategy). Please note this is an advanced feature." +" For all details, please refer to the Ray documentation: " +"https://docs.ray.io/en/latest/ray-core/scheduling/index.html" msgstr "" -#: ../../source/ref-changelog.md:626 -msgid "" -"Similar to `start_server`, `start_simulation` now accepts a full `Server`" -" instance. This enables users to heavily customize the execution of " -"eperiments and opens the door to running, for example, async FL using the" -" Virtual Client Engine." +#: flwr.simulation.app.start_simulation:66 of +msgid "**hist** -- Object containing metrics from training." msgstr "" -#: ../../source/ref-changelog.md:628 -msgid "" -"**Update code examples** " -"([#1291](https://github.com/adap/flower/pull/1291), " -"[#1286](https://github.com/adap/flower/pull/1286), " -"[#1282](https://github.com/adap/flower/pull/1282))" +#: ../../source/ref-changelog.md:1 +msgid "Changelog" msgstr "" -#: ../../source/ref-changelog.md:630 -msgid "" -"Many code examples received small or even large maintenance updates, " -"among them are" +#: ../../source/ref-changelog.md:3 +msgid "v1.9.0 (2024-06-10)" msgstr "" -#: ../../source/ref-changelog.md:632 -msgid "`scikit-learn`" +#: ../../source/ref-changelog.md:5 ../../source/ref-changelog.md:105 +#: ../../source/ref-changelog.md:169 ../../source/ref-changelog.md:262 +#: ../../source/ref-changelog.md:362 ../../source/ref-changelog.md:446 +#: ../../source/ref-changelog.md:510 ../../source/ref-changelog.md:568 +#: ../../source/ref-changelog.md:637 ../../source/ref-changelog.md:706 +msgid "Thanks to our contributors" msgstr "" -#: ../../source/ref-changelog.md:633 -msgid "`simulation_pytorch`" +#: ../../source/ref-changelog.md:7 ../../source/ref-changelog.md:107 +#: ../../source/ref-changelog.md:171 ../../source/ref-changelog.md:264 +#: ../../source/ref-changelog.md:364 ../../source/ref-changelog.md:448 +#: ../../source/ref-changelog.md:512 ../../source/ref-changelog.md:570 +msgid "" +"We would like to give our special thanks to all the contributors who made" +" the new version of Flower possible (in `git shortlog` order):" msgstr "" -#: ../../source/ref-changelog.md:634 -msgid "`quickstart_pytorch`" +#: ../../source/ref-changelog.md:9 +msgid "" +"`Adam Narozniak`, `Charles Beauville`, `Chong Shen Ng`, `Daniel J. " +"Beutel`, `Daniel Nata Nugraha`, `Heng Pan`, `Javier`, `Mahdi Beitollahi`," +" `Robert Steiner`, `Taner Topal`, `Yan Gao`, `bapic`, `mohammadnaseri` " +msgstr "" + +#: ../../source/ref-changelog.md:11 ../../source/ref-changelog.md:111 +#: ../../source/ref-changelog.md:175 ../../source/ref-changelog.md:268 +#: ../../source/ref-changelog.md:368 ../../source/ref-changelog.md:452 +#: ../../source/ref-changelog.md:516 ../../source/ref-changelog.md:574 +#: ../../source/ref-changelog.md:643 ../../source/ref-changelog.md:772 +#: ../../source/ref-changelog.md:814 ../../source/ref-changelog.md:881 +#: ../../source/ref-changelog.md:947 ../../source/ref-changelog.md:992 +#: ../../source/ref-changelog.md:1031 ../../source/ref-changelog.md:1064 +#: ../../source/ref-changelog.md:1114 +msgid "What's new?" msgstr "" -#: ../../source/ref-changelog.md:635 -msgid "`quickstart_simulation`" +#: ../../source/ref-changelog.md:13 +msgid "" +"**Introduce built-in authentication (preview)** " +"([#2946](https://github.com/adap/flower/pull/2946), " +"[#3388](https://github.com/adap/flower/pull/3388), " +"[#2948](https://github.com/adap/flower/pull/2948), " +"[#2917](https://github.com/adap/flower/pull/2917), " +"[#3386](https://github.com/adap/flower/pull/3386), " +"[#3308](https://github.com/adap/flower/pull/3308), " +"[#3001](https://github.com/adap/flower/pull/3001), " +"[#3409](https://github.com/adap/flower/pull/3409), " +"[#2999](https://github.com/adap/flower/pull/2999), " +"[#2979](https://github.com/adap/flower/pull/2979), " +"[#3389](https://github.com/adap/flower/pull/3389), " +"[#3503](https://github.com/adap/flower/pull/3503), " +"[#3366](https://github.com/adap/flower/pull/3366), " +"[#3357](https://github.com/adap/flower/pull/3357))" msgstr "" -#: ../../source/ref-changelog.md:636 -msgid "`quickstart_tensorflow`" +#: ../../source/ref-changelog.md:15 +msgid "" +"Flower 1.9 introduces the first build-in version of client node " +"authentication. In previous releases, users often wrote glue code to " +"connect Flower to external authentication systems. With this release, the" +" SuperLink can authenticate SuperNodes using a built-in authentication " +"system. A new [how-to guide](https://flower.ai/docs/framework/how-to-" +"authenticate-supernodes.html) and a new [code " +"example](https://github.com/adap/flower/tree/main/examples/flower-" +"authentication) help you to get started." msgstr "" -#: ../../source/ref-changelog.md:637 -msgid "`advanced_tensorflow`" +#: ../../source/ref-changelog.md:17 +msgid "" +"This is the first preview release of the Flower-native authentication " +"system. Many additional features are on the roadmap for upcoming Flower " +"releases - stay tuned." msgstr "" -#: ../../source/ref-changelog.md:639 +#: ../../source/ref-changelog.md:19 msgid "" -"**Remove the obsolete simulation example** " -"([#1328](https://github.com/adap/flower/pull/1328))" +"**Introduce end-to-end Docker support** " +"([#3483](https://github.com/adap/flower/pull/3483), " +"[#3266](https://github.com/adap/flower/pull/3266), " +"[#3390](https://github.com/adap/flower/pull/3390), " +"[#3283](https://github.com/adap/flower/pull/3283), " +"[#3285](https://github.com/adap/flower/pull/3285), " +"[#3391](https://github.com/adap/flower/pull/3391), " +"[#3403](https://github.com/adap/flower/pull/3403), " +"[#3458](https://github.com/adap/flower/pull/3458), " +"[#3533](https://github.com/adap/flower/pull/3533), " +"[#3453](https://github.com/adap/flower/pull/3453), " +"[#3486](https://github.com/adap/flower/pull/3486), " +"[#3290](https://github.com/adap/flower/pull/3290))" msgstr "" -#: ../../source/ref-changelog.md:641 +#: ../../source/ref-changelog.md:21 msgid "" -"Removes the obsolete `simulation` example and renames " -"`quickstart_simulation` to `simulation_tensorflow` so it fits withs the " -"naming of `simulation_pytorch`" +"Full Flower Next Docker support is here! With the release of Flower 1.9, " +"Flower provides stable Docker images for the Flower SuperLink, the Flower" +" SuperNode, and the Flower `ServerApp`. This set of images enables you to" +" run all Flower components in Docker. Check out the new [how-to " +"guide](https://flower.ai/docs/framework/how-to-run-flower-using-" +"docker.html) to get stated." msgstr "" -#: ../../source/ref-changelog.md:643 +#: ../../source/ref-changelog.md:23 msgid "" -"**Update documentation** " -"([#1223](https://github.com/adap/flower/pull/1223), " -"[#1209](https://github.com/adap/flower/pull/1209), " -"[#1251](https://github.com/adap/flower/pull/1251), " -"[#1257](https://github.com/adap/flower/pull/1257), " -"[#1267](https://github.com/adap/flower/pull/1267), " -"[#1268](https://github.com/adap/flower/pull/1268), " -"[#1300](https://github.com/adap/flower/pull/1300), " -"[#1304](https://github.com/adap/flower/pull/1304), " -"[#1305](https://github.com/adap/flower/pull/1305), " -"[#1307](https://github.com/adap/flower/pull/1307))" +"**Re-architect Flower Next simulation engine** " +"([#3307](https://github.com/adap/flower/pull/3307), " +"[#3355](https://github.com/adap/flower/pull/3355), " +"[#3272](https://github.com/adap/flower/pull/3272), " +"[#3273](https://github.com/adap/flower/pull/3273), " +"[#3417](https://github.com/adap/flower/pull/3417), " +"[#3281](https://github.com/adap/flower/pull/3281), " +"[#3343](https://github.com/adap/flower/pull/3343), " +"[#3326](https://github.com/adap/flower/pull/3326))" msgstr "" -#: ../../source/ref-changelog.md:645 +#: ../../source/ref-changelog.md:25 msgid "" -"One substantial documentation update fixes multiple smaller rendering " -"issues, makes titles more succinct to improve navigation, removes a " -"deprecated library, updates documentation dependencies, includes the " -"`flwr.common` module in the API reference, includes support for markdown-" -"based documentation, migrates the changelog from `.rst` to `.md`, and " -"fixes a number of smaller details!" +"Flower Next simulations now use a new in-memory `Driver` that improves " +"the reliability of simulations, especially in notebook environments. This" +" is a significant step towards a complete overhaul of the Flower Next " +"simulation architecture." msgstr "" -#: ../../source/ref-changelog.md:647 ../../source/ref-changelog.md:702 -#: ../../source/ref-changelog.md:771 ../../source/ref-changelog.md:810 -msgid "**Minor updates**" +#: ../../source/ref-changelog.md:27 +msgid "" +"**Upgrade simulation engine** " +"([#3354](https://github.com/adap/flower/pull/3354), " +"[#3378](https://github.com/adap/flower/pull/3378), " +"[#3262](https://github.com/adap/flower/pull/3262), " +"[#3435](https://github.com/adap/flower/pull/3435), " +"[#3501](https://github.com/adap/flower/pull/3501), " +"[#3482](https://github.com/adap/flower/pull/3482), " +"[#3494](https://github.com/adap/flower/pull/3494))" msgstr "" -#: ../../source/ref-changelog.md:649 +#: ../../source/ref-changelog.md:29 msgid "" -"Add round number to fit and evaluate log messages " -"([#1266](https://github.com/adap/flower/pull/1266))" +"The Flower Next simulation engine comes with improved and configurable " +"logging. The Ray-based simulation backend in Flower 1.9 was updated to " +"use Ray 2.10." msgstr "" -#: ../../source/ref-changelog.md:650 +#: ../../source/ref-changelog.md:31 msgid "" -"Add secure gRPC connection to the `advanced_tensorflow` code example " -"([#847](https://github.com/adap/flower/pull/847))" +"**Introduce FedPFT baseline** " +"([#3268](https://github.com/adap/flower/pull/3268))" msgstr "" -#: ../../source/ref-changelog.md:651 +#: ../../source/ref-changelog.md:33 msgid "" -"Update developer tooling " -"([#1231](https://github.com/adap/flower/pull/1231), " -"[#1276](https://github.com/adap/flower/pull/1276), " -"[#1301](https://github.com/adap/flower/pull/1301), " -"[#1310](https://github.com/adap/flower/pull/1310))" +"FedPFT allows you to perform one-shot Federated Learning by leveraging " +"widely available foundational models, dramatically reducing communication" +" costs while delivering high performing models. This is work led by Mahdi" +" Beitollahi from Huawei Noah's Ark Lab (Montreal, Canada). Read all the " +"details in their paper: \"Parametric Feature Transfer: One-shot Federated" +" Learning with Foundation Models\" " +"([arxiv](https://arxiv.org/abs/2402.01862))" msgstr "" -#: ../../source/ref-changelog.md:652 +#: ../../source/ref-changelog.md:35 msgid "" -"Rename ProtoBuf messages to improve consistency " -"([#1214](https://github.com/adap/flower/pull/1214), " -"[#1258](https://github.com/adap/flower/pull/1258), " -"[#1259](https://github.com/adap/flower/pull/1259))" +"**Launch additional** `flwr new` **templates for Apple MLX, Hugging Face " +"Transformers, scikit-learn and TensorFlow** " +"([#3291](https://github.com/adap/flower/pull/3291), " +"[#3139](https://github.com/adap/flower/pull/3139), " +"[#3284](https://github.com/adap/flower/pull/3284), " +"[#3251](https://github.com/adap/flower/pull/3251), " +"[#3376](https://github.com/adap/flower/pull/3376), " +"[#3287](https://github.com/adap/flower/pull/3287))" msgstr "" -#: ../../source/ref-changelog.md:654 -msgid "v0.19.0 (2022-05-18)" +#: ../../source/ref-changelog.md:37 +msgid "" +"The `flwr` CLI's `flwr new` command is starting to become everone's " +"favorite way of creating new Flower projects. This release introduces " +"additional `flwr new` templates for Apple MLX, Hugging Face Transformers," +" scikit-learn and TensorFlow. In addition to that, existing templates " +"also received updates." msgstr "" -#: ../../source/ref-changelog.md:658 +#: ../../source/ref-changelog.md:39 msgid "" -"**Flower Baselines (preview): FedOpt, FedBN, FedAvgM** " -"([#919](https://github.com/adap/flower/pull/919), " -"[#1127](https://github.com/adap/flower/pull/1127), " -"[#914](https://github.com/adap/flower/pull/914))" +"**Refine** `RecordSet` **API** " +"([#3209](https://github.com/adap/flower/pull/3209), " +"[#3331](https://github.com/adap/flower/pull/3331), " +"[#3334](https://github.com/adap/flower/pull/3334), " +"[#3335](https://github.com/adap/flower/pull/3335), " +"[#3375](https://github.com/adap/flower/pull/3375), " +"[#3368](https://github.com/adap/flower/pull/3368))" msgstr "" -#: ../../source/ref-changelog.md:660 +#: ../../source/ref-changelog.md:41 msgid "" -"The first preview release of Flower Baselines has arrived! We're " -"kickstarting Flower Baselines with implementations of FedOpt (FedYogi, " -"FedAdam, FedAdagrad), FedBN, and FedAvgM. Check the documentation on how " -"to use [Flower Baselines](https://flower.ai/docs/using-baselines.html). " -"With this first preview release we're also inviting the community to " -"[contribute their own baselines](https://flower.ai/docs/baselines/how-to-" -"contribute-baselines.html)." +"`RecordSet` is part of the Flower Next low-level API preview release. In " +"Flower 1.9, `RecordSet` received a number of usability improvements that " +"make it easier to build `RecordSet`-based `ServerApp`s and `ClientApp`s." msgstr "" -#: ../../source/ref-changelog.md:662 +#: ../../source/ref-changelog.md:43 msgid "" -"**C++ client SDK (preview) and code example** " -"([#1111](https://github.com/adap/flower/pull/1111))" +"**Beautify logging** ([#3379](https://github.com/adap/flower/pull/3379), " +"[#3430](https://github.com/adap/flower/pull/3430), " +"[#3461](https://github.com/adap/flower/pull/3461), " +"[#3360](https://github.com/adap/flower/pull/3360), " +"[#3433](https://github.com/adap/flower/pull/3433))" msgstr "" -#: ../../source/ref-changelog.md:664 +#: ../../source/ref-changelog.md:45 msgid "" -"Preview support for Flower clients written in C++. The C++ preview " -"includes a Flower client SDK and a quickstart code example that " -"demonstrates a simple C++ client using the SDK." +"Logs received a substantial update. Not only are logs now much nicer to " +"look at, but they are also more configurable." msgstr "" -#: ../../source/ref-changelog.md:666 +#: ../../source/ref-changelog.md:47 msgid "" -"**Add experimental support for Python 3.10 and Python 3.11** " -"([#1135](https://github.com/adap/flower/pull/1135))" +"**Improve reliability** " +"([#3564](https://github.com/adap/flower/pull/3564), " +"[#3561](https://github.com/adap/flower/pull/3561), " +"[#3566](https://github.com/adap/flower/pull/3566), " +"[#3462](https://github.com/adap/flower/pull/3462), " +"[#3225](https://github.com/adap/flower/pull/3225), " +"[#3514](https://github.com/adap/flower/pull/3514), " +"[#3535](https://github.com/adap/flower/pull/3535), " +"[#3372](https://github.com/adap/flower/pull/3372))" msgstr "" -#: ../../source/ref-changelog.md:668 +#: ../../source/ref-changelog.md:49 msgid "" -"Python 3.10 is the latest stable release of Python and Python 3.11 is due" -" to be released in October. This Flower release adds experimental support" -" for both Python versions." +"Flower 1.9 includes reliability improvements across many parts of the " +"system. One example is a much improved SuperNode shutdown procedure." msgstr "" -#: ../../source/ref-changelog.md:670 +#: ../../source/ref-changelog.md:51 msgid "" -"**Aggregate custom metrics through user-provided functions** " -"([#1144](https://github.com/adap/flower/pull/1144))" +"**Update Swift and C++ SDKs** " +"([#3321](https://github.com/adap/flower/pull/3321), " +"[#2763](https://github.com/adap/flower/pull/2763))" msgstr "" -#: ../../source/ref-changelog.md:672 +#: ../../source/ref-changelog.md:53 msgid "" -"Custom metrics (e.g., `accuracy`) can now be aggregated without having to" -" customize the strategy. Built-in strategies support two new arguments, " -"`fit_metrics_aggregation_fn` and `evaluate_metrics_aggregation_fn`, that " -"allow passing custom metric aggregation functions." +"In the C++ SDK, communication-related code is now separate from main " +"client logic. A new abstract class `Communicator` has been introduced " +"alongside a gRPC implementation of it." msgstr "" -#: ../../source/ref-changelog.md:674 +#: ../../source/ref-changelog.md:55 msgid "" -"**User-configurable round timeout** " -"([#1162](https://github.com/adap/flower/pull/1162))" +"**Improve testing, tooling and CI/CD infrastructure** " +"([#3294](https://github.com/adap/flower/pull/3294), " +"[#3282](https://github.com/adap/flower/pull/3282), " +"[#3311](https://github.com/adap/flower/pull/3311), " +"[#2878](https://github.com/adap/flower/pull/2878), " +"[#3333](https://github.com/adap/flower/pull/3333), " +"[#3255](https://github.com/adap/flower/pull/3255), " +"[#3349](https://github.com/adap/flower/pull/3349), " +"[#3400](https://github.com/adap/flower/pull/3400), " +"[#3401](https://github.com/adap/flower/pull/3401), " +"[#3399](https://github.com/adap/flower/pull/3399), " +"[#3346](https://github.com/adap/flower/pull/3346), " +"[#3398](https://github.com/adap/flower/pull/3398), " +"[#3397](https://github.com/adap/flower/pull/3397), " +"[#3347](https://github.com/adap/flower/pull/3347), " +"[#3502](https://github.com/adap/flower/pull/3502), " +"[#3387](https://github.com/adap/flower/pull/3387), " +"[#3542](https://github.com/adap/flower/pull/3542), " +"[#3396](https://github.com/adap/flower/pull/3396), " +"[#3496](https://github.com/adap/flower/pull/3496), " +"[#3465](https://github.com/adap/flower/pull/3465), " +"[#3473](https://github.com/adap/flower/pull/3473), " +"[#3484](https://github.com/adap/flower/pull/3484), " +"[#3521](https://github.com/adap/flower/pull/3521), " +"[#3363](https://github.com/adap/flower/pull/3363), " +"[#3497](https://github.com/adap/flower/pull/3497), " +"[#3464](https://github.com/adap/flower/pull/3464), " +"[#3495](https://github.com/adap/flower/pull/3495), " +"[#3478](https://github.com/adap/flower/pull/3478), " +"[#3271](https://github.com/adap/flower/pull/3271))" msgstr "" -#: ../../source/ref-changelog.md:676 +#: ../../source/ref-changelog.md:57 msgid "" -"A new configuration value allows the round timeout to be set for " -"`start_server` and `start_simulation`. If the `config` dictionary " -"contains a `round_timeout` key (with a `float` value in seconds), the " -"server will wait *at least* `round_timeout` seconds before it closes the " -"connection." +"As always, the Flower tooling, testing, and CI/CD infrastructure has " +"received many updates." msgstr "" -#: ../../source/ref-changelog.md:678 +#: ../../source/ref-changelog.md:59 msgid "" -"**Enable both federated evaluation and centralized evaluation to be used " -"at the same time in all built-in strategies** " -"([#1091](https://github.com/adap/flower/pull/1091))" +"**Improve documentation** " +"([#3530](https://github.com/adap/flower/pull/3530), " +"[#3539](https://github.com/adap/flower/pull/3539), " +"[#3425](https://github.com/adap/flower/pull/3425), " +"[#3520](https://github.com/adap/flower/pull/3520), " +"[#3286](https://github.com/adap/flower/pull/3286), " +"[#3516](https://github.com/adap/flower/pull/3516), " +"[#3523](https://github.com/adap/flower/pull/3523), " +"[#3545](https://github.com/adap/flower/pull/3545), " +"[#3498](https://github.com/adap/flower/pull/3498), " +"[#3439](https://github.com/adap/flower/pull/3439), " +"[#3440](https://github.com/adap/flower/pull/3440), " +"[#3382](https://github.com/adap/flower/pull/3382), " +"[#3559](https://github.com/adap/flower/pull/3559), " +"[#3432](https://github.com/adap/flower/pull/3432), " +"[#3278](https://github.com/adap/flower/pull/3278), " +"[#3371](https://github.com/adap/flower/pull/3371), " +"[#3519](https://github.com/adap/flower/pull/3519), " +"[#3267](https://github.com/adap/flower/pull/3267), " +"[#3204](https://github.com/adap/flower/pull/3204), " +"[#3274](https://github.com/adap/flower/pull/3274))" +msgstr "" + +#: ../../source/ref-changelog.md:61 +msgid "" +"As always, the Flower documentation has received many updates. Notable " +"new pages include:" msgstr "" -#: ../../source/ref-changelog.md:680 +#: ../../source/ref-changelog.md:63 msgid "" -"Built-in strategies can now perform both federated evaluation (i.e., " -"client-side) and centralized evaluation (i.e., server-side) in the same " -"round. Federated evaluation can be disabled by setting `fraction_eval` to" -" `0.0`." +"[How-to upgrate to Flower Next (Flower Next migration " +"guide)](https://flower.ai/docs/framework/how-to-upgrade-to-flower-" +"next.html)" msgstr "" -#: ../../source/ref-changelog.md:682 +#: ../../source/ref-changelog.md:65 msgid "" -"**Two new Jupyter Notebook tutorials** " -"([#1141](https://github.com/adap/flower/pull/1141))" +"[How-to run Flower using Docker](https://flower.ai/docs/framework/how-to-" +"run-flower-using-docker.html)" msgstr "" -#: ../../source/ref-changelog.md:684 +#: ../../source/ref-changelog.md:67 msgid "" -"Two Jupyter Notebook tutorials (compatible with Google Colab) explain " -"basic and intermediate Flower features:" +"[Flower Mods reference](https://flower.ai/docs/framework/ref-" +"api/flwr.client.mod.html#module-flwr.client.mod)" msgstr "" -#: ../../source/ref-changelog.md:686 +#: ../../source/ref-changelog.md:69 msgid "" -"*An Introduction to Federated Learning*: [Open in " -"Colab](https://colab.research.google.com/github/adap/flower/blob/main/tutorials/Flower-1" -"-Intro-to-FL-PyTorch.ipynb)" +"**General updates to Flower Examples** " +"([#3205](https://github.com/adap/flower/pull/3205), " +"[#3226](https://github.com/adap/flower/pull/3226), " +"[#3211](https://github.com/adap/flower/pull/3211), " +"[#3252](https://github.com/adap/flower/pull/3252), " +"[#3427](https://github.com/adap/flower/pull/3427), " +"[#3410](https://github.com/adap/flower/pull/3410), " +"[#3426](https://github.com/adap/flower/pull/3426), " +"[#3228](https://github.com/adap/flower/pull/3228), " +"[#3342](https://github.com/adap/flower/pull/3342), " +"[#3200](https://github.com/adap/flower/pull/3200), " +"[#3202](https://github.com/adap/flower/pull/3202), " +"[#3394](https://github.com/adap/flower/pull/3394), " +"[#3488](https://github.com/adap/flower/pull/3488), " +"[#3329](https://github.com/adap/flower/pull/3329), " +"[#3526](https://github.com/adap/flower/pull/3526), " +"[#3392](https://github.com/adap/flower/pull/3392), " +"[#3474](https://github.com/adap/flower/pull/3474), " +"[#3269](https://github.com/adap/flower/pull/3269))" +msgstr "" + +#: ../../source/ref-changelog.md:71 +msgid "As always, Flower code examples have received many updates." +msgstr "" + +#: ../../source/ref-changelog.md:73 +msgid "" +"**General improvements** " +"([#3532](https://github.com/adap/flower/pull/3532), " +"[#3318](https://github.com/adap/flower/pull/3318), " +"[#3565](https://github.com/adap/flower/pull/3565), " +"[#3296](https://github.com/adap/flower/pull/3296), " +"[#3305](https://github.com/adap/flower/pull/3305), " +"[#3246](https://github.com/adap/flower/pull/3246), " +"[#3224](https://github.com/adap/flower/pull/3224), " +"[#3475](https://github.com/adap/flower/pull/3475), " +"[#3297](https://github.com/adap/flower/pull/3297), " +"[#3317](https://github.com/adap/flower/pull/3317), " +"[#3429](https://github.com/adap/flower/pull/3429), " +"[#3196](https://github.com/adap/flower/pull/3196), " +"[#3534](https://github.com/adap/flower/pull/3534), " +"[#3240](https://github.com/adap/flower/pull/3240), " +"[#3365](https://github.com/adap/flower/pull/3365), " +"[#3407](https://github.com/adap/flower/pull/3407), " +"[#3563](https://github.com/adap/flower/pull/3563), " +"[#3344](https://github.com/adap/flower/pull/3344), " +"[#3330](https://github.com/adap/flower/pull/3330), " +"[#3436](https://github.com/adap/flower/pull/3436), " +"[#3300](https://github.com/adap/flower/pull/3300), " +"[#3327](https://github.com/adap/flower/pull/3327), " +"[#3254](https://github.com/adap/flower/pull/3254), " +"[#3253](https://github.com/adap/flower/pull/3253), " +"[#3419](https://github.com/adap/flower/pull/3419), " +"[#3289](https://github.com/adap/flower/pull/3289), " +"[#3208](https://github.com/adap/flower/pull/3208), " +"[#3245](https://github.com/adap/flower/pull/3245), " +"[#3319](https://github.com/adap/flower/pull/3319), " +"[#3203](https://github.com/adap/flower/pull/3203), " +"[#3423](https://github.com/adap/flower/pull/3423), " +"[#3352](https://github.com/adap/flower/pull/3352), " +"[#3292](https://github.com/adap/flower/pull/3292), " +"[#3261](https://github.com/adap/flower/pull/3261))" +msgstr "" + +#: ../../source/ref-changelog.md:75 ../../source/ref-changelog.md:1058 +msgid "Deprecations" msgstr "" -#: ../../source/ref-changelog.md:688 -msgid "" -"*Using Strategies in Federated Learning*: [Open in " -"Colab](https://colab.research.google.com/github/adap/flower/blob/main/tutorials/Flower-2" -"-Strategies-in-FL-PyTorch.ipynb)" +#: ../../source/ref-changelog.md:77 +msgid "**Deprecate Python 3.8 support**" msgstr "" -#: ../../source/ref-changelog.md:690 +#: ../../source/ref-changelog.md:79 msgid "" -"**New FedAvgM strategy (Federated Averaging with Server Momentum)** " -"([#1076](https://github.com/adap/flower/pull/1076))" +"Python 3.8 will stop receiving security fixes in [October " +"2024](https://devguide.python.org/versions/). Support for Python 3.8 is " +"now deprecated and will be removed in an upcoming release." msgstr "" -#: ../../source/ref-changelog.md:692 +#: ../../source/ref-changelog.md:81 msgid "" -"The new `FedAvgM` strategy implements Federated Averaging with Server " -"Momentum \\[Hsu et al., 2019\\]." +"**Deprecate (experimental)** `flower-driver-api` **and** `flower-fleet-" +"api` ([#3416](https://github.com/adap/flower/pull/3416), " +"[#3420](https://github.com/adap/flower/pull/3420))" msgstr "" -#: ../../source/ref-changelog.md:694 +#: ../../source/ref-changelog.md:83 msgid "" -"**New advanced PyTorch code example** " -"([#1007](https://github.com/adap/flower/pull/1007))" +"Flower 1.9 deprecates the two (experimental) commands `flower-driver-api`" +" and `flower-fleet-api`. Both commands will be removed in an upcoming " +"release. Use `flower-superlink` instead." msgstr "" -#: ../../source/ref-changelog.md:696 +#: ../../source/ref-changelog.md:85 msgid "" -"A new code example (`advanced_pytorch`) demonstrates advanced Flower " -"concepts with PyTorch." +"**Deprecate** `--server` **in favor of** `--superlink` " +"([#3518](https://github.com/adap/flower/pull/3518))" msgstr "" -#: ../../source/ref-changelog.md:698 +#: ../../source/ref-changelog.md:87 msgid "" -"**New JAX code example** " -"([#906](https://github.com/adap/flower/pull/906), " -"[#1143](https://github.com/adap/flower/pull/1143))" +"The commands `flower-server-app` and `flower-client-app` should use " +"`--superlink` instead of the now deprecated `--server`. Support for " +"`--server` will be removed in a future release." msgstr "" -#: ../../source/ref-changelog.md:700 -msgid "" -"A new code example (`jax_from_centralized_to_federated`) shows federated " -"learning with JAX and Flower." +#: ../../source/ref-changelog.md:89 ../../source/ref-changelog.md:163 +#: ../../source/ref-changelog.md:238 ../../source/ref-changelog.md:350 +#: ../../source/ref-changelog.md:440 ../../source/ref-changelog.md:504 +#: ../../source/ref-changelog.md:562 ../../source/ref-changelog.md:631 +#: ../../source/ref-changelog.md:693 ../../source/ref-changelog.md:712 +#: ../../source/ref-changelog.md:868 ../../source/ref-changelog.md:939 +#: ../../source/ref-changelog.md:976 ../../source/ref-changelog.md:1019 +msgid "Incompatible changes" msgstr "" -#: ../../source/ref-changelog.md:704 +#: ../../source/ref-changelog.md:91 msgid "" -"New option to keep Ray running if Ray was already initialized in " -"`start_simulation` ([#1177](https://github.com/adap/flower/pull/1177))" +"**Replace** `flower-superlink` **CLI option** `--certificates` **with** " +"`--ssl-ca-certfile` **,** `--ssl-certfile` **and** `--ssl-keyfile` " +"([#3512](https://github.com/adap/flower/pull/3512), " +"[#3408](https://github.com/adap/flower/pull/3408))" msgstr "" -#: ../../source/ref-changelog.md:705 +#: ../../source/ref-changelog.md:93 msgid "" -"Add support for custom `ClientManager` as a `start_simulation` parameter " -"([#1171](https://github.com/adap/flower/pull/1171))" +"SSL-related `flower-superlink` CLI arguments were restructured in an " +"incompatible way. Instead of passing a single `--certificates` flag with " +"three values, you now need to pass three flags (`--ssl-ca-certfile`, " +"`--ssl-certfile` and `--ssl-keyfile`) with one value each. Check out the " +"[SSL connections](https://flower.ai/docs/framework/how-to-enable-ssl-" +"connections.html) documentation page for details." msgstr "" -#: ../../source/ref-changelog.md:706 +#: ../../source/ref-changelog.md:95 msgid "" -"New documentation for [implementing " -"strategies](https://flower.ai/docs/framework/how-to-implement-" -"strategies.html) ([#1097](https://github.com/adap/flower/pull/1097), " -"[#1175](https://github.com/adap/flower/pull/1175))" +"**Remove SuperLink** `--vce` **option** " +"([#3513](https://github.com/adap/flower/pull/3513))" msgstr "" -#: ../../source/ref-changelog.md:707 +#: ../../source/ref-changelog.md:97 msgid "" -"New mobile-friendly documentation theme " -"([#1174](https://github.com/adap/flower/pull/1174))" +"Instead of separately starting a SuperLink and a `ServerApp` for " +"simulation, simulations must now be started using the single `flower-" +"simulation` command." msgstr "" -#: ../../source/ref-changelog.md:708 +#: ../../source/ref-changelog.md:99 msgid "" -"Limit version range for (optional) `ray` dependency to include only " -"compatible releases (`>=1.9.2,<1.12.0`) " -"([#1205](https://github.com/adap/flower/pull/1205))" +"**Merge** `--grpc-rere` **and** `--rest` **SuperLink options** " +"([#3527](https://github.com/adap/flower/pull/3527))" msgstr "" -#: ../../source/ref-changelog.md:712 +#: ../../source/ref-changelog.md:101 msgid "" -"**Remove deprecated support for Python 3.6** " -"([#871](https://github.com/adap/flower/pull/871))" +"To simplify the usage of `flower-superlink`, previously separate sets of " +"CLI options for gRPC and REST were merged into one unified set of " +"options. Consult the [Flower CLI reference " +"documentation](https://flower.ai/docs/framework/ref-api-cli.html) for " +"details." +msgstr "" + +#: ../../source/ref-changelog.md:103 +msgid "v1.8.0 (2024-04-03)" msgstr "" -#: ../../source/ref-changelog.md:713 +#: ../../source/ref-changelog.md:109 +msgid "" +"`Adam Narozniak`, `Charles Beauville`, `Daniel J. Beutel`, `Daniel Nata " +"Nugraha`, `Danny`, `Gustavo Bertoli`, `Heng Pan`, `Ikko Eltociear " +"Ashimine`, `Jack Cook`, `Javier`, `Raj Parekh`, `Robert Steiner`, " +"`Sebastian van der Voort`, `Taner Topal`, `Yan Gao`, `mohammadnaseri`, " +"`tabdar-khan` " +msgstr "" + +#: ../../source/ref-changelog.md:113 +msgid "" +"**Introduce Flower Next high-level API (stable)** " +"([#3002](https://github.com/adap/flower/pull/3002), " +"[#2934](https://github.com/adap/flower/pull/2934), " +"[#2958](https://github.com/adap/flower/pull/2958), " +"[#3173](https://github.com/adap/flower/pull/3173), " +"[#3174](https://github.com/adap/flower/pull/3174), " +"[#2923](https://github.com/adap/flower/pull/2923), " +"[#2691](https://github.com/adap/flower/pull/2691), " +"[#3079](https://github.com/adap/flower/pull/3079), " +"[#2961](https://github.com/adap/flower/pull/2961), " +"[#2924](https://github.com/adap/flower/pull/2924), " +"[#3166](https://github.com/adap/flower/pull/3166), " +"[#3031](https://github.com/adap/flower/pull/3031), " +"[#3057](https://github.com/adap/flower/pull/3057), " +"[#3000](https://github.com/adap/flower/pull/3000), " +"[#3113](https://github.com/adap/flower/pull/3113), " +"[#2957](https://github.com/adap/flower/pull/2957), " +"[#3183](https://github.com/adap/flower/pull/3183), " +"[#3180](https://github.com/adap/flower/pull/3180), " +"[#3035](https://github.com/adap/flower/pull/3035), " +"[#3189](https://github.com/adap/flower/pull/3189), " +"[#3185](https://github.com/adap/flower/pull/3185), " +"[#3190](https://github.com/adap/flower/pull/3190), " +"[#3191](https://github.com/adap/flower/pull/3191), " +"[#3195](https://github.com/adap/flower/pull/3195), " +"[#3197](https://github.com/adap/flower/pull/3197))" +msgstr "" + +#: ../../source/ref-changelog.md:115 +msgid "" +"The Flower Next high-level API is stable! Flower Next is the future of " +"Flower - all new features (like Flower Mods) will be built on top of it. " +"You can start to migrate your existing projects to Flower Next by using " +"`ServerApp` and `ClientApp` (check out `quickstart-pytorch` or " +"`quickstart-tensorflow`, a detailed migration guide will follow shortly)." +" Flower Next allows you to run multiple projects concurrently (we call " +"this multi-run) and execute the same project in either simulation " +"environments or deployment environments without having to change a single" +" line of code. The best part? It's fully compatible with existing Flower " +"projects that use `Strategy`, `NumPyClient` & co." +msgstr "" + +#: ../../source/ref-changelog.md:117 msgid "" -"**Remove deprecated KerasClient** " -"([#857](https://github.com/adap/flower/pull/857))" +"**Introduce Flower Next low-level API (preview)** " +"([#3062](https://github.com/adap/flower/pull/3062), " +"[#3034](https://github.com/adap/flower/pull/3034), " +"[#3069](https://github.com/adap/flower/pull/3069))" msgstr "" -#: ../../source/ref-changelog.md:714 +#: ../../source/ref-changelog.md:119 +msgid "" +"In addition to the Flower Next *high-level* API that uses `Strategy`, " +"`NumPyClient` & co, Flower 1.8 also comes with a preview version of the " +"new Flower Next *low-level* API. The low-level API allows for granular " +"control of every aspect of the learning process by sending/receiving " +"individual messages to/from client nodes. The new `ServerApp` supports " +"registering a custom `main` function that allows writing custom training " +"loops for methods like async FL, cyclic training, or federated analytics." +" The new `ClientApp` supports registering `train`, `evaluate` and `query`" +" functions that can access the raw message received from the `ServerApp`." +" New abstractions like `RecordSet`, `Message` and `Context` further " +"enable sending multiple models, multiple sets of config values and " +"metrics, stateful computations on the client node and implementations of " +"custom SMPC protocols, to name just a few." +msgstr "" + +#: ../../source/ref-changelog.md:121 +msgid "" +"**Introduce Flower Mods (preview)** " +"([#3054](https://github.com/adap/flower/pull/3054), " +"[#2911](https://github.com/adap/flower/pull/2911), " +"[#3083](https://github.com/adap/flower/pull/3083))" +msgstr "" + +#: ../../source/ref-changelog.md:123 msgid "" -"**Remove deprecated no-op extra installs** " -"([#973](https://github.com/adap/flower/pull/973))" +"Flower Modifiers (we call them Mods) can intercept messages and analyze, " +"edit or handle them directly. Mods can be used to develop pluggable " +"modules that work across different projects. Flower 1.8 already includes " +"mods to log the size of a message, the number of parameters sent over the" +" network, differential privacy with fixed clipping and adaptive clipping," +" local differential privacy and secure aggregation protocols SecAgg and " +"SecAgg+. The Flower Mods API is released as a preview, but researchers " +"can already use it to experiment with arbirtrary SMPC protocols." +msgstr "" + +#: ../../source/ref-changelog.md:125 +msgid "" +"**Fine-tune LLMs with LLM FlowerTune** " +"([#3029](https://github.com/adap/flower/pull/3029), " +"[#3089](https://github.com/adap/flower/pull/3089), " +"[#3092](https://github.com/adap/flower/pull/3092), " +"[#3100](https://github.com/adap/flower/pull/3100), " +"[#3114](https://github.com/adap/flower/pull/3114), " +"[#3162](https://github.com/adap/flower/pull/3162), " +"[#3172](https://github.com/adap/flower/pull/3172))" +msgstr "" + +#: ../../source/ref-changelog.md:127 +msgid "" +"We are introducing LLM FlowerTune, an introductory example that " +"demonstrates federated LLM fine-tuning of pre-trained Llama2 models on " +"the Alpaca-GPT4 dataset. The example is built to be easily adapted to use" +" different models and/or datasets. Read our blog post [LLM FlowerTune: " +"Federated LLM Fine-tuning with Flower](https://flower.ai/blog/2024-03-14" +"-llm-flowertune-federated-llm-finetuning-with-flower/) for more details." msgstr "" -#: ../../source/ref-changelog.md:715 +#: ../../source/ref-changelog.md:129 msgid "" -"**Remove deprecated proto fields from** `FitRes` **and** `EvaluateRes` " -"([#869](https://github.com/adap/flower/pull/869))" +"**Introduce built-in Differential Privacy (preview)** " +"([#2798](https://github.com/adap/flower/pull/2798), " +"[#2959](https://github.com/adap/flower/pull/2959), " +"[#3038](https://github.com/adap/flower/pull/3038), " +"[#3147](https://github.com/adap/flower/pull/3147), " +"[#2909](https://github.com/adap/flower/pull/2909), " +"[#2893](https://github.com/adap/flower/pull/2893), " +"[#2892](https://github.com/adap/flower/pull/2892), " +"[#3039](https://github.com/adap/flower/pull/3039), " +"[#3074](https://github.com/adap/flower/pull/3074))" msgstr "" -#: ../../source/ref-changelog.md:716 +#: ../../source/ref-changelog.md:131 msgid "" -"**Remove deprecated QffedAvg strategy (replaced by QFedAvg)** " -"([#1107](https://github.com/adap/flower/pull/1107))" +"Built-in Differential Privacy is here! Flower supports both central and " +"local differential privacy (DP). Central DP can be configured with either" +" fixed or adaptive clipping. The clipping can happen either on the " +"server-side or the client-side. Local DP does both clipping and noising " +"on the client-side. A new documentation page [explains Differential " +"Privacy approaches](https://flower.ai/docs/framework/explanation-" +"differential-privacy.html) and a new how-to guide describes [how to use " +"the new Differential Privacy components](https://flower.ai/docs/framework" +"/how-to-use-differential-privacy.html) in Flower." msgstr "" -#: ../../source/ref-changelog.md:717 +#: ../../source/ref-changelog.md:133 msgid "" -"**Remove deprecated DefaultStrategy strategy** " -"([#1142](https://github.com/adap/flower/pull/1142))" +"**Introduce built-in Secure Aggregation (preview)** " +"([#3120](https://github.com/adap/flower/pull/3120), " +"[#3110](https://github.com/adap/flower/pull/3110), " +"[#3108](https://github.com/adap/flower/pull/3108))" msgstr "" -#: ../../source/ref-changelog.md:718 +#: ../../source/ref-changelog.md:135 msgid "" -"**Remove deprecated support for eval_fn accuracy return value** " -"([#1142](https://github.com/adap/flower/pull/1142))" +"Built-in Secure Aggregation is here! Flower now supports different secure" +" aggregation protocols out-of-the-box. The best part? You can add secure " +"aggregation to your Flower projects with only a few lines of code. In " +"this initial release, we inlcude support for SecAgg and SecAgg+, but more" +" protocols will be implemented shortly. We'll also add detailed docs that" +" explain secure aggregation and how to use it in Flower. You can already " +"check out the new code example that shows how to use Flower to easily " +"combine Federated Learning, Differential Privacy and Secure Aggregation " +"in the same project." msgstr "" -#: ../../source/ref-changelog.md:719 +#: ../../source/ref-changelog.md:137 msgid "" -"**Remove deprecated support for passing initial parameters as NumPy " -"ndarrays** ([#1142](https://github.com/adap/flower/pull/1142))" +"**Introduce** `flwr` **CLI (preview)** " +"([#2942](https://github.com/adap/flower/pull/2942), " +"[#3055](https://github.com/adap/flower/pull/3055), " +"[#3111](https://github.com/adap/flower/pull/3111), " +"[#3130](https://github.com/adap/flower/pull/3130), " +"[#3136](https://github.com/adap/flower/pull/3136), " +"[#3094](https://github.com/adap/flower/pull/3094), " +"[#3059](https://github.com/adap/flower/pull/3059), " +"[#3049](https://github.com/adap/flower/pull/3049), " +"[#3142](https://github.com/adap/flower/pull/3142))" msgstr "" -#: ../../source/ref-changelog.md:721 -msgid "v0.18.0 (2022-02-28)" +#: ../../source/ref-changelog.md:139 +msgid "" +"A new `flwr` CLI command allows creating new Flower projects (`flwr new`)" +" and then running them using the Simulation Engine (`flwr run`)." msgstr "" -#: ../../source/ref-changelog.md:725 +#: ../../source/ref-changelog.md:141 msgid "" -"**Improved Virtual Client Engine compatibility with Jupyter Notebook / " -"Google Colab** ([#866](https://github.com/adap/flower/pull/866), " -"[#872](https://github.com/adap/flower/pull/872), " -"[#833](https://github.com/adap/flower/pull/833), " -"[#1036](https://github.com/adap/flower/pull/1036))" +"**Introduce Flower Next Simulation Engine** " +"([#3024](https://github.com/adap/flower/pull/3024), " +"[#3061](https://github.com/adap/flower/pull/3061), " +"[#2997](https://github.com/adap/flower/pull/2997), " +"[#2783](https://github.com/adap/flower/pull/2783), " +"[#3184](https://github.com/adap/flower/pull/3184), " +"[#3075](https://github.com/adap/flower/pull/3075), " +"[#3047](https://github.com/adap/flower/pull/3047), " +"[#2998](https://github.com/adap/flower/pull/2998), " +"[#3009](https://github.com/adap/flower/pull/3009), " +"[#3008](https://github.com/adap/flower/pull/3008))" msgstr "" -#: ../../source/ref-changelog.md:727 +#: ../../source/ref-changelog.md:143 msgid "" -"Simulations (using the Virtual Client Engine through `start_simulation`) " -"now work more smoothly on Jupyter Notebooks (incl. Google Colab) after " -"installing Flower with the `simulation` extra (`pip install " -"flwr[simulation]`)." +"The Flower Simulation Engine can now run Flower Next projects. For " +"notebook environments, there's also a new `run_simulation` function that " +"can run `ServerApp` and `ClientApp`." msgstr "" -#: ../../source/ref-changelog.md:729 +#: ../../source/ref-changelog.md:145 msgid "" -"**New Jupyter Notebook code example** " -"([#833](https://github.com/adap/flower/pull/833))" +"**Handle SuperNode connection errors** " +"([#2969](https://github.com/adap/flower/pull/2969))" msgstr "" -#: ../../source/ref-changelog.md:731 +#: ../../source/ref-changelog.md:147 msgid "" -"A new code example (`quickstart_simulation`) demonstrates Flower " -"simulations using the Virtual Client Engine through Jupyter Notebook " -"(incl. Google Colab)." +"A SuperNode will now try to reconnect indefinitely to the SuperLink in " +"case of connection errors. The arguments `--max-retries` and `--max-wait-" +"time` can now be passed to the `flower-client-app` command. `--max-" +"retries` will define the number of tentatives the client should make " +"before it gives up trying to reconnect to the SuperLink, and, `--max-" +"wait-time` defines the time before the SuperNode gives up trying to " +"reconnect to the SuperLink." msgstr "" -#: ../../source/ref-changelog.md:733 +#: ../../source/ref-changelog.md:149 msgid "" -"**Client properties (feature preview)** " -"([#795](https://github.com/adap/flower/pull/795))" +"**General updates to Flower Baselines** " +"([#2904](https://github.com/adap/flower/pull/2904), " +"[#2482](https://github.com/adap/flower/pull/2482), " +"[#2985](https://github.com/adap/flower/pull/2985), " +"[#2968](https://github.com/adap/flower/pull/2968))" msgstr "" -#: ../../source/ref-changelog.md:735 +#: ../../source/ref-changelog.md:151 msgid "" -"Clients can implement a new method `get_properties` to enable server-side" -" strategies to query client properties." +"There's a new [FedStar](https://flower.ai/docs/baselines/fedstar.html) " +"baseline. Several other baselined have been updated as well." msgstr "" -#: ../../source/ref-changelog.md:737 +#: ../../source/ref-changelog.md:153 msgid "" -"**Experimental Android support with TFLite** " -"([#865](https://github.com/adap/flower/pull/865))" +"**Improve documentation and translations** " +"([#3050](https://github.com/adap/flower/pull/3050), " +"[#3044](https://github.com/adap/flower/pull/3044), " +"[#3043](https://github.com/adap/flower/pull/3043), " +"[#2986](https://github.com/adap/flower/pull/2986), " +"[#3041](https://github.com/adap/flower/pull/3041), " +"[#3046](https://github.com/adap/flower/pull/3046), " +"[#3042](https://github.com/adap/flower/pull/3042), " +"[#2978](https://github.com/adap/flower/pull/2978), " +"[#2952](https://github.com/adap/flower/pull/2952), " +"[#3167](https://github.com/adap/flower/pull/3167), " +"[#2953](https://github.com/adap/flower/pull/2953), " +"[#3045](https://github.com/adap/flower/pull/3045), " +"[#2654](https://github.com/adap/flower/pull/2654), " +"[#3082](https://github.com/adap/flower/pull/3082), " +"[#2990](https://github.com/adap/flower/pull/2990), " +"[#2989](https://github.com/adap/flower/pull/2989))" msgstr "" -#: ../../source/ref-changelog.md:739 +#: ../../source/ref-changelog.md:155 msgid "" -"Android support has finally arrived in `main`! Flower is both client-" -"agnostic and framework-agnostic by design. One can integrate arbitrary " -"client platforms and with this release, using Flower on Android has " -"become a lot easier." +"As usual, we merged many smaller and larger improvements to the " +"documentation. A special thank you goes to [Sebastian van der " +"Voort](https://github.com/svdvoort) for landing a big documentation PR!" msgstr "" -#: ../../source/ref-changelog.md:741 +#: ../../source/ref-changelog.md:157 msgid "" -"The example uses TFLite on the client side, along with a new " -"`FedAvgAndroid` strategy. The Android client and `FedAvgAndroid` are " -"still experimental, but they are a first step towards a fully-fledged " -"Android SDK and a unified `FedAvg` implementation that integrated the new" -" functionality from `FedAvgAndroid`." +"**General updates to Flower Examples** " +"([3134](https://github.com/adap/flower/pull/3134), " +"[2996](https://github.com/adap/flower/pull/2996), " +"[2930](https://github.com/adap/flower/pull/2930), " +"[2967](https://github.com/adap/flower/pull/2967), " +"[2467](https://github.com/adap/flower/pull/2467), " +"[2910](https://github.com/adap/flower/pull/2910), " +"[#2918](https://github.com/adap/flower/pull/2918), " +"[#2773](https://github.com/adap/flower/pull/2773), " +"[#3063](https://github.com/adap/flower/pull/3063), " +"[#3116](https://github.com/adap/flower/pull/3116), " +"[#3117](https://github.com/adap/flower/pull/3117))" msgstr "" -#: ../../source/ref-changelog.md:743 +#: ../../source/ref-changelog.md:159 msgid "" -"**Make gRPC keepalive time user-configurable and decrease default " -"keepalive time** ([#1069](https://github.com/adap/flower/pull/1069))" +"Two new examples show federated training of a Vision Transformer (ViT) " +"and federated learning in a medical context using the popular MONAI " +"library. `quickstart-pytorch` and `quickstart-tensorflow` demonstrate the" +" new Flower Next `ServerApp` and `ClientApp`. Many other examples " +"received considerable updates as well." msgstr "" -#: ../../source/ref-changelog.md:745 +#: ../../source/ref-changelog.md:161 msgid "" -"The default gRPC keepalive time has been reduced to increase the " -"compatibility of Flower with more cloud environments (for example, " -"Microsoft Azure). Users can configure the keepalive time to customize the" -" gRPC stack based on specific requirements." +"**General improvements** " +"([#3171](https://github.com/adap/flower/pull/3171), " +"[3099](https://github.com/adap/flower/pull/3099), " +"[3003](https://github.com/adap/flower/pull/3003), " +"[3145](https://github.com/adap/flower/pull/3145), " +"[3017](https://github.com/adap/flower/pull/3017), " +"[3085](https://github.com/adap/flower/pull/3085), " +"[3012](https://github.com/adap/flower/pull/3012), " +"[3119](https://github.com/adap/flower/pull/3119), " +"[2991](https://github.com/adap/flower/pull/2991), " +"[2970](https://github.com/adap/flower/pull/2970), " +"[2980](https://github.com/adap/flower/pull/2980), " +"[3086](https://github.com/adap/flower/pull/3086), " +"[2932](https://github.com/adap/flower/pull/2932), " +"[2928](https://github.com/adap/flower/pull/2928), " +"[2941](https://github.com/adap/flower/pull/2941), " +"[2933](https://github.com/adap/flower/pull/2933), " +"[3181](https://github.com/adap/flower/pull/3181), " +"[2973](https://github.com/adap/flower/pull/2973), " +"[2992](https://github.com/adap/flower/pull/2992), " +"[2915](https://github.com/adap/flower/pull/2915), " +"[3040](https://github.com/adap/flower/pull/3040), " +"[3022](https://github.com/adap/flower/pull/3022), " +"[3032](https://github.com/adap/flower/pull/3032), " +"[2902](https://github.com/adap/flower/pull/2902), " +"[2931](https://github.com/adap/flower/pull/2931), " +"[3005](https://github.com/adap/flower/pull/3005), " +"[3132](https://github.com/adap/flower/pull/3132), " +"[3115](https://github.com/adap/flower/pull/3115), " +"[2944](https://github.com/adap/flower/pull/2944), " +"[3064](https://github.com/adap/flower/pull/3064), " +"[3106](https://github.com/adap/flower/pull/3106), " +"[2974](https://github.com/adap/flower/pull/2974), " +"[3178](https://github.com/adap/flower/pull/3178), " +"[2993](https://github.com/adap/flower/pull/2993), " +"[3186](https://github.com/adap/flower/pull/3186), " +"[3091](https://github.com/adap/flower/pull/3091), " +"[3125](https://github.com/adap/flower/pull/3125), " +"[3093](https://github.com/adap/flower/pull/3093), " +"[3013](https://github.com/adap/flower/pull/3013), " +"[3033](https://github.com/adap/flower/pull/3033), " +"[3133](https://github.com/adap/flower/pull/3133), " +"[3068](https://github.com/adap/flower/pull/3068), " +"[2916](https://github.com/adap/flower/pull/2916), " +"[2975](https://github.com/adap/flower/pull/2975), " +"[2984](https://github.com/adap/flower/pull/2984), " +"[2846](https://github.com/adap/flower/pull/2846), " +"[3077](https://github.com/adap/flower/pull/3077), " +"[3143](https://github.com/adap/flower/pull/3143), " +"[2921](https://github.com/adap/flower/pull/2921), " +"[3101](https://github.com/adap/flower/pull/3101), " +"[2927](https://github.com/adap/flower/pull/2927), " +"[2995](https://github.com/adap/flower/pull/2995), " +"[2972](https://github.com/adap/flower/pull/2972), " +"[2912](https://github.com/adap/flower/pull/2912), " +"[3065](https://github.com/adap/flower/pull/3065), " +"[3028](https://github.com/adap/flower/pull/3028), " +"[2922](https://github.com/adap/flower/pull/2922), " +"[2982](https://github.com/adap/flower/pull/2982), " +"[2914](https://github.com/adap/flower/pull/2914), " +"[3179](https://github.com/adap/flower/pull/3179), " +"[3080](https://github.com/adap/flower/pull/3080), " +"[2994](https://github.com/adap/flower/pull/2994), " +"[3187](https://github.com/adap/flower/pull/3187), " +"[2926](https://github.com/adap/flower/pull/2926), " +"[3018](https://github.com/adap/flower/pull/3018), " +"[3144](https://github.com/adap/flower/pull/3144), " +"[3011](https://github.com/adap/flower/pull/3011), " +"[#3152](https://github.com/adap/flower/pull/3152), " +"[#2836](https://github.com/adap/flower/pull/2836), " +"[#2929](https://github.com/adap/flower/pull/2929), " +"[#2943](https://github.com/adap/flower/pull/2943), " +"[#2955](https://github.com/adap/flower/pull/2955), " +"[#2954](https://github.com/adap/flower/pull/2954))" +msgstr "" + +#: ../../source/ref-changelog.md:165 ../../source/ref-changelog.md:442 +#: ../../source/ref-changelog.md:506 ../../source/ref-changelog.md:564 +#: ../../source/ref-changelog.md:633 ../../source/ref-changelog.md:695 +msgid "None" msgstr "" -#: ../../source/ref-changelog.md:747 -msgid "" -"**New differential privacy example using Opacus and PyTorch** " -"([#805](https://github.com/adap/flower/pull/805))" +#: ../../source/ref-changelog.md:167 +msgid "v1.7.0 (2024-02-05)" msgstr "" -#: ../../source/ref-changelog.md:749 +#: ../../source/ref-changelog.md:173 msgid "" -"A new code example (`opacus`) demonstrates differentially-private " -"federated learning with Opacus, PyTorch, and Flower." +"`Aasheesh Singh`, `Adam Narozniak`, `Aml Hassan Esmil`, `Charles " +"Beauville`, `Daniel J. Beutel`, `Daniel Nata Nugraha`, `Edoardo " +"Gabrielli`, `Gustavo Bertoli`, `HelinLin`, `Heng Pan`, `Javier`, `M S " +"Chaitanya Kumar`, `Mohammad Naseri`, `Nikos Vlachakis`, `Pritam Neog`, " +"`Robert Kuska`, `Robert Steiner`, `Taner Topal`, `Yahia Salaheldin " +"Shaaban`, `Yan Gao`, `Yasar Abbas` " msgstr "" -#: ../../source/ref-changelog.md:751 +#: ../../source/ref-changelog.md:177 msgid "" -"**New Hugging Face Transformers code example** " -"([#863](https://github.com/adap/flower/pull/863))" +"**Introduce stateful clients (experimental)** " +"([#2770](https://github.com/adap/flower/pull/2770), " +"[#2686](https://github.com/adap/flower/pull/2686), " +"[#2696](https://github.com/adap/flower/pull/2696), " +"[#2643](https://github.com/adap/flower/pull/2643), " +"[#2769](https://github.com/adap/flower/pull/2769))" msgstr "" -#: ../../source/ref-changelog.md:753 +#: ../../source/ref-changelog.md:179 msgid "" -"A new code example (`quickstart_huggingface`) demonstrates usage of " -"Hugging Face Transformers with Flower." +"Subclasses of `Client` and `NumPyClient` can now store local state that " +"remains on the client. Let's start with the highlight first: this new " +"feature is compatible with both simulated clients (via " +"`start_simulation`) and networked clients (via `start_client`). It's also" +" the first preview of new abstractions like `Context` and `RecordSet`. " +"Clients can access state of type `RecordSet` via `state: RecordSet = " +"self.context.state`. Changes to this `RecordSet` are preserved across " +"different rounds of execution to enable stateful computations in a " +"unified way across simulation and deployment." msgstr "" -#: ../../source/ref-changelog.md:755 +#: ../../source/ref-changelog.md:181 msgid "" -"**New MLCube code example** " -"([#779](https://github.com/adap/flower/pull/779), " -"[#1034](https://github.com/adap/flower/pull/1034), " -"[#1065](https://github.com/adap/flower/pull/1065), " -"[#1090](https://github.com/adap/flower/pull/1090))" +"**Improve performance** " +"([#2293](https://github.com/adap/flower/pull/2293))" msgstr "" -#: ../../source/ref-changelog.md:757 +#: ../../source/ref-changelog.md:183 msgid "" -"A new code example (`quickstart_mlcube`) demonstrates usage of MLCube " -"with Flower." +"Flower is faster than ever. All `FedAvg`-derived strategies now use in-" +"place aggregation to reduce memory consumption. The Flower client " +"serialization/deserialization has been rewritten from the ground up, " +"which results in significant speedups, especially when the client-side " +"training time is short." msgstr "" -#: ../../source/ref-changelog.md:759 +#: ../../source/ref-changelog.md:185 msgid "" -"**SSL-enabled server and client** " -"([#842](https://github.com/adap/flower/pull/842), " -"[#844](https://github.com/adap/flower/pull/844), " -"[#845](https://github.com/adap/flower/pull/845), " -"[#847](https://github.com/adap/flower/pull/847), " -"[#993](https://github.com/adap/flower/pull/993), " -"[#994](https://github.com/adap/flower/pull/994))" +"**Support Federated Learning with Apple MLX and Flower** " +"([#2693](https://github.com/adap/flower/pull/2693))" msgstr "" -#: ../../source/ref-changelog.md:761 +#: ../../source/ref-changelog.md:187 msgid "" -"SSL enables secure encrypted connections between clients and servers. " -"This release open-sources the Flower secure gRPC implementation to make " -"encrypted communication channels accessible to all Flower users." +"Flower has official support for federated learning using [Apple " +"MLX](https://ml-explore.github.io/mlx) via the new `quickstart-mlx` code " +"example." msgstr "" -#: ../../source/ref-changelog.md:763 +#: ../../source/ref-changelog.md:189 msgid "" -"**Updated** `FedAdam` **and** `FedYogi` **strategies** " -"([#885](https://github.com/adap/flower/pull/885), " -"[#895](https://github.com/adap/flower/pull/895))" +"**Introduce new XGBoost cyclic strategy** " +"([#2666](https://github.com/adap/flower/pull/2666), " +"[#2668](https://github.com/adap/flower/pull/2668))" msgstr "" -#: ../../source/ref-changelog.md:765 +#: ../../source/ref-changelog.md:191 msgid "" -"`FedAdam` and `FedAdam` match the latest version of the Adaptive " -"Federated Optimization paper." +"A new strategy called `FedXgbCyclic` supports a client-by-client style of" +" training (often called cyclic). The `xgboost-comprehensive` code example" +" shows how to use it in a full project. In addition to that, `xgboost-" +"comprehensive` now also supports simulation mode. With this, Flower " +"offers best-in-class XGBoost support." msgstr "" -#: ../../source/ref-changelog.md:767 +#: ../../source/ref-changelog.md:193 msgid "" -"**Initialize** `start_simulation` **with a list of client IDs** " -"([#860](https://github.com/adap/flower/pull/860))" +"**Support Python 3.11** " +"([#2394](https://github.com/adap/flower/pull/2394))" msgstr "" -#: ../../source/ref-changelog.md:769 +#: ../../source/ref-changelog.md:195 msgid "" -"`start_simulation` can now be called with a list of client IDs " -"(`clients_ids`, type: `List[str]`). Those IDs will be passed to the " -"`client_fn` whenever a client needs to be initialized, which can make it " -"easier to load data partitions that are not accessible through `int` " -"identifiers." +"Framework tests now run on Python 3.8, 3.9, 3.10, and 3.11. This will " +"ensure better support for users using more recent Python versions." msgstr "" -#: ../../source/ref-changelog.md:773 +#: ../../source/ref-changelog.md:197 msgid "" -"Update `num_examples` calculation in PyTorch code examples in " -"([#909](https://github.com/adap/flower/pull/909))" +"**Update gRPC and ProtoBuf dependencies** " +"([#2814](https://github.com/adap/flower/pull/2814))" msgstr "" -#: ../../source/ref-changelog.md:774 +#: ../../source/ref-changelog.md:199 msgid "" -"Expose Flower version through `flwr.__version__` " -"([#952](https://github.com/adap/flower/pull/952))" +"The `grpcio` and `protobuf` dependencies were updated to their latest " +"versions for improved security and performance." msgstr "" -#: ../../source/ref-changelog.md:775 +#: ../../source/ref-changelog.md:201 msgid "" -"`start_server` in `app.py` now returns a `History` object containing " -"metrics from training ([#974](https://github.com/adap/flower/pull/974))" +"**Introduce Docker image for Flower server** " +"([#2700](https://github.com/adap/flower/pull/2700), " +"[#2688](https://github.com/adap/flower/pull/2688), " +"[#2705](https://github.com/adap/flower/pull/2705), " +"[#2695](https://github.com/adap/flower/pull/2695), " +"[#2747](https://github.com/adap/flower/pull/2747), " +"[#2746](https://github.com/adap/flower/pull/2746), " +"[#2680](https://github.com/adap/flower/pull/2680), " +"[#2682](https://github.com/adap/flower/pull/2682), " +"[#2701](https://github.com/adap/flower/pull/2701))" msgstr "" -#: ../../source/ref-changelog.md:776 +#: ../../source/ref-changelog.md:203 msgid "" -"Make `max_workers` (used by `ThreadPoolExecutor`) configurable " -"([#978](https://github.com/adap/flower/pull/978))" +"The Flower server can now be run using an official Docker image. A new " +"how-to guide explains [how to run Flower using " +"Docker](https://flower.ai/docs/framework/how-to-run-flower-using-" +"docker.html). An official Flower client Docker image will follow." msgstr "" -#: ../../source/ref-changelog.md:777 +#: ../../source/ref-changelog.md:205 msgid "" -"Increase sleep time after server start to three seconds in all code " -"examples ([#1086](https://github.com/adap/flower/pull/1086))" +"**Introduce** `flower-via-docker-compose` **example** " +"([#2626](https://github.com/adap/flower/pull/2626))" msgstr "" -#: ../../source/ref-changelog.md:778 +#: ../../source/ref-changelog.md:207 msgid "" -"Added a new FAQ section to the documentation " -"([#948](https://github.com/adap/flower/pull/948))" +"**Introduce** `quickstart-sklearn-tabular` **example** " +"([#2719](https://github.com/adap/flower/pull/2719))" msgstr "" -#: ../../source/ref-changelog.md:779 +#: ../../source/ref-changelog.md:209 msgid "" -"And many more under-the-hood changes, library updates, documentation " -"changes, and tooling improvements!" +"**Introduce** `custom-metrics` **example** " +"([#1958](https://github.com/adap/flower/pull/1958))" msgstr "" -#: ../../source/ref-changelog.md:783 +#: ../../source/ref-changelog.md:211 msgid "" -"**Removed** `flwr_example` **and** `flwr_experimental` **from release " -"build** ([#869](https://github.com/adap/flower/pull/869))" +"**Update code examples to use Flower Datasets** " +"([#2450](https://github.com/adap/flower/pull/2450), " +"[#2456](https://github.com/adap/flower/pull/2456), " +"[#2318](https://github.com/adap/flower/pull/2318), " +"[#2712](https://github.com/adap/flower/pull/2712))" msgstr "" -#: ../../source/ref-changelog.md:785 +#: ../../source/ref-changelog.md:213 msgid "" -"The packages `flwr_example` and `flwr_experimental` have been deprecated " -"since Flower 0.12.0 and they are not longer included in Flower release " -"builds. The associated extras (`baseline`, `examples-pytorch`, `examples-" -"tensorflow`, `http-logger`, `ops`) are now no-op and will be removed in " -"an upcoming release." +"Several code examples were updated to use [Flower " +"Datasets](https://flower.ai/docs/datasets/)." msgstr "" -#: ../../source/ref-changelog.md:787 -msgid "v0.17.0 (2021-09-24)" +#: ../../source/ref-changelog.md:215 +msgid "" +"**General updates to Flower Examples** " +"([#2381](https://github.com/adap/flower/pull/2381), " +"[#2805](https://github.com/adap/flower/pull/2805), " +"[#2782](https://github.com/adap/flower/pull/2782), " +"[#2806](https://github.com/adap/flower/pull/2806), " +"[#2829](https://github.com/adap/flower/pull/2829), " +"[#2825](https://github.com/adap/flower/pull/2825), " +"[#2816](https://github.com/adap/flower/pull/2816), " +"[#2726](https://github.com/adap/flower/pull/2726), " +"[#2659](https://github.com/adap/flower/pull/2659), " +"[#2655](https://github.com/adap/flower/pull/2655))" msgstr "" -#: ../../source/ref-changelog.md:791 -msgid "" -"**Experimental virtual client engine** " -"([#781](https://github.com/adap/flower/pull/781) " -"[#790](https://github.com/adap/flower/pull/790) " -"[#791](https://github.com/adap/flower/pull/791))" +#: ../../source/ref-changelog.md:217 +msgid "Many Flower code examples received substantial updates." msgstr "" -#: ../../source/ref-changelog.md:793 -msgid "" -"One of Flower's goals is to enable research at scale. This release " -"enables a first (experimental) peek at a major new feature, codenamed the" -" virtual client engine. Virtual clients enable simulations that scale to " -"a (very) large number of clients on a single machine or compute cluster. " -"The easiest way to test the new functionality is to look at the two new " -"code examples called `quickstart_simulation` and `simulation_pytorch`." +#: ../../source/ref-changelog.md:219 ../../source/ref-changelog.md:312 +msgid "**Update Flower Baselines**" msgstr "" -#: ../../source/ref-changelog.md:795 +#: ../../source/ref-changelog.md:221 msgid "" -"The feature is still experimental, so there's no stability guarantee for " -"the API. It's also not quite ready for prime time and comes with a few " -"known caveats. However, those who are curious are encouraged to try it " -"out and share their thoughts." +"HFedXGBoost ([#2226](https://github.com/adap/flower/pull/2226), " +"[#2771](https://github.com/adap/flower/pull/2771))" msgstr "" -#: ../../source/ref-changelog.md:797 -msgid "" -"**New built-in strategies** " -"([#828](https://github.com/adap/flower/pull/828) " -"[#822](https://github.com/adap/flower/pull/822))" +#: ../../source/ref-changelog.md:222 +msgid "FedVSSL ([#2412](https://github.com/adap/flower/pull/2412))" msgstr "" -#: ../../source/ref-changelog.md:799 -msgid "" -"FedYogi - Federated learning strategy using Yogi on server-side. " -"Implementation based on https://arxiv.org/abs/2003.00295" +#: ../../source/ref-changelog.md:223 +msgid "FedNova ([#2179](https://github.com/adap/flower/pull/2179))" msgstr "" -#: ../../source/ref-changelog.md:800 -msgid "" -"FedAdam - Federated learning strategy using Adam on server-side. " -"Implementation based on https://arxiv.org/abs/2003.00295" +#: ../../source/ref-changelog.md:224 +msgid "HeteroFL ([#2439](https://github.com/adap/flower/pull/2439))" msgstr "" -#: ../../source/ref-changelog.md:802 -msgid "" -"**New PyTorch Lightning code example** " -"([#617](https://github.com/adap/flower/pull/617))" +#: ../../source/ref-changelog.md:225 +msgid "FedAvgM ([#2246](https://github.com/adap/flower/pull/2246))" msgstr "" -#: ../../source/ref-changelog.md:804 -msgid "" -"**New Variational Auto-Encoder code example** " -"([#752](https://github.com/adap/flower/pull/752))" +#: ../../source/ref-changelog.md:226 +msgid "FedPara ([#2722](https://github.com/adap/flower/pull/2722))" msgstr "" -#: ../../source/ref-changelog.md:806 +#: ../../source/ref-changelog.md:228 msgid "" -"**New scikit-learn code example** " -"([#748](https://github.com/adap/flower/pull/748))" +"**Improve documentation** " +"([#2674](https://github.com/adap/flower/pull/2674), " +"[#2480](https://github.com/adap/flower/pull/2480), " +"[#2826](https://github.com/adap/flower/pull/2826), " +"[#2727](https://github.com/adap/flower/pull/2727), " +"[#2761](https://github.com/adap/flower/pull/2761), " +"[#2900](https://github.com/adap/flower/pull/2900))" msgstr "" -#: ../../source/ref-changelog.md:808 +#: ../../source/ref-changelog.md:230 msgid "" -"**New experimental TensorBoard strategy** " -"([#789](https://github.com/adap/flower/pull/789))" +"**Improved testing and development infrastructure** " +"([#2797](https://github.com/adap/flower/pull/2797), " +"[#2676](https://github.com/adap/flower/pull/2676), " +"[#2644](https://github.com/adap/flower/pull/2644), " +"[#2656](https://github.com/adap/flower/pull/2656), " +"[#2848](https://github.com/adap/flower/pull/2848), " +"[#2675](https://github.com/adap/flower/pull/2675), " +"[#2735](https://github.com/adap/flower/pull/2735), " +"[#2767](https://github.com/adap/flower/pull/2767), " +"[#2732](https://github.com/adap/flower/pull/2732), " +"[#2744](https://github.com/adap/flower/pull/2744), " +"[#2681](https://github.com/adap/flower/pull/2681), " +"[#2699](https://github.com/adap/flower/pull/2699), " +"[#2745](https://github.com/adap/flower/pull/2745), " +"[#2734](https://github.com/adap/flower/pull/2734), " +"[#2731](https://github.com/adap/flower/pull/2731), " +"[#2652](https://github.com/adap/flower/pull/2652), " +"[#2720](https://github.com/adap/flower/pull/2720), " +"[#2721](https://github.com/adap/flower/pull/2721), " +"[#2717](https://github.com/adap/flower/pull/2717), " +"[#2864](https://github.com/adap/flower/pull/2864), " +"[#2694](https://github.com/adap/flower/pull/2694), " +"[#2709](https://github.com/adap/flower/pull/2709), " +"[#2658](https://github.com/adap/flower/pull/2658), " +"[#2796](https://github.com/adap/flower/pull/2796), " +"[#2692](https://github.com/adap/flower/pull/2692), " +"[#2657](https://github.com/adap/flower/pull/2657), " +"[#2813](https://github.com/adap/flower/pull/2813), " +"[#2661](https://github.com/adap/flower/pull/2661), " +"[#2398](https://github.com/adap/flower/pull/2398))" msgstr "" -#: ../../source/ref-changelog.md:812 +#: ../../source/ref-changelog.md:232 msgid "" -"Improved advanced TensorFlow code example " -"([#769](https://github.com/adap/flower/pull/769))" +"The Flower testing and development infrastructure has received " +"substantial updates. This makes Flower 1.7 the most tested release ever." msgstr "" -#: ../../source/ref-changelog.md:813 +#: ../../source/ref-changelog.md:234 msgid "" -"Warning when `min_available_clients` is misconfigured " -"([#830](https://github.com/adap/flower/pull/830))" +"**Update dependencies** " +"([#2753](https://github.com/adap/flower/pull/2753), " +"[#2651](https://github.com/adap/flower/pull/2651), " +"[#2739](https://github.com/adap/flower/pull/2739), " +"[#2837](https://github.com/adap/flower/pull/2837), " +"[#2788](https://github.com/adap/flower/pull/2788), " +"[#2811](https://github.com/adap/flower/pull/2811), " +"[#2774](https://github.com/adap/flower/pull/2774), " +"[#2790](https://github.com/adap/flower/pull/2790), " +"[#2751](https://github.com/adap/flower/pull/2751), " +"[#2850](https://github.com/adap/flower/pull/2850), " +"[#2812](https://github.com/adap/flower/pull/2812), " +"[#2872](https://github.com/adap/flower/pull/2872), " +"[#2736](https://github.com/adap/flower/pull/2736), " +"[#2756](https://github.com/adap/flower/pull/2756), " +"[#2857](https://github.com/adap/flower/pull/2857), " +"[#2757](https://github.com/adap/flower/pull/2757), " +"[#2810](https://github.com/adap/flower/pull/2810), " +"[#2740](https://github.com/adap/flower/pull/2740), " +"[#2789](https://github.com/adap/flower/pull/2789))" msgstr "" -#: ../../source/ref-changelog.md:814 +#: ../../source/ref-changelog.md:236 msgid "" -"Improved gRPC server docs " -"([#841](https://github.com/adap/flower/pull/841))" +"**General improvements** " +"([#2803](https://github.com/adap/flower/pull/2803), " +"[#2847](https://github.com/adap/flower/pull/2847), " +"[#2877](https://github.com/adap/flower/pull/2877), " +"[#2690](https://github.com/adap/flower/pull/2690), " +"[#2889](https://github.com/adap/flower/pull/2889), " +"[#2874](https://github.com/adap/flower/pull/2874), " +"[#2819](https://github.com/adap/flower/pull/2819), " +"[#2689](https://github.com/adap/flower/pull/2689), " +"[#2457](https://github.com/adap/flower/pull/2457), " +"[#2870](https://github.com/adap/flower/pull/2870), " +"[#2669](https://github.com/adap/flower/pull/2669), " +"[#2876](https://github.com/adap/flower/pull/2876), " +"[#2885](https://github.com/adap/flower/pull/2885), " +"[#2858](https://github.com/adap/flower/pull/2858), " +"[#2867](https://github.com/adap/flower/pull/2867), " +"[#2351](https://github.com/adap/flower/pull/2351), " +"[#2886](https://github.com/adap/flower/pull/2886), " +"[#2860](https://github.com/adap/flower/pull/2860), " +"[#2828](https://github.com/adap/flower/pull/2828), " +"[#2869](https://github.com/adap/flower/pull/2869), " +"[#2875](https://github.com/adap/flower/pull/2875), " +"[#2733](https://github.com/adap/flower/pull/2733), " +"[#2488](https://github.com/adap/flower/pull/2488), " +"[#2646](https://github.com/adap/flower/pull/2646), " +"[#2879](https://github.com/adap/flower/pull/2879), " +"[#2821](https://github.com/adap/flower/pull/2821), " +"[#2855](https://github.com/adap/flower/pull/2855), " +"[#2800](https://github.com/adap/flower/pull/2800), " +"[#2807](https://github.com/adap/flower/pull/2807), " +"[#2801](https://github.com/adap/flower/pull/2801), " +"[#2804](https://github.com/adap/flower/pull/2804), " +"[#2851](https://github.com/adap/flower/pull/2851), " +"[#2787](https://github.com/adap/flower/pull/2787), " +"[#2852](https://github.com/adap/flower/pull/2852), " +"[#2672](https://github.com/adap/flower/pull/2672), " +"[#2759](https://github.com/adap/flower/pull/2759))" msgstr "" -#: ../../source/ref-changelog.md:815 +#: ../../source/ref-changelog.md:240 msgid "" -"Improved error message in `NumPyClient` " -"([#851](https://github.com/adap/flower/pull/851))" +"**Deprecate** `start_numpy_client` " +"([#2563](https://github.com/adap/flower/pull/2563), " +"[#2718](https://github.com/adap/flower/pull/2718))" msgstr "" -#: ../../source/ref-changelog.md:816 +#: ../../source/ref-changelog.md:242 msgid "" -"Improved PyTorch quickstart code example " -"([#852](https://github.com/adap/flower/pull/852))" +"Until now, clients of type `NumPyClient` needed to be started via " +"`start_numpy_client`. In our efforts to consolidate framework APIs, we " +"have introduced changes, and now all client types should start via " +"`start_client`. To continue using `NumPyClient` clients, you simply need " +"to first call the `.to_client()` method and then pass returned `Client` " +"object to `start_client`. The examples and the documentation have been " +"updated accordingly." msgstr "" -#: ../../source/ref-changelog.md:820 +#: ../../source/ref-changelog.md:244 msgid "" -"**Disabled final distributed evaluation** " -"([#800](https://github.com/adap/flower/pull/800))" +"**Deprecate legacy DP wrappers** " +"([#2749](https://github.com/adap/flower/pull/2749))" msgstr "" -#: ../../source/ref-changelog.md:822 +#: ../../source/ref-changelog.md:246 msgid "" -"Prior behaviour was to perform a final round of distributed evaluation on" -" all connected clients, which is often not required (e.g., when using " -"server-side evaluation). The prior behaviour can be enabled by passing " -"`force_final_distributed_eval=True` to `start_server`." +"Legacy DP wrapper classes are deprecated, but still functional. This is " +"in preparation for an all-new pluggable version of differential privacy " +"support in Flower." msgstr "" -#: ../../source/ref-changelog.md:824 +#: ../../source/ref-changelog.md:248 msgid "" -"**Renamed q-FedAvg strategy** " -"([#802](https://github.com/adap/flower/pull/802))" +"**Make optional arg** `--callable` **in** `flower-client` **a required " +"positional arg** ([#2673](https://github.com/adap/flower/pull/2673))" msgstr "" -#: ../../source/ref-changelog.md:826 +#: ../../source/ref-changelog.md:250 msgid "" -"The strategy named `QffedAvg` was renamed to `QFedAvg` to better reflect " -"the notation given in the original paper (q-FFL is the optimization " -"objective, q-FedAvg is the proposed solver). Note the original (now " -"deprecated) `QffedAvg` class is still available for compatibility reasons" -" (it will be removed in a future release)." +"**Rename** `certificates` **to** `root_certificates` **in** `Driver` " +"([#2890](https://github.com/adap/flower/pull/2890))" msgstr "" -#: ../../source/ref-changelog.md:828 +#: ../../source/ref-changelog.md:252 msgid "" -"**Deprecated and renamed code example** `simulation_pytorch` **to** " -"`simulation_pytorch_legacy` " -"([#791](https://github.com/adap/flower/pull/791))" +"**Drop experimental** `Task` **fields** " +"([#2866](https://github.com/adap/flower/pull/2866), " +"[#2865](https://github.com/adap/flower/pull/2865))" msgstr "" -#: ../../source/ref-changelog.md:830 +#: ../../source/ref-changelog.md:254 msgid "" -"This example has been replaced by a new example. The new example is based" -" on the experimental virtual client engine, which will become the new " -"default way of doing most types of large-scale simulations in Flower. The" -" existing example was kept for reference purposes, but it might be " -"removed in the future." +"Experimental fields `sa`, `legacy_server_message` and " +"`legacy_client_message` were removed from `Task` message. The removed " +"fields are superseded by the new `RecordSet` abstraction." msgstr "" -#: ../../source/ref-changelog.md:832 -msgid "v0.16.0 (2021-05-11)" +#: ../../source/ref-changelog.md:256 +msgid "" +"**Retire MXNet examples** " +"([#2724](https://github.com/adap/flower/pull/2724))" msgstr "" -#: ../../source/ref-changelog.md:836 +#: ../../source/ref-changelog.md:258 msgid "" -"**New built-in strategies** " -"([#549](https://github.com/adap/flower/pull/549))" +"The development of the MXNet fremework has ended and the project is now " +"[archived on GitHub](https://github.com/apache/mxnet). Existing MXNet " +"examples won't receive updates." msgstr "" -#: ../../source/ref-changelog.md:838 -msgid "(abstract) FedOpt" +#: ../../source/ref-changelog.md:260 +msgid "v1.6.0 (2023-11-28)" msgstr "" -#: ../../source/ref-changelog.md:841 +#: ../../source/ref-changelog.md:266 msgid "" -"**Custom metrics for server and strategies** " -"([#717](https://github.com/adap/flower/pull/717))" +"`Aashish Kolluri`, `Adam Narozniak`, `Alessio Mora`, `Barathwaja S`, " +"`Charles Beauville`, `Daniel J. Beutel`, `Daniel Nata Nugraha`, `Gabriel " +"Mota`, `Heng Pan`, `Ivan Agarský`, `JS.KIM`, `Javier`, `Marius Schlegel`," +" `Navin Chandra`, `Nic Lane`, `Peterpan828`, `Qinbin Li`, `Shaz-hash`, " +"`Steve Laskaridis`, `Taner Topal`, `William Lindskog`, `Yan Gao`, " +"`cnxdeveloper`, `k3nfalt` " msgstr "" -#: ../../source/ref-changelog.md:843 +#: ../../source/ref-changelog.md:270 msgid "" -"The Flower server is now fully task-agnostic, all remaining instances of " -"task-specific metrics (such as `accuracy`) have been replaced by custom " -"metrics dictionaries. Flower 0.15 introduced the capability to pass a " -"dictionary containing custom metrics from client to server. As of this " -"release, custom metrics replace task-specific metrics on the server." +"**Add experimental support for Python 3.12** " +"([#2565](https://github.com/adap/flower/pull/2565))" msgstr "" -#: ../../source/ref-changelog.md:845 +#: ../../source/ref-changelog.md:272 msgid "" -"Custom metric dictionaries are now used in two user-facing APIs: they are" -" returned from Strategy methods `aggregate_fit`/`aggregate_evaluate` and " -"they enable evaluation functions passed to built-in strategies (via " -"`eval_fn`) to return more than two evaluation metrics. Strategies can " -"even return *aggregated* metrics dictionaries for the server to keep " -"track of." +"**Add new XGBoost examples** " +"([#2612](https://github.com/adap/flower/pull/2612), " +"[#2554](https://github.com/adap/flower/pull/2554), " +"[#2617](https://github.com/adap/flower/pull/2617), " +"[#2618](https://github.com/adap/flower/pull/2618), " +"[#2619](https://github.com/adap/flower/pull/2619), " +"[#2567](https://github.com/adap/flower/pull/2567))" msgstr "" -#: ../../source/ref-changelog.md:847 +#: ../../source/ref-changelog.md:274 msgid "" -"Strategy implementations should migrate their `aggregate_fit` and " -"`aggregate_evaluate` methods to the new return type (e.g., by simply " -"returning an empty `{}`), server-side evaluation functions should migrate" -" from `return loss, accuracy` to `return loss, {\"accuracy\": accuracy}`." +"We have added a new `xgboost-quickstart` example alongside a new " +"`xgboost-comprehensive` example that goes more in-depth." msgstr "" -#: ../../source/ref-changelog.md:849 +#: ../../source/ref-changelog.md:276 msgid "" -"Flower 0.15-style return types are deprecated (but still supported), " -"compatibility will be removed in a future release." +"**Add Vertical FL example** " +"([#2598](https://github.com/adap/flower/pull/2598))" msgstr "" -#: ../../source/ref-changelog.md:851 +#: ../../source/ref-changelog.md:278 msgid "" -"**Migration warnings for deprecated functionality** " -"([#690](https://github.com/adap/flower/pull/690))" +"We had many questions about Vertical Federated Learning using Flower, so " +"we decided to add an simple example for it on the [Titanic " +"dataset](https://www.kaggle.com/competitions/titanic/data) alongside a " +"tutorial (in the README)." msgstr "" -#: ../../source/ref-changelog.md:853 +#: ../../source/ref-changelog.md:280 msgid "" -"Earlier versions of Flower were often migrated to new APIs, while " -"maintaining compatibility with legacy APIs. This release introduces " -"detailed warning messages if usage of deprecated APIs is detected. The " -"new warning messages often provide details on how to migrate to more " -"recent APIs, thus easing the transition from one release to another." -msgstr "" - -#: ../../source/ref-changelog.md:855 -msgid "" -"Improved docs and docstrings " -"([#691](https://github.com/adap/flower/pull/691) " -"[#692](https://github.com/adap/flower/pull/692) " -"[#713](https://github.com/adap/flower/pull/713))" -msgstr "" - -#: ../../source/ref-changelog.md:857 -msgid "MXNet example and documentation" -msgstr "" - -#: ../../source/ref-changelog.md:859 -msgid "" -"FedBN implementation in example PyTorch: From Centralized To Federated " -"([#696](https://github.com/adap/flower/pull/696) " -"[#702](https://github.com/adap/flower/pull/702) " -"[#705](https://github.com/adap/flower/pull/705))" +"**Support custom** `ClientManager` **in** `start_driver()` " +"([#2292](https://github.com/adap/flower/pull/2292))" msgstr "" -#: ../../source/ref-changelog.md:863 +#: ../../source/ref-changelog.md:282 msgid "" -"**Serialization-agnostic server** " -"([#721](https://github.com/adap/flower/pull/721))" +"**Update REST API to support create and delete nodes** " +"([#2283](https://github.com/adap/flower/pull/2283))" msgstr "" -#: ../../source/ref-changelog.md:865 +#: ../../source/ref-changelog.md:284 msgid "" -"The Flower server is now fully serialization-agnostic. Prior usage of " -"class `Weights` (which represents parameters as deserialized NumPy " -"ndarrays) was replaced by class `Parameters` (e.g., in `Strategy`). " -"`Parameters` objects are fully serialization-agnostic and represents " -"parameters as byte arrays, the `tensor_type` attributes indicates how " -"these byte arrays should be interpreted (e.g., for " -"serialization/deserialization)." +"**Update the Android SDK** " +"([#2187](https://github.com/adap/flower/pull/2187))" msgstr "" -#: ../../source/ref-changelog.md:867 -msgid "" -"Built-in strategies implement this approach by handling serialization and" -" deserialization to/from `Weights` internally. Custom/3rd-party Strategy " -"implementations should update to the slightly changed Strategy method " -"definitions. Strategy authors can consult PR " -"[#721](https://github.com/adap/flower/pull/721) to see how strategies can" -" easily migrate to the new format." +#: ../../source/ref-changelog.md:286 +msgid "Add gRPC request-response capability to the Android SDK." msgstr "" -#: ../../source/ref-changelog.md:869 +#: ../../source/ref-changelog.md:288 msgid "" -"Deprecated `flwr.server.Server.evaluate`, use " -"`flwr.server.Server.evaluate_round` instead " -"([#717](https://github.com/adap/flower/pull/717))" +"**Update the C++ SDK** " +"([#2537](https://github.com/adap/flower/pull/2537), " +"[#2528](https://github.com/adap/flower/pull/2528), " +"[#2523](https://github.com/adap/flower/pull/2523), " +"[#2522](https://github.com/adap/flower/pull/2522))" msgstr "" -#: ../../source/ref-changelog.md:871 -msgid "v0.15.0 (2021-03-12)" +#: ../../source/ref-changelog.md:290 +msgid "Add gRPC request-response capability to the C++ SDK." msgstr "" -#: ../../source/ref-changelog.md:875 +#: ../../source/ref-changelog.md:292 msgid "" -"**Server-side parameter initialization** " -"([#658](https://github.com/adap/flower/pull/658))" +"**Make HTTPS the new default** " +"([#2591](https://github.com/adap/flower/pull/2591), " +"[#2636](https://github.com/adap/flower/pull/2636))" msgstr "" -#: ../../source/ref-changelog.md:877 +#: ../../source/ref-changelog.md:294 msgid "" -"Model parameters can now be initialized on the server-side. Server-side " -"parameter initialization works via a new `Strategy` method called " -"`initialize_parameters`." +"Flower is moving to HTTPS by default. The new `flower-server` requires " +"passing `--certificates`, but users can enable `--insecure` to use HTTP " +"for prototyping. The same applies to `flower-client`, which can either " +"use user-provided credentials or gRPC-bundled certificates to connect to " +"an HTTPS-enabled server or requires opt-out via passing `--insecure` to " +"enable insecure HTTP connections." msgstr "" -#: ../../source/ref-changelog.md:879 +#: ../../source/ref-changelog.md:296 msgid "" -"Built-in strategies support a new constructor argument called " -"`initial_parameters` to set the initial parameters. Built-in strategies " -"will provide these initial parameters to the server on startup and then " -"delete them to free the memory afterwards." +"For backward compatibility, `start_client()` and `start_numpy_client()` " +"will still start in insecure mode by default. In a future release, " +"insecure connections will require user opt-in by passing `insecure=True`." msgstr "" -#: ../../source/ref-changelog.md:898 +#: ../../source/ref-changelog.md:298 msgid "" -"If no initial parameters are provided to the strategy, the server will " -"continue to use the current behaviour (namely, it will ask one of the " -"connected clients for its parameters and use these as the initial global " -"parameters)." -msgstr "" - -#: ../../source/ref-changelog.md:900 -msgid "Deprecations" +"**Unify client API** ([#2303](https://github.com/adap/flower/pull/2303), " +"[#2390](https://github.com/adap/flower/pull/2390), " +"[#2493](https://github.com/adap/flower/pull/2493))" msgstr "" -#: ../../source/ref-changelog.md:902 +#: ../../source/ref-changelog.md:300 msgid "" -"Deprecate `flwr.server.strategy.DefaultStrategy` (migrate to " -"`flwr.server.strategy.FedAvg`, which is equivalent)" -msgstr "" - -#: ../../source/ref-changelog.md:904 -msgid "v0.14.0 (2021-02-18)" +"Using the `client_fn`, Flower clients can interchangeably run as " +"standalone processes (i.e. via `start_client`) or in simulation (i.e. via" +" `start_simulation`) without requiring changes to how the client class is" +" defined and instantiated. The `to_client()` function is introduced to " +"convert a `NumPyClient` to a `Client`." msgstr "" -#: ../../source/ref-changelog.md:908 +#: ../../source/ref-changelog.md:302 msgid "" -"**Generalized** `Client.fit` **and** `Client.evaluate` **return values** " -"([#610](https://github.com/adap/flower/pull/610) " -"[#572](https://github.com/adap/flower/pull/572) " -"[#633](https://github.com/adap/flower/pull/633))" +"**Add new** `Bulyan` **strategy** " +"([#1817](https://github.com/adap/flower/pull/1817), " +"[#1891](https://github.com/adap/flower/pull/1891))" msgstr "" -#: ../../source/ref-changelog.md:910 +#: ../../source/ref-changelog.md:304 msgid "" -"Clients can now return an additional dictionary mapping `str` keys to " -"values of the following types: `bool`, `bytes`, `float`, `int`, `str`. " -"This means one can return almost arbitrary values from `fit`/`evaluate` " -"and make use of them on the server side!" +"The new `Bulyan` strategy implements Bulyan by [El Mhamdi et al., " +"2018](https://arxiv.org/abs/1802.07927)" msgstr "" -#: ../../source/ref-changelog.md:912 +#: ../../source/ref-changelog.md:306 msgid "" -"This improvement also allowed for more consistent return types between " -"`fit` and `evaluate`: `evaluate` should now return a tuple `(float, int, " -"dict)` representing the loss, number of examples, and a dictionary " -"holding arbitrary problem-specific values like accuracy." +"**Add new** `XGB Bagging` **strategy** " +"([#2611](https://github.com/adap/flower/pull/2611))" msgstr "" -#: ../../source/ref-changelog.md:914 +#: ../../source/ref-changelog.md:308 ../../source/ref-changelog.md:310 msgid "" -"In case you wondered: this feature is compatible with existing projects, " -"the additional dictionary return value is optional. New code should " -"however migrate to the new return types to be compatible with upcoming " -"Flower releases (`fit`: `List[np.ndarray], int, Dict[str, Scalar]`, " -"`evaluate`: `float, int, Dict[str, Scalar]`). See the example below for " -"details." +"**Introduce `WorkloadState`** " +"([#2564](https://github.com/adap/flower/pull/2564), " +"[#2632](https://github.com/adap/flower/pull/2632))" msgstr "" -#: ../../source/ref-changelog.md:916 +#: ../../source/ref-changelog.md:314 msgid "" -"*Code example:* note the additional dictionary return values in both " -"`FlwrClient.fit` and `FlwrClient.evaluate`:" +"FedProx ([#2210](https://github.com/adap/flower/pull/2210), " +"[#2286](https://github.com/adap/flower/pull/2286), " +"[#2509](https://github.com/adap/flower/pull/2509))" msgstr "" -#: ../../source/ref-changelog.md:931 +#: ../../source/ref-changelog.md:316 msgid "" -"**Generalized** `config` **argument in** `Client.fit` **and** " -"`Client.evaluate` ([#595](https://github.com/adap/flower/pull/595))" +"Baselines Docs ([#2290](https://github.com/adap/flower/pull/2290), " +"[#2400](https://github.com/adap/flower/pull/2400))" msgstr "" -#: ../../source/ref-changelog.md:933 +#: ../../source/ref-changelog.md:318 msgid "" -"The `config` argument used to be of type `Dict[str, str]`, which means " -"that dictionary values were expected to be strings. The new release " -"generalizes this to enable values of the following types: `bool`, " -"`bytes`, `float`, `int`, `str`." +"FedMLB ([#2340](https://github.com/adap/flower/pull/2340), " +"[#2507](https://github.com/adap/flower/pull/2507))" msgstr "" -#: ../../source/ref-changelog.md:935 +#: ../../source/ref-changelog.md:320 msgid "" -"This means one can now pass almost arbitrary values to `fit`/`evaluate` " -"using the `config` dictionary. Yay, no more `str(epochs)` on the server-" -"side and `int(config[\"epochs\"])` on the client side!" +"TAMUNA ([#2254](https://github.com/adap/flower/pull/2254), " +"[#2508](https://github.com/adap/flower/pull/2508))" msgstr "" -#: ../../source/ref-changelog.md:937 -msgid "" -"*Code example:* note that the `config` dictionary now contains non-`str` " -"values in both `Client.fit` and `Client.evaluate`:" +#: ../../source/ref-changelog.md:322 +msgid "FedMeta [#2438](https://github.com/adap/flower/pull/2438)" msgstr "" -#: ../../source/ref-changelog.md:954 -msgid "v0.13.0 (2021-01-08)" +#: ../../source/ref-changelog.md:324 +msgid "FjORD [#2431](https://github.com/adap/flower/pull/2431)" msgstr "" -#: ../../source/ref-changelog.md:958 -msgid "" -"New example: PyTorch From Centralized To Federated " -"([#549](https://github.com/adap/flower/pull/549))" +#: ../../source/ref-changelog.md:326 +msgid "MOON [#2421](https://github.com/adap/flower/pull/2421)" msgstr "" -#: ../../source/ref-changelog.md:959 -msgid "Improved documentation" +#: ../../source/ref-changelog.md:328 +msgid "DepthFL [#2295](https://github.com/adap/flower/pull/2295)" msgstr "" -#: ../../source/ref-changelog.md:960 -msgid "New documentation theme ([#551](https://github.com/adap/flower/pull/551))" +#: ../../source/ref-changelog.md:330 +msgid "FedPer [#2266](https://github.com/adap/flower/pull/2266)" msgstr "" -#: ../../source/ref-changelog.md:961 -msgid "New API reference ([#554](https://github.com/adap/flower/pull/554))" +#: ../../source/ref-changelog.md:332 +msgid "FedWav2vec [#2551](https://github.com/adap/flower/pull/2551)" msgstr "" -#: ../../source/ref-changelog.md:962 -msgid "" -"Updated examples documentation " -"([#549](https://github.com/adap/flower/pull/549))" +#: ../../source/ref-changelog.md:334 +msgid "niid-Bench [#2428](https://github.com/adap/flower/pull/2428)" msgstr "" -#: ../../source/ref-changelog.md:963 +#: ../../source/ref-changelog.md:336 msgid "" -"Removed obsolete documentation " -"([#548](https://github.com/adap/flower/pull/548))" -msgstr "" - -#: ../../source/ref-changelog.md:965 -msgid "Bugfix:" +"FedBN ([#2608](https://github.com/adap/flower/pull/2608), " +"[#2615](https://github.com/adap/flower/pull/2615))" msgstr "" -#: ../../source/ref-changelog.md:967 +#: ../../source/ref-changelog.md:338 msgid "" -"`Server.fit` does not disconnect clients when finished, disconnecting the" -" clients is now handled in `flwr.server.start_server` " -"([#553](https://github.com/adap/flower/pull/553) " -"[#540](https://github.com/adap/flower/issues/540))." -msgstr "" - -#: ../../source/ref-changelog.md:969 -msgid "v0.12.0 (2020-12-07)" -msgstr "" - -#: ../../source/ref-changelog.md:971 ../../source/ref-changelog.md:987 -msgid "Important changes:" +"**General updates to Flower Examples** " +"([#2384](https://github.com/adap/flower/pull/2384), " +"[#2425](https://github.com/adap/flower/pull/2425), " +"[#2526](https://github.com/adap/flower/pull/2526), " +"[#2302](https://github.com/adap/flower/pull/2302), " +"[#2545](https://github.com/adap/flower/pull/2545))" msgstr "" -#: ../../source/ref-changelog.md:973 +#: ../../source/ref-changelog.md:340 msgid "" -"Added an example for embedded devices " -"([#507](https://github.com/adap/flower/pull/507))" +"**General updates to Flower Baselines** " +"([#2301](https://github.com/adap/flower/pull/2301), " +"[#2305](https://github.com/adap/flower/pull/2305), " +"[#2307](https://github.com/adap/flower/pull/2307), " +"[#2327](https://github.com/adap/flower/pull/2327), " +"[#2435](https://github.com/adap/flower/pull/2435), " +"[#2462](https://github.com/adap/flower/pull/2462), " +"[#2463](https://github.com/adap/flower/pull/2463), " +"[#2461](https://github.com/adap/flower/pull/2461), " +"[#2469](https://github.com/adap/flower/pull/2469), " +"[#2466](https://github.com/adap/flower/pull/2466), " +"[#2471](https://github.com/adap/flower/pull/2471), " +"[#2472](https://github.com/adap/flower/pull/2472), " +"[#2470](https://github.com/adap/flower/pull/2470))" msgstr "" -#: ../../source/ref-changelog.md:974 +#: ../../source/ref-changelog.md:342 msgid "" -"Added a new NumPyClient (in addition to the existing KerasClient) " -"([#504](https://github.com/adap/flower/pull/504) " -"[#508](https://github.com/adap/flower/pull/508))" +"**General updates to the simulation engine** " +"([#2331](https://github.com/adap/flower/pull/2331), " +"[#2447](https://github.com/adap/flower/pull/2447), " +"[#2448](https://github.com/adap/flower/pull/2448), " +"[#2294](https://github.com/adap/flower/pull/2294))" msgstr "" -#: ../../source/ref-changelog.md:975 +#: ../../source/ref-changelog.md:344 msgid "" -"Deprecated `flwr_example` package and started to migrate examples into " -"the top-level `examples` directory " -"([#494](https://github.com/adap/flower/pull/494) " -"[#512](https://github.com/adap/flower/pull/512))" +"**General updates to Flower SDKs** " +"([#2288](https://github.com/adap/flower/pull/2288), " +"[#2429](https://github.com/adap/flower/pull/2429), " +"[#2555](https://github.com/adap/flower/pull/2555), " +"[#2543](https://github.com/adap/flower/pull/2543), " +"[#2544](https://github.com/adap/flower/pull/2544), " +"[#2597](https://github.com/adap/flower/pull/2597), " +"[#2623](https://github.com/adap/flower/pull/2623))" msgstr "" -#: ../../source/ref-changelog.md:977 -msgid "v0.11.0 (2020-11-30)" +#: ../../source/ref-changelog.md:346 +msgid "" +"**General improvements** " +"([#2309](https://github.com/adap/flower/pull/2309), " +"[#2310](https://github.com/adap/flower/pull/2310), " +"[#2313](https://github.com/adap/flower/pull/2313), " +"[#2316](https://github.com/adap/flower/pull/2316), " +"[#2317](https://github.com/adap/flower/pull/2317), " +"[#2349](https://github.com/adap/flower/pull/2349), " +"[#2360](https://github.com/adap/flower/pull/2360), " +"[#2402](https://github.com/adap/flower/pull/2402), " +"[#2446](https://github.com/adap/flower/pull/2446), " +"[#2561](https://github.com/adap/flower/pull/2561), " +"[#2273](https://github.com/adap/flower/pull/2273), " +"[#2267](https://github.com/adap/flower/pull/2267), " +"[#2274](https://github.com/adap/flower/pull/2274), " +"[#2275](https://github.com/adap/flower/pull/2275), " +"[#2432](https://github.com/adap/flower/pull/2432), " +"[#2251](https://github.com/adap/flower/pull/2251), " +"[#2321](https://github.com/adap/flower/pull/2321), " +"[#1936](https://github.com/adap/flower/pull/1936), " +"[#2408](https://github.com/adap/flower/pull/2408), " +"[#2413](https://github.com/adap/flower/pull/2413), " +"[#2401](https://github.com/adap/flower/pull/2401), " +"[#2531](https://github.com/adap/flower/pull/2531), " +"[#2534](https://github.com/adap/flower/pull/2534), " +"[#2535](https://github.com/adap/flower/pull/2535), " +"[#2521](https://github.com/adap/flower/pull/2521), " +"[#2553](https://github.com/adap/flower/pull/2553), " +"[#2596](https://github.com/adap/flower/pull/2596))" msgstr "" -#: ../../source/ref-changelog.md:979 -msgid "Incompatible changes:" +#: ../../source/ref-changelog.md:348 ../../source/ref-changelog.md:438 +#: ../../source/ref-changelog.md:502 ../../source/ref-changelog.md:556 +#: ../../source/ref-changelog.md:623 +msgid "Flower received many improvements under the hood, too many to list here." msgstr "" -#: ../../source/ref-changelog.md:981 +#: ../../source/ref-changelog.md:352 msgid "" -"Renamed strategy methods " -"([#486](https://github.com/adap/flower/pull/486)) to unify the naming of " -"Flower's public APIs. Other public methods/functions (e.g., every method " -"in `Client`, but also `Strategy.evaluate`) do not use the `on_` prefix, " -"which is why we're removing it from the four methods in Strategy. To " -"migrate rename the following `Strategy` methods accordingly:" +"**Remove support for Python 3.7** " +"([#2280](https://github.com/adap/flower/pull/2280), " +"[#2299](https://github.com/adap/flower/pull/2299), " +"[#2304](https://github.com/adap/flower/pull/2304), " +"[#2306](https://github.com/adap/flower/pull/2306), " +"[#2355](https://github.com/adap/flower/pull/2355), " +"[#2356](https://github.com/adap/flower/pull/2356))" msgstr "" -#: ../../source/ref-changelog.md:982 -msgid "`on_configure_evaluate` => `configure_evaluate`" +#: ../../source/ref-changelog.md:354 +msgid "" +"Python 3.7 support was deprecated in Flower 1.5, and this release removes" +" support. Flower now requires Python 3.8." msgstr "" -#: ../../source/ref-changelog.md:983 -msgid "`on_aggregate_evaluate` => `aggregate_evaluate`" +#: ../../source/ref-changelog.md:356 +msgid "" +"**Remove experimental argument** `rest` **from** `start_client` " +"([#2324](https://github.com/adap/flower/pull/2324))" msgstr "" -#: ../../source/ref-changelog.md:984 -msgid "`on_configure_fit` => `configure_fit`" +#: ../../source/ref-changelog.md:358 +msgid "" +"The (still experimental) argument `rest` was removed from `start_client` " +"and `start_numpy_client`. Use `transport=\"rest\"` to opt into the " +"experimental REST API instead." msgstr "" -#: ../../source/ref-changelog.md:985 -msgid "`on_aggregate_fit` => `aggregate_fit`" +#: ../../source/ref-changelog.md:360 +msgid "v1.5.0 (2023-08-31)" msgstr "" -#: ../../source/ref-changelog.md:989 +#: ../../source/ref-changelog.md:366 msgid "" -"Deprecated `DefaultStrategy` " -"([#479](https://github.com/adap/flower/pull/479)). To migrate use " -"`FedAvg` instead." +"`Adam Narozniak`, `Anass Anhari`, `Charles Beauville`, `Dana-Farber`, " +"`Daniel J. Beutel`, `Daniel Nata Nugraha`, `Edoardo Gabrielli`, `Gustavo " +"Bertoli`, `Heng Pan`, `Javier`, `Mahdi`, `Steven Hé (Sīchàng)`, `Taner " +"Topal`, `achiverram28`, `danielnugraha`, `eunchung`, `ruthgal` " msgstr "" -#: ../../source/ref-changelog.md:990 +#: ../../source/ref-changelog.md:370 msgid "" -"Simplified examples and baselines " -"([#484](https://github.com/adap/flower/pull/484))." +"**Introduce new simulation engine** " +"([#1969](https://github.com/adap/flower/pull/1969), " +"[#2221](https://github.com/adap/flower/pull/2221), " +"[#2248](https://github.com/adap/flower/pull/2248))" msgstr "" -#: ../../source/ref-changelog.md:991 +#: ../../source/ref-changelog.md:372 msgid "" -"Removed presently unused `on_conclude_round` from strategy interface " -"([#483](https://github.com/adap/flower/pull/483))." +"The new simulation engine has been rewritten from the ground up, yet it " +"remains fully backwards compatible. It offers much improved stability and" +" memory handling, especially when working with GPUs. Simulations " +"transparently adapt to different settings to scale simulation in CPU-" +"only, CPU+GPU, multi-GPU, or multi-node multi-GPU environments." msgstr "" -#: ../../source/ref-changelog.md:992 +#: ../../source/ref-changelog.md:374 msgid "" -"Set minimal Python version to 3.6.1 instead of 3.6.9 " -"([#471](https://github.com/adap/flower/pull/471))." +"Comprehensive documentation includes a new [how-to run " +"simulations](https://flower.ai/docs/framework/how-to-run-" +"simulations.html) guide, new [simulation-" +"pytorch](https://flower.ai/docs/examples/simulation-pytorch.html) and " +"[simulation-tensorflow](https://flower.ai/docs/examples/simulation-" +"tensorflow.html) notebooks, and a new [YouTube tutorial " +"series](https://www.youtube.com/watch?v=cRebUIGB5RU&list=PLNG4feLHqCWlnj8a_E1A_n5zr2-8pafTB)." msgstr "" -#: ../../source/ref-changelog.md:993 +#: ../../source/ref-changelog.md:376 msgid "" -"Improved `Strategy` docstrings " -"([#470](https://github.com/adap/flower/pull/470))." +"**Restructure Flower Docs** " +"([#1824](https://github.com/adap/flower/pull/1824), " +"[#1865](https://github.com/adap/flower/pull/1865), " +"[#1884](https://github.com/adap/flower/pull/1884), " +"[#1887](https://github.com/adap/flower/pull/1887), " +"[#1919](https://github.com/adap/flower/pull/1919), " +"[#1922](https://github.com/adap/flower/pull/1922), " +"[#1920](https://github.com/adap/flower/pull/1920), " +"[#1923](https://github.com/adap/flower/pull/1923), " +"[#1924](https://github.com/adap/flower/pull/1924), " +"[#1962](https://github.com/adap/flower/pull/1962), " +"[#2006](https://github.com/adap/flower/pull/2006), " +"[#2133](https://github.com/adap/flower/pull/2133), " +"[#2203](https://github.com/adap/flower/pull/2203), " +"[#2215](https://github.com/adap/flower/pull/2215), " +"[#2122](https://github.com/adap/flower/pull/2122), " +"[#2223](https://github.com/adap/flower/pull/2223), " +"[#2219](https://github.com/adap/flower/pull/2219), " +"[#2232](https://github.com/adap/flower/pull/2232), " +"[#2233](https://github.com/adap/flower/pull/2233), " +"[#2234](https://github.com/adap/flower/pull/2234), " +"[#2235](https://github.com/adap/flower/pull/2235), " +"[#2237](https://github.com/adap/flower/pull/2237), " +"[#2238](https://github.com/adap/flower/pull/2238), " +"[#2242](https://github.com/adap/flower/pull/2242), " +"[#2231](https://github.com/adap/flower/pull/2231), " +"[#2243](https://github.com/adap/flower/pull/2243), " +"[#2227](https://github.com/adap/flower/pull/2227))" msgstr "" -#: ../../source/ref-example-projects.rst:2 -msgid "Example projects" +#: ../../source/ref-changelog.md:378 +msgid "" +"Much effort went into a completely restructured Flower docs experience. " +"The documentation on [flower.ai/docs](https://flower.ai/docs) is now " +"divided into Flower Framework, Flower Baselines, Flower Android SDK, " +"Flower iOS SDK, and code example projects." msgstr "" -#: ../../source/ref-example-projects.rst:4 +#: ../../source/ref-changelog.md:380 msgid "" -"Flower comes with a number of usage examples. The examples demonstrate " -"how Flower can be used to federate different kinds of existing machine " -"learning pipelines, usually leveraging popular machine learning " -"frameworks such as `PyTorch `_ or `TensorFlow " -"`_." +"**Introduce Flower Swift SDK** " +"([#1858](https://github.com/adap/flower/pull/1858), " +"[#1897](https://github.com/adap/flower/pull/1897))" msgstr "" -#: ../../source/ref-example-projects.rst:10 +#: ../../source/ref-changelog.md:382 msgid "" -"The following examples are available as standalone projects. Quickstart " -"TensorFlow/Keras ---------------------------" +"This is the first preview release of the Flower Swift SDK. Flower support" +" on iOS is improving, and alongside the Swift SDK and code example, there" +" is now also an iOS quickstart tutorial." msgstr "" -#: ../../source/ref-example-projects.rst:14 +#: ../../source/ref-changelog.md:384 msgid "" -"The TensorFlow/Keras quickstart example shows CIFAR-10 image " -"classification with MobileNetV2:" +"**Introduce Flower Android SDK** " +"([#2131](https://github.com/adap/flower/pull/2131))" msgstr "" -#: ../../source/ref-example-projects.rst:17 +#: ../../source/ref-changelog.md:386 msgid "" -"`Quickstart TensorFlow (Code) " -"`_" +"This is the first preview release of the Flower Kotlin SDK. Flower " +"support on Android is improving, and alongside the Kotlin SDK and code " +"example, there is now also an Android quickstart tutorial." msgstr "" -#: ../../source/ref-example-projects.rst:18 -msgid ":doc:`Quickstart TensorFlow (Tutorial) `" +#: ../../source/ref-changelog.md:388 +msgid "" +"**Introduce new end-to-end testing infrastructure** " +"([#1842](https://github.com/adap/flower/pull/1842), " +"[#2071](https://github.com/adap/flower/pull/2071), " +"[#2072](https://github.com/adap/flower/pull/2072), " +"[#2068](https://github.com/adap/flower/pull/2068), " +"[#2067](https://github.com/adap/flower/pull/2067), " +"[#2069](https://github.com/adap/flower/pull/2069), " +"[#2073](https://github.com/adap/flower/pull/2073), " +"[#2070](https://github.com/adap/flower/pull/2070), " +"[#2074](https://github.com/adap/flower/pull/2074), " +"[#2082](https://github.com/adap/flower/pull/2082), " +"[#2084](https://github.com/adap/flower/pull/2084), " +"[#2093](https://github.com/adap/flower/pull/2093), " +"[#2109](https://github.com/adap/flower/pull/2109), " +"[#2095](https://github.com/adap/flower/pull/2095), " +"[#2140](https://github.com/adap/flower/pull/2140), " +"[#2137](https://github.com/adap/flower/pull/2137), " +"[#2165](https://github.com/adap/flower/pull/2165))" msgstr "" -#: ../../source/ref-example-projects.rst:19 +#: ../../source/ref-changelog.md:390 msgid "" -"`Quickstart TensorFlow (Blog Post) `_" +"A new testing infrastructure ensures that new changes stay compatible " +"with existing framework integrations or strategies." msgstr "" -#: ../../source/ref-example-projects.rst:23 -#: ../../source/tutorial-quickstart-pytorch.rst:5 -msgid "Quickstart PyTorch" +#: ../../source/ref-changelog.md:392 +msgid "**Deprecate Python 3.7**" msgstr "" -#: ../../source/ref-example-projects.rst:25 +#: ../../source/ref-changelog.md:394 msgid "" -"The PyTorch quickstart example shows CIFAR-10 image classification with a" -" simple Convolutional Neural Network:" +"Since Python 3.7 reached its end of life (EOL) on 2023-06-27, support for" +" Python 3.7 is now deprecated and will be removed in an upcoming release." msgstr "" -#: ../../source/ref-example-projects.rst:28 +#: ../../source/ref-changelog.md:396 msgid "" -"`Quickstart PyTorch (Code) " -"`_" -msgstr "" - -#: ../../source/ref-example-projects.rst:29 -msgid ":doc:`Quickstart PyTorch (Tutorial) `" +"**Add new** `FedTrimmedAvg` **strategy** " +"([#1769](https://github.com/adap/flower/pull/1769), " +"[#1853](https://github.com/adap/flower/pull/1853))" msgstr "" -#: ../../source/ref-example-projects.rst:33 -msgid "PyTorch: From Centralized To Federated" +#: ../../source/ref-changelog.md:398 +msgid "" +"The new `FedTrimmedAvg` strategy implements Trimmed Mean by [Dong Yin, " +"2018](https://arxiv.org/abs/1803.01498)." msgstr "" -#: ../../source/ref-example-projects.rst:35 +#: ../../source/ref-changelog.md:400 msgid "" -"This example shows how a regular PyTorch project can be federated using " -"Flower:" +"**Introduce start_driver** " +"([#1697](https://github.com/adap/flower/pull/1697))" msgstr "" -#: ../../source/ref-example-projects.rst:37 +#: ../../source/ref-changelog.md:402 msgid "" -"`PyTorch: From Centralized To Federated (Code) " -"`_" +"In addition to `start_server` and using the raw Driver API, there is a " +"new `start_driver` function that allows for running `start_server` " +"scripts as a Flower driver with only a single-line code change. Check out" +" the `mt-pytorch` code example to see a working example using " +"`start_driver`." msgstr "" -#: ../../source/ref-example-projects.rst:38 +#: ../../source/ref-changelog.md:404 msgid "" -":doc:`PyTorch: From Centralized To Federated (Tutorial) `" +"**Add parameter aggregation to** `mt-pytorch` **code example** " +"([#1785](https://github.com/adap/flower/pull/1785))" msgstr "" -#: ../../source/ref-example-projects.rst:42 -msgid "Federated Learning on Raspberry Pi and Nvidia Jetson" +#: ../../source/ref-changelog.md:406 +msgid "" +"The `mt-pytorch` example shows how to aggregate parameters when writing a" +" driver script. The included `driver.py` and `server.py` have been " +"aligned to demonstrate both the low-level way and the high-level way of " +"building server-side logic." msgstr "" -#: ../../source/ref-example-projects.rst:44 +#: ../../source/ref-changelog.md:408 msgid "" -"This example shows how Flower can be used to build a federated learning " -"system that run across Raspberry Pi and Nvidia Jetson:" +"**Migrate experimental REST API to Starlette** " +"([2171](https://github.com/adap/flower/pull/2171))" msgstr "" -#: ../../source/ref-example-projects.rst:46 +#: ../../source/ref-changelog.md:410 msgid "" -"`Federated Learning on Raspberry Pi and Nvidia Jetson (Code) " -"`_" +"The (experimental) REST API used to be implemented in " +"[FastAPI](https://fastapi.tiangolo.com/), but it has now been migrated to" +" use [Starlette](https://www.starlette.io/) directly." msgstr "" -#: ../../source/ref-example-projects.rst:47 +#: ../../source/ref-changelog.md:412 msgid "" -"`Federated Learning on Raspberry Pi and Nvidia Jetson (Blog Post) " -"`_" +"Please note: The REST request-response API is still experimental and will" +" likely change significantly over time." msgstr "" -#: ../../source/ref-faq.rst:4 +#: ../../source/ref-changelog.md:414 msgid "" -"This page collects answers to commonly asked questions about Federated " -"Learning with Flower." +"**Introduce experimental gRPC request-response API** " +"([#1867](https://github.com/adap/flower/pull/1867), " +"[#1901](https://github.com/adap/flower/pull/1901))" msgstr "" -#: ../../source/ref-faq.rst -msgid ":fa:`eye,mr-1` Can Flower run on Jupyter Notebooks / Google Colab?" +#: ../../source/ref-changelog.md:416 +msgid "" +"In addition to the existing gRPC API (based on bidirectional streaming) " +"and the experimental REST API, there is now a new gRPC API that uses a " +"request-response model to communicate with client nodes." msgstr "" -#: ../../source/ref-faq.rst:8 +#: ../../source/ref-changelog.md:418 msgid "" -"Yes, it can! Flower even comes with a few under-the-hood optimizations to" -" make it work even better on Colab. Here's a quickstart example:" +"Please note: The gRPC request-response API is still experimental and will" +" likely change significantly over time." msgstr "" -#: ../../source/ref-faq.rst:10 +#: ../../source/ref-changelog.md:420 msgid "" -"`Flower simulation PyTorch " -"`_" +"**Replace the experimental** `start_client(rest=True)` **with the new** " +"`start_client(transport=\"rest\")` " +"([#1880](https://github.com/adap/flower/pull/1880))" msgstr "" -#: ../../source/ref-faq.rst:11 +#: ../../source/ref-changelog.md:422 msgid "" -"`Flower simulation TensorFlow/Keras " -"`_" +"The (experimental) `start_client` argument `rest` was deprecated in " +"favour of a new argument `transport`. `start_client(transport=\"rest\")` " +"will yield the same behaviour as `start_client(rest=True)` did before. " +"All code should migrate to the new argument `transport`. The deprecated " +"argument `rest` will be removed in a future release." msgstr "" -#: ../../source/ref-faq.rst -msgid ":fa:`eye,mr-1` How can I run Federated Learning on a Raspberry Pi?" +#: ../../source/ref-changelog.md:424 +msgid "" +"**Add a new gRPC option** " +"([#2197](https://github.com/adap/flower/pull/2197))" msgstr "" -#: ../../source/ref-faq.rst:15 +#: ../../source/ref-changelog.md:426 msgid "" -"Find the `blog post about federated learning on embedded device here " -"`_" -" and the corresponding `GitHub code example " -"`_." +"We now start a gRPC server with the `grpc.keepalive_permit_without_calls`" +" option set to 0 by default. This prevents the clients from sending " +"keepalive pings when there is no outstanding stream." msgstr "" -#: ../../source/ref-faq.rst -msgid ":fa:`eye,mr-1` Does Flower support federated learning on Android devices?" +#: ../../source/ref-changelog.md:428 +msgid "" +"**Improve example notebooks** " +"([#2005](https://github.com/adap/flower/pull/2005))" msgstr "" -#: ../../source/ref-faq.rst:19 +#: ../../source/ref-changelog.md:430 +msgid "There's a new 30min Federated Learning PyTorch tutorial!" +msgstr "" + +#: ../../source/ref-changelog.md:432 msgid "" -"Yes, it does. Please take a look at our `blog post " -"`_ or check out the code examples:" +"**Example updates** ([#1772](https://github.com/adap/flower/pull/1772), " +"[#1873](https://github.com/adap/flower/pull/1873), " +"[#1981](https://github.com/adap/flower/pull/1981), " +"[#1988](https://github.com/adap/flower/pull/1988), " +"[#1984](https://github.com/adap/flower/pull/1984), " +"[#1982](https://github.com/adap/flower/pull/1982), " +"[#2112](https://github.com/adap/flower/pull/2112), " +"[#2144](https://github.com/adap/flower/pull/2144), " +"[#2174](https://github.com/adap/flower/pull/2174), " +"[#2225](https://github.com/adap/flower/pull/2225), " +"[#2183](https://github.com/adap/flower/pull/2183))" msgstr "" -#: ../../source/ref-faq.rst:21 +#: ../../source/ref-changelog.md:434 msgid "" -"`Android Kotlin example `_" +"Many examples have received significant updates, including simplified " +"advanced-tensorflow and advanced-pytorch examples, improved macOS " +"compatibility of TensorFlow examples, and code examples for simulation. A" +" major upgrade is that all code examples now have a `requirements.txt` " +"(in addition to `pyproject.toml`)." msgstr "" -#: ../../source/ref-faq.rst:22 -msgid "`Android Java example `_" +#: ../../source/ref-changelog.md:436 +msgid "" +"**General improvements** " +"([#1872](https://github.com/adap/flower/pull/1872), " +"[#1866](https://github.com/adap/flower/pull/1866), " +"[#1884](https://github.com/adap/flower/pull/1884), " +"[#1837](https://github.com/adap/flower/pull/1837), " +"[#1477](https://github.com/adap/flower/pull/1477), " +"[#2171](https://github.com/adap/flower/pull/2171))" msgstr "" -#: ../../source/ref-faq.rst -msgid ":fa:`eye,mr-1` Can I combine federated learning with blockchain?" +#: ../../source/ref-changelog.md:444 +msgid "v1.4.0 (2023-04-21)" msgstr "" -#: ../../source/ref-faq.rst:26 +#: ../../source/ref-changelog.md:450 msgid "" -"Yes, of course. A list of available examples using Flower within a " -"blockchain environment is available here:" +"`Adam Narozniak`, `Alexander Viala Bellander`, `Charles Beauville`, " +"`Chenyang Ma (Danny)`, `Daniel J. Beutel`, `Edoardo`, `Gautam Jajoo`, " +"`Iacob-Alexandru-Andrei`, `JDRanpariya`, `Jean Charle Yaacoub`, `Kunal " +"Sarkhel`, `L. Jiang`, `Lennart Behme`, `Max Kapsecker`, `Michał`, `Nic " +"Lane`, `Nikolaos Episkopos`, `Ragy`, `Saurav Maheshkar`, `Semo Yang`, " +"`Steve Laskaridis`, `Steven Hé (Sīchàng)`, `Taner Topal`" msgstr "" -#: ../../source/ref-faq.rst:28 +#: ../../source/ref-changelog.md:454 msgid "" -"`Flower meets Nevermined GitHub Repository `_." +"**Introduce support for XGBoost (**`FedXgbNnAvg` **strategy and " +"example)** ([#1694](https://github.com/adap/flower/pull/1694), " +"[#1709](https://github.com/adap/flower/pull/1709), " +"[#1715](https://github.com/adap/flower/pull/1715), " +"[#1717](https://github.com/adap/flower/pull/1717), " +"[#1763](https://github.com/adap/flower/pull/1763), " +"[#1795](https://github.com/adap/flower/pull/1795))" msgstr "" -#: ../../source/ref-faq.rst:29 +#: ../../source/ref-changelog.md:456 msgid "" -"`Flower meets Nevermined YouTube video " -"`_." +"XGBoost is a tree-based ensemble machine learning algorithm that uses " +"gradient boosting to improve model accuracy. We added a new `FedXgbNnAvg`" +" " +"[strategy](https://github.com/adap/flower/tree/main/src/py/flwr/server/strategy/fedxgb_nn_avg.py)," +" and a [code example](https://github.com/adap/flower/tree/main/examples" +"/xgboost-quickstart) that demonstrates the usage of this new strategy in " +"an XGBoost project." msgstr "" -#: ../../source/ref-faq.rst:30 +#: ../../source/ref-changelog.md:458 msgid "" -"`Flower meets KOSMoS `_." +"**Introduce iOS SDK (preview)** " +"([#1621](https://github.com/adap/flower/pull/1621), " +"[#1764](https://github.com/adap/flower/pull/1764))" msgstr "" -#: ../../source/ref-faq.rst:31 +#: ../../source/ref-changelog.md:460 msgid "" -"`Flower meets Talan blog post `_ ." +"This is a major update for anyone wanting to implement Federated Learning" +" on iOS mobile devices. We now have a swift iOS SDK present under " +"[src/swift/flwr](https://github.com/adap/flower/tree/main/src/swift/flwr)" +" that will facilitate greatly the app creating process. To showcase its " +"use, the [iOS " +"example](https://github.com/adap/flower/tree/main/examples/ios) has also " +"been updated!" msgstr "" -#: ../../source/ref-faq.rst:32 +#: ../../source/ref-changelog.md:462 msgid "" -"`Flower meets Talan GitHub Repository " -"`_ ." +"**Introduce new \"What is Federated Learning?\" tutorial** " +"([#1657](https://github.com/adap/flower/pull/1657), " +"[#1721](https://github.com/adap/flower/pull/1721))" msgstr "" -#: ../../source/ref-telemetry.md:1 -msgid "Telemetry" +#: ../../source/ref-changelog.md:464 +msgid "" +"A new [entry-level tutorial](https://flower.ai/docs/framework/tutorial-" +"what-is-federated-learning.html) in our documentation explains the basics" +" of Fedetated Learning. It enables anyone who's unfamiliar with Federated" +" Learning to start their journey with Flower. Forward it to anyone who's " +"interested in Federated Learning!" msgstr "" -#: ../../source/ref-telemetry.md:3 +#: ../../source/ref-changelog.md:466 msgid "" -"The Flower open-source project collects **anonymous** usage metrics to " -"make well-informed decisions to improve Flower. Doing this enables the " -"Flower team to understand how Flower is used and what challenges users " -"might face." +"**Introduce new Flower Baseline: FedProx MNIST** " +"([#1513](https://github.com/adap/flower/pull/1513), " +"[#1680](https://github.com/adap/flower/pull/1680), " +"[#1681](https://github.com/adap/flower/pull/1681), " +"[#1679](https://github.com/adap/flower/pull/1679))" msgstr "" -#: ../../source/ref-telemetry.md:5 +#: ../../source/ref-changelog.md:468 msgid "" -"**Flower is a friendly framework for collaborative AI and data science.**" -" Staying true to this statement, Flower makes it easy to disable " -"telemetry for users that do not want to share anonymous usage metrics." +"This new baseline replicates the MNIST+CNN task from the paper [Federated" +" Optimization in Heterogeneous Networks (Li et al., " +"2018)](https://arxiv.org/abs/1812.06127). It uses the `FedProx` strategy," +" which aims at making convergence more robust in heterogeneous settings." msgstr "" -#: ../../source/ref-telemetry.md:7 -msgid "Principles" +#: ../../source/ref-changelog.md:470 +msgid "" +"**Introduce new Flower Baseline: FedAvg FEMNIST** " +"([#1655](https://github.com/adap/flower/pull/1655))" msgstr "" -#: ../../source/ref-telemetry.md:9 -msgid "We follow strong principles guarding anonymous usage metrics collection:" +#: ../../source/ref-changelog.md:472 +msgid "" +"This new baseline replicates an experiment evaluating the performance of " +"the FedAvg algorithm on the FEMNIST dataset from the paper [LEAF: A " +"Benchmark for Federated Settings (Caldas et al., " +"2018)](https://arxiv.org/abs/1812.01097)." msgstr "" -#: ../../source/ref-telemetry.md:11 +#: ../../source/ref-changelog.md:474 msgid "" -"**Optional:** You will always be able to disable telemetry; read on to " -"learn “[How to opt-out](#how-to-opt-out)”." +"**Introduce (experimental) REST API** " +"([#1594](https://github.com/adap/flower/pull/1594), " +"[#1690](https://github.com/adap/flower/pull/1690), " +"[#1695](https://github.com/adap/flower/pull/1695), " +"[#1712](https://github.com/adap/flower/pull/1712), " +"[#1802](https://github.com/adap/flower/pull/1802), " +"[#1770](https://github.com/adap/flower/pull/1770), " +"[#1733](https://github.com/adap/flower/pull/1733))" msgstr "" -#: ../../source/ref-telemetry.md:12 +#: ../../source/ref-changelog.md:476 msgid "" -"**Anonymous:** The reported usage metrics are anonymous and do not " -"contain any personally identifiable information (PII). See “[Collected " -"metrics](#collected-metrics)” to understand what metrics are being " -"reported." +"A new REST API has been introduced as an alternative to the gRPC-based " +"communication stack. In this initial version, the REST API only supports " +"anonymous clients." msgstr "" -#: ../../source/ref-telemetry.md:13 +#: ../../source/ref-changelog.md:478 msgid "" -"**Transparent:** You can easily inspect what anonymous metrics are being " -"reported; see the section “[How to inspect what is being reported](#how-" -"to-inspect-what-is-being-reported)”" +"Please note: The REST API is still experimental and will likely change " +"significantly over time." msgstr "" -#: ../../source/ref-telemetry.md:14 +#: ../../source/ref-changelog.md:480 msgid "" -"**Open for feedback:** You can always reach out to us if you have " -"feedback; see the section “[How to contact us](#how-to-contact-us)” for " -"details." +"**Improve the (experimental) Driver API** " +"([#1663](https://github.com/adap/flower/pull/1663), " +"[#1666](https://github.com/adap/flower/pull/1666), " +"[#1667](https://github.com/adap/flower/pull/1667), " +"[#1664](https://github.com/adap/flower/pull/1664), " +"[#1675](https://github.com/adap/flower/pull/1675), " +"[#1676](https://github.com/adap/flower/pull/1676), " +"[#1693](https://github.com/adap/flower/pull/1693), " +"[#1662](https://github.com/adap/flower/pull/1662), " +"[#1794](https://github.com/adap/flower/pull/1794))" msgstr "" -#: ../../source/ref-telemetry.md:16 -msgid "How to opt-out" +#: ../../source/ref-changelog.md:482 +msgid "" +"The Driver API is still an experimental feature, but this release " +"introduces some major upgrades. One of the main improvements is the " +"introduction of an SQLite database to store server state on disk (instead" +" of in-memory). Another improvement is that tasks (instructions or " +"results) that have been delivered will now be deleted. This greatly " +"improves the memory efficiency of a long-running Flower server." msgstr "" -#: ../../source/ref-telemetry.md:18 +#: ../../source/ref-changelog.md:484 msgid "" -"When Flower starts, it will check for an environment variable called " -"`FLWR_TELEMETRY_ENABLED`. Telemetry can easily be disabled by setting " -"`FLWR_TELEMETRY_ENABLED=0`. Assuming you are starting a Flower server or " -"client, simply do so by prepending your command as in:" +"**Fix spilling issues related to Ray during simulations** " +"([#1698](https://github.com/adap/flower/pull/1698))" msgstr "" -#: ../../source/ref-telemetry.md:24 +#: ../../source/ref-changelog.md:486 msgid "" -"Alternatively, you can export `FLWR_TELEMETRY_ENABLED=0` in, for example," -" `.bashrc` (or whatever configuration file applies to your environment) " -"to disable Flower telemetry permanently." -msgstr "" - -#: ../../source/ref-telemetry.md:26 -msgid "Collected metrics" +"While running long simulations, `ray` was sometimes spilling huge amounts" +" of data that would make the training unable to continue. This is now " +"fixed! 🎉" msgstr "" -#: ../../source/ref-telemetry.md:28 -msgid "Flower telemetry collects the following metrics:" +#: ../../source/ref-changelog.md:488 +msgid "" +"**Add new example using** `TabNet` **and Flower** " +"([#1725](https://github.com/adap/flower/pull/1725))" msgstr "" -#: ../../source/ref-telemetry.md:30 +#: ../../source/ref-changelog.md:490 msgid "" -"**Flower version.** Understand which versions of Flower are currently " -"being used. This helps us to decide whether we should invest effort into " -"releasing a patch version for an older version of Flower or instead use " -"the bandwidth to build new features." +"TabNet is a powerful and flexible framework for training machine learning" +" models on tabular data. We now have a federated example using Flower: " +"[quickstart-tabnet](https://github.com/adap/flower/tree/main/examples" +"/quickstart-tabnet)." msgstr "" -#: ../../source/ref-telemetry.md:32 +#: ../../source/ref-changelog.md:492 msgid "" -"**Operating system.** Enables us to answer questions such as: *Should we " -"create more guides for Linux, macOS, or Windows?*" +"**Add new how-to guide for monitoring simulations** " +"([#1649](https://github.com/adap/flower/pull/1649))" msgstr "" -#: ../../source/ref-telemetry.md:34 +#: ../../source/ref-changelog.md:494 msgid "" -"**Python version.** Knowing the Python version helps us, for example, to " -"decide whether we should invest effort into supporting old versions of " -"Python or stop supporting them and start taking advantage of new Python " -"features." +"We now have a documentation guide to help users monitor their performance" +" during simulations." msgstr "" -#: ../../source/ref-telemetry.md:36 +#: ../../source/ref-changelog.md:496 msgid "" -"**Hardware properties.** Understanding the hardware environment that " -"Flower is being used in helps to decide whether we should, for example, " -"put more effort into supporting low-resource environments." +"**Add training metrics to** `History` **object during simulations** " +"([#1696](https://github.com/adap/flower/pull/1696))" msgstr "" -#: ../../source/ref-telemetry.md:38 +#: ../../source/ref-changelog.md:498 msgid "" -"**Execution mode.** Knowing what execution mode Flower starts in enables " -"us to understand how heavily certain features are being used and better " -"prioritize based on that." +"The `fit_metrics_aggregation_fn` can be used to aggregate training " +"metrics, but previous releases did not save the results in the `History` " +"object. This is now the case!" msgstr "" -#: ../../source/ref-telemetry.md:40 +#: ../../source/ref-changelog.md:500 msgid "" -"**Cluster.** Flower telemetry assigns a random in-memory cluster ID each " -"time a Flower workload starts. This allows us to understand which device " -"types not only start Flower workloads but also successfully complete " -"them." +"**General improvements** " +"([#1659](https://github.com/adap/flower/pull/1659), " +"[#1646](https://github.com/adap/flower/pull/1646), " +"[#1647](https://github.com/adap/flower/pull/1647), " +"[#1471](https://github.com/adap/flower/pull/1471), " +"[#1648](https://github.com/adap/flower/pull/1648), " +"[#1651](https://github.com/adap/flower/pull/1651), " +"[#1652](https://github.com/adap/flower/pull/1652), " +"[#1653](https://github.com/adap/flower/pull/1653), " +"[#1659](https://github.com/adap/flower/pull/1659), " +"[#1665](https://github.com/adap/flower/pull/1665), " +"[#1670](https://github.com/adap/flower/pull/1670), " +"[#1672](https://github.com/adap/flower/pull/1672), " +"[#1677](https://github.com/adap/flower/pull/1677), " +"[#1684](https://github.com/adap/flower/pull/1684), " +"[#1683](https://github.com/adap/flower/pull/1683), " +"[#1686](https://github.com/adap/flower/pull/1686), " +"[#1682](https://github.com/adap/flower/pull/1682), " +"[#1685](https://github.com/adap/flower/pull/1685), " +"[#1692](https://github.com/adap/flower/pull/1692), " +"[#1705](https://github.com/adap/flower/pull/1705), " +"[#1708](https://github.com/adap/flower/pull/1708), " +"[#1711](https://github.com/adap/flower/pull/1711), " +"[#1713](https://github.com/adap/flower/pull/1713), " +"[#1714](https://github.com/adap/flower/pull/1714), " +"[#1718](https://github.com/adap/flower/pull/1718), " +"[#1716](https://github.com/adap/flower/pull/1716), " +"[#1723](https://github.com/adap/flower/pull/1723), " +"[#1735](https://github.com/adap/flower/pull/1735), " +"[#1678](https://github.com/adap/flower/pull/1678), " +"[#1750](https://github.com/adap/flower/pull/1750), " +"[#1753](https://github.com/adap/flower/pull/1753), " +"[#1736](https://github.com/adap/flower/pull/1736), " +"[#1766](https://github.com/adap/flower/pull/1766), " +"[#1760](https://github.com/adap/flower/pull/1760), " +"[#1775](https://github.com/adap/flower/pull/1775), " +"[#1776](https://github.com/adap/flower/pull/1776), " +"[#1777](https://github.com/adap/flower/pull/1777), " +"[#1779](https://github.com/adap/flower/pull/1779), " +"[#1784](https://github.com/adap/flower/pull/1784), " +"[#1773](https://github.com/adap/flower/pull/1773), " +"[#1755](https://github.com/adap/flower/pull/1755), " +"[#1789](https://github.com/adap/flower/pull/1789), " +"[#1788](https://github.com/adap/flower/pull/1788), " +"[#1798](https://github.com/adap/flower/pull/1798), " +"[#1799](https://github.com/adap/flower/pull/1799), " +"[#1739](https://github.com/adap/flower/pull/1739), " +"[#1800](https://github.com/adap/flower/pull/1800), " +"[#1804](https://github.com/adap/flower/pull/1804), " +"[#1805](https://github.com/adap/flower/pull/1805))" msgstr "" -#: ../../source/ref-telemetry.md:42 -msgid "" -"**Source.** Flower telemetry tries to store a random source ID in " -"`~/.flwr/source` the first time a telemetry event is generated. The " -"source ID is important to identify whether an issue is recurring or " -"whether an issue is triggered by multiple clusters running concurrently " -"(which often happens in simulation). For example, if a device runs " -"multiple workloads at the same time, and this results in an issue, then, " -"in order to reproduce the issue, multiple workloads must be started at " -"the same time." +#: ../../source/ref-changelog.md:508 +msgid "v1.3.0 (2023-02-06)" msgstr "" -#: ../../source/ref-telemetry.md:44 +#: ../../source/ref-changelog.md:514 msgid "" -"You may delete the source ID at any time. If you wish for all events " -"logged under a specific source ID to be deleted, you can send a deletion " -"request mentioning the source ID to `telemetry@flower.ai`. All events " -"related to that source ID will then be permanently deleted." +"`Adam Narozniak`, `Alexander Viala Bellander`, `Charles Beauville`, " +"`Daniel J. Beutel`, `JDRanpariya`, `Lennart Behme`, `Taner Topal`" msgstr "" -#: ../../source/ref-telemetry.md:46 +#: ../../source/ref-changelog.md:518 msgid "" -"We will not collect any personally identifiable information. If you think" -" any of the metrics collected could be misused in any way, please [get in" -" touch with us](#how-to-contact-us). We will update this page to reflect " -"any changes to the metrics collected and publish changes in the " -"changelog." +"**Add support for** `workload_id` **and** `group_id` **in Driver API** " +"([#1595](https://github.com/adap/flower/pull/1595))" msgstr "" -#: ../../source/ref-telemetry.md:48 +#: ../../source/ref-changelog.md:520 msgid "" -"If you think other metrics would be helpful for us to better guide our " -"decisions, please let us know! We will carefully review them; if we are " -"confident that they do not compromise user privacy, we may add them." +"The (experimental) Driver API now supports a `workload_id` that can be " +"used to identify which workload a task belongs to. It also supports a new" +" `group_id` that can be used, for example, to indicate the current " +"training round. Both the `workload_id` and `group_id` enable client nodes" +" to decide whether they want to handle a task or not." msgstr "" -#: ../../source/ref-telemetry.md:50 -msgid "How to inspect what is being reported" +#: ../../source/ref-changelog.md:522 +msgid "" +"**Make Driver API and Fleet API address configurable** " +"([#1637](https://github.com/adap/flower/pull/1637))" msgstr "" -#: ../../source/ref-telemetry.md:52 +#: ../../source/ref-changelog.md:524 msgid "" -"We wanted to make it very easy for you to inspect what anonymous usage " -"metrics are reported. You can view all the reported telemetry information" -" by setting the environment variable `FLWR_TELEMETRY_LOGGING=1`. Logging " -"is disabled by default. You may use logging independently from " -"`FLWR_TELEMETRY_ENABLED` so that you can inspect the telemetry feature " -"without sending any metrics." +"The (experimental) long-running Flower server (Driver API and Fleet API) " +"can now configure the server address of both Driver API (via `--driver-" +"api-address`) and Fleet API (via `--fleet-api-address`) when starting:" msgstr "" -#: ../../source/ref-telemetry.md:58 +#: ../../source/ref-changelog.md:526 msgid "" -"The inspect Flower telemetry without sending any anonymous usage metrics," -" use both environment variables:" +"`flower-server --driver-api-address \"0.0.0.0:8081\" --fleet-api-address " +"\"0.0.0.0:8086\"`" msgstr "" -#: ../../source/ref-telemetry.md:64 -msgid "How to contact us" +#: ../../source/ref-changelog.md:528 +msgid "Both IPv4 and IPv6 addresses are supported." msgstr "" -#: ../../source/ref-telemetry.md:66 +#: ../../source/ref-changelog.md:530 msgid "" -"We want to hear from you. If you have any feedback or ideas on how to " -"improve the way we handle anonymous usage metrics, reach out to us via " -"[Slack](https://flower.ai/join-slack/) (channel `#telemetry`) or email " -"(`telemetry@flower.ai`)." +"**Add new example of Federated Learning using fastai and Flower** " +"([#1598](https://github.com/adap/flower/pull/1598))" msgstr "" -#: ../../source/tutorial-quickstart-android.rst:-1 +#: ../../source/ref-changelog.md:532 msgid "" -"Read this Federated Learning quickstart tutorial for creating an Android " -"app using Flower." -msgstr "" - -#: ../../source/tutorial-quickstart-android.rst:5 -msgid "Quickstart Android" +"A new code example (`quickstart-fastai`) demonstrates federated learning " +"with [fastai](https://www.fast.ai/) and Flower. You can find it here: " +"[quickstart-fastai](https://github.com/adap/flower/tree/main/examples" +"/quickstart-fastai)." msgstr "" -#: ../../source/tutorial-quickstart-android.rst:10 +#: ../../source/ref-changelog.md:534 msgid "" -"Let's build a federated learning system using TFLite and Flower on " -"Android!" +"**Make Android example compatible with** `flwr >= 1.0.0` **and the latest" +" versions of Android** " +"([#1603](https://github.com/adap/flower/pull/1603))" msgstr "" -#: ../../source/tutorial-quickstart-android.rst:12 +#: ../../source/ref-changelog.md:536 msgid "" -"Please refer to the `full code example " -"`_ to learn " -"more." +"The Android code example has received a substantial update: the project " +"is compatible with Flower 1.0 (and later), the UI received a full " +"refresh, and the project is updated to be compatible with newer Android " +"tooling." msgstr "" -#: ../../source/tutorial-quickstart-fastai.rst:-1 +#: ../../source/ref-changelog.md:538 msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with FastAI to train a vision model on CIFAR-10." -msgstr "" - -#: ../../source/tutorial-quickstart-fastai.rst:5 -msgid "Quickstart fastai" +"**Add new `FedProx` strategy** " +"([#1619](https://github.com/adap/flower/pull/1619))" msgstr "" -#: ../../source/tutorial-quickstart-fastai.rst:10 -msgid "Let's build a federated learning system using fastai and Flower!" +#: ../../source/ref-changelog.md:540 +msgid "" +"This " +"[strategy](https://github.com/adap/flower/blob/main/src/py/flwr/server/strategy/fedprox.py)" +" is almost identical to " +"[`FedAvg`](https://github.com/adap/flower/blob/main/src/py/flwr/server/strategy/fedavg.py)," +" but helps users replicate what is described in this " +"[paper](https://arxiv.org/abs/1812.06127). It essentially adds a " +"parameter called `proximal_mu` to regularize the local models with " +"respect to the global models." msgstr "" -#: ../../source/tutorial-quickstart-fastai.rst:12 +#: ../../source/ref-changelog.md:542 msgid "" -"Please refer to the `full code example " -"`_ " -"to learn more." +"**Add new metrics to telemetry events** " +"([#1640](https://github.com/adap/flower/pull/1640))" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:-1 +#: ../../source/ref-changelog.md:544 msgid "" -"Check out this Federating Learning quickstart tutorial for using Flower " -"with HuggingFace Transformers in order to fine-tune an LLM." +"An updated event structure allows, for example, the clustering of events " +"within the same workload." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:5 -msgid "Quickstart 🤗 Transformers" +#: ../../source/ref-changelog.md:546 +msgid "" +"**Add new custom strategy tutorial section** " +"[#1623](https://github.com/adap/flower/pull/1623)" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:10 +#: ../../source/ref-changelog.md:548 msgid "" -"Let's build a federated learning system using Hugging Face Transformers " -"and Flower!" +"The Flower tutorial now has a new section that covers implementing a " +"custom strategy from scratch: [Open in " +"Colab](https://colab.research.google.com/github/adap/flower/blob/main/doc/source" +"/tutorial-build-a-strategy-from-scratch-pytorch.ipynb)" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:12 +#: ../../source/ref-changelog.md:550 msgid "" -"We will leverage Hugging Face to federate the training of language models" -" over multiple clients using Flower. More specifically, we will fine-tune" -" a pre-trained Transformer model (distilBERT) for sequence classification" -" over a dataset of IMDB ratings. The end goal is to detect if a movie " -"rating is positive or negative." +"**Add new custom serialization tutorial section** " +"([#1622](https://github.com/adap/flower/pull/1622))" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:18 -msgid "Dependencies" +#: ../../source/ref-changelog.md:552 +msgid "" +"The Flower tutorial now has a new section that covers custom " +"serialization: [Open in " +"Colab](https://colab.research.google.com/github/adap/flower/blob/main/doc/source" +"/tutorial-customize-the-client-pytorch.ipynb)" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:20 +#: ../../source/ref-changelog.md:554 msgid "" -"To follow along this tutorial you will need to install the following " -"packages: :code:`datasets`, :code:`evaluate`, :code:`flwr`, " -":code:`torch`, and :code:`transformers`. This can be done using " -":code:`pip`:" -msgstr "" - -#: ../../source/tutorial-quickstart-huggingface.rst:30 -msgid "Standard Hugging Face workflow" +"**General improvements** " +"([#1638](https://github.com/adap/flower/pull/1638), " +"[#1634](https://github.com/adap/flower/pull/1634), " +"[#1636](https://github.com/adap/flower/pull/1636), " +"[#1635](https://github.com/adap/flower/pull/1635), " +"[#1633](https://github.com/adap/flower/pull/1633), " +"[#1632](https://github.com/adap/flower/pull/1632), " +"[#1631](https://github.com/adap/flower/pull/1631), " +"[#1630](https://github.com/adap/flower/pull/1630), " +"[#1627](https://github.com/adap/flower/pull/1627), " +"[#1593](https://github.com/adap/flower/pull/1593), " +"[#1616](https://github.com/adap/flower/pull/1616), " +"[#1615](https://github.com/adap/flower/pull/1615), " +"[#1607](https://github.com/adap/flower/pull/1607), " +"[#1609](https://github.com/adap/flower/pull/1609), " +"[#1608](https://github.com/adap/flower/pull/1608), " +"[#1603](https://github.com/adap/flower/pull/1603), " +"[#1590](https://github.com/adap/flower/pull/1590), " +"[#1580](https://github.com/adap/flower/pull/1580), " +"[#1599](https://github.com/adap/flower/pull/1599), " +"[#1600](https://github.com/adap/flower/pull/1600), " +"[#1601](https://github.com/adap/flower/pull/1601), " +"[#1597](https://github.com/adap/flower/pull/1597), " +"[#1595](https://github.com/adap/flower/pull/1595), " +"[#1591](https://github.com/adap/flower/pull/1591), " +"[#1588](https://github.com/adap/flower/pull/1588), " +"[#1589](https://github.com/adap/flower/pull/1589), " +"[#1587](https://github.com/adap/flower/pull/1587), " +"[#1573](https://github.com/adap/flower/pull/1573), " +"[#1581](https://github.com/adap/flower/pull/1581), " +"[#1578](https://github.com/adap/flower/pull/1578), " +"[#1574](https://github.com/adap/flower/pull/1574), " +"[#1572](https://github.com/adap/flower/pull/1572), " +"[#1586](https://github.com/adap/flower/pull/1586))" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:33 -msgid "Handling the data" +#: ../../source/ref-changelog.md:558 +msgid "" +"**Updated documentation** " +"([#1629](https://github.com/adap/flower/pull/1629), " +"[#1628](https://github.com/adap/flower/pull/1628), " +"[#1620](https://github.com/adap/flower/pull/1620), " +"[#1618](https://github.com/adap/flower/pull/1618), " +"[#1617](https://github.com/adap/flower/pull/1617), " +"[#1613](https://github.com/adap/flower/pull/1613), " +"[#1614](https://github.com/adap/flower/pull/1614))" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:35 +#: ../../source/ref-changelog.md:560 ../../source/ref-changelog.md:627 msgid "" -"To fetch the IMDB dataset, we will use Hugging Face's :code:`datasets` " -"library. We then need to tokenize the data and create :code:`PyTorch` " -"dataloaders, this is all done in the :code:`load_data` function:" +"As usual, the documentation has improved quite a bit. It is another step " +"in our effort to make the Flower documentation the best documentation of " +"any project. Stay tuned and as always, feel free to provide feedback!" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:81 -msgid "Training and testing the model" +#: ../../source/ref-changelog.md:566 +msgid "v1.2.0 (2023-01-13)" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:83 +#: ../../source/ref-changelog.md:572 msgid "" -"Once we have a way of creating our trainloader and testloader, we can " -"take care of the training and testing. This is very similar to any " -":code:`PyTorch` training or testing loop:" +"`Adam Narozniak`, `Charles Beauville`, `Daniel J. Beutel`, `Edoardo`, `L." +" Jiang`, `Ragy`, `Taner Topal`, `dannymcy`" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:121 -msgid "Creating the model itself" +#: ../../source/ref-changelog.md:576 +msgid "" +"**Introduce new Flower Baseline: FedAvg MNIST** " +"([#1497](https://github.com/adap/flower/pull/1497), " +"[#1552](https://github.com/adap/flower/pull/1552))" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:123 +#: ../../source/ref-changelog.md:578 msgid "" -"To create the model itself, we will just load the pre-trained distillBERT" -" model using Hugging Face’s :code:`AutoModelForSequenceClassification` :" +"Over the coming weeks, we will be releasing a number of new reference " +"implementations useful especially to FL newcomers. They will typically " +"revisit well known papers from the literature, and be suitable for " +"integration in your own application or for experimentation, in order to " +"deepen your knowledge of FL in general. Today's release is the first in " +"this series. [Read more.](https://flower.ai/blog/2023-01-12-fl-starter-" +"pack-fedavg-mnist-cnn/)" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:136 -msgid "Federating the example" +#: ../../source/ref-changelog.md:580 +msgid "" +"**Improve GPU support in simulations** " +"([#1555](https://github.com/adap/flower/pull/1555))" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:139 -msgid "Creating the IMDBClient" +#: ../../source/ref-changelog.md:582 +msgid "" +"The Ray-based Virtual Client Engine (`start_simulation`) has been updated" +" to improve GPU support. The update includes some of the hard-earned " +"lessons from scaling simulations in GPU cluster environments. New " +"defaults make running GPU-based simulations substantially more robust." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:141 +#: ../../source/ref-changelog.md:584 msgid "" -"To federate our example to multiple clients, we first need to write our " -"Flower client class (inheriting from :code:`flwr.client.NumPyClient`). " -"This is very easy, as our model is a standard :code:`PyTorch` model:" +"**Improve GPU support in Jupyter Notebook tutorials** " +"([#1527](https://github.com/adap/flower/pull/1527), " +"[#1558](https://github.com/adap/flower/pull/1558))" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:169 +#: ../../source/ref-changelog.md:586 msgid "" -"The :code:`get_parameters` function lets the server get the client's " -"parameters. Inversely, the :code:`set_parameters` function allows the " -"server to send its parameters to the client. Finally, the :code:`fit` " -"function trains the model locally for the client, and the " -":code:`evaluate` function tests the model locally and returns the " -"relevant metrics." +"Some users reported that Jupyter Notebooks have not always been easy to " +"use on GPU instances. We listened and made improvements to all of our " +"Jupyter notebooks! Check out the updated notebooks here:" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:175 -msgid "Starting the server" +#: ../../source/ref-changelog.md:588 +msgid "" +"[An Introduction to Federated Learning](https://flower.ai/docs/framework" +"/tutorial-get-started-with-flower-pytorch.html)" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:177 +#: ../../source/ref-changelog.md:589 msgid "" -"Now that we have a way to instantiate clients, we need to create our " -"server in order to aggregate the results. Using Flower, this can be done " -"very easily by first choosing a strategy (here, we are using " -":code:`FedAvg`, which will define the global weights as the average of " -"all the clients' weights at each round) and then using the " -":code:`flwr.server.start_server` function:" +"[Strategies in Federated Learning](https://flower.ai/docs/framework" +"/tutorial-use-a-federated-learning-strategy-pytorch.html)" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:205 +#: ../../source/ref-changelog.md:590 msgid "" -"The :code:`weighted_average` function is there to provide a way to " -"aggregate the metrics distributed amongst the clients (basically this " -"allows us to display a nice average accuracy and loss for every round)." +"[Building a Strategy](https://flower.ai/docs/framework/tutorial-build-a" +"-strategy-from-scratch-pytorch.html)" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:209 -msgid "Putting everything together" +#: ../../source/ref-changelog.md:591 +msgid "" +"[Client and NumPyClient](https://flower.ai/docs/framework/tutorial-" +"customize-the-client-pytorch.html)" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:211 -msgid "We can now start client instances using:" +#: ../../source/ref-changelog.md:593 +msgid "" +"**Introduce optional telemetry** " +"([#1533](https://github.com/adap/flower/pull/1533), " +"[#1544](https://github.com/adap/flower/pull/1544), " +"[#1584](https://github.com/adap/flower/pull/1584))" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:221 +#: ../../source/ref-changelog.md:595 msgid "" -"And they will be able to connect to the server and start the federated " -"training." +"After a [request for " +"feedback](https://github.com/adap/flower/issues/1534) from the community," +" the Flower open-source project introduces optional collection of " +"*anonymous* usage metrics to make well-informed decisions to improve " +"Flower. Doing this enables the Flower team to understand how Flower is " +"used and what challenges users might face." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:223 +#: ../../source/ref-changelog.md:597 msgid "" -"If you want to check out everything put together, you should check out " -"the `full code example `_ ." +"**Flower is a friendly framework for collaborative AI and data science.**" +" Staying true to this statement, Flower makes it easy to disable " +"telemetry for users who do not want to share anonymous usage metrics. " +"[Read more.](https://flower.ai/docs/telemetry.html)." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:226 +#: ../../source/ref-changelog.md:599 msgid "" -"Of course, this is a very basic example, and a lot can be added or " -"modified, it was just to showcase how simply we could federate a Hugging " -"Face workflow using Flower." +"**Introduce (experimental) Driver API** " +"([#1520](https://github.com/adap/flower/pull/1520), " +"[#1525](https://github.com/adap/flower/pull/1525), " +"[#1545](https://github.com/adap/flower/pull/1545), " +"[#1546](https://github.com/adap/flower/pull/1546), " +"[#1550](https://github.com/adap/flower/pull/1550), " +"[#1551](https://github.com/adap/flower/pull/1551), " +"[#1567](https://github.com/adap/flower/pull/1567))" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:229 +#: ../../source/ref-changelog.md:601 msgid "" -"Note that in this example we used :code:`PyTorch`, but we could have very" -" well used :code:`TensorFlow`." +"Flower now has a new (experimental) Driver API which will enable fully " +"programmable, async, and multi-tenant Federated Learning and Federated " +"Analytics applications. Phew, that's a lot! Going forward, the Driver API" +" will be the abstraction that many upcoming features will be built on - " +"and you can start building those things now, too." msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:-1 +#: ../../source/ref-changelog.md:603 msgid "" -"Read this Federated Learning quickstart tutorial for creating an iOS app " -"using Flower to train a neural network on MNIST." +"The Driver API also enables a new execution mode in which the server runs" +" indefinitely. Multiple individual workloads can run concurrently and " +"start and stop their execution independent of the server. This is " +"especially useful for users who want to deploy Flower in production." msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:5 -msgid "Quickstart iOS" +#: ../../source/ref-changelog.md:605 +msgid "" +"To learn more, check out the `mt-pytorch` code example. We look forward " +"to you feedback!" msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:10 +#: ../../source/ref-changelog.md:607 msgid "" -"In this tutorial we will learn how to train a Neural Network on MNIST " -"using Flower and CoreML on iOS devices." +"Please note: *The Driver API is still experimental and will likely change" +" significantly over time.*" msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:12 +#: ../../source/ref-changelog.md:609 msgid "" -"First of all, for running the Flower Python server, it is recommended to " -"create a virtual environment and run everything within a :doc:`virtualenv" -" `. For the Flower client " -"implementation in iOS, it is recommended to use Xcode as our IDE." +"**Add new Federated Analytics with Pandas example** " +"([#1469](https://github.com/adap/flower/pull/1469), " +"[#1535](https://github.com/adap/flower/pull/1535))" msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:15 +#: ../../source/ref-changelog.md:611 msgid "" -"Our example consists of one Python *server* and two iPhone *clients* that" -" all have the same model." +"A new code example (`quickstart-pandas`) demonstrates federated analytics" +" with Pandas and Flower. You can find it here: [quickstart-" +"pandas](https://github.com/adap/flower/tree/main/examples/quickstart-" +"pandas)." msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:17 +#: ../../source/ref-changelog.md:613 msgid "" -"*Clients* are responsible for generating individual weight updates for " -"the model based on their local datasets. These updates are then sent to " -"the *server* which will aggregate them to produce a better model. " -"Finally, the *server* sends this improved version of the model back to " -"each *client*. A complete cycle of weight updates is called a *round*." +"**Add new strategies: Krum and MultiKrum** " +"([#1481](https://github.com/adap/flower/pull/1481))" msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:21 +#: ../../source/ref-changelog.md:615 msgid "" -"Now that we have a rough idea of what is going on, let's get started to " -"setup our Flower server environment. We first need to install Flower. You" -" can do this by using pip:" +"Edoardo, a computer science student at the Sapienza University of Rome, " +"contributed a new `Krum` strategy that enables users to easily use Krum " +"and MultiKrum in their workloads." msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:27 -msgid "Or Poetry:" +#: ../../source/ref-changelog.md:617 +msgid "" +"**Update C++ example to be compatible with Flower v1.2.0** " +"([#1495](https://github.com/adap/flower/pull/1495))" msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:34 -#: ../../source/tutorial-quickstart-mxnet.rst:36 -#: ../../source/tutorial-quickstart-pytorch.rst:37 -#: ../../source/tutorial-quickstart-scikitlearn.rst:40 -#: ../../source/tutorial-quickstart-tensorflow.rst:29 -#: ../../source/tutorial-quickstart-xgboost.rst:55 -msgid "Flower Client" +#: ../../source/ref-changelog.md:619 +msgid "" +"The C++ code example has received a substantial update to make it " +"compatible with the latest version of Flower." msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:36 +#: ../../source/ref-changelog.md:621 msgid "" -"Now that we have all our dependencies installed, let's run a simple " -"distributed training using CoreML as our local training pipeline and " -"MNIST as our dataset. For simplicity reasons we will use the complete " -"Flower client with CoreML, that has been implemented and stored inside " -"the Swift SDK. The client implementation can be seen below:" +"**General improvements** " +"([#1491](https://github.com/adap/flower/pull/1491), " +"[#1504](https://github.com/adap/flower/pull/1504), " +"[#1506](https://github.com/adap/flower/pull/1506), " +"[#1514](https://github.com/adap/flower/pull/1514), " +"[#1522](https://github.com/adap/flower/pull/1522), " +"[#1523](https://github.com/adap/flower/pull/1523), " +"[#1526](https://github.com/adap/flower/pull/1526), " +"[#1528](https://github.com/adap/flower/pull/1528), " +"[#1547](https://github.com/adap/flower/pull/1547), " +"[#1549](https://github.com/adap/flower/pull/1549), " +"[#1560](https://github.com/adap/flower/pull/1560), " +"[#1564](https://github.com/adap/flower/pull/1564), " +"[#1566](https://github.com/adap/flower/pull/1566))" msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:72 +#: ../../source/ref-changelog.md:625 msgid "" -"Let's create a new application project in Xcode and add :code:`flwr` as a" -" dependency in your project. For our application, we will store the logic" -" of our app in :code:`FLiOSModel.swift` and the UI elements in " -":code:`ContentView.swift`. We will focus more on :code:`FLiOSModel.swift`" -" in this quickstart. Please refer to the `full code example " -"`_ to learn more " -"about the app." +"**Updated documentation** " +"([#1494](https://github.com/adap/flower/pull/1494), " +"[#1496](https://github.com/adap/flower/pull/1496), " +"[#1500](https://github.com/adap/flower/pull/1500), " +"[#1503](https://github.com/adap/flower/pull/1503), " +"[#1505](https://github.com/adap/flower/pull/1505), " +"[#1524](https://github.com/adap/flower/pull/1524), " +"[#1518](https://github.com/adap/flower/pull/1518), " +"[#1519](https://github.com/adap/flower/pull/1519), " +"[#1515](https://github.com/adap/flower/pull/1515))" msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:75 -msgid "Import Flower and CoreML related packages in :code:`FLiOSModel.swift`:" +#: ../../source/ref-changelog.md:629 +msgid "" +"One highlight is the new [first time contributor " +"guide](https://flower.ai/docs/first-time-contributors.html): if you've " +"never contributed on GitHub before, this is the perfect place to start!" msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:83 -msgid "" -"Then add the mlmodel to the project simply by drag-and-drop, the mlmodel " -"will be bundled inside the application during deployment to your iOS " -"device. We need to pass the url to access mlmodel and run CoreML machine " -"learning processes, it can be retrieved by calling the function " -":code:`Bundle.main.url`. For the MNIST dataset, we need to preprocess it " -"into :code:`MLBatchProvider` object. The preprocessing is done inside " -":code:`DataLoader.swift`." +#: ../../source/ref-changelog.md:635 +msgid "v1.1.0 (2022-10-31)" msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:99 +#: ../../source/ref-changelog.md:639 msgid "" -"Since CoreML does not allow the model parameters to be seen before " -"training, and accessing the model parameters during or after the training" -" can only be done by specifying the layer name, we need to know this " -"information beforehand, through looking at the model specification, which" -" are written as proto files. The implementation can be seen in " -":code:`MLModelInspect`." +"We would like to give our **special thanks** to all the contributors who " +"made the new version of Flower possible (in `git shortlog` order):" msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:102 +#: ../../source/ref-changelog.md:641 msgid "" -"After we have all of the necessary information, let's create our Flower " -"client." +"`Akis Linardos`, `Christopher S`, `Daniel J. Beutel`, `George`, `Jan " +"Schlicht`, `Mohammad Fares`, `Pedro Porto Buarque de Gusmão`, `Philipp " +"Wiesner`, `Rob Luke`, `Taner Topal`, `VasundharaAgarwal`, " +"`danielnugraha`, `edogab33`" msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:117 +#: ../../source/ref-changelog.md:645 msgid "" -"Then start the Flower gRPC client and start communicating to the server " -"by passing our Flower client to the function :code:`startFlwrGRPC`." +"**Introduce Differential Privacy wrappers (preview)** " +"([#1357](https://github.com/adap/flower/pull/1357), " +"[#1460](https://github.com/adap/flower/pull/1460))" msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:124 +#: ../../source/ref-changelog.md:647 msgid "" -"That's it for the client. We only have to implement :code:`Client` or " -"call the provided :code:`MLFlwrClient` and call :code:`startFlwrGRPC()`. " -"The attribute :code:`hostname` and :code:`port` tells the client which " -"server to connect to. This can be done by entering the hostname and port " -"in the application before clicking the start button to start the " -"federated learning process." +"The first (experimental) preview of pluggable Differential Privacy " +"wrappers enables easy configuration and usage of differential privacy " +"(DP). The pluggable DP wrappers enable framework-agnostic **and** " +"strategy-agnostic usage of both client-side DP and server-side DP. Head " +"over to the Flower docs, a new explainer goes into more detail." msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:129 -#: ../../source/tutorial-quickstart-mxnet.rst:226 -#: ../../source/tutorial-quickstart-pytorch.rst:203 -#: ../../source/tutorial-quickstart-scikitlearn.rst:157 -#: ../../source/tutorial-quickstart-tensorflow.rst:98 -#: ../../source/tutorial-quickstart-xgboost.rst:309 -msgid "Flower Server" +#: ../../source/ref-changelog.md:649 +msgid "" +"**New iOS CoreML code example** " +"([#1289](https://github.com/adap/flower/pull/1289))" msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:131 -#: ../../source/tutorial-quickstart-mxnet.rst:228 -#: ../../source/tutorial-quickstart-pytorch.rst:205 -#: ../../source/tutorial-quickstart-tensorflow.rst:100 +#: ../../source/ref-changelog.md:651 msgid "" -"For simple workloads we can start a Flower server and leave all the " -"configuration possibilities at their default values. In a file named " -":code:`server.py`, import Flower and start the server:" +"Flower goes iOS! A massive new code example shows how Flower clients can " +"be built for iOS. The code example contains both Flower iOS SDK " +"components that can be used for many tasks, and one task example running " +"on CoreML." msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:142 -#: ../../source/tutorial-quickstart-mxnet.rst:239 -#: ../../source/tutorial-quickstart-pytorch.rst:216 -#: ../../source/tutorial-quickstart-scikitlearn.rst:215 -#: ../../source/tutorial-quickstart-tensorflow.rst:112 -msgid "Train the model, federated!" +#: ../../source/ref-changelog.md:653 +msgid "" +"**New FedMedian strategy** " +"([#1461](https://github.com/adap/flower/pull/1461))" msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:144 -#: ../../source/tutorial-quickstart-pytorch.rst:218 -#: ../../source/tutorial-quickstart-tensorflow.rst:114 -#: ../../source/tutorial-quickstart-xgboost.rst:525 +#: ../../source/ref-changelog.md:655 msgid "" -"With both client and server ready, we can now run everything and see " -"federated learning in action. FL systems usually have a server and " -"multiple clients. We therefore have to start the server first:" +"The new `FedMedian` strategy implements Federated Median (FedMedian) by " +"[Yin et al., 2018](https://arxiv.org/pdf/1803.01498v1.pdf)." msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:152 +#: ../../source/ref-changelog.md:657 msgid "" -"Once the server is running we can start the clients in different " -"terminals. Build and run the client through your Xcode, one through Xcode" -" Simulator and the other by deploying it to your iPhone. To see more " -"about how to deploy your app to iPhone or Simulator visit `here " -"`_." +"**Log** `Client` **exceptions in Virtual Client Engine** " +"([#1493](https://github.com/adap/flower/pull/1493))" msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:156 +#: ../../source/ref-changelog.md:659 msgid "" -"Congratulations! You've successfully built and run your first federated " -"learning system in your ios device. The full `source code " -"`_ for this " -"example can be found in :code:`examples/ios`." +"All `Client` exceptions happening in the VCE are now logged by default " +"and not just exposed to the configured `Strategy` (via the `failures` " +"argument)." msgstr "" -#: ../../source/tutorial-quickstart-jax.rst:-1 +#: ../../source/ref-changelog.md:661 msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with Jax to train a linear regression model on a scikit-learn dataset." +"**Improve Virtual Client Engine internals** " +"([#1401](https://github.com/adap/flower/pull/1401), " +"[#1453](https://github.com/adap/flower/pull/1453))" msgstr "" -#: ../../source/tutorial-quickstart-jax.rst:5 -msgid "Quickstart JAX" +#: ../../source/ref-changelog.md:663 +msgid "" +"Some internals of the Virtual Client Engine have been revamped. The VCE " +"now uses Ray 2.0 under the hood, the value type of the `client_resources`" +" dictionary changed to `float` to allow fractions of resources to be " +"allocated." msgstr "" -#: ../../source/tutorial-quickstart-mxnet.rst:-1 +#: ../../source/ref-changelog.md:665 msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with MXNet to train a Sequential model on MNIST." +"**Support optional** `Client`**/**`NumPyClient` **methods in Virtual " +"Client Engine**" msgstr "" -#: ../../source/tutorial-quickstart-mxnet.rst:5 -msgid "Quickstart MXNet" +#: ../../source/ref-changelog.md:667 +msgid "" +"The Virtual Client Engine now has full support for optional `Client` (and" +" `NumPyClient`) methods." msgstr "" -#: ../../source/tutorial-quickstart-mxnet.rst:7 +#: ../../source/ref-changelog.md:669 msgid "" -"MXNet is no longer maintained and has been moved into `Attic " -"`_. As a result, we would " -"encourage you to use other ML frameworks alongside Flower, for example, " -"PyTorch. This tutorial might be removed in future versions of Flower." +"**Provide type information to packages using** `flwr` " +"([#1377](https://github.com/adap/flower/pull/1377))" msgstr "" -#: ../../source/tutorial-quickstart-mxnet.rst:12 +#: ../../source/ref-changelog.md:671 msgid "" -"In this tutorial, we will learn how to train a :code:`Sequential` model " -"on MNIST using Flower and MXNet." +"The package `flwr` is now bundled with a `py.typed` file indicating that " +"the package is typed. This enables typing support for projects or " +"packages that use `flwr` by enabling them to improve their code using " +"static type checkers like `mypy`." msgstr "" -#: ../../source/tutorial-quickstart-mxnet.rst:14 -#: ../../source/tutorial-quickstart-scikitlearn.rst:12 +#: ../../source/ref-changelog.md:673 msgid "" -"It is recommended to create a virtual environment and run everything " -"within this :doc:`virtualenv `." +"**Updated code example** " +"([#1344](https://github.com/adap/flower/pull/1344), " +"[#1347](https://github.com/adap/flower/pull/1347))" msgstr "" -#: ../../source/tutorial-quickstart-mxnet.rst:16 -#: ../../source/tutorial-quickstart-pytorch.rst:17 -#: ../../source/tutorial-quickstart-scikitlearn.rst:14 +#: ../../source/ref-changelog.md:675 msgid "" -"Our example consists of one *server* and two *clients* all having the " -"same model." +"The code examples covering scikit-learn and PyTorch Lightning have been " +"updated to work with the latest version of Flower." msgstr "" -#: ../../source/tutorial-quickstart-mxnet.rst:18 -#: ../../source/tutorial-quickstart-scikitlearn.rst:16 +#: ../../source/ref-changelog.md:677 msgid "" -"*Clients* are responsible for generating individual model parameter " -"updates for the model based on their local datasets. These updates are " -"then sent to the *server* which will aggregate them to produce an updated" -" global model. Finally, the *server* sends this improved version of the " -"model back to each *client*. A complete cycle of parameters updates is " -"called a *round*." +"**Updated documentation** " +"([#1355](https://github.com/adap/flower/pull/1355), " +"[#1558](https://github.com/adap/flower/pull/1558), " +"[#1379](https://github.com/adap/flower/pull/1379), " +"[#1380](https://github.com/adap/flower/pull/1380), " +"[#1381](https://github.com/adap/flower/pull/1381), " +"[#1332](https://github.com/adap/flower/pull/1332), " +"[#1391](https://github.com/adap/flower/pull/1391), " +"[#1403](https://github.com/adap/flower/pull/1403), " +"[#1364](https://github.com/adap/flower/pull/1364), " +"[#1409](https://github.com/adap/flower/pull/1409), " +"[#1419](https://github.com/adap/flower/pull/1419), " +"[#1444](https://github.com/adap/flower/pull/1444), " +"[#1448](https://github.com/adap/flower/pull/1448), " +"[#1417](https://github.com/adap/flower/pull/1417), " +"[#1449](https://github.com/adap/flower/pull/1449), " +"[#1465](https://github.com/adap/flower/pull/1465), " +"[#1467](https://github.com/adap/flower/pull/1467))" msgstr "" -#: ../../source/tutorial-quickstart-mxnet.rst:22 -#: ../../source/tutorial-quickstart-scikitlearn.rst:20 +#: ../../source/ref-changelog.md:679 msgid "" -"Now that we have a rough idea of what is going on, let's get started. We " -"first need to install Flower. You can do this by running:" +"There have been so many documentation updates that it doesn't even make " +"sense to list them individually." msgstr "" -#: ../../source/tutorial-quickstart-mxnet.rst:28 -msgid "Since we want to use MXNet, let's go ahead and install it:" +#: ../../source/ref-changelog.md:681 +msgid "" +"**Restructured documentation** " +"([#1387](https://github.com/adap/flower/pull/1387))" msgstr "" -#: ../../source/tutorial-quickstart-mxnet.rst:38 +#: ../../source/ref-changelog.md:683 msgid "" -"Now that we have all our dependencies installed, let's run a simple " -"distributed training with two clients and one server. Our training " -"procedure and network architecture are based on MXNet´s `Hand-written " -"Digit Recognition tutorial " -"`_." +"The documentation has been restructured to make it easier to navigate. " +"This is just the first step in a larger effort to make the Flower " +"documentation the best documentation of any project ever. Stay tuned!" msgstr "" -#: ../../source/tutorial-quickstart-mxnet.rst:40 +#: ../../source/ref-changelog.md:685 msgid "" -"In a file called :code:`client.py`, import Flower and MXNet related " -"packages:" +"**Open in Colab button** " +"([#1389](https://github.com/adap/flower/pull/1389))" msgstr "" -#: ../../source/tutorial-quickstart-mxnet.rst:55 -msgid "In addition, define the device allocation in MXNet with:" +#: ../../source/ref-changelog.md:687 +msgid "" +"The four parts of the Flower Federated Learning Tutorial now come with a " +"new `Open in Colab` button. No need to install anything on your local " +"machine, you can now use and learn about Flower in your browser, it's " +"only a single click away." msgstr "" -#: ../../source/tutorial-quickstart-mxnet.rst:61 +#: ../../source/ref-changelog.md:689 msgid "" -"We use MXNet to load MNIST, a popular image classification dataset of " -"handwritten digits for machine learning. The MXNet utility " -":code:`mx.test_utils.get_mnist()` downloads the training and test data." +"**Improved tutorial** ([#1468](https://github.com/adap/flower/pull/1468)," +" [#1470](https://github.com/adap/flower/pull/1470), " +"[#1472](https://github.com/adap/flower/pull/1472), " +"[#1473](https://github.com/adap/flower/pull/1473), " +"[#1474](https://github.com/adap/flower/pull/1474), " +"[#1475](https://github.com/adap/flower/pull/1475))" msgstr "" -#: ../../source/tutorial-quickstart-mxnet.rst:75 +#: ../../source/ref-changelog.md:691 msgid "" -"Define the training and loss with MXNet. We train the model by looping " -"over the dataset, measure the corresponding loss, and optimize it." +"The Flower Federated Learning Tutorial has two brand-new parts covering " +"custom strategies (still WIP) and the distinction between `Client` and " +"`NumPyClient`. The existing parts one and two have also been improved " +"(many small changes and fixes)." msgstr "" -#: ../../source/tutorial-quickstart-mxnet.rst:113 -msgid "" -"Next, we define the validation of our machine learning model. We loop " -"over the test set and measure both loss and accuracy on the test set." +#: ../../source/ref-changelog.md:697 +msgid "v1.0.0 (2022-07-28)" msgstr "" -#: ../../source/tutorial-quickstart-mxnet.rst:137 -msgid "" -"After defining the training and testing of a MXNet machine learning " -"model, we use these functions to implement a Flower client." +#: ../../source/ref-changelog.md:699 +msgid "Highlights" msgstr "" -#: ../../source/tutorial-quickstart-mxnet.rst:139 -msgid "Our Flower clients will use a simple :code:`Sequential` model:" +#: ../../source/ref-changelog.md:701 +msgid "Stable **Virtual Client Engine** (accessible via `start_simulation`)" msgstr "" -#: ../../source/tutorial-quickstart-mxnet.rst:158 -msgid "" -"After loading the dataset with :code:`load_data()` we perform one forward" -" propagation to initialize the model and model parameters with " -":code:`model(init)`. Next, we implement a Flower client." +#: ../../source/ref-changelog.md:702 +msgid "All `Client`/`NumPyClient` methods are now optional" msgstr "" -#: ../../source/tutorial-quickstart-mxnet.rst:160 -#: ../../source/tutorial-quickstart-pytorch.rst:144 -#: ../../source/tutorial-quickstart-tensorflow.rst:54 -msgid "" -"The Flower server interacts with clients through an interface called " -":code:`Client`. When the server selects a particular client for training," -" it sends training instructions over the network. The client receives " -"those instructions and calls one of the :code:`Client` methods to run " -"your code (i.e., to train the neural network we defined earlier)." +#: ../../source/ref-changelog.md:703 +msgid "Configurable `get_parameters`" msgstr "" -#: ../../source/tutorial-quickstart-mxnet.rst:166 +#: ../../source/ref-changelog.md:704 msgid "" -"Flower provides a convenience class called :code:`NumPyClient` which " -"makes it easier to implement the :code:`Client` interface when your " -"workload uses MXNet. Implementing :code:`NumPyClient` usually means " -"defining the following methods (:code:`set_parameters` is optional " -"though):" +"Tons of small API cleanups resulting in a more coherent developer " +"experience" msgstr "" -#: ../../source/tutorial-quickstart-mxnet.rst:172 -#: ../../source/tutorial-quickstart-pytorch.rst:156 -#: ../../source/tutorial-quickstart-scikitlearn.rst:109 -msgid "return the model weight as a list of NumPy ndarrays" +#: ../../source/ref-changelog.md:708 +msgid "" +"We would like to give our **special thanks** to all the contributors who " +"made Flower 1.0 possible (in reverse [GitHub " +"Contributors](https://github.com/adap/flower/graphs/contributors) order):" msgstr "" -#: ../../source/tutorial-quickstart-mxnet.rst:173 -#: ../../source/tutorial-quickstart-pytorch.rst:157 -#: ../../source/tutorial-quickstart-scikitlearn.rst:111 -msgid ":code:`set_parameters` (optional)" +#: ../../source/ref-changelog.md:710 +msgid "" +"[@rtaiello](https://github.com/rtaiello), " +"[@g-pichler](https://github.com/g-pichler), [@rob-" +"luke](https://github.com/rob-luke), [@andreea-zaharia](https://github.com" +"/andreea-zaharia), [@kinshukdua](https://github.com/kinshukdua), " +"[@nfnt](https://github.com/nfnt), " +"[@tatiana-s](https://github.com/tatiana-s), " +"[@TParcollet](https://github.com/TParcollet), " +"[@vballoli](https://github.com/vballoli), " +"[@negedng](https://github.com/negedng), " +"[@RISHIKESHAVAN](https://github.com/RISHIKESHAVAN), " +"[@hei411](https://github.com/hei411), " +"[@SebastianSpeitel](https://github.com/SebastianSpeitel), " +"[@AmitChaulwar](https://github.com/AmitChaulwar), " +"[@Rubiel1](https://github.com/Rubiel1), [@FANTOME-PAN](https://github.com" +"/FANTOME-PAN), [@Rono-BC](https://github.com/Rono-BC), " +"[@lbhm](https://github.com/lbhm), " +"[@sishtiaq](https://github.com/sishtiaq), " +"[@remde](https://github.com/remde), [@Jueun-Park](https://github.com" +"/Jueun-Park), [@architjen](https://github.com/architjen), " +"[@PratikGarai](https://github.com/PratikGarai), " +"[@mrinaald](https://github.com/mrinaald), " +"[@zliel](https://github.com/zliel), " +"[@MeiruiJiang](https://github.com/MeiruiJiang), " +"[@sancarlim](https://github.com/sancarlim), " +"[@gubertoli](https://github.com/gubertoli), " +"[@Vingt100](https://github.com/Vingt100), " +"[@MakGulati](https://github.com/MakGulati), " +"[@cozek](https://github.com/cozek), " +"[@jafermarq](https://github.com/jafermarq), " +"[@sisco0](https://github.com/sisco0), " +"[@akhilmathurs](https://github.com/akhilmathurs), " +"[@CanTuerk](https://github.com/CanTuerk), " +"[@mariaboerner1987](https://github.com/mariaboerner1987), " +"[@pedropgusmao](https://github.com/pedropgusmao), " +"[@tanertopal](https://github.com/tanertopal), " +"[@danieljanes](https://github.com/danieljanes)." msgstr "" -#: ../../source/tutorial-quickstart-mxnet.rst:174 -#: ../../source/tutorial-quickstart-pytorch.rst:158 -#: ../../source/tutorial-quickstart-scikitlearn.rst:111 +#: ../../source/ref-changelog.md:714 msgid "" -"update the local model weights with the parameters received from the " -"server" +"**All arguments must be passed as keyword arguments** " +"([#1338](https://github.com/adap/flower/pull/1338))" msgstr "" -#: ../../source/tutorial-quickstart-mxnet.rst:176 -#: ../../source/tutorial-quickstart-pytorch.rst:160 -#: ../../source/tutorial-quickstart-scikitlearn.rst:114 -msgid "set the local model weights" +#: ../../source/ref-changelog.md:716 +msgid "" +"Pass all arguments as keyword arguments, positional arguments are not " +"longer supported. Code that uses positional arguments (e.g., " +"`start_client(\"127.0.0.1:8080\", FlowerClient())`) must add the keyword " +"for each positional argument (e.g., " +"`start_client(server_address=\"127.0.0.1:8080\", " +"client=FlowerClient())`)." msgstr "" -#: ../../source/tutorial-quickstart-mxnet.rst:177 -#: ../../source/tutorial-quickstart-pytorch.rst:161 -#: ../../source/tutorial-quickstart-scikitlearn.rst:115 -msgid "train the local model" +#: ../../source/ref-changelog.md:718 +msgid "" +"**Introduce configuration object** `ServerConfig` **in** `start_server` " +"**and** `start_simulation` " +"([#1317](https://github.com/adap/flower/pull/1317))" msgstr "" -#: ../../source/tutorial-quickstart-mxnet.rst:178 -#: ../../source/tutorial-quickstart-pytorch.rst:162 -#: ../../source/tutorial-quickstart-scikitlearn.rst:116 -msgid "receive the updated local model weights" +#: ../../source/ref-changelog.md:720 +msgid "" +"Instead of a config dictionary `{\"num_rounds\": 3, \"round_timeout\": " +"600.0}`, `start_server` and `start_simulation` now expect a configuration" +" object of type `flwr.server.ServerConfig`. `ServerConfig` takes the same" +" arguments that as the previous config dict, but it makes writing type-" +"safe code easier and the default parameters values more transparent." msgstr "" -#: ../../source/tutorial-quickstart-mxnet.rst:180 -#: ../../source/tutorial-quickstart-pytorch.rst:164 -#: ../../source/tutorial-quickstart-scikitlearn.rst:118 -msgid "test the local model" +#: ../../source/ref-changelog.md:722 +msgid "" +"**Rename built-in strategy parameters for clarity** " +"([#1334](https://github.com/adap/flower/pull/1334))" msgstr "" -#: ../../source/tutorial-quickstart-mxnet.rst:182 -msgid "They can be implemented in the following way:" +#: ../../source/ref-changelog.md:724 +msgid "" +"The following built-in strategy parameters were renamed to improve " +"readability and consistency with other API's:" msgstr "" -#: ../../source/tutorial-quickstart-mxnet.rst:212 -msgid "" -"We can now create an instance of our class :code:`MNISTClient` and add " -"one line to actually run this client:" +#: ../../source/ref-changelog.md:726 +msgid "`fraction_eval` --> `fraction_evaluate`" msgstr "" -#: ../../source/tutorial-quickstart-mxnet.rst:219 -msgid "" -"That's it for the client. We only have to implement :code:`Client` or " -":code:`NumPyClient` and call :code:`fl.client.start_client()` or " -":code:`fl.client.start_numpy_client()`. The string " -":code:`\"0.0.0.0:8080\"` tells the client which server to connect to. In " -"our case we can run the server and the client on the same machine, " -"therefore we use :code:`\"0.0.0.0:8080\"`. If we run a truly federated " -"workload with the server and clients running on different machines, all " -"that needs to change is the :code:`server_address` we pass to the client." +#: ../../source/ref-changelog.md:727 +msgid "`min_eval_clients` --> `min_evaluate_clients`" +msgstr "" + +#: ../../source/ref-changelog.md:728 +msgid "`eval_fn` --> `evaluate_fn`" msgstr "" -#: ../../source/tutorial-quickstart-mxnet.rst:241 +#: ../../source/ref-changelog.md:730 msgid "" -"With both client and server ready, we can now run everything and see " -"federated learning in action. Federated learning systems usually have a " -"server and multiple clients. We therefore have to start the server first:" +"**Update default arguments of built-in strategies** " +"([#1278](https://github.com/adap/flower/pull/1278))" msgstr "" -#: ../../source/tutorial-quickstart-mxnet.rst:249 -#: ../../source/tutorial-quickstart-pytorch.rst:226 -#: ../../source/tutorial-quickstart-scikitlearn.rst:224 -#: ../../source/tutorial-quickstart-tensorflow.rst:122 -#: ../../source/tutorial-quickstart-xgboost.rst:533 +#: ../../source/ref-changelog.md:732 msgid "" -"Once the server is running we can start the clients in different " -"terminals. Open a new terminal and start the first client:" +"All built-in strategies now use `fraction_fit=1.0` and " +"`fraction_evaluate=1.0`, which means they select *all* currently " +"available clients for training and evaluation. Projects that relied on " +"the previous default values can get the previous behaviour by " +"initializing the strategy in the following way:" msgstr "" -#: ../../source/tutorial-quickstart-mxnet.rst:256 -#: ../../source/tutorial-quickstart-pytorch.rst:233 -#: ../../source/tutorial-quickstart-scikitlearn.rst:231 -#: ../../source/tutorial-quickstart-tensorflow.rst:129 -#: ../../source/tutorial-quickstart-xgboost.rst:540 -msgid "Open another terminal and start the second client:" +#: ../../source/ref-changelog.md:734 +msgid "`strategy = FedAvg(fraction_fit=0.1, fraction_evaluate=0.1)`" msgstr "" -#: ../../source/tutorial-quickstart-mxnet.rst:262 -#: ../../source/tutorial-quickstart-pytorch.rst:239 -#: ../../source/tutorial-quickstart-scikitlearn.rst:237 -#: ../../source/tutorial-quickstart-xgboost.rst:546 +#: ../../source/ref-changelog.md:736 msgid "" -"Each client will have its own dataset. You should now see how the " -"training does in the very first terminal (the one that started the " -"server):" +"**Add** `server_round` **to** `Strategy.evaluate` " +"([#1334](https://github.com/adap/flower/pull/1334))" msgstr "" -#: ../../source/tutorial-quickstart-mxnet.rst:294 +#: ../../source/ref-changelog.md:738 msgid "" -"Congratulations! You've successfully built and run your first federated " -"learning system. The full `source code " -"`_ for this example can be found in :code:`examples" -"/quickstart-mxnet`." +"The `Strategy` method `evaluate` now receives the current round of " +"federated learning/evaluation as the first parameter." msgstr "" -#: ../../source/tutorial-quickstart-pandas.rst:-1 +#: ../../source/ref-changelog.md:740 msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with Pandas to perform Federated Analytics." +"**Add** `server_round` **and** `config` **parameters to** `evaluate_fn` " +"([#1334](https://github.com/adap/flower/pull/1334))" msgstr "" -#: ../../source/tutorial-quickstart-pandas.rst:5 -msgid "Quickstart Pandas" +#: ../../source/ref-changelog.md:742 +msgid "" +"The `evaluate_fn` passed to built-in strategies like `FedAvg` now takes " +"three parameters: (1) The current round of federated learning/evaluation " +"(`server_round`), (2) the model parameters to evaluate (`parameters`), " +"and (3) a config dictionary (`config`)." msgstr "" -#: ../../source/tutorial-quickstart-pandas.rst:10 -msgid "Let's build a federated analytics system using Pandas and Flower!" +#: ../../source/ref-changelog.md:744 +msgid "" +"**Rename** `rnd` **to** `server_round` " +"([#1321](https://github.com/adap/flower/pull/1321))" msgstr "" -#: ../../source/tutorial-quickstart-pandas.rst:12 +#: ../../source/ref-changelog.md:746 msgid "" -"Please refer to the `full code example " -"`_ " -"to learn more." +"Several Flower methods and functions (`evaluate_fn`, `configure_fit`, " +"`aggregate_fit`, `configure_evaluate`, `aggregate_evaluate`) receive the " +"current round of federated learning/evaluation as their first parameter. " +"To improve reaability and avoid confusion with *random*, this parameter " +"has been renamed from `rnd` to `server_round`." msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:-1 +#: ../../source/ref-changelog.md:748 msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with PyTorch to train a CNN model on MNIST." +"**Move** `flwr.dataset` **to** `flwr_baselines` " +"([#1273](https://github.com/adap/flower/pull/1273))" msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:13 -msgid "" -"In this tutorial we will learn how to train a Convolutional Neural " -"Network on CIFAR10 using Flower and PyTorch." +#: ../../source/ref-changelog.md:750 +msgid "The experimental package `flwr.dataset` was migrated to Flower Baselines." msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:15 -#: ../../source/tutorial-quickstart-xgboost.rst:39 +#: ../../source/ref-changelog.md:752 msgid "" -"First of all, it is recommended to create a virtual environment and run " -"everything within a :doc:`virtualenv `." +"**Remove experimental strategies** " +"([#1280](https://github.com/adap/flower/pull/1280))" msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:19 +#: ../../source/ref-changelog.md:754 msgid "" -"*Clients* are responsible for generating individual weight-updates for " -"the model based on their local datasets. These updates are then sent to " -"the *server* which will aggregate them to produce a better model. " -"Finally, the *server* sends this improved version of the model back to " -"each *client*. A complete cycle of weight updates is called a *round*." +"Remove unmaintained experimental strategies (`FastAndSlow`, `FedFSv0`, " +"`FedFSv1`)." msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:23 +#: ../../source/ref-changelog.md:756 msgid "" -"Now that we have a rough idea of what is going on, let's get started. We " -"first need to install Flower. You can do this by running :" +"**Rename** `Weights` **to** `NDArrays` " +"([#1258](https://github.com/adap/flower/pull/1258), " +"[#1259](https://github.com/adap/flower/pull/1259))" msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:29 +#: ../../source/ref-changelog.md:758 msgid "" -"Since we want to use PyTorch to solve a computer vision task, let's go " -"ahead and install PyTorch and the **torchvision** library:" +"`flwr.common.Weights` was renamed to `flwr.common.NDArrays` to better " +"capture what this type is all about." msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:39 +#: ../../source/ref-changelog.md:760 msgid "" -"Now that we have all our dependencies installed, let's run a simple " -"distributed training with two clients and one server. Our training " -"procedure and network architecture are based on PyTorch's `Deep Learning " -"with PyTorch " -"`_." +"**Remove antiquated** `force_final_distributed_eval` **from** " +"`start_server` ([#1258](https://github.com/adap/flower/pull/1258), " +"[#1259](https://github.com/adap/flower/pull/1259))" msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:41 +#: ../../source/ref-changelog.md:762 msgid "" -"In a file called :code:`client.py`, import Flower and PyTorch related " -"packages:" +"The `start_server` parameter `force_final_distributed_eval` has long been" +" a historic artefact, in this release it is finally gone for good." msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:56 -msgid "In addition, we define the device allocation in PyTorch with:" +#: ../../source/ref-changelog.md:764 +msgid "" +"**Make** `get_parameters` **configurable** " +"([#1242](https://github.com/adap/flower/pull/1242))" msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:62 +#: ../../source/ref-changelog.md:766 msgid "" -"We use PyTorch to load CIFAR10, a popular colored image classification " -"dataset for machine learning. The PyTorch :code:`DataLoader()` downloads " -"the training and test data that are then normalized." +"The `get_parameters` method now accepts a configuration dictionary, just " +"like `get_properties`, `fit`, and `evaluate`." msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:78 +#: ../../source/ref-changelog.md:768 msgid "" -"Define the loss and optimizer with PyTorch. The training of the dataset " -"is done by looping over the dataset, measure the corresponding loss and " -"optimize it." +"**Replace** `num_rounds` **in** `start_simulation` **with new** `config` " +"**parameter** ([#1281](https://github.com/adap/flower/pull/1281))" msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:94 +#: ../../source/ref-changelog.md:770 msgid "" -"Define then the validation of the machine learning network. We loop over" -" the test set and measure the loss and accuracy of the test set." +"The `start_simulation` function now accepts a configuration dictionary " +"`config` instead of the `num_rounds` integer. This improves the " +"consistency between `start_simulation` and `start_server` and makes " +"transitioning between the two easier." msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:113 +#: ../../source/ref-changelog.md:774 msgid "" -"After defining the training and testing of a PyTorch machine learning " -"model, we use the functions for the Flower clients." +"**Support Python 3.10** " +"([#1320](https://github.com/adap/flower/pull/1320))" msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:115 +#: ../../source/ref-changelog.md:776 msgid "" -"The Flower clients will use a simple CNN adapted from 'PyTorch: A 60 " -"Minute Blitz':" +"The previous Flower release introduced experimental support for Python " +"3.10, this release declares Python 3.10 support as stable." msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:142 +#: ../../source/ref-changelog.md:778 msgid "" -"After loading the data set with :code:`load_data()` we define the Flower " -"interface." +"**Make all** `Client` **and** `NumPyClient` **methods optional** " +"([#1260](https://github.com/adap/flower/pull/1260), " +"[#1277](https://github.com/adap/flower/pull/1277))" msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:150 +#: ../../source/ref-changelog.md:780 msgid "" -"Flower provides a convenience class called :code:`NumPyClient` which " -"makes it easier to implement the :code:`Client` interface when your " -"workload uses PyTorch. Implementing :code:`NumPyClient` usually means " -"defining the following methods (:code:`set_parameters` is optional " -"though):" +"The `Client`/`NumPyClient` methods `get_properties`, `get_parameters`, " +"`fit`, and `evaluate` are all optional. This enables writing clients that" +" implement, for example, only `fit`, but no other method. No need to " +"implement `evaluate` when using centralized evaluation!" msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:166 -msgid "which can be implemented in the following way:" +#: ../../source/ref-changelog.md:782 +msgid "" +"**Enable passing a** `Server` **instance to** `start_simulation` " +"([#1281](https://github.com/adap/flower/pull/1281))" msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:189 -#: ../../source/tutorial-quickstart-tensorflow.rst:82 +#: ../../source/ref-changelog.md:784 msgid "" -"We can now create an instance of our class :code:`CifarClient` and add " -"one line to actually run this client:" +"Similar to `start_server`, `start_simulation` now accepts a full `Server`" +" instance. This enables users to heavily customize the execution of " +"eperiments and opens the door to running, for example, async FL using the" +" Virtual Client Engine." msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:196 -#: ../../source/tutorial-quickstart-tensorflow.rst:90 +#: ../../source/ref-changelog.md:786 msgid "" -"That's it for the client. We only have to implement :code:`Client` or " -":code:`NumPyClient` and call :code:`fl.client.start_client()`. If you " -"implement a client of type :code:`NumPyClient` you'll need to first call " -"its :code:`to_client()` method. The string :code:`\"[::]:8080\"` tells " -"the client which server to connect to. In our case we can run the server " -"and the client on the same machine, therefore we use " -":code:`\"[::]:8080\"`. If we run a truly federated workload with the " -"server and clients running on different machines, all that needs to " -"change is the :code:`server_address` we point the client at." +"**Update code examples** " +"([#1291](https://github.com/adap/flower/pull/1291), " +"[#1286](https://github.com/adap/flower/pull/1286), " +"[#1282](https://github.com/adap/flower/pull/1282))" msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:271 +#: ../../source/ref-changelog.md:788 msgid "" -"Congratulations! You've successfully built and run your first federated " -"learning system. The full `source code " -"`_ for this example can be found in :code:`examples" -"/quickstart-pytorch`." +"Many code examples received small or even large maintenance updates, " +"among them are" msgstr "" -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:-1 -msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with PyTorch Lightning to train an Auto Encoder model on MNIST." +#: ../../source/ref-changelog.md:790 +msgid "`scikit-learn`" msgstr "" -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:5 -msgid "Quickstart PyTorch Lightning" +#: ../../source/ref-changelog.md:791 +msgid "`simulation_pytorch`" msgstr "" -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:10 -msgid "" -"Let's build a horizontal federated learning system using PyTorch " -"Lightning and Flower!" +#: ../../source/ref-changelog.md:792 +msgid "`quickstart_pytorch`" msgstr "" -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:12 -msgid "" -"Please refer to the `full code example " -"`_ to learn more." +#: ../../source/ref-changelog.md:793 +msgid "`quickstart_simulation`" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:-1 -msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with scikit-learn to train a linear regression model." +#: ../../source/ref-changelog.md:794 +msgid "`quickstart_tensorflow`" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:5 -msgid "Quickstart scikit-learn" +#: ../../source/ref-changelog.md:795 +msgid "`advanced_tensorflow`" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:10 +#: ../../source/ref-changelog.md:797 msgid "" -"In this tutorial, we will learn how to train a :code:`Logistic " -"Regression` model on MNIST using Flower and scikit-learn." +"**Remove the obsolete simulation example** " +"([#1328](https://github.com/adap/flower/pull/1328))" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:26 -msgid "Since we want to use scikit-learn, let's go ahead and install it:" +#: ../../source/ref-changelog.md:799 +msgid "" +"Removes the obsolete `simulation` example and renames " +"`quickstart_simulation` to `simulation_tensorflow` so it fits withs the " +"naming of `simulation_pytorch`" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:32 -msgid "Or simply install all dependencies using Poetry:" +#: ../../source/ref-changelog.md:801 +msgid "" +"**Update documentation** " +"([#1223](https://github.com/adap/flower/pull/1223), " +"[#1209](https://github.com/adap/flower/pull/1209), " +"[#1251](https://github.com/adap/flower/pull/1251), " +"[#1257](https://github.com/adap/flower/pull/1257), " +"[#1267](https://github.com/adap/flower/pull/1267), " +"[#1268](https://github.com/adap/flower/pull/1268), " +"[#1300](https://github.com/adap/flower/pull/1300), " +"[#1304](https://github.com/adap/flower/pull/1304), " +"[#1305](https://github.com/adap/flower/pull/1305), " +"[#1307](https://github.com/adap/flower/pull/1307))" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:42 +#: ../../source/ref-changelog.md:803 msgid "" -"Now that we have all our dependencies installed, let's run a simple " -"distributed training with two clients and one server. However, before " -"setting up the client and server, we will define all functionalities that" -" we need for our federated learning setup within :code:`utils.py`. The " -":code:`utils.py` contains different functions defining all the machine " -"learning basics:" +"One substantial documentation update fixes multiple smaller rendering " +"issues, makes titles more succinct to improve navigation, removes a " +"deprecated library, updates documentation dependencies, includes the " +"`flwr.common` module in the API reference, includes support for markdown-" +"based documentation, migrates the changelog from `.rst` to `.md`, and " +"fixes a number of smaller details!" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:45 -msgid ":code:`get_model_parameters()`" +#: ../../source/ref-changelog.md:805 ../../source/ref-changelog.md:860 +#: ../../source/ref-changelog.md:929 ../../source/ref-changelog.md:968 +msgid "**Minor updates**" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:46 -msgid "Returns the parameters of a :code:`sklearn` LogisticRegression model" +#: ../../source/ref-changelog.md:807 +msgid "" +"Add round number to fit and evaluate log messages " +"([#1266](https://github.com/adap/flower/pull/1266))" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:47 -msgid ":code:`set_model_params()`" +#: ../../source/ref-changelog.md:808 +msgid "" +"Add secure gRPC connection to the `advanced_tensorflow` code example " +"([#847](https://github.com/adap/flower/pull/847))" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:48 -msgid "Sets the parameters of a :code:`sklean` LogisticRegression model" +#: ../../source/ref-changelog.md:809 +msgid "" +"Update developer tooling " +"([#1231](https://github.com/adap/flower/pull/1231), " +"[#1276](https://github.com/adap/flower/pull/1276), " +"[#1301](https://github.com/adap/flower/pull/1301), " +"[#1310](https://github.com/adap/flower/pull/1310))" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:49 -msgid ":code:`set_initial_params()`" +#: ../../source/ref-changelog.md:810 +msgid "" +"Rename ProtoBuf messages to improve consistency " +"([#1214](https://github.com/adap/flower/pull/1214), " +"[#1258](https://github.com/adap/flower/pull/1258), " +"[#1259](https://github.com/adap/flower/pull/1259))" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:50 -msgid "Initializes the model parameters that the Flower server will ask for" +#: ../../source/ref-changelog.md:812 +msgid "v0.19.0 (2022-05-18)" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:51 -msgid ":code:`load_mnist()`" +#: ../../source/ref-changelog.md:816 +msgid "" +"**Flower Baselines (preview): FedOpt, FedBN, FedAvgM** " +"([#919](https://github.com/adap/flower/pull/919), " +"[#1127](https://github.com/adap/flower/pull/1127), " +"[#914](https://github.com/adap/flower/pull/914))" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:52 -msgid "Loads the MNIST dataset using OpenML" +#: ../../source/ref-changelog.md:818 +msgid "" +"The first preview release of Flower Baselines has arrived! We're " +"kickstarting Flower Baselines with implementations of FedOpt (FedYogi, " +"FedAdam, FedAdagrad), FedBN, and FedAvgM. Check the documentation on how " +"to use [Flower Baselines](https://flower.ai/docs/using-baselines.html). " +"With this first preview release we're also inviting the community to " +"[contribute their own baselines](https://flower.ai/docs/baselines/how-to-" +"contribute-baselines.html)." msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:53 -msgid ":code:`shuffle()`" +#: ../../source/ref-changelog.md:820 +msgid "" +"**C++ client SDK (preview) and code example** " +"([#1111](https://github.com/adap/flower/pull/1111))" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:54 -msgid "Shuffles data and its label" +#: ../../source/ref-changelog.md:822 +msgid "" +"Preview support for Flower clients written in C++. The C++ preview " +"includes a Flower client SDK and a quickstart code example that " +"demonstrates a simple C++ client using the SDK." msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:56 -msgid ":code:`partition()`" +#: ../../source/ref-changelog.md:824 +msgid "" +"**Add experimental support for Python 3.10 and Python 3.11** " +"([#1135](https://github.com/adap/flower/pull/1135))" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:56 -msgid "Splits datasets into a number of partitions" +#: ../../source/ref-changelog.md:826 +msgid "" +"Python 3.10 is the latest stable release of Python and Python 3.11 is due" +" to be released in October. This Flower release adds experimental support" +" for both Python versions." msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:58 +#: ../../source/ref-changelog.md:828 msgid "" -"Please check out :code:`utils.py` `here " -"`_ for more details. The pre-defined functions are used in" -" the :code:`client.py` and imported. The :code:`client.py` also requires " -"to import several packages such as Flower and scikit-learn:" +"**Aggregate custom metrics through user-provided functions** " +"([#1144](https://github.com/adap/flower/pull/1144))" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:73 +#: ../../source/ref-changelog.md:830 msgid "" -"We load the MNIST dataset from `OpenML " -"`_, a popular " -"image classification dataset of handwritten digits for machine learning. " -"The utility :code:`utils.load_mnist()` downloads the training and test " -"data. The training set is split afterwards into 10 partitions with " -":code:`utils.partition()`." +"Custom metrics (e.g., `accuracy`) can now be aggregated without having to" +" customize the strategy. Built-in strategies support two new arguments, " +"`fit_metrics_aggregation_fn` and `evaluate_metrics_aggregation_fn`, that " +"allow passing custom metric aggregation functions." msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:85 +#: ../../source/ref-changelog.md:832 msgid "" -"Next, the logistic regression model is defined and initialized with " -":code:`utils.set_initial_params()`." +"**User-configurable round timeout** " +"([#1162](https://github.com/adap/flower/pull/1162))" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:97 +#: ../../source/ref-changelog.md:834 msgid "" -"The Flower server interacts with clients through an interface called " -":code:`Client`. When the server selects a particular client for training," -" it sends training instructions over the network. The client receives " -"those instructions and calls one of the :code:`Client` methods to run " -"your code (i.e., to fit the logistic regression we defined earlier)." +"A new configuration value allows the round timeout to be set for " +"`start_server` and `start_simulation`. If the `config` dictionary " +"contains a `round_timeout` key (with a `float` value in seconds), the " +"server will wait *at least* `round_timeout` seconds before it closes the " +"connection." msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:103 +#: ../../source/ref-changelog.md:836 msgid "" -"Flower provides a convenience class called :code:`NumPyClient` which " -"makes it easier to implement the :code:`Client` interface when your " -"workload uses scikit-learn. Implementing :code:`NumPyClient` usually " -"means defining the following methods (:code:`set_parameters` is optional " -"though):" +"**Enable both federated evaluation and centralized evaluation to be used " +"at the same time in all built-in strategies** " +"([#1091](https://github.com/adap/flower/pull/1091))" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:112 -msgid "is directly imported with :code:`utils.set_model_params()`" +#: ../../source/ref-changelog.md:838 +msgid "" +"Built-in strategies can now perform both federated evaluation (i.e., " +"client-side) and centralized evaluation (i.e., server-side) in the same " +"round. Federated evaluation can be disabled by setting `fraction_eval` to" +" `0.0`." msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:120 -msgid "The methods can be implemented in the following way:" +#: ../../source/ref-changelog.md:840 +msgid "" +"**Two new Jupyter Notebook tutorials** " +"([#1141](https://github.com/adap/flower/pull/1141))" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:143 +#: ../../source/ref-changelog.md:842 msgid "" -"We can now create an instance of our class :code:`MnistClient` and add " -"one line to actually run this client:" +"Two Jupyter Notebook tutorials (compatible with Google Colab) explain " +"basic and intermediate Flower features:" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:150 +#: ../../source/ref-changelog.md:844 msgid "" -"That's it for the client. We only have to implement :code:`Client` or " -":code:`NumPyClient` and call :code:`fl.client.start_client()`. If you " -"implement a client of type :code:`NumPyClient` you'll need to first call " -"its :code:`to_client()` method. The string :code:`\"0.0.0.0:8080\"` tells" -" the client which server to connect to. In our case we can run the server" -" and the client on the same machine, therefore we use " -":code:`\"0.0.0.0:8080\"`. If we run a truly federated workload with the " -"server and clients running on different machines, all that needs to " -"change is the :code:`server_address` we pass to the client." +"*An Introduction to Federated Learning*: [Open in " +"Colab](https://colab.research.google.com/github/adap/flower/blob/main/tutorials/Flower-1" +"-Intro-to-FL-PyTorch.ipynb)" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:159 +#: ../../source/ref-changelog.md:846 msgid "" -"The following Flower server is a little bit more advanced and returns an " -"evaluation function for the server-side evaluation. First, we import " -"again all required libraries such as Flower and scikit-learn." +"*Using Strategies in Federated Learning*: [Open in " +"Colab](https://colab.research.google.com/github/adap/flower/blob/main/tutorials/Flower-2" +"-Strategies-in-FL-PyTorch.ipynb)" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:162 -msgid ":code:`server.py`, import Flower and start the server:" +#: ../../source/ref-changelog.md:848 +msgid "" +"**New FedAvgM strategy (Federated Averaging with Server Momentum)** " +"([#1076](https://github.com/adap/flower/pull/1076))" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:173 +#: ../../source/ref-changelog.md:850 msgid "" -"The number of federated learning rounds is set in :code:`fit_round()` and" -" the evaluation is defined in :code:`get_evaluate_fn()`. The evaluation " -"function is called after each federated learning round and gives you " -"information about loss and accuracy." +"The new `FedAvgM` strategy implements Federated Averaging with Server " +"Momentum \\[Hsu et al., 2019\\]." msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:198 +#: ../../source/ref-changelog.md:852 msgid "" -"The :code:`main` contains the server-side parameter initialization " -":code:`utils.set_initial_params()` as well as the aggregation strategy " -":code:`fl.server.strategy:FedAvg()`. The strategy is the default one, " -"federated averaging (or FedAvg), with two clients and evaluation after " -"each federated learning round. The server can be started with the command" -" :code:`fl.server.start_server(server_address=\"0.0.0.0:8080\", " -"strategy=strategy, config=fl.server.ServerConfig(num_rounds=3))`." +"**New advanced PyTorch code example** " +"([#1007](https://github.com/adap/flower/pull/1007))" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:217 +#: ../../source/ref-changelog.md:854 msgid "" -"With both client and server ready, we can now run everything and see " -"federated learning in action. Federated learning systems usually have a " -"server and multiple clients. We, therefore, have to start the server " -"first:" +"A new code example (`advanced_pytorch`) demonstrates advanced Flower " +"concepts with PyTorch." msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:271 +#: ../../source/ref-changelog.md:856 msgid "" -"Congratulations! You've successfully built and run your first federated " -"learning system. The full `source code " -"`_ for this example can be found in :code:`examples/sklearn-logreg-" -"mnist`." +"**New JAX code example** " +"([#906](https://github.com/adap/flower/pull/906), " +"[#1143](https://github.com/adap/flower/pull/1143))" msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:-1 +#: ../../source/ref-changelog.md:858 msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with TensorFlow to train a MobilNetV2 model on CIFAR-10." +"A new code example (`jax_from_centralized_to_federated`) shows federated " +"learning with JAX and Flower." msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:5 -msgid "Quickstart TensorFlow" +#: ../../source/ref-changelog.md:862 +msgid "" +"New option to keep Ray running if Ray was already initialized in " +"`start_simulation` ([#1177](https://github.com/adap/flower/pull/1177))" msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:13 -msgid "Let's build a federated learning system in less than 20 lines of code!" +#: ../../source/ref-changelog.md:863 +msgid "" +"Add support for custom `ClientManager` as a `start_simulation` parameter " +"([#1171](https://github.com/adap/flower/pull/1171))" msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:15 -msgid "Before Flower can be imported we have to install it:" +#: ../../source/ref-changelog.md:864 +msgid "" +"New documentation for [implementing " +"strategies](https://flower.ai/docs/framework/how-to-implement-" +"strategies.html) ([#1097](https://github.com/adap/flower/pull/1097), " +"[#1175](https://github.com/adap/flower/pull/1175))" msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:21 +#: ../../source/ref-changelog.md:865 msgid "" -"Since we want to use the Keras API of TensorFlow (TF), we have to install" -" TF as well:" +"New mobile-friendly documentation theme " +"([#1174](https://github.com/adap/flower/pull/1174))" msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:31 -msgid "Next, in a file called :code:`client.py`, import Flower and TensorFlow:" +#: ../../source/ref-changelog.md:866 +msgid "" +"Limit version range for (optional) `ray` dependency to include only " +"compatible releases (`>=1.9.2,<1.12.0`) " +"([#1205](https://github.com/adap/flower/pull/1205))" msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:38 +#: ../../source/ref-changelog.md:870 msgid "" -"We use the Keras utilities of TF to load CIFAR10, a popular colored image" -" classification dataset for machine learning. The call to " -":code:`tf.keras.datasets.cifar10.load_data()` downloads CIFAR10, caches " -"it locally, and then returns the entire training and test set as NumPy " -"ndarrays." +"**Remove deprecated support for Python 3.6** " +"([#871](https://github.com/adap/flower/pull/871))" msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:47 +#: ../../source/ref-changelog.md:871 msgid "" -"Next, we need a model. For the purpose of this tutorial, we use " -"MobilNetV2 with 10 output classes:" +"**Remove deprecated KerasClient** " +"([#857](https://github.com/adap/flower/pull/857))" msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:60 +#: ../../source/ref-changelog.md:872 msgid "" -"Flower provides a convenience class called :code:`NumPyClient` which " -"makes it easier to implement the :code:`Client` interface when your " -"workload uses Keras. The :code:`NumPyClient` interface defines three " -"methods which can be implemented in the following way:" +"**Remove deprecated no-op extra installs** " +"([#973](https://github.com/adap/flower/pull/973))" msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:135 -msgid "Each client will have its own dataset." +#: ../../source/ref-changelog.md:873 +msgid "" +"**Remove deprecated proto fields from** `FitRes` **and** `EvaluateRes` " +"([#869](https://github.com/adap/flower/pull/869))" msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:137 +#: ../../source/ref-changelog.md:874 msgid "" -"You should now see how the training does in the very first terminal (the " -"one that started the server):" +"**Remove deprecated QffedAvg strategy (replaced by QFedAvg)** " +"([#1107](https://github.com/adap/flower/pull/1107))" msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:169 +#: ../../source/ref-changelog.md:875 msgid "" -"Congratulations! You've successfully built and run your first federated " -"learning system. The full `source code " -"`_ for this can be found in :code:`examples" -"/quickstart-tensorflow/client.py`." +"**Remove deprecated DefaultStrategy strategy** " +"([#1142](https://github.com/adap/flower/pull/1142))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:-1 +#: ../../source/ref-changelog.md:876 msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with XGBoost to train classification models on trees." +"**Remove deprecated support for eval_fn accuracy return value** " +"([#1142](https://github.com/adap/flower/pull/1142))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:5 -msgid "Quickstart XGBoost" +#: ../../source/ref-changelog.md:877 +msgid "" +"**Remove deprecated support for passing initial parameters as NumPy " +"ndarrays** ([#1142](https://github.com/adap/flower/pull/1142))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:14 -msgid "Federated XGBoost" +#: ../../source/ref-changelog.md:879 +msgid "v0.18.0 (2022-02-28)" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:16 +#: ../../source/ref-changelog.md:883 msgid "" -"EXtreme Gradient Boosting (**XGBoost**) is a robust and efficient " -"implementation of gradient-boosted decision tree (**GBDT**), that " -"maximises the computational boundaries for boosted tree methods. It's " -"primarily designed to enhance both the performance and computational " -"speed of machine learning models. In XGBoost, trees are constructed " -"concurrently, unlike the sequential approach taken by GBDT." +"**Improved Virtual Client Engine compatibility with Jupyter Notebook / " +"Google Colab** ([#866](https://github.com/adap/flower/pull/866), " +"[#872](https://github.com/adap/flower/pull/872), " +"[#833](https://github.com/adap/flower/pull/833), " +"[#1036](https://github.com/adap/flower/pull/1036))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:20 +#: ../../source/ref-changelog.md:885 msgid "" -"Often, for tabular data on medium-sized datasets with fewer than 10k " -"training examples, XGBoost surpasses the results of deep learning " -"techniques." +"Simulations (using the Virtual Client Engine through `start_simulation`) " +"now work more smoothly on Jupyter Notebooks (incl. Google Colab) after " +"installing Flower with the `simulation` extra (`pip install " +"flwr[simulation]`)." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:23 -msgid "Why federated XGBoost?" +#: ../../source/ref-changelog.md:887 +msgid "" +"**New Jupyter Notebook code example** " +"([#833](https://github.com/adap/flower/pull/833))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:25 +#: ../../source/ref-changelog.md:889 msgid "" -"Indeed, as the demand for data privacy and decentralized learning grows, " -"there's an increasing requirement to implement federated XGBoost systems " -"for specialised applications, like survival analysis and financial fraud " -"detection." +"A new code example (`quickstart_simulation`) demonstrates Flower " +"simulations using the Virtual Client Engine through Jupyter Notebook " +"(incl. Google Colab)." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:27 +#: ../../source/ref-changelog.md:891 msgid "" -"Federated learning ensures that raw data remains on the local device, " -"making it an attractive approach for sensitive domains where data " -"security and privacy are paramount. Given the robustness and efficiency " -"of XGBoost, combining it with federated learning offers a promising " -"solution for these specific challenges." +"**Client properties (feature preview)** " +"([#795](https://github.com/adap/flower/pull/795))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:30 +#: ../../source/ref-changelog.md:893 msgid "" -"In this tutorial we will learn how to train a federated XGBoost model on " -"HIGGS dataset using Flower and :code:`xgboost` package. We use a simple " -"example (`full code xgboost-quickstart " -"`_)" -" with two *clients* and one *server* to demonstrate how federated XGBoost" -" works, and then we dive into a more complex example (`full code xgboost-" -"comprehensive `_) to run various experiments." +"Clients can implement a new method `get_properties` to enable server-side" +" strategies to query client properties." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:37 -msgid "Environment Setup" +#: ../../source/ref-changelog.md:895 +msgid "" +"**Experimental Android support with TFLite** " +"([#865](https://github.com/adap/flower/pull/865))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:41 +#: ../../source/ref-changelog.md:897 msgid "" -"We first need to install Flower and Flower Datasets. You can do this by " -"running :" +"Android support has finally arrived in `main`! Flower is both client-" +"agnostic and framework-agnostic by design. One can integrate arbitrary " +"client platforms and with this release, using Flower on Android has " +"become a lot easier." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:47 +#: ../../source/ref-changelog.md:899 msgid "" -"Since we want to use :code:`xgboost` package to build up XGBoost trees, " -"let's go ahead and install :code:`xgboost`:" +"The example uses TFLite on the client side, along with a new " +"`FedAvgAndroid` strategy. The Android client and `FedAvgAndroid` are " +"still experimental, but they are a first step towards a fully-fledged " +"Android SDK and a unified `FedAvg` implementation that integrated the new" +" functionality from `FedAvgAndroid`." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:57 +#: ../../source/ref-changelog.md:901 msgid "" -"*Clients* are responsible for generating individual weight-updates for " -"the model based on their local datasets. Now that we have all our " -"dependencies installed, let's run a simple distributed training with two " -"clients and one server." +"**Make gRPC keepalive time user-configurable and decrease default " +"keepalive time** ([#1069](https://github.com/adap/flower/pull/1069))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:60 +#: ../../source/ref-changelog.md:903 msgid "" -"In a file called :code:`client.py`, import xgboost, Flower, Flower " -"Datasets and other related functions:" +"The default gRPC keepalive time has been reduced to increase the " +"compatibility of Flower with more cloud environments (for example, " +"Microsoft Azure). Users can configure the keepalive time to customize the" +" gRPC stack based on specific requirements." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:87 -msgid "Dataset partition and hyper-parameter selection" +#: ../../source/ref-changelog.md:905 +msgid "" +"**New differential privacy example using Opacus and PyTorch** " +"([#805](https://github.com/adap/flower/pull/805))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:89 +#: ../../source/ref-changelog.md:907 msgid "" -"Prior to local training, we require loading the HIGGS dataset from Flower" -" Datasets and conduct data partitioning for FL:" +"A new code example (`opacus`) demonstrates differentially-private " +"federated learning with Opacus, PyTorch, and Flower." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:102 +#: ../../source/ref-changelog.md:909 msgid "" -"In this example, we split the dataset into two partitions with uniform " -"distribution (:code:`IidPartitioner(num_partitions=2)`). Then, we load " -"the partition for the given client based on :code:`node_id`:" +"**New Hugging Face Transformers code example** " +"([#863](https://github.com/adap/flower/pull/863))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:121 +#: ../../source/ref-changelog.md:911 msgid "" -"After that, we do train/test splitting on the given partition (client's " -"local data), and transform data format for :code:`xgboost` package." +"A new code example (`quickstart_huggingface`) demonstrates usage of " +"Hugging Face Transformers with Flower." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:134 +#: ../../source/ref-changelog.md:913 msgid "" -"The functions of :code:`train_test_split` and " -":code:`transform_dataset_to_dmatrix` are defined as below:" +"**New MLCube code example** " +"([#779](https://github.com/adap/flower/pull/779), " +"[#1034](https://github.com/adap/flower/pull/1034), " +"[#1065](https://github.com/adap/flower/pull/1065), " +"[#1090](https://github.com/adap/flower/pull/1090))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:158 -msgid "Finally, we define the hyper-parameters used for XGBoost training." +#: ../../source/ref-changelog.md:915 +msgid "" +"A new code example (`quickstart_mlcube`) demonstrates usage of MLCube " +"with Flower." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:174 +#: ../../source/ref-changelog.md:917 msgid "" -"The :code:`num_local_round` represents the number of iterations for local" -" tree boost. We use CPU for the training in default. One can shift it to " -"GPU by setting :code:`tree_method` to :code:`gpu_hist`. We use AUC as " -"evaluation metric." +"**SSL-enabled server and client** " +"([#842](https://github.com/adap/flower/pull/842), " +"[#844](https://github.com/adap/flower/pull/844), " +"[#845](https://github.com/adap/flower/pull/845), " +"[#847](https://github.com/adap/flower/pull/847), " +"[#993](https://github.com/adap/flower/pull/993), " +"[#994](https://github.com/adap/flower/pull/994))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:181 -msgid "Flower client definition for XGBoost" +#: ../../source/ref-changelog.md:919 +msgid "" +"SSL enables secure encrypted connections between clients and servers. " +"This release open-sources the Flower secure gRPC implementation to make " +"encrypted communication channels accessible to all Flower users." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:183 +#: ../../source/ref-changelog.md:921 msgid "" -"After loading the dataset we define the Flower client. We follow the " -"general rule to define :code:`XgbClient` class inherited from " -":code:`fl.client.Client`." +"**Updated** `FedAdam` **and** `FedYogi` **strategies** " +"([#885](https://github.com/adap/flower/pull/885), " +"[#895](https://github.com/adap/flower/pull/895))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:193 +#: ../../source/ref-changelog.md:923 msgid "" -"The :code:`self.bst` is used to keep the Booster objects that remain " -"consistent across rounds, allowing them to store predictions from trees " -"integrated in earlier rounds and maintain other essential data structures" -" for training." +"`FedAdam` and `FedAdam` match the latest version of the Adaptive " +"Federated Optimization paper." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:196 +#: ../../source/ref-changelog.md:925 msgid "" -"Then, we override :code:`get_parameters`, :code:`fit` and " -":code:`evaluate` methods insides :code:`XgbClient` class as follows." +"**Initialize** `start_simulation` **with a list of client IDs** " +"([#860](https://github.com/adap/flower/pull/860))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:210 +#: ../../source/ref-changelog.md:927 msgid "" -"Unlike neural network training, XGBoost trees are not started from a " -"specified random weights. In this case, we do not use " -":code:`get_parameters` and :code:`set_parameters` to initialise model " -"parameters for XGBoost. As a result, let's return an empty tensor in " -":code:`get_parameters` when it is called by the server at the first " -"round." +"`start_simulation` can now be called with a list of client IDs " +"(`clients_ids`, type: `List[str]`). Those IDs will be passed to the " +"`client_fn` whenever a client needs to be initialized, which can make it " +"easier to load data partitions that are not accessible through `int` " +"identifiers." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:251 +#: ../../source/ref-changelog.md:931 msgid "" -"In :code:`fit`, at the first round, we call :code:`xgb.train()` to build " -"up the first set of trees. the returned Booster object and config are " -"stored in :code:`self.bst` and :code:`self.config`, respectively. From " -"the second round, we load the global model sent from server to " -":code:`self.bst`, and then update model weights on local training data " -"with function :code:`local_boost` as follows:" +"Update `num_examples` calculation in PyTorch code examples in " +"([#909](https://github.com/adap/flower/pull/909))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:269 +#: ../../source/ref-changelog.md:932 msgid "" -"Given :code:`num_local_round`, we update trees by calling " -":code:`self.bst.update` method. After training, the last " -":code:`N=num_local_round` trees will be extracted to send to the server." +"Expose Flower version through `flwr.__version__` " +"([#952](https://github.com/adap/flower/pull/952))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:291 +#: ../../source/ref-changelog.md:933 msgid "" -"In :code:`evaluate`, we call :code:`self.bst.eval_set` function to " -"conduct evaluation on valid set. The AUC value will be returned." +"`start_server` in `app.py` now returns a `History` object containing " +"metrics from training ([#974](https://github.com/adap/flower/pull/974))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:294 +#: ../../source/ref-changelog.md:934 msgid "" -"Now, we can create an instance of our class :code:`XgbClient` and add one" -" line to actually run this client:" +"Make `max_workers` (used by `ThreadPoolExecutor`) configurable " +"([#978](https://github.com/adap/flower/pull/978))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:300 +#: ../../source/ref-changelog.md:935 msgid "" -"That's it for the client. We only have to implement :code:`Client`and " -"call :code:`fl.client.start_client()`. The string :code:`\"[::]:8080\"` " -"tells the client which server to connect to. In our case we can run the " -"server and the client on the same machine, therefore we use " -":code:`\"[::]:8080\"`. If we run a truly federated workload with the " -"server and clients running on different machines, all that needs to " -"change is the :code:`server_address` we point the client at." +"Increase sleep time after server start to three seconds in all code " +"examples ([#1086](https://github.com/adap/flower/pull/1086))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:311 +#: ../../source/ref-changelog.md:936 msgid "" -"These updates are then sent to the *server* which will aggregate them to " -"produce a better model. Finally, the *server* sends this improved version" -" of the model back to each *client* to finish a complete FL round." +"Added a new FAQ section to the documentation " +"([#948](https://github.com/adap/flower/pull/948))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:314 +#: ../../source/ref-changelog.md:937 msgid "" -"In a file named :code:`server.py`, import Flower and FedXgbBagging from " -":code:`flwr.server.strategy`." +"And many more under-the-hood changes, library updates, documentation " +"changes, and tooling improvements!" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:316 -msgid "We first define a strategy for XGBoost bagging aggregation." +#: ../../source/ref-changelog.md:941 +msgid "" +"**Removed** `flwr_example` **and** `flwr_experimental` **from release " +"build** ([#869](https://github.com/adap/flower/pull/869))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:339 +#: ../../source/ref-changelog.md:943 msgid "" -"We use two clients for this example. An " -":code:`evaluate_metrics_aggregation` function is defined to collect and " -"wighted average the AUC values from clients." +"The packages `flwr_example` and `flwr_experimental` have been deprecated " +"since Flower 0.12.0 and they are not longer included in Flower release " +"builds. The associated extras (`baseline`, `examples-pytorch`, `examples-" +"tensorflow`, `http-logger`, `ops`) are now no-op and will be removed in " +"an upcoming release." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:342 -msgid "Then, we start the server:" +#: ../../source/ref-changelog.md:945 +msgid "v0.17.0 (2021-09-24)" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:354 -msgid "Tree-based bagging aggregation" +#: ../../source/ref-changelog.md:949 +msgid "" +"**Experimental virtual client engine** " +"([#781](https://github.com/adap/flower/pull/781) " +"[#790](https://github.com/adap/flower/pull/790) " +"[#791](https://github.com/adap/flower/pull/791))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:356 +#: ../../source/ref-changelog.md:951 msgid "" -"You must be curious about how bagging aggregation works. Let's look into " -"the details." +"One of Flower's goals is to enable research at scale. This release " +"enables a first (experimental) peek at a major new feature, codenamed the" +" virtual client engine. Virtual clients enable simulations that scale to " +"a (very) large number of clients on a single machine or compute cluster. " +"The easiest way to test the new functionality is to look at the two new " +"code examples called `quickstart_simulation` and `simulation_pytorch`." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:358 +#: ../../source/ref-changelog.md:953 msgid "" -"In file :code:`flwr.server.strategy.fedxgb_bagging.py`, we define " -":code:`FedXgbBagging` inherited from :code:`flwr.server.strategy.FedAvg`." -" Then, we override the :code:`aggregate_fit`, :code:`aggregate_evaluate` " -"and :code:`evaluate` methods as follows:" +"The feature is still experimental, so there's no stability guarantee for " +"the API. It's also not quite ready for prime time and comes with a few " +"known caveats. However, those who are curious are encouraged to try it " +"out and share their thoughts." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:454 +#: ../../source/ref-changelog.md:955 msgid "" -"In :code:`aggregate_fit`, we sequentially aggregate the clients' XGBoost " -"trees by calling :code:`aggregate()` function:" +"**New built-in strategies** " +"([#828](https://github.com/adap/flower/pull/828) " +"[#822](https://github.com/adap/flower/pull/822))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:513 +#: ../../source/ref-changelog.md:957 msgid "" -"In this function, we first fetch the number of trees and the number of " -"parallel trees for the current and previous model by calling " -":code:`_get_tree_nums`. Then, the fetched information will be aggregated." -" After that, the trees (containing model weights) are aggregated to " -"generate a new tree model." +"FedYogi - Federated learning strategy using Yogi on server-side. " +"Implementation based on https://arxiv.org/abs/2003.00295" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:518 +#: ../../source/ref-changelog.md:958 msgid "" -"After traversal of all clients' models, a new global model is generated, " -"followed by the serialisation, and sending back to each client." +"FedAdam - Federated learning strategy using Adam on server-side. " +"Implementation based on https://arxiv.org/abs/2003.00295" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:523 -msgid "Launch Federated XGBoost!" +#: ../../source/ref-changelog.md:960 +msgid "" +"**New PyTorch Lightning code example** " +"([#617](https://github.com/adap/flower/pull/617))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:585 +#: ../../source/ref-changelog.md:962 msgid "" -"Congratulations! You've successfully built and run your first federated " -"XGBoost system. The AUC values can be checked in " -":code:`metrics_distributed`. One can see that the average AUC increases " -"over FL rounds." +"**New Variational Auto-Encoder code example** " +"([#752](https://github.com/adap/flower/pull/752))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:590 +#: ../../source/ref-changelog.md:964 msgid "" -"The full `source code `_ for this example can be found in :code:`examples" -"/xgboost-quickstart`." +"**New scikit-learn code example** " +"([#748](https://github.com/adap/flower/pull/748))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:594 -msgid "Comprehensive Federated XGBoost" +#: ../../source/ref-changelog.md:966 +msgid "" +"**New experimental TensorBoard strategy** " +"([#789](https://github.com/adap/flower/pull/789))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:596 +#: ../../source/ref-changelog.md:970 msgid "" -"Now that you have known how federated XGBoost work with Flower, it's time" -" to run some more comprehensive experiments by customising the " -"experimental settings. In the xgboost-comprehensive example (`full code " -"`_), we provide more options to define various experimental" -" setups, including aggregation strategies, data partitioning and " -"centralised/distributed evaluation. We also support :doc:`Flower " -"simulation ` making it easy to simulate large " -"client cohorts in a resource-aware manner. Let's take a look!" +"Improved advanced TensorFlow code example " +"([#769](https://github.com/adap/flower/pull/769))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:603 -msgid "Cyclic training" +#: ../../source/ref-changelog.md:971 +msgid "" +"Warning when `min_available_clients` is misconfigured " +"([#830](https://github.com/adap/flower/pull/830))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:605 +#: ../../source/ref-changelog.md:972 msgid "" -"In addition to bagging aggregation, we offer a cyclic training scheme, " -"which performs FL in a client-by-client fashion. Instead of aggregating " -"multiple clients, there is only one single client participating in the " -"training per round in the cyclic training scenario. The trained local " -"XGBoost trees will be passed to the next client as an initialised model " -"for next round's boosting." +"Improved gRPC server docs " +"([#841](https://github.com/adap/flower/pull/841))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:609 +#: ../../source/ref-changelog.md:973 msgid "" -"To do this, we first customise a :code:`ClientManager` in " -":code:`server_utils.py`:" +"Improved error message in `NumPyClient` " +"([#851](https://github.com/adap/flower/pull/851))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:649 +#: ../../source/ref-changelog.md:974 msgid "" -"The customised :code:`ClientManager` samples all available clients in " -"each FL round based on the order of connection to the server. Then, we " -"define a new strategy :code:`FedXgbCyclic` in " -":code:`flwr.server.strategy.fedxgb_cyclic.py`, in order to sequentially " -"select only one client in given round and pass the received model to next" -" client." +"Improved PyTorch quickstart code example " +"([#852](https://github.com/adap/flower/pull/852))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:690 +#: ../../source/ref-changelog.md:978 msgid "" -"Unlike the original :code:`FedAvg`, we don't perform aggregation here. " -"Instead, we just make a copy of the received client model as global model" -" by overriding :code:`aggregate_fit`." +"**Disabled final distributed evaluation** " +"([#800](https://github.com/adap/flower/pull/800))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:693 +#: ../../source/ref-changelog.md:980 msgid "" -"Also, the customised :code:`configure_fit` and :code:`configure_evaluate`" -" methods ensure the clients to be sequentially selected given FL round:" +"Prior behaviour was to perform a final round of distributed evaluation on" +" all connected clients, which is often not required (e.g., when using " +"server-side evaluation). The prior behaviour can be enabled by passing " +"`force_final_distributed_eval=True` to `start_server`." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:757 -msgid "Customised data partitioning" +#: ../../source/ref-changelog.md:982 +msgid "" +"**Renamed q-FedAvg strategy** " +"([#802](https://github.com/adap/flower/pull/802))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:759 +#: ../../source/ref-changelog.md:984 msgid "" -"In :code:`dataset.py`, we have a function :code:`instantiate_partitioner`" -" to instantiate the data partitioner based on the given " -":code:`num_partitions` and :code:`partitioner_type`. Currently, we " -"provide four supported partitioner type to simulate the uniformity/non-" -"uniformity in data quantity (uniform, linear, square, exponential)." +"The strategy named `QffedAvg` was renamed to `QFedAvg` to better reflect " +"the notation given in the original paper (q-FFL is the optimization " +"objective, q-FedAvg is the proposed solver). Note the original (now " +"deprecated) `QffedAvg` class is still available for compatibility reasons" +" (it will be removed in a future release)." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:790 -msgid "Customised centralised/distributed evaluation" +#: ../../source/ref-changelog.md:986 +msgid "" +"**Deprecated and renamed code example** `simulation_pytorch` **to** " +"`simulation_pytorch_legacy` " +"([#791](https://github.com/adap/flower/pull/791))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:792 +#: ../../source/ref-changelog.md:988 msgid "" -"To facilitate centralised evaluation, we define a function in " -":code:`server_utils.py`:" +"This example has been replaced by a new example. The new example is based" +" on the experimental virtual client engine, which will become the new " +"default way of doing most types of large-scale simulations in Flower. The" +" existing example was kept for reference purposes, but it might be " +"removed in the future." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:824 -msgid "" -"This function returns a evaluation function which instantiates a " -":code:`Booster` object and loads the global model weights to it. The " -"evaluation is conducted by calling :code:`eval_set()` method, and the " -"tested AUC value is reported." +#: ../../source/ref-changelog.md:990 +msgid "v0.16.0 (2021-05-11)" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:827 +#: ../../source/ref-changelog.md:994 msgid "" -"As for distributed evaluation on the clients, it's same as the quick-" -"start example by overriding the :code:`evaluate()` method insides the " -":code:`XgbClient` class in :code:`client_utils.py`." +"**New built-in strategies** " +"([#549](https://github.com/adap/flower/pull/549))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:831 -msgid "Flower simulation" +#: ../../source/ref-changelog.md:996 +msgid "(abstract) FedOpt" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:832 +#: ../../source/ref-changelog.md:999 msgid "" -"We also provide an example code (:code:`sim.py`) to use the simulation " -"capabilities of Flower to simulate federated XGBoost training on either a" -" single machine or a cluster of machines." +"**Custom metrics for server and strategies** " +"([#717](https://github.com/adap/flower/pull/717))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:866 +#: ../../source/ref-changelog.md:1001 msgid "" -"After importing all required packages, we define a :code:`main()` " -"function to perform the simulation process:" +"The Flower server is now fully task-agnostic, all remaining instances of " +"task-specific metrics (such as `accuracy`) have been replaced by custom " +"metrics dictionaries. Flower 0.15 introduced the capability to pass a " +"dictionary containing custom metrics from client to server. As of this " +"release, custom metrics replace task-specific metrics on the server." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:921 +#: ../../source/ref-changelog.md:1003 msgid "" -"We first load the dataset and perform data partitioning, and the pre-" -"processed data is stored in a :code:`list`. After the simulation begins, " -"the clients won't need to pre-process their partitions again." +"Custom metric dictionaries are now used in two user-facing APIs: they are" +" returned from Strategy methods `aggregate_fit`/`aggregate_evaluate` and " +"they enable evaluation functions passed to built-in strategies (via " +"`eval_fn`) to return more than two evaluation metrics. Strategies can " +"even return *aggregated* metrics dictionaries for the server to keep " +"track of." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:924 -msgid "Then, we define the strategies and other hyper-parameters:" +#: ../../source/ref-changelog.md:1005 +msgid "" +"Strategy implementations should migrate their `aggregate_fit` and " +"`aggregate_evaluate` methods to the new return type (e.g., by simply " +"returning an empty `{}`), server-side evaluation functions should migrate" +" from `return loss, accuracy` to `return loss, {\"accuracy\": accuracy}`." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:975 +#: ../../source/ref-changelog.md:1007 msgid "" -"After that, we start the simulation by calling " -":code:`fl.simulation.start_simulation`:" +"Flower 0.15-style return types are deprecated (but still supported), " +"compatibility will be removed in a future release." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:995 +#: ../../source/ref-changelog.md:1009 msgid "" -"One of key parameters for :code:`start_simulation` is :code:`client_fn` " -"which returns a function to construct a client. We define it as follows:" +"**Migration warnings for deprecated functionality** " +"([#690](https://github.com/adap/flower/pull/690))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1038 -msgid "Arguments parser" +#: ../../source/ref-changelog.md:1011 +msgid "" +"Earlier versions of Flower were often migrated to new APIs, while " +"maintaining compatibility with legacy APIs. This release introduces " +"detailed warning messages if usage of deprecated APIs is detected. The " +"new warning messages often provide details on how to migrate to more " +"recent APIs, thus easing the transition from one release to another." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1040 +#: ../../source/ref-changelog.md:1013 msgid "" -"In :code:`utils.py`, we define the arguments parsers for clients, server " -"and simulation, allowing users to specify different experimental " -"settings. Let's first see the sever side:" +"Improved docs and docstrings " +"([#691](https://github.com/adap/flower/pull/691) " +"[#692](https://github.com/adap/flower/pull/692) " +"[#713](https://github.com/adap/flower/pull/713))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1086 +#: ../../source/ref-changelog.md:1015 +msgid "MXNet example and documentation" +msgstr "" + +#: ../../source/ref-changelog.md:1017 msgid "" -"This allows user to specify training strategies / the number of total " -"clients / FL rounds / participating clients / clients for evaluation, and" -" evaluation fashion. Note that with :code:`--centralised-eval`, the sever" -" will do centralised evaluation and all functionalities for client " -"evaluation will be disabled." +"FedBN implementation in example PyTorch: From Centralized To Federated " +"([#696](https://github.com/adap/flower/pull/696) " +"[#702](https://github.com/adap/flower/pull/702) " +"[#705](https://github.com/adap/flower/pull/705))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1090 -msgid "Then, the argument parser on client side:" +#: ../../source/ref-changelog.md:1021 +msgid "" +"**Serialization-agnostic server** " +"([#721](https://github.com/adap/flower/pull/721))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1144 +#: ../../source/ref-changelog.md:1023 msgid "" -"This defines various options for client data partitioning. Besides, " -"clients also have an option to conduct evaluation on centralised test set" -" by setting :code:`--centralised-eval`, as well as an option to perform " -"scaled learning rate based on the number of clients by setting :code" -":`--scaled-lr`." +"The Flower server is now fully serialization-agnostic. Prior usage of " +"class `Weights` (which represents parameters as deserialized NumPy " +"ndarrays) was replaced by class `Parameters` (e.g., in `Strategy`). " +"`Parameters` objects are fully serialization-agnostic and represents " +"parameters as byte arrays, the `tensor_type` attributes indicates how " +"these byte arrays should be interpreted (e.g., for " +"serialization/deserialization)." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1148 -msgid "We also have an argument parser for simulation:" +#: ../../source/ref-changelog.md:1025 +msgid "" +"Built-in strategies implement this approach by handling serialization and" +" deserialization to/from `Weights` internally. Custom/3rd-party Strategy " +"implementations should update to the slightly changed Strategy method " +"definitions. Strategy authors can consult PR " +"[#721](https://github.com/adap/flower/pull/721) to see how strategies can" +" easily migrate to the new format." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1226 -msgid "This integrates all arguments for both client and server sides." +#: ../../source/ref-changelog.md:1027 +msgid "" +"Deprecated `flwr.server.Server.evaluate`, use " +"`flwr.server.Server.evaluate_round` instead " +"([#717](https://github.com/adap/flower/pull/717))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1229 -msgid "Example commands" +#: ../../source/ref-changelog.md:1029 +msgid "v0.15.0 (2021-03-12)" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1231 +#: ../../source/ref-changelog.md:1033 msgid "" -"To run a centralised evaluated experiment with bagging strategy on 5 " -"clients with exponential distribution for 50 rounds, we first start the " -"server as below:" +"**Server-side parameter initialization** " +"([#658](https://github.com/adap/flower/pull/658))" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1238 -msgid "Then, on each client terminal, we start the clients:" +#: ../../source/ref-changelog.md:1035 +msgid "" +"Model parameters can now be initialized on the server-side. Server-side " +"parameter initialization works via a new `Strategy` method called " +"`initialize_parameters`." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1244 -msgid "To run the same experiment with Flower simulation:" +#: ../../source/ref-changelog.md:1037 +msgid "" +"Built-in strategies support a new constructor argument called " +"`initial_parameters` to set the initial parameters. Built-in strategies " +"will provide these initial parameters to the server on startup and then " +"delete them to free the memory afterwards." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1250 +#: ../../source/ref-changelog.md:1056 msgid "" -"The full `code `_ for this comprehensive example can be found in" -" :code:`examples/xgboost-comprehensive`." +"If no initial parameters are provided to the strategy, the server will " +"continue to use the current behaviour (namely, it will ask one of the " +"connected clients for its parameters and use these as the initial global " +"parameters)." msgstr "" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:9 -msgid "Build a strategy from scratch" +#: ../../source/ref-changelog.md:1060 +msgid "" +"Deprecate `flwr.server.strategy.DefaultStrategy` (migrate to " +"`flwr.server.strategy.FedAvg`, which is equivalent)" msgstr "" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:11 -msgid "" -"Welcome to the third part of the Flower federated learning tutorial. In " -"previous parts of this tutorial, we introduced federated learning with " -"PyTorch and Flower (`part 1 `__) and we learned how strategies " -"can be used to customize the execution on both the server and the clients" -" (`part 2 `__)." +#: ../../source/ref-changelog.md:1062 +msgid "v0.14.0 (2021-02-18)" msgstr "" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:13 +#: ../../source/ref-changelog.md:1066 msgid "" -"In this notebook, we'll continue to customize the federated learning " -"system we built previously by creating a custom version of FedAvg (again," -" using `Flower `__ and `PyTorch " -"`__)." +"**Generalized** `Client.fit` **and** `Client.evaluate` **return values** " +"([#610](https://github.com/adap/flower/pull/610) " +"[#572](https://github.com/adap/flower/pull/572) " +"[#633](https://github.com/adap/flower/pull/633))" msgstr "" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:15 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:16 -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:15 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:15 +#: ../../source/ref-changelog.md:1068 msgid "" -"`Star Flower on GitHub `__ ⭐️ and join " -"the Flower community on Slack to connect, ask questions, and get help: " -"`Join Slack `__ 🌼 We'd love to hear from " -"you in the ``#introductions`` channel! And if anything is unclear, head " -"over to the ``#questions`` channel." +"Clients can now return an additional dictionary mapping `str` keys to " +"values of the following types: `bool`, `bytes`, `float`, `int`, `str`. " +"This means one can return almost arbitrary values from `fit`/`evaluate` " +"and make use of them on the server side!" msgstr "" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:17 -msgid "Let's build a new ``Strategy`` from scratch!" +#: ../../source/ref-changelog.md:1070 +msgid "" +"This improvement also allowed for more consistent return types between " +"`fit` and `evaluate`: `evaluate` should now return a tuple `(float, int, " +"dict)` representing the loss, number of examples, and a dictionary " +"holding arbitrary problem-specific values like accuracy." msgstr "" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:29 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:29 -msgid "Preparation" +#: ../../source/ref-changelog.md:1072 +msgid "" +"In case you wondered: this feature is compatible with existing projects, " +"the additional dictionary return value is optional. New code should " +"however migrate to the new return types to be compatible with upcoming " +"Flower releases (`fit`: `List[np.ndarray], int, Dict[str, Scalar]`, " +"`evaluate`: `float, int, Dict[str, Scalar]`). See the example below for " +"details." msgstr "" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:31 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:32 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:31 +#: ../../source/ref-changelog.md:1074 msgid "" -"Before we begin with the actual code, let's make sure that we have " -"everything we need." +"*Code example:* note the additional dictionary return values in both " +"`FlwrClient.fit` and `FlwrClient.evaluate`:" msgstr "" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:43 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:44 -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:43 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:43 -msgid "Installing dependencies" +#: ../../source/ref-changelog.md:1089 +msgid "" +"**Generalized** `config` **argument in** `Client.fit` **and** " +"`Client.evaluate` ([#595](https://github.com/adap/flower/pull/595))" msgstr "" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:45 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:46 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:45 -msgid "First, we install the necessary packages:" +#: ../../source/ref-changelog.md:1091 +msgid "" +"The `config` argument used to be of type `Dict[str, str]`, which means " +"that dictionary values were expected to be strings. The new release " +"generalizes this to enable values of the following types: `bool`, " +"`bytes`, `float`, `int`, `str`." msgstr "" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:65 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:66 -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:65 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:65 +#: ../../source/ref-changelog.md:1093 msgid "" -"Now that we have all dependencies installed, we can import everything we " -"need for this tutorial:" +"This means one can now pass almost arbitrary values to `fit`/`evaluate` " +"using the `config` dictionary. Yay, no more `str(epochs)` on the server-" +"side and `int(config[\"epochs\"])` on the client side!" msgstr "" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:101 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:102 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:101 +#: ../../source/ref-changelog.md:1095 msgid "" -"It is possible to switch to a runtime that has GPU acceleration enabled " -"(on Google Colab: ``Runtime > Change runtime type > Hardware acclerator: " -"GPU > Save``). Note, however, that Google Colab is not always able to " -"offer GPU acceleration. If you see an error related to GPU availability " -"in one of the following sections, consider switching back to CPU-based " -"execution by setting ``DEVICE = torch.device(\"cpu\")``. If the runtime " -"has GPU acceleration enabled, you should see the output ``Training on " -"cuda``, otherwise it'll say ``Training on cpu``." +"*Code example:* note that the `config` dictionary now contains non-`str` " +"values in both `Client.fit` and `Client.evaluate`:" msgstr "" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:114 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:115 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:114 -msgid "Data loading" +#: ../../source/ref-changelog.md:1112 +msgid "v0.13.0 (2021-01-08)" msgstr "" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:116 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:116 +#: ../../source/ref-changelog.md:1116 msgid "" -"Let's now load the CIFAR-10 training and test set, partition them into " -"ten smaller datasets (each split into training and validation set), and " -"wrap everything in their own ``DataLoader``. We introduce a new parameter" -" ``num_clients`` which allows us to call ``load_datasets`` with different" -" numbers of clients." +"New example: PyTorch From Centralized To Federated " +"([#549](https://github.com/adap/flower/pull/549))" msgstr "" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:167 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:168 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:167 -msgid "Model training/evaluation" +#: ../../source/ref-changelog.md:1117 +msgid "Improved documentation" msgstr "" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:169 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:170 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:169 -msgid "" -"Let's continue with the usual model definition (including " -"``set_parameters`` and ``get_parameters``), training and test functions:" +#: ../../source/ref-changelog.md:1118 +msgid "New documentation theme ([#551](https://github.com/adap/flower/pull/551))" msgstr "" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:258 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:258 -msgid "Flower client" +#: ../../source/ref-changelog.md:1119 +msgid "New API reference ([#554](https://github.com/adap/flower/pull/554))" msgstr "" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:260 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:260 +#: ../../source/ref-changelog.md:1120 msgid "" -"To implement the Flower client, we (again) create a subclass of " -"``flwr.client.NumPyClient`` and implement the three methods " -"``get_parameters``, ``fit``, and ``evaluate``. Here, we also pass the " -"``cid`` to the client and use it log additional details:" +"Updated examples documentation " +"([#549](https://github.com/adap/flower/pull/549))" msgstr "" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:308 -msgid "Let's test what we have so far before we continue:" +#: ../../source/ref-changelog.md:1121 +msgid "" +"Removed obsolete documentation " +"([#548](https://github.com/adap/flower/pull/548))" msgstr "" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:339 -msgid "Build a Strategy from scratch" +#: ../../source/ref-changelog.md:1123 +msgid "Bugfix:" msgstr "" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:341 +#: ../../source/ref-changelog.md:1125 msgid "" -"Let’s overwrite the ``configure_fit`` method such that it passes a higher" -" learning rate (potentially also other hyperparameters) to the optimizer " -"of a fraction of the clients. We will keep the sampling of the clients as" -" it is in ``FedAvg`` and then change the configuration dictionary (one of" -" the ``FitIns`` attributes)." +"`Server.fit` does not disconnect clients when finished, disconnecting the" +" clients is now handled in `flwr.server.start_server` " +"([#553](https://github.com/adap/flower/pull/553) " +"[#540](https://github.com/adap/flower/issues/540))." msgstr "" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:507 -msgid "" -"The only thing left is to use the newly created custom Strategy " -"``FedCustom`` when starting the experiment:" +#: ../../source/ref-changelog.md:1127 +msgid "v0.12.0 (2020-12-07)" msgstr "" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:534 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:932 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:697 -msgid "Recap" +#: ../../source/ref-changelog.md:1129 ../../source/ref-changelog.md:1145 +msgid "Important changes:" msgstr "" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:536 +#: ../../source/ref-changelog.md:1131 msgid "" -"In this notebook, we’ve seen how to implement a custom strategy. A custom" -" strategy enables granular control over client node configuration, result" -" aggregation, and more. To define a custom strategy, you only have to " -"overwrite the abstract methods of the (abstract) base class ``Strategy``." -" To make custom strategies even more powerful, you can pass custom " -"functions to the constructor of your new class (``__init__``) and then " -"call these functions whenever needed." +"Added an example for embedded devices " +"([#507](https://github.com/adap/flower/pull/507))" msgstr "" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:550 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:948 -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:729 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:715 -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:369 +#: ../../source/ref-changelog.md:1132 msgid "" -"Before you continue, make sure to join the Flower community on Slack: " -"`Join Slack `__" +"Added a new NumPyClient (in addition to the existing KerasClient) " +"([#504](https://github.com/adap/flower/pull/504) " +"[#508](https://github.com/adap/flower/pull/508))" msgstr "" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:552 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:950 -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:731 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:717 -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:371 +#: ../../source/ref-changelog.md:1133 msgid "" -"There's a dedicated ``#questions`` channel if you need help, but we'd " -"also love to hear who you are in ``#introductions``!" +"Deprecated `flwr_example` package and started to migrate examples into " +"the top-level `examples` directory " +"([#494](https://github.com/adap/flower/pull/494) " +"[#512](https://github.com/adap/flower/pull/512))" msgstr "" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:554 -msgid "" -"The `Flower Federated Learning Tutorial - Part 4 " -"`__ introduces ``Client``, the flexible API underlying " -"``NumPyClient``." +#: ../../source/ref-changelog.md:1135 +msgid "v0.11.0 (2020-11-30)" msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:9 -msgid "Customize the client" +#: ../../source/ref-changelog.md:1137 +msgid "Incompatible changes:" msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:11 +#: ../../source/ref-changelog.md:1139 msgid "" -"Welcome to the fourth part of the Flower federated learning tutorial. In " -"the previous parts of this tutorial, we introduced federated learning " -"with PyTorch and Flower (`part 1 `__), we learned how " -"strategies can be used to customize the execution on both the server and " -"the clients (`part 2 `__), and we built our own " -"custom strategy from scratch (`part 3 `__)." +"Renamed strategy methods " +"([#486](https://github.com/adap/flower/pull/486)) to unify the naming of " +"Flower's public APIs. Other public methods/functions (e.g., every method " +"in `Client`, but also `Strategy.evaluate`) do not use the `on_` prefix, " +"which is why we're removing it from the four methods in Strategy. To " +"migrate rename the following `Strategy` methods accordingly:" msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:14 -msgid "" -"In this notebook, we revisit ``NumPyClient`` and introduce a new " -"baseclass for building clients, simply named ``Client``. In previous " -"parts of this tutorial, we've based our client on ``NumPyClient``, a " -"convenience class which makes it easy to work with machine learning " -"libraries that have good NumPy interoperability. With ``Client``, we gain" -" a lot of flexibility that we didn't have before, but we'll also have to " -"do a few things the we didn't have to do before." +#: ../../source/ref-changelog.md:1140 +msgid "`on_configure_evaluate` => `configure_evaluate`" msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:18 -msgid "" -"Let's go deeper and see what it takes to move from ``NumPyClient`` to " -"``Client``!" +#: ../../source/ref-changelog.md:1141 +msgid "`on_aggregate_evaluate` => `aggregate_evaluate`" msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:30 -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:29 -msgid "Step 0: Preparation" +#: ../../source/ref-changelog.md:1142 +msgid "`on_configure_fit` => `configure_fit`" msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:117 -msgid "" -"Let's now load the CIFAR-10 training and test set, partition them into " -"ten smaller datasets (each split into training and validation set), and " -"wrap everything in their own ``DataLoader``." +#: ../../source/ref-changelog.md:1143 +msgid "`on_aggregate_fit` => `aggregate_fit`" msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:259 -msgid "Step 1: Revisiting NumPyClient" +#: ../../source/ref-changelog.md:1147 +msgid "" +"Deprecated `DefaultStrategy` " +"([#479](https://github.com/adap/flower/pull/479)). To migrate use " +"`FedAvg` instead." msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:261 +#: ../../source/ref-changelog.md:1148 msgid "" -"So far, we've implemented our client by subclassing " -"``flwr.client.NumPyClient``. The three methods we implemented are " -"``get_parameters``, ``fit``, and ``evaluate``. Finally, we wrap the " -"creation of instances of this class in a function called ``client_fn``:" +"Simplified examples and baselines " +"([#484](https://github.com/adap/flower/pull/484))." msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:309 +#: ../../source/ref-changelog.md:1149 msgid "" -"We've seen this before, there's nothing new so far. The only *tiny* " -"difference compared to the previous notebook is naming, we've changed " -"``FlowerClient`` to ``FlowerNumPyClient`` and ``client_fn`` to " -"``numpyclient_fn``. Let's run it to see the output we get:" +"Removed presently unused `on_conclude_round` from strategy interface " +"([#483](https://github.com/adap/flower/pull/483))." msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:339 +#: ../../source/ref-changelog.md:1150 msgid "" -"This works as expected, two clients are training for three rounds of " -"federated learning." +"Set minimal Python version to 3.6.1 instead of 3.6.9 " +"([#471](https://github.com/adap/flower/pull/471))." msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:341 +#: ../../source/ref-changelog.md:1151 msgid "" -"Let's dive a little bit deeper and discuss how Flower executes this " -"simulation. Whenever a client is selected to do some work, " -"``start_simulation`` calls the function ``numpyclient_fn`` to create an " -"instance of our ``FlowerNumPyClient`` (along with loading the model and " -"the data)." +"Improved `Strategy` docstrings " +"([#470](https://github.com/adap/flower/pull/470))." msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:343 -msgid "" -"But here's the perhaps surprising part: Flower doesn't actually use the " -"``FlowerNumPyClient`` object directly. Instead, it wraps the object to " -"makes it look like a subclass of ``flwr.client.Client``, not " -"``flwr.client.NumPyClient``. In fact, the Flower core framework doesn't " -"know how to handle ``NumPyClient``'s, it only knows how to handle " -"``Client``'s. ``NumPyClient`` is just a convenience abstraction built on " -"top of ``Client``." +#: ../../source/ref-example-projects.rst:2 +msgid "Example projects" msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:345 +#: ../../source/ref-example-projects.rst:4 msgid "" -"Instead of building on top of ``NumPyClient``, we can directly build on " -"top of ``Client``." +"Flower comes with a number of usage examples. The examples demonstrate " +"how Flower can be used to federate different kinds of existing machine " +"learning pipelines, usually leveraging popular machine learning " +"frameworks such as `PyTorch `_ or `TensorFlow " +"`_." msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:357 -msgid "Step 2: Moving from ``NumPyClient`` to ``Client``" +#: ../../source/ref-example-projects.rst:10 +msgid "" +"The following examples are available as standalone projects. Quickstart " +"TensorFlow/Keras ---------------------------" msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:359 +#: ../../source/ref-example-projects.rst:14 msgid "" -"Let's try to do the same thing using ``Client`` instead of " -"``NumPyClient``." +"The TensorFlow/Keras quickstart example shows CIFAR-10 image " +"classification with MobileNetV2:" msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:465 +#: ../../source/ref-example-projects.rst:17 msgid "" -"Before we discuss the code in more detail, let's try to run it! Gotta " -"make sure our new ``Client``-based client works, right?" +"`Quickstart TensorFlow (Code) " +"`_" msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:490 -msgid "" -"That's it, we're now using ``Client``. It probably looks similar to what " -"we've done with ``NumPyClient``. So what's the difference?" +#: ../../source/ref-example-projects.rst:18 +msgid ":doc:`Quickstart TensorFlow (Tutorial) `" msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:492 +#: ../../source/ref-example-projects.rst:19 msgid "" -"First of all, it's more code. But why? The difference comes from the fact" -" that ``Client`` expects us to take care of parameter serialization and " -"deserialization. For Flower to be able to send parameters over the " -"network, it eventually needs to turn these parameters into ``bytes``. " -"Turning parameters (e.g., NumPy ``ndarray``'s) into raw bytes is called " -"serialization. Turning raw bytes into something more useful (like NumPy " -"``ndarray``'s) is called deserialization. Flower needs to do both: it " -"needs to serialize parameters on the server-side and send them to the " -"client, the client needs to deserialize them to use them for local " -"training, and then serialize the updated parameters again to send them " -"back to the server, which (finally!) deserializes them again in order to " -"aggregate them with the updates received from other clients." +"`Quickstart TensorFlow (Blog Post) `_" msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:495 +#: ../../source/ref-example-projects.rst:23 +#: ../../source/tutorial-quickstart-pytorch.rst:5 +msgid "Quickstart PyTorch" +msgstr "" + +#: ../../source/ref-example-projects.rst:25 msgid "" -"The only *real* difference between Client and NumPyClient is that " -"NumPyClient takes care of serialization and deserialization for you. It " -"can do so because it expects you to return parameters as NumPy ndarray's," -" and it knows how to handle these. This makes working with machine " -"learning libraries that have good NumPy support (most of them) a breeze." +"The PyTorch quickstart example shows CIFAR-10 image classification with a" +" simple Convolutional Neural Network:" msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:497 +#: ../../source/ref-example-projects.rst:28 msgid "" -"In terms of API, there's one major difference: all methods in Client take" -" exactly one argument (e.g., ``FitIns`` in ``Client.fit``) and return " -"exactly one value (e.g., ``FitRes`` in ``Client.fit``). The methods in " -"``NumPyClient`` on the other hand have multiple arguments (e.g., " -"``parameters`` and ``config`` in ``NumPyClient.fit``) and multiple return" -" values (e.g., ``parameters``, ``num_example``, and ``metrics`` in " -"``NumPyClient.fit``) if there are multiple things to handle. These " -"``*Ins`` and ``*Res`` objects in ``Client`` wrap all the individual " -"values you're used to from ``NumPyClient``." +"`Quickstart PyTorch (Code) " +"`_" msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:510 -msgid "Step 3: Custom serialization" +#: ../../source/ref-example-projects.rst:29 +msgid ":doc:`Quickstart PyTorch (Tutorial) `" msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:512 -msgid "" -"Here we will explore how to implement custom serialization with a simple " -"example." +#: ../../source/ref-example-projects.rst:33 +msgid "PyTorch: From Centralized To Federated" msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:514 +#: ../../source/ref-example-projects.rst:35 msgid "" -"But first what is serialization? Serialization is just the process of " -"converting an object into raw bytes, and equally as important, " -"deserialization is the process of converting raw bytes back into an " -"object. This is very useful for network communication. Indeed, without " -"serialization, you could not just a Python object through the internet." +"This example shows how a regular PyTorch project can be federated using " +"Flower:" msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:516 +#: ../../source/ref-example-projects.rst:37 msgid "" -"Federated Learning relies heavily on internet communication for training " -"by sending Python objects back and forth between the clients and the " -"server. This means that serialization is an essential part of Federated " -"Learning." +"`PyTorch: From Centralized To Federated (Code) " +"`_" msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:518 +#: ../../source/ref-example-projects.rst:38 msgid "" -"In the following section, we will write a basic example where instead of " -"sending a serialized version of our ``ndarray``\\ s containing our " -"parameters, we will first convert the ``ndarray`` into sparse matrices, " -"before sending them. This technique can be used to save bandwidth, as in " -"certain cases where the weights of a model are sparse (containing many 0 " -"entries), converting them to a sparse matrix can greatly improve their " -"bytesize." +":doc:`PyTorch: From Centralized To Federated (Tutorial) `" msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:521 -msgid "Our custom serialization/deserialization functions" +#: ../../source/ref-example-projects.rst:42 +msgid "Federated Learning on Raspberry Pi and Nvidia Jetson" msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:523 +#: ../../source/ref-example-projects.rst:44 msgid "" -"This is where the real serialization/deserialization will happen, " -"especially in ``ndarray_to_sparse_bytes`` for serialization and " -"``sparse_bytes_to_ndarray`` for deserialization." +"This example shows how Flower can be used to build a federated learning " +"system that run across Raspberry Pi and Nvidia Jetson:" msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:525 +#: ../../source/ref-example-projects.rst:46 msgid "" -"Note that we imported the ``scipy.sparse`` library in order to convert " -"our arrays." -msgstr "" - -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:613 -msgid "Client-side" +"`Federated Learning on Raspberry Pi and Nvidia Jetson (Code) " +"`_" msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:615 +#: ../../source/ref-example-projects.rst:47 msgid "" -"To be able to serialize our ``ndarray``\\ s into sparse parameters, we " -"will just have to call our custom functions in our " -"``flwr.client.Client``." +"`Federated Learning on Raspberry Pi and Nvidia Jetson (Blog Post) " +"`_" msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:617 +#: ../../source/ref-faq.rst:4 msgid "" -"Indeed, in ``get_parameters`` we need to serialize the parameters we got " -"from our network using our custom ``ndarrays_to_sparse_parameters`` " -"defined above." +"This page collects answers to commonly asked questions about Federated " +"Learning with Flower." msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:619 -msgid "" -"In ``fit``, we first need to deserialize the parameters coming from the " -"server using our custom ``sparse_parameters_to_ndarrays`` and then we " -"need to serialize our local results with " -"``ndarrays_to_sparse_parameters``." +#: ../../source/ref-faq.rst +msgid ":fa:`eye,mr-1` Can Flower run on Jupyter Notebooks / Google Colab?" msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:621 +#: ../../source/ref-faq.rst:8 msgid "" -"In ``evaluate``, we will only need to deserialize the global parameters " -"with our custom function." +"Yes, it can! Flower even comes with a few under-the-hood optimizations to" +" make it work even better on Colab. Here's a quickstart example:" msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:725 -msgid "Server-side" +#: ../../source/ref-faq.rst:10 +msgid "" +"`Flower simulation PyTorch " +"`_" msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:727 +#: ../../source/ref-faq.rst:11 msgid "" -"For this example, we will just use ``FedAvg`` as a strategy. To change " -"the serialization and deserialization here, we only need to reimplement " -"the ``evaluate`` and ``aggregate_fit`` functions of ``FedAvg``. The other" -" functions of the strategy will be inherited from the super class " -"``FedAvg``." +"`Flower simulation TensorFlow/Keras " +"`_" msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:729 -msgid "As you can see only one line as change in ``evaluate``:" +#: ../../source/ref-faq.rst +msgid ":fa:`eye,mr-1` How can I run Federated Learning on a Raspberry Pi?" msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:735 +#: ../../source/ref-faq.rst:15 msgid "" -"And for ``aggregate_fit``, we will first deserialize every result we " -"received:" +"Find the `blog post about federated learning on embedded device here " +"`_" +" and the corresponding `GitHub code example " +"`_." msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:744 -msgid "And then serialize the aggregated result:" +#: ../../source/ref-faq.rst +msgid ":fa:`eye,mr-1` Does Flower support federated learning on Android devices?" msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:903 -msgid "We can now run our custom serialization example!" +#: ../../source/ref-faq.rst:19 +msgid "" +"Yes, it does. Please take a look at our `blog post " +"`_ or check out the code examples:" msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:934 +#: ../../source/ref-faq.rst:21 msgid "" -"In this part of the tutorial, we've seen how we can build clients by " -"subclassing either ``NumPyClient`` or ``Client``. ``NumPyClient`` is a " -"convenience abstraction that makes it easier to work with machine " -"learning libraries that have good NumPy interoperability. ``Client`` is a" -" more flexible abstraction that allows us to do things that are not " -"possible in ``NumPyClient``. In order to do so, it requires us to handle " -"parameter serialization and deserialization ourselves." +"`Android Kotlin example `_" msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:952 -msgid "" -"This is the final part of the Flower tutorial (for now!), " -"congratulations! You're now well equipped to understand the rest of the " -"documentation. There are many topics we didn't cover in the tutorial, we " -"recommend the following resources:" +#: ../../source/ref-faq.rst:22 +msgid "`Android Java example `_" msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:954 -msgid "`Read Flower Docs `__" +#: ../../source/ref-faq.rst +msgid ":fa:`eye,mr-1` Can I combine federated learning with blockchain?" msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:955 +#: ../../source/ref-faq.rst:26 msgid "" -"`Check out Flower Code Examples " -"`__" +"Yes, of course. A list of available examples using Flower within a " +"blockchain environment is available here:" msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:956 +#: ../../source/ref-faq.rst:28 msgid "" -"`Use Flower Baselines for your research " -"`__" +"`Flower meets Nevermined GitHub Repository `_." msgstr "" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:957 +#: ../../source/ref-faq.rst:29 msgid "" -"`Watch Flower Summit 2023 videos `__" +"`Flower meets Nevermined YouTube video " +"`_." msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:9 -msgid "Get started with Flower" +#: ../../source/ref-faq.rst:30 +msgid "" +"`Flower meets KOSMoS `_." msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:11 -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:11 -msgid "Welcome to the Flower federated learning tutorial!" +#: ../../source/ref-faq.rst:31 +msgid "" +"`Flower meets Talan blog post `_ ." msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:13 +#: ../../source/ref-faq.rst:32 msgid "" -"In this notebook, we'll build a federated learning system using Flower, " -"`Flower Datasets `__ and PyTorch. In " -"part 1, we use PyTorch for the model training pipeline and data loading. " -"In part 2, we continue to federate the PyTorch-based pipeline using " -"Flower." +"`Flower meets Talan GitHub Repository " +"`_ ." msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:17 -msgid "Let's get stated!" +#: ../../source/ref-telemetry.md:1 +msgid "Telemetry" msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:31 +#: ../../source/ref-telemetry.md:3 msgid "" -"Before we begin with any actual code, let's make sure that we have " -"everything we need." +"The Flower open-source project collects **anonymous** usage metrics to " +"make well-informed decisions to improve Flower. Doing this enables the " +"Flower team to understand how Flower is used and what challenges users " +"might face." msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:45 +#: ../../source/ref-telemetry.md:5 msgid "" -"Next, we install the necessary packages for PyTorch (``torch`` and " -"``torchvision``), Flower Datasets (``flwr-datasets``) and Flower " -"(``flwr``):" +"**Flower is a friendly framework for collaborative AI and data science.**" +" Staying true to this statement, Flower makes it easy to disable " +"telemetry for users that do not want to share anonymous usage metrics." msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:105 -msgid "" -"It is possible to switch to a runtime that has GPU acceleration enabled " -"(on Google Colab: ``Runtime > Change runtime type > Hardware accelerator:" -" GPU > Save``). Note, however, that Google Colab is not always able to " -"offer GPU acceleration. If you see an error related to GPU availability " -"in one of the following sections, consider switching back to CPU-based " -"execution by setting ``DEVICE = torch.device(\"cpu\")``. If the runtime " -"has GPU acceleration enabled, you should see the output ``Training on " -"cuda``, otherwise it'll say ``Training on cpu``." +#: ../../source/ref-telemetry.md:7 +msgid "Principles" msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:118 -msgid "Loading the data" +#: ../../source/ref-telemetry.md:9 +msgid "We follow strong principles guarding anonymous usage metrics collection:" msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:120 +#: ../../source/ref-telemetry.md:11 msgid "" -"Federated learning can be applied to many different types of tasks across" -" different domains. In this tutorial, we introduce federated learning by " -"training a simple convolutional neural network (CNN) on the popular " -"CIFAR-10 dataset. CIFAR-10 can be used to train image classifiers that " -"distinguish between images from ten different classes: 'airplane', " -"'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', and " -"'truck'." +"**Optional:** You will always be able to disable telemetry; read on to " +"learn “[How to opt-out](#how-to-opt-out)”." msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:131 +#: ../../source/ref-telemetry.md:12 msgid "" -"We simulate having multiple datasets from multiple organizations (also " -"called the \"cross-silo\" setting in federated learning) by splitting the" -" original CIFAR-10 dataset into multiple partitions. Each partition will " -"represent the data from a single organization. We're doing this purely " -"for experimentation purposes, in the real world there's no need for data " -"splitting because each organization already has their own data (so the " -"data is naturally partitioned)." +"**Anonymous:** The reported usage metrics are anonymous and do not " +"contain any personally identifiable information (PII). See “[Collected " +"metrics](#collected-metrics)” to understand what metrics are being " +"reported." msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:133 +#: ../../source/ref-telemetry.md:13 msgid "" -"Each organization will act as a client in the federated learning system. " -"So having ten organizations participate in a federation means having ten " -"clients connected to the federated learning server." -msgstr "" +"**Transparent:** You can easily inspect what anonymous metrics are being " +"reported; see the section “[How to inspect what is being reported](#how-" +"to-inspect-what-is-being-reported)”" +msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:144 +#: ../../source/ref-telemetry.md:14 msgid "" -"Let's now create the Federated Dataset abstraction that from ``flwr-" -"datasets`` that partitions the CIFAR-10. We will create small training " -"and test set for each edge device and wrap each of them into a PyTorch " -"``DataLoader``:" +"**Open for feedback:** You can always reach out to us if you have " +"feedback; see the section “[How to contact us](#how-to-contact-us)” for " +"details." msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:198 -msgid "" -"We now have a list of ten training sets and ten validation sets " -"(``trainloaders`` and ``valloaders``) representing the data of ten " -"different organizations. Each ``trainloader``/``valloader`` pair contains" -" 4500 training examples and 500 validation examples. There's also a " -"single ``testloader`` (we did not split the test set). Again, this is " -"only necessary for building research or educational systems, actual " -"federated learning systems have their data naturally distributed across " -"multiple partitions." +#: ../../source/ref-telemetry.md:16 +msgid "How to opt-out" msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:201 +#: ../../source/ref-telemetry.md:18 msgid "" -"Let's take a look at the first batch of images and labels in the first " -"training set (i.e., ``trainloaders[0]``) before we move on:" +"When Flower starts, it will check for an environment variable called " +"`FLWR_TELEMETRY_ENABLED`. Telemetry can easily be disabled by setting " +"`FLWR_TELEMETRY_ENABLED=0`. Assuming you are starting a Flower server or " +"client, simply do so by prepending your command as in:" msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:240 +#: ../../source/ref-telemetry.md:24 msgid "" -"The output above shows a random batch of images from the first " -"``trainloader`` in our list of ten ``trainloaders``. It also prints the " -"labels associated with each image (i.e., one of the ten possible labels " -"we've seen above). If you run the cell again, you should see another " -"batch of images." +"Alternatively, you can export `FLWR_TELEMETRY_ENABLED=0` in, for example," +" `.bashrc` (or whatever configuration file applies to your environment) " +"to disable Flower telemetry permanently." msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:252 -msgid "Step 1: Centralized Training with PyTorch" +#: ../../source/ref-telemetry.md:26 +msgid "Collected metrics" msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:263 -msgid "" -"Next, we're going to use PyTorch to define a simple convolutional neural " -"network. This introduction assumes basic familiarity with PyTorch, so it " -"doesn't cover the PyTorch-related aspects in full detail. If you want to " -"dive deeper into PyTorch, we recommend `DEEP LEARNING WITH PYTORCH: A 60 " -"MINUTE BLITZ " -"`__." +#: ../../source/ref-telemetry.md:28 +msgid "Flower telemetry collects the following metrics:" msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:275 -msgid "Defining the model" +#: ../../source/ref-telemetry.md:30 +msgid "" +"**Flower version.** Understand which versions of Flower are currently " +"being used. This helps us to decide whether we should invest effort into " +"releasing a patch version for an older version of Flower or instead use " +"the bandwidth to build new features." msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:277 +#: ../../source/ref-telemetry.md:32 msgid "" -"We use the simple CNN described in the `PyTorch tutorial " -"`__:" +"**Operating system.** Enables us to answer questions such as: *Should we " +"create more guides for Linux, macOS, or Windows?*" msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:314 -msgid "Let's continue with the usual training and test functions:" +#: ../../source/ref-telemetry.md:34 +msgid "" +"**Python version.** Knowing the Python version helps us, for example, to " +"decide whether we should invest effort into supporting old versions of " +"Python or stop supporting them and start taking advantage of new Python " +"features." msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:374 -msgid "Training the model" +#: ../../source/ref-telemetry.md:36 +msgid "" +"**Hardware properties.** Understanding the hardware environment that " +"Flower is being used in helps to decide whether we should, for example, " +"put more effort into supporting low-resource environments." msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:376 +#: ../../source/ref-telemetry.md:38 msgid "" -"We now have all the basic building blocks we need: a dataset, a model, a " -"training function, and a test function. Let's put them together to train " -"the model on the dataset of one of our organizations " -"(``trainloaders[0]``). This simulates the reality of most machine " -"learning projects today: each organization has their own data and trains " -"models only on this internal data:" +"**Execution mode.** Knowing what execution mode Flower starts in enables " +"us to understand how heavily certain features are being used and better " +"prioritize based on that." msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:406 +#: ../../source/ref-telemetry.md:40 msgid "" -"Training the simple CNN on our CIFAR-10 split for 5 epochs should result " -"in a test set accuracy of about 41%, which is not good, but at the same " -"time, it doesn't really matter for the purposes of this tutorial. The " -"intent was just to show a simplistic centralized training pipeline that " -"sets the stage for what comes next - federated learning!" +"**Cluster.** Flower telemetry assigns a random in-memory cluster ID each " +"time a Flower workload starts. This allows us to understand which device " +"types not only start Flower workloads but also successfully complete " +"them." msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:418 -msgid "Step 2: Federated Learning with Flower" +#: ../../source/ref-telemetry.md:42 +msgid "" +"**Source.** Flower telemetry tries to store a random source ID in " +"`~/.flwr/source` the first time a telemetry event is generated. The " +"source ID is important to identify whether an issue is recurring or " +"whether an issue is triggered by multiple clusters running concurrently " +"(which often happens in simulation). For example, if a device runs " +"multiple workloads at the same time, and this results in an issue, then, " +"in order to reproduce the issue, multiple workloads must be started at " +"the same time." msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:420 +#: ../../source/ref-telemetry.md:44 msgid "" -"Step 1 demonstrated a simple centralized training pipeline. All data was " -"in one place (i.e., a single ``trainloader`` and a single ``valloader``)." -" Next, we'll simulate a situation where we have multiple datasets in " -"multiple organizations and where we train a model over these " -"organizations using federated learning." +"You may delete the source ID at any time. If you wish for all events " +"logged under a specific source ID to be deleted, you can send a deletion " +"request mentioning the source ID to `telemetry@flower.ai`. All events " +"related to that source ID will then be permanently deleted." msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:432 -msgid "Updating model parameters" +#: ../../source/ref-telemetry.md:46 +msgid "" +"We will not collect any personally identifiable information. If you think" +" any of the metrics collected could be misused in any way, please [get in" +" touch with us](#how-to-contact-us). We will update this page to reflect " +"any changes to the metrics collected and publish changes in the " +"changelog." msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:434 +#: ../../source/ref-telemetry.md:48 msgid "" -"In federated learning, the server sends the global model parameters to " -"the client, and the client updates the local model with the parameters " -"received from the server. It then trains the model on the local data " -"(which changes the model parameters locally) and sends the " -"updated/changed model parameters back to the server (or, alternatively, " -"it sends just the gradients back to the server, not the full model " -"parameters)." +"If you think other metrics would be helpful for us to better guide our " +"decisions, please let us know! We will carefully review them; if we are " +"confident that they do not compromise user privacy, we may add them." msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:436 +#: ../../source/ref-telemetry.md:50 +msgid "How to inspect what is being reported" +msgstr "" + +#: ../../source/ref-telemetry.md:52 msgid "" -"We need two helper functions to update the local model with parameters " -"received from the server and to get the updated model parameters from the" -" local model: ``set_parameters`` and ``get_parameters``. The following " -"two functions do just that for the PyTorch model above." +"We wanted to make it very easy for you to inspect what anonymous usage " +"metrics are reported. You can view all the reported telemetry information" +" by setting the environment variable `FLWR_TELEMETRY_LOGGING=1`. Logging " +"is disabled by default. You may use logging independently from " +"`FLWR_TELEMETRY_ENABLED` so that you can inspect the telemetry feature " +"without sending any metrics." msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:438 +#: ../../source/ref-telemetry.md:58 msgid "" -"The details of how this works are not really important here (feel free to" -" consult the PyTorch documentation if you want to learn more). In " -"essence, we use ``state_dict`` to access PyTorch model parameter tensors." -" The parameter tensors are then converted to/from a list of NumPy " -"ndarray's (which Flower knows how to serialize/deserialize):" +"The inspect Flower telemetry without sending any anonymous usage metrics," +" use both environment variables:" msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:466 -msgid "Implementing a Flower client" +#: ../../source/ref-telemetry.md:64 +msgid "How to contact us" msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:468 +#: ../../source/ref-telemetry.md:66 msgid "" -"With that out of the way, let's move on to the interesting part. " -"Federated learning systems consist of a server and multiple clients. In " -"Flower, we create clients by implementing subclasses of " -"``flwr.client.Client`` or ``flwr.client.NumPyClient``. We use " -"``NumPyClient`` in this tutorial because it is easier to implement and " -"requires us to write less boilerplate." +"We want to hear from you. If you have any feedback or ideas on how to " +"improve the way we handle anonymous usage metrics, reach out to us via " +"[Slack](https://flower.ai/join-slack/) (channel `#telemetry`) or email " +"(`telemetry@flower.ai`)." msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:470 +#: ../../source/tutorial-quickstart-android.rst:-1 msgid "" -"To implement the Flower client, we create a subclass of " -"``flwr.client.NumPyClient`` and implement the three methods " -"``get_parameters``, ``fit``, and ``evaluate``:" +"Read this Federated Learning quickstart tutorial for creating an Android " +"app using Flower." msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:472 -msgid "``get_parameters``: Return the current local model parameters" +#: ../../source/tutorial-quickstart-android.rst:5 +msgid "Quickstart Android" msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:473 +#: ../../source/tutorial-quickstart-android.rst:10 msgid "" -"``fit``: Receive model parameters from the server, train the model " -"parameters on the local data, and return the (updated) model parameters " -"to the server" +"Let's build a federated learning system using TFLite and Flower on " +"Android!" msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:474 +#: ../../source/tutorial-quickstart-android.rst:12 msgid "" -"``evaluate``: Receive model parameters from the server, evaluate the " -"model parameters on the local data, and return the evaluation result to " -"the server" +"Please refer to the `full code example " +"`_ to learn " +"more." msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:476 +#: ../../source/tutorial-quickstart-fastai.rst:-1 msgid "" -"We mentioned that our clients will use the previously defined PyTorch " -"components for model training and evaluation. Let's see a simple Flower " -"client implementation that brings everything together:" +"Check out this Federated Learning quickstart tutorial for using Flower " +"with FastAI to train a vision model on CIFAR-10." msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:513 -msgid "" -"Our class ``FlowerClient`` defines how local training/evaluation will be " -"performed and allows Flower to call the local training/evaluation through" -" ``fit`` and ``evaluate``. Each instance of ``FlowerClient`` represents a" -" *single client* in our federated learning system. Federated learning " -"systems have multiple clients (otherwise, there's not much to federate), " -"so each client will be represented by its own instance of " -"``FlowerClient``. If we have, for example, three clients in our workload," -" then we'd have three instances of ``FlowerClient``. Flower calls " -"``FlowerClient.fit`` on the respective instance when the server selects a" -" particular client for training (and ``FlowerClient.evaluate`` for " -"evaluation)." +#: ../../source/tutorial-quickstart-fastai.rst:5 +msgid "Quickstart fastai" msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:517 -msgid "Using the Virtual Client Engine" +#: ../../source/tutorial-quickstart-fastai.rst:10 +msgid "Let's build a federated learning system using fastai and Flower!" msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:519 +#: ../../source/tutorial-quickstart-fastai.rst:12 msgid "" -"In this notebook, we want to simulate a federated learning system with 10" -" clients on a single machine. This means that the server and all 10 " -"clients will live on a single machine and share resources such as CPU, " -"GPU, and memory. Having 10 clients would mean having 10 instances of " -"``FlowerClient`` in memory. Doing this on a single machine can quickly " -"exhaust the available memory resources, even if only a subset of these " -"clients participates in a single round of federated learning." -msgstr "" - -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:521 +"Please refer to the `full code example " +"`_ " +"to learn more." +msgstr "" + +#: ../../source/tutorial-quickstart-huggingface.rst:-1 msgid "" -"In addition to the regular capabilities where server and clients run on " -"multiple machines, Flower, therefore, provides special simulation " -"capabilities that create ``FlowerClient`` instances only when they are " -"actually necessary for training or evaluation. To enable the Flower " -"framework to create clients when necessary, we need to implement a " -"function called ``client_fn`` that creates a ``FlowerClient`` instance on" -" demand. Flower calls ``client_fn`` whenever it needs an instance of one " -"particular client to call ``fit`` or ``evaluate`` (those instances are " -"usually discarded after use, so they should not keep any local state). " -"Clients are identified by a client ID, or short ``cid``. The ``cid`` can " -"be used, for example, to load different local data partitions for " -"different clients, as can be seen below:" +"Check out this Federating Learning quickstart tutorial for using Flower " +"with HuggingFace Transformers in order to fine-tune an LLM." msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:556 -msgid "Starting the training" +#: ../../source/tutorial-quickstart-huggingface.rst:5 +msgid "Quickstart 🤗 Transformers" msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:558 +#: ../../source/tutorial-quickstart-huggingface.rst:10 msgid "" -"We now have the class ``FlowerClient`` which defines client-side " -"training/evaluation and ``client_fn`` which allows Flower to create " -"``FlowerClient`` instances whenever it needs to call ``fit`` or " -"``evaluate`` on one particular client. The last step is to start the " -"actual simulation using ``flwr.simulation.start_simulation``." +"Let's build a federated learning system using Hugging Face Transformers " +"and Flower!" msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:560 +#: ../../source/tutorial-quickstart-huggingface.rst:12 msgid "" -"The function ``start_simulation`` accepts a number of arguments, amongst " -"them the ``client_fn`` used to create ``FlowerClient`` instances, the " -"number of clients to simulate (``num_clients``), the number of federated " -"learning rounds (``num_rounds``), and the strategy. The strategy " -"encapsulates the federated learning approach/algorithm, for example, " -"*Federated Averaging* (FedAvg)." +"We will leverage Hugging Face to federate the training of language models" +" over multiple clients using Flower. More specifically, we will fine-tune" +" a pre-trained Transformer model (distilBERT) for sequence classification" +" over a dataset of IMDB ratings. The end goal is to detect if a movie " +"rating is positive or negative." msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:562 -msgid "" -"Flower has a number of built-in strategies, but we can also use our own " -"strategy implementations to customize nearly all aspects of the federated" -" learning approach. For this example, we use the built-in ``FedAvg`` " -"implementation and customize it using a few basic parameters. The last " -"step is the actual call to ``start_simulation`` which - you guessed it - " -"starts the simulation:" +#: ../../source/tutorial-quickstart-huggingface.rst:18 +msgid "Dependencies" msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:608 -msgid "Behind the scenes" +#: ../../source/tutorial-quickstart-huggingface.rst:20 +msgid "" +"To follow along this tutorial you will need to install the following " +"packages: :code:`datasets`, :code:`evaluate`, :code:`flwr`, " +":code:`torch`, and :code:`transformers`. This can be done using " +":code:`pip`:" msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:610 -msgid "So how does this work? How does Flower execute this simulation?" +#: ../../source/tutorial-quickstart-huggingface.rst:30 +msgid "Standard Hugging Face workflow" msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:612 -#, python-format -msgid "" -"When we call ``start_simulation``, we tell Flower that there are 10 " -"clients (``num_clients=10``). Flower then goes ahead an asks the " -"``FedAvg`` strategy to select clients. ``FedAvg`` knows that it should " -"select 100% of the available clients (``fraction_fit=1.0``), so it goes " -"ahead and selects 10 random clients (i.e., 100% of 10)." +#: ../../source/tutorial-quickstart-huggingface.rst:33 +msgid "Handling the data" msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:614 +#: ../../source/tutorial-quickstart-huggingface.rst:35 msgid "" -"Flower then asks the selected 10 clients to train the model. When the " -"server receives the model parameter updates from the clients, it hands " -"those updates over to the strategy (*FedAvg*) for aggregation. The " -"strategy aggregates those updates and returns the new global model, which" -" then gets used in the next round of federated learning." +"To fetch the IMDB dataset, we will use Hugging Face's :code:`datasets` " +"library. We then need to tokenize the data and create :code:`PyTorch` " +"dataloaders, this is all done in the :code:`load_data` function:" msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:626 -msgid "Where's the accuracy?" +#: ../../source/tutorial-quickstart-huggingface.rst:81 +msgid "Training and testing the model" msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:628 +#: ../../source/tutorial-quickstart-huggingface.rst:83 msgid "" -"You may have noticed that all metrics except for ``losses_distributed`` " -"are empty. Where did the ``{\"accuracy\": float(accuracy)}`` go?" +"Once we have a way of creating our trainloader and testloader, we can " +"take care of the training and testing. This is very similar to any " +":code:`PyTorch` training or testing loop:" msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:630 -msgid "" -"Flower can automatically aggregate losses returned by individual clients," -" but it cannot do the same for metrics in the generic metrics dictionary " -"(the one with the ``accuracy`` key). Metrics dictionaries can contain " -"very different kinds of metrics and even key/value pairs that are not " -"metrics at all, so the framework does not (and can not) know how to " -"handle these automatically." +#: ../../source/tutorial-quickstart-huggingface.rst:121 +msgid "Creating the model itself" msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:632 +#: ../../source/tutorial-quickstart-huggingface.rst:123 msgid "" -"As users, we need to tell the framework how to handle/aggregate these " -"custom metrics, and we do so by passing metric aggregation functions to " -"the strategy. The strategy will then call these functions whenever it " -"receives fit or evaluate metrics from clients. The two possible functions" -" are ``fit_metrics_aggregation_fn`` and " -"``evaluate_metrics_aggregation_fn``." +"To create the model itself, we will just load the pre-trained distillBERT" +" model using Hugging Face’s :code:`AutoModelForSequenceClassification` :" msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:634 -msgid "" -"Let's create a simple weighted averaging function to aggregate the " -"``accuracy`` metric we return from ``evaluate``:" +#: ../../source/tutorial-quickstart-huggingface.rst:136 +msgid "Federating the example" msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:660 -msgid "" -"The only thing left to do is to tell the strategy to call this function " -"whenever it receives evaluation metric dictionaries from the clients:" +#: ../../source/tutorial-quickstart-huggingface.rst:139 +msgid "Creating the IMDBClient" msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:697 +#: ../../source/tutorial-quickstart-huggingface.rst:141 msgid "" -"We now have a full system that performs federated training and federated " -"evaluation. It uses the ``weighted_average`` function to aggregate custom" -" evaluation metrics and calculates a single ``accuracy`` metric across " -"all clients on the server side." +"To federate our example to multiple clients, we first need to write our " +"Flower client class (inheriting from :code:`flwr.client.NumPyClient`). " +"This is very easy, as our model is a standard :code:`PyTorch` model:" msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:699 +#: ../../source/tutorial-quickstart-huggingface.rst:169 msgid "" -"The other two categories of metrics (``losses_centralized`` and " -"``metrics_centralized``) are still empty because they only apply when " -"centralized evaluation is being used. Part two of the Flower tutorial " -"will cover centralized evaluation." +"The :code:`get_parameters` function lets the server get the client's " +"parameters. Inversely, the :code:`set_parameters` function allows the " +"server to send its parameters to the client. Finally, the :code:`fit` " +"function trains the model locally for the client, and the " +":code:`evaluate` function tests the model locally and returns the " +"relevant metrics." msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:711 -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:351 -msgid "Final remarks" +#: ../../source/tutorial-quickstart-huggingface.rst:175 +msgid "Starting the server" msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:713 +#: ../../source/tutorial-quickstart-huggingface.rst:177 msgid "" -"Congratulations, you just trained a convolutional neural network, " -"federated over 10 clients! With that, you understand the basics of " -"federated learning with Flower. The same approach you've seen can be used" -" with other machine learning frameworks (not just PyTorch) and tasks (not" -" just CIFAR-10 images classification), for example NLP with Hugging Face " -"Transformers or speech with SpeechBrain." +"Now that we have a way to instantiate clients, we need to create our " +"server in order to aggregate the results. Using Flower, this can be done " +"very easily by first choosing a strategy (here, we are using " +":code:`FedAvg`, which will define the global weights as the average of " +"all the clients' weights at each round) and then using the " +":code:`flwr.server.start_server` function:" msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:715 +#: ../../source/tutorial-quickstart-huggingface.rst:205 msgid "" -"In the next notebook, we're going to cover some more advanced concepts. " -"Want to customize your strategy? Initialize parameters on the server " -"side? Or evaluate the aggregated model on the server side? We'll cover " -"all this and more in the next tutorial." +"The :code:`weighted_average` function is there to provide a way to " +"aggregate the metrics distributed amongst the clients (basically this " +"allows us to display a nice average accuracy and loss for every round)." msgstr "" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:733 -msgid "" -"The `Flower Federated Learning Tutorial - Part 2 " -"`__ goes into more depth about strategies and all " -"the advanced things you can build with them." +#: ../../source/tutorial-quickstart-huggingface.rst:209 +msgid "Putting everything together" msgstr "" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:9 -msgid "Use a federated learning strategy" +#: ../../source/tutorial-quickstart-huggingface.rst:211 +msgid "We can now start client instances using:" msgstr "" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:11 +#: ../../source/tutorial-quickstart-huggingface.rst:221 msgid "" -"Welcome to the next part of the federated learning tutorial. In previous " -"parts of this tutorial, we introduced federated learning with PyTorch and" -" Flower (`part 1 `__)." +"And they will be able to connect to the server and start the federated " +"training." msgstr "" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:13 +#: ../../source/tutorial-quickstart-huggingface.rst:223 msgid "" -"In this notebook, we'll begin to customize the federated learning system " -"we built in the introductory notebook (again, using `Flower " -"`__ and `PyTorch `__)." +"If you want to check out everything put together, you should check out " +"the `full code example `_ ." msgstr "" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:17 -msgid "Let's move beyond FedAvg with Flower strategies!" +#: ../../source/tutorial-quickstart-huggingface.rst:226 +msgid "" +"Of course, this is a very basic example, and a lot can be added or " +"modified, it was just to showcase how simply we could federate a Hugging " +"Face workflow using Flower." msgstr "" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:309 -msgid "Strategy customization" +#: ../../source/tutorial-quickstart-huggingface.rst:229 +msgid "" +"Note that in this example we used :code:`PyTorch`, but we could have very" +" well used :code:`TensorFlow`." msgstr "" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:311 +#: ../../source/tutorial-quickstart-ios.rst:-1 msgid "" -"So far, everything should look familiar if you've worked through the " -"introductory notebook. With that, we're ready to introduce a number of " -"new features." +"Read this Federated Learning quickstart tutorial for creating an iOS app " +"using Flower to train a neural network on MNIST." msgstr "" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:323 -msgid "Server-side parameter **initialization**" +#: ../../source/tutorial-quickstart-ios.rst:5 +msgid "Quickstart iOS" msgstr "" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:325 +#: ../../source/tutorial-quickstart-ios.rst:10 msgid "" -"Flower, by default, initializes the global model by asking one random " -"client for the initial parameters. In many cases, we want more control " -"over parameter initialization though. Flower therefore allows you to " -"directly pass the initial parameters to the Strategy:" +"In this tutorial we will learn how to train a Neural Network on MNIST " +"using Flower and CoreML on iOS devices." msgstr "" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:370 +#: ../../source/tutorial-quickstart-ios.rst:12 msgid "" -"Passing ``initial_parameters`` to the ``FedAvg`` strategy prevents Flower" -" from asking one of the clients for the initial parameters. If we look " -"closely, we can see that the logs do not show any calls to the " -"``FlowerClient.get_parameters`` method." -msgstr "" - -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:382 -msgid "Starting with a customized strategy" +"First of all, for running the Flower Python server, it is recommended to " +"create a virtual environment and run everything within a :doc:`virtualenv" +" `. For the Flower client " +"implementation in iOS, it is recommended to use Xcode as our IDE." msgstr "" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:384 +#: ../../source/tutorial-quickstart-ios.rst:15 msgid "" -"We've seen the function ``start_simulation`` before. It accepts a number " -"of arguments, amongst them the ``client_fn`` used to create " -"``FlowerClient`` instances, the number of clients to simulate " -"``num_clients``, the number of rounds ``num_rounds``, and the strategy." +"Our example consists of one Python *server* and two iPhone *clients* that" +" all have the same model." msgstr "" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:386 +#: ../../source/tutorial-quickstart-ios.rst:17 msgid "" -"The strategy encapsulates the federated learning approach/algorithm, for " -"example, ``FedAvg`` or ``FedAdagrad``. Let's try to use a different " -"strategy this time:" +"*Clients* are responsible for generating individual weight updates for " +"the model based on their local datasets. These updates are then sent to " +"the *server* which will aggregate them to produce a better model. " +"Finally, the *server* sends this improved version of the model back to " +"each *client*. A complete cycle of weight updates is called a *round*." msgstr "" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:424 -msgid "Server-side parameter **evaluation**" +#: ../../source/tutorial-quickstart-ios.rst:21 +msgid "" +"Now that we have a rough idea of what is going on, let's get started to " +"setup our Flower server environment. We first need to install Flower. You" +" can do this by using pip:" msgstr "" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:426 -msgid "" -"Flower can evaluate the aggregated model on the server-side or on the " -"client-side. Client-side and server-side evaluation are similar in some " -"ways, but different in others." +#: ../../source/tutorial-quickstart-ios.rst:27 +msgid "Or Poetry:" msgstr "" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:428 -msgid "" -"**Centralized Evaluation** (or *server-side evaluation*) is conceptually " -"simple: it works the same way that evaluation in centralized machine " -"learning does. If there is a server-side dataset that can be used for " -"evaluation purposes, then that's great. We can evaluate the newly " -"aggregated model after each round of training without having to send the " -"model to clients. We're also fortunate in the sense that our entire " -"evaluation dataset is available at all times." +#: ../../source/tutorial-quickstart-ios.rst:34 +#: ../../source/tutorial-quickstart-pytorch.rst:37 +#: ../../source/tutorial-quickstart-scikitlearn.rst:40 +#: ../../source/tutorial-quickstart-tensorflow.rst:29 +#: ../../source/tutorial-quickstart-xgboost.rst:55 +msgid "Flower Client" msgstr "" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:430 +#: ../../source/tutorial-quickstart-ios.rst:36 msgid "" -"**Federated Evaluation** (or *client-side evaluation*) is more complex, " -"but also more powerful: it doesn't require a centralized dataset and " -"allows us to evaluate models over a larger set of data, which often " -"yields more realistic evaluation results. In fact, many scenarios require" -" us to use **Federated Evaluation** if we want to get representative " -"evaluation results at all. But this power comes at a cost: once we start " -"to evaluate on the client side, we should be aware that our evaluation " -"dataset can change over consecutive rounds of learning if those clients " -"are not always available. Moreover, the dataset held by each client can " -"also change over consecutive rounds. This can lead to evaluation results " -"that are not stable, so even if we would not change the model, we'd see " -"our evaluation results fluctuate over consecutive rounds." +"Now that we have all our dependencies installed, let's run a simple " +"distributed training using CoreML as our local training pipeline and " +"MNIST as our dataset. For simplicity reasons we will use the complete " +"Flower client with CoreML, that has been implemented and stored inside " +"the Swift SDK. The client implementation can be seen below:" msgstr "" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:433 +#: ../../source/tutorial-quickstart-ios.rst:72 msgid "" -"We've seen how federated evaluation works on the client side (i.e., by " -"implementing the ``evaluate`` method in ``FlowerClient``). Now let's see " -"how we can evaluate aggregated model parameters on the server-side:" +"Let's create a new application project in Xcode and add :code:`flwr` as a" +" dependency in your project. For our application, we will store the logic" +" of our app in :code:`FLiOSModel.swift` and the UI elements in " +":code:`ContentView.swift`. We will focus more on :code:`FLiOSModel.swift`" +" in this quickstart. Please refer to the `full code example " +"`_ to learn more " +"about the app." msgstr "" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:490 -msgid "Sending/receiving arbitrary values to/from clients" +#: ../../source/tutorial-quickstart-ios.rst:75 +msgid "Import Flower and CoreML related packages in :code:`FLiOSModel.swift`:" msgstr "" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:492 +#: ../../source/tutorial-quickstart-ios.rst:83 msgid "" -"In some situations, we want to configure client-side execution (training," -" evaluation) from the server-side. One example for that is the server " -"asking the clients to train for a certain number of local epochs. Flower " -"provides a way to send configuration values from the server to the " -"clients using a dictionary. Let's look at an example where the clients " -"receive values from the server through the ``config`` parameter in " -"``fit`` (``config`` is also available in ``evaluate``). The ``fit`` " -"method receives the configuration dictionary through the ``config`` " -"parameter and can then read values from this dictionary. In this example," -" it reads ``server_round`` and ``local_epochs`` and uses those values to " -"improve the logging and configure the number of local training epochs:" +"Then add the mlmodel to the project simply by drag-and-drop, the mlmodel " +"will be bundled inside the application during deployment to your iOS " +"device. We need to pass the url to access mlmodel and run CoreML machine " +"learning processes, it can be retrieved by calling the function " +":code:`Bundle.main.url`. For the MNIST dataset, we need to preprocess it " +"into :code:`MLBatchProvider` object. The preprocessing is done inside " +":code:`DataLoader.swift`." msgstr "" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:546 +#: ../../source/tutorial-quickstart-ios.rst:99 msgid "" -"So how can we send this config dictionary from server to clients? The " -"built-in Flower Strategies provide way to do this, and it works similarly" -" to the way server-side evaluation works. We provide a function to the " -"strategy, and the strategy calls this function for every round of " -"federated learning:" +"Since CoreML does not allow the model parameters to be seen before " +"training, and accessing the model parameters during or after the training" +" can only be done by specifying the layer name, we need to know this " +"information beforehand, through looking at the model specification, which" +" are written as proto files. The implementation can be seen in " +":code:`MLModelInspect`." msgstr "" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:576 +#: ../../source/tutorial-quickstart-ios.rst:102 msgid "" -"Next, we'll just pass this function to the FedAvg strategy before " -"starting the simulation:" +"After we have all of the necessary information, let's create our Flower " +"client." msgstr "" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:613 +#: ../../source/tutorial-quickstart-ios.rst:117 msgid "" -"As we can see, the client logs now include the current round of federated" -" learning (which they read from the ``config`` dictionary). We can also " -"configure local training to run for one epoch during the first and second" -" round of federated learning, and then for two epochs during the third " -"round." +"Then start the Flower gRPC client and start communicating to the server " +"by passing our Flower client to the function :code:`startFlwrGRPC`." msgstr "" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:615 +#: ../../source/tutorial-quickstart-ios.rst:124 msgid "" -"Clients can also return arbitrary values to the server. To do so, they " -"return a dictionary from ``fit`` and/or ``evaluate``. We have seen and " -"used this concept throughout this notebook without mentioning it " -"explicitly: our ``FlowerClient`` returns a dictionary containing a custom" -" key/value pair as the third return value in ``evaluate``." +"That's it for the client. We only have to implement :code:`Client` or " +"call the provided :code:`MLFlwrClient` and call :code:`startFlwrGRPC()`. " +"The attribute :code:`hostname` and :code:`port` tells the client which " +"server to connect to. This can be done by entering the hostname and port " +"in the application before clicking the start button to start the " +"federated learning process." msgstr "" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:627 -msgid "Scaling federated learning" +#: ../../source/tutorial-quickstart-ios.rst:129 +#: ../../source/tutorial-quickstart-pytorch.rst:203 +#: ../../source/tutorial-quickstart-scikitlearn.rst:167 +#: ../../source/tutorial-quickstart-tensorflow.rst:98 +#: ../../source/tutorial-quickstart-xgboost.rst:309 +msgid "Flower Server" msgstr "" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:629 +#: ../../source/tutorial-quickstart-ios.rst:131 +#: ../../source/tutorial-quickstart-pytorch.rst:205 +#: ../../source/tutorial-quickstart-tensorflow.rst:100 msgid "" -"As a last step in this notebook, let's see how we can use Flower to " -"experiment with a large number of clients." +"For simple workloads we can start a Flower server and leave all the " +"configuration possibilities at their default values. In a file named " +":code:`server.py`, import Flower and start the server:" msgstr "" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:651 -#, python-format -msgid "" -"We now have 1000 partitions, each holding 45 training and 5 validation " -"examples. Given that the number of training examples on each client is " -"quite small, we should probably train the model a bit longer, so we " -"configure the clients to perform 3 local training epochs. We should also " -"adjust the fraction of clients selected for training during each round " -"(we don't want all 1000 clients participating in every round), so we " -"adjust ``fraction_fit`` to ``0.05``, which means that only 5% of " -"available clients (so 50 clients) will be selected for training each " -"round:" +#: ../../source/tutorial-quickstart-ios.rst:142 +#: ../../source/tutorial-quickstart-pytorch.rst:216 +#: ../../source/tutorial-quickstart-scikitlearn.rst:230 +#: ../../source/tutorial-quickstart-tensorflow.rst:112 +msgid "Train the model, federated!" msgstr "" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:699 +#: ../../source/tutorial-quickstart-ios.rst:144 +#: ../../source/tutorial-quickstart-pytorch.rst:218 +#: ../../source/tutorial-quickstart-tensorflow.rst:114 +#: ../../source/tutorial-quickstart-xgboost.rst:525 msgid "" -"In this notebook, we've seen how we can gradually enhance our system by " -"customizing the strategy, initializing parameters on the server side, " -"choosing a different strategy, and evaluating models on the server-side. " -"That's quite a bit of flexibility with so little code, right?" +"With both client and server ready, we can now run everything and see " +"federated learning in action. FL systems usually have a server and " +"multiple clients. We therefore have to start the server first:" msgstr "" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:701 +#: ../../source/tutorial-quickstart-ios.rst:152 msgid "" -"In the later sections, we've seen how we can communicate arbitrary values" -" between server and clients to fully customize client-side execution. " -"With that capability, we built a large-scale Federated Learning " -"simulation using the Flower Virtual Client Engine and ran an experiment " -"involving 1000 clients in the same workload - all in a Jupyter Notebook!" +"Once the server is running we can start the clients in different " +"terminals. Build and run the client through your Xcode, one through Xcode" +" Simulator and the other by deploying it to your iPhone. To see more " +"about how to deploy your app to iPhone or Simulator visit `here " +"`_." msgstr "" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:719 +#: ../../source/tutorial-quickstart-ios.rst:156 msgid "" -"The `Flower Federated Learning Tutorial - Part 3 " -"`__ shows how to build a fully custom ``Strategy`` from " -"scratch." -msgstr "" - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:9 -msgid "What is Federated Learning?" +"Congratulations! You've successfully built and run your first federated " +"learning system in your ios device. The full `source code " +"`_ for this " +"example can be found in :code:`examples/ios`." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:13 +#: ../../source/tutorial-quickstart-jax.rst:-1 msgid "" -"In this tutorial, you will learn what federated learning is, build your " -"first system in Flower, and gradually extend it. If you work through all " -"parts of the tutorial, you will be able to build advanced federated " -"learning systems that approach the current state of the art in the field." +"Check out this Federated Learning quickstart tutorial for using Flower " +"with Jax to train a linear regression model on a scikit-learn dataset." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:15 -msgid "" -"🧑‍🏫 This tutorial starts at zero and expects no familiarity with " -"federated learning. Only a basic understanding of data science and Python" -" programming is assumed." +#: ../../source/tutorial-quickstart-jax.rst:5 +msgid "Quickstart JAX" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:17 +#: ../../source/tutorial-quickstart-pandas.rst:-1 msgid "" -"`Star Flower on GitHub `__ ⭐️ and join " -"the open-source Flower community on Slack to connect, ask questions, and " -"get help: `Join Slack `__ 🌼 We'd love to " -"hear from you in the ``#introductions`` channel! And if anything is " -"unclear, head over to the ``#questions`` channel." +"Check out this Federated Learning quickstart tutorial for using Flower " +"with Pandas to perform Federated Analytics." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:19 -msgid "Let's get started!" +#: ../../source/tutorial-quickstart-pandas.rst:5 +msgid "Quickstart Pandas" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:31 -msgid "Classic machine learning" +#: ../../source/tutorial-quickstart-pandas.rst:10 +msgid "Let's build a federated analytics system using Pandas and Flower!" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:33 +#: ../../source/tutorial-quickstart-pandas.rst:12 msgid "" -"Before we begin to discuss federated learning, let us quickly recap how " -"most machine learning works today." +"Please refer to the `full code example " +"`_ " +"to learn more." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:35 +#: ../../source/tutorial-quickstart-pytorch.rst:-1 msgid "" -"In machine learning, we have a model, and we have data. The model could " -"be a neural network (as depicted here), or something else, like classical" -" linear regression." +"Check out this Federated Learning quickstart tutorial for using Flower " +"with PyTorch to train a CNN model on MNIST." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:41 -msgid "|2b5c62c529f6416f840c594cce062fbb|" +#: ../../source/tutorial-quickstart-pytorch.rst:13 +msgid "" +"In this tutorial we will learn how to train a Convolutional Neural " +"Network on CIFAR10 using Flower and PyTorch." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:109 -msgid "Model and data" +#: ../../source/tutorial-quickstart-pytorch.rst:15 +#: ../../source/tutorial-quickstart-xgboost.rst:39 +msgid "" +"First of all, it is recommended to create a virtual environment and run " +"everything within a :doc:`virtualenv `." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:47 +#: ../../source/tutorial-quickstart-pytorch.rst:17 +#: ../../source/tutorial-quickstart-scikitlearn.rst:14 msgid "" -"We train the model using the data to perform a useful task. A task could " -"be to detect objects in images, transcribe an audio recording, or play a " -"game like Go." +"Our example consists of one *server* and two *clients* all having the " +"same model." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:53 -msgid "|90b334680cb7467d9a04d39b8e8dca9f|" +#: ../../source/tutorial-quickstart-pytorch.rst:19 +msgid "" +"*Clients* are responsible for generating individual weight-updates for " +"the model based on their local datasets. These updates are then sent to " +"the *server* which will aggregate them to produce a better model. " +"Finally, the *server* sends this improved version of the model back to " +"each *client*. A complete cycle of weight updates is called a *round*." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:111 -msgid "Train model using data" +#: ../../source/tutorial-quickstart-pytorch.rst:23 +msgid "" +"Now that we have a rough idea of what is going on, let's get started. We " +"first need to install Flower. You can do this by running :" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:59 +#: ../../source/tutorial-quickstart-pytorch.rst:29 msgid "" -"Now, in practice, the training data we work with doesn't originate on the" -" machine we train the model on. It gets created somewhere else." +"Since we want to use PyTorch to solve a computer vision task, let's go " +"ahead and install PyTorch and the **torchvision** library:" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:61 +#: ../../source/tutorial-quickstart-pytorch.rst:39 msgid "" -"It originates on a smartphone by the user interacting with an app, a car " -"collecting sensor data, a laptop receiving input via the keyboard, or a " -"smart speaker listening to someone trying to sing a song." +"Now that we have all our dependencies installed, let's run a simple " +"distributed training with two clients and one server. Our training " +"procedure and network architecture are based on PyTorch's `Deep Learning " +"with PyTorch " +"`_." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:67 -msgid "|65764ceee89f4335bfd93fd0b115e831|" +#: ../../source/tutorial-quickstart-pytorch.rst:41 +msgid "" +"In a file called :code:`client.py`, import Flower and PyTorch related " +"packages:" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:113 -msgid "Data on a phone" +#: ../../source/tutorial-quickstart-pytorch.rst:56 +msgid "In addition, we define the device allocation in PyTorch with:" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:73 +#: ../../source/tutorial-quickstart-pytorch.rst:62 msgid "" -"What's also important to mention, this \"somewhere else\" is usually not " -"just one place, it's many places. It could be several devices all running" -" the same app. But it could also be several organizations, all generating" -" data for the same task." +"We use PyTorch to load CIFAR10, a popular colored image classification " +"dataset for machine learning. The PyTorch :code:`DataLoader()` downloads " +"the training and test data that are then normalized." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:79 -msgid "|d97319ec28bb407ea0ab9705e38f3bcf|" +#: ../../source/tutorial-quickstart-pytorch.rst:78 +msgid "" +"Define the loss and optimizer with PyTorch. The training of the dataset " +"is done by looping over the dataset, measure the corresponding loss and " +"optimize it." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:115 -msgid "Data is on many devices" +#: ../../source/tutorial-quickstart-pytorch.rst:94 +msgid "" +"Define then the validation of the machine learning network. We loop over" +" the test set and measure the loss and accuracy of the test set." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:85 +#: ../../source/tutorial-quickstart-pytorch.rst:113 msgid "" -"So to use machine learning, or any kind of data analysis, the approach " -"that has been used in the past was to collect all data on a central " -"server. This server can be somewhere in a data center, or somewhere in " -"the cloud." +"After defining the training and testing of a PyTorch machine learning " +"model, we use the functions for the Flower clients." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:91 -msgid "|11e95ac83a8548d8b3505b4663187d07|" +#: ../../source/tutorial-quickstart-pytorch.rst:115 +msgid "" +"The Flower clients will use a simple CNN adapted from 'PyTorch: A 60 " +"Minute Blitz':" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:117 -msgid "Central data collection" +#: ../../source/tutorial-quickstart-pytorch.rst:142 +msgid "" +"After loading the data set with :code:`load_data()` we define the Flower " +"interface." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:97 +#: ../../source/tutorial-quickstart-pytorch.rst:144 +#: ../../source/tutorial-quickstart-tensorflow.rst:54 msgid "" -"Once all the data is collected in one place, we can finally use machine " -"learning algorithms to train our model on the data. This is the machine " -"learning approach that we've basically always relied on." +"The Flower server interacts with clients through an interface called " +":code:`Client`. When the server selects a particular client for training," +" it sends training instructions over the network. The client receives " +"those instructions and calls one of the :code:`Client` methods to run " +"your code (i.e., to train the neural network we defined earlier)." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:103 -msgid "|1dab2f3a23674abc8a6731f20fa10730|" +#: ../../source/tutorial-quickstart-pytorch.rst:150 +msgid "" +"Flower provides a convenience class called :code:`NumPyClient` which " +"makes it easier to implement the :code:`Client` interface when your " +"workload uses PyTorch. Implementing :code:`NumPyClient` usually means " +"defining the following methods (:code:`set_parameters` is optional " +"though):" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:119 -msgid "Central model training" +#: ../../source/tutorial-quickstart-pytorch.rst:156 +#: ../../source/tutorial-quickstart-scikitlearn.rst:119 +msgid "return the model weight as a list of NumPy ndarrays" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:130 -msgid "Challenges of classical machine learning" +#: ../../source/tutorial-quickstart-pytorch.rst:157 +#: ../../source/tutorial-quickstart-scikitlearn.rst:121 +msgid ":code:`set_parameters` (optional)" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:132 +#: ../../source/tutorial-quickstart-pytorch.rst:158 +#: ../../source/tutorial-quickstart-scikitlearn.rst:121 msgid "" -"The classic machine learning approach we've just seen can be used in some" -" cases. Great examples include categorizing holiday photos, or analyzing " -"web traffic. Cases, where all the data is naturally available on a " -"centralized server." -msgstr "" - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:138 -msgid "|7f0ee162da38450788493a21627306f7|" +"update the local model weights with the parameters received from the " +"server" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:173 -msgid "Centralized possible" +#: ../../source/tutorial-quickstart-pytorch.rst:160 +#: ../../source/tutorial-quickstart-scikitlearn.rst:124 +msgid "set the local model weights" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:144 -msgid "" -"But the approach can not be used in many other cases. Cases, where the " -"data is not available on a centralized server, or cases where the data " -"available on one server is not enough to train a good model." +#: ../../source/tutorial-quickstart-pytorch.rst:161 +#: ../../source/tutorial-quickstart-scikitlearn.rst:125 +msgid "train the local model" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:150 -msgid "|296a1fb72c514b23b3d8905ff0ff98c6|" +#: ../../source/tutorial-quickstart-pytorch.rst:162 +#: ../../source/tutorial-quickstart-scikitlearn.rst:126 +msgid "receive the updated local model weights" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:175 -msgid "Centralized impossible" +#: ../../source/tutorial-quickstart-pytorch.rst:164 +#: ../../source/tutorial-quickstart-scikitlearn.rst:128 +msgid "test the local model" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:156 -msgid "" -"There are many reasons why the classic centralized machine learning " -"approach does not work for a large number of highly important real-world " -"use cases. Those reasons include:" +#: ../../source/tutorial-quickstart-pytorch.rst:166 +msgid "which can be implemented in the following way:" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:158 +#: ../../source/tutorial-quickstart-pytorch.rst:189 +#: ../../source/tutorial-quickstart-tensorflow.rst:82 msgid "" -"**Regulations**: GDPR (Europe), CCPA (California), PIPEDA (Canada), LGPD " -"(Brazil), PDPL (Argentina), KVKK (Turkey), POPI (South Africa), FSS " -"(Russia), CDPR (China), PDPB (India), PIPA (Korea), APPI (Japan), PDP " -"(Indonesia), PDPA (Singapore), APP (Australia), and other regulations " -"protect sensitive data from being moved. In fact, those regulations " -"sometimes even prevent single organizations from combining their own " -"users' data for artificial intelligence training because those users live" -" in different parts of the world, and their data is governed by different" -" data protection regulations." +"We can now create an instance of our class :code:`CifarClient` and add " +"one line to actually run this client:" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:160 +#: ../../source/tutorial-quickstart-pytorch.rst:196 +#: ../../source/tutorial-quickstart-tensorflow.rst:90 msgid "" -"**User preference**: In addition to regulation, there are use cases where" -" users just expect that no data leaves their device, ever. If you type " -"your passwords and credit card info into the digital keyboard of your " -"phone, you don't expect those passwords to end up on the server of the " -"company that developed that keyboard, do you? In fact, that use case was " -"the reason federated learning was invented in the first place." +"That's it for the client. We only have to implement :code:`Client` or " +":code:`NumPyClient` and call :code:`fl.client.start_client()`. If you " +"implement a client of type :code:`NumPyClient` you'll need to first call " +"its :code:`to_client()` method. The string :code:`\"[::]:8080\"` tells " +"the client which server to connect to. In our case we can run the server " +"and the client on the same machine, therefore we use " +":code:`\"[::]:8080\"`. If we run a truly federated workload with the " +"server and clients running on different machines, all that needs to " +"change is the :code:`server_address` we point the client at." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:161 +#: ../../source/tutorial-quickstart-pytorch.rst:226 +#: ../../source/tutorial-quickstart-scikitlearn.rst:239 +#: ../../source/tutorial-quickstart-tensorflow.rst:122 +#: ../../source/tutorial-quickstart-xgboost.rst:533 msgid "" -"**Data volume**: Some sensors, like cameras, produce such a high data " -"volume that it is neither feasible nor economic to collect all the data " -"(due to, for example, bandwidth or communication efficiency). Think about" -" a national rail service with hundreds of train stations across the " -"country. If each of these train stations is outfitted with a number of " -"security cameras, the volume of raw on-device data they produce requires " -"incredibly powerful and exceedingly expensive infrastructure to process " -"and store. And most of the data isn't even useful." +"Once the server is running we can start the clients in different " +"terminals. Open a new terminal and start the first client:" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:164 -msgid "Examples where centralized machine learning does not work include:" +#: ../../source/tutorial-quickstart-pytorch.rst:233 +#: ../../source/tutorial-quickstart-scikitlearn.rst:246 +#: ../../source/tutorial-quickstart-tensorflow.rst:129 +#: ../../source/tutorial-quickstart-xgboost.rst:540 +msgid "Open another terminal and start the second client:" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:166 +#: ../../source/tutorial-quickstart-pytorch.rst:239 +#: ../../source/tutorial-quickstart-scikitlearn.rst:252 +#: ../../source/tutorial-quickstart-xgboost.rst:546 msgid "" -"Sensitive healthcare records from multiple hospitals to train cancer " -"detection models" +"Each client will have its own dataset. You should now see how the " +"training does in the very first terminal (the one that started the " +"server):" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:167 +#: ../../source/tutorial-quickstart-pytorch.rst:271 msgid "" -"Financial information from different organizations to detect financial " -"fraud" +"Congratulations! You've successfully built and run your first federated " +"learning system. The full `source code " +"`_ for this example can be found in :code:`examples" +"/quickstart-pytorch`." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:168 -msgid "Location data from your electric car to make better range prediction" +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:-1 +msgid "" +"Check out this Federated Learning quickstart tutorial for using Flower " +"with PyTorch Lightning to train an Auto Encoder model on MNIST." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:169 -msgid "End-to-end encrypted messages to train better auto-complete models" +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:5 +msgid "Quickstart PyTorch Lightning" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:171 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:10 msgid "" -"The popularity of privacy-enhancing systems like the `Brave " -"`__ browser or the `Signal `__ " -"messenger shows that users care about privacy. In fact, they choose the " -"privacy-enhancing version over other alternatives, if such an alternative" -" exists. But what can we do to apply machine learning and data science to" -" these cases to utilize private data? After all, these are all areas that" -" would benefit significantly from recent advances in AI." +"Let's build a horizontal federated learning system using PyTorch " +"Lightning and Flower!" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:186 -msgid "Federated learning" +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:12 +msgid "" +"Please refer to the `full code example " +"`_ to learn more." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:188 +#: ../../source/tutorial-quickstart-scikitlearn.rst:-1 msgid "" -"Federated learning simply reverses this approach. It enables machine " -"learning on distributed data by moving the training to the data, instead " -"of moving the data to the training. Here's the single-sentence " -"explanation:" +"Check out this Federated Learning quickstart tutorial for using Flower " +"with scikit-learn to train a linear regression model." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:190 -msgid "Central machine learning: move the data to the computation" +#: ../../source/tutorial-quickstart-scikitlearn.rst:5 +msgid "Quickstart scikit-learn" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:191 -msgid "Federated (machine) learning: move the computation to the data" +#: ../../source/tutorial-quickstart-scikitlearn.rst:10 +msgid "" +"In this tutorial, we will learn how to train a :code:`Logistic " +"Regression` model on MNIST using Flower and scikit-learn." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:193 +#: ../../source/tutorial-quickstart-scikitlearn.rst:12 msgid "" -"By doing so, it enables us to use machine learning (and other data " -"science approaches) in areas where it wasn't possible before. We can now " -"train excellent medical AI models by enabling different hospitals to work" -" together. We can solve financial fraud by training AI models on the data" -" of different financial institutions. We can build novel privacy-" -"enhancing applications (such as secure messaging) that have better built-" -"in AI than their non-privacy-enhancing alternatives. And those are just a" -" few of the examples that come to mind. As we deploy federated learning, " -"we discover more and more areas that can suddenly be reinvented because " -"they now have access to vast amounts of previously inaccessible data." +"It is recommended to create a virtual environment and run everything " +"within this :doc:`virtualenv `." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:196 +#: ../../source/tutorial-quickstart-scikitlearn.rst:16 msgid "" -"So how does federated learning work, exactly? Let's start with an " -"intuitive explanation." +"*Clients* are responsible for generating individual model parameter " +"updates for the model based on their local datasets. These updates are " +"then sent to the *server* which will aggregate them to produce an updated" +" global model. Finally, the *server* sends this improved version of the " +"model back to each *client*. A complete cycle of parameters updates is " +"called a *round*." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:199 -msgid "Federated learning in five steps" +#: ../../source/tutorial-quickstart-scikitlearn.rst:20 +msgid "" +"Now that we have a rough idea of what is going on, let's get started. We " +"first need to install Flower. You can do this by running:" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:202 -msgid "Step 0: Initialize global model" +#: ../../source/tutorial-quickstart-scikitlearn.rst:26 +msgid "Since we want to use scikit-learn, let's go ahead and install it:" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:204 -msgid "" -"We start by initializing the model on the server. This is exactly the " -"same in classic centralized learning: we initialize the model parameters," -" either randomly or from a previously saved checkpoint." +#: ../../source/tutorial-quickstart-scikitlearn.rst:32 +msgid "Or simply install all dependencies using Poetry:" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:210 -msgid "|5b1408eec0d746cdb91162a9107b6089|" +#: ../../source/tutorial-quickstart-scikitlearn.rst:42 +msgid "" +"Now that we have all our dependencies installed, let's run a simple " +"distributed training with two clients and one server. However, before " +"setting up the client and server, we will define all functionalities that" +" we need for our federated learning setup within :code:`utils.py`. The " +":code:`utils.py` contains different functions defining all the machine " +"learning basics:" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:307 -msgid "Initialize global model" +#: ../../source/tutorial-quickstart-scikitlearn.rst:45 +msgid ":code:`get_model_parameters()`" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:217 -msgid "" -"Step 1: Send model to a number of connected organizations/devices (client" -" nodes)" +#: ../../source/tutorial-quickstart-scikitlearn.rst:46 +msgid "Returns the parameters of a :code:`sklearn` LogisticRegression model" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:219 -msgid "" -"Next, we send the parameters of the global model to the connected client " -"nodes (think: edge devices like smartphones or servers belonging to " -"organizations). This is to ensure that each participating node starts " -"their local training using the same model parameters. We often use only a" -" few of the connected nodes instead of all nodes. The reason for this is " -"that selecting more and more client nodes has diminishing returns." +#: ../../source/tutorial-quickstart-scikitlearn.rst:47 +msgid ":code:`set_model_params()`" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:225 -msgid "|aef19f4b122c4e8d9f4c57f99bcd5dd2|" +#: ../../source/tutorial-quickstart-scikitlearn.rst:48 +msgid "Sets the parameters of a :code:`sklearn` LogisticRegression model" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:309 -msgid "Send global model" +#: ../../source/tutorial-quickstart-scikitlearn.rst:50 +msgid ":code:`set_initial_params()`" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:232 -msgid "" -"Step 2: Train model locally on the data of each organization/device " -"(client node)" +#: ../../source/tutorial-quickstart-scikitlearn.rst:50 +msgid "Initializes the model parameters that the Flower server will ask for" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:234 +#: ../../source/tutorial-quickstart-scikitlearn.rst:52 msgid "" -"Now that all (selected) client nodes have the latest version of the " -"global model parameters, they start the local training. They use their " -"own local dataset to train their own local model. They don't train the " -"model until full convergence, but they only train for a little while. " -"This could be as little as one epoch on the local data, or even just a " -"few steps (mini-batches)." +"Please check out :code:`utils.py` `here " +"`_ for more details. The pre-defined functions are used in" +" the :code:`client.py` and imported. The :code:`client.py` also requires " +"to import several packages such as Flower and scikit-learn:" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:240 -msgid "|2881a86d8fc54ba29d96b29fc2819f4a|" +#: ../../source/tutorial-quickstart-scikitlearn.rst:67 +msgid "" +"Prior to local training, we need to load the MNIST dataset, a popular " +"image classification dataset of handwritten digits for machine learning, " +"and partition the dataset for FL. This can be conveniently achieved using" +" `Flower Datasets `_. The " +":code:`FederatedDataset.load_partition()` method loads the partitioned " +"training set for each partition ID defined in the :code:`--partition-id` " +"argument." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:311 -msgid "Train on local data" +#: ../../source/tutorial-quickstart-scikitlearn.rst:95 +msgid "" +"Next, the logistic regression model is defined and initialized with " +":code:`utils.set_initial_params()`." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:247 -msgid "Step 3: Return model updates back to the server" +#: ../../source/tutorial-quickstart-scikitlearn.rst:107 +msgid "" +"The Flower server interacts with clients through an interface called " +":code:`Client`. When the server selects a particular client for training," +" it sends training instructions over the network. The client receives " +"those instructions and calls one of the :code:`Client` methods to run " +"your code (i.e., to fit the logistic regression we defined earlier)." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:249 +#: ../../source/tutorial-quickstart-scikitlearn.rst:113 msgid "" -"After local training, each client node has a slightly different version " -"of the model parameters they originally received. The parameters are all " -"different because each client node has different examples in its local " -"dataset. The client nodes then send those model updates back to the " -"server. The model updates they send can either be the full model " -"parameters or just the gradients that were accumulated during local " -"training." +"Flower provides a convenience class called :code:`NumPyClient` which " +"makes it easier to implement the :code:`Client` interface when your " +"workload uses scikit-learn. Implementing :code:`NumPyClient` usually " +"means defining the following methods (:code:`set_parameters` is optional " +"though):" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:255 -msgid "|ec1fe880237247e0975f52766775ab84|" +#: ../../source/tutorial-quickstart-scikitlearn.rst:122 +msgid "is directly imported with :code:`utils.set_model_params()`" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:313 -msgid "Send model updates" +#: ../../source/tutorial-quickstart-scikitlearn.rst:130 +msgid "The methods can be implemented in the following way:" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:262 -msgid "Step 4: Aggregate model updates into a new global model" +#: ../../source/tutorial-quickstart-scikitlearn.rst:153 +msgid "" +"We can now create an instance of our class :code:`MnistClient` and add " +"one line to actually run this client:" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:264 +#: ../../source/tutorial-quickstart-scikitlearn.rst:160 msgid "" -"The server receives model updates from the selected client nodes. If it " -"selected 100 client nodes, it now has 100 slightly different versions of " -"the original global model, each trained on the local data of one client. " -"But didn't we want to have one model that contains the learnings from the" -" data of all 100 client nodes?" +"That's it for the client. We only have to implement :code:`Client` or " +":code:`NumPyClient` and call :code:`fl.client.start_client()`. If you " +"implement a client of type :code:`NumPyClient` you'll need to first call " +"its :code:`to_client()` method. The string :code:`\"0.0.0.0:8080\"` tells" +" the client which server to connect to. In our case we can run the server" +" and the client on the same machine, therefore we use " +":code:`\"0.0.0.0:8080\"`. If we run a truly federated workload with the " +"server and clients running on different machines, all that needs to " +"change is the :code:`server_address` we pass to the client." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:266 +#: ../../source/tutorial-quickstart-scikitlearn.rst:169 msgid "" -"In order to get one single model, we have to combine all the model " -"updates we received from the client nodes. This process is called " -"*aggregation*, and there are many different ways to do it. The most basic" -" way to do it is called *Federated Averaging* (`McMahan et al., 2016 " -"`__), often abbreviated as *FedAvg*. " -"*FedAvg* takes the 100 model updates and, as the name suggests, averages " -"them. To be more precise, it takes the *weighted average* of the model " -"updates, weighted by the number of examples each client used for " -"training. The weighting is important to make sure that each data example " -"has the same \"influence\" on the resulting global model. If one client " -"has 10 examples, and another client has 100 examples, then - without " -"weighting - each of the 10 examples would influence the global model ten " -"times as much as each of the 100 examples." +"The following Flower server is a little bit more advanced and returns an " +"evaluation function for the server-side evaluation. First, we import " +"again all required libraries such as Flower and scikit-learn." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:273 -msgid "|9fdf048ed58d4467b2718cdf4aaf1ec3|" +#: ../../source/tutorial-quickstart-scikitlearn.rst:172 +msgid ":code:`server.py`, import Flower and start the server:" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:315 -msgid "Aggregate model updates" +#: ../../source/tutorial-quickstart-scikitlearn.rst:185 +msgid "" +"The number of federated learning rounds is set in :code:`fit_round()` and" +" the evaluation is defined in :code:`get_evaluate_fn()`. The evaluation " +"function is called after each federated learning round and gives you " +"information about loss and accuracy. Note that we also make use of Flower" +" Datasets here to load the test split of the MNIST dataset for server-" +"side evaluation." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:280 -msgid "Step 5: Repeat steps 1 to 4 until the model converges" +#: ../../source/tutorial-quickstart-scikitlearn.rst:213 +msgid "" +"The :code:`main` contains the server-side parameter initialization " +":code:`utils.set_initial_params()` as well as the aggregation strategy " +":code:`fl.server.strategy:FedAvg()`. The strategy is the default one, " +"federated averaging (or FedAvg), with two clients and evaluation after " +"each federated learning round. The server can be started with the command" +" :code:`fl.server.start_server(server_address=\"0.0.0.0:8080\", " +"strategy=strategy, config=fl.server.ServerConfig(num_rounds=3))`." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:282 +#: ../../source/tutorial-quickstart-scikitlearn.rst:232 msgid "" -"Steps 1 to 4 are what we call a single round of federated learning. The " -"global model parameters get sent to the participating client nodes (step " -"1), the client nodes train on their local data (step 2), they send their " -"updated models to the server (step 3), and the server then aggregates the" -" model updates to get a new version of the global model (step 4)." +"With both client and server ready, we can now run everything and see " +"federated learning in action. Federated learning systems usually have a " +"server and multiple clients. We, therefore, have to start the server " +"first:" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:284 +#: ../../source/tutorial-quickstart-scikitlearn.rst:286 msgid "" -"During a single round, each client node that participates in that " -"iteration only trains for a little while. This means that after the " -"aggregation step (step 4), we have a model that has been trained on all " -"the data of all participating client nodes, but only for a little while. " -"We then have to repeat this training process over and over again to " -"eventually arrive at a fully trained model that performs well across the " -"data of all client nodes." +"Congratulations! You've successfully built and run your first federated " +"learning system. The full `source code " +"`_ for this example can be found in :code:`examples/sklearn-logreg-" +"mnist`." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:289 +#: ../../source/tutorial-quickstart-tensorflow.rst:-1 msgid "" -"Congratulations, you now understand the basics of federated learning. " -"There's a lot more to discuss, of course, but that was federated learning" -" in a nutshell. In later parts of this tutorial, we will go into more " -"detail. Interesting questions include: How can we select the best client " -"nodes that should participate in the next round? What's the best way to " -"aggregate model updates? How can we handle failing client nodes " -"(stragglers)?" +"Check out this Federated Learning quickstart tutorial for using Flower " +"with TensorFlow to train a MobilNetV2 model on CIFAR-10." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:294 -msgid "" -"Just like we can train a model on the decentralized data of different " -"client nodes, we can also evaluate the model on that data to receive " -"valuable metrics. This is called federated evaluation, sometimes " -"abbreviated as FE. In fact, federated evaluation is an integral part of " -"most federated learning systems." +#: ../../source/tutorial-quickstart-tensorflow.rst:5 +msgid "Quickstart TensorFlow" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:297 -msgid "Federated analytics" +#: ../../source/tutorial-quickstart-tensorflow.rst:13 +msgid "Let's build a federated learning system in less than 20 lines of code!" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:299 +#: ../../source/tutorial-quickstart-tensorflow.rst:15 +msgid "Before Flower can be imported we have to install it:" +msgstr "" + +#: ../../source/tutorial-quickstart-tensorflow.rst:21 msgid "" -"In many cases, machine learning isn't necessary to derive value from " -"data. Data analysis can yield valuable insights, but again, there's often" -" not enough data to get a clear answer. What's the average age at which " -"people develop a certain type of health condition? Federated analytics " -"enables such queries over multiple client nodes. It is usually used in " -"conjunction with other privacy-enhancing technologies like secure " -"aggregation to prevent the server from seeing the results submitted by " -"individual client nodes." +"Since we want to use the Keras API of TensorFlow (TF), we have to install" +" TF as well:" msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:305 +#: ../../source/tutorial-quickstart-tensorflow.rst:31 +msgid "Next, in a file called :code:`client.py`, import Flower and TensorFlow:" +msgstr "" + +#: ../../source/tutorial-quickstart-tensorflow.rst:38 msgid "" -"Differential privacy (DP) is often mentioned in the context of Federated " -"Learning. It is a privacy-preserving method used when analyzing and " -"sharing statistical data, ensuring the privacy of individual " -"participants. DP achieves this by adding statistical noise to the model " -"updates, ensuring any individual participants’ information cannot be " -"distinguished or re-identified. This technique can be considered an " -"optimization that provides a quantifiable privacy protection measure." +"We use the Keras utilities of TF to load CIFAR10, a popular colored image" +" classification dataset for machine learning. The call to " +":code:`tf.keras.datasets.cifar10.load_data()` downloads CIFAR10, caches " +"it locally, and then returns the entire training and test set as NumPy " +"ndarrays." +msgstr "" + +#: ../../source/tutorial-quickstart-tensorflow.rst:47 +msgid "" +"Next, we need a model. For the purpose of this tutorial, we use " +"MobilNetV2 with 10 output classes:" +msgstr "" + +#: ../../source/tutorial-quickstart-tensorflow.rst:60 +msgid "" +"Flower provides a convenience class called :code:`NumPyClient` which " +"makes it easier to implement the :code:`Client` interface when your " +"workload uses Keras. The :code:`NumPyClient` interface defines three " +"methods which can be implemented in the following way:" +msgstr "" + +#: ../../source/tutorial-quickstart-tensorflow.rst:135 +msgid "Each client will have its own dataset." +msgstr "" + +#: ../../source/tutorial-quickstart-tensorflow.rst:137 +msgid "" +"You should now see how the training does in the very first terminal (the " +"one that started the server):" +msgstr "" + +#: ../../source/tutorial-quickstart-tensorflow.rst:169 +msgid "" +"Congratulations! You've successfully built and run your first federated " +"learning system. The full `source code " +"`_ for this can be found in :code:`examples" +"/quickstart-tensorflow/client.py`." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:-1 +msgid "" +"Check out this Federated Learning quickstart tutorial for using Flower " +"with XGBoost to train classification models on trees." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:5 +msgid "Quickstart XGBoost" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:14 +msgid "Federated XGBoost" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:16 +msgid "" +"EXtreme Gradient Boosting (**XGBoost**) is a robust and efficient " +"implementation of gradient-boosted decision tree (**GBDT**), that " +"maximises the computational boundaries for boosted tree methods. It's " +"primarily designed to enhance both the performance and computational " +"speed of machine learning models. In XGBoost, trees are constructed " +"concurrently, unlike the sequential approach taken by GBDT." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:20 +msgid "" +"Often, for tabular data on medium-sized datasets with fewer than 10k " +"training examples, XGBoost surpasses the results of deep learning " +"techniques." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:23 +msgid "Why federated XGBoost?" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:25 +msgid "" +"Indeed, as the demand for data privacy and decentralized learning grows, " +"there's an increasing requirement to implement federated XGBoost systems " +"for specialised applications, like survival analysis and financial fraud " +"detection." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:27 +msgid "" +"Federated learning ensures that raw data remains on the local device, " +"making it an attractive approach for sensitive domains where data " +"security and privacy are paramount. Given the robustness and efficiency " +"of XGBoost, combining it with federated learning offers a promising " +"solution for these specific challenges." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:30 +msgid "" +"In this tutorial we will learn how to train a federated XGBoost model on " +"HIGGS dataset using Flower and :code:`xgboost` package. We use a simple " +"example (`full code xgboost-quickstart " +"`_)" +" with two *clients* and one *server* to demonstrate how federated XGBoost" +" works, and then we dive into a more complex example (`full code xgboost-" +"comprehensive `_) to run various experiments." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:37 +msgid "Environment Setup" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:41 +msgid "" +"We first need to install Flower and Flower Datasets. You can do this by " +"running :" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:47 +msgid "" +"Since we want to use :code:`xgboost` package to build up XGBoost trees, " +"let's go ahead and install :code:`xgboost`:" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:57 +msgid "" +"*Clients* are responsible for generating individual weight-updates for " +"the model based on their local datasets. Now that we have all our " +"dependencies installed, let's run a simple distributed training with two " +"clients and one server." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:60 +msgid "" +"In a file called :code:`client.py`, import xgboost, Flower, Flower " +"Datasets and other related functions:" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:87 +msgid "Dataset partition and hyper-parameter selection" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:89 +msgid "" +"Prior to local training, we require loading the HIGGS dataset from Flower" +" Datasets and conduct data partitioning for FL:" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:102 +msgid "" +"In this example, we split the dataset into two partitions with uniform " +"distribution (:code:`IidPartitioner(num_partitions=2)`). Then, we load " +"the partition for the given client based on :code:`node_id`:" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:121 +msgid "" +"After that, we do train/test splitting on the given partition (client's " +"local data), and transform data format for :code:`xgboost` package." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:134 +msgid "" +"The functions of :code:`train_test_split` and " +":code:`transform_dataset_to_dmatrix` are defined as below:" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:158 +msgid "Finally, we define the hyper-parameters used for XGBoost training." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:174 +msgid "" +"The :code:`num_local_round` represents the number of iterations for local" +" tree boost. We use CPU for the training in default. One can shift it to " +"GPU by setting :code:`tree_method` to :code:`gpu_hist`. We use AUC as " +"evaluation metric." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:181 +msgid "Flower client definition for XGBoost" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:183 +msgid "" +"After loading the dataset we define the Flower client. We follow the " +"general rule to define :code:`XgbClient` class inherited from " +":code:`fl.client.Client`." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:193 +msgid "" +"The :code:`self.bst` is used to keep the Booster objects that remain " +"consistent across rounds, allowing them to store predictions from trees " +"integrated in earlier rounds and maintain other essential data structures" +" for training." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:196 +msgid "" +"Then, we override :code:`get_parameters`, :code:`fit` and " +":code:`evaluate` methods insides :code:`XgbClient` class as follows." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:210 +msgid "" +"Unlike neural network training, XGBoost trees are not started from a " +"specified random weights. In this case, we do not use " +":code:`get_parameters` and :code:`set_parameters` to initialise model " +"parameters for XGBoost. As a result, let's return an empty tensor in " +":code:`get_parameters` when it is called by the server at the first " +"round." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:251 +msgid "" +"In :code:`fit`, at the first round, we call :code:`xgb.train()` to build " +"up the first set of trees. the returned Booster object and config are " +"stored in :code:`self.bst` and :code:`self.config`, respectively. From " +"the second round, we load the global model sent from server to " +":code:`self.bst`, and then update model weights on local training data " +"with function :code:`local_boost` as follows:" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:269 +msgid "" +"Given :code:`num_local_round`, we update trees by calling " +":code:`self.bst.update` method. After training, the last " +":code:`N=num_local_round` trees will be extracted to send to the server." msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:326 -msgid "Flower" -msgstr "" +#: ../../source/tutorial-quickstart-xgboost.rst:291 +msgid "" +"In :code:`evaluate`, we call :code:`self.bst.eval_set` function to " +"conduct evaluation on valid set. The AUC value will be returned." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:294 +msgid "" +"Now, we can create an instance of our class :code:`XgbClient` and add one" +" line to actually run this client:" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:300 +msgid "" +"That's it for the client. We only have to implement :code:`Client`and " +"call :code:`fl.client.start_client()`. The string :code:`\"[::]:8080\"` " +"tells the client which server to connect to. In our case we can run the " +"server and the client on the same machine, therefore we use " +":code:`\"[::]:8080\"`. If we run a truly federated workload with the " +"server and clients running on different machines, all that needs to " +"change is the :code:`server_address` we point the client at." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:311 +msgid "" +"These updates are then sent to the *server* which will aggregate them to " +"produce a better model. Finally, the *server* sends this improved version" +" of the model back to each *client* to finish a complete FL round." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:314 +msgid "" +"In a file named :code:`server.py`, import Flower and FedXgbBagging from " +":code:`flwr.server.strategy`." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:316 +msgid "We first define a strategy for XGBoost bagging aggregation." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:339 +msgid "" +"We use two clients for this example. An " +":code:`evaluate_metrics_aggregation` function is defined to collect and " +"wighted average the AUC values from clients." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:342 +msgid "Then, we start the server:" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:354 +msgid "Tree-based bagging aggregation" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:356 +msgid "" +"You must be curious about how bagging aggregation works. Let's look into " +"the details." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:358 +msgid "" +"In file :code:`flwr.server.strategy.fedxgb_bagging.py`, we define " +":code:`FedXgbBagging` inherited from :code:`flwr.server.strategy.FedAvg`." +" Then, we override the :code:`aggregate_fit`, :code:`aggregate_evaluate` " +"and :code:`evaluate` methods as follows:" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:454 +msgid "" +"In :code:`aggregate_fit`, we sequentially aggregate the clients' XGBoost " +"trees by calling :code:`aggregate()` function:" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:513 +msgid "" +"In this function, we first fetch the number of trees and the number of " +"parallel trees for the current and previous model by calling " +":code:`_get_tree_nums`. Then, the fetched information will be aggregated." +" After that, the trees (containing model weights) are aggregated to " +"generate a new tree model." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:518 +msgid "" +"After traversal of all clients' models, a new global model is generated, " +"followed by the serialisation, and sending back to each client." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:523 +msgid "Launch Federated XGBoost!" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:585 +msgid "" +"Congratulations! You've successfully built and run your first federated " +"XGBoost system. The AUC values can be checked in " +":code:`metrics_distributed`. One can see that the average AUC increases " +"over FL rounds." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:590 +msgid "" +"The full `source code `_ for this example can be found in :code:`examples" +"/xgboost-quickstart`." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:594 +msgid "Comprehensive Federated XGBoost" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:596 +msgid "" +"Now that you have known how federated XGBoost work with Flower, it's time" +" to run some more comprehensive experiments by customising the " +"experimental settings. In the xgboost-comprehensive example (`full code " +"`_), we provide more options to define various experimental" +" setups, including aggregation strategies, data partitioning and " +"centralised/distributed evaluation. We also support :doc:`Flower " +"simulation ` making it easy to simulate large " +"client cohorts in a resource-aware manner. Let's take a look!" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:603 +msgid "Cyclic training" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:605 +msgid "" +"In addition to bagging aggregation, we offer a cyclic training scheme, " +"which performs FL in a client-by-client fashion. Instead of aggregating " +"multiple clients, there is only one single client participating in the " +"training per round in the cyclic training scenario. The trained local " +"XGBoost trees will be passed to the next client as an initialised model " +"for next round's boosting." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:609 +msgid "" +"To do this, we first customise a :code:`ClientManager` in " +":code:`server_utils.py`:" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:649 +msgid "" +"The customised :code:`ClientManager` samples all available clients in " +"each FL round based on the order of connection to the server. Then, we " +"define a new strategy :code:`FedXgbCyclic` in " +":code:`flwr.server.strategy.fedxgb_cyclic.py`, in order to sequentially " +"select only one client in given round and pass the received model to next" +" client." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:690 +msgid "" +"Unlike the original :code:`FedAvg`, we don't perform aggregation here. " +"Instead, we just make a copy of the received client model as global model" +" by overriding :code:`aggregate_fit`." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:693 +msgid "" +"Also, the customised :code:`configure_fit` and :code:`configure_evaluate`" +" methods ensure the clients to be sequentially selected given FL round:" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:757 +msgid "Customised data partitioning" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:759 +msgid "" +"In :code:`dataset.py`, we have a function :code:`instantiate_partitioner`" +" to instantiate the data partitioner based on the given " +":code:`num_partitions` and :code:`partitioner_type`. Currently, we " +"provide four supported partitioner type to simulate the uniformity/non-" +"uniformity in data quantity (uniform, linear, square, exponential)." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:790 +msgid "Customised centralised/distributed evaluation" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:792 +msgid "" +"To facilitate centralised evaluation, we define a function in " +":code:`server_utils.py`:" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:824 +msgid "" +"This function returns a evaluation function which instantiates a " +":code:`Booster` object and loads the global model weights to it. The " +"evaluation is conducted by calling :code:`eval_set()` method, and the " +"tested AUC value is reported." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:827 +msgid "" +"As for distributed evaluation on the clients, it's same as the quick-" +"start example by overriding the :code:`evaluate()` method insides the " +":code:`XgbClient` class in :code:`client_utils.py`." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:831 +msgid "Flower simulation" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:832 +msgid "" +"We also provide an example code (:code:`sim.py`) to use the simulation " +"capabilities of Flower to simulate federated XGBoost training on either a" +" single machine or a cluster of machines." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:866 +msgid "" +"After importing all required packages, we define a :code:`main()` " +"function to perform the simulation process:" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:921 +msgid "" +"We first load the dataset and perform data partitioning, and the pre-" +"processed data is stored in a :code:`list`. After the simulation begins, " +"the clients won't need to pre-process their partitions again." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:924 +msgid "Then, we define the strategies and other hyper-parameters:" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:975 +msgid "" +"After that, we start the simulation by calling " +":code:`fl.simulation.start_simulation`:" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:995 +msgid "" +"One of key parameters for :code:`start_simulation` is :code:`client_fn` " +"which returns a function to construct a client. We define it as follows:" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:1038 +msgid "Arguments parser" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:1040 +msgid "" +"In :code:`utils.py`, we define the arguments parsers for clients, server " +"and simulation, allowing users to specify different experimental " +"settings. Let's first see the sever side:" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:1086 +msgid "" +"This allows user to specify training strategies / the number of total " +"clients / FL rounds / participating clients / clients for evaluation, and" +" evaluation fashion. Note that with :code:`--centralised-eval`, the sever" +" will do centralised evaluation and all functionalities for client " +"evaluation will be disabled." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:1090 +msgid "Then, the argument parser on client side:" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:1144 +msgid "" +"This defines various options for client data partitioning. Besides, " +"clients also have an option to conduct evaluation on centralised test set" +" by setting :code:`--centralised-eval`, as well as an option to perform " +"scaled learning rate based on the number of clients by setting :code" +":`--scaled-lr`." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:1148 +msgid "We also have an argument parser for simulation:" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:1226 +msgid "This integrates all arguments for both client and server sides." +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:1229 +msgid "Example commands" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:1231 +msgid "" +"To run a centralised evaluated experiment with bagging strategy on 5 " +"clients with exponential distribution for 50 rounds, we first start the " +"server as below:" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:1238 +msgid "Then, on each client terminal, we start the clients:" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:1244 +msgid "To run the same experiment with Flower simulation:" +msgstr "" + +#: ../../source/tutorial-quickstart-xgboost.rst:1250 +msgid "" +"The full `code `_ for this comprehensive example can be found in" +" :code:`examples/xgboost-comprehensive`." +msgstr "" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:9 +msgid "Build a strategy from scratch" +msgstr "" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:11 +msgid "" +"Welcome to the third part of the Flower federated learning tutorial. In " +"previous parts of this tutorial, we introduced federated learning with " +"PyTorch and Flower (`part 1 `__) and we learned how strategies " +"can be used to customize the execution on both the server and the clients" +" (`part 2 `__)." +msgstr "" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:13 +msgid "" +"In this notebook, we'll continue to customize the federated learning " +"system we built previously by creating a custom version of FedAvg (again," +" using `Flower `__ and `PyTorch " +"`__)." +msgstr "" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:15 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:16 +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:15 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:15 +msgid "" +"`Star Flower on GitHub `__ ⭐️ and join " +"the Flower community on Slack to connect, ask questions, and get help: " +"`Join Slack `__ 🌼 We'd love to hear from " +"you in the ``#introductions`` channel! And if anything is unclear, head " +"over to the ``#questions`` channel." +msgstr "" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:17 +msgid "Let's build a new ``Strategy`` from scratch!" +msgstr "" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:29 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:29 +msgid "Preparation" +msgstr "" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:31 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:32 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:31 +msgid "" +"Before we begin with the actual code, let's make sure that we have " +"everything we need." +msgstr "" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:43 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:44 +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:43 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:43 +msgid "Installing dependencies" +msgstr "" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:45 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:46 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:45 +msgid "First, we install the necessary packages:" +msgstr "" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:65 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:66 +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:65 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:65 +msgid "" +"Now that we have all dependencies installed, we can import everything we " +"need for this tutorial:" +msgstr "" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:101 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:102 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:101 +msgid "" +"It is possible to switch to a runtime that has GPU acceleration enabled " +"(on Google Colab: ``Runtime > Change runtime type > Hardware acclerator: " +"GPU > Save``). Note, however, that Google Colab is not always able to " +"offer GPU acceleration. If you see an error related to GPU availability " +"in one of the following sections, consider switching back to CPU-based " +"execution by setting ``DEVICE = torch.device(\"cpu\")``. If the runtime " +"has GPU acceleration enabled, you should see the output ``Training on " +"cuda``, otherwise it'll say ``Training on cpu``." +msgstr "" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:114 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:115 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:114 +msgid "Data loading" +msgstr "" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:116 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:116 +msgid "" +"Let's now load the CIFAR-10 training and test set, partition them into " +"ten smaller datasets (each split into training and validation set), and " +"wrap everything in their own ``DataLoader``. We introduce a new parameter" +" ``num_clients`` which allows us to call ``load_datasets`` with different" +" numbers of clients." +msgstr "" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:167 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:168 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:167 +msgid "Model training/evaluation" +msgstr "" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:169 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:170 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:169 +msgid "" +"Let's continue with the usual model definition (including " +"``set_parameters`` and ``get_parameters``), training and test functions:" +msgstr "" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:258 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:258 +msgid "Flower client" +msgstr "" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:260 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:260 +msgid "" +"To implement the Flower client, we (again) create a subclass of " +"``flwr.client.NumPyClient`` and implement the three methods " +"``get_parameters``, ``fit``, and ``evaluate``. Here, we also pass the " +"``cid`` to the client and use it log additional details:" +msgstr "" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:308 +msgid "Let's test what we have so far before we continue:" +msgstr "" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:339 +msgid "Build a Strategy from scratch" +msgstr "" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:341 +msgid "" +"Let’s overwrite the ``configure_fit`` method such that it passes a higher" +" learning rate (potentially also other hyperparameters) to the optimizer " +"of a fraction of the clients. We will keep the sampling of the clients as" +" it is in ``FedAvg`` and then change the configuration dictionary (one of" +" the ``FitIns`` attributes)." +msgstr "" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:507 +msgid "" +"The only thing left is to use the newly created custom Strategy " +"``FedCustom`` when starting the experiment:" +msgstr "" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:534 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:932 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:697 +msgid "Recap" +msgstr "" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:536 +msgid "" +"In this notebook, we’ve seen how to implement a custom strategy. A custom" +" strategy enables granular control over client node configuration, result" +" aggregation, and more. To define a custom strategy, you only have to " +"overwrite the abstract methods of the (abstract) base class ``Strategy``." +" To make custom strategies even more powerful, you can pass custom " +"functions to the constructor of your new class (``__init__``) and then " +"call these functions whenever needed." +msgstr "" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:550 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:948 +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:729 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:715 +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:369 +msgid "" +"Before you continue, make sure to join the Flower community on Slack: " +"`Join Slack `__" +msgstr "" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:552 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:950 +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:731 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:717 +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:371 +msgid "" +"There's a dedicated ``#questions`` channel if you need help, but we'd " +"also love to hear who you are in ``#introductions``!" +msgstr "" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:554 +msgid "" +"The `Flower Federated Learning Tutorial - Part 4 " +"`__ introduces ``Client``, the flexible API underlying " +"``NumPyClient``." +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:9 +msgid "Customize the client" +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:11 +msgid "" +"Welcome to the fourth part of the Flower federated learning tutorial. In " +"the previous parts of this tutorial, we introduced federated learning " +"with PyTorch and Flower (`part 1 `__), we learned how " +"strategies can be used to customize the execution on both the server and " +"the clients (`part 2 `__), and we built our own " +"custom strategy from scratch (`part 3 `__)." +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:14 +msgid "" +"In this notebook, we revisit ``NumPyClient`` and introduce a new " +"baseclass for building clients, simply named ``Client``. In previous " +"parts of this tutorial, we've based our client on ``NumPyClient``, a " +"convenience class which makes it easy to work with machine learning " +"libraries that have good NumPy interoperability. With ``Client``, we gain" +" a lot of flexibility that we didn't have before, but we'll also have to " +"do a few things the we didn't have to do before." +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:18 +msgid "" +"Let's go deeper and see what it takes to move from ``NumPyClient`` to " +"``Client``!" +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:30 +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:29 +msgid "Step 0: Preparation" +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:117 +msgid "" +"Let's now load the CIFAR-10 training and test set, partition them into " +"ten smaller datasets (each split into training and validation set), and " +"wrap everything in their own ``DataLoader``." +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:259 +msgid "Step 1: Revisiting NumPyClient" +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:261 +msgid "" +"So far, we've implemented our client by subclassing " +"``flwr.client.NumPyClient``. The three methods we implemented are " +"``get_parameters``, ``fit``, and ``evaluate``. Finally, we wrap the " +"creation of instances of this class in a function called ``client_fn``:" +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:309 +msgid "" +"We've seen this before, there's nothing new so far. The only *tiny* " +"difference compared to the previous notebook is naming, we've changed " +"``FlowerClient`` to ``FlowerNumPyClient`` and ``client_fn`` to " +"``numpyclient_fn``. Let's run it to see the output we get:" +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:339 +msgid "" +"This works as expected, two clients are training for three rounds of " +"federated learning." +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:341 +msgid "" +"Let's dive a little bit deeper and discuss how Flower executes this " +"simulation. Whenever a client is selected to do some work, " +"``start_simulation`` calls the function ``numpyclient_fn`` to create an " +"instance of our ``FlowerNumPyClient`` (along with loading the model and " +"the data)." +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:343 +msgid "" +"But here's the perhaps surprising part: Flower doesn't actually use the " +"``FlowerNumPyClient`` object directly. Instead, it wraps the object to " +"makes it look like a subclass of ``flwr.client.Client``, not " +"``flwr.client.NumPyClient``. In fact, the Flower core framework doesn't " +"know how to handle ``NumPyClient``'s, it only knows how to handle " +"``Client``'s. ``NumPyClient`` is just a convenience abstraction built on " +"top of ``Client``." +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:345 +msgid "" +"Instead of building on top of ``NumPyClient``, we can directly build on " +"top of ``Client``." +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:357 +msgid "Step 2: Moving from ``NumPyClient`` to ``Client``" +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:359 +msgid "" +"Let's try to do the same thing using ``Client`` instead of " +"``NumPyClient``." +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:465 +msgid "" +"Before we discuss the code in more detail, let's try to run it! Gotta " +"make sure our new ``Client``-based client works, right?" +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:490 +msgid "" +"That's it, we're now using ``Client``. It probably looks similar to what " +"we've done with ``NumPyClient``. So what's the difference?" +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:492 +msgid "" +"First of all, it's more code. But why? The difference comes from the fact" +" that ``Client`` expects us to take care of parameter serialization and " +"deserialization. For Flower to be able to send parameters over the " +"network, it eventually needs to turn these parameters into ``bytes``. " +"Turning parameters (e.g., NumPy ``ndarray``'s) into raw bytes is called " +"serialization. Turning raw bytes into something more useful (like NumPy " +"``ndarray``'s) is called deserialization. Flower needs to do both: it " +"needs to serialize parameters on the server-side and send them to the " +"client, the client needs to deserialize them to use them for local " +"training, and then serialize the updated parameters again to send them " +"back to the server, which (finally!) deserializes them again in order to " +"aggregate them with the updates received from other clients." +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:495 +msgid "" +"The only *real* difference between Client and NumPyClient is that " +"NumPyClient takes care of serialization and deserialization for you. It " +"can do so because it expects you to return parameters as NumPy ndarray's," +" and it knows how to handle these. This makes working with machine " +"learning libraries that have good NumPy support (most of them) a breeze." +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:497 +msgid "" +"In terms of API, there's one major difference: all methods in Client take" +" exactly one argument (e.g., ``FitIns`` in ``Client.fit``) and return " +"exactly one value (e.g., ``FitRes`` in ``Client.fit``). The methods in " +"``NumPyClient`` on the other hand have multiple arguments (e.g., " +"``parameters`` and ``config`` in ``NumPyClient.fit``) and multiple return" +" values (e.g., ``parameters``, ``num_example``, and ``metrics`` in " +"``NumPyClient.fit``) if there are multiple things to handle. These " +"``*Ins`` and ``*Res`` objects in ``Client`` wrap all the individual " +"values you're used to from ``NumPyClient``." +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:510 +msgid "Step 3: Custom serialization" +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:512 +msgid "" +"Here we will explore how to implement custom serialization with a simple " +"example." +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:514 +msgid "" +"But first what is serialization? Serialization is just the process of " +"converting an object into raw bytes, and equally as important, " +"deserialization is the process of converting raw bytes back into an " +"object. This is very useful for network communication. Indeed, without " +"serialization, you could not just a Python object through the internet." +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:516 +msgid "" +"Federated Learning relies heavily on internet communication for training " +"by sending Python objects back and forth between the clients and the " +"server. This means that serialization is an essential part of Federated " +"Learning." +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:518 +msgid "" +"In the following section, we will write a basic example where instead of " +"sending a serialized version of our ``ndarray``\\ s containing our " +"parameters, we will first convert the ``ndarray`` into sparse matrices, " +"before sending them. This technique can be used to save bandwidth, as in " +"certain cases where the weights of a model are sparse (containing many 0 " +"entries), converting them to a sparse matrix can greatly improve their " +"bytesize." +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:521 +msgid "Our custom serialization/deserialization functions" +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:523 +msgid "" +"This is where the real serialization/deserialization will happen, " +"especially in ``ndarray_to_sparse_bytes`` for serialization and " +"``sparse_bytes_to_ndarray`` for deserialization." +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:525 +msgid "" +"Note that we imported the ``scipy.sparse`` library in order to convert " +"our arrays." +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:613 +msgid "Client-side" +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:615 +msgid "" +"To be able to serialize our ``ndarray``\\ s into sparse parameters, we " +"will just have to call our custom functions in our " +"``flwr.client.Client``." +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:617 +msgid "" +"Indeed, in ``get_parameters`` we need to serialize the parameters we got " +"from our network using our custom ``ndarrays_to_sparse_parameters`` " +"defined above." +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:619 +msgid "" +"In ``fit``, we first need to deserialize the parameters coming from the " +"server using our custom ``sparse_parameters_to_ndarrays`` and then we " +"need to serialize our local results with " +"``ndarrays_to_sparse_parameters``." +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:621 +msgid "" +"In ``evaluate``, we will only need to deserialize the global parameters " +"with our custom function." +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:725 +msgid "Server-side" +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:727 +msgid "" +"For this example, we will just use ``FedAvg`` as a strategy. To change " +"the serialization and deserialization here, we only need to reimplement " +"the ``evaluate`` and ``aggregate_fit`` functions of ``FedAvg``. The other" +" functions of the strategy will be inherited from the super class " +"``FedAvg``." +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:729 +msgid "As you can see only one line as change in ``evaluate``:" +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:735 +msgid "" +"And for ``aggregate_fit``, we will first deserialize every result we " +"received:" +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:744 +msgid "And then serialize the aggregated result:" +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:903 +msgid "We can now run our custom serialization example!" +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:934 +msgid "" +"In this part of the tutorial, we've seen how we can build clients by " +"subclassing either ``NumPyClient`` or ``Client``. ``NumPyClient`` is a " +"convenience abstraction that makes it easier to work with machine " +"learning libraries that have good NumPy interoperability. ``Client`` is a" +" more flexible abstraction that allows us to do things that are not " +"possible in ``NumPyClient``. In order to do so, it requires us to handle " +"parameter serialization and deserialization ourselves." +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:952 +msgid "" +"This is the final part of the Flower tutorial (for now!), " +"congratulations! You're now well equipped to understand the rest of the " +"documentation. There are many topics we didn't cover in the tutorial, we " +"recommend the following resources:" +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:954 +msgid "`Read Flower Docs `__" +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:955 +msgid "" +"`Check out Flower Code Examples " +"`__" +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:956 +msgid "" +"`Use Flower Baselines for your research " +"`__" +msgstr "" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:957 +msgid "" +"`Watch Flower Summit 2023 videos `__" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:9 +msgid "Get started with Flower" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:11 +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:11 +msgid "Welcome to the Flower federated learning tutorial!" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:13 +msgid "" +"In this notebook, we'll build a federated learning system using Flower, " +"`Flower Datasets `__ and PyTorch. In " +"part 1, we use PyTorch for the model training pipeline and data loading. " +"In part 2, we continue to federate the PyTorch-based pipeline using " +"Flower." +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:17 +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:19 +msgid "Let's get started!" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:31 +msgid "" +"Before we begin with any actual code, let's make sure that we have " +"everything we need." +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:45 +msgid "" +"Next, we install the necessary packages for PyTorch (``torch`` and " +"``torchvision``), Flower Datasets (``flwr-datasets``) and Flower " +"(``flwr``):" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:105 +msgid "" +"It is possible to switch to a runtime that has GPU acceleration enabled " +"(on Google Colab: ``Runtime > Change runtime type > Hardware accelerator:" +" GPU > Save``). Note, however, that Google Colab is not always able to " +"offer GPU acceleration. If you see an error related to GPU availability " +"in one of the following sections, consider switching back to CPU-based " +"execution by setting ``DEVICE = torch.device(\"cpu\")``. If the runtime " +"has GPU acceleration enabled, you should see the output ``Training on " +"cuda``, otherwise it'll say ``Training on cpu``." +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:118 +msgid "Loading the data" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:120 +msgid "" +"Federated learning can be applied to many different types of tasks across" +" different domains. In this tutorial, we introduce federated learning by " +"training a simple convolutional neural network (CNN) on the popular " +"CIFAR-10 dataset. CIFAR-10 can be used to train image classifiers that " +"distinguish between images from ten different classes: 'airplane', " +"'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', and " +"'truck'." +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:131 +msgid "" +"We simulate having multiple datasets from multiple organizations (also " +"called the \"cross-silo\" setting in federated learning) by splitting the" +" original CIFAR-10 dataset into multiple partitions. Each partition will " +"represent the data from a single organization. We're doing this purely " +"for experimentation purposes, in the real world there's no need for data " +"splitting because each organization already has their own data (so the " +"data is naturally partitioned)." +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:133 +msgid "" +"Each organization will act as a client in the federated learning system. " +"So having ten organizations participate in a federation means having ten " +"clients connected to the federated learning server." +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:144 +msgid "" +"Let's now create the Federated Dataset abstraction that from ``flwr-" +"datasets`` that partitions the CIFAR-10. We will create small training " +"and test set for each edge device and wrap each of them into a PyTorch " +"``DataLoader``:" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:198 +msgid "" +"We now have a list of ten training sets and ten validation sets " +"(``trainloaders`` and ``valloaders``) representing the data of ten " +"different organizations. Each ``trainloader``/``valloader`` pair contains" +" 4000 training examples and 1000 validation examples. There's also a " +"single ``testloader`` (we did not split the test set). Again, this is " +"only necessary for building research or educational systems, actual " +"federated learning systems have their data naturally distributed across " +"multiple partitions." +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:201 +msgid "" +"Let's take a look at the first batch of images and labels in the first " +"training set (i.e., ``trainloaders[0]``) before we move on:" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:240 +msgid "" +"The output above shows a random batch of images from the first " +"``trainloader`` in our list of ten ``trainloaders``. It also prints the " +"labels associated with each image (i.e., one of the ten possible labels " +"we've seen above). If you run the cell again, you should see another " +"batch of images." +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:252 +msgid "Step 1: Centralized Training with PyTorch" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:263 +msgid "" +"Next, we're going to use PyTorch to define a simple convolutional neural " +"network. This introduction assumes basic familiarity with PyTorch, so it " +"doesn't cover the PyTorch-related aspects in full detail. If you want to " +"dive deeper into PyTorch, we recommend `DEEP LEARNING WITH PYTORCH: A 60 " +"MINUTE BLITZ " +"`__." +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:275 +msgid "Defining the model" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:277 +msgid "" +"We use the simple CNN described in the `PyTorch tutorial " +"`__:" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:314 +msgid "Let's continue with the usual training and test functions:" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:374 +msgid "Training the model" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:376 +msgid "" +"We now have all the basic building blocks we need: a dataset, a model, a " +"training function, and a test function. Let's put them together to train " +"the model on the dataset of one of our organizations " +"(``trainloaders[0]``). This simulates the reality of most machine " +"learning projects today: each organization has their own data and trains " +"models only on this internal data:" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:406 +msgid "" +"Training the simple CNN on our CIFAR-10 split for 5 epochs should result " +"in a test set accuracy of about 41%, which is not good, but at the same " +"time, it doesn't really matter for the purposes of this tutorial. The " +"intent was just to show a simplistic centralized training pipeline that " +"sets the stage for what comes next - federated learning!" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:418 +msgid "Step 2: Federated Learning with Flower" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:420 +msgid "" +"Step 1 demonstrated a simple centralized training pipeline. All data was " +"in one place (i.e., a single ``trainloader`` and a single ``valloader``)." +" Next, we'll simulate a situation where we have multiple datasets in " +"multiple organizations and where we train a model over these " +"organizations using federated learning." +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:432 +msgid "Updating model parameters" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:434 +msgid "" +"In federated learning, the server sends the global model parameters to " +"the client, and the client updates the local model with the parameters " +"received from the server. It then trains the model on the local data " +"(which changes the model parameters locally) and sends the " +"updated/changed model parameters back to the server (or, alternatively, " +"it sends just the gradients back to the server, not the full model " +"parameters)." +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:436 +msgid "" +"We need two helper functions to update the local model with parameters " +"received from the server and to get the updated model parameters from the" +" local model: ``set_parameters`` and ``get_parameters``. The following " +"two functions do just that for the PyTorch model above." +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:438 +msgid "" +"The details of how this works are not really important here (feel free to" +" consult the PyTorch documentation if you want to learn more). In " +"essence, we use ``state_dict`` to access PyTorch model parameter tensors." +" The parameter tensors are then converted to/from a list of NumPy " +"ndarray's (which Flower knows how to serialize/deserialize):" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:466 +msgid "Implementing a Flower client" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:468 +msgid "" +"With that out of the way, let's move on to the interesting part. " +"Federated learning systems consist of a server and multiple clients. In " +"Flower, we create clients by implementing subclasses of " +"``flwr.client.Client`` or ``flwr.client.NumPyClient``. We use " +"``NumPyClient`` in this tutorial because it is easier to implement and " +"requires us to write less boilerplate." +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:470 +msgid "" +"To implement the Flower client, we create a subclass of " +"``flwr.client.NumPyClient`` and implement the three methods " +"``get_parameters``, ``fit``, and ``evaluate``:" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:472 +msgid "``get_parameters``: Return the current local model parameters" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:473 +msgid "" +"``fit``: Receive model parameters from the server, train the model " +"parameters on the local data, and return the (updated) model parameters " +"to the server" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:474 +msgid "" +"``evaluate``: Receive model parameters from the server, evaluate the " +"model parameters on the local data, and return the evaluation result to " +"the server" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:476 +msgid "" +"We mentioned that our clients will use the previously defined PyTorch " +"components for model training and evaluation. Let's see a simple Flower " +"client implementation that brings everything together:" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:513 +msgid "" +"Our class ``FlowerClient`` defines how local training/evaluation will be " +"performed and allows Flower to call the local training/evaluation through" +" ``fit`` and ``evaluate``. Each instance of ``FlowerClient`` represents a" +" *single client* in our federated learning system. Federated learning " +"systems have multiple clients (otherwise, there's not much to federate), " +"so each client will be represented by its own instance of " +"``FlowerClient``. If we have, for example, three clients in our workload," +" then we'd have three instances of ``FlowerClient``. Flower calls " +"``FlowerClient.fit`` on the respective instance when the server selects a" +" particular client for training (and ``FlowerClient.evaluate`` for " +"evaluation)." +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:517 +msgid "Using the Virtual Client Engine" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:519 +msgid "" +"In this notebook, we want to simulate a federated learning system with 10" +" clients on a single machine. This means that the server and all 10 " +"clients will live on a single machine and share resources such as CPU, " +"GPU, and memory. Having 10 clients would mean having 10 instances of " +"``FlowerClient`` in memory. Doing this on a single machine can quickly " +"exhaust the available memory resources, even if only a subset of these " +"clients participates in a single round of federated learning." +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:521 +msgid "" +"In addition to the regular capabilities where server and clients run on " +"multiple machines, Flower, therefore, provides special simulation " +"capabilities that create ``FlowerClient`` instances only when they are " +"actually necessary for training or evaluation. To enable the Flower " +"framework to create clients when necessary, we need to implement a " +"function called ``client_fn`` that creates a ``FlowerClient`` instance on" +" demand. Flower calls ``client_fn`` whenever it needs an instance of one " +"particular client to call ``fit`` or ``evaluate`` (those instances are " +"usually discarded after use, so they should not keep any local state). " +"Clients are identified by a client ID, or short ``cid``. The ``cid`` can " +"be used, for example, to load different local data partitions for " +"different clients, as can be seen below:" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:556 +msgid "Starting the training" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:558 +msgid "" +"We now have the class ``FlowerClient`` which defines client-side " +"training/evaluation and ``client_fn`` which allows Flower to create " +"``FlowerClient`` instances whenever it needs to call ``fit`` or " +"``evaluate`` on one particular client. The last step is to start the " +"actual simulation using ``flwr.simulation.start_simulation``." +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:560 +msgid "" +"The function ``start_simulation`` accepts a number of arguments, amongst " +"them the ``client_fn`` used to create ``FlowerClient`` instances, the " +"number of clients to simulate (``num_clients``), the number of federated " +"learning rounds (``num_rounds``), and the strategy. The strategy " +"encapsulates the federated learning approach/algorithm, for example, " +"*Federated Averaging* (FedAvg)." +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:562 +msgid "" +"Flower has a number of built-in strategies, but we can also use our own " +"strategy implementations to customize nearly all aspects of the federated" +" learning approach. For this example, we use the built-in ``FedAvg`` " +"implementation and customize it using a few basic parameters. The last " +"step is the actual call to ``start_simulation`` which - you guessed it - " +"starts the simulation:" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:608 +msgid "Behind the scenes" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:610 +msgid "So how does this work? How does Flower execute this simulation?" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:612 +#, python-format +msgid "" +"When we call ``start_simulation``, we tell Flower that there are 10 " +"clients (``num_clients=10``). Flower then goes ahead an asks the " +"``FedAvg`` strategy to select clients. ``FedAvg`` knows that it should " +"select 100% of the available clients (``fraction_fit=1.0``), so it goes " +"ahead and selects 10 random clients (i.e., 100% of 10)." +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:614 +msgid "" +"Flower then asks the selected 10 clients to train the model. When the " +"server receives the model parameter updates from the clients, it hands " +"those updates over to the strategy (*FedAvg*) for aggregation. The " +"strategy aggregates those updates and returns the new global model, which" +" then gets used in the next round of federated learning." +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:626 +msgid "Where's the accuracy?" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:628 +msgid "" +"You may have noticed that all metrics except for ``losses_distributed`` " +"are empty. Where did the ``{\"accuracy\": float(accuracy)}`` go?" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:630 +msgid "" +"Flower can automatically aggregate losses returned by individual clients," +" but it cannot do the same for metrics in the generic metrics dictionary " +"(the one with the ``accuracy`` key). Metrics dictionaries can contain " +"very different kinds of metrics and even key/value pairs that are not " +"metrics at all, so the framework does not (and can not) know how to " +"handle these automatically." +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:632 +msgid "" +"As users, we need to tell the framework how to handle/aggregate these " +"custom metrics, and we do so by passing metric aggregation functions to " +"the strategy. The strategy will then call these functions whenever it " +"receives fit or evaluate metrics from clients. The two possible functions" +" are ``fit_metrics_aggregation_fn`` and " +"``evaluate_metrics_aggregation_fn``." +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:634 +msgid "" +"Let's create a simple weighted averaging function to aggregate the " +"``accuracy`` metric we return from ``evaluate``:" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:660 +msgid "" +"The only thing left to do is to tell the strategy to call this function " +"whenever it receives evaluation metric dictionaries from the clients:" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:697 +msgid "" +"We now have a full system that performs federated training and federated " +"evaluation. It uses the ``weighted_average`` function to aggregate custom" +" evaluation metrics and calculates a single ``accuracy`` metric across " +"all clients on the server side." +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:699 +msgid "" +"The other two categories of metrics (``losses_centralized`` and " +"``metrics_centralized``) are still empty because they only apply when " +"centralized evaluation is being used. Part two of the Flower tutorial " +"will cover centralized evaluation." +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:711 +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:351 +msgid "Final remarks" +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:713 +msgid "" +"Congratulations, you just trained a convolutional neural network, " +"federated over 10 clients! With that, you understand the basics of " +"federated learning with Flower. The same approach you've seen can be used" +" with other machine learning frameworks (not just PyTorch) and tasks (not" +" just CIFAR-10 images classification), for example NLP with Hugging Face " +"Transformers or speech with SpeechBrain." +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:715 +msgid "" +"In the next notebook, we're going to cover some more advanced concepts. " +"Want to customize your strategy? Initialize parameters on the server " +"side? Or evaluate the aggregated model on the server side? We'll cover " +"all this and more in the next tutorial." +msgstr "" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:733 +msgid "" +"The `Flower Federated Learning Tutorial - Part 2 " +"`__ goes into more depth about strategies and all " +"the advanced things you can build with them." +msgstr "" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:9 +msgid "Use a federated learning strategy" +msgstr "" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:11 +msgid "" +"Welcome to the next part of the federated learning tutorial. In previous " +"parts of this tutorial, we introduced federated learning with PyTorch and" +" Flower (`part 1 `__)." +msgstr "" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:13 +msgid "" +"In this notebook, we'll begin to customize the federated learning system " +"we built in the introductory notebook (again, using `Flower " +"`__ and `PyTorch `__)." +msgstr "" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:17 +msgid "Let's move beyond FedAvg with Flower strategies!" +msgstr "" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:309 +msgid "Strategy customization" +msgstr "" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:311 +msgid "" +"So far, everything should look familiar if you've worked through the " +"introductory notebook. With that, we're ready to introduce a number of " +"new features." +msgstr "" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:323 +msgid "Server-side parameter **initialization**" +msgstr "" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:325 +msgid "" +"Flower, by default, initializes the global model by asking one random " +"client for the initial parameters. In many cases, we want more control " +"over parameter initialization though. Flower therefore allows you to " +"directly pass the initial parameters to the Strategy:" +msgstr "" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:370 +msgid "" +"Passing ``initial_parameters`` to the ``FedAvg`` strategy prevents Flower" +" from asking one of the clients for the initial parameters. If we look " +"closely, we can see that the logs do not show any calls to the " +"``FlowerClient.get_parameters`` method." +msgstr "" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:382 +msgid "Starting with a customized strategy" +msgstr "" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:384 +msgid "" +"We've seen the function ``start_simulation`` before. It accepts a number " +"of arguments, amongst them the ``client_fn`` used to create " +"``FlowerClient`` instances, the number of clients to simulate " +"``num_clients``, the number of rounds ``num_rounds``, and the strategy." +msgstr "" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:386 +msgid "" +"The strategy encapsulates the federated learning approach/algorithm, for " +"example, ``FedAvg`` or ``FedAdagrad``. Let's try to use a different " +"strategy this time:" +msgstr "" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:424 +msgid "Server-side parameter **evaluation**" +msgstr "" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:426 +msgid "" +"Flower can evaluate the aggregated model on the server-side or on the " +"client-side. Client-side and server-side evaluation are similar in some " +"ways, but different in others." +msgstr "" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:428 +msgid "" +"**Centralized Evaluation** (or *server-side evaluation*) is conceptually " +"simple: it works the same way that evaluation in centralized machine " +"learning does. If there is a server-side dataset that can be used for " +"evaluation purposes, then that's great. We can evaluate the newly " +"aggregated model after each round of training without having to send the " +"model to clients. We're also fortunate in the sense that our entire " +"evaluation dataset is available at all times." +msgstr "" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:430 +msgid "" +"**Federated Evaluation** (or *client-side evaluation*) is more complex, " +"but also more powerful: it doesn't require a centralized dataset and " +"allows us to evaluate models over a larger set of data, which often " +"yields more realistic evaluation results. In fact, many scenarios require" +" us to use **Federated Evaluation** if we want to get representative " +"evaluation results at all. But this power comes at a cost: once we start " +"to evaluate on the client side, we should be aware that our evaluation " +"dataset can change over consecutive rounds of learning if those clients " +"are not always available. Moreover, the dataset held by each client can " +"also change over consecutive rounds. This can lead to evaluation results " +"that are not stable, so even if we would not change the model, we'd see " +"our evaluation results fluctuate over consecutive rounds." +msgstr "" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:433 +msgid "" +"We've seen how federated evaluation works on the client side (i.e., by " +"implementing the ``evaluate`` method in ``FlowerClient``). Now let's see " +"how we can evaluate aggregated model parameters on the server-side:" +msgstr "" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:490 +msgid "Sending/receiving arbitrary values to/from clients" +msgstr "" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:492 +msgid "" +"In some situations, we want to configure client-side execution (training," +" evaluation) from the server-side. One example for that is the server " +"asking the clients to train for a certain number of local epochs. Flower " +"provides a way to send configuration values from the server to the " +"clients using a dictionary. Let's look at an example where the clients " +"receive values from the server through the ``config`` parameter in " +"``fit`` (``config`` is also available in ``evaluate``). The ``fit`` " +"method receives the configuration dictionary through the ``config`` " +"parameter and can then read values from this dictionary. In this example," +" it reads ``server_round`` and ``local_epochs`` and uses those values to " +"improve the logging and configure the number of local training epochs:" +msgstr "" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:546 +msgid "" +"So how can we send this config dictionary from server to clients? The " +"built-in Flower Strategies provide way to do this, and it works similarly" +" to the way server-side evaluation works. We provide a function to the " +"strategy, and the strategy calls this function for every round of " +"federated learning:" +msgstr "" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:576 +msgid "" +"Next, we'll just pass this function to the FedAvg strategy before " +"starting the simulation:" +msgstr "" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:613 +msgid "" +"As we can see, the client logs now include the current round of federated" +" learning (which they read from the ``config`` dictionary). We can also " +"configure local training to run for one epoch during the first and second" +" round of federated learning, and then for two epochs during the third " +"round." +msgstr "" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:615 +msgid "" +"Clients can also return arbitrary values to the server. To do so, they " +"return a dictionary from ``fit`` and/or ``evaluate``. We have seen and " +"used this concept throughout this notebook without mentioning it " +"explicitly: our ``FlowerClient`` returns a dictionary containing a custom" +" key/value pair as the third return value in ``evaluate``." +msgstr "" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:627 +msgid "Scaling federated learning" +msgstr "" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:629 +msgid "" +"As a last step in this notebook, let's see how we can use Flower to " +"experiment with a large number of clients." +msgstr "" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:651 +#, python-format +msgid "" +"We now have 1000 partitions, each holding 45 training and 5 validation " +"examples. Given that the number of training examples on each client is " +"quite small, we should probably train the model a bit longer, so we " +"configure the clients to perform 3 local training epochs. We should also " +"adjust the fraction of clients selected for training during each round " +"(we don't want all 1000 clients participating in every round), so we " +"adjust ``fraction_fit`` to ``0.05``, which means that only 5% of " +"available clients (so 50 clients) will be selected for training each " +"round:" +msgstr "" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:699 +msgid "" +"In this notebook, we've seen how we can gradually enhance our system by " +"customizing the strategy, initializing parameters on the server side, " +"choosing a different strategy, and evaluating models on the server-side. " +"That's quite a bit of flexibility with so little code, right?" +msgstr "" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:701 +msgid "" +"In the later sections, we've seen how we can communicate arbitrary values" +" between server and clients to fully customize client-side execution. " +"With that capability, we built a large-scale Federated Learning " +"simulation using the Flower Virtual Client Engine and ran an experiment " +"involving 1000 clients in the same workload - all in a Jupyter Notebook!" +msgstr "" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:719 +msgid "" +"The `Flower Federated Learning Tutorial - Part 3 " +"`__ shows how to build a fully custom ``Strategy`` from " +"scratch." +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:9 +msgid "What is Federated Learning?" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:13 +msgid "" +"In this tutorial, you will learn what federated learning is, build your " +"first system in Flower, and gradually extend it. If you work through all " +"parts of the tutorial, you will be able to build advanced federated " +"learning systems that approach the current state of the art in the field." +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:15 +msgid "" +"🧑‍🏫 This tutorial starts at zero and expects no familiarity with " +"federated learning. Only a basic understanding of data science and Python" +" programming is assumed." +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:17 +msgid "" +"`Star Flower on GitHub `__ ⭐️ and join " +"the open-source Flower community on Slack to connect, ask questions, and " +"get help: `Join Slack `__ 🌼 We'd love to " +"hear from you in the ``#introductions`` channel! And if anything is " +"unclear, head over to the ``#questions`` channel." +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:31 +msgid "Classic machine learning" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:33 +msgid "" +"Before we begin to discuss federated learning, let us quickly recap how " +"most machine learning works today." +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:35 +msgid "" +"In machine learning, we have a model, and we have data. The model could " +"be a neural network (as depicted here), or something else, like classical" +" linear regression." +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:41 +msgid "|93b02017c78049bbbd5ae456dcb2c91b|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:109 +msgid "Model and data" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:47 +msgid "" +"We train the model using the data to perform a useful task. A task could " +"be to detect objects in images, transcribe an audio recording, or play a " +"game like Go." +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:53 +msgid "|01471150fd5144c080a176b43e92a3ff|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:111 +msgid "Train model using data" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:59 +msgid "" +"Now, in practice, the training data we work with doesn't originate on the" +" machine we train the model on. It gets created somewhere else." +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:61 +msgid "" +"It originates on a smartphone by the user interacting with an app, a car " +"collecting sensor data, a laptop receiving input via the keyboard, or a " +"smart speaker listening to someone trying to sing a song." +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:67 +msgid "|9bc21c7dbd17444a8f070c60786e3484|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:113 +msgid "Data on a phone" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:73 +msgid "" +"What's also important to mention, this \"somewhere else\" is usually not " +"just one place, it's many places. It could be several devices all running" +" the same app. But it could also be several organizations, all generating" +" data for the same task." +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:79 +msgid "|3047bbce54b34099ae559963d0420d79|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:115 +msgid "Data is on many devices" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:85 +msgid "" +"So to use machine learning, or any kind of data analysis, the approach " +"that has been used in the past was to collect all data on a central " +"server. This server can be somewhere in a data center, or somewhere in " +"the cloud." +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:91 +msgid "|e9f8ce948593444fb838d2f354c7ec5d|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:117 +msgid "Central data collection" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:97 +msgid "" +"Once all the data is collected in one place, we can finally use machine " +"learning algorithms to train our model on the data. This is the machine " +"learning approach that we've basically always relied on." +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:103 +msgid "|c24c1478b30e4f74839208628a842d1e|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:119 +msgid "Central model training" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:130 +msgid "Challenges of classical machine learning" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:132 +msgid "" +"The classic machine learning approach we've just seen can be used in some" +" cases. Great examples include categorizing holiday photos, or analyzing " +"web traffic. Cases, where all the data is naturally available on a " +"centralized server." +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:138 +msgid "|1b3613d7a58847b59e1d3180802dbc09|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:173 +msgid "Centralized possible" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:144 +msgid "" +"But the approach can not be used in many other cases. Cases, where the " +"data is not available on a centralized server, or cases where the data " +"available on one server is not enough to train a good model." +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:150 +msgid "|9980b5213db547d0b8024a50992b9e3f|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:175 +msgid "Centralized impossible" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:156 +msgid "" +"There are many reasons why the classic centralized machine learning " +"approach does not work for a large number of highly important real-world " +"use cases. Those reasons include:" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:158 +msgid "" +"**Regulations**: GDPR (Europe), CCPA (California), PIPEDA (Canada), LGPD " +"(Brazil), PDPL (Argentina), KVKK (Turkey), POPI (South Africa), FSS " +"(Russia), CDPR (China), PDPB (India), PIPA (Korea), APPI (Japan), PDP " +"(Indonesia), PDPA (Singapore), APP (Australia), and other regulations " +"protect sensitive data from being moved. In fact, those regulations " +"sometimes even prevent single organizations from combining their own " +"users' data for artificial intelligence training because those users live" +" in different parts of the world, and their data is governed by different" +" data protection regulations." +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:160 +msgid "" +"**User preference**: In addition to regulation, there are use cases where" +" users just expect that no data leaves their device, ever. If you type " +"your passwords and credit card info into the digital keyboard of your " +"phone, you don't expect those passwords to end up on the server of the " +"company that developed that keyboard, do you? In fact, that use case was " +"the reason federated learning was invented in the first place." +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:161 +msgid "" +"**Data volume**: Some sensors, like cameras, produce such a high data " +"volume that it is neither feasible nor economic to collect all the data " +"(due to, for example, bandwidth or communication efficiency). Think about" +" a national rail service with hundreds of train stations across the " +"country. If each of these train stations is outfitted with a number of " +"security cameras, the volume of raw on-device data they produce requires " +"incredibly powerful and exceedingly expensive infrastructure to process " +"and store. And most of the data isn't even useful." +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:164 +msgid "Examples where centralized machine learning does not work include:" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:166 +msgid "" +"Sensitive healthcare records from multiple hospitals to train cancer " +"detection models" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:167 +msgid "" +"Financial information from different organizations to detect financial " +"fraud" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:168 +msgid "Location data from your electric car to make better range prediction" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:169 +msgid "End-to-end encrypted messages to train better auto-complete models" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:171 +msgid "" +"The popularity of privacy-enhancing systems like the `Brave " +"`__ browser or the `Signal `__ " +"messenger shows that users care about privacy. In fact, they choose the " +"privacy-enhancing version over other alternatives, if such an alternative" +" exists. But what can we do to apply machine learning and data science to" +" these cases to utilize private data? After all, these are all areas that" +" would benefit significantly from recent advances in AI." +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:186 +msgid "Federated learning" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:188 +msgid "" +"Federated learning simply reverses this approach. It enables machine " +"learning on distributed data by moving the training to the data, instead " +"of moving the data to the training. Here's the single-sentence " +"explanation:" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:190 +msgid "Central machine learning: move the data to the computation" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:191 +msgid "Federated (machine) learning: move the computation to the data" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:193 +msgid "" +"By doing so, it enables us to use machine learning (and other data " +"science approaches) in areas where it wasn't possible before. We can now " +"train excellent medical AI models by enabling different hospitals to work" +" together. We can solve financial fraud by training AI models on the data" +" of different financial institutions. We can build novel privacy-" +"enhancing applications (such as secure messaging) that have better built-" +"in AI than their non-privacy-enhancing alternatives. And those are just a" +" few of the examples that come to mind. As we deploy federated learning, " +"we discover more and more areas that can suddenly be reinvented because " +"they now have access to vast amounts of previously inaccessible data." +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:196 +msgid "" +"So how does federated learning work, exactly? Let's start with an " +"intuitive explanation." +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:199 +msgid "Federated learning in five steps" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:202 +msgid "Step 0: Initialize global model" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:204 +msgid "" +"We start by initializing the model on the server. This is exactly the " +"same in classic centralized learning: we initialize the model parameters," +" either randomly or from a previously saved checkpoint." +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:210 +msgid "|c7afb4c92d154bfaa5e8cb9a150e17f1|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:307 +msgid "Initialize global model" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:217 +msgid "" +"Step 1: Send model to a number of connected organizations/devices (client" +" nodes)" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:219 +msgid "" +"Next, we send the parameters of the global model to the connected client " +"nodes (think: edge devices like smartphones or servers belonging to " +"organizations). This is to ensure that each participating node starts " +"their local training using the same model parameters. We often use only a" +" few of the connected nodes instead of all nodes. The reason for this is " +"that selecting more and more client nodes has diminishing returns." +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:225 +msgid "|032eb6fed6924ac387b9f13854919196|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:309 +msgid "Send global model" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:232 +msgid "" +"Step 2: Train model locally on the data of each organization/device " +"(client node)" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:234 +msgid "" +"Now that all (selected) client nodes have the latest version of the " +"global model parameters, they start the local training. They use their " +"own local dataset to train their own local model. They don't train the " +"model until full convergence, but they only train for a little while. " +"This could be as little as one epoch on the local data, or even just a " +"few steps (mini-batches)." +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:240 +msgid "|fbf225add7fd4df5a9bf25a95597d954|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:311 +msgid "Train on local data" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:247 +msgid "Step 3: Return model updates back to the server" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:249 +msgid "" +"After local training, each client node has a slightly different version " +"of the model parameters they originally received. The parameters are all " +"different because each client node has different examples in its local " +"dataset. The client nodes then send those model updates back to the " +"server. The model updates they send can either be the full model " +"parameters or just the gradients that were accumulated during local " +"training." +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:255 +msgid "|7efbe3d29d8349b89594e8947e910525|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:313 +msgid "Send model updates" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:262 +msgid "Step 4: Aggregate model updates into a new global model" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:264 +msgid "" +"The server receives model updates from the selected client nodes. If it " +"selected 100 client nodes, it now has 100 slightly different versions of " +"the original global model, each trained on the local data of one client. " +"But didn't we want to have one model that contains the learnings from the" +" data of all 100 client nodes?" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:266 +msgid "" +"In order to get one single model, we have to combine all the model " +"updates we received from the client nodes. This process is called " +"*aggregation*, and there are many different ways to do it. The most basic" +" way to do it is called *Federated Averaging* (`McMahan et al., 2016 " +"`__), often abbreviated as *FedAvg*. " +"*FedAvg* takes the 100 model updates and, as the name suggests, averages " +"them. To be more precise, it takes the *weighted average* of the model " +"updates, weighted by the number of examples each client used for " +"training. The weighting is important to make sure that each data example " +"has the same \"influence\" on the resulting global model. If one client " +"has 10 examples, and another client has 100 examples, then - without " +"weighting - each of the 10 examples would influence the global model ten " +"times as much as each of the 100 examples." +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:273 +msgid "|329fb3c04c744eda83bb51fa444c2266|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:315 +msgid "Aggregate model updates" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:280 +msgid "Step 5: Repeat steps 1 to 4 until the model converges" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:282 +msgid "" +"Steps 1 to 4 are what we call a single round of federated learning. The " +"global model parameters get sent to the participating client nodes (step " +"1), the client nodes train on their local data (step 2), they send their " +"updated models to the server (step 3), and the server then aggregates the" +" model updates to get a new version of the global model (step 4)." +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:284 +msgid "" +"During a single round, each client node that participates in that " +"iteration only trains for a little while. This means that after the " +"aggregation step (step 4), we have a model that has been trained on all " +"the data of all participating client nodes, but only for a little while. " +"We then have to repeat this training process over and over again to " +"eventually arrive at a fully trained model that performs well across the " +"data of all client nodes." +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:289 +msgid "" +"Congratulations, you now understand the basics of federated learning. " +"There's a lot more to discuss, of course, but that was federated learning" +" in a nutshell. In later parts of this tutorial, we will go into more " +"detail. Interesting questions include: How can we select the best client " +"nodes that should participate in the next round? What's the best way to " +"aggregate model updates? How can we handle failing client nodes " +"(stragglers)?" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:294 +msgid "" +"Just like we can train a model on the decentralized data of different " +"client nodes, we can also evaluate the model on that data to receive " +"valuable metrics. This is called federated evaluation, sometimes " +"abbreviated as FE. In fact, federated evaluation is an integral part of " +"most federated learning systems." +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:297 +msgid "Federated analytics" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:299 +msgid "" +"In many cases, machine learning isn't necessary to derive value from " +"data. Data analysis can yield valuable insights, but again, there's often" +" not enough data to get a clear answer. What's the average age at which " +"people develop a certain type of health condition? Federated analytics " +"enables such queries over multiple client nodes. It is usually used in " +"conjunction with other privacy-enhancing technologies like secure " +"aggregation to prevent the server from seeing the results submitted by " +"individual client nodes." +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:305 +msgid "" +"Differential privacy (DP) is often mentioned in the context of Federated " +"Learning. It is a privacy-preserving method used when analyzing and " +"sharing statistical data, ensuring the privacy of individual " +"participants. DP achieves this by adding statistical noise to the model " +"updates, ensuring any individual participants’ information cannot be " +"distinguished or re-identified. This technique can be considered an " +"optimization that provides a quantifiable privacy protection measure." +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:326 +msgid "Flower" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:328 +msgid "" +"Federated learning, federated evaluation, and federated analytics require" +" infrastructure to move machine learning models back and forth, train and" +" evaluate them on local data, and then aggregate the updated models. " +"Flower provides the infrastructure to do exactly that in an easy, " +"scalable, and secure way. In short, Flower presents a unified approach to" +" federated learning, analytics, and evaluation. It allows the user to " +"federate any workload, any ML framework, and any programming language." +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:334 +msgid "|c00bf2750bc24d229737a0fe1395f0fc|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:340 +msgid "" +"Flower federated learning server and client nodes (car, scooter, personal" +" computer, roomba, and phone)" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:353 +msgid "" +"Congratulations, you just learned the basics of federated learning and " +"how it relates to the classic (centralized) machine learning!" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:355 +msgid "" +"In the next part of this tutorial, we are going to build a first " +"federated learning system with Flower." +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:373 +msgid "" +"The `Flower Federated Learning Tutorial - Part 1 " +"`__ shows how to build a simple federated learning system " +"with PyTorch and Flower." +msgstr "" + +#~ msgid "" +#~ "Configuring and setting up the " +#~ ":code:`Dockerfile` as well the configuration" +#~ " for the devcontainer can be a " +#~ "bit more involved. The good thing " +#~ "is you want have to do it. " +#~ "Usually it should be enough to " +#~ "install Docker on your system and " +#~ "ensure its available on your command " +#~ "line. Additionally, install the `VSCode " +#~ "Containers Extension `_." +#~ msgstr "" + +#~ msgid "" +#~ "``flwr = { path = " +#~ "\"../../dist/flwr-1.0.0-py3-none-any.whl\" }`` " +#~ "(without extras)" +#~ msgstr "" + +#~ msgid "" +#~ "``flwr = { path = " +#~ "\"../../dist/flwr-1.0.0-py3-none-any.whl\", extras =" +#~ " [\"simulation\"] }`` (with extras)" +#~ msgstr "" + +#~ msgid "Upload the whl (e.g., ``flwr-1.7.0-py3-none-any.whl``)" +#~ msgstr "" + +#~ msgid "" +#~ "Change ``!pip install -q 'flwr[simulation]'" +#~ " torch torchvision matplotlib`` to ``!pip" +#~ " install -q 'flwr-1.7.0-py3-none-" +#~ "any.whl[simulation]' torch torchvision matplotlib``" +#~ msgstr "" + +#~ msgid "Before the release" +#~ msgstr "" + +#~ msgid "" +#~ "Update the changelog (``changelog.md``) with" +#~ " all relevant changes that happened " +#~ "after the last release. If the " +#~ "last release was tagged ``v1.2.0``, you" +#~ " can use the following URL to " +#~ "see all commits that got merged " +#~ "into ``main`` since then:" +#~ msgstr "" + +#~ msgid "" +#~ "`GitHub: Compare v1.2.0...main " +#~ "`_" +#~ msgstr "" + +#~ msgid "" +#~ "Thank the authors who contributed since" +#~ " the last release. This can be " +#~ "done by running the ``./dev/add-" +#~ "shortlog.sh`` convenience script (it can " +#~ "be ran multiple times and will " +#~ "update the names in the list if" +#~ " new contributors were added in the" +#~ " meantime)." +#~ msgstr "" + +#~ msgid "" +#~ "Update the ``changelog.md`` section header " +#~ "``Unreleased`` to contain the version " +#~ "number and date for the release " +#~ "you are building. Create a pull " +#~ "request with the change." +#~ msgstr "" + +#~ msgid "" +#~ "Tag the release commit with the " +#~ "version number as soon as the PR" +#~ " is merged: ``git tag v0.12.3``, then" +#~ " ``git push --tags``. This will " +#~ "create a draft release on GitHub " +#~ "containing the correct artifacts and the" +#~ " relevant part of the changelog." +#~ msgstr "" + +#~ msgid "" +#~ "Note that, in order to build the" +#~ " documentation locally (with ``poetry run" +#~ " make html``, like described below), " +#~ "`Pandoc _` needs " +#~ "to be installed on the system." +#~ msgstr "" + +#~ msgid "" +#~ "If you're familiar with how contributing" +#~ " on GitHub works, you can directly" +#~ " checkout our `getting started guide " +#~ "for contributors `_ and examples " +#~ "of `good first contributions " +#~ "`_." +#~ msgstr "" + +#~ msgid "" +#~ "This will create a `flower/` (or " +#~ "the name of your fork if you " +#~ "renamed it) folder in the current " +#~ "working directory." +#~ msgstr "" + +#~ msgid "Otherwise you can always find this option in the `Branches` page." +#~ msgstr "" + +#~ msgid "" +#~ "Once you click the `Compare & pull" +#~ " request` button, you should see " +#~ "something similar to this:" +#~ msgstr "" + +#~ msgid "Find the source file in `doc/source`" +#~ msgstr "" + +#~ msgid "" +#~ "Make the change in the `.rst` file" +#~ " (beware, the dashes under the title" +#~ " should be the same length as " +#~ "the title itself)" +#~ msgstr "" + +#~ msgid "Change the file name to `save-progress.rst`" +#~ msgstr "" + +#~ msgid "Add a redirect rule to `doc/source/conf.py`" +#~ msgstr "" + +#~ msgid "" +#~ "This will cause a redirect from " +#~ "`saving-progress.html` to `save-progress.html`," +#~ " old links will continue to work." +#~ msgstr "" + +#~ msgid "" +#~ "For the lateral navigation bar to " +#~ "work properly, it is very important " +#~ "to update the `index.rst` file as " +#~ "well. This is where we define the" +#~ " whole arborescence of the navbar." +#~ msgstr "" + +#~ msgid "Find and modify the file name in `index.rst`" +#~ msgstr "" + +#~ msgid "Add CI job to deploy the staging system when the `main` branch changes" +#~ msgstr "" + +#~ msgid "`Python 3.7 `_ or above" +#~ msgstr "" + +#~ msgid "" +#~ "First, clone the `Flower repository " +#~ "`_ from GitHub::" +#~ msgstr "" + +#~ msgid "" +#~ "Second, create a virtual environment " +#~ "(and activate it). If you chose to" +#~ " use :code:`pyenv` (with the :code" +#~ ":`pyenv-virtualenv` plugin) and already " +#~ "have it installed , you can use" +#~ " the following convenience script (by " +#~ "default it will use :code:`Python " +#~ "3.8.17`, but you can change it by" +#~ " providing a specific :code:``)::" +#~ msgstr "" + +#~ msgid "" +#~ "If you don't have :code:`pyenv` " +#~ "installed, you can use the following " +#~ "script that will install pyenv, set " +#~ "it up and create the virtual " +#~ "environment (with :code:`Python 3.8.17` by " +#~ "default)::" +#~ msgstr "" + +#~ msgid "" +#~ "Third, install the Flower package in " +#~ "development mode (think :code:`pip install " +#~ "-e`) along with all necessary " +#~ "dependencies::" +#~ msgstr "" + +#~ msgid "" +#~ "Developers could run the full set " +#~ "of Github Actions workflows under their" +#~ " local environment by using `Act " +#~ "_`. Please refer to" +#~ " the installation instructions under the" +#~ " linked repository and run the next" +#~ " command under Flower main cloned " +#~ "repository folder::" +#~ msgstr "" + +#~ msgid "" +#~ "Please note that these components are" +#~ " still experimental, the correct " +#~ "configuration of DP for a specific " +#~ "task is still an unsolved problem." +#~ msgstr "" + +#~ msgid "" +#~ "The distribution of the update norm " +#~ "has been shown to vary from " +#~ "task-to-task and to evolve as " +#~ "training progresses. Therefore, we use " +#~ "an adaptive approach [andrew]_ that " +#~ "continuously adjusts the clipping threshold" +#~ " to track a prespecified quantile of" +#~ " the update norm distribution." +#~ msgstr "" + +#~ msgid "" +#~ "We make (and attempt to enforce) a" +#~ " number of assumptions that must be" +#~ " satisfied to ensure that the " +#~ "training process actually realises the " +#~ ":math:`(\\epsilon, \\delta)` guarantees the " +#~ "user has in mind when configuring " +#~ "the setup." +#~ msgstr "" + +#~ msgid "" +#~ "The first two are useful for " +#~ "eliminating a multitude of complications " +#~ "associated with calibrating the noise to" +#~ " the clipping threshold while the " +#~ "third one is required to comply " +#~ "with the assumptions of the privacy " +#~ "analysis." +#~ msgstr "" + +#~ msgid "" +#~ "The first version of our solution " +#~ "was to define a decorator whose " +#~ "constructor accepted, among other things, " +#~ "a boolean valued variable indicating " +#~ "whether adaptive clipping was to be " +#~ "enabled or not. We quickly realized " +#~ "that this would clutter its " +#~ ":code:`__init__()` function with variables " +#~ "corresponding to hyperparameters of adaptive" +#~ " clipping that would remain unused " +#~ "when it was disabled. A cleaner " +#~ "implementation could be achieved by " +#~ "splitting the functionality into two " +#~ "decorators, :code:`DPFedAvgFixed` and " +#~ ":code:`DPFedAvgAdaptive`, with the latter sub-" +#~ " classing the former. The constructors " +#~ "for both classes accept a boolean " +#~ "parameter :code:`server_side_noising`, which, as " +#~ "the name suggests, determines where " +#~ "noising is to be performed." +#~ msgstr "" + +#~ msgid "" +#~ ":code:`aggregate_fit()`: We check whether any" +#~ " of the sampled clients dropped out" +#~ " or failed to upload an update " +#~ "before the round timed out. In " +#~ "that case, we need to abort the" +#~ " current round, discarding any successful" +#~ " updates that were received, and move" +#~ " on to the next one. On the " +#~ "other hand, if all clients responded " +#~ "successfully, we must force the " +#~ "averaging of the updates to happen " +#~ "in an unweighted manner by intercepting" +#~ " the :code:`parameters` field of " +#~ ":code:`FitRes` for each received update " +#~ "and setting it to 1. Furthermore, " +#~ "if :code:`server_side_noising=true`, each update " +#~ "is perturbed with an amount of " +#~ "noise equal to what it would have" +#~ " been subjected to had client-side" +#~ " noising being enabled. This entails " +#~ "*pre*-processing of the arguments to " +#~ "this method before passing them on " +#~ "to the wrappee's implementation of " +#~ ":code:`aggregate_fit()`." +#~ msgstr "" + +#~ msgid "" +#~ "McMahan, H. Brendan, et al. \"Learning" +#~ " differentially private recurrent language " +#~ "models.\" arXiv preprint arXiv:1710.06963 " +#~ "(2017)." +#~ msgstr "" + +#~ msgid "" +#~ "Andrew, Galen, et al. \"Differentially " +#~ "private learning with adaptive clipping.\" " +#~ "Advances in Neural Information Processing " +#~ "Systems 34 (2021): 17455-17466." +#~ msgstr "" + +#~ msgid "" +#~ "The following command can be used " +#~ "to verfiy if Flower was successfully " +#~ "installed. If everything worked, it " +#~ "should print the version of Flower " +#~ "to the command line::" +#~ msgstr "" + +#~ msgid "flwr (Python API reference)" +#~ msgstr "" + +#~ msgid "start_client" +#~ msgstr "" + +#~ msgid "start_numpy_client" +#~ msgstr "" + +#~ msgid "start_simulation" +#~ msgstr "" + +#~ msgid "server.start_server" +#~ msgstr "" + +#~ msgid "server.strategy" +#~ msgstr "" + +#~ msgid "server.strategy.Strategy" +#~ msgstr "" + +#~ msgid "server.strategy.FedAvg" +#~ msgstr "" + +#~ msgid "server.strategy.FedAvgM" +#~ msgstr "" + +#~ msgid "server.strategy.FedMedian" +#~ msgstr "" + +#~ msgid "server.strategy.QFedAvg" +#~ msgstr "" + +#~ msgid "server.strategy.FaultTolerantFedAvg" +#~ msgstr "" + +#~ msgid "server.strategy.FedOpt" +#~ msgstr "" + +#~ msgid "server.strategy.FedProx" +#~ msgstr "" + +#~ msgid "server.strategy.FedAdagrad" +#~ msgstr "" + +#~ msgid "server.strategy.FedAdam" +#~ msgstr "" + +#~ msgid "server.strategy.FedYogi" +#~ msgstr "" + +#~ msgid "server.strategy.FedTrimmedAvg" +#~ msgstr "" + +#~ msgid "server.strategy.Krum" +#~ msgstr "" + +#~ msgid "server.strategy.FedXgbNnAvg" +#~ msgstr "" + +#~ msgid "server.strategy.DPFedAvgAdaptive" +#~ msgstr "" + +#~ msgid "server.strategy.DPFedAvgFixed" +#~ msgstr "" + +#~ msgid "" +#~ "**Fix the incorrect return types of " +#~ "Strategy** " +#~ "([#2432](https://github.com/adap/flower/pull/2432/files))" +#~ msgstr "" + +#~ msgid "" +#~ "The types of the return values in" +#~ " the docstrings in two methods " +#~ "(`aggregate_fit` and `aggregate_evaluate`) now " +#~ "match the hint types in the code." +#~ msgstr "" + +#~ msgid "" +#~ "Using the `client_fn`, Flower clients " +#~ "can interchangeably run as standalone " +#~ "processes (i.e. via `start_client`) or " +#~ "in simulation (i.e. via `start_simulation`)" +#~ " without requiring changes to how the" +#~ " client class is defined and " +#~ "instantiated. Calling `start_numpy_client` is " +#~ "now deprecated." +#~ msgstr "" + +#~ msgid "" +#~ "**Update Flower Examples** " +#~ "([#2384](https://github.com/adap/flower/pull/2384)), " +#~ "([#2425](https://github.com/adap/flower/pull/2425))" +#~ msgstr "" + +#~ msgid "" +#~ "**General updates to baselines** " +#~ "([#2301](https://github.com/adap/flower/pull/2301), " +#~ "[#2305](https://github.com/adap/flower/pull/2305), " +#~ "[#2307](https://github.com/adap/flower/pull/2307), " +#~ "[#2327](https://github.com/adap/flower/pull/2327), " +#~ "[#2435](https://github.com/adap/flower/pull/2435))" +#~ msgstr "" + +#~ msgid "" +#~ "**General updates to the simulation " +#~ "engine** ([#2331](https://github.com/adap/flower/pull/2331), " +#~ "[#2447](https://github.com/adap/flower/pull/2447), " +#~ "[#2448](https://github.com/adap/flower/pull/2448))" +#~ msgstr "" + +#~ msgid "" +#~ "**General improvements** " +#~ "([#2309](https://github.com/adap/flower/pull/2309), " +#~ "[#2310](https://github.com/adap/flower/pull/2310), " +#~ "[2313](https://github.com/adap/flower/pull/2313), " +#~ "[#2316](https://github.com/adap/flower/pull/2316), " +#~ "[2317](https://github.com/adap/flower/pull/2317),[#2349](https://github.com/adap/flower/pull/2349)," +#~ " [#2360](https://github.com/adap/flower/pull/2360), " +#~ "[#2402](https://github.com/adap/flower/pull/2402), " +#~ "[#2446](https://github.com/adap/flower/pull/2446))" +#~ msgstr "" + +#~ msgid "" +#~ "`flower-superlink --driver-api-address " +#~ "\"0.0.0.0:8081\" --fleet-api-address " +#~ "\"0.0.0.0:8086\"`" +#~ msgstr "" + +#~ msgid "" +#~ "That's it for the client. We only" +#~ " have to implement :code:`Client` or " +#~ ":code:`NumPyClient` and call " +#~ ":code:`fl.client.start_client()`. The string " +#~ ":code:`\"0.0.0.0:8080\"` tells the client " +#~ "which server to connect to. In our" +#~ " case we can run the server and" +#~ " the client on the same machine, " +#~ "therefore we use :code:`\"0.0.0.0:8080\"`. If" +#~ " we run a truly federated workload" +#~ " with the server and clients running" +#~ " on different machines, all that " +#~ "needs to change is the " +#~ ":code:`server_address` we pass to the " +#~ "client." +#~ msgstr "" + +#~ msgid "" +#~ "That's it for the client. We only" +#~ " have to implement :code:`Client` or " +#~ ":code:`NumPyClient` and call " +#~ ":code:`fl.client.start_client()`. The string " +#~ ":code:`\"[::]:8080\"` tells the client which" +#~ " server to connect to. In our " +#~ "case we can run the server and " +#~ "the client on the same machine, " +#~ "therefore we use :code:`\"[::]:8080\"`. If " +#~ "we run a truly federated workload " +#~ "with the server and clients running " +#~ "on different machines, all that needs" +#~ " to change is the :code:`server_address`" +#~ " we point the client at." +#~ msgstr "" + +#~ msgid "" +#~ "Let's build a horizontal federated " +#~ "learning system using XGBoost and " +#~ "Flower!" +#~ msgstr "" + +#~ msgid "" +#~ "Please refer to the `full code " +#~ "example `_ to learn " +#~ "more." +#~ msgstr "" + +#~ msgid "" +#~ "In this notebook, we'll build a " +#~ "federated learning system using Flower " +#~ "and PyTorch. In part 1, we use " +#~ "PyTorch for the model training pipeline" +#~ " and data loading. In part 2, " +#~ "we continue to federate the PyTorch-" +#~ "based pipeline using Flower." +#~ msgstr "" + +#~ msgid "" +#~ "Next, we install the necessary packages" +#~ " for PyTorch (``torch`` and " +#~ "``torchvision``) and Flower (``flwr``):" +#~ msgstr "" + +#~ msgid "" +#~ "Federated learning can be applied to " +#~ "many different types of tasks across " +#~ "different domains. In this tutorial, we" +#~ " introduce federated learning by training" +#~ " a simple convolutional neural network " +#~ "(CNN) on the popular CIFAR-10 dataset." +#~ " CIFAR-10 can be used to train " +#~ "image classifiers that distinguish between " +#~ "images from ten different classes:" +#~ msgstr "" + +#~ msgid "" +#~ "Each organization will act as a " +#~ "client in the federated learning system." +#~ " So having ten organizations participate" +#~ " in a federation means having ten " +#~ "clients connected to the federated " +#~ "learning server:" +#~ msgstr "" + +#~ msgid "" +#~ "Let's now load the CIFAR-10 training " +#~ "and test set, partition them into " +#~ "ten smaller datasets (each split into" +#~ " training and validation set), and " +#~ "wrap the resulting partitions by " +#~ "creating a PyTorch ``DataLoader`` for " +#~ "each of them:" +#~ msgstr "" + +#~ msgid "|ed6498a023f2477a9ccd57ee4514bda4|" +#~ msgstr "" + +#~ msgid "|5a4f742489ac4f819afefdd4dc9ab272|" +#~ msgstr "" + +#~ msgid "|3331c80cd05045f6a56524d8e3e76d0c|" +#~ msgstr "" + +#~ msgid "|4987b26884ec4b2c8f06c1264bcebe60|" +#~ msgstr "" + +#~ msgid "|ec8ae2d778aa493a986eb2fa29c220e5|" +#~ msgstr "" + +#~ msgid "|b8949d0669fe4f8eadc9a4932f4e9c57|" +#~ msgstr "" + +#~ msgid "|94ff30bdcd09443e8488b5f29932a541|" +#~ msgstr "" + +#~ msgid "|48dccf1d6d0544bba8917d2783a47719|" +#~ msgstr "" + +#~ msgid "|0366618db96b4f329f0d4372d1150fde|" +#~ msgstr "" + +#~ msgid "|ac80eddc76e6478081b1ca35eed029c0|" +#~ msgstr "" + +#~ msgid "|1ac94140c317450e89678db133c7f3c2|" +#~ msgstr "" + +#~ msgid "|f8850c6e96fc4430b55e53bba237a7c0|" +#~ msgstr "" + +#~ msgid "|4a368fdd3fc34adabd20a46752a68582|" +#~ msgstr "" + +#~ msgid "|40f69c17bb444652a7c8dfe577cd120e|" +#~ msgstr "" + +#~ msgid "" +#~ "Please follow the first section on " +#~ "`Run Flower using Docker " +#~ "`_ which covers this" +#~ " step in more detail." +#~ msgstr "" + +#~ msgid "" +#~ "Since `Flower 1.5 `_ we have " +#~ "introduced translations to our doc " +#~ "pages, but, as you might have " +#~ "noticed, the translations are often " +#~ "imperfect. If you speak languages other" +#~ " than English, you might be able " +#~ "to help us in our effort to " +#~ "make Federated Learning accessible to as" +#~ " many people as possible by " +#~ "contributing to those translations! This " +#~ "might also be a great opportunity " +#~ "for those wanting to become open " +#~ "source contributors with little prerequistes." +#~ msgstr "" + +#~ msgid "" +#~ "You input your translation in the " +#~ "textbox at the top and then, once" +#~ " you are happy with it, you " +#~ "either press ``Save and continue`` (to" +#~ " save the translation and go to " +#~ "the next untranslated string), ``Save " +#~ "and stay`` (to save the translation " +#~ "and stay on the same page), " +#~ "``Suggest`` (to add your translation to" +#~ " suggestions for other users to " +#~ "view), or ``Skip`` (to go to the" +#~ " next untranslated string without saving" +#~ " anything)." +#~ msgstr "" + +#~ msgid "" +#~ "The first thing we need to do " +#~ "is to define a message type for" +#~ " the RPC system in :code:`transport.proto`." +#~ " Note that we have to do it " +#~ "for both the request and response " +#~ "messages. For more details on the " +#~ "syntax of proto3, please see the " +#~ "`official documentation `_." +#~ msgstr "" + +#~ msgid "" +#~ "Source: `Official VSCode documentation " +#~ "`_" +#~ msgstr "" + +#~ msgid "" +#~ "`Developing inside a Container " +#~ "`_" +#~ msgstr "" + +#~ msgid "" +#~ "`Remote development in Containers " +#~ "`_" +#~ msgstr "" + +#~ msgid "" +#~ "If you are not familiar with " +#~ "Flower Baselines, you should probably " +#~ "check-out our `contributing guide for " +#~ "baselines `_." +#~ msgstr "" + +#~ msgid "" +#~ "You should then check out the open" +#~ " `issues " +#~ "`_" +#~ " for baseline requests. If you find" +#~ " a baseline that you'd like to " +#~ "work on and that has no assignes," +#~ " feel free to assign it to " +#~ "yourself and start working on it!" +#~ msgstr "" + +#~ msgid "" +#~ "If you're familiar with how contributing" +#~ " on GitHub works, you can directly" +#~ " checkout our `getting started guide " +#~ "for contributors `_." +#~ msgstr "" + +#~ msgid "" +#~ "Git is a distributed version control " +#~ "tool. This allows for an entire " +#~ "codebase's history to be stored and " +#~ "every developer's machine. It is a " +#~ "software that will need to be " +#~ "installed on your local machine, you " +#~ "can follow this `guide " +#~ "`_ to set it up." +#~ msgstr "" + +#~ msgid "" +#~ "A fork is a personal copy of " +#~ "a GitHub repository. To create one " +#~ "for Flower, you must navigate to " +#~ "https://github.com/adap/flower (while connected to" +#~ " your GitHub account) and click the" +#~ " ``Fork`` button situated on the top" +#~ " right of the page." +#~ msgstr "" + +#~ msgid "" +#~ "Now we will add an upstream " +#~ "address to our repository. Still in " +#~ "the same directroy, we must run " +#~ "the following command:" +#~ msgstr "" + +#~ msgid "" +#~ "This can be achieved by following " +#~ "this `getting started guide for " +#~ "contributors`_ (note that you won't need" +#~ " to clone the repository). Once you" +#~ " are able to write code and " +#~ "test it, you can finally start " +#~ "making changes!" +#~ msgstr "" + +#~ msgid "" +#~ "For our documentation, we’ve started to" +#~ " use the `Diàtaxis framework " +#~ "`_." +#~ msgstr "" + +#~ msgid "" +#~ "Our “How to” guides should have " +#~ "titles that continue the sencence “How" +#~ " to …”, for example, “How to " +#~ "upgrade to Flower 1.0”." +#~ msgstr "" + +#~ msgid "" +#~ "This issue is about changing the " +#~ "title of a doc from present " +#~ "continious to present simple." +#~ msgstr "" + +#~ msgid "" +#~ "Let's take the example of “Saving " +#~ "Progress” which we changed to “Save " +#~ "Progress”. Does this pass our check?" +#~ msgstr "" + +#~ msgid "Before: ”How to saving progress” ❌" +#~ msgstr "" + +#~ msgid "After: ”How to save progress” ✅" +#~ msgstr "" + +#~ msgid "" +#~ "This is a tiny change, but it’ll" +#~ " allow us to test your end-" +#~ "to-end setup. After cloning and " +#~ "setting up the Flower repo, here’s " +#~ "what you should do:" +#~ msgstr "" + +#~ msgid "" +#~ "Build the docs and check the " +#~ "result: ``_" +#~ msgstr "" + +#~ msgid "Here’s how to change the file name:" +#~ msgstr "" + +#~ msgid "" +#~ "Commit the changes (commit messages are" +#~ " always imperative: “Do something”, in " +#~ "this case “Change …”)" +#~ msgstr "" + +#~ msgid "" +#~ "`Good first contributions " +#~ "`_, where you should" +#~ " particularly look into the " +#~ ":code:`baselines` contributions." +#~ msgstr "" + +#~ msgid "" +#~ "If the section is completely empty " +#~ "(without any token) or non-existant, " +#~ "the changelog will just contain the " +#~ "title of the PR for the changelog" +#~ " entry, without any description." +#~ msgstr "" + +#~ msgid "" +#~ "Flower uses :code:`pyproject.toml` to manage" +#~ " dependencies and configure development " +#~ "tools (the ones which support it). " +#~ "Poetry is a build tool which " +#~ "supports `PEP 517 " +#~ "`_." +#~ msgstr "" + +#~ msgid "" +#~ "This tutorial will show you how to" +#~ " use Flower to build a federated " +#~ "version of an existing machine learning" +#~ " workload with `FedBN `_, a federated training strategy" +#~ " designed for non-iid data. We " +#~ "are using PyTorch to train a " +#~ "Convolutional Neural Network(with Batch " +#~ "Normalization layers) on the CIFAR-10 " +#~ "dataset. When applying FedBN, only few" +#~ " changes needed compared to `Example: " +#~ "PyTorch - From Centralized To Federated" +#~ " `_." +#~ msgstr "" + +#~ msgid "" +#~ "All files are revised based on " +#~ "`Example: PyTorch - From Centralized To" +#~ " Federated `_. The " +#~ "only thing to do is modifying the" +#~ " file called :code:`cifar.py`, revised part" +#~ " is shown below:" +#~ msgstr "" + +#~ msgid "" +#~ "So far this should all look fairly" +#~ " familiar if you've used PyTorch " +#~ "before. Let's take the next step " +#~ "and use what we've built to create" +#~ " a federated learning system within " +#~ "FedBN, the sytstem consists of one " +#~ "server and two clients." +#~ msgstr "" + +#~ msgid "" +#~ "If you have read `Example: PyTorch " +#~ "- From Centralized To Federated " +#~ "`_, the following" +#~ " parts are easy to follow, onyl " +#~ ":code:`get_parameters` and :code:`set_parameters` " +#~ "function in :code:`client.py` needed to " +#~ "revise. If not, please read the " +#~ "`Example: PyTorch - From Centralized To" +#~ " Federated `_. first." +#~ msgstr "" + +#~ msgid "Example: Walk-Through PyTorch & MNIST" +#~ msgstr "" + +#~ msgid "" +#~ "In this tutorial we will learn, " +#~ "how to train a Convolutional Neural " +#~ "Network on MNIST using Flower and " +#~ "PyTorch." +#~ msgstr "" + +#~ msgid "" +#~ "Since we want to use PyTorch to" +#~ " solve a computer vision task, let's" +#~ " go ahead an install PyTorch and " +#~ "the **torchvision** library:" +#~ msgstr "" + +#~ msgid "Ready... Set... Train!" +#~ msgstr "" + +#~ msgid "" +#~ "Now that we have all our " +#~ "dependencies installed, let's run a " +#~ "simple distributed training with two " +#~ "clients and one server. Our training " +#~ "procedure and network architecture are " +#~ "based on PyTorch's `Basic MNIST Example" +#~ " `_. " +#~ "This will allow you see how easy" +#~ " it is to wrap your code with" +#~ " Flower and begin training in a " +#~ "federated way. We provide you with " +#~ "two helper scripts, namely *run-" +#~ "server.sh*, and *run-clients.sh*. Don't " +#~ "be afraid to look inside, they are" +#~ " simple enough =)." +#~ msgstr "" + +#~ msgid "" +#~ "Go ahead and launch on a terminal" +#~ " the *run-server.sh* script first as" +#~ " follows:" +#~ msgstr "" + +#~ msgid "Now that the server is up and running, go ahead and launch the clients." +#~ msgstr "" + +#~ msgid "" +#~ "Et voilà! You should be seeing the" +#~ " training procedure and, after a few" +#~ " iterations, the test accuracy for " +#~ "each client." +#~ msgstr "" + +#~ msgid "Now, let's see what is really happening inside." +#~ msgstr "" + +#~ msgid "" +#~ "Inside the server helper script *run-" +#~ "server.sh* you will find the following" +#~ " code that basically runs the " +#~ ":code:`server.py`" +#~ msgstr "" + +#~ msgid "" +#~ "We can go a bit deeper and " +#~ "see that :code:`server.py` simply launches " +#~ "a server that will coordinate three " +#~ "rounds of training. Flower Servers are" +#~ " very customizable, but for simple " +#~ "workloads, we can start a server " +#~ "using the :ref:`start_server ` function and leave " +#~ "all the configuration possibilities at " +#~ "their default values, as seen below." +#~ msgstr "" + +#~ msgid "" +#~ "Next, let's take a look at the " +#~ "*run-clients.sh* file. You will see " +#~ "that it contains the main loop " +#~ "that starts a set of *clients*." +#~ msgstr "" + +#~ msgid "" +#~ "**cid**: is the client ID. It is" +#~ " an integer that uniquely identifies " +#~ "client identifier." +#~ msgstr "" + +#~ msgid "**sever_address**: String that identifies IP and port of the server." +#~ msgstr "" + +#~ msgid "" +#~ "**nb_clients**: This defines the number " +#~ "of clients being created. This piece " +#~ "of information is not required by " +#~ "the client, but it helps us " +#~ "partition the original MNIST dataset to" +#~ " make sure that every client is " +#~ "working on unique subsets of both " +#~ "*training* and *test* sets." +#~ msgstr "" + +#~ msgid "" +#~ "Again, we can go deeper and look" +#~ " inside :code:`flwr_example/quickstart-" +#~ "pytorch/client.py`. After going through the" +#~ " argument parsing code at the " +#~ "beginning of our :code:`main` function, " +#~ "you will find a call to " +#~ ":code:`mnist.load_data`. This function is " +#~ "responsible for partitioning the original " +#~ "MNIST datasets (*training* and *test*) " +#~ "and returning a :code:`torch.utils.data.DataLoader`" +#~ " s for each of them. We then" +#~ " instantiate a :code:`PytorchMNISTClient` object" +#~ " with our client ID, our DataLoaders," +#~ " the number of epochs in each " +#~ "round, and which device we want to" +#~ " use for training (CPU or GPU)." +#~ msgstr "" + +#~ msgid "" +#~ "The :code:`PytorchMNISTClient` object when " +#~ "finally passed to :code:`fl.client.start_client` " +#~ "along with the server's address as " +#~ "the training process begins." +#~ msgstr "" + +#~ msgid "A Closer Look" +#~ msgstr "" + +#~ msgid "" +#~ "Now, let's look closely into the " +#~ ":code:`PytorchMNISTClient` inside :code:`flwr_example" +#~ ".quickstart-pytorch.mnist` and see what it" +#~ " is doing:" +#~ msgstr "" + +#~ msgid "" +#~ "The first thing to notice is that" +#~ " :code:`PytorchMNISTClient` instantiates a CNN" +#~ " model inside its constructor" +#~ msgstr "" + +#~ msgid "" +#~ "The code for the CNN is available" +#~ " under :code:`quickstart-pytorch.mnist` and " +#~ "it is reproduced below. It is the" +#~ " same network found in `Basic MNIST" +#~ " Example " +#~ "`_." +#~ msgstr "" + +#~ msgid "" +#~ "The second thing to notice is that" +#~ " :code:`PytorchMNISTClient` class inherits from" +#~ " the :code:`fl.client.Client`, and hence it" +#~ " must implement the following methods:" +#~ msgstr "" + +#~ msgid "" +#~ "When comparing the abstract class to " +#~ "its derived class :code:`PytorchMNISTClient` " +#~ "you will notice that :code:`fit` calls" +#~ " a :code:`train` function and that " +#~ ":code:`evaluate` calls a :code:`test`: " +#~ "function." +#~ msgstr "" + +#~ msgid "" +#~ "These functions can both be found " +#~ "inside the same :code:`quickstart-" +#~ "pytorch.mnist` module:" +#~ msgstr "" + +#~ msgid "" +#~ "Observe that these functions encapsulate " +#~ "regular training and test loops and " +#~ "provide :code:`fit` and :code:`evaluate` with" +#~ " final statistics for each round. You" +#~ " could substitute them with your " +#~ "custom train and test loops and " +#~ "change the network architecture, and the" +#~ " entire example would still work " +#~ "flawlessly. As a matter of fact, " +#~ "why not try and modify the code" +#~ " to an example of your liking?" +#~ msgstr "" + +#~ msgid "Give It a Try" +#~ msgstr "" + +#~ msgid "" +#~ "Looking through the quickstart code " +#~ "description above will have given a " +#~ "good understanding of how *clients* and" +#~ " *servers* work in Flower, how to " +#~ "run a simple experiment, and the " +#~ "internals of a client wrapper. Here " +#~ "are a few things you could try " +#~ "on your own and get more " +#~ "experience with Flower:" +#~ msgstr "" + +#~ msgid "" +#~ "Try and change :code:`PytorchMNISTClient` so" +#~ " it can accept different architectures." +#~ msgstr "" + +#~ msgid "" +#~ "Modify the :code:`train` function so " +#~ "that it accepts different optimizers" +#~ msgstr "" + +#~ msgid "" +#~ "Modify the :code:`test` function so that" +#~ " it proves not only the top-1 " +#~ "(regular accuracy) but also the top-5" +#~ " accuracy?" +#~ msgstr "" + +#~ msgid "" +#~ "Go larger! Try to adapt the code" +#~ " to larger images and datasets. Why" +#~ " not try training on ImageNet with" +#~ " a ResNet-50?" +#~ msgstr "" + +#~ msgid "You are ready now. Enjoy learning in a federated way!" +#~ msgstr "" + +#~ msgid "Differential privacy" +#~ msgstr "" + +#~ msgid "" +#~ "Flower provides differential privacy (DP) " +#~ "wrapper classes for the easy integration" +#~ " of the central DP guarantees " +#~ "provided by DP-FedAvg into training " +#~ "pipelines defined in any of the " +#~ "various ML frameworks that Flower is " +#~ "compatible with." +#~ msgstr "" + +#~ msgid "" +#~ "Please note that these components are" +#~ " still experimental; the correct " +#~ "configuration of DP for a specific " +#~ "task is still an unsolved problem." +#~ msgstr "" + +#~ msgid "" +#~ "The name DP-FedAvg is misleading " +#~ "since it can be applied on top " +#~ "of any FL algorithm that conforms " +#~ "to the general structure prescribed by" +#~ " the FedOpt family of algorithms." +#~ msgstr "" + +#~ msgid "DP-FedAvg" +#~ msgstr "" + +#~ msgid "" +#~ "DP-FedAvg, originally proposed by " +#~ "McMahan et al. [mcmahan]_ and extended" +#~ " by Andrew et al. [andrew]_, is " +#~ "essentially FedAvg with the following " +#~ "modifications." +#~ msgstr "" + +#~ msgid "" +#~ "**Clipping** : The influence of each " +#~ "client's update is bounded by clipping" +#~ " it. This is achieved by enforcing" +#~ " a cap on the L2 norm of " +#~ "the update, scaling it down if " +#~ "needed." +#~ msgstr "" + +#~ msgid "" +#~ "**Noising** : Gaussian noise, calibrated " +#~ "to the clipping threshold, is added " +#~ "to the average computed at the " +#~ "server." +#~ msgstr "" + +#~ msgid "" +#~ "The distribution of the update norm " +#~ "has been shown to vary from " +#~ "task-to-task and to evolve as " +#~ "training progresses. This variability is " +#~ "crucial in understanding its impact on" +#~ " differential privacy guarantees, emphasizing " +#~ "the need for an adaptive approach " +#~ "[andrew]_ that continuously adjusts the " +#~ "clipping threshold to track a " +#~ "prespecified quantile of the update norm" +#~ " distribution." +#~ msgstr "" + +#~ msgid "Simplifying Assumptions" +#~ msgstr "" + +#~ msgid "" +#~ "We make (and attempt to enforce) a" +#~ " number of assumptions that must be" +#~ " satisfied to ensure that the " +#~ "training process actually realizes the " +#~ ":math:`(\\epsilon, \\delta)` guarantees the " +#~ "user has in mind when configuring " +#~ "the setup." +#~ msgstr "" + +#~ msgid "" +#~ "**Fixed-size subsampling** :Fixed-size " +#~ "subsamples of the clients must be " +#~ "taken at each round, as opposed to" +#~ " variable-sized Poisson subsamples." +#~ msgstr "" + +#~ msgid "" +#~ "**Unweighted averaging** : The contributions" +#~ " from all the clients must weighted" +#~ " equally in the aggregate to " +#~ "eliminate the requirement for the server" +#~ " to know in advance the sum of" +#~ " the weights of all clients available" +#~ " for selection." +#~ msgstr "" + +#~ msgid "" +#~ "**No client failures** : The set " +#~ "of available clients must stay constant" +#~ " across all rounds of training. In" +#~ " other words, clients cannot drop out" +#~ " or fail." +#~ msgstr "" + +#~ msgid "" +#~ "The first two are useful for " +#~ "eliminating a multitude of complications " +#~ "associated with calibrating the noise to" +#~ " the clipping threshold, while the " +#~ "third one is required to comply " +#~ "with the assumptions of the privacy " +#~ "analysis." +#~ msgstr "" + +#~ msgid "" +#~ "These restrictions are in line with " +#~ "constraints imposed by Andrew et al. " +#~ "[andrew]_." +#~ msgstr "" + +#~ msgid "Customizable Responsibility for Noise injection" +#~ msgstr "" + +#~ msgid "" +#~ "In contrast to other implementations " +#~ "where the addition of noise is " +#~ "performed at the server, you can " +#~ "configure the site of noise injection" +#~ " to better match your threat model." +#~ " We provide users with the " +#~ "flexibility to set up the training " +#~ "such that each client independently adds" +#~ " a small amount of noise to the" +#~ " clipped update, with the result that" +#~ " simply aggregating the noisy updates " +#~ "is equivalent to the explicit addition" +#~ " of noise to the non-noisy " +#~ "aggregate at the server." +#~ msgstr "" + +#~ msgid "" +#~ "To be precise, if we let :math:`m`" +#~ " be the number of clients sampled " +#~ "each round and :math:`\\sigma_\\Delta` be " +#~ "the scale of the total Gaussian " +#~ "noise that needs to be added to" +#~ " the sum of the model updates, " +#~ "we can use simple maths to show" +#~ " that this is equivalent to each " +#~ "client adding noise with scale " +#~ ":math:`\\sigma_\\Delta/\\sqrt{m}`." +#~ msgstr "" + +#~ msgid "Wrapper-based approach" +#~ msgstr "" + +#~ msgid "" +#~ "Introducing DP to an existing workload" +#~ " can be thought of as adding an" +#~ " extra layer of security around it." +#~ " This inspired us to provide the " +#~ "additional server and client-side logic" +#~ " needed to make the training process" +#~ " differentially private as wrappers for " +#~ "instances of the :code:`Strategy` and " +#~ ":code:`NumPyClient` abstract classes respectively." +#~ " This wrapper-based approach has the" +#~ " advantage of being easily composable " +#~ "with other wrappers that someone might" +#~ " contribute to the Flower library in" +#~ " the future, e.g., for secure " +#~ "aggregation. Using Inheritance instead can " +#~ "be tedious because that would require" +#~ " the creation of new sub- classes " +#~ "every time a new class implementing " +#~ ":code:`Strategy` or :code:`NumPyClient` is " +#~ "defined." +#~ msgstr "" + +#~ msgid "Server-side logic" +#~ msgstr "" + +#~ msgid "" +#~ "The first version of our solution " +#~ "was to define a decorator whose " +#~ "constructor accepted, among other things, " +#~ "a boolean-valued variable indicating " +#~ "whether adaptive clipping was to be " +#~ "enabled or not. We quickly realized " +#~ "that this would clutter its " +#~ ":code:`__init__()` function with variables " +#~ "corresponding to hyperparameters of adaptive" +#~ " clipping that would remain unused " +#~ "when it was disabled. A cleaner " +#~ "implementation could be achieved by " +#~ "splitting the functionality into two " +#~ "decorators, :code:`DPFedAvgFixed` and " +#~ ":code:`DPFedAvgAdaptive`, with the latter sub-" +#~ " classing the former. The constructors " +#~ "for both classes accept a boolean " +#~ "parameter :code:`server_side_noising`, which, as " +#~ "the name suggests, determines where " +#~ "noising is to be performed." +#~ msgstr "" + +#~ msgid "" +#~ "The server-side capabilities required " +#~ "for the original version of DP-" +#~ "FedAvg, i.e., the one which performed" +#~ " fixed clipping, can be completely " +#~ "captured with the help of wrapper " +#~ "logic for just the following two " +#~ "methods of the :code:`Strategy` abstract " +#~ "class." +#~ msgstr "" + +#~ msgid "" +#~ ":code:`configure_fit()` : The config " +#~ "dictionary being sent by the wrapped " +#~ ":code:`Strategy` to each client needs to" +#~ " be augmented with an additional " +#~ "value equal to the clipping threshold" +#~ " (keyed under :code:`dpfedavg_clip_norm`) and," +#~ " if :code:`server_side_noising=true`, another one" +#~ " equal to the scale of the " +#~ "Gaussian noise that needs to be " +#~ "added at the client (keyed under " +#~ ":code:`dpfedavg_noise_stddev`). This entails " +#~ "*post*-processing of the results returned " +#~ "by the wrappee's implementation of " +#~ ":code:`configure_fit()`." +#~ msgstr "" + +#~ msgid "" +#~ ":code:`aggregate_fit()`: We check whether any" +#~ " of the sampled clients dropped out" +#~ " or failed to upload an update " +#~ "before the round timed out. In " +#~ "that case, we need to abort the" +#~ " current round, discarding any successful" +#~ " updates that were received, and move" +#~ " on to the next one. On the " +#~ "other hand, if all clients responded " +#~ "successfully, we must force the " +#~ "averaging of the updates to happen " +#~ "in an unweighted manner by intercepting" +#~ " the :code:`parameters` field of " +#~ ":code:`FitRes` for each received update " +#~ "and setting it to 1. Furthermore, " +#~ "if :code:`server_side_noising=true`, each update " +#~ "is perturbed with an amount of " +#~ "noise equal to what it would have" +#~ " been subjected to had client-side" +#~ " noising being enabled. This entails " +#~ "*pre*-processing of the arguments to " +#~ "this method before passing them on " +#~ "to the wrappee's implementation of " +#~ ":code:`aggregate_fit()`." +#~ msgstr "" + +#~ msgid "" +#~ "We can't directly change the aggregation" +#~ " function of the wrapped strategy to" +#~ " force it to add noise to the" +#~ " aggregate, hence we simulate client-" +#~ "side noising to implement server-side" +#~ " noising." +#~ msgstr "" + +#~ msgid "" +#~ "These changes have been put together " +#~ "into a class called :code:`DPFedAvgFixed`, " +#~ "whose constructor accepts the strategy " +#~ "being decorated, the clipping threshold " +#~ "and the number of clients sampled " +#~ "every round as compulsory arguments. The" +#~ " user is expected to specify the " +#~ "clipping threshold since the order of" +#~ " magnitude of the update norms is " +#~ "highly dependent on the model being " +#~ "trained and providing a default value" +#~ " would be misleading. The number of" +#~ " clients sampled at every round is" +#~ " required to calculate the amount of" +#~ " noise that must be added to " +#~ "each individual update, either by the" +#~ " server or the clients." +#~ msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:328 -msgid "" -"Federated learning, federated evaluation, and federated analytics require" -" infrastructure to move machine learning models back and forth, train and" -" evaluate them on local data, and then aggregate the updated models. " -"Flower provides the infrastructure to do exactly that in an easy, " -"scalable, and secure way. In short, Flower presents a unified approach to" -" federated learning, analytics, and evaluation. It allows the user to " -"federate any workload, any ML framework, and any programming language." -msgstr "" +#~ msgid "" +#~ "The additional functionality required to " +#~ "facilitate adaptive clipping has been " +#~ "provided in :code:`DPFedAvgAdaptive`, a " +#~ "subclass of :code:`DPFedAvgFixed`. It " +#~ "overrides the above-mentioned methods to" +#~ " do the following." +#~ msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:334 -msgid "|ff726bc5505e432388ee2fdd6ef420b9|" -msgstr "" +#~ msgid "" +#~ ":code:`configure_fit()` : It intercepts the" +#~ " config dict returned by " +#~ ":code:`super.configure_fit()` to add the " +#~ "key-value pair " +#~ ":code:`dpfedavg_adaptive_clip_enabled:True` to it, " +#~ "which the client interprets as an " +#~ "instruction to include an indicator bit" +#~ " (1 if update norm <= clipping " +#~ "threshold, 0 otherwise) in the results" +#~ " returned by it." +#~ msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:340 -msgid "" -"Flower federated learning server and client nodes (car, scooter, personal" -" computer, roomba, and phone)" -msgstr "" +#~ msgid "" +#~ ":code:`aggregate_fit()` : It follows a " +#~ "call to :code:`super.aggregate_fit()` with one" +#~ " to :code:`__update_clip_norm__()`, a procedure" +#~ " which adjusts the clipping threshold " +#~ "on the basis of the indicator bits" +#~ " received from the sampled clients." +#~ msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:353 -msgid "" -"Congratulations, you just learned the basics of federated learning and " -"how it relates to the classic (centralized) machine learning!" -msgstr "" +#~ msgid "Client-side logic" +#~ msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:355 -msgid "" -"In the next part of this tutorial, we are going to build a first " -"federated learning system with Flower." -msgstr "" +#~ msgid "" +#~ "The client-side capabilities required " +#~ "can be completely captured through " +#~ "wrapper logic for just the :code:`fit()`" +#~ " method of the :code:`NumPyClient` abstract" +#~ " class. To be precise, we need " +#~ "to *post-process* the update computed" +#~ " by the wrapped client to clip " +#~ "it, if necessary, to the threshold " +#~ "value supplied by the server as " +#~ "part of the config dictionary. In " +#~ "addition to this, it may need to" +#~ " perform some extra work if either" +#~ " (or both) of the following keys " +#~ "are also present in the dict." +#~ msgstr "" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:373 -msgid "" -"The `Flower Federated Learning Tutorial - Part 1 " -"`__ shows how to build a simple federated learning system " -"with PyTorch and Flower." -msgstr "" +#~ msgid "" +#~ ":code:`dpfedavg_noise_stddev` : Generate and " +#~ "add the specified amount of noise " +#~ "to the clipped update." +#~ msgstr "" #~ msgid "" -#~ "Configuring and setting up the " -#~ ":code:`Dockerfile` as well the configuration" -#~ " for the devcontainer can be a " -#~ "bit more involved. The good thing " -#~ "is you want have to do it. " -#~ "Usually it should be enough to " -#~ "install Docker on your system and " -#~ "ensure its available on your command " -#~ "line. Additionally, install the `VSCode " -#~ "Containers Extension `_." +#~ ":code:`dpfedavg_adaptive_clip_enabled` : Augment the" +#~ " metrics dict in the :code:`FitRes` " +#~ "object being returned to the server " +#~ "with an indicator bit, calculated as " +#~ "described earlier." +#~ msgstr "" + +#~ msgid "Performing the :math:`(\\epsilon, \\delta)` analysis" #~ msgstr "" #~ msgid "" -#~ "``flwr = { path = " -#~ "\"../../dist/flwr-1.0.0-py3-none-any.whl\" }`` " -#~ "(without extras)" +#~ "Assume you have trained for :math:`n`" +#~ " rounds with sampling fraction :math:`q`" +#~ " and noise multiplier :math:`z`. In " +#~ "order to calculate the :math:`\\epsilon` " +#~ "value this would result in for a" +#~ " particular :math:`\\delta`, the following " +#~ "script may be used." #~ msgstr "" #~ msgid "" -#~ "``flwr = { path = " -#~ "\"../../dist/flwr-1.0.0-py3-none-any.whl\", extras =" -#~ " [\"simulation\"] }`` (with extras)" +#~ "McMahan et al. \"Learning Differentially " +#~ "Private Recurrent Language Models.\" " +#~ "International Conference on Learning " +#~ "Representations (ICLR), 2017." #~ msgstr "" -#~ msgid "Upload the whl (e.g., ``flwr-1.7.0-py3-none-any.whl``)" +#~ msgid "" +#~ "Andrew, Galen, et al. \"Differentially " +#~ "Private Learning with Adaptive Clipping.\" " +#~ "Advances in Neural Information Processing " +#~ "Systems (NeurIPS), 2021." #~ msgstr "" #~ msgid "" -#~ "Change ``!pip install -q 'flwr[simulation]'" -#~ " torch torchvision matplotlib`` to ``!pip" -#~ " install -q 'flwr-1.7.0-py3-none-" -#~ "any.whl[simulation]' torch torchvision matplotlib``" +#~ "This can be achieved by customizing " +#~ "an existing strategy or by `implementing" +#~ " a custom strategy from scratch " +#~ "`_. Here's a nonsensical " +#~ "example that customizes :code:`FedAvg` by " +#~ "adding a custom ``\"hello\": \"world\"`` " +#~ "configuration key/value pair to the " +#~ "config dict of a *single client* " +#~ "(only the first client in the " +#~ "list, the other clients in this " +#~ "round to not receive this \"special\"" +#~ " config value):" #~ msgstr "" -#~ msgid "Before the release" +#~ msgid "" +#~ "More sophisticated implementations can use " +#~ ":code:`configure_fit` to implement custom " +#~ "client selection logic. A client will" +#~ " only participate in a round if " +#~ "the corresponding :code:`ClientProxy` is " +#~ "included in the the list returned " +#~ "from :code:`configure_fit`." #~ msgstr "" #~ msgid "" -#~ "Update the changelog (``changelog.md``) with" -#~ " all relevant changes that happened " -#~ "after the last release. If the " -#~ "last release was tagged ``v1.2.0``, you" -#~ " can use the following URL to " -#~ "see all commits that got merged " -#~ "into ``main`` since then:" +#~ "More sophisticated implementations can use " +#~ ":code:`configure_evaluate` to implement custom " +#~ "client selection logic. A client will" +#~ " only participate in a round if " +#~ "the corresponding :code:`ClientProxy` is " +#~ "included in the the list returned " +#~ "from :code:`configure_evaluate`." #~ msgstr "" #~ msgid "" -#~ "`GitHub: Compare v1.2.0...main " -#~ "`_" +#~ "`How to run Flower using Docker " +#~ "`_" #~ msgstr "" #~ msgid "" -#~ "Thank the authors who contributed since" -#~ " the last release. This can be " -#~ "done by running the ``./dev/add-" -#~ "shortlog.sh`` convenience script (it can " -#~ "be ran multiple times and will " -#~ "update the names in the list if" -#~ " new contributors were added in the" -#~ " meantime)." +#~ "Ray Dashboard: ``_" #~ msgstr "" #~ msgid "" -#~ "Update the ``changelog.md`` section header " -#~ "``Unreleased`` to contain the version " -#~ "number and date for the release " -#~ "you are building. Create a pull " -#~ "request with the change." +#~ "Ray Metrics: ``_" +#~ msgstr "" + +#~ msgid "Enjoy building more robust and flexible ``ClientApp``s with mods!" #~ msgstr "" #~ msgid "" -#~ "Tag the release commit with the " -#~ "version number as soon as the PR" -#~ " is merged: ``git tag v0.12.3``, then" -#~ " ``git push --tags``. This will " -#~ "create a draft release on GitHub " -#~ "containing the correct artifacts and the" -#~ " relevant part of the changelog." +#~ ":py:obj:`ClientApp `\\ " +#~ "\\(client\\_fn\\[\\, mods\\]\\)" +#~ msgstr "" + +#~ msgid ":py:obj:`flwr.server.driver `\\" +#~ msgstr "" + +#~ msgid "Flower driver SDK." +#~ msgstr "" + +#~ msgid "driver" #~ msgstr "" #~ msgid "" -#~ "Note that, in order to build the" -#~ " documentation locally (with ``poetry run" -#~ " make html``, like described below), " -#~ "`Pandoc _` needs " -#~ "to be installed on the system." +#~ ":py:obj:`start_driver `\\ " +#~ "\\(\\*\\[\\, server\\_address\\, server\\, ...\\]\\)" #~ msgstr "" #~ msgid "" -#~ "If you're familiar with how contributing" -#~ " on GitHub works, you can directly" -#~ " checkout our `getting started guide " -#~ "for contributors `_ and examples " -#~ "of `good first contributions " -#~ "`_." +#~ ":py:obj:`Driver `\\ " +#~ "\\(\\[driver\\_service\\_address\\, ...\\]\\)" #~ msgstr "" #~ msgid "" -#~ "This will create a `flower/` (or " -#~ "the name of your fork if you " -#~ "renamed it) folder in the current " -#~ "working directory." +#~ ":py:obj:`GrpcDriver `\\ " +#~ "\\(\\[driver\\_service\\_address\\, ...\\]\\)" +#~ msgstr "" + +#~ msgid "`GrpcDriver` provides access to the gRPC Driver API/service." #~ msgstr "" -#~ msgid "Otherwise you can always find this option in the `Branches` page." +#~ msgid ":py:obj:`get_nodes `\\ \\(\\)" #~ msgstr "" #~ msgid "" -#~ "Once you click the `Compare & pull" -#~ " request` button, you should see " -#~ "something similar to this:" +#~ ":py:obj:`pull_task_res " +#~ "`\\ \\(task\\_ids\\)" #~ msgstr "" -#~ msgid "Find the source file in `doc/source`" +#~ msgid "Get task results." #~ msgstr "" #~ msgid "" -#~ "Make the change in the `.rst` file" -#~ " (beware, the dashes under the title" -#~ " should be the same length as " -#~ "the title itself)" +#~ ":py:obj:`push_task_ins " +#~ "`\\ " +#~ "\\(task\\_ins\\_list\\)" #~ msgstr "" -#~ msgid "Change the file name to `save-progress.rst`" +#~ msgid "Schedule tasks." #~ msgstr "" -#~ msgid "Add a redirect rule to `doc/source/conf.py`" +#~ msgid "GrpcDriver" #~ msgstr "" -#~ msgid "" -#~ "This will cause a redirect from " -#~ "`saving-progress.html` to `save-progress.html`," -#~ " old links will continue to work." +#~ msgid ":py:obj:`connect `\\ \\(\\)" +#~ msgstr "" + +#~ msgid "Connect to the Driver API." #~ msgstr "" #~ msgid "" -#~ "For the lateral navigation bar to " -#~ "work properly, it is very important " -#~ "to update the `index.rst` file as " -#~ "well. This is where we define the" -#~ " whole arborescence of the navbar." +#~ ":py:obj:`create_run " +#~ "`\\ \\(req\\)" #~ msgstr "" -#~ msgid "Find and modify the file name in `index.rst`" +#~ msgid "Request for run ID." #~ msgstr "" -#~ msgid "Add CI job to deploy the staging system when the `main` branch changes" +#~ msgid "" +#~ ":py:obj:`disconnect " +#~ "`\\ \\(\\)" #~ msgstr "" -#~ msgid "`Python 3.7 `_ or above" +#~ msgid "Disconnect from the Driver API." #~ msgstr "" #~ msgid "" -#~ "First, clone the `Flower repository " -#~ "`_ from GitHub::" +#~ ":py:obj:`get_nodes `\\" +#~ " \\(req\\)" #~ msgstr "" -#~ msgid "" -#~ "Second, create a virtual environment " -#~ "(and activate it). If you chose to" -#~ " use :code:`pyenv` (with the :code" -#~ ":`pyenv-virtualenv` plugin) and already " -#~ "have it installed , you can use" -#~ " the following convenience script (by " -#~ "default it will use :code:`Python " -#~ "3.8.17`, but you can change it by" -#~ " providing a specific :code:``)::" +#~ msgid "Get client IDs." #~ msgstr "" #~ msgid "" -#~ "If you don't have :code:`pyenv` " -#~ "installed, you can use the following " -#~ "script that will install pyenv, set " -#~ "it up and create the virtual " -#~ "environment (with :code:`Python 3.8.17` by " -#~ "default)::" +#~ ":py:obj:`pull_task_res " +#~ "`\\ \\(req\\)" #~ msgstr "" #~ msgid "" -#~ "Third, install the Flower package in " -#~ "development mode (think :code:`pip install " -#~ "-e`) along with all necessary " -#~ "dependencies::" +#~ ":py:obj:`push_task_ins " +#~ "`\\ \\(req\\)" #~ msgstr "" #~ msgid "" -#~ "Developers could run the full set " -#~ "of Github Actions workflows under their" -#~ " local environment by using `Act " -#~ "_`. Please refer to" -#~ " the installation instructions under the" -#~ " linked repository and run the next" -#~ " command under Flower main cloned " -#~ "repository folder::" +#~ "Optionally specify the type of actor " +#~ "to use. The actor object, which " +#~ "persists throughout the simulation, will " +#~ "be the process in charge of " +#~ "running the clients' jobs (i.e. their" +#~ " `fit()` method)." #~ msgstr "" #~ msgid "" -#~ "Please note that these components are" -#~ " still experimental, the correct " -#~ "configuration of DP for a specific " -#~ "task is still an unsolved problem." +#~ "Much effort went into a completely " +#~ "restructured Flower docs experience. The " +#~ "documentation on [flower.ai/docs](flower.ai/docs) is" +#~ " now divided into Flower Framework, " +#~ "Flower Baselines, Flower Android SDK, " +#~ "Flower iOS SDK, and code example " +#~ "projects." #~ msgstr "" #~ msgid "" -#~ "The distribution of the update norm " -#~ "has been shown to vary from " -#~ "task-to-task and to evolve as " -#~ "training progresses. Therefore, we use " -#~ "an adaptive approach [andrew]_ that " -#~ "continuously adjusts the clipping threshold" -#~ " to track a prespecified quantile of" -#~ " the update norm distribution." +#~ "The first preview release of Flower " +#~ "Baselines has arrived! We're kickstarting " +#~ "Flower Baselines with implementations of " +#~ "FedOpt (FedYogi, FedAdam, FedAdagrad), FedBN," +#~ " and FedAvgM. Check the documentation " +#~ "on how to use [Flower " +#~ "Baselines](https://flower.ai/docs/using-baselines.html). " +#~ "With this first preview release we're" +#~ " also inviting the community to " +#~ "[contribute their own " +#~ "baselines](https://flower.ai/docs/contributing-baselines.html)." #~ msgstr "" #~ msgid "" -#~ "We make (and attempt to enforce) a" -#~ " number of assumptions that must be" -#~ " satisfied to ensure that the " -#~ "training process actually realises the " -#~ ":math:`(\\epsilon, \\delta)` guarantees the " -#~ "user has in mind when configuring " -#~ "the setup." +#~ "Flower usage examples used to be " +#~ "bundled with Flower in a package " +#~ "called ``flwr_example``. We are migrating " +#~ "those examples to standalone projects to" +#~ " make them easier to use. All " +#~ "new examples are based in the " +#~ "directory `examples " +#~ "`_." #~ msgstr "" -#~ msgid "" -#~ "The first two are useful for " -#~ "eliminating a multitude of complications " -#~ "associated with calibrating the noise to" -#~ " the clipping threshold while the " -#~ "third one is required to comply " -#~ "with the assumptions of the privacy " -#~ "analysis." +#~ msgid "The following examples are available as standalone projects." #~ msgstr "" -#~ msgid "" -#~ "The first version of our solution " -#~ "was to define a decorator whose " -#~ "constructor accepted, among other things, " -#~ "a boolean valued variable indicating " -#~ "whether adaptive clipping was to be " -#~ "enabled or not. We quickly realized " -#~ "that this would clutter its " -#~ ":code:`__init__()` function with variables " -#~ "corresponding to hyperparameters of adaptive" -#~ " clipping that would remain unused " -#~ "when it was disabled. A cleaner " -#~ "implementation could be achieved by " -#~ "splitting the functionality into two " -#~ "decorators, :code:`DPFedAvgFixed` and " -#~ ":code:`DPFedAvgAdaptive`, with the latter sub-" -#~ " classing the former. The constructors " -#~ "for both classes accept a boolean " -#~ "parameter :code:`server_side_noising`, which, as " -#~ "the name suggests, determines where " -#~ "noising is to be performed." +#~ msgid "Quickstart TensorFlow/Keras" #~ msgstr "" #~ msgid "" -#~ ":code:`aggregate_fit()`: We check whether any" -#~ " of the sampled clients dropped out" -#~ " or failed to upload an update " -#~ "before the round timed out. In " -#~ "that case, we need to abort the" -#~ " current round, discarding any successful" -#~ " updates that were received, and move" -#~ " on to the next one. On the " -#~ "other hand, if all clients responded " -#~ "successfully, we must force the " -#~ "averaging of the updates to happen " -#~ "in an unweighted manner by intercepting" -#~ " the :code:`parameters` field of " -#~ ":code:`FitRes` for each received update " -#~ "and setting it to 1. Furthermore, " -#~ "if :code:`server_side_noising=true`, each update " -#~ "is perturbed with an amount of " -#~ "noise equal to what it would have" -#~ " been subjected to had client-side" -#~ " noising being enabled. This entails " -#~ "*pre*-processing of the arguments to " -#~ "this method before passing them on " -#~ "to the wrappee's implementation of " -#~ ":code:`aggregate_fit()`." +#~ "`Quickstart TensorFlow (Tutorial) " +#~ "`_" #~ msgstr "" #~ msgid "" -#~ "McMahan, H. Brendan, et al. \"Learning" -#~ " differentially private recurrent language " -#~ "models.\" arXiv preprint arXiv:1710.06963 " -#~ "(2017)." +#~ "`Quickstart PyTorch (Tutorial) " +#~ "`_" #~ msgstr "" #~ msgid "" -#~ "Andrew, Galen, et al. \"Differentially " -#~ "private learning with adaptive clipping.\" " -#~ "Advances in Neural Information Processing " -#~ "Systems 34 (2021): 17455-17466." +#~ "`PyTorch: From Centralized To Federated " +#~ "(Tutorial) `_" #~ msgstr "" -#~ msgid "" -#~ "The following command can be used " -#~ "to verfiy if Flower was successfully " -#~ "installed. If everything worked, it " -#~ "should print the version of Flower " -#~ "to the command line::" +#~ msgid "Legacy Examples (`flwr_example`)" #~ msgstr "" -#~ msgid "flwr (Python API reference)" +#~ msgid "" +#~ "The useage examples in `flwr_example` " +#~ "are deprecated and will be removed " +#~ "in the future. New examples are " +#~ "provided as standalone projects in " +#~ "`examples `_." #~ msgstr "" -#~ msgid "start_client" +#~ msgid "Extra Dependencies" #~ msgstr "" -#~ msgid "start_numpy_client" +#~ msgid "" +#~ "The core Flower framework keeps a " +#~ "minimal set of dependencies. The " +#~ "examples demonstrate Flower in the " +#~ "context of different machine learning " +#~ "frameworks, so additional dependencies need" +#~ " to be installed before an example" +#~ " can be run." #~ msgstr "" -#~ msgid "start_simulation" +#~ msgid "For PyTorch examples::" #~ msgstr "" -#~ msgid "server.start_server" +#~ msgid "For TensorFlow examples::" #~ msgstr "" -#~ msgid "server.strategy" +#~ msgid "For both PyTorch and TensorFlow examples::" #~ msgstr "" -#~ msgid "server.strategy.Strategy" +#~ msgid "" +#~ "Please consult :code:`pyproject.toml` for a" +#~ " full list of possible extras " +#~ "(section :code:`[tool.poetry.extras]`)." #~ msgstr "" -#~ msgid "server.strategy.FedAvg" +#~ msgid "PyTorch Examples" #~ msgstr "" -#~ msgid "server.strategy.FedAvgM" +#~ msgid "" +#~ "Our PyTorch examples are based on " +#~ "PyTorch 1.7. They should work with " +#~ "other releases as well. So far, we" +#~ " provide the following examples." #~ msgstr "" -#~ msgid "server.strategy.FedMedian" +#~ msgid "CIFAR-10 Image Classification" #~ msgstr "" -#~ msgid "server.strategy.QFedAvg" +#~ msgid "" +#~ "`CIFAR-10 and CIFAR-100 " +#~ "`_ are " +#~ "popular RGB image datasets. The Flower" +#~ " CIFAR-10 example uses PyTorch to " +#~ "train a simple CNN classifier in a" +#~ " federated learning setup with two " +#~ "clients." #~ msgstr "" -#~ msgid "server.strategy.FaultTolerantFedAvg" +#~ msgid "First, start a Flower server:" #~ msgstr "" -#~ msgid "server.strategy.FedOpt" +#~ msgid "$ ./src/py/flwr_example/pytorch_cifar/run-server.sh" #~ msgstr "" -#~ msgid "server.strategy.FedProx" +#~ msgid "Then, start the two clients in a new terminal window:" #~ msgstr "" -#~ msgid "server.strategy.FedAdagrad" +#~ msgid "$ ./src/py/flwr_example/pytorch_cifar/run-clients.sh" #~ msgstr "" -#~ msgid "server.strategy.FedAdam" +#~ msgid "For more details, see :code:`src/py/flwr_example/pytorch_cifar`." #~ msgstr "" -#~ msgid "server.strategy.FedYogi" +#~ msgid "ImageNet-2012 Image Classification" #~ msgstr "" -#~ msgid "server.strategy.FedTrimmedAvg" +#~ msgid "" +#~ "`ImageNet-2012 `_ is " +#~ "one of the major computer vision " +#~ "datasets. The Flower ImageNet example " +#~ "uses PyTorch to train a ResNet-18 " +#~ "classifier in a federated learning setup" +#~ " with ten clients." #~ msgstr "" -#~ msgid "server.strategy.Krum" +#~ msgid "$ ./src/py/flwr_example/pytorch_imagenet/run-server.sh" #~ msgstr "" -#~ msgid "server.strategy.FedXgbNnAvg" +#~ msgid "$ ./src/py/flwr_example/pytorch_imagenet/run-clients.sh" #~ msgstr "" -#~ msgid "server.strategy.DPFedAvgAdaptive" +#~ msgid "For more details, see :code:`src/py/flwr_example/pytorch_imagenet`." #~ msgstr "" -#~ msgid "server.strategy.DPFedAvgFixed" +#~ msgid "TensorFlow Examples" #~ msgstr "" #~ msgid "" -#~ "**Fix the incorrect return types of " -#~ "Strategy** " -#~ "([#2432](https://github.com/adap/flower/pull/2432/files))" +#~ "Our TensorFlow examples are based on " +#~ "TensorFlow 2.0 or newer. So far, " +#~ "we provide the following examples." #~ msgstr "" -#~ msgid "" -#~ "The types of the return values in" -#~ " the docstrings in two methods " -#~ "(`aggregate_fit` and `aggregate_evaluate`) now " -#~ "match the hint types in the code." +#~ msgid "Fashion-MNIST Image Classification" #~ msgstr "" #~ msgid "" -#~ "Using the `client_fn`, Flower clients " -#~ "can interchangeably run as standalone " -#~ "processes (i.e. via `start_client`) or " -#~ "in simulation (i.e. via `start_simulation`)" -#~ " without requiring changes to how the" -#~ " client class is defined and " -#~ "instantiated. Calling `start_numpy_client` is " -#~ "now deprecated." +#~ "`Fashion-MNIST `_ is often used as " +#~ "the \"Hello, world!\" of machine " +#~ "learning. We follow this tradition and" +#~ " provide an example which samples " +#~ "random local datasets from Fashion-MNIST" +#~ " and trains a simple image " +#~ "classification model over those partitions." #~ msgstr "" -#~ msgid "" -#~ "**Update Flower Examples** " -#~ "([#2384](https://github.com/adap/flower/pull/2384)), " -#~ "([#2425](https://github.com/adap/flower/pull/2425))" +#~ msgid "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-server.sh" #~ msgstr "" -#~ msgid "" -#~ "**General updates to baselines** " -#~ "([#2301](https://github.com/adap/flower/pull/2301), " -#~ "[#2305](https://github.com/adap/flower/pull/2305), " -#~ "[#2307](https://github.com/adap/flower/pull/2307), " -#~ "[#2327](https://github.com/adap/flower/pull/2327), " -#~ "[#2435](https://github.com/adap/flower/pull/2435))" +#~ msgid "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-clients.sh" #~ msgstr "" #~ msgid "" -#~ "**General updates to the simulation " -#~ "engine** ([#2331](https://github.com/adap/flower/pull/2331), " -#~ "[#2447](https://github.com/adap/flower/pull/2447), " -#~ "[#2448](https://github.com/adap/flower/pull/2448))" +#~ "For more details, see " +#~ ":code:`src/py/flwr_example/tensorflow_fashion_mnist`." #~ msgstr "" -#~ msgid "" -#~ "**General improvements** " -#~ "([#2309](https://github.com/adap/flower/pull/2309), " -#~ "[#2310](https://github.com/adap/flower/pull/2310), " -#~ "[2313](https://github.com/adap/flower/pull/2313), " -#~ "[#2316](https://github.com/adap/flower/pull/2316), " -#~ "[2317](https://github.com/adap/flower/pull/2317),[#2349](https://github.com/adap/flower/pull/2349)," -#~ " [#2360](https://github.com/adap/flower/pull/2360), " -#~ "[#2402](https://github.com/adap/flower/pull/2402), " -#~ "[#2446](https://github.com/adap/flower/pull/2446))" +#~ msgid ":fa:`eye,mr-1` Can Flower run on Juptyter Notebooks / Google Colab?" #~ msgstr "" #~ msgid "" -#~ "`flower-superlink --driver-api-address " -#~ "\"0.0.0.0:8081\" --fleet-api-address " -#~ "\"0.0.0.0:8086\"`" +#~ "`Flower meets KOSMoS `_." #~ msgstr "" #~ msgid "" -#~ "That's it for the client. We only" -#~ " have to implement :code:`Client` or " -#~ ":code:`NumPyClient` and call " -#~ ":code:`fl.client.start_client()`. The string " -#~ ":code:`\"0.0.0.0:8080\"` tells the client " -#~ "which server to connect to. In our" -#~ " case we can run the server and" -#~ " the client on the same machine, " -#~ "therefore we use :code:`\"0.0.0.0:8080\"`. If" -#~ " we run a truly federated workload" -#~ " with the server and clients running" -#~ " on different machines, all that " -#~ "needs to change is the " -#~ ":code:`server_address` we pass to the " -#~ "client." +#~ "If you want to check out " +#~ "everything put together, you should " +#~ "check out the full code example: " +#~ "[https://github.com/adap/flower/tree/main/examples/quickstart-" +#~ "huggingface](https://github.com/adap/flower/tree/main/examples" +#~ "/quickstart-huggingface)." #~ msgstr "" #~ msgid "" -#~ "That's it for the client. We only" -#~ " have to implement :code:`Client` or " -#~ ":code:`NumPyClient` and call " -#~ ":code:`fl.client.start_client()`. The string " -#~ ":code:`\"[::]:8080\"` tells the client which" -#~ " server to connect to. In our " -#~ "case we can run the server and " -#~ "the client on the same machine, " -#~ "therefore we use :code:`\"[::]:8080\"`. If " -#~ "we run a truly federated workload " -#~ "with the server and clients running " -#~ "on different machines, all that needs" -#~ " to change is the :code:`server_address`" -#~ " we point the client at." +#~ "First of all, for running the " +#~ "Flower Python server, it is recommended" +#~ " to create a virtual environment and" +#~ " run everything within a `virtualenv " +#~ "`_. " +#~ "For the Flower client implementation in" +#~ " iOS, it is recommended to use " +#~ "Xcode as our IDE." #~ msgstr "" #~ msgid "" -#~ "Let's build a horizontal federated " -#~ "learning system using XGBoost and " -#~ "Flower!" +#~ "Since CoreML does not allow the " +#~ "model parameters to be seen before " +#~ "training, and accessing the model " +#~ "parameters during or after the training" +#~ " can only be done by specifying " +#~ "the layer name, we need to know" +#~ " this informations beforehand, through " +#~ "looking at the model specification, " +#~ "which are written as proto files. " +#~ "The implementation can be seen in " +#~ ":code:`MLModelInspect`." #~ msgstr "" #~ msgid "" -#~ "Please refer to the `full code " -#~ "example `_ to learn " -#~ "more." +#~ "After we have all of the necessary" +#~ " informations, let's create our Flower " +#~ "client." #~ msgstr "" #~ msgid "" -#~ "In this notebook, we'll build a " -#~ "federated learning system using Flower " -#~ "and PyTorch. In part 1, we use " -#~ "PyTorch for the model training pipeline" -#~ " and data loading. In part 2, " -#~ "we continue to federate the PyTorch-" -#~ "based pipeline using Flower." +#~ "MXNet is no longer maintained and " +#~ "has been moved into `Attic " +#~ "`_. As a " +#~ "result, we would encourage you to " +#~ "use other ML frameworks alongise Flower," +#~ " for example, PyTorch. This tutorial " +#~ "might be removed in future versions " +#~ "of Flower." #~ msgstr "" #~ msgid "" -#~ "Next, we install the necessary packages" -#~ " for PyTorch (``torch`` and " -#~ "``torchvision``) and Flower (``flwr``):" +#~ "It is recommended to create a " +#~ "virtual environment and run everything " +#~ "within this `virtualenv `_." #~ msgstr "" #~ msgid "" -#~ "Federated learning can be applied to " -#~ "many different types of tasks across " -#~ "different domains. In this tutorial, we" -#~ " introduce federated learning by training" -#~ " a simple convolutional neural network " -#~ "(CNN) on the popular CIFAR-10 dataset." -#~ " CIFAR-10 can be used to train " -#~ "image classifiers that distinguish between " -#~ "images from ten different classes:" +#~ "First of all, it is recommended to" +#~ " create a virtual environment and run" +#~ " everything within a `virtualenv " +#~ "`_." #~ msgstr "" -#~ msgid "" -#~ "Each organization will act as a " -#~ "client in the federated learning system." -#~ " So having ten organizations participate" -#~ " in a federation means having ten " -#~ "clients connected to the federated " -#~ "learning server:" +#~ msgid "Since we want to use scikt-learn, let's go ahead and install it:" #~ msgstr "" #~ msgid "" -#~ "Let's now load the CIFAR-10 training " -#~ "and test set, partition them into " -#~ "ten smaller datasets (each split into" -#~ " training and validation set), and " -#~ "wrap the resulting partitions by " -#~ "creating a PyTorch ``DataLoader`` for " -#~ "each of them:" +#~ "We load the MNIST dataset from " +#~ "`OpenML `_, a popular" +#~ " image classification dataset of " +#~ "handwritten digits for machine learning. " +#~ "The utility :code:`utils.load_mnist()` downloads " +#~ "the training and test data. The " +#~ "training set is split afterwards into" +#~ " 10 partitions with :code:`utils.partition()`." #~ msgstr "" -#~ msgid "|ed6498a023f2477a9ccd57ee4514bda4|" +#~ msgid "" +#~ "Now that you have known how " +#~ "federated XGBoost work with Flower, it's" +#~ " time to run some more comprehensive" +#~ " experiments by customising the " +#~ "experimental settings. In the xgboost-" +#~ "comprehensive example (`full code " +#~ "`_), we provide more options " +#~ "to define various experimental setups, " +#~ "including aggregation strategies, data " +#~ "partitioning and centralised/distributed evaluation." +#~ " We also support `Flower simulation " +#~ "`_ making it easy to " +#~ "simulate large client cohorts in a " +#~ "resource-aware manner. Let's take a " +#~ "look!" #~ msgstr "" -#~ msgid "|5a4f742489ac4f819afefdd4dc9ab272|" +#~ msgid "|31e4b1afa87c4b968327bbeafbf184d4|" #~ msgstr "" -#~ msgid "|3331c80cd05045f6a56524d8e3e76d0c|" +#~ msgid "|c9d935b4284e4c389a33d86b33e07c0a|" #~ msgstr "" -#~ msgid "|4987b26884ec4b2c8f06c1264bcebe60|" +#~ msgid "|00727b5faffb468f84dd1b03ded88638|" #~ msgstr "" -#~ msgid "|ec8ae2d778aa493a986eb2fa29c220e5|" +#~ msgid "|daf0cf0ff4c24fd29439af78416cf47b|" #~ msgstr "" -#~ msgid "|b8949d0669fe4f8eadc9a4932f4e9c57|" +#~ msgid "|9f093007080d471d94ca90d3e9fde9b6|" #~ msgstr "" -#~ msgid "|94ff30bdcd09443e8488b5f29932a541|" +#~ msgid "|46a26e6150e0479fbd3dfd655f36eb13|" #~ msgstr "" -#~ msgid "|48dccf1d6d0544bba8917d2783a47719|" +#~ msgid "|3daba297595c4c7fb845d90404a6179a|" #~ msgstr "" -#~ msgid "|0366618db96b4f329f0d4372d1150fde|" +#~ msgid "|5769874fa9c4455b80b2efda850d39d7|" #~ msgstr "" -#~ msgid "|ac80eddc76e6478081b1ca35eed029c0|" +#~ msgid "|ba47ffb421814b0f8f9fa5719093d839|" #~ msgstr "" -#~ msgid "|1ac94140c317450e89678db133c7f3c2|" +#~ msgid "|aeac5bf79cbf497082e979834717e01b|" #~ msgstr "" -#~ msgid "|f8850c6e96fc4430b55e53bba237a7c0|" +#~ msgid "|ce27ed4bbe95459dba016afc42486ba2|" #~ msgstr "" -#~ msgid "|4a368fdd3fc34adabd20a46752a68582|" +#~ msgid "|ae94a7f71dda443cbec2385751427d41|" #~ msgstr "" -#~ msgid "|40f69c17bb444652a7c8dfe577cd120e|" +#~ msgid "|e61fce4d43d243e7bb08bdde97d81ce6|" #~ msgstr "" -#~ msgid "" -#~ "Please follow the first section on " -#~ "`Run Flower using Docker " -#~ "`_ which covers this" -#~ " step in more detail." +#~ msgid "|08cb60859b07461588fe44e55810b050|" #~ msgstr "" +#~ msgid "``BASE_IMAGE_TAG``" +#~ msgstr "``BASE_IMAGE_TAG``" + +#~ msgid "The image tag of the base image." +#~ msgstr "A tag da imagem da imagem base." + #~ msgid "" -#~ "Since `Flower 1.5 `_ we have " -#~ "introduced translations to our doc " -#~ "pages, but, as you might have " -#~ "noticed, the translations are often " -#~ "imperfect. If you speak languages other" -#~ " than English, you might be able " -#~ "to help us in our effort to " -#~ "make Federated Learning accessible to as" -#~ " many people as possible by " -#~ "contributing to those translations! This " -#~ "might also be a great opportunity " -#~ "for those wanting to become open " -#~ "source contributors with little prerequistes." +#~ "Open the notebook ``doc/source/tutorial-" +#~ "get-started-with-flower-pytorch.ipynb``:" #~ msgstr "" #~ msgid "" -#~ "You input your translation in the " -#~ "textbox at the top and then, once" -#~ " you are happy with it, you " -#~ "either press ``Save and continue`` (to" -#~ " save the translation and go to " -#~ "the next untranslated string), ``Save " -#~ "and stay`` (to save the translation " -#~ "and stay on the same page), " -#~ "``Suggest`` (to add your translation to" -#~ " suggestions for other users to " -#~ "view), or ``Skip`` (to go to the" -#~ " next untranslated string without saving" -#~ " anything)." +#~ "https://colab.research.google.com/github/adap/flower/blob/main/doc/source" +#~ "/tutorial-get-started-with-flower-" +#~ "pytorch.ipynb" #~ msgstr "" #~ msgid "" -#~ "The first thing we need to do " -#~ "is to define a message type for" -#~ " the RPC system in :code:`transport.proto`." -#~ " Note that we have to do it " -#~ "for both the request and response " -#~ "messages. For more details on the " -#~ "syntax of proto3, please see the " -#~ "`official documentation `_." +#~ "https://colab.research.google.com/github/adap/flower/blob/branch-" +#~ "name/doc/source/tutorial-get-started-with-" +#~ "flower-pytorch.ipynb" #~ msgstr "" -#~ msgid "" -#~ "Source: `Official VSCode documentation " -#~ "`_" +#~ msgid "Virutualenv with Pyenv/Virtualenv" #~ msgstr "" #~ msgid "" -#~ "`Developing inside a Container " -#~ "`_" +#~ "It is important to follow the " +#~ "instructions described in comments. For " +#~ "instance, in order to not break " +#~ "how our changelog system works, you " +#~ "should read the information above the" +#~ " ``Changelog entry`` section carefully. You" +#~ " can also checkout some examples and" +#~ " details in the :ref:`changelogentry` " +#~ "appendix." #~ msgstr "" -#~ msgid "" -#~ "`Remote development in Containers " -#~ "`_" +#~ msgid "Open a PR (as shown above)" #~ msgstr "" -#~ msgid "" -#~ "If you are not familiar with " -#~ "Flower Baselines, you should probably " -#~ "check-out our `contributing guide for " -#~ "baselines `_." +#~ msgid "How to write a good PR title" #~ msgstr "" #~ msgid "" -#~ "You should then check out the open" -#~ " `issues " -#~ "`_" -#~ " for baseline requests. If you find" -#~ " a baseline that you'd like to " -#~ "work on and that has no assignes," -#~ " feel free to assign it to " -#~ "yourself and start working on it!" +#~ "A well-crafted PR title helps team" +#~ " members quickly understand the purpose " +#~ "and scope of the changes being " +#~ "proposed. Here's a guide to help " +#~ "you write a good GitHub PR title:" #~ msgstr "" #~ msgid "" -#~ "If you're familiar with how contributing" -#~ " on GitHub works, you can directly" -#~ " checkout our `getting started guide " -#~ "for contributors `_." +#~ "1. Be Clear and Concise: Provide a" +#~ " clear summary of the changes in " +#~ "a concise manner. 1. Use Actionable " +#~ "Verbs: Start with verbs like \"Add,\"" +#~ " \"Update,\" or \"Fix\" to indicate " +#~ "the purpose. 1. Include Relevant " +#~ "Information: Mention the affected feature " +#~ "or module for context. 1. Keep it" +#~ " Short: Avoid lengthy titles for easy" +#~ " readability. 1. Use Proper Capitalization" +#~ " and Punctuation: Follow grammar rules " +#~ "for clarity." #~ msgstr "" #~ msgid "" -#~ "Git is a distributed version control " -#~ "tool. This allows for an entire " -#~ "codebase's history to be stored and " -#~ "every developer's machine. It is a " -#~ "software that will need to be " -#~ "installed on your local machine, you " -#~ "can follow this `guide " -#~ "`_ to set it up." +#~ "Let's start with a few examples " +#~ "for titles that should be avoided " +#~ "because they do not provide meaningful" +#~ " information:" #~ msgstr "" -#~ msgid "" -#~ "A fork is a personal copy of " -#~ "a GitHub repository. To create one " -#~ "for Flower, you must navigate to " -#~ "https://github.com/adap/flower (while connected to" -#~ " your GitHub account) and click the" -#~ " ``Fork`` button situated on the top" -#~ " right of the page." +#~ msgid "Implement Algorithm" #~ msgstr "" -#~ msgid "" -#~ "Now we will add an upstream " -#~ "address to our repository. Still in " -#~ "the same directroy, we must run " -#~ "the following command:" +#~ msgid "Database" #~ msgstr "" -#~ msgid "" -#~ "This can be achieved by following " -#~ "this `getting started guide for " -#~ "contributors`_ (note that you won't need" -#~ " to clone the repository). Once you" -#~ " are able to write code and " -#~ "test it, you can finally start " -#~ "making changes!" +#~ msgid "Add my_new_file.py to codebase" #~ msgstr "" -#~ msgid "" -#~ "For our documentation, we’ve started to" -#~ " use the `Diàtaxis framework " -#~ "`_." +#~ msgid "Improve code in module" #~ msgstr "" -#~ msgid "" -#~ "Our “How to” guides should have " -#~ "titles that continue the sencence “How" -#~ " to …”, for example, “How to " -#~ "upgrade to Flower 1.0”." +#~ msgid "Change SomeModule" #~ msgstr "" #~ msgid "" -#~ "This issue is about changing the " -#~ "title of a doc from present " -#~ "continious to present simple." +#~ "Here are a few positive examples " +#~ "which provide helpful information without " +#~ "repeating how they do it, as that" +#~ " is already visible in the \"Files" +#~ " changed\" section of the PR:" #~ msgstr "" -#~ msgid "" -#~ "Let's take the example of “Saving " -#~ "Progress” which we changed to “Save " -#~ "Progress”. Does this pass our check?" +#~ msgid "Update docs banner to mention Flower Summit 2023" #~ msgstr "" -#~ msgid "Before: ”How to saving progress” ❌" +#~ msgid "Remove unnecessary XGBoost dependency" #~ msgstr "" -#~ msgid "After: ”How to save progress” ✅" +#~ msgid "Remove redundant attributes in strategies subclassing FedAvg" #~ msgstr "" #~ msgid "" -#~ "This is a tiny change, but it’ll" -#~ " allow us to test your end-" -#~ "to-end setup. After cloning and " -#~ "setting up the Flower repo, here’s " -#~ "what you should do:" +#~ "Add CI job to deploy the staging" +#~ " system when the ``main`` branch " +#~ "changes" #~ msgstr "" #~ msgid "" -#~ "Build the docs and check the " -#~ "result: ``_" +#~ "Add new amazing library which will " +#~ "be used to improve the simulation " +#~ "engine" #~ msgstr "" -#~ msgid "Here’s how to change the file name:" +#~ msgid "Changelog entry" #~ msgstr "" #~ msgid "" -#~ "Commit the changes (commit messages are" -#~ " always imperative: “Do something”, in " -#~ "this case “Change …”)" +#~ "When opening a new PR, inside its" +#~ " description, there should be a " +#~ "``Changelog entry`` header." #~ msgstr "" #~ msgid "" -#~ "`Good first contributions " -#~ "`_, where you should" -#~ " particularly look into the " -#~ ":code:`baselines` contributions." +#~ "Above this header you should see " +#~ "the following comment that explains how" +#~ " to write your changelog entry:" +#~ msgstr "" + +#~ msgid "" +#~ "Inside the following 'Changelog entry' " +#~ "section, you should put the description" +#~ " of your changes that will be " +#~ "added to the changelog alongside your" +#~ " PR title." #~ msgstr "" #~ msgid "" #~ "If the section is completely empty " -#~ "(without any token) or non-existant, " +#~ "(without any token) or non-existent, " #~ "the changelog will just contain the " #~ "title of the PR for the changelog" #~ " entry, without any description." #~ msgstr "" #~ msgid "" -#~ "Flower uses :code:`pyproject.toml` to manage" -#~ " dependencies and configure development " -#~ "tools (the ones which support it). " -#~ "Poetry is a build tool which " -#~ "supports `PEP 517 " -#~ "`_." +#~ "If the section contains some text " +#~ "other than tokens, it will use it" +#~ " to add a description to the " +#~ "change." #~ msgstr "" #~ msgid "" -#~ "This tutorial will show you how to" -#~ " use Flower to build a federated " -#~ "version of an existing machine learning" -#~ " workload with `FedBN `_, a federated training strategy" -#~ " designed for non-iid data. We " -#~ "are using PyTorch to train a " -#~ "Convolutional Neural Network(with Batch " -#~ "Normalization layers) on the CIFAR-10 " -#~ "dataset. When applying FedBN, only few" -#~ " changes needed compared to `Example: " -#~ "PyTorch - From Centralized To Federated" -#~ " `_." +#~ "If the section contains one of the" +#~ " following tokens it will ignore any" +#~ " other text and put the PR " +#~ "under the corresponding section of the" +#~ " changelog:" #~ msgstr "" -#~ msgid "" -#~ "All files are revised based on " -#~ "`Example: PyTorch - From Centralized To" -#~ " Federated `_. The " -#~ "only thing to do is modifying the" -#~ " file called :code:`cifar.py`, revised part" -#~ " is shown below:" +#~ msgid " is for classifying a PR as a general improvement." #~ msgstr "" -#~ msgid "" -#~ "So far this should all look fairly" -#~ " familiar if you've used PyTorch " -#~ "before. Let's take the next step " -#~ "and use what we've built to create" -#~ " a federated learning system within " -#~ "FedBN, the sytstem consists of one " -#~ "server and two clients." +#~ msgid " is to not add the PR to the changelog" #~ msgstr "" -#~ msgid "" -#~ "If you have read `Example: PyTorch " -#~ "- From Centralized To Federated " -#~ "`_, the following" -#~ " parts are easy to follow, onyl " -#~ ":code:`get_parameters` and :code:`set_parameters` " -#~ "function in :code:`client.py` needed to " -#~ "revise. If not, please read the " -#~ "`Example: PyTorch - From Centralized To" -#~ " Federated `_. first." +#~ msgid " is to add a general baselines change to the PR" #~ msgstr "" -#~ msgid "Example: Walk-Through PyTorch & MNIST" +#~ msgid " is to add a general examples change to the PR" #~ msgstr "" -#~ msgid "" -#~ "In this tutorial we will learn, " -#~ "how to train a Convolutional Neural " -#~ "Network on MNIST using Flower and " -#~ "PyTorch." +#~ msgid " is to add a general sdk change to the PR" #~ msgstr "" -#~ msgid "" -#~ "Since we want to use PyTorch to" -#~ " solve a computer vision task, let's" -#~ " go ahead an install PyTorch and " -#~ "the **torchvision** library:" +#~ msgid " is to add a general simulations change to the PR" #~ msgstr "" -#~ msgid "Ready... Set... Train!" +#~ msgid "Note that only one token should be used." #~ msgstr "" #~ msgid "" -#~ "Now that we have all our " -#~ "dependencies installed, let's run a " -#~ "simple distributed training with two " -#~ "clients and one server. Our training " -#~ "procedure and network architecture are " -#~ "based on PyTorch's `Basic MNIST Example" -#~ " `_. " -#~ "This will allow you see how easy" -#~ " it is to wrap your code with" -#~ " Flower and begin training in a " -#~ "federated way. We provide you with " -#~ "two helper scripts, namely *run-" -#~ "server.sh*, and *run-clients.sh*. Don't " -#~ "be afraid to look inside, they are" -#~ " simple enough =)." +#~ "Its content must have a specific " +#~ "format. We will break down what " +#~ "each possibility does:" #~ msgstr "" #~ msgid "" -#~ "Go ahead and launch on a terminal" -#~ " the *run-server.sh* script first as" -#~ " follows:" +#~ "If the ``### Changelog entry`` section" +#~ " contains nothing or doesn't exist, " +#~ "the following text will be added " +#~ "to the changelog::" #~ msgstr "" -#~ msgid "Now that the server is up and running, go ahead and launch the clients." +#~ msgid "" +#~ "If the ``### Changelog entry`` section" +#~ " contains a description (and no " +#~ "token), the following text will be " +#~ "added to the changelog::" #~ msgstr "" #~ msgid "" -#~ "Et voilà! You should be seeing the" -#~ " training procedure and, after a few" -#~ " iterations, the test accuracy for " -#~ "each client." +#~ "If the ``### Changelog entry`` section" +#~ " contains ````, nothing will change" +#~ " in the changelog." #~ msgstr "" -#~ msgid "Now, let's see what is really happening inside." +#~ msgid "" +#~ "If the ``### Changelog entry`` section" +#~ " contains ````, the following text" +#~ " will be added to the changelog::" #~ msgstr "" #~ msgid "" -#~ "Inside the server helper script *run-" -#~ "server.sh* you will find the following" -#~ " code that basically runs the " -#~ ":code:`server.py`" +#~ "If the ``### Changelog entry`` section" +#~ " contains ````, the following " +#~ "text will be added to the " +#~ "changelog::" #~ msgstr "" #~ msgid "" -#~ "We can go a bit deeper and " -#~ "see that :code:`server.py` simply launches " -#~ "a server that will coordinate three " -#~ "rounds of training. Flower Servers are" -#~ " very customizable, but for simple " -#~ "workloads, we can start a server " -#~ "using the :ref:`start_server ` function and leave " -#~ "all the configuration possibilities at " -#~ "their default values, as seen below." +#~ "If the ``### Changelog entry`` section" +#~ " contains ````, the following " +#~ "text will be added to the " +#~ "changelog::" #~ msgstr "" #~ msgid "" -#~ "Next, let's take a look at the " -#~ "*run-clients.sh* file. You will see " -#~ "that it contains the main loop " -#~ "that starts a set of *clients*." +#~ "If the ``### Changelog entry`` section" +#~ " contains ````, the following text " +#~ "will be added to the changelog::" +#~ msgstr "" + +#~ msgid "" +#~ "If the ``### Changelog entry`` section" +#~ " contains ````, the following " +#~ "text will be added to the " +#~ "changelog::" #~ msgstr "" #~ msgid "" -#~ "**cid**: is the client ID. It is" -#~ " an integer that uniquely identifies " -#~ "client identifier." +#~ "Note that only one token must be" +#~ " provided, otherwise, only the first " +#~ "action (in the order listed above), " +#~ "will be performed." #~ msgstr "" -#~ msgid "**sever_address**: String that identifies IP and port of the server." +#~ msgid "Example: MXNet - Run MXNet Federated" #~ msgstr "" #~ msgid "" -#~ "**nb_clients**: This defines the number " -#~ "of clients being created. This piece " -#~ "of information is not required by " -#~ "the client, but it helps us " -#~ "partition the original MNIST dataset to" -#~ " make sure that every client is " -#~ "working on unique subsets of both " -#~ "*training* and *test* sets." +#~ "This tutorial will show you how to" +#~ " use Flower to build a federated " +#~ "version of an existing MXNet workload." +#~ " We are using MXNet to train a" +#~ " Sequential model on the MNIST " +#~ "dataset. We will structure the example" +#~ " similar to our `PyTorch - From " +#~ "Centralized To Federated " +#~ "`_ walkthrough. " +#~ "MXNet and PyTorch are very similar " +#~ "and a very good comparison between " +#~ "MXNet and PyTorch is given `here " +#~ "`_. First, " +#~ "we build a centralized training approach" +#~ " based on the `Handwritten Digit " +#~ "Recognition " +#~ "`_" +#~ " tutorial. Then, we build upon the" +#~ " centralized training code to run the" +#~ " training in a federated fashion." #~ msgstr "" #~ msgid "" -#~ "Again, we can go deeper and look" -#~ " inside :code:`flwr_example/quickstart-" -#~ "pytorch/client.py`. After going through the" -#~ " argument parsing code at the " -#~ "beginning of our :code:`main` function, " -#~ "you will find a call to " -#~ ":code:`mnist.load_data`. This function is " -#~ "responsible for partitioning the original " -#~ "MNIST datasets (*training* and *test*) " -#~ "and returning a :code:`torch.utils.data.DataLoader`" -#~ " s for each of them. We then" -#~ " instantiate a :code:`PytorchMNISTClient` object" -#~ " with our client ID, our DataLoaders," -#~ " the number of epochs in each " -#~ "round, and which device we want to" -#~ " use for training (CPU or GPU)." +#~ "Before we start setting up our " +#~ "MXNet example, we install the " +#~ ":code:`mxnet` and :code:`flwr` packages:" #~ msgstr "" -#~ msgid "" -#~ "The :code:`PytorchMNISTClient` object when " -#~ "finally passed to :code:`fl.client.start_client` " -#~ "along with the server's address as " -#~ "the training process begins." +#~ msgid "MNIST Training with MXNet" #~ msgstr "" -#~ msgid "A Closer Look" +#~ msgid "" +#~ "We begin with a brief description " +#~ "of the centralized training code based" +#~ " on a :code:`Sequential` model. If " +#~ "you want a more in-depth " +#~ "explanation of what's going on then " +#~ "have a look at the official `MXNet" +#~ " tutorial " +#~ "`_." #~ msgstr "" #~ msgid "" -#~ "Now, let's look closely into the " -#~ ":code:`PytorchMNISTClient` inside :code:`flwr_example" -#~ ".quickstart-pytorch.mnist` and see what it" -#~ " is doing:" +#~ "Let's create a new file " +#~ "called:code:`mxnet_mnist.py` with all the " +#~ "components required for a traditional " +#~ "(centralized) MNIST training. First, the " +#~ "MXNet package :code:`mxnet` needs to be" +#~ " imported. You can see that we " +#~ "do not yet import the :code:`flwr` " +#~ "package for federated learning. This " +#~ "will be done later." #~ msgstr "" #~ msgid "" -#~ "The first thing to notice is that" -#~ " :code:`PytorchMNISTClient` instantiates a CNN" -#~ " model inside its constructor" +#~ "The :code:`load_data()` function loads the " +#~ "MNIST training and test sets." #~ msgstr "" #~ msgid "" -#~ "The code for the CNN is available" -#~ " under :code:`quickstart-pytorch.mnist` and " -#~ "it is reproduced below. It is the" -#~ " same network found in `Basic MNIST" -#~ " Example " -#~ "`_." +#~ "As already mentioned, we will use " +#~ "the MNIST dataset for this machine " +#~ "learning workload. The model architecture " +#~ "(a very simple :code:`Sequential` model) " +#~ "is defined in :code:`model()`." #~ msgstr "" #~ msgid "" -#~ "The second thing to notice is that" -#~ " :code:`PytorchMNISTClient` class inherits from" -#~ " the :code:`fl.client.Client`, and hence it" -#~ " must implement the following methods:" +#~ "We now need to define the training" +#~ " (function :code:`train()`) which loops " +#~ "over the training set and measures " +#~ "the loss for each batch of " +#~ "training examples." #~ msgstr "" #~ msgid "" -#~ "When comparing the abstract class to " -#~ "its derived class :code:`PytorchMNISTClient` " -#~ "you will notice that :code:`fit` calls" -#~ " a :code:`train` function and that " -#~ ":code:`evaluate` calls a :code:`test`: " -#~ "function." +#~ "The evaluation of the model is " +#~ "defined in function :code:`test()`. The " +#~ "function loops over all test samples " +#~ "and measures the loss and accuracy " +#~ "of the model based on the test " +#~ "dataset." #~ msgstr "" #~ msgid "" -#~ "These functions can both be found " -#~ "inside the same :code:`quickstart-" -#~ "pytorch.mnist` module:" +#~ "Having defined the data loading, model" +#~ " architecture, training, and evaluation we" +#~ " can put everything together and " +#~ "train our model on MNIST. Note " +#~ "that the GPU/CPU device for the " +#~ "training and testing is defined within" +#~ " the :code:`ctx` (context)." +#~ msgstr "" + +#~ msgid "You can now run your (centralized) MXNet machine learning workload:" #~ msgstr "" #~ msgid "" -#~ "Observe that these functions encapsulate " -#~ "regular training and test loops and " -#~ "provide :code:`fit` and :code:`evaluate` with" -#~ " final statistics for each round. You" -#~ " could substitute them with your " -#~ "custom train and test loops and " -#~ "change the network architecture, and the" -#~ " entire example would still work " -#~ "flawlessly. As a matter of fact, " -#~ "why not try and modify the code" -#~ " to an example of your liking?" +#~ "So far this should all look fairly" +#~ " familiar if you've used MXNet (or" +#~ " even PyTorch) before. Let's take the" +#~ " next step and use what we've " +#~ "built to create a simple federated " +#~ "learning system consisting of one server" +#~ " and two clients." #~ msgstr "" -#~ msgid "Give It a Try" +#~ msgid "MXNet meets Flower" #~ msgstr "" #~ msgid "" -#~ "Looking through the quickstart code " -#~ "description above will have given a " -#~ "good understanding of how *clients* and" -#~ " *servers* work in Flower, how to " -#~ "run a simple experiment, and the " -#~ "internals of a client wrapper. Here " -#~ "are a few things you could try " -#~ "on your own and get more " -#~ "experience with Flower:" +#~ "So far, it was not easily possible" +#~ " to use MXNet workloads for federated" +#~ " learning because federated learning is " +#~ "not supported in MXNet. Since Flower " +#~ "is fully agnostic towards the underlying" +#~ " machine learning framework, it can " +#~ "be used to federated arbitrary machine" +#~ " learning workloads. This section will " +#~ "show you how Flower can be used" +#~ " to federate our centralized MXNet " +#~ "workload." #~ msgstr "" #~ msgid "" -#~ "Try and change :code:`PytorchMNISTClient` so" -#~ " it can accept different architectures." +#~ "The concept to federate an existing " +#~ "workload is always the same and " +#~ "easy to understand. We have to " +#~ "start a *server* and then use the" +#~ " code in :code:`mxnet_mnist.py` for the " +#~ "*clients* that are connected to the " +#~ "*server*. The *server* sends model " +#~ "parameters to the clients. The *clients*" +#~ " run the training and update the " +#~ "parameters. The updated parameters are " +#~ "sent back to the *server* which " +#~ "averages all received parameter updates. " +#~ "This describes one round of the " +#~ "federated learning process and we repeat" +#~ " this for multiple rounds." #~ msgstr "" #~ msgid "" -#~ "Modify the :code:`train` function so " -#~ "that it accepts different optimizers" +#~ "Finally, we will define our *client* " +#~ "logic in :code:`client.py` and build " +#~ "upon the previously defined MXNet " +#~ "training in :code:`mxnet_mnist.py`. Our " +#~ "*client* needs to import :code:`flwr`, " +#~ "but also :code:`mxnet` to update the " +#~ "parameters on our MXNet model:" #~ msgstr "" #~ msgid "" -#~ "Modify the :code:`test` function so that" -#~ " it proves not only the top-1 " -#~ "(regular accuracy) but also the top-5" -#~ " accuracy?" +#~ "Implementing a Flower *client* basically " +#~ "means implementing a subclass of either" +#~ " :code:`flwr.client.Client` or " +#~ ":code:`flwr.client.NumPyClient`. Our implementation " +#~ "will be based on " +#~ ":code:`flwr.client.NumPyClient` and we'll call " +#~ "it :code:`MNISTClient`. :code:`NumPyClient` is " +#~ "slightly easier to implement than " +#~ ":code:`Client` if you use a framework" +#~ " with good NumPy interoperability (like " +#~ "PyTorch or MXNet) because it avoids " +#~ "some of the boilerplate that would " +#~ "otherwise be necessary. :code:`MNISTClient` " +#~ "needs to implement four methods, two " +#~ "methods for getting/setting model parameters," +#~ " one method for training the model," +#~ " and one method for testing the " +#~ "model:" +#~ msgstr "" + +#~ msgid "transform MXNet :code:`NDArray`'s to NumPy :code:`ndarray`'s" #~ msgstr "" #~ msgid "" -#~ "Go larger! Try to adapt the code" -#~ " to larger images and datasets. Why" -#~ " not try training on ImageNet with" -#~ " a ResNet-50?" +#~ "The challenging part is to transform " +#~ "the MXNet parameters from :code:`NDArray` " +#~ "to :code:`NumPy Arrays` to make it " +#~ "readable for Flower." #~ msgstr "" -#~ msgid "You are ready now. Enjoy learning in a federated way!" +#~ msgid "" +#~ "The two :code:`NumPyClient` methods " +#~ ":code:`fit` and :code:`evaluate` make use " +#~ "of the functions :code:`train()` and " +#~ ":code:`test()` previously defined in " +#~ ":code:`mxnet_mnist.py`. So what we really " +#~ "do here is we tell Flower through" +#~ " our :code:`NumPyClient` subclass which of" +#~ " our already defined functions to " +#~ "call for training and evaluation. We " +#~ "included type annotations to give you" +#~ " a better understanding of the data" +#~ " types that get passed around." #~ msgstr "" -#~ msgid "Differential privacy" +#~ msgid "" +#~ "Having defined data loading, model " +#~ "architecture, training, and evaluation we " +#~ "can put everything together and train" +#~ " our :code:`Sequential` model on MNIST." #~ msgstr "" #~ msgid "" -#~ "Flower provides differential privacy (DP) " -#~ "wrapper classes for the easy integration" -#~ " of the central DP guarantees " -#~ "provided by DP-FedAvg into training " -#~ "pipelines defined in any of the " -#~ "various ML frameworks that Flower is " -#~ "compatible with." +#~ "in each window (make sure that the" +#~ " server is still running before you" +#~ " do so) and see your MXNet " +#~ "project run federated learning across " +#~ "two clients. Congratulations!" #~ msgstr "" #~ msgid "" -#~ "Please note that these components are" -#~ " still experimental; the correct " -#~ "configuration of DP for a specific " -#~ "task is still an unsolved problem." +#~ "The full source code for this " +#~ "example: `MXNet: From Centralized To " +#~ "Federated (Code) " +#~ "`_. Our " +#~ "example is of course somewhat over-" +#~ "simplified because both clients load the" +#~ " exact same dataset, which isn't " +#~ "realistic. You're now prepared to " +#~ "explore this topic further. How about" +#~ " using a CNN or using a " +#~ "different dataset? How about adding more" +#~ " clients?" #~ msgstr "" #~ msgid "" -#~ "The name DP-FedAvg is misleading " -#~ "since it can be applied on top " -#~ "of any FL algorithm that conforms " -#~ "to the general structure prescribed by" -#~ " the FedOpt family of algorithms." +#~ "This guide describes how to a " +#~ "SSL-enabled secure Flower server can " +#~ "be started and how a Flower client" +#~ " can establish a secure connections " +#~ "to it." #~ msgstr "" -#~ msgid "DP-FedAvg" +#~ msgid "" +#~ "The code example comes with a " +#~ "README.md file which will explain how" +#~ " to start it. Although it is " +#~ "already SSL-enabled, it might be " +#~ "less descriptive on how. Stick to " +#~ "this guide for a deeper introduction " +#~ "to the topic." #~ msgstr "" #~ msgid "" -#~ "DP-FedAvg, originally proposed by " -#~ "McMahan et al. [mcmahan]_ and extended" -#~ " by Andrew et al. [andrew]_, is " -#~ "essentially FedAvg with the following " -#~ "modifications." +#~ "Using SSL-enabled connections requires " +#~ "certificates to be passed to the " +#~ "server and client. For the purpose " +#~ "of this guide we are going to " +#~ "generate self-signed certificates. As " +#~ "this can become quite complex we " +#~ "are going to ask you to run " +#~ "the script in :code:`examples/advanced-" +#~ "tensorflow/certificates/generate.sh`" #~ msgstr "" -#~ msgid "" -#~ "**Clipping** : The influence of each " -#~ "client's update is bounded by clipping" -#~ " it. This is achieved by enforcing" -#~ " a cap on the L2 norm of " -#~ "the update, scaling it down if " -#~ "needed." +#~ msgid "with the following command sequence:" #~ msgstr "" #~ msgid "" -#~ "**Noising** : Gaussian noise, calibrated " -#~ "to the clipping threshold, is added " -#~ "to the average computed at the " -#~ "server." +#~ "The approach how the SSL certificates" +#~ " are generated in this example can" +#~ " serve as an inspiration and starting" +#~ " point but should not be taken " +#~ "as complete for production environments. " +#~ "Please refer to other sources regarding" +#~ " the issue of correctly generating " +#~ "certificates for production environments." #~ msgstr "" #~ msgid "" -#~ "The distribution of the update norm " -#~ "has been shown to vary from " -#~ "task-to-task and to evolve as " -#~ "training progresses. This variability is " -#~ "crucial in understanding its impact on" -#~ " differential privacy guarantees, emphasizing " -#~ "the need for an adaptive approach " -#~ "[andrew]_ that continuously adjusts the " -#~ "clipping threshold to track a " -#~ "prespecified quantile of the update norm" -#~ " distribution." +#~ "In case you are a researcher you" +#~ " might be just fine using the " +#~ "self-signed certificates generated using " +#~ "the scripts which are part of this" +#~ " guide." #~ msgstr "" -#~ msgid "Simplifying Assumptions" +#~ msgid "" +#~ "We are now going to show how " +#~ "to write a sever which uses the" +#~ " previously generated scripts." #~ msgstr "" #~ msgid "" -#~ "We make (and attempt to enforce) a" -#~ " number of assumptions that must be" -#~ " satisfied to ensure that the " -#~ "training process actually realizes the " -#~ ":math:`(\\epsilon, \\delta)` guarantees the " -#~ "user has in mind when configuring " -#~ "the setup." +#~ "When providing certificates, the server " +#~ "expects a tuple of three certificates." +#~ " :code:`Path` can be used to easily" +#~ " read the contents of those files " +#~ "into byte strings, which is the " +#~ "data type :code:`start_server` expects." #~ msgstr "" #~ msgid "" -#~ "**Fixed-size subsampling** :Fixed-size " -#~ "subsamples of the clients must be " -#~ "taken at each round, as opposed to" -#~ " variable-sized Poisson subsamples." +#~ "We are now going to show how " +#~ "to write a client which uses the" +#~ " previously generated scripts:" #~ msgstr "" #~ msgid "" -#~ "**Unweighted averaging** : The contributions" -#~ " from all the clients must weighted" -#~ " equally in the aggregate to " -#~ "eliminate the requirement for the server" -#~ " to know in advance the sum of" -#~ " the weights of all clients available" -#~ " for selection." +#~ "When setting :code:`root_certificates`, the " +#~ "client expects the PEM-encoded root " +#~ "certificates as a byte string. We " +#~ "are again using :code:`Path` to simplify" +#~ " reading those as byte strings." #~ msgstr "" #~ msgid "" -#~ "**No client failures** : The set " -#~ "of available clients must stay constant" -#~ " across all rounds of training. In" -#~ " other words, clients cannot drop out" -#~ " or fail." +#~ "You should now have learned how to" +#~ " generate self-signed certificates using" +#~ " the given script, start a SSL-" +#~ "enabled server, and have a client " +#~ "establish a secure connection to it." #~ msgstr "" #~ msgid "" -#~ "The first two are useful for " -#~ "eliminating a multitude of complications " -#~ "associated with calibrating the noise to" -#~ " the clipping threshold, while the " -#~ "third one is required to comply " -#~ "with the assumptions of the privacy " -#~ "analysis." +#~ "The simplest way to get started " +#~ "with Flower is by using the " +#~ "pre-made Docker images, which you can" +#~ " find on `Docker Hub " +#~ "`_." #~ msgstr "" -#~ msgid "" -#~ "These restrictions are in line with " -#~ "constraints imposed by Andrew et al. " -#~ "[andrew]_." +#~ msgid "Flower server" #~ msgstr "" -#~ msgid "Customizable Responsibility for Noise injection" +#~ msgid "" +#~ "The command will pull the Docker " +#~ "image with the tag " +#~ "``1.7.0-py3.11-ubuntu22.04`` from Docker Hub. " +#~ "The tag contains the information which" +#~ " Flower, Python and Ubuntu is used." +#~ " In this case, it uses Flower " +#~ "1.7.0, Python 3.11 and Ubuntu 22.04. " +#~ "The ``--rm`` flag tells Docker to " +#~ "remove the container after it exits." #~ msgstr "" #~ msgid "" -#~ "In contrast to other implementations " -#~ "where the addition of noise is " -#~ "performed at the server, you can " -#~ "configure the site of noise injection" -#~ " to better match your threat model." -#~ " We provide users with the " -#~ "flexibility to set up the training " -#~ "such that each client independently adds" -#~ " a small amount of noise to the" -#~ " clipped update, with the result that" -#~ " simply aggregating the noisy updates " -#~ "is equivalent to the explicit addition" -#~ " of noise to the non-noisy " -#~ "aggregate at the server." +#~ "By default, the Flower server keeps " +#~ "state in-memory. When using the " +#~ "Docker flag ``--rm``, the state is " +#~ "not persisted between container starts. " +#~ "We will show below how to save " +#~ "the state in a file on your " +#~ "host system." #~ msgstr "" #~ msgid "" -#~ "To be precise, if we let :math:`m`" -#~ " be the number of clients sampled " -#~ "each round and :math:`\\sigma_\\Delta` be " -#~ "the scale of the total Gaussian " -#~ "noise that needs to be added to" -#~ " the sum of the model updates, " -#~ "we can use simple maths to show" -#~ " that this is equivalent to each " -#~ "client adding noise with scale " -#~ ":math:`\\sigma_\\Delta/\\sqrt{m}`." +#~ "The ``-p :`` flag tells " +#~ "Docker to map the ports " +#~ "``9091``/``9092`` of the host to " +#~ "``9091``/``9092`` of the container, allowing" +#~ " you to access the Driver API " +#~ "on ``http://localhost:9091`` and the Fleet " +#~ "API on ``http://localhost:9092``. Lastly, any" +#~ " flag that comes after the tag " +#~ "is passed to the Flower server. " +#~ "Here, we are passing the flag " +#~ "``--insecure``." #~ msgstr "" -#~ msgid "Wrapper-based approach" +#~ msgid "" +#~ "The ``--insecure`` flag enables insecure " +#~ "communication (using HTTP, not HTTPS) " +#~ "and should only be used for " +#~ "testing purposes. We strongly recommend " +#~ "enabling `SSL `_ when " +#~ "deploying to a production environment." #~ msgstr "" #~ msgid "" -#~ "Introducing DP to an existing workload" -#~ " can be thought of as adding an" -#~ " extra layer of security around it." -#~ " This inspired us to provide the " -#~ "additional server and client-side logic" -#~ " needed to make the training process" -#~ " differentially private as wrappers for " -#~ "instances of the :code:`Strategy` and " -#~ ":code:`NumPyClient` abstract classes respectively." -#~ " This wrapper-based approach has the" -#~ " advantage of being easily composable " -#~ "with other wrappers that someone might" -#~ " contribute to the Flower library in" -#~ " the future, e.g., for secure " -#~ "aggregation. Using Inheritance instead can " -#~ "be tedious because that would require" -#~ " the creation of new sub- classes " -#~ "every time a new class implementing " -#~ ":code:`Strategy` or :code:`NumPyClient` is " -#~ "defined." +#~ "You can use ``--help`` to view all" +#~ " available flags that the server " +#~ "supports:" #~ msgstr "" -#~ msgid "Server-side logic" +#~ msgid "" +#~ "If you want to persist the state" +#~ " of the server on your host " +#~ "system, all you need to do is " +#~ "specify a path where you want to" +#~ " save the file on your host " +#~ "system and a name for the database" +#~ " file. In the example below, we " +#~ "tell Docker via the flag ``-v`` to" +#~ " mount the user's home directory " +#~ "(``~/`` on your host) into the " +#~ "``/app/`` directory of the container. " +#~ "Furthermore, we use the flag " +#~ "``--database`` to specify the name of" +#~ " the database file." #~ msgstr "" #~ msgid "" -#~ "The first version of our solution " -#~ "was to define a decorator whose " -#~ "constructor accepted, among other things, " -#~ "a boolean-valued variable indicating " -#~ "whether adaptive clipping was to be " -#~ "enabled or not. We quickly realized " -#~ "that this would clutter its " -#~ ":code:`__init__()` function with variables " -#~ "corresponding to hyperparameters of adaptive" -#~ " clipping that would remain unused " -#~ "when it was disabled. A cleaner " -#~ "implementation could be achieved by " -#~ "splitting the functionality into two " -#~ "decorators, :code:`DPFedAvgFixed` and " -#~ ":code:`DPFedAvgAdaptive`, with the latter sub-" -#~ " classing the former. The constructors " -#~ "for both classes accept a boolean " -#~ "parameter :code:`server_side_noising`, which, as " -#~ "the name suggests, determines where " -#~ "noising is to be performed." +#~ "As soon as the server starts, the" +#~ " file ``state.db`` is created in the" +#~ " user's home directory on your host" +#~ " system. If the file already exists," +#~ " the server tries to restore the " +#~ "state from the file. To start the" +#~ " server with an empty database, " +#~ "simply remove the ``state.db`` file." +#~ msgstr "" + +#~ msgid "" +#~ "To enable SSL, you will need a " +#~ "CA certificate, a server certificate and" +#~ " a server private key." #~ msgstr "" #~ msgid "" -#~ "The server-side capabilities required " -#~ "for the original version of DP-" -#~ "FedAvg, i.e., the one which performed" -#~ " fixed clipping, can be completely " -#~ "captured with the help of wrapper " -#~ "logic for just the following two " -#~ "methods of the :code:`Strategy` abstract " -#~ "class." +#~ "For testing purposes, you can generate" +#~ " your own self-signed certificates. " +#~ "The `Enable SSL connections " +#~ "`_ page contains " +#~ "a section that will guide you " +#~ "through the process." #~ msgstr "" #~ msgid "" -#~ ":code:`configure_fit()` : The config " -#~ "dictionary being sent by the wrapped " -#~ ":code:`Strategy` to each client needs to" -#~ " be augmented with an additional " -#~ "value equal to the clipping threshold" -#~ " (keyed under :code:`dpfedavg_clip_norm`) and," -#~ " if :code:`server_side_noising=true`, another one" -#~ " equal to the scale of the " -#~ "Gaussian noise that needs to be " -#~ "added at the client (keyed under " -#~ ":code:`dpfedavg_noise_stddev`). This entails " -#~ "*post*-processing of the results returned " -#~ "by the wrappee's implementation of " -#~ ":code:`configure_fit()`." +#~ "Assuming all files we need are in" +#~ " the local ``certificates`` directory, we" +#~ " can use the flag ``-v`` to " +#~ "mount the local directory into the " +#~ "``/app/`` directory of the container. " +#~ "This allows the server to access " +#~ "the files within the container. Finally," +#~ " we pass the names of the " +#~ "certificates to the server with the " +#~ "``--certificates`` flag." +#~ msgstr "" + +#~ msgid "Using a different Flower or Python version" #~ msgstr "" #~ msgid "" -#~ ":code:`aggregate_fit()`: We check whether any" -#~ " of the sampled clients dropped out" -#~ " or failed to upload an update " -#~ "before the round timed out. In " -#~ "that case, we need to abort the" -#~ " current round, discarding any successful" -#~ " updates that were received, and move" -#~ " on to the next one. On the " -#~ "other hand, if all clients responded " -#~ "successfully, we must force the " -#~ "averaging of the updates to happen " -#~ "in an unweighted manner by intercepting" -#~ " the :code:`parameters` field of " -#~ ":code:`FitRes` for each received update " -#~ "and setting it to 1. Furthermore, " -#~ "if :code:`server_side_noising=true`, each update " -#~ "is perturbed with an amount of " -#~ "noise equal to what it would have" -#~ " been subjected to had client-side" -#~ " noising being enabled. This entails " -#~ "*pre*-processing of the arguments to " -#~ "this method before passing them on " -#~ "to the wrappee's implementation of " -#~ ":code:`aggregate_fit()`." +#~ "If you want to use a different " +#~ "version of Flower or Python, you " +#~ "can do so by changing the tag. " +#~ "All versions we provide are available" +#~ " on `Docker Hub " +#~ "`_." #~ msgstr "" #~ msgid "" -#~ "We can't directly change the aggregation" -#~ " function of the wrapped strategy to" -#~ " force it to add noise to the" -#~ " aggregate, hence we simulate client-" -#~ "side noising to implement server-side" -#~ " noising." +#~ "The following command returns the " +#~ "current image hash referenced by the " +#~ "``server:1.7.0-py3.11-ubuntu22.04`` tag:" +#~ msgstr "" + +#~ msgid "Next, we can pin the hash when running a new server container:" #~ msgstr "" #~ msgid "" -#~ "These changes have been put together " -#~ "into a class called :code:`DPFedAvgFixed`, " -#~ "whose constructor accepts the strategy " -#~ "being decorated, the clipping threshold " -#~ "and the number of clients sampled " -#~ "every round as compulsory arguments. The" -#~ " user is expected to specify the " -#~ "clipping threshold since the order of" -#~ " magnitude of the update norms is " -#~ "highly dependent on the model being " -#~ "trained and providing a default value" -#~ " would be misleading. The number of" -#~ " clients sampled at every round is" -#~ " required to calculate the amount of" -#~ " noise that must be added to " -#~ "each individual update, either by the" -#~ " server or the clients." +#~ "QUICKSTART TUTORIALS: :doc:`PyTorch ` | :doc:`TensorFlow " +#~ "` | :doc:`🤗 " +#~ "Transformers ` " +#~ "| :doc:`JAX ` |" +#~ " :doc:`Pandas ` " +#~ "| :doc:`fastai `" +#~ " | :doc:`PyTorch Lightning ` | :doc:`MXNet " +#~ "` | :doc" +#~ ":`scikit-learn `" +#~ " | :doc:`XGBoost ` | :doc:`Android ` | :doc:`iOS `" +#~ msgstr "" + +#~ msgid "flower-driver-api" +#~ msgstr "" + +#~ msgid "flower-fleet-api" #~ msgstr "" #~ msgid "" -#~ "The additional functionality required to " -#~ "facilitate adaptive clipping has been " -#~ "provided in :code:`DPFedAvgAdaptive`, a " -#~ "subclass of :code:`DPFedAvgFixed`. It " -#~ "overrides the above-mentioned methods to" -#~ " do the following." +#~ "Bases: :py:class:`~flwr.common.record.typeddict.TypedDict`\\ " +#~ "[:py:class:`str`, :py:obj:`~typing.Union`\\ " +#~ "[:py:class:`int`, :py:class:`float`, :py:class:`str`, " +#~ ":py:class:`bytes`, :py:class:`bool`, " +#~ ":py:class:`~typing.List`\\ [:py:class:`int`], " +#~ ":py:class:`~typing.List`\\ [:py:class:`float`], " +#~ ":py:class:`~typing.List`\\ [:py:class:`str`], " +#~ ":py:class:`~typing.List`\\ [:py:class:`bytes`], " +#~ ":py:class:`~typing.List`\\ [:py:class:`bool`]]]" #~ msgstr "" #~ msgid "" -#~ ":code:`configure_fit()` : It intercepts the" -#~ " config dict returned by " -#~ ":code:`super.configure_fit()` to add the " -#~ "key-value pair " -#~ ":code:`dpfedavg_adaptive_clip_enabled:True` to it, " -#~ "which the client interprets as an " -#~ "instruction to include an indicator bit" -#~ " (1 if update norm <= clipping " -#~ "threshold, 0 otherwise) in the results" -#~ " returned by it." +#~ ":py:obj:`create_error_reply " +#~ "`\\ \\(error\\, " +#~ "ttl\\)" #~ msgstr "" #~ msgid "" -#~ ":code:`aggregate_fit()` : It follows a " -#~ "call to :code:`super.aggregate_fit()` with one" -#~ " to :code:`__update_clip_norm__()`, a procedure" -#~ " which adjusts the clipping threshold " -#~ "on the basis of the indicator bits" -#~ " received from the sampled clients." +#~ ":py:obj:`create_reply `\\ " +#~ "\\(content\\, ttl\\)" #~ msgstr "" -#~ msgid "Client-side logic" +#~ msgid "" +#~ "Bases: :py:class:`~flwr.common.record.typeddict.TypedDict`\\ " +#~ "[:py:class:`str`, :py:obj:`~typing.Union`\\ " +#~ "[:py:class:`int`, :py:class:`float`, " +#~ ":py:class:`~typing.List`\\ [:py:class:`int`], " +#~ ":py:class:`~typing.List`\\ [:py:class:`float`]]]" +#~ msgstr "" + +#~ msgid "Run Flower server (Driver API and Fleet API)." #~ msgstr "" #~ msgid "" -#~ "The client-side capabilities required " -#~ "can be completely captured through " -#~ "wrapper logic for just the :code:`fit()`" -#~ " method of the :code:`NumPyClient` abstract" -#~ " class. To be precise, we need " -#~ "to *post-process* the update computed" -#~ " by the wrapped client to clip " -#~ "it, if necessary, to the threshold " -#~ "value supplied by the server as " -#~ "part of the config dictionary. In " -#~ "addition to this, it may need to" -#~ " perform some extra work if either" -#~ " (or both) of the following keys " -#~ "are also present in the dict." +#~ ":py:obj:`start_driver `\\ " +#~ "\\(\\*\\[\\, server\\_address\\, server\\, ...\\]\\)" +#~ msgstr "" + +#~ msgid "Start a Flower Driver API server." #~ msgstr "" #~ msgid "" -#~ ":code:`dpfedavg_noise_stddev` : Generate and " -#~ "add the specified amount of noise " -#~ "to the clipped update." +#~ ":py:obj:`Driver `\\ " +#~ "\\(\\[driver\\_service\\_address\\, ...\\]\\)" +#~ msgstr "" + +#~ msgid "`Driver` class provides an interface to the Driver API." #~ msgstr "" #~ msgid "" -#~ ":code:`dpfedavg_adaptive_clip_enabled` : Augment the" -#~ " metrics dict in the :code:`FitRes` " -#~ "object being returned to the server " -#~ "with an indicator bit, calculated as " -#~ "described earlier." +#~ "The IPv4 or IPv6 address of the" +#~ " Driver API server. Defaults to " +#~ "`\"[::]:9091\"`." #~ msgstr "" -#~ msgid "Performing the :math:`(\\epsilon, \\delta)` analysis" +#~ msgid ":py:obj:`close `\\ \\(\\)" +#~ msgstr "" + +#~ msgid "Disconnect from the SuperLink if connected." #~ msgstr "" #~ msgid "" -#~ "Assume you have trained for :math:`n`" -#~ " rounds with sampling fraction :math:`q`" -#~ " and noise multiplier :math:`z`. In " -#~ "order to calculate the :math:`\\epsilon` " -#~ "value this would result in for a" -#~ " particular :math:`\\delta`, the following " -#~ "script may be used." +#~ ":py:obj:`create_message `\\" +#~ " \\(content\\, message\\_type\\, ...\\)" #~ msgstr "" #~ msgid "" -#~ "McMahan et al. \"Learning Differentially " -#~ "Private Recurrent Language Models.\" " -#~ "International Conference on Learning " -#~ "Representations (ICLR), 2017." +#~ "Time-to-live for the round trip" +#~ " of this message, i.e., the time " +#~ "from sending this message to receiving" +#~ " a reply. It specifies the duration" +#~ " for which the message and its " +#~ "potential reply are considered valid." +#~ msgstr "" + +#~ msgid "start\\_driver" #~ msgstr "" #~ msgid "" -#~ "Andrew, Galen, et al. \"Differentially " -#~ "Private Learning with Adaptive Clipping.\" " -#~ "Advances in Neural Information Processing " -#~ "Systems (NeurIPS), 2021." +#~ "The IPv4 or IPv6 address of the" +#~ " Driver API server. Defaults to " +#~ "`\"[::]:8080\"`." #~ msgstr "" #~ msgid "" -#~ "This can be achieved by customizing " -#~ "an existing strategy or by `implementing" -#~ " a custom strategy from scratch " -#~ "`_. Here's a nonsensical " -#~ "example that customizes :code:`FedAvg` by " -#~ "adding a custom ``\"hello\": \"world\"`` " -#~ "configuration key/value pair to the " -#~ "config dict of a *single client* " -#~ "(only the first client in the " -#~ "list, the other clients in this " -#~ "round to not receive this \"special\"" -#~ " config value):" +#~ "A server implementation, either " +#~ "`flwr.server.Server` or a subclass thereof." +#~ " If no instance is provided, then " +#~ "`start_driver` will create one." #~ msgstr "" #~ msgid "" -#~ "More sophisticated implementations can use " -#~ ":code:`configure_fit` to implement custom " -#~ "client selection logic. A client will" -#~ " only participate in a round if " -#~ "the corresponding :code:`ClientProxy` is " -#~ "included in the the list returned " -#~ "from :code:`configure_fit`." +#~ "An implementation of the class " +#~ "`flwr.server.ClientManager`. If no implementation" +#~ " is provided, then `start_driver` will " +#~ "use `flwr.server.SimpleClientManager`." +#~ msgstr "" + +#~ msgid "The Driver object to use." +#~ msgstr "" + +#~ msgid "Starting a driver that connects to an insecure server:" +#~ msgstr "" + +#~ msgid "Starting a driver that connects to an SSL-enabled server:" #~ msgstr "" #~ msgid "" -#~ "More sophisticated implementations can use " -#~ ":code:`configure_evaluate` to implement custom " -#~ "client selection logic. A client will" -#~ " only participate in a round if " -#~ "the corresponding :code:`ClientProxy` is " -#~ "included in the the list returned " -#~ "from :code:`configure_evaluate`." +#~ ":py:obj:`run_simulation_from_cli " +#~ "`\\ \\(\\)" +#~ msgstr "" + +#~ msgid "Run Simulation Engine from the CLI." +#~ msgstr "" + +#~ msgid "run\\_simulation\\_from\\_cli" #~ msgstr "" #~ msgid "" -#~ "`How to run Flower using Docker " -#~ "`_" +#~ "Check out this Federated Learning " +#~ "quickstart tutorial for using Flower " +#~ "with MXNet to train a Sequential " +#~ "model on MNIST." +#~ msgstr "" + +#~ msgid "Quickstart MXNet" #~ msgstr "" #~ msgid "" -#~ "Ray Dashboard: ``_" +#~ "MXNet is no longer maintained and " +#~ "has been moved into `Attic " +#~ "`_. As a " +#~ "result, we would encourage you to " +#~ "use other ML frameworks alongside " +#~ "Flower, for example, PyTorch. This " +#~ "tutorial might be removed in future " +#~ "versions of Flower." #~ msgstr "" #~ msgid "" -#~ "Ray Metrics: ``_" +#~ "In this tutorial, we will learn " +#~ "how to train a :code:`Sequential` model" +#~ " on MNIST using Flower and MXNet." #~ msgstr "" -#~ msgid "Enjoy building more robust and flexible ``ClientApp``s with mods!" +#~ msgid "Since we want to use MXNet, let's go ahead and install it:" #~ msgstr "" #~ msgid "" -#~ ":py:obj:`ClientApp `\\ " -#~ "\\(client\\_fn\\[\\, mods\\]\\)" +#~ "Now that we have all our " +#~ "dependencies installed, let's run a " +#~ "simple distributed training with two " +#~ "clients and one server. Our training " +#~ "procedure and network architecture are " +#~ "based on MXNet´s `Hand-written Digit " +#~ "Recognition tutorial " +#~ "`_." #~ msgstr "" -#~ msgid ":py:obj:`flwr.server.driver `\\" +#~ msgid "" +#~ "In a file called :code:`client.py`, " +#~ "import Flower and MXNet related " +#~ "packages:" #~ msgstr "" -#~ msgid "Flower driver SDK." +#~ msgid "In addition, define the device allocation in MXNet with:" #~ msgstr "" -#~ msgid "driver" +#~ msgid "" +#~ "We use MXNet to load MNIST, a " +#~ "popular image classification dataset of " +#~ "handwritten digits for machine learning. " +#~ "The MXNet utility :code:`mx.test_utils.get_mnist()`" +#~ " downloads the training and test " +#~ "data." #~ msgstr "" #~ msgid "" -#~ ":py:obj:`start_driver `\\ " -#~ "\\(\\*\\[\\, server\\_address\\, server\\, ...\\]\\)" +#~ "Define the training and loss with " +#~ "MXNet. We train the model by " +#~ "looping over the dataset, measure the" +#~ " corresponding loss, and optimize it." #~ msgstr "" #~ msgid "" -#~ ":py:obj:`Driver `\\ " -#~ "\\(\\[driver\\_service\\_address\\, ...\\]\\)" +#~ "Next, we define the validation of " +#~ "our machine learning model. We loop " +#~ "over the test set and measure both" +#~ " loss and accuracy on the test " +#~ "set." #~ msgstr "" #~ msgid "" -#~ ":py:obj:`GrpcDriver `\\ " -#~ "\\(\\[driver\\_service\\_address\\, ...\\]\\)" +#~ "After defining the training and testing" +#~ " of a MXNet machine learning model," +#~ " we use these functions to implement" +#~ " a Flower client." #~ msgstr "" -#~ msgid "`GrpcDriver` provides access to the gRPC Driver API/service." +#~ msgid "Our Flower clients will use a simple :code:`Sequential` model:" #~ msgstr "" -#~ msgid ":py:obj:`get_nodes `\\ \\(\\)" +#~ msgid "" +#~ "After loading the dataset with " +#~ ":code:`load_data()` we perform one forward " +#~ "propagation to initialize the model and" +#~ " model parameters with :code:`model(init)`. " +#~ "Next, we implement a Flower client." #~ msgstr "" #~ msgid "" -#~ ":py:obj:`pull_task_res " -#~ "`\\ \\(task\\_ids\\)" +#~ "Flower provides a convenience class " +#~ "called :code:`NumPyClient` which makes it " +#~ "easier to implement the :code:`Client` " +#~ "interface when your workload uses MXNet." +#~ " Implementing :code:`NumPyClient` usually means" +#~ " defining the following methods " +#~ "(:code:`set_parameters` is optional though):" #~ msgstr "" -#~ msgid "Get task results." +#~ msgid "They can be implemented in the following way:" #~ msgstr "" #~ msgid "" -#~ ":py:obj:`push_task_ins " -#~ "`\\ " -#~ "\\(task\\_ins\\_list\\)" +#~ "We can now create an instance of" +#~ " our class :code:`MNISTClient` and add " +#~ "one line to actually run this " +#~ "client:" #~ msgstr "" -#~ msgid "Schedule tasks." +#~ msgid "" +#~ "That's it for the client. We only" +#~ " have to implement :code:`Client` or " +#~ ":code:`NumPyClient` and call " +#~ ":code:`fl.client.start_client()` or " +#~ ":code:`fl.client.start_numpy_client()`. The string " +#~ ":code:`\"0.0.0.0:8080\"` tells the client " +#~ "which server to connect to. In our" +#~ " case we can run the server and" +#~ " the client on the same machine, " +#~ "therefore we use :code:`\"0.0.0.0:8080\"`. If" +#~ " we run a truly federated workload" +#~ " with the server and clients running" +#~ " on different machines, all that " +#~ "needs to change is the " +#~ ":code:`server_address` we pass to the " +#~ "client." #~ msgstr "" -#~ msgid "GrpcDriver" +#~ msgid "" +#~ "With both client and server ready, " +#~ "we can now run everything and see" +#~ " federated learning in action. Federated" +#~ " learning systems usually have a " +#~ "server and multiple clients. We " +#~ "therefore have to start the server " +#~ "first:" #~ msgstr "" -#~ msgid ":py:obj:`connect `\\ \\(\\)" +#~ msgid "" +#~ "Congratulations! You've successfully built and" +#~ " run your first federated learning " +#~ "system. The full `source code " +#~ "`_ for this example can " +#~ "be found in :code:`examples/quickstart-mxnet`." #~ msgstr "" -#~ msgid "Connect to the Driver API." +#~ msgid "Sets the parameters of a :code:`sklean` LogisticRegression model" #~ msgstr "" - -#~ msgid "" -#~ ":py:obj:`create_run " -#~ "`\\ \\(req\\)" + +#~ msgid ":code:`load_mnist()`" #~ msgstr "" -#~ msgid "Request for run ID." +#~ msgid "Loads the MNIST dataset using OpenML" #~ msgstr "" -#~ msgid "" -#~ ":py:obj:`disconnect " -#~ "`\\ \\(\\)" +#~ msgid ":code:`shuffle()`" #~ msgstr "" -#~ msgid "Disconnect from the Driver API." +#~ msgid "Shuffles data and its label" #~ msgstr "" -#~ msgid "" -#~ ":py:obj:`get_nodes `\\" -#~ " \\(req\\)" +#~ msgid ":code:`partition()`" #~ msgstr "" -#~ msgid "Get client IDs." +#~ msgid "Splits datasets into a number of partitions" #~ msgstr "" #~ msgid "" -#~ ":py:obj:`pull_task_res " -#~ "`\\ \\(req\\)" +#~ "We load the MNIST dataset from " +#~ "`OpenML " +#~ "`_, a" +#~ " popular image classification dataset of" +#~ " handwritten digits for machine learning." +#~ " The utility :code:`utils.load_mnist()` downloads" +#~ " the training and test data. The " +#~ "training set is split afterwards into" +#~ " 10 partitions with :code:`utils.partition()`." #~ msgstr "" #~ msgid "" -#~ ":py:obj:`push_task_ins " -#~ "`\\ \\(req\\)" +#~ "The number of federated learning rounds" +#~ " is set in :code:`fit_round()` and " +#~ "the evaluation is defined in " +#~ ":code:`get_evaluate_fn()`. The evaluation function" +#~ " is called after each federated " +#~ "learning round and gives you information" +#~ " about loss and accuracy." #~ msgstr "" -#~ msgid "" -#~ "Optionally specify the type of actor " -#~ "to use. The actor object, which " -#~ "persists throughout the simulation, will " -#~ "be the process in charge of " -#~ "running the clients' jobs (i.e. their" -#~ " `fit()` method)." +#~ msgid "Let's get stated!" #~ msgstr "" #~ msgid "" -#~ "Much effort went into a completely " -#~ "restructured Flower docs experience. The " -#~ "documentation on [flower.ai/docs](flower.ai/docs) is" -#~ " now divided into Flower Framework, " -#~ "Flower Baselines, Flower Android SDK, " -#~ "Flower iOS SDK, and code example " -#~ "projects." +#~ "We now have a list of ten " +#~ "training sets and ten validation sets" +#~ " (``trainloaders`` and ``valloaders``) " +#~ "representing the data of ten different" +#~ " organizations. Each ``trainloader``/``valloader`` " +#~ "pair contains 4500 training examples and" +#~ " 500 validation examples. There's also " +#~ "a single ``testloader`` (we did not " +#~ "split the test set). Again, this " +#~ "is only necessary for building research" +#~ " or educational systems, actual federated" +#~ " learning systems have their data " +#~ "naturally distributed across multiple " +#~ "partitions." #~ msgstr "" -#~ msgid "" -#~ "The first preview release of Flower " -#~ "Baselines has arrived! We're kickstarting " -#~ "Flower Baselines with implementations of " -#~ "FedOpt (FedYogi, FedAdam, FedAdagrad), FedBN," -#~ " and FedAvgM. Check the documentation " -#~ "on how to use [Flower " -#~ "Baselines](https://flower.ai/docs/using-baselines.html). " -#~ "With this first preview release we're" -#~ " also inviting the community to " -#~ "[contribute their own " -#~ "baselines](https://flower.ai/docs/contributing-baselines.html)." +#~ msgid "|2b5c62c529f6416f840c594cce062fbb|" #~ msgstr "" -#~ msgid "" -#~ "Flower usage examples used to be " -#~ "bundled with Flower in a package " -#~ "called ``flwr_example``. We are migrating " -#~ "those examples to standalone projects to" -#~ " make them easier to use. All " -#~ "new examples are based in the " -#~ "directory `examples " -#~ "`_." +#~ msgid "|90b334680cb7467d9a04d39b8e8dca9f|" #~ msgstr "" -#~ msgid "The following examples are available as standalone projects." +#~ msgid "|65764ceee89f4335bfd93fd0b115e831|" #~ msgstr "" -#~ msgid "Quickstart TensorFlow/Keras" +#~ msgid "|d97319ec28bb407ea0ab9705e38f3bcf|" #~ msgstr "" -#~ msgid "" -#~ "`Quickstart TensorFlow (Tutorial) " -#~ "`_" +#~ msgid "|11e95ac83a8548d8b3505b4663187d07|" #~ msgstr "" -#~ msgid "" -#~ "`Quickstart PyTorch (Tutorial) " -#~ "`_" +#~ msgid "|1dab2f3a23674abc8a6731f20fa10730|" #~ msgstr "" -#~ msgid "" -#~ "`PyTorch: From Centralized To Federated " -#~ "(Tutorial) `_" +#~ msgid "|7f0ee162da38450788493a21627306f7|" #~ msgstr "" -#~ msgid "Legacy Examples (`flwr_example`)" +#~ msgid "|296a1fb72c514b23b3d8905ff0ff98c6|" #~ msgstr "" -#~ msgid "" -#~ "The useage examples in `flwr_example` " -#~ "are deprecated and will be removed " -#~ "in the future. New examples are " -#~ "provided as standalone projects in " -#~ "`examples `_." +#~ msgid "|5b1408eec0d746cdb91162a9107b6089|" #~ msgstr "" -#~ msgid "Extra Dependencies" +#~ msgid "|aef19f4b122c4e8d9f4c57f99bcd5dd2|" #~ msgstr "" -#~ msgid "" -#~ "The core Flower framework keeps a " -#~ "minimal set of dependencies. The " -#~ "examples demonstrate Flower in the " -#~ "context of different machine learning " -#~ "frameworks, so additional dependencies need" -#~ " to be installed before an example" -#~ " can be run." +#~ msgid "|2881a86d8fc54ba29d96b29fc2819f4a|" #~ msgstr "" -#~ msgid "For PyTorch examples::" +#~ msgid "|ec1fe880237247e0975f52766775ab84|" #~ msgstr "" -#~ msgid "For TensorFlow examples::" +#~ msgid "|9fdf048ed58d4467b2718cdf4aaf1ec3|" #~ msgstr "" -#~ msgid "For both PyTorch and TensorFlow examples::" +#~ msgid "|ff726bc5505e432388ee2fdd6ef420b9|" #~ msgstr "" #~ msgid "" -#~ "Please consult :code:`pyproject.toml` for a" -#~ " full list of possible extras " -#~ "(section :code:`[tool.poetry.extras]`)." +#~ "Currently, Flower provides two images, a" +#~ " ``base`` image and a ``superlink`` " +#~ "image. The base image, as the name" +#~ " suggests, contains basic dependencies that" +#~ " the SuperLink needs. This includes " +#~ "system dependencies, Python and Python " +#~ "tools. The SuperLink image is based " +#~ "on the base image, but it " +#~ "additionally installs the SuperLink using " +#~ "``pip``." #~ msgstr "" +#~ "Atualmente, Flower fornece duas imagens, " +#~ "uma imagem base e uma imagem de" +#~ " servidor. Também haverá uma imagem " +#~ "de cliente em breve. A imagem " +#~ "base, como o nome sugere, contém " +#~ "dependências básicas que tanto o " +#~ "servidor quanto o cliente precisam. Isso" +#~ " inclui dependências do sistema, Python " +#~ "e ferramentas Python. A imagem do " +#~ "servidor é baseada na imagem base, " +#~ "mas também instala o servidor Flower " +#~ "usando ``pip```." -#~ msgid "PyTorch Examples" +#~ msgid "``3.11``" +#~ msgstr "``3.11``" + +#~ msgid "Defaults to ``22.04``." +#~ msgstr "Como padrão ``22.04``." + +#~ msgid "Building the SuperLink image" +#~ msgstr "Construindo a imagem do servidor" + +#~ msgid "Defaults to ``flwr/base``." +#~ msgstr "Pré-definido para ``flwr/server``." + +#~ msgid "The Python version of the base image." +#~ msgstr "O nome do repositório da imagem base." + +#~ msgid "Defaults to ``py3.11``." +#~ msgstr "Como padrão ``22.04``." + +#~ msgid "Defaults to ``ubuntu22.04``." +#~ msgstr "Pré-definido para ``py3.11-ubuntu22.04``." + +#~ msgid "The PyPI package to install." #~ msgstr "" +#~ msgid "Defaults to ``flwr``." +#~ msgstr "Pré-definido para ``flwr/server``." + #~ msgid "" -#~ "Our PyTorch examples are based on " -#~ "PyTorch 1.7. They should work with " -#~ "other releases as well. So far, we" -#~ " provide the following examples." -#~ msgstr "" +#~ "The name of image is ``flwr_superlink``" +#~ " and the tag ``0.1.0``. Remember that" +#~ " the build arguments as well as " +#~ "the name and tag can be adapted" +#~ " to your needs. These values serve" +#~ " as examples only." +#~ msgstr "" +#~ "O nome da imagem é ``flwr_server`` " +#~ "e a tag ``0.1.0``. Lembre-se que" +#~ " os argumentos de compilação, bem " +#~ "como o nome e a tag podem " +#~ "ser adaptados às suas necessidades. " +#~ "Esses valores servem apenas como " +#~ "exemplos." + +#~ msgid "Creating New Messages" +#~ msgstr "Criando novas mensagens" -#~ msgid "CIFAR-10 Image Classification" +#~ msgid "" +#~ "This is a simple guide for " +#~ "creating a new type of message " +#~ "between the server and clients in " +#~ "Flower." #~ msgstr "" #~ msgid "" -#~ "`CIFAR-10 and CIFAR-100 " -#~ "`_ are " -#~ "popular RGB image datasets. The Flower" -#~ " CIFAR-10 example uses PyTorch to " -#~ "train a simple CNN classifier in a" -#~ " federated learning setup with two " -#~ "clients." +#~ "Let's suppose we have the following " +#~ "example functions in :code:`server.py` and " +#~ ":code:`numpy_client.py`..." #~ msgstr "" -#~ msgid "First, start a Flower server:" +#~ msgid "Server's side:" #~ msgstr "" -#~ msgid "$ ./src/py/flwr_example/pytorch_cifar/run-server.sh" +#~ msgid "Client's side:" #~ msgstr "" -#~ msgid "Then, start the two clients in a new terminal window:" +#~ msgid "" +#~ "Let's now see what we need to " +#~ "implement in order to get this " +#~ "simple function between the server and" +#~ " client to work!" #~ msgstr "" -#~ msgid "$ ./src/py/flwr_example/pytorch_cifar/run-clients.sh" +#~ msgid "Message Types for Protocol Buffers" #~ msgstr "" -#~ msgid "For more details, see :code:`src/py/flwr_example/pytorch_cifar`." +#~ msgid "" +#~ "The first thing we need to do " +#~ "is to define a message type for" +#~ " the RPC system in :code:`transport.proto`." +#~ " Note that we have to do it " +#~ "for both the request and response " +#~ "messages. For more details on the " +#~ "syntax of proto3, please see the " +#~ "`official documentation `_." #~ msgstr "" -#~ msgid "ImageNet-2012 Image Classification" +#~ msgid "Within the :code:`ServerMessage` block:" +#~ msgstr "" + +#~ msgid "Within the ClientMessage block:" #~ msgstr "" #~ msgid "" -#~ "`ImageNet-2012 `_ is " -#~ "one of the major computer vision " -#~ "datasets. The Flower ImageNet example " -#~ "uses PyTorch to train a ResNet-18 " -#~ "classifier in a federated learning setup" -#~ " with ten clients." +#~ "Make sure to also add a field " +#~ "of the newly created message type " +#~ "in :code:`oneof msg`." #~ msgstr "" -#~ msgid "$ ./src/py/flwr_example/pytorch_imagenet/run-server.sh" +#~ msgid "Once that is done, we will compile the file with:" #~ msgstr "" -#~ msgid "$ ./src/py/flwr_example/pytorch_imagenet/run-clients.sh" +#~ msgid "If it compiles successfully, you should see the following message:" #~ msgstr "" -#~ msgid "For more details, see :code:`src/py/flwr_example/pytorch_imagenet`." +#~ msgid "Serialization and Deserialization Functions" #~ msgstr "" -#~ msgid "TensorFlow Examples" +#~ msgid "" +#~ "Our next step is to add functions" +#~ " to serialize and deserialize Python " +#~ "datatypes to or from our defined " +#~ "RPC message types. You should add " +#~ "these functions in :code:`serde.py`." +#~ msgstr "" + +#~ msgid "The four functions:" +#~ msgstr "" + +#~ msgid "Sending the Message from the Server" #~ msgstr "" #~ msgid "" -#~ "Our TensorFlow examples are based on " -#~ "TensorFlow 2.0 or newer. So far, " -#~ "we provide the following examples." +#~ "Now write the request function in " +#~ "your Client Proxy class (e.g., " +#~ ":code:`grpc_client_proxy.py`) using the serde " +#~ "functions you just created:" #~ msgstr "" -#~ msgid "Fashion-MNIST Image Classification" +#~ msgid "Receiving the Message by the Client" #~ msgstr "" #~ msgid "" -#~ "`Fashion-MNIST `_ is often used as " -#~ "the \"Hello, world!\" of machine " -#~ "learning. We follow this tradition and" -#~ " provide an example which samples " -#~ "random local datasets from Fashion-MNIST" -#~ " and trains a simple image " -#~ "classification model over those partitions." +#~ "Last step! Modify the code in " +#~ ":code:`message_handler.py` to check the field" +#~ " of your message and call the " +#~ ":code:`example_response` function. Remember to " +#~ "use the serde functions!" #~ msgstr "" -#~ msgid "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-server.sh" +#~ msgid "Within the handle function:" #~ msgstr "" -#~ msgid "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-clients.sh" +#~ msgid "And add a new function:" +#~ msgstr "" + +#~ msgid "Hopefully, when you run your program you will get the intended result!" #~ msgstr "" #~ msgid "" -#~ "For more details, see " -#~ ":code:`src/py/flwr_example/tensorflow_fashion_mnist`." +#~ "The simplest way to get started " +#~ "with Flower is by using the " +#~ "pre-made Docker images, which you can" +#~ " find on `Docker Hub " +#~ "`__." #~ msgstr "" -#~ msgid ":fa:`eye,mr-1` Can Flower run on Juptyter Notebooks / Google Colab?" +#~ msgid "" +#~ "If you want to persist the state" +#~ " of the SuperLink on your host " +#~ "system, all you need to do is " +#~ "specify a path where you want to" +#~ " save the file on your host " +#~ "system and a name for the database" +#~ " file. In the example below, we " +#~ "tell Docker via the flag ``--volume``" +#~ " to mount the user's home directory" +#~ " (``~/`` on your host) into the " +#~ "``/app/`` directory of the container. " +#~ "Furthermore, we use the flag " +#~ "``--database`` to specify the name of" +#~ " the database file." #~ msgstr "" #~ msgid "" -#~ "`Flower meets KOSMoS `_." +#~ "As soon as the SuperLink starts, " +#~ "the file ``state.db`` is created in " +#~ "the user's home directory on your " +#~ "host system. If the file already " +#~ "exists, the SuperLink tries to restore" +#~ " the state from the file. To " +#~ "start the SuperLink with an empty " +#~ "database, simply remove the ``state.db`` " +#~ "file." #~ msgstr "" #~ msgid "" -#~ "If you want to check out " -#~ "everything put together, you should " -#~ "check out the full code example: " -#~ "[https://github.com/adap/flower/tree/main/examples/quickstart-" -#~ "huggingface](https://github.com/adap/flower/tree/main/examples" -#~ "/quickstart-huggingface)." +#~ "Assuming all files we need are in" +#~ " the local ``certificates`` directory, we" +#~ " can use the flag ``--volume`` to " +#~ "mount the local directory into the " +#~ "``/app/`` directory of the container. " +#~ "This allows the SuperLink to access " +#~ "the files within the container. Finally," +#~ " we pass the names of the " +#~ "certificates to the SuperLink with the" +#~ " ``--certificates`` flag." #~ msgstr "" #~ msgid "" -#~ "First of all, for running the " -#~ "Flower Python server, it is recommended" -#~ " to create a virtual environment and" -#~ " run everything within a `virtualenv " -#~ "`_. " -#~ "For the Flower client implementation in" -#~ " iOS, it is recommended to use " -#~ "Xcode as our IDE." +#~ "``--server 192.168.1.100:9092``: This option " +#~ "specifies the address of the SuperLinks" +#~ " Fleet" #~ msgstr "" #~ msgid "" -#~ "Since CoreML does not allow the " -#~ "model parameters to be seen before " -#~ "training, and accessing the model " -#~ "parameters during or after the training" -#~ " can only be done by specifying " -#~ "the layer name, we need to know" -#~ " this informations beforehand, through " -#~ "looking at the model specification, " -#~ "which are written as proto files. " -#~ "The implementation can be seen in " -#~ ":code:`MLModelInspect`." +#~ "Assuming the certificate already exists " +#~ "locally, we can use the flag " +#~ "``--volume`` to mount the local " +#~ "certificate into the container's ``/app/`` " +#~ "directory. This allows the SuperNode to" +#~ " access the certificate within the " +#~ "container. Use the ``--certificates`` flag " +#~ "when starting the container." #~ msgstr "" #~ msgid "" -#~ "After we have all of the necessary" -#~ " informations, let's create our Flower " -#~ "client." +#~ "``--server 192.168.1.100:9091``: This option " +#~ "specifies the address of the SuperLinks" +#~ " Driver" #~ msgstr "" #~ msgid "" -#~ "MXNet is no longer maintained and " -#~ "has been moved into `Attic " -#~ "`_. As a " -#~ "result, we would encourage you to " -#~ "use other ML frameworks alongise Flower," -#~ " for example, PyTorch. This tutorial " -#~ "might be removed in future versions " -#~ "of Flower." +#~ "Assuming the certificate already exists " +#~ "locally, we can use the flag " +#~ "``--volume`` to mount the local " +#~ "certificate into the container's ``/app/`` " +#~ "directory. This allows the ServerApp to" +#~ " access the certificate within the " +#~ "container. Use the ``--certificates`` flag " +#~ "when starting the container." #~ msgstr "" #~ msgid "" -#~ "It is recommended to create a " -#~ "virtual environment and run everything " -#~ "within this `virtualenv `_." +#~ "If you want to use a different " +#~ "version of Flower, for example Flower" +#~ " nightly, you can do so by " +#~ "changing the tag. All available versions" +#~ " are on `Docker Hub " +#~ "`__." #~ msgstr "" #~ msgid "" -#~ "First of all, it is recommended to" -#~ " create a virtual environment and run" -#~ " everything within a `virtualenv " -#~ "`_." +#~ "Here's another example to start with " +#~ "HTTPS. Use the ``--certificates`` command " +#~ "line argument to pass paths to (CA" +#~ " certificate, server certificate, and " +#~ "server private key)." #~ msgstr "" -#~ msgid "Since we want to use scikt-learn, let's go ahead and install it:" +#~ msgid ":py:obj:`run_driver_api `\\ \\(\\)" #~ msgstr "" -#~ msgid "" -#~ "We load the MNIST dataset from " -#~ "`OpenML `_, a popular" -#~ " image classification dataset of " -#~ "handwritten digits for machine learning. " -#~ "The utility :code:`utils.load_mnist()` downloads " -#~ "the training and test data. The " -#~ "training set is split afterwards into" -#~ " 10 partitions with :code:`utils.partition()`." +#~ msgid "Run Flower server (Driver API)." #~ msgstr "" -#~ msgid "" -#~ "Now that you have known how " -#~ "federated XGBoost work with Flower, it's" -#~ " time to run some more comprehensive" -#~ " experiments by customising the " -#~ "experimental settings. In the xgboost-" -#~ "comprehensive example (`full code " -#~ "`_), we provide more options " -#~ "to define various experimental setups, " -#~ "including aggregation strategies, data " -#~ "partitioning and centralised/distributed evaluation." -#~ " We also support `Flower simulation " -#~ "`_ making it easy to " -#~ "simulate large client cohorts in a " -#~ "resource-aware manner. Let's take a " -#~ "look!" +#~ msgid ":py:obj:`run_fleet_api `\\ \\(\\)" #~ msgstr "" -#~ msgid "|31e4b1afa87c4b968327bbeafbf184d4|" +#~ msgid "Run Flower server (Fleet API)." #~ msgstr "" -#~ msgid "|c9d935b4284e4c389a33d86b33e07c0a|" +#~ msgid "Unreleased" #~ msgstr "" -#~ msgid "|00727b5faffb468f84dd1b03ded88638|" +#~ msgid "|d8bf04f23d9b46d8a23cc6f4887d7873|" #~ msgstr "" -#~ msgid "|daf0cf0ff4c24fd29439af78416cf47b|" +#~ msgid "|5aa1711387d74d0f8b9c499e1a51627e|" #~ msgstr "" -#~ msgid "|9f093007080d471d94ca90d3e9fde9b6|" +#~ msgid "|2bc8e069228d4873804061ff4a95048c|" #~ msgstr "" -#~ msgid "|46a26e6150e0479fbd3dfd655f36eb13|" +#~ msgid "|c258488766324dc9a6807f0e7c4fd5f4|" #~ msgstr "" -#~ msgid "|3daba297595c4c7fb845d90404a6179a|" +#~ msgid "|d5f962c3f4ec48529efda980868c14b0|" #~ msgstr "" -#~ msgid "|5769874fa9c4455b80b2efda850d39d7|" +#~ msgid "|a5eccea18d4c43a68b54b65043cabef8|" #~ msgstr "" -#~ msgid "|ba47ffb421814b0f8f9fa5719093d839|" +#~ msgid "|f17662f7df2d42f68cac70a1fdeda8a7|" #~ msgstr "" -#~ msgid "|aeac5bf79cbf497082e979834717e01b|" +#~ msgid "|241fc906441a4f038c625a19d30d01b2|" #~ msgstr "" -#~ msgid "|ce27ed4bbe95459dba016afc42486ba2|" +#~ msgid "|0aa5aa05810b44b6a835cecce28f3137|" #~ msgstr "" -#~ msgid "|ae94a7f71dda443cbec2385751427d41|" +#~ msgid "|c742940dd4bf4de09d8d0d5e8d179638|" #~ msgstr "" -#~ msgid "|e61fce4d43d243e7bb08bdde97d81ce6|" +#~ msgid "|1f169ab4601a47e1a226f1628f4ebddb|" #~ msgstr "" -#~ msgid "|08cb60859b07461588fe44e55810b050|" +#~ msgid "|12cfa9cde14440ecb8c8f6c1d7185bec|" +#~ msgstr "" + +#~ msgid "|72939caf6e294b0986fee6dde96614d7|" +#~ msgstr "" + +#~ msgid "|83a8daee45da4a98b8d6f24ae098fc50|" #~ msgstr "" diff --git a/doc/locales/pt_BR/LC_MESSAGES/sphinx.po b/doc/locales/pt_BR/LC_MESSAGES/sphinx.po index 79be6ffce144..45f362cca809 100644 --- a/doc/locales/pt_BR/LC_MESSAGES/sphinx.po +++ b/doc/locales/pt_BR/LC_MESSAGES/sphinx.po @@ -8,7 +8,7 @@ msgid "" msgstr "" "Project-Id-Version: Flower main\n" "Report-Msgid-Bugs-To: \n" -"POT-Creation-Date: 2023-10-19 07:55+0200\n" +"POT-Creation-Date: 2024-05-28 11:47+0200\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: pt_BR\n" @@ -17,7 +17,7 @@ msgstr "" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" -"Generated-By: Babel 2.12.1\n" +"Generated-By: Babel 2.15.0\n" #: ../../source/_templates/base.html:18 msgid "About these documents" @@ -28,6 +28,7 @@ msgid "Index" msgstr "" #: ../../source/_templates/base.html:24 +#: ../../source/_templates/sidebar/search.html:2 msgid "Search" msgstr "" @@ -35,3 +36,7 @@ msgstr "" msgid "Copyright" msgstr "" +#: ../../source/_templates/sidebar/search.html:2 +msgid "Type / to search" +msgstr "" + diff --git a/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po b/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po index 86d96e5e6865..d07217ea35f7 100644 --- a/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po +++ b/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po @@ -7,8 +7,8 @@ msgid "" msgstr "" "Project-Id-Version: Flower main\n" "Report-Msgid-Bugs-To: \n" -"POT-Creation-Date: 2024-03-15 14:23+0000\n" -"PO-Revision-Date: 2024-02-19 11:37+0000\n" +"POT-Creation-Date: 2024-06-17 16:09+0200\n" +"PO-Revision-Date: 2024-06-12 10:09+0000\n" "Last-Translator: Yan Gao \n" "Language: zh_Hans\n" "Language-Team: Chinese (Simplified) `_ that include all necessary " -"dependencies for running the server. You can also build your own custom " -"docker images from scratch with a different version of Python or Ubuntu " -"if that is what you need. In this guide, we will explain what images " -"exist and how to build them locally." +"`_ that include all necessary dependencies" +" for running the SuperLink, SuperNode or ServerApp. You can also build " +"your own custom docker images from scratch with a different version of " +"Python or Linux distribution (Ubuntu/Alpine) if that is what you need. In" +" this guide, we will explain what images exist and how to build them " +"locally." msgstr "" +"Flower 在 `Docker Hub `_ " +"上提供了预制的 docker 镜像，其中包括运行服务器所需的所有依赖项。如果你需要，也可以使用不同版本的 Python 或 Ubuntu " +"从头开始构建自己的定制 docker 镜像。在本指南中，我们将介绍有哪些镜像，以及如何在本地构建它们。" -#: ../../source/contributor-how-to-build-docker-images.rst:9 +#: ../../source/contributor-how-to-build-docker-images.rst:10 +#, fuzzy msgid "" "Before we can start, we need to meet a few prerequisites in our local " "development environment." -msgstr "" +msgstr "在开始之前，我们需要在本地开发环境中满足一些先决条件。" -#: ../../source/contributor-how-to-build-docker-images.rst:11 +#: ../../source/contributor-how-to-build-docker-images.rst:12 #, fuzzy msgid "Clone the flower repository." msgstr "**叉花仓库**" -#: ../../source/contributor-how-to-build-docker-images.rst:17 +#: ../../source/contributor-how-to-build-docker-images.rst:18 +#: ../../source/how-to-run-flower-using-docker.rst:165 +#, fuzzy msgid "Verify the Docker daemon is running." -msgstr "" +msgstr "验证 Docker 守护进程是否正在运行。" -#: ../../source/contributor-how-to-build-docker-images.rst:19 +#: ../../source/contributor-how-to-build-docker-images.rst:20 +#: ../../source/how-to-run-flower-using-docker.rst:167 +#, fuzzy msgid "" "Please follow the first section on :doc:`Run Flower using Docker ` which covers this step in more detail." msgstr "" +"请阅读 :doc:`Run Flower using Docker ` " +"的第一节，其中更详细地介绍了这一步骤。" -#: ../../source/contributor-how-to-build-docker-images.rst:23 -msgid "" -"Currently, Flower provides two images, a base image and a server image. " -"There will also be a client image soon. The base image, as the name " -"suggests, contains basic dependencies that both the server and the client" -" need. This includes system dependencies, Python and Python tools. The " -"server image is based on the base image, but it additionally installs the" -" Flower server using ``pip``." -msgstr "" - -#: ../../source/contributor-how-to-build-docker-images.rst:28 +#: ../../source/contributor-how-to-build-docker-images.rst:25 +#, fuzzy msgid "" "The build instructions that assemble the images are located in the " "respective Dockerfiles. You can find them in the subdirectories of " "``src/docker``." -msgstr "" +msgstr "组装镜像的构建说明位于各自的 Dockerfile 中。你可以在 ``src/docker`` 的子目录中找到它们。" -#: ../../source/contributor-how-to-build-docker-images.rst:31 +#: ../../source/contributor-how-to-build-docker-images.rst:28 +#, fuzzy msgid "" -"Both, base and server image are configured via build arguments. Through " -"build arguments, we can make our build more flexible. For example, in the" -" base image, we can specify the version of Python to install using the " -"``PYTHON_VERSION`` build argument. Some of the build arguments have " +"Flower Docker images are configured via build arguments. Through build " +"arguments, we can make the creation of images more flexible. For example," +" in the base image, we can specify the version of Python to install using" +" the ``PYTHON_VERSION`` build argument. Some of the build arguments have " "default values, others must be specified when building the image. All " "available build arguments for each image are listed in one of the tables " "below." msgstr "" +"基础镜像和服务器镜像都是通过构建参数配置的。通过联编参数，我们可以使联编更加灵活。例如，在基础镜像中，我们可以使用 " +"``PYTHON_VERSION`` 联编参数指定要安装的 Python " +"版本。有些联编参数有默认值，有些则必须在联编映像时指定。每个映像的所有可用联编参数都列在下表中。" -#: ../../source/contributor-how-to-build-docker-images.rst:38 +#: ../../source/contributor-how-to-build-docker-images.rst:35 #, fuzzy msgid "Building the base image" msgstr "加载数据" -#: ../../source/contributor-how-to-build-docker-images.rst:44 -#: ../../source/contributor-how-to-build-docker-images.rst:86 +#: ../../source/contributor-how-to-build-docker-images.rst:41 +#: ../../source/contributor-how-to-build-docker-images.rst:98 #, fuzzy msgid "Build argument" msgstr "构建文档" -#: ../../source/contributor-how-to-build-docker-images.rst:45 -#: ../../source/contributor-how-to-build-docker-images.rst:87 +#: ../../source/contributor-how-to-build-docker-images.rst:42 +#: ../../source/contributor-how-to-build-docker-images.rst:99 #, fuzzy msgid "Description" msgstr "停用" -#: ../../source/contributor-how-to-build-docker-images.rst:46 -#: ../../source/contributor-how-to-build-docker-images.rst:88 +#: ../../source/contributor-how-to-build-docker-images.rst:43 +#: ../../source/contributor-how-to-build-docker-images.rst:100 #, fuzzy msgid "Required" msgstr "所需变更" -#: ../../source/contributor-how-to-build-docker-images.rst:47 -#: ../../source/contributor-how-to-build-docker-images.rst:89 +#: ../../source/contributor-how-to-build-docker-images.rst:44 +#: ../../source/contributor-how-to-build-docker-images.rst:101 #, fuzzy msgid "Example" msgstr "实例" +#: ../../source/contributor-how-to-build-docker-images.rst:45 +msgid "``DISTRO``" +msgstr "" + +#: ../../source/contributor-how-to-build-docker-images.rst:46 +#, fuzzy +msgid "The Linux distribution to use as the base image." +msgstr "基础镜像的存储库名称。" + +#: ../../source/contributor-how-to-build-docker-images.rst:47 +#: ../../source/contributor-how-to-build-docker-images.rst:51 +#: ../../source/contributor-how-to-build-docker-images.rst:55 +#: ../../source/contributor-how-to-build-docker-images.rst:71 +#: ../../source/contributor-how-to-build-docker-images.rst:104 +#, fuzzy +msgid "No" +msgstr "现在" + #: ../../source/contributor-how-to-build-docker-images.rst:48 #, fuzzy -msgid "``PYTHON_VERSION``" -msgstr "Python 版本" +msgid "``ubuntu``" +msgstr "``UBUNTU_VERSION``" #: ../../source/contributor-how-to-build-docker-images.rst:49 -msgid "Version of ``python`` to be installed." -msgstr "" +#, fuzzy +msgid "``DISTRO_VERSION``" +msgstr "``PIP_VERSION``" #: ../../source/contributor-how-to-build-docker-images.rst:50 -#: ../../source/contributor-how-to-build-docker-images.rst:54 -#: ../../source/contributor-how-to-build-docker-images.rst:58 -#: ../../source/contributor-how-to-build-docker-images.rst:100 -#, fuzzy -msgid "Yes" -msgstr "类型" +msgid "Version of the Linux distribution." +msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:51 +#: ../../source/contributor-how-to-build-docker-images.rst:52 #, fuzzy -msgid "``3.11``" +msgid "``22.04``" msgstr "``1.0.0rc1``" -#: ../../source/contributor-how-to-build-docker-images.rst:52 -msgid "``PIP_VERSION``" +#: ../../source/contributor-how-to-build-docker-images.rst:53 +#, fuzzy +msgid "``PYTHON_VERSION``" +msgstr "Python 版本" + +#: ../../source/contributor-how-to-build-docker-images.rst:54 +#, fuzzy +msgid "Version of ``python`` to be installed." +msgstr "要安装的 ``python`` 版本。" + +#: ../../source/contributor-how-to-build-docker-images.rst:56 +msgid "``3.11`` or ``3.11.1``" msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:53 +#: ../../source/contributor-how-to-build-docker-images.rst:57 +#, fuzzy +msgid "``PIP_VERSION``" +msgstr "``PIP_VERSION``" + +#: ../../source/contributor-how-to-build-docker-images.rst:58 +#, fuzzy msgid "Version of ``pip`` to be installed." -msgstr "" +msgstr "要安装的 ``pip` 版本。" -#: ../../source/contributor-how-to-build-docker-images.rst:55 +#: ../../source/contributor-how-to-build-docker-images.rst:59 +#: ../../source/contributor-how-to-build-docker-images.rst:63 +#: ../../source/contributor-how-to-build-docker-images.rst:67 +#: ../../source/contributor-how-to-build-docker-images.rst:108 +#, fuzzy +msgid "Yes" +msgstr "类型" + +#: ../../source/contributor-how-to-build-docker-images.rst:60 #, fuzzy msgid "``23.0.1``" msgstr "``1.0.0rc1``" -#: ../../source/contributor-how-to-build-docker-images.rst:56 +#: ../../source/contributor-how-to-build-docker-images.rst:61 +#, fuzzy msgid "``SETUPTOOLS_VERSION``" -msgstr "" +msgstr "设置工具版本" -#: ../../source/contributor-how-to-build-docker-images.rst:57 +#: ../../source/contributor-how-to-build-docker-images.rst:62 +#, fuzzy msgid "Version of ``setuptools`` to be installed." -msgstr "" +msgstr "要安装的 `setuptools`` 版本。" -#: ../../source/contributor-how-to-build-docker-images.rst:59 +#: ../../source/contributor-how-to-build-docker-images.rst:64 #, fuzzy msgid "``69.0.2``" msgstr "``1.0.0b0``" -#: ../../source/contributor-how-to-build-docker-images.rst:60 -msgid "``UBUNTU_VERSION``" -msgstr "" +#: ../../source/contributor-how-to-build-docker-images.rst:65 +#, fuzzy +msgid "``FLWR_VERSION``" +msgstr "``FLWR_VERSION``" -#: ../../source/contributor-how-to-build-docker-images.rst:61 -msgid "Version of the official Ubuntu Docker image." -msgstr "" +#: ../../source/contributor-how-to-build-docker-images.rst:66 +#, fuzzy +msgid "Version of Flower to be installed." +msgstr "要安装的 Flower 版本。" -#: ../../source/contributor-how-to-build-docker-images.rst:62 -msgid "Defaults to ``22.04``." +#: ../../source/contributor-how-to-build-docker-images.rst:68 +#, fuzzy +msgid "``1.8.0``" +msgstr "``1.0.0b0``" + +#: ../../source/contributor-how-to-build-docker-images.rst:69 +#, fuzzy +msgid "``FLWR_PACKAGE``" +msgstr "``FLWR_VERSION``" + +#: ../../source/contributor-how-to-build-docker-images.rst:70 +#, fuzzy +msgid "The Flower package to be installed." +msgstr "要安装的 PyPI 软件包。" + +#: ../../source/contributor-how-to-build-docker-images.rst:72 +msgid "``flwr`` or ``flwr-nightly``" msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:65 +#: ../../source/contributor-how-to-build-docker-images.rst:75 +#, fuzzy msgid "" -"The following example creates a base image with Python 3.11.0, pip 23.0.1" -" and setuptools 69.0.2:" -msgstr "" +"The following example creates a base Ubuntu/Alpine image with Python " +"3.11.0, pip 23.0.1, setuptools 69.0.2 and Flower 1.8.0:" +msgstr "下面的示例使用 Python 3.11.0、pip 23.0.1 和 setuptools 69.0.2 创建了基本映像：" -#: ../../source/contributor-how-to-build-docker-images.rst:76 +#: ../../source/contributor-how-to-build-docker-images.rst:88 +#, fuzzy msgid "" "The name of image is ``flwr_base`` and the tag ``0.1.0``. Remember that " "the build arguments as well as the name and tag can be adapted to your " "needs. These values serve as examples only." -msgstr "" +msgstr "图像名称为 ``flwr_base``，标记为 ``0.1.0``。请记住，编译参数以及名称和标记都可以根据需要进行调整。这些值仅供参考。" -#: ../../source/contributor-how-to-build-docker-images.rst:80 +#: ../../source/contributor-how-to-build-docker-images.rst:92 #, fuzzy -msgid "Building the server image" +msgid "Building the SuperLink/SuperNode or ServerApp image" msgstr "启动服务器" -#: ../../source/contributor-how-to-build-docker-images.rst:90 +#: ../../source/contributor-how-to-build-docker-images.rst:102 +#, fuzzy msgid "``BASE_REPOSITORY``" -msgstr "" +msgstr "基础存储库" -#: ../../source/contributor-how-to-build-docker-images.rst:91 +#: ../../source/contributor-how-to-build-docker-images.rst:103 +#, fuzzy msgid "The repository name of the base image." -msgstr "" - -#: ../../source/contributor-how-to-build-docker-images.rst:92 -msgid "Defaults to ``flwr/server``." -msgstr "" - -#: ../../source/contributor-how-to-build-docker-images.rst:94 -msgid "``BASE_IMAGE_TAG``" -msgstr "" - -#: ../../source/contributor-how-to-build-docker-images.rst:95 -msgid "The image tag of the base image." -msgstr "" - -#: ../../source/contributor-how-to-build-docker-images.rst:96 -msgid "Defaults to ``py3.11-ubuntu22.04``." -msgstr "" +msgstr "基础镜像的存储库名称。" -#: ../../source/contributor-how-to-build-docker-images.rst:98 -msgid "``FLWR_VERSION``" -msgstr "" +#: ../../source/contributor-how-to-build-docker-images.rst:105 +#, fuzzy +msgid "``flwr/base``" +msgstr "``FLWR_VERSION``" -#: ../../source/contributor-how-to-build-docker-images.rst:99 -msgid "Version of Flower to be installed." -msgstr "" +#: ../../source/contributor-how-to-build-docker-images.rst:106 +#, fuzzy +msgid "``BASE_IMAGE``" +msgstr "基础存储库" -#: ../../source/contributor-how-to-build-docker-images.rst:101 +#: ../../source/contributor-how-to-build-docker-images.rst:107 #, fuzzy -msgid "``1.7.0``" -msgstr "``1.0.0b0``" +msgid "The Tag of the Flower base image." +msgstr "基础镜像的存储库名称。" -#: ../../source/contributor-how-to-build-docker-images.rst:103 -msgid "" -"The following example creates a server image with the official Flower " -"base image py3.11-ubuntu22.04 and Flower 1.7.0:" +#: ../../source/contributor-how-to-build-docker-images.rst:109 +msgid "``1.8.0-py3.10-ubuntu22.04``" msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:114 +#: ../../source/contributor-how-to-build-docker-images.rst:111 +#, fuzzy msgid "" -"The name of image is ``flwr_server`` and the tag ``0.1.0``. Remember that" -" the build arguments as well as the name and tag can be adapted to your " -"needs. These values serve as examples only." -msgstr "" +"The following example creates a SuperLink/SuperNode or ServerApp image " +"with the official Flower base image:" +msgstr "下面的示例使用官方的 Flower 基本镜像 py3.11-ubuntu22.04 和 Flower 1.7.0 创建了一个服务器镜像：" -#: ../../source/contributor-how-to-build-docker-images.rst:117 +#: ../../source/contributor-how-to-build-docker-images.rst:122 +#, fuzzy msgid "" "If you want to use your own base image instead of the official Flower " -"base image, all you need to do is set the ``BASE_REPOSITORY`` and " -"``BASE_IMAGE_TAG`` build arguments. The value of ``BASE_REPOSITORY`` must" -" match the name of your image and the value of ``BASE_IMAGE_TAG`` must " -"match the tag of your image." +"base image, all you need to do is set the ``BASE_REPOSITORY`` build " +"argument." msgstr "" +"如果您想使用自己的基础图片而不是 Flower 官方的基础图片，只需设置 ``BASE_REPOSITORY`` 和 " +"``BASE_IMAGE_TAG`` " +"联编参数即可。`BASE_REPOSITORY``的值必须与您的图像名称一致，`BASE_IMAGE_TAG``的值必须与您的图像标签一致。" -#: ../../source/contributor-how-to-build-docker-images.rst:131 +#: ../../source/contributor-how-to-build-docker-images.rst:133 +#, fuzzy msgid "After creating the image, we can test whether the image is working:" -msgstr "" +msgstr "创建图像后，我们可以测试图像是否正常工作：" #: ../../source/contributor-how-to-contribute-translations.rst:2 msgid "Contribute translations" @@ -414,127 +470,6 @@ msgstr "" "如果您想添加新语言，请先联系我们，可以在 `Slack `_ 上联系，也可以在我们的 " "`GitHub repo `_ 上提交问题。" -#: ../../source/contributor-how-to-create-new-messages.rst:2 -msgid "Creating New Messages" -msgstr "创建新信息" - -#: ../../source/contributor-how-to-create-new-messages.rst:4 -msgid "" -"This is a simple guide for creating a new type of message between the " -"server and clients in Flower." -msgstr "这是一个如何用Flower在服务器和客户端之间创建新类型的信息的简要指导。" - -#: ../../source/contributor-how-to-create-new-messages.rst:6 -msgid "" -"Let's suppose we have the following example functions in " -":code:`server.py` and :code:`numpy_client.py`..." -msgstr "假设我们在脚本code:`server.py`和code:`numpy_client.py`中有以下的示例函数..." - -#: ../../source/contributor-how-to-create-new-messages.rst:8 -msgid "Server's side:" -msgstr "在服务器端：" - -#: ../../source/contributor-how-to-create-new-messages.rst:17 -msgid "Client's side:" -msgstr "在客户端：" - -#: ../../source/contributor-how-to-create-new-messages.rst:26 -msgid "" -"Let's now see what we need to implement in order to get this simple " -"function between the server and client to work!" -msgstr "现在让我们来看看，为了让服务器和客户端之间的这个简单的函数正常工作，我们需要实现哪些功能！" - -#: ../../source/contributor-how-to-create-new-messages.rst:30 -msgid "Message Types for Protocol Buffers" -msgstr "协议缓冲区的信息类型" - -#: ../../source/contributor-how-to-create-new-messages.rst:32 -#, fuzzy -msgid "" -"The first thing we need to do is to define a message type for the RPC " -"system in :code:`transport.proto`. Note that we have to do it for both " -"the request and response messages. For more details on the syntax of " -"proto3, please see the `official documentation `_." -msgstr "" -"我们需要做的第一件事是在脚本code:`transport.proto`中定义 RPC " -"系统的消息类型。请注意，我们必须对请求信息和响应信息都这样做。有关 proto3 语法的更多详情，请参阅官方文档 " -"`_。" - -#: ../../source/contributor-how-to-create-new-messages.rst:35 -msgid "Within the :code:`ServerMessage` block:" -msgstr "在 :code:`ServerMessage` 代码块中：" - -#: ../../source/contributor-how-to-create-new-messages.rst:52 -msgid "Within the ClientMessage block:" -msgstr "在 ClientMessage 代码块中：" - -#: ../../source/contributor-how-to-create-new-messages.rst:70 -msgid "" -"Make sure to also add a field of the newly created message type in " -":code:`oneof msg`." -msgstr "确保在 :code:`oneof msg` 中也添加一个新创建的消息类型字段。" - -#: ../../source/contributor-how-to-create-new-messages.rst:72 -msgid "Once that is done, we will compile the file with:" -msgstr "完成后，我们将使用：" - -#: ../../source/contributor-how-to-create-new-messages.rst:78 -msgid "If it compiles successfully, you should see the following message:" -msgstr "如果编译成功，你应该会看到以下信息：" - -#: ../../source/contributor-how-to-create-new-messages.rst:87 -msgid "Serialization and Deserialization Functions" -msgstr "序列化和反序列化函数" - -#: ../../source/contributor-how-to-create-new-messages.rst:89 -msgid "" -"Our next step is to add functions to serialize and deserialize Python " -"datatypes to or from our defined RPC message types. You should add these " -"functions in :code:`serde.py`." -msgstr "" -"下一步是添加函数，以便将 Python 数据类型序列化和反序列化为我们定义的 RPC 消息类型或从我们定义的 RPC 消息类型反序列化和反序列化 " -"Python 数据类型。您应该在 :code:`serde.py` 中添加这些函数。" - -#: ../../source/contributor-how-to-create-new-messages.rst:91 -msgid "The four functions:" -msgstr "四种函数：" - -#: ../../source/contributor-how-to-create-new-messages.rst:112 -msgid "Sending the Message from the Server" -msgstr "从服务器发送信息" - -#: ../../source/contributor-how-to-create-new-messages.rst:114 -msgid "" -"Now write the request function in your Client Proxy class (e.g., " -":code:`grpc_client_proxy.py`) using the serde functions you just created:" -msgstr "现在，在客户端代理类（例如 :code:`grpc_client_proxy.py`）中使用刚才创建的 serde 函数编写请求函数：" - -#: ../../source/contributor-how-to-create-new-messages.rst:128 -msgid "Receiving the Message by the Client" -msgstr "由客户端接收信息" - -#: ../../source/contributor-how-to-create-new-messages.rst:130 -msgid "" -"Last step! Modify the code in :code:`message_handler.py` to check the " -"field of your message and call the :code:`example_response` function. " -"Remember to use the serde functions!" -msgstr "" -"最后一步 修改 :code:`message_handler.py` 中的代码，检查信息的字段并调用 " -":code:`example_response` 函数。记住使用 serde 函数！" - -#: ../../source/contributor-how-to-create-new-messages.rst:132 -msgid "Within the handle function:" -msgstr "在句柄函数内：" - -#: ../../source/contributor-how-to-create-new-messages.rst:139 -msgid "And add a new function:" -msgstr "并增加一个新函数：" - -#: ../../source/contributor-how-to-create-new-messages.rst:149 -msgid "Hopefully, when you run your program you will get the intended result!" -msgstr "希望您在运行程序时能得到预期的结果！" - #: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:2 msgid "Develop in VSCode Dev Containers" msgstr "使用 VSCode Dev Containers 进行开发" @@ -782,15 +717,17 @@ msgid "Open Jupyter Notebooks on Google Colab" msgstr "在谷歌 Colab 上打开 Jupyter 笔记本" #: ../../source/contributor-how-to-install-development-versions.rst:51 +#, fuzzy msgid "" -"Open the notebook ``doc/source/tutorial-get-started-with-flower-" +"Open the notebook ``doc/source/tutorial-series-get-started-with-flower-" "pytorch.ipynb``:" msgstr "打开笔记本 ``doc/source/tutorial-get-started-with-flower-pytorch.ipynb``：" #: ../../source/contributor-how-to-install-development-versions.rst:53 +#, fuzzy msgid "" "https://colab.research.google.com/github/adap/flower/blob/main/doc/source" -"/tutorial-get-started-with-flower-pytorch.ipynb" +"/tutorial-series-get-started-with-flower-pytorch.ipynb" msgstr "" "https://colab.research.google.com/github/adap/flower/blob/main/doc/source" "/tutorial-get-started-with-flower-pytorch.ipynb" @@ -804,9 +741,10 @@ msgstr "" "打开同一笔记本的开发版本：" #: ../../source/contributor-how-to-install-development-versions.rst:57 +#, fuzzy msgid "" "https://colab.research.google.com/github/adap/flower/blob/branch-" -"name/doc/source/tutorial-get-started-with-flower-pytorch.ipynb" +"name/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb" msgstr "" "https://colab.research.google.com/github/adap/flower/blob/branch-" "name/doc/source/tutorial-get-started-with-flower-pytorch.ipynb" @@ -859,13 +797,17 @@ msgid "" msgstr "版本号在 ``pyproject.toml`` 中说明。要发布 Flower 的新版本，需要完成以下工作（按顺序排列）：" #: ../../source/contributor-how-to-release-flower.rst:11 +#, fuzzy msgid "" "Run ``python3 src/py/flwr_tool/update_changelog.py `` in " "order to add every new change to the changelog (feel free to make manual " "changes to the changelog afterwards until it looks good)." msgstr "" +"运行 ``python3 src/py/flwr_tool/update_changelog.py `` " +"以将每项新更改添加到更新日志中（之后可对更新日志进行手动更改，直到看起来不错为止）。" #: ../../source/contributor-how-to-release-flower.rst:12 +#, fuzzy msgid "" "Once the changelog has been updated with all the changes, run ``./dev" "/prepare-release-changelog.sh v``, where ```` " @@ -874,6 +816,9 @@ msgid "" "by the version and current date, and it will add a thanking message for " "the contributors. Open a pull request with those changes." msgstr "" +"更新更新日志后，运行``./dev/prepare-release-changelog.sh " +"v``，其中````是``pyproject.toml``中的版本（注意前面的``v``）。这将用版本和当前日期替换更新日志中的" +" ``Unreleased`` 标头，并为贡献者添加一条感谢信息。打开一个包含这些更改的拉取请求。" #: ../../source/contributor-how-to-release-flower.rst:13 #, fuzzy @@ -891,89 +836,139 @@ msgstr "" msgid "Check the draft release on GitHub, and if everything is good, publish it." msgstr "检查 GitHub 上的发布稿，如果一切正常，就发布它。" +#: ../../source/contributor-how-to-release-flower.rst:15 +#, fuzzy +msgid "Trigger the CI for building the Docker images." +msgstr "官方 Ubuntu Docker 映像的版本。" + #: ../../source/contributor-how-to-release-flower.rst:17 +msgid "" +"To trigger the workflow, a collaborator must create a " +"``workflow_dispatch`` event in the GitHub CI. This can be done either " +"through the UI or via the GitHub CLI. The event requires only one input, " +"the Flower version, to be released." +msgstr "" + +#: ../../source/contributor-how-to-release-flower.rst:21 +#, fuzzy +msgid "**Via the UI**" +msgstr "**审查 PR**" + +#: ../../source/contributor-how-to-release-flower.rst:23 +msgid "" +"Go to the ``Build docker images`` workflow `page " +"`_." +msgstr "" + +#: ../../source/contributor-how-to-release-flower.rst:24 +msgid "" +"Click on the ``Run workflow`` button and type the new version of Flower " +"in the ``Version of Flower`` input field." +msgstr "" + +#: ../../source/contributor-how-to-release-flower.rst:25 +msgid "Click on the **green** ``Run workflow`` button." +msgstr "" + +#: ../../source/contributor-how-to-release-flower.rst:29 +msgid "**Via the GitHub CI**" +msgstr "" + +#: ../../source/contributor-how-to-release-flower.rst:31 +msgid "" +"Make sure you are logged in via ``gh auth login`` and that the current " +"working directory is the root of the Flower repository." +msgstr "" + +#: ../../source/contributor-how-to-release-flower.rst:32 +msgid "" +"Trigger the workflow via ``gh workflow run docker-images.yml -f flwr-" +"version=``." +msgstr "" + +#: ../../source/contributor-how-to-release-flower.rst:35 msgid "After the release" msgstr "发布后" -#: ../../source/contributor-how-to-release-flower.rst:19 +#: ../../source/contributor-how-to-release-flower.rst:37 msgid "Create a pull request which contains the following changes:" msgstr "创建包含以下更改的拉取请求：" -#: ../../source/contributor-how-to-release-flower.rst:21 +#: ../../source/contributor-how-to-release-flower.rst:39 msgid "Increase the minor version in ``pyproject.toml`` by one." msgstr "将 ``pyproject.toml`` 中的次要版本增加一个。" -#: ../../source/contributor-how-to-release-flower.rst:22 +#: ../../source/contributor-how-to-release-flower.rst:40 msgid "Update all files which contain the current version number if necessary." msgstr "如有必要，更新包含当前版本号的所有文件。" -#: ../../source/contributor-how-to-release-flower.rst:23 +#: ../../source/contributor-how-to-release-flower.rst:41 msgid "Add a new ``Unreleased`` section in ``changelog.md``." msgstr "在 ``changelog.md`` 中添加新的 ``Unreleased`` 部分。" -#: ../../source/contributor-how-to-release-flower.rst:25 +#: ../../source/contributor-how-to-release-flower.rst:43 msgid "" "Merge the pull request on the same day (i.e., before a new nightly " "release gets published to PyPI)." msgstr "在同一天合并拉取请求（即在新版本发布到 PyPI 之前）。" -#: ../../source/contributor-how-to-release-flower.rst:28 +#: ../../source/contributor-how-to-release-flower.rst:46 msgid "Publishing a pre-release" msgstr "发布预发布版本" -#: ../../source/contributor-how-to-release-flower.rst:31 +#: ../../source/contributor-how-to-release-flower.rst:49 msgid "Pre-release naming" msgstr "释放前命名" -#: ../../source/contributor-how-to-release-flower.rst:33 +#: ../../source/contributor-how-to-release-flower.rst:51 msgid "" "PyPI supports pre-releases (alpha, beta, release candidate). Pre-releases" " MUST use one of the following naming patterns:" msgstr "PyPI 支持预发布版本（alpha、beta、release candidate）。预发布版本必须使用以下命名模式之一：" -#: ../../source/contributor-how-to-release-flower.rst:35 +#: ../../source/contributor-how-to-release-flower.rst:53 msgid "Alpha: ``MAJOR.MINOR.PATCHaN``" msgstr "阿尔法 ``MAJOR.MINOR.PATCHaN``" -#: ../../source/contributor-how-to-release-flower.rst:36 +#: ../../source/contributor-how-to-release-flower.rst:54 msgid "Beta: ``MAJOR.MINOR.PATCHbN``" msgstr "贝塔： ``MAJOR.MINOR.PATCHbN``" -#: ../../source/contributor-how-to-release-flower.rst:37 +#: ../../source/contributor-how-to-release-flower.rst:55 msgid "Release candidate (RC): ``MAJOR.MINOR.PATCHrcN``" msgstr "版本代号 (RC)： ``MAJOR.MINOR.PATCHrcN``" -#: ../../source/contributor-how-to-release-flower.rst:39 +#: ../../source/contributor-how-to-release-flower.rst:57 msgid "Examples include:" msgstr "例子包括：" -#: ../../source/contributor-how-to-release-flower.rst:41 +#: ../../source/contributor-how-to-release-flower.rst:59 msgid "``1.0.0a0``" msgstr "``1.0.0a0``" -#: ../../source/contributor-how-to-release-flower.rst:42 +#: ../../source/contributor-how-to-release-flower.rst:60 msgid "``1.0.0b0``" msgstr "``1.0.0b0``" -#: ../../source/contributor-how-to-release-flower.rst:43 +#: ../../source/contributor-how-to-release-flower.rst:61 msgid "``1.0.0rc0``" msgstr "``1.0.0rc0``" -#: ../../source/contributor-how-to-release-flower.rst:44 +#: ../../source/contributor-how-to-release-flower.rst:62 msgid "``1.0.0rc1``" msgstr "``1.0.0rc1``" -#: ../../source/contributor-how-to-release-flower.rst:46 +#: ../../source/contributor-how-to-release-flower.rst:64 msgid "" "This is in line with PEP-440 and the recommendations from the Python " "Packaging Authority (PyPA):" msgstr "这符合 PEP-440 和 Python 包装管理局 (PyPA) 的建议：" -#: ../../source/contributor-how-to-release-flower.rst:49 +#: ../../source/contributor-how-to-release-flower.rst:67 msgid "`PEP-440 `_" msgstr "`PEP-440 `_" -#: ../../source/contributor-how-to-release-flower.rst:50 +#: ../../source/contributor-how-to-release-flower.rst:68 msgid "" "`PyPA Choosing a versioning scheme " "`_" -#: ../../source/contributor-how-to-release-flower.rst:52 +#: ../../source/contributor-how-to-release-flower.rst:70 msgid "" "Note that the approach defined by PyPA is not compatible with SemVer " "2.0.0 spec, for details consult the `Semantic Versioning Specification " @@ -993,26 +988,26 @@ msgstr "" "规范不兼容，详情请查阅《语义版本规范》`_（特别是关于优先级的第 11 项）。" -#: ../../source/contributor-how-to-release-flower.rst:55 +#: ../../source/contributor-how-to-release-flower.rst:73 msgid "Pre-release classification" msgstr "发布前分类" -#: ../../source/contributor-how-to-release-flower.rst:57 +#: ../../source/contributor-how-to-release-flower.rst:75 msgid "Should the next pre-release be called alpha, beta, or release candidate?" msgstr "下一个预发布版应该叫阿尔法版、贝塔版还是候选发布版？" -#: ../../source/contributor-how-to-release-flower.rst:59 +#: ../../source/contributor-how-to-release-flower.rst:77 msgid "" "RC: feature complete, no known issues (apart from issues that are " "classified as \"won't fix\" for the next stable release) - if no issues " "surface this will become the next stable release" msgstr "RC：功能完整，无已知问题（除了下一个稳定版中被列为 \"不会修复 \"的问题）--如果没有问题出现，这将成为下一个稳定版" -#: ../../source/contributor-how-to-release-flower.rst:60 +#: ../../source/contributor-how-to-release-flower.rst:78 msgid "Beta: feature complete, allowed to have known issues" msgstr "贝塔版：功能完整，允许存在已知问题" -#: ../../source/contributor-how-to-release-flower.rst:61 +#: ../../source/contributor-how-to-release-flower.rst:79 msgid "Alpha: not feature complete, allowed to have known issues" msgstr "阿尔法版：功能不完整，允许存在已知问题" @@ -1045,10 +1040,22 @@ msgstr "" "3.10 `_或更高版本。" #: ../../source/contributor-how-to-set-up-a-virtual-env.rst:14 -msgid "Virutualenv with Pyenv/Virtualenv" +#, fuzzy +msgid "" +"Due to a known incompatibility with `ray " +"`_, we currently recommend utilizing at " +"most `Python 3.11 `_ for running Flower " +"simulations." +msgstr "" +"由于已知与 `ray `_ 不兼容，我们目前建议最多使用 `Python 3.11" +" `_ 运行 Flower 仿真。" + +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:19 +#, fuzzy +msgid "Virtualenv with Pyenv/Virtualenv" msgstr "Virutualenv 和 Pyenv/Virtualenv" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:16 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:21 msgid "" "One of the recommended virtual environment is `pyenv " "`_/`virtualenv `_。详情请参见 `Flower 示例 " "`_。" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:18 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:23 msgid "" "Once Pyenv is set up, you can use it to install `Python Version 3.10 " "`_ or above:" msgstr "一旦设置好 Pyenv，就可以用它来安装 `Python 3.10 `_ 或更高版本：" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:24 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:29 msgid "Create the virtualenv with:" msgstr "创建虚拟环境：" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:31 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:36 msgid "Activate the virtualenv by running the following command:" msgstr "运行以下命令激活 virtualenv：" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:39 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:44 msgid "Virtualenv with Poetry" msgstr "有诗意的 Virtualenv" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:41 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:46 msgid "" "The Flower examples are based on `Poetry `_ to manage dependencies. After installing Poetry you " @@ -1086,17 +1093,17 @@ msgstr "" "Flower 示例基于 `Poetry `_ 来管理依赖关系。安装 Poetry" " 后，只需创建一个虚拟环境即可：" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:47 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:52 msgid "" "If you open a new terminal you can activate the previously created " "virtual environment with the following command:" msgstr "如果打开一个新终端，可以使用以下命令激活之前创建的虚拟环境：" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:55 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:60 msgid "Virtualenv with Anaconda" msgstr "使用 Anaconda 的 Virtualenv" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:57 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:62 msgid "" "If you prefer to use Anaconda for your virtual environment then install " "and setup the `conda `_ 软件包。设置完成后，您就可以使用以下工具创建虚拟环境：" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:63 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:68 msgid "and activate the virtual environment with:" msgstr "并激活虚拟环境：" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:71 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:76 msgid "And then?" msgstr "然后呢？" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:73 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:78 msgid "" "As soon as you created your virtual environment you clone one of the " "`Flower examples `_." @@ -1141,7 +1148,7 @@ msgstr "" "Markdown（``.md`` 文件）。" #: ../../source/contributor-how-to-write-documentation.rst:10 -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:142 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:169 #, fuzzy msgid "" "Note that, in order to build the documentation locally (with ``poetry run" @@ -1622,7 +1629,7 @@ msgstr "完成此操作后，您将在 GitHub 上看到您的分叉仓库已根 msgid "Creating and merging a pull request (PR)" msgstr "创建和合并拉取请求 (PR)" -#: ../../source/contributor-tutorial-contribute-on-github.rst:205 +#: ../../source/contributor-tutorial-contribute-on-github.rst:206 msgid "**Create the PR**" msgstr "**创建 PR**" @@ -1656,6 +1663,16 @@ msgid "" msgstr "在这个例子中，你可以看到请求将我分叉的版本库中的分支 ``doc-fixes`` 合并到 Flower 版本库中的分支 ``main``。" #: ../../source/contributor-tutorial-contribute-on-github.rst:193 +#, fuzzy +msgid "" +"The title should be changed to adhere to the :ref:`pr_title_format` " +"guidelines, otherwise it won't be possible to merge the PR. So in this " +"case, a correct title might be ``docs(framework:skip) Fix typos``." +msgstr "" +"应该修改标题以符合 :ref:`pr_title_format` 准则，否则将无法合并 PR。因此，在这种情况下，正确的标题可能是 " +"``docs(framework:skip)修复错字``。" + +#: ../../source/contributor-tutorial-contribute-on-github.rst:196 msgid "" "The input box in the middle is there for you to describe what your PR " "does and to link it to existing issues. We have placed comments (that " @@ -1663,171 +1680,167 @@ msgid "" "process." msgstr "中间的输入框供您描述 PR 的作用，并将其与现有问题联系起来。我们在此放置了注释（一旦 PR 打开，注释将不会显示），以指导您完成整个过程。" -#: ../../source/contributor-tutorial-contribute-on-github.rst:196 -msgid "" -"It is important to follow the instructions described in comments. For " -"instance, in order to not break how our changelog system works, you " -"should read the information above the ``Changelog entry`` section " -"carefully. You can also checkout some examples and details in the " -":ref:`changelogentry` appendix." -msgstr "" +#: ../../source/contributor-tutorial-contribute-on-github.rst:199 +#, fuzzy +msgid "It is important to follow the instructions described in comments." +msgstr "请务必遵守注释中的说明。" -#: ../../source/contributor-tutorial-contribute-on-github.rst:200 +#: ../../source/contributor-tutorial-contribute-on-github.rst:201 msgid "" "At the bottom you will find the button to open the PR. This will notify " "reviewers that a new PR has been opened and that they should look over it" " to merge or to request changes." msgstr "在底部，您可以找到打开 PR 的按钮。这将通知审核人员新的 PR 已经打开，他们应该查看该 PR 以进行合并或要求修改。" -#: ../../source/contributor-tutorial-contribute-on-github.rst:203 +#: ../../source/contributor-tutorial-contribute-on-github.rst:204 msgid "" "If your PR is not yet ready for review, and you don't want to notify " "anyone, you have the option to create a draft pull request:" msgstr "如果您的 PR 尚未准备好接受审核，而且您不想通知任何人，您可以选择创建一个草案拉取请求：" -#: ../../source/contributor-tutorial-contribute-on-github.rst:208 +#: ../../source/contributor-tutorial-contribute-on-github.rst:209 msgid "**Making new changes**" msgstr "**作出新的改变**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:208 +#: ../../source/contributor-tutorial-contribute-on-github.rst:209 msgid "" "Once the PR has been opened (as draft or not), you can still push new " "commits to it the same way we did before, by making changes to the branch" " associated with the PR." msgstr "一旦 PR 被打开（无论是否作为草案），你仍然可以像以前一样，通过修改与 PR 关联的分支来推送新的提交。" -#: ../../source/contributor-tutorial-contribute-on-github.rst:230 +#: ../../source/contributor-tutorial-contribute-on-github.rst:231 msgid "**Review the PR**" msgstr "**审查 PR**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:211 +#: ../../source/contributor-tutorial-contribute-on-github.rst:212 msgid "" "Once the PR has been opened or once the draft PR has been marked as " "ready, a review from code owners will be automatically requested:" msgstr "一旦 PR 被打开或 PR 草案被标记为就绪，就会自动要求代码所有者进行审核：" -#: ../../source/contributor-tutorial-contribute-on-github.rst:215 +#: ../../source/contributor-tutorial-contribute-on-github.rst:216 msgid "" "Code owners will then look into the code, ask questions, request changes " "or validate the PR." msgstr "然后，代码所有者会查看代码、提出问题、要求修改或验证 PR。" -#: ../../source/contributor-tutorial-contribute-on-github.rst:217 +#: ../../source/contributor-tutorial-contribute-on-github.rst:218 msgid "Merging will be blocked if there are ongoing requested changes." msgstr "如果有正在进行的更改请求，合并将被阻止。" -#: ../../source/contributor-tutorial-contribute-on-github.rst:221 +#: ../../source/contributor-tutorial-contribute-on-github.rst:222 msgid "" "To resolve them, just push the necessary changes to the branch associated" " with the PR:" msgstr "要解决这些问题，只需将必要的更改推送到与 PR 关联的分支即可：" -#: ../../source/contributor-tutorial-contribute-on-github.rst:225 +#: ../../source/contributor-tutorial-contribute-on-github.rst:226 msgid "And resolve the conversation:" msgstr "并解决对话：" -#: ../../source/contributor-tutorial-contribute-on-github.rst:229 +#: ../../source/contributor-tutorial-contribute-on-github.rst:230 msgid "" "Once all the conversations have been resolved, you can re-request a " "review." msgstr "一旦所有对话都得到解决，您就可以重新申请审核。" -#: ../../source/contributor-tutorial-contribute-on-github.rst:250 +#: ../../source/contributor-tutorial-contribute-on-github.rst:251 msgid "**Once the PR is merged**" msgstr "**一旦 PR 被合并**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:233 +#: ../../source/contributor-tutorial-contribute-on-github.rst:234 msgid "" "If all the automatic tests have passed and reviewers have no more changes" " to request, they can approve the PR and merge it." msgstr "如果所有自动测试都已通过，且审核员不再需要修改，他们就可以批准 PR 并将其合并。" -#: ../../source/contributor-tutorial-contribute-on-github.rst:237 +#: ../../source/contributor-tutorial-contribute-on-github.rst:238 msgid "" "Once it is merged, you can delete the branch on GitHub (a button should " "appear to do so) and also delete it locally by doing:" msgstr "合并后，您可以在 GitHub 上删除该分支（会出现一个删除按钮），也可以在本地删除该分支：" -#: ../../source/contributor-tutorial-contribute-on-github.rst:244 +#: ../../source/contributor-tutorial-contribute-on-github.rst:245 msgid "Then you should update your forked repository by doing:" msgstr "然后，你应该更新你的分叉仓库：" -#: ../../source/contributor-tutorial-contribute-on-github.rst:253 +#: ../../source/contributor-tutorial-contribute-on-github.rst:254 msgid "Example of first contribution" msgstr "首次捐款实例" -#: ../../source/contributor-tutorial-contribute-on-github.rst:256 +#: ../../source/contributor-tutorial-contribute-on-github.rst:257 msgid "Problem" msgstr "问题" -#: ../../source/contributor-tutorial-contribute-on-github.rst:258 +#: ../../source/contributor-tutorial-contribute-on-github.rst:259 #, fuzzy msgid "" "For our documentation, we've started to use the `Diàtaxis framework " "`_." msgstr "对于我们的文档，我们已经开始使用 \"Diàtaxis 框架 `_\"。" -#: ../../source/contributor-tutorial-contribute-on-github.rst:260 +#: ../../source/contributor-tutorial-contribute-on-github.rst:261 #, fuzzy msgid "" "Our \"How to\" guides should have titles that continue the sentence \"How" " to …\", for example, \"How to upgrade to Flower 1.0\"." msgstr "我们的 \"如何 \"指南的标题应延续 \"如何...... \"的句式，例如 \"如何升级到 Flower 1.0\"。" -#: ../../source/contributor-tutorial-contribute-on-github.rst:262 +#: ../../source/contributor-tutorial-contribute-on-github.rst:263 msgid "" "Most of our guides do not follow this new format yet, and changing their " "title is (unfortunately) more involved than one might think." msgstr "我们的大多数指南还没有采用这种新格式，而更改其标题（不幸的是）比人们想象的要复杂得多。" -#: ../../source/contributor-tutorial-contribute-on-github.rst:264 +#: ../../source/contributor-tutorial-contribute-on-github.rst:265 #, fuzzy msgid "" "This issue is about changing the title of a doc from present continuous " "to present simple." msgstr "这个问题是关于将文档标题从现在进行时改为现在进行时。" -#: ../../source/contributor-tutorial-contribute-on-github.rst:266 +#: ../../source/contributor-tutorial-contribute-on-github.rst:267 #, fuzzy msgid "" "Let's take the example of \"Saving Progress\" which we changed to \"Save " "Progress\". Does this pass our check?" msgstr "以 \"保存进度 \"为例，我们将其改为 \"保存进度\"。这是否通过了我们的检查？" -#: ../../source/contributor-tutorial-contribute-on-github.rst:268 +#: ../../source/contributor-tutorial-contribute-on-github.rst:269 #, fuzzy msgid "Before: \"How to saving progress\" ❌" msgstr "之前： \"如何保存进度\" ❌" -#: ../../source/contributor-tutorial-contribute-on-github.rst:270 +#: ../../source/contributor-tutorial-contribute-on-github.rst:271 #, fuzzy msgid "After: \"How to save progress\" ✅" msgstr "之后： \"如何保存进度\"✅" -#: ../../source/contributor-tutorial-contribute-on-github.rst:273 +#: ../../source/contributor-tutorial-contribute-on-github.rst:274 msgid "Solution" msgstr "解决方案" -#: ../../source/contributor-tutorial-contribute-on-github.rst:275 +#: ../../source/contributor-tutorial-contribute-on-github.rst:276 #, fuzzy msgid "" "This is a tiny change, but it'll allow us to test your end-to-end setup. " "After cloning and setting up the Flower repo, here's what you should do:" msgstr "这只是一个很小的改动，但可以让我们测试你的端到端设置。克隆并设置好 Flower repo 后，你应该这样做：" -#: ../../source/contributor-tutorial-contribute-on-github.rst:277 +#: ../../source/contributor-tutorial-contribute-on-github.rst:278 #, fuzzy msgid "Find the source file in ``doc/source``" msgstr "在 `doc/source` 中查找源文件" -#: ../../source/contributor-tutorial-contribute-on-github.rst:278 +#: ../../source/contributor-tutorial-contribute-on-github.rst:279 #, fuzzy msgid "" "Make the change in the ``.rst`` file (beware, the dashes under the title " "should be the same length as the title itself)" msgstr "在 `.rst` 文件中进行修改（注意，标题下的破折号应与标题本身的长度相同）" -#: ../../source/contributor-tutorial-contribute-on-github.rst:279 +#: ../../source/contributor-tutorial-contribute-on-github.rst:280 #, fuzzy msgid "" "Build the docs and `check the result `_" -#: ../../source/contributor-tutorial-contribute-on-github.rst:282 +#: ../../source/contributor-tutorial-contribute-on-github.rst:283 msgid "Rename file" msgstr "重命名文件" -#: ../../source/contributor-tutorial-contribute-on-github.rst:284 +#: ../../source/contributor-tutorial-contribute-on-github.rst:285 msgid "" "You might have noticed that the file name still reflects the old wording." " If we just change the file, then we break all existing links to it - it " @@ -1850,33 +1863,33 @@ msgstr "" "您可能已经注意到，文件名仍然反映了旧的措辞。如果我们只是更改文件，那么就会破坏与该文件的所有现有链接--" "避免这种情况是***重要的，破坏链接会损害我们的搜索引擎排名。" -#: ../../source/contributor-tutorial-contribute-on-github.rst:287 +#: ../../source/contributor-tutorial-contribute-on-github.rst:288 #, fuzzy msgid "Here's how to change the file name:" msgstr "下面是更改文件名的方法：" -#: ../../source/contributor-tutorial-contribute-on-github.rst:289 +#: ../../source/contributor-tutorial-contribute-on-github.rst:290 #, fuzzy msgid "Change the file name to ``save-progress.rst``" msgstr "将文件名改为`save-progress.rst`" -#: ../../source/contributor-tutorial-contribute-on-github.rst:290 +#: ../../source/contributor-tutorial-contribute-on-github.rst:291 #, fuzzy msgid "Add a redirect rule to ``doc/source/conf.py``" msgstr "在 `doc/source/conf.py` 中添加重定向规则" -#: ../../source/contributor-tutorial-contribute-on-github.rst:292 +#: ../../source/contributor-tutorial-contribute-on-github.rst:293 #, fuzzy msgid "" "This will cause a redirect from ``saving-progress.html`` to ``save-" "progress.html``, old links will continue to work." msgstr "这将导致从 `saving-progress.html` 重定向到 `save-progress.html`，旧链接将继续工作。" -#: ../../source/contributor-tutorial-contribute-on-github.rst:295 +#: ../../source/contributor-tutorial-contribute-on-github.rst:296 msgid "Apply changes in the index file" msgstr "应用索引文件中的更改" -#: ../../source/contributor-tutorial-contribute-on-github.rst:297 +#: ../../source/contributor-tutorial-contribute-on-github.rst:298 #, fuzzy msgid "" "For the lateral navigation bar to work properly, it is very important to " @@ -1884,119 +1897,42 @@ msgid "" "arborescence of the navbar." msgstr "要使横向导航栏正常工作，更新 `index.rst` 文件也非常重要。我们就是在这里定义整个导航栏的结构。" -#: ../../source/contributor-tutorial-contribute-on-github.rst:300 +#: ../../source/contributor-tutorial-contribute-on-github.rst:301 #, fuzzy msgid "Find and modify the file name in ``index.rst``" msgstr "查找并修改 `index.rst` 中的文件名" -#: ../../source/contributor-tutorial-contribute-on-github.rst:303 +#: ../../source/contributor-tutorial-contribute-on-github.rst:304 msgid "Open PR" msgstr "开放式 PR" -#: ../../source/contributor-tutorial-contribute-on-github.rst:305 +#: ../../source/contributor-tutorial-contribute-on-github.rst:306 #, fuzzy msgid "" "Commit the changes (commit messages are always imperative: \"Do " "something\", in this case \"Change …\")" msgstr "提交更改（提交信息总是命令式的：\"做某事\"，这里是 \"更改......\"）" -#: ../../source/contributor-tutorial-contribute-on-github.rst:306 +#: ../../source/contributor-tutorial-contribute-on-github.rst:307 msgid "Push the changes to your fork" msgstr "将更改推送到分叉" -#: ../../source/contributor-tutorial-contribute-on-github.rst:307 -msgid "Open a PR (as shown above)" -msgstr "打开 PR（如上图所示）" - #: ../../source/contributor-tutorial-contribute-on-github.rst:308 +#, fuzzy +msgid "" +"Open a PR (as shown above) with title ``docs(framework) Update how-to " +"guide title``" +msgstr "打开一个 PR（如上图所示），标题为\"`docs(framework) Update how-to guide title```\"。" + +#: ../../source/contributor-tutorial-contribute-on-github.rst:309 msgid "Wait for it to be approved!" msgstr "等待审批！" -#: ../../source/contributor-tutorial-contribute-on-github.rst:309 +#: ../../source/contributor-tutorial-contribute-on-github.rst:310 msgid "Congrats! 🥳 You're now officially a Flower contributor!" msgstr "祝贺你 🥳 您现在正式成为 \"Flower \"贡献者！" -#: ../../source/contributor-tutorial-contribute-on-github.rst:313 -msgid "How to write a good PR title" -msgstr "如何撰写好的公关标题" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:315 -msgid "" -"A well-crafted PR title helps team members quickly understand the purpose" -" and scope of the changes being proposed. Here's a guide to help you " -"write a good GitHub PR title:" -msgstr "一个精心撰写的公关标题能帮助团队成员迅速了解所提修改的目的和范围。以下指南可帮助您撰写一个好的 GitHub PR 标题：" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:317 -msgid "" -"1. Be Clear and Concise: Provide a clear summary of the changes in a " -"concise manner. 1. Use Actionable Verbs: Start with verbs like \"Add,\" " -"\"Update,\" or \"Fix\" to indicate the purpose. 1. Include Relevant " -"Information: Mention the affected feature or module for context. 1. Keep " -"it Short: Avoid lengthy titles for easy readability. 1. Use Proper " -"Capitalization and Punctuation: Follow grammar rules for clarity." -msgstr "" -"1. 简明扼要： 以简明扼要的方式清楚地概述变化。1. 使用可操作的动词： 使用 \"添加\"、\"更新 \"或 \"修复 " -"\"等动词来表明目的。1. 包含相关信息： 提及受影响的功能或模块以了解上下文。1. 简短：避免冗长的标题，以方便阅读。1. " -"使用正确的大小写和标点符号： 遵守语法规则，以确保清晰。" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:323 -msgid "" -"Let's start with a few examples for titles that should be avoided because" -" they do not provide meaningful information:" -msgstr "让我们先举例说明几个应该避免使用的标题，因为它们不能提供有意义的信息：" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:325 -msgid "Implement Algorithm" -msgstr "执行算法" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:326 -msgid "Database" -msgstr "数据库" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:327 -msgid "Add my_new_file.py to codebase" -msgstr "在代码库中添加 my_new_file.py" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:328 -msgid "Improve code in module" -msgstr "改进模块中的代码" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:329 -msgid "Change SomeModule" -msgstr "更改 SomeModule" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:331 -msgid "" -"Here are a few positive examples which provide helpful information " -"without repeating how they do it, as that is already visible in the " -"\"Files changed\" section of the PR:" -msgstr "这里有几个正面的例子，提供了有用的信息，但没有重复他们是如何做的，因为在 PR 的 \"已更改文件 \"部分已经可以看到：" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:333 -msgid "Update docs banner to mention Flower Summit 2023" -msgstr "更新文件横幅，提及 2023 年 Flower 峰会" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:334 -msgid "Remove unnecessary XGBoost dependency" -msgstr "移除不必要的 XGBoost 依赖性" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:335 -msgid "Remove redundant attributes in strategies subclassing FedAvg" -msgstr "删除 FedAvg 子类化策略中的多余属性" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:336 -#, fuzzy -msgid "Add CI job to deploy the staging system when the ``main`` branch changes" -msgstr "添加 CI 作业，以便在 \"主 \"分支发生变化时部署暂存系统" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:337 -msgid "" -"Add new amazing library which will be used to improve the simulation " -"engine" -msgstr "添加新的惊人库，用于改进模拟引擎" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:341 +#: ../../source/contributor-tutorial-contribute-on-github.rst:314 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:548 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:946 #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:727 @@ -2005,13 +1941,13 @@ msgstr "添加新的惊人库，用于改进模拟引擎" msgid "Next steps" msgstr "接下来的步骤" -#: ../../source/contributor-tutorial-contribute-on-github.rst:343 +#: ../../source/contributor-tutorial-contribute-on-github.rst:316 msgid "" "Once you have made your first PR, and want to contribute more, be sure to" " check out the following :" msgstr "一旦您完成了第一份 PR，并希望做出更多贡献，请务必查看以下内容：" -#: ../../source/contributor-tutorial-contribute-on-github.rst:345 +#: ../../source/contributor-tutorial-contribute-on-github.rst:318 #, fuzzy msgid "" ":doc:`Good first contributions `_，在这里你应该特别看看 :code:`baselines` 的贡献。" -#: ../../source/contributor-tutorial-contribute-on-github.rst:349 +#: ../../source/contributor-tutorial-contribute-on-github.rst:322 #: ../../source/fed/0000-20200102-fed-template.md:60 msgid "Appendix" msgstr "附录" -#: ../../source/contributor-tutorial-contribute-on-github.rst:354 +#: ../../source/contributor-tutorial-contribute-on-github.rst:327 #, fuzzy -msgid "Changelog entry" -msgstr "更新日志" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:356 -msgid "" -"When opening a new PR, inside its description, there should be a " -"``Changelog entry`` header." -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:358 -msgid "" -"Above this header you should see the following comment that explains how " -"to write your changelog entry:" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:360 -msgid "" -"Inside the following 'Changelog entry' section, you should put the " -"description of your changes that will be added to the changelog alongside" -" your PR title." -msgstr "" +msgid "PR title format" +msgstr "PR 标题格式" -#: ../../source/contributor-tutorial-contribute-on-github.rst:363 -msgid "" -"If the section is completely empty (without any token) or non-existent, " -"the changelog will just contain the title of the PR for the changelog " -"entry, without any description." -msgstr "" +#: ../../source/contributor-tutorial-contribute-on-github.rst:329 +#, fuzzy +msgid "We enforce the following PR title format:" +msgstr "我们执行以下 PR 标题格式：" -#: ../../source/contributor-tutorial-contribute-on-github.rst:366 +#: ../../source/contributor-tutorial-contribute-on-github.rst:335 +#, fuzzy msgid "" -"If the section contains some text other than tokens, it will use it to " -"add a description to the change." -msgstr "" +"(or ``(:skip) `` to ignore the PR in the " +"changelog)" +msgstr "(或 ``(:skip) `` 忽略更新日志中的 PR）。" -#: ../../source/contributor-tutorial-contribute-on-github.rst:368 +#: ../../source/contributor-tutorial-contribute-on-github.rst:337 +#, fuzzy msgid "" -"If the section contains one of the following tokens it will ignore any " -"other text and put the PR under the corresponding section of the " -"changelog:" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:370 -msgid " is for classifying a PR as a general improvement." -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:372 -msgid " is to not add the PR to the changelog" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:374 -msgid " is to add a general baselines change to the PR" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:376 -msgid " is to add a general examples change to the PR" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:378 -msgid " is to add a general sdk change to the PR" -msgstr "" - -#: ../../source/contributor-tutorial-contribute-on-github.rst:380 -msgid " is to add a general simulations change to the PR" +"Where ```` needs to be in ``{ci, fix, feat, docs, refactor, " +"break}``, ```` should be in ``{framework, baselines, datasets, " +"examples, or '*' when modifying multiple projects which requires the " +"':skip' flag to be used}``, and ```` starts with a capitalised " +"verb in the imperative mood." msgstr "" +"其中 ```` 需要使用 ``{ci, fix, feat, docs, refactor, break}``, " +"```` 应该使用 ``{framework, baselines, datasets, examples, 或者 '*' " +"当修改多个项目时需要使用 ':skip'标记}``, 并且 ```` 应该以一个大写的动词开始。" -#: ../../source/contributor-tutorial-contribute-on-github.rst:382 -msgid "Note that only one token should be used." -msgstr "" +#: ../../source/contributor-tutorial-contribute-on-github.rst:341 +#, fuzzy +msgid "Valid examples:" +msgstr "实例" -#: ../../source/contributor-tutorial-contribute-on-github.rst:384 -msgid "" -"Its content must have a specific format. We will break down what each " -"possibility does:" -msgstr "" +#: ../../source/contributor-tutorial-contribute-on-github.rst:343 +#, fuzzy +msgid "``feat(framework) Add flwr build CLI command``" +msgstr "`feat(框架) 添加 flwr build CLI 命令```" -#: ../../source/contributor-tutorial-contribute-on-github.rst:386 -msgid "" -"If the ``### Changelog entry`` section contains nothing or doesn't exist," -" the following text will be added to the changelog::" -msgstr "" +#: ../../source/contributor-tutorial-contribute-on-github.rst:344 +#, fuzzy +msgid "``refactor(examples:skip) Improve quickstart-pytorch logging``" +msgstr "``refactor(examples:skip) Improve quickstart-pytorch logging``." -#: ../../source/contributor-tutorial-contribute-on-github.rst:390 -msgid "" -"If the ``### Changelog entry`` section contains a description (and no " -"token), the following text will be added to the changelog::" -msgstr "" +#: ../../source/contributor-tutorial-contribute-on-github.rst:345 +#, fuzzy +msgid "``ci(*:skip) Enforce PR title format``" +msgstr "`ci(*:skip)执行 PR 标题格式``。" -#: ../../source/contributor-tutorial-contribute-on-github.rst:396 -msgid "" -"If the ``### Changelog entry`` section contains ````, nothing will " -"change in the changelog." -msgstr "" +#: ../../source/contributor-tutorial-contribute-on-github.rst:347 +#, fuzzy +msgid "Invalid examples:" +msgstr "模拟示例" -#: ../../source/contributor-tutorial-contribute-on-github.rst:398 -msgid "" -"If the ``### Changelog entry`` section contains ````, the " -"following text will be added to the changelog::" -msgstr "" +#: ../../source/contributor-tutorial-contribute-on-github.rst:349 +#, fuzzy +msgid "``feat(framework): Add flwr build CLI command`` (extra ``:``)" +msgstr "`feat(框架)： 添加 flwr build CLI 命令``（额外的``:``)" -#: ../../source/contributor-tutorial-contribute-on-github.rst:402 +#: ../../source/contributor-tutorial-contribute-on-github.rst:350 +#, fuzzy msgid "" -"If the ``### Changelog entry`` section contains ````, the " -"following text will be added to the changelog::" -msgstr "" +"``feat(*) Add flwr build CLI command`` (missing ``skip`` flag along with " +"``*``)" +msgstr "`feat(*)添加flwr构建CLI命令``（缺少``skip``标志和``*``）。" -#: ../../source/contributor-tutorial-contribute-on-github.rst:406 -msgid "" -"If the ``### Changelog entry`` section contains ````, the " -"following text will be added to the changelog::" -msgstr "" +#: ../../source/contributor-tutorial-contribute-on-github.rst:351 +#, fuzzy +msgid "``feat(skip) Add flwr build CLI command`` (missing ````)" +msgstr "`feat(skip)添加flwr构建CLI命令``（缺少```）。" -#: ../../source/contributor-tutorial-contribute-on-github.rst:410 -msgid "" -"If the ``### Changelog entry`` section contains ````, the following " -"text will be added to the changelog::" -msgstr "" +#: ../../source/contributor-tutorial-contribute-on-github.rst:352 +#, fuzzy +msgid "``feat(framework) add flwr build CLI command`` (non capitalised verb)" +msgstr "`feat(framework)添加 flwr 构建 CLI 命令``（非大写动词）" -#: ../../source/contributor-tutorial-contribute-on-github.rst:414 -msgid "" -"If the ``### Changelog entry`` section contains ````, the " -"following text will be added to the changelog::" -msgstr "" +#: ../../source/contributor-tutorial-contribute-on-github.rst:353 +#, fuzzy +msgid "``feat(framework) Add flwr build CLI command.`` (dot at the end)" +msgstr "feat(框架) 添加 flwr 构建 CLI 命令。" -#: ../../source/contributor-tutorial-contribute-on-github.rst:418 -msgid "" -"Note that only one token must be provided, otherwise, only the first " -"action (in the order listed above), will be performed." +#: ../../source/contributor-tutorial-contribute-on-github.rst:354 +#, fuzzy +msgid "``Add flwr build CLI command.`` (missing ``()``)" msgstr "" +"``添加 flwr build CLI 命令.``（缺少``()``） ``Add flwr build CLI " +"command.`` (missing ``()``)" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:2 msgid "Get started as a contributor" msgstr "成为贡献者" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:5 +#: ../../source/how-to-run-flower-using-docker.rst:153 msgid "Prerequisites" msgstr "先决条件" @@ -2198,38 +2092,45 @@ msgid "Developer Machine Setup" msgstr "开发者机器设置" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:21 +#, fuzzy msgid "Preliminarities" -msgstr "" +msgstr "前言" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:22 +#, fuzzy msgid "Some system-wide dependencies are needed." -msgstr "" +msgstr "需要一些全系统依赖性。" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:25 +#, fuzzy msgid "For macOS" -msgstr "" +msgstr "适用于 macOS" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:27 +#, fuzzy msgid "" "Install `homebrew `_. Don't forget the post-" "installation actions to add `brew` to your PATH." -msgstr "" +msgstr "安装 `homebrew `_。别忘了安装后的操作，将 `brew` 添加到你的 PATH。" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:28 +#, fuzzy msgid "" "Install `xz` (to install different Python versions) and `pandoc` to build" " the docs::" -msgstr "" +msgstr "安装 `xz`（用于安装不同的 Python 版本）和 `pandoc` 以构建文档：：" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:34 +#, fuzzy msgid "For Ubuntu" -msgstr "" +msgstr "针对 Ubuntu" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:35 +#, fuzzy msgid "" "Ensure you system (Ubuntu 22.04+) is up-to-date, and you have all " "necessary packages::" -msgstr "" +msgstr "确保您的系统（Ubuntu 22.04+）为最新版本，并安装了所有必要的软件包：：" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:44 #, fuzzy @@ -2244,6 +2145,7 @@ msgid "" msgstr "首先，从 GitHub 克隆 \"Flower 存储库 `_\"：" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:52 +#, fuzzy msgid "" "Let's create the Python environment for all-things Flower. If you wish to" " use :code:`pyenv`, we provide two convenience scripts that you can use. " @@ -2251,6 +2153,8 @@ msgid "" "environment, activate and skip to the last point where all packages are " "installed." msgstr "" +"让我们为 Flower 创建一个 Python 环境。如果您想使用 :code:`pyenv`，我们提供了两个方便的脚本供您使用。如果你不喜欢使用" +" :code:`pyenv`，请创建一个新环境，激活并跳到最后一点，即安装所有软件包。" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:54 #, fuzzy @@ -2308,10 +2212,66 @@ msgid "Run Linters and Tests" msgstr "运行分类器和测试" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:106 +#, fuzzy +msgid "Add a pre-commit hook" +msgstr "添加预先提交钩子" + +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:108 +#, fuzzy +msgid "" +"Developers may integrate a pre-commit hook into their workflow utilizing " +"the `pre-commit `_ library. The pre-" +"commit hook is configured to execute two primary operations: " +"``./dev/format.sh`` and ``./dev/test.sh`` scripts." +msgstr "" +"开发人员可利用 `pre-commit `_ " +"库将预提交钩子集成到工作流程中。预提交钩子被配置为执行两个主要操作： `./dev/format.sh`` 和 ``./dev/test.sh``" +" 脚本。" + +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:110 +#, fuzzy +msgid "There are multiple ways developers can use this:" +msgstr "开发人员可以通过多种方式使用它：" + +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:112 +#, fuzzy +msgid "Install the pre-commit hook to your local git directory by simply running:" +msgstr "在本地 git 目录中安装预提交钩子，只需运行" + +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:118 +#, fuzzy +msgid "" +"Each ``git commit`` will trigger the execution of formatting and " +"linting/test scripts." +msgstr "每次 \"git 提交 \"都会触发格式化和内核/测试脚本的执行。" + +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:119 +#, fuzzy +msgid "" +"If in a hurry, bypass the hook using ``--no-verify`` with the ``git " +"commit`` command. ::" +msgstr "如果赶时间，可使用 ``--no-verify`` 和 ``git commit` 命令绕过钩子：" + +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:124 +#, fuzzy +msgid "" +"For developers who prefer not to install the hook permanently, it is " +"possible to execute a one-time check prior to committing changes by using" +" the following command:" +msgstr "对于不想永久安装钩子的开发人员，可以使用以下命令在提交更改之前执行一次性检查：" + +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:130 +#, fuzzy +msgid "" +"This executes the formatting and linting checks/tests on all the files " +"without modifying the default behavior of ``git commit``." +msgstr "这将在不修改 ``git commit`` 默认行为的情况下对所有文件执行格式化和词排检查/测试。" + +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:133 msgid "Run Github Actions (CI) locally" msgstr "在本地运行 Github 操作 (CI)" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:108 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:135 #, fuzzy msgid "" "Developers could run the full set of Github Actions workflows under their" @@ -2322,33 +2282,33 @@ msgstr "" "开发人员可以使用 `Act _` 在本地环境下运行全套 Github Actions" " 工作流程。请参考链接仓库下的安装说明，并在 Flower 主克隆仓库文件夹下运行下一条命令：：" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:115 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:142 msgid "" "The Flower default workflow would run by setting up the required Docker " "machines underneath." msgstr "Flower 默认工作流程将通过在下面设置所需的 Docker 机器来运行。" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:120 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:147 msgid "Build Release" msgstr "版本发布" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:122 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:149 msgid "" "Flower uses Poetry to build releases. The necessary command is wrapped in" " a simple script::" msgstr "Flower 使用 Poetry 创建发布版本。必要的命令封装在一个简单的脚本中：：" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:127 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:154 msgid "" "The resulting :code:`.whl` and :code:`.tar.gz` releases will be stored in" " the :code:`/dist` subdirectory." msgstr "生成的 :code:`.whl` 和 :code:`.tar.gz` 版本将存储在 :code:`/dist` 子目录中。" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:132 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:159 msgid "Build Documentation" msgstr "构建文档" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:134 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:161 msgid "" "Flower's documentation uses `Sphinx `_. " "There's no convenience script to re-build the documentation yet, but it's" @@ -2357,7 +2317,7 @@ msgstr "" "Flower 的文档使用 `Sphinx `_。目前还没有很方便的脚本来重新构建文档，不过这很容易：：" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:140 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:167 msgid "This will generate HTML documentation in ``doc/build/html``." msgstr "这将在 ``doc/build/html`` 中生成 HTML 文档。" @@ -2473,7 +2433,6 @@ msgstr "确保服务器仍在运行后，然后您就能看到您的 PyTorch 项 #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:94 #: ../../source/example-jax-from-centralized-to-federated.rst:277 -#: ../../source/example-mxnet-walk-through.rst:356 #: ../../source/example-pytorch-from-centralized-to-federated.rst:310 #: ../../source/tutorial-quickstart-jax.rst:283 msgid "Next Steps" @@ -2642,7 +2601,6 @@ msgstr "" ":code:`jax_training.py`中的代码。*服务器*向客户端发送模型参数，*客户端*运行训练并更新参数。更新后的参数被发回*服务器*，然后服务器对所有收到的参数进行平均聚合。以上的描述构成了一轮联邦学习，我们将重复进行多轮学习。" #: ../../source/example-jax-from-centralized-to-federated.rst:123 -#: ../../source/example-mxnet-walk-through.rst:204 #: ../../source/example-pytorch-from-centralized-to-federated.rst:181 #: ../../source/tutorial-quickstart-jax.rst:129 msgid "" @@ -2655,7 +2613,6 @@ msgstr "" ":code:`flwr`。接下来，我们使用 :code:`start_server` 函数启动服务器，并让它执行三轮联邦学习。" #: ../../source/example-jax-from-centralized-to-federated.rst:133 -#: ../../source/example-mxnet-walk-through.rst:214 #: ../../source/example-pytorch-from-centralized-to-federated.rst:191 #: ../../source/tutorial-quickstart-jax.rst:139 msgid "We can already start the *server*:" @@ -2695,13 +2652,11 @@ msgstr "" "需要实现四个方法，两个用于获取/设置模型参数，一个用于训练模型，一个用于测试模型：" #: ../../source/example-jax-from-centralized-to-federated.rst:161 -#: ../../source/example-mxnet-walk-through.rst:242 #: ../../source/tutorial-quickstart-jax.rst:167 msgid ":code:`set_parameters (optional)`" msgstr ":code:`set_parameters (可选)`" #: ../../source/example-jax-from-centralized-to-federated.rst:160 -#: ../../source/example-mxnet-walk-through.rst:241 #: ../../source/example-pytorch-from-centralized-to-federated.rst:219 #: ../../source/tutorial-quickstart-jax.rst:166 msgid "" @@ -2715,7 +2670,6 @@ msgid "transform parameters to NumPy :code:`ndarray`'s" msgstr "将参数转换为 NumPy :code:`ndarray`格式" #: ../../source/example-jax-from-centralized-to-federated.rst:162 -#: ../../source/example-mxnet-walk-through.rst:243 #: ../../source/example-pytorch-from-centralized-to-federated.rst:220 #: ../../source/tutorial-quickstart-jax.rst:168 msgid "" @@ -2724,17 +2678,14 @@ msgid "" msgstr "循环遍历以 NumPy :code:`ndarray` 形式接收的模型参数列表（可以看作神经网络的列表）" #: ../../source/example-jax-from-centralized-to-federated.rst:163 -#: ../../source/example-mxnet-walk-through.rst:244 #: ../../source/example-pytorch-from-centralized-to-federated.rst:221 #: ../../source/tutorial-quickstart-jax.rst:169 -#: ../../source/tutorial-quickstart-mxnet.rst:171 #: ../../source/tutorial-quickstart-pytorch.rst:155 -#: ../../source/tutorial-quickstart-scikitlearn.rst:108 +#: ../../source/tutorial-quickstart-scikitlearn.rst:118 msgid ":code:`get_parameters`" msgstr ":code:`get_parameters`" #: ../../source/example-jax-from-centralized-to-federated.rst:164 -#: ../../source/example-mxnet-walk-through.rst:245 #: ../../source/example-pytorch-from-centralized-to-federated.rst:222 #: ../../source/tutorial-quickstart-jax.rst:170 msgid "" @@ -2745,19 +2696,15 @@ msgstr "" ":code:`flwr.client.NumPyClient`所匹配的格式）" #: ../../source/example-jax-from-centralized-to-federated.rst:167 -#: ../../source/example-mxnet-walk-through.rst:248 #: ../../source/example-pytorch-from-centralized-to-federated.rst:225 #: ../../source/tutorial-quickstart-jax.rst:173 -#: ../../source/tutorial-quickstart-mxnet.rst:177 #: ../../source/tutorial-quickstart-pytorch.rst:161 -#: ../../source/tutorial-quickstart-scikitlearn.rst:115 +#: ../../source/tutorial-quickstart-scikitlearn.rst:125 msgid ":code:`fit`" msgstr ":code:`fit`" #: ../../source/example-jax-from-centralized-to-federated.rst:166 #: ../../source/example-jax-from-centralized-to-federated.rst:170 -#: ../../source/example-mxnet-walk-through.rst:247 -#: ../../source/example-mxnet-walk-through.rst:251 #: ../../source/example-pytorch-from-centralized-to-federated.rst:224 #: ../../source/example-pytorch-from-centralized-to-federated.rst:228 #: ../../source/tutorial-quickstart-jax.rst:172 @@ -2768,7 +2715,6 @@ msgid "" msgstr "用从服务器接收到的参数更新本地模型的参数" #: ../../source/example-jax-from-centralized-to-federated.rst:167 -#: ../../source/example-mxnet-walk-through.rst:248 #: ../../source/example-pytorch-from-centralized-to-federated.rst:225 #: ../../source/tutorial-quickstart-jax.rst:173 msgid "train the model on the local training set" @@ -2780,17 +2726,14 @@ msgid "get the updated local model parameters and return them to the server" msgstr "获取更新后的本地模型参数并返回服务器" #: ../../source/example-jax-from-centralized-to-federated.rst:172 -#: ../../source/example-mxnet-walk-through.rst:253 #: ../../source/example-pytorch-from-centralized-to-federated.rst:230 #: ../../source/tutorial-quickstart-jax.rst:178 -#: ../../source/tutorial-quickstart-mxnet.rst:180 #: ../../source/tutorial-quickstart-pytorch.rst:164 -#: ../../source/tutorial-quickstart-scikitlearn.rst:118 +#: ../../source/tutorial-quickstart-scikitlearn.rst:128 msgid ":code:`evaluate`" msgstr ":code:`evaluate`" #: ../../source/example-jax-from-centralized-to-federated.rst:171 -#: ../../source/example-mxnet-walk-through.rst:252 #: ../../source/example-pytorch-from-centralized-to-federated.rst:229 #: ../../source/tutorial-quickstart-jax.rst:177 msgid "evaluate the updated model on the local test set" @@ -2833,7 +2776,6 @@ msgid "Having defined the federation process, we can run it." msgstr "定义了联邦进程后，我们就可以运行它了。" #: ../../source/example-jax-from-centralized-to-federated.rst:268 -#: ../../source/example-mxnet-walk-through.rst:347 #: ../../source/example-pytorch-from-centralized-to-federated.rst:301 #: ../../source/tutorial-quickstart-jax.rst:274 msgid "And that's it. You can now open two additional terminal windows and run" @@ -2867,373 +2809,131 @@ msgid "" "clients?" msgstr "现在，您已准备好进行更深一步探索了。例如使用更复杂的模型或使用不同的数据集会如何？增加更多客户端会如何？" -#: ../../source/example-mxnet-walk-through.rst:2 -msgid "Example: MXNet - Run MXNet Federated" -msgstr "示例： MXNet - 运行联邦式 MXNet" +#: ../../source/example-pytorch-from-centralized-to-federated.rst:2 +msgid "Example: PyTorch - From Centralized To Federated" +msgstr "实例： PyTorch - 从集中式到联邦式" -#: ../../source/example-mxnet-walk-through.rst:4 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:4 msgid "" "This tutorial will show you how to use Flower to build a federated " -"version of an existing MXNet workload. We are using MXNet to train a " -"Sequential model on the MNIST dataset. We will structure the example " -"similar to our `PyTorch - From Centralized To Federated " -"`_ walkthrough. MXNet and PyTorch are very " -"similar and a very good comparison between MXNet and PyTorch is given " -"`here `_. First, we build a centralized " -"training approach based on the `Handwritten Digit Recognition " -"`_" -" tutorial. Then, we build upon the centralized training code to run the " +"version of an existing machine learning workload. We are using PyTorch to" +" train a Convolutional Neural Network on the CIFAR-10 dataset. First, we " +"introduce this machine learning task with a centralized training approach" +" based on the `Deep Learning with PyTorch " +"`_ " +"tutorial. Then, we build upon the centralized training code to run the " "training in a federated fashion." msgstr "" -"本教程将向您展示如何使用 Flower 构建现有 MXNet 的联学习版本。我们将使用 MXNet 在 MNIST " -"数据集上训练一个序列模型。另外，我们将采用与我们的 `PyTorch - 从集中式到联邦式 " -"`_ 教程类似的示例结构。MXNet 和 PyTorch 非常相似，参考 `此处 " -"`_对 MXNet 和 PyTorch " -"进行了详细的比较。首先，我们根据 `手写数字识别 " -"`" -" 教程 建立了集中式训练方法。然后，我们在集中式训练代码的基础上，以联邦方式运行训练。" +"本教程将向您展示如何使用 Flower 构建现有机器学习工作的联邦版本。我们使用 PyTorch 在 CIFAR-10 " +"数据集上训练一个卷积神经网络。首先，我们基于 \"Deep Learning with PyTorch " +"`_\"教程，采用集中式训练方法介绍了这项机器学习任务。然后，我们在集中式训练代码的基础上以联邦方式运行训练。" -#: ../../source/example-mxnet-walk-through.rst:10 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:12 msgid "" -"Before we start setting up our MXNet example, we install the " -":code:`mxnet` and :code:`flwr` packages:" -msgstr "在开始设置 MXNet 示例之前，我们先安装 :code:`mxnet` 和 :code:`flwr` 软件包：" - -#: ../../source/example-mxnet-walk-through.rst:19 -msgid "MNIST Training with MXNet" -msgstr "使用 MXNet 进行 MNIST 训练" +"We begin with a brief description of the centralized CNN training code. " +"If you want a more in-depth explanation of what's going on then have a " +"look at the official `PyTorch tutorial " +"`_." +msgstr "" +"我们首先简要介绍一下集中式 CNN 训练代码。如果您想获得更深入的解释，请参阅 PyTorch 官方教程`PyTorch tutorial " +"`_。" -#: ../../source/example-mxnet-walk-through.rst:21 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:15 msgid "" -"We begin with a brief description of the centralized training code based " -"on a :code:`Sequential` model. If you want a more in-depth explanation of" -" what's going on then have a look at the official `MXNet tutorial " -"`_." +"Let's create a new file called :code:`cifar.py` with all the components " +"required for a traditional (centralized) training on CIFAR-10. First, all" +" required packages (such as :code:`torch` and :code:`torchvision`) need " +"to be imported. You can see that we do not import any package for " +"federated learning. You can keep all these imports as they are even when " +"we add the federated learning components at a later point." msgstr "" -"首先，我们将简要介绍基于 :code:`Sequential` 模型的集中式训练代码。如果您想获得更深入的解释，请参阅官方的 `MXNet教程 " -"`_。" +"让我们创建一个名为 :code:`cifar.py` 的新文件，其中包含 CIFAR-10 " +"传统（集中）培训所需的所有组件。首先，需要导入所有必需的软件包（如 :code:`torch` 和 " +":code:`torchvision`）。您可以看到，我们没有导入任何用于联邦学习的软件包。即使在以后添加联邦学习组件时，也可以保留所有这些导入。" -#: ../../source/example-mxnet-walk-through.rst:24 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:32 msgid "" -"Let's create a new file called:code:`mxnet_mnist.py` with all the " -"components required for a traditional (centralized) MNIST training. " -"First, the MXNet package :code:`mxnet` needs to be imported. You can see " -"that we do not yet import the :code:`flwr` package for federated " -"learning. This will be done later." +"As already mentioned we will use the CIFAR-10 dataset for this machine " +"learning workload. The model architecture (a very simple Convolutional " +"Neural Network) is defined in :code:`class Net()`." msgstr "" -"让我们创建一个名为:code:`mxnet_mnist.py`的新文件，其中包含传统（集中式）MNIST 训练所需的所有组件。首先，需要导入 " -"MXNet 包 :code:`mxnet`。您可以看到，我们尚未导入用于联合学习的 :code:`flwr` 包，这将在稍后完成。" - -#: ../../source/example-mxnet-walk-through.rst:42 -msgid "The :code:`load_data()` function loads the MNIST training and test sets." -msgstr ":code:`load_data()` 函数加载 MNIST 训练集和测试集。" +"如前所述，我们将使用 CIFAR-10 数据集进行机器学习。模型架构（一个非常简单的卷积神经网络）在 :code:`class Net()` " +"中定义。" -#: ../../source/example-mxnet-walk-through.rst:57 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:56 msgid "" -"As already mentioned, we will use the MNIST dataset for this machine " -"learning workload. The model architecture (a very simple " -":code:`Sequential` model) is defined in :code:`model()`." +"The :code:`load_data()` function loads the CIFAR-10 training and test " +"sets. The :code:`transform` normalized the data after loading." msgstr "" -"如前所述，我们将使用 MNIST 数据集进行机器学习。模型架构（一个非常简单的 :code:`Sequential` 模型）在 " -":code:`model()` 中定义。" +":code:`load_data()` 函数加载 CIFAR-10 " +"训练集和测试集。加载数据后，:code:`transform`函数对数据进行了归一化处理。" -#: ../../source/example-mxnet-walk-through.rst:70 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:74 msgid "" "We now need to define the training (function :code:`train()`) which loops" -" over the training set and measures the loss for each batch of training " -"examples." -msgstr "现在，我们需要定义训练函数（ :code:`train()`），该函数在训练集上循环训练，并计算每批训练示例的损失值。" +" over the training set, measures the loss, backpropagates it, and then " +"takes one optimizer step for each batch of training examples." +msgstr "现在，我们需要定义训练函数（:code:`train()`），该函数在训练集上循环训练，计算损失值并反向传播，然后为每批训练数据在优化器上执行一个优化步骤。" -#: ../../source/example-mxnet-walk-through.rst:123 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:76 msgid "" -"The evaluation of the model is defined in function :code:`test()`. The " -"function loops over all test samples and measures the loss and accuracy " -"of the model based on the test dataset." -msgstr "模型的评估在函数 :code:`test()` 中定义。该函数循环遍历所有测试样本，并根据测试数据集计算模型的损失值和准确度。" +"The evaluation of the model is defined in the function :code:`test()`. " +"The function loops over all test samples and measures the loss of the " +"model based on the test dataset." +msgstr "模型的评估在函数 :code:`test()` 中定义。该函数循环遍历所有测试样本，并计算测试数据集的模型损失值。" -#: ../../source/example-mxnet-walk-through.rst:158 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:136 msgid "" "Having defined the data loading, model architecture, training, and " -"evaluation we can put everything together and train our model on MNIST. " -"Note that the GPU/CPU device for the training and testing is defined " -"within the :code:`ctx` (context)." -msgstr "" -"在定义了数据加载、模型架构、训练和评估之后，我们就可以把所有放在一起，在 MNIST 上训练我们的模型了。请注意，用于训练和测试的 GPU/CPU" -" 设备是在 :code:`ctx`中定义的。" - -#: ../../source/example-mxnet-walk-through.rst:184 -msgid "You can now run your (centralized) MXNet machine learning workload:" -msgstr "现在，您可以运行（集中式）MXNet 机器学习工作：" +"evaluation we can put everything together and train our CNN on CIFAR-10." +msgstr "在确定了数据加载、模型架构、训练和评估之后，我们就可以将所有整合在一起，在 CIFAR-10 上训练我们的 CNN。" -#: ../../source/example-mxnet-walk-through.rst:190 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:163 msgid "" -"So far this should all look fairly familiar if you've used MXNet (or even" -" PyTorch) before. Let's take the next step and use what we've built to " -"create a simple federated learning system consisting of one server and " -"two clients." +"So far, this should all look fairly familiar if you've used PyTorch " +"before. Let's take the next step and use what we've built to create a " +"simple federated learning system consisting of one server and two " +"clients." msgstr "" -"到目前为止，如果你以前使用过 MXNet（甚至 " -"PyTorch），这一切看起来应该相当熟悉。下一步，让我们利用已构建的内容创建一个简单联邦学习系统（由一个服务器和两个客户端组成）。" +"到目前为止，如果你以前用过 " +"PyTorch，这一切看起来应该相当熟悉。让我们进行下一步，利用我们所构建的内容创建一个简单联邦学习系统（由一个服务器和两个客户端组成）。" -#: ../../source/example-mxnet-walk-through.rst:194 -msgid "MXNet meets Flower" -msgstr "MXNet 结合 Flower" +#: ../../source/example-pytorch-from-centralized-to-federated.rst:169 +msgid "" +"The simple machine learning project discussed in the previous section " +"trains the model on a single dataset (CIFAR-10), we call this centralized" +" learning. This concept of centralized learning, as shown in the previous" +" section, is probably known to most of you, and many of you have used it " +"previously. Normally, if you'd want to run machine learning workloads in " +"a federated fashion, then you'd have to change most of your code and set " +"everything up from scratch. This can be a considerable effort." +msgstr "上一节讨论的简单机器学习项目在单一数据集（CIFAR-10）上训练模型，我们称之为集中学习。如上一节所示，集中学习的概念可能为大多数人所熟知，而且很多人以前都使用过。通常情况下，如果要以联邦方式运行机器学习工作，就必须更改大部分代码，并从头开始设置一切。这可能是一个相当大的工作量。" -#: ../../source/example-mxnet-walk-through.rst:196 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:173 msgid "" -"So far, it was not easily possible to use MXNet workloads for federated " -"learning because federated learning is not supported in MXNet. Since " -"Flower is fully agnostic towards the underlying machine learning " -"framework, it can be used to federated arbitrary machine learning " -"workloads. This section will show you how Flower can be used to federate " -"our centralized MXNet workload." -msgstr "" -"由于 MXNet 目前不支持联邦学习，因此无法轻松地直接将 MXNet 用于联邦学习之中。Flower " -"与底层机器学习框架完全无关，因此它可用于任意联邦式机器学习工作。本节将向你展示如何使用 Flower 将我们的集中式 MXNet 改为联邦式训练。" +"However, with Flower you can evolve your pre-existing code into a " +"federated learning setup without the need for a major rewrite." +msgstr "不过，有了 Flower，您可以轻松地将已有的代码转变成联邦学习的模式，无需进行大量重写。" -#: ../../source/example-mxnet-walk-through.rst:198 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:175 msgid "" -"The concept to federate an existing workload is always the same and easy " -"to understand. We have to start a *server* and then use the code in " -":code:`mxnet_mnist.py` for the *clients* that are connected to the " -"*server*. The *server* sends model parameters to the clients. The " +"The concept is easy to understand. We have to start a *server* and then " +"use the code in :code:`cifar.py` for the *clients* that are connected to " +"the *server*. The *server* sends model parameters to the clients. The " "*clients* run the training and update the parameters. The updated " "parameters are sent back to the *server* which averages all received " "parameter updates. This describes one round of the federated learning " "process and we repeat this for multiple rounds." msgstr "" -"将现有模型框架联邦化的概念始终是相同的，也很容易理解。我们必须启动一个*服务器*，然后对连接到*服务器*的*客户端*使用 " -":code:`mxnet_mnist.py`中的代码。*服务器*向客户端发送模型参数，然后*客户端*运行训练并更新参数。更新后的参数被发回*服务器*，然后会对所有收到的参数更新进行平均聚合。以上描述的是一轮联邦学习过程，我们将重复进行多轮学习。" +"这个概念很容易理解。我们必须启动一个*服务器*，然后对连接到*服务器*的*客户端*使用 " +":code:`cifar.py`中的代码。*服务器*向客户端发送模型参数，*客户端*运行训练并更新参数。更新后的参数被发回*服务器*，然后会对所有收到的参数更新进行平均聚合。以上描述的是一轮联邦学习过程，我们将重复进行多轮学习。" -#: ../../source/example-mxnet-walk-through.rst:220 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:197 msgid "" "Finally, we will define our *client* logic in :code:`client.py` and build" -" upon the previously defined MXNet training in :code:`mxnet_mnist.py`. " -"Our *client* needs to import :code:`flwr`, but also :code:`mxnet` to " -"update the parameters on our MXNet model:" -msgstr "" -"最后，我们将在 :code:`client.py` 中定义我们的 *client* 逻辑，并以之前在 :code:`mxnet_mnist.py`" -" 中定义的 MXNet 训练为基础。我们的 *client* 不仅需要导入 :code:`flwr`，还需要导入 " -":code:`mxnet`，以更新 MXNet 模型的参数：" - -#: ../../source/example-mxnet-walk-through.rst:235 -msgid "" -"Implementing a Flower *client* basically means implementing a subclass of" -" either :code:`flwr.client.Client` or :code:`flwr.client.NumPyClient`. " -"Our implementation will be based on :code:`flwr.client.NumPyClient` and " -"we'll call it :code:`MNISTClient`. :code:`NumPyClient` is slightly easier" -" to implement than :code:`Client` if you use a framework with good NumPy " -"interoperability (like PyTorch or MXNet) because it avoids some of the " -"boilerplate that would otherwise be necessary. :code:`MNISTClient` needs " -"to implement four methods, two methods for getting/setting model " -"parameters, one method for training the model, and one method for testing" -" the model:" -msgstr "" -"实现 Flower *client*基本上意味着要实现 :code:`flwr.client.Client` 或 " -":code:`flwr.client.NumPyClient` 的子类。我们的代码实现将基于 " -":code:`flwr.client.NumPyClient`，并将其命名为 :code:`MNISTClient`。如果使用具有良好 NumPy" -" 互操作性的框架（如 PyTorch 或 MXNet），:code:`NumPyClient` 比 " -":code:`Client`更容易实现，因为它避免了一些不必要的操作。:code:`MNISTClient` " -"需要实现四个方法，两个用于获取/设置模型参数，一个用于训练模型，一个用于测试模型：" - -#: ../../source/example-mxnet-walk-through.rst:242 -msgid "transform MXNet :code:`NDArray`'s to NumPy :code:`ndarray`'s" -msgstr "将 MXNet :code:`NDArray` 转换为 NumPy :code:`ndarray`" - -#: ../../source/example-mxnet-walk-through.rst:249 -#: ../../source/example-pytorch-from-centralized-to-federated.rst:226 -msgid "get the updated local model weights and return them to the server" -msgstr "获取更新后的本地模型参数并发送回服务器" - -#: ../../source/example-mxnet-walk-through.rst:253 -#: ../../source/example-pytorch-from-centralized-to-federated.rst:230 -msgid "return the local loss and accuracy to the server" -msgstr "向服务器返回本地损失值和精确度" - -#: ../../source/example-mxnet-walk-through.rst:255 -msgid "" -"The challenging part is to transform the MXNet parameters from " -":code:`NDArray` to :code:`NumPy Arrays` to make it readable for Flower." -msgstr "" -"具有挑战性的部分是将 MXNet 参数从 :code:`NDArray` 转换为 :code:`NumPy Arrays` 以便 Flower " -"可以读取。" - -#: ../../source/example-mxnet-walk-through.rst:257 -msgid "" -"The two :code:`NumPyClient` methods :code:`fit` and :code:`evaluate` make" -" use of the functions :code:`train()` and :code:`test()` previously " -"defined in :code:`mxnet_mnist.py`. So what we really do here is we tell " -"Flower through our :code:`NumPyClient` subclass which of our already " -"defined functions to call for training and evaluation. We included type " -"annotations to give you a better understanding of the data types that get" -" passed around." -msgstr "" -"这两个 :code:`NumPyClient` 方法 :code:`fit` 和 :code:`evaluate` 使用了之前在 " -":code:`mxnet_mnist.py` 中定义的函数 :code:`train()` 和 " -":code:`test()`。因此，我们要做的就是通过 :code:`NumPyClient` 子类告知 Flower " -"在训练和评估时要调用哪些已定义的函数。我们加入了类型注解，以便让您更好地理解传递的数据类型。" - -#: ../../source/example-mxnet-walk-through.rst:319 -msgid "" -"Having defined data loading, model architecture, training, and evaluation" -" we can put everything together and train our :code:`Sequential` model on" -" MNIST." -msgstr "" -"在定义了数据加载、模型架构、训练和评估之后，我们就可以将所有内容整合在一起，在 MNIST 上训练我们的 :code:`Sequential` " -"模型。" - -#: ../../source/example-mxnet-walk-through.rst:353 -msgid "" -"in each window (make sure that the server is still running before you do " -"so) and see your MXNet project run federated learning across two clients." -" Congratulations!" -msgstr "确保服务器仍在运行后，然后就能在每个窗口中看到 MXNet 项目在两个客户端上运行联邦学习了。祝贺!" - -#: ../../source/example-mxnet-walk-through.rst:358 -msgid "" -"The full source code for this example: `MXNet: From Centralized To " -"Federated (Code) `_. Our example is of course " -"somewhat over-simplified because both clients load the exact same " -"dataset, which isn't realistic. You're now prepared to explore this topic" -" further. How about using a CNN or using a different dataset? How about " -"adding more clients?" -msgstr "" -"此示例的完整源代码在：\"MXNet： From Centralized To Federated (Code) " -"`_。当然，我们的示例有些过于简单，因为两个客户端都加载了完全相同的数据集，这并不真实。现在您已经准备好进一步探讨了。使用 " -"CNN 或使用不同的数据集会如何？添加更多客户端会如何？" - -#: ../../source/example-pytorch-from-centralized-to-federated.rst:2 -msgid "Example: PyTorch - From Centralized To Federated" -msgstr "实例： PyTorch - 从集中式到联邦式" - -#: ../../source/example-pytorch-from-centralized-to-federated.rst:4 -msgid "" -"This tutorial will show you how to use Flower to build a federated " -"version of an existing machine learning workload. We are using PyTorch to" -" train a Convolutional Neural Network on the CIFAR-10 dataset. First, we " -"introduce this machine learning task with a centralized training approach" -" based on the `Deep Learning with PyTorch " -"`_ " -"tutorial. Then, we build upon the centralized training code to run the " -"training in a federated fashion." -msgstr "" -"本教程将向您展示如何使用 Flower 构建现有机器学习工作的联邦版本。我们使用 PyTorch 在 CIFAR-10 " -"数据集上训练一个卷积神经网络。首先，我们基于 \"Deep Learning with PyTorch " -"`_\"教程，采用集中式训练方法介绍了这项机器学习任务。然后，我们在集中式训练代码的基础上以联邦方式运行训练。" - -#: ../../source/example-pytorch-from-centralized-to-federated.rst:12 -msgid "" -"We begin with a brief description of the centralized CNN training code. " -"If you want a more in-depth explanation of what's going on then have a " -"look at the official `PyTorch tutorial " -"`_." -msgstr "" -"我们首先简要介绍一下集中式 CNN 训练代码。如果您想获得更深入的解释，请参阅 PyTorch 官方教程`PyTorch tutorial " -"`_。" - -#: ../../source/example-pytorch-from-centralized-to-federated.rst:15 -msgid "" -"Let's create a new file called :code:`cifar.py` with all the components " -"required for a traditional (centralized) training on CIFAR-10. First, all" -" required packages (such as :code:`torch` and :code:`torchvision`) need " -"to be imported. You can see that we do not import any package for " -"federated learning. You can keep all these imports as they are even when " -"we add the federated learning components at a later point." -msgstr "" -"让我们创建一个名为 :code:`cifar.py` 的新文件，其中包含 CIFAR-10 " -"传统（集中）培训所需的所有组件。首先，需要导入所有必需的软件包（如 :code:`torch` 和 " -":code:`torchvision`）。您可以看到，我们没有导入任何用于联邦学习的软件包。即使在以后添加联邦学习组件时，也可以保留所有这些导入。" - -#: ../../source/example-pytorch-from-centralized-to-federated.rst:32 -msgid "" -"As already mentioned we will use the CIFAR-10 dataset for this machine " -"learning workload. The model architecture (a very simple Convolutional " -"Neural Network) is defined in :code:`class Net()`." -msgstr "" -"如前所述，我们将使用 CIFAR-10 数据集进行机器学习。模型架构（一个非常简单的卷积神经网络）在 :code:`class Net()` " -"中定义。" - -#: ../../source/example-pytorch-from-centralized-to-federated.rst:56 -msgid "" -"The :code:`load_data()` function loads the CIFAR-10 training and test " -"sets. The :code:`transform` normalized the data after loading." -msgstr "" -":code:`load_data()` 函数加载 CIFAR-10 " -"训练集和测试集。加载数据后，:code:`transform`函数对数据进行了归一化处理。" - -#: ../../source/example-pytorch-from-centralized-to-federated.rst:74 -msgid "" -"We now need to define the training (function :code:`train()`) which loops" -" over the training set, measures the loss, backpropagates it, and then " -"takes one optimizer step for each batch of training examples." -msgstr "现在，我们需要定义训练函数（:code:`train()`），该函数在训练集上循环训练，计算损失值并反向传播，然后为每批训练数据在优化器上执行一个优化步骤。" - -#: ../../source/example-pytorch-from-centralized-to-federated.rst:76 -msgid "" -"The evaluation of the model is defined in the function :code:`test()`. " -"The function loops over all test samples and measures the loss of the " -"model based on the test dataset." -msgstr "模型的评估在函数 :code:`test()` 中定义。该函数循环遍历所有测试样本，并计算测试数据集的模型损失值。" - -#: ../../source/example-pytorch-from-centralized-to-federated.rst:136 -msgid "" -"Having defined the data loading, model architecture, training, and " -"evaluation we can put everything together and train our CNN on CIFAR-10." -msgstr "在确定了数据加载、模型架构、训练和评估之后，我们就可以将所有整合在一起，在 CIFAR-10 上训练我们的 CNN。" - -#: ../../source/example-pytorch-from-centralized-to-federated.rst:163 -msgid "" -"So far, this should all look fairly familiar if you've used PyTorch " -"before. Let's take the next step and use what we've built to create a " -"simple federated learning system consisting of one server and two " -"clients." -msgstr "" -"到目前为止，如果你以前用过 " -"PyTorch，这一切看起来应该相当熟悉。让我们进行下一步，利用我们所构建的内容创建一个简单联邦学习系统（由一个服务器和两个客户端组成）。" - -#: ../../source/example-pytorch-from-centralized-to-federated.rst:169 -msgid "" -"The simple machine learning project discussed in the previous section " -"trains the model on a single dataset (CIFAR-10), we call this centralized" -" learning. This concept of centralized learning, as shown in the previous" -" section, is probably known to most of you, and many of you have used it " -"previously. Normally, if you'd want to run machine learning workloads in " -"a federated fashion, then you'd have to change most of your code and set " -"everything up from scratch. This can be a considerable effort." -msgstr "上一节讨论的简单机器学习项目在单一数据集（CIFAR-10）上训练模型，我们称之为集中学习。如上一节所示，集中学习的概念可能为大多数人所熟知，而且很多人以前都使用过。通常情况下，如果要以联邦方式运行机器学习工作，就必须更改大部分代码，并从头开始设置一切。这可能是一个相当大的工作量。" - -#: ../../source/example-pytorch-from-centralized-to-federated.rst:173 -msgid "" -"However, with Flower you can evolve your pre-existing code into a " -"federated learning setup without the need for a major rewrite." -msgstr "不过，有了 Flower，您可以轻松地将已有的代码转变成联邦学习的模式，无需进行大量重写。" - -#: ../../source/example-pytorch-from-centralized-to-federated.rst:175 -msgid "" -"The concept is easy to understand. We have to start a *server* and then " -"use the code in :code:`cifar.py` for the *clients* that are connected to " -"the *server*. The *server* sends model parameters to the clients. The " -"*clients* run the training and update the parameters. The updated " -"parameters are sent back to the *server* which averages all received " -"parameter updates. This describes one round of the federated learning " -"process and we repeat this for multiple rounds." -msgstr "" -"这个概念很容易理解。我们必须启动一个*服务器*，然后对连接到*服务器*的*客户端*使用 " -":code:`cifar.py`中的代码。*服务器*向客户端发送模型参数，*客户端*运行训练并更新参数。更新后的参数被发回*服务器*，然后会对所有收到的参数更新进行平均聚合。以上描述的是一轮联邦学习过程，我们将重复进行多轮学习。" - -#: ../../source/example-pytorch-from-centralized-to-federated.rst:197 -msgid "" -"Finally, we will define our *client* logic in :code:`client.py` and build" -" upon the previously defined centralized training in :code:`cifar.py`. " -"Our *client* needs to import :code:`flwr`, but also :code:`torch` to " -"update the parameters on our PyTorch model:" +" upon the previously defined centralized training in :code:`cifar.py`. " +"Our *client* needs to import :code:`flwr`, but also :code:`torch` to " +"update the parameters on our PyTorch model:" msgstr "" "最后，我们将在 :code:`client.py` 中定义我们的 *client* 逻辑，并以之前在 :code:`cifar.py` " "中定义的集中式训练为基础。我们的 *client* 不仅需要导入 :code:`flwr`，还需要导入 :code:`torch`，以更新 " @@ -3263,6 +2963,14 @@ msgstr "" msgid ":code:`set_parameters`" msgstr ":code:`set_parameters`" +#: ../../source/example-pytorch-from-centralized-to-federated.rst:226 +msgid "get the updated local model weights and return them to the server" +msgstr "获取更新后的本地模型参数并发送回服务器" + +#: ../../source/example-pytorch-from-centralized-to-federated.rst:230 +msgid "return the local loss and accuracy to the server" +msgstr "向服务器返回本地损失值和精确度" + #: ../../source/example-pytorch-from-centralized-to-federated.rst:232 msgid "" "The two :code:`NumPyClient` methods :code:`fit` and :code:`evaluate` make" @@ -3317,23 +3025,26 @@ msgid "Differential Privacy" msgstr "差分隐私" #: ../../source/explanation-differential-privacy.rst:3 +#, fuzzy msgid "" "The information in datasets like healthcare, financial transactions, user" " preferences, etc., is valuable and has the potential for scientific " "breakthroughs and provides important business insights. However, such " "data is also sensitive and there is a risk of compromising individual " "privacy." -msgstr "" +msgstr "医疗保健、金融交易、用户偏好等数据集中的信息非常宝贵，有可能带来科学突破并提供重要的商业见解。然而，这些数据也是敏感数据，存在泄露个人隐私的风险。" #: ../../source/explanation-differential-privacy.rst:6 +#, fuzzy msgid "" "Traditional methods like anonymization alone would not work because of " "attacks like Re-identification and Data Linkage. That's where " "differential privacy comes in. It provides the possibility of analyzing " "data while ensuring the privacy of individuals." -msgstr "" +msgstr "单靠匿名等传统方法是行不通的，因为会受到重新识别和数据链接等攻击。这就是差异化隐私的用武之地。它提供了在分析数据的同时确保个人隐私的可能性。" #: ../../source/explanation-differential-privacy.rst:12 +#, fuzzy msgid "" "Imagine two datasets that are identical except for a single record (for " "instance, Alice's data). Differential Privacy (DP) guarantees that any " @@ -3342,18 +3053,23 @@ msgid "" "preserves group patterns while obscuring individual details, ensuring the" " individual's information remains hidden in the crowd." msgstr "" +"试想一下，两个数据集除了一条记录（例如 Alice " +"的数据）之外完全相同。差分隐私（DP）可以保证任何分析（M），比如计算平均收入，对两个数据集都会产生几乎相同的结果（O 和 O' " +"将是相似的）。这既保留了群体模式，又掩盖了个人细节，确保个人的信息隐藏在人群中。" #: ../../source/explanation-differential-privacy.rst:-1 +#, fuzzy msgid "DP Intro" -msgstr "" +msgstr "DP 介绍" #: ../../source/explanation-differential-privacy.rst:22 +#, fuzzy msgid "" "One of the most commonly used mechanisms to achieve DP is adding enough " "noise to the output of the analysis to mask the contribution of each " "individual in the data while preserving the overall accuracy of the " "analysis." -msgstr "" +msgstr "实现 DP 的最常用机制之一是在分析输出中加入足够的噪音，以掩盖数据中每个个体的贡献，同时保持分析的整体准确性。" #: ../../source/explanation-differential-privacy.rst:25 #, fuzzy @@ -3361,6 +3077,7 @@ msgid "Formal Definition" msgstr "编译 ProtoBuf 定义" #: ../../source/explanation-differential-privacy.rst:26 +#, fuzzy msgid "" "Differential Privacy (DP) provides statistical guarantees against the " "information an adversary can infer through the output of a randomized " @@ -3371,14 +3088,22 @@ msgid "" "databases, D :sub:`1` and D :sub:`2`, that differ in only a single " "record, and for all possible outputs S ⊆ Range(A):" msgstr "" +"差分隐私（Differential " +"Privacy，DP）针对对手通过随机算法的输出所能推断出的信息提供统计保证。它为单个个体通过添加噪声对算法输出的影响提供了一个无条件的上限[1]。如果任意两个相邻的数据库D" +" :sub:`1`和D :sub:`2`只有一条记录不同，并且对于所有可能的输出S ⊆ " +"Range(A)，随机化机制M提供（:math:`epsilon`，:math:`\\delta`）差异隐私：" #: ../../source/explanation-differential-privacy.rst:32 +#, fuzzy msgid "" "\\small\n" "P[M(D_{1} \\in A)] \\leq e^{\\delta} P[M(D_{2} \\in A)] + \\delta" msgstr "" +"\\small\n" +"P[M(D_{1} \\in A)] \\leq e^{\\delta} P[M(D_{2} \\in A)] + \\delta" #: ../../source/explanation-differential-privacy.rst:38 +#, fuzzy msgid "" "The :math:`\\epsilon` parameter, also known as the privacy budget, is a " "metric of privacy loss. It also controls the privacy-utility trade-off; " @@ -3389,6 +3114,8 @@ msgid "" "proportional to the sensitivity of the output, which measures the maximum" " change in the output due to the inclusion or removal of a single record." msgstr "" +":math:`\\epsilon`参数也称为隐私预算，是衡量隐私损失的指标。较低的 :math:`\\epsilon` " +"值表示较高的隐私级别，但也可能降低效用。:math:`\\delta`参数考虑了:math:`\\epsilon`上限不成立的小概率。实现差异化隐私所需的噪声量与输出的灵敏度成正比，而输出的灵敏度是指由于包含或删除一条记录而导致的输出的最大变化。" #: ../../source/explanation-differential-privacy.rst:45 #, fuzzy @@ -3396,6 +3123,7 @@ msgid "Differential Privacy in Machine Learning" msgstr "差分隐私" #: ../../source/explanation-differential-privacy.rst:46 +#, fuzzy msgid "" "DP can be utilized in machine learning to preserve the privacy of the " "training data. Differentially private machine learning algorithms are " @@ -3409,6 +3137,10 @@ msgid "" "model training. Additionally, such noise can be incorporated into the " "model's output." msgstr "" +"机器学习中可以利用 DP " +"来保护训练数据的隐私。差分保密机器学习算法的设计方式是防止算法学习到任何单个数据点的任何特定信息，从而防止模型泄露敏感信息。根据引入噪声的阶段，有多种方法可将" +" DP " +"应用于机器学习算法。一种方法是在训练数据（特征或标签）中添加噪声，另一种方法是在模型训练过程中向损失函数的梯度注入噪声。此外，这种噪声还可以被纳入模型的输出中。" #: ../../source/explanation-differential-privacy.rst:53 #, fuzzy @@ -3416,6 +3148,7 @@ msgid "Differential Privacy in Federated Learning" msgstr "扩大联邦学习的规模" #: ../../source/explanation-differential-privacy.rst:54 +#, fuzzy msgid "" "Federated learning is a data minimization approach that allows multiple " "parties to collaboratively train a model without sharing their raw data. " @@ -3424,15 +3157,17 @@ msgid "" " about the local data. These leaks can be exploited by attacks such as " "membership inference and property inference attacks, or model inversion " "attacks." -msgstr "" +msgstr "联合学习是一种数据最小化方法，允许多方在不共享原始数据的情况下合作训练一个模型。然而，联合学习也带来了新的隐私挑战。各方与中央服务器之间的模型更新可能会泄露本地数据信息。这些泄漏信息可能会被攻击利用，如成员推断攻击、属性推断攻击或模型反转攻击。" #: ../../source/explanation-differential-privacy.rst:58 +#, fuzzy msgid "" "DP can play a crucial role in federated learning to provide privacy for " "the clients' data." -msgstr "" +msgstr "DP 可以在联合学习中发挥重要作用，为客户数据提供隐私保护。" #: ../../source/explanation-differential-privacy.rst:60 +#, fuzzy msgid "" "Depending on the granularity of privacy provision or the location of " "noise addition, different forms of DP exist in federated learning. In " @@ -3440,21 +3175,25 @@ msgid "" " learning based on where the noise is added: at the server (also known as" " the center) or at the client (also known as the local)." msgstr "" +"根据提供隐私的粒度或添加噪声的位置，联合学习中存在不同形式的 DP。在本说明中，我们将根据添加噪声的位置，重点介绍联合学习中利用 DP " +"的两种方法：在服务器（也称为中心）或客户端（也称为本地）。" #: ../../source/explanation-differential-privacy.rst:63 +#, fuzzy msgid "" "**Central Differential Privacy**: DP is applied by the server and the " "goal is to prevent the aggregated model from leaking information about " "each client's data." -msgstr "" +msgstr "**中央差分隐私**： DP 由服务器应用，目标是防止聚合模型泄露每个客户的数据信息。" #: ../../source/explanation-differential-privacy.rst:65 +#, fuzzy msgid "" "**Local Differential Privacy**: DP is applied on the client side before " "sending any information to the server and the goal is to prevent the " "updates that are sent to the server from leaking any information about " "the client's data." -msgstr "" +msgstr "**本地差分隐私**： 在向服务器发送任何信息之前，在客户端应用 DP，目的是防止向服务器发送的更新泄露任何有关客户端数据的信息。" #: ../../source/explanation-differential-privacy.rst:-1 #: ../../source/explanation-differential-privacy.rst:68 @@ -3464,13 +3203,15 @@ msgid "Central Differential Privacy" msgstr "差分隐私" #: ../../source/explanation-differential-privacy.rst:69 +#, fuzzy msgid "" "In this approach, which is also known as user-level DP, the central " "server is responsible for adding noise to the globally aggregated " "parameters. It should be noted that trust in the server is required." -msgstr "" +msgstr "在这种方法（也称为用户级 DP）中，中央服务器负责在全局汇总参数中添加噪声。需要注意的是，这需要对服务器的信任。" #: ../../source/explanation-differential-privacy.rst:76 +#, fuzzy msgid "" "While there are various ways to implement central DP in federated " "learning, we concentrate on the algorithms proposed by [2] and [3]. The " @@ -3484,12 +3225,16 @@ msgid "" " by restricting the `L2` norm of the clients' model updates, ensuring " "that larger updates are scaled down to fit within the norm `S`." msgstr "" +"虽然在联合学习中实现中央数据处理的方法有很多种，但我们将重点放在[2]和[3]提出的算法上。总体方法是剪辑客户端发送的模型更新，并在聚合模型中添加一定量的噪声。在每次迭代中，以特定概率随机选择一组客户端进行训练。每个客户端对自己的数据进行局部训练。然后，每个客户端的更新会被某个值`S`（灵敏度`S`）剪切。这将限制任何单个客户端的影响，这对隐私至关重要，通常也有利于稳健性。实现这一点的常用方法是限制客户机模型更新的" +" `L2` 准则，确保较大的更新被缩减以适应 `S` 准则。" #: ../../source/explanation-differential-privacy.rst:-1 +#, fuzzy msgid "clipping" -msgstr "" +msgstr "剪贴" #: ../../source/explanation-differential-privacy.rst:89 +#, fuzzy msgid "" "Afterwards, the Gaussian mechanism is used to add noise in order to " "distort the sum of all clients' updates. The amount of noise is scaled to" @@ -3497,38 +3242,45 @@ msgid "" "mechanism is used with a noise sampled from `N (0, σ²)` where `σ = ( " "noise_scale * S ) / (number of sampled clients)`." msgstr "" +"然后，使用高斯机制添加噪声，以扭曲所有客户端的更新总和。噪声量与灵敏度值成正比，以获得隐私保证。高斯机制的噪声采样范围为 `N (0, σ²)` " +"，其中 σ = ( 噪声规模 * S ) / (采样客户数)`。" #: ../../source/explanation-differential-privacy.rst:94 +#, fuzzy msgid "Clipping" -msgstr "" +msgstr "剪贴" #: ../../source/explanation-differential-privacy.rst:96 +#, fuzzy msgid "" "There are two forms of clipping commonly used in Central DP: Fixed " "Clipping and Adaptive Clipping." -msgstr "" +msgstr "中央处理器常用的剪切有两种形式：固定剪切和自适应剪切。" #: ../../source/explanation-differential-privacy.rst:98 +#, fuzzy msgid "" "**Fixed Clipping** : A predefined fix threshold is set for the magnitude " "of clients' updates. Any update exceeding this threshold is clipped back " "to the threshold value." -msgstr "" +msgstr "** 固定削波** ： 为客户端更新的大小设置了一个预定义的固定阈值。任何超过该阈值的更新都会被剪切回阈值。" #: ../../source/explanation-differential-privacy.rst:100 +#, fuzzy msgid "" "**Adaptive Clipping** : The clipping threshold dynamically adjusts based " "on the observed update distribution [4]. It means that the clipping value" " is tuned during the rounds with respect to the quantile of the update " "norm distribution." -msgstr "" +msgstr "** 自适应削波** ： 削波阈值根据观察到的更新分布动态调整[4]。这意味着，在各轮中，会根据更新规范分布的量化值调整削波值。" #: ../../source/explanation-differential-privacy.rst:102 +#, fuzzy msgid "" "The choice between fixed and adaptive clipping depends on various factors" " such as privacy requirements, data distribution, model complexity, and " "others." -msgstr "" +msgstr "在固定剪切和自适应剪切之间做出选择取决于各种因素，如隐私要求、数据分布、模型复杂性等。" #: ../../source/explanation-differential-privacy.rst:-1 #: ../../source/explanation-differential-privacy.rst:105 @@ -3538,45 +3290,59 @@ msgid "Local Differential Privacy" msgstr "差分隐私" #: ../../source/explanation-differential-privacy.rst:107 +#, fuzzy msgid "" "In this approach, each client is responsible for performing DP. Local DP " "avoids the need for a fully trusted aggregator, but it should be noted " "that local DP leads to a decrease in accuracy but better privacy in " "comparison to central DP." msgstr "" +"在这种方法中，每个客户端都负责执行 DP。本地 DP 避免了对完全可信的聚合器的需求，但需要注意的是，与中央 DP 相比，本地 DP " +"会降低准确性，但却能更好地保护隐私。" #: ../../source/explanation-differential-privacy.rst:116 +#, fuzzy msgid "In this explainer, we focus on two forms of achieving Local DP:" -msgstr "" +msgstr "在本说明中，我们将重点介绍实现本地 DP 的两种形式：" #: ../../source/explanation-differential-privacy.rst:118 +#, fuzzy msgid "" "Each client adds noise to the local updates before sending them to the " "server. To achieve (:math:`\\epsilon`, :math:`\\delta`)-DP, considering " "the sensitivity of the local model to be ∆, Gaussian noise is applied " "with a noise scale of σ where:" msgstr "" +"每个客户端在向服务器发送本地更新之前，都会在本地更新中加入噪声。为了实现（:math:`\\epsilon`, " +":math:`\\delta`）-DP，考虑到本地模型的灵敏度为 ∆，应用了高斯噪声，噪声尺度为 σ，其中：" #: ../../source/explanation-differential-privacy.rst:120 +#, fuzzy msgid "" "\\small\n" "\\frac{∆ \\times \\sqrt{2 \\times " "\\log\\left(\\frac{1.25}{\\delta}\\right)}}{\\epsilon}\n" "\n" msgstr "" +"\\small\n" +"\\frac{∆ \\times \\sqrt{2 \\times " +"\\log\\left(\\frac{1.25}{\\delta}\\right)}}{\\epsilon}\n" +"\n" #: ../../source/explanation-differential-privacy.rst:125 +#, fuzzy msgid "" "Each client adds noise to the gradients of the model during the local " "training (DP-SGD). More specifically, in this approach, gradients are " "clipped and an amount of calibrated noise is injected into the gradients." -msgstr "" +msgstr "在局部训练过程中，每个客户端都会向模型的梯度添加噪声（DP-SGD）。更具体地说，在这种方法中，梯度会被剪切，并在梯度中注入一定量的校准噪声。" #: ../../source/explanation-differential-privacy.rst:128 +#, fuzzy msgid "" "Please note that these two approaches are providing privacy at different " "levels." -msgstr "" +msgstr "请注意，这两种方法提供了不同层次的隐私。" #: ../../source/explanation-differential-privacy.rst:131 #, fuzzy @@ -3584,8 +3350,9 @@ msgid "**References:**" msgstr "参考资料" #: ../../source/explanation-differential-privacy.rst:133 +#, fuzzy msgid "[1] Dwork et al. The Algorithmic Foundations of Differential Privacy." -msgstr "" +msgstr "[1] Dwork 等：《差分隐私的算法基础》。" #: ../../source/explanation-differential-privacy.rst:135 #, fuzzy @@ -3597,10 +3364,11 @@ msgstr "" "language models.\" arXiv preprint arXiv:1710.06963 (2017)." #: ../../source/explanation-differential-privacy.rst:137 +#, fuzzy msgid "" "[3] Geyer et al. Differentially Private Federated Learning: A Client " "Level Perspective." -msgstr "" +msgstr "[3] Geyer 等人。差异化化私人联合学习：客户层面的视角。" #: ../../source/explanation-differential-privacy.rst:139 #, fuzzy @@ -4258,25 +4026,230 @@ msgid "" "provided in these dictionaries:" msgstr "然后，服务器可以使用定制的策略来汇总这些字典中提供的指标：" -#: ../../source/how-to-configure-clients.rst:2 -msgid "Configure clients" -msgstr "配置客户端" +#: ../../source/how-to-authenticate-supernodes.rst:2 +#, fuzzy +msgid "Authenticate SuperNodes" +msgstr "验证超级节点" -#: ../../source/how-to-configure-clients.rst:4 +#: ../../source/how-to-authenticate-supernodes.rst:4 +#, fuzzy msgid "" -"Along with model parameters, Flower can send configuration values to " -"clients. Configuration values can be used for various purposes. They are," -" for example, a popular way to control client-side hyperparameters from " -"the server." -msgstr "除了模型参数，Flower 还可以向客户端发送配置值。配置值有多种用途。它们是一种从服务器控制客户端超参数的常用方法。" +"Flower has built-in support for authenticated SuperNodes that you can use" +" to verify the identities of each SuperNode connecting to a SuperLink. " +"Flower node authentication works similar to how GitHub SSH authentication" +" works:" +msgstr "" +"Flower 内置了对经过身份验证的超级节点的支持，您可以用它来验证连接到超级链接的每个超级节点的身份。Flower 节点身份验证的工作方式与 " +"GitHub SSH 身份验证的工作方式类似：" -#: ../../source/how-to-configure-clients.rst:7 -msgid "Configuration values" -msgstr "配置值" +#: ../../source/how-to-authenticate-supernodes.rst:7 +#, fuzzy +msgid "SuperLink (server) stores a list of known (client) node public keys" +msgstr "超级链接（服务器）存储已知（客户端）节点公钥列表" -#: ../../source/how-to-configure-clients.rst:9 +#: ../../source/how-to-authenticate-supernodes.rst:8 +#, fuzzy msgid "" -"Configuration values are represented as a dictionary with ``str`` keys " +"Using ECDH, both SuperNode and SuperLink independently derive a shared " +"secret" +msgstr "使用 ECDH，超级节点和超级链路可独立生成共享秘密" + +#: ../../source/how-to-authenticate-supernodes.rst:9 +#, fuzzy +msgid "" +"Shared secret is used to compute the HMAC value of the message sent from " +"SuperNode to SuperLink as a token" +msgstr "共享秘密用于计算作为令牌从超级节点发送到超级链接的信息的 HMAC 值" + +#: ../../source/how-to-authenticate-supernodes.rst:10 +#, fuzzy +msgid "SuperLink verifies the token" +msgstr "超级链接验证令牌" + +#: ../../source/how-to-authenticate-supernodes.rst:12 +#, fuzzy +msgid "" +"We recommend you to check out the complete `code example " +"`_ demonstrating federated learning with Flower in an " +"authenticated setting." +msgstr "" +"请参阅`完整代码示例 " +"`_了解更多信息。" + +#: ../../source/how-to-authenticate-supernodes.rst:15 +#, fuzzy +msgid "" +"This guide covers a preview feature that might change in future versions " +"of Flower." +msgstr "本指南涵盖的预览功能可能会在 Flower 的未来版本中有所改变。" + +#: ../../source/how-to-authenticate-supernodes.rst:18 +#, fuzzy +msgid "" +"For increased security, node authentication can only be used when " +"encrypted connections (SSL/TLS) are enabled." +msgstr "为提高安全性，只有启用加密连接（SSL/TLS）时才能使用节点验证。" + +#: ../../source/how-to-authenticate-supernodes.rst:21 +#, fuzzy +msgid "Enable node authentication in :code:`SuperLink`" +msgstr "在 :code:`SuperLink` 中启用节点验证" + +#: ../../source/how-to-authenticate-supernodes.rst:23 +#, fuzzy +msgid "" +"To enable node authentication, first you need to configure SSL/TLS " +"connections to secure the SuperLink<>SuperNode communication. You can " +"find the complete guide `here `_. After configuring secure connections, you" +" can enable client authentication in a long-running Flower " +":code:`SuperLink`. Use the following terminal command to start a Flower " +":code:`SuperNode` that has both secure connections and node " +"authentication enabled:" +msgstr "" +"要启用节点验证，首先需要配置 SSL/TLS 连接，以确保 SuperLink<>SuperNode 通信的安全。您可以在 " +"`_ " +"找到完整的指南。配置安全连接后，您就可以在长期运行的 Flower " +":code:`SuperLink`中启用客户端身份验证。使用以下终端命令启动一个同时启用了安全连接和节点验证的 Flower " +":code:`SuperNode`：" + +#: ../../source/how-to-authenticate-supernodes.rst:38 +#, fuzzy +msgid "Let's break down the authentication flags:" +msgstr "让我们来分析一下身份验证标志：" + +#: ../../source/how-to-authenticate-supernodes.rst:40 +#, fuzzy +msgid "" +"The first flag :code:`--auth-list-public-keys` expects a path to a CSV " +"file storing all known node public keys. You need to store all known node" +" public keys that are allowed to participate in a federation in one CSV " +"file (:code:`.csv`)." +msgstr "" +"第一个标志 :code:`--auth-list-public-keys`（密码：`--auth-list-public-keys`）需要一个 " +"CSV 文件路径，该文件存储了所有已知节点的公钥。您需要在一个 CSV 文件（:code:`.csv`）中存储所有允许参与联盟的已知节点公钥。" + +#: ../../source/how-to-authenticate-supernodes.rst:42 +#, fuzzy +msgid "" +"A valid CSV file storing known node public keys should list the keys in " +"OpenSSH format, separated by commas and without any comments. For an " +"example, refer to our code sample, which contains a CSV file with two " +"known node public keys." +msgstr "" +"存储已知节点公开密钥的有效 CSV 文件应以 OpenSSH " +"格式列出密钥，以逗号分隔，不含任何注释。有关示例，请参阅我们的代码示例，其中包含一个包含两个已知节点公钥的 CSV 文件。" + +#: ../../source/how-to-authenticate-supernodes.rst:44 +#, fuzzy +msgid "" +"The second and third flags :code:`--auth-superlink-private-key` and :code" +":`--auth-superlink-public-key` expect paths to the server's private and " +"public keys. For development purposes, you can generate a private and " +"public key pair using :code:`ssh-keygen -t ecdsa -b 384`." +msgstr "" +"第二和第三个标记 :code:`--auth-superlink-private-key` 和 :code:`--auth-superlink-" +"public-key` 希望指向服务器私钥和公钥的路径。出于开发目的，您可以使用 :code:`ssh-keygen -t ecdsa -b " +"384` 生成一对私钥和公钥。" + +#: ../../source/how-to-authenticate-supernodes.rst:47 +#, fuzzy +msgid "" +"In Flower 1.9, there is no support for dynamically removing, editing, or " +"adding known node public keys to the SuperLink. To change the set of " +"known nodes, you need to shut the server down, edit the CSV file, and " +"start the server again. Support for dynamically changing the set of known" +" nodes is on the roadmap to be released in Flower 1.10 (ETA: June)." +msgstr "" +"在 Flower 1.9 中，超级链接不支持动态删除、编辑或添加已知节点公钥。要更改已知节点集，您需要关闭服务器，编辑 CSV " +"文件，然后重新启动服务器。动态更改已知节点集的支持已列入 Flower 1.10（预计发布时间：6 月）的路线图。" + +#: ../../source/how-to-authenticate-supernodes.rst:53 +#, fuzzy +msgid "Enable node authentication in :code:`SuperNode`" +msgstr "在 :code:`SuperNode` 中启用节点验证" + +#: ../../source/how-to-authenticate-supernodes.rst:55 +#, fuzzy +msgid "" +"Similar to the long-running Flower server (:code:`SuperLink`), you can " +"easily enable node authentication in the long-running Flower client " +"(:code:`SuperNode`). Use the following terminal command to start an " +"authenticated :code:`SuperNode`:" +msgstr "" +"与长期运行的 Flower 服务器（:code:`SuperLink`）类似，您也可以在长期运行的 Flower " +"客户端（:code:`SuperNode`）中轻松启用节点身份验证。使用以下终端命令启动已验证的 :code:`SuperNode`：" + +#: ../../source/how-to-authenticate-supernodes.rst:66 +#, fuzzy +msgid "" +"The :code:`--auth-supernode-private-key` flag expects a path to the " +"node's private key file and the :code:`--auth-supernode-public-key` flag " +"expects a path to the node's public key file. For development purposes, " +"you can generate a private and public key pair using :code:`ssh-keygen -t" +" ecdsa -b 384`." +msgstr "" +":code:`--auth-supernode-private-key`标志需要节点私钥文件的路径，:code:`-auth-supernode-" +"public-key`标志需要节点公钥文件的路径。出于开发目的，可以使用 :code:`ssh-keygen -t ecdsa -b 384` " +"生成一对私钥和公钥。" + +#: ../../source/how-to-authenticate-supernodes.rst:70 +#, fuzzy +msgid "Security notice" +msgstr "安全通知" + +#: ../../source/how-to-authenticate-supernodes.rst:72 +#, fuzzy +msgid "" +"The system's security relies on the credentials of the SuperLink and each" +" SuperNode. Therefore, it is imperative to safeguard and safely store the" +" credentials to avoid security risks such as Public Key Infrastructure " +"(PKI) impersonation attacks. The node authentication mechanism also " +"involves human interaction, so please ensure that all of the " +"communication is done in a secure manner, using trusted communication " +"methods." +msgstr "" +"系统的安全性依赖于超级链接和每个超级节点的凭证。因此，必须保护和安全存储凭证，以避免公钥基础设施 (PKI) " +"假冒攻击等安全风险。节点验证机制还涉及人机交互，因此请确保使用可信的通信方法，以安全的方式进行所有通信。" + +#: ../../source/how-to-authenticate-supernodes.rst:77 +#: ../../source/how-to-enable-ssl-connections.rst:68 +#: ../../source/how-to-use-built-in-mods.rst:85 +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:287 +msgid "Conclusion" +msgstr "总结" + +#: ../../source/how-to-authenticate-supernodes.rst:79 +#, fuzzy +msgid "" +"You should now have learned how to start a long-running Flower server " +"(:code:`SuperLink`) and client (:code:`SuperNode`) with node " +"authentication enabled. You should also know the significance of the " +"private key and store it safely to minimize security risks." +msgstr "" +"现在，您应该已经学会了如何启动长期运行的 Flower " +"服务器（:code:`SuperLink`）和客户端（:code:`SuperNode`）并启用节点身份验证。您还应该知道私钥的重要性，并将其安全存储，以尽量减少安全风险。" + +#: ../../source/how-to-configure-clients.rst:2 +msgid "Configure clients" +msgstr "配置客户端" + +#: ../../source/how-to-configure-clients.rst:4 +msgid "" +"Along with model parameters, Flower can send configuration values to " +"clients. Configuration values can be used for various purposes. They are," +" for example, a popular way to control client-side hyperparameters from " +"the server." +msgstr "除了模型参数，Flower 还可以向客户端发送配置值。配置值有多种用途。它们是一种从服务器控制客户端超参数的常用方法。" + +#: ../../source/how-to-configure-clients.rst:7 +msgid "Configuration values" +msgstr "配置值" + +#: ../../source/how-to-configure-clients.rst:9 +msgid "" +"Configuration values are represented as a dictionary with ``str`` keys " "and values of type ``bool``, ``bytes``, ``double`` (64-bit precision " "float), ``int``, or ``str`` (or equivalent types in different languages)." " Here is an example of a configuration dictionary in Python:" @@ -4499,9 +4472,11 @@ msgid "Enable SSL connections" msgstr "启用 SSL 连接" #: ../../source/how-to-enable-ssl-connections.rst:4 +#, fuzzy msgid "" -"This guide describes how to a SSL-enabled secure Flower server can be " -"started and how a Flower client can establish a secure connections to it." +"This guide describes how to a SSL-enabled secure Flower server " +"(:code:`SuperLink`) can be started and how a Flower client " +"(:code:`SuperNode`) can establish a secure connections to it." msgstr "本指南介绍如何启动启用 SSL 的安全 Flower 服务器，以及 Flower 客户端如何与其建立安全连接。" #: ../../source/how-to-enable-ssl-connections.rst:7 @@ -4514,11 +4489,12 @@ msgstr "" "/advanced-tensorflow>`_ 。" #: ../../source/how-to-enable-ssl-connections.rst:10 +#, fuzzy msgid "" -"The code example comes with a README.md file which will explain how to " -"start it. Although it is already SSL-enabled, it might be less " -"descriptive on how. Stick to this guide for a deeper introduction to the " -"topic." +"The code example comes with a :code:`README.md` file which explains how " +"to start it. Although it is already SSL-enabled, it might be less " +"descriptive on how it does so. Stick to this guide for a deeper " +"introduction to the topic." msgstr "代码示例附带的 README.md 文件将解释如何启动它。虽然它已经启用了 SSL，但对如何启用可能描述较少。请参考本指南，了解更深入的相关介绍。" #: ../../source/how-to-enable-ssl-connections.rst:16 @@ -4526,111 +4502,99 @@ msgid "Certificates" msgstr "证书" #: ../../source/how-to-enable-ssl-connections.rst:18 +#, fuzzy msgid "" "Using SSL-enabled connections requires certificates to be passed to the " "server and client. For the purpose of this guide we are going to generate" " self-signed certificates. As this can become quite complex we are going " "to ask you to run the script in :code:`examples/advanced-" -"tensorflow/certificates/generate.sh`" +"tensorflow/certificates/generate.sh` with the following command sequence:" msgstr "" "使用支持 SSL 的连接需要向服务器和客户端传递证书。在本指南中，我们将生成自签名证书。由于这可能会变得相当复杂，我们将要求你运行 " ":code:`examples/advanced-tensorflow/certificates/generate.sh` 中的脚本" -#: ../../source/how-to-enable-ssl-connections.rst:23 -msgid "with the following command sequence:" -msgstr "使用以下命令序列：" - -#: ../../source/how-to-enable-ssl-connections.rst:30 +#: ../../source/how-to-enable-ssl-connections.rst:29 msgid "" "This will generate the certificates in :code:`examples/advanced-" "tensorflow/.cache/certificates`." msgstr "这将在 :code:`examples/advanced-tensorflow/.cache/certificates` 中生成证书。" -#: ../../source/how-to-enable-ssl-connections.rst:32 +#: ../../source/how-to-enable-ssl-connections.rst:31 +#, fuzzy msgid "" -"The approach how the SSL certificates are generated in this example can " -"serve as an inspiration and starting point but should not be taken as " -"complete for production environments. Please refer to other sources " -"regarding the issue of correctly generating certificates for production " -"environments." +"The approach for generating SSL certificates in the context of this " +"example can serve as an inspiration and starting point, but it should not" +" be used as a reference for production environments. Please refer to " +"other sources regarding the issue of correctly generating certificates " +"for production environments. For non-critical prototyping or research " +"projects, it might be sufficient to use the self-signed certificates " +"generated using the scripts mentioned in this guide." msgstr "本示例中生成 SSL 证书的方法可作为启发和起点，但不应被视为生产环境的完整方法。有关在生产环境中正确生成证书的问题，请参考其他资料。" -#: ../../source/how-to-enable-ssl-connections.rst:36 -msgid "" -"In case you are a researcher you might be just fine using the self-signed" -" certificates generated using the scripts which are part of this guide." -msgstr "如果你是一名研究人员，使用本指南中的脚本生成的自签名证书就可以了。" +#: ../../source/how-to-enable-ssl-connections.rst:39 +#, fuzzy +msgid "Server (SuperLink)" +msgstr "flower-superlink" #: ../../source/how-to-enable-ssl-connections.rst:41 -#: ../../source/ref-api/flwr.server.Server.rst:2 -msgid "Server" -msgstr "服务器" - -#: ../../source/how-to-enable-ssl-connections.rst:43 +#, fuzzy msgid "" -"We are now going to show how to write a sever which uses the previously " -"generated scripts." -msgstr "现在，我们将展示如何编写一个使用先前生成的脚本的服务器。" +"Use the following terminal command to start a sever (SuperLink) that uses" +" the previously generated certificates:" +msgstr "现在我们将演示如何编写一个客户端，使用之前生成的脚本：" -#: ../../source/how-to-enable-ssl-connections.rst:61 +#: ../../source/how-to-enable-ssl-connections.rst:50 +#, fuzzy msgid "" "When providing certificates, the server expects a tuple of three " -"certificates. :code:`Path` can be used to easily read the contents of " -"those files into byte strings, which is the data type " -":code:`start_server` expects." -msgstr "" -"在提供证书时，服务器希望得到由三个证书组成的元组。 :code:`Path` 可用于轻松地将这些文件的内容读取为字节字符串，这就是 " -":code:`start_server` 期望的数据类型。" +"certificates paths: CA certificate, server certificate and server private" +" key." +msgstr "要启用 SSL，需要 CA 证书、服务器证书和服务器私钥。" -#: ../../source/how-to-enable-ssl-connections.rst:65 -#: ../../source/how-to-upgrade-to-flower-1.0.rst:37 -#: ../../source/ref-api/flwr.client.Client.rst:2 -msgid "Client" -msgstr "客户端" +#: ../../source/how-to-enable-ssl-connections.rst:54 +#, fuzzy +msgid "Client (SuperNode)" +msgstr "客户端状态代码。" -#: ../../source/how-to-enable-ssl-connections.rst:67 +#: ../../source/how-to-enable-ssl-connections.rst:56 +#, fuzzy msgid "" -"We are now going to show how to write a client which uses the previously " -"generated scripts:" +"Use the following terminal command to start a client (SuperNode) that " +"uses the previously generated certificates:" msgstr "现在我们将演示如何编写一个客户端，使用之前生成的脚本：" -#: ../../source/how-to-enable-ssl-connections.rst:84 +#: ../../source/how-to-enable-ssl-connections.rst:64 +#, fuzzy msgid "" -"When setting :code:`root_certificates`, the client expects the PEM-" -"encoded root certificates as a byte string. We are again using " -":code:`Path` to simplify reading those as byte strings." +"When setting :code:`root_certificates`, the client expects a file path to" +" PEM-encoded root certificates." msgstr "" "当设置 :code:`root_certificates` 时，客户端希望 PEM 编码的根证书是字节字符串。我们再次使用 " ":code:`Path` 来简化以字节字符串形式读取证书的过程。" -#: ../../source/how-to-enable-ssl-connections.rst:89 -#: ../../source/how-to-use-built-in-mods.rst:85 -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:287 -msgid "Conclusion" -msgstr "总结" - -#: ../../source/how-to-enable-ssl-connections.rst:91 +#: ../../source/how-to-enable-ssl-connections.rst:70 +#, fuzzy msgid "" "You should now have learned how to generate self-signed certificates " -"using the given script, start a SSL-enabled server, and have a client " +"using the given script, start an SSL-enabled server and have a client " "establish a secure connection to it." msgstr "现在，你应该已经学会了如何使用给定的脚本生成自签名证书、启动启用 SSL 的服务器并让客户端与其建立安全连接。" -#: ../../source/how-to-enable-ssl-connections.rst:96 +#: ../../source/how-to-enable-ssl-connections.rst:75 msgid "Additional resources" msgstr "补充资源" -#: ../../source/how-to-enable-ssl-connections.rst:98 +#: ../../source/how-to-enable-ssl-connections.rst:77 msgid "" "These additional sources might be relevant if you would like to dive " "deeper into the topic of certificates:" msgstr "如果您想更深入地了解证书主题，这些额外的资料来源可能有帮助：" -#: ../../source/how-to-enable-ssl-connections.rst:100 +#: ../../source/how-to-enable-ssl-connections.rst:79 msgid "`Let's Encrypt `_" msgstr "`让我们加密 `_" -#: ../../source/how-to-enable-ssl-connections.rst:101 +#: ../../source/how-to-enable-ssl-connections.rst:80 msgid "`certbot `_" msgstr "`certbot `_" @@ -4952,8 +4916,10 @@ msgid "Install stable release" msgstr "安装稳定版" #: ../../source/how-to-install-flower.rst:15 +#: ../../source/how-to-upgrade-to-flower-next.rst:46 +#, fuzzy msgid "Using pip" -msgstr "" +msgstr "使用 pip" #: ../../source/how-to-install-flower.rst:17 msgid "" @@ -4968,28 +4934,33 @@ msgid "" msgstr "对于使用虚拟客户端引擎的模拟，`flwr`` 应与`simulation`` 一起安装：" #: ../../source/how-to-install-flower.rst:27 +#, fuzzy msgid "Using conda (or mamba)" -msgstr "" +msgstr "使用 conda（或 mamba）" #: ../../source/how-to-install-flower.rst:29 +#, fuzzy msgid "Flower can also be installed from the ``conda-forge`` channel." -msgstr "" +msgstr "Flower 也可以从 ``conda-forge`` 频道安装。" #: ../../source/how-to-install-flower.rst:31 +#, fuzzy msgid "" "If you have not added ``conda-forge`` to your channels, you will first " "need to run the following::" -msgstr "" +msgstr "如果您尚未在频道中添加 ``conda-forge``，则首先需要运行以下程序：：" #: ../../source/how-to-install-flower.rst:36 +#, fuzzy msgid "" "Once the ``conda-forge`` channel has been enabled, ``flwr`` can be " "installed with ``conda``::" -msgstr "" +msgstr "一旦启用了 ``conda-forge`` 频道，就可以使用 ``conda``: 安装 ``flwr``：" #: ../../source/how-to-install-flower.rst:40 +#, fuzzy msgid "or with ``mamba``::" -msgstr "" +msgstr "或用 ``mamba`` ：：" #: ../../source/how-to-install-flower.rst:46 msgid "Verify installation" @@ -5304,253 +5275,766 @@ msgstr "" "metrics.html>`_" #: ../../source/how-to-run-flower-using-docker.rst:2 +#, fuzzy msgid "Run Flower using Docker" -msgstr "" +msgstr "使用 Docker 运行 Flower" #: ../../source/how-to-run-flower-using-docker.rst:4 +#, fuzzy msgid "" "The simplest way to get started with Flower is by using the pre-made " "Docker images, which you can find on `Docker Hub " -"`_." +"`__. Supported architectures include " +"``amd64`` and ``arm64v8``." msgstr "" +"开始使用 Flower 的最简单方法是使用预制的 Docker 镜像，您可以在 `Docker Hub " +"`_ 上找到这些镜像。" -#: ../../source/how-to-run-flower-using-docker.rst:7 +#: ../../source/how-to-run-flower-using-docker.rst:8 +#, fuzzy msgid "Before you start, make sure that the Docker daemon is running:" -msgstr "" +msgstr "开始之前，请确保 Docker 守护进程正在运行：" -#: ../../source/how-to-run-flower-using-docker.rst:14 +#: ../../source/how-to-run-flower-using-docker.rst:15 +#, fuzzy msgid "" "If you do not see the version of Docker but instead get an error saying " "that the command was not found, you will need to install Docker first. " "You can find installation instruction `here `_." msgstr "" +"如果没有看到 Docker 的版本，而是出现找不到命令的错误，则需要先安装 Docker。你可以在 " +"`_ 找到安装说明。" -#: ../../source/how-to-run-flower-using-docker.rst:20 +#: ../../source/how-to-run-flower-using-docker.rst:21 +#, fuzzy msgid "" "On Linux, Docker commands require ``sudo`` privilege. If you want to " "avoid using ``sudo``, you can follow the `Post-installation steps " "`_ on the " "official Docker website." msgstr "" +"在 Linux 上，Docker 命令需要 ``sudo`` 权限。如果你想避免使用 ``sudo``，可以按照 Docker 官方网站上的 " +"`安装后步骤 `_进行操作。" -#: ../../source/how-to-run-flower-using-docker.rst:25 +#: ../../source/how-to-run-flower-using-docker.rst:27 #, fuzzy -msgid "Flower server" -msgstr "Flower 服务器" +msgid "" +"To ensure optimal performance and compatibility, the SuperLink, SuperNode" +" and ServerApp image must have the same version when running together. " +"This guarantees seamless integration and avoids potential conflicts or " +"issues that may arise from using different versions." +msgstr "" +"为确保最佳性能和兼容性，SuperLink、SuperNode 和 ServerApp " +"映像在一起运行时必须具有相同的版本。这可确保无缝集成，并避免因使用不同版本而可能产生的潜在冲突或问题。" + +#: ../../source/how-to-run-flower-using-docker.rst:32 +#, fuzzy +msgid "Flower SuperLink" +msgstr "flower-superlink" -#: ../../source/how-to-run-flower-using-docker.rst:28 +#: ../../source/how-to-run-flower-using-docker.rst:35 #, fuzzy msgid "Quickstart" msgstr "快速入门 JAX" -#: ../../source/how-to-run-flower-using-docker.rst:30 +#: ../../source/how-to-run-flower-using-docker.rst:37 +#, fuzzy msgid "If you're looking to try out Flower, you can use the following command:" -msgstr "" +msgstr "如果您想试用 Flower，可以使用以下命令：" -#: ../../source/how-to-run-flower-using-docker.rst:37 +#: ../../source/how-to-run-flower-using-docker.rst:43 +#, fuzzy msgid "" -"The command will pull the Docker image with the tag " -"``1.7.0-py3.11-ubuntu22.04`` from Docker Hub. The tag contains the " -"information which Flower, Python and Ubuntu is used. In this case, it " -"uses Flower 1.7.0, Python 3.11 and Ubuntu 22.04. The ``--rm`` flag tells " -"Docker to remove the container after it exits." +"The command pulls the Docker image with the tag ``1.8.0`` from Docker " +"Hub. The tag specifies the Flower version. In this case, Flower 1.8.0. " +"The ``--rm`` flag tells Docker to remove the container after it exits." msgstr "" +"该命令将从 Docker Hub 提取标签为``1.7.0-py3.11-ubuntu22.04``的 Docker 镜像。标签包含使用 " +"Flower、Python 和 Ubuntu 的信息。在本例中，它使用了 Flower 1.7.0、Python 3.11 和 Ubuntu " +"22.04。rm \"标记告诉 Docker 在退出后移除容器。" -#: ../../source/how-to-run-flower-using-docker.rst:44 +#: ../../source/how-to-run-flower-using-docker.rst:49 +#, fuzzy msgid "" -"By default, the Flower server keeps state in-memory. When using the " +"By default, the Flower SuperLink keeps state in-memory. When using the " "Docker flag ``--rm``, the state is not persisted between container " "starts. We will show below how to save the state in a file on your host " "system." msgstr "" +"默认情况下，Flower 服务器会将状态保存在内存中。使用 Docker 标志 ``--rm`` " +"时，状态不会在容器启动之间持久化。下面我们将展示如何将状态保存到主机系统上的文件中。" -#: ../../source/how-to-run-flower-using-docker.rst:48 +#: ../../source/how-to-run-flower-using-docker.rst:53 +#, fuzzy msgid "" "The ``-p :`` flag tells Docker to map the ports " "``9091``/``9092`` of the host to ``9091``/``9092`` of the container, " "allowing you to access the Driver API on ``http://localhost:9091`` and " "the Fleet API on ``http://localhost:9092``. Lastly, any flag that comes " -"after the tag is passed to the Flower server. Here, we are passing the " -"flag ``--insecure``." +"after the tag is passed to the Flower SuperLink. Here, we are passing the" +" flag ``--insecure``." msgstr "" +"``-p :`` 标记会告诉 Docker 将主机的端口 ``9091``/``9092`` 映射到容器的端口 " +"``9091``/`9092``，这样你就可以在 ``http://localhost:9091`` 上访问 Driver API，在 " +"``http://localhost:9092`` 上访问 Fleet API。最后，标签后面的任何标志都会传递给 Flower " +"服务器。在这里，我们传递的标志是 ``--insecure`` 。" -#: ../../source/how-to-run-flower-using-docker.rst:55 +#: ../../source/how-to-run-flower-using-docker.rst:60 +#: ../../source/how-to-run-flower-using-docker.rst:259 +#: ../../source/how-to-run-flower-using-docker.rst:376 +#, fuzzy msgid "" "The ``--insecure`` flag enables insecure communication (using HTTP, not " "HTTPS) and should only be used for testing purposes. We strongly " "recommend enabling `SSL `_ when " +"flower-using-docker.html#enabling-ssl-for-secure-connections>`__ when " "deploying to a production environment." msgstr "" +"不安全 \"标志启用不安全通信（使用 HTTP，而非 HTTPS），只能用于测试目的。我们强烈建议在部署到生产环境时启用 `SSL " +"`_。" -#: ../../source/how-to-run-flower-using-docker.rst:60 +#: ../../source/how-to-run-flower-using-docker.rst:65 +#, fuzzy msgid "" -"You can use ``--help`` to view all available flags that the server " +"You can use ``--help`` to view all available flags that the SuperLink " "supports:" -msgstr "" +msgstr "您可以使用 ``--help`` 查看服务器支持的所有可用标记：" -#: ../../source/how-to-run-flower-using-docker.rst:67 +#: ../../source/how-to-run-flower-using-docker.rst:72 +#, fuzzy msgid "Mounting a volume to store the state on the host system" +msgstr "在主机系统上挂载卷以存储状态" + +#: ../../source/how-to-run-flower-using-docker.rst:74 +msgid "" +"If you want to persist the state of the SuperLink on your host system, " +"all you need to do is specify a directory where you want to save the file" +" on your host system and a name for the database file. By default, the " +"SuperLink container runs with a non-root user called ``app`` with the " +"user ID ``49999``. It is recommended to create new directory and change " +"the user ID of the directory to ``49999`` to ensure the mounted directory" +" has the proper permissions. If you later want to delete the directory, " +"you can change the user ID back to the current user ID by running ``sudo " +"chown -R $USER:$(id -gn) state``." msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:69 +#: ../../source/how-to-run-flower-using-docker.rst:82 +#, fuzzy msgid "" -"If you want to persist the state of the server on your host system, all " -"you need to do is specify a path where you want to save the file on your " -"host system and a name for the database file. In the example below, we " -"tell Docker via the flag ``-v`` to mount the user's home directory " -"(``~/`` on your host) into the ``/app/`` directory of the container. " +"In the example below, we create a new directory, change the user ID and " +"tell Docker via the flag ``--volume`` to mount the local ``state`` " +"directory into the ``/app/state`` directory of the container. " "Furthermore, we use the flag ``--database`` to specify the name of the " "database file." msgstr "" +"如果想在主机系统上持久保存服务器的状态，只需在主机系统上指定保存文件的路径和数据库文件的名称即可。在下面的示例中，我们通过标志 ``-v`` 告诉" +" Docker 将用户的主目录（主机上的 ``~/``）挂载到容器的 ``/app/`` 目录中。此外，我们使用标志 ``--database``" +" 来指定数据库文件的名称。" -#: ../../source/how-to-run-flower-using-docker.rst:82 +#: ../../source/how-to-run-flower-using-docker.rst:95 +#, fuzzy msgid "" -"As soon as the server starts, the file ``state.db`` is created in the " -"user's home directory on your host system. If the file already exists, " -"the server tries to restore the state from the file. To start the server " -"with an empty database, simply remove the ``state.db`` file." +"As soon as the SuperLink starts, the file ``state.db`` is created in the " +"``state`` directory on your host system. If the file already exists, the " +"SuperLink tries to restore the state from the file. To start the " +"SuperLink with an empty database, simply remove the ``state.db`` file." msgstr "" +"服务器一启动，就会在主机系统的用户主目录下创建文件 " +"``state.db``。如果该文件已经存在，服务器会尝试从该文件恢复状态。要以空数据库启动服务器，只需删除 ``state.db`` 文件即可。" -#: ../../source/how-to-run-flower-using-docker.rst:87 +#: ../../source/how-to-run-flower-using-docker.rst:100 +#: ../../source/how-to-run-flower-using-docker.rst:281 +#: ../../source/how-to-run-flower-using-docker.rst:397 #, fuzzy msgid "Enabling SSL for secure connections" msgstr "启用 SSL 连接" -#: ../../source/how-to-run-flower-using-docker.rst:89 +#: ../../source/how-to-run-flower-using-docker.rst:102 +#, fuzzy msgid "" -"To enable SSL, you will need a CA certificate, a server certificate and a" -" server private key." -msgstr "" +"To enable SSL, you will need a PEM-encoded root certificate, a PEM-" +"encoded private key and a PEM-encoded certificate chain." +msgstr "要启用 SSL，需要 CA 证书、服务器证书和服务器私钥。" -#: ../../source/how-to-run-flower-using-docker.rst:92 +#: ../../source/how-to-run-flower-using-docker.rst:106 +#, fuzzy msgid "" "For testing purposes, you can generate your own self-signed certificates." " The `Enable SSL connections `_ page contains a section that " -"will guide you through the process." +"enable-ssl-connections.html#certificates>`__ page contains a section that" +" will guide you through the process." msgstr "" +"出于测试目的，你可以生成自己的自签名证书。启用 SSL 连接 `_ 页面中有一个部分将指导你完成这一过程。" -#: ../../source/how-to-run-flower-using-docker.rst:96 +#: ../../source/how-to-run-flower-using-docker.rst:110 +#, fuzzy msgid "" "Assuming all files we need are in the local ``certificates`` directory, " -"we can use the flag ``-v`` to mount the local directory into the " -"``/app/`` directory of the container. This allows the server to access " -"the files within the container. Finally, we pass the names of the " -"certificates to the server with the ``--certificates`` flag." -msgstr "" - -#: ../../source/how-to-run-flower-using-docker.rst:108 -msgid "Using a different Flower or Python version" +"we can use the flag ``--volume`` to mount the local directory into the " +"``/app/certificates/`` directory of the container. This allows the " +"SuperLink to access the files within the container. The ``ro`` stands for" +" ``read-only``. Docker volumes default to ``read-write``; that option " +"tells Docker to make the volume ``read-only`` instead. Finally, we pass " +"the names of the certificates and key file to the SuperLink with the " +"``--ssl-ca-certfile``, ``--ssl-certfile`` and ``--ssl-keyfile`` flag." msgstr "" +"假设我们需要的所有文件都在本地的 ``certificates`` 目录中，我们可以使用标记 ``-v`` 将本地目录挂载到容器的 " +"``/app/`` 目录中。这样，服务器就可以访问容器内的文件。最后，我们使用 ``--certificates`` 标志将证书名称传递给服务器。" -#: ../../source/how-to-run-flower-using-docker.rst:110 +#: ../../source/how-to-run-flower-using-docker.rst:128 msgid "" -"If you want to use a different version of Flower or Python, you can do so" -" by changing the tag. All versions we provide are available on `Docker " -"Hub `_." +"Because Flower containers, by default, run with a non-root user ``app``, " +"the mounted files and directories must have the proper permissions for " +"the user ID ``49999``. For example, to change the user ID of all files in" +" the ``certificates/`` directory, you can run ``sudo chown -R 49999:49999" +" certificates/*``." msgstr "" -#: ../../source/how-to-run-flower-using-docker.rst:114 -msgid "Pinning a Docker image to a specific version" -msgstr "" +#: ../../source/how-to-run-flower-using-docker.rst:134 +#, fuzzy +msgid "Flower SuperNode" +msgstr "Flower 服务器" -#: ../../source/how-to-run-flower-using-docker.rst:116 +#: ../../source/how-to-run-flower-using-docker.rst:136 +#, fuzzy msgid "" -"It may happen that we update the images behind the tags. Such updates " -"usually include security updates of system dependencies that should not " -"change the functionality of Flower. However, if you want to ensure that " -"you always use the same image, you can specify the hash of the image " -"instead of the tag." -msgstr "" +"The SuperNode Docker image comes with a pre-installed version of Flower " +"and serves as a base for building your own SuperNode image." +msgstr "超级节点 Docker 镜像预装了 Flower 版本，可作为构建自己的超级节点镜像的基础。" -#: ../../source/how-to-run-flower-using-docker.rst:121 +#: ../../source/how-to-run-flower-using-docker.rst:141 +#, fuzzy msgid "" -"The following command returns the current image hash referenced by the " -"``server:1.7.0-py3.11-ubuntu22.04`` tag:" -msgstr "" - -#: ../../source/how-to-run-flower-using-docker.rst:128 -msgid "Next, we can pin the hash when running a new server container:" +"The SuperNode Docker image currently works only with the 1.9.0-nightly " +"release. A stable version will be available when Flower 1.9.0 (stable) " +"gets released (ETA: May). A SuperNode nightly image must be paired with " +"the corresponding SuperLink and ServerApp nightly images released on the " +"same day. To ensure the versions are in sync, using the concrete tag, " +"e.g., ``1.9.0.dev20240501`` instead of ``nightly`` is recommended." msgstr "" +"超级节点 Docker 映像目前仅适用于 1.9.0-nightly 版本。稳定版将在 Flower " +"1.9.0（稳定版）发布时推出（预计发布时间：5 " +"月）。超级节点夜间镜像必须与同一天发布的相应超级链接和服务器应用程序夜间镜像配对。为确保版本同步，建议使用具体标签，例如``1.9.0.dev20240501``，而不是``nightly``。" -#: ../../source/how-to-run-flower-using-docker.rst:137 +#: ../../source/how-to-run-flower-using-docker.rst:147 #, fuzzy -msgid "Setting environment variables" -msgstr "设置编码环境" +msgid "" +"We will use the ``quickstart-pytorch`` example, which you can find in the" +" Flower repository, to illustrate how you can dockerize your ClientApp." +msgstr "我们将使用 \"quickstart-pytorch\"（快速启动-pytorch）示例来说明如何对 ClientApp 进行 docker 化。" -#: ../../source/how-to-run-flower-using-docker.rst:139 +#: ../../source/how-to-run-flower-using-docker.rst:155 +#, fuzzy msgid "" -"To set a variable inside a Docker container, you can use the ``-e " -"=`` flag." -msgstr "" +"Before we can start, we need to meet a few prerequisites in our local " +"development environment. You can skip the first part if you want to run " +"your ClientApp instead of the ``quickstart-pytorch`` example." +msgstr "在开始之前，我们需要在本地开发环境中满足一些先决条件。" -#: ../../source/how-to-run-simulations.rst:2 -msgid "Run simulations" -msgstr "运行模拟" +#: ../../source/how-to-run-flower-using-docker.rst:159 +#, fuzzy +msgid "Clone the Flower repository." +msgstr "**叉花仓库**" -#: ../../source/how-to-run-simulations.rst:8 +#: ../../source/how-to-run-flower-using-docker.rst:173 +#, fuzzy +msgid "Creating a SuperNode Dockerfile" +msgstr "创建超级节点 Dockerfile" + +#: ../../source/how-to-run-flower-using-docker.rst:175 +#: ../../source/how-to-run-flower-using-docker.rst:311 +#, fuzzy +msgid "Let's assume the following project layout:" +msgstr "假设项目布局如下" + +#: ../../source/how-to-run-flower-using-docker.rst:184 +#, fuzzy msgid "" -"Simulating Federated Learning workloads is useful for a multitude of use-" -"cases: you might want to run your workload on a large cohort of clients " -"but without having to source, configure and mange a large number of " -"physical devices; you might want to run your FL workloads as fast as " -"possible on the compute systems you have access to without having to go " -"through a complex setup process; you might want to validate your " -"algorithm on different scenarios at varying levels of data and system " -"heterogeneity, client availability, privacy budgets, etc. These are among" -" some of the use-cases where simulating FL workloads makes sense. Flower " -"can accommodate these scenarios by means of its `VirtualClientEngine " -"`_ or " -"VCE." +"First, we need to create a ``requirements.txt`` file in the directory " +"where the ``ClientApp`` code is located. In the file, we list all the " +"dependencies that the ClientApp requires." msgstr "" -"模拟联邦学习工作负载可用于多种案例：您可能希望在大量客户端上运行您的工作负载，但无需采购、配置和管理大量物理设备；您可能希望在您可以访问的计算系统上尽可能快地运行您的" -" FL 工作负载，而无需经过复杂的设置过程；您可能希望在不同数据和系统异构性、客户端可用性、隐私预算等不同水平的场景中验证您的算法。这些都是模拟 " -"FL 工作负载的一些案例。Flower 可以通过其 \"虚拟客户端引擎\"（VirtualClientEngine）_或 VCE 来匹配这些情况。" +"首先，我们需要在 ``ClientApp`` 代码所在的目录中创建一个 ``requirements.txt`` 文件。在该文件中，我们列出了 " +"ClientApp 需要的所有依赖项。" -#: ../../source/how-to-run-simulations.rst:10 +#: ../../source/how-to-run-flower-using-docker.rst:196 +#, fuzzy msgid "" -"The :code:`VirtualClientEngine` schedules, launches and manages `virtual`" -" clients. These clients are identical to `non-virtual` clients (i.e. the " -"ones you launch via the command `flwr.client.start_client `_) in the sense that they can be configure by " -"creating a class inheriting, for example, from `flwr.client.NumPyClient " -"`_ and therefore behave in an " -"identical way. In addition to that, clients managed by the " -":code:`VirtualClientEngine` are:" +"Note that `flwr `__ is already installed " +"in the ``flwr/supernode`` base image, so you only need to include other " +"package dependencies in your ``requirements.txt``, such as ``torch``, " +"``tensorflow``, etc." msgstr "" -":code:`VirtualClientEngine`用来规划，启动和管理`虚拟`客户端。这些客户端跟`非虚拟`客户端是一样的（即为您通过`flwr.client.start_client" -" `_启动的客户端），因为它们可以通过创建一个继承自 " -"`flwr.client.NumPyClient `_ " -"的类进行配置，因此其行为方式相同。另外，由 `VirtualClientEngine` 管理的客户端有：" +"请注意，`flwr `__ " +"已经安装在`flwr/supernode``基础镜像中，因此只需在`requirements.txt``中包含其他依赖包，如`torch``、`tensorflow`等。" -#: ../../source/how-to-run-simulations.rst:12 +#: ../../source/how-to-run-flower-using-docker.rst:200 +#, fuzzy msgid "" -"resource-aware: this means that each client gets assigned a portion of " -"the compute and memory on your system. You as a user can control this at " -"the beginning of the simulation and allows you to control the degree of " -"parallelism of your Flower FL simulation. The fewer the resources per " -"client, the more clients can run concurrently on the same hardware." +"Next, we create a Dockerfile. If you use the ``quickstart-pytorch`` " +"example, create a new file called ``Dockerfile.supernode`` in ``examples" +"/quickstart-pytorch``." msgstr "" -"资源感知：这意味着每个客户端都会分配到系统中的一部分计算和内存。作为用户，您可以在模拟开始时对其进行控制，从而控制 Flower FL " -"模拟的并行程度。每个客户端的资源越少，在同一硬件上并发运行的客户端就越多。" +"接下来，我们创建一个 Dockerfile。如果使用 ``quickstart-pytorch`` 示例，请在 ``examples" +"/quickstart-pytorch`` 中创建一个名为 ``Dockerfile.supernode`` 的新文件。" -#: ../../source/how-to-run-simulations.rst:13 +#: ../../source/how-to-run-flower-using-docker.rst:203 +#, fuzzy msgid "" -"self-managed: this means that you as a user do not need to launch clients" -" manually, instead this gets delegated to :code:`VirtualClientEngine`'s " -"internals." -msgstr "自管理：这意味着用户无需手动启动客户端，而是由 :code:`VirtualClientEngine` 负责。" +"The ``Dockerfile.supernode`` contains the instructions that assemble the " +"SuperNode image." +msgstr "Dockerfile.supernode \"包含组装超级节点映像的指令。" -#: ../../source/how-to-run-simulations.rst:14 +#: ../../source/how-to-run-flower-using-docker.rst:217 +#, fuzzy msgid "" -"ephemeral: this means that a client is only materialized when it is " -"required in the FL process (e.g. to do `fit() `_). The object is destroyed afterwards," -" releasing the resources it was assigned and allowing in this way other " -"clients to participate." +"In the first two lines, we instruct Docker to use the SuperNode image " +"tagged ``nightly`` as a base image and set our working directory to " +"``/app``. The following instructions will now be executed in the ``/app``" +" directory. Next, we install the ClientApp dependencies by copying the " +"``requirements.txt`` file into the image and run ``pip install``. In the " +"last two lines, we copy the ``client.py`` module into the image and set " +"the entry point to ``flower-client-app`` with the argument " +"``client:app``. The argument is the object reference of the ClientApp " +"(``:``) that will be run inside the ClientApp." +msgstr "" +"在前两行中，我们指示 Docker 使用标记为 ``nightly`` 的 SuperNode 镜像作为基础镜像，并将工作目录设置为 " +"``/app``。下面的指令将在 ``/app`` 目录中执行。接下来，我们通过将 ``requirements.txt`` " +"文件复制到映像中并运行 ``pip install`` 来安装 ClientApp 依赖项。最后两行，我们将 ``client.py`` " +"模块复制到映像中，并将入口点设置为 ``flower-client-app``，参数为 ``client:app``。参数是将在 " +"ClientApp 内运行的 ClientApp 的对象引用（``<模块>:<属性>``）。" + +#: ../../source/how-to-run-flower-using-docker.rst:226 +#, fuzzy +msgid "Building the SuperNode Docker image" +msgstr "启动服务器" + +#: ../../source/how-to-run-flower-using-docker.rst:228 +#, fuzzy +msgid "" +"Next, we build the SuperNode Docker image by running the following " +"command in the directory where Dockerfile and ClientApp code are located." +msgstr "接下来，我们在 Dockerfile 和 ClientApp 代码所在的目录下运行以下命令，构建 SuperNode Docker 映像。" + +#: ../../source/how-to-run-flower-using-docker.rst:235 +#, fuzzy +msgid "" +"We gave the image the name ``flwr_supernode``, and the tag ``0.0.1``. " +"Remember that the here chosen values only serve as an example. You can " +"change them to your needs." +msgstr "我们将图像命名为 ``flwr_supernode``，标签为 ``0.0.1``。请记住，这里选择的值只是一个示例。您可以根据自己的需要进行更改。" + +#: ../../source/how-to-run-flower-using-docker.rst:240 +#, fuzzy +msgid "Running the SuperNode Docker image" +msgstr "启动服务器" + +#: ../../source/how-to-run-flower-using-docker.rst:242 +#, fuzzy +msgid "Now that we have built the SuperNode image, we can finally run it." +msgstr "现在，我们已经构建了超级节点镜像，终于可以运行它了。" + +#: ../../source/how-to-run-flower-using-docker.rst:250 +#: ../../source/how-to-run-flower-using-docker.rst:367 +#, fuzzy +msgid "Let's break down each part of this command:" +msgstr "让我们来分析一下这条命令的各个部分：" + +#: ../../source/how-to-run-flower-using-docker.rst:252 +#: ../../source/how-to-run-flower-using-docker.rst:369 +#, fuzzy +msgid "``docker run``: This is the command to run a new Docker container." +msgstr "`docker run``： 这是运行新 Docker 容器的命令。" + +#: ../../source/how-to-run-flower-using-docker.rst:253 +#: ../../source/how-to-run-flower-using-docker.rst:370 +#, fuzzy +msgid "" +"``--rm``: This option specifies that the container should be " +"automatically removed when it stops." +msgstr "`-rm``： 该选项指定容器停止时应自动移除。" + +#: ../../source/how-to-run-flower-using-docker.rst:254 +#, fuzzy +msgid "``flwr_supernode:0.0.1``: The name the tag of the Docker image to use." +msgstr "flwr_supernode:0.0.1``： 要使用的 Docker 映像的名称和标记。" + +#: ../../source/how-to-run-flower-using-docker.rst:255 +#: ../../source/how-to-run-flower-using-docker.rst:372 +#, fuzzy +msgid "``--insecure``: This option enables insecure communication." +msgstr "不安全\"： 该选项启用不安全通信。" + +#: ../../source/how-to-run-flower-using-docker.rst +#, fuzzy +msgid "" +"``--superlink 192.168.1.100:9092``: This option specifies the address of " +"the SuperLinks Fleet" +msgstr "``--server 192.168.1.100:9092``: 该选项指定超级链接舰队的地址" + +#: ../../source/how-to-run-flower-using-docker.rst +#, fuzzy +msgid "API to connect to. Remember to update it with your SuperLink IP." +msgstr "要连接的 API。记住用您的超级链接 IP 更新它。" + +#: ../../source/how-to-run-flower-using-docker.rst:269 +#, fuzzy +msgid "" +"To test running Flower locally, you can create a `bridge network " +"`__, use the ``--network`` argument and pass the " +"name of the Docker network to run your SuperNodes." +msgstr "" +"要测试在本地运行 Flower，可以创建一个 \"桥接网络 `__\"，使用\"--网络 " +"\"参数并传递 Docker 网络的名称，以运行超级节点。" + +#: ../../source/how-to-run-flower-using-docker.rst:273 +#, fuzzy +msgid "" +"Any argument that comes after the tag is passed to the Flower SuperNode " +"binary. To see all available flags that the SuperNode supports, run:" +msgstr "标记后的任何参数都将传递给 Flower 超级节点二进制文件。要查看超级节点支持的所有可用标记，请运行" + +#: ../../source/how-to-run-flower-using-docker.rst:283 +#, fuzzy +msgid "" +"To enable SSL, we will need to mount a PEM-encoded root certificate into " +"your SuperNode container." +msgstr "要启用 SSL，我们需要将 PEM 编码的根证书挂载到 SuperNode 容器中。" + +#: ../../source/how-to-run-flower-using-docker.rst:285 +#, fuzzy +msgid "" +"Assuming the certificate already exists locally, we can use the flag " +"``--volume`` to mount the local certificate into the container's " +"``/app/`` directory. This allows the SuperNode to access the certificate " +"within the container. Use the ``--root-certificates`` flag when starting " +"the container." +msgstr "" +"假设我们需要的所有文件都在本地的 ``certificates`` 目录中，我们可以使用标记 ``-v`` 将本地目录挂载到容器的 " +"``/app/`` 目录中。这样，服务器就可以访问容器内的文件。最后，我们使用 ``--certificates`` 标志将证书名称传递给服务器。" + +#: ../../source/how-to-run-flower-using-docker.rst:297 +#, fuzzy +msgid "Flower ServerApp" +msgstr "Flower 服务器。" + +#: ../../source/how-to-run-flower-using-docker.rst:299 +#, fuzzy +msgid "" +"The procedure for building and running a ServerApp image is almost " +"identical to the SuperNode image." +msgstr "构建和运行 ServerApp 映像的程序与 SuperNode 映像几乎完全相同。" + +#: ../../source/how-to-run-flower-using-docker.rst:301 +#, fuzzy +msgid "" +"Similar to the SuperNode image, the ServerApp Docker image comes with a " +"pre-installed version of Flower and serves as a base for building your " +"own ServerApp image." +msgstr "" +"与 SuperNode 映像类似，ServerApp Docker 映像也预装了 Flower 版本，可作为构建自己的 ServerApp " +"映像的基础。" + +#: ../../source/how-to-run-flower-using-docker.rst:304 +#, fuzzy +msgid "" +"We will use the same ``quickstart-pytorch`` example as we do in the " +"Flower SuperNode section. If you have not already done so, please follow " +"the `SuperNode Prerequisites`_ before proceeding." +msgstr "" +"我们将使用与 \"Flower SuperNode \"部分相同的 \"quickstart-pytorch " +"\"示例。如果您还没有这样做，请在继续之前遵循 \"SuperNode 先决条件\"。" + +#: ../../source/how-to-run-flower-using-docker.rst:309 +#, fuzzy +msgid "Creating a ServerApp Dockerfile" +msgstr "创建 ServerApp Dockerfile" + +#: ../../source/how-to-run-flower-using-docker.rst:320 +#, fuzzy +msgid "" +"First, we need to create a Dockerfile in the directory where the " +"``ServerApp`` code is located. If you use the ``quickstart-pytorch`` " +"example, create a new file called ``Dockerfile.serverapp`` in ``examples" +"/quickstart-pytorch``." +msgstr "" +"首先，我们需要在 ``ServerApp`` 代码所在的目录中创建一个 Dockerfile。如果使用 ``quickstart-" +"pytorch`` 示例，请在 ``examples/quickstart-pytorch`` 中创建一个名为 " +"``Dockerfile.serverapp`` 的新文件。" + +#: ../../source/how-to-run-flower-using-docker.rst:324 +#, fuzzy +msgid "" +"The ``Dockerfile.serverapp`` contains the instructions that assemble the " +"ServerApp image." +msgstr "Dockerfile.serverapp \"包含组装 ServerApp 镜像的说明。" + +#: ../../source/how-to-run-flower-using-docker.rst:335 +#, fuzzy +msgid "" +"In the first two lines, we instruct Docker to use the ServerApp image " +"tagged ``1.8.0`` as a base image and set our working directory to " +"``/app``. The following instructions will now be executed in the ``/app``" +" directory. In the last two lines, we copy the ``server.py`` module into " +"the image and set the entry point to ``flower-server-app`` with the " +"argument ``server:app``. The argument is the object reference of the " +"ServerApp (``:``) that will be run inside the " +"ServerApp container." +msgstr "" +"在前两行中，我们指示 Docker 使用标记为 ``1.8.0`` 的 ServerApp 镜像作为基础镜像，并将工作目录设置为 " +"``/app``。下面的指令将在 ``/app`` 目录中执行。在最后两行中，我们将 ``server.py`` " +"模块复制到映像中，并将入口点设置为 ``flower-server-app``，参数为 ``server:app``。参数是将在 " +"ServerApp 容器内运行的 ServerApp 的对象引用（``<模块>:<属性>``）。" + +#: ../../source/how-to-run-flower-using-docker.rst:343 +#, fuzzy +msgid "Building the ServerApp Docker image" +msgstr "启动服务器" + +#: ../../source/how-to-run-flower-using-docker.rst:345 +#, fuzzy +msgid "" +"Next, we build the ServerApp Docker image by running the following " +"command in the directory where Dockerfile and ServerApp code are located." +msgstr "接下来，我们在 Dockerfile 和 ServerApp 代码所在的目录下运行以下命令，构建 ServerApp Docker 镜像。" + +#: ../../source/how-to-run-flower-using-docker.rst:352 +#, fuzzy +msgid "" +"We gave the image the name ``flwr_serverapp``, and the tag ``0.0.1``. " +"Remember that the here chosen values only serve as an example. You can " +"change them to your needs." +msgstr "我们给图片命名为 ``flwr_serverapp``，标签为 ``0.0.1``。请记住，这里选择的值只是一个示例。您可以根据自己的需要进行更改。" + +#: ../../source/how-to-run-flower-using-docker.rst:357 +#, fuzzy +msgid "Running the ServerApp Docker image" +msgstr "启动服务器" + +#: ../../source/how-to-run-flower-using-docker.rst:359 +#, fuzzy +msgid "Now that we have built the ServerApp image, we can finally run it." +msgstr "现在我们已经构建了 ServerApp 镜像，终于可以运行它了。" + +#: ../../source/how-to-run-flower-using-docker.rst:371 +#, fuzzy +msgid "``flwr_serverapp:0.0.1``: The name the tag of the Docker image to use." +msgstr "flwr_serverapp:0.0.1``： 要使用的 Docker 映像的名称和标记。" + +#: ../../source/how-to-run-flower-using-docker.rst +#, fuzzy +msgid "" +"``--superlink 192.168.1.100:9091``: This option specifies the address of " +"the SuperLinks Driver" +msgstr "``--server 192.168.1.100:9091``: 此选项指定超级链接驱动程序的地址" + +#: ../../source/how-to-run-flower-using-docker.rst:385 +#, fuzzy +msgid "" +"To test running Flower locally, you can create a `bridge network " +"`__, use the ``--network`` argument and pass the " +"name of the Docker network to run your ServerApps." +msgstr "" +"要测试在本地运行 Flower，可以创建一个 ``bridge network `___，使用 " +"``--network`` 参数并传递 Docker 网络的名称，以运行 ServerApps。" + +#: ../../source/how-to-run-flower-using-docker.rst:389 +#, fuzzy +msgid "" +"Any argument that comes after the tag is passed to the Flower ServerApp " +"binary. To see all available flags that the ServerApp supports, run:" +msgstr "标记后的任何参数都将传递给 Flower ServerApp 二进制文件。要查看 ServerApp 支持的所有可用标记，请运行" + +#: ../../source/how-to-run-flower-using-docker.rst:399 +#, fuzzy +msgid "" +"To enable SSL, we will need to mount a PEM-encoded root certificate into " +"your ServerApp container." +msgstr "要启用 SSL，需要 CA 证书、服务器证书和服务器私钥。" + +#: ../../source/how-to-run-flower-using-docker.rst:401 +#, fuzzy +msgid "" +"Assuming the certificate already exists locally, we can use the flag " +"``--volume`` to mount the local certificate into the container's " +"``/app/`` directory. This allows the ServerApp to access the certificate " +"within the container. Use the ``--root-certificates`` flags when starting" +" the container." +msgstr "" +"假设我们需要的所有文件都在本地的 ``certificates`` 目录中，我们可以使用标记 ``-v`` 将本地目录挂载到容器的 " +"``/app/`` 目录中。这样，服务器就可以访问容器内的文件。最后，我们使用 ``--certificates`` 标志将证书名称传递给服务器。" + +#: ../../source/how-to-run-flower-using-docker.rst:412 +#, fuzzy +msgid "Advanced Docker options" +msgstr "高级安装选项" + +#: ../../source/how-to-run-flower-using-docker.rst:415 +msgid "Run with root user privileges" +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:417 +msgid "" +"Flower Docker images, by default, run with a non-root user " +"(username/groupname: ``app``, UID/GID: ``49999``). Using root user is not" +" recommended unless it is necessary for specific tasks during the build " +"process. Always make sure to run the container as a non-root user in " +"production to maintain security best practices." +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:422 +msgid "**Run a container with root user privileges**" +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:424 +msgid "" +"Run the Docker image with the ``-u`` flag and specify ``root`` as the " +"username:" +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:430 +msgid "This command will run the Docker container with root user privileges." +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:432 +msgid "**Run the build process with root user privileges**" +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:434 +msgid "" +"If you want to switch to the root user during the build process of the " +"Docker image to install missing system dependencies, you can use the " +"``USER root`` directive within your Dockerfile." +msgstr "" + +#: ../../source/how-to-run-flower-using-docker.rst:454 +#, fuzzy +msgid "Using a different Flower version" +msgstr "使用不同的 Flower 或 Python 版本" + +#: ../../source/how-to-run-flower-using-docker.rst:456 +#, fuzzy +msgid "" +"If you want to use a different version of Flower, for example Flower " +"nightly, you can do so by changing the tag. All available versions are on" +" `Docker Hub `__." +msgstr "" +"如果您想使用不同版本的 Flower 或 Python，可以通过更改标签来实现。我们提供的所有版本都可以在 `Docker Hub " +"`_ 上找到。" + +#: ../../source/how-to-run-flower-using-docker.rst:460 +#, fuzzy +msgid "Pinning a Docker image to a specific version" +msgstr "将 Docker 映像固定到特定版本" + +#: ../../source/how-to-run-flower-using-docker.rst:462 +#, fuzzy +msgid "" +"It may happen that we update the images behind the tags. Such updates " +"usually include security updates of system dependencies that should not " +"change the functionality of Flower. However, if you want to ensure that " +"you always use the same image, you can specify the hash of the image " +"instead of the tag." +msgstr "" +"我们可能会更新标签后面的图像。此类更新通常包括系统依赖项的安全更新，不会改变 Flower " +"的功能。不过，如果您想确保始终使用同一张图片，可以指定图片的哈希值而不是标签。" + +#: ../../source/how-to-run-flower-using-docker.rst:467 +#, fuzzy +msgid "" +"The following command returns the current image hash referenced by the " +"``superlink:1.8.0`` tag:" +msgstr "下面的命令将返回由 ``server:1.7.0-py3.11-ubuntu22.04`` 标记引用的当前图像哈希值：" + +#: ../../source/how-to-run-flower-using-docker.rst:474 +#, fuzzy +msgid "Next, we can pin the hash when running a new SuperLink container:" +msgstr "接下来，我们可以在运行新服务器容器时将哈希值固定下来：" + +#: ../../source/how-to-run-flower-using-docker.rst:483 +#, fuzzy +msgid "Setting environment variables" +msgstr "设置编码环境" + +#: ../../source/how-to-run-flower-using-docker.rst:485 +#, fuzzy +msgid "" +"To set a variable inside a Docker container, you can use the ``-e " +"=`` flag." +msgstr "要在 Docker 容器内设置变量，可以使用 ``-e =`` 标志。" + +#: ../../source/how-to-run-simulations.rst:2 +msgid "Run simulations" +msgstr "运行模拟" + +#: ../../source/how-to-run-simulations.rst:8 +msgid "" +"Simulating Federated Learning workloads is useful for a multitude of use-" +"cases: you might want to run your workload on a large cohort of clients " +"but without having to source, configure and mange a large number of " +"physical devices; you might want to run your FL workloads as fast as " +"possible on the compute systems you have access to without having to go " +"through a complex setup process; you might want to validate your " +"algorithm on different scenarios at varying levels of data and system " +"heterogeneity, client availability, privacy budgets, etc. These are among" +" some of the use-cases where simulating FL workloads makes sense. Flower " +"can accommodate these scenarios by means of its `VirtualClientEngine " +"`_ or " +"VCE." +msgstr "" +"模拟联邦学习工作负载可用于多种案例：您可能希望在大量客户端上运行您的工作负载，但无需采购、配置和管理大量物理设备；您可能希望在您可以访问的计算系统上尽可能快地运行您的" +" FL 工作负载，而无需经过复杂的设置过程；您可能希望在不同数据和系统异构性、客户端可用性、隐私预算等不同水平的场景中验证您的算法。这些都是模拟 " +"FL 工作负载的一些案例。Flower 可以通过其 \"虚拟客户端引擎\"（VirtualClientEngine）_或 VCE 来匹配这些情况。" + +#: ../../source/how-to-run-simulations.rst:10 +msgid "" +"The :code:`VirtualClientEngine` schedules, launches and manages `virtual`" +" clients. These clients are identical to `non-virtual` clients (i.e. the " +"ones you launch via the command `flwr.client.start_client `_) in the sense that they can be configure by " +"creating a class inheriting, for example, from `flwr.client.NumPyClient " +"`_ and therefore behave in an " +"identical way. In addition to that, clients managed by the " +":code:`VirtualClientEngine` are:" +msgstr "" +":code:`VirtualClientEngine`用来规划，启动和管理`虚拟`客户端。这些客户端跟`非虚拟`客户端是一样的（即为您通过`flwr.client.start_client" +" `_启动的客户端），因为它们可以通过创建一个继承自 " +"`flwr.client.NumPyClient `_ " +"的类进行配置，因此其行为方式相同。另外，由 `VirtualClientEngine` 管理的客户端有：" + +#: ../../source/how-to-run-simulations.rst:12 +msgid "" +"resource-aware: this means that each client gets assigned a portion of " +"the compute and memory on your system. You as a user can control this at " +"the beginning of the simulation and allows you to control the degree of " +"parallelism of your Flower FL simulation. The fewer the resources per " +"client, the more clients can run concurrently on the same hardware." +msgstr "" +"资源感知：这意味着每个客户端都会分配到系统中的一部分计算和内存。作为用户，您可以在模拟开始时对其进行控制，从而控制 Flower FL " +"模拟的并行程度。每个客户端的资源越少，在同一硬件上并发运行的客户端就越多。" + +#: ../../source/how-to-run-simulations.rst:13 +msgid "" +"self-managed: this means that you as a user do not need to launch clients" +" manually, instead this gets delegated to :code:`VirtualClientEngine`'s " +"internals." +msgstr "自管理：这意味着用户无需手动启动客户端，而是由 :code:`VirtualClientEngine` 负责。" + +#: ../../source/how-to-run-simulations.rst:14 +msgid "" +"ephemeral: this means that a client is only materialized when it is " +"required in the FL process (e.g. to do `fit() `_). The object is destroyed afterwards," +" releasing the resources it was assigned and allowing in this way other " +"clients to participate." msgstr "" "即时性：这意味着客户端只有在 FL 进程中需要它时才会被实体化（例如执行 `fit() `_ " @@ -6032,10 +6516,13 @@ msgid "" msgstr "要加载进度，只需在代码中添加以下几行。请注意，这将遍历所有已保存的检查点，并加载最新的检查点：" #: ../../source/how-to-save-and-load-model-checkpoints.rst:97 +#, fuzzy msgid "" "Return/use this object of type ``Parameters`` wherever necessary, such as" " in the ``initial_parameters`` when defining a ``Strategy``." msgstr "" +"在必要时返回/使用此 ``Parameters`` 类型的对象，例如在定义 ``Strategy` 时的 " +"``initial_parameters` 中。" #: ../../source/how-to-upgrade-to-flower-1.0.rst:2 msgid "Upgrade to Flower 1.0" @@ -6052,6 +6539,7 @@ msgstr "" "系列版本）相比，有一些破坏性改动需要修改现有 0.x 系列项目的代码。" #: ../../source/how-to-upgrade-to-flower-1.0.rst:8 +#: ../../source/how-to-upgrade-to-flower-next.rst:43 msgid "Install update" msgstr "安装更新" @@ -6099,6 +6587,7 @@ msgstr "" "}``（当使用``start_simulation``时）" #: ../../source/how-to-upgrade-to-flower-1.0.rst:24 +#: ../../source/how-to-upgrade-to-flower-next.rst:100 msgid "Required changes" msgstr "所需变更" @@ -6131,6 +6620,11 @@ msgstr "" "Flower 1.0（关键字参数）： ``start_client(server_address=\"127.0.0.1:8080\", " "client=FlowerClient())``" +#: ../../source/how-to-upgrade-to-flower-1.0.rst:37 +#: ../../source/ref-api/flwr.client.Client.rst:2 +msgid "Client" +msgstr "客户端" + #: ../../source/how-to-upgrade-to-flower-1.0.rst:39 msgid "" "Subclasses of ``NumPyClient``: change ``def get_parameters(self):``` to " @@ -6337,6 +6831,7 @@ msgstr "" "...)``" #: ../../source/how-to-upgrade-to-flower-1.0.rst:89 +#: ../../source/how-to-upgrade-to-flower-next.rst:317 msgid "Further help" msgstr "更多帮助" @@ -6352,74 +6847,329 @@ msgstr "" " 已经更新到 Flower 1.0，它们可以作为使用 Flower 1.0 API 的参考。如果还有其他问题，请加入 Flower Slack " "`_ 并使用 \"#questions``\"。" -#: ../../source/how-to-use-built-in-mods.rst:2 -msgid "Use Built-in Mods" -msgstr "" +#: ../../source/how-to-upgrade-to-flower-next.rst:2 +#, fuzzy +msgid "Upgrade to Flower Next" +msgstr "升级至 Flower 1.0" -#: ../../source/how-to-use-built-in-mods.rst:4 +#: ../../source/how-to-upgrade-to-flower-next.rst:4 +#, fuzzy msgid "" -"**Note: This tutorial covers experimental features. The functionality and" -" interfaces may change in future versions.**" +"Welcome to the migration guide for updating Flower to Flower Next! " +"Whether you're a seasoned user or just getting started, this guide will " +"help you smoothly transition your existing setup to take advantage of the" +" latest features and improvements in Flower Next, starting from version " +"1.8." msgstr "" +"欢迎阅读从 Flower 升级到 Flower Next 的迁移指南！无论您是经验丰富的用户还是刚刚开始使用 " +"Flower，本指南都将帮助您顺利过渡现有设置，以利用 Flower Next 从 1.8 版开始的最新功能和改进。" -#: ../../source/how-to-use-built-in-mods.rst:6 +#: ../../source/how-to-upgrade-to-flower-next.rst:9 +#, fuzzy msgid "" -"In this tutorial, we will learn how to utilize built-in mods to augment " -"the behavior of a ``ClientApp``. Mods (sometimes also called Modifiers) " -"allow us to perform operations before and after a task is processed in " -"the ``ClientApp``." +"This guide shows how to reuse pre-``1.8`` Flower code with minimum code " +"changes by using the *compatibility layer* in Flower Next. In another " +"guide, we will show how to run Flower Next end-to-end with pure Flower " +"Next APIs." msgstr "" +"本指南展示了如何通过使用 Flower Next 中的*可兼容层*，以最小的代码改动重用```1.8```前的 Flower " +"代码。在另一个指南中，我们将介绍如何使用纯 Flower Next API 端到端运行 Flower Next。" -#: ../../source/how-to-use-built-in-mods.rst:9 -msgid "What are Mods?" -msgstr "" +#: ../../source/how-to-upgrade-to-flower-next.rst:13 +#, fuzzy +msgid "Let's dive in!" +msgstr "让我们深入了解一下！" -#: ../../source/how-to-use-built-in-mods.rst:11 +#: ../../source/how-to-upgrade-to-flower-next.rst:48 +#, fuzzy msgid "" -"A Mod is a callable that wraps around a ``ClientApp``. It can manipulate " -"or inspect the incoming ``Message`` and the resulting outgoing " -"``Message``. The signature for a ``Mod`` is as follows:" -msgstr "" +"Here's how to update an existing installation of Flower to Flower Next " +"with ``pip``:" +msgstr "下面介绍如何使用 pip 或 Poetry 将现有安装更新到 Flower 1.0：" + +#: ../../source/how-to-upgrade-to-flower-next.rst:54 +#, fuzzy +msgid "or if you need Flower Next with simulation:" +msgstr "启动 Flower 模拟" + +#: ../../source/how-to-upgrade-to-flower-next.rst:61 +#, fuzzy +msgid "" +"Ensure you set the following version constraint in your " +"``requirements.txt``" +msgstr "确保在 ``requirements.txt`` 中设置了以下版本限制" + +#: ../../source/how-to-upgrade-to-flower-next.rst:71 +#, fuzzy +msgid "or ``pyproject.toml``:" +msgstr "或 ``pyproject.toml```：" + +#: ../../source/how-to-upgrade-to-flower-next.rst:82 +#, fuzzy +msgid "Using Poetry" +msgstr "使用 pip" + +#: ../../source/how-to-upgrade-to-flower-next.rst:84 +#, fuzzy +msgid "" +"Update the ``flwr`` dependency in ``pyproject.toml`` and then reinstall " +"(don't forget to delete ``poetry.lock`` via ``rm poetry.lock`` before " +"running ``poetry install``)." +msgstr "" +"Poetry：更新 ``pyproject.toml`` 中的 ``flwr`` 依赖包，然后重新安装（运行 ``poetry install``" +" 前，别忘了通过 ``rm poetry.lock` 删除 ``poetry.lock`）。" + +#: ../../source/how-to-upgrade-to-flower-next.rst:86 +#, fuzzy +msgid "" +"Ensure you set the following version constraint in your " +"``pyproject.toml``:" +msgstr "将 ``pyproject.toml`` 中的次要版本增加一个。" + +#: ../../source/how-to-upgrade-to-flower-next.rst:102 +#, fuzzy +msgid "" +"In Flower Next, the *infrastructure* and *application layers* have been " +"decoupled. Instead of starting a client in code via ``start_client()``, " +"you create a |clientapp_link|_ and start it via the command line. Instead" +" of starting a server in code via ``start_server()``, you create a " +"|serverapp_link|_ and start it via the command line. The long-running " +"components of server and client are called SuperLink and SuperNode. The " +"following non-breaking changes that require manual updates and allow you " +"to run your project both in the traditional way and in the Flower Next " +"way:" +msgstr "" +"在 Flower Next " +"中，*基础架构层*和*应用层*已经解耦。你不再需要在代码中通过``start_client()``启动客户端，而是创建一个|clientapp_link|_，然后通过命令行启动它。无需通过``start_server()``在代码中启动服务器，而是创建一个" +" |serverapp_link|_ " +"并通过命令行启动它。服务器和客户端的长期运行组件被称为超级链接（SuperLink）和超级节点（SuperNode）。以下是无需手动更新的非破坏性更改，可让您以传统方式和" +" Flower Next 方式运行项目：" + +#: ../../source/how-to-upgrade-to-flower-next.rst:109 +#, fuzzy +msgid "|clientapp_link|_" +msgstr "客户端" + +#: ../../source/how-to-upgrade-to-flower-next.rst:110 +#, fuzzy +msgid "" +"Wrap your existing client with |clientapp_link|_ instead of launching it " +"via |startclient_link|_. Here's an example:" +msgstr "用 |clientapp_link|_ 封装现有客户端，而不是通过 |startclient_link|_ 启动。下面是一个例子：" + +#: ../../source/how-to-upgrade-to-flower-next.rst:132 +#, fuzzy +msgid "|serverapp_link|_" +msgstr "服务器" + +#: ../../source/how-to-upgrade-to-flower-next.rst:133 +#, fuzzy +msgid "" +"Wrap your existing strategy with |serverapp_link|_ instead of starting " +"the server via |startserver_link|_. Here's an example:" +msgstr "用 |serverapp_link|_ 包住现有策略，而不是通过 |startserver_link|_ 启动服务器。下面是一个例子：" + +#: ../../source/how-to-upgrade-to-flower-next.rst:154 +#, fuzzy +msgid "Deployment" +msgstr "调配" + +#: ../../source/how-to-upgrade-to-flower-next.rst:155 +#, fuzzy +msgid "" +"Run the ``SuperLink`` using |flowernext_superlink_link|_ before running, " +"in sequence, |flowernext_clientapp_link|_ (2x) and " +"|flowernext_serverapp_link|_. There is no need to execute `client.py` and" +" `server.py` as Python scripts." +msgstr "" +"在依次运行 |flowernext_clientapp_link|_ (2x) 和 |flowernext_serverapp_link|_ " +"之前，使用 |flowernext_superlink_link|_ 运行 ``SuperLink`` 。无需将 |client.py` 和 " +"`server.py` 作为 Python 脚本执行。" + +#: ../../source/how-to-upgrade-to-flower-next.rst:158 +#, fuzzy +msgid "" +"Here's an example to start the server without HTTPS (only for " +"prototyping):" +msgstr "下面是一个在不使用 HTTPS 的情况下启动服务器的示例（仅用于原型开发）：" + +#: ../../source/how-to-upgrade-to-flower-next.rst:174 +#, fuzzy +msgid "" +"Here's another example to start with HTTPS. Use the ``--ssl-ca-" +"certfile``, ``--ssl-certfile``, and ``--ssl-keyfile`` command line " +"options to pass paths to (CA certificate, server certificate, and server " +"private key)." +msgstr "下面是另一个使用 HTTPS 的示例。使用 ``--certificates`` 命令行参数传递路径（CA 证书、服务器证书和服务器私钥）。" + +#: ../../source/how-to-upgrade-to-flower-next.rst:201 +#, fuzzy +msgid "Simulation in CLI" +msgstr "运行模拟" + +#: ../../source/how-to-upgrade-to-flower-next.rst:202 +#, fuzzy +msgid "" +"Wrap your existing client and strategy with |clientapp_link|_ and " +"|serverapp_link|_, respectively. There is no need to use |startsim_link|_" +" anymore. Here's an example:" +msgstr "" +"分别用 |clientapp_link|_ 和 |serverapp_link|_ 封装现有的客户端和策略。无需再使用 " +"|startsim_link|_。下面是一个示例：" + +#: ../../source/how-to-upgrade-to-flower-next.rst:232 +#, fuzzy +msgid "" +"Run |flower_simulation_link|_ in CLI and point to the ``server_app`` / " +"``client_app`` object in the code instead of executing the Python script." +" Here's an example (assuming the ``server_app`` and ``client_app`` " +"objects are in a ``sim.py`` module):" +msgstr "" +"在 CLI 中运行 |flower_simulation_link|_ 并指向代码中的 ``server_app`` " +"/``client_app`` 对象，而不是执行 Python 脚本。下面是一个示例（假定 `server_app`` 和 " +"`client_app`` 对象位于 `sim.py`` 模块中）：" + +#: ../../source/how-to-upgrade-to-flower-next.rst:249 +#, fuzzy +msgid "" +"Set default resources for each |clientapp_link|_ using the ``--backend-" +"config`` command line argument instead of setting the " +"``client_resources`` argument in |startsim_link|_. Here's an example:" +msgstr "" +"使用 ``--backend-config`` 命令行参数为每个 |clientapp_link|_ 设置默认资源，而不是在 " +"|startsim_link|_ 中设置 ``client_resources`` 参数。下面是一个例子：" + +#: ../../source/how-to-upgrade-to-flower-next.rst:275 +#, fuzzy +msgid "Simulation in a Notebook" +msgstr "笔记本中的模拟" + +#: ../../source/how-to-upgrade-to-flower-next.rst:276 +#, fuzzy +msgid "" +"Run |runsim_link|_ in your notebook instead of |startsim_link|_. Here's " +"an example:" +msgstr "在笔记本中运行 |runsim_link|_，而不是 |startsim_link|_。下面是一个例子：" + +#: ../../source/how-to-upgrade-to-flower-next.rst:319 +#, fuzzy +msgid "" +"Some official `Flower code examples `_ " +"are already updated to Flower Next so they can serve as a reference for " +"using the Flower Next API. If there are further questions, `join the " +"Flower Slack `_ and use the channel " +"``#questions``. You can also `participate in Flower Discuss " +"`_ where you can find us answering questions," +" or share and learn from others about migrating to Flower Next." +msgstr "" +"大多数官方的 `Flower 代码示例 `_" +" 已经更新到 Flower 1.0，它们可以作为使用 Flower 1.0 API 的参考。如果还有其他问题，请加入 Flower Slack " +"`_ 并使用 \"#questions``\"。" + +#: ../../source/how-to-upgrade-to-flower-next.rst:325 +#, fuzzy +msgid "Important" +msgstr "重要变更：" + +#: ../../source/how-to-upgrade-to-flower-next.rst:328 +#, fuzzy +msgid "" +"As we continuously enhance Flower Next at a rapid pace, we'll be " +"periodically updating this guide. Please feel free to share any feedback " +"with us!" +msgstr "随着 Flower Next 的不断快速改进，我们将定期更新本指南。如有任何反馈，请随时与我们分享！" + +#: ../../source/how-to-upgrade-to-flower-next.rst:334 +#, fuzzy +msgid "Happy migrating! 🚀" +msgstr "移民愉快！🚀" + +#: ../../source/how-to-use-built-in-mods.rst:2 +#, fuzzy +msgid "Use Built-in Mods" +msgstr "使用内置调制器" + +#: ../../source/how-to-use-built-in-mods.rst:4 +#, fuzzy +msgid "" +"**Note: This tutorial covers experimental features. The functionality and" +" interfaces may change in future versions.**" +msgstr "**注：本教程涵盖实验性功能。功能和界面可能会在未来版本中发生变化。" + +#: ../../source/how-to-use-built-in-mods.rst:6 +#, fuzzy +msgid "" +"In this tutorial, we will learn how to utilize built-in mods to augment " +"the behavior of a ``ClientApp``. Mods (sometimes also called Modifiers) " +"allow us to perform operations before and after a task is processed in " +"the ``ClientApp``." +msgstr "" +"在本教程中，我们将学习如何利用内置模块来增强 ``ClientApp`` 的行为。修改器（有时也称为修改器）允许我们在 ``ClientApp``" +" 处理任务之前和之后执行操作。" + +#: ../../source/how-to-use-built-in-mods.rst:9 +#, fuzzy +msgid "What are Mods?" +msgstr "什么是 Mods？" + +#: ../../source/how-to-use-built-in-mods.rst:11 +#, fuzzy +msgid "" +"A Mod is a callable that wraps around a ``ClientApp``. It can manipulate " +"or inspect the incoming ``Message`` and the resulting outgoing " +"``Message``. The signature for a ``Mod`` is as follows:" +msgstr "" +"Mod 是包裹在 ``ClientApp`` 周围的可调用程序。它可以操作或检查传入的 ``Message`` 和由此产生的传出的 " +"``Message`` 。一个 ``Mod`` 的签名如下：" #: ../../source/how-to-use-built-in-mods.rst:18 +#, fuzzy msgid "A typical mod function might look something like this:" -msgstr "" +msgstr "一个典型的修改函数可能是这样的：" #: ../../source/how-to-use-built-in-mods.rst:31 +#, fuzzy msgid "Using Mods" -msgstr "" +msgstr "使用修改器" #: ../../source/how-to-use-built-in-mods.rst:33 +#, fuzzy msgid "To use mods in your ``ClientApp``, you can follow these steps:" -msgstr "" +msgstr "要在您的 ``ClientApp`` 中使用 mod，可以按照以下步骤操作：" #: ../../source/how-to-use-built-in-mods.rst:36 +#, fuzzy msgid "1. Import the required mods" -msgstr "" +msgstr "1. 导入所需修改" #: ../../source/how-to-use-built-in-mods.rst:38 +#, fuzzy msgid "First, import the built-in mod you intend to use:" -msgstr "" +msgstr "首先，导入您打算使用的内置模式：" #: ../../source/how-to-use-built-in-mods.rst:46 +#, fuzzy msgid "2. Define your client function" -msgstr "" +msgstr "2. 定义客户功能" #: ../../source/how-to-use-built-in-mods.rst:48 +#, fuzzy msgid "" "Define your client function (``client_fn``) that will be wrapped by the " "mod(s):" -msgstr "" +msgstr "定义将被 mod 封装的客户端函数（``client_fn``）：" #: ../../source/how-to-use-built-in-mods.rst:57 +#, fuzzy msgid "3. Create the ``ClientApp`` with mods" -msgstr "" +msgstr "3. 用模块创建 ``ClientApp``" #: ../../source/how-to-use-built-in-mods.rst:59 +#, fuzzy msgid "" "Create your ``ClientApp`` and pass the mods as a list to the ``mods`` " "argument. The order in which you provide the mods matters:" -msgstr "" +msgstr "创建您的 ``ClientApp`` 并将 mods 作为列表传递给 ``mods`` 参数。提供 mod 的顺序很重要：" #: ../../source/how-to-use-built-in-mods.rst:72 #, fuzzy @@ -6427,50 +7177,61 @@ msgid "Order of execution" msgstr "停用" #: ../../source/how-to-use-built-in-mods.rst:74 +#, fuzzy msgid "" "When the ``ClientApp`` runs, the mods are executed in the order they are " "provided in the list:" -msgstr "" +msgstr "当运行 ``ClientApp`` 时，会按照列表中提供的顺序执行模块：" #: ../../source/how-to-use-built-in-mods.rst:76 +#, fuzzy msgid "``example_mod_1`` (outermost mod)" -msgstr "" +msgstr "``example_mod_1`` （最外层模块）" #: ../../source/how-to-use-built-in-mods.rst:77 +#, fuzzy msgid "``example_mod_2`` (next mod)" -msgstr "" +msgstr "示例模式 2（下一个模式）" #: ../../source/how-to-use-built-in-mods.rst:78 +#, fuzzy msgid "" "Message handler (core function that handles the incoming ``Message`` and " "returns the outgoing ``Message``)" -msgstr "" +msgstr "消息处理程序（处理传入的 \"消息 \"并返回传出的 \"消息 \"的核心函数）" #: ../../source/how-to-use-built-in-mods.rst:79 +#, fuzzy msgid "``example_mod_2`` (on the way back)" -msgstr "" +msgstr "``example_mod_2`` （返回途中）" #: ../../source/how-to-use-built-in-mods.rst:80 +#, fuzzy msgid "``example_mod_1`` (outermost mod on the way back)" -msgstr "" +msgstr "``example_mod_1`` （返回途中最外层的模式）" #: ../../source/how-to-use-built-in-mods.rst:82 +#, fuzzy msgid "" "Each mod has a chance to inspect and modify the incoming ``Message`` " "before passing it to the next mod, and likewise with the outgoing " "``Message`` before returning it up the stack." -msgstr "" +msgstr "每个模块都有机会检查和修改传入的 \"信息\"，然后再将其传递给下一个模块，同样，也有机会检查和修改传出的 \"信息\"，然后再将其返回堆栈。" #: ../../source/how-to-use-built-in-mods.rst:87 +#, fuzzy msgid "" "By following this guide, you have learned how to effectively use mods to " "enhance your ``ClientApp``'s functionality. Remember that the order of " "mods is crucial and affects how the input and output are processed." msgstr "" +"通过本指南，您已学会如何有效地使用 mod 来增强您的 ``ClientApp`` 的功能。请记住，mod " +"的顺序至关重要，它会影响输入和输出的处理方式。" #: ../../source/how-to-use-built-in-mods.rst:89 +#, fuzzy msgid "Enjoy building a more robust and flexible ``ClientApp`` with mods!" -msgstr "" +msgstr "使用 mods 构建更强大、更灵活的 \"客户端应用程序\"！" #: ../../source/how-to-use-differential-privacy.rst:2 #, fuzzy @@ -6478,29 +7239,37 @@ msgid "Use Differential Privacy" msgstr "差分隐私" #: ../../source/how-to-use-differential-privacy.rst:3 +#, fuzzy msgid "" "This guide explains how you can utilize differential privacy in the " "Flower framework. If you are not yet familiar with differential privacy, " "you can refer to :doc:`explanation-differential-privacy`." msgstr "" +"本指南解释了如何在 Flower 框架中使用差分隐私。如果您还不熟悉差分隐私，可以参考 :doc:`explanation-" +"differential-privacy` 。" #: ../../source/how-to-use-differential-privacy.rst:7 +#, fuzzy msgid "" "Differential Privacy in Flower is in a preview phase. If you plan to use " "these features in a production environment with sensitive data, feel free" " contact us to discuss your requirements and to receive guidance on how " "to best use these features." msgstr "" +"Flower " +"中的差异隐私处于预览阶段。如果您计划在生产环境中使用这些敏感数据功能，请随时联系我们，讨论您的需求，并获得如何最好地使用这些功能的指导。" #: ../../source/how-to-use-differential-privacy.rst:12 +#, fuzzy msgid "" "This approach consists of two seprate phases: clipping of the updates and" " adding noise to the aggregated model. For the clipping phase, Flower " "framework has made it possible to decide whether to perform clipping on " "the server side or the client side." -msgstr "" +msgstr "这种方法包括两个独立的阶段：对更新进行剪切和在聚合模型中添加噪声。在剪切阶段，Flower 框架可以决定是在服务器端还是在客户端执行剪切。" #: ../../source/how-to-use-differential-privacy.rst:15 +#, fuzzy msgid "" "**Server-side Clipping**: This approach has the advantage of the server " "enforcing uniform clipping across all clients' updates and reducing the " @@ -6508,14 +7277,17 @@ msgid "" "disadvantage of increasing the computational load on the server due to " "the need to perform the clipping operation for all clients." msgstr "" +"** 服务器端剪切**： " +"这种方法的优点是服务器可对所有客户端的更新执行统一的剪切，并减少剪切值的通信开销。不过，这种方法也有缺点，那就是需要为所有客户端执行剪切操作，从而增加了服务器的计算负荷。" #: ../../source/how-to-use-differential-privacy.rst:16 +#, fuzzy msgid "" "**Client-side Clipping**: This approach has the advantage of reducing the" " computational overhead on the server. However, it also has the " "disadvantage of lacking centralized control, as the server has less " "control over the clipping process." -msgstr "" +msgstr "**客户端剪切**： 这种方法的优点是可以减少服务器的计算开销。不过，它也有缺乏集中控制的缺点，因为服务器对剪切过程的控制较少。" #: ../../source/how-to-use-differential-privacy.rst:21 #, fuzzy @@ -6523,6 +7295,7 @@ msgid "Server-side Clipping" msgstr "服务器端逻辑" #: ../../source/how-to-use-differential-privacy.rst:22 +#, fuzzy msgid "" "For central DP with server-side clipping, there are two :code:`Strategy` " "classes that act as wrappers around the actual :code:`Strategy` instance " @@ -6531,6 +7304,10 @@ msgid "" ":code:`DifferentialPrivacyServerSideAdaptiveClipping` for fixed and " "adaptive clipping." msgstr "" +"对于具有服务器端剪裁功能的中央 DP，有两个 :code:`Strategy` 类作为实际 :code:`Strategy` 实例（例如 " +":code:`FedAvg`）的包装器。这两个封装类分别是 " +":code:`DifferentialPrivacyServerSideFixedClipping` 和 " +":code:`DifferentialPrivacyServerSideAdaptiveClipping` ，用于固定剪辑和自适应剪辑。" #: ../../source/how-to-use-differential-privacy.rst:-1 #, fuzzy @@ -6538,6 +7315,7 @@ msgid "server side clipping" msgstr "服务器端逻辑" #: ../../source/how-to-use-differential-privacy.rst:31 +#, fuzzy msgid "" "The code sample below enables the :code:`FedAvg` strategy to use server-" "side fixed clipping using the " @@ -6546,6 +7324,9 @@ msgid "" ":code:`DifferentialPrivacyServerSideAdaptiveClipping` by adjusting the " "corresponding input parameters." msgstr "" +"下面的代码示例使用 :code:`DifferentialPrivacyServerSideFixedClipping` 封装类使 " +":code:`FedAvg` 策略使用服务器端固定剪辑。通过调整相应的输入参数，同样的方法也可用于 " +":code:`DifferentialPrivacyServerSideAdaptiveClipping`。" #: ../../source/how-to-use-differential-privacy.rst:52 #, fuzzy @@ -6553,6 +7334,7 @@ msgid "Client-side Clipping" msgstr "客户端逻辑" #: ../../source/how-to-use-differential-privacy.rst:53 +#, fuzzy msgid "" "For central DP with client-side clipping, the server sends the clipping " "value to selected clients on each round. Clients can use existing Flower " @@ -6562,6 +7344,11 @@ msgid "" ":code:`DifferentialPrivacyClientSideFixedClipping` and " ":code:`DifferentialPrivacyClientSideAdaptiveClipping`." msgstr "" +"对于带有客户端剪裁功能的中央 DP，服务器会在每一轮向选定的客户端发送剪裁值。客户端可以使用现有的 Flower " +":code:`Mods`来执行剪裁。有两种模式可用于固定和自适应客户端剪辑：:code:`fixedclipping_mod` 和 " +":code:`adaptiveclipping_mod`，以及相应的服务器端封装 " +":code:`DifferentialPrivacyClientSideFixedClipping` 和 " +":code:`DifferentialPrivacyClientSideAdaptiveClipping`。" #: ../../source/how-to-use-differential-privacy.rst:-1 #, fuzzy @@ -6569,49 +7356,63 @@ msgid "client side clipping" msgstr "客户端逻辑" #: ../../source/how-to-use-differential-privacy.rst:63 +#, fuzzy msgid "" "The code sample below enables the :code:`FedAvg` strategy to use " "differential privacy with client-side fixed clipping using both the " ":code:`DifferentialPrivacyClientSideFixedClipping` wrapper class and, on " "the client, :code:`fixedclipping_mod`:" msgstr "" +"下面的代码示例使用 :code:`DifferentialPrivacyClientSideFixedClipping` 封装类和客户端的 " +":code:`fixedclipping_mod` 使 :code:`FedAvg` 策略在客户端固定剪辑的情况下使用差分隐私：" #: ../../source/how-to-use-differential-privacy.rst:80 +#, fuzzy msgid "" "In addition to the server-side strategy wrapper, the :code:`ClientApp` " "needs to configure the matching :code:`fixedclipping_mod` to perform the " "client-side clipping:" msgstr "" +"除了服务器端策略包装器外，:code:`ClientApp` 还需要配置匹配的 :code:`fixedclipping_mod` " +"以执行客户端剪切：" #: ../../source/how-to-use-differential-privacy.rst:97 +#, fuzzy msgid "" "To utilize local differential privacy (DP) and add noise to the client " "model parameters before transmitting them to the server in Flower, you " "can use the `LocalDpMod`. The following hyperparameters need to be set: " "clipping norm value, sensitivity, epsilon, and delta." msgstr "" +"要利用本地差分隐私（DP）并在将客户端模型参数传输到 Flower 服务器之前为其添加噪声，可以使用 " +"`LocalDpMod`。需要设置以下超参数：剪切规范值、灵敏度、ε 和 delta。" #: ../../source/how-to-use-differential-privacy.rst:-1 +#, fuzzy msgid "local DP mod" -msgstr "" +msgstr "本地 DP 模式" #: ../../source/how-to-use-differential-privacy.rst:104 +#, fuzzy msgid "Below is a code example that shows how to use :code:`LocalDpMod`:" -msgstr "" +msgstr "下面的代码示例展示了如何使用 :code:`LocalDpMod`：" #: ../../source/how-to-use-differential-privacy.rst:122 +#, fuzzy msgid "" "Please note that the order of mods, especially those that modify " "parameters, is important when using multiple modifiers. Typically, " "differential privacy (DP) modifiers should be the last to operate on " "parameters." -msgstr "" +msgstr "请注意，在使用多个修改器时，修改器（尤其是修改参数的修改器）的顺序非常重要。通常情况下，差分隐私 (DP) 修改器应最后对参数进行操作。" #: ../../source/how-to-use-differential-privacy.rst:125 +#, fuzzy msgid "Local Training using Privacy Engines" -msgstr "" +msgstr "使用隐私引擎进行本地培训" #: ../../source/how-to-use-differential-privacy.rst:126 +#, fuzzy msgid "" "For ensuring data instance-level privacy during local model training on " "the client side, consider leveraging privacy engines such as Opacus and " @@ -6621,6 +7422,11 @@ msgid "" " Privacy `_)." msgstr "" +"要在客户端本地模型训练期间确保数据实例级隐私，可考虑利用 Opacus 和 TensorFlow Privacy 等隐私引擎。有关将 Flower" +" 与这些引擎结合使用的示例，请参阅 Flower 示例目录（`Opacus " +"`_, `Tensorflow" +" Privacy `_）。" #: ../../source/how-to-use-strategies.rst:2 msgid "Use strategies" @@ -6742,15 +7548,15 @@ msgstr "教程" msgid "Quickstart tutorials" msgstr "快速入门教程" -#: ../../source/index.rst:75 ../../source/index.rst:79 +#: ../../source/index.rst:74 ../../source/index.rst:78 msgid "How-to guides" msgstr "操作指南" -#: ../../source/index.rst:98 +#: ../../source/index.rst:99 msgid "Legacy example guides" msgstr "旧版指南范例" -#: ../../source/index.rst:109 ../../source/index.rst:113 +#: ../../source/index.rst:108 ../../source/index.rst:112 msgid "Explanations" msgstr "说明" @@ -6758,23 +7564,23 @@ msgstr "说明" msgid "API reference" msgstr "应用程序接口参考" -#: ../../source/index.rst:138 +#: ../../source/index.rst:137 msgid "Reference docs" msgstr "参考文档" -#: ../../source/index.rst:154 +#: ../../source/index.rst:153 msgid "Contributor tutorials" msgstr "贡献者教程" -#: ../../source/index.rst:161 +#: ../../source/index.rst:160 msgid "Contributor how-to guides" msgstr "投稿指南" -#: ../../source/index.rst:174 +#: ../../source/index.rst:172 msgid "Contributor explanations" msgstr "贡献者解释" -#: ../../source/index.rst:180 +#: ../../source/index.rst:178 msgid "Contributor references" msgstr "贡献者参考资料" @@ -6833,17 +7639,18 @@ msgid "" "place to start." msgstr "以学习为导向的联邦学习教程系列，最好的起点。" -#: ../../source/index.rst:62 +#: ../../source/index.rst:61 +#, fuzzy msgid "" "QUICKSTART TUTORIALS: :doc:`PyTorch ` | " ":doc:`TensorFlow ` | :doc:`🤗 Transformers" " ` | :doc:`JAX ` | :doc:`Pandas ` | :doc:`fastai " "` | :doc:`PyTorch Lightning ` | :doc:`MXNet `" -" | :doc:`scikit-learn ` | :doc:`XGBoost " -"` | :doc:`Android ` | :doc:`iOS `" +"quickstart-pytorch-lightning>` | :doc:`scikit-learn ` | :doc:`XGBoost ` | " +":doc:`Android ` | :doc:`iOS `" msgstr "" "快速入门教程: :doc:`PyTorch ` | :doc:`TensorFlow " "` | :doc:`🤗 Transformers ` | :doc:`Android ` | :doc:`iOS `" -#: ../../source/index.rst:64 +#: ../../source/index.rst:63 msgid "We also made video tutorials for PyTorch:" msgstr "我们还为 PyTorch 制作了视频教程：" -#: ../../source/index.rst:69 +#: ../../source/index.rst:68 msgid "And TensorFlow:" msgstr "还有 TensorFlow：" -#: ../../source/index.rst:77 +#: ../../source/index.rst:76 msgid "" "Problem-oriented how-to guides show step-by-step how to achieve a " "specific goal." msgstr "以问题为导向的 \"如何做 \"指南逐步展示如何实现特定目标。" -#: ../../source/index.rst:111 +#: ../../source/index.rst:110 msgid "" "Understanding-oriented concept guides explain and discuss key topics and " "underlying ideas behind Flower and collaborative AI." msgstr "以理解为导向的概念指南解释并讨论了Flower和协作式人工智能背后的关键主题和基本思想。" -#: ../../source/index.rst:121 +#: ../../source/index.rst:120 msgid "References" msgstr "参考资料" -#: ../../source/index.rst:123 +#: ../../source/index.rst:122 msgid "Information-oriented API reference and other reference material." msgstr "以信息为导向的 API 参考资料和其他参考资料。" -#: ../../source/index.rst:132::1 +#: ../../source/index.rst:131::1 +#, fuzzy msgid ":py:obj:`flwr `\\" -msgstr "" +msgstr ":py:obj:`flwr `\\" -#: ../../source/index.rst:132::1 flwr:1 of +#: ../../source/index.rst:131::1 flwr:1 of +#, fuzzy msgid "Flower main package." -msgstr "" +msgstr "Flower 主包装。" -#: ../../source/index.rst:149 +#: ../../source/index.rst:148 msgid "Contributor docs" msgstr "贡献者文档" -#: ../../source/index.rst:151 +#: ../../source/index.rst:150 msgid "" "The Flower community welcomes contributions. The following docs are " "intended to help along the way." @@ -6906,23 +7715,20 @@ msgid "Flower CLI reference" msgstr "Flower CLI 参考" #: ../../source/ref-api-cli.rst:7 -msgid "flower-superlink" -msgstr "flower-superlink" +#, fuzzy +msgid "flower-simulation" +msgstr "运行模拟" #: ../../source/ref-api-cli.rst:17 -msgid "flower-driver-api" -msgstr "flower-driver-api" +msgid "flower-superlink" +msgstr "flower-superlink" #: ../../source/ref-api-cli.rst:27 -msgid "flower-fleet-api" -msgstr "flower-fleet-api" - -#: ../../source/ref-api-cli.rst:37 #, fuzzy msgid "flower-client-app" msgstr "Flower 客户端。" -#: ../../source/ref-api-cli.rst:47 +#: ../../source/ref-api-cli.rst:37 #, fuzzy msgid "flower-server-app" msgstr "flower-driver-api" @@ -6932,39 +7738,45 @@ msgstr "flower-driver-api" msgid "flwr" msgstr "Flower" -#: ../../source/ref-api/flwr.rst:25 ../../source/ref-api/flwr.server.rst:52 +#: ../../source/ref-api/flwr.client.rst:45 ../../source/ref-api/flwr.rst:25 +#: ../../source/ref-api/flwr.server.rst:49 +#, fuzzy msgid "Modules" -msgstr "" +msgstr "模块" #: ../../source/ref-api/flwr.rst:35::1 +#, fuzzy msgid ":py:obj:`flwr.client `\\" -msgstr "" +msgstr ":py:obj:`flwr.client `\\" #: ../../source/ref-api/flwr.rst:35::1 flwr.client:1 of msgid "Flower client." msgstr "Flower 客户端。" #: ../../source/ref-api/flwr.rst:35::1 +#, fuzzy msgid ":py:obj:`flwr.common `\\" -msgstr "" +msgstr ":py:obj:`flwr.common `\\" #: ../../source/ref-api/flwr.rst:35::1 flwr.common:1 of msgid "Common components shared between server and client." msgstr "服务器和客户端共享的通用组件。" #: ../../source/ref-api/flwr.rst:35::1 +#, fuzzy msgid ":py:obj:`flwr.server `\\" -msgstr "" +msgstr ":py:obj:`flwr.server `\\" #: ../../source/ref-api/flwr.rst:35::1 -#: ../../source/ref-api/flwr.server.rst:41::1 flwr.server:1 +#: ../../source/ref-api/flwr.server.rst:38::1 flwr.server:1 #: flwr.server.server.Server:1 of msgid "Flower server." msgstr "Flower 服务器。" #: ../../source/ref-api/flwr.rst:35::1 +#, fuzzy msgid ":py:obj:`flwr.simulation `\\" -msgstr "" +msgstr ":py:obj:`flwr.simulation `\\" #: ../../source/ref-api/flwr.rst:35::1 flwr.simulation:1 of #, fuzzy @@ -6975,6 +7787,7 @@ msgstr "运行模拟" msgid "client" msgstr "客户端" +#: ../../source/ref-api/flwr.client.mod.rst:13 #: ../../source/ref-api/flwr.client.rst:13 #: ../../source/ref-api/flwr.common.rst:13 #: ../../source/ref-api/flwr.server.rst:13 @@ -6983,85 +7796,123 @@ msgstr "客户端" msgid "Functions" msgstr "四种函数：" -#: ../../source/ref-api/flwr.client.rst:24::1 +#: ../../source/ref-api/flwr.client.rst:25::1 +#, fuzzy msgid ":py:obj:`run_client_app `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`run_client_app `\\ \\(\\)" -#: ../../source/ref-api/flwr.client.rst:24::1 -#: flwr.client.app.run_client_app:1 of +#: ../../source/ref-api/flwr.client.rst:25::1 +#: flwr.client.supernode.app.run_client_app:1 of #, fuzzy msgid "Run Flower client app." msgstr "Flower 客户端。" -#: ../../source/ref-api/flwr.client.rst:24::1 +#: ../../source/ref-api/flwr.client.rst:25::1 +#, fuzzy +msgid ":py:obj:`run_supernode `\\ \\(\\)" +msgstr ":py:obj:`run_superlink `\\ \\(\\)" + +#: ../../source/ref-api/flwr.client.rst:25::1 +#: flwr.client.supernode.app.run_supernode:1 of +#, fuzzy +msgid "Run Flower SuperNode." +msgstr "Flower 服务器。" + +#: ../../source/ref-api/flwr.client.rst:25::1 +#, fuzzy msgid "" ":py:obj:`start_client `\\ \\(\\*\\, " "server\\_address\\[\\, client\\_fn\\, ...\\]\\)" msgstr "" +":py:obj:`start_client `\\ \\(\\*\\, " +"server\\_address\\[\\, client\\_fn\\, ...\\]\\)" -#: ../../source/ref-api/flwr.client.rst:24::1 +#: ../../source/ref-api/flwr.client.rst:25::1 #: flwr.client.app.start_client:1 of msgid "Start a Flower client node which connects to a Flower server." msgstr "启动一个 Flower 客户节点，连接到 Flower 服务器。" -#: ../../source/ref-api/flwr.client.rst:24::1 +#: ../../source/ref-api/flwr.client.rst:25::1 +#, fuzzy msgid "" ":py:obj:`start_numpy_client `\\ \\(\\*\\," " server\\_address\\, client\\)" msgstr "" +":py:obj:`start_numpy_client `\\ \\(\\*\\," +" server\\_address\\, client\\)" -#: ../../source/ref-api/flwr.client.rst:24::1 +#: ../../source/ref-api/flwr.client.rst:25::1 #: flwr.client.app.start_numpy_client:1 of msgid "Start a Flower NumPyClient which connects to a gRPC server." msgstr "启动 Flower NumPyClient，连接到 gRPC 服务器。" -#: ../../source/ref-api/flwr.client.rst:26 +#: ../../source/ref-api/flwr.client.mod.rst:30 +#: ../../source/ref-api/flwr.client.rst:27 #: ../../source/ref-api/flwr.common.rst:32 -#: ../../source/ref-api/flwr.server.rst:29 +#: ../../source/ref-api/flwr.server.rst:26 #: ../../source/ref-api/flwr.server.strategy.rst:17 #: ../../source/ref-api/flwr.server.workflow.rst:17 +#, fuzzy msgid "Classes" -msgstr "" +msgstr "类别" -#: ../../source/ref-api/flwr.client.rst:33::1 +#: ../../source/ref-api/flwr.client.rst:34::1 +#, fuzzy msgid ":py:obj:`Client `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`Client `\\ \\(\\)" -#: ../../source/ref-api/flwr.client.rst:33::1 +#: ../../source/ref-api/flwr.client.rst:34::1 #: flwr.client.client.Client:1 of msgid "Abstract base class for Flower clients." msgstr "Flower 客户端的抽象基类。" -#: ../../source/ref-api/flwr.client.rst:33::1 +#: ../../source/ref-api/flwr.client.rst:34::1 +#, fuzzy msgid "" ":py:obj:`ClientApp `\\ \\(\\[client\\_fn\\, " "mods\\]\\)" msgstr "" +":py:obj:`ClientApp `\\ \\(\\[client\\_fn\\, " +"mods\\]\\)" -#: ../../source/ref-api/flwr.client.rst:33::1 +#: ../../source/ref-api/flwr.client.rst:34::1 #: flwr.client.client_app.ClientApp:1 of #, fuzzy msgid "Flower ClientApp." msgstr "Flower 客户端。" -#: ../../source/ref-api/flwr.client.rst:33::1 +#: ../../source/ref-api/flwr.client.rst:34::1 +#, fuzzy msgid ":py:obj:`NumPyClient `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`NumPyClient `\\ \\(\\)" -#: ../../source/ref-api/flwr.client.rst:33::1 +#: ../../source/ref-api/flwr.client.rst:34::1 #: flwr.client.numpy_client.NumPyClient:1 of msgid "Abstract base class for Flower clients using NumPy." msgstr "使用 NumPy 的 Flower 客户端的抽象基类。" +#: ../../source/ref-api/flwr.client.rst:52::1 +#, fuzzy +msgid ":py:obj:`flwr.client.mod `\\" +msgstr ":py:obj:`flwr.client `\\" + +#: ../../source/ref-api/flwr.client.rst:52::1 flwr.client.mod:1 of +#, fuzzy +msgid "Flower Built-in Mods." +msgstr "使用内置调制器" + #: flwr.client.client.Client:1 flwr.client.numpy_client.NumPyClient:1 #: flwr.server.client_manager.ClientManager:1 -#: flwr.server.strategy.strategy.Strategy:1 of +#: flwr.server.driver.driver.Driver:1 flwr.server.strategy.strategy.Strategy:1 +#: of +#, fuzzy msgid "Bases: :py:class:`~abc.ABC`" -msgstr "" +msgstr "Bases: :py:class:`~abc.ABC`" #: ../../source/ref-api/flwr.client.Client.rst:15 #: ../../source/ref-api/flwr.client.ClientApp.rst:15 #: ../../source/ref-api/flwr.client.NumPyClient.rst:15 +#: ../../source/ref-api/flwr.client.mod.LocalDpMod.rst:15 #: ../../source/ref-api/flwr.common.Array.rst:15 #: ../../source/ref-api/flwr.common.ClientMessage.rst:15 #: ../../source/ref-api/flwr.common.ConfigsRecord.rst:15 @@ -7070,6 +7921,7 @@ msgstr "" #: ../../source/ref-api/flwr.common.Error.rst:15 #: ../../source/ref-api/flwr.common.EvaluateIns.rst:15 #: ../../source/ref-api/flwr.common.EvaluateRes.rst:15 +#: ../../source/ref-api/flwr.common.EventType.rst:15 #: ../../source/ref-api/flwr.common.FitIns.rst:15 #: ../../source/ref-api/flwr.common.FitRes.rst:15 #: ../../source/ref-api/flwr.common.GetParametersIns.rst:15 @@ -7122,12 +7974,14 @@ msgstr "" #: ../../source/ref-api/flwr.server.workflow.DefaultWorkflow.rst:15 #: ../../source/ref-api/flwr.server.workflow.SecAggPlusWorkflow.rst:15 #: ../../source/ref-api/flwr.server.workflow.SecAggWorkflow.rst:15 +#, fuzzy msgid "Methods" -msgstr "" +msgstr "方法" #: ../../source/ref-api/flwr.client.Client.rst:44::1 +#, fuzzy msgid ":py:obj:`evaluate `\\ \\(ins\\)" -msgstr "" +msgstr ":py:obj:`evaluate `\\ \\(ins\\)" #: ../../source/ref-api/flwr.client.Client.rst:44::1 #: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 @@ -7137,8 +7991,9 @@ msgid "Evaluate the provided parameters using the locally held dataset." msgstr "使用本地数据集评估所提供的参数。" #: ../../source/ref-api/flwr.client.Client.rst:44::1 +#, fuzzy msgid ":py:obj:`fit `\\ \\(ins\\)" -msgstr "" +msgstr ":py:obj:`fit `\\ \\(ins\\)" #: ../../source/ref-api/flwr.client.Client.rst:44::1 #: flwr.client.client.Client.fit:1 of @@ -7146,8 +8001,9 @@ msgid "Refine the provided parameters using the locally held dataset." msgstr "利用本地数据集完善所提供的参数。" #: ../../source/ref-api/flwr.client.Client.rst:44::1 +#, fuzzy msgid ":py:obj:`get_context `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`get_context `\\ \\(\\)" #: ../../source/ref-api/flwr.client.Client.rst:44::1 #: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 @@ -7158,8 +8014,9 @@ msgid "Get the run context from this client." msgstr "评估客户端的反应。" #: ../../source/ref-api/flwr.client.Client.rst:44::1 +#, fuzzy msgid ":py:obj:`get_parameters `\\ \\(ins\\)" -msgstr "" +msgstr ":py:obj:`get_parameters `\\ \\(ins\\)" #: ../../source/ref-api/flwr.client.Client.rst:44::1 #: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 @@ -7169,8 +8026,9 @@ msgid "Return the current local model parameters." msgstr "返回当前本地模型参数。" #: ../../source/ref-api/flwr.client.Client.rst:44::1 +#, fuzzy msgid ":py:obj:`get_properties `\\ \\(ins\\)" -msgstr "" +msgstr ":py:obj:`get_properties `\\ \\(ins\\)" #: ../../source/ref-api/flwr.client.Client.rst:44::1 #: flwr.client.client.Client.get_properties:1 of @@ -7178,19 +8036,22 @@ msgid "Return set of client's properties." msgstr "返回客户端的属性集。" #: ../../source/ref-api/flwr.client.Client.rst:44::1 +#, fuzzy msgid ":py:obj:`set_context `\\ \\(context\\)" -msgstr "" +msgstr ":py:obj:`set_context `\\ \\(context\\)" #: ../../source/ref-api/flwr.client.Client.rst:44::1 #: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 #: flwr.client.client.Client.set_context:1 #: flwr.client.numpy_client.NumPyClient.set_context:1 of +#, fuzzy msgid "Apply a run context to this client." -msgstr "" +msgstr "将运行上下文应用于该客户端。" #: ../../source/ref-api/flwr.client.Client.rst:44::1 +#, fuzzy msgid ":py:obj:`to_client `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`to_client `\\ \\(\\)" #: ../../source/ref-api/flwr.client.Client.rst:44::1 #: flwr.client.client.Client.to_client:1 of @@ -7207,7 +8068,7 @@ msgstr "返回客户端（本身）。" #: ../../source/ref-api/flwr.common.Error.rst:25 #: ../../source/ref-api/flwr.common.EvaluateIns.rst:25 #: ../../source/ref-api/flwr.common.EvaluateRes.rst:25 -#: ../../source/ref-api/flwr.common.EventType.rst:19 +#: ../../source/ref-api/flwr.common.EventType.rst:165 #: ../../source/ref-api/flwr.common.FitIns.rst:25 #: ../../source/ref-api/flwr.common.FitRes.rst:25 #: ../../source/ref-api/flwr.common.GetParametersIns.rst:25 @@ -7225,18 +8086,21 @@ msgstr "返回客户端（本身）。" #: ../../source/ref-api/flwr.common.Status.rst:25 #: ../../source/ref-api/flwr.server.LegacyContext.rst:25 #: ../../source/ref-api/flwr.server.ServerConfig.rst:25 +#, fuzzy msgid "Attributes" -msgstr "" +msgstr "属性" #: flwr.client.client.Client.evaluate:1::1 of +#, fuzzy msgid ":py:obj:`context `\\" -msgstr "" +msgstr ":py:obj:`context `\\" #: ../../source/ref-api/flwr.common.Parameters.rst:2 #: flwr.client.app.start_client flwr.client.app.start_numpy_client #: flwr.client.client.Client.evaluate flwr.client.client.Client.fit #: flwr.client.client.Client.get_parameters #: flwr.client.client.Client.get_properties +#: flwr.client.mod.localdp_mod.LocalDpMod #: flwr.client.numpy_client.NumPyClient.evaluate #: flwr.client.numpy_client.NumPyClient.fit #: flwr.client.numpy_client.NumPyClient.get_parameters @@ -7250,7 +8114,6 @@ msgstr "" #: flwr.server.client_manager.SimpleClientManager.register #: flwr.server.client_manager.SimpleClientManager.unregister #: flwr.server.client_manager.SimpleClientManager.wait_for -#: flwr.server.compat.app.start_driver flwr.server.driver.driver.Driver #: flwr.server.driver.driver.Driver.create_message #: flwr.server.driver.driver.Driver.pull_messages #: flwr.server.driver.driver.Driver.push_messages @@ -7302,7 +8165,6 @@ msgstr "评估指令包含从服务器接收的（全局）模型参数，以及 #: flwr.server.client_manager.SimpleClientManager.num_available #: flwr.server.client_manager.SimpleClientManager.register #: flwr.server.client_manager.SimpleClientManager.wait_for -#: flwr.server.compat.app.start_driver #: flwr.server.driver.driver.Driver.create_message #: flwr.server.driver.driver.Driver.pull_messages #: flwr.server.driver.driver.Driver.push_messages @@ -7336,7 +8198,6 @@ msgstr "评估结果包含本地数据集上的损失值和其他详细信息， #: flwr.server.client_manager.SimpleClientManager.num_available #: flwr.server.client_manager.SimpleClientManager.register #: flwr.server.client_manager.SimpleClientManager.wait_for -#: flwr.server.compat.app.start_driver #: flwr.server.driver.driver.Driver.create_message #: flwr.server.driver.driver.Driver.pull_messages #: flwr.server.driver.driver.Driver.push_messages @@ -7391,10 +8252,11 @@ msgstr "当前客户端属性。" msgid "ClientApp" msgstr "客户端" -#: flwr.client.client_app.ClientApp:1 flwr.common.constant.MessageType:1 -#: flwr.common.constant.MessageTypeLegacy:1 flwr.common.context.Context:1 -#: flwr.common.message.Error:1 flwr.common.message.Message:1 -#: flwr.common.message.Metadata:1 flwr.common.record.parametersrecord.Array:1 +#: flwr.client.client_app.ClientApp:1 flwr.client.mod.localdp_mod.LocalDpMod:1 +#: flwr.common.constant.MessageType:1 flwr.common.constant.MessageTypeLegacy:1 +#: flwr.common.context.Context:1 flwr.common.message.Error:1 +#: flwr.common.message.Message:1 flwr.common.message.Metadata:1 +#: flwr.common.record.parametersrecord.Array:1 #: flwr.common.record.recordset.RecordSet:1 flwr.common.typing.ClientMessage:1 #: flwr.common.typing.DisconnectRes:1 flwr.common.typing.EvaluateIns:1 #: flwr.common.typing.EvaluateRes:1 flwr.common.typing.FitIns:1 @@ -7402,22 +8264,23 @@ msgstr "客户端" #: flwr.common.typing.GetParametersRes:1 flwr.common.typing.GetPropertiesIns:1 #: flwr.common.typing.GetPropertiesRes:1 flwr.common.typing.Parameters:1 #: flwr.common.typing.ReconnectIns:1 flwr.common.typing.ServerMessage:1 -#: flwr.common.typing.Status:1 flwr.server.driver.driver.Driver:1 -#: flwr.server.history.History:1 flwr.server.server.Server:1 -#: flwr.server.server_app.ServerApp:1 flwr.server.server_config.ServerConfig:1 +#: flwr.common.typing.Status:1 flwr.server.history.History:1 +#: flwr.server.server.Server:1 flwr.server.server_app.ServerApp:1 +#: flwr.server.server_config.ServerConfig:1 #: flwr.server.workflow.default_workflows.DefaultWorkflow:1 #: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:1 #: of +#, fuzzy msgid "Bases: :py:class:`object`" -msgstr "" +msgstr "Bases: :py:class:`object`" #: flwr.client.app.start_client:41 flwr.client.app.start_numpy_client:36 #: flwr.client.client_app.ClientApp:4 #: flwr.client.client_app.ClientApp.evaluate:4 #: flwr.client.client_app.ClientApp.query:4 -#: flwr.client.client_app.ClientApp.train:4 flwr.server.app.start_server:41 -#: flwr.server.compat.app.start_driver:32 flwr.server.server_app.ServerApp:4 -#: flwr.server.server_app.ServerApp.main:4 +#: flwr.client.client_app.ClientApp.train:4 +#: flwr.client.mod.localdp_mod.LocalDpMod:22 flwr.server.app.start_server:41 +#: flwr.server.server_app.ServerApp:4 flwr.server.server_app.ServerApp.main:4 #: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:29 #: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:22 #: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:21 @@ -7427,41 +8290,50 @@ msgid "Examples" msgstr "实例" #: flwr.client.client_app.ClientApp:5 of +#, fuzzy msgid "" "Assuming a typical `Client` implementation named `FlowerClient`, you can " "wrap it in a `ClientApp` as follows:" -msgstr "" +msgstr "假定有一个名为 `FlowerClient` 的典型 `Client` 实现，可以将其封装在一个 `ClientApp` 中，如下所示：" #: flwr.client.client_app.ClientApp:16 of +#, fuzzy msgid "" "If the above code is in a Python module called `client`, it can be " "started as follows:" -msgstr "" +msgstr "如果上述代码位于一个名为 \"客户端 \"的 Python 模块中，则可以按如下方式启动它：" #: flwr.client.client_app.ClientApp:21 of +#, fuzzy msgid "" "In this `client:app` example, `client` refers to the Python module " "`client.py` in which the previous code lives in and `app` refers to the " "global attribute `app` that points to an object of type `ClientApp`." msgstr "" +"在这个 `client:app` 例子中，`client` 指的是前面代码所在的 Python 模块 `client.py`，而 `app` " +"指的是指向 `ClientApp` 类型对象的全局属性 `app` 。" #: flwr.client.client_app.ClientApp.evaluate:1::1 of +#, fuzzy msgid ":py:obj:`evaluate `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`evaluate `\\ \\(\\)" #: flwr.client.client_app.ClientApp.evaluate:1 #: flwr.client.client_app.ClientApp.evaluate:1::1 of +#, fuzzy msgid "Return a decorator that registers the evaluate fn with the client app." -msgstr "" +msgstr "返回一个装饰器，用于向客户端程序注册评估 fn。" #: flwr.client.client_app.ClientApp.evaluate:1::1 of +#, fuzzy msgid ":py:obj:`query `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`query `\\ \\(\\)" #: flwr.client.client_app.ClientApp.evaluate:1::1 #: flwr.client.client_app.ClientApp.query:1 of +#, fuzzy msgid "Return a decorator that registers the query fn with the client app." -msgstr "" +msgstr "返回一个向客户端应用程序注册查询 fn 的装饰器。" #: flwr.client.client_app.ClientApp.evaluate:1::1 of #, fuzzy @@ -7470,22 +8342,27 @@ msgstr "server.strategy.Strategy" #: flwr.client.client_app.ClientApp.evaluate:1::1 #: flwr.client.client_app.ClientApp.train:1 of +#, fuzzy msgid "Return a decorator that registers the train fn with the client app." -msgstr "" +msgstr "返回一个装饰器，用于在客户端应用程序中注册火车 fn。" #: ../../source/ref-api/flwr.client.NumPyClient.rst:2 msgid "NumPyClient" msgstr "NumPyClient" #: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 +#, fuzzy msgid "" ":py:obj:`evaluate `\\ \\(parameters\\, " "config\\)" msgstr "" +":py:obj:`evaluate `\\ \\(parameters\\, " +"config\\)" #: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 +#, fuzzy msgid ":py:obj:`fit `\\ \\(parameters\\, config\\)" -msgstr "" +msgstr ":py:obj:`fit `\\ \\(parameters\\, config\\)" #: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 #: flwr.client.numpy_client.NumPyClient.fit:1 of @@ -7493,20 +8370,27 @@ msgid "Train the provided parameters using the locally held dataset." msgstr "使用本地数据集训练所提供的参数。" #: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 +#, fuzzy msgid ":py:obj:`get_context `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`get_context `\\ \\(\\)" #: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 +#, fuzzy msgid "" ":py:obj:`get_parameters `\\ " "\\(config\\)" msgstr "" +":py:obj:`get_parameters `\\ " +"\\(config\\)" #: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 +#, fuzzy msgid "" ":py:obj:`get_properties `\\ " "\\(config\\)" msgstr "" +":py:obj:`get_properties `\\ " +"\\(config\\)" #: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 #: flwr.client.numpy_client.NumPyClient.get_properties:1 of @@ -7514,14 +8398,18 @@ msgid "Return a client's set of properties." msgstr "返回客户端的属性集。" #: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 +#, fuzzy msgid "" ":py:obj:`set_context `\\ " "\\(context\\)" msgstr "" +":py:obj:`set_context `\\ " +"\\(context\\)" #: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 +#, fuzzy msgid ":py:obj:`to_client `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`to_client `\\ \\(\\)" #: ../../source/ref-api/flwr.client.NumPyClient.rst:44::1 #: flwr.client.numpy_client.NumPyClient.to_client:1 of @@ -7529,8 +8417,9 @@ msgid "Convert to object to Client type and return it." msgstr "将对象转换为客户类型并返回。" #: flwr.client.numpy_client.NumPyClient.evaluate:1::1 of +#, fuzzy msgid ":py:obj:`context `\\" -msgstr "" +msgstr ":py:obj:`context `\\" #: flwr.client.numpy_client.NumPyClient.evaluate:3 #: flwr.client.numpy_client.NumPyClient.fit:3 @@ -7646,9 +8535,269 @@ msgstr "" "**properties** -- 将任意字符串键映射到 bool、bytes、float、int 或 str " "类型值的字典。它可用于将任意属性值传回服务器。" +#: ../../source/ref-api/flwr.client.mod.rst:2 +#, fuzzy +msgid "mod" +msgstr "模块" + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +msgid "" +":py:obj:`adaptiveclipping_mod `\\ " +"\\(msg\\, ctxt\\, call\\_next\\)" +msgstr "" + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#: flwr.client.mod.centraldp_mods.adaptiveclipping_mod:1 of +#, fuzzy +msgid "Client-side adaptive clipping modifier." +msgstr "客户端逻辑" + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +msgid "" +":py:obj:`fixedclipping_mod `\\ " +"\\(msg\\, ctxt\\, call\\_next\\)" +msgstr "" + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#: flwr.client.mod.centraldp_mods.fixedclipping_mod:1 of +#, fuzzy +msgid "Client-side fixed clipping modifier." +msgstr "客户端逻辑" + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#, fuzzy +msgid ":py:obj:`make_ffn `\\ \\(ffn\\, mods\\)" +msgstr ":py:obj:`Client `\\ \\(\\)" + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#: flwr.client.mod.utils.make_ffn:1 of +msgid "." +msgstr "" + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#, fuzzy +msgid "" +":py:obj:`secagg_mod `\\ \\(msg\\, ctxt\\, " +"call\\_next\\)" +msgstr ":py:obj:`set_context `\\ \\(context\\)" + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#: flwr.client.mod.secure_aggregation.secagg_mod.secagg_mod:1 of +msgid "Handle incoming message and return results, following the SecAgg protocol." +msgstr "" + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#, fuzzy +msgid "" +":py:obj:`secaggplus_mod `\\ \\(msg\\, " +"ctxt\\, call\\_next\\)" +msgstr "" +":py:obj:`SecAggPlusWorkflow `\\ " +"\\(num\\_shares\\, ...\\[\\, ...\\]\\)" + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#: flwr.client.mod.secure_aggregation.secaggplus_mod.secaggplus_mod:1 of +msgid "" +"Handle incoming message and return results, following the SecAgg+ " +"protocol." +msgstr "" + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#, fuzzy +msgid "" +":py:obj:`message_size_mod `\\ \\(msg\\," +" ctxt\\, call\\_next\\)" +msgstr "" +":py:obj:`Message `\\ \\(metadata\\[\\, content\\, " +"error\\]\\)" + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#: flwr.client.mod.comms_mods.message_size_mod:1 of +#, fuzzy +msgid "Message size mod." +msgstr "信息类型。" + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#, fuzzy +msgid "" +":py:obj:`parameters_size_mod `\\ " +"\\(msg\\, ctxt\\, call\\_next\\)" +msgstr "" +":py:obj:`ParametersRecord `\\ " +"\\(\\[array\\_dict\\, keep\\_input\\]\\)" + +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#: flwr.client.mod.comms_mods.parameters_size_mod:1 of +#, fuzzy +msgid "Parameters size mod." +msgstr "参数" + +#: ../../source/ref-api/flwr.client.mod.rst:35::1 +#, fuzzy +msgid "" +":py:obj:`LocalDpMod `\\ \\(clipping\\_norm\\," +" sensitivity\\, ...\\)" +msgstr "" +":py:obj:`ClientApp `\\ \\(\\[client\\_fn\\, " +"mods\\]\\)" + +#: ../../source/ref-api/flwr.client.mod.rst:35::1 +#: flwr.client.mod.localdp_mod.LocalDpMod:1 of +#, fuzzy +msgid "Modifier for local differential privacy." +msgstr "差分隐私" + +#: ../../source/ref-api/flwr.client.mod.LocalDpMod.rst:2 +#, fuzzy +msgid "LocalDpMod" +msgstr "本地 DP 模式" + +#: flwr.client.mod.localdp_mod.LocalDpMod:3 of +msgid "" +"This mod clips the client model updates and adds noise to the params " +"before sending them to the server." +msgstr "" + +#: flwr.client.mod.centraldp_mods.adaptiveclipping_mod:12 +#: flwr.client.mod.centraldp_mods.fixedclipping_mod:10 +#: flwr.client.mod.localdp_mod.LocalDpMod:6 of +msgid "It operates on messages of type `MessageType.TRAIN`." +msgstr "" + +#: flwr.client.mod.localdp_mod.LocalDpMod:8 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:15 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:8 +#: of +#, fuzzy +msgid "The value of the clipping norm." +msgstr "削波法线的值。" + +#: flwr.client.mod.localdp_mod.LocalDpMod:10 of +msgid "The sensitivity of the client model." +msgstr "" + +#: flwr.client.mod.localdp_mod.LocalDpMod:12 of +msgid "" +"The privacy budget. Smaller value of epsilon indicates a higher level of " +"privacy protection." +msgstr "" + +#: flwr.client.mod.localdp_mod.LocalDpMod:15 of +msgid "" +"The failure probability. The probability that the privacy mechanism fails" +" to provide the desired level of privacy. A smaller value of delta " +"indicates a stricter privacy guarantee." +msgstr "" + +#: flwr.client.mod.localdp_mod.LocalDpMod:23 of +msgid "Create an instance of the local DP mod and add it to the client-side mods:" +msgstr "" + +#: ../../source/ref-api/flwr.client.mod.adaptiveclipping_mod.rst:2 +msgid "adaptiveclipping\\_mod" +msgstr "" + +#: flwr.client.mod.centraldp_mods.adaptiveclipping_mod:3 of +#, fuzzy +msgid "" +"This mod needs to be used with the " +"DifferentialPrivacyClientSideAdaptiveClipping server-side strategy " +"wrapper." +msgstr "用 \"DifferentialPrivacyClientSideAdaptiveClipping \"包装器对策略进行包装：" + +#: flwr.client.mod.centraldp_mods.adaptiveclipping_mod:6 +#: flwr.client.mod.centraldp_mods.fixedclipping_mod:6 of +#, fuzzy +msgid "The wrapper sends the clipping_norm value to the client." +msgstr "向客户发送近端因子mu" + +#: flwr.client.mod.centraldp_mods.adaptiveclipping_mod:8 +#: flwr.client.mod.centraldp_mods.fixedclipping_mod:8 of +msgid "This mod clips the client model updates before sending them to the server." +msgstr "" + +#: flwr.client.mod.centraldp_mods.adaptiveclipping_mod:10 of +msgid "" +"It also sends KEY_NORM_BIT to the server for computing the new clipping " +"value." +msgstr "" + +#: flwr.client.mod.centraldp_mods.adaptiveclipping_mod:15 +#: flwr.client.mod.centraldp_mods.fixedclipping_mod:13 +#: flwr.server.driver.driver.Driver.send_and_receive:18 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:53 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:60 +#: of +#, fuzzy +msgid "Notes" +msgstr "无" + +#: flwr.client.mod.centraldp_mods.adaptiveclipping_mod:16 +#: flwr.client.mod.centraldp_mods.fixedclipping_mod:14 of +msgid "Consider the order of mods when using multiple." +msgstr "" + +#: flwr.client.mod.centraldp_mods.adaptiveclipping_mod:18 of +msgid "Typically, adaptiveclipping_mod should be the last to operate on params." +msgstr "" + +#: ../../source/ref-api/flwr.client.mod.fixedclipping_mod.rst:2 +#, fuzzy +msgid "fixedclipping\\_mod" +msgstr "剪贴" + +#: flwr.client.mod.centraldp_mods.fixedclipping_mod:3 of +#, fuzzy +msgid "" +"This mod needs to be used with the " +"DifferentialPrivacyClientSideFixedClipping server-side strategy wrapper." +msgstr "用 \"DifferentialPrivacyClientSideFixedClipping \"包装器包装策略：" + +#: flwr.client.mod.centraldp_mods.fixedclipping_mod:16 of +msgid "Typically, fixedclipping_mod should be the last to operate on params." +msgstr "" + +#: ../../source/ref-api/flwr.client.mod.make_ffn.rst:2 +msgid "make\\_ffn" +msgstr "" + +#: ../../source/ref-api/flwr.client.mod.message_size_mod.rst:2 +msgid "message\\_size\\_mod" +msgstr "" + +#: flwr.client.mod.comms_mods.message_size_mod:3 of +msgid "This mod logs the size in bytes of the message being transmited." +msgstr "" + +#: ../../source/ref-api/flwr.client.mod.parameters_size_mod.rst:2 +#, fuzzy +msgid "parameters\\_size\\_mod" +msgstr "参数" + +#: flwr.client.mod.comms_mods.parameters_size_mod:3 of +msgid "" +"This mod logs the number of parameters transmitted in the message as well" +" as their size in bytes." +msgstr "" + +#: ../../source/ref-api/flwr.client.mod.secagg_mod.rst:2 +msgid "secagg\\_mod" +msgstr "" + +#: ../../source/ref-api/flwr.client.mod.secaggplus_mod.rst:2 +#, fuzzy +msgid "secaggplus\\_mod" +msgstr "工作流程" + #: ../../source/ref-api/flwr.client.run_client_app.rst:2 +#, fuzzy msgid "run\\_client\\_app" -msgstr "" +msgstr "run\\_client\\_app" + +#: ../../source/ref-api/flwr.client.run_supernode.rst:2 +#, fuzzy +msgid "run\\_supernode" +msgstr "flower-superlink" #: ../../source/ref-api/flwr.client.start_client.rst:2 #, fuzzy @@ -7686,8 +8835,7 @@ msgstr "" "可与 Flower 服务器交换的 gRPC 信息的最大长度：默认值对大多数模型都足够了。训练超大模型的用户可能需要增加该值。请注意，Flower " "服务器需要以相同的值启动（请参阅 `flwr.server.start_server`），否则它将不知道增加的限制并阻止更大的消息。" -#: flwr.client.app.start_client:19 flwr.client.app.start_numpy_client:22 -#: flwr.server.compat.app.start_driver:21 of +#: flwr.client.app.start_client:19 flwr.client.app.start_numpy_client:22 of msgid "" "The PEM-encoded root certificates as a byte string or a path string. If " "provided, a secure connection using the certificates will be established " @@ -7695,10 +8843,13 @@ msgid "" msgstr "字节字符串或路径字符串形式的 PEM 编码根证书。如果提供，将使用这些证书与启用 SSL 的 Flower 服务器建立安全连接。" #: flwr.client.app.start_client:23 flwr.client.app.start_numpy_client:26 of +#, fuzzy msgid "" "Starts an insecure gRPC connection when True. Enables HTTPS connection " "when False, using system certificates if `root_certificates` is None." msgstr "" +"为 True 时启动不安全的 gRPC 连接。False 时启用 HTTPS 连接，如果 `root_certificates` 为 " +"None，则使用系统证书。" #: flwr.client.app.start_client:26 flwr.client.app.start_numpy_client:29 of msgid "" @@ -7710,18 +8861,20 @@ msgstr "" "'rest': HTTP（实验性）" #: flwr.client.app.start_client:31 of +#, fuzzy msgid "" "The maximum number of times the client will try to connect to the server " "before giving up in case of a connection error. If set to None, there is " "no limit to the number of tries." -msgstr "" +msgstr "客户端在出现连接错误时放弃连接服务器的最大尝试次数。如果设置为 \"无\"，则不限制尝试次数。" #: flwr.client.app.start_client:35 of +#, fuzzy msgid "" "The maximum duration before the client stops trying to connect to the " "server in case of connection error. If set to None, there is no limit to " "the total time." -msgstr "" +msgstr "在出现连接错误时，客户端停止尝试连接服务器之前的最长持续时间。如果设置为 \"无\"，则总时间没有限制。" #: flwr.client.app.start_client:42 flwr.client.app.start_numpy_client:37 of msgid "Starting a gRPC client with an insecure server connection:" @@ -7743,12 +8896,15 @@ msgid "start\\_numpy\\_client" msgstr "start_numpy_client" #: flwr.client.app.start_numpy_client:5 of +#, fuzzy msgid "" "This function is deprecated since 1.7.0. Use " ":code:`flwr.client.start_client` instead and first convert your " ":code:`NumPyClient` to type :code:`flwr.client.Client` by executing its " ":code:`to_client()` method." msgstr "" +"自 1.7.0 起该函数已被弃用。请使用 :code:`flwr.client.start_client`，并首先通过执行 " +":code:`to_client()`方法将 :code:`NumPyClient`转换为 :code:`flwr.client.Client`。" #: flwr.client.app.start_numpy_client:13 of msgid "An implementation of the abstract base class `flwr.client.NumPyClient`." @@ -7759,8 +8915,9 @@ msgid "common" msgstr "常见" #: ../../source/ref-api/flwr.common.rst:30::1 +#, fuzzy msgid ":py:obj:`array_from_numpy `\\ \\(ndarray\\)" -msgstr "" +msgstr ":py:obj:`array_from_numpy `\\ \\(ndarray\\)" #: ../../source/ref-api/flwr.common.rst:30::1 #: flwr.common.record.conversion_utils.array_from_numpy:1 of @@ -7769,8 +8926,9 @@ msgid "Create Array from NumPy ndarray." msgstr "将参数对象转换为 NumPy ndarrays。" #: ../../source/ref-api/flwr.common.rst:30::1 +#, fuzzy msgid ":py:obj:`bytes_to_ndarray `\\ \\(tensor\\)" -msgstr "" +msgstr ":py:obj:`bytes_to_ndarray `\\ \\(tensor\\)" #: ../../source/ref-api/flwr.common.rst:30::1 #: flwr.common.parameter.bytes_to_ndarray:1 of @@ -7778,10 +8936,13 @@ msgid "Deserialize NumPy ndarray from bytes." msgstr "从字节反序列化 NumPy ndarray。" #: ../../source/ref-api/flwr.common.rst:30::1 +#, fuzzy msgid "" ":py:obj:`configure `\\ \\(identifier\\[\\, " "filename\\, host\\]\\)" msgstr "" +":py:obj:`configure `\\ \\(identifier\\[\\, " +"filename\\, host\\]\\)" #: ../../source/ref-api/flwr.common.rst:30::1 #: flwr.common.logger.configure:1 of @@ -7789,21 +8950,28 @@ msgid "Configure logging to file and/or remote log server." msgstr "配置将日志记录到文件和/或远程日志服务器。" #: ../../source/ref-api/flwr.common.rst:30::1 +#, fuzzy msgid "" ":py:obj:`event `\\ \\(event\\_type\\[\\, " "event\\_details\\]\\)" msgstr "" +":py:obj:`event `\\ \\(event\\_type\\[\\, " +"event\\_details\\]\\)" #: ../../source/ref-api/flwr.common.rst:30::1 #: flwr.common.telemetry.event:1 of +#, fuzzy msgid "Submit create_event to ThreadPoolExecutor to avoid blocking." -msgstr "" +msgstr "将 create_event 提交给 ThreadPoolExecutor 以避免阻塞。" #: ../../source/ref-api/flwr.common.rst:30::1 +#, fuzzy msgid "" ":py:obj:`log `\\ \\(level\\, msg\\, \\*args\\, " "\\*\\*kwargs\\)" msgstr "" +":py:obj:`log `\\ \\(level\\, msg\\, \\*args\\, " +"\\*\\*kwargs\\)" #: ../../source/ref-api/flwr.common.rst:30::1 logging.Logger.log:1 #: of @@ -7811,8 +8979,9 @@ msgid "Log 'msg % args' with the integer severity 'level'." msgstr "以整数严重性 \"级别 \"记录 \"msg % args\"。" #: ../../source/ref-api/flwr.common.rst:30::1 +#, fuzzy msgid ":py:obj:`ndarray_to_bytes `\\ \\(ndarray\\)" -msgstr "" +msgstr ":py:obj:`ndarray_to_bytes `\\ \\(ndarray\\)" #: ../../source/ref-api/flwr.common.rst:30::1 #: flwr.common.parameter.ndarray_to_bytes:1 of @@ -7820,8 +8989,9 @@ msgid "Serialize NumPy ndarray to bytes." msgstr "将 NumPy ndarray 序列化为字节。" #: ../../source/ref-api/flwr.common.rst:30::1 +#, fuzzy msgid ":py:obj:`now `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`now `\\ \\(\\)" #: ../../source/ref-api/flwr.common.rst:30::1 #: flwr.common.date.now:1 of @@ -7829,10 +8999,13 @@ msgid "Construct a datetime from time.time() with time zone set to UTC." msgstr "从 time.time() 生成日期时间，时区设置为 UTC。" #: ../../source/ref-api/flwr.common.rst:30::1 +#, fuzzy msgid "" ":py:obj:`ndarrays_to_parameters `\\ " "\\(ndarrays\\)" msgstr "" +":py:obj:`ndarrays_to_parameters `\\ " +"\\(ndarrays\\)" #: ../../source/ref-api/flwr.common.rst:30::1 #: flwr.common.parameter.ndarrays_to_parameters:1 @@ -7843,10 +9016,13 @@ msgid "Convert NumPy ndarrays to parameters object." msgstr "将 NumPy ndarrays 转换为参数对象。" #: ../../source/ref-api/flwr.common.rst:30::1 +#, fuzzy msgid "" ":py:obj:`parameters_to_ndarrays `\\ " "\\(parameters\\)" msgstr "" +":py:obj:`parameters_to_ndarrays `\\ " +"\\(parameters\\)" #: ../../source/ref-api/flwr.common.rst:30::1 #: flwr.common.parameter.parameters_to_ndarrays:1 of @@ -7854,10 +9030,13 @@ msgid "Convert parameters object to NumPy ndarrays." msgstr "将参数对象转换为 NumPy ndarrays。" #: ../../source/ref-api/flwr.common.rst:64::1 +#, fuzzy msgid "" ":py:obj:`Array `\\ \\(dtype\\, shape\\, stype\\, " "data\\)" msgstr "" +":py:obj:`Array `\\ \\(dtype\\, shape\\, stype\\, " +"data\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.record.parametersrecord.Array:1 of @@ -7866,10 +9045,13 @@ msgid "Array type." msgstr "返回类型" #: ../../source/ref-api/flwr.common.rst:64::1 +#, fuzzy msgid "" ":py:obj:`ClientMessage `\\ " "\\(\\[get\\_properties\\_res\\, ...\\]\\)" msgstr "" +":py:obj:`ClientMessage `\\ " +"\\(\\[get\\_properties\\_res\\, ...\\]\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.typing.ClientMessage:1 of @@ -7877,8 +9059,9 @@ msgid "ClientMessage is a container used to hold one result message." msgstr "ClientMessage 是用于容纳一条结果信息的容器。" #: ../../source/ref-api/flwr.common.rst:64::1 +#, fuzzy msgid ":py:obj:`Code `\\ \\(value\\)" -msgstr "" +msgstr ":py:obj:`Code `\\ \\(value\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.typing.Code:1 of @@ -7902,17 +9085,20 @@ msgid "Configs record." msgstr "配置日志记录" #: ../../source/ref-api/flwr.common.rst:64::1 +#, fuzzy msgid ":py:obj:`Context `\\ \\(state\\)" -msgstr "" +msgstr ":py:obj:`Context `\\ \\(state\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.context.Context:1 of +#, fuzzy msgid "State of your run." -msgstr "" +msgstr "您的运行状态。" #: ../../source/ref-api/flwr.common.rst:64::1 +#, fuzzy msgid ":py:obj:`DisconnectRes `\\ \\(reason\\)" -msgstr "" +msgstr ":py:obj:`DisconnectRes `\\ \\(reason\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.typing.DisconnectRes:1 of @@ -7920,10 +9106,13 @@ msgid "DisconnectRes message from client to server." msgstr "客户端向服务器发送 DisconnectRes 信息。" #: ../../source/ref-api/flwr.common.rst:64::1 +#, fuzzy msgid "" ":py:obj:`EvaluateIns `\\ \\(parameters\\, " "config\\)" msgstr "" +":py:obj:`EvaluateIns `\\ \\(parameters\\, " +"config\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.typing.EvaluateIns:1 of @@ -7931,10 +9120,13 @@ msgid "Evaluate instructions for a client." msgstr "评估客户端的指示。" #: ../../source/ref-api/flwr.common.rst:64::1 +#, fuzzy msgid "" ":py:obj:`EvaluateRes `\\ \\(status\\, loss\\, " "num\\_examples\\, metrics\\)" msgstr "" +":py:obj:`EvaluateRes `\\ \\(status\\, loss\\, " +"num\\_examples\\, metrics\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.typing.EvaluateRes:1 of @@ -7942,8 +9134,9 @@ msgid "Evaluate response from a client." msgstr "评估客户端的反应。" #: ../../source/ref-api/flwr.common.rst:64::1 +#, fuzzy msgid ":py:obj:`EventType `\\ \\(value\\)" -msgstr "" +msgstr ":py:obj:`EventType `\\ \\(value\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.telemetry.EventType:1 of @@ -7951,8 +9144,9 @@ msgid "Types of telemetry events." msgstr "遥测事件类型。" #: ../../source/ref-api/flwr.common.rst:64::1 +#, fuzzy msgid ":py:obj:`FitIns `\\ \\(parameters\\, config\\)" -msgstr "" +msgstr ":py:obj:`FitIns `\\ \\(parameters\\, config\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.typing.FitIns:1 of @@ -7960,10 +9154,13 @@ msgid "Fit instructions for a client." msgstr "为客户提供安装说明。" #: ../../source/ref-api/flwr.common.rst:64::1 +#, fuzzy msgid "" ":py:obj:`FitRes `\\ \\(status\\, parameters\\, " "num\\_examples\\, metrics\\)" msgstr "" +":py:obj:`FitRes `\\ \\(status\\, parameters\\, " +"num\\_examples\\, metrics\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.typing.FitRes:1 of @@ -7971,17 +9168,20 @@ msgid "Fit response from a client." msgstr "来自客户端的合适回复。" #: ../../source/ref-api/flwr.common.rst:64::1 +#, fuzzy msgid ":py:obj:`Error `\\ \\(code\\[\\, reason\\]\\)" -msgstr "" +msgstr ":py:obj:`Error `\\ \\(code\\[\\, reason\\]\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.message.Error:1 of +#, fuzzy msgid "A dataclass that stores information about an error that occurred." -msgstr "" +msgstr "数据类，用于存储所发生错误的相关信息。" #: ../../source/ref-api/flwr.common.rst:64::1 +#, fuzzy msgid ":py:obj:`GetParametersIns `\\ \\(config\\)" -msgstr "" +msgstr ":py:obj:`GetParametersIns `\\ \\(config\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.typing.GetParametersIns:1 of @@ -7989,10 +9189,13 @@ msgid "Parameters request for a client." msgstr "客户端的参数请求。" #: ../../source/ref-api/flwr.common.rst:64::1 +#, fuzzy msgid "" ":py:obj:`GetParametersRes `\\ \\(status\\, " "parameters\\)" msgstr "" +":py:obj:`GetParametersRes `\\ \\(status\\, " +"parameters\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.typing.GetParametersRes:1 of @@ -8000,8 +9203,9 @@ msgid "Response when asked to return parameters." msgstr "要求返回参数时的响应。" #: ../../source/ref-api/flwr.common.rst:64::1 +#, fuzzy msgid ":py:obj:`GetPropertiesIns `\\ \\(config\\)" -msgstr "" +msgstr ":py:obj:`GetPropertiesIns `\\ \\(config\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.typing.GetPropertiesIns:1 of @@ -8009,10 +9213,13 @@ msgid "Properties request for a client." msgstr "客户端的属性请求。" #: ../../source/ref-api/flwr.common.rst:64::1 +#, fuzzy msgid "" ":py:obj:`GetPropertiesRes `\\ \\(status\\, " "properties\\)" msgstr "" +":py:obj:`GetPropertiesRes `\\ \\(status\\, " +"properties\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.typing.GetPropertiesRes:1 of @@ -8020,71 +9227,94 @@ msgid "Properties response from a client." msgstr "来自客户端的属性响应。" #: ../../source/ref-api/flwr.common.rst:64::1 +#, fuzzy msgid "" ":py:obj:`Message `\\ \\(metadata\\[\\, content\\, " "error\\]\\)" msgstr "" +":py:obj:`Message `\\ \\(metadata\\[\\, content\\, " +"error\\]\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.message.Message:1 of +#, fuzzy msgid "State of your application from the viewpoint of the entity using it." -msgstr "" +msgstr "从使用实体的角度看应用程序的状态。" #: ../../source/ref-api/flwr.common.rst:64::1 +#, fuzzy msgid ":py:obj:`MessageType `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`MessageType `\\ \\(\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.constant.MessageType:1 of +#, fuzzy msgid "Message type." -msgstr "" +msgstr "信息类型。" #: ../../source/ref-api/flwr.common.rst:64::1 +#, fuzzy msgid ":py:obj:`MessageTypeLegacy `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`MessageTypeLegacy `\\ \\(\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.constant.MessageTypeLegacy:1 of +#, fuzzy msgid "Legacy message type." -msgstr "" +msgstr "传统信息类型。" #: ../../source/ref-api/flwr.common.rst:64::1 +#, fuzzy msgid "" ":py:obj:`Metadata `\\ \\(run\\_id\\, " "message\\_id\\, src\\_node\\_id\\, ...\\)" msgstr "" +":py:obj:`Metadata `\\ \\(run\\_id\\, " +"message\\_id\\, src\\_node\\_id\\, ...\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.message.Metadata:1 of +#, fuzzy msgid "A dataclass holding metadata associated with the current message." -msgstr "" +msgstr "数据类型，包含与当前报文相关的元数据。" #: ../../source/ref-api/flwr.common.rst:64::1 +#, fuzzy msgid "" ":py:obj:`MetricsRecord `\\ " "\\(\\[metrics\\_dict\\, keep\\_input\\]\\)" msgstr "" +":py:obj:`MetricsRecord `\\ " +"\\(\\[metrics\\_dict\\, keep\\_input\\]\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.record.metricsrecord.MetricsRecord:1 of +#, fuzzy msgid "Metrics record." -msgstr "" +msgstr "指标记录。" #: ../../source/ref-api/flwr.common.rst:64::1 +#, fuzzy msgid ":py:obj:`NDArray `\\" -msgstr "" +msgstr ":py:obj:`NDArray `\\" #: ../../source/ref-api/flwr.common.rst:64::1 +#, fuzzy msgid "" "alias of :py:class:`~numpy.ndarray`\\ [:py:obj:`~typing.Any`, " ":py:class:`~numpy.dtype`\\ [:py:obj:`~typing.Any`]]" msgstr "" +"alias of :py:class:`~numpy.ndarray`\\ [:py:obj:`~typing.Any`, " +":py:class:`~numpy.dtype`\\ [:py:obj:`~typing.Any`]]" #: ../../source/ref-api/flwr.common.rst:64::1 +#, fuzzy msgid "" ":py:obj:`Parameters `\\ \\(tensors\\, " "tensor\\_type\\)" msgstr "" +":py:obj:`Parameters `\\ \\(tensors\\, " +"tensor\\_type\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.typing.Parameters:1 of @@ -8092,10 +9322,13 @@ msgid "Model parameters." msgstr "模型参数。" #: ../../source/ref-api/flwr.common.rst:64::1 +#, fuzzy msgid "" ":py:obj:`ParametersRecord `\\ " "\\(\\[array\\_dict\\, keep\\_input\\]\\)" msgstr "" +":py:obj:`ParametersRecord `\\ " +"\\(\\[array\\_dict\\, keep\\_input\\]\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.record.parametersrecord.ParametersRecord:1 of @@ -8104,8 +9337,9 @@ msgid "Parameters record." msgstr "参数" #: ../../source/ref-api/flwr.common.rst:64::1 +#, fuzzy msgid ":py:obj:`ReconnectIns `\\ \\(seconds\\)" -msgstr "" +msgstr ":py:obj:`ReconnectIns `\\ \\(seconds\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.typing.ReconnectIns:1 of @@ -8113,21 +9347,28 @@ msgid "ReconnectIns message from server to client." msgstr "服务器发送给客户端的重新连接信息。" #: ../../source/ref-api/flwr.common.rst:64::1 +#, fuzzy msgid "" ":py:obj:`RecordSet `\\ " "\\(\\[parameters\\_records\\, ...\\]\\)" msgstr "" +":py:obj:`RecordSet `\\ " +"\\(\\[parameters\\_records\\, ...\\]\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.record.recordset.RecordSet:1 of +#, fuzzy msgid "RecordSet stores groups of parameters, metrics and configs." -msgstr "" +msgstr "RecordSet 可存储参数、指标和配置组。" #: ../../source/ref-api/flwr.common.rst:64::1 +#, fuzzy msgid "" ":py:obj:`ServerMessage `\\ " "\\(\\[get\\_properties\\_ins\\, ...\\]\\)" msgstr "" +":py:obj:`ServerMessage `\\ " +"\\(\\[get\\_properties\\_ins\\, ...\\]\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.typing.ServerMessage:1 of @@ -8135,8 +9376,9 @@ msgid "ServerMessage is a container used to hold one instruction message." msgstr "ServerMessage 是用于容纳一条指令信息的容器。" #: ../../source/ref-api/flwr.common.rst:64::1 +#, fuzzy msgid ":py:obj:`Status `\\ \\(code\\, message\\)" -msgstr "" +msgstr ":py:obj:`Status `\\ \\(code\\, message\\)" #: ../../source/ref-api/flwr.common.rst:64::1 #: flwr.common.typing.Status:1 of @@ -8144,37 +9386,43 @@ msgid "Client status." msgstr "客户端状态。" #: ../../source/ref-api/flwr.common.Array.rst:2 +#, fuzzy msgid "Array" -msgstr "" +msgstr "数组" #: flwr.common.record.parametersrecord.Array:3 of +#, fuzzy msgid "" "A dataclass containing serialized data from an array-like or tensor-like " "object along with some metadata about it." -msgstr "" +msgstr "数据类，包含数组类或张量类对象的序列化数据以及相关元数据。" #: flwr.common.record.parametersrecord.Array:6 of +#, fuzzy msgid "" "A string representing the data type of the serialised object (e.g. " "`np.float32`)" -msgstr "" +msgstr "表示序列化对象数据类型的字符串（例如 `np.float32`)" #: flwr.common.record.parametersrecord.Array:8 of +#, fuzzy msgid "" "A list representing the shape of the unserialized array-like object. This" " is used to deserialize the data (depending on the serialization method) " "or simply as a metadata field." -msgstr "" +msgstr "代表未序列化数组对象形状的列表。它可用于反序列化数据（取决于序列化方法），或仅作为元数据字段使用。" #: flwr.common.record.parametersrecord.Array:12 of +#, fuzzy msgid "" "A string indicating the type of serialisation mechanism used to generate " "the bytes in `data` from an array-like or tensor-like object." -msgstr "" +msgstr "表示序列化机制类型的字符串，用于从类似数组或类似张量的对象中生成 `data` 中的字节。" #: flwr.common.record.parametersrecord.Array:15 of +#, fuzzy msgid "A buffer of bytes containing the data." -msgstr "" +msgstr "包含数据的字节缓冲区。" #: ../../source/ref-api/flwr.common.Array.rst:26::1 #, fuzzy @@ -8188,8 +9436,9 @@ msgid "Return the array as a NumPy array." msgstr "以 NumPy ndarrays 列表形式返回模型参数" #: flwr.common.record.parametersrecord.Array.numpy:1::1 of +#, fuzzy msgid ":py:obj:`dtype `\\" -msgstr "" +msgstr ":py:obj:`dtype `\\" #: flwr.common.record.parametersrecord.Array.numpy:1::1 of #, fuzzy @@ -8202,8 +9451,9 @@ msgid ":py:obj:`stype `\\" msgstr "server.strategy.Strategy" #: flwr.common.record.parametersrecord.Array.numpy:1::1 of +#, fuzzy msgid ":py:obj:`data `\\" -msgstr "" +msgstr ":py:obj:`data `\\" #: ../../source/ref-api/flwr.common.ClientMessage.rst:2 #, fuzzy @@ -8211,58 +9461,79 @@ msgid "ClientMessage" msgstr "客户端" #: ../../source/ref-api/flwr.common.ClientMessage.rst:31::1 +#, fuzzy msgid ":py:obj:`evaluate_res `\\" -msgstr "" +msgstr ":py:obj:`evaluate_res `\\" #: ../../source/ref-api/flwr.common.ClientMessage.rst:31::1 +#, fuzzy msgid ":py:obj:`fit_res `\\" -msgstr "" +msgstr ":py:obj:`fit_res `\\" #: ../../source/ref-api/flwr.common.ClientMessage.rst:31::1 +#, fuzzy msgid "" ":py:obj:`get_parameters_res " "`\\" msgstr "" +":py:obj:`get_parameters_res " +"`\\" #: ../../source/ref-api/flwr.common.ClientMessage.rst:31::1 +#, fuzzy msgid "" ":py:obj:`get_properties_res " "`\\" msgstr "" +":py:obj:`get_properties_res " +"`\\" #: ../../source/ref-api/flwr.common.Code.rst:2 +#, fuzzy msgid "Code" -msgstr "" +msgstr "代码" #: flwr.common.typing.Code:1 of +#, fuzzy msgid "Bases: :py:class:`~enum.Enum`" -msgstr "" +msgstr "Bases: :py:class:`~enum.Enum`" #: ../../source/ref-api/flwr.common.Code.rst:26::1 +#, fuzzy msgid ":py:obj:`OK `\\" -msgstr "" +msgstr ":py:obj:`OK `\\" #: ../../source/ref-api/flwr.common.Code.rst:26::1 +#, fuzzy msgid "" ":py:obj:`GET_PROPERTIES_NOT_IMPLEMENTED " "`\\" msgstr "" +":py:obj:`GET_PROPERTIES_NOT_IMPLEMENTED " +"`\\" #: ../../source/ref-api/flwr.common.Code.rst:26::1 +#, fuzzy msgid "" ":py:obj:`GET_PARAMETERS_NOT_IMPLEMENTED " "`\\" msgstr "" +":py:obj:`GET_PARAMETERS_NOT_IMPLEMENTED " +"`\\" #: ../../source/ref-api/flwr.common.Code.rst:26::1 +#, fuzzy msgid ":py:obj:`FIT_NOT_IMPLEMENTED `\\" -msgstr "" +msgstr ":py:obj:`FIT_NOT_IMPLEMENTED `\\" #: ../../source/ref-api/flwr.common.Code.rst:26::1 +#, fuzzy msgid "" ":py:obj:`EVALUATE_NOT_IMPLEMENTED " "`\\" msgstr "" +":py:obj:`EVALUATE_NOT_IMPLEMENTED " +"`\\" #: ../../source/ref-api/flwr.common.ConfigsRecord.rst:2 #, fuzzy @@ -8270,86 +9541,111 @@ msgid "ConfigsRecord" msgstr "配置日志记录" #: flwr.common.record.configsrecord.ConfigsRecord:1 of +#, fuzzy msgid "" "Bases: :py:class:`~flwr.common.record.typeddict.TypedDict`\\ " +"[:py:class:`str`, :py:class:`int` | :py:class:`float` | :py:class:`str` |" +" :py:class:`bytes` | :py:class:`bool` | :py:class:`~typing.List`\\ " +"[:py:class:`int`] | :py:class:`~typing.List`\\ [:py:class:`float`] | " +":py:class:`~typing.List`\\ [:py:class:`str`] | :py:class:`~typing.List`\\" +" [:py:class:`bytes`] | :py:class:`~typing.List`\\ [:py:class:`bool`]]" +msgstr "" +"Bases: :py:class:`~flwr.common.record.typeddict.TypedDict`\\ " "[:py:class:`str`, :py:obj:`~typing.Union`\\ [:py:class:`int`, " ":py:class:`float`, :py:class:`str`, :py:class:`bytes`, :py:class:`bool`, " ":py:class:`~typing.List`\\ [:py:class:`int`], :py:class:`~typing.List`\\ " "[:py:class:`float`], :py:class:`~typing.List`\\ [:py:class:`str`], " ":py:class:`~typing.List`\\ [:py:class:`bytes`], " ":py:class:`~typing.List`\\ [:py:class:`bool`]]]" -msgstr "" #: flwr.common.record.typeddict.TypedDict.clear:1::1 of +#, fuzzy msgid ":py:obj:`clear `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`clear `\\ \\(\\)" #: flwr.common.record.typeddict.TypedDict.clear:1 #: flwr.common.record.typeddict.TypedDict.clear:1::1 of +#, fuzzy msgid "Remove all items from R." -msgstr "" +msgstr "从 R 中删除所有项目。" #: flwr.common.record.typeddict.TypedDict.clear:1::1 of +#, fuzzy msgid ":py:obj:`count_bytes `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`count_bytes `\\ \\(\\)" #: flwr.common.record.configsrecord.ConfigsRecord.count_bytes:1 #: flwr.common.record.metricsrecord.MetricsRecord.count_bytes:1 #: flwr.common.record.parametersrecord.ParametersRecord.count_bytes:1 #: flwr.common.record.typeddict.TypedDict.clear:1::1 of +#, fuzzy msgid "Return number of Bytes stored in this object." -msgstr "" +msgstr "返回存储在此对象中的字节数。" #: flwr.common.record.typeddict.TypedDict.clear:1::1 of +#, fuzzy msgid ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" -msgstr "" +msgstr ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" #: flwr.common.record.typeddict.TypedDict.clear:1::1 #: flwr.common.record.typeddict.TypedDict.get:1 of +#, fuzzy msgid "d defaults to None." -msgstr "" +msgstr "d 默认为 \"无\"。" #: flwr.common.record.typeddict.TypedDict.clear:1::1 of +#, fuzzy msgid ":py:obj:`items `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`items `\\ \\(\\)" #: flwr.common.record.typeddict.TypedDict.clear:1::1 of +#, fuzzy msgid ":py:obj:`keys `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`keys `\\ \\(\\)" #: flwr.common.record.typeddict.TypedDict.clear:1::1 of +#, fuzzy msgid ":py:obj:`pop `\\ \\(k\\[\\,d\\]\\)" -msgstr "" +msgstr ":py:obj:`pop `\\ \\(k\\[\\,d\\]\\)" #: flwr.common.record.typeddict.TypedDict.clear:1::1 #: flwr.common.record.typeddict.TypedDict.pop:1 of +#, fuzzy msgid "If key is not found, d is returned if given, otherwise KeyError is raised." -msgstr "" +msgstr "如果未找到 key，则返回 d（如果给定），否则引发 KeyError。" #: flwr.common.record.typeddict.TypedDict.clear:1::1 of +#, fuzzy msgid "" ":py:obj:`update `\\ \\(\\[E\\, " "\\]\\*\\*F\\)" msgstr "" +":py:obj:`update `\\ \\(\\[E\\, " +"\\]\\*\\*F\\)" #: flwr.common.record.typeddict.TypedDict.clear:1::1 #: flwr.common.record.typeddict.TypedDict.update:1 of +#, fuzzy msgid "Update R from dict/iterable E and F." -msgstr "" +msgstr "根据二进制/可迭代 E 和 F 更新 R。" #: flwr.common.record.typeddict.TypedDict.clear:1::1 of +#, fuzzy msgid ":py:obj:`values `\\ \\(\\)" -msgstr "" +msgstr ":py:obj:`values `\\ \\(\\)" #: flwr.common.record.configsrecord.ConfigsRecord.count_bytes:3 of +#, fuzzy msgid "This function counts booleans as occupying 1 Byte." -msgstr "" +msgstr "该函数将布尔值计算为占用 1 个字节。" #: ../../source/ref-api/flwr.common.Context.rst:2 +#, fuzzy msgid "Context" -msgstr "" +msgstr "背景" #: flwr.common.context.Context:3 of +#, fuzzy msgid "" "Holds records added by the entity in a given run and that will stay " "local. This means that the data it holds will never leave the system it's" @@ -8357,7 +9653,7 @@ msgid "" "when executing mods. It can also be used as a memory to access at " "different points during the lifecycle of this entity (e.g. across " "multiple rounds)" -msgstr "" +msgstr "保存实体在给定运行中添加的记录，这些记录将保留在本地。这意味着它保存的数据永远不会离开运行的系统。在执行模式时，它可用作中间存储或抓取板。它还可以作为存储器，在实体生命周期的不同阶段（如多轮）进行访问。" #: ../../source/ref-api/flwr.common.Context.rst:28::1 #, fuzzy @@ -8365,40 +9661,49 @@ msgid ":py:obj:`state `\\" msgstr "server.strategy.Strategy" #: ../../source/ref-api/flwr.common.DisconnectRes.rst:2 +#, fuzzy msgid "DisconnectRes" -msgstr "" +msgstr "断开Res" #: ../../source/ref-api/flwr.common.DisconnectRes.rst:28::1 +#, fuzzy msgid ":py:obj:`reason `\\" -msgstr "" +msgstr ":py:obj:`reason `\\" #: ../../source/ref-api/flwr.common.Error.rst:2 +#, fuzzy msgid "Error" -msgstr "" +msgstr "错误" #: flwr.common.message.Error:3 of +#, fuzzy msgid "An identifier for the error." -msgstr "" +msgstr "错误的标识符。" #: flwr.common.message.Error:5 of +#, fuzzy msgid "A reason for why the error arose (e.g. an exception stack-trace)" -msgstr "" +msgstr "出错原因（如异常堆栈跟踪）" #: flwr.common.Error.code:1::1 of +#, fuzzy msgid ":py:obj:`code `\\" -msgstr "" +msgstr ":py:obj:`code `\\" #: flwr.common.Error.code:1 flwr.common.Error.code:1::1 of +#, fuzzy msgid "Error code." -msgstr "" +msgstr "错误代码。" #: flwr.common.Error.code:1::1 of +#, fuzzy msgid ":py:obj:`reason `\\" -msgstr "" +msgstr ":py:obj:`reason `\\" #: flwr.common.Error.code:1::1 flwr.common.Error.reason:1 of +#, fuzzy msgid "Reason reported about the error." -msgstr "" +msgstr "报告的错误原因。" #: ../../source/ref-api/flwr.common.EvaluateIns.rst:2 #, fuzzy @@ -8406,32 +9711,39 @@ msgid "EvaluateIns" msgstr "说明" #: ../../source/ref-api/flwr.common.EvaluateIns.rst:29::1 +#, fuzzy msgid ":py:obj:`parameters `\\" -msgstr "" +msgstr ":py:obj:`parameters `\\" #: ../../source/ref-api/flwr.common.EvaluateIns.rst:29::1 +#, fuzzy msgid ":py:obj:`config `\\" -msgstr "" +msgstr ":py:obj:`config `\\" #: ../../source/ref-api/flwr.common.EvaluateRes.rst:2 +#, fuzzy msgid "EvaluateRes" -msgstr "" +msgstr "评估Res" #: ../../source/ref-api/flwr.common.EvaluateRes.rst:31::1 +#, fuzzy msgid ":py:obj:`status `\\" -msgstr "" +msgstr ":py:obj:`status `\\" #: ../../source/ref-api/flwr.common.EvaluateRes.rst:31::1 +#, fuzzy msgid ":py:obj:`loss `\\" -msgstr "" +msgstr ":py:obj:`loss `\\" #: ../../source/ref-api/flwr.common.EvaluateRes.rst:31::1 +#, fuzzy msgid ":py:obj:`num_examples `\\" -msgstr "" +msgstr ":py:obj:`num_examples `\\" #: ../../source/ref-api/flwr.common.EvaluateRes.rst:31::1 +#, fuzzy msgid ":py:obj:`metrics `\\" -msgstr "" +msgstr ":py:obj:`metrics `\\" #: ../../source/ref-api/flwr.common.EventType.rst:2 #, fuzzy @@ -8439,14676 +9751,18906 @@ msgid "EventType" msgstr "返回类型" #: flwr.common.telemetry.EventType:1 of +#, fuzzy msgid "Bases: :py:class:`str`, :py:class:`~enum.Enum`" -msgstr "" - -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid ":py:obj:`PING `\\" -msgstr "" +msgstr "Bases: :py:class:`str`, :py:class:`~enum.Enum`" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid ":py:obj:`START_CLIENT_ENTER `\\" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid "" +":py:obj:`encode `\\ \\(\\[encoding\\, " +"errors\\]\\)" +msgstr ":py:obj:`EventType `\\ \\(value\\)" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid ":py:obj:`START_CLIENT_LEAVE `\\" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.encode:1 of +#, fuzzy +msgid "Encode the string using the codec registered for encoding." +msgstr "使用注册的编码解码器对字符串进行编码。" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid ":py:obj:`START_SERVER_ENTER `\\" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid "" +":py:obj:`replace `\\ \\(old\\, new\\[\\, " +"count\\]\\)" +msgstr ":py:obj:`EventType `\\ \\(value\\)" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid ":py:obj:`START_SERVER_LEAVE `\\" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.replace:1 of +#, fuzzy +msgid "Return a copy with all occurrences of substring old replaced by new." +msgstr "返回用 new 替换子串 old 的所有出现次数的副本。" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy msgid "" -":py:obj:`RUN_DRIVER_API_ENTER " -"`\\" -msgstr "" +":py:obj:`split `\\ \\(\\[sep\\, " +"maxsplit\\]\\)" +msgstr ":py:obj:`PING `\\" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.rsplit:1 flwr.common.EventType.split:1 of +#, fuzzy msgid "" -":py:obj:`RUN_DRIVER_API_LEAVE " -"`\\" -msgstr "" +"Return a list of the substrings in the string, using sep as the separator" +" string." +msgstr "使用 sep 作为分隔符，返回字符串中的子字符串列表。" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy msgid "" -":py:obj:`RUN_FLEET_API_ENTER " -"`\\" -msgstr "" +":py:obj:`rsplit `\\ \\(\\[sep\\, " +"maxsplit\\]\\)" +msgstr ":py:obj:`PING `\\" + +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`join `\\ \\(iterable\\, \\/\\)" +msgstr ":py:obj:`EventType `\\ \\(value\\)" + +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.join:1 of +#, fuzzy +msgid "Concatenate any number of strings." +msgstr "连接任意数量的字符串。" + +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`capitalize `\\ \\(\\)" +msgstr ":py:obj:`PING `\\" + +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.capitalize:1 of +#, fuzzy +msgid "Return a capitalized version of the string." +msgstr "返回字符串的大写版本。" + +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`casefold `\\ \\(\\)" +msgstr ":py:obj:`EventType `\\ \\(value\\)" + +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.casefold:1 of +#, fuzzy +msgid "Return a version of the string suitable for caseless comparisons." +msgstr "返回适合无例比较的字符串版本。" + +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`title `\\ \\(\\)" +msgstr ":py:obj:`EventType `\\ \\(value\\)" + +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.title:1 of +#, fuzzy +msgid "Return a version of the string where each word is titlecased." +msgstr "返回字符串的版本，其中每个单词都使用了标题大小写。" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy msgid "" -":py:obj:`RUN_FLEET_API_LEAVE " -"`\\" -msgstr "" +":py:obj:`center `\\ \\(width\\[\\, " +"fillchar\\]\\)" +msgstr ":py:obj:`EventType `\\ \\(value\\)" + +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.center:1 of +#, fuzzy +msgid "Return a centered string of length width." +msgstr "返回客户端的属性集。" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy msgid "" -":py:obj:`RUN_SUPERLINK_ENTER " -"`\\" -msgstr "" +":py:obj:`count `\\ \\(sub\\[\\, start\\[\\, " +"end\\]\\]\\)" +msgstr ":py:obj:`Context `\\ \\(state\\)" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy msgid "" -":py:obj:`RUN_SUPERLINK_LEAVE " -"`\\" -msgstr "" +"Return the number of non-overlapping occurrences of substring sub in " +"string S[start:end]." +msgstr "返回子字符串 sub 在字符串 S[start:end] 中非重叠出现的次数。" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy msgid "" -":py:obj:`START_SIMULATION_ENTER " -"`\\" -msgstr "" +":py:obj:`expandtabs `\\ " +"\\(\\[tabsize\\]\\)" +msgstr ":py:obj:`EventType `\\ \\(value\\)" + +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.expandtabs:1 of +#, fuzzy +msgid "Return a copy where all tab characters are expanded using spaces." +msgstr "返回使用空格扩展所有制表符的副本。" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy msgid "" -":py:obj:`START_SIMULATION_LEAVE " -"`\\" -msgstr "" +":py:obj:`find `\\ \\(sub\\[\\, start\\[\\, " +"end\\]\\]\\)" +msgstr ":py:obj:`PING `\\" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid ":py:obj:`DRIVER_CONNECT `\\" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid "" +"Return the lowest index in S where substring sub is found, such that sub " +"is contained within S[start:end]." +msgstr "返回在 S 中找到子串 sub 的最低索引，且 sub 包含在 S[start:end] 中。" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid ":py:obj:`DRIVER_DISCONNECT `\\" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`partition `\\ \\(sep\\, \\/\\)" +msgstr ":py:obj:`partition_id `\\" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid ":py:obj:`START_DRIVER_ENTER `\\" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.partition:1 flwr.common.EventType.rpartition:1 of +#, fuzzy +msgid "Partition the string into three parts using the given separator." +msgstr "使用给定的分隔符将字符串分为三部分。" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 -msgid ":py:obj:`START_DRIVER_LEAVE `\\" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid "" +":py:obj:`index `\\ \\(sub\\[\\, start\\[\\, " +"end\\]\\]\\)" +msgstr ":py:obj:`Context `\\ \\(state\\)" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy msgid "" -":py:obj:`RUN_CLIENT_APP_ENTER " -"`\\" -msgstr "" +":py:obj:`ljust `\\ \\(width\\[\\, " +"fillchar\\]\\)" +msgstr ":py:obj:`EventType `\\ \\(value\\)" + +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.ljust:1 of +#, fuzzy +msgid "Return a left-justified string of length width." +msgstr "返回长度为 width 的左对齐字符串。" + +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`lower `\\ \\(\\)" +msgstr ":py:obj:`now `\\ \\(\\)" + +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.lower:1 of +#, fuzzy +msgid "Return a copy of the string converted to lowercase." +msgstr "返回转换为小写的字符串副本。" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`lstrip `\\ \\(\\[chars\\]\\)" +msgstr ":py:obj:`EventType `\\ \\(value\\)" + +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.lstrip:1 of +#, fuzzy +msgid "Return a copy of the string with leading whitespace removed." +msgstr "返回去掉前导空白的字符串副本。" + +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy msgid "" -":py:obj:`RUN_CLIENT_APP_LEAVE " -"`\\" -msgstr "" +":py:obj:`rfind `\\ \\(sub\\[\\, start\\[\\, " +"end\\]\\]\\)" +msgstr ":py:obj:`PING `\\" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy msgid "" -":py:obj:`RUN_SERVER_APP_ENTER " -"`\\" -msgstr "" +"Return the highest index in S where substring sub is found, such that sub" +" is contained within S[start:end]." +msgstr "返回在 S 中找到子串 sub 的最高索引，且 sub 包含在 S[start:end] 中。" -#: ../../source/ref-api/flwr.common.EventType.rst:42::1 +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy msgid "" -":py:obj:`RUN_SERVER_APP_LEAVE " -"`\\" -msgstr "" +":py:obj:`rindex `\\ \\(sub\\[\\, " +"start\\[\\, end\\]\\]\\)" +msgstr ":py:obj:`Context `\\ \\(state\\)" -#: ../../source/ref-api/flwr.common.FitIns.rst:2 -msgid "FitIns" -msgstr "" - -#: ../../source/ref-api/flwr.common.FitIns.rst:29::1 -msgid ":py:obj:`parameters `\\" -msgstr "" - -#: ../../source/ref-api/flwr.common.FitIns.rst:29::1 -msgid ":py:obj:`config `\\" -msgstr "" - -#: ../../source/ref-api/flwr.common.FitRes.rst:2 -msgid "FitRes" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid "" +":py:obj:`rjust `\\ \\(width\\[\\, " +"fillchar\\]\\)" +msgstr ":py:obj:`EventType `\\ \\(value\\)" -#: ../../source/ref-api/flwr.common.FitRes.rst:31::1 -msgid ":py:obj:`status `\\" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.rjust:1 of +#, fuzzy +msgid "Return a right-justified string of length width." +msgstr "返回长度为 width 的右对齐字符串。" -#: ../../source/ref-api/flwr.common.FitRes.rst:31::1 -msgid ":py:obj:`parameters `\\" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`rstrip `\\ \\(\\[chars\\]\\)" +msgstr ":py:obj:`EventType `\\ \\(value\\)" -#: ../../source/ref-api/flwr.common.FitRes.rst:31::1 -msgid ":py:obj:`num_examples `\\" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.rstrip:1 of +#, fuzzy +msgid "Return a copy of the string with trailing whitespace removed." +msgstr "返回去掉尾部空白的字符串副本。" -#: ../../source/ref-api/flwr.common.FitRes.rst:31::1 -msgid ":py:obj:`metrics `\\" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`rpartition `\\ \\(sep\\, \\/\\)" +msgstr ":py:obj:`partition_id `\\" -#: ../../source/ref-api/flwr.common.GetParametersIns.rst:2 +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 #, fuzzy -msgid "GetParametersIns" -msgstr "参数" +msgid "" +":py:obj:`splitlines `\\ " +"\\(\\[keepends\\]\\)" +msgstr ":py:obj:`PING `\\" -#: ../../source/ref-api/flwr.common.GetParametersIns.rst:28::1 -msgid ":py:obj:`config `\\" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.splitlines:1 of +#, fuzzy +msgid "Return a list of the lines in the string, breaking at line boundaries." +msgstr "返回字符串中的行列表，以行为分界线。" -#: ../../source/ref-api/flwr.common.GetParametersRes.rst:2 +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 #, fuzzy -msgid "GetParametersRes" -msgstr "参数" +msgid ":py:obj:`strip `\\ \\(\\[chars\\]\\)" +msgstr ":py:obj:`EventType `\\ \\(value\\)" -#: ../../source/ref-api/flwr.common.GetParametersRes.rst:29::1 -msgid ":py:obj:`status `\\" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.strip:1 of +#, fuzzy +msgid "Return a copy of the string with leading and trailing whitespace removed." +msgstr "返回去掉前导和尾部空白的字符串副本。" -#: ../../source/ref-api/flwr.common.GetParametersRes.rst:29::1 -msgid ":py:obj:`parameters `\\" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`swapcase `\\ \\(\\)" +msgstr ":py:obj:`EventType `\\ \\(value\\)" -#: ../../source/ref-api/flwr.common.GetPropertiesIns.rst:2 -msgid "GetPropertiesIns" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.swapcase:1 of +#, fuzzy +msgid "" +"Convert uppercase characters to lowercase and lowercase characters to " +"uppercase." +msgstr "将大写字母转换为小写字母，将小写字母转换为大写字母。" -#: ../../source/ref-api/flwr.common.GetPropertiesIns.rst:28::1 -msgid ":py:obj:`config `\\" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`translate `\\ \\(table\\, \\/\\)" +msgstr ":py:obj:`EventType `\\ \\(value\\)" -#: ../../source/ref-api/flwr.common.GetPropertiesRes.rst:2 -msgid "GetPropertiesRes" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.translate:1 of +#, fuzzy +msgid "Replace each character in the string using the given translation table." +msgstr "使用给定的翻译表替换字符串中的每个字符。" -#: ../../source/ref-api/flwr.common.GetPropertiesRes.rst:29::1 -msgid ":py:obj:`status `\\" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`upper `\\ \\(\\)" +msgstr ":py:obj:`PING `\\" -#: ../../source/ref-api/flwr.common.GetPropertiesRes.rst:29::1 -msgid ":py:obj:`properties `\\" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.upper:1 of +#, fuzzy +msgid "Return a copy of the string converted to uppercase." +msgstr "返回转换为大写字符串的副本。" -#: ../../source/ref-api/flwr.common.Message.rst:2 +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 #, fuzzy -msgid "Message" -msgstr "服务器端" +msgid "" +":py:obj:`startswith `\\ \\(prefix\\[\\," +" start\\[\\, end\\]\\]\\)" +msgstr ":py:obj:`Status `\\ \\(code\\, message\\)" -#: flwr.common.Message.content:1::1 flwr.common.Message.metadata:1 -#: flwr.common.message.Message:3 of -msgid "A dataclass including information about the message to be executed." -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid "Return True if S starts with the specified prefix, False otherwise." +msgstr "如果 S 以指定前缀开头，则返回 True，否则返回 False。" -#: flwr.common.message.Message:5 of +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy msgid "" -"Holds records either sent by another entity (e.g. sent by the server-side" -" logic to a client, or vice-versa) or that will be sent to it." -msgstr "" +":py:obj:`endswith `\\ \\(suffix\\[\\, " +"start\\[\\, end\\]\\]\\)" +msgstr ":py:obj:`EventType `\\ \\(value\\)" -#: flwr.common.message.Message:8 of -msgid "" -"A dataclass that captures information about an error that took place when" -" processing another message." -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid "Return True if S ends with the specified suffix, False otherwise." +msgstr "如果 S 以指定后缀结束，则返回 True，否则返回 False。" -#: ../../source/ref-api/flwr.common.Message.rst:35::1 +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy msgid "" -":py:obj:`create_error_reply `\\ " -"\\(error\\, ttl\\)" -msgstr "" +":py:obj:`removeprefix `\\ " +"\\(prefix\\, \\/\\)" +msgstr ":py:obj:`EventType `\\ \\(value\\)" -#: ../../source/ref-api/flwr.common.Message.rst:35::1 -#: flwr.common.message.Message.create_error_reply:1 of -msgid "Construct a reply message indicating an error happened." -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.removeprefix:1 of +#, fuzzy +msgid "Return a str with the given prefix string removed if present." +msgstr "返回一个字符串，如果存在，则去掉给定的前缀字符串。" -#: ../../source/ref-api/flwr.common.Message.rst:35::1 +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy msgid "" -":py:obj:`create_reply `\\ \\(content\\," -" ttl\\)" -msgstr "" - -#: ../../source/ref-api/flwr.common.Message.rst:35::1 -#: flwr.common.message.Message.create_reply:1 of -msgid "Create a reply to this message with specified content and TTL." -msgstr "" +":py:obj:`removesuffix `\\ " +"\\(suffix\\, \\/\\)" +msgstr ":py:obj:`EventType `\\ \\(value\\)" -#: ../../source/ref-api/flwr.common.Message.rst:35::1 -msgid ":py:obj:`has_content `\\ \\(\\)" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.removesuffix:1 of +#, fuzzy +msgid "Return a str with the given suffix string removed if present." +msgstr "返回一个字符串，如果存在给定的后缀字符串，则将其删除。" -#: ../../source/ref-api/flwr.common.Message.rst:35::1 -#: flwr.common.message.Message.has_content:1 of -msgid "Return True if message has content, else False." -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`isascii `\\ \\(\\)" +msgstr ":py:obj:`PING `\\" -#: ../../source/ref-api/flwr.common.Message.rst:35::1 -msgid ":py:obj:`has_error `\\ \\(\\)" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.isascii:1 of +#, fuzzy +msgid "Return True if all characters in the string are ASCII, False otherwise." +msgstr "如果字符串中的所有字符都是 ASCII 码，则返回 True，否则返回 False。" -#: ../../source/ref-api/flwr.common.Message.rst:35::1 -#: flwr.common.message.Message.has_error:1 of -msgid "Return True if message has an error, else False." -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`islower `\\ \\(\\)" +msgstr ":py:obj:`now `\\ \\(\\)" -#: flwr.common.Message.content:1::1 of -msgid ":py:obj:`content `\\" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.islower:1 of +#, fuzzy +msgid "Return True if the string is a lowercase string, False otherwise." +msgstr "如果字符串是小写字符串，则返回 True，否则返回 False。" -#: flwr.common.Message.content:1 flwr.common.Message.content:1::1 -#: of +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 #, fuzzy -msgid "The content of this message." -msgstr "评估客户端的反应。" +msgid ":py:obj:`isupper `\\ \\(\\)" +msgstr ":py:obj:`EventType `\\ \\(value\\)" -#: flwr.common.Message.content:1::1 of -msgid ":py:obj:`error `\\" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.isupper:1 of +#, fuzzy +msgid "Return True if the string is an uppercase string, False otherwise." +msgstr "如果字符串是大写字符串，则返回 True，否则返回 False。" -#: flwr.common.Message.content:1::1 flwr.common.Message.error:1 of -msgid "Error captured by this message." -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`istitle `\\ \\(\\)" +msgstr ":py:obj:`EventType `\\ \\(value\\)" -#: flwr.common.Message.content:1::1 of -msgid ":py:obj:`metadata `\\" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.istitle:1 of +#, fuzzy +msgid "Return True if the string is a title-cased string, False otherwise." +msgstr "如果字符串是带标题的字符串，则返回 True，否则返回 False。" -#: flwr.common.message.Message.create_error_reply:3 of -msgid "The error that was encountered." -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`isspace `\\ \\(\\)" +msgstr ":py:obj:`EventType `\\ \\(value\\)" -#: flwr.common.Metadata.dst_node_id:1::1 -#: flwr.common.Metadata.ttl:1 flwr.common.message.Message.create_error_reply:5 -#: flwr.common.message.Message.create_reply:9 flwr.common.message.Metadata:16 -#: of -msgid "Time-to-live for this message." -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.isspace:1 of +#, fuzzy +msgid "Return True if the string is a whitespace string, False otherwise." +msgstr "如果字符串是空白字符串，则返回 True，否则返回 False。" -#: flwr.common.message.Message.create_reply:3 of -msgid "" -"The method generates a new `Message` as a reply to this message. It " -"inherits 'run_id', 'src_node_id', 'dst_node_id', and 'message_type' from " -"this message and sets 'reply_to_message' to the ID of this message." -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`isdecimal `\\ \\(\\)" +msgstr ":py:obj:`PING `\\" -#: flwr.common.message.Message.create_reply:7 of -msgid "The content for the reply message." -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.isdecimal:1 of +#, fuzzy +msgid "Return True if the string is a decimal string, False otherwise." +msgstr "如果字符串是十进制字符串，则返回 True，否则返回 False。" -#: flwr.common.message.Message.create_reply:12 of -msgid "A new `Message` instance representing the reply." -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`isdigit `\\ \\(\\)" +msgstr ":py:obj:`PING `\\" -#: ../../source/ref-api/flwr.common.MessageType.rst:2 +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.isdigit:1 of #, fuzzy -msgid "MessageType" -msgstr "返回类型" +msgid "Return True if the string is a digit string, False otherwise." +msgstr "如果字符串是数字字符串，则返回 True，否则返回 False。" -#: ../../source/ref-api/flwr.common.MessageType.rst:30::1 -msgid ":py:obj:`EVALUATE `\\" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`isnumeric `\\ \\(\\)" +msgstr ":py:obj:`PING `\\" -#: ../../source/ref-api/flwr.common.MessageType.rst:30::1 -msgid ":py:obj:`QUERY `\\" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.isnumeric:1 of +#, fuzzy +msgid "Return True if the string is a numeric string, False otherwise." +msgstr "如果字符串是数字字符串，则返回 True，否则返回 False。" -#: ../../source/ref-api/flwr.common.MessageType.rst:30::1 -msgid ":py:obj:`TRAIN `\\" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`isalpha `\\ \\(\\)" +msgstr ":py:obj:`PING `\\" -#: ../../source/ref-api/flwr.common.MessageTypeLegacy.rst:2 -msgid "MessageTypeLegacy" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.isalpha:1 of +#, fuzzy +msgid "Return True if the string is an alphabetic string, False otherwise." +msgstr "如果字符串是字母字符串，则返回 True，否则返回 False。" -#: ../../source/ref-api/flwr.common.MessageTypeLegacy.rst:29::1 -msgid ":py:obj:`GET_PARAMETERS `\\" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`isalnum `\\ \\(\\)" +msgstr ":py:obj:`PING `\\" -#: ../../source/ref-api/flwr.common.MessageTypeLegacy.rst:29::1 -msgid ":py:obj:`GET_PROPERTIES `\\" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.isalnum:1 of +#, fuzzy +msgid "Return True if the string is an alpha-numeric string, False otherwise." +msgstr "如果字符串是字母数字字符串，则返回 True，否则返回 False。" -#: flwr.common.Metadata.dst_node_id:1::1 -#: flwr.common.Metadata.run_id:1 flwr.common.message.Metadata:3 of -msgid "An identifier for the current run." -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`isidentifier `\\ \\(\\)" +msgstr ":py:obj:`EventType `\\ \\(value\\)" -#: flwr.common.Metadata.dst_node_id:1::1 -#: flwr.common.Metadata.message_id:1 flwr.common.message.Metadata:5 of -msgid "An identifier for the current message." -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.isidentifier:1 of +#, fuzzy +msgid "Return True if the string is a valid Python identifier, False otherwise." +msgstr "如果字符串是有效的 Python 标识符，则返回 True，否则返回 False。" -#: flwr.common.Metadata.dst_node_id:1::1 -#: flwr.common.Metadata.src_node_id:1 flwr.common.message.Metadata:7 of -msgid "An identifier for the node sending this message." -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`isprintable `\\ \\(\\)" +msgstr ":py:obj:`PING `\\" -#: flwr.common.Metadata.dst_node_id:1 -#: flwr.common.Metadata.dst_node_id:1::1 -#: flwr.common.message.Metadata:9 of -msgid "An identifier for the node receiving this message." -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.isprintable:1 of +#, fuzzy +msgid "Return True if the string is printable, False otherwise." +msgstr "如果字符串可打印，则返回 True，否则返回 False。" -#: flwr.common.Metadata.dst_node_id:1::1 -#: flwr.common.Metadata.reply_to_message:1 flwr.common.message.Metadata:11 of -msgid "An identifier for the message this message replies to." -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`zfill `\\ \\(width\\, \\/\\)" +msgstr ":py:obj:`PING `\\" -#: flwr.common.message.Metadata:13 of +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.zfill:1 of +#, fuzzy msgid "" -"An identifier for grouping messages. In some settings, this is used as " -"the FL round." -msgstr "" +"Pad a numeric string with zeros on the left, to fill a field of the given" +" width." +msgstr "在数字字符串左侧填充零，以填满给定宽度的字段。" -#: flwr.common.Metadata.dst_node_id:1::1 -#: flwr.common.Metadata.message_type:1 flwr.common.message.Metadata:18 of -msgid "A string that encodes the action to be executed on the receiving end." -msgstr "" - -#: flwr.common.message.Metadata:21 of +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy msgid "" -"An identifier that can be used when loading a particular data partition " -"for a ClientApp. Making use of this identifier is more relevant when " -"conducting simulations." +":py:obj:`format `\\ \\(\\*args\\, " +"\\*\\*kwargs\\)" msgstr "" +":py:obj:`log `\\ \\(level\\, msg\\, \\*args\\, " +"\\*\\*kwargs\\)" -#: flwr.common.Metadata.dst_node_id:1::1 of -msgid ":py:obj:`dst_node_id `\\" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid "Return a formatted version of S, using substitutions from args and kwargs." +msgstr "使用 args 和 kwargs 的替换，返回 S 的格式化版本。" -#: flwr.common.Metadata.dst_node_id:1::1 of -msgid ":py:obj:`group_id `\\" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`format_map `\\ \\(mapping\\)" +msgstr ":py:obj:`EventType `\\ \\(value\\)" -#: flwr.common.Metadata.dst_node_id:1::1 -#: flwr.common.Metadata.group_id:1 of -msgid "An identifier for grouping messages." -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid "Return a formatted version of S, using substitutions from mapping." +msgstr "使用映射中的替换，返回 S 的格式化版本。" -#: flwr.common.Metadata.dst_node_id:1::1 of -msgid ":py:obj:`message_id `\\" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#, fuzzy +msgid ":py:obj:`maketrans `\\" +msgstr ":py:obj:`TRAIN `\\" -#: flwr.common.Metadata.dst_node_id:1::1 of -msgid ":py:obj:`message_type `\\" -msgstr "" +#: ../../source/ref-api/flwr.common.EventType.rst:163::1 +#: flwr.common.EventType.maketrans:1 of +#, fuzzy +msgid "Return a translation table usable for str.translate()." +msgstr "返回可用于 str.translate() 的翻译表。" -#: flwr.common.Metadata.dst_node_id:1::1 of -msgid ":py:obj:`partition_id `\\" -msgstr "" +#: flwr.common.EventType.capitalize:1::1 of +#, fuzzy +msgid ":py:obj:`PING `\\" +msgstr ":py:obj:`PING `\\" -#: flwr.common.Metadata.dst_node_id:1::1 -#: flwr.common.Metadata.partition_id:1 of -msgid "An identifier telling which data partition a ClientApp should use." -msgstr "" - -#: flwr.common.Metadata.dst_node_id:1::1 of -msgid ":py:obj:`reply_to_message `\\" -msgstr "" - -#: flwr.common.Metadata.dst_node_id:1::1 of -msgid ":py:obj:`run_id `\\" -msgstr "" +#: flwr.common.EventType.capitalize:1::1 of +#, fuzzy +msgid ":py:obj:`START_CLIENT_ENTER `\\" +msgstr ":py:obj:`START_CLIENT_ENTER `\\" -#: flwr.common.Metadata.dst_node_id:1::1 of -msgid ":py:obj:`src_node_id `\\" -msgstr "" +#: flwr.common.EventType.capitalize:1::1 of +#, fuzzy +msgid ":py:obj:`START_CLIENT_LEAVE `\\" +msgstr ":py:obj:`START_CLIENT_LEAVE `\\" -#: flwr.common.Metadata.dst_node_id:1::1 of -msgid ":py:obj:`ttl `\\" -msgstr "" +#: flwr.common.EventType.capitalize:1::1 of +#, fuzzy +msgid ":py:obj:`START_SERVER_ENTER `\\" +msgstr ":py:obj:`START_SERVER_ENTER `\\" -#: ../../source/ref-api/flwr.common.MetricsRecord.rst:2 -msgid "MetricsRecord" -msgstr "" +#: flwr.common.EventType.capitalize:1::1 of +#, fuzzy +msgid ":py:obj:`START_SERVER_LEAVE `\\" +msgstr ":py:obj:`START_SERVER_LEAVE `\\" -#: flwr.common.record.metricsrecord.MetricsRecord:1 of +#: flwr.common.EventType.capitalize:1::1 of +#, fuzzy msgid "" -"Bases: :py:class:`~flwr.common.record.typeddict.TypedDict`\\ " -"[:py:class:`str`, :py:obj:`~typing.Union`\\ [:py:class:`int`, " -":py:class:`float`, :py:class:`~typing.List`\\ [:py:class:`int`], " -":py:class:`~typing.List`\\ [:py:class:`float`]]]" -msgstr "" - -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`clear `\\ \\(\\)" +":py:obj:`RUN_DRIVER_API_ENTER " +"`\\" msgstr "" +":py:obj:`RUN_DRIVER_API_ENTER " +"`\\" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`count_bytes `\\ \\(\\)" +#: flwr.common.EventType.capitalize:1::1 of +#, fuzzy +msgid "" +":py:obj:`RUN_DRIVER_API_LEAVE " +"`\\" msgstr "" +":py:obj:`RUN_DRIVER_API_LEAVE " +"`\\" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" +#: flwr.common.EventType.capitalize:1::1 of +#, fuzzy +msgid "" +":py:obj:`RUN_FLEET_API_ENTER " +"`\\" msgstr "" +":py:obj:`RUN_FLEET_API_ENTER " +"`\\" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`items `\\ \\(\\)" +#: flwr.common.EventType.capitalize:1::1 of +#, fuzzy +msgid "" +":py:obj:`RUN_FLEET_API_LEAVE " +"`\\" msgstr "" +":py:obj:`RUN_FLEET_API_LEAVE " +"`\\" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`keys `\\ \\(\\)" +#: flwr.common.EventType.capitalize:1::1 of +#, fuzzy +msgid "" +":py:obj:`RUN_SUPERLINK_ENTER " +"`\\" msgstr "" +":py:obj:`RUN_SUPERLINK_ENTER " +"`\\" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`pop `\\ \\(k\\[\\,d\\]\\)" +#: flwr.common.EventType.capitalize:1::1 of +#, fuzzy +msgid "" +":py:obj:`RUN_SUPERLINK_LEAVE " +"`\\" msgstr "" +":py:obj:`RUN_SUPERLINK_LEAVE " +"`\\" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +#: flwr.common.EventType.capitalize:1::1 of +#, fuzzy msgid "" -":py:obj:`update `\\ \\(\\[E\\, " -"\\]\\*\\*F\\)" +":py:obj:`START_SIMULATION_ENTER " +"`\\" msgstr "" +":py:obj:`START_SIMULATION_ENTER " +"`\\" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`values `\\ \\(\\)" +#: flwr.common.EventType.capitalize:1::1 of +#, fuzzy +msgid "" +":py:obj:`START_SIMULATION_LEAVE " +"`\\" msgstr "" +":py:obj:`START_SIMULATION_LEAVE " +"`\\" -#: ../../source/ref-api/flwr.common.NDArray.rst:2 -msgid "NDArray" -msgstr "" +#: flwr.common.EventType.capitalize:1::1 of +#, fuzzy +msgid ":py:obj:`DRIVER_CONNECT `\\" +msgstr ":py:obj:`DRIVER_CONNECT `\\" -#: ../../source/ref-api/flwr.common.Parameters.rst:29::1 -msgid ":py:obj:`tensors `\\" -msgstr "" +#: flwr.common.EventType.capitalize:1::1 of +#, fuzzy +msgid ":py:obj:`DRIVER_DISCONNECT `\\" +msgstr ":py:obj:`DRIVER_DISCONNECT `\\" -#: ../../source/ref-api/flwr.common.Parameters.rst:29::1 -msgid ":py:obj:`tensor_type `\\" -msgstr "" +#: flwr.common.EventType.capitalize:1::1 of +#, fuzzy +msgid ":py:obj:`START_DRIVER_ENTER `\\" +msgstr ":py:obj:`START_DRIVER_ENTER `\\" -#: ../../source/ref-api/flwr.common.ParametersRecord.rst:2 +#: flwr.common.EventType.capitalize:1::1 of #, fuzzy -msgid "ParametersRecord" -msgstr "参数" +msgid ":py:obj:`START_DRIVER_LEAVE `\\" +msgstr ":py:obj:`START_DRIVER_LEAVE `\\" -#: flwr.common.record.parametersrecord.ParametersRecord:1 of +#: flwr.common.EventType.capitalize:1::1 of +#, fuzzy msgid "" -"Bases: :py:class:`~flwr.common.record.typeddict.TypedDict`\\ " -"[:py:class:`str`, :py:class:`~flwr.common.record.parametersrecord.Array`]" +":py:obj:`RUN_CLIENT_APP_ENTER " +"`\\" msgstr "" +":py:obj:`RUN_CLIENT_APP_ENTER " +"`\\" -#: flwr.common.record.parametersrecord.ParametersRecord:3 of +#: flwr.common.EventType.capitalize:1::1 of +#, fuzzy msgid "" -"A dataclass storing named Arrays in order. This means that it holds " -"entries as an OrderedDict[str, Array]. ParametersRecord objects can be " -"viewed as an equivalent to PyTorch's state_dict, but holding serialised " -"tensors instead." +":py:obj:`RUN_CLIENT_APP_LEAVE " +"`\\" msgstr "" +":py:obj:`RUN_CLIENT_APP_LEAVE " +"`\\" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`clear `\\ \\(\\)" +#: flwr.common.EventType.capitalize:1::1 of +#, fuzzy +msgid "" +":py:obj:`RUN_SERVER_APP_ENTER " +"`\\" msgstr "" +":py:obj:`RUN_SERVER_APP_ENTER " +"`\\" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`count_bytes `\\ \\(\\)" +#: flwr.common.EventType.capitalize:1::1 of +#, fuzzy +msgid "" +":py:obj:`RUN_SERVER_APP_LEAVE " +"`\\" msgstr "" +":py:obj:`RUN_SERVER_APP_LEAVE " +"`\\" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" +#: flwr.common.EventType.capitalize:1::1 of +#, fuzzy +msgid "" +":py:obj:`RUN_SUPERNODE_ENTER " +"`\\" msgstr "" +":py:obj:`RUN_SUPERLINK_ENTER " +"`\\" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`items `\\ \\(\\)" +#: flwr.common.EventType.capitalize:1::1 of +#, fuzzy +msgid "" +":py:obj:`RUN_SUPERNODE_LEAVE " +"`\\" msgstr "" +":py:obj:`RUN_SUPERLINK_LEAVE " +"`\\" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`keys `\\ \\(\\)" +#: flwr.common.EventType.capitalize:1::1 of +#, fuzzy +msgid "" +":py:obj:`RUN_SUPEREXEC_ENTER " +"`\\" msgstr "" +":py:obj:`RUN_SUPERLINK_ENTER " +"`\\" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`pop `\\ \\(k\\[\\,d\\]\\)" +#: flwr.common.EventType.capitalize:1::1 of +#, fuzzy +msgid "" +":py:obj:`RUN_SUPEREXEC_LEAVE " +"`\\" msgstr "" +":py:obj:`RUN_SUPERLINK_LEAVE " +"`\\" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +#: flwr.common.EventType.capitalize:3 of +#, fuzzy msgid "" -":py:obj:`update `\\ \\(\\[E\\, " -"\\]\\*\\*F\\)" -msgstr "" +"More specifically, make the first character have upper case and the rest " +"lower case." +msgstr "更具体地说，让第一个字符大写，其余字符小写。" -#: flwr.common.record.typeddict.TypedDict.clear:1::1 of -msgid ":py:obj:`values `\\ \\(\\)" -msgstr "" +#: flwr.common.EventType.center:3 flwr.common.EventType.ljust:3 +#: flwr.common.EventType.rjust:3 of +#, fuzzy +msgid "Padding is done using the specified fill character (default is a space)." +msgstr "使用指定的填充字符（默认为空格）进行填充。" -#: flwr.common.record.parametersrecord.ParametersRecord.count_bytes:3 of +#: flwr.common.EventType.count:1 of +#, fuzzy msgid "" -"Note that a small amount of Bytes might also be included in this counting" -" that correspond to metadata of the serialized object (e.g. of NumPy " -"array) needed for deseralization." -msgstr "" +"Return the number of non-overlapping occurrences of substring sub in " +"string S[start:end]. Optional arguments start and end are interpreted as" +" in slice notation." +msgstr "返回子串 sub 在字符串 S[start:end] 中非重叠出现的次数。 可选参数 start 和 end 按切分符号解释。" -#: ../../source/ref-api/flwr.common.ReconnectIns.rst:2 +#: flwr.common.EventType.encode:3 of #, fuzzy -msgid "ReconnectIns" -msgstr "启用 SSL 连接" +msgid "encoding" +msgstr "编码" -#: ../../source/ref-api/flwr.common.ReconnectIns.rst:28::1 -msgid ":py:obj:`seconds `\\" -msgstr "" +#: flwr.common.EventType.encode:4 of +#, fuzzy +msgid "The encoding in which to encode the string." +msgstr "字符串的编码。" -#: ../../source/ref-api/flwr.common.RecordSet.rst:2 -msgid "RecordSet" -msgstr "" +#: flwr.common.EventType.encode:9 of +#, fuzzy +msgid "errors" +msgstr "错误" -#: flwr.common.RecordSet.configs_records:1::1 of -msgid ":py:obj:`configs_records `\\" +#: flwr.common.EventType.encode:6 of +#, fuzzy +msgid "" +"The error handling scheme to use for encoding errors. The default is " +"'strict' meaning that encoding errors raise a UnicodeEncodeError. Other " +"possible values are 'ignore', 'replace' and 'xmlcharrefreplace' as well " +"as any other name registered with codecs.register_error that can handle " +"UnicodeEncodeErrors." msgstr "" +"编码错误的错误处理方案。默认值为 \"strict\"，即编码错误会引发 UnicodeEncodeError。 其他可能的值包括 " +"\"ignore\"、\"replace \"和 \"xmlcharrefreplace\"，以及通过 codecs.register_error" +" 注册的、可处理 UnicodeEncodeErrror 的其他名称。" -#: flwr.common.RecordSet.configs_records:1 -#: flwr.common.RecordSet.configs_records:1::1 of -msgid "Dictionary holding ConfigsRecord instances." +#: flwr.common.EventType.endswith:1 of +#, fuzzy +msgid "" +"Return True if S ends with the specified suffix, False otherwise. With " +"optional start, test S beginning at that position. With optional end, " +"stop comparing S at that position. suffix can also be a tuple of strings " +"to try." msgstr "" +"如果 S 以指定后缀结束，则返回 True，否则返回 False。如果起始位置可选，则从该位置开始测试 S。如果使用可选的 " +"end，则在该位置停止比较 S。后缀也可以是要尝试的字符串元组。" -#: flwr.common.RecordSet.configs_records:1::1 of -msgid ":py:obj:`metrics_records `\\" -msgstr "" +#: flwr.common.EventType.expandtabs:3 of +#, fuzzy +msgid "If tabsize is not given, a tab size of 8 characters is assumed." +msgstr "如果未给出制表符大小，则假定制表符大小为 8 个字符。" -#: flwr.common.RecordSet.configs_records:1::1 -#: flwr.common.RecordSet.metrics_records:1 of -msgid "Dictionary holding MetricsRecord instances." -msgstr "" +#: flwr.common.EventType.find:1 flwr.common.EventType.index:1 of +#, fuzzy +msgid "" +"Return the lowest index in S where substring sub is found, such that sub " +"is contained within S[start:end]. Optional arguments start and end are " +"interpreted as in slice notation." +msgstr "返回在 S 中找到子串 sub 的最低索引，即 sub 包含在 S[start:end] 中。 可选参数 start 和 end 按切分符号解释。" -#: flwr.common.RecordSet.configs_records:1::1 of -msgid ":py:obj:`parameters_records `\\" -msgstr "" +#: flwr.common.EventType.find:5 flwr.common.EventType.rfind:5 of +#, fuzzy +msgid "Return -1 on failure." +msgstr "失败时返回-1。" -#: flwr.common.RecordSet.configs_records:1::1 -#: flwr.common.RecordSet.parameters_records:1 of -msgid "Dictionary holding ParametersRecord instances." -msgstr "" +#: flwr.common.EventType.format:1 of +#, fuzzy +msgid "" +"Return a formatted version of S, using substitutions from args and " +"kwargs. The substitutions are identified by braces ('{' and '}')." +msgstr "使用来自 args 和 kwargs 的替换，返回 S 的格式化版本。替换用大括号（'{'和'}'）标识。" -#: ../../source/ref-api/flwr.common.ServerMessage.rst:2 +#: flwr.common.EventType.format_map:1 of #, fuzzy -msgid "ServerMessage" -msgstr "服务器端" +msgid "" +"Return a formatted version of S, using substitutions from mapping. The " +"substitutions are identified by braces ('{' and '}')." +msgstr "使用映射中的替换，返回 S 的格式化版本。替换用大括号（'{'和'}'）标识。" -#: ../../source/ref-api/flwr.common.ServerMessage.rst:31::1 -msgid ":py:obj:`evaluate_ins `\\" -msgstr "" +#: flwr.common.EventType.index:5 flwr.common.EventType.rindex:5 of +#, fuzzy +msgid "Raises ValueError when the substring is not found." +msgstr "如果未找到子串，则引发 ValueError。" -#: ../../source/ref-api/flwr.common.ServerMessage.rst:31::1 -msgid ":py:obj:`fit_ins `\\" -msgstr "" +#: flwr.common.EventType.isalnum:3 of +#, fuzzy +msgid "" +"A string is alpha-numeric if all characters in the string are alpha-" +"numeric and there is at least one character in the string." +msgstr "如果字符串中的所有字符都是字母数字，且字符串中至少有一个字符，则该字符串为字母数字字符串。" -#: ../../source/ref-api/flwr.common.ServerMessage.rst:31::1 +#: flwr.common.EventType.isalpha:3 of +#, fuzzy msgid "" -":py:obj:`get_parameters_ins " -"`\\" -msgstr "" +"A string is alphabetic if all characters in the string are alphabetic and" +" there is at least one character in the string." +msgstr "如果字符串中的所有字符都是字母，并且字符串中至少有一个字符，那么该字符串就是字母字符串。" -#: ../../source/ref-api/flwr.common.ServerMessage.rst:31::1 +#: flwr.common.EventType.isascii:3 of +#, fuzzy msgid "" -":py:obj:`get_properties_ins " -"`\\" -msgstr "" +"ASCII characters have code points in the range U+0000-U+007F. Empty " +"string is ASCII too." +msgstr "ASCII 字符的码位范围为 U+0000-U+007F。空字符串也是 ASCII 字符。" -#: ../../source/ref-api/flwr.common.Status.rst:2 +#: flwr.common.EventType.isdecimal:3 of #, fuzzy -msgid "Status" -msgstr "客户端状态。" +msgid "" +"A string is a decimal string if all characters in the string are decimal " +"and there is at least one character in the string." +msgstr "如果字符串中的所有字符都是十进制，并且字符串中至少有一个字符是十进制，那么该字符串就是十进制字符串。" -#: ../../source/ref-api/flwr.common.Status.rst:29::1 -msgid ":py:obj:`code `\\" -msgstr "" - -#: ../../source/ref-api/flwr.common.Status.rst:29::1 -msgid ":py:obj:`message `\\" -msgstr "" - -#: ../../source/ref-api/flwr.common.array_from_numpy.rst:2 -msgid "array\\_from\\_numpy" -msgstr "" - -#: ../../source/ref-api/flwr.common.bytes_to_ndarray.rst:2 -msgid "bytes\\_to\\_ndarray" -msgstr "" +#: flwr.common.EventType.isdigit:3 of +#, fuzzy +msgid "" +"A string is a digit string if all characters in the string are digits and" +" there is at least one character in the string." +msgstr "如果字符串中的所有字符都是数字，并且字符串中至少有一个字符，那么该字符串就是数字字符串。" -#: ../../source/ref-api/flwr.common.configure.rst:2 +#: flwr.common.EventType.isidentifier:3 of #, fuzzy -msgid "configure" -msgstr "配置日志记录" +msgid "" +"Call keyword.iskeyword(s) to test whether string s is a reserved " +"identifier, such as \"def\" or \"class\"." +msgstr "调用 keyword.iskeyword(s) 测试字符串 s 是否为保留标识符，如 \"def \"或 \"class\"。" -#: ../../source/ref-api/flwr.common.event.rst:2 -msgid "event" -msgstr "" +#: flwr.common.EventType.islower:3 of +#, fuzzy +msgid "" +"A string is lowercase if all cased characters in the string are lowercase" +" and there is at least one cased character in the string." +msgstr "如果字符串中的所有大小写字符都是小写，且字符串中至少有一个大小写字符，则该字符串为小写字符串。" -#: ../../source/ref-api/flwr.common.log.rst:2 -msgid "log" -msgstr "" +#: flwr.common.EventType.isnumeric:3 of +#, fuzzy +msgid "" +"A string is numeric if all characters in the string are numeric and there" +" is at least one character in the string." +msgstr "如果字符串中的所有字符都是数字，且字符串中至少有一个字符，则该字符串为数字字符串。" -#: logging.Logger.log:3 of +#: flwr.common.EventType.isprintable:3 of +#, fuzzy msgid "" -"To pass exception information, use the keyword argument exc_info with a " -"true value, e.g." -msgstr "要传递异常信息，请使用带 true 值的关键字参数 exc_info，例如。" +"A string is printable if all of its characters are considered printable " +"in repr() or if it is empty." +msgstr "如果字符串的所有字符在 repr() 中都被认为是可打印的，或者字符串为空，那么该字符串就是可打印的。" -#: logging.Logger.log:6 of -#, python-format -msgid "logger.log(level, \"We have a %s\", \"mysterious problem\", exc_info=1)" -msgstr "logger.log(level, \"We have a %s\", \"mysterious problem\", exc_info=1)" +#: flwr.common.EventType.isspace:3 of +#, fuzzy +msgid "" +"A string is whitespace if all characters in the string are whitespace and" +" there is at least one character in the string." +msgstr "如果字符串中的所有字符都是空格，且字符串中至少有一个字符，则该字符串为空格。" -#: ../../source/ref-api/flwr.common.ndarray_to_bytes.rst:2 -msgid "ndarray\\_to\\_bytes" -msgstr "" +#: flwr.common.EventType.istitle:3 of +#, fuzzy +msgid "" +"In a title-cased string, upper- and title-case characters may only follow" +" uncased characters and lowercase characters only cased ones." +msgstr "在标题大小写字符串中，大写和标题大小写字符只能跟在无大小写字符之后，小写字符只能跟在有大小写字符之后。" -#: ../../source/ref-api/flwr.common.ndarrays_to_parameters.rst:2 -msgid "ndarrays\\_to\\_parameters" -msgstr "" +#: flwr.common.EventType.isupper:3 of +#, fuzzy +msgid "" +"A string is uppercase if all cased characters in the string are uppercase" +" and there is at least one cased character in the string." +msgstr "如果字符串中所有带大小写的字符都是大写，并且字符串中至少有一个带大小写的字符，则该字符串为大写字符串。" -#: ../../source/ref-api/flwr.common.now.rst:2 -msgid "now" -msgstr "" +#: flwr.common.EventType.join:3 of +#, fuzzy +msgid "" +"The string whose method is called is inserted in between each given " +"string. The result is returned as a new string." +msgstr "方法被调用的字符串会被插入每个给定的字符串之间。结果将以新字符串的形式返回。" -#: ../../source/ref-api/flwr.common.parameters_to_ndarrays.rst:2 -msgid "parameters\\_to\\_ndarrays" -msgstr "" +#: flwr.common.EventType.join:6 of +#, fuzzy +msgid "Example: '.'.join(['ab', 'pq', 'rs']) -> 'ab.pq.rs'" +msgstr "示例：'.'.join(['ab', 'pq', 'rs']) -> 'ab.pq.rs'" -#: ../../source/ref-api/flwr.server.rst:2 -msgid "server" -msgstr "服务器" +#: flwr.common.EventType.lstrip:3 flwr.common.EventType.rstrip:3 +#: flwr.common.EventType.strip:3 of +#, fuzzy +msgid "If chars is given and not None, remove characters in chars instead." +msgstr "如果给定的是 chars 而不是 None，则删除 chars 中的字符。" -#: ../../source/ref-api/flwr.server.rst:27::1 -msgid ":py:obj:`run_driver_api `\\ \\(\\)" +#: flwr.common.EventType.maketrans:3 of +#, fuzzy +msgid "" +"If there is only one argument, it must be a dictionary mapping Unicode " +"ordinals (integers) or characters to Unicode ordinals, strings or None. " +"Character keys will be then converted to ordinals. If there are two " +"arguments, they must be strings of equal length, and in the resulting " +"dictionary, each character in x will be mapped to the character at the " +"same position in y. If there is a third argument, it must be a string, " +"whose characters will be mapped to None in the result." msgstr "" +"如果只有一个参数，则必须是一个将 Unicode 序号（整数）或字符映射到 Unicode 序号、字符串或 None " +"的字典。字符键将被转换为序号。如果有两个参数，它们必须是长度相等的字符串，在生成的字典中，x 中的每个字符将被映射到 y 中相同位置的字符。" -#: ../../source/ref-api/flwr.server.rst:27::1 -#: flwr.server.app.run_driver_api:1 of +#: flwr.common.EventType.partition:3 of #, fuzzy -msgid "Run Flower server (Driver API)." -msgstr "flower-driver-api" - -#: ../../source/ref-api/flwr.server.rst:27::1 -msgid ":py:obj:`run_fleet_api `\\ \\(\\)" -msgstr "" +msgid "" +"This will search for the separator in the string. If the separator is " +"found, returns a 3-tuple containing the part before the separator, the " +"separator itself, and the part after it." +msgstr "它会在字符串中搜索分隔符。 如果找到分隔符，则返回一个包含分隔符前部分、分隔符本身和分隔符后部分的 3 元组。" -#: ../../source/ref-api/flwr.server.rst:27::1 -#: flwr.server.app.run_fleet_api:1 of +#: flwr.common.EventType.partition:7 of #, fuzzy -msgid "Run Flower server (Fleet API)." -msgstr "Flower 服务器。" +msgid "" +"If the separator is not found, returns a 3-tuple containing the original " +"string and two empty strings." +msgstr "如果找不到分隔符，则返回一个包含原始字符串和两个空字符串的 3 元组。" -#: ../../source/ref-api/flwr.server.rst:27::1 -msgid ":py:obj:`run_server_app `\\ \\(\\)" -msgstr "" +#: flwr.common.EventType.removeprefix:3 of +#, fuzzy +msgid "" +"If the string starts with the prefix string, return string[len(prefix):]." +" Otherwise, return a copy of the original string." +msgstr "如果字符串以前缀字符串开始，则返回 string[len(prefix):]。否则，返回原始字符串的副本。" -#: ../../source/ref-api/flwr.server.rst:27::1 -#: flwr.server.run_serverapp.run_server_app:1 of +#: flwr.common.EventType.removesuffix:3 of #, fuzzy -msgid "Run Flower server app." -msgstr "Flower 服务器。" +msgid "" +"If the string ends with the suffix string and that suffix is not empty, " +"return string[:-len(suffix)]. Otherwise, return a copy of the original " +"string." +msgstr "如果字符串以后缀字符串结尾，且后缀不为空，则返回 string[:-len(suffix)]。否则，返回原始字符串的副本。" -#: ../../source/ref-api/flwr.server.rst:27::1 -msgid ":py:obj:`run_superlink `\\ \\(\\)" -msgstr "" +#: flwr.common.EventType.replace:5 of +#, fuzzy +msgid "count" +msgstr "背景" -#: ../../source/ref-api/flwr.server.rst:27::1 -#: flwr.server.app.run_superlink:1 of -msgid "Run Flower server (Driver API and Fleet API)." -msgstr "" +#: flwr.common.EventType.replace:4 of +#, fuzzy +msgid "" +"Maximum number of occurrences to replace. -1 (the default value) means " +"replace all occurrences." +msgstr "要替换的最大出现次数。-1（默认值）表示替换所有出现次数。" -#: ../../source/ref-api/flwr.server.rst:27::1 +#: flwr.common.EventType.replace:7 of +#, fuzzy msgid "" -":py:obj:`start_driver `\\ \\(\\*\\[\\, " -"server\\_address\\, server\\, ...\\]\\)" -msgstr "" +"If the optional argument count is given, only the first count occurrences" +" are replaced." +msgstr "如果给出可选参数 count，则只替换第一个计数出现的次数。" -#: ../../source/ref-api/flwr.server.rst:27::1 -#: flwr.server.compat.app.start_driver:1 of +#: flwr.common.EventType.rfind:1 flwr.common.EventType.rindex:1 of #, fuzzy -msgid "Start a Flower Driver API server." -msgstr "启动基于 Ray 的Flower模拟服务器。" +msgid "" +"Return the highest index in S where substring sub is found, such that sub" +" is contained within S[start:end]. Optional arguments start and end are " +"interpreted as in slice notation." +msgstr "返回在 S 中找到子串 sub 且 sub 包含在 S[start:end] 中的最高索引。 可选参数 start 和 end 按切分符号解释。" -#: ../../source/ref-api/flwr.server.rst:27::1 +#: flwr.common.EventType.rpartition:3 of +#, fuzzy msgid "" -":py:obj:`start_server `\\ \\(\\*\\[\\, " -"server\\_address\\, server\\, ...\\]\\)" -msgstr "" +"This will search for the separator in the string, starting at the end. If" +" the separator is found, returns a 3-tuple containing the part before the" +" separator, the separator itself, and the part after it." +msgstr "它会从字符串的末尾开始搜索分隔符。如果找到分隔符，则返回一个包含分隔符前部分、分隔符本身和分隔符后部分的 3 元组。" -#: ../../source/ref-api/flwr.server.rst:27::1 -#: flwr.server.app.start_server:1 of -msgid "Start a Flower server using the gRPC transport layer." -msgstr "使用 gRPC 传输层启动 Flower 服务器。" +#: flwr.common.EventType.rpartition:7 of +#, fuzzy +msgid "" +"If the separator is not found, returns a 3-tuple containing two empty " +"strings and the original string." +msgstr "如果找不到分隔符，则返回一个包含两个空字符串和原始字符串的 3 元组。" -#: ../../source/ref-api/flwr.server.rst:41::1 -msgid ":py:obj:`ClientManager `\\ \\(\\)" -msgstr "" +#: flwr.common.EventType.rsplit:7 flwr.common.EventType.split:7 of +#, fuzzy +msgid "sep" +msgstr "sep" -#: ../../source/ref-api/flwr.server.rst:41::1 -#: flwr.server.client_manager.ClientManager:1 of +#: flwr.common.EventType.rsplit:4 flwr.common.EventType.split:4 of #, fuzzy -msgid "Abstract base class for managing Flower clients." -msgstr "Flower 客户端的抽象基类。" +msgid "The separator used to split the string." +msgstr "用于分割字符串的分隔符。" -#: ../../source/ref-api/flwr.server.rst:41::1 +#: flwr.common.EventType.rsplit:6 flwr.common.EventType.split:6 of #, fuzzy msgid "" -":py:obj:`Driver `\\ " -"\\(\\[driver\\_service\\_address\\, ...\\]\\)" -msgstr "" -"Flower 1.0: ``start_server(..., " -"config=flwr.server.ServerConfig(num_rounds=3, round_timeout=600.0), " -"...)``" +"When set to None (the default value), will split on any whitespace " +"character (including \\\\n \\\\r \\\\t \\\\f and spaces) and will discard" +" empty strings from the result." +msgstr "当设置为 \"无\"（默认值）时，将对任何空白字符（包括 \\n \\r \\t \\f 和空格）进行分割，并从结果中剔除空字符串。" -#: ../../source/ref-api/flwr.server.rst:41::1 -#: flwr.server.driver.driver.Driver:1 of -msgid "`Driver` class provides an interface to the Driver API." -msgstr "" +#: flwr.common.EventType.rsplit:11 flwr.common.EventType.split:11 of +#, fuzzy +msgid "maxsplit" +msgstr "最大分割" -#: ../../source/ref-api/flwr.server.rst:41::1 -msgid ":py:obj:`History `\\ \\(\\)" -msgstr "" +#: flwr.common.EventType.rsplit:10 flwr.common.EventType.split:10 of +#, fuzzy +msgid "" +"Maximum number of splits (starting from the left). -1 (the default value)" +" means no limit." +msgstr "最大分割次数（从左边开始）。-1（默认值）表示没有限制。" -#: ../../source/ref-api/flwr.server.rst:41::1 -#: flwr.server.history.History:1 of +#: flwr.common.EventType.rsplit:13 of #, fuzzy -msgid "History class for training and/or evaluation metrics collection." -msgstr "**hist** -- 包含训练和评估指标的对象。" +msgid "Splitting starts at the end of the string and works to the front." +msgstr "从琴弦末端开始分弦，一直到琴弦前端。" -#: ../../source/ref-api/flwr.server.rst:41::1 +#: flwr.common.EventType.split:13 of +#, fuzzy msgid "" -":py:obj:`LegacyContext `\\ \\(state\\[\\, " -"config\\, strategy\\, ...\\]\\)" -msgstr "" +"Note, str.split() is mainly useful for data that has been intentionally " +"delimited. With natural text that includes punctuation, consider using " +"the regular expression module." +msgstr "注意，str.split() 主要适用于有意分隔的数据。 对于包含标点符号的自然文本，可以考虑使用正则表达式模块。" -#: ../../source/ref-api/flwr.server.rst:41::1 -#: flwr.server.compat.legacy_context.LegacyContext:1 of -msgid "Legacy Context." -msgstr "" +#: flwr.common.EventType.splitlines:3 of +#, fuzzy +msgid "" +"Line breaks are not included in the resulting list unless keepends is " +"given and true." +msgstr "除非指定 keepends 为 true，否则换行符不会包含在生成的列表中。" -#: ../../source/ref-api/flwr.server.rst:41::1 +#: flwr.common.EventType.startswith:1 of +#, fuzzy msgid "" -":py:obj:`Server `\\ \\(\\*\\, client\\_manager\\[\\, " -"strategy\\]\\)" +"Return True if S starts with the specified prefix, False otherwise. With " +"optional start, test S beginning at that position. With optional end, " +"stop comparing S at that position. prefix can also be a tuple of strings " +"to try." msgstr "" +"如果 S 以指定的前缀开始，则返回 True，否则返回 False。如果选择 start，则从该位置开始测试 S。如果使用可选的 " +"end，则在该位置停止比较 S。" -#: ../../source/ref-api/flwr.server.rst:41::1 +#: flwr.common.EventType.title:3 of #, fuzzy msgid "" -":py:obj:`ServerApp `\\ \\(\\[server\\, config\\, " -"strategy\\, ...\\]\\)" -msgstr "server.strategy.Strategy" +"More specifically, words start with uppercased characters and all " +"remaining cased characters have lower case." +msgstr "更具体地说，单词以大写字母开头，其余所有大小写字符均为小写。" -#: ../../source/ref-api/flwr.server.rst:41::1 -#: flwr.server.server_app.ServerApp:1 of +#: flwr.common.EventType.translate:5 of #, fuzzy -msgid "Flower ServerApp." -msgstr "Flower 服务器。" +msgid "table" +msgstr "数据库" -#: ../../source/ref-api/flwr.server.rst:41::1 +#: flwr.common.EventType.translate:4 of #, fuzzy msgid "" -":py:obj:`ServerConfig `\\ \\(\\[num\\_rounds\\," -" round\\_timeout\\]\\)" -msgstr "" -"Flower 1.0: ``start_server(..., " -"config=flwr.server.ServerConfig(num_rounds=3, round_timeout=600.0), " -"...)``" +"Translation table, which must be a mapping of Unicode ordinals to Unicode" +" ordinals, strings, or None." +msgstr "翻译表，必须是 Unicode 序号到 Unicode 序号、字符串或无的映射。" -#: ../../source/ref-api/flwr.server.rst:41::1 -#: flwr.server.server_config.ServerConfig:1 of +#: flwr.common.EventType.translate:7 of #, fuzzy -msgid "Flower server config." -msgstr "Flower 服务器。" - -#: ../../source/ref-api/flwr.server.rst:41::1 -msgid ":py:obj:`SimpleClientManager `\\ \\(\\)" +msgid "" +"The table must implement lookup/indexing via __getitem__, for instance a " +"dictionary or list. If this operation raises LookupError, the character " +"is left untouched. Characters mapped to None are deleted." msgstr "" +"表必须通过 __getitem__ 实现查找/索引，例如字典或列表。 如果该操作引发 LookupError，该字符将保持不变。 映射为 None" +" 的字符将被删除。" -#: ../../source/ref-api/flwr.server.rst:41::1 -#: flwr.server.client_manager.SimpleClientManager:1 of +#: flwr.common.EventType.zfill:3 of #, fuzzy -msgid "Provides a pool of available clients." -msgstr "使用部分可用客户进行评估。" +msgid "The string is never truncated." +msgstr "字符串不会被截断。" -#: ../../source/ref-api/flwr.server.rst:60::1 +#: ../../source/ref-api/flwr.common.FitIns.rst:2 #, fuzzy -msgid ":py:obj:`flwr.server.strategy `\\" -msgstr "server.strategy.Strategy" +msgid "FitIns" +msgstr "FitIns" -#: ../../source/ref-api/flwr.server.rst:60::1 -#: flwr.server.strategy:1 of -msgid "Contains the strategy abstraction and different implementations." -msgstr "包含策略抽象和不同的实现方法。" +#: ../../source/ref-api/flwr.common.FitIns.rst:29::1 +#, fuzzy +msgid ":py:obj:`parameters `\\" +msgstr ":py:obj:`parameters `\\" -#: ../../source/ref-api/flwr.server.rst:60::1 +#: ../../source/ref-api/flwr.common.FitIns.rst:29::1 #, fuzzy -msgid ":py:obj:`flwr.server.workflow `\\" -msgstr "server.strategy.Strategy" +msgid ":py:obj:`config `\\" +msgstr ":py:obj:`config `\\" -#: ../../source/ref-api/flwr.server.rst:60::1 -#: flwr.server.workflow:1 of +#: ../../source/ref-api/flwr.common.FitRes.rst:2 #, fuzzy -msgid "Workflows." -msgstr "工作流程" +msgid "FitRes" +msgstr "FitRes" -#: ../../source/ref-api/flwr.server.ClientManager.rst:2 +#: ../../source/ref-api/flwr.common.FitRes.rst:31::1 #, fuzzy -msgid "ClientManager" -msgstr "客户端" +msgid ":py:obj:`status `\\" +msgstr ":py:obj:`status `\\" -#: flwr.server.client_manager.ClientManager.all:1::1 of -msgid ":py:obj:`all `\\ \\(\\)" -msgstr "" +#: ../../source/ref-api/flwr.common.FitRes.rst:31::1 +#, fuzzy +msgid ":py:obj:`parameters `\\" +msgstr ":py:obj:`parameters `\\" -#: flwr.server.client_manager.ClientManager.all:1 -#: flwr.server.client_manager.ClientManager.all:1::1 -#: flwr.server.client_manager.SimpleClientManager.all:1 -#: flwr.server.client_manager.SimpleClientManager.all:1::1 of -msgid "Return all available clients." -msgstr "" +#: ../../source/ref-api/flwr.common.FitRes.rst:31::1 +#, fuzzy +msgid ":py:obj:`num_examples `\\" +msgstr ":py:obj:`num_examples `\\" -#: flwr.server.client_manager.ClientManager.all:1::1 of -msgid ":py:obj:`num_available `\\ \\(\\)" -msgstr "" +#: ../../source/ref-api/flwr.common.FitRes.rst:31::1 +#, fuzzy +msgid ":py:obj:`metrics `\\" +msgstr ":py:obj:`metrics `\\" -#: flwr.server.client_manager.ClientManager.all:1::1 -#: flwr.server.client_manager.ClientManager.num_available:1 -#: flwr.server.client_manager.SimpleClientManager.all:1::1 -#: flwr.server.client_manager.SimpleClientManager.num_available:1 of +#: ../../source/ref-api/flwr.common.GetParametersIns.rst:2 #, fuzzy -msgid "Return the number of available clients." -msgstr "返回样本大小和所需的可用客户数量。" +msgid "GetParametersIns" +msgstr "参数" -#: flwr.server.client_manager.ClientManager.all:1::1 of -msgid ":py:obj:`register `\\ \\(client\\)" -msgstr "" +#: ../../source/ref-api/flwr.common.GetParametersIns.rst:28::1 +#, fuzzy +msgid ":py:obj:`config `\\" +msgstr ":py:obj:`config `\\" -#: flwr.server.client_manager.ClientManager.all:1::1 -#: flwr.server.client_manager.ClientManager.register:1 -#: flwr.server.client_manager.SimpleClientManager.all:1::1 -#: flwr.server.client_manager.SimpleClientManager.register:1 of -msgid "Register Flower ClientProxy instance." -msgstr "" +#: ../../source/ref-api/flwr.common.GetParametersRes.rst:2 +#, fuzzy +msgid "GetParametersRes" +msgstr "参数" -#: flwr.server.client_manager.ClientManager.all:1::1 of -msgid "" -":py:obj:`sample `\\ " -"\\(num\\_clients\\[\\, min\\_num\\_clients\\, criterion\\]\\)" -msgstr "" +#: ../../source/ref-api/flwr.common.GetParametersRes.rst:29::1 +#, fuzzy +msgid ":py:obj:`status `\\" +msgstr ":py:obj:`status `\\" -#: flwr.server.client_manager.ClientManager.all:1::1 -#: flwr.server.client_manager.ClientManager.sample:1 -#: flwr.server.client_manager.SimpleClientManager.all:1::1 -#: flwr.server.client_manager.SimpleClientManager.sample:1 of -msgid "Sample a number of Flower ClientProxy instances." -msgstr "" +#: ../../source/ref-api/flwr.common.GetParametersRes.rst:29::1 +#, fuzzy +msgid ":py:obj:`parameters `\\" +msgstr ":py:obj:`parameters `\\" -#: flwr.server.client_manager.ClientManager.all:1::1 of -msgid ":py:obj:`unregister `\\ \\(client\\)" -msgstr "" +#: ../../source/ref-api/flwr.common.GetPropertiesIns.rst:2 +#, fuzzy +msgid "GetPropertiesIns" +msgstr "GetPropertiesIns" -#: flwr.server.client_manager.ClientManager.all:1::1 -#: flwr.server.client_manager.ClientManager.unregister:1 -#: flwr.server.client_manager.SimpleClientManager.all:1::1 -#: flwr.server.client_manager.SimpleClientManager.unregister:1 of -msgid "Unregister Flower ClientProxy instance." -msgstr "" +#: ../../source/ref-api/flwr.common.GetPropertiesIns.rst:28::1 +#, fuzzy +msgid ":py:obj:`config `\\" +msgstr ":py:obj:`config `\\" -#: flwr.server.client_manager.ClientManager.all:1::1 of -msgid "" -":py:obj:`wait_for `\\ " -"\\(num\\_clients\\, timeout\\)" -msgstr "" +#: ../../source/ref-api/flwr.common.GetPropertiesRes.rst:2 +#, fuzzy +msgid "GetPropertiesRes" +msgstr "GetPropertiesRes" -#: flwr.server.client_manager.ClientManager.all:1::1 -#: flwr.server.client_manager.ClientManager.wait_for:1 -#: flwr.server.client_manager.SimpleClientManager.all:1::1 -#: flwr.server.client_manager.SimpleClientManager.wait_for:1 of -msgid "Wait until at least `num_clients` are available." -msgstr "" +#: ../../source/ref-api/flwr.common.GetPropertiesRes.rst:29::1 +#, fuzzy +msgid ":py:obj:`status `\\" +msgstr ":py:obj:`status `\\" -#: flwr.server.client_manager.ClientManager.num_available:3 -#: flwr.server.client_manager.SimpleClientManager.num_available:3 of -msgid "**num_available** -- The number of currently available clients." -msgstr "" +#: ../../source/ref-api/flwr.common.GetPropertiesRes.rst:29::1 +#, fuzzy +msgid ":py:obj:`properties `\\" +msgstr ":py:obj:`properties `\\" -#: flwr.server.client_manager.ClientManager.register:6 -#: flwr.server.client_manager.SimpleClientManager.register:6 of -msgid "" -"**success** -- Indicating if registration was successful. False if " -"ClientProxy is already registered or can not be registered for any " -"reason." -msgstr "" +#: ../../source/ref-api/flwr.common.Message.rst:2 +#, fuzzy +msgid "Message" +msgstr "服务器端" -#: flwr.server.client_manager.ClientManager.unregister:3 -#: flwr.server.client_manager.SimpleClientManager.unregister:3 of -msgid "This method is idempotent." -msgstr "" +#: flwr.common.Message.content:1::1 flwr.common.Message.metadata:1 +#: flwr.common.message.Message:3 of +#, fuzzy +msgid "A dataclass including information about the message to be executed." +msgstr "数据类型，包括要执行的信息的相关信息。" -#: ../../source/ref-api/flwr.server.Driver.rst:2 +#: flwr.common.message.Message:5 of #, fuzzy -msgid "Driver" -msgstr "服务器" +msgid "" +"Holds records either sent by another entity (e.g. sent by the server-side" +" logic to a client, or vice-versa) or that will be sent to it." +msgstr "保存由其他实体发送的记录（如由服务器端逻辑发送到客户端，反之亦然）或将发送到该实体的记录。" -#: flwr.server.driver.driver.Driver:3 of +#: flwr.common.message.Message:8 of #, fuzzy msgid "" -"The IPv4 or IPv6 address of the Driver API server. Defaults to " -"`\"[::]:9091\"`." -msgstr "服务器的 IPv4 或 IPv6 地址。默认为 `\"[::]:8080\"。" +"A dataclass that captures information about an error that took place when" +" processing another message." +msgstr "数据类，用于捕捉处理其他报文时发生的错误信息。" -#: flwr.server.app.start_server:28 flwr.server.driver.driver.Driver:6 of +#: ../../source/ref-api/flwr.common.Message.rst:35::1 +#, fuzzy msgid "" -"Tuple containing root certificate, server certificate, and private key to" -" start a secure SSL-enabled server. The tuple is expected to have three " -"bytes elements in the following order: * CA certificate. * " -"server certificate. * server private key." +":py:obj:`create_error_reply `\\ " +"\\(error\\[\\, ttl\\]\\)" msgstr "" -"包含根证书、服务器证书和私钥的元组，用于启动启用 SSL 的安全服务器。元组应按以下顺序包含三个字节元素： * CA 证书，* 服务器证书， * " -"服务器私钥。" +":py:obj:`create_error_reply `\\ " +"\\(error\\, ttl\\)" -#: flwr.server.app.start_server:28 flwr.server.driver.driver.Driver:6 of +#: ../../source/ref-api/flwr.common.Message.rst:35::1 +#: flwr.common.message.Message.create_error_reply:1 of +#, fuzzy +msgid "Construct a reply message indicating an error happened." +msgstr "构建一条回复信息，说明发生了错误。" + +#: ../../source/ref-api/flwr.common.Message.rst:35::1 +#, fuzzy msgid "" -"Tuple containing root certificate, server certificate, and private key to" -" start a secure SSL-enabled server. The tuple is expected to have three " -"bytes elements in the following order:" -msgstr "包含根证书、服务器证书和私钥的元组，用于启动启用 SSL 的安全服务器。元组应按以下顺序包含三个字节元素：" +":py:obj:`create_reply `\\ " +"\\(content\\[\\, ttl\\]\\)" +msgstr "" +":py:obj:`create_reply `\\ \\(content\\," +" ttl\\)" -#: flwr.server.app.start_server:32 flwr.server.driver.driver.Driver:10 of -msgid "CA certificate." -msgstr "CA 证书。" +#: ../../source/ref-api/flwr.common.Message.rst:35::1 +#: flwr.common.message.Message.create_reply:1 of +#, fuzzy +msgid "Create a reply to this message with specified content and TTL." +msgstr "以指定的内容和 TTL 创建对该信息的回复。" -#: flwr.server.app.start_server:33 flwr.server.driver.driver.Driver:11 of -msgid "server certificate." -msgstr "服务器证书。" +#: ../../source/ref-api/flwr.common.Message.rst:35::1 +#, fuzzy +msgid ":py:obj:`has_content `\\ \\(\\)" +msgstr ":py:obj:`has_content `\\ \\(\\)" -#: flwr.server.app.start_server:34 flwr.server.driver.driver.Driver:12 of -msgid "server private key." -msgstr "服务器私人密钥。" +#: ../../source/ref-api/flwr.common.Message.rst:35::1 +#: flwr.common.message.Message.has_content:1 of +#, fuzzy +msgid "Return True if message has content, else False." +msgstr "如果信息有内容，则返回 True，否则返回 False。" -#: flwr.server.driver.driver.Driver.close:1::1 of +#: ../../source/ref-api/flwr.common.Message.rst:35::1 #, fuzzy -msgid ":py:obj:`close `\\ \\(\\)" -msgstr "server.strategy.Strategy" +msgid ":py:obj:`has_error `\\ \\(\\)" +msgstr ":py:obj:`has_error `\\ \\(\\)" -#: flwr.server.driver.driver.Driver.close:1 -#: flwr.server.driver.driver.Driver.close:1::1 of -msgid "Disconnect from the SuperLink if connected." -msgstr "" +#: ../../source/ref-api/flwr.common.Message.rst:35::1 +#: flwr.common.message.Message.has_error:1 of +#, fuzzy +msgid "Return True if message has an error, else False." +msgstr "如果信息有错误，则返回 True，否则返回 False。" -#: flwr.server.driver.driver.Driver.close:1::1 of -msgid "" -":py:obj:`create_message `\\ " -"\\(content\\, message\\_type\\, ...\\)" -msgstr "" +#: flwr.common.Message.content:1::1 of +#, fuzzy +msgid ":py:obj:`content `\\" +msgstr ":py:obj:`content `\\" -#: flwr.server.driver.driver.Driver.close:1::1 -#: flwr.server.driver.driver.Driver.create_message:1 of -msgid "Create a new message with specified parameters." -msgstr "" +#: flwr.common.Message.content:1 flwr.common.Message.content:1::1 +#: of +#, fuzzy +msgid "The content of this message." +msgstr "评估客户端的反应。" -#: flwr.server.driver.driver.Driver.close:1::1 of -msgid ":py:obj:`get_node_ids `\\ \\(\\)" -msgstr "" +#: flwr.common.Message.content:1::1 of +#, fuzzy +msgid ":py:obj:`error `\\" +msgstr ":py:obj:`error `\\" -#: flwr.server.driver.driver.Driver.close:1::1 -#: flwr.server.driver.driver.Driver.get_node_ids:1 of -msgid "Get node IDs." -msgstr "" +#: flwr.common.Message.content:1::1 flwr.common.Message.error:1 of +#, fuzzy +msgid "Error captured by this message." +msgstr "该信息捕捉到的错误。" -#: flwr.server.driver.driver.Driver.close:1::1 of -msgid "" -":py:obj:`pull_messages `\\ " -"\\(message\\_ids\\)" -msgstr "" +#: flwr.common.Message.content:1::1 of +#, fuzzy +msgid ":py:obj:`metadata `\\" +msgstr ":py:obj:`metadata `\\" -#: flwr.server.driver.driver.Driver.close:1::1 -#: flwr.server.driver.driver.Driver.pull_messages:1 of -msgid "Pull messages based on message IDs." -msgstr "" +#: flwr.common.message.Message.create_error_reply:3 of +#, fuzzy +msgid "The error that was encountered." +msgstr "遇到的错误。" -#: flwr.server.driver.driver.Driver.close:1::1 of +#: flwr.common.message.Message.create_error_reply:5 +#: flwr.common.message.Message.create_reply:9 of +#, fuzzy msgid "" -":py:obj:`push_messages `\\ " -"\\(messages\\)" +"Time-to-live for this message in seconds. If unset, it will be set based " +"on the remaining time for the received message before it expires. This " +"follows the equation: ttl = msg.meta.ttl - (reply.meta.created_at - " +"msg.meta.created_at)" msgstr "" +"该信息的有效时间（秒）。如果未设置，则将根据收到的信息过期前的剩余时间来设置。其计算公式为：ttl = msg.meta.ttl - " +"(reply.meta.created_at - msg.meta.created_at)" -#: flwr.server.driver.driver.Driver.close:1::1 -#: flwr.server.driver.driver.Driver.push_messages:1 of -msgid "Push messages to specified node IDs." -msgstr "" - -#: flwr.server.driver.driver.Driver.close:1::1 of +#: flwr.common.message.Message.create_error_reply:5 +#: flwr.common.message.Message.create_reply:9 of #, fuzzy msgid "" -":py:obj:`send_and_receive `\\ " -"\\(messages\\, \\*\\[\\, timeout\\]\\)" -msgstr "" -"Flower 1.0: ``start_server(..., " -"config=flwr.server.ServerConfig(num_rounds=3, round_timeout=600.0), " -"...)``" +"Time-to-live for this message in seconds. If unset, it will be set based " +"on the remaining time for the received message before it expires. This " +"follows the equation:" +msgstr "该信息的有效时间（秒）。如果未设置，则将根据接收到的信息过期前的剩余时间来设置。其计算公式如下" -#: flwr.server.driver.driver.Driver.close:1::1 -#: flwr.server.driver.driver.Driver.send_and_receive:1 of -msgid "Push messages to specified node IDs and pull the reply messages." -msgstr "" +#: flwr.common.message.Message.create_error_reply:9 +#: flwr.common.message.Message.create_reply:13 of +#, fuzzy +msgid "ttl = msg.meta.ttl - (reply.meta.created_at - msg.meta.created_at)" +msgstr "ttl = msg.meta.ttl - (reply.meta.created_at - msg.meta.created_at)" -#: flwr.server.driver.driver.Driver.create_message:3 of +#: flwr.common.message.Message.create_reply:3 of +#, fuzzy msgid "" -"This method constructs a new `Message` with given content and metadata. " -"The `run_id` and `src_node_id` will be set automatically." +"The method generates a new `Message` as a reply to this message. It " +"inherits 'run_id', 'src_node_id', 'dst_node_id', and 'message_type' from " +"this message and sets 'reply_to_message' to the ID of this message." msgstr "" +"该方法会生成一条新的 \"信息\"，作为对该信息的回复。该方法继承了该消息的 " +"\"run_id\"、\"src_node_id\"、\"dst_node_id \"和 \"message_type\"，并将 " +"\"reply_to_message \"设置为该消息的 ID。" -#: flwr.server.driver.driver.Driver.create_message:6 of -msgid "" -"The content for the new message. This holds records that are to be sent " -"to the destination node." -msgstr "" +#: flwr.common.message.Message.create_reply:7 of +#, fuzzy +msgid "The content for the reply message." +msgstr "回复信息的内容。" -#: flwr.server.driver.driver.Driver.create_message:9 of -msgid "" -"The type of the message, defining the action to be executed on the " -"receiving end." -msgstr "" +#: flwr.common.message.Message.create_reply:16 of +#, fuzzy +msgid "A new `Message` instance representing the reply." +msgstr "代表回复的新的 `Message` 实例。" -#: flwr.server.driver.driver.Driver.create_message:12 of -msgid "The ID of the destination node to which the message is being sent." -msgstr "" +#: ../../source/ref-api/flwr.common.MessageType.rst:2 +#, fuzzy +msgid "MessageType" +msgstr "返回类型" -#: flwr.server.driver.driver.Driver.create_message:14 of -msgid "" -"The ID of the group to which this message is associated. In some " -"settings, this is used as the FL round." -msgstr "" +#: ../../source/ref-api/flwr.common.MessageType.rst:30::1 +#, fuzzy +msgid ":py:obj:`EVALUATE `\\" +msgstr ":py:obj:`EVALUATE `\\" -#: flwr.server.driver.driver.Driver.create_message:17 of -msgid "" -"Time-to-live for the round trip of this message, i.e., the time from " -"sending this message to receiving a reply. It specifies the duration for " -"which the message and its potential reply are considered valid." -msgstr "" +#: ../../source/ref-api/flwr.common.MessageType.rst:30::1 +#, fuzzy +msgid ":py:obj:`QUERY `\\" +msgstr ":py:obj:`QUERY `\\" -#: flwr.server.driver.driver.Driver.create_message:22 of -msgid "" -"**message** -- A new `Message` instance with the specified content and " -"metadata." -msgstr "" +#: ../../source/ref-api/flwr.common.MessageType.rst:30::1 +#, fuzzy +msgid ":py:obj:`TRAIN `\\" +msgstr ":py:obj:`TRAIN `\\" -#: flwr.server.driver.driver.Driver.pull_messages:3 of -msgid "" -"This method is used to collect messages from the SuperLink that " -"correspond to a set of given message IDs." -msgstr "" +#: ../../source/ref-api/flwr.common.MessageTypeLegacy.rst:2 +#, fuzzy +msgid "MessageTypeLegacy" +msgstr "MessageTypeLegacy" -#: flwr.server.driver.driver.Driver.pull_messages:6 of -msgid "An iterable of message IDs for which reply messages are to be retrieved." -msgstr "" +#: ../../source/ref-api/flwr.common.MessageTypeLegacy.rst:29::1 +#, fuzzy +msgid ":py:obj:`GET_PARAMETERS `\\" +msgstr ":py:obj:`GET_PARAMETERS `\\" -#: flwr.server.driver.driver.Driver.pull_messages:9 of -msgid "**messages** -- An iterable of messages received." -msgstr "" +#: ../../source/ref-api/flwr.common.MessageTypeLegacy.rst:29::1 +#, fuzzy +msgid ":py:obj:`GET_PROPERTIES `\\" +msgstr ":py:obj:`GET_PROPERTIES `\\" -#: flwr.server.driver.driver.Driver.push_messages:3 of -msgid "" -"This method takes an iterable of messages and sends each message to the " -"node specified in `dst_node_id`." -msgstr "" +#: flwr.common.Metadata.created_at:1::1 +#: flwr.common.Metadata.run_id:1 flwr.common.message.Metadata:3 of +#, fuzzy +msgid "An identifier for the current run." +msgstr "当前运行的标识符。" -#: flwr.server.driver.driver.Driver.push_messages:6 -#: flwr.server.driver.driver.Driver.send_and_receive:7 of -msgid "An iterable of messages to be sent." -msgstr "" +#: flwr.common.Metadata.created_at:1::1 +#: flwr.common.Metadata.message_id:1 flwr.common.message.Metadata:5 of +#, fuzzy +msgid "An identifier for the current message." +msgstr "当前信息的标识符。" -#: flwr.server.driver.driver.Driver.push_messages:9 of -msgid "" -"**message_ids** -- An iterable of IDs for the messages that were sent, " -"which can be used to pull replies." -msgstr "" +#: flwr.common.Metadata.created_at:1::1 +#: flwr.common.Metadata.src_node_id:1 flwr.common.message.Metadata:7 of +#, fuzzy +msgid "An identifier for the node sending this message." +msgstr "发送此信息的节点的标识符。" -#: flwr.server.driver.driver.Driver.send_and_receive:3 of -msgid "" -"This method sends a list of messages to their destination node IDs and " -"then waits for the replies. It continues to pull replies until either all" -" replies are received or the specified timeout duration is exceeded." -msgstr "" +#: flwr.common.Metadata.created_at:1::1 +#: flwr.common.Metadata.dst_node_id:1 flwr.common.message.Metadata:9 of +#, fuzzy +msgid "An identifier for the node receiving this message." +msgstr "接收此信息的节点的标识符。" -#: flwr.server.driver.driver.Driver.send_and_receive:9 of +#: flwr.common.Metadata.created_at:1::1 +#: flwr.common.Metadata.reply_to_message:1 flwr.common.message.Metadata:11 of +#, fuzzy +msgid "An identifier for the message this message replies to." +msgstr "该信息回复的信息的标识符。" + +#: flwr.common.message.Metadata:13 of +#, fuzzy msgid "" -"The timeout duration in seconds. If specified, the method will wait for " -"replies for this duration. If `None`, there is no time limit and the " -"method will wait until replies for all messages are received." -msgstr "" +"An identifier for grouping messages. In some settings, this is used as " +"the FL round." +msgstr "用于分组报文的标识符。在某些设置中，它被用作 FL 轮。" -#: flwr.server.driver.driver.Driver.send_and_receive:14 of -msgid "**replies** -- An iterable of reply messages received from the SuperLink." -msgstr "" +#: flwr.common.message.Metadata:16 of +#, fuzzy +msgid "Time-to-live for this message in seconds." +msgstr "该信息的有效时间。" -#: flwr.server.driver.driver.Driver.send_and_receive:18 -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:53 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:60 -#: of +#: flwr.common.Metadata.created_at:1::1 +#: flwr.common.Metadata.message_type:1 flwr.common.message.Metadata:18 of #, fuzzy -msgid "Notes" -msgstr "无" +msgid "A string that encodes the action to be executed on the receiving end." +msgstr "编码接收端要执行的操作的字符串。" -#: flwr.server.driver.driver.Driver.send_and_receive:19 of +#: flwr.common.message.Metadata:21 of +#, fuzzy msgid "" -"This method uses `push_messages` to send the messages and `pull_messages`" -" to collect the replies. If `timeout` is set, the method may not return " -"replies for all sent messages. A message remains valid until its TTL, " -"which is not affected by `timeout`." -msgstr "" +"An identifier that can be used when loading a particular data partition " +"for a ClientApp. Making use of this identifier is more relevant when " +"conducting simulations." +msgstr "为 ClientApp 加载特定数据分区时可使用的标识符。在进行模拟时，使用该标识符更有意义。" -#: ../../source/ref-api/flwr.server.History.rst:2 -msgid "History" -msgstr "" +#: flwr.common.Metadata.created_at:1::1 of +#, fuzzy +msgid ":py:obj:`created_at `\\" +msgstr ":py:obj:`ttl `\\" -#: flwr.server.history.History.add_loss_centralized:1::1 of -msgid "" -":py:obj:`add_loss_centralized " -"`\\ \\(server\\_round\\, " -"loss\\)" -msgstr "" +#: flwr.common.Metadata.created_at:1 +#: flwr.common.Metadata.created_at:1::1 of +#, fuzzy +msgid "Unix timestamp when the message was created." +msgstr "创建信息时的 Unix 时间戳。" -#: flwr.server.history.History.add_loss_centralized:1 -#: flwr.server.history.History.add_loss_centralized:1::1 of +#: flwr.common.Metadata.created_at:1::1 of #, fuzzy -msgid "Add one loss entry (from centralized evaluation)." -msgstr "集中评估" +msgid ":py:obj:`dst_node_id `\\" +msgstr ":py:obj:`dst_node_id `\\" -#: flwr.server.history.History.add_loss_centralized:1::1 of -msgid "" -":py:obj:`add_loss_distributed " -"`\\ \\(server\\_round\\, " -"loss\\)" -msgstr "" +#: flwr.common.Metadata.created_at:1::1 of +#, fuzzy +msgid ":py:obj:`group_id `\\" +msgstr ":py:obj:`group_id `\\" -#: flwr.server.history.History.add_loss_centralized:1::1 -#: flwr.server.history.History.add_loss_distributed:1 of -msgid "Add one loss entry (from distributed evaluation)." -msgstr "" +#: flwr.common.Metadata.created_at:1::1 +#: flwr.common.Metadata.group_id:1 of +#, fuzzy +msgid "An identifier for grouping messages." +msgstr "用于分组信息的标识符。" -#: flwr.server.history.History.add_loss_centralized:1::1 of -msgid "" -":py:obj:`add_metrics_centralized " -"`\\ \\(server\\_round\\, " -"metrics\\)" -msgstr "" +#: flwr.common.Metadata.created_at:1::1 of +#, fuzzy +msgid ":py:obj:`message_id `\\" +msgstr ":py:obj:`message_id `\\" -#: flwr.server.history.History.add_loss_centralized:1::1 -#: flwr.server.history.History.add_metrics_centralized:1 of +#: flwr.common.Metadata.created_at:1::1 of #, fuzzy -msgid "Add metrics entries (from centralized evaluation)." -msgstr "集中评估" +msgid ":py:obj:`message_type `\\" +msgstr ":py:obj:`message_type `\\" -#: flwr.server.history.History.add_loss_centralized:1::1 of -msgid "" -":py:obj:`add_metrics_distributed " -"`\\ \\(server\\_round\\, " -"metrics\\)" -msgstr "" +#: flwr.common.Metadata.created_at:1::1 of +#, fuzzy +msgid ":py:obj:`partition_id `\\" +msgstr ":py:obj:`partition_id `\\" -#: flwr.server.history.History.add_loss_centralized:1::1 -#: flwr.server.history.History.add_metrics_distributed:1 of +#: flwr.common.Metadata.created_at:1::1 +#: flwr.common.Metadata.partition_id:1 of #, fuzzy -msgid "Add metrics entries (from distributed evaluation)." -msgstr "定制的集中/分布式评估" +msgid "An identifier telling which data partition a ClientApp should use." +msgstr "告诉 ClientApp 应使用哪个数据分区的标识符。" -#: flwr.server.history.History.add_loss_centralized:1::1 of -msgid "" -":py:obj:`add_metrics_distributed_fit " -"`\\ \\(server\\_round\\," -" ...\\)" -msgstr "" +#: flwr.common.Metadata.created_at:1::1 of +#, fuzzy +msgid ":py:obj:`reply_to_message `\\" +msgstr ":py:obj:`reply_to_message `\\" -#: flwr.server.history.History.add_loss_centralized:1::1 -#: flwr.server.history.History.add_metrics_distributed_fit:1 of -msgid "Add metrics entries (from distributed fit)." -msgstr "" +#: flwr.common.Metadata.created_at:1::1 of +#, fuzzy +msgid ":py:obj:`run_id `\\" +msgstr ":py:obj:`run_id `\\" -#: ../../source/ref-api/flwr.server.LegacyContext.rst:2 -msgid "LegacyContext" -msgstr "" +#: flwr.common.Metadata.created_at:1::1 of +#, fuzzy +msgid ":py:obj:`src_node_id `\\" +msgstr ":py:obj:`src_node_id `\\" -#: flwr.server.compat.legacy_context.LegacyContext:1 of -msgid "Bases: :py:class:`~flwr.common.context.Context`" -msgstr "" +#: flwr.common.Metadata.created_at:1::1 of +#, fuzzy +msgid ":py:obj:`ttl `\\" +msgstr ":py:obj:`ttl `\\" -#: ../../source/ref-api/flwr.server.LegacyContext.rst:32::1 +#: flwr.common.Metadata.created_at:1::1 flwr.common.Metadata.ttl:1 +#: of #, fuzzy -msgid ":py:obj:`config `\\" -msgstr "server.strategy.Strategy" +msgid "Time-to-live for this message." +msgstr "该信息的有效时间。" -#: ../../source/ref-api/flwr.server.LegacyContext.rst:32::1 +#: ../../source/ref-api/flwr.common.MetricsRecord.rst:2 #, fuzzy -msgid ":py:obj:`strategy `\\" -msgstr "server.strategy.Strategy" +msgid "MetricsRecord" +msgstr "MetricsRecord" -#: ../../source/ref-api/flwr.server.LegacyContext.rst:32::1 -msgid ":py:obj:`client_manager `\\" +#: flwr.common.record.metricsrecord.MetricsRecord:1 of +#, fuzzy +msgid "" +"Bases: :py:class:`~flwr.common.record.typeddict.TypedDict`\\ " +"[:py:class:`str`, :py:class:`int` | :py:class:`float` | " +":py:class:`~typing.List`\\ [:py:class:`int`] | :py:class:`~typing.List`\\" +" [:py:class:`float`]]" msgstr "" +"Bases: :py:class:`~flwr.common.record.typeddict.TypedDict`\\ " +"[:py:class:`str`, :py:obj:`~typing.Union`\\ [:py:class:`int`, " +":py:class:`float`, :py:class:`~typing.List`\\ [:py:class:`int`], " +":py:class:`~typing.List`\\ [:py:class:`float`]]]" -#: ../../source/ref-api/flwr.server.LegacyContext.rst:32::1 +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of #, fuzzy -msgid ":py:obj:`history `\\" -msgstr "server.strategy.Strategy" +msgid ":py:obj:`clear `\\ \\(\\)" +msgstr ":py:obj:`clear `\\ \\(\\)" -#: ../../source/ref-api/flwr.server.LegacyContext.rst:32::1 +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of #, fuzzy -msgid ":py:obj:`state `\\" -msgstr "server.strategy.Strategy" +msgid ":py:obj:`count_bytes `\\ \\(\\)" +msgstr ":py:obj:`count_bytes `\\ \\(\\)" -#: flwr.server.server.Server.client_manager:1::1 of -msgid ":py:obj:`client_manager `\\ \\(\\)" -msgstr "" +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +#, fuzzy +msgid ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" +msgstr ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" -#: flwr.server.server.Server.client_manager:1 -#: flwr.server.server.Server.client_manager:1::1 of +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of #, fuzzy -msgid "Return ClientManager." -msgstr "返回客户端（本身）。" +msgid ":py:obj:`items `\\ \\(\\)" +msgstr ":py:obj:`items `\\ \\(\\)" -#: flwr.server.server.Server.client_manager:1::1 of -msgid "" -":py:obj:`disconnect_all_clients " -"`\\ \\(timeout\\)" -msgstr "" +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +#, fuzzy +msgid ":py:obj:`keys `\\ \\(\\)" +msgstr ":py:obj:`keys `\\ \\(\\)" -#: flwr.server.server.Server.client_manager:1::1 -#: flwr.server.server.Server.disconnect_all_clients:1 of -msgid "Send shutdown signal to all clients." -msgstr "" +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +#, fuzzy +msgid ":py:obj:`pop `\\ \\(k\\[\\,d\\]\\)" +msgstr ":py:obj:`pop `\\ \\(k\\[\\,d\\]\\)" -#: flwr.server.server.Server.client_manager:1::1 of +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +#, fuzzy msgid "" -":py:obj:`evaluate_round `\\ " -"\\(server\\_round\\, timeout\\)" +":py:obj:`update `\\ \\(\\[E\\, " +"\\]\\*\\*F\\)" msgstr "" +":py:obj:`update `\\ \\(\\[E\\, " +"\\]\\*\\*F\\)" -#: flwr.server.server.Server.client_manager:1::1 -#: flwr.server.server.Server.evaluate_round:1 of +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of #, fuzzy -msgid "Validate current global model on a number of clients." -msgstr "当前（全局）模型参数。" +msgid ":py:obj:`values `\\ \\(\\)" +msgstr ":py:obj:`values `\\ \\(\\)" -#: flwr.server.server.Server.client_manager:1::1 of -msgid ":py:obj:`fit `\\ \\(num\\_rounds\\, timeout\\)" -msgstr "" +#: ../../source/ref-api/flwr.common.NDArray.rst:2 +#, fuzzy +msgid "NDArray" +msgstr "NDArray" -#: flwr.server.server.Server.client_manager:1::1 -#: flwr.server.server.Server.fit:1 of +#: ../../source/ref-api/flwr.common.Parameters.rst:29::1 #, fuzzy -msgid "Run federated averaging for a number of rounds." -msgstr "联邦平均动量策略。" +msgid ":py:obj:`tensors `\\" +msgstr ":py:obj:`tensors `\\" -#: flwr.server.server.Server.client_manager:1::1 of -msgid "" -":py:obj:`fit_round `\\ \\(server\\_round\\," -" timeout\\)" -msgstr "" +#: ../../source/ref-api/flwr.common.Parameters.rst:29::1 +#, fuzzy +msgid ":py:obj:`tensor_type `\\" +msgstr ":py:obj:`tensor_type `\\" -#: flwr.server.server.Server.client_manager:1::1 -#: flwr.server.server.Server.fit_round:1 of +#: ../../source/ref-api/flwr.common.ParametersRecord.rst:2 #, fuzzy -msgid "Perform a single round of federated averaging." -msgstr "本轮联邦学习。" +msgid "ParametersRecord" +msgstr "参数" -#: flwr.server.server.Server.client_manager:1::1 of +#: flwr.common.record.parametersrecord.ParametersRecord:1 of +#, fuzzy msgid "" -":py:obj:`set_max_workers `\\ " -"\\(max\\_workers\\)" -msgstr "" - -#: flwr.server.server.Server.client_manager:1::1 -#: flwr.server.server.Server.set_max_workers:1 of -msgid "Set the max_workers used by ThreadPoolExecutor." +"Bases: :py:class:`~flwr.common.record.typeddict.TypedDict`\\ " +"[:py:class:`str`, :py:class:`~flwr.common.record.parametersrecord.Array`]" msgstr "" +"Bases: :py:class:`~flwr.common.record.typeddict.TypedDict`\\ " +"[:py:class:`str`, :py:class:`~flwr.common.record.parametersrecord.Array`]" -#: flwr.server.server.Server.client_manager:1::1 of -msgid ":py:obj:`set_strategy `\\ \\(strategy\\)" +#: flwr.common.record.parametersrecord.ParametersRecord:3 of +#, fuzzy +msgid "" +"A dataclass storing named Arrays in order. This means that it holds " +"entries as an OrderedDict[str, Array]. ParametersRecord objects can be " +"viewed as an equivalent to PyTorch's state_dict, but holding serialised " +"tensors instead." msgstr "" +"按顺序存储命名数组的数据类。这意味着它以 OrderedDict[str, Array] 的形式保存条目。ParametersRecord " +"对象相当于 PyTorch 的 state_dict，但它保存的是序列化的张量。" -#: flwr.server.server.Server.client_manager:1::1 -#: flwr.server.server.Server.set_strategy:1 of +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of #, fuzzy -msgid "Replace server strategy." -msgstr "server.strategy" +msgid ":py:obj:`clear `\\ \\(\\)" +msgstr ":py:obj:`clear `\\ \\(\\)" -#: ../../source/ref-api/flwr.server.ServerApp.rst:2 +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of #, fuzzy -msgid "ServerApp" -msgstr "服务器" +msgid ":py:obj:`count_bytes `\\ \\(\\)" +msgstr ":py:obj:`count_bytes `\\ \\(\\)" -#: flwr.server.server_app.ServerApp:5 of +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of #, fuzzy -msgid "Use the `ServerApp` with an existing `Strategy`:" -msgstr "使用现有策略" +msgid ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" +msgstr ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" -#: flwr.server.server_app.ServerApp:15 of -msgid "Use the `ServerApp` with a custom main function:" -msgstr "" +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +#, fuzzy +msgid ":py:obj:`items `\\ \\(\\)" +msgstr ":py:obj:`items `\\ \\(\\)" -#: flwr.server.server_app.ServerApp.main:1::1 of +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of #, fuzzy -msgid ":py:obj:`main `\\ \\(\\)" -msgstr "server.strategy.Strategy" +msgid ":py:obj:`keys `\\ \\(\\)" +msgstr ":py:obj:`keys `\\ \\(\\)" -#: flwr.server.server_app.ServerApp.main:1 -#: flwr.server.server_app.ServerApp.main:1::1 of -msgid "Return a decorator that registers the main fn with the server app." +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +#, fuzzy +msgid ":py:obj:`pop `\\ \\(k\\[\\,d\\]\\)" +msgstr ":py:obj:`pop `\\ \\(k\\[\\,d\\]\\)" + +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of +#, fuzzy +msgid "" +":py:obj:`update `\\ \\(\\[E\\, " +"\\]\\*\\*F\\)" msgstr "" +":py:obj:`update `\\ \\(\\[E\\, " +"\\]\\*\\*F\\)" -#: ../../source/ref-api/flwr.server.ServerConfig.rst:2 +#: flwr.common.record.typeddict.TypedDict.clear:1::1 of #, fuzzy -msgid "ServerConfig" -msgstr "服务器" +msgid ":py:obj:`values `\\ \\(\\)" +msgstr ":py:obj:`values `\\ \\(\\)" -#: flwr.server.server_config.ServerConfig:3 of +#: flwr.common.record.parametersrecord.ParametersRecord.count_bytes:3 of +#, fuzzy msgid "" -"All attributes have default values which allows users to configure just " -"the ones they care about." -msgstr "" +"Note that a small amount of Bytes might also be included in this counting" +" that correspond to metadata of the serialized object (e.g. of NumPy " +"array) needed for deseralization." +msgstr "请注意，该计数中还可能包含少量字节，这些字节与序列化对象（如 NumPy 数组）的元数据相对应，需要进行去eralization。" -#: ../../source/ref-api/flwr.server.ServerConfig.rst:29::1 -msgid ":py:obj:`num_rounds `\\" -msgstr "" +#: ../../source/ref-api/flwr.common.ReconnectIns.rst:2 +#, fuzzy +msgid "ReconnectIns" +msgstr "启用 SSL 连接" -#: ../../source/ref-api/flwr.server.ServerConfig.rst:29::1 -msgid ":py:obj:`round_timeout `\\" -msgstr "" +#: ../../source/ref-api/flwr.common.ReconnectIns.rst:28::1 +#, fuzzy +msgid ":py:obj:`seconds `\\" +msgstr ":py:obj:`seconds `\\" -#: ../../source/ref-api/flwr.server.SimpleClientManager.rst:2 -msgid "SimpleClientManager" -msgstr "" +#: ../../source/ref-api/flwr.common.RecordSet.rst:2 +#, fuzzy +msgid "RecordSet" +msgstr "RecordSet" -#: flwr.server.client_manager.SimpleClientManager:1 of -msgid "Bases: :py:class:`~flwr.server.client_manager.ClientManager`" -msgstr "" +#: flwr.common.RecordSet.configs_records:1::1 of +#, fuzzy +msgid ":py:obj:`configs_records `\\" +msgstr ":py:obj:`configs_records `\\" -#: flwr.server.client_manager.SimpleClientManager.all:1::1 of -msgid ":py:obj:`all `\\ \\(\\)" -msgstr "" +#: flwr.common.RecordSet.configs_records:1 +#: flwr.common.RecordSet.configs_records:1::1 of +#, fuzzy +msgid "Dictionary holding ConfigsRecord instances." +msgstr "包含 ConfigsRecord 实例的字典。" -#: flwr.server.client_manager.SimpleClientManager.all:1::1 of -msgid "" -":py:obj:`num_available `\\" -" \\(\\)" -msgstr "" +#: flwr.common.RecordSet.configs_records:1::1 of +#, fuzzy +msgid ":py:obj:`metrics_records `\\" +msgstr ":py:obj:`metrics_records `\\" -#: flwr.server.client_manager.SimpleClientManager.all:1::1 of -msgid "" -":py:obj:`register `\\ " -"\\(client\\)" -msgstr "" +#: flwr.common.RecordSet.configs_records:1::1 +#: flwr.common.RecordSet.metrics_records:1 of +#, fuzzy +msgid "Dictionary holding MetricsRecord instances." +msgstr "保存 MetricsRecord 实例的字典。" -#: flwr.server.client_manager.SimpleClientManager.all:1::1 of -msgid "" -":py:obj:`sample `\\ " -"\\(num\\_clients\\[\\, min\\_num\\_clients\\, criterion\\]\\)" -msgstr "" +#: flwr.common.RecordSet.configs_records:1::1 of +#, fuzzy +msgid ":py:obj:`parameters_records `\\" +msgstr ":py:obj:`parameters_records `\\" -#: flwr.server.client_manager.SimpleClientManager.all:1::1 of -msgid "" -":py:obj:`unregister `\\ " -"\\(client\\)" -msgstr "" +#: flwr.common.RecordSet.configs_records:1::1 +#: flwr.common.RecordSet.parameters_records:1 of +#, fuzzy +msgid "Dictionary holding ParametersRecord instances." +msgstr "存放 ParametersRecord 实例的字典。" -#: flwr.server.client_manager.SimpleClientManager.all:1::1 of -msgid "" -":py:obj:`wait_for `\\ " -"\\(num\\_clients\\[\\, timeout\\]\\)" -msgstr "" +#: ../../source/ref-api/flwr.common.ServerMessage.rst:2 +#, fuzzy +msgid "ServerMessage" +msgstr "服务器端" -#: flwr.server.client_manager.SimpleClientManager.wait_for:3 of -msgid "" -"Blocks until the requested number of clients is available or until a " -"timeout is reached. Current timeout default: 1 day." -msgstr "" +#: ../../source/ref-api/flwr.common.ServerMessage.rst:31::1 +#, fuzzy +msgid ":py:obj:`evaluate_ins `\\" +msgstr ":py:obj:`evaluate_ins `\\" -#: flwr.server.client_manager.SimpleClientManager.wait_for:6 of -msgid "The number of clients to wait for." -msgstr "" +#: ../../source/ref-api/flwr.common.ServerMessage.rst:31::1 +#, fuzzy +msgid ":py:obj:`fit_ins `\\" +msgstr ":py:obj:`fit_ins `\\" -#: flwr.server.client_manager.SimpleClientManager.wait_for:8 of -msgid "The time in seconds to wait for, defaults to 86400 (24h)." +#: ../../source/ref-api/flwr.common.ServerMessage.rst:31::1 +#, fuzzy +msgid "" +":py:obj:`get_parameters_ins " +"`\\" msgstr "" +":py:obj:`get_parameters_ins " +"`\\" -#: flwr.server.client_manager.SimpleClientManager.wait_for:11 of -msgid "**success**" +#: ../../source/ref-api/flwr.common.ServerMessage.rst:31::1 +#, fuzzy +msgid "" +":py:obj:`get_properties_ins " +"`\\" msgstr "" +":py:obj:`get_properties_ins " +"`\\" -#: ../../source/ref-api/flwr.server.run_driver_api.rst:2 +#: ../../source/ref-api/flwr.common.Status.rst:2 #, fuzzy -msgid "run\\_driver\\_api" -msgstr "flower-driver-api" +msgid "Status" +msgstr "客户端状态。" -#: ../../source/ref-api/flwr.server.run_fleet_api.rst:2 -msgid "run\\_fleet\\_api" -msgstr "" +#: ../../source/ref-api/flwr.common.Status.rst:29::1 +#, fuzzy +msgid ":py:obj:`code `\\" +msgstr ":py:obj:`code `\\" -#: ../../source/ref-api/flwr.server.run_server_app.rst:2 -msgid "run\\_server\\_app" -msgstr "" +#: ../../source/ref-api/flwr.common.Status.rst:29::1 +#, fuzzy +msgid ":py:obj:`message `\\" +msgstr ":py:obj:`message `\\" -#: ../../source/ref-api/flwr.server.run_superlink.rst:2 +#: ../../source/ref-api/flwr.common.array_from_numpy.rst:2 #, fuzzy -msgid "run\\_superlink" -msgstr "flower-superlink" +msgid "array\\_from\\_numpy" +msgstr "array\\_from\\_numpy" -#: ../../source/ref-api/flwr.server.start_driver.rst:2 +#: ../../source/ref-api/flwr.common.bytes_to_ndarray.rst:2 #, fuzzy -msgid "start\\_driver" -msgstr "启动客户端" +msgid "bytes\\_to\\_ndarray" +msgstr "bytes\\_to\\_ndarray" -#: flwr.server.compat.app.start_driver:3 of +#: ../../source/ref-api/flwr.common.configure.rst:2 #, fuzzy -msgid "" -"The IPv4 or IPv6 address of the Driver API server. Defaults to " -"`\"[::]:8080\"`." -msgstr "服务器的 IPv4 或 IPv6 地址。默认为 `\"[::]:8080\"。" +msgid "configure" +msgstr "配置日志记录" -#: flwr.server.compat.app.start_driver:6 of +#: ../../source/ref-api/flwr.common.event.rst:2 #, fuzzy -msgid "" -"A server implementation, either `flwr.server.Server` or a subclass " -"thereof. If no instance is provided, then `start_driver` will create one." -msgstr "服务器实现，可以是 `flwr.server.Server` 或其子类。如果没有提供实例，`start_server` 将创建一个。" +msgid "event" +msgstr "事件" -#: flwr.server.app.start_server:9 flwr.server.compat.app.start_driver:10 -#: flwr.simulation.app.start_simulation:28 of -msgid "" -"Currently supported values are `num_rounds` (int, default: 1) and " -"`round_timeout` in seconds (float, default: None)." -msgstr "目前支持的值有：`num_rounds`（int，默认值：1）和以秒为单位的`round_timeout`（float，默认值：无）。" +#: ../../source/ref-api/flwr.common.log.rst:2 +#, fuzzy +msgid "log" +msgstr "登录" -#: flwr.server.app.start_server:12 flwr.server.compat.app.start_driver:13 of +#: logging.Logger.log:3 of msgid "" -"An implementation of the abstract base class " -"`flwr.server.strategy.Strategy`. If no strategy is provided, then " -"`start_server` will use `flwr.server.strategy.FedAvg`." -msgstr "" -"抽象基类 `flwr.server.strategy.Strategy` 的实现。如果没有提供策略，`start_server` 将使用 " -"`flwr.server.strategy.FedAvg`。" +"To pass exception information, use the keyword argument exc_info with a " +"true value, e.g." +msgstr "要传递异常信息，请使用带 true 值的关键字参数 exc_info，例如。" + +#: logging.Logger.log:6 of +#, python-format +msgid "logger.log(level, \"We have a %s\", \"mysterious problem\", exc_info=1)" +msgstr "logger.log(level, \"We have a %s\", \"mysterious problem\", exc_info=1)" -#: flwr.server.compat.app.start_driver:17 of +#: ../../source/ref-api/flwr.common.ndarray_to_bytes.rst:2 #, fuzzy -msgid "" -"An implementation of the class `flwr.server.ClientManager`. If no " -"implementation is provided, then `start_driver` will use " -"`flwr.server.SimpleClientManager`." -msgstr "" -"抽象基类 `flwr.server.ClientManager` 的实现。如果没有提供实现，`start_server` 将使用 " -"`flwr.server.client_manager.SimpleClientManager`。" +msgid "ndarray\\_to\\_bytes" +msgstr "ndarray\\_to\\_bytes" -#: flwr.server.compat.app.start_driver:25 of -msgid "The Driver object to use." -msgstr "" +#: ../../source/ref-api/flwr.common.ndarrays_to_parameters.rst:2 +#, fuzzy +msgid "ndarrays\\_to\\_parameters" +msgstr "ndarrays\\_to\\_parameters" -#: flwr.server.app.start_server:37 flwr.server.compat.app.start_driver:28 of -msgid "**hist** -- Object containing training and evaluation metrics." -msgstr "**hist** -- 包含训练和评估指标的对象。" +#: ../../source/ref-api/flwr.common.now.rst:2 +#, fuzzy +msgid "now" +msgstr "现在" -#: flwr.server.compat.app.start_driver:33 of +#: ../../source/ref-api/flwr.common.parameters_to_ndarrays.rst:2 #, fuzzy -msgid "Starting a driver that connects to an insecure server:" -msgstr "启动不安全的服务器：" +msgid "parameters\\_to\\_ndarrays" +msgstr "parameters\\_to\\_ndarrays" + +#: ../../source/ref-api/flwr.server.rst:2 +msgid "server" +msgstr "服务器" -#: flwr.server.compat.app.start_driver:37 of +#: ../../source/ref-api/flwr.server.rst:24::1 #, fuzzy -msgid "Starting a driver that connects to an SSL-enabled server:" -msgstr "启动支持 SSL 的服务器：" +msgid ":py:obj:`run_server_app `\\ \\(\\)" +msgstr ":py:obj:`run_server_app `\\ \\(\\)" -#: ../../source/ref-api/flwr.server.start_server.rst:2 +#: ../../source/ref-api/flwr.server.rst:24::1 +#: flwr.server.run_serverapp.run_server_app:1 of #, fuzzy -msgid "start\\_server" -msgstr "server.start_server" +msgid "Run Flower server app." +msgstr "Flower 服务器。" -#: flwr.server.app.start_server:3 of -msgid "The IPv4 or IPv6 address of the server. Defaults to `\"[::]:8080\"`." -msgstr "服务器的 IPv4 或 IPv6 地址。默认为 `\"[::]:8080\"。" +#: ../../source/ref-api/flwr.server.rst:24::1 +#, fuzzy +msgid ":py:obj:`run_superlink `\\ \\(\\)" +msgstr ":py:obj:`run_superlink `\\ \\(\\)" -#: flwr.server.app.start_server:5 of -msgid "" -"A server implementation, either `flwr.server.Server` or a subclass " -"thereof. If no instance is provided, then `start_server` will create one." -msgstr "服务器实现，可以是 `flwr.server.Server` 或其子类。如果没有提供实例，`start_server` 将创建一个。" +#: ../../source/ref-api/flwr.server.rst:24::1 +#: flwr.server.app.run_superlink:1 of +#, fuzzy +msgid "Run Flower SuperLink (Driver API and Fleet API)." +msgstr "运行 Flower 服务器（Driver API 和 Fleet API）。" -#: flwr.server.app.start_server:16 of +#: ../../source/ref-api/flwr.server.rst:24::1 +#, fuzzy msgid "" -"An implementation of the abstract base class `flwr.server.ClientManager`." -" If no implementation is provided, then `start_server` will use " -"`flwr.server.client_manager.SimpleClientManager`." -msgstr "" -"抽象基类 `flwr.server.ClientManager` 的实现。如果没有提供实现，`start_server` 将使用 " -"`flwr.server.client_manager.SimpleClientManager`。" - -#: flwr.server.app.start_server:21 of -msgid "" -"The maximum length of gRPC messages that can be exchanged with the Flower" -" clients. The default should be sufficient for most models. Users who " -"train very large models might need to increase this value. Note that the " -"Flower clients need to be started with the same value (see " -"`flwr.client.start_client`), otherwise clients will not know about the " -"increased limit and block larger messages." +":py:obj:`start_server `\\ \\(\\*\\[\\, " +"server\\_address\\, server\\, ...\\]\\)" msgstr "" -"可与 Flower 客户端交换的 gRPC 消息的最大长度：默认值对大多数模型都足够了。训练超大模型的用户可能需要增加该值。请注意，Flower " -"客户端需要以相同的值启动（请参阅 `flwr.client.start_client`），否则客户端将不知道已增加的限制并阻止更大的消息。" +":py:obj:`start_server `\\ \\(\\*\\[\\, " +"server\\_address\\, server\\, ...\\]\\)" -#: flwr.server.app.start_server:42 of -msgid "Starting an insecure server:" -msgstr "启动不安全的服务器：" +#: ../../source/ref-api/flwr.server.rst:24::1 +#: flwr.server.app.start_server:1 of +msgid "Start a Flower server using the gRPC transport layer." +msgstr "使用 gRPC 传输层启动 Flower 服务器。" -#: flwr.server.app.start_server:46 of -msgid "Starting an SSL-enabled server:" -msgstr "启动支持 SSL 的服务器：" +#: ../../source/ref-api/flwr.server.rst:38::1 +#, fuzzy +msgid ":py:obj:`ClientManager `\\ \\(\\)" +msgstr ":py:obj:`ClientManager `\\ \\(\\)" -#: ../../source/ref-api/flwr.server.strategy.rst:2 +#: ../../source/ref-api/flwr.server.rst:38::1 +#: flwr.server.client_manager.ClientManager:1 of #, fuzzy -msgid "strategy" -msgstr "Krum 策略。" +msgid "Abstract base class for managing Flower clients." +msgstr "Flower 客户端的抽象基类。" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -msgid "" -":py:obj:`Bulyan `\\ \\(\\*\\, " -"fraction\\_fit\\, fraction\\_evaluate\\, ...\\)" -msgstr "" +#: ../../source/ref-api/flwr.server.rst:38::1 +#, fuzzy +msgid ":py:obj:`Driver `\\ \\(\\)" +msgstr ":py:obj:`run_driver_api `\\ \\(\\)" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.bulyan.Bulyan:1 of -msgid "Bulyan strategy." -msgstr "Bulyan 策略。" +#: ../../source/ref-api/flwr.server.rst:38::1 +#: flwr.server.driver.driver.Driver:1 of +#, fuzzy +msgid "Abstract base Driver class for the Driver API." +msgstr "Flower 客户端的抽象基类。" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -msgid "" -":py:obj:`DPFedAvgAdaptive `\\ " -"\\(strategy\\, num\\_sampled\\_clients\\)" -msgstr "" +#: ../../source/ref-api/flwr.server.rst:38::1 +#, fuzzy +msgid ":py:obj:`History `\\ \\(\\)" +msgstr ":py:obj:`History `\\ \\(\\)" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive:1 of -msgid "Wrapper for configuring a Strategy for DP with Adaptive Clipping." -msgstr "用于配置具有自适应剪切功能的 DP 策略的包装器。" +#: ../../source/ref-api/flwr.server.rst:38::1 +#: flwr.server.history.History:1 of +#, fuzzy +msgid "History class for training and/or evaluation metrics collection." +msgstr "**hist** -- 包含训练和评估指标的对象。" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: ../../source/ref-api/flwr.server.rst:38::1 +#, fuzzy msgid "" -":py:obj:`DPFedAvgFixed `\\ " -"\\(strategy\\, num\\_sampled\\_clients\\, ...\\)" +":py:obj:`LegacyContext `\\ \\(state\\[\\, " +"config\\, strategy\\, ...\\]\\)" msgstr "" +":py:obj:`LegacyContext `\\ \\(state\\[\\, " +"config\\, strategy\\, ...\\]\\)" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed:1 of -msgid "Wrapper for configuring a Strategy for DP with Fixed Clipping." -msgstr "封装器，用于为具有固定剪切功能的 DP 配置策略。" +#: ../../source/ref-api/flwr.server.rst:38::1 +#: flwr.server.compat.legacy_context.LegacyContext:1 of +#, fuzzy +msgid "Legacy Context." +msgstr "传承背景。" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: ../../source/ref-api/flwr.server.rst:38::1 +#, fuzzy msgid "" -":py:obj:`DifferentialPrivacyClientSideAdaptiveClipping " -"`\\ " -"\\(...\\)" +":py:obj:`Server `\\ \\(\\*\\, client\\_manager\\[\\, " +"strategy\\]\\)" msgstr "" +":py:obj:`Server `\\ \\(\\*\\, client\\_manager\\[\\, " +"strategy\\]\\)" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:1 -#: of +#: ../../source/ref-api/flwr.server.rst:38::1 #, fuzzy -msgid "Strategy wrapper for central DP with client-side adaptive clipping." -msgstr "用于配置具有自适应剪切功能的 DP 策略的包装器。" - -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 msgid "" -":py:obj:`DifferentialPrivacyServerSideAdaptiveClipping " -"`\\ " -"\\(...\\)" -msgstr "" +":py:obj:`ServerApp `\\ \\(\\[server\\, config\\, " +"strategy\\, ...\\]\\)" +msgstr "server.strategy.Strategy" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:1 -#: of +#: ../../source/ref-api/flwr.server.rst:38::1 +#: flwr.server.server_app.ServerApp:1 of #, fuzzy -msgid "Strategy wrapper for central DP with server-side adaptive clipping." -msgstr "用于配置具有自适应剪切功能的 DP 策略的包装器。" +msgid "Flower ServerApp." +msgstr "Flower 服务器。" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: ../../source/ref-api/flwr.server.rst:38::1 +#, fuzzy msgid "" -":py:obj:`DifferentialPrivacyClientSideFixedClipping " -"`\\ " -"\\(...\\)" +":py:obj:`ServerConfig `\\ \\(\\[num\\_rounds\\," +" round\\_timeout\\]\\)" msgstr "" +"Flower 1.0: ``start_server(..., " +"config=flwr.server.ServerConfig(num_rounds=3, round_timeout=600.0), " +"...)``" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:1 -#: of +#: ../../source/ref-api/flwr.server.rst:38::1 +#: flwr.server.server_config.ServerConfig:1 of #, fuzzy -msgid "Strategy wrapper for central DP with client-side fixed clipping." -msgstr "封装器，用于为具有固定剪切功能的 DP 配置策略。" +msgid "Flower server config." +msgstr "Flower 服务器。" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -msgid "" -":py:obj:`DifferentialPrivacyServerSideFixedClipping " -"`\\ " -"\\(...\\)" -msgstr "" +#: ../../source/ref-api/flwr.server.rst:38::1 +#, fuzzy +msgid ":py:obj:`SimpleClientManager `\\ \\(\\)" +msgstr ":py:obj:`SimpleClientManager `\\ \\(\\)" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:1 -#: of +#: ../../source/ref-api/flwr.server.rst:38::1 +#: flwr.server.client_manager.SimpleClientManager:1 of #, fuzzy -msgid "Strategy wrapper for central DP with server-side fixed clipping." -msgstr "封装器，用于为具有固定剪切功能的 DP 配置策略。" +msgid "Provides a pool of available clients." +msgstr "使用部分可用客户进行评估。" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -msgid "" -":py:obj:`FedAdagrad `\\ \\(\\*\\[\\, " -"fraction\\_fit\\, ...\\]\\)" -msgstr "" +#: ../../source/ref-api/flwr.server.rst:57::1 +#, fuzzy +msgid ":py:obj:`flwr.server.strategy `\\" +msgstr "server.strategy.Strategy" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fedadagrad.FedAdagrad:1 of -msgid "FedAdagrad strategy - Adaptive Federated Optimization using Adagrad." -msgstr "FedAdagrad 策略 - 使用 Adagrad 进行自适应联合优化。" +#: ../../source/ref-api/flwr.server.rst:57::1 +#: flwr.server.strategy:1 of +msgid "Contains the strategy abstraction and different implementations." +msgstr "包含策略抽象和不同的实现方法。" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -msgid "" -":py:obj:`FedAdam `\\ \\(\\*\\[\\, " -"fraction\\_fit\\, ...\\]\\)" -msgstr "" +#: ../../source/ref-api/flwr.server.rst:57::1 +#, fuzzy +msgid ":py:obj:`flwr.server.workflow `\\" +msgstr "server.strategy.Strategy" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fedadam.FedAdam:1 of -msgid "FedAdam - Adaptive Federated Optimization using Adam." -msgstr "FedAdam - 使用 Adam 进行自适应联合优化。" +#: ../../source/ref-api/flwr.server.rst:57::1 +#: flwr.server.workflow:1 of +#, fuzzy +msgid "Workflows." +msgstr "工作流程" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -msgid "" -":py:obj:`FedAvg `\\ \\(\\*\\[\\, " -"fraction\\_fit\\, fraction\\_evaluate\\, ...\\]\\)" -msgstr "" +#: ../../source/ref-api/flwr.server.ClientManager.rst:2 +#, fuzzy +msgid "ClientManager" +msgstr "客户端" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fedavg.FedAvg:1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:1 of -msgid "Federated Averaging strategy." -msgstr "联邦平均策略。" +#: flwr.server.client_manager.ClientManager.all:1::1 of +#, fuzzy +msgid ":py:obj:`all `\\ \\(\\)" +msgstr ":py:obj:`all `\\ \\(\\)" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -msgid "" -":py:obj:`FedAvgAndroid `\\ " -"\\(\\*\\[\\, fraction\\_fit\\, ...\\]\\)" -msgstr "" +#: flwr.server.client_manager.ClientManager.all:1 +#: flwr.server.client_manager.ClientManager.all:1::1 +#: flwr.server.client_manager.SimpleClientManager.all:1 +#: flwr.server.client_manager.SimpleClientManager.all:1::1 of +#, fuzzy +msgid "Return all available clients." +msgstr "返回所有可用客户。" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -msgid "" -":py:obj:`FedAvgM `\\ \\(\\*\\[\\, " -"fraction\\_fit\\, ...\\]\\)" -msgstr "" +#: flwr.server.client_manager.ClientManager.all:1::1 of +#, fuzzy +msgid ":py:obj:`num_available `\\ \\(\\)" +msgstr ":py:obj:`num_available `\\ \\(\\)" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fedavgm.FedAvgM:1 of -msgid "Federated Averaging with Momentum strategy." -msgstr "联邦平均动量策略。" +#: flwr.server.client_manager.ClientManager.all:1::1 +#: flwr.server.client_manager.ClientManager.num_available:1 +#: flwr.server.client_manager.SimpleClientManager.all:1::1 +#: flwr.server.client_manager.SimpleClientManager.num_available:1 of +#, fuzzy +msgid "Return the number of available clients." +msgstr "返回样本大小和所需的可用客户数量。" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -msgid "" -":py:obj:`FedMedian `\\ \\(\\*\\[\\, " -"fraction\\_fit\\, ...\\]\\)" -msgstr "" +#: flwr.server.client_manager.ClientManager.all:1::1 of +#, fuzzy +msgid ":py:obj:`register `\\ \\(client\\)" +msgstr ":py:obj:`register `\\ \\(client\\)" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fedmedian.FedMedian:1 of +#: flwr.server.client_manager.ClientManager.all:1::1 +#: flwr.server.client_manager.ClientManager.register:1 +#: flwr.server.client_manager.SimpleClientManager.all:1::1 +#: flwr.server.client_manager.SimpleClientManager.register:1 of #, fuzzy -msgid "Configurable FedMedian strategy implementation." -msgstr "可配置的 FedAvg 策略实施。" +msgid "Register Flower ClientProxy instance." +msgstr "注册 Flower ClientProxy 实例。" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.client_manager.ClientManager.all:1::1 of +#, fuzzy msgid "" -":py:obj:`FedOpt `\\ \\(\\*\\[\\, " -"fraction\\_fit\\, fraction\\_evaluate\\, ...\\]\\)" +":py:obj:`sample `\\ " +"\\(num\\_clients\\[\\, min\\_num\\_clients\\, criterion\\]\\)" msgstr "" +":py:obj:`sample `\\ " +"\\(num\\_clients\\[\\, min\\_num\\_clients\\, criterion\\]\\)" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fedopt.FedOpt:1 of +#: flwr.server.client_manager.ClientManager.all:1::1 +#: flwr.server.client_manager.ClientManager.sample:1 +#: flwr.server.client_manager.SimpleClientManager.all:1::1 +#: flwr.server.client_manager.SimpleClientManager.sample:1 of #, fuzzy -msgid "Federated Optim strategy." -msgstr "联邦优化策略。" +msgid "Sample a number of Flower ClientProxy instances." +msgstr "取样若干 Flower ClientProxy 实例。" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -msgid "" -":py:obj:`FedProx `\\ \\(\\*\\[\\, " -"fraction\\_fit\\, ...\\]\\)" -msgstr "" +#: flwr.server.client_manager.ClientManager.all:1::1 of +#, fuzzy +msgid ":py:obj:`unregister `\\ \\(client\\)" +msgstr ":py:obj:`unregister `\\ \\(client\\)" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fedprox.FedProx:1 of -msgid "Federated Optimization strategy." -msgstr "联邦优化策略。" +#: flwr.server.client_manager.ClientManager.all:1::1 +#: flwr.server.client_manager.ClientManager.unregister:1 +#: flwr.server.client_manager.SimpleClientManager.all:1::1 +#: flwr.server.client_manager.SimpleClientManager.unregister:1 of +#, fuzzy +msgid "Unregister Flower ClientProxy instance." +msgstr "取消注册 Flower ClientProxy 实例。" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.client_manager.ClientManager.all:1::1 of +#, fuzzy msgid "" -":py:obj:`FedTrimmedAvg `\\ " -"\\(\\*\\[\\, fraction\\_fit\\, ...\\]\\)" +":py:obj:`wait_for `\\ " +"\\(num\\_clients\\, timeout\\)" msgstr "" +":py:obj:`wait_for `\\ " +"\\(num\\_clients\\, timeout\\)" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:1 of -msgid "Federated Averaging with Trimmed Mean [Dong Yin, et al., 2021]." -msgstr "带修剪均值的联邦平均法[Dong Yin 等，2021]。" +#: flwr.server.client_manager.ClientManager.all:1::1 +#: flwr.server.client_manager.ClientManager.wait_for:1 +#: flwr.server.client_manager.SimpleClientManager.all:1::1 +#: flwr.server.client_manager.SimpleClientManager.wait_for:1 of +#, fuzzy +msgid "Wait until at least `num_clients` are available." +msgstr "等待至少 `num_clients` 可用。" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.client_manager.ClientManager.num_available:3 +#: flwr.server.client_manager.SimpleClientManager.num_available:3 of +#, fuzzy +msgid "**num_available** -- The number of currently available clients." +msgstr "**num_available** -- 当前可用客户端的数量。" + +#: flwr.server.client_manager.ClientManager.register:6 +#: flwr.server.client_manager.SimpleClientManager.register:6 of +#, fuzzy msgid "" -":py:obj:`FedXgbBagging `\\ " -"\\(\\[evaluate\\_function\\]\\)" -msgstr "" +"**success** -- Indicating if registration was successful. False if " +"ClientProxy is already registered or can not be registered for any " +"reason." +msgstr "**success** -- 表示注册是否成功。如果 ClientProxy 已注册或因故无法注册，则为 False。" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging:1 of +#: flwr.server.client_manager.ClientManager.unregister:3 +#: flwr.server.client_manager.SimpleClientManager.unregister:3 of #, fuzzy -msgid "Configurable FedXgbBagging strategy implementation." -msgstr "可配置的 FedXgbNAvg 策略实施。" +msgid "This method is idempotent." +msgstr "这种方法是幂等的。" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: ../../source/ref-api/flwr.server.Driver.rst:2 +#, fuzzy +msgid "Driver" +msgstr "服务器" + +#: flwr.server.driver.driver.Driver.create_message:1::1 of +#, fuzzy msgid "" -":py:obj:`FedXgbCyclic `\\ " -"\\(\\*\\*kwargs\\)" +":py:obj:`create_message `\\ " +"\\(content\\, message\\_type\\, ...\\[\\, ttl\\]\\)" msgstr "" +":py:obj:`create_message `\\ " +"\\(content\\, message\\_type\\, ...\\)" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic:1 of +#: flwr.server.driver.driver.Driver.create_message:1 +#: flwr.server.driver.driver.Driver.create_message:1::1 of #, fuzzy -msgid "Configurable FedXgbCyclic strategy implementation." -msgstr "可配置的 FedAvg 策略实施。" +msgid "Create a new message with specified parameters." +msgstr "使用指定参数创建新信息。" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -msgid "" -":py:obj:`FedXgbNnAvg `\\ \\(\\*args\\, " -"\\*\\*kwargs\\)" -msgstr "" +#: flwr.server.driver.driver.Driver.create_message:1::1 of +#, fuzzy +msgid ":py:obj:`get_node_ids `\\ \\(\\)" +msgstr ":py:obj:`get_node_ids `\\ \\(\\)" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg:1 of -msgid "Configurable FedXgbNnAvg strategy implementation." -msgstr "可配置的 FedXgbNAvg 策略实施。" +#: flwr.server.driver.driver.Driver.create_message:1::1 +#: flwr.server.driver.driver.Driver.get_node_ids:1 of +#, fuzzy +msgid "Get node IDs." +msgstr "获取节点 ID。" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.driver.driver.Driver.create_message:1::1 of +#, fuzzy msgid "" -":py:obj:`FedYogi `\\ \\(\\*\\[\\, " -"fraction\\_fit\\, ...\\]\\)" +":py:obj:`pull_messages `\\ " +"\\(message\\_ids\\)" msgstr "" +":py:obj:`pull_messages `\\ " +"\\(message\\_ids\\)" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fedyogi.FedYogi:1 of -msgid "FedYogi [Reddi et al., 2020] strategy." -msgstr "FedYogi [Reddi 等人，2020] 策略。" +#: flwr.server.driver.driver.Driver.create_message:1::1 +#: flwr.server.driver.driver.Driver.pull_messages:1 of +#, fuzzy +msgid "Pull messages based on message IDs." +msgstr "根据信息 ID 提取信息。" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.driver.driver.Driver.create_message:1::1 of +#, fuzzy msgid "" -":py:obj:`FaultTolerantFedAvg " -"`\\ \\(\\*\\[\\, " -"fraction\\_fit\\, ...\\]\\)" +":py:obj:`push_messages `\\ " +"\\(messages\\)" msgstr "" +":py:obj:`push_messages `\\ " +"\\(messages\\)" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg:1 of -msgid "Configurable fault-tolerant FedAvg strategy implementation." -msgstr "可配置的容错 FedAvg 策略实施。" +#: flwr.server.driver.driver.Driver.create_message:1::1 +#: flwr.server.driver.driver.Driver.push_messages:1 of +#, fuzzy +msgid "Push messages to specified node IDs." +msgstr "向指定的节点 ID 推送信息。" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.driver.driver.Driver.create_message:1::1 of +#, fuzzy msgid "" -":py:obj:`Krum `\\ \\(\\*\\[\\, " -"fraction\\_fit\\, fraction\\_evaluate\\, ...\\]\\)" +":py:obj:`send_and_receive `\\ " +"\\(messages\\, \\*\\[\\, timeout\\]\\)" msgstr "" +"Flower 1.0: ``start_server(..., " +"config=flwr.server.ServerConfig(num_rounds=3, round_timeout=600.0), " +"...)``" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.krum.Krum:1 of +#: flwr.server.driver.driver.Driver.create_message:1::1 +#: flwr.server.driver.driver.Driver.send_and_receive:1 of #, fuzzy -msgid "Krum [Blanchard et al., 2017] strategy." -msgstr "FedYogi [Reddi 等人，2020] 策略。" +msgid "Push messages to specified node IDs and pull the reply messages." +msgstr "向指定的节点 ID 推送信息并提取回复信息。" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.driver.driver.Driver.create_message:3 of +#, fuzzy msgid "" -":py:obj:`QFedAvg `\\ \\(\\*\\[\\, " -"q\\_param\\, qffl\\_learning\\_rate\\, ...\\]\\)" -msgstr "" +"This method constructs a new `Message` with given content and metadata. " +"The `run_id` and `src_node_id` will be set automatically." +msgstr "本方法使用给定的内容和元数据构建新的 `Message` 。run_id \"和 \"src_node_id \"将自动设置。" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.qfedavg.QFedAvg:1 of -msgid "Configurable QFedAvg strategy implementation." -msgstr "可配置的 QFedAvg 策略实施。" +#: flwr.server.driver.driver.Driver.create_message:6 of +#, fuzzy +msgid "" +"The content for the new message. This holds records that are to be sent " +"to the destination node." +msgstr "新信息的内容。其中包含要发送到目的节点的记录。" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.driver.driver.Driver.create_message:9 of #, fuzzy -msgid ":py:obj:`Strategy `\\ \\(\\)" -msgstr "server.strategy.Strategy" +msgid "" +"The type of the message, defining the action to be executed on the " +"receiving end." +msgstr "信息类型，定义接收端要执行的操作。" -#: ../../source/ref-api/flwr.server.strategy.rst:45::1 -#: flwr.server.strategy.strategy.Strategy:1 of -msgid "Abstract base class for server strategy implementations." -msgstr "服务器策略实现的抽象基类。" +#: flwr.server.driver.driver.Driver.create_message:12 of +#, fuzzy +msgid "The ID of the destination node to which the message is being sent." +msgstr "信息发送目的地节点的 ID。" -#: ../../source/ref-api/flwr.server.strategy.Bulyan.rst:2 -msgid "Bulyan" -msgstr "" +#: flwr.server.driver.driver.Driver.create_message:14 of +#, fuzzy +msgid "" +"The ID of the group to which this message is associated. In some " +"settings, this is used as the FL round." +msgstr "与该信息相关联的组的 ID。在某些设置中，它被用作 FL 轮。" -#: flwr.server.strategy.bulyan.Bulyan:1 -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg:1 -#: flwr.server.strategy.fedavgm.FedAvgM:1 -#: flwr.server.strategy.fedmedian.FedMedian:1 -#: flwr.server.strategy.fedopt.FedOpt:1 flwr.server.strategy.fedprox.FedProx:1 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:1 -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging:1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic:1 -#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg:1 -#: flwr.server.strategy.krum.Krum:1 flwr.server.strategy.qfedavg.QFedAvg:1 of +#: flwr.server.driver.driver.Driver.create_message:17 of #, fuzzy -msgid "Bases: :py:class:`~flwr.server.strategy.fedavg.FedAvg`" -msgstr "server.strategy.DPFedAvgFixed" +msgid "" +"Time-to-live for the round trip of this message, i.e., the time from " +"sending this message to receiving a reply. It specifies in seconds the " +"duration for which the message and its potential reply are considered " +"valid. If unset, the default TTL (i.e., `common.DEFAULT_TTL`) will be " +"used." +msgstr "此报文往返的有效时间，即从发送此报文到收到回复的时间。它规定了信息及其潜在回复被视为有效的持续时间。" -#: flwr.server.strategy.bulyan.Bulyan:3 of -msgid "Implementation based on https://arxiv.org/abs/1802.07927." -msgstr "实施基于 https://arxiv.org/abs/1802.07927。" +#: flwr.server.driver.driver.Driver.create_message:23 of +#, fuzzy +msgid "" +"**message** -- A new `Message` instance with the specified content and " +"metadata." +msgstr "**message** -- 具有指定内容和元数据的新 \"信息 \"实例。" -#: flwr.server.strategy.bulyan.Bulyan:5 -#: flwr.server.strategy.fedadagrad.FedAdagrad:5 -#: flwr.server.strategy.fedadam.FedAdam:5 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:5 -#: flwr.server.strategy.fedavgm.FedAvgM:5 flwr.server.strategy.fedopt.FedOpt:5 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:5 -#: flwr.server.strategy.fedyogi.FedYogi:5 flwr.server.strategy.krum.Krum:5 of -msgid "Fraction of clients used during training. Defaults to 1.0." -msgstr "训练期间使用客户的比例。默认为 1.0。" +#: flwr.server.driver.driver.Driver.pull_messages:3 of +#, fuzzy +msgid "" +"This method is used to collect messages from the SuperLink that " +"correspond to a set of given message IDs." +msgstr "该方法用于从超级链接中收集与一组给定消息 ID 相对应的消息。" -#: flwr.server.strategy.bulyan.Bulyan:7 -#: flwr.server.strategy.fedadagrad.FedAdagrad:7 -#: flwr.server.strategy.fedadam.FedAdam:7 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:7 -#: flwr.server.strategy.fedavgm.FedAvgM:7 flwr.server.strategy.fedopt.FedOpt:7 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:7 -#: flwr.server.strategy.fedyogi.FedYogi:7 flwr.server.strategy.krum.Krum:7 of -msgid "Fraction of clients used during validation. Defaults to 1.0." -msgstr "验证过程中使用的客户端比例。默认为 1.0。" +#: flwr.server.driver.driver.Driver.pull_messages:6 of +#, fuzzy +msgid "An iterable of message IDs for which reply messages are to be retrieved." +msgstr "要检索回复信息的信息 ID 的可迭代项。" -#: flwr.server.strategy.bulyan.Bulyan:9 -#: flwr.server.strategy.fedadagrad.FedAdagrad:9 -#: flwr.server.strategy.fedadam.FedAdam:9 flwr.server.strategy.fedavg.FedAvg:13 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:9 -#: flwr.server.strategy.fedavgm.FedAvgM:9 flwr.server.strategy.fedopt.FedOpt:9 -#: flwr.server.strategy.fedprox.FedProx:45 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:9 -#: flwr.server.strategy.fedyogi.FedYogi:9 flwr.server.strategy.krum.Krum:9 of -msgid "Minimum number of clients used during training. Defaults to 2." -msgstr "训练期间使用的最少客户数。默认为 2。" +#: flwr.server.driver.driver.Driver.pull_messages:9 of +#, fuzzy +msgid "**messages** -- An iterable of messages received." +msgstr "**messages** -- 收到的信息迭代。" -#: flwr.server.strategy.bulyan.Bulyan:11 -#: flwr.server.strategy.fedadagrad.FedAdagrad:11 -#: flwr.server.strategy.fedadam.FedAdam:11 -#: flwr.server.strategy.fedavg.FedAvg:15 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:11 -#: flwr.server.strategy.fedavgm.FedAvgM:11 -#: flwr.server.strategy.fedopt.FedOpt:11 -#: flwr.server.strategy.fedprox.FedProx:47 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:11 -#: flwr.server.strategy.fedyogi.FedYogi:11 flwr.server.strategy.krum.Krum:11 of -msgid "Minimum number of clients used during validation. Defaults to 2." -msgstr "验证过程中使用的最少客户端数量。默认为 2。" +#: flwr.server.driver.driver.Driver.push_messages:3 of +#, fuzzy +msgid "" +"This method takes an iterable of messages and sends each message to the " +"node specified in `dst_node_id`." +msgstr "该方法接收一个可迭代的消息，并将每条消息发送到 `dst_node_id` 中指定的节点。" -#: flwr.server.strategy.bulyan.Bulyan:13 -#: flwr.server.strategy.fedadagrad.FedAdagrad:13 -#: flwr.server.strategy.fedadam.FedAdam:13 -#: flwr.server.strategy.fedavg.FedAvg:17 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:13 -#: flwr.server.strategy.fedavgm.FedAvgM:13 -#: flwr.server.strategy.fedopt.FedOpt:13 -#: flwr.server.strategy.fedprox.FedProx:49 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:13 -#: flwr.server.strategy.fedyogi.FedYogi:13 flwr.server.strategy.krum.Krum:13 of -msgid "Minimum number of total clients in the system. Defaults to 2." -msgstr "系统中客户总数的最小值。默认为 2。" - -#: flwr.server.strategy.bulyan.Bulyan:15 flwr.server.strategy.krum.Krum:15 of -msgid "Number of malicious clients in the system. Defaults to 0." -msgstr "系统中恶意客户端的数量。默认为 0。" - -#: flwr.server.strategy.bulyan.Bulyan:17 -#: flwr.server.strategy.fedadagrad.FedAdagrad:15 -#: flwr.server.strategy.fedadam.FedAdam:15 -#: flwr.server.strategy.fedavg.FedAvg:19 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:15 -#: flwr.server.strategy.fedavgm.FedAvgM:15 -#: flwr.server.strategy.fedopt.FedOpt:15 -#: flwr.server.strategy.fedprox.FedProx:51 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:15 -#: flwr.server.strategy.fedyogi.FedYogi:17 -#: flwr.server.strategy.fedyogi.FedYogi:18 -#: flwr.server.strategy.fedyogi.FedYogi:19 flwr.server.strategy.krum.Krum:20 of -msgid "Optional function used for validation. Defaults to None." -msgstr "用于验证的可选函数。默认为 \"无\"。" +#: flwr.server.driver.driver.Driver.push_messages:6 +#: flwr.server.driver.driver.Driver.send_and_receive:7 of +#, fuzzy +msgid "An iterable of messages to be sent." +msgstr "要发送的信息迭代。" -#: flwr.server.strategy.bulyan.Bulyan:19 -#: flwr.server.strategy.fedadagrad.FedAdagrad:17 -#: flwr.server.strategy.fedadam.FedAdam:17 -#: flwr.server.strategy.fedavg.FedAvg:21 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:17 -#: flwr.server.strategy.fedavgm.FedAvgM:17 -#: flwr.server.strategy.fedopt.FedOpt:17 -#: flwr.server.strategy.fedprox.FedProx:53 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:17 -#: flwr.server.strategy.fedyogi.FedYogi:20 flwr.server.strategy.krum.Krum:22 of -msgid "Function used to configure training. Defaults to None." -msgstr "用于配置训练的功能。默认为 \"无\"。" +#: flwr.server.driver.driver.Driver.push_messages:9 of +#, fuzzy +msgid "" +"**message_ids** -- An iterable of IDs for the messages that were sent, " +"which can be used to pull replies." +msgstr "**message_ids** -- 已发送信息的可迭代 ID，可用于提取回复信息。" -#: flwr.server.strategy.bulyan.Bulyan:21 -#: flwr.server.strategy.fedadagrad.FedAdagrad:19 -#: flwr.server.strategy.fedadam.FedAdam:19 -#: flwr.server.strategy.fedavg.FedAvg:23 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:19 -#: flwr.server.strategy.fedavgm.FedAvgM:19 -#: flwr.server.strategy.fedopt.FedOpt:19 -#: flwr.server.strategy.fedprox.FedProx:55 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:19 -#: flwr.server.strategy.fedyogi.FedYogi:22 flwr.server.strategy.krum.Krum:24 of -msgid "Function used to configure validation. Defaults to None." -msgstr "用于配置验证的函数。默认为 \"无\"。" +#: flwr.server.driver.driver.Driver.send_and_receive:3 of +#, fuzzy +msgid "" +"This method sends a list of messages to their destination node IDs and " +"then waits for the replies. It continues to pull replies until either all" +" replies are received or the specified timeout duration is exceeded." +msgstr "该方法会向目标节点 ID 发送信息列表，然后等待回复。它会继续提取回复，直到收到所有回复或超过指定的超时时间。" -#: flwr.server.strategy.bulyan.Bulyan:23 -#: flwr.server.strategy.fedadagrad.FedAdagrad:25 -#: flwr.server.strategy.fedadam.FedAdam:21 -#: flwr.server.strategy.fedavg.FedAvg:25 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:21 -#: flwr.server.strategy.fedavgm.FedAvgM:21 -#: flwr.server.strategy.fedopt.FedOpt:21 -#: flwr.server.strategy.fedprox.FedProx:57 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:21 -#: flwr.server.strategy.fedyogi.FedYogi:24 flwr.server.strategy.krum.Krum:26 of -msgid "Whether or not accept rounds containing failures. Defaults to True." -msgstr "是否接受包含失败的轮。默认为 True。" +#: flwr.server.driver.driver.Driver.send_and_receive:9 of +#, fuzzy +msgid "" +"The timeout duration in seconds. If specified, the method will wait for " +"replies for this duration. If `None`, there is no time limit and the " +"method will wait until replies for all messages are received." +msgstr "超时时间（秒）。如果指定，该方法将在此期限内等待回复。如果指定为 \"无\"，则没有时间限制，该方法将等待直到收到所有信息的回复。" -#: flwr.server.strategy.bulyan.Bulyan:25 -#: flwr.server.strategy.fedadagrad.FedAdagrad:27 -#: flwr.server.strategy.fedadam.FedAdam:23 -#: flwr.server.strategy.fedavg.FedAvg:27 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:24 -#: flwr.server.strategy.fedavgm.FedAvgM:23 -#: flwr.server.strategy.fedopt.FedOpt:23 -#: flwr.server.strategy.fedprox.FedProx:59 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:23 -#: flwr.server.strategy.fedyogi.FedYogi:26 flwr.server.strategy.krum.Krum:28 of -msgid "Initial global model parameters." -msgstr "初始全局模型参数。" +#: flwr.server.driver.driver.Driver.send_and_receive:14 of +#, fuzzy +msgid "**replies** -- An iterable of reply messages received from the SuperLink." +msgstr "**replies** -- 从超级链接收到的回复信息的迭代。" -#: flwr.server.strategy.bulyan.Bulyan:27 of +#: flwr.server.driver.driver.Driver.send_and_receive:19 of +#, fuzzy msgid "" -"Byzantine resilient aggregation rule that is used as the first step of " -"the Bulyan (e.g., Krum)" -msgstr "Byzantine弹性聚合规则，用作 Bulyan 的第一步（如 Krum）" +"This method uses `push_messages` to send the messages and `pull_messages`" +" to collect the replies. If `timeout` is set, the method may not return " +"replies for all sent messages. A message remains valid until its TTL, " +"which is not affected by `timeout`." +msgstr "" +"该方法使用 `push_messages` 发送信息，并使用 `pull_messages` 收集回复。如果设置了 " +"`timeout`，该方法可能不会返回所有已发送消息的回复。消息在其 TTL 之前一直有效，不受 `timeout` 影响。" -#: flwr.server.strategy.bulyan.Bulyan:29 of -msgid "arguments to the first_aggregation rule" -msgstr "第一聚类规则的参数" +#: ../../source/ref-api/flwr.server.History.rst:2 +#, fuzzy +msgid "History" +msgstr "历史" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.history.History.add_loss_centralized:1::1 of +#, fuzzy msgid "" -":py:obj:`aggregate_evaluate " -"`\\ \\(server\\_round\\, " -"results\\, ...\\)" +":py:obj:`add_loss_centralized " +"`\\ \\(server\\_round\\, " +"loss\\)" msgstr "" +":py:obj:`add_loss_centralized " +"`\\ \\(server\\_round\\, " +"loss\\)" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1 -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1 -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1 -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of -msgid "Aggregate evaluation losses using weighted average." -msgstr "采用加权平均法计算评估损失总额。" +#: flwr.server.history.History.add_loss_centralized:1 +#: flwr.server.history.History.add_loss_centralized:1::1 of +#, fuzzy +msgid "Add one loss entry (from centralized evaluation)." +msgstr "集中评估" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.history.History.add_loss_centralized:1::1 of +#, fuzzy msgid "" -":py:obj:`aggregate_fit `\\ " -"\\(server\\_round\\, results\\, failures\\)" +":py:obj:`add_loss_distributed " +"`\\ \\(server\\_round\\, " +"loss\\)" msgstr "" +":py:obj:`add_loss_distributed " +"`\\ \\(server\\_round\\, " +"loss\\)" -#: flwr.server.strategy.bulyan.Bulyan.aggregate_fit:1 -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "Aggregate fit results using Bulyan." -msgstr "使用 Bulyan 技术汇总拟合结果。" +#: flwr.server.history.History.add_loss_centralized:1::1 +#: flwr.server.history.History.add_loss_distributed:1 of +#, fuzzy +msgid "Add one loss entry (from distributed evaluation)." +msgstr "增加一个损失条目（来自分布式评估）。" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.history.History.add_loss_centralized:1::1 of +#, fuzzy msgid "" -":py:obj:`configure_evaluate " -"`\\ \\(server\\_round\\, " -"parameters\\, ...\\)" +":py:obj:`add_metrics_centralized " +"`\\ \\(server\\_round\\, " +"metrics\\)" msgstr "" +":py:obj:`add_metrics_centralized " +"`\\ \\(server\\_round\\, " +"metrics\\)" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.configure_evaluate:1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.configure_evaluate:1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.configure_evaluate:1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.configure_evaluate:1 -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.configure_evaluate:1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.configure_evaluate:1 -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.configure_evaluate:1 -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.qfedavg.QFedAvg.configure_evaluate:1 -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 -#: flwr.server.strategy.strategy.Strategy.configure_evaluate:1 of -msgid "Configure the next round of evaluation." -msgstr "配置下一轮评估。" +#: flwr.server.history.History.add_loss_centralized:1::1 +#: flwr.server.history.History.add_metrics_centralized:1 of +#, fuzzy +msgid "Add metrics entries (from centralized evaluation)." +msgstr "集中评估" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.history.History.add_loss_centralized:1::1 of +#, fuzzy msgid "" -":py:obj:`configure_fit `\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`add_metrics_distributed " +"`\\ \\(server\\_round\\, " +"metrics\\)" msgstr "" +":py:obj:`add_metrics_distributed " +"`\\ \\(server\\_round\\, " +"metrics\\)" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.configure_fit:1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.configure_fit:1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.configure_fit:1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.configure_fit:1 -#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive.configure_fit:1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.configure_fit:1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.configure_fit:1 -#: flwr.server.strategy.fedprox.FedProx.configure_fit:1 -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.configure_fit:1 -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.qfedavg.QFedAvg.configure_fit:1 -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 -#: flwr.server.strategy.strategy.Strategy.configure_fit:1 of -msgid "Configure the next round of training." -msgstr "配置下一轮训练。" +#: flwr.server.history.History.add_loss_centralized:1::1 +#: flwr.server.history.History.add_metrics_distributed:1 of +#, fuzzy +msgid "Add metrics entries (from distributed evaluation)." +msgstr "定制的集中/分布式评估" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.history.History.add_loss_centralized:1::1 of +#, fuzzy msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" +":py:obj:`add_metrics_distributed_fit " +"`\\ \\(server\\_round\\," +" ...\\)" msgstr "" +":py:obj:`add_metrics_distributed_fit " +"`\\ \\(server\\_round\\," +" ...\\)" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.evaluate:1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.evaluate:1 -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.evaluate:1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg.evaluate:1 -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of -msgid "Evaluate model parameters using an evaluation function." -msgstr "使用评估函数评估模型参数。" - -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" -msgstr "" +#: flwr.server.history.History.add_loss_centralized:1::1 +#: flwr.server.history.History.add_metrics_distributed_fit:1 of +#, fuzzy +msgid "Add metrics entries (from distributed fit)." +msgstr "添加度量条目（来自分布式拟合）。" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.initialize_parameters:1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.initialize_parameters:1 -#: flwr.server.strategy.fedavgm.FedAvgM.initialize_parameters:1 -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of -msgid "Initialize global model parameters." -msgstr "初始化全局模型参数。" +#: ../../source/ref-api/flwr.server.LegacyContext.rst:2 +#, fuzzy +msgid "LegacyContext" +msgstr "遗留上下文" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" -msgstr "" +#: flwr.server.compat.legacy_context.LegacyContext:1 of +#, fuzzy +msgid "Bases: :py:class:`~flwr.common.context.Context`" +msgstr "Bases: :py:class:`~flwr.common.context.Context`" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.num_evaluation_clients:1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.num_evaluation_clients:1 -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.qfedavg.QFedAvg.num_evaluation_clients:1 of -msgid "Use a fraction of available clients for evaluation." -msgstr "使用部分可用客户进行评估。" +#: ../../source/ref-api/flwr.server.LegacyContext.rst:32::1 +#, fuzzy +msgid ":py:obj:`config `\\" +msgstr "server.strategy.Strategy" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`num_fit_clients `\\" -" \\(num\\_available\\_clients\\)" -msgstr "" +#: ../../source/ref-api/flwr.server.LegacyContext.rst:32::1 +#, fuzzy +msgid ":py:obj:`strategy `\\" +msgstr "server.strategy.Strategy" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.num_fit_clients:1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.num_fit_clients:1 -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.qfedavg.QFedAvg.num_fit_clients:1 of -msgid "Return the sample size and the required number of available clients." -msgstr "返回样本大小和所需的可用客户数量。" +#: ../../source/ref-api/flwr.server.LegacyContext.rst:32::1 +#, fuzzy +msgid ":py:obj:`client_manager `\\" +msgstr ":py:obj:`client_manager `\\" -#: ../../source/ref-api/flwr.server.strategy.DPFedAvgAdaptive.rst:2 -msgid "DPFedAvgAdaptive" -msgstr "DPFedAvgAdaptive" +#: ../../source/ref-api/flwr.server.LegacyContext.rst:32::1 +#, fuzzy +msgid ":py:obj:`history `\\" +msgstr "server.strategy.Strategy" -#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive:1 of -msgid "Bases: :py:class:`~flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed`" -msgstr "" +#: ../../source/ref-api/flwr.server.LegacyContext.rst:32::1 +#, fuzzy +msgid ":py:obj:`state `\\" +msgstr "server.strategy.Strategy" -#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive:3 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed:3 of -msgid "This class is deprecated and will be removed in a future release." -msgstr "" +#: ../../source/ref-api/flwr.server.Server.rst:2 +msgid "Server" +msgstr "服务器" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" -msgstr "" +#: flwr.server.server.Server.client_manager:1::1 of +#, fuzzy +msgid ":py:obj:`client_manager `\\ \\(\\)" +msgstr ":py:obj:`client_manager `\\ \\(\\)" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of -msgid "Aggregate evaluation losses using the given strategy." -msgstr "使用给定的策略汇总评估损失。" +#: flwr.server.server.Server.client_manager:1 +#: flwr.server.server.Server.client_manager:1::1 of +#, fuzzy +msgid "Return ClientManager." +msgstr "返回客户端（本身）。" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of +#: flwr.server.server.Server.client_manager:1::1 of +#, fuzzy msgid "" -":py:obj:`aggregate_fit " -"`\\ " -"\\(server\\_round\\, results\\, failures\\)" +":py:obj:`disconnect_all_clients " +"`\\ \\(timeout\\)" msgstr "" +":py:obj:`disconnect_all_clients " +"`\\ \\(timeout\\)" -#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive.aggregate_fit:1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of -msgid "Aggregate training results as in DPFedAvgFixed and update clip norms." -msgstr "汇总 DPFedAvgFixed 中的训练结果并更新片段标准。" +#: flwr.server.server.Server.client_manager:1::1 +#: flwr.server.server.Server.disconnect_all_clients:1 of +#, fuzzy +msgid "Send shutdown signal to all clients." +msgstr "向所有客户端发送关闭信号。" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of +#: flwr.server.server.Server.client_manager:1::1 of +#, fuzzy msgid "" -":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`evaluate_round `\\ " +"\\(server\\_round\\, timeout\\)" msgstr "" +":py:obj:`evaluate_round `\\ " +"\\(server\\_round\\, timeout\\)" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:1 of -msgid "Configure the next round of evaluation using the specified strategy." -msgstr "使用指定策略配置下一轮评估。" +#: flwr.server.server.Server.client_manager:1::1 +#: flwr.server.server.Server.evaluate_round:1 of +#, fuzzy +msgid "Validate current global model on a number of clients." +msgstr "当前（全局）模型参数。" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`configure_fit " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" -msgstr "" +#: flwr.server.server.Server.client_manager:1::1 of +#, fuzzy +msgid ":py:obj:`fit `\\ \\(num\\_rounds\\, timeout\\)" +msgstr ":py:obj:`fit `\\ \\(num\\_rounds\\, timeout\\)" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of +#: flwr.server.server.Server.client_manager:1::1 +#: flwr.server.server.Server.fit:1 of +#, fuzzy +msgid "Run federated averaging for a number of rounds." +msgstr "联邦平均动量策略。" + +#: flwr.server.server.Server.client_manager:1::1 of +#, fuzzy msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" +":py:obj:`fit_round `\\ \\(server\\_round\\," +" timeout\\)" msgstr "" +":py:obj:`fit_round `\\ \\(server\\_round\\," +" timeout\\)" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.evaluate:1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.evaluate:1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.evaluate:1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.evaluate:1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.evaluate:1 of -msgid "Evaluate model parameters using an evaluation function from the strategy." -msgstr "使用策略中的评估函数评估模型参数。" +#: flwr.server.server.Server.client_manager:1::1 +#: flwr.server.server.Server.fit_round:1 of +#, fuzzy +msgid "Perform a single round of federated averaging." +msgstr "本轮联邦学习。" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of +#: flwr.server.server.Server.client_manager:1::1 of +#, fuzzy msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +":py:obj:`set_max_workers `\\ " +"\\(max\\_workers\\)" msgstr "" +":py:obj:`set_max_workers `\\ " +"\\(max\\_workers\\)" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.initialize_parameters:1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.initialize_parameters:1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.initialize_parameters:1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.initialize_parameters:1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.initialize_parameters:1 of -msgid "Initialize global model parameters using given strategy." -msgstr "使用给定的策略初始化全局模型参数。" +#: flwr.server.server.Server.client_manager:1::1 +#: flwr.server.server.Server.set_max_workers:1 of +#, fuzzy +msgid "Set the max_workers used by ThreadPoolExecutor." +msgstr "设置 ThreadPoolExecutor 使用的最大工作器数。" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:3 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:6 -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:3 -#: flwr.server.strategy.strategy.Strategy.aggregate_fit:3 -#: flwr.server.strategy.strategy.Strategy.configure_evaluate:3 -#: flwr.server.strategy.strategy.Strategy.configure_fit:3 -#: flwr.server.strategy.strategy.Strategy.evaluate:6 of -msgid "The current round of federated learning." -msgstr "本轮联邦学习。" +#: flwr.server.server.Server.client_manager:1::1 of +#, fuzzy +msgid ":py:obj:`set_strategy `\\ \\(strategy\\)" +msgstr ":py:obj:`set_strategy `\\ \\(strategy\\)" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:7 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:10 -#: flwr.server.strategy.strategy.Strategy.configure_evaluate:7 -#: flwr.server.strategy.strategy.Strategy.configure_fit:7 -#: flwr.server.strategy.strategy.Strategy.initialize_parameters:3 of -msgid "The client manager which holds all currently connected clients." -msgstr "客户端管理器，用于管理当前连接的所有客户端。" +#: flwr.server.server.Server.client_manager:1::1 +#: flwr.server.server.Server.set_strategy:1 of +#, fuzzy +msgid "Replace server strategy." +msgstr "server.strategy" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:10 -#: flwr.server.strategy.strategy.Strategy.configure_evaluate:10 of -msgid "" -"**evaluate_configuration** -- A list of tuples. Each tuple in the list " -"identifies a `ClientProxy` and the `EvaluateIns` for this particular " -"`ClientProxy`. If a particular `ClientProxy` is not included in this " -"list, it means that this `ClientProxy` will not participate in the next " -"round of federated evaluation." -msgstr "" -"**evaluate_configuration** -- " -"一个元组列表。列表中的每个元组都标识了一个`ClientProxy`和该特定`ClientProxy`的`EvaluateIns`。如果某个特定的" -" `ClientProxy` 未包含在此列表中，则表示该 `ClientProxy` 将不参与下一轮联合评估。" +#: ../../source/ref-api/flwr.server.ServerApp.rst:2 +#, fuzzy +msgid "ServerApp" +msgstr "服务器" -#: ../../source/ref-api/flwr.server.strategy.DPFedAvgFixed.rst:2 -msgid "DPFedAvgFixed" -msgstr "DPFedAvgFixed" +#: flwr.server.server_app.ServerApp:5 of +#, fuzzy +msgid "Use the `ServerApp` with an existing `Strategy`:" +msgstr "使用现有策略" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed:1 -#: flwr.server.strategy.fedavg.FedAvg:1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:1 of -msgid "Bases: :py:class:`~flwr.server.strategy.strategy.Strategy`" -msgstr "" +#: flwr.server.server_app.ServerApp:15 of +#, fuzzy +msgid "Use the `ServerApp` with a custom main function:" +msgstr "使用带有自定义主函数的 `ServerApp`：" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" -msgstr "" +#: flwr.server.server_app.ServerApp.main:1::1 of +#, fuzzy +msgid ":py:obj:`main `\\ \\(\\)" +msgstr "server.strategy.Strategy" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of +#: flwr.server.server_app.ServerApp.main:1 +#: flwr.server.server_app.ServerApp.main:1::1 of +#, fuzzy +msgid "Return a decorator that registers the main fn with the server app." +msgstr "返回向服务器应用程序注册 main fn 的装饰器。" + +#: ../../source/ref-api/flwr.server.ServerConfig.rst:2 +#, fuzzy +msgid "ServerConfig" +msgstr "服务器" + +#: flwr.server.server_config.ServerConfig:3 of +#, fuzzy msgid "" -":py:obj:`aggregate_fit " -"`\\ " -"\\(server\\_round\\, results\\, failures\\)" -msgstr "" +"All attributes have default values which allows users to configure just " +"the ones they care about." +msgstr "所有属性都有默认值，用户只需配置自己关心的属性即可。" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_fit:1 of -msgid "Aggregate training results using unweighted aggregation." -msgstr "使用非加权汇总法汇总训练结果。" +#: ../../source/ref-api/flwr.server.ServerConfig.rst:29::1 +#, fuzzy +msgid ":py:obj:`num_rounds `\\" +msgstr ":py:obj:`num_rounds `\\" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.ServerConfig.rst:29::1 +#, fuzzy +msgid ":py:obj:`round_timeout `\\" +msgstr ":py:obj:`round_timeout `\\" + +#: ../../source/ref-api/flwr.server.SimpleClientManager.rst:2 +#, fuzzy +msgid "SimpleClientManager" +msgstr "SimpleClientManager" + +#: flwr.server.client_manager.SimpleClientManager:1 of +#, fuzzy +msgid "Bases: :py:class:`~flwr.server.client_manager.ClientManager`" +msgstr "Bases: :py:class:`~flwr.server.client_manager.ClientManager`" + +#: flwr.server.client_manager.SimpleClientManager.all:1::1 of +#, fuzzy +msgid ":py:obj:`all `\\ \\(\\)" +msgstr ":py:obj:`all `\\ \\(\\)" + +#: flwr.server.client_manager.SimpleClientManager.all:1::1 of +#, fuzzy msgid "" -":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`num_available `\\" +" \\(\\)" msgstr "" +":py:obj:`num_available `\\" +" \\(\\)" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of +#: flwr.server.client_manager.SimpleClientManager.all:1::1 of +#, fuzzy msgid "" -":py:obj:`configure_fit " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`register `\\ " +"\\(client\\)" msgstr "" +":py:obj:`register `\\ " +"\\(client\\)" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:1 of +#: flwr.server.client_manager.SimpleClientManager.all:1::1 of +#, fuzzy msgid "" -"Configure the next round of training incorporating Differential Privacy " -"(DP)." -msgstr "配置包含差分隐私 (DP) 的下一轮训练。" +":py:obj:`sample `\\ " +"\\(num\\_clients\\[\\, min\\_num\\_clients\\, criterion\\]\\)" +msgstr "" +":py:obj:`sample `\\ " +"\\(num\\_clients\\[\\, min\\_num\\_clients\\, criterion\\]\\)" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of +#: flwr.server.client_manager.SimpleClientManager.all:1::1 of +#, fuzzy msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" +":py:obj:`unregister `\\ " +"\\(client\\)" msgstr "" +":py:obj:`unregister `\\ " +"\\(client\\)" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 -#: of +#: flwr.server.client_manager.SimpleClientManager.all:1::1 of +#, fuzzy msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +":py:obj:`wait_for `\\ " +"\\(num\\_clients\\[\\, timeout\\]\\)" msgstr "" +":py:obj:`wait_for `\\ " +"\\(num\\_clients\\[\\, timeout\\]\\)" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:3 of +#: flwr.server.client_manager.SimpleClientManager.wait_for:3 of +#, fuzzy msgid "" -"Configuration of the next training round includes information related to " -"DP, such as clip norm and noise stddev." -msgstr "下一轮训练的配置包括与 DP 相关的信息，如片段规范和噪声 stddev。" +"Blocks until the requested number of clients is available or until a " +"timeout is reached. Current timeout default: 1 day." +msgstr "阻塞，直到请求的客户端数量可用或达到超时为止。当前超时默认值：1 天。" -#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:13 -#: flwr.server.strategy.strategy.Strategy.configure_fit:10 of -msgid "" -"**fit_configuration** -- A list of tuples. Each tuple in the list " -"identifies a `ClientProxy` and the `FitIns` for this particular " -"`ClientProxy`. If a particular `ClientProxy` is not included in this " -"list, it means that this `ClientProxy` will not participate in the next " -"round of federated learning." -msgstr "" -"**fit_configuration** -- " -"一个元组列表。列表中的每个元组都标识了一个`ClientProxy`和该特定`ClientProxy`的`FitIns'。如果某个特定的`ClientProxy`不在此列表中，则表示该`ClientProxy`将不参加下一轮联合学习。" +#: flwr.server.client_manager.SimpleClientManager.wait_for:6 of +#, fuzzy +msgid "The number of clients to wait for." +msgstr "需要等待的客户数量。" -#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyClientSideAdaptiveClipping.rst:2 -msgid "DifferentialPrivacyClientSideAdaptiveClipping" -msgstr "" +#: flwr.server.client_manager.SimpleClientManager.wait_for:8 of +#, fuzzy +msgid "The time in seconds to wait for, defaults to 86400 (24h)." +msgstr "以秒为单位的等待时间，默认为 86400（24 小时）。" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:3 -#: of -msgid "Use `adaptiveclipping_mod` modifier at the client side." -msgstr "" +#: flwr.server.client_manager.SimpleClientManager.wait_for:11 of +#, fuzzy +msgid "**success**" +msgstr "**success**" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:5 -#: of -msgid "" -"In comparison to `DifferentialPrivacyServerSideAdaptiveClipping`, which " -"performs clipping on the server-side, " -"`DifferentialPrivacyClientSideAdaptiveClipping` expects clipping to " -"happen on the client-side, usually by using the built-in " -"`adaptiveclipping_mod`." -msgstr "" +#: ../../source/ref-api/flwr.server.run_driver_api.rst:2 +#, fuzzy +msgid "run\\_driver\\_api" +msgstr "flower-driver-api" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:10 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:3 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:10 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:3 -#: of -msgid "The strategy to which DP functionalities will be added by this wrapper." -msgstr "" +#: ../../source/ref-api/flwr.server.run_fleet_api.rst:2 +#, fuzzy +msgid "run\\_fleet\\_api" +msgstr "run\\_fleet\\_api" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:12 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:5 -#: of -msgid "The noise multiplier for the Gaussian mechanism for model updates." -msgstr "" +#: ../../source/ref-api/flwr.server.run_server_app.rst:2 +#, fuzzy +msgid "run\\_server\\_app" +msgstr "run\\_server\\_app" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:14 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:7 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:17 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:10 -#: of -msgid "The number of clients that are sampled on each round." -msgstr "" +#: ../../source/ref-api/flwr.server.run_superlink.rst:2 +#, fuzzy +msgid "run\\_superlink" +msgstr "flower-superlink" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:16 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:9 -#: of -msgid "" -"The initial value of clipping norm. Defaults to 0.1. Andrew et al. " -"recommends to set to 0.1." -msgstr "" +#: ../../source/ref-api/flwr.server.start_server.rst:2 +#, fuzzy +msgid "start\\_server" +msgstr "server.start_server" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:19 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:12 -#: of -msgid "The desired quantile of updates which should be clipped. Defaults to 0.5." -msgstr "" +#: flwr.server.app.start_server:3 of +msgid "The IPv4 or IPv6 address of the server. Defaults to `\"[::]:8080\"`." +msgstr "服务器的 IPv4 或 IPv6 地址。默认为 `\"[::]:8080\"。" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:21 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:14 -#: of +#: flwr.server.app.start_server:5 of msgid "" -"The learning rate for the clipping norm adaptation. Defaults to 0.2. " -"Andrew et al. recommends to set to 0.2." -msgstr "" +"A server implementation, either `flwr.server.Server` or a subclass " +"thereof. If no instance is provided, then `start_server` will create one." +msgstr "服务器实现，可以是 `flwr.server.Server` 或其子类。如果没有提供实例，`start_server` 将创建一个。" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:24 -#: of +#: flwr.server.app.start_server:9 flwr.simulation.app.start_simulation:28 of msgid "" -"The stddev of the noise added to the count of updates currently below the" -" estimate. Andrew et al. recommends to set to `expected_num_records/20`" -msgstr "" - -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:30 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:23 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:22 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:15 -#: of -#, fuzzy -msgid "Create a strategy:" -msgstr "server.strategy" +"Currently supported values are `num_rounds` (int, default: 1) and " +"`round_timeout` in seconds (float, default: None)." +msgstr "目前支持的值有：`num_rounds`（int，默认值：1）和以秒为单位的`round_timeout`（float，默认值：无）。" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:34 -#: of +#: flwr.server.app.start_server:12 of msgid "" -"Wrap the strategy with the " -"`DifferentialPrivacyClientSideAdaptiveClipping` wrapper:" -msgstr "" - -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:40 -#: of -msgid "On the client, add the `adaptiveclipping_mod` to the client-side mods:" +"An implementation of the abstract base class " +"`flwr.server.strategy.Strategy`. If no strategy is provided, then " +"`start_server` will use `flwr.server.strategy.FedAvg`." msgstr "" +"抽象基类 `flwr.server.strategy.Strategy` 的实现。如果没有提供策略，`start_server` 将使用 " +"`flwr.server.strategy.FedAvg`。" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 -#: of +#: flwr.server.app.start_server:16 of msgid "" -":py:obj:`aggregate_evaluate " -"`\\" -" \\(server\\_round\\, results\\, ...\\)" +"An implementation of the abstract base class `flwr.server.ClientManager`." +" If no implementation is provided, then `start_server` will use " +"`flwr.server.client_manager.SimpleClientManager`." msgstr "" +"抽象基类 `flwr.server.ClientManager` 的实现。如果没有提供实现，`start_server` 将使用 " +"`flwr.server.client_manager.SimpleClientManager`。" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 -#: of +#: flwr.server.app.start_server:21 of msgid "" -":py:obj:`aggregate_fit " -"`\\" -" \\(server\\_round\\, results\\, failures\\)" +"The maximum length of gRPC messages that can be exchanged with the Flower" +" clients. The default should be sufficient for most models. Users who " +"train very large models might need to increase this value. Note that the " +"Flower clients need to be started with the same value (see " +"`flwr.client.start_client`), otherwise clients will not know about the " +"increased limit and block larger messages." msgstr "" +"可与 Flower 客户端交换的 gRPC 消息的最大长度：默认值对大多数模型都足够了。训练超大模型的用户可能需要增加该值。请注意，Flower " +"客户端需要以相同的值启动（请参阅 `flwr.client.start_client`），否则客户端将不知道已增加的限制并阻止更大的消息。" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_fit:1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_fit:1 -#: of -#, fuzzy -msgid "Aggregate training results and update clip norms." -msgstr "汇总 DPFedAvgFixed 中的训练结果并更新片段标准。" - -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 -#: of +#: flwr.server.app.start_server:28 of msgid "" -":py:obj:`configure_evaluate " -"`\\" -" \\(server\\_round\\, parameters\\, ...\\)" +"Tuple containing root certificate, server certificate, and private key to" +" start a secure SSL-enabled server. The tuple is expected to have three " +"bytes elements in the following order: * CA certificate. * " +"server certificate. * server private key." msgstr "" +"包含根证书、服务器证书和私钥的元组，用于启动启用 SSL 的安全服务器。元组应按以下顺序包含三个字节元素： * CA 证书，* 服务器证书， * " +"服务器私钥。" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 -#: of +#: flwr.server.app.start_server:28 of msgid "" -":py:obj:`configure_fit " -"`\\" -" \\(server\\_round\\, parameters\\, ...\\)" -msgstr "" +"Tuple containing root certificate, server certificate, and private key to" +" start a secure SSL-enabled server. The tuple is expected to have three " +"bytes elements in the following order:" +msgstr "包含根证书、服务器证书和私钥的元组，用于启动启用 SSL 的安全服务器。元组应按以下顺序包含三个字节元素：" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`evaluate " -"`\\" -" \\(server\\_round\\, parameters\\)" -msgstr "" +#: flwr.server.app.start_server:32 of +msgid "CA certificate." +msgstr "CA 证书。" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`initialize_parameters " -"`\\" -" \\(client\\_manager\\)" -msgstr "" +#: flwr.server.app.start_server:33 of +msgid "server certificate." +msgstr "服务器证书。" -#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyClientSideFixedClipping.rst:2 +#: flwr.server.app.start_server:34 of +msgid "server private key." +msgstr "服务器私人密钥。" + +#: flwr.server.app.start_server:37 of +msgid "**hist** -- Object containing training and evaluation metrics." +msgstr "**hist** -- 包含训练和评估指标的对象。" + +#: flwr.server.app.start_server:42 of +msgid "Starting an insecure server:" +msgstr "启动不安全的服务器：" + +#: flwr.server.app.start_server:46 of +msgid "Starting an SSL-enabled server:" +msgstr "启动支持 SSL 的服务器：" + +#: ../../source/ref-api/flwr.server.strategy.rst:2 #, fuzzy -msgid "DifferentialPrivacyClientSideFixedClipping" -msgstr "差分隐私" +msgid "strategy" +msgstr "Krum 策略。" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:3 -#: of -msgid "Use `fixedclipping_mod` modifier at the client side." +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#, fuzzy +msgid "" +":py:obj:`Bulyan `\\ \\(\\*\\, " +"fraction\\_fit\\, fraction\\_evaluate\\, ...\\)" msgstr "" +":py:obj:`Bulyan `\\ \\(\\*\\, " +"fraction\\_fit\\, fraction\\_evaluate\\, ...\\)" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:5 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.bulyan.Bulyan:1 of +msgid "Bulyan strategy." +msgstr "Bulyan 策略。" + +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#, fuzzy msgid "" -"In comparison to `DifferentialPrivacyServerSideFixedClipping`, which " -"performs clipping on the server-side, " -"`DifferentialPrivacyClientSideFixedClipping` expects clipping to happen " -"on the client-side, usually by using the built-in `fixedclipping_mod`." +":py:obj:`DPFedAvgAdaptive `\\ " +"\\(strategy\\, num\\_sampled\\_clients\\)" msgstr "" +":py:obj:`DPFedAvgAdaptive `\\ " +"\\(strategy\\, num\\_sampled\\_clients\\)" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:12 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:5 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive:1 of +msgid "Wrapper for configuring a Strategy for DP with Adaptive Clipping." +msgstr "用于配置具有自适应剪切功能的 DP 策略的包装器。" + +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#, fuzzy msgid "" -"The noise multiplier for the Gaussian mechanism for model updates. A " -"value of 1.0 or higher is recommended for strong privacy." +":py:obj:`DPFedAvgFixed `\\ " +"\\(strategy\\, num\\_sampled\\_clients\\, ...\\)" msgstr "" +":py:obj:`DPFedAvgFixed `\\ " +"\\(strategy\\, num\\_sampled\\_clients\\, ...\\)" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:15 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:8 -#: of -msgid "The value of the clipping norm." -msgstr "" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed:1 of +msgid "Wrapper for configuring a Strategy for DP with Fixed Clipping." +msgstr "封装器，用于为具有固定剪切功能的 DP 配置策略。" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:26 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#, fuzzy msgid "" -"Wrap the strategy with the `DifferentialPrivacyClientSideFixedClipping` " -"wrapper:" +":py:obj:`DifferentialPrivacyClientSideAdaptiveClipping " +"`\\ " +"\\(...\\)" msgstr "" +":py:obj:`DifferentialPrivacyClientSideAdaptiveClipping " +"`\\ " +"\\(...\\)" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:32 +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:1 #: of -msgid "On the client, add the `fixedclipping_mod` to the client-side mods:" -msgstr "" +#, fuzzy +msgid "Strategy wrapper for central DP with client-side adaptive clipping." +msgstr "用于配置具有自适应剪切功能的 DP 策略的包装器。" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#, fuzzy msgid "" -":py:obj:`aggregate_evaluate " -"`\\" -" \\(server\\_round\\, results\\, ...\\)" +":py:obj:`DifferentialPrivacyServerSideAdaptiveClipping " +"`\\ " +"\\(...\\)" msgstr "" +":py:obj:`DifferentialPrivacyServerSideAdaptiveClipping " +"`\\ " +"\\(...\\)" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:1 #: of +#, fuzzy +msgid "Strategy wrapper for central DP with server-side adaptive clipping." +msgstr "用于配置具有自适应剪切功能的 DP 策略的包装器。" + +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#, fuzzy msgid "" -":py:obj:`aggregate_fit " -"`\\" -" \\(server\\_round\\, results\\, failures\\)" +":py:obj:`DifferentialPrivacyClientSideFixedClipping " +"`\\ " +"\\(...\\)" msgstr "" +":py:obj:`DifferentialPrivacyClientSideFixedClipping " +"`\\ " +"\\(...\\)" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_fit:1 +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:1 #: of #, fuzzy -msgid "Add noise to the aggregated parameters." -msgstr "然后将汇总结果序列化：" +msgid "Strategy wrapper for central DP with client-side fixed clipping." +msgstr "封装器，用于为具有固定剪切功能的 DP 配置策略。" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#, fuzzy msgid "" -":py:obj:`configure_evaluate " -"`\\" -" \\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`DifferentialPrivacyServerSideFixedClipping " +"`\\ " +"\\(...\\)" msgstr "" +":py:obj:`DifferentialPrivacyServerSideFixedClipping " +"`\\ " +"\\(...\\)" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:1 #: of -msgid "" -":py:obj:`configure_fit " -"`\\" -" \\(server\\_round\\, parameters\\, ...\\)" -msgstr "" +#, fuzzy +msgid "Strategy wrapper for central DP with server-side fixed clipping." +msgstr "封装器，用于为具有固定剪切功能的 DP 配置策略。" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#, fuzzy msgid "" -":py:obj:`evaluate " -"`\\" -" \\(server\\_round\\, parameters\\)" +":py:obj:`FedAdagrad `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, ...\\]\\)" msgstr "" +":py:obj:`FedAdagrad `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, ...\\]\\)" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fedadagrad.FedAdagrad:1 of +msgid "FedAdagrad strategy - Adaptive Federated Optimization using Adagrad." +msgstr "FedAdagrad 策略 - 使用 Adagrad 进行自适应联合优化。" + +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#, fuzzy msgid "" -":py:obj:`initialize_parameters " -"`\\" -" \\(client\\_manager\\)" +":py:obj:`FedAdam `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, ...\\]\\)" msgstr "" +":py:obj:`FedAdam `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, ...\\]\\)" -#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyServerSideAdaptiveClipping.rst:2 -msgid "DifferentialPrivacyServerSideAdaptiveClipping" -msgstr "" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fedadam.FedAdam:1 of +msgid "FedAdam - Adaptive Federated Optimization using Adam." +msgstr "FedAdam - 使用 Adam 进行自适应联合优化。" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:17 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#, fuzzy msgid "" -"The standard deviation of the noise added to the count of updates below " -"the estimate. Andrew et al. recommends to set to " -"`expected_num_records/20`" +":py:obj:`FedAvg `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, fraction\\_evaluate\\, ...\\]\\)" msgstr "" +":py:obj:`FedAvg `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, fraction\\_evaluate\\, ...\\]\\)" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:27 -#: of -msgid "" -"Wrap the strategy with the DifferentialPrivacyServerSideAdaptiveClipping " -"wrapper" -msgstr "" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fedavg.FedAvg:1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:1 of +msgid "Federated Averaging strategy." +msgstr "联邦平均策略。" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#, fuzzy msgid "" -":py:obj:`aggregate_evaluate " -"`\\" -" \\(server\\_round\\, results\\, ...\\)" +":py:obj:`FedAvgAndroid `\\ " +"\\(\\*\\[\\, fraction\\_fit\\, ...\\]\\)" msgstr "" +":py:obj:`FedAvgAndroid `\\ " +"\\(\\*\\[\\, fraction\\_fit\\, ...\\]\\)" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#, fuzzy msgid "" -":py:obj:`aggregate_fit " -"`\\" -" \\(server\\_round\\, results\\, failures\\)" +":py:obj:`FedAvgM `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, ...\\]\\)" msgstr "" +":py:obj:`FedAvgM `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, ...\\]\\)" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`configure_evaluate " -"`\\" -" \\(server\\_round\\, parameters\\, ...\\)" -msgstr "" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fedavgm.FedAvgM:1 of +msgid "Federated Averaging with Momentum strategy." +msgstr "联邦平均动量策略。" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#, fuzzy msgid "" -":py:obj:`configure_fit " -"`\\" -" \\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`FedMedian `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, ...\\]\\)" msgstr "" +":py:obj:`FedMedian `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, ...\\]\\)" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`evaluate " -"`\\" -" \\(server\\_round\\, parameters\\)" -msgstr "" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fedmedian.FedMedian:1 of +#, fuzzy +msgid "Configurable FedMedian strategy implementation." +msgstr "可配置的 FedAvg 策略实施。" -#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#, fuzzy msgid "" -":py:obj:`initialize_parameters " -"`\\" -" \\(client\\_manager\\)" +":py:obj:`FedOpt `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, fraction\\_evaluate\\, ...\\]\\)" msgstr "" +":py:obj:`FedOpt `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, fraction\\_evaluate\\, ...\\]\\)" -#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyServerSideFixedClipping.rst:2 +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fedopt.FedOpt:1 of #, fuzzy -msgid "DifferentialPrivacyServerSideFixedClipping" -msgstr "差分隐私" +msgid "Federated Optim strategy." +msgstr "联邦优化策略。" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:19 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#, fuzzy msgid "" -"Wrap the strategy with the DifferentialPrivacyServerSideFixedClipping " -"wrapper" +":py:obj:`FedProx `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, ...\\]\\)" msgstr "" +":py:obj:`FedProx `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, ...\\]\\)" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`aggregate_evaluate " -"`\\" -" \\(server\\_round\\, results\\, ...\\)" -msgstr "" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fedprox.FedProx:1 of +msgid "Federated Optimization strategy." +msgstr "联邦优化策略。" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#, fuzzy msgid "" -":py:obj:`aggregate_fit " -"`\\" -" \\(server\\_round\\, results\\, failures\\)" +":py:obj:`FedTrimmedAvg `\\ " +"\\(\\*\\[\\, fraction\\_fit\\, ...\\]\\)" msgstr "" +":py:obj:`FedTrimmedAvg `\\ " +"\\(\\*\\[\\, fraction\\_fit\\, ...\\]\\)" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_fit:1 -#: of -msgid "Compute the updates, clip, and pass them for aggregation." -msgstr "" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:1 of +msgid "Federated Averaging with Trimmed Mean [Dong Yin, et al., 2021]." +msgstr "带修剪均值的联邦平均法[Dong Yin 等，2021]。" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#, fuzzy msgid "" -":py:obj:`configure_evaluate " -"`\\" -" \\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`FedXgbBagging `\\ " +"\\(\\[evaluate\\_function\\]\\)" msgstr "" +":py:obj:`FedXgbBagging `\\ " +"\\(\\[evaluate\\_function\\]\\)" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`configure_fit " -"`\\" -" \\(server\\_round\\, parameters\\, ...\\)" -msgstr "" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging:1 of +#, fuzzy +msgid "Configurable FedXgbBagging strategy implementation." +msgstr "可配置的 FedXgbNAvg 策略实施。" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#, fuzzy msgid "" -":py:obj:`evaluate " -"`\\" -" \\(server\\_round\\, parameters\\)" +":py:obj:`FedXgbCyclic `\\ " +"\\(\\*\\*kwargs\\)" msgstr "" +":py:obj:`FedXgbCyclic `\\ " +"\\(\\*\\*kwargs\\)" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic:1 of +#, fuzzy +msgid "Configurable FedXgbCyclic strategy implementation." +msgstr "可配置的 FedAvg 策略实施。" + +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#, fuzzy msgid "" -":py:obj:`initialize_parameters " -"`\\" -" \\(client\\_manager\\)" +":py:obj:`FedXgbNnAvg `\\ \\(\\*args\\, " +"\\*\\*kwargs\\)" msgstr "" +":py:obj:`FedXgbNnAvg `\\ \\(\\*args\\, " +"\\*\\*kwargs\\)" -#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_fit:3 -#: of -msgid "Afterward, add noise to the aggregated parameters." -msgstr "" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg:1 of +msgid "Configurable FedXgbNnAvg strategy implementation." +msgstr "可配置的 FedXgbNAvg 策略实施。" -#: ../../source/ref-api/flwr.server.strategy.FaultTolerantFedAvg.rst:2 +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 #, fuzzy -msgid "FaultTolerantFedAvg" -msgstr "server.strategy.FaultTolerantFedAvg" - -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: of msgid "" -":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" +":py:obj:`FedYogi `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, ...\\]\\)" msgstr "" +":py:obj:`FedYogi `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, ...\\]\\)" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`aggregate_fit " -"`\\ " -"\\(server\\_round\\, results\\, failures\\)" -msgstr "" - -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_fit:1 -#: flwr.server.strategy.fedadagrad.FedAdagrad.aggregate_fit:1 -#: flwr.server.strategy.fedadam.FedAdam.aggregate_fit:1 -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg.FedAvg.aggregate_fit:1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_fit:1 -#: flwr.server.strategy.fedavgm.FedAvgM.aggregate_fit:1 -#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg.aggregate_fit:1 -#: flwr.server.strategy.fedyogi.FedYogi.aggregate_fit:1 -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_fit:1 of -msgid "Aggregate fit results using weighted average." -msgstr "使用加权平均法汇总拟合结果。" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fedyogi.FedYogi:1 of +msgid "FedYogi [Reddi et al., 2020] strategy." +msgstr "FedYogi [Reddi 等人，2020] 策略。" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#, fuzzy msgid "" -":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`FaultTolerantFedAvg " +"`\\ \\(\\*\\[\\, " +"fraction\\_fit\\, ...\\]\\)" msgstr "" +":py:obj:`FaultTolerantFedAvg " +"`\\ \\(\\*\\[\\, " +"fraction\\_fit\\, ...\\]\\)" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`configure_fit " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" -msgstr "" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg:1 of +msgid "Configurable fault-tolerant FedAvg strategy implementation." +msgstr "可配置的容错 FedAvg 策略实施。" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#, fuzzy msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" +":py:obj:`Krum `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, fraction\\_evaluate\\, ...\\]\\)" msgstr "" +":py:obj:`Krum `\\ \\(\\*\\[\\, " +"fraction\\_fit\\, fraction\\_evaluate\\, ...\\]\\)" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" -msgstr "" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.krum.Krum:1 of +#, fuzzy +msgid "Krum [Blanchard et al., 2017] strategy." +msgstr "FedYogi [Reddi 等人，2020] 策略。" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: of +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#, fuzzy msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +":py:obj:`QFedAvg `\\ \\(\\*\\[\\, " +"q\\_param\\, qffl\\_learning\\_rate\\, ...\\]\\)" msgstr "" +":py:obj:`QFedAvg `\\ \\(\\*\\[\\, " +"q\\_param\\, qffl\\_learning\\_rate\\, ...\\]\\)" -#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`num_fit_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" -msgstr "" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.qfedavg.QFedAvg:1 of +msgid "Configurable QFedAvg strategy implementation." +msgstr "可配置的 QFedAvg 策略实施。" -#: ../../source/ref-api/flwr.server.strategy.FedAdagrad.rst:2 -#: ../../source/ref-changelog.md:839 -msgid "FedAdagrad" -msgstr "FedAdagrad" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#, fuzzy +msgid ":py:obj:`Strategy `\\ \\(\\)" +msgstr "server.strategy.Strategy" -#: flwr.server.strategy.fedadagrad.FedAdagrad:1 -#: flwr.server.strategy.fedadam.FedAdam:1 -#: flwr.server.strategy.fedyogi.FedYogi:1 of -msgid "Bases: :py:class:`~flwr.server.strategy.fedopt.FedOpt`" -msgstr "" +#: ../../source/ref-api/flwr.server.strategy.rst:45::1 +#: flwr.server.strategy.strategy.Strategy:1 of +msgid "Abstract base class for server strategy implementations." +msgstr "服务器策略实现的抽象基类。" -#: flwr.server.strategy.fedadagrad.FedAdagrad:3 -#: flwr.server.strategy.fedadam.FedAdam:3 flwr.server.strategy.fedopt.FedOpt:3 -#: flwr.server.strategy.fedyogi.FedYogi:3 of -msgid "Implementation based on https://arxiv.org/abs/2003.00295v5" -msgstr "实施基于 https://arxiv.org/abs/2003.00295v5" +#: ../../source/ref-api/flwr.server.strategy.Bulyan.rst:2 +#, fuzzy +msgid "Bulyan" +msgstr "Bulyan" -#: flwr.server.strategy.fedadagrad.FedAdagrad:21 -#: flwr.server.strategy.fedadagrad.FedAdagrad:23 -#: flwr.server.strategy.fedadam.FedAdam:25 -#: flwr.server.strategy.fedadam.FedAdam:27 -#: flwr.server.strategy.fedavg.FedAvg:29 flwr.server.strategy.fedavg.FedAvg:31 -#: flwr.server.strategy.fedopt.FedOpt:25 flwr.server.strategy.fedopt.FedOpt:27 -#: flwr.server.strategy.fedprox.FedProx:61 -#: flwr.server.strategy.fedprox.FedProx:63 -#: flwr.server.strategy.fedyogi.FedYogi:28 -#: flwr.server.strategy.fedyogi.FedYogi:30 of -msgid "Metrics aggregation function, optional." -msgstr "指标汇总功能，可选。" +#: flwr.server.strategy.bulyan.Bulyan:1 +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg:1 +#: flwr.server.strategy.fedavgm.FedAvgM:1 +#: flwr.server.strategy.fedmedian.FedMedian:1 +#: flwr.server.strategy.fedopt.FedOpt:1 flwr.server.strategy.fedprox.FedProx:1 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:1 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging:1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic:1 +#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg:1 +#: flwr.server.strategy.krum.Krum:1 flwr.server.strategy.qfedavg.QFedAvg:1 of +#, fuzzy +msgid "Bases: :py:class:`~flwr.server.strategy.fedavg.FedAvg`" +msgstr "server.strategy.DPFedAvgFixed" -#: flwr.server.strategy.fedadagrad.FedAdagrad:29 -#: flwr.server.strategy.fedadam.FedAdam:29 -#: flwr.server.strategy.fedopt.FedOpt:29 of -msgid "Server-side learning rate. Defaults to 1e-1." -msgstr "服务器端学习率。默认为 1e-1。" +#: flwr.server.strategy.bulyan.Bulyan:3 of +msgid "Implementation based on https://arxiv.org/abs/1802.07927." +msgstr "实施基于 https://arxiv.org/abs/1802.07927。" -#: flwr.server.strategy.fedadagrad.FedAdagrad:31 -#: flwr.server.strategy.fedadam.FedAdam:31 -#: flwr.server.strategy.fedopt.FedOpt:31 of -msgid "Client-side learning rate. Defaults to 1e-1." -msgstr "客户端学习率。默认为 1e-1。" +#: flwr.server.strategy.bulyan.Bulyan:5 +#: flwr.server.strategy.fedadagrad.FedAdagrad:5 +#: flwr.server.strategy.fedadam.FedAdam:5 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:5 +#: flwr.server.strategy.fedavgm.FedAvgM:5 flwr.server.strategy.fedopt.FedOpt:5 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:5 +#: flwr.server.strategy.fedyogi.FedYogi:5 flwr.server.strategy.krum.Krum:5 of +msgid "Fraction of clients used during training. Defaults to 1.0." +msgstr "训练期间使用客户的比例。默认为 1.0。" -#: flwr.server.strategy.fedadagrad.FedAdagrad:33 -#: flwr.server.strategy.fedadam.FedAdam:37 -#: flwr.server.strategy.fedopt.FedOpt:37 of -msgid "Controls the algorithm's degree of adaptability. Defaults to 1e-9." -msgstr "控制算法的适应度。默认为 1e-9。" +#: flwr.server.strategy.bulyan.Bulyan:7 +#: flwr.server.strategy.fedadagrad.FedAdagrad:7 +#: flwr.server.strategy.fedadam.FedAdam:7 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:7 +#: flwr.server.strategy.fedavgm.FedAvgM:7 flwr.server.strategy.fedopt.FedOpt:7 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:7 +#: flwr.server.strategy.fedyogi.FedYogi:7 flwr.server.strategy.krum.Krum:7 of +msgid "Fraction of clients used during validation. Defaults to 1.0." +msgstr "验证过程中使用的客户端比例。默认为 1.0。" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" -msgstr "" +#: flwr.server.strategy.bulyan.Bulyan:9 +#: flwr.server.strategy.fedadagrad.FedAdagrad:9 +#: flwr.server.strategy.fedadam.FedAdam:9 flwr.server.strategy.fedavg.FedAvg:13 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:9 +#: flwr.server.strategy.fedavgm.FedAvgM:9 flwr.server.strategy.fedopt.FedOpt:9 +#: flwr.server.strategy.fedprox.FedProx:45 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:9 +#: flwr.server.strategy.fedyogi.FedYogi:9 flwr.server.strategy.krum.Krum:9 of +msgid "Minimum number of clients used during training. Defaults to 2." +msgstr "训练期间使用的最少客户数。默认为 2。" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`aggregate_fit `\\" -" \\(server\\_round\\, results\\, failures\\)" -msgstr "" +#: flwr.server.strategy.bulyan.Bulyan:11 +#: flwr.server.strategy.fedadagrad.FedAdagrad:11 +#: flwr.server.strategy.fedadam.FedAdam:11 +#: flwr.server.strategy.fedavg.FedAvg:15 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:11 +#: flwr.server.strategy.fedavgm.FedAvgM:11 +#: flwr.server.strategy.fedopt.FedOpt:11 +#: flwr.server.strategy.fedprox.FedProx:47 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:11 +#: flwr.server.strategy.fedyogi.FedYogi:11 flwr.server.strategy.krum.Krum:11 of +msgid "Minimum number of clients used during validation. Defaults to 2." +msgstr "验证过程中使用的最少客户端数量。默认为 2。" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" -msgstr "" +#: flwr.server.strategy.bulyan.Bulyan:13 +#: flwr.server.strategy.fedadagrad.FedAdagrad:13 +#: flwr.server.strategy.fedadam.FedAdam:13 +#: flwr.server.strategy.fedavg.FedAvg:17 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:13 +#: flwr.server.strategy.fedavgm.FedAvgM:13 +#: flwr.server.strategy.fedopt.FedOpt:13 +#: flwr.server.strategy.fedprox.FedProx:49 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:13 +#: flwr.server.strategy.fedyogi.FedYogi:13 flwr.server.strategy.krum.Krum:13 of +msgid "Minimum number of total clients in the system. Defaults to 2." +msgstr "系统中客户总数的最小值。默认为 2。" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`configure_fit `\\" -" \\(server\\_round\\, parameters\\, ...\\)" -msgstr "" +#: flwr.server.strategy.bulyan.Bulyan:15 flwr.server.strategy.krum.Krum:15 of +msgid "Number of malicious clients in the system. Defaults to 0." +msgstr "系统中恶意客户端的数量。默认为 0。" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" -msgstr "" +#: flwr.server.strategy.bulyan.Bulyan:17 +#: flwr.server.strategy.fedadagrad.FedAdagrad:15 +#: flwr.server.strategy.fedadam.FedAdam:15 +#: flwr.server.strategy.fedavg.FedAvg:19 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:15 +#: flwr.server.strategy.fedavgm.FedAvgM:15 +#: flwr.server.strategy.fedopt.FedOpt:15 +#: flwr.server.strategy.fedprox.FedProx:51 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:15 +#: flwr.server.strategy.fedyogi.FedYogi:17 +#: flwr.server.strategy.fedyogi.FedYogi:18 +#: flwr.server.strategy.fedyogi.FedYogi:19 flwr.server.strategy.krum.Krum:20 of +msgid "Optional function used for validation. Defaults to None." +msgstr "用于验证的可选函数。默认为 \"无\"。" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" -msgstr "" +#: flwr.server.strategy.bulyan.Bulyan:19 +#: flwr.server.strategy.fedadagrad.FedAdagrad:17 +#: flwr.server.strategy.fedadam.FedAdam:17 +#: flwr.server.strategy.fedavg.FedAvg:21 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:17 +#: flwr.server.strategy.fedavgm.FedAvgM:17 +#: flwr.server.strategy.fedopt.FedOpt:17 +#: flwr.server.strategy.fedprox.FedProx:53 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:17 +#: flwr.server.strategy.fedyogi.FedYogi:20 flwr.server.strategy.krum.Krum:22 of +msgid "Function used to configure training. Defaults to None." +msgstr "用于配置训练的功能。默认为 \"无\"。" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" -msgstr "" +#: flwr.server.strategy.bulyan.Bulyan:21 +#: flwr.server.strategy.fedadagrad.FedAdagrad:19 +#: flwr.server.strategy.fedadam.FedAdam:19 +#: flwr.server.strategy.fedavg.FedAvg:23 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:19 +#: flwr.server.strategy.fedavgm.FedAvgM:19 +#: flwr.server.strategy.fedopt.FedOpt:19 +#: flwr.server.strategy.fedprox.FedProx:55 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:19 +#: flwr.server.strategy.fedyogi.FedYogi:22 flwr.server.strategy.krum.Krum:24 of +msgid "Function used to configure validation. Defaults to None." +msgstr "用于配置验证的函数。默认为 \"无\"。" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`num_fit_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" -msgstr "" +#: flwr.server.strategy.bulyan.Bulyan:23 +#: flwr.server.strategy.fedadagrad.FedAdagrad:25 +#: flwr.server.strategy.fedadam.FedAdam:21 +#: flwr.server.strategy.fedavg.FedAvg:25 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:21 +#: flwr.server.strategy.fedavgm.FedAvgM:21 +#: flwr.server.strategy.fedopt.FedOpt:21 +#: flwr.server.strategy.fedprox.FedProx:57 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:21 +#: flwr.server.strategy.fedyogi.FedYogi:24 flwr.server.strategy.krum.Krum:26 of +msgid "Whether or not accept rounds containing failures. Defaults to True." +msgstr "是否接受包含失败的轮。默认为 True。" -#: ../../source/ref-api/flwr.server.strategy.FedAdam.rst:2 -#, fuzzy -msgid "FedAdam" -msgstr "FedAdagrad" +#: flwr.server.strategy.bulyan.Bulyan:25 +#: flwr.server.strategy.fedadagrad.FedAdagrad:27 +#: flwr.server.strategy.fedadam.FedAdam:23 +#: flwr.server.strategy.fedavg.FedAvg:27 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:24 +#: flwr.server.strategy.fedavgm.FedAvgM:23 +#: flwr.server.strategy.fedopt.FedOpt:23 +#: flwr.server.strategy.fedprox.FedProx:59 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:23 +#: flwr.server.strategy.fedyogi.FedYogi:26 flwr.server.strategy.krum.Krum:28 of +msgid "Initial global model parameters." +msgstr "初始全局模型参数。" -#: flwr.server.strategy.fedadam.FedAdam:33 -#: flwr.server.strategy.fedyogi.FedYogi:36 of -msgid "Momentum parameter. Defaults to 0.9." -msgstr "动量参数。默认为 0.9。" +#: flwr.server.strategy.bulyan.Bulyan:27 of +msgid "" +"Byzantine resilient aggregation rule that is used as the first step of " +"the Bulyan (e.g., Krum)" +msgstr "Byzantine弹性聚合规则，用作 Bulyan 的第一步（如 Krum）" -#: flwr.server.strategy.fedadam.FedAdam:35 -#: flwr.server.strategy.fedyogi.FedYogi:38 of -msgid "Second moment parameter. Defaults to 0.99." -msgstr "第二动量参数。默认为 0.99。" +#: flwr.server.strategy.bulyan.Bulyan:29 of +msgid "arguments to the first_aggregation rule" +msgstr "第一聚类规则的参数" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" ":py:obj:`aggregate_evaluate " -"`\\ \\(server\\_round\\," -" results\\, ...\\)" +"`\\ \\(server\\_round\\, " +"results\\, ...\\)" msgstr "" +":py:obj:`aggregate_evaluate " +"`\\ \\(server\\_round\\, " +"results\\, ...\\)" + +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1 +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of +msgid "Aggregate evaluation losses using weighted average." +msgstr "采用加权平均法计算评估损失总额。" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -":py:obj:`aggregate_fit `\\ " +":py:obj:`aggregate_fit `\\ " "\\(server\\_round\\, results\\, failures\\)" msgstr "" +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" + +#: flwr.server.strategy.bulyan.Bulyan.aggregate_fit:1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +msgid "Aggregate fit results using Bulyan." +msgstr "使用 Bulyan 技术汇总拟合结果。" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" ":py:obj:`configure_evaluate " -"`\\ \\(server\\_round\\," -" parameters\\, ...\\)" +"`\\ \\(server\\_round\\, " +"parameters\\, ...\\)" msgstr "" +":py:obj:`configure_evaluate " +"`\\ \\(server\\_round\\, " +"parameters\\, ...\\)" + +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.configure_evaluate:1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.configure_evaluate:1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.configure_evaluate:1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.configure_evaluate:1 +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.configure_evaluate:1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.configure_evaluate:1 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.configure_evaluate:1 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.qfedavg.QFedAvg.configure_evaluate:1 +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 +#: flwr.server.strategy.strategy.Strategy.configure_evaluate:1 of +msgid "Configure the next round of evaluation." +msgstr "配置下一轮评估。" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -":py:obj:`configure_fit `\\ " +":py:obj:`configure_fit `\\ " "\\(server\\_round\\, parameters\\, ...\\)" msgstr "" +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" + +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.configure_fit:1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.configure_fit:1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.configure_fit:1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.configure_fit:1 +#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive.configure_fit:1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.configure_fit:1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.configure_fit:1 +#: flwr.server.strategy.fedprox.FedProx.configure_fit:1 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.configure_fit:1 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.qfedavg.QFedAvg.configure_fit:1 +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 +#: flwr.server.strategy.strategy.Strategy.configure_fit:1 of +msgid "Configure the next round of training." +msgstr "配置下一轮训练。" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -":py:obj:`evaluate `\\ " +":py:obj:`evaluate `\\ " "\\(server\\_round\\, parameters\\)" msgstr "" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" + +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.evaluate:1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.evaluate:1 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.evaluate:1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg.evaluate:1 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of +msgid "Evaluate model parameters using an evaluation function." +msgstr "使用评估函数评估模型参数。" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" ":py:obj:`initialize_parameters " -"`\\ " +"`\\ " "\\(client\\_manager\\)" msgstr "" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" + +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.initialize_parameters:1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.initialize_parameters:1 +#: flwr.server.strategy.fedavgm.FedAvgM.initialize_parameters:1 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of +msgid "Initialize global model parameters." +msgstr "初始化全局模型参数。" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" ":py:obj:`num_evaluation_clients " -"`\\ " +"`\\ " "\\(num\\_available\\_clients\\)" msgstr "" - -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`num_fit_clients " -"`\\ " +":py:obj:`num_evaluation_clients " +"`\\ " "\\(num\\_available\\_clients\\)" -msgstr "" - -#: ../../source/ref-api/flwr.server.strategy.FedAvg.rst:2 -#, fuzzy -msgid "FedAvg" -msgstr "DP-FedAvg" -#: flwr.server.strategy.fedavg.FedAvg:3 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid:3 of -msgid "Implementation based on https://arxiv.org/abs/1602.05629" -msgstr "实施基于 https://arxiv.org/abs/1602.05629" +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.num_evaluation_clients:1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.num_evaluation_clients:1 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.qfedavg.QFedAvg.num_evaluation_clients:1 of +msgid "Use a fraction of available clients for evaluation." +msgstr "使用部分可用客户进行评估。" -#: flwr.server.strategy.fedavg.FedAvg:5 flwr.server.strategy.fedprox.FedProx:37 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -"Fraction of clients used during training. In case `min_fit_clients` is " -"larger than `fraction_fit * available_clients`, `min_fit_clients` will " -"still be sampled. Defaults to 1.0." +":py:obj:`num_fit_clients `\\" +" \\(num\\_available\\_clients\\)" msgstr "" -"训练过程中使用的客户端比例。如果 `min_fit_clients` 大于 `fraction_fit * " -"available_clients`，则仍会对 `min_fit_clients` 进行采样。默认为 1.0。" +":py:obj:`num_fit_clients `\\" +" \\(num\\_available\\_clients\\)" -#: flwr.server.strategy.fedavg.FedAvg:9 flwr.server.strategy.fedprox.FedProx:41 -#: of -msgid "" -"Fraction of clients used during validation. In case " -"`min_evaluate_clients` is larger than `fraction_evaluate * " -"available_clients`, `min_evaluate_clients` will still be sampled. " -"Defaults to 1.0." -msgstr "" -"验证过程中使用的客户端的比例。如果 `min_evaluate_clients` 大于 `fraction_evaluate * " -"available_clients`，则仍会对 `min_evaluate_clients` 进行采样。默认为 1.0。" +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.num_fit_clients:1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.num_fit_clients:1 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.qfedavg.QFedAvg.num_fit_clients:1 of +msgid "Return the sample size and the required number of available clients." +msgstr "返回样本大小和所需的可用客户数量。" -#: flwr.server.strategy.fedavg.FedAvg:33 of -msgid "Enable (True) or disable (False) in-place aggregation of model updates." -msgstr "" +#: ../../source/ref-api/flwr.server.strategy.DPFedAvgAdaptive.rst:2 +msgid "DPFedAvgAdaptive" +msgstr "DPFedAvgAdaptive" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive:1 of +#, fuzzy +msgid "Bases: :py:class:`~flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed`" +msgstr "Bases: :py:class:`~flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed`" + +#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive:3 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed:3 of +#, fuzzy +msgid "This class is deprecated and will be removed in a future release." +msgstr "该类已被弃用，将在以后的版本中删除。" + +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" ":py:obj:`aggregate_evaluate " -"`\\ \\(server\\_round\\, " -"results\\, ...\\)" +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" msgstr "" +":py:obj:`aggregate_evaluate " +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: of +msgid "Aggregate evaluation losses using the given strategy." +msgstr "使用给定的策略汇总评估损失。" + +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" -":py:obj:`aggregate_fit `\\ " +":py:obj:`aggregate_fit " +"`\\ " "\\(server\\_round\\, results\\, failures\\)" msgstr "" +":py:obj:`aggregate_fit " +"`\\ " +"\\(server\\_round\\, results\\, failures\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive.aggregate_fit:1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: of +msgid "Aggregate training results as in DPFedAvgFixed and update clip norms." +msgstr "汇总 DPFedAvgFixed 中的训练结果并更新片段标准。" + +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" ":py:obj:`configure_evaluate " -"`\\ \\(server\\_round\\, " -"parameters\\, ...\\)" +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" +":py:obj:`configure_evaluate " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:1 of +msgid "Configure the next round of evaluation using the specified strategy." +msgstr "使用指定策略配置下一轮评估。" + +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" -":py:obj:`configure_fit `\\ " +":py:obj:`configure_fit " +"`\\ " "\\(server\\_round\\, parameters\\, ...\\)" msgstr "" +":py:obj:`configure_fit " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" -":py:obj:`evaluate `\\ " +":py:obj:`evaluate `\\ " "\\(server\\_round\\, parameters\\)" msgstr "" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.evaluate:1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.evaluate:1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.evaluate:1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.evaluate:1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.evaluate:1 of +msgid "Evaluate model parameters using an evaluation function from the strategy." +msgstr "使用策略中的评估函数评估模型参数。" + +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" ":py:obj:`initialize_parameters " -"`\\ " +"`\\ " "\\(client\\_manager\\)" msgstr "" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" -msgstr "" +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.initialize_parameters:1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.initialize_parameters:1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.initialize_parameters:1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.initialize_parameters:1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.initialize_parameters:1 of +msgid "Initialize global model parameters using given strategy." +msgstr "使用给定的策略初始化全局模型参数。" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:3 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:6 +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:3 +#: flwr.server.strategy.strategy.Strategy.aggregate_fit:3 +#: flwr.server.strategy.strategy.Strategy.configure_evaluate:3 +#: flwr.server.strategy.strategy.Strategy.configure_fit:3 +#: flwr.server.strategy.strategy.Strategy.evaluate:6 of +msgid "The current round of federated learning." +msgstr "本轮联邦学习。" + +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:7 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:10 +#: flwr.server.strategy.strategy.Strategy.configure_evaluate:7 +#: flwr.server.strategy.strategy.Strategy.configure_fit:7 +#: flwr.server.strategy.strategy.Strategy.initialize_parameters:3 of +msgid "The client manager which holds all currently connected clients." +msgstr "客户端管理器，用于管理当前连接的所有客户端。" + +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:10 +#: flwr.server.strategy.strategy.Strategy.configure_evaluate:10 of msgid "" -":py:obj:`num_fit_clients `\\" -" \\(num\\_available\\_clients\\)" +"**evaluate_configuration** -- A list of tuples. Each tuple in the list " +"identifies a `ClientProxy` and the `EvaluateIns` for this particular " +"`ClientProxy`. If a particular `ClientProxy` is not included in this " +"list, it means that this `ClientProxy` will not participate in the next " +"round of federated evaluation." msgstr "" +"**evaluate_configuration** -- " +"一个元组列表。列表中的每个元组都标识了一个`ClientProxy`和该特定`ClientProxy`的`EvaluateIns`。如果某个特定的" +" `ClientProxy` 未包含在此列表中，则表示该 `ClientProxy` 将不参与下一轮联合评估。" -#: ../../source/ref-api/flwr.server.strategy.FedAvgAndroid.rst:2 +#: ../../source/ref-api/flwr.server.strategy.DPFedAvgFixed.rst:2 +msgid "DPFedAvgFixed" +msgstr "DPFedAvgFixed" + +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed:1 +#: flwr.server.strategy.fedavg.FedAvg:1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:1 of #, fuzzy -msgid "FedAvgAndroid" -msgstr "DPFedAvgAdaptive" +msgid "Bases: :py:class:`~flwr.server.strategy.strategy.Strategy`" +msgstr "Bases: :py:class:`~flwr.server.strategy.strategy.Strategy`" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 #: of +#, fuzzy msgid "" ":py:obj:`aggregate_evaluate " -"`\\ " +"`\\ " "\\(server\\_round\\, results\\, ...\\)" msgstr "" +":py:obj:`aggregate_evaluate " +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 #: of +#, fuzzy msgid "" ":py:obj:`aggregate_fit " -"`\\ " +"`\\ " "\\(server\\_round\\, results\\, failures\\)" msgstr "" +":py:obj:`aggregate_fit " +"`\\ " +"\\(server\\_round\\, results\\, failures\\)" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`bytes_to_ndarray " -"`\\ \\(tensor\\)" -msgstr "" - -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.bytes_to_ndarray:1 of -#, fuzzy -msgid "Deserialize NumPy array from bytes." -msgstr "从字节反序列化 NumPy ndarray。" +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_fit:1 of +msgid "Aggregate training results using unweighted aggregation." +msgstr "使用非加权汇总法汇总训练结果。" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 #: of +#, fuzzy msgid "" ":py:obj:`configure_evaluate " -"`\\ " +"`\\ " "\\(server\\_round\\, parameters\\, ...\\)" msgstr "" +":py:obj:`configure_evaluate " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 #: of +#, fuzzy msgid "" ":py:obj:`configure_fit " -"`\\ " +"`\\ " "\\(server\\_round\\, parameters\\, ...\\)" msgstr "" +":py:obj:`configure_fit " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:1 of msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" -msgstr "" +"Configure the next round of training incorporating Differential Privacy " +"(DP)." +msgstr "配置包含差分隐私 (DP) 的下一轮训练。" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 #: of +#, fuzzy +msgid "" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" +msgstr "" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" + +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" ":py:obj:`initialize_parameters " -"`\\ " +"`\\ " "\\(client\\_manager\\)" msgstr "" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:3 of msgid "" -":py:obj:`ndarray_to_bytes " -"`\\ \\(ndarray\\)" +"Configuration of the next training round includes information related to " +"DP, such as clip norm and noise stddev." +msgstr "下一轮训练的配置包括与 DP 相关的信息，如片段规范和噪声 stddev。" + +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:13 +#: flwr.server.strategy.strategy.Strategy.configure_fit:10 of +msgid "" +"**fit_configuration** -- A list of tuples. Each tuple in the list " +"identifies a `ClientProxy` and the `FitIns` for this particular " +"`ClientProxy`. If a particular `ClientProxy` is not included in this " +"list, it means that this `ClientProxy` will not participate in the next " +"round of federated learning." msgstr "" +"**fit_configuration** -- " +"一个元组列表。列表中的每个元组都标识了一个`ClientProxy`和该特定`ClientProxy`的`FitIns'。如果某个特定的`ClientProxy`不在此列表中，则表示该`ClientProxy`将不参加下一轮联合学习。" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.ndarray_to_bytes:1 of +#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyClientSideAdaptiveClipping.rst:2 #, fuzzy -msgid "Serialize NumPy array to bytes." -msgstr "将 NumPy ndarray 序列化为字节。" +msgid "DifferentialPrivacyClientSideAdaptiveClipping" +msgstr "DifferentialPrivacyClientSideAdaptiveClipping" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:3 #: of -msgid "" -":py:obj:`ndarrays_to_parameters " -"`\\ " -"\\(ndarrays\\)" -msgstr "" +#, fuzzy +msgid "Use `adaptiveclipping_mod` modifier at the client side." +msgstr "在客户端使用 \"adaptiveclipping_mod \"修改器。" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:5 #: of +#, fuzzy msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +"In comparison to `DifferentialPrivacyServerSideAdaptiveClipping`, which " +"performs clipping on the server-side, " +"`DifferentialPrivacyClientSideAdaptiveClipping` expects clipping to " +"happen on the client-side, usually by using the built-in " +"`adaptiveclipping_mod`." msgstr "" +"与在服务器端执行剪切的 `DifferentialPrivacyServerSideAdaptiveClipping` " +"相比，`DifferentialPrivacyClientSideAdaptiveClipping` 希望在客户端进行剪切，通常使用内置的 " +"`adaptiveclipping_mod`。" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:10 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:3 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:10 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:3 #: of -msgid "" -":py:obj:`num_fit_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" -msgstr "" +#, fuzzy +msgid "The strategy to which DP functionalities will be added by this wrapper." +msgstr "该包装器将添加 DP 功能的策略。" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:12 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:5 +#: of +#, fuzzy +msgid "The noise multiplier for the Gaussian mechanism for model updates." +msgstr "用于模型更新的高斯机制的噪声乘数。" + +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:14 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:7 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:17 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:10 +#: of +#, fuzzy +msgid "The number of clients that are sampled on each round." +msgstr "每轮取样的客户数。" + +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:16 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:9 #: of +#, fuzzy msgid "" -":py:obj:`parameters_to_ndarrays " -"`\\ " -"\\(parameters\\)" -msgstr "" +"The initial value of clipping norm. Defaults to 0.1. Andrew et al. " +"recommends to set to 0.1." +msgstr "剪切规范的初始值。默认为 0.1。安德鲁等人建议设置为 0.1。" -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedavg_android.FedAvgAndroid.parameters_to_ndarrays:1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:19 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:12 #: of #, fuzzy -msgid "Convert parameters object to NumPy weights." -msgstr "将参数对象转换为 NumPy ndarrays。" +msgid "The desired quantile of updates which should be clipped. Defaults to 0.5." +msgstr "需要剪切的更新量化值。默认为 0.5。" -#: ../../source/ref-api/flwr.server.strategy.FedAvgM.rst:2 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:21 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:14 +#: of #, fuzzy -msgid "FedAvgM" -msgstr "DP-FedAvg" +msgid "" +"The learning rate for the clipping norm adaptation. Defaults to 0.2. " +"Andrew et al. recommends to set to 0.2." +msgstr "剪切规范适应的学习率。默认为 0.2。安德鲁等人建议设置为 0.2。" -#: flwr.server.strategy.fedavgm.FedAvgM:3 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:24 +#: of #, fuzzy -msgid "Implementation based on https://arxiv.org/abs/1909.06335" -msgstr "实施基于 https://arxiv.org/pdf/1909.06335.pdf" +msgid "" +"The stddev of the noise added to the count of updates currently below the" +" estimate. Andrew et al. recommends to set to `expected_num_records/20`" +msgstr "添加到当前低于估计值的更新计数中的噪声的 stddev。安德鲁等人建议设置为 \"expected_num_records/20" -#: flwr.server.strategy.fedavgm.FedAvgM:25 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:30 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:23 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:22 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:15 +#: of +#, fuzzy +msgid "Create a strategy:" +msgstr "server.strategy" + +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:34 +#: of +#, fuzzy msgid "" -"Server-side learning rate used in server-side optimization. Defaults to " -"1.0." -msgstr "服务器端优化中使用的服务器端学习率。默认为 1.0。" +"Wrap the strategy with the " +"`DifferentialPrivacyClientSideAdaptiveClipping` wrapper:" +msgstr "用 \"DifferentialPrivacyClientSideAdaptiveClipping \"包装器对策略进行包装：" -#: flwr.server.strategy.fedavgm.FedAvgM:28 of -msgid "Server-side momentum factor used for FedAvgM. Defaults to 0.0." -msgstr "用于 FedAvgM 的服务器端动量因子。默认为 0.0。" +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping:40 +#: of +#, fuzzy +msgid "On the client, add the `adaptiveclipping_mod` to the client-side mods:" +msgstr "在客户端，将 \"adaptiveclipping_mod \"添加到客户端模块中：" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" ":py:obj:`aggregate_evaluate " -"`\\ \\(server\\_round\\," -" results\\, ...\\)" +"`\\" +" \\(server\\_round\\, results\\, ...\\)" msgstr "" +":py:obj:`aggregate_evaluate " +"`\\" +" \\(server\\_round\\, results\\, ...\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" -":py:obj:`aggregate_fit `\\ " -"\\(server\\_round\\, results\\, failures\\)" +":py:obj:`aggregate_fit " +"`\\" +" \\(server\\_round\\, results\\, failures\\)" msgstr "" +":py:obj:`aggregate_fit " +"`\\" +" \\(server\\_round\\, results\\, failures\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_fit:1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_fit:1 +#: of +#, fuzzy +msgid "Aggregate training results and update clip norms." +msgstr "汇总 DPFedAvgFixed 中的训练结果并更新片段标准。" + +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" ":py:obj:`configure_evaluate " -"`\\ \\(server\\_round\\," -" parameters\\, ...\\)" +"`\\" +" \\(server\\_round\\, parameters\\, ...\\)" msgstr "" +":py:obj:`configure_evaluate " +"`\\" +" \\(server\\_round\\, parameters\\, ...\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" -":py:obj:`configure_fit `\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`configure_fit " +"`\\" +" \\(server\\_round\\, parameters\\, ...\\)" msgstr "" +":py:obj:`configure_fit " +"`\\" +" \\(server\\_round\\, parameters\\, ...\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" +":py:obj:`evaluate " +"`\\" +" \\(server\\_round\\, parameters\\)" msgstr "" +":py:obj:`evaluate " +"`\\" +" \\(server\\_round\\, parameters\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" ":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +"`\\" +" \\(client\\_manager\\)" msgstr "" +":py:obj:`initialize_parameters " +"`\\" +" \\(client\\_manager\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyClientSideFixedClipping.rst:2 +#, fuzzy +msgid "DifferentialPrivacyClientSideFixedClipping" +msgstr "差分隐私" + +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:3 +#: of +#, fuzzy +msgid "Use `fixedclipping_mod` modifier at the client side." +msgstr "在客户端使用 `fixedclipping_mod` 修改器。" + +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:5 +#: of +#, fuzzy msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +"In comparison to `DifferentialPrivacyServerSideFixedClipping`, which " +"performs clipping on the server-side, " +"`DifferentialPrivacyClientSideFixedClipping` expects clipping to happen " +"on the client-side, usually by using the built-in `fixedclipping_mod`." msgstr "" +"与在服务器端执行剪切的 \"DifferentialPrivacyServerSideFixedClipping " +"\"相比，\"DifferentialPrivacyClientSideFixedClipping \"希望在客户端进行剪切，通常是使用内置的 " +"\"fixedclipping_mod\"。" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:12 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:5 +#: of +#, fuzzy msgid "" -":py:obj:`num_fit_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" -msgstr "" +"The noise multiplier for the Gaussian mechanism for model updates. A " +"value of 1.0 or higher is recommended for strong privacy." +msgstr "模型更新高斯机制的噪声乘数。建议使用 1.0 或更高的值，以获得较强的隐私性。" -#: ../../source/ref-api/flwr.server.strategy.FedMedian.rst:2 -msgid "FedMedian" -msgstr "" +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:26 +#: of +#, fuzzy +msgid "" +"Wrap the strategy with the `DifferentialPrivacyClientSideFixedClipping` " +"wrapper:" +msgstr "用 \"DifferentialPrivacyClientSideFixedClipping \"包装器包装策略：" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping:32 +#: of +#, fuzzy +msgid "On the client, add the `fixedclipping_mod` to the client-side mods:" +msgstr "在客户端，将 \"fixedclipping_mod \"添加到客户端模块中：" + +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" ":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" +"`\\" +" \\(server\\_round\\, results\\, ...\\)" msgstr "" +":py:obj:`aggregate_evaluate " +"`\\" +" \\(server\\_round\\, results\\, ...\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" -":py:obj:`aggregate_fit `\\ " -"\\(server\\_round\\, results\\, failures\\)" +":py:obj:`aggregate_fit " +"`\\" +" \\(server\\_round\\, results\\, failures\\)" msgstr "" +":py:obj:`aggregate_fit " +"`\\" +" \\(server\\_round\\, results\\, failures\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedmedian.FedMedian.aggregate_fit:1 of -msgid "Aggregate fit results using median." -msgstr "使用中位数汇总拟合结果。" +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_fit:1 +#: of +#, fuzzy +msgid "Add noise to the aggregated parameters." +msgstr "然后将汇总结果序列化：" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" ":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +"`\\" +" \\(server\\_round\\, parameters\\, ...\\)" msgstr "" +":py:obj:`configure_evaluate " +"`\\" +" \\(server\\_round\\, parameters\\, ...\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" -":py:obj:`configure_fit `\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`configure_fit " +"`\\" +" \\(server\\_round\\, parameters\\, ...\\)" msgstr "" +":py:obj:`configure_fit " +"`\\" +" \\(server\\_round\\, parameters\\, ...\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" +":py:obj:`evaluate " +"`\\" +" \\(server\\_round\\, parameters\\)" msgstr "" +":py:obj:`evaluate " +"`\\" +" \\(server\\_round\\, parameters\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" ":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +"`\\" +" \\(client\\_manager\\)" msgstr "" +":py:obj:`initialize_parameters " +"`\\" +" \\(client\\_manager\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of -msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" -msgstr "" +#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyServerSideAdaptiveClipping.rst:2 +#, fuzzy +msgid "DifferentialPrivacyServerSideAdaptiveClipping" +msgstr "DifferentialPrivacyServerSideAdaptiveClipping" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:17 +#: of +#, fuzzy msgid "" -":py:obj:`num_fit_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" -msgstr "" - -#: ../../source/ref-api/flwr.server.strategy.FedOpt.rst:2 -msgid "FedOpt" -msgstr "" - -#: flwr.server.strategy.fedopt.FedOpt:33 of -msgid "Momentum parameter. Defaults to 0.0." -msgstr "动量参数。默认为 0.0。" +"The standard deviation of the noise added to the count of updates below " +"the estimate. Andrew et al. recommends to set to " +"`expected_num_records/20`" +msgstr "添加到低于估计值的更新计数中的噪声标准偏差。安德鲁等人建议设置为 \"expected_num_records/20" -#: flwr.server.strategy.fedopt.FedOpt:35 of -msgid "Second moment parameter. Defaults to 0.0." -msgstr "第二动量参数。默认为 0.0。" +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping:27 +#: of +#, fuzzy +msgid "" +"Wrap the strategy with the DifferentialPrivacyServerSideAdaptiveClipping " +"wrapper" +msgstr "用 DifferentialPrivacyServerSideAdaptiveClipping 封装器封装策略" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" ":py:obj:`aggregate_evaluate " -"`\\ \\(server\\_round\\, " -"results\\, ...\\)" +"`\\" +" \\(server\\_round\\, results\\, ...\\)" msgstr "" +":py:obj:`aggregate_evaluate " +"`\\" +" \\(server\\_round\\, results\\, ...\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" -":py:obj:`aggregate_fit `\\ " -"\\(server\\_round\\, results\\, failures\\)" +":py:obj:`aggregate_fit " +"`\\" +" \\(server\\_round\\, results\\, failures\\)" msgstr "" +":py:obj:`aggregate_fit " +"`\\" +" \\(server\\_round\\, results\\, failures\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" ":py:obj:`configure_evaluate " -"`\\ \\(server\\_round\\, " -"parameters\\, ...\\)" +"`\\" +" \\(server\\_round\\, parameters\\, ...\\)" msgstr "" +":py:obj:`configure_evaluate " +"`\\" +" \\(server\\_round\\, parameters\\, ...\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" -":py:obj:`configure_fit `\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`configure_fit " +"`\\" +" \\(server\\_round\\, parameters\\, ...\\)" msgstr "" +":py:obj:`configure_fit " +"`\\" +" \\(server\\_round\\, parameters\\, ...\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" +":py:obj:`evaluate " +"`\\" +" \\(server\\_round\\, parameters\\)" msgstr "" +":py:obj:`evaluate " +"`\\" +" \\(server\\_round\\, parameters\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" ":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" +"`\\" +" \\(client\\_manager\\)" msgstr "" +":py:obj:`initialize_parameters " +"`\\" +" \\(client\\_manager\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyServerSideFixedClipping.rst:2 +#, fuzzy +msgid "DifferentialPrivacyServerSideFixedClipping" +msgstr "差分隐私" + +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping:19 +#: of +#, fuzzy msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" -msgstr "" +"Wrap the strategy with the DifferentialPrivacyServerSideFixedClipping " +"wrapper" +msgstr "用 DifferentialPrivacyServerSideFixedClipping 封装器封装策略" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" -":py:obj:`num_fit_clients `\\" -" \\(num\\_available\\_clients\\)" +":py:obj:`aggregate_evaluate " +"`\\" +" \\(server\\_round\\, results\\, ...\\)" msgstr "" +":py:obj:`aggregate_evaluate " +"`\\" +" \\(server\\_round\\, results\\, ...\\)" -#: ../../source/ref-api/flwr.server.strategy.FedProx.rst:2 -msgid "FedProx" +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 +#: of +#, fuzzy +msgid "" +":py:obj:`aggregate_fit " +"`\\" +" \\(server\\_round\\, results\\, failures\\)" msgstr "" +":py:obj:`aggregate_fit " +"`\\" +" \\(server\\_round\\, results\\, failures\\)" -#: flwr.server.strategy.fedprox.FedProx:3 of -msgid "Implementation based on https://arxiv.org/abs/1812.06127" -msgstr "实施基于 https://arxiv.org/abs/1812.06127" - -#: flwr.server.strategy.fedprox.FedProx:5 of -msgid "" -"The strategy in itself will not be different than FedAvg, the client " -"needs to be adjusted. A proximal term needs to be added to the loss " -"function during the training:" -msgstr "策略本身与 FedAvg 并无不同，客户端需要进行调整。在训练过程中，需要在损失函数中添加一个近端项：" +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_fit:1 +#: of +#, fuzzy +msgid "Compute the updates, clip, and pass them for aggregation." +msgstr "计算更新、剪辑并将其传递给聚合。" -#: flwr.server.strategy.fedprox.FedProx:9 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" -"\\\\frac{\\\\mu}{2} || w - w^t ||^2\n" -"\n" +":py:obj:`configure_evaluate " +"`\\" +" \\(server\\_round\\, parameters\\, ...\\)" msgstr "" -"\\\\frac{\\\\mu}{2} || w - w^t ||^2\n" -"\n" +":py:obj:`configure_evaluate " +"`\\" +" \\(server\\_round\\, parameters\\, ...\\)" -#: flwr.server.strategy.fedprox.FedProx:12 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" -"Where $w^t$ are the global parameters and $w$ are the local weights the " -"function will be optimized with." -msgstr "其中，$w^t$ 是全局参数，$w$ 是优化函数的局部权重。" - -#: flwr.server.strategy.fedprox.FedProx:15 of -msgid "In PyTorch, for example, the loss would go from:" -msgstr "例如，在 PyTorch 中，损失将从：" - -#: flwr.server.strategy.fedprox.FedProx:21 of -msgid "To:" -msgstr "致：" +":py:obj:`configure_fit " +"`\\" +" \\(server\\_round\\, parameters\\, ...\\)" +msgstr "" +":py:obj:`configure_fit " +"`\\" +" \\(server\\_round\\, parameters\\, ...\\)" -#: flwr.server.strategy.fedprox.FedProx:30 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" -"With `global_params` being a copy of the parameters before the training " -"takes place." -msgstr "其中，\"global_params \"是训练前的参数副本。" +":py:obj:`evaluate " +"`\\" +" \\(server\\_round\\, parameters\\)" +msgstr "" +":py:obj:`evaluate " +"`\\" +" \\(server\\_round\\, parameters\\)" -#: flwr.server.strategy.fedprox.FedProx:65 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" -"The weight of the proximal term used in the optimization. 0.0 makes this " -"strategy equivalent to FedAvg, and the higher the coefficient, the more " -"regularization will be used (that is, the client parameters will need to " -"be closer to the server parameters during training)." +":py:obj:`initialize_parameters " +"`\\" +" \\(client\\_manager\\)" msgstr "" -"优化中使用的近端项权重。0.0 使该策略等同于 " -"FedAvg，系数越大，使用的正则化就越多（也就是说，在训练过程中，客户端参数需要更接近服务器参数）。" +":py:obj:`initialize_parameters " +"`\\" +" \\(client\\_manager\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping.aggregate_fit:3 +#: of +#, fuzzy +msgid "Afterward, add noise to the aggregated parameters." +msgstr "然后，在汇总参数中添加噪声。" + +#: ../../source/ref-api/flwr.server.strategy.FaultTolerantFedAvg.rst:2 +#, fuzzy +msgid "FaultTolerantFedAvg" +msgstr "server.strategy.FaultTolerantFedAvg" + +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" ":py:obj:`aggregate_evaluate " -"`\\ \\(server\\_round\\," -" results\\, ...\\)" +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" msgstr "" +":py:obj:`aggregate_evaluate " +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" -":py:obj:`aggregate_fit `\\ " +":py:obj:`aggregate_fit " +"`\\ " "\\(server\\_round\\, results\\, failures\\)" msgstr "" +":py:obj:`aggregate_fit " +"`\\ " +"\\(server\\_round\\, results\\, failures\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_fit:1 +#: flwr.server.strategy.fedadagrad.FedAdagrad.aggregate_fit:1 +#: flwr.server.strategy.fedadam.FedAdam.aggregate_fit:1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_fit:1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_fit:1 +#: flwr.server.strategy.fedavgm.FedAvgM.aggregate_fit:1 +#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg.aggregate_fit:1 +#: flwr.server.strategy.fedyogi.FedYogi.aggregate_fit:1 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_fit:1 of +msgid "Aggregate fit results using weighted average." +msgstr "使用加权平均法汇总拟合结果。" + +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" ":py:obj:`configure_evaluate " -"`\\ \\(server\\_round\\," -" parameters\\, ...\\)" +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" +":py:obj:`configure_evaluate " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" -":py:obj:`configure_fit `\\ " +":py:obj:`configure_fit " +"`\\ " "\\(server\\_round\\, parameters\\, ...\\)" msgstr "" +":py:obj:`configure_fit " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" -":py:obj:`evaluate `\\ " +":py:obj:`evaluate `\\ " "\\(server\\_round\\, parameters\\)" msgstr "" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" ":py:obj:`initialize_parameters " -"`\\ " +"`\\ " "\\(client\\_manager\\)" msgstr "" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" ":py:obj:`num_evaluation_clients " -"`\\ " +"`\\ " "\\(num\\_available\\_clients\\)" msgstr "" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" ":py:obj:`num_fit_clients " -"`\\ " +"`\\ " "\\(num\\_available\\_clients\\)" msgstr "" +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" -#: flwr.server.strategy.fedprox.FedProx.configure_fit:3 of -msgid "Sends the proximal factor mu to the clients" -msgstr "向客户发送近端因子mu" +#: ../../source/ref-api/flwr.server.strategy.FedAdagrad.rst:2 +#: ../../source/ref-changelog.md:997 +msgid "FedAdagrad" +msgstr "FedAdagrad" -#: ../../source/ref-api/flwr.server.strategy.FedTrimmedAvg.rst:2 +#: flwr.server.strategy.fedadagrad.FedAdagrad:1 +#: flwr.server.strategy.fedadam.FedAdam:1 +#: flwr.server.strategy.fedyogi.FedYogi:1 of #, fuzzy -msgid "FedTrimmedAvg" -msgstr "server.strategy.FedTrimmedAvg" +msgid "Bases: :py:class:`~flwr.server.strategy.fedopt.FedOpt`" +msgstr "Bases: :py:class:`~flwr.server.strategy.fedopt.FedOpt`" -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:3 of -#, fuzzy -msgid "Implemented based on: https://arxiv.org/abs/1803.01498" -msgstr "实施基于 https://arxiv.org/abs/1802.07927。" +#: flwr.server.strategy.fedadagrad.FedAdagrad:3 +#: flwr.server.strategy.fedadam.FedAdam:3 flwr.server.strategy.fedopt.FedOpt:3 +#: flwr.server.strategy.fedyogi.FedYogi:3 of +msgid "Implementation based on https://arxiv.org/abs/2003.00295v5" +msgstr "实施基于 https://arxiv.org/abs/2003.00295v5" -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:25 of -msgid "Fraction to cut off of both tails of the distribution. Defaults to 0.2." -msgstr "截取分布两个尾部的分数。默认为 0.2。" +#: flwr.server.strategy.fedadagrad.FedAdagrad:21 +#: flwr.server.strategy.fedadagrad.FedAdagrad:23 +#: flwr.server.strategy.fedadam.FedAdam:25 +#: flwr.server.strategy.fedadam.FedAdam:27 +#: flwr.server.strategy.fedavg.FedAvg:29 flwr.server.strategy.fedavg.FedAvg:31 +#: flwr.server.strategy.fedopt.FedOpt:25 flwr.server.strategy.fedopt.FedOpt:27 +#: flwr.server.strategy.fedprox.FedProx:61 +#: flwr.server.strategy.fedprox.FedProx:63 +#: flwr.server.strategy.fedyogi.FedYogi:28 +#: flwr.server.strategy.fedyogi.FedYogi:30 of +msgid "Metrics aggregation function, optional." +msgstr "指标汇总功能，可选。" + +#: flwr.server.strategy.fedadagrad.FedAdagrad:29 +#: flwr.server.strategy.fedadam.FedAdam:29 +#: flwr.server.strategy.fedopt.FedOpt:29 of +msgid "Server-side learning rate. Defaults to 1e-1." +msgstr "服务器端学习率。默认为 1e-1。" + +#: flwr.server.strategy.fedadagrad.FedAdagrad:31 +#: flwr.server.strategy.fedadam.FedAdam:31 +#: flwr.server.strategy.fedopt.FedOpt:31 of +msgid "Client-side learning rate. Defaults to 1e-1." +msgstr "客户端学习率。默认为 1e-1。" + +#: flwr.server.strategy.fedadagrad.FedAdagrad:33 +#: flwr.server.strategy.fedadam.FedAdam:37 +#: flwr.server.strategy.fedopt.FedOpt:37 of +msgid "Controls the algorithm's degree of adaptability. Defaults to 1e-9." +msgstr "控制算法的适应度。默认为 1e-9。" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" ":py:obj:`aggregate_evaluate " -"`\\ " +"`\\ " "\\(server\\_round\\, results\\, ...\\)" msgstr "" +":py:obj:`aggregate_evaluate " +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -":py:obj:`aggregate_fit " -"`\\ " -"\\(server\\_round\\, results\\, failures\\)" +":py:obj:`aggregate_fit `\\" +" \\(server\\_round\\, results\\, failures\\)" msgstr "" - -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.aggregate_fit:1 of -msgid "Aggregate fit results using trimmed average." -msgstr "使用修剪平均值汇总拟合结果。" +":py:obj:`aggregate_fit `\\" +" \\(server\\_round\\, results\\, failures\\)" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" ":py:obj:`configure_evaluate " -"`\\ " +"`\\ " "\\(server\\_round\\, parameters\\, ...\\)" msgstr "" +":py:obj:`configure_evaluate " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -":py:obj:`configure_fit " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +":py:obj:`configure_fit `\\" +" \\(server\\_round\\, parameters\\, ...\\)" msgstr "" +":py:obj:`configure_fit `\\" +" \\(server\\_round\\, parameters\\, ...\\)" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -":py:obj:`evaluate `\\ " +":py:obj:`evaluate `\\ " "\\(server\\_round\\, parameters\\)" msgstr "" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" ":py:obj:`initialize_parameters " -"`\\ " +"`\\ " "\\(client\\_manager\\)" msgstr "" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" ":py:obj:`num_evaluation_clients " -"`\\ " +"`\\ " "\\(num\\_available\\_clients\\)" msgstr "" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" ":py:obj:`num_fit_clients " -"`\\ " +"`\\ " "\\(num\\_available\\_clients\\)" msgstr "" +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" -#: ../../source/ref-api/flwr.server.strategy.FedXgbBagging.rst:2 -msgid "FedXgbBagging" -msgstr "" +#: ../../source/ref-api/flwr.server.strategy.FedAdam.rst:2 +#, fuzzy +msgid "FedAdam" +msgstr "FedAdagrad" -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.fedadam.FedAdam:33 +#: flwr.server.strategy.fedyogi.FedYogi:36 of +msgid "Momentum parameter. Defaults to 0.9." +msgstr "动量参数。默认为 0.9。" + +#: flwr.server.strategy.fedadam.FedAdam:35 +#: flwr.server.strategy.fedyogi.FedYogi:38 of +msgid "Second moment parameter. Defaults to 0.99." +msgstr "第二动量参数。默认为 0.99。" + +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" ":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" +"`\\ \\(server\\_round\\," +" results\\, ...\\)" msgstr "" +":py:obj:`aggregate_evaluate " +"`\\ \\(server\\_round\\," +" results\\, ...\\)" -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1 -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of #, fuzzy -msgid "Aggregate evaluation metrics using average." -msgstr "采用加权平均法计算评估损失总额。" - -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: of msgid "" -":py:obj:`aggregate_fit " -"`\\ " +":py:obj:`aggregate_fit `\\ " "\\(server\\_round\\, results\\, failures\\)" msgstr "" +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_fit:1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_fit:1 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of #, fuzzy -msgid "Aggregate fit results using bagging." -msgstr "使用 Bulyan 技术汇总拟合结果。" - -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: of msgid "" ":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +"`\\ \\(server\\_round\\," +" parameters\\, ...\\)" msgstr "" +":py:obj:`configure_evaluate " +"`\\ \\(server\\_round\\," +" parameters\\, ...\\)" -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -":py:obj:`configure_fit " -"`\\ " +":py:obj:`configure_fit `\\ " "\\(server\\_round\\, parameters\\, ...\\)" msgstr "" +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -":py:obj:`evaluate `\\ " +":py:obj:`evaluate `\\ " "\\(server\\_round\\, parameters\\)" msgstr "" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" ":py:obj:`initialize_parameters " -"`\\ " +"`\\ " "\\(client\\_manager\\)" msgstr "" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" ":py:obj:`num_evaluation_clients " -"`\\ " +"`\\ " "\\(num\\_available\\_clients\\)" msgstr "" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" -#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" ":py:obj:`num_fit_clients " -"`\\ " +"`\\ " "\\(num\\_available\\_clients\\)" msgstr "" +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" -#: ../../source/ref-api/flwr.server.strategy.FedXgbCyclic.rst:2 -msgid "FedXgbCyclic" -msgstr "" - -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" -msgstr "" - -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`aggregate_fit " -"`\\ \\(server\\_round\\," -" results\\, failures\\)" -msgstr "" - -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" -msgstr "" - -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`configure_fit " -"`\\ \\(server\\_round\\," -" parameters\\, ...\\)" -msgstr "" - -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`evaluate `\\ " -"\\(server\\_round\\, parameters\\)" -msgstr "" +#: ../../source/ref-api/flwr.server.strategy.FedAvg.rst:2 +#, fuzzy +msgid "FedAvg" +msgstr "DP-FedAvg" -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 -#: of -msgid "" -":py:obj:`initialize_parameters " -"`\\ " -"\\(client\\_manager\\)" -msgstr "" +#: flwr.server.strategy.fedavg.FedAvg:3 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid:3 of +msgid "Implementation based on https://arxiv.org/abs/1602.05629" +msgstr "实施基于 https://arxiv.org/abs/1602.05629" -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg:5 flwr.server.strategy.fedprox.FedProx:37 #: of msgid "" -":py:obj:`num_evaluation_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +"Fraction of clients used during training. In case `min_fit_clients` is " +"larger than `fraction_fit * available_clients`, `min_fit_clients` will " +"still be sampled. Defaults to 1.0." msgstr "" +"训练过程中使用的客户端比例。如果 `min_fit_clients` 大于 `fraction_fit * " +"available_clients`，则仍会对 `min_fit_clients` 进行采样。默认为 1.0。" -#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg.FedAvg:9 flwr.server.strategy.fedprox.FedProx:41 #: of msgid "" -":py:obj:`num_fit_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" +"Fraction of clients used during validation. In case " +"`min_evaluate_clients` is larger than `fraction_evaluate * " +"available_clients`, `min_evaluate_clients` will still be sampled. " +"Defaults to 1.0." msgstr "" +"验证过程中使用的客户端的比例。如果 `min_evaluate_clients` 大于 `fraction_evaluate * " +"available_clients`，则仍会对 `min_evaluate_clients` 进行采样。默认为 1.0。" -#: ../../source/ref-api/flwr.server.strategy.FedXgbNnAvg.rst:2 +#: flwr.server.strategy.fedavg.FedAvg:33 of #, fuzzy -msgid "FedXgbNnAvg" -msgstr "DP-FedAvg" - -#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg:5 of -msgid "" -"This strategy is deprecated, but a copy of it is available in Flower " -"Baselines: " -"https://github.com/adap/flower/tree/main/baselines/hfedxgboost." -msgstr "" +msgid "Enable (True) or disable (False) in-place aggregation of model updates." +msgstr "启用（真）或禁用（假）模型更新的就地聚合。" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" ":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" +"`\\ \\(server\\_round\\, " +"results\\, ...\\)" msgstr "" +":py:obj:`aggregate_evaluate " +"`\\ \\(server\\_round\\, " +"results\\, ...\\)" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -":py:obj:`aggregate_fit " -"`\\ \\(server\\_round\\, " -"results\\, failures\\)" +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" ":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +"`\\ \\(server\\_round\\, " +"parameters\\, ...\\)" msgstr "" +":py:obj:`configure_evaluate " +"`\\ \\(server\\_round\\, " +"parameters\\, ...\\)" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -":py:obj:`configure_fit " -"`\\ \\(server\\_round\\, " -"parameters\\, ...\\)" +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -":py:obj:`evaluate `\\ " +":py:obj:`evaluate `\\ " "\\(server\\_round\\, parameters\\)" msgstr "" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" ":py:obj:`initialize_parameters " -"`\\ " +"`\\ " "\\(client\\_manager\\)" msgstr "" - +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" + #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" ":py:obj:`num_evaluation_clients " -"`\\ " +"`\\ " "\\(num\\_available\\_clients\\)" msgstr "" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -":py:obj:`num_fit_clients " -"`\\ " -"\\(num\\_available\\_clients\\)" -msgstr "" - -#: ../../source/ref-api/flwr.server.strategy.FedYogi.rst:2 -msgid "FedYogi" +":py:obj:`num_fit_clients `\\" +" \\(num\\_available\\_clients\\)" msgstr "" +":py:obj:`num_fit_clients `\\" +" \\(num\\_available\\_clients\\)" -#: flwr.server.strategy.fedyogi.FedYogi:32 of -#, fuzzy -msgid "Server-side learning rate. Defaults to 1e-2." -msgstr "服务器端学习率。默认为 1e-1。" - -#: flwr.server.strategy.fedyogi.FedYogi:34 of +#: ../../source/ref-api/flwr.server.strategy.FedAvgAndroid.rst:2 #, fuzzy -msgid "Client-side learning rate. Defaults to 0.0316." -msgstr "客户端学习率。默认为 1e-1。" +msgid "FedAvgAndroid" +msgstr "DPFedAvgAdaptive" -#: flwr.server.strategy.fedyogi.FedYogi:40 of +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: of #, fuzzy -msgid "Controls the algorithm's degree of adaptability. Defaults to 1e-3." -msgstr "控制算法的适应度。默认为 1e-9。" - -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of msgid "" ":py:obj:`aggregate_evaluate " -"`\\ \\(server\\_round\\," -" results\\, ...\\)" +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" msgstr "" +":py:obj:`aggregate_evaluate " +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" -":py:obj:`aggregate_fit `\\ " +":py:obj:`aggregate_fit " +"`\\ " "\\(server\\_round\\, results\\, failures\\)" msgstr "" +":py:obj:`aggregate_fit " +"`\\ " +"\\(server\\_round\\, results\\, failures\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: of +#, fuzzy +msgid "" +":py:obj:`bytes_to_ndarray " +"`\\ \\(tensor\\)" +msgstr "" +":py:obj:`bytes_to_ndarray " +"`\\ \\(tensor\\)" + +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.bytes_to_ndarray:1 of +#, fuzzy +msgid "Deserialize NumPy array from bytes." +msgstr "从字节反序列化 NumPy ndarray。" + +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" ":py:obj:`configure_evaluate " -"`\\ \\(server\\_round\\," -" parameters\\, ...\\)" +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" +":py:obj:`configure_evaluate " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" -":py:obj:`configure_fit `\\ " +":py:obj:`configure_fit " +"`\\ " "\\(server\\_round\\, parameters\\, ...\\)" msgstr "" +":py:obj:`configure_fit " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" -":py:obj:`evaluate `\\ " +":py:obj:`evaluate `\\ " "\\(server\\_round\\, parameters\\)" msgstr "" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" ":py:obj:`initialize_parameters " -"`\\ " +"`\\ " "\\(client\\_manager\\)" msgstr "" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: of +#, fuzzy +msgid "" +":py:obj:`ndarray_to_bytes " +"`\\ \\(ndarray\\)" +msgstr "" +":py:obj:`ndarray_to_bytes " +"`\\ \\(ndarray\\)" + +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.ndarray_to_bytes:1 of +#, fuzzy +msgid "Serialize NumPy array to bytes." +msgstr "将 NumPy ndarray 序列化为字节。" + +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: of +#, fuzzy +msgid "" +":py:obj:`ndarrays_to_parameters " +"`\\ " +"\\(ndarrays\\)" +msgstr "" +":py:obj:`ndarrays_to_parameters " +"`\\ " +"\\(ndarrays\\)" + +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" ":py:obj:`num_evaluation_clients " -"`\\ " +"`\\ " "\\(num\\_available\\_clients\\)" msgstr "" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" ":py:obj:`num_fit_clients " -"`\\ " +"`\\ " "\\(num\\_available\\_clients\\)" msgstr "" +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" -#: ../../source/ref-api/flwr.server.strategy.Krum.rst:2 -msgid "Krum" +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: of +#, fuzzy +msgid "" +":py:obj:`parameters_to_ndarrays " +"`\\ " +"\\(parameters\\)" msgstr "" +":py:obj:`parameters_to_ndarrays " +"`\\ " +"\\(parameters\\)" -#: flwr.server.strategy.krum.Krum:3 of +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedavg_android.FedAvgAndroid.parameters_to_ndarrays:1 +#: of #, fuzzy -msgid "Implementation based on https://arxiv.org/abs/1703.02757" -msgstr "实施基于 https://arxiv.org/abs/2304.07537。" +msgid "Convert parameters object to NumPy weights." +msgstr "将参数对象转换为 NumPy ndarrays。" -#: flwr.server.strategy.krum.Krum:17 of +#: ../../source/ref-api/flwr.server.strategy.FedAvgM.rst:2 +#, fuzzy +msgid "FedAvgM" +msgstr "DP-FedAvg" + +#: flwr.server.strategy.fedavgm.FedAvgM:3 of +#, fuzzy +msgid "Implementation based on https://arxiv.org/abs/1909.06335" +msgstr "实施基于 https://arxiv.org/pdf/1909.06335.pdf" + +#: flwr.server.strategy.fedavgm.FedAvgM:25 of msgid "" -"Number of clients to keep before averaging (MultiKrum). Defaults to 0, in" -" that case classical Krum is applied." -msgstr "求平均值前保留的客户端数量（MultiKrum）。默认值为 0，在这种情况下会应用经典 Krum。" +"Server-side learning rate used in server-side optimization. Defaults to " +"1.0." +msgstr "服务器端优化中使用的服务器端学习率。默认为 1.0。" + +#: flwr.server.strategy.fedavgm.FedAvgM:28 of +msgid "Server-side momentum factor used for FedAvgM. Defaults to 0.0." +msgstr "用于 FedAvgM 的服务器端动量因子。默认为 0.0。" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" ":py:obj:`aggregate_evaluate " -"`\\ \\(server\\_round\\, " -"results\\, ...\\)" +"`\\ \\(server\\_round\\," +" results\\, ...\\)" msgstr "" +":py:obj:`aggregate_evaluate " +"`\\ \\(server\\_round\\," +" results\\, ...\\)" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -":py:obj:`aggregate_fit `\\ " +":py:obj:`aggregate_fit `\\ " "\\(server\\_round\\, results\\, failures\\)" msgstr "" - -#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 -#: flwr.server.strategy.krum.Krum.aggregate_fit:1 of -msgid "Aggregate fit results using Krum." -msgstr "使用 Krum 汇总拟合结果。" +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" ":py:obj:`configure_evaluate " -"`\\ \\(server\\_round\\, " -"parameters\\, ...\\)" +"`\\ \\(server\\_round\\," +" parameters\\, ...\\)" msgstr "" +":py:obj:`configure_evaluate " +"`\\ \\(server\\_round\\," +" parameters\\, ...\\)" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -":py:obj:`configure_fit `\\ " +":py:obj:`configure_fit `\\ " "\\(server\\_round\\, parameters\\, ...\\)" msgstr "" +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -":py:obj:`evaluate `\\ " +":py:obj:`evaluate `\\ " "\\(server\\_round\\, parameters\\)" msgstr "" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" ":py:obj:`initialize_parameters " -"`\\ " +"`\\ " "\\(client\\_manager\\)" msgstr "" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" ":py:obj:`num_evaluation_clients " -"`\\ " +"`\\ " "\\(num\\_available\\_clients\\)" msgstr "" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" #: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -":py:obj:`num_fit_clients `\\ " +":py:obj:`num_fit_clients " +"`\\ " "\\(num\\_available\\_clients\\)" msgstr "" +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" -#: ../../source/ref-api/flwr.server.strategy.QFedAvg.rst:2 +#: ../../source/ref-api/flwr.server.strategy.FedMedian.rst:2 #, fuzzy -msgid "QFedAvg" -msgstr "DP-FedAvg" +msgid "FedMedian" +msgstr "联邦医保" -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" ":py:obj:`aggregate_evaluate " -"`\\ \\(server\\_round\\," -" results\\, ...\\)" +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" msgstr "" +":py:obj:`aggregate_evaluate " +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -":py:obj:`aggregate_fit `\\ " +":py:obj:`aggregate_fit `\\ " "\\(server\\_round\\, results\\, failures\\)" msgstr "" +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedmedian.FedMedian.aggregate_fit:1 of +msgid "Aggregate fit results using median." +msgstr "使用中位数汇总拟合结果。" + +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" ":py:obj:`configure_evaluate " -"`\\ \\(server\\_round\\," -" parameters\\, ...\\)" +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" +":py:obj:`configure_evaluate " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -":py:obj:`configure_fit `\\ " +":py:obj:`configure_fit `\\ " "\\(server\\_round\\, parameters\\, ...\\)" msgstr "" +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -":py:obj:`evaluate `\\ " +":py:obj:`evaluate `\\ " "\\(server\\_round\\, parameters\\)" msgstr "" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" ":py:obj:`initialize_parameters " -"`\\ " +"`\\ " "\\(client\\_manager\\)" msgstr "" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" ":py:obj:`num_evaluation_clients " -"`\\ " +"`\\ " "\\(num\\_available\\_clients\\)" msgstr "" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" -#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" ":py:obj:`num_fit_clients " -"`\\ " +"`\\ " "\\(num\\_available\\_clients\\)" msgstr "" +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" -#: ../../source/ref-api/flwr.server.strategy.Strategy.rst:2 +#: ../../source/ref-api/flwr.server.strategy.FedOpt.rst:2 #, fuzzy -msgid "Strategy" -msgstr "Krum 策略。" +msgid "FedOpt" +msgstr "FedOpt" -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.fedopt.FedOpt:33 of +msgid "Momentum parameter. Defaults to 0.0." +msgstr "动量参数。默认为 0.0。" + +#: flwr.server.strategy.fedopt.FedOpt:35 of +msgid "Second moment parameter. Defaults to 0.0." +msgstr "第二动量参数。默认为 0.0。" + +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" ":py:obj:`aggregate_evaluate " -"`\\ " -"\\(server\\_round\\, results\\, ...\\)" +"`\\ \\(server\\_round\\, " +"results\\, ...\\)" msgstr "" +":py:obj:`aggregate_evaluate " +"`\\ \\(server\\_round\\, " +"results\\, ...\\)" -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1 -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 -#: of -msgid "Aggregate evaluation results." -msgstr "聚合评估结果。" - -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -":py:obj:`aggregate_fit `\\ " +":py:obj:`aggregate_fit `\\ " "\\(server\\_round\\, results\\, failures\\)" msgstr "" +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 -#: flwr.server.strategy.strategy.Strategy.aggregate_fit:1 of -msgid "Aggregate training results." -msgstr "汇总训练结果。" - -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" ":py:obj:`configure_evaluate " -"`\\ " -"\\(server\\_round\\, parameters\\, ...\\)" +"`\\ \\(server\\_round\\, " +"parameters\\, ...\\)" msgstr "" +":py:obj:`configure_evaluate " +"`\\ \\(server\\_round\\, " +"parameters\\, ...\\)" -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -":py:obj:`configure_fit `\\ " +":py:obj:`configure_fit `\\ " "\\(server\\_round\\, parameters\\, ...\\)" msgstr "" +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -":py:obj:`evaluate `\\ " +":py:obj:`evaluate `\\ " "\\(server\\_round\\, parameters\\)" msgstr "" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 -#: flwr.server.strategy.strategy.Strategy.evaluate:1 of -msgid "Evaluate the current model parameters." -msgstr "评估当前的模型参数。" - -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" ":py:obj:`initialize_parameters " -"`\\ " +"`\\ " "\\(client\\_manager\\)" msgstr "" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 -#: flwr.server.strategy.strategy.Strategy.initialize_parameters:1 of -msgid "Initialize the (global) model parameters." -msgstr "初始化（全局）模型参数。" +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy +msgid "" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" +msgstr "" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:5 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -"Successful updates from the previously selected and configured clients. " -"Each pair of `(ClientProxy, FitRes` constitutes a successful update from " -"one of the previously selected clients. Not that not all previously " -"selected clients are necessarily included in this list: a client might " -"drop out and not submit a result. For each client that did not submit an " -"update, there should be an `Exception` in `failures`." +":py:obj:`num_fit_clients `\\" +" \\(num\\_available\\_clients\\)" msgstr "" -"从先前选定和配置的客户端进行的成功更新。每一对`(ClientProxy, " -"FitRes)`都是来自先前选定客户端的一次成功更新。但并非所有先前选定的客户机都一定包含在此列表中：客户机可能会退出，不提交结果。对于每个没有提交更新的客户端，`failures`中都应该有一个`Exception`。" +":py:obj:`num_fit_clients `\\" +" \\(num\\_available\\_clients\\)" -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:13 -#: flwr.server.strategy.strategy.Strategy.aggregate_fit:13 of -msgid "Exceptions that occurred while the server was waiting for client updates." -msgstr "服务器等待客户端更新时发生的异常。" +#: ../../source/ref-api/flwr.server.strategy.FedProx.rst:2 +#, fuzzy +msgid "FedProx" +msgstr "FedProx" -#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:16 of -msgid "" -"**aggregation_result** -- The aggregated evaluation result. Aggregation " -"typically uses some variant of a weighted average." -msgstr "**aggregation_result** -- 汇总的评估结果。聚合通常使用某种加权平均值。" +#: flwr.server.strategy.fedprox.FedProx:3 of +msgid "Implementation based on https://arxiv.org/abs/1812.06127" +msgstr "实施基于 https://arxiv.org/abs/1812.06127" -#: flwr.server.strategy.strategy.Strategy.aggregate_fit:5 of +#: flwr.server.strategy.fedprox.FedProx:5 of msgid "" -"Successful updates from the previously selected and configured clients. " -"Each pair of `(ClientProxy, FitRes)` constitutes a successful update from" -" one of the previously selected clients. Not that not all previously " -"selected clients are necessarily included in this list: a client might " -"drop out and not submit a result. For each client that did not submit an " -"update, there should be an `Exception` in `failures`." -msgstr "" -"来自先前选定和配置的客户端的成功更新。每一对`(ClientProxy, " -"FitRes)`都构成先前选定的客户端之一的一次成功更新。但并非所有先前选定的客户机都一定包含在此列表中：客户机可能会退出，不提交结果。对于每个没有提交更新的客户端，\"失败" -" \"中都应该有一个 \"异常\"。" +"The strategy in itself will not be different than FedAvg, the client " +"needs to be adjusted. A proximal term needs to be added to the loss " +"function during the training:" +msgstr "策略本身与 FedAvg 并无不同，客户端需要进行调整。在训练过程中，需要在损失函数中添加一个近端项：" -#: flwr.server.strategy.strategy.Strategy.aggregate_fit:17 of +#: flwr.server.strategy.fedprox.FedProx:9 of msgid "" -"**parameters** -- If parameters are returned, then the server will treat " -"these as the new global model parameters (i.e., it will replace the " -"previous parameters with the ones returned from this method). If `None` " -"is returned (e.g., because there were only failures and no viable " -"results) then the server will no update the previous model parameters, " -"the updates received in this round are discarded, and the global model " -"parameters remain the same." +"\\\\frac{\\\\mu}{2} || w - w^t ||^2\n" +"\n" msgstr "" -"**parameters** -- 如果返回参数，那么服务器将把这些参数作为新的全局模型参数（即用本方法返回的参数替换之前的参数）。如果返回 " -"\"无\"（例如，因为只有失败而没有可行的结果），那么服务器将不再更新之前的模型参数，本轮收到的更新将被丢弃，全局模型参数保持不变。" +"\\\\frac{\\\\mu}{2} || w - w^t ||^2\n" +"\n" -#: flwr.server.strategy.strategy.Strategy.evaluate:3 of +#: flwr.server.strategy.fedprox.FedProx:12 of msgid "" -"This function can be used to perform centralized (i.e., server-side) " -"evaluation of model parameters." -msgstr "该函数可用于对模型参数进行集中（即服务器端）评估。" +"Where $w^t$ are the global parameters and $w$ are the local weights the " +"function will be optimized with." +msgstr "其中，$w^t$ 是全局参数，$w$ 是优化函数的局部权重。" -#: flwr.server.strategy.strategy.Strategy.evaluate:11 of -msgid "" -"**evaluation_result** -- The evaluation result, usually a Tuple " -"containing loss and a dictionary containing task-specific metrics (e.g., " -"accuracy)." -msgstr "**evaluation_result** -- 评估结果，通常是一个元组，包含损失值和一个字典，字典中包含特定任务的指标（如准确率）。" +#: flwr.server.strategy.fedprox.FedProx:15 of +msgid "In PyTorch, for example, the loss would go from:" +msgstr "例如，在 PyTorch 中，损失将从：" -#: flwr.server.strategy.strategy.Strategy.initialize_parameters:6 of +#: flwr.server.strategy.fedprox.FedProx:21 of +msgid "To:" +msgstr "致：" + +#: flwr.server.strategy.fedprox.FedProx:30 of msgid "" -"**parameters** -- If parameters are returned, then the server will treat " -"these as the initial global model parameters." -msgstr "**parameters** -- 如果返回参数，服务器将把这些参数视为初始全局模型参数。" +"With `global_params` being a copy of the parameters before the training " +"takes place." +msgstr "其中，\"global_params \"是训练前的参数副本。" -#: ../../source/ref-api/flwr.server.workflow.rst:2 +#: flwr.server.strategy.fedprox.FedProx:65 of +msgid "" +"The weight of the proximal term used in the optimization. 0.0 makes this " +"strategy equivalent to FedAvg, and the higher the coefficient, the more " +"regularization will be used (that is, the client parameters will need to " +"be closer to the server parameters during training)." +msgstr "" +"优化中使用的近端项权重。0.0 使该策略等同于 " +"FedAvg，系数越大，使用的正则化就越多（也就是说，在训练过程中，客户端参数需要更接近服务器参数）。" + +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of #, fuzzy -msgid "workflow" -msgstr "工作流程" +msgid "" +":py:obj:`aggregate_evaluate " +"`\\ \\(server\\_round\\," +" results\\, ...\\)" +msgstr "" +":py:obj:`aggregate_evaluate " +"`\\ \\(server\\_round\\," +" results\\, ...\\)" -#: ../../source/ref-api/flwr.server.workflow.rst:24::1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -":py:obj:`DefaultWorkflow `\\ " -"\\(\\[fit\\_workflow\\, ...\\]\\)" +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" -#: ../../source/ref-api/flwr.server.workflow.rst:24::1 -#: flwr.server.workflow.default_workflows.DefaultWorkflow:1 of -msgid "Default workflow in Flower." +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy +msgid "" +":py:obj:`configure_evaluate " +"`\\ \\(server\\_round\\," +" parameters\\, ...\\)" msgstr "" +":py:obj:`configure_evaluate " +"`\\ \\(server\\_round\\," +" parameters\\, ...\\)" -#: ../../source/ref-api/flwr.server.workflow.rst:24::1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -":py:obj:`SecAggPlusWorkflow `\\ " -"\\(num\\_shares\\, ...\\[\\, ...\\]\\)" +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" -#: ../../source/ref-api/flwr.server.workflow.rst:24::1 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:1 -#: of -msgid "The workflow for the SecAgg+ protocol." +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy +msgid "" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" -#: ../../source/ref-api/flwr.server.workflow.rst:24::1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -":py:obj:`SecAggWorkflow `\\ " -"\\(reconstruction\\_threshold\\, \\*\\)" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" -#: ../../source/ref-api/flwr.server.workflow.rst:24::1 -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:1 of -msgid "The workflow for the SecAgg protocol." +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy +msgid "" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" -#: ../../source/ref-api/flwr.server.workflow.DefaultWorkflow.rst:2 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of #, fuzzy -msgid "DefaultWorkflow" -msgstr "工作流程" +msgid "" +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" +msgstr "" +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" -#: ../../source/ref-api/flwr.server.workflow.SecAggPlusWorkflow.rst:2 +#: flwr.server.strategy.fedprox.FedProx.configure_fit:3 of +msgid "Sends the proximal factor mu to the clients" +msgstr "向客户发送近端因子mu" + +#: ../../source/ref-api/flwr.server.strategy.FedTrimmedAvg.rst:2 #, fuzzy -msgid "SecAggPlusWorkflow" -msgstr "工作流程" +msgid "FedTrimmedAvg" +msgstr "server.strategy.FedTrimmedAvg" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:3 -#: of +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:3 of +#, fuzzy +msgid "Implemented based on: https://arxiv.org/abs/1803.01498" +msgstr "实施基于 https://arxiv.org/abs/1802.07927。" + +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:25 of +msgid "Fraction to cut off of both tails of the distribution. Defaults to 0.2." +msgstr "截取分布两个尾部的分数。默认为 0.2。" + +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -"The SecAgg+ protocol ensures the secure summation of integer vectors " -"owned by multiple parties, without accessing any individual integer " -"vector. This workflow allows the server to compute the weighted average " -"of model parameters across all clients, ensuring individual contributions" -" remain private. This is achieved by clients sending both, a weighting " -"factor and a weighted version of the locally updated parameters, both of " -"which are masked for privacy. Specifically, each client uploads \"[w, w *" -" params]\" with masks, where weighting factor 'w' is the number of " -"examples ('num_examples') and 'params' represents the model parameters " -"('parameters') from the client's `FitRes`. The server then aggregates " -"these contributions to compute the weighted average of model parameters." +":py:obj:`aggregate_evaluate " +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" msgstr "" +":py:obj:`aggregate_evaluate " +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:14 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -"The protocol involves four main stages: - 'setup': Send SecAgg+ " -"configuration to clients and collect their public keys. - 'share keys': " -"Broadcast public keys among clients and collect encrypted secret" +":py:obj:`aggregate_fit " +"`\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" +":py:obj:`aggregate_fit " +"`\\ " +"\\(server\\_round\\, results\\, failures\\)" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:17 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:17 -#: of -msgid "key shares." -msgstr "" +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.aggregate_fit:1 of +msgid "Aggregate fit results using trimmed average." +msgstr "使用修剪平均值汇总拟合结果。" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:18 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:18 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -"'collect masked vectors': Forward encrypted secret key shares to target " -"clients and collect masked model parameters." +":py:obj:`configure_evaluate " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" +":py:obj:`configure_evaluate " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:20 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:20 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -"'unmask': Collect secret key shares to decrypt and aggregate the model " -"parameters." +":py:obj:`configure_fit " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" +":py:obj:`configure_fit " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:22 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:22 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -"Only the aggregated model parameters are exposed and passed to " -"`Strategy.aggregate_fit`, ensuring individual data privacy." +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:25 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -"The number of shares into which each client's private key is split under " -"the SecAgg+ protocol. If specified as a float, it represents the " -"proportion of all selected clients, and the number of shares will be set " -"dynamically in the run time. A private key can be reconstructed from " -"these shares, allowing for the secure aggregation of model updates. Each " -"client sends one share to each of its neighbors while retaining one." +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:25 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:32 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -"The minimum number of shares required to reconstruct a client's private " -"key, or, if specified as a float, it represents the proportion of the " -"total number of shares needed for reconstruction. This threshold ensures " -"privacy by allowing for the recovery of contributions from dropped " -"clients during aggregation, without compromising individual client data." +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:31 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:38 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -"The maximum value of the weight that can be assigned to any single " -"client's update during the weighted average calculation on the server " -"side, e.g., in the FedAvg algorithm." +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:35 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:42 +#: ../../source/ref-api/flwr.server.strategy.FedXgbBagging.rst:2 +#, fuzzy +msgid "FedXgbBagging" +msgstr "FedXgbBagging" + +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 #: of +#, fuzzy msgid "" -"The range within which model parameters are clipped before quantization. " -"This parameter ensures each model parameter is bounded within " -"[-clipping_range, clipping_range], facilitating quantization." +":py:obj:`aggregate_evaluate " +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" msgstr "" +":py:obj:`aggregate_evaluate " +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:39 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:46 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 #: of -msgid "" -"The size of the range into which floating-point model parameters are " -"quantized, mapping each parameter to an integer in [0, " -"quantization_range-1]. This facilitates cryptographic operations on the " -"model updates." -msgstr "" +#, fuzzy +msgid "Aggregate evaluation metrics using average." +msgstr "采用加权平均法计算评估损失总额。" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:43 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:50 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 #: of +#, fuzzy msgid "" -"The range of values from which random mask entries are uniformly sampled " -"([0, modulus_range-1]). `modulus_range` must be less than 4294967296. " -"Please use 2**n values for `modulus_range` to prevent overflow issues." +":py:obj:`aggregate_fit " +"`\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" +":py:obj:`aggregate_fit " +"`\\ " +"\\(server\\_round\\, results\\, failures\\)" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:47 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:54 -#: of -msgid "" -"The timeout duration in seconds. If specified, the workflow will wait for" -" replies for this duration each time. If `None`, there is no time limit " -"and the workflow will wait until replies for all messages are received." -msgstr "" +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_fit:1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_fit:1 of +#, fuzzy +msgid "Aggregate fit results using bagging." +msgstr "使用 Bulyan 技术汇总拟合结果。" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:61 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 #: of +#, fuzzy msgid "" -"Generally, higher `num_shares` means more robust to dropouts while " -"increasing the computational costs; higher `reconstruction_threshold` " -"means better privacy guarantees but less tolerance to dropouts." +":py:obj:`configure_evaluate " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" +":py:obj:`configure_evaluate " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:58 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:64 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 #: of -msgid "Too large `max_weight` may compromise the precision of the quantization." +#, fuzzy +msgid "" +":py:obj:`configure_fit " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" +":py:obj:`configure_fit " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:59 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:65 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 #: of -msgid "`modulus_range` must be 2**n and larger than `quantization_range`." +#, fuzzy +msgid "" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:66 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 #: of +#, fuzzy msgid "" -"When `num_shares` is a float, it is interpreted as the proportion of all " -"selected clients, and hence the number of shares will be determined in " -"the runtime. This allows for dynamic adjustment based on the total number" -" of participating clients." +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:69 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 #: of +#, fuzzy msgid "" -"Similarly, when `reconstruction_threshold` is a float, it is interpreted " -"as the proportion of the number of shares needed for the reconstruction " -"of a private key. This feature enables flexibility in setting the " -"security threshold relative to the number of distributed shares." +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:73 +#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1::1 #: of +#, fuzzy msgid "" -"`num_shares`, `reconstruction_threshold`, and the quantization parameters" -" (`clipping_range`, `quantization_range`, `modulus_range`) play critical " -"roles in balancing privacy, robustness, and efficiency within the SecAgg+" -" protocol." +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: ../../source/ref-api/flwr.server.strategy.FedXgbCyclic.rst:2 +#, fuzzy +msgid "FedXgbCyclic" +msgstr "FedXgbCyclic" + +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 #: of +#, fuzzy msgid "" -":py:obj:`collect_masked_vectors_stage " -"`\\" -" \\(driver\\, ...\\)" +":py:obj:`aggregate_evaluate " +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" msgstr "" +":py:obj:`aggregate_evaluate " +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 #: of -msgid "Execute the 'collect masked vectors' stage." +#, fuzzy +msgid "" +":py:obj:`aggregate_fit " +"`\\ \\(server\\_round\\," +" results\\, failures\\)" msgstr "" +":py:obj:`aggregate_fit " +"`\\ \\(server\\_round\\," +" results\\, failures\\)" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 #: of +#, fuzzy msgid "" -":py:obj:`setup_stage " -"`\\ \\(driver\\, " -"context\\, state\\)" +":py:obj:`configure_evaluate " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" +":py:obj:`configure_evaluate " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.setup_stage:1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 #: of -msgid "Execute the 'setup' stage." +#, fuzzy +msgid "" +":py:obj:`configure_fit " +"`\\ \\(server\\_round\\," +" parameters\\, ...\\)" msgstr "" +":py:obj:`configure_fit " +"`\\ \\(server\\_round\\," +" parameters\\, ...\\)" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 #: of +#, fuzzy msgid "" -":py:obj:`share_keys_stage " -"`\\ " -"\\(driver\\, context\\, state\\)" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.share_keys_stage:1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 #: of -msgid "Execute the 'share keys' stage." +#, fuzzy +msgid "" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 #: of +#, fuzzy msgid "" -":py:obj:`unmask_stage " -"`\\ \\(driver\\, " -"context\\, state\\)" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.unmask_stage:1 +#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1::1 #: of -msgid "Execute the 'unmask' stage." +#, fuzzy +msgid "" +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" -#: ../../source/ref-api/flwr.server.workflow.SecAggWorkflow.rst:2 +#: ../../source/ref-api/flwr.server.strategy.FedXgbNnAvg.rst:2 #, fuzzy -msgid "SecAggWorkflow" -msgstr "工作流程" +msgid "FedXgbNnAvg" +msgstr "DP-FedAvg" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:1 of +#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg:5 of +#, fuzzy msgid "" -"Bases: " -":py:class:`~flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow`" +"This strategy is deprecated, but a copy of it is available in Flower " +"Baselines: " +"https://github.com/adap/flower/tree/main/baselines/hfedxgboost." msgstr "" +"该策略已被弃用，但在 Flower Baselines: " +"https://github.com/adap/flower/tree/main/baselines/hfedxgboost 中有其副本。" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:3 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -"The SecAgg protocol ensures the secure summation of integer vectors owned" -" by multiple parties, without accessing any individual integer vector. " -"This workflow allows the server to compute the weighted average of model " -"parameters across all clients, ensuring individual contributions remain " -"private. This is achieved by clients sending both, a weighting factor and" -" a weighted version of the locally updated parameters, both of which are " -"masked for privacy. Specifically, each client uploads \"[w, w * params]\"" -" with masks, where weighting factor 'w' is the number of examples " -"('num_examples') and 'params' represents the model parameters " -"('parameters') from the client's `FitRes`. The server then aggregates " -"these contributions to compute the weighted average of model parameters." -msgstr "" - -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:14 of -msgid "" -"The protocol involves four main stages: - 'setup': Send SecAgg " -"configuration to clients and collect their public keys. - 'share keys': " -"Broadcast public keys among clients and collect encrypted secret" +":py:obj:`aggregate_evaluate " +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" msgstr "" +":py:obj:`aggregate_evaluate " +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:54 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -"Each client's private key is split into N shares under the SecAgg " -"protocol, where N is the number of selected clients." +":py:obj:`aggregate_fit " +"`\\ \\(server\\_round\\, " +"results\\, failures\\)" msgstr "" +":py:obj:`aggregate_fit " +"`\\ \\(server\\_round\\, " +"results\\, failures\\)" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:56 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -"Generally, higher `reconstruction_threshold` means better privacy " -"guarantees but less tolerance to dropouts." +":py:obj:`configure_evaluate " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" +":py:obj:`configure_evaluate " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:60 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -"When `reconstruction_threshold` is a float, it is interpreted as the " -"proportion of the number of all selected clients needed for the " -"reconstruction of a private key. This feature enables flexibility in " -"setting the security threshold relative to the number of selected " -"clients." +":py:obj:`configure_fit " +"`\\ \\(server\\_round\\, " +"parameters\\, ...\\)" msgstr "" +":py:obj:`configure_fit " +"`\\ \\(server\\_round\\, " +"parameters\\, ...\\)" -#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:64 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -"`reconstruction_threshold`, and the quantization parameters " -"(`clipping_range`, `quantization_range`, `modulus_range`) play critical " -"roles in balancing privacy, robustness, and efficiency within the SecAgg " -"protocol." +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -":py:obj:`collect_masked_vectors_stage " -"`\\ " -"\\(driver\\, ...\\)" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -":py:obj:`setup_stage `\\" -" \\(driver\\, context\\, state\\)" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 -#: of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -":py:obj:`share_keys_stage " -"`\\ \\(driver\\, " -"context\\, state\\)" +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" -#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 -#: of -msgid "" -":py:obj:`unmask_stage " -"`\\ \\(driver\\, " -"context\\, state\\)" -msgstr "" +#: ../../source/ref-api/flwr.server.strategy.FedYogi.rst:2 +#, fuzzy +msgid "FedYogi" +msgstr "FedYogi" -#: ../../source/ref-api/flwr.simulation.rst:2 +#: flwr.server.strategy.fedyogi.FedYogi:32 of #, fuzzy -msgid "simulation" -msgstr "运行模拟" +msgid "Server-side learning rate. Defaults to 1e-2." +msgstr "服务器端学习率。默认为 1e-1。" -#: ../../source/ref-api/flwr.simulation.rst:19::1 -msgid "" -":py:obj:`start_simulation `\\ \\(\\*\\," -" client\\_fn\\[\\, ...\\]\\)" -msgstr "" +#: flwr.server.strategy.fedyogi.FedYogi:34 of +#, fuzzy +msgid "Client-side learning rate. Defaults to 0.0316." +msgstr "客户端学习率。默认为 1e-1。" -#: ../../source/ref-api/flwr.simulation.rst:19::1 -#: flwr.simulation.app.start_simulation:1 of -msgid "Start a Ray-based Flower simulation server." -msgstr "启动基于 Ray 的Flower模拟服务器。" +#: flwr.server.strategy.fedyogi.FedYogi:40 of +#, fuzzy +msgid "Controls the algorithm's degree of adaptability. Defaults to 1e-3." +msgstr "控制算法的适应度。默认为 1e-9。" -#: ../../source/ref-api/flwr.simulation.rst:19::1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -":py:obj:`run_simulation_from_cli " -"`\\ \\(\\)" -msgstr "" - -#: ../../source/ref-api/flwr.simulation.rst:19::1 -#: flwr.simulation.run_simulation.run_simulation_from_cli:1 of -msgid "Run Simulation Engine from the CLI." +":py:obj:`aggregate_evaluate " +"`\\ \\(server\\_round\\," +" results\\, ...\\)" msgstr "" +":py:obj:`aggregate_evaluate " +"`\\ \\(server\\_round\\," +" results\\, ...\\)" -#: ../../source/ref-api/flwr.simulation.rst:19::1 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -":py:obj:`run_simulation `\\ " -"\\(server\\_app\\, client\\_app\\, ...\\)" -msgstr "" - -#: ../../source/ref-api/flwr.simulation.rst:19::1 -#: flwr.simulation.run_simulation.run_simulation:1 of -msgid "Run a Flower App using the Simulation Engine." +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" -#: ../../source/ref-api/flwr.simulation.run_simulation.rst:2 +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of #, fuzzy -msgid "run\\_simulation" -msgstr "运行模拟" - -#: flwr.simulation.run_simulation.run_simulation:3 of msgid "" -"The `ServerApp` to be executed. It will send messages to different " -"`ClientApp` instances running on different (virtual) SuperNodes." +":py:obj:`configure_evaluate " +"`\\ \\(server\\_round\\," +" parameters\\, ...\\)" msgstr "" +":py:obj:`configure_evaluate " +"`\\ \\(server\\_round\\," +" parameters\\, ...\\)" -#: flwr.simulation.run_simulation.run_simulation:6 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -"The `ClientApp` to be executed by each of the SuperNodes. It will receive" -" messages sent by the `ServerApp`." +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" -#: flwr.simulation.run_simulation.run_simulation:9 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -"Number of nodes that run a ClientApp. They can be sampled by a Driver in " -"the ServerApp and receive a Message describing what the ClientApp should " -"perform." -msgstr "" - -#: flwr.simulation.run_simulation.run_simulation:13 of -msgid "A simulation backend that runs `ClientApp`s." +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" -#: flwr.simulation.run_simulation.run_simulation:15 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -"'A dictionary, e.g {\"\": , \"\": } to " -"configure a backend. Values supported in are those included by " -"`flwr.common.typing.ConfigsRecordValues`." +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" -#: flwr.simulation.run_simulation.run_simulation:19 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -"A boolean to indicate whether to enable GPU growth on the main thread. " -"This is desirable if you make use of a TensorFlow model on your " -"`ServerApp` while having your `ClientApp` running on the same GPU. " -"Without enabling this, you might encounter an out-of-memory error because" -" TensorFlow, by default, allocates all GPU memory. Read more about how " -"`tf.config.experimental.set_memory_growth()` works in the TensorFlow " -"documentation: https://www.tensorflow.org/api/stable." +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" -#: flwr.simulation.run_simulation.run_simulation:26 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -"When diabled, only INFO, WARNING and ERROR log messages will be shown. If" -" enabled, DEBUG-level logs will be displayed." +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" -#: ../../source/ref-api/flwr.simulation.run_simulation_from_cli.rst:2 +#: ../../source/ref-api/flwr.server.strategy.Krum.rst:2 #, fuzzy -msgid "run\\_simulation\\_from\\_cli" -msgstr "运行模拟" +msgid "Krum" +msgstr "Krum" -#: ../../source/ref-api/flwr.simulation.start_simulation.rst:2 +#: flwr.server.strategy.krum.Krum:3 of #, fuzzy -msgid "start\\_simulation" -msgstr "start_simulation" +msgid "Implementation based on https://arxiv.org/abs/1703.02757" +msgstr "实施基于 https://arxiv.org/abs/2304.07537。" -#: flwr.simulation.app.start_simulation:3 of +#: flwr.server.strategy.krum.Krum:17 of msgid "" -"A function creating client instances. The function must take a single " -"`str` argument called `cid`. It should return a single client instance of" -" type Client. Note that the created client instances are ephemeral and " -"will often be destroyed after a single method invocation. Since client " -"instances are not long-lived, they should not attempt to carry state over" -" method invocations. Any state required by the instance (model, dataset, " -"hyperparameters, ...) should be (re-)created in either the call to " -"`client_fn` or the call to any of the client methods (e.g., load " -"evaluation data in the `evaluate` method itself)." -msgstr "" -"创建客户端实例的函数。该函数必须接受一个名为 `cid` 的 `str` 参数。它应返回一个 Client " -"类型的客户端实例。请注意，创建的客户端实例是短暂的，通常在调用一个方法后就会被销毁。由于客户机实例不是长期存在的，它们不应试图在方法调用时携带状态数据。实例所需的任何状态数据（模型、数据集、超参数......）都应在调用" -" `client_fn` 或任何客户端方法（例如，在 `evaluate` 方法中加载评估数据）时（重新）创建。" +"Number of clients to keep before averaging (MultiKrum). Defaults to 0, in" +" that case classical Krum is applied." +msgstr "求平均值前保留的客户端数量（MultiKrum）。默认值为 0，在这种情况下会应用经典 Krum。" -#: flwr.simulation.app.start_simulation:13 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -"The total number of clients in this simulation. This must be set if " -"`clients_ids` is not set and vice-versa." -msgstr "本次模拟的客户总数。如果未设置 `clients_ids`，则必须设置该参数，反之亦然。" - -#: flwr.simulation.app.start_simulation:16 of -msgid "" -"List `client_id`s for each client. This is only required if `num_clients`" -" is not set. Setting both `num_clients` and `clients_ids` with " -"`len(clients_ids)` not equal to `num_clients` generates an error." +":py:obj:`aggregate_evaluate " +"`\\ \\(server\\_round\\, " +"results\\, ...\\)" msgstr "" -"列出每个客户的 `client_id`。只有在未设置 `num_clients` " -"时才需要这样做。同时设置`num_clients`和`clients_ids`，且`len(clients_ids)`不等于`num_clients`，会产生错误。" +":py:obj:`aggregate_evaluate " +"`\\ \\(server\\_round\\, " +"results\\, ...\\)" -#: flwr.simulation.app.start_simulation:20 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of #, fuzzy msgid "" -"CPU and GPU resources for a single client. Supported keys are `num_cpus` " -"and `num_gpus`. To understand the GPU utilization caused by `num_gpus`, " -"as well as using custom resources, please consult the Ray documentation." +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" -"\"num_gpus\"： 0.0` 单个客户端的 CPU 和 GPU 资源。支持的键值为 `num_cpus` 和 `num_gpus`。要了解" -" `num_gpus` 所导致的 GPU 利用率，以及使用自定义资源的情况，请查阅 Ray 文档。" +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" -#: flwr.simulation.app.start_simulation:25 of -msgid "" -"An implementation of the abstract base class `flwr.server.Server`. If no " -"instance is provided, then `start_server` will create one." -msgstr "抽象基类 `flwr.server.Server`的实现。如果没有提供实例，`start_server` 将创建一个。" +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 +#: flwr.server.strategy.krum.Krum.aggregate_fit:1 of +msgid "Aggregate fit results using Krum." +msgstr "使用 Krum 汇总拟合结果。" -#: flwr.simulation.app.start_simulation:31 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -"An implementation of the abstract base class `flwr.server.Strategy`. If " -"no strategy is provided, then `start_server` will use " -"`flwr.server.strategy.FedAvg`." +":py:obj:`configure_evaluate " +"`\\ \\(server\\_round\\, " +"parameters\\, ...\\)" msgstr "" -"抽象基类 `flwr.server.strategy` 的实现。如果没有提供策略，`start_server` 将使用 " -"`flwr.server.strategy.FedAvg`。" +":py:obj:`configure_evaluate " +"`\\ \\(server\\_round\\, " +"parameters\\, ...\\)" -#: flwr.simulation.app.start_simulation:35 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -"An implementation of the abstract base class `flwr.server.ClientManager`." -" If no implementation is provided, then `start_simulation` will use " -"`flwr.server.client_manager.SimpleClientManager`." +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" -"抽象基类 `flwr.server.ClientManager` 的实现。如果没有提供实现，`start_simulation` 将使用 " -"`flwr.server.client_manager.SimpleClientManager`。" +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" -#: flwr.simulation.app.start_simulation:39 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -"Optional dictionary containing arguments for the call to `ray.init`. If " -"ray_init_args is None (the default), Ray will be initialized with the " -"following default args: { \"ignore_reinit_error\": True, " -"\"include_dashboard\": False } An empty dictionary can be used " -"(ray_init_args={}) to prevent any arguments from being passed to " -"ray.init." +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" -"可选字典，包含调用 `ray.init` 时的参数。如果 ray_init_args 为 None（默认值），则将使用以下默认参数初始化 Ray：" -" { \"ignore_reinit_error\"： True, \"include_dashboard\"： False } " -"可以使用空字典（ray_init_args={}）来防止向 ray.init 传递任何参数。" - -#: flwr.simulation.app.start_simulation:39 of -msgid "" -"Optional dictionary containing arguments for the call to `ray.init`. If " -"ray_init_args is None (the default), Ray will be initialized with the " -"following default args:" -msgstr "可选字典，包含调用 `ray.init` 时的参数。如果 ray_init_args 为 None（默认值），则将使用以下默认参数初始化 Ray：" - -#: flwr.simulation.app.start_simulation:43 of -msgid "{ \"ignore_reinit_error\": True, \"include_dashboard\": False }" -msgstr "{ \"ignore_reinit_error\"： True, \"include_dashboard\"： False }" - -#: flwr.simulation.app.start_simulation:45 of -msgid "" -"An empty dictionary can be used (ray_init_args={}) to prevent any " -"arguments from being passed to ray.init." -msgstr "可以使用空字典 (ray_init_args={}) 来防止向 ray.init 传递任何参数。" - -#: flwr.simulation.app.start_simulation:48 of -msgid "" -"Set to True to prevent `ray.shutdown()` in case " -"`ray.is_initialized()=True`." -msgstr "设为 True 可在 `ray.is_initialized()=True` 情况下阻止 `ray.shutdown()` 。" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" -#: flwr.simulation.app.start_simulation:50 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of #, fuzzy msgid "" -"Optionally specify the type of actor to use. The actor object, which " -"persists throughout the simulation, will be the process in charge of " -"executing a ClientApp wrapping input argument `client_fn`." -msgstr "可选择指定要使用的actor类型。actor对象将在整个模拟过程中持续存在，它将是负责运行客户端作业（即其 `fit()`方法）的进程。" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" +msgstr "" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" -#: flwr.simulation.app.start_simulation:54 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -"If you want to create your own Actor classes, you might need to pass some" -" input argument. You can use this dictionary for such purpose." -msgstr "如果您想创建自己的 Actor 类，可能需要传递一些输入参数。为此，您可以使用本字典。" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" +msgstr "" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" -#: flwr.simulation.app.start_simulation:57 of +#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -"(default: \"DEFAULT\") Optional string (\"DEFAULT\" or \"SPREAD\") for " -"the VCE to choose in which node the actor is placed. If you are an " -"advanced user needed more control you can use lower-level scheduling " -"strategies to pin actors to specific compute nodes (e.g. via " -"NodeAffinitySchedulingStrategy). Please note this is an advanced feature." -" For all details, please refer to the Ray documentation: " -"https://docs.ray.io/en/latest/ray-core/scheduling/index.html" +":py:obj:`num_fit_clients `\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -"(默认：\"DEFAULT\"）可选字符串（\"DEFAULT \"或 \"SPREAD\"），供 VCE " -"选择将行为体放置在哪个节点上。如果你是需要更多控制权的高级用户，可以使用低级调度策略将actor固定到特定计算节点（例如，通过 " -"NodeAffinitySchedulingStrategy）。请注意，这是一项高级功能。有关详细信息，请参阅 Ray " -"文档：https://docs.ray.io/en/latest/ray-core/scheduling/index.html" - -#: flwr.simulation.app.start_simulation:66 of -msgid "**hist** -- Object containing metrics from training." -msgstr "**hist** -- 包含训练指标的对象。" - -#: ../../source/ref-changelog.md:1 -msgid "Changelog" -msgstr "更新日志" - -#: ../../source/ref-changelog.md:3 -msgid "Unreleased" -msgstr "尚未发布" - -#: ../../source/ref-changelog.md:5 ../../source/ref-changelog.md:17 -#: ../../source/ref-changelog.md:110 ../../source/ref-changelog.md:210 -#: ../../source/ref-changelog.md:294 ../../source/ref-changelog.md:358 -#: ../../source/ref-changelog.md:416 ../../source/ref-changelog.md:485 -#: ../../source/ref-changelog.md:614 ../../source/ref-changelog.md:656 -#: ../../source/ref-changelog.md:723 ../../source/ref-changelog.md:789 -#: ../../source/ref-changelog.md:834 ../../source/ref-changelog.md:873 -#: ../../source/ref-changelog.md:906 ../../source/ref-changelog.md:956 -msgid "What's new?" -msgstr "有什么新内容？" - -#: ../../source/ref-changelog.md:7 ../../source/ref-changelog.md:80 -#: ../../source/ref-changelog.md:192 ../../source/ref-changelog.md:282 -#: ../../source/ref-changelog.md:346 ../../source/ref-changelog.md:404 -#: ../../source/ref-changelog.md:473 ../../source/ref-changelog.md:535 -#: ../../source/ref-changelog.md:554 ../../source/ref-changelog.md:710 -#: ../../source/ref-changelog.md:781 ../../source/ref-changelog.md:818 -#: ../../source/ref-changelog.md:861 -msgid "Incompatible changes" -msgstr "不兼容的更改" +":py:obj:`num_fit_clients `\\ " +"\\(num\\_available\\_clients\\)" -#: ../../source/ref-changelog.md:9 +#: ../../source/ref-api/flwr.server.strategy.QFedAvg.rst:2 #, fuzzy -msgid "v1.7.0 (2024-02-05)" -msgstr "v1.3.0 (2023-02-06)" - -#: ../../source/ref-changelog.md:11 ../../source/ref-changelog.md:104 -#: ../../source/ref-changelog.md:204 ../../source/ref-changelog.md:288 -#: ../../source/ref-changelog.md:352 ../../source/ref-changelog.md:410 -#: ../../source/ref-changelog.md:479 ../../source/ref-changelog.md:548 -msgid "Thanks to our contributors" -msgstr "感谢我们的贡献者" +msgid "QFedAvg" +msgstr "DP-FedAvg" -#: ../../source/ref-changelog.md:13 ../../source/ref-changelog.md:106 -#: ../../source/ref-changelog.md:206 ../../source/ref-changelog.md:290 -#: ../../source/ref-changelog.md:354 ../../source/ref-changelog.md:412 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -"We would like to give our special thanks to all the contributors who made" -" the new version of Flower possible (in `git shortlog` order):" -msgstr "在此，我们要特别感谢所有为 Flower 的新版本做出贡献的人员（按 `git shortlog` 顺序排列）：" +":py:obj:`aggregate_evaluate " +"`\\ \\(server\\_round\\," +" results\\, ...\\)" +msgstr "" +":py:obj:`aggregate_evaluate " +"`\\ \\(server\\_round\\," +" results\\, ...\\)" -#: ../../source/ref-changelog.md:15 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of #, fuzzy msgid "" -"`Aasheesh Singh`, `Adam Narozniak`, `Aml Hassan Esmil`, `Charles " -"Beauville`, `Daniel J. Beutel`, `Daniel Nata Nugraha`, `Edoardo " -"Gabrielli`, `Gustavo Bertoli`, `HelinLin`, `Heng Pan`, `Javier`, `M S " -"Chaitanya Kumar`, `Mohammad Naseri`, `Nikos Vlachakis`, `Pritam Neog`, " -"`Robert Kuska`, `Robert Steiner`, `Taner Topal`, `Yahia Salaheldin " -"Shaaban`, `Yan Gao`, `Yasar Abbas` " +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" -"`Adam Narozniak`, `Anass Anhari`, `Charles Beauville`, `Dana-Farber`, " -"`Daniel J. Beutel`, `Daniel Nata Nugraha`, `Edoardo Gabrielli`, `Gustavo " -"Bertoli`, `Heng Pan`, `Javier`, `Mahdi`, `Steven Hé (Sīchàng)`, `Taner " -"Topal`, `achiverram28`, `danielnugraha`, `eunchung`, `ruthgal` " +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" -#: ../../source/ref-changelog.md:19 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of #, fuzzy msgid "" -"**Introduce stateful clients (experimental)** " -"([#2770](https://github.com/adap/flower/pull/2770), " -"[#2686](https://github.com/adap/flower/pull/2686), " -"[#2696](https://github.com/adap/flower/pull/2696), " -"[#2643](https://github.com/adap/flower/pull/2643), " -"[#2769](https://github.com/adap/flower/pull/2769))" +":py:obj:`configure_evaluate " +"`\\ \\(server\\_round\\," +" parameters\\, ...\\)" msgstr "" -"** baselines的普通更新** ([#2301](https://github.com/adap/flower/pull/2301), " -"[#2305](https://github.com/adap/flower/pull/2305), " -"[#2307](https://github.com/adap/flower/pull/2307), " -"[#2327](https://github.com/adap/flower/pull/2327), " -"[#2435](https://github.com/adap/flower/pull/2435))" +":py:obj:`configure_evaluate " +"`\\ \\(server\\_round\\," +" parameters\\, ...\\)" -#: ../../source/ref-changelog.md:21 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -"Subclasses of `Client` and `NumPyClient` can now store local state that " -"remains on the client. Let's start with the highlight first: this new " -"feature is compatible with both simulated clients (via " -"`start_simulation`) and networked clients (via `start_client`). It's also" -" the first preview of new abstractions like `Context` and `RecordSet`. " -"Clients can access state of type `RecordSet` via `state: RecordSet = " -"self.context.state`. Changes to this `RecordSet` are preserved across " -"different rounds of execution to enable stateful computations in a " -"unified way across simulation and deployment." +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" -#: ../../source/ref-changelog.md:23 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of #, fuzzy msgid "" -"**Improve performance** " -"([#2293](https://github.com/adap/flower/pull/2293))" -msgstr "**改进示例笔记** ([#2005](https://github.com/adap/flower/pull/2005))" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" +msgstr "" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" -#: ../../source/ref-changelog.md:25 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -"Flower is faster than ever. All `FedAvg`-derived strategies now use in-" -"place aggregation to reduce memory consumption. The Flower client " -"serialization/deserialization has been rewritten from the ground up, " -"which results in significant speedups, especially when the client-side " -"training time is short." +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" -#: ../../source/ref-changelog.md:27 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of #, fuzzy msgid "" -"**Support Federated Learning with Apple MLX and Flower** " -"([#2693](https://github.com/adap/flower/pull/2693))" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" -"** 添加使用 fastai 和 Flower 进行联邦学习的新示例** " -"([#1598](https://github.com/adap/flower/pull/1598))" +":py:obj:`num_evaluation_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" -#: ../../source/ref-changelog.md:29 +#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1::1 of +#, fuzzy msgid "" -"Flower has official support for federated learning using [Apple " -"MLX](https://ml-explore.github.io/mlx) via the new `quickstart-mlx` code " -"example." +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" msgstr "" +":py:obj:`num_fit_clients " +"`\\ " +"\\(num\\_available\\_clients\\)" -#: ../../source/ref-changelog.md:31 +#: ../../source/ref-api/flwr.server.strategy.Strategy.rst:2 +#, fuzzy +msgid "Strategy" +msgstr "Krum 策略。" + +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 +#: of #, fuzzy msgid "" -"**Introduce new XGBoost cyclic strategy** " -"([#2666](https://github.com/adap/flower/pull/2666), " -"[#2668](https://github.com/adap/flower/pull/2668))" +":py:obj:`aggregate_evaluate " +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" msgstr "" -"**介绍 iOS SDK（预览版）** ([#1621](https://github.com/adap/flower/pull/1621), " -"[#1764](https://github.com/adap/flower/pull/1764))" +":py:obj:`aggregate_evaluate " +"`\\ " +"\\(server\\_round\\, results\\, ...\\)" -#: ../../source/ref-changelog.md:33 +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1 +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 +#: of +msgid "Aggregate evaluation results." +msgstr "聚合评估结果。" + +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" -"A new strategy called `FedXgbCyclic` supports a client-by-client style of" -" training (often called cyclic). The `xgboost-comprehensive` code example" -" shows how to use it in a full project. In addition to that, `xgboost-" -"comprehensive` now also supports simulation mode. With this, Flower " -"offers best-in-class XGBoost support." +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" msgstr "" +":py:obj:`aggregate_fit `\\ " +"\\(server\\_round\\, results\\, failures\\)" -#: ../../source/ref-changelog.md:35 +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 +#: flwr.server.strategy.strategy.Strategy.aggregate_fit:1 of +msgid "Aggregate training results." +msgstr "汇总训练结果。" + +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 +#: of #, fuzzy msgid "" -"**Support Python 3.11** " -"([#2394](https://github.com/adap/flower/pull/2394))" -msgstr "** 支持 Python 3.10** ([#1320](https://github.com/adap/flower/pull/1320))" +":py:obj:`configure_evaluate " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" +msgstr "" +":py:obj:`configure_evaluate " +"`\\ " +"\\(server\\_round\\, parameters\\, ...\\)" -#: ../../source/ref-changelog.md:37 +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" -"Framework tests now run on Python 3.8, 3.9, 3.10, and 3.11. This will " -"ensure better support for users using more recent Python versions." +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" msgstr "" +":py:obj:`configure_fit `\\ " +"\\(server\\_round\\, parameters\\, ...\\)" -#: ../../source/ref-changelog.md:39 +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 +#: of #, fuzzy msgid "" -"**Update gRPC and ProtoBuf dependencies** " -"([#2814](https://github.com/adap/flower/pull/2814))" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" msgstr "" -"**更新 REST API 以支持创建和删除节点** " -"([#2283](https://github.com/adap/flower/pull/2283))" +":py:obj:`evaluate `\\ " +"\\(server\\_round\\, parameters\\)" -#: ../../source/ref-changelog.md:41 +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 +#: flwr.server.strategy.strategy.Strategy.evaluate:1 of +msgid "Evaluate the current model parameters." +msgstr "评估当前的模型参数。" + +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 +#: of +#, fuzzy msgid "" -"The `grpcio` and `protobuf` dependencies were updated to their latest " -"versions for improved security and performance." +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" msgstr "" +":py:obj:`initialize_parameters " +"`\\ " +"\\(client\\_manager\\)" -#: ../../source/ref-changelog.md:43 -#, fuzzy +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1::1 +#: flwr.server.strategy.strategy.Strategy.initialize_parameters:1 of +msgid "Initialize the (global) model parameters." +msgstr "初始化（全局）模型参数。" + +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:5 of msgid "" -"**Introduce Docker image for Flower server** " -"([#2700](https://github.com/adap/flower/pull/2700), " -"[#2688](https://github.com/adap/flower/pull/2688), " -"[#2705](https://github.com/adap/flower/pull/2705), " -"[#2695](https://github.com/adap/flower/pull/2695), " -"[#2747](https://github.com/adap/flower/pull/2747), " -"[#2746](https://github.com/adap/flower/pull/2746), " -"[#2680](https://github.com/adap/flower/pull/2680), " -"[#2682](https://github.com/adap/flower/pull/2682), " -"[#2701](https://github.com/adap/flower/pull/2701))" +"Successful updates from the previously selected and configured clients. " +"Each pair of `(ClientProxy, FitRes` constitutes a successful update from " +"one of the previously selected clients. Not that not all previously " +"selected clients are necessarily included in this list: a client might " +"drop out and not submit a result. For each client that did not submit an " +"update, there should be an `Exception` in `failures`." msgstr "" -"** 支持 SSL 的服务器和客户端** ([#842](https://github.com/adap/flower/pull/842), " -"[#844](https://github.com/adap/flower/pull/844), " -"[#845](https://github.com/adap/flower/pull/845), " -"[#847](https://github.com/adap/flower/pull/847), " -"[#993](https://github.com/adap/flower/pull/993), " -"[#994](https://github.com/adap/flower/pull/994))" +"从先前选定和配置的客户端进行的成功更新。每一对`(ClientProxy, " +"FitRes)`都是来自先前选定客户端的一次成功更新。但并非所有先前选定的客户机都一定包含在此列表中：客户机可能会退出，不提交结果。对于每个没有提交更新的客户端，`failures`中都应该有一个`Exception`。" -#: ../../source/ref-changelog.md:45 +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:13 +#: flwr.server.strategy.strategy.Strategy.aggregate_fit:13 of +msgid "Exceptions that occurred while the server was waiting for client updates." +msgstr "服务器等待客户端更新时发生的异常。" + +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:16 of msgid "" -"The Flower server can now be run using an official Docker image. A new " -"how-to guide explains [how to run Flower using " -"Docker](https://flower.ai/docs/framework/how-to-run-flower-using-" -"docker.html). An official Flower client Docker image will follow." +"**aggregation_result** -- The aggregated evaluation result. Aggregation " +"typically uses some variant of a weighted average." +msgstr "**aggregation_result** -- 汇总的评估结果。聚合通常使用某种加权平均值。" + +#: flwr.server.strategy.strategy.Strategy.aggregate_fit:5 of +msgid "" +"Successful updates from the previously selected and configured clients. " +"Each pair of `(ClientProxy, FitRes)` constitutes a successful update from" +" one of the previously selected clients. Not that not all previously " +"selected clients are necessarily included in this list: a client might " +"drop out and not submit a result. For each client that did not submit an " +"update, there should be an `Exception` in `failures`." msgstr "" +"来自先前选定和配置的客户端的成功更新。每一对`(ClientProxy, " +"FitRes)`都构成先前选定的客户端之一的一次成功更新。但并非所有先前选定的客户机都一定包含在此列表中：客户机可能会退出，不提交结果。对于每个没有提交更新的客户端，\"失败" +" \"中都应该有一个 \"异常\"。" -#: ../../source/ref-changelog.md:47 -#, fuzzy +#: flwr.server.strategy.strategy.Strategy.aggregate_fit:17 of msgid "" -"**Introduce** `flower-via-docker-compose` **example** " -"([#2626](https://github.com/adap/flower/pull/2626))" +"**parameters** -- If parameters are returned, then the server will treat " +"these as the new global model parameters (i.e., it will replace the " +"previous parameters with the ones returned from this method). If `None` " +"is returned (e.g., because there were only failures and no viable " +"results) then the server will no update the previous model parameters, " +"the updates received in this round are discarded, and the global model " +"parameters remain the same." msgstr "" -"**介绍Flower Android SDK** " -"([#2131](https://github.com/adap/flower/pull/2131))" +"**parameters** -- 如果返回参数，那么服务器将把这些参数作为新的全局模型参数（即用本方法返回的参数替换之前的参数）。如果返回 " +"\"无\"（例如，因为只有失败而没有可行的结果），那么服务器将不再更新之前的模型参数，本轮收到的更新将被丢弃，全局模型参数保持不变。" -#: ../../source/ref-changelog.md:49 -#, fuzzy +#: flwr.server.strategy.strategy.Strategy.evaluate:3 of msgid "" -"**Introduce** `quickstart-sklearn-tabular` **example** " -"([#2719](https://github.com/adap/flower/pull/2719))" -msgstr "**引入 start_driver**（[#1697](https://github.com/adap/flower/pull/1697)）" +"This function can be used to perform centralized (i.e., server-side) " +"evaluation of model parameters." +msgstr "该函数可用于对模型参数进行集中（即服务器端）评估。" -#: ../../source/ref-changelog.md:51 -#, fuzzy +#: flwr.server.strategy.strategy.Strategy.evaluate:11 of msgid "" -"**Introduce** `custom-metrics` **example** " -"([#1958](https://github.com/adap/flower/pull/1958))" -msgstr "**引入 start_driver**（[#1697](https://github.com/adap/flower/pull/1697)）" +"**evaluation_result** -- The evaluation result, usually a Tuple " +"containing loss and a dictionary containing task-specific metrics (e.g., " +"accuracy)." +msgstr "**evaluation_result** -- 评估结果，通常是一个元组，包含损失值和一个字典，字典中包含特定任务的指标（如准确率）。" -#: ../../source/ref-changelog.md:53 +#: flwr.server.strategy.strategy.Strategy.initialize_parameters:6 of +msgid "" +"**parameters** -- If parameters are returned, then the server will treat " +"these as the initial global model parameters." +msgstr "**parameters** -- 如果返回参数，服务器将把这些参数视为初始全局模型参数。" + +#: ../../source/ref-api/flwr.server.workflow.rst:2 +#, fuzzy +msgid "workflow" +msgstr "工作流程" + +#: ../../source/ref-api/flwr.server.workflow.rst:24::1 #, fuzzy msgid "" -"**Update code examples to use Flower Datasets** " -"([#2450](https://github.com/adap/flower/pull/2450), " -"[#2456](https://github.com/adap/flower/pull/2456), " -"[#2318](https://github.com/adap/flower/pull/2318), " -"[#2712](https://github.com/adap/flower/pull/2712))" +":py:obj:`DefaultWorkflow `\\ " +"\\(\\[fit\\_workflow\\, ...\\]\\)" msgstr "" -"更新开发人员工具（[#1231](https://github.com/adap/flower/pull/1231), " -"[#1276](https://github.com/adap/flower/pull/1276), " -"[#1301](https://github.com/adap/flower/pull/1301), " -"[#1310](https://github.com/adap/flower/pull/1310)" +":py:obj:`DefaultWorkflow `\\ " +"\\(\\[fit\\_workflow\\, ...\\]\\)" -#: ../../source/ref-changelog.md:55 +#: ../../source/ref-api/flwr.server.workflow.rst:24::1 +#: flwr.server.workflow.default_workflows.DefaultWorkflow:1 of +#, fuzzy +msgid "Default workflow in Flower." +msgstr "Flower 中的默认工作流程。" + +#: ../../source/ref-api/flwr.server.workflow.rst:24::1 +#, fuzzy msgid "" -"Several code examples were updated to use [Flower " -"Datasets](https://flower.ai/docs/datasets/)." +":py:obj:`SecAggPlusWorkflow `\\ " +"\\(num\\_shares\\, ...\\[\\, ...\\]\\)" msgstr "" +":py:obj:`SecAggPlusWorkflow `\\ " +"\\(num\\_shares\\, ...\\[\\, ...\\]\\)" -#: ../../source/ref-changelog.md:57 +#: ../../source/ref-api/flwr.server.workflow.rst:24::1 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:1 +#: of #, fuzzy -msgid "" -"**General updates to Flower Examples** " -"([#2381](https://github.com/adap/flower/pull/2381), " -"[#2805](https://github.com/adap/flower/pull/2805), " -"[#2782](https://github.com/adap/flower/pull/2782), " -"[#2806](https://github.com/adap/flower/pull/2806), " -"[#2829](https://github.com/adap/flower/pull/2829), " -"[#2825](https://github.com/adap/flower/pull/2825), " -"[#2816](https://github.com/adap/flower/pull/2816), " -"[#2726](https://github.com/adap/flower/pull/2726), " -"[#2659](https://github.com/adap/flower/pull/2659), " -"[#2655](https://github.com/adap/flower/pull/2655))" -msgstr "" -"**改进（试验性）驱动程序应用程序接口** ([#1663](https://github.com/adap/flower/pull/1663)," -" [#1666](https://github.com/adap/flower/pull/1666), " -"[#1667](https://github.com/adap/flower/pull/1667), " -"[#1664](https://github.com/adap/flower/pull/1664), " -"[#1675](https://github.com/adap/flower/pull/1675), " -"[#1676](https://github.com/adap/flower/pull/1676), " -"[#1693](https://github.com/adap/flower/pull/1693), " -"[#1662](https://github.com/adap/flower/pull/1662), " -"[#1794](https://github.com/adap/flower/pull/1794))" - -#: ../../source/ref-changelog.md:59 -msgid "Many Flower code examples received substantial updates." -msgstr "" - -#: ../../source/ref-changelog.md:61 ../../source/ref-changelog.md:154 -msgid "**Update Flower Baselines**" -msgstr "**更新 Flower Baselines**" +msgid "The workflow for the SecAgg+ protocol." +msgstr "SecAgg+ 协议的工作流程。" -#: ../../source/ref-changelog.md:63 +#: ../../source/ref-api/flwr.server.workflow.rst:24::1 #, fuzzy msgid "" -"HFedXGBoost ([#2226](https://github.com/adap/flower/pull/2226), " -"[#2771](https://github.com/adap/flower/pull/2771))" +":py:obj:`SecAggWorkflow `\\ " +"\\(reconstruction\\_threshold\\, \\*\\)" msgstr "" -"FedBN ([#2608](https://github.com/adap/flower/pull/2608), " -"[#2615](https://github.com/adap/flower/pull/2615))" - -#: ../../source/ref-changelog.md:64 -#, fuzzy -msgid "FedVSSL ([#2412](https://github.com/adap/flower/pull/2412))" -msgstr "FjORD [#2431](https://github.com/adap/flower/pull/2431)" - -#: ../../source/ref-changelog.md:65 -#, fuzzy -msgid "FedNova ([#2179](https://github.com/adap/flower/pull/2179))" -msgstr "FjORD [#2431](https://github.com/adap/flower/pull/2431)" +":py:obj:`SecAggWorkflow `\\ " +"\\(reconstruction\\_threshold\\, \\*\\)" -#: ../../source/ref-changelog.md:66 +#: ../../source/ref-api/flwr.server.workflow.rst:24::1 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:1 of #, fuzzy -msgid "HeteroFL ([#2439](https://github.com/adap/flower/pull/2439))" -msgstr "FedMeta [#2438](https://github.com/adap/flower/pull/2438)" +msgid "The workflow for the SecAgg protocol." +msgstr "SecAgg 协议的工作流程。" -#: ../../source/ref-changelog.md:67 +#: ../../source/ref-api/flwr.server.workflow.DefaultWorkflow.rst:2 #, fuzzy -msgid "FedAvgM ([#2246](https://github.com/adap/flower/pull/2246))" -msgstr "FedPer [#2266](https://github.com/adap/flower/pull/2266)" +msgid "DefaultWorkflow" +msgstr "工作流程" -#: ../../source/ref-changelog.md:68 +#: ../../source/ref-api/flwr.server.workflow.SecAggPlusWorkflow.rst:2 #, fuzzy -msgid "FedPara ([#2722](https://github.com/adap/flower/pull/2722))" -msgstr "FedPer [#2266](https://github.com/adap/flower/pull/2266)" +msgid "SecAggPlusWorkflow" +msgstr "工作流程" -#: ../../source/ref-changelog.md:70 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:3 +#: of #, fuzzy msgid "" -"**Improve documentation** " -"([#2674](https://github.com/adap/flower/pull/2674), " -"[#2480](https://github.com/adap/flower/pull/2480), " -"[#2826](https://github.com/adap/flower/pull/2826), " -"[#2727](https://github.com/adap/flower/pull/2727), " -"[#2761](https://github.com/adap/flower/pull/2761), " -"[#2900](https://github.com/adap/flower/pull/2900))" -msgstr "" -"** 更新文档** ([#1629](https://github.com/adap/flower/pull/1629), " -"[#1628](https://github.com/adap/flower/pull/1628), " -"[#1620](https://github.com/adap/flower/pull/1620), " -"[#1618](https://github.com/adap/flower/pull/1618), " -"[#1617](https://github.com/adap/flower/pull/1617), " -"[#1613](https://github.com/adap/flower/pull/1613), " -"[#1614](https://github.com/adap/flower/pull/1614)))" - -#: ../../source/ref-changelog.md:72 -msgid "" -"**Improved testing and development infrastructure** " -"([#2797](https://github.com/adap/flower/pull/2797), " -"[#2676](https://github.com/adap/flower/pull/2676), " -"[#2644](https://github.com/adap/flower/pull/2644), " -"[#2656](https://github.com/adap/flower/pull/2656), " -"[#2848](https://github.com/adap/flower/pull/2848), " -"[#2675](https://github.com/adap/flower/pull/2675), " -"[#2735](https://github.com/adap/flower/pull/2735), " -"[#2767](https://github.com/adap/flower/pull/2767), " -"[#2732](https://github.com/adap/flower/pull/2732), " -"[#2744](https://github.com/adap/flower/pull/2744), " -"[#2681](https://github.com/adap/flower/pull/2681), " -"[#2699](https://github.com/adap/flower/pull/2699), " -"[#2745](https://github.com/adap/flower/pull/2745), " -"[#2734](https://github.com/adap/flower/pull/2734), " -"[#2731](https://github.com/adap/flower/pull/2731), " -"[#2652](https://github.com/adap/flower/pull/2652), " -"[#2720](https://github.com/adap/flower/pull/2720), " -"[#2721](https://github.com/adap/flower/pull/2721), " -"[#2717](https://github.com/adap/flower/pull/2717), " -"[#2864](https://github.com/adap/flower/pull/2864), " -"[#2694](https://github.com/adap/flower/pull/2694), " -"[#2709](https://github.com/adap/flower/pull/2709), " -"[#2658](https://github.com/adap/flower/pull/2658), " -"[#2796](https://github.com/adap/flower/pull/2796), " -"[#2692](https://github.com/adap/flower/pull/2692), " -"[#2657](https://github.com/adap/flower/pull/2657), " -"[#2813](https://github.com/adap/flower/pull/2813), " -"[#2661](https://github.com/adap/flower/pull/2661), " -"[#2398](https://github.com/adap/flower/pull/2398))" -msgstr "" - -#: ../../source/ref-changelog.md:74 -msgid "" -"The Flower testing and development infrastructure has received " -"substantial updates. This makes Flower 1.7 the most tested release ever." +"The SecAgg+ protocol ensures the secure summation of integer vectors " +"owned by multiple parties, without accessing any individual integer " +"vector. This workflow allows the server to compute the weighted average " +"of model parameters across all clients, ensuring individual contributions" +" remain private. This is achieved by clients sending both, a weighting " +"factor and a weighted version of the locally updated parameters, both of " +"which are masked for privacy. Specifically, each client uploads \"[w, w *" +" params]\" with masks, where weighting factor 'w' is the number of " +"examples ('num_examples') and 'params' represents the model parameters " +"('parameters') from the client's `FitRes`. The server then aggregates " +"these contributions to compute the weighted average of model parameters." msgstr "" +"SecAgg+ " +"协议可确保对多方拥有的整数向量进行安全求和，而不会访问任何单个整数向量。该工作流程允许服务器计算所有客户端模型参数的加权平均值，确保个人贡献保持私密。这可以通过客户端同时发送加权因子和本地更新参数的加权版本来实现，为了保护隐私，两者都会被屏蔽。具体来说，每个客户端都会上传带掩码的\"[w," +" w * params]\"，其中加权因子 \"w \"是示例数（\"num_examples\"），\"params \"代表客户端 " +"\"FitRes \"中的模型参数（\"parameters\"）。然后，服务器会汇总这些贡献，计算模型参数的加权平均值。" -#: ../../source/ref-changelog.md:76 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:14 +#: of #, fuzzy msgid "" -"**Update dependencies** " -"([#2753](https://github.com/adap/flower/pull/2753), " -"[#2651](https://github.com/adap/flower/pull/2651), " -"[#2739](https://github.com/adap/flower/pull/2739), " -"[#2837](https://github.com/adap/flower/pull/2837), " -"[#2788](https://github.com/adap/flower/pull/2788), " -"[#2811](https://github.com/adap/flower/pull/2811), " -"[#2774](https://github.com/adap/flower/pull/2774), " -"[#2790](https://github.com/adap/flower/pull/2790), " -"[#2751](https://github.com/adap/flower/pull/2751), " -"[#2850](https://github.com/adap/flower/pull/2850), " -"[#2812](https://github.com/adap/flower/pull/2812), " -"[#2872](https://github.com/adap/flower/pull/2872), " -"[#2736](https://github.com/adap/flower/pull/2736), " -"[#2756](https://github.com/adap/flower/pull/2756), " -"[#2857](https://github.com/adap/flower/pull/2857), " -"[#2757](https://github.com/adap/flower/pull/2757), " -"[#2810](https://github.com/adap/flower/pull/2810), " -"[#2740](https://github.com/adap/flower/pull/2740), " -"[#2789](https://github.com/adap/flower/pull/2789))" -msgstr "" -"**更新Example** ([#1772](https://github.com/adap/flower/pull/1772), " -"[#1873](https://github.com/adap/flower/pull/1873), " -"[#1981](https://github.com/adap/flower/pull/1981), " -"[#1988](https://github.com/adap/flower/pull/1988), " -"[#1984](https://github.com/adap/flower/pull/1984), " -"[#1982](https://github.com/adap/flower/pull/1982), " -"[#2112](https://github.com/adap/flower/pull/2112), " -"[#2144](https://github.com/adap/flower/pull/2144), " -"[#2174](https://github.com/adap/flower/pull/2174), " -"[#2225](https://github.com/adap/flower/pull/2225), " -"[#2183](https://github.com/adap/flower/pull/2183))" +"The protocol involves four main stages: - 'setup': Send SecAgg+ " +"configuration to clients and collect their public keys. - 'share keys': " +"Broadcast public keys among clients and collect encrypted secret" +msgstr "协议包括四个主要阶段： - 设置\"： 向客户端发送 SecAgg+ 配置并收集其公钥。- 共享密钥\"： 在客户端之间广播公钥，并收集加密密钥。" -#: ../../source/ref-changelog.md:78 -msgid "" -"**General improvements** " -"([#2803](https://github.com/adap/flower/pull/2803), " -"[#2847](https://github.com/adap/flower/pull/2847), " -"[#2877](https://github.com/adap/flower/pull/2877), " -"[#2690](https://github.com/adap/flower/pull/2690), " -"[#2889](https://github.com/adap/flower/pull/2889), " -"[#2874](https://github.com/adap/flower/pull/2874), " -"[#2819](https://github.com/adap/flower/pull/2819), " -"[#2689](https://github.com/adap/flower/pull/2689), " -"[#2457](https://github.com/adap/flower/pull/2457), " -"[#2870](https://github.com/adap/flower/pull/2870), " -"[#2669](https://github.com/adap/flower/pull/2669), " -"[#2876](https://github.com/adap/flower/pull/2876), " -"[#2885](https://github.com/adap/flower/pull/2885), " -"[#2858](https://github.com/adap/flower/pull/2858), " -"[#2867](https://github.com/adap/flower/pull/2867), " -"[#2351](https://github.com/adap/flower/pull/2351), " -"[#2886](https://github.com/adap/flower/pull/2886), " -"[#2860](https://github.com/adap/flower/pull/2860), " -"[#2828](https://github.com/adap/flower/pull/2828), " -"[#2869](https://github.com/adap/flower/pull/2869), " -"[#2875](https://github.com/adap/flower/pull/2875), " -"[#2733](https://github.com/adap/flower/pull/2733), " -"[#2488](https://github.com/adap/flower/pull/2488), " -"[#2646](https://github.com/adap/flower/pull/2646), " -"[#2879](https://github.com/adap/flower/pull/2879), " -"[#2821](https://github.com/adap/flower/pull/2821), " -"[#2855](https://github.com/adap/flower/pull/2855), " -"[#2800](https://github.com/adap/flower/pull/2800), " -"[#2807](https://github.com/adap/flower/pull/2807), " -"[#2801](https://github.com/adap/flower/pull/2801), " -"[#2804](https://github.com/adap/flower/pull/2804), " -"[#2851](https://github.com/adap/flower/pull/2851), " -"[#2787](https://github.com/adap/flower/pull/2787), " -"[#2852](https://github.com/adap/flower/pull/2852), " -"[#2672](https://github.com/adap/flower/pull/2672), " -"[#2759](https://github.com/adap/flower/pull/2759))" -msgstr "" +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:17 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:17 +#: of +#, fuzzy +msgid "key shares." +msgstr "关键股份。" -#: ../../source/ref-changelog.md:82 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:18 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:18 +#: of #, fuzzy msgid "" -"**Deprecate** `start_numpy_client` " -"([#2563](https://github.com/adap/flower/pull/2563), " -"[#2718](https://github.com/adap/flower/pull/2718))" -msgstr "" -"TAMUNA ([#2254](https://github.com/adap/flower/pull/2254), " -"[#2508](https://github.com/adap/flower/pull/2508))" +"'collect masked vectors': Forward encrypted secret key shares to target " +"clients and collect masked model parameters." +msgstr "收集屏蔽向量\"： 向目标客户端转发加密密钥共享，并收集屏蔽模型参数。" -#: ../../source/ref-changelog.md:84 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:20 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:20 +#: of +#, fuzzy msgid "" -"Until now, clients of type `NumPyClient` needed to be started via " -"`start_numpy_client`. In our efforts to consolidate framework APIs, we " -"have introduced changes, and now all client types should start via " -"`start_client`. To continue using `NumPyClient` clients, you simply need " -"to first call the `.to_client()` method and then pass returned `Client` " -"object to `start_client`. The examples and the documentation have been " -"updated accordingly." -msgstr "" +"'unmask': Collect secret key shares to decrypt and aggregate the model " +"parameters." +msgstr "解密\"： 收集密钥共享，解密并汇总模型参数。" -#: ../../source/ref-changelog.md:86 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:22 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:22 +#: of #, fuzzy msgid "" -"**Deprecate legacy DP wrappers** " -"([#2749](https://github.com/adap/flower/pull/2749))" -msgstr "**移除过时的 KerasClient**（[#857](https://github.com/adap/flower/pull/857)）" +"Only the aggregated model parameters are exposed and passed to " +"`Strategy.aggregate_fit`, ensuring individual data privacy." +msgstr "只有聚合模型参数才会公开并传递给 `Strategy.aggregate_fit`，从而确保个人数据隐私。" -#: ../../source/ref-changelog.md:88 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:25 +#: of +#, fuzzy msgid "" -"Legacy DP wrapper classes are deprecated, but still functional. This is " -"in preparation for an all-new pluggable version of differential privacy " -"support in Flower." +"The number of shares into which each client's private key is split under " +"the SecAgg+ protocol. If specified as a float, it represents the " +"proportion of all selected clients, and the number of shares will be set " +"dynamically in the run time. A private key can be reconstructed from " +"these shares, allowing for the secure aggregation of model updates. Each " +"client sends one share to each of its neighbors while retaining one." msgstr "" +"在 SecAgg+ " +"协议下，每个客户的私钥被分成的份数。如果指定为浮点数，则代表所有选定客户的比例，份额数将在运行时动态设置。私钥可以从这些份额中重建，从而实现模型更新的安全聚合。每个客户端向其每个邻居发送一份，同时保留一份。" -#: ../../source/ref-changelog.md:90 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:25 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:32 +#: of #, fuzzy msgid "" -"**Make optional arg** `--callable` **in** `flower-client` **a required " -"positional arg** ([#2673](https://github.com/adap/flower/pull/2673))" -msgstr "" -"**从** `start_client` 中移除** `rest` **实验参数 " -"([#2324](https://github.com/adap/flower/pull/2324))" +"The minimum number of shares required to reconstruct a client's private " +"key, or, if specified as a float, it represents the proportion of the " +"total number of shares needed for reconstruction. This threshold ensures " +"privacy by allowing for the recovery of contributions from dropped " +"clients during aggregation, without compromising individual client data." +msgstr "重建客户私钥所需的最小份数，如果指定为浮动，则表示重建所需的份数占总份数的比例。这个阈值允许在聚合过程中恢复掉线客户的贡献，从而确保隐私，而不会泄露单个客户的数据。" -#: ../../source/ref-changelog.md:92 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:31 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:38 +#: of #, fuzzy msgid "" -"**Rename** `certificates` **to** `root_certificates` **in** `Driver` " -"([#2890](https://github.com/adap/flower/pull/2890))" -msgstr "" -"**重新命名** `rnd` ** to** `server_round` " -"([#1321](https://github.com/adap/flower/pull/1321))" +"The maximum value of the weight that can be assigned to any single " +"client's update during the weighted average calculation on the server " +"side, e.g., in the FedAvg algorithm." +msgstr "在服务器端进行加权平均计算（如 FedAvg 算法）时，可分配给任何单个客户端更新的权重的最大值。" -#: ../../source/ref-changelog.md:94 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:35 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:42 +#: of #, fuzzy msgid "" -"**Drop experimental** `Task` **fields** " -"([#2866](https://github.com/adap/flower/pull/2866), " -"[#2865](https://github.com/adap/flower/pull/2865))" -msgstr "" -"FedBN ([#2608](https://github.com/adap/flower/pull/2608), " -"[#2615](https://github.com/adap/flower/pull/2615))" - -#: ../../source/ref-changelog.md:96 -msgid "" -"Experimental fields `sa`, `legacy_server_message` and " -"`legacy_client_message` were removed from `Task` message. The removed " -"fields are superseded by the new `RecordSet` abstraction." -msgstr "" +"The range within which model parameters are clipped before quantization. " +"This parameter ensures each model parameter is bounded within " +"[-clipping_range, clipping_range], facilitating quantization." +msgstr "量化前模型参数的裁剪范围。该参数可确保每个模型参数都在 [-clipping_range, clipping_range] 范围内，便于量化。" -#: ../../source/ref-changelog.md:98 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:39 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:46 +#: of #, fuzzy msgid "" -"**Retire MXNet examples** " -"([#2724](https://github.com/adap/flower/pull/2724))" -msgstr "**新的 scikit-learn 代码示例** ([#748](https://github.com/adap/flower/pull/748))" +"The size of the range into which floating-point model parameters are " +"quantized, mapping each parameter to an integer in [0, " +"quantization_range-1]. This facilitates cryptographic operations on the " +"model updates." +msgstr "浮点模型参数量化范围的大小，将每个参数映射为 [0, quantization_range-1] 中的整数。这有助于对模型更新进行加密操作。" -#: ../../source/ref-changelog.md:100 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:43 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:50 +#: of +#, fuzzy msgid "" -"The development of the MXNet fremework has ended and the project is now " -"[archived on GitHub](https://github.com/apache/mxnet). Existing MXNet " -"examples won't receive updates." +"The range of values from which random mask entries are uniformly sampled " +"([0, modulus_range-1]). `modulus_range` must be less than 4294967296. " +"Please use 2**n values for `modulus_range` to prevent overflow issues." msgstr "" +"对随机掩码条目进行均匀采样的数值范围（[0, modulus_range-1]）。modulus_range \"必须小于 " +"4294967296。为防止出现溢出问题，请为 `modulus_range` 使用 2**n 的值。" -#: ../../source/ref-changelog.md:102 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:47 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:54 +#: of #, fuzzy -msgid "v1.6.0 (2023-11-28)" -msgstr "v1.4.0 (2023-04-21)" - -#: ../../source/ref-changelog.md:108 msgid "" -"`Aashish Kolluri`, `Adam Narozniak`, `Alessio Mora`, `Barathwaja S`, " -"`Charles Beauville`, `Daniel J. Beutel`, `Daniel Nata Nugraha`, `Gabriel " -"Mota`, `Heng Pan`, `Ivan Agarský`, `JS.KIM`, `Javier`, `Marius Schlegel`," -" `Navin Chandra`, `Nic Lane`, `Peterpan828`, `Qinbin Li`, `Shaz-hash`, " -"`Steve Laskaridis`, `Taner Topal`, `William Lindskog`, `Yan Gao`, " -"`cnxdeveloper`, `k3nfalt` " -msgstr "" +"The timeout duration in seconds. If specified, the workflow will wait for" +" replies for this duration each time. If `None`, there is no time limit " +"and the workflow will wait until replies for all messages are received." +msgstr "超时时间（秒）。如果指定，工作流将在每次等待回复的时间内等待回复。如果指定为 \"无\"，则没有时间限制，工作流程将一直等待到收到所有信息的回复。" -#: ../../source/ref-changelog.md:112 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:61 +#: of +#, fuzzy msgid "" -"**Add experimental support for Python 3.12** " -"([#2565](https://github.com/adap/flower/pull/2565))" -msgstr "" -"** 增加对 Python 3.12 的实验支持** " -"([#2565](https://github.com/adap/flower/pull/2565))" +"Generally, higher `num_shares` means more robust to dropouts while " +"increasing the computational costs; higher `reconstruction_threshold` " +"means better privacy guarantees but less tolerance to dropouts." +msgstr "一般来说，\"份额数 \"越高，意味着对丢弃的鲁棒性越强，同时计算成本也会增加；\"重构阈值 \"越高，意味着隐私保证越好，但对丢弃的容忍度越低。" -#: ../../source/ref-changelog.md:114 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:58 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:64 +#: of #, fuzzy -msgid "" -"**Add new XGBoost examples** " -"([#2612](https://github.com/adap/flower/pull/2612), " -"[#2554](https://github.com/adap/flower/pull/2554), " -"[#2617](https://github.com/adap/flower/pull/2617), " -"[#2618](https://github.com/adap/flower/pull/2618), " -"[#2619](https://github.com/adap/flower/pull/2619), " -"[#2567](https://github.com/adap/flower/pull/2567))" -msgstr "" -"**引入（试验性）Driver API** ([#1520](https://github.com/adap/flower/pull/1520)," -" [#1525](https://github.com/adap/flower/pull/1525), " -"[#1545](https://github.com/adap/flower/pull/1545), " -"[#1546](https://github.com/adap/flower/pull/1546), " -"[#1550](https://github.com/adap/flower/pull/1550), " -"[#1551](https://github.com/adap/flower/pull/1551), " -"[#1567](https://github.com/adap/flower/pull/1567))" +msgid "Too large `max_weight` may compromise the precision of the quantization." +msgstr "过大的 `max_weight` 可能会影响量化的精度。" -#: ../../source/ref-changelog.md:116 -msgid "" -"We have added a new `xgboost-quickstart` example alongside a new " -"`xgboost-comprehensive` example that goes more in-depth." -msgstr "" +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:59 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:65 +#: of +#, fuzzy +msgid "`modulus_range` must be 2**n and larger than `quantization_range`." +msgstr "modulus_range \"必须为 2**n，且大于 \"quantization_range\"。" -#: ../../source/ref-changelog.md:118 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:66 +#: of #, fuzzy msgid "" -"**Add Vertical FL example** " -"([#2598](https://github.com/adap/flower/pull/2598))" -msgstr "**新的 iOS CoreML 代码示例**（[#1289](https://github.com/adap/flower/pull/1289)）" +"When `num_shares` is a float, it is interpreted as the proportion of all " +"selected clients, and hence the number of shares will be determined in " +"the runtime. This allows for dynamic adjustment based on the total number" +" of participating clients." +msgstr "当 `num_shares` 为浮点数时，它被解释为所有选定客户端的比例，因此份额数将在运行时确定。这样就可以根据参与客户端的总数进行动态调整。" -#: ../../source/ref-changelog.md:120 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:69 +#: of +#, fuzzy msgid "" -"We had many questions about Vertical Federated Learning using Flower, so " -"we decided to add an simple example for it on the [Titanic " -"dataset](https://www.kaggle.com/competitions/titanic/data) alongside a " -"tutorial (in the README)." +"Similarly, when `reconstruction_threshold` is a float, it is interpreted " +"as the proportion of the number of shares needed for the reconstruction " +"of a private key. This feature enables flexibility in setting the " +"security threshold relative to the number of distributed shares." msgstr "" +"同样，当 `reconstruction_threshold` " +"为浮点数时，它被解释为重建私钥所需的份额数比例。这一功能使我们可以根据分发的份额数灵活设置安全阈值。" -#: ../../source/ref-changelog.md:122 -msgid "" -"**Support custom** `ClientManager` **in** `start_driver()` " -"([#2292](https://github.com/adap/flower/pull/2292))" -msgstr "**在***`start_driver()`中支持自定义***`ClientManager([#2292](https://github.com/adap/flower/pull/2292))" - -#: ../../source/ref-changelog.md:124 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow:73 +#: of +#, fuzzy msgid "" -"**Update REST API to support create and delete nodes** " -"([#2283](https://github.com/adap/flower/pull/2283))" +"`num_shares`, `reconstruction_threshold`, and the quantization parameters" +" (`clipping_range`, `quantization_range`, `modulus_range`) play critical " +"roles in balancing privacy, robustness, and efficiency within the SecAgg+" +" protocol." msgstr "" -"**更新 REST API 以支持创建和删除节点** " -"([#2283](https://github.com/adap/flower/pull/2283))" +"份额数\"、\"重建阈值 \"和量化参数（\"裁剪范围\"、\"量化范围\"、\"模数范围\"）在平衡 SecAgg+ " +"协议的隐私性、稳健性和效率方面发挥着关键作用。" -#: ../../source/ref-changelog.md:126 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: of #, fuzzy msgid "" -"**Update the Android SDK** " -"([#2187](https://github.com/adap/flower/pull/2187))" +":py:obj:`collect_masked_vectors_stage " +"`\\" +" \\(driver\\, ...\\)" msgstr "" -"**介绍Flower Android SDK** " -"([#2131](https://github.com/adap/flower/pull/2131))" +":py:obj:`collect_masked_vectors_stage " +"`\\" +" \\(driver\\, ...\\)" -#: ../../source/ref-changelog.md:128 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: of #, fuzzy -msgid "Add gRPC request-response capability to the Android SDK." -msgstr "为 C++ SDK 添加 gRPC 请求-响应功能。" +msgid "Execute the 'collect masked vectors' stage." +msgstr "执行 \"收集屏蔽向量 \"阶段。" -#: ../../source/ref-changelog.md:130 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: of #, fuzzy msgid "" -"**Update the C++ SDK** " -"([#2537](https://github.com/adap/flower/pull/2537), " -"[#2528](https://github.com/adap/flower/pull/2528), " -"[#2523](https://github.com/adap/flower/pull/2523), " -"[#2522](https://github.com/adap/flower/pull/2522))" +":py:obj:`setup_stage " +"`\\ \\(driver\\, " +"context\\, state\\)" msgstr "" -"** 更新 C++ SDK** ([#2537](https://github/com/adap/flower/pull/2537), " -"[#2528](https://github/com/adap/flower/pull/2528), " -"[#2523](https://github.com/adap/flower/pull/2523), " -"[#2522](https://github.com/adap/flower/pull/2522))" +":py:obj:`setup_stage " +"`\\ \\(driver\\, " +"context\\, state\\)" -#: ../../source/ref-changelog.md:132 -msgid "Add gRPC request-response capability to the C++ SDK." -msgstr "为 C++ SDK 添加 gRPC 请求-响应功能。" +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.setup_stage:1 +#: of +#, fuzzy +msgid "Execute the 'setup' stage." +msgstr "执行 \"设置 \"阶段。" -#: ../../source/ref-changelog.md:134 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: of #, fuzzy msgid "" -"**Make HTTPS the new default** " -"([#2591](https://github.com/adap/flower/pull/2591), " -"[#2636](https://github.com/adap/flower/pull/2636))" +":py:obj:`share_keys_stage " +"`\\ " +"\\(driver\\, context\\, state\\)" msgstr "" -"Baselines文档（[#2290](https://github.com/adap/flower/pull/2290), " -"[#2400](https://github.com/adap/flower/pull/2400)" +":py:obj:`share_keys_stage " +"`\\ " +"\\(driver\\, context\\, state\\)" -#: ../../source/ref-changelog.md:136 -msgid "" -"Flower is moving to HTTPS by default. The new `flower-server` requires " -"passing `--certificates`, but users can enable `--insecure` to use HTTP " -"for prototyping. The same applies to `flower-client`, which can either " -"use user-provided credentials or gRPC-bundled certificates to connect to " -"an HTTPS-enabled server or requires opt-out via passing `--insecure` to " -"enable insecure HTTP connections." -msgstr "" +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.share_keys_stage:1 +#: of +#, fuzzy +msgid "Execute the 'share keys' stage." +msgstr "执行 \"共享密钥 \"阶段。" -#: ../../source/ref-changelog.md:138 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: of +#, fuzzy msgid "" -"For backward compatibility, `start_client()` and `start_numpy_client()` " -"will still start in insecure mode by default. In a future release, " -"insecure connections will require user opt-in by passing `insecure=True`." +":py:obj:`unmask_stage " +"`\\ \\(driver\\, " +"context\\, state\\)" msgstr "" +":py:obj:`unmask_stage " +"`\\ \\(driver\\, " +"context\\, state\\)" + +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.unmask_stage:1 +#: of +#, fuzzy +msgid "Execute the 'unmask' stage." +msgstr "执行 \"解除屏蔽 \"阶段。" + +#: ../../source/ref-api/flwr.server.workflow.SecAggWorkflow.rst:2 +#, fuzzy +msgid "SecAggWorkflow" +msgstr "工作流程" -#: ../../source/ref-changelog.md:140 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:1 of +#, fuzzy msgid "" -"**Unify client API** ([#2303](https://github.com/adap/flower/pull/2303), " -"[#2390](https://github.com/adap/flower/pull/2390), " -"[#2493](https://github.com/adap/flower/pull/2493))" +"Bases: " +":py:class:`~flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow`" msgstr "" -"** 统一客户端应用程序接口** ([#2303](https://github.com/adap/flower/pull/2303), " -"[#2390](https://github.com/adap/flower/pull/2390), " -"[#2493](https://github.com/adap/flower/pull/2493))" +"基础： " +":py:class:`~flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow`." -#: ../../source/ref-changelog.md:142 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:3 of #, fuzzy msgid "" -"Using the `client_fn`, Flower clients can interchangeably run as " -"standalone processes (i.e. via `start_client`) or in simulation (i.e. via" -" `start_simulation`) without requiring changes to how the client class is" -" defined and instantiated. The `to_client()` function is introduced to " -"convert a `NumPyClient` to a `Client`." +"The SecAgg protocol ensures the secure summation of integer vectors owned" +" by multiple parties, without accessing any individual integer vector. " +"This workflow allows the server to compute the weighted average of model " +"parameters across all clients, ensuring individual contributions remain " +"private. This is achieved by clients sending both, a weighting factor and" +" a weighted version of the locally updated parameters, both of which are " +"masked for privacy. Specifically, each client uploads \"[w, w * params]\"" +" with masks, where weighting factor 'w' is the number of examples " +"('num_examples') and 'params' represents the model parameters " +"('parameters') from the client's `FitRes`. The server then aggregates " +"these contributions to compute the weighted average of model parameters." msgstr "" -"使用 `client_fn`，Flower 客户端可以作为独立进程（即通过 `start_client`）或在模拟中（即通过 " -"`start_simulation`）交替运行，而无需更改客户端类的定义和实例化方式。调用 `start_numpy_client` 现已过时。" +"SecAgg " +"协议可确保对多方拥有的整数向量进行安全求和，而不会访问任何单个整数向量。该工作流程允许服务器计算所有客户端模型参数的加权平均值，确保个人贡献保持私密。这可以通过客户端同时发送加权因子和本地更新参数的加权版本来实现，为了保护隐私，两者都会被屏蔽。具体来说，每个客户端都会上传带掩码的\"[w," +" w * params]\"，其中加权因子 \"w \"是示例数（\"num_examples\"），\"params \"代表客户端 " +"\"FitRes \"中的模型参数（\"parameters\"）。然后，服务器会汇总这些贡献，计算模型参数的加权平均值。" -#: ../../source/ref-changelog.md:144 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:14 of +#, fuzzy msgid "" -"**Add new** `Bulyan` **strategy** " -"([#1817](https://github.com/adap/flower/pull/1817), " -"[#1891](https://github.com/adap/flower/pull/1891))" -msgstr "" -"**添加新**\"Bulyan " -"\"**策略**（[#1817](https://github.com/adap/flower/pull/1817), " -"[#1891](https://github.com/adap/flower/pull/1891)" +"The protocol involves four main stages: - 'setup': Send SecAgg " +"configuration to clients and collect their public keys. - 'share keys': " +"Broadcast public keys among clients and collect encrypted secret" +msgstr "协议包括四个主要阶段： - 设置\"： 向客户端发送 SecAgg 配置并收集它们的公钥。- 共享密钥\"： 在客户端之间广播公钥并收集加密密钥。" -#: ../../source/ref-changelog.md:146 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:54 of +#, fuzzy msgid "" -"The new `Bulyan` strategy implements Bulyan by [El Mhamdi et al., " -"2018](https://arxiv.org/abs/1802.07927)" -msgstr "新的 \"Bulyan\"策略通过[El Mhamdi 等人，2018]（https://arxiv.org/abs/1802.07927）实现" +"Each client's private key is split into N shares under the SecAgg " +"protocol, where N is the number of selected clients." +msgstr "根据 SecAgg 协议，每个客户的私人密钥被分成 N 份，其中 N 是所选客户的数量。" -#: ../../source/ref-changelog.md:148 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:56 of #, fuzzy msgid "" -"**Add new** `XGB Bagging` **strategy** " -"([#2611](https://github.com/adap/flower/pull/2611))" -msgstr "**添加新的`FedProx`策略** （[#1619](https://github.com/adap/flower/pull/1619)）" +"Generally, higher `reconstruction_threshold` means better privacy " +"guarantees but less tolerance to dropouts." +msgstr "一般来说，\"重建阈值 \"越高，隐私保证就越好，但对丢包的容忍度就越低。" -#: ../../source/ref-changelog.md:150 ../../source/ref-changelog.md:152 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:60 of #, fuzzy msgid "" -"**Introduce `WorkloadState`** " -"([#2564](https://github.com/adap/flower/pull/2564), " -"[#2632](https://github.com/adap/flower/pull/2632))" +"When `reconstruction_threshold` is a float, it is interpreted as the " +"proportion of the number of all selected clients needed for the " +"reconstruction of a private key. This feature enables flexibility in " +"setting the security threshold relative to the number of selected " +"clients." msgstr "" -"**新的内置策略**（[#828](https://github.com/adap/flower/pull/828) " -"[#822](https://github.com/adap/flower/pull/822)" +"当 `reconstruction_threshold` " +"为浮点数时，它被解释为重建私钥所需的所有选定客户端数量的比例。此功能可根据所选客户端的数量灵活设置安全阈值。" -#: ../../source/ref-changelog.md:156 +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow:64 of +#, fuzzy msgid "" -"FedProx ([#2210](https://github.com/adap/flower/pull/2210), " -"[#2286](https://github.com/adap/flower/pull/2286), " -"[#2509](https://github.com/adap/flower/pull/2509))" +"`reconstruction_threshold`, and the quantization parameters " +"(`clipping_range`, `quantization_range`, `modulus_range`) play critical " +"roles in balancing privacy, robustness, and efficiency within the SecAgg " +"protocol." msgstr "" -"FedProx ([#2210](https://github.com/adap/flower/pull/2210), " -"[#2286](https://github.com/adap/flower/pull/2286), " -"[#2509](https://github.com/adap/flower/pull/2509))" +"重构阈值 \"和量化参数（\"裁剪范围\"、\"量化范围\"、\"模量范围\"）在 SecAgg " +"协议中平衡隐私性、鲁棒性和效率方面起着至关重要的作用。" -#: ../../source/ref-changelog.md:158 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: of +#, fuzzy msgid "" -"Baselines Docs ([#2290](https://github.com/adap/flower/pull/2290), " -"[#2400](https://github.com/adap/flower/pull/2400))" +":py:obj:`collect_masked_vectors_stage " +"`\\ " +"\\(driver\\, ...\\)" msgstr "" -"Baselines文档（[#2290](https://github.com/adap/flower/pull/2290), " -"[#2400](https://github.com/adap/flower/pull/2400)" +":py:obj:`collect_masked_vectors_stage " +"`\\(driver\\," +" ...\\)" -#: ../../source/ref-changelog.md:160 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: of +#, fuzzy msgid "" -"FedMLB ([#2340](https://github.com/adap/flower/pull/2340), " -"[#2507](https://github.com/adap/flower/pull/2507))" +":py:obj:`setup_stage `\\" +" \\(driver\\, context\\, state\\)" msgstr "" -"FedMLB ([#2340](https://github.com/adap/flower/pull/2340), " -"[#2507](https://github.com/adap/flower/pull/2507))" +":py:obj:`setup_stage " +"`\\(driver\\, context\\," +" state\\)" -#: ../../source/ref-changelog.md:162 +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: of +#, fuzzy msgid "" -"TAMUNA ([#2254](https://github.com/adap/flower/pull/2254), " -"[#2508](https://github.com/adap/flower/pull/2508))" +":py:obj:`share_keys_stage " +"`\\ \\(driver\\, " +"context\\, state\\)" msgstr "" -"TAMUNA ([#2254](https://github.com/adap/flower/pull/2254), " -"[#2508](https://github.com/adap/flower/pull/2508))" +"py:obj:`share_keys_stage " +"`\\(driver\\, " +"context\\, state\\)" -#: ../../source/ref-changelog.md:164 -msgid "FedMeta [#2438](https://github.com/adap/flower/pull/2438)" -msgstr "FedMeta [#2438](https://github.com/adap/flower/pull/2438)" +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow.collect_masked_vectors_stage:1::1 +#: of +#, fuzzy +msgid "" +":py:obj:`unmask_stage " +"`\\ \\(driver\\, " +"context\\, state\\)" +msgstr "" +":py:obj:`unmask_stage " +"`\\ \\(driver\\, " +"context\\, state\\)" -#: ../../source/ref-changelog.md:166 -msgid "FjORD [#2431](https://github.com/adap/flower/pull/2431)" -msgstr "FjORD [#2431](https://github.com/adap/flower/pull/2431)" +#: ../../source/ref-api/flwr.simulation.rst:2 +#, fuzzy +msgid "simulation" +msgstr "运行模拟" -#: ../../source/ref-changelog.md:168 -msgid "MOON [#2421](https://github.com/adap/flower/pull/2421)" -msgstr "MOON [#2421](https://github.com/adap/flower/pull/2421)" +#: ../../source/ref-api/flwr.simulation.rst:18::1 +#, fuzzy +msgid "" +":py:obj:`start_simulation `\\ \\(\\*\\," +" client\\_fn\\[\\, ...\\]\\)" +msgstr "" +":py:obj:`start_simulation `\\ \\(\\*\\," +" client\\_fn\\[\\, ...\\]\\)" -#: ../../source/ref-changelog.md:170 -msgid "DepthFL [#2295](https://github.com/adap/flower/pull/2295)" -msgstr "DepthFL [#2295](https://github.com/adap/flower/pull/2295)" +#: ../../source/ref-api/flwr.simulation.rst:18::1 +#: flwr.simulation.app.start_simulation:1 of +msgid "Start a Ray-based Flower simulation server." +msgstr "启动基于 Ray 的Flower模拟服务器。" -#: ../../source/ref-changelog.md:172 -msgid "FedPer [#2266](https://github.com/adap/flower/pull/2266)" -msgstr "FedPer [#2266](https://github.com/adap/flower/pull/2266)" +#: ../../source/ref-api/flwr.simulation.rst:18::1 +#, fuzzy +msgid "" +":py:obj:`run_simulation `\\ " +"\\(server\\_app\\, client\\_app\\, ...\\)" +msgstr "" +":py:obj:`run_simulation `\\ " +"\\(server\\_app\\, client\\_app\\, ...\\)" -#: ../../source/ref-changelog.md:174 -msgid "FedWav2vec [#2551](https://github.com/adap/flower/pull/2551)" -msgstr "FedWav2vec [#2551](https://github.com/adap/flower/pull/2551)" +#: ../../source/ref-api/flwr.simulation.rst:18::1 +#: flwr.simulation.run_simulation.run_simulation:1 of +#, fuzzy +msgid "Run a Flower App using the Simulation Engine." +msgstr "使用模拟引擎运行花朵应用程序。" -#: ../../source/ref-changelog.md:176 -msgid "niid-Bench [#2428](https://github.com/adap/flower/pull/2428)" -msgstr "niid-Bench [#2428](https://github.com/adap/flower/pull/2428)" +#: ../../source/ref-api/flwr.simulation.run_simulation.rst:2 +#, fuzzy +msgid "run\\_simulation" +msgstr "运行模拟" -#: ../../source/ref-changelog.md:178 +#: flwr.simulation.run_simulation.run_simulation:3 of +#, fuzzy msgid "" -"FedBN ([#2608](https://github.com/adap/flower/pull/2608), " -"[#2615](https://github.com/adap/flower/pull/2615))" -msgstr "" -"FedBN ([#2608](https://github.com/adap/flower/pull/2608), " -"[#2615](https://github.com/adap/flower/pull/2615))" +"The `ServerApp` to be executed. It will send messages to different " +"`ClientApp` instances running on different (virtual) SuperNodes." +msgstr "要执行的 `ServerApp`。它将向运行在不同（虚拟）超级节点上的不同 `ClientApp`实例发送消息。" -#: ../../source/ref-changelog.md:180 +#: flwr.simulation.run_simulation.run_simulation:6 of #, fuzzy msgid "" -"**General updates to Flower Examples** " -"([#2384](https://github.com/adap/flower/pull/2384), " -"[#2425](https://github.com/adap/flower/pull/2425), " -"[#2526](https://github.com/adap/flower/pull/2526), " -"[#2302](https://github.com/adap/flower/pull/2302), " -"[#2545](https://github.com/adap/flower/pull/2545))" -msgstr "" -"** 更新 C++ SDK** ([#2537](https://github/com/adap/flower/pull/2537), " -"[#2528](https://github/com/adap/flower/pull/2528), " -"[#2523](https://github.com/adap/flower/pull/2523), " -"[#2522](https://github.com/adap/flower/pull/2522))" +"The `ClientApp` to be executed by each of the SuperNodes. It will receive" +" messages sent by the `ServerApp`." +msgstr "由每个超级节点执行的 `ClientApp`。它将接收由 `ServerApp` 发送的信息。" -#: ../../source/ref-changelog.md:182 +#: flwr.simulation.run_simulation.run_simulation:9 of #, fuzzy msgid "" -"**General updates to Flower Baselines** " -"([#2301](https://github.com/adap/flower/pull/2301), " -"[#2305](https://github.com/adap/flower/pull/2305), " -"[#2307](https://github.com/adap/flower/pull/2307), " -"[#2327](https://github.com/adap/flower/pull/2327), " -"[#2435](https://github.com/adap/flower/pull/2435), " -"[#2462](https://github.com/adap/flower/pull/2462), " -"[#2463](https://github.com/adap/flower/pull/2463), " -"[#2461](https://github.com/adap/flower/pull/2461), " -"[#2469](https://github.com/adap/flower/pull/2469), " -"[#2466](https://github.com/adap/flower/pull/2466), " -"[#2471](https://github.com/adap/flower/pull/2471), " -"[#2472](https://github.com/adap/flower/pull/2472), " -"[#2470](https://github.com/adap/flower/pull/2470))" -msgstr "" -"**普通改进**（[#2309](https://github.com/adap/flower/pull/2309), " -"[#2310](https://github.com/adap/flower/pull/2310), " -"[2313](https://github.com/adap/flower/pull/2313), " -"[#2316](https://github.com/adap/flower/pull/2316), " -"[2317](https://github.com/adap/flower/pull/2317),[#2349](https://github.com/adap/flower/pull/2349)," -" [#2360](https://github.com/adap/flower/pull/2360), " -"[#2402](https://github.com/adap/flower/pull/2402), " -"[#2446](https://github.com/adap/flower/pull/2446) " -"[#2561](https://github.com/adap/flower/pull/2561))" +"Number of nodes that run a ClientApp. They can be sampled by a Driver in " +"the ServerApp and receive a Message describing what the ClientApp should " +"perform." +msgstr "运行 ClientApp 的节点数。它们可被 ServerApp 中的驱动程序采样，并接收描述 ClientApp 应执行的操作的信息。" -#: ../../source/ref-changelog.md:184 +#: flwr.simulation.run_simulation.run_simulation:13 of +#, fuzzy +msgid "A simulation backend that runs `ClientApp`s." +msgstr "运行 \"客户端应用程序 \"的模拟后台。" + +#: flwr.simulation.run_simulation.run_simulation:15 of #, fuzzy msgid "" -"**General updates to the simulation engine** " -"([#2331](https://github.com/adap/flower/pull/2331), " -"[#2447](https://github.com/adap/flower/pull/2447), " -"[#2448](https://github.com/adap/flower/pull/2448), " -"[#2294](https://github.com/adap/flower/pull/2294))" +"'A dictionary, e.g {\"\": , \"\": } to " +"configure a backend. Values supported in are those included by " +"`flwr.common.typing.ConfigsRecordValues`." msgstr "" -"**模拟引擎的普通更新** ([#2331](https://github.com/adap/flower/pull/2331), " -"[#2447](https://github.com/adap/flower/pull/2447), " -"[#2448](https://github.com/adap/flower/pull/2448))" +"字典，例如 {\"\"： , \"\"： } 来配置后端。 中支持的值是 " +"`flwr.common.typing.ConfigsRecordValues`中包含的值。" -#: ../../source/ref-changelog.md:186 +#: flwr.simulation.run_simulation.run_simulation:19 of #, fuzzy msgid "" -"**General updates to Flower SDKs** " -"([#2288](https://github.com/adap/flower/pull/2288), " -"[#2429](https://github.com/adap/flower/pull/2429), " -"[#2555](https://github.com/adap/flower/pull/2555), " -"[#2543](https://github.com/adap/flower/pull/2543), " -"[#2544](https://github.com/adap/flower/pull/2544), " -"[#2597](https://github.com/adap/flower/pull/2597), " -"[#2623](https://github.com/adap/flower/pull/2623))" +"A boolean to indicate whether to enable GPU growth on the main thread. " +"This is desirable if you make use of a TensorFlow model on your " +"`ServerApp` while having your `ClientApp` running on the same GPU. " +"Without enabling this, you might encounter an out-of-memory error because" +" TensorFlow, by default, allocates all GPU memory. Read more about how " +"`tf.config.experimental.set_memory_growth()` works in the TensorFlow " +"documentation: https://www.tensorflow.org/api/stable." msgstr "" -"**改进教程** ([#1468](https://github.com/adap/flower/pull/1468), " -"[#1470](https://github.com/adap/flower/pull/1470), " -"[#1472](https://github.com/adap/flower/pull/1472), " -"[#1473](https://github.com/adap/flower/pull/1473), " -"[#1474](https://github.com/adap/flower/pull/1474), " -"[#1475](https://github.com/adap/flower/pull/1475)))" +"布尔值，用于指示是否在主线程上启用 GPU 增长。如果您在 \"ServerApp \"上使用 TensorFlow 模型，同时让 " +"\"ClientApp \"在同一 GPU 上运行，则最好启用此选项。如果不启用此功能，您可能会遇到内存不足的错误，因为 TensorFlow " +"默认会分配所有 GPU 内存。有关 `tf.config.experimental.set_memory_growth()` " +"如何工作的更多信息，请参阅 TensorFlow 文档：https://www.tensorflow.org/api/stable。" -#: ../../source/ref-changelog.md:188 +#: flwr.simulation.run_simulation.run_simulation:26 of +#, fuzzy msgid "" -"**General improvements** " -"([#2309](https://github.com/adap/flower/pull/2309), " -"[#2310](https://github.com/adap/flower/pull/2310), " -"[#2313](https://github.com/adap/flower/pull/2313), " -"[#2316](https://github.com/adap/flower/pull/2316), " -"[#2317](https://github.com/adap/flower/pull/2317), " -"[#2349](https://github.com/adap/flower/pull/2349), " -"[#2360](https://github.com/adap/flower/pull/2360), " -"[#2402](https://github.com/adap/flower/pull/2402), " -"[#2446](https://github.com/adap/flower/pull/2446), " -"[#2561](https://github.com/adap/flower/pull/2561), " -"[#2273](https://github.com/adap/flower/pull/2273), " -"[#2267](https://github.com/adap/flower/pull/2267), " -"[#2274](https://github.com/adap/flower/pull/2274), " -"[#2275](https://github.com/adap/flower/pull/2275), " -"[#2432](https://github.com/adap/flower/pull/2432), " -"[#2251](https://github.com/adap/flower/pull/2251), " -"[#2321](https://github.com/adap/flower/pull/2321), " -"[#1936](https://github.com/adap/flower/pull/1936), " -"[#2408](https://github.com/adap/flower/pull/2408), " -"[#2413](https://github.com/adap/flower/pull/2413), " -"[#2401](https://github.com/adap/flower/pull/2401), " -"[#2531](https://github.com/adap/flower/pull/2531), " -"[#2534](https://github.com/adap/flower/pull/2534), " -"[#2535](https://github.com/adap/flower/pull/2535), " -"[#2521](https://github.com/adap/flower/pull/2521), " -"[#2553](https://github.com/adap/flower/pull/2553), " -"[#2596](https://github.com/adap/flower/pull/2596))" -msgstr "" +"When diabled, only INFO, WARNING and ERROR log messages will be shown. If" +" enabled, DEBUG-level logs will be displayed." +msgstr "启用后，将只显示 INFO、WARNING 和 ERROR 日志信息。启用后，将显示 DEBUG 级日志。" -#: ../../source/ref-changelog.md:190 ../../source/ref-changelog.md:280 -#: ../../source/ref-changelog.md:344 ../../source/ref-changelog.md:398 -#: ../../source/ref-changelog.md:465 -msgid "Flower received many improvements under the hood, too many to list here." -msgstr "Flower 进行了许多改进，这里就不一一列举了。" +#: ../../source/ref-api/flwr.simulation.start_simulation.rst:2 +#, fuzzy +msgid "start\\_simulation" +msgstr "start_simulation" -#: ../../source/ref-changelog.md:194 +#: flwr.simulation.app.start_simulation:3 of msgid "" -"**Remove support for Python 3.7** " -"([#2280](https://github.com/adap/flower/pull/2280), " -"[#2299](https://github.com/adap/flower/pull/2299), " -"[#2304](https://github.com/adap/flower/pull/2304), " -"[#2306](https://github.com/adap/flower/pull/2306), " -"[#2355](https://github.com/adap/flower/pull/2355), " -"[#2356](https://github.com/adap/flower/pull/2356))" +"A function creating client instances. The function must take a single " +"`str` argument called `cid`. It should return a single client instance of" +" type Client. Note that the created client instances are ephemeral and " +"will often be destroyed after a single method invocation. Since client " +"instances are not long-lived, they should not attempt to carry state over" +" method invocations. Any state required by the instance (model, dataset, " +"hyperparameters, ...) should be (re-)created in either the call to " +"`client_fn` or the call to any of the client methods (e.g., load " +"evaluation data in the `evaluate` method itself)." msgstr "" -"**移除对 Python 3.7 的支持** " -"([#2280](https://github.com/adap/flower/pull/2280), " -"[#2299](https://github.com/adap/flower/pull/2299), " -"[#2304](https://github.com/adap/flower/pull/2304), " -"[#2306](https://github.com/adap/flower/pull/2306), " -"[#2355](https://github.com/adap/flower/pull/2355), " -"[#2356](https://github.com/adap/flower/pull/2356))" +"创建客户端实例的函数。该函数必须接受一个名为 `cid` 的 `str` 参数。它应返回一个 Client " +"类型的客户端实例。请注意，创建的客户端实例是短暂的，通常在调用一个方法后就会被销毁。由于客户机实例不是长期存在的，它们不应试图在方法调用时携带状态数据。实例所需的任何状态数据（模型、数据集、超参数......）都应在调用" +" `client_fn` 或任何客户端方法（例如，在 `evaluate` 方法中加载评估数据）时（重新）创建。" -#: ../../source/ref-changelog.md:196 +#: flwr.simulation.app.start_simulation:13 of msgid "" -"Python 3.7 support was deprecated in Flower 1.5, and this release removes" -" support. Flower now requires Python 3.8." -msgstr "在 Flower 1.5 中，Python 3.7 支持已被弃用，本版本将删除该支持。Flower 现在需要 Python 3.8。" +"The total number of clients in this simulation. This must be set if " +"`clients_ids` is not set and vice-versa." +msgstr "本次模拟的客户总数。如果未设置 `clients_ids`，则必须设置该参数，反之亦然。" -#: ../../source/ref-changelog.md:198 +#: flwr.simulation.app.start_simulation:16 of msgid "" -"**Remove experimental argument** `rest` **from** `start_client` " -"([#2324](https://github.com/adap/flower/pull/2324))" +"List `client_id`s for each client. This is only required if `num_clients`" +" is not set. Setting both `num_clients` and `clients_ids` with " +"`len(clients_ids)` not equal to `num_clients` generates an error." msgstr "" -"**从** `start_client` 中移除** `rest` **实验参数 " -"([#2324](https://github.com/adap/flower/pull/2324))" +"列出每个客户的 `client_id`。只有在未设置 `num_clients` " +"时才需要这样做。同时设置`num_clients`和`clients_ids`，且`len(clients_ids)`不等于`num_clients`，会产生错误。" -#: ../../source/ref-changelog.md:200 +#: flwr.simulation.app.start_simulation:20 of +#, fuzzy msgid "" -"The (still experimental) argument `rest` was removed from `start_client` " -"and `start_numpy_client`. Use `transport=\"rest\"` to opt into the " -"experimental REST API instead." +"CPU and GPU resources for a single client. Supported keys are `num_cpus` " +"and `num_gpus`. To understand the GPU utilization caused by `num_gpus`, " +"as well as using custom resources, please consult the Ray documentation." msgstr "" -"删除了 `start_client` 和 `start_numpy_client` 中的参数 `rest`（仍属试验性质）。请使用 " -"`transport=\"rest\"` 来选择使用试验性 REST API。" - -#: ../../source/ref-changelog.md:202 -msgid "v1.5.0 (2023-08-31)" -msgstr "v1.5.0 (2023-08-31)" +"\"num_gpus\"： 0.0` 单个客户端的 CPU 和 GPU 资源。支持的键值为 `num_cpus` 和 `num_gpus`。要了解" +" `num_gpus` 所导致的 GPU 利用率，以及使用自定义资源的情况，请查阅 Ray 文档。" -#: ../../source/ref-changelog.md:208 +#: flwr.simulation.app.start_simulation:25 of msgid "" -"`Adam Narozniak`, `Anass Anhari`, `Charles Beauville`, `Dana-Farber`, " -"`Daniel J. Beutel`, `Daniel Nata Nugraha`, `Edoardo Gabrielli`, `Gustavo " -"Bertoli`, `Heng Pan`, `Javier`, `Mahdi`, `Steven Hé (Sīchàng)`, `Taner " -"Topal`, `achiverram28`, `danielnugraha`, `eunchung`, `ruthgal` " -msgstr "" -"`Adam Narozniak`, `Anass Anhari`, `Charles Beauville`, `Dana-Farber`, " -"`Daniel J. Beutel`, `Daniel Nata Nugraha`, `Edoardo Gabrielli`, `Gustavo " -"Bertoli`, `Heng Pan`, `Javier`, `Mahdi`, `Steven Hé (Sīchàng)`, `Taner " -"Topal`, `achiverram28`, `danielnugraha`, `eunchung`, `ruthgal` " +"An implementation of the abstract base class `flwr.server.Server`. If no " +"instance is provided, then `start_server` will create one." +msgstr "抽象基类 `flwr.server.Server`的实现。如果没有提供实例，`start_server` 将创建一个。" -#: ../../source/ref-changelog.md:212 +#: flwr.simulation.app.start_simulation:31 of msgid "" -"**Introduce new simulation engine** " -"([#1969](https://github.com/adap/flower/pull/1969), " -"[#2221](https://github.com/adap/flower/pull/2221), " -"[#2248](https://github.com/adap/flower/pull/2248))" +"An implementation of the abstract base class `flwr.server.Strategy`. If " +"no strategy is provided, then `start_server` will use " +"`flwr.server.strategy.FedAvg`." msgstr "" -"**引入新的模拟引擎** ([#1969](https://github.com/adap/flower/pull/1969), " -"[#2221](https://github.com/adap/flower/pull/2221), " -"[#2248](https://github.com/adap/flower/pull/2248))" +"抽象基类 `flwr.server.strategy` 的实现。如果没有提供策略，`start_server` 将使用 " +"`flwr.server.strategy.FedAvg`。" -#: ../../source/ref-changelog.md:214 +#: flwr.simulation.app.start_simulation:35 of msgid "" -"The new simulation engine has been rewritten from the ground up, yet it " -"remains fully backwards compatible. It offers much improved stability and" -" memory handling, especially when working with GPUs. Simulations " -"transparently adapt to different settings to scale simulation in CPU-" -"only, CPU+GPU, multi-GPU, or multi-node multi-GPU environments." +"An implementation of the abstract base class `flwr.server.ClientManager`." +" If no implementation is provided, then `start_simulation` will use " +"`flwr.server.client_manager.SimpleClientManager`." msgstr "" -"新的模拟引擎从头开始重新编写，但仍完全向后兼容。它的稳定性和内存处理能力大大提高，尤其是在使用 GPU 时。仿真可透明地适应不同的设置，以在仅 " -"CPU、CPU+GPU、多 GPU 或多节点多 GPU 环境中扩展模拟。" +"抽象基类 `flwr.server.ClientManager` 的实现。如果没有提供实现，`start_simulation` 将使用 " +"`flwr.server.client_manager.SimpleClientManager`。" -#: ../../source/ref-changelog.md:216 +#: flwr.simulation.app.start_simulation:39 of msgid "" -"Comprehensive documentation includes a new [how-to run " -"simulations](https://flower.ai/docs/framework/how-to-run-" -"simulations.html) guide, new [simulation-" -"pytorch](https://flower.ai/docs/examples/simulation-pytorch.html) and " -"[simulation-tensorflow](https://flower.ai/docs/examples/simulation-" -"tensorflow.html) notebooks, and a new [YouTube tutorial " -"series](https://www.youtube.com/watch?v=cRebUIGB5RU&list=PLNG4feLHqCWlnj8a_E1A_n5zr2-8pafTB)." +"Optional dictionary containing arguments for the call to `ray.init`. If " +"ray_init_args is None (the default), Ray will be initialized with the " +"following default args: { \"ignore_reinit_error\": True, " +"\"include_dashboard\": False } An empty dictionary can be used " +"(ray_init_args={}) to prevent any arguments from being passed to " +"ray.init." msgstr "" -"综合文档包括新的[how-to run simulations](https://flower.ai/docs/framework/how-to-" -"run-simulations.html) guide, new [simulation-" -"pytorch](https://flower.ai/docs/examples/simulation-pytorch.html) and " -"[simulation-tensorflow](https://flower.ai/docs/examples/simulation-" -"tensorflow.html) notebooks, and a new [YouTube tutorial " -"series](https://www.youtube.com/watch?v=cRebUIGB5RU&list=PLNG4feLHqCWlnj8a_E1A_n5zr2-8pafTB)。" +"可选字典，包含调用 `ray.init` 时的参数。如果 ray_init_args 为 None（默认值），则将使用以下默认参数初始化 Ray：" +" { \"ignore_reinit_error\"： True, \"include_dashboard\"： False } " +"可以使用空字典（ray_init_args={}）来防止向 ray.init 传递任何参数。" -#: ../../source/ref-changelog.md:218 +#: flwr.simulation.app.start_simulation:39 of msgid "" -"**Restructure Flower Docs** " -"([#1824](https://github.com/adap/flower/pull/1824), " -"[#1865](https://github.com/adap/flower/pull/1865), " -"[#1884](https://github.com/adap/flower/pull/1884), " -"[#1887](https://github.com/adap/flower/pull/1887), " -"[#1919](https://github.com/adap/flower/pull/1919), " -"[#1922](https://github.com/adap/flower/pull/1922), " -"[#1920](https://github.com/adap/flower/pull/1920), " -"[#1923](https://github.com/adap/flower/pull/1923), " -"[#1924](https://github.com/adap/flower/pull/1924), " -"[#1962](https://github.com/adap/flower/pull/1962), " -"[#2006](https://github.com/adap/flower/pull/2006), " -"[#2133](https://github.com/adap/flower/pull/2133), " -"[#2203](https://github.com/adap/flower/pull/2203), " -"[#2215](https://github.com/adap/flower/pull/2215), " -"[#2122](https://github.com/adap/flower/pull/2122), " -"[#2223](https://github.com/adap/flower/pull/2223), " -"[#2219](https://github.com/adap/flower/pull/2219), " -"[#2232](https://github.com/adap/flower/pull/2232), " -"[#2233](https://github.com/adap/flower/pull/2233), " -"[#2234](https://github.com/adap/flower/pull/2234), " -"[#2235](https://github.com/adap/flower/pull/2235), " -"[#2237](https://github.com/adap/flower/pull/2237), " -"[#2238](https://github.com/adap/flower/pull/2238), " -"[#2242](https://github.com/adap/flower/pull/2242), " -"[#2231](https://github.com/adap/flower/pull/2231), " -"[#2243](https://github.com/adap/flower/pull/2243), " -"[#2227](https://github.com/adap/flower/pull/2227))" -msgstr "" -"**重构 Flower 文档** ([#1824](https://github.com/adap/flower/pull/1824), " -"[#1865](https://github.com/adap/flower/pull/1865), " -"[#1884](https://github.com/adap/flower/pull/1884), " -"[#1887](https://github.com/adap/flower/pull/1887), " -"[#1919](https://github.com/adap/flower/pull/1919), " -"[#1922](https://github.com/adap/flower/pull/1922), " -"[#1920](https://github.com/adap/flower/pull/1920), " -"[#1923](https://github.com/adap/flower/pull/1923), " -"[#1924](https://github.com/adap/flower/pull/1924), " -"[#1962](https://github.com/adap/flower/pull/1962), " -"[#2006](https://github.com/adap/flower/pull/2006), " -"[#2133](https://github.com/adap/flower/pull/2133), " -"[#2203](https://github.com/adap/flower/pull/2203), " -"[#2215](https://github.com/adap/flower/pull/2215), " -"[#2122](https://github.com/adap/flower/pull/2122), " -"[#2223](https://github.com/adap/flower/pull/2223), " -"[#2219](https://github.com/adap/flower/pull/2219), " -"[#2232](https://github.com/adap/flower/pull/2232), " -"[#2233](https://github.com/adap/flower/pull/2233), " -"[#2234](https://github.com/adap/flower/pull/2234), " -"[#2235](https://github.com/adap/flower/pull/2235), " -"[#2237](https://github.com/adap/flower/pull/2237), " -"[#2238](https://github.com/adap/flower/pull/2238), " -"[#2242](https://github.com/adap/flower/pull/2242), " -"[#2231](https://github.com/adap/flower/pull/2231), " -"[#2243](https://github.com/adap/flower/pull/2243), " -"[#2227](https://github.com/adap/flower/pull/2227))" +"Optional dictionary containing arguments for the call to `ray.init`. If " +"ray_init_args is None (the default), Ray will be initialized with the " +"following default args:" +msgstr "可选字典，包含调用 `ray.init` 时的参数。如果 ray_init_args 为 None（默认值），则将使用以下默认参数初始化 Ray：" -#: ../../source/ref-changelog.md:220 -#, fuzzy -msgid "" -"Much effort went into a completely restructured Flower docs experience. " -"The documentation on [flower.ai/docs](https://flower.ai/docs) is now " -"divided into Flower Framework, Flower Baselines, Flower Android SDK, " -"Flower iOS SDK, and code example projects." -msgstr "" -"Flower 文档体验的全面重构耗费了大量精力。现在，[flower.ai/docs](flower.ai/docs)上的文档分为 Flower " -"Framework、Flower Baselines、Flower Android SDK、Flower iOS SDK 和代码示例项目。" +#: flwr.simulation.app.start_simulation:43 of +msgid "{ \"ignore_reinit_error\": True, \"include_dashboard\": False }" +msgstr "{ \"ignore_reinit_error\"： True, \"include_dashboard\"： False }" -#: ../../source/ref-changelog.md:222 +#: flwr.simulation.app.start_simulation:45 of msgid "" -"**Introduce Flower Swift SDK** " -"([#1858](https://github.com/adap/flower/pull/1858), " -"[#1897](https://github.com/adap/flower/pull/1897))" -msgstr "" -"**介绍 Flower Swift SDK** " -"([#1858](https://github.com/adap/flower/pull/1858), " -"[#1897](https://github.com/adap/flower/pull/1897))" +"An empty dictionary can be used (ray_init_args={}) to prevent any " +"arguments from being passed to ray.init." +msgstr "可以使用空字典 (ray_init_args={}) 来防止向 ray.init 传递任何参数。" -#: ../../source/ref-changelog.md:224 +#: flwr.simulation.app.start_simulation:48 of msgid "" -"This is the first preview release of the Flower Swift SDK. Flower support" -" on iOS is improving, and alongside the Swift SDK and code example, there" -" is now also an iOS quickstart tutorial." -msgstr "" -"这是 Flower Swift SDK 的首个预览版。Flower 对 iOS 的支持正在不断改进，除了 Swift SDK " -"和代码示例外，现在还有 iOS 快速入门教程。" +"Set to True to prevent `ray.shutdown()` in case " +"`ray.is_initialized()=True`." +msgstr "设为 True 可在 `ray.is_initialized()=True` 情况下阻止 `ray.shutdown()` 。" -#: ../../source/ref-changelog.md:226 +#: flwr.simulation.app.start_simulation:50 of +#, fuzzy msgid "" -"**Introduce Flower Android SDK** " -"([#2131](https://github.com/adap/flower/pull/2131))" -msgstr "" -"**介绍Flower Android SDK** " -"([#2131](https://github.com/adap/flower/pull/2131))" +"Optionally specify the type of actor to use. The actor object, which " +"persists throughout the simulation, will be the process in charge of " +"executing a ClientApp wrapping input argument `client_fn`." +msgstr "可选择指定要使用的actor类型。actor对象将在整个模拟过程中持续存在，它将是负责运行客户端作业（即其 `fit()`方法）的进程。" -#: ../../source/ref-changelog.md:228 +#: flwr.simulation.app.start_simulation:54 of msgid "" -"This is the first preview release of the Flower Kotlin SDK. Flower " -"support on Android is improving, and alongside the Kotlin SDK and code " -"example, there is now also an Android quickstart tutorial." -msgstr "" -"这是 Flower Kotlin SDK 的首个预览版。Flower 对 Android 的支持正在不断改进，除了 Kotlin SDK " -"和代码示例，现在还有 Android 快速入门教程。" +"If you want to create your own Actor classes, you might need to pass some" +" input argument. You can use this dictionary for such purpose." +msgstr "如果您想创建自己的 Actor 类，可能需要传递一些输入参数。为此，您可以使用本字典。" -#: ../../source/ref-changelog.md:230 +#: flwr.simulation.app.start_simulation:57 of msgid "" -"**Introduce new end-to-end testing infrastructure** " -"([#1842](https://github.com/adap/flower/pull/1842), " -"[#2071](https://github.com/adap/flower/pull/2071), " -"[#2072](https://github.com/adap/flower/pull/2072), " -"[#2068](https://github.com/adap/flower/pull/2068), " -"[#2067](https://github.com/adap/flower/pull/2067), " -"[#2069](https://github.com/adap/flower/pull/2069), " -"[#2073](https://github.com/adap/flower/pull/2073), " -"[#2070](https://github.com/adap/flower/pull/2070), " -"[#2074](https://github.com/adap/flower/pull/2074), " -"[#2082](https://github.com/adap/flower/pull/2082), " -"[#2084](https://github.com/adap/flower/pull/2084), " -"[#2093](https://github.com/adap/flower/pull/2093), " -"[#2109](https://github.com/adap/flower/pull/2109), " -"[#2095](https://github.com/adap/flower/pull/2095), " -"[#2140](https://github.com/adap/flower/pull/2140), " -"[#2137](https://github.com/adap/flower/pull/2137), " -"[#2165](https://github.com/adap/flower/pull/2165))" +"(default: \"DEFAULT\") Optional string (\"DEFAULT\" or \"SPREAD\") for " +"the VCE to choose in which node the actor is placed. If you are an " +"advanced user needed more control you can use lower-level scheduling " +"strategies to pin actors to specific compute nodes (e.g. via " +"NodeAffinitySchedulingStrategy). Please note this is an advanced feature." +" For all details, please refer to the Ray documentation: " +"https://docs.ray.io/en/latest/ray-core/scheduling/index.html" msgstr "" -"*介绍新的端到端测试** ([#1842](https://github.com/adap/flower/pull/1842), " -"[#2071](https://github.com/adap/flower/pull/2071), " -"[#2072](https://github.com/adap/flower/pull/2072), " -"[#2068](https://github.com/adap/flower/pull/2068), " -"[#2067](https://github.com/adap/flower/pull/2067), " -"[#2069](https://github.com/adap/flower/pull/2069), " -"[#2073](https://github.com/adap/flower/pull/2073), " -"[#2070](https://github.com/adap/flower/pull/2070), " -"[#2074](https://github.com/adap/flower/pull/2074), " -"[#2082](https://github.com/adap/flower/pull/2082), " -"[#2084](https://github.com/adap/flower/pull/2084), " -"[#2093](https://github.com/adap/flower/pull/2093), " -"[#2109](https://github.com/adap/flower/pull/2109), " -"[#2095](https://github.com/adap/flower/pull/2095), " -"[#2140](https://github.com/adap/flower/pull/2140), " -"[#2137](https://github.com/adap/flower/pull/2137), " -"[#2165](https://github.com/adap/flower/pull/2165))" +"(默认：\"DEFAULT\"）可选字符串（\"DEFAULT \"或 \"SPREAD\"），供 VCE " +"选择将行为体放置在哪个节点上。如果你是需要更多控制权的高级用户，可以使用低级调度策略将actor固定到特定计算节点（例如，通过 " +"NodeAffinitySchedulingStrategy）。请注意，这是一项高级功能。有关详细信息，请参阅 Ray " +"文档：https://docs.ray.io/en/latest/ray-core/scheduling/index.html" -#: ../../source/ref-changelog.md:232 -msgid "" -"A new testing infrastructure ensures that new changes stay compatible " -"with existing framework integrations or strategies." -msgstr "新的测试设施可确保新的变更与现有的框架集成或策略保持兼容。" +#: flwr.simulation.app.start_simulation:66 of +msgid "**hist** -- Object containing metrics from training." +msgstr "**hist** -- 包含训练指标的对象。" -#: ../../source/ref-changelog.md:234 -msgid "**Deprecate Python 3.7**" -msgstr "** 过时的 Python 3.7**" +#: ../../source/ref-changelog.md:1 +msgid "Changelog" +msgstr "更新日志" -#: ../../source/ref-changelog.md:236 -msgid "" -"Since Python 3.7 reached its end of life (EOL) on 2023-06-27, support for" -" Python 3.7 is now deprecated and will be removed in an upcoming release." -msgstr "由于 Python 3.7 已于 2023-06-27 弃用 (EOL)，对 Python 3.7 的支持现已废弃，并将在即将发布的版本中移除。" +#: ../../source/ref-changelog.md:3 +#, fuzzy +msgid "v1.9.0 (2024-06-10)" +msgstr "v1.3.0 (2023-02-06)" + +#: ../../source/ref-changelog.md:5 ../../source/ref-changelog.md:105 +#: ../../source/ref-changelog.md:169 ../../source/ref-changelog.md:262 +#: ../../source/ref-changelog.md:362 ../../source/ref-changelog.md:446 +#: ../../source/ref-changelog.md:510 ../../source/ref-changelog.md:568 +#: ../../source/ref-changelog.md:637 ../../source/ref-changelog.md:706 +msgid "Thanks to our contributors" +msgstr "感谢我们的贡献者" -#: ../../source/ref-changelog.md:238 +#: ../../source/ref-changelog.md:7 ../../source/ref-changelog.md:107 +#: ../../source/ref-changelog.md:171 ../../source/ref-changelog.md:264 +#: ../../source/ref-changelog.md:364 ../../source/ref-changelog.md:448 +#: ../../source/ref-changelog.md:512 ../../source/ref-changelog.md:570 msgid "" -"**Add new** `FedTrimmedAvg` **strategy** " -"([#1769](https://github.com/adap/flower/pull/1769), " -"[#1853](https://github.com/adap/flower/pull/1853))" -msgstr "" -"**添加新的**`FedTrimmedAvg`**策略**（[#1769](https://github.com/adap/flower/pull/1769)," -" [#1853](https://github.com/adap/flower/pull/1853)" +"We would like to give our special thanks to all the contributors who made" +" the new version of Flower possible (in `git shortlog` order):" +msgstr "在此，我们要特别感谢所有为 Flower 的新版本做出贡献的人员（按 `git shortlog` 顺序排列）：" -#: ../../source/ref-changelog.md:240 +#: ../../source/ref-changelog.md:9 +#, fuzzy msgid "" -"The new `FedTrimmedAvg` strategy implements Trimmed Mean by [Dong Yin, " -"2018](https://arxiv.org/abs/1803.01498)." +"`Adam Narozniak`, `Charles Beauville`, `Chong Shen Ng`, `Daniel J. " +"Beutel`, `Daniel Nata Nugraha`, `Heng Pan`, `Javier`, `Mahdi Beitollahi`," +" `Robert Steiner`, `Taner Topal`, `Yan Gao`, `bapic`, `mohammadnaseri` " msgstr "" -"新的 \"FedTrimmedAvg \"策略实现了[Dong Yin, " -"2018]（https://arxiv.org/abs/1803.01498）的 \"Trimmed Mean\"。" +"`Adam Narozniak`, `Anass Anhari`, `Charles Beauville`, `Dana-Farber`, " +"`Daniel J. Beutel`, `Daniel Nata Nugraha`, `Edoardo Gabrielli`, `Gustavo " +"Bertoli`, `Heng Pan`, `Javier`, `Mahdi`, `Steven Hé (Sīchàng)`, `Taner " +"Topal`, `achiverram28`, `danielnugraha`, `eunchung`, `ruthgal` " -#: ../../source/ref-changelog.md:242 -msgid "" -"**Introduce start_driver** " -"([#1697](https://github.com/adap/flower/pull/1697))" -msgstr "**引入 start_driver**（[#1697](https://github.com/adap/flower/pull/1697)）" +#: ../../source/ref-changelog.md:11 ../../source/ref-changelog.md:111 +#: ../../source/ref-changelog.md:175 ../../source/ref-changelog.md:268 +#: ../../source/ref-changelog.md:368 ../../source/ref-changelog.md:452 +#: ../../source/ref-changelog.md:516 ../../source/ref-changelog.md:574 +#: ../../source/ref-changelog.md:643 ../../source/ref-changelog.md:772 +#: ../../source/ref-changelog.md:814 ../../source/ref-changelog.md:881 +#: ../../source/ref-changelog.md:947 ../../source/ref-changelog.md:992 +#: ../../source/ref-changelog.md:1031 ../../source/ref-changelog.md:1064 +#: ../../source/ref-changelog.md:1114 +msgid "What's new?" +msgstr "有什么新内容？" -#: ../../source/ref-changelog.md:244 +#: ../../source/ref-changelog.md:13 +#, fuzzy +msgid "" +"**Introduce built-in authentication (preview)** " +"([#2946](https://github.com/adap/flower/pull/2946), " +"[#3388](https://github.com/adap/flower/pull/3388), " +"[#2948](https://github.com/adap/flower/pull/2948), " +"[#2917](https://github.com/adap/flower/pull/2917), " +"[#3386](https://github.com/adap/flower/pull/3386), " +"[#3308](https://github.com/adap/flower/pull/3308), " +"[#3001](https://github.com/adap/flower/pull/3001), " +"[#3409](https://github.com/adap/flower/pull/3409), " +"[#2999](https://github.com/adap/flower/pull/2999), " +"[#2979](https://github.com/adap/flower/pull/2979), " +"[#3389](https://github.com/adap/flower/pull/3389), " +"[#3503](https://github.com/adap/flower/pull/3503), " +"[#3366](https://github.com/adap/flower/pull/3366), " +"[#3357](https://github.com/adap/flower/pull/3357))" +msgstr "" +"** 更新文档** ([#1494](https://github.com/adap/flower/pull/1494), " +"[#1496](https://github.com/adap/flower/pull/1496), " +"[#1500](https://github.com/adap/flower/pull/1500), " +"[#1503](https://github.com/adap/flower/pull/1503), " +"[#1505](https://github.com/adap/flower/pull/1505), " +"[#1524](https://github.com/adap/flower/pull/1524), " +"[#1518](https://github.com/adap/flower/pull/1518), " +"[#1519](https://github.com/adap/flower/pull/1519), " +"[#1515](https://github.com/adap/flower/pull/1515))" + +#: ../../source/ref-changelog.md:15 msgid "" -"In addition to `start_server` and using the raw Driver API, there is a " -"new `start_driver` function that allows for running `start_server` " -"scripts as a Flower driver with only a single-line code change. Check out" -" the `mt-pytorch` code example to see a working example using " -"`start_driver`." +"Flower 1.9 introduces the first build-in version of client node " +"authentication. In previous releases, users often wrote glue code to " +"connect Flower to external authentication systems. With this release, the" +" SuperLink can authenticate SuperNodes using a built-in authentication " +"system. A new [how-to guide](https://flower.ai/docs/framework/how-to-" +"authenticate-supernodes.html) and a new [code " +"example](https://github.com/adap/flower/tree/main/examples/flower-" +"authentication) help you to get started." msgstr "" -"除了 `start_server` 和使用原始驱动 API 之外，还有一个新的 `start_driver` 函数，只需修改一行代码，就能将 " -"`start_server` 脚本作为 Flower 驱动程序运行。请查看 `mt-pytorch` 代码示例，了解使用 " -"`start_driver` 的工作示例。" -#: ../../source/ref-changelog.md:246 +#: ../../source/ref-changelog.md:17 msgid "" -"**Add parameter aggregation to** `mt-pytorch` **code example** " -"([#1785](https://github.com/adap/flower/pull/1785))" +"This is the first preview release of the Flower-native authentication " +"system. Many additional features are on the roadmap for upcoming Flower " +"releases - stay tuned." msgstr "" -"为 `mt-pytorch` **代码示例**添加参数聚合 " -"([#1785](https://github.com/adap/flower/pull/1785))" -#: ../../source/ref-changelog.md:248 +#: ../../source/ref-changelog.md:19 +#, fuzzy msgid "" -"The `mt-pytorch` example shows how to aggregate parameters when writing a" -" driver script. The included `driver.py` and `server.py` have been " -"aligned to demonstrate both the low-level way and the high-level way of " -"building server-side logic." +"**Introduce end-to-end Docker support** " +"([#3483](https://github.com/adap/flower/pull/3483), " +"[#3266](https://github.com/adap/flower/pull/3266), " +"[#3390](https://github.com/adap/flower/pull/3390), " +"[#3283](https://github.com/adap/flower/pull/3283), " +"[#3285](https://github.com/adap/flower/pull/3285), " +"[#3391](https://github.com/adap/flower/pull/3391), " +"[#3403](https://github.com/adap/flower/pull/3403), " +"[#3458](https://github.com/adap/flower/pull/3458), " +"[#3533](https://github.com/adap/flower/pull/3533), " +"[#3453](https://github.com/adap/flower/pull/3453), " +"[#3486](https://github.com/adap/flower/pull/3486), " +"[#3290](https://github.com/adap/flower/pull/3290))" msgstr "" -"`mt-pytorch`示例展示了如何在编写驱动程序脚本时聚合参数。附带的 `driver.py` 和 `server.py` " -"已经进行了调整，以演示构建服务器端逻辑的低级方法和高级方法。" +"**引入（试验性）REST API** ([#1594](https://github.com/adap/flower/pull/1594), " +"[#1690](https://github.com/adap/flower/pull/1690), " +"[#1695](https://github.com/adap/flower/pull/1695), " +"[#1712](https://github.com/adap/flower/pull/1712), " +"[#1802](https://github.com/adap/flower/pull/1802), " +"[#1770](https://github.com/adap/flower/pull/1770), " +"[#1733](https://github.com/adap/flower/pull/1733))" -#: ../../source/ref-changelog.md:250 +#: ../../source/ref-changelog.md:21 msgid "" -"**Migrate experimental REST API to Starlette** " -"([2171](https://github.com/adap/flower/pull/2171))" +"Full Flower Next Docker support is here! With the release of Flower 1.9, " +"Flower provides stable Docker images for the Flower SuperLink, the Flower" +" SuperNode, and the Flower `ServerApp`. This set of images enables you to" +" run all Flower components in Docker. Check out the new [how-to " +"guide](https://flower.ai/docs/framework/how-to-run-flower-using-" +"docker.html) to get stated." msgstr "" -"**将实验性 REST API 移植到 Starlette** " -"([2171](https://github.com/adap/flower/pull/2171))" -#: ../../source/ref-changelog.md:252 +#: ../../source/ref-changelog.md:23 +#, fuzzy msgid "" -"The (experimental) REST API used to be implemented in " -"[FastAPI](https://fastapi.tiangolo.com/), but it has now been migrated to" -" use [Starlette](https://www.starlette.io/) directly." +"**Re-architect Flower Next simulation engine** " +"([#3307](https://github.com/adap/flower/pull/3307), " +"[#3355](https://github.com/adap/flower/pull/3355), " +"[#3272](https://github.com/adap/flower/pull/3272), " +"[#3273](https://github.com/adap/flower/pull/3273), " +"[#3417](https://github.com/adap/flower/pull/3417), " +"[#3281](https://github.com/adap/flower/pull/3281), " +"[#3343](https://github.com/adap/flower/pull/3343), " +"[#3326](https://github.com/adap/flower/pull/3326))" msgstr "" -"REST API（试验性）曾在 [FastAPI](https://fastapi.tiangolo.com/) 中实现，但现在已迁移到直接使用 " -"[Starlette](https://www.starlette.io/) 。" +"** 更新文档** ([#1629](https://github.com/adap/flower/pull/1629), " +"[#1628](https://github.com/adap/flower/pull/1628), " +"[#1620](https://github.com/adap/flower/pull/1620), " +"[#1618](https://github.com/adap/flower/pull/1618), " +"[#1617](https://github.com/adap/flower/pull/1617), " +"[#1613](https://github.com/adap/flower/pull/1613), " +"[#1614](https://github.com/adap/flower/pull/1614)))" -#: ../../source/ref-changelog.md:254 +#: ../../source/ref-changelog.md:25 msgid "" -"Please note: The REST request-response API is still experimental and will" -" likely change significantly over time." -msgstr "请注意：REST 请求-响应 API 仍处于试验阶段，随着时间的推移可能会发生重大变化。" +"Flower Next simulations now use a new in-memory `Driver` that improves " +"the reliability of simulations, especially in notebook environments. This" +" is a significant step towards a complete overhaul of the Flower Next " +"simulation architecture." +msgstr "" -#: ../../source/ref-changelog.md:256 +#: ../../source/ref-changelog.md:27 +#, fuzzy msgid "" -"**Introduce experimental gRPC request-response API** " -"([#1867](https://github.com/adap/flower/pull/1867), " -"[#1901](https://github.com/adap/flower/pull/1901))" +"**Upgrade simulation engine** " +"([#3354](https://github.com/adap/flower/pull/3354), " +"[#3378](https://github.com/adap/flower/pull/3378), " +"[#3262](https://github.com/adap/flower/pull/3262), " +"[#3435](https://github.com/adap/flower/pull/3435), " +"[#3501](https://github.com/adap/flower/pull/3501), " +"[#3482](https://github.com/adap/flower/pull/3482), " +"[#3494](https://github.com/adap/flower/pull/3494))" msgstr "" -"**引入实验性 gRPC 请求-响应 API** " -"（[#1867](https://github.com/adap/flower/pull/1867), " -"[#1901](https://github.com/adap/flower/pull/1901)" +"** 更新文档** ([#1629](https://github.com/adap/flower/pull/1629), " +"[#1628](https://github.com/adap/flower/pull/1628), " +"[#1620](https://github.com/adap/flower/pull/1620), " +"[#1618](https://github.com/adap/flower/pull/1618), " +"[#1617](https://github.com/adap/flower/pull/1617), " +"[#1613](https://github.com/adap/flower/pull/1613), " +"[#1614](https://github.com/adap/flower/pull/1614)))" -#: ../../source/ref-changelog.md:258 +#: ../../source/ref-changelog.md:29 msgid "" -"In addition to the existing gRPC API (based on bidirectional streaming) " -"and the experimental REST API, there is now a new gRPC API that uses a " -"request-response model to communicate with client nodes." +"The Flower Next simulation engine comes with improved and configurable " +"logging. The Ray-based simulation backend in Flower 1.9 was updated to " +"use Ray 2.10." msgstr "" -"除了现有的 gRPC 应用程序接口（基于双向流）和试验性 REST 应用程序接口外，现在还有一个新的 gRPC " -"应用程序接口，它使用请求-响应模型与客户端节点通信。" -#: ../../source/ref-changelog.md:260 +#: ../../source/ref-changelog.md:31 +#, fuzzy msgid "" -"Please note: The gRPC request-response API is still experimental and will" -" likely change significantly over time." -msgstr "请注意：gRPC 请求-响应 API 仍处于试验阶段，随着时间的推移可能会发生重大变化。" +"**Introduce FedPFT baseline** " +"([#3268](https://github.com/adap/flower/pull/3268))" +msgstr "**引入 start_driver**（[#1697](https://github.com/adap/flower/pull/1697)）" -#: ../../source/ref-changelog.md:262 +#: ../../source/ref-changelog.md:33 msgid "" -"**Replace the experimental** `start_client(rest=True)` **with the new** " -"`start_client(transport=\"rest\")` " -"([#1880](https://github.com/adap/flower/pull/1880))" +"FedPFT allows you to perform one-shot Federated Learning by leveraging " +"widely available foundational models, dramatically reducing communication" +" costs while delivering high performing models. This is work led by Mahdi" +" Beitollahi from Huawei Noah's Ark Lab (Montreal, Canada). Read all the " +"details in their paper: \"Parametric Feature Transfer: One-shot Federated" +" Learning with Foundation Models\" " +"([arxiv](https://arxiv.org/abs/2402.01862))" msgstr "" -"**用新的** `start_client(transport=\"rest\")` 替换实验性** " -"`start_client(rest=True)` " -"([#1880](https://github.com/adap/flower/pull/1880))" -#: ../../source/ref-changelog.md:264 +#: ../../source/ref-changelog.md:35 +#, fuzzy msgid "" -"The (experimental) `start_client` argument `rest` was deprecated in " -"favour of a new argument `transport`. `start_client(transport=\"rest\")` " -"will yield the same behaviour as `start_client(rest=True)` did before. " -"All code should migrate to the new argument `transport`. The deprecated " -"argument `rest` will be removed in a future release." +"**Launch additional** `flwr new` **templates for Apple MLX, Hugging Face " +"Transformers, scikit-learn and TensorFlow** " +"([#3291](https://github.com/adap/flower/pull/3291), " +"[#3139](https://github.com/adap/flower/pull/3139), " +"[#3284](https://github.com/adap/flower/pull/3284), " +"[#3251](https://github.com/adap/flower/pull/3251), " +"[#3376](https://github.com/adap/flower/pull/3376), " +"[#3287](https://github.com/adap/flower/pull/3287))" msgstr "" -"已废弃（试验性的）`start_client`参数`rest`，改用新参数`transport`。`start_client(transport=\"rest\")`将产生与以前的`start_client(rest=True)`相同的行为。所有代码都应迁移到新参数" -" `transport`。过时的参数 `rest` 将在今后的版本中删除。" +"**移除对 Python 3.7 的支持** " +"([#2280](https://github.com/adap/flower/pull/2280), " +"[#2299](https://github.com/adap/flower/pull/2299), " +"[#2304](https://github.com/adap/flower/pull/2304), " +"[#2306](https://github.com/adap/flower/pull/2306), " +"[#2355](https://github.com/adap/flower/pull/2355), " +"[#2356](https://github.com/adap/flower/pull/2356))" -#: ../../source/ref-changelog.md:266 +#: ../../source/ref-changelog.md:37 msgid "" -"**Add a new gRPC option** " -"([#2197](https://github.com/adap/flower/pull/2197))" -msgstr "** 添加一个新的 gRPC 选项**（[#2197](https://github.com/adap/flower/pull/2197)）" +"The `flwr` CLI's `flwr new` command is starting to become everone's " +"favorite way of creating new Flower projects. This release introduces " +"additional `flwr new` templates for Apple MLX, Hugging Face Transformers," +" scikit-learn and TensorFlow. In addition to that, existing templates " +"also received updates." +msgstr "" -#: ../../source/ref-changelog.md:268 +#: ../../source/ref-changelog.md:39 +#, fuzzy msgid "" -"We now start a gRPC server with the `grpc.keepalive_permit_without_calls`" -" option set to 0 by default. This prevents the clients from sending " -"keepalive pings when there is no outstanding stream." +"**Refine** `RecordSet` **API** " +"([#3209](https://github.com/adap/flower/pull/3209), " +"[#3331](https://github.com/adap/flower/pull/3331), " +"[#3334](https://github.com/adap/flower/pull/3334), " +"[#3335](https://github.com/adap/flower/pull/3335), " +"[#3375](https://github.com/adap/flower/pull/3375), " +"[#3368](https://github.com/adap/flower/pull/3368))" msgstr "" -"现在我们启动一个 gRPC 服务器，并将 `grpc.keepalive_permit_without_calls` 选项默认设置为 " -"0。这将防止客户端在没有未处理数据流时发送 keepalive pings。" +"**普通改进**（[#1872](https://github.com/adap/flower/pull/1872), " +"[#1866](https://github.com/adap/flower/pull/1866), " +"[#1884](https://github.com/adap/flower/pull/1884), " +"[#1837](https://github.com/adap/flower/pull/1837), " +"[#1477](https://github.com/adap/flower/pull/1477), " +"[#2171](https://github.com/adap/flower/pull/2171))" -#: ../../source/ref-changelog.md:270 +#: ../../source/ref-changelog.md:41 msgid "" -"**Improve example notebooks** " -"([#2005](https://github.com/adap/flower/pull/2005))" -msgstr "**改进示例笔记** ([#2005](https://github.com/adap/flower/pull/2005))" +"`RecordSet` is part of the Flower Next low-level API preview release. In " +"Flower 1.9, `RecordSet` received a number of usability improvements that " +"make it easier to build `RecordSet`-based `ServerApp`s and `ClientApp`s." +msgstr "" -#: ../../source/ref-changelog.md:272 -msgid "There's a new 30min Federated Learning PyTorch tutorial!" -msgstr "有一个新的 30 分钟的联邦学习 PyTorch 教程！" +#: ../../source/ref-changelog.md:43 +#, fuzzy +msgid "" +"**Beautify logging** ([#3379](https://github.com/adap/flower/pull/3379), " +"[#3430](https://github.com/adap/flower/pull/3430), " +"[#3461](https://github.com/adap/flower/pull/3461), " +"[#3360](https://github.com/adap/flower/pull/3360), " +"[#3433](https://github.com/adap/flower/pull/3433))" +msgstr "" +"** 更新 C++ SDK** ([#2537](https://github/com/adap/flower/pull/2537), " +"[#2528](https://github/com/adap/flower/pull/2528), " +"[#2523](https://github.com/adap/flower/pull/2523), " +"[#2522](https://github.com/adap/flower/pull/2522))" -#: ../../source/ref-changelog.md:274 +#: ../../source/ref-changelog.md:45 msgid "" -"**Example updates** ([#1772](https://github.com/adap/flower/pull/1772), " -"[#1873](https://github.com/adap/flower/pull/1873), " -"[#1981](https://github.com/adap/flower/pull/1981), " -"[#1988](https://github.com/adap/flower/pull/1988), " -"[#1984](https://github.com/adap/flower/pull/1984), " -"[#1982](https://github.com/adap/flower/pull/1982), " -"[#2112](https://github.com/adap/flower/pull/2112), " -"[#2144](https://github.com/adap/flower/pull/2144), " -"[#2174](https://github.com/adap/flower/pull/2174), " -"[#2225](https://github.com/adap/flower/pull/2225), " -"[#2183](https://github.com/adap/flower/pull/2183))" +"Logs received a substantial update. Not only are logs now much nicer to " +"look at, but they are also more configurable." msgstr "" -"**更新Example** ([#1772](https://github.com/adap/flower/pull/1772), " -"[#1873](https://github.com/adap/flower/pull/1873), " -"[#1981](https://github.com/adap/flower/pull/1981), " -"[#1988](https://github.com/adap/flower/pull/1988), " -"[#1984](https://github.com/adap/flower/pull/1984), " -"[#1982](https://github.com/adap/flower/pull/1982), " -"[#2112](https://github.com/adap/flower/pull/2112), " -"[#2144](https://github.com/adap/flower/pull/2144), " -"[#2174](https://github.com/adap/flower/pull/2174), " -"[#2225](https://github.com/adap/flower/pull/2225), " -"[#2183](https://github.com/adap/flower/pull/2183))" -#: ../../source/ref-changelog.md:276 +#: ../../source/ref-changelog.md:47 +#, fuzzy msgid "" -"Many examples have received significant updates, including simplified " -"advanced-tensorflow and advanced-pytorch examples, improved macOS " -"compatibility of TensorFlow examples, and code examples for simulation. A" -" major upgrade is that all code examples now have a `requirements.txt` " -"(in addition to `pyproject.toml`)." +"**Improve reliability** " +"([#3564](https://github.com/adap/flower/pull/3564), " +"[#3561](https://github.com/adap/flower/pull/3561), " +"[#3566](https://github.com/adap/flower/pull/3566), " +"[#3462](https://github.com/adap/flower/pull/3462), " +"[#3225](https://github.com/adap/flower/pull/3225), " +"[#3514](https://github.com/adap/flower/pull/3514), " +"[#3535](https://github.com/adap/flower/pull/3535), " +"[#3372](https://github.com/adap/flower/pull/3372))" msgstr "" -"许多示例都进行了重大更新，包括简化了 advanced-tensorflow 和 advanced-pytorch 示例，改进了 " -"TensorFlow 示例的 macOS 兼容性，以及模拟代码示例。一项重大升级是所有代码示例现在都有了 " -"\"requirements.txt\"（除 \"pyproject.toml \"外）。" +"**改进教程** ([#1468](https://github.com/adap/flower/pull/1468), " +"[#1470](https://github.com/adap/flower/pull/1470), " +"[#1472](https://github.com/adap/flower/pull/1472), " +"[#1473](https://github.com/adap/flower/pull/1473), " +"[#1474](https://github.com/adap/flower/pull/1474), " +"[#1475](https://github.com/adap/flower/pull/1475)))" -#: ../../source/ref-changelog.md:278 +#: ../../source/ref-changelog.md:49 msgid "" -"**General improvements** " -"([#1872](https://github.com/adap/flower/pull/1872), " -"[#1866](https://github.com/adap/flower/pull/1866), " -"[#1884](https://github.com/adap/flower/pull/1884), " -"[#1837](https://github.com/adap/flower/pull/1837), " -"[#1477](https://github.com/adap/flower/pull/1477), " -"[#2171](https://github.com/adap/flower/pull/2171))" +"Flower 1.9 includes reliability improvements across many parts of the " +"system. One example is a much improved SuperNode shutdown procedure." msgstr "" -"**普通改进**（[#1872](https://github.com/adap/flower/pull/1872), " -"[#1866](https://github.com/adap/flower/pull/1866), " -"[#1884](https://github.com/adap/flower/pull/1884), " -"[#1837](https://github.com/adap/flower/pull/1837), " -"[#1477](https://github.com/adap/flower/pull/1477), " -"[#2171](https://github.com/adap/flower/pull/2171))" -#: ../../source/ref-changelog.md:284 ../../source/ref-changelog.md:348 -#: ../../source/ref-changelog.md:406 ../../source/ref-changelog.md:475 -#: ../../source/ref-changelog.md:537 -msgid "None" -msgstr "无" +#: ../../source/ref-changelog.md:51 +#, fuzzy +msgid "" +"**Update Swift and C++ SDKs** " +"([#3321](https://github.com/adap/flower/pull/3321), " +"[#2763](https://github.com/adap/flower/pull/2763))" +msgstr "" +"** 更新代码示例** ([#1344](https://github.com/adap/flower/pull/1344), " +"[#1347](https://github.com/adap/flower/pull/1347))" -#: ../../source/ref-changelog.md:286 -msgid "v1.4.0 (2023-04-21)" -msgstr "v1.4.0 (2023-04-21)" +#: ../../source/ref-changelog.md:53 +msgid "" +"In the C++ SDK, communication-related code is now separate from main " +"client logic. A new abstract class `Communicator` has been introduced " +"alongside a gRPC implementation of it." +msgstr "" -#: ../../source/ref-changelog.md:292 +#: ../../source/ref-changelog.md:55 msgid "" -"`Adam Narozniak`, `Alexander Viala Bellander`, `Charles Beauville`, " -"`Chenyang Ma (Danny)`, `Daniel J. Beutel`, `Edoardo`, `Gautam Jajoo`, " -"`Iacob-Alexandru-Andrei`, `JDRanpariya`, `Jean Charle Yaacoub`, `Kunal " -"Sarkhel`, `L. Jiang`, `Lennart Behme`, `Max Kapsecker`, `Michał`, `Nic " -"Lane`, `Nikolaos Episkopos`, `Ragy`, `Saurav Maheshkar`, `Semo Yang`, " -"`Steve Laskaridis`, `Steven Hé (Sīchàng)`, `Taner Topal`" +"**Improve testing, tooling and CI/CD infrastructure** " +"([#3294](https://github.com/adap/flower/pull/3294), " +"[#3282](https://github.com/adap/flower/pull/3282), " +"[#3311](https://github.com/adap/flower/pull/3311), " +"[#2878](https://github.com/adap/flower/pull/2878), " +"[#3333](https://github.com/adap/flower/pull/3333), " +"[#3255](https://github.com/adap/flower/pull/3255), " +"[#3349](https://github.com/adap/flower/pull/3349), " +"[#3400](https://github.com/adap/flower/pull/3400), " +"[#3401](https://github.com/adap/flower/pull/3401), " +"[#3399](https://github.com/adap/flower/pull/3399), " +"[#3346](https://github.com/adap/flower/pull/3346), " +"[#3398](https://github.com/adap/flower/pull/3398), " +"[#3397](https://github.com/adap/flower/pull/3397), " +"[#3347](https://github.com/adap/flower/pull/3347), " +"[#3502](https://github.com/adap/flower/pull/3502), " +"[#3387](https://github.com/adap/flower/pull/3387), " +"[#3542](https://github.com/adap/flower/pull/3542), " +"[#3396](https://github.com/adap/flower/pull/3396), " +"[#3496](https://github.com/adap/flower/pull/3496), " +"[#3465](https://github.com/adap/flower/pull/3465), " +"[#3473](https://github.com/adap/flower/pull/3473), " +"[#3484](https://github.com/adap/flower/pull/3484), " +"[#3521](https://github.com/adap/flower/pull/3521), " +"[#3363](https://github.com/adap/flower/pull/3363), " +"[#3497](https://github.com/adap/flower/pull/3497), " +"[#3464](https://github.com/adap/flower/pull/3464), " +"[#3495](https://github.com/adap/flower/pull/3495), " +"[#3478](https://github.com/adap/flower/pull/3478), " +"[#3271](https://github.com/adap/flower/pull/3271))" msgstr "" -"`Adam Narozniak`, `Alexander Viala Bellander`, `Charles Beauville`, " -"`Chenyang Ma (Danny)`, `Daniel J. Beutel`, `Edoardo`, `Gautam Jajoo`, " -"`Iacob-Alexandru-Andrei`, `JDRanpariya`, `Jean Charle Yaacoub`, `Kunal " -"Sarkhel`, `L. Jiang`, `Lennart Behme`, `Max Kapsecker`, `Michał`, `Nic " -"Lane`, `Nikolaos Episkopos`, `Ragy`, `Saurav Maheshkar`, `Semo Yang`, " -"`Steve Laskaridis`, `Steven Hé (Sīchàng)`, `Taner Topal`" -#: ../../source/ref-changelog.md:296 +#: ../../source/ref-changelog.md:57 msgid "" -"**Introduce support for XGBoost (**`FedXgbNnAvg` **strategy and " -"example)** ([#1694](https://github.com/adap/flower/pull/1694), " -"[#1709](https://github.com/adap/flower/pull/1709), " -"[#1715](https://github.com/adap/flower/pull/1715), " -"[#1717](https://github.com/adap/flower/pull/1717), " -"[#1763](https://github.com/adap/flower/pull/1763), " -"[#1795](https://github.com/adap/flower/pull/1795))" +"As always, the Flower tooling, testing, and CI/CD infrastructure has " +"received many updates." msgstr "" -"**引入对XGBoost的支持（**`FedXgbNnAvg` **策略和示例）** " -"([#1694](https://github.com/adap/flower/pull/1694), " -"[#1709](https://github.com/adap/flower/pull/1709), " -"[#1715](https://github.com/adap/flower/pull/1715), " -"[#1717](https://github.com/adap/flower/pull/1717), " -"[#1763](https://github.com/adap/flower/pull/1763), " -"[#1795](https://github.com/adap/flower/pull/1795))" -#: ../../source/ref-changelog.md:298 +#: ../../source/ref-changelog.md:59 msgid "" -"XGBoost is a tree-based ensemble machine learning algorithm that uses " -"gradient boosting to improve model accuracy. We added a new `FedXgbNnAvg`" -" " -"[strategy](https://github.com/adap/flower/tree/main/src/py/flwr/server/strategy/fedxgb_nn_avg.py)," -" and a [code example](https://github.com/adap/flower/tree/main/examples" -"/xgboost-quickstart) that demonstrates the usage of this new strategy in " -"an XGBoost project." +"**Improve documentation** " +"([#3530](https://github.com/adap/flower/pull/3530), " +"[#3539](https://github.com/adap/flower/pull/3539), " +"[#3425](https://github.com/adap/flower/pull/3425), " +"[#3520](https://github.com/adap/flower/pull/3520), " +"[#3286](https://github.com/adap/flower/pull/3286), " +"[#3516](https://github.com/adap/flower/pull/3516), " +"[#3523](https://github.com/adap/flower/pull/3523), " +"[#3545](https://github.com/adap/flower/pull/3545), " +"[#3498](https://github.com/adap/flower/pull/3498), " +"[#3439](https://github.com/adap/flower/pull/3439), " +"[#3440](https://github.com/adap/flower/pull/3440), " +"[#3382](https://github.com/adap/flower/pull/3382), " +"[#3559](https://github.com/adap/flower/pull/3559), " +"[#3432](https://github.com/adap/flower/pull/3432), " +"[#3278](https://github.com/adap/flower/pull/3278), " +"[#3371](https://github.com/adap/flower/pull/3371), " +"[#3519](https://github.com/adap/flower/pull/3519), " +"[#3267](https://github.com/adap/flower/pull/3267), " +"[#3204](https://github.com/adap/flower/pull/3204), " +"[#3274](https://github.com/adap/flower/pull/3274))" +msgstr "" + +#: ../../source/ref-changelog.md:61 +msgid "" +"As always, the Flower documentation has received many updates. Notable " +"new pages include:" msgstr "" -"XGBoost 是一种基于树的集合机器学习算法，它使用梯度提升来提高模型的准确性。我们添加了一个新的 " -"\"FedXgbNnAvg\"[策略](https://github.com/adap/flower/tree/main/src/py/flwr/server/strategy/fedxgb_nn_avg.py)和一个[代码示例](https://github.com/adap/flower/tree/main/examples" -"/xgboost-quickstart)，演示如何在 XGBoost 项目中使用这个新策略。" -#: ../../source/ref-changelog.md:300 +#: ../../source/ref-changelog.md:63 msgid "" -"**Introduce iOS SDK (preview)** " -"([#1621](https://github.com/adap/flower/pull/1621), " -"[#1764](https://github.com/adap/flower/pull/1764))" +"[How-to upgrate to Flower Next (Flower Next migration " +"guide)](https://flower.ai/docs/framework/how-to-upgrade-to-flower-" +"next.html)" msgstr "" -"**介绍 iOS SDK（预览版）** ([#1621](https://github.com/adap/flower/pull/1621), " -"[#1764](https://github.com/adap/flower/pull/1764))" -#: ../../source/ref-changelog.md:302 +#: ../../source/ref-changelog.md:65 +#, fuzzy msgid "" -"This is a major update for anyone wanting to implement Federated Learning" -" on iOS mobile devices. We now have a swift iOS SDK present under " -"[src/swift/flwr](https://github.com/adap/flower/tree/main/src/swift/flwr)" -" that will facilitate greatly the app creating process. To showcase its " -"use, the [iOS " -"example](https://github.com/adap/flower/tree/main/examples/ios) has also " -"been updated!" +"[How-to run Flower using Docker](https://flower.ai/docs/framework/how-to-" +"run-flower-using-docker.html)" msgstr "" -"对于想要在 iOS 移动设备上实施联邦学习的人来说，这是一次重大更新。现在，我们在 " -"[src/swift/flwr](https://github.com/adap/flower/tree/main/src/swift/flwr)" -" 下提供了一个迅捷的 iOS SDK，这将大大方便应用程序的创建过程。为了展示其使用情况，我们还更新了 [iOS " -"示例](https://github.com/adap/flower/tree/main/examples/ios)！" +"`TensorFlow快速入门 (教程) `_" -#: ../../source/ref-changelog.md:304 +#: ../../source/ref-changelog.md:67 msgid "" -"**Introduce new \"What is Federated Learning?\" tutorial** " -"([#1657](https://github.com/adap/flower/pull/1657), " -"[#1721](https://github.com/adap/flower/pull/1721))" +"[Flower Mods reference](https://flower.ai/docs/framework/ref-" +"api/flwr.client.mod.html#module-flwr.client.mod)" msgstr "" -"**引入新的 " -"\"什么是联邦学习？\"教程**（[#1657](https://github.com/adap/flower/pull/1657), " -"[#1721](https://github.com/adap/flower/pull/1721)" -#: ../../source/ref-changelog.md:306 +#: ../../source/ref-changelog.md:69 +#, fuzzy msgid "" -"A new [entry-level tutorial](https://flower.ai/docs/framework/tutorial-" -"what-is-federated-learning.html) in our documentation explains the basics" -" of Fedetated Learning. It enables anyone who's unfamiliar with Federated" -" Learning to start their journey with Flower. Forward it to anyone who's " -"interested in Federated Learning!" +"**General updates to Flower Examples** " +"([#3205](https://github.com/adap/flower/pull/3205), " +"[#3226](https://github.com/adap/flower/pull/3226), " +"[#3211](https://github.com/adap/flower/pull/3211), " +"[#3252](https://github.com/adap/flower/pull/3252), " +"[#3427](https://github.com/adap/flower/pull/3427), " +"[#3410](https://github.com/adap/flower/pull/3410), " +"[#3426](https://github.com/adap/flower/pull/3426), " +"[#3228](https://github.com/adap/flower/pull/3228), " +"[#3342](https://github.com/adap/flower/pull/3342), " +"[#3200](https://github.com/adap/flower/pull/3200), " +"[#3202](https://github.com/adap/flower/pull/3202), " +"[#3394](https://github.com/adap/flower/pull/3394), " +"[#3488](https://github.com/adap/flower/pull/3488), " +"[#3329](https://github.com/adap/flower/pull/3329), " +"[#3526](https://github.com/adap/flower/pull/3526), " +"[#3392](https://github.com/adap/flower/pull/3392), " +"[#3474](https://github.com/adap/flower/pull/3474), " +"[#3269](https://github.com/adap/flower/pull/3269))" msgstr "" -"我们的文档中新增了一个[入门级教程](https://flower.ai/docs/framework/tutorial-what-is-" -"federated-learning.html)，解释了联邦学习的基础知识。它让任何不熟悉联邦学习的人都能开始 Flower " -"之旅。请转发给对联邦学习感兴趣的人！" +"**更新文档** ([#1223](https://github.com/adap/flower/pull/1223), " +"[#1209](https://github.com/adap/flower/pull/1209), " +"[#1251](https://github.com/adap/flower/pull/1251), " +"[#1257](https://github.com/adap/flower/pull/1257), " +"[#1267](https://github.com/adap/flower/pull/1267), " +"[#1268](https://github.com/adap/flower/pull/1268), " +"[#1300](https://github.com/adap/flower/pull/1300), " +"[#1304](https://github.com/adap/flower/pull/1304), " +"[#1305](https://github.com/adap/flower/pull/1305), " +"[#1307](https://github.com/adap/flower/pull/1307))" + +#: ../../source/ref-changelog.md:71 +#, fuzzy +msgid "As always, Flower code examples have received many updates." +msgstr "许多 \"Flower \"代码示例得到了大幅更新。" -#: ../../source/ref-changelog.md:308 +#: ../../source/ref-changelog.md:73 msgid "" -"**Introduce new Flower Baseline: FedProx MNIST** " -"([#1513](https://github.com/adap/flower/pull/1513), " -"[#1680](https://github.com/adap/flower/pull/1680), " -"[#1681](https://github.com/adap/flower/pull/1681), " -"[#1679](https://github.com/adap/flower/pull/1679))" +"**General improvements** " +"([#3532](https://github.com/adap/flower/pull/3532), " +"[#3318](https://github.com/adap/flower/pull/3318), " +"[#3565](https://github.com/adap/flower/pull/3565), " +"[#3296](https://github.com/adap/flower/pull/3296), " +"[#3305](https://github.com/adap/flower/pull/3305), " +"[#3246](https://github.com/adap/flower/pull/3246), " +"[#3224](https://github.com/adap/flower/pull/3224), " +"[#3475](https://github.com/adap/flower/pull/3475), " +"[#3297](https://github.com/adap/flower/pull/3297), " +"[#3317](https://github.com/adap/flower/pull/3317), " +"[#3429](https://github.com/adap/flower/pull/3429), " +"[#3196](https://github.com/adap/flower/pull/3196), " +"[#3534](https://github.com/adap/flower/pull/3534), " +"[#3240](https://github.com/adap/flower/pull/3240), " +"[#3365](https://github.com/adap/flower/pull/3365), " +"[#3407](https://github.com/adap/flower/pull/3407), " +"[#3563](https://github.com/adap/flower/pull/3563), " +"[#3344](https://github.com/adap/flower/pull/3344), " +"[#3330](https://github.com/adap/flower/pull/3330), " +"[#3436](https://github.com/adap/flower/pull/3436), " +"[#3300](https://github.com/adap/flower/pull/3300), " +"[#3327](https://github.com/adap/flower/pull/3327), " +"[#3254](https://github.com/adap/flower/pull/3254), " +"[#3253](https://github.com/adap/flower/pull/3253), " +"[#3419](https://github.com/adap/flower/pull/3419), " +"[#3289](https://github.com/adap/flower/pull/3289), " +"[#3208](https://github.com/adap/flower/pull/3208), " +"[#3245](https://github.com/adap/flower/pull/3245), " +"[#3319](https://github.com/adap/flower/pull/3319), " +"[#3203](https://github.com/adap/flower/pull/3203), " +"[#3423](https://github.com/adap/flower/pull/3423), " +"[#3352](https://github.com/adap/flower/pull/3352), " +"[#3292](https://github.com/adap/flower/pull/3292), " +"[#3261](https://github.com/adap/flower/pull/3261))" +msgstr "" + +#: ../../source/ref-changelog.md:75 ../../source/ref-changelog.md:1058 +msgid "Deprecations" +msgstr "停用" + +#: ../../source/ref-changelog.md:77 +#, fuzzy +msgid "**Deprecate Python 3.8 support**" +msgstr "** 过时的 Python 3.7**" + +#: ../../source/ref-changelog.md:79 +#, fuzzy +msgid "" +"Python 3.8 will stop receiving security fixes in [October " +"2024](https://devguide.python.org/versions/). Support for Python 3.8 is " +"now deprecated and will be removed in an upcoming release." +msgstr "由于 Python 3.7 已于 2023-06-27 弃用 (EOL)，对 Python 3.7 的支持现已废弃，并将在即将发布的版本中移除。" + +#: ../../source/ref-changelog.md:81 +#, fuzzy +msgid "" +"**Deprecate (experimental)** `flower-driver-api` **and** `flower-fleet-" +"api` ([#3416](https://github.com/adap/flower/pull/3416), " +"[#3420](https://github.com/adap/flower/pull/3420))" msgstr "" -"**引入新的 Flower Baseline： FedProx MNIST** " -"（[#1513](https://github.com/adap/flower/pull/1513), " -"[#1680](https://github.com/adap/flower/pull/1680), " -"[#1681](https://github.com/adap/flower/pull/1681), " -"[#1679](https://github.com/adap/flower/pull/1679)" +"FedBN ([#2608](https://github.com/adap/flower/pull/2608), " +"[#2615](https://github.com/adap/flower/pull/2615))" -#: ../../source/ref-changelog.md:310 +#: ../../source/ref-changelog.md:83 msgid "" -"This new baseline replicates the MNIST+CNN task from the paper [Federated" -" Optimization in Heterogeneous Networks (Li et al., " -"2018)](https://arxiv.org/abs/1812.06127). It uses the `FedProx` strategy," -" which aims at making convergence more robust in heterogeneous settings." +"Flower 1.9 deprecates the two (experimental) commands `flower-driver-api`" +" and `flower-fleet-api`. Both commands will be removed in an upcoming " +"release. Use `flower-superlink` instead." msgstr "" -"这条新Baseline复现了论文[Federated Optimization in Heterogeneous Networks (Li et " -"al., 2018)](https://arxiv.org/abs/1812.06127)中的 MNIST+CNN 任务。它使用 " -"\"FedProx \"策略，旨在使收敛在异构环境中更加稳健。" -#: ../../source/ref-changelog.md:312 +#: ../../source/ref-changelog.md:85 +#, fuzzy msgid "" -"**Introduce new Flower Baseline: FedAvg FEMNIST** " -"([#1655](https://github.com/adap/flower/pull/1655))" +"**Deprecate** `--server` **in favor of** `--superlink` " +"([#3518](https://github.com/adap/flower/pull/3518))" msgstr "" -"**引入新的 Flower Baseline： FedAvg FEMNIST** " -"([#1655](https://github.com/adap/flower/pull/1655))" +"**启用向** `start_simulation` 传递** `Server` 实例 " +"([#1281](https://github.com/adap/flower/pull/1281))" -#: ../../source/ref-changelog.md:314 +#: ../../source/ref-changelog.md:87 msgid "" -"This new baseline replicates an experiment evaluating the performance of " -"the FedAvg algorithm on the FEMNIST dataset from the paper [LEAF: A " -"Benchmark for Federated Settings (Caldas et al., " -"2018)](https://arxiv.org/abs/1812.01097)." +"The commands `flower-server-app` and `flower-client-app` should use " +"`--superlink` instead of the now deprecated `--server`. Support for " +"`--server` will be removed in a future release." msgstr "" -"这一新Baseline复现了论文[LEAF: A Benchmark for Federated Settings（Caldas 等人，2018 " -"年）](https://arxiv.org/abs/1812.01097)中评估 FedAvg 算法在 FEMNIST 数据集上性能的实验。" -#: ../../source/ref-changelog.md:316 +#: ../../source/ref-changelog.md:89 ../../source/ref-changelog.md:163 +#: ../../source/ref-changelog.md:238 ../../source/ref-changelog.md:350 +#: ../../source/ref-changelog.md:440 ../../source/ref-changelog.md:504 +#: ../../source/ref-changelog.md:562 ../../source/ref-changelog.md:631 +#: ../../source/ref-changelog.md:693 ../../source/ref-changelog.md:712 +#: ../../source/ref-changelog.md:868 ../../source/ref-changelog.md:939 +#: ../../source/ref-changelog.md:976 ../../source/ref-changelog.md:1019 +msgid "Incompatible changes" +msgstr "不兼容的更改" + +#: ../../source/ref-changelog.md:91 msgid "" -"**Introduce (experimental) REST API** " -"([#1594](https://github.com/adap/flower/pull/1594), " -"[#1690](https://github.com/adap/flower/pull/1690), " -"[#1695](https://github.com/adap/flower/pull/1695), " -"[#1712](https://github.com/adap/flower/pull/1712), " -"[#1802](https://github.com/adap/flower/pull/1802), " -"[#1770](https://github.com/adap/flower/pull/1770), " -"[#1733](https://github.com/adap/flower/pull/1733))" +"**Replace** `flower-superlink` **CLI option** `--certificates` **with** " +"`--ssl-ca-certfile` **,** `--ssl-certfile` **and** `--ssl-keyfile` " +"([#3512](https://github.com/adap/flower/pull/3512), " +"[#3408](https://github.com/adap/flower/pull/3408))" msgstr "" -"**引入（试验性）REST API** ([#1594](https://github.com/adap/flower/pull/1594), " -"[#1690](https://github.com/adap/flower/pull/1690), " -"[#1695](https://github.com/adap/flower/pull/1695), " -"[#1712](https://github.com/adap/flower/pull/1712), " -"[#1802](https://github.com/adap/flower/pull/1802), " -"[#1770](https://github.com/adap/flower/pull/1770), " -"[#1733](https://github.com/adap/flower/pull/1733))" -#: ../../source/ref-changelog.md:318 +#: ../../source/ref-changelog.md:93 msgid "" -"A new REST API has been introduced as an alternative to the gRPC-based " -"communication stack. In this initial version, the REST API only supports " -"anonymous clients." -msgstr "作为基于 gRPC 的通信栈的替代方案，我们引入了新的 REST API。在初始版本中，REST API 仅支持匿名客户端。" +"SSL-related `flower-superlink` CLI arguments were restructured in an " +"incompatible way. Instead of passing a single `--certificates` flag with " +"three values, you now need to pass three flags (`--ssl-ca-certfile`, " +"`--ssl-certfile` and `--ssl-keyfile`) with one value each. Check out the " +"[SSL connections](https://flower.ai/docs/framework/how-to-enable-ssl-" +"connections.html) documentation page for details." +msgstr "" -#: ../../source/ref-changelog.md:320 +#: ../../source/ref-changelog.md:95 +#, fuzzy msgid "" -"Please note: The REST API is still experimental and will likely change " -"significantly over time." -msgstr "请注意：REST API 仍处于试验阶段，随着时间的推移可能会发生重大变化。" +"**Remove SuperLink** `--vce` **option** " +"([#3513](https://github.com/adap/flower/pull/3513))" +msgstr "**重构文档**（[#1387](https://github.com/adap/flower/pull/1387)）" -#: ../../source/ref-changelog.md:322 +#: ../../source/ref-changelog.md:97 msgid "" -"**Improve the (experimental) Driver API** " -"([#1663](https://github.com/adap/flower/pull/1663), " -"[#1666](https://github.com/adap/flower/pull/1666), " -"[#1667](https://github.com/adap/flower/pull/1667), " -"[#1664](https://github.com/adap/flower/pull/1664), " -"[#1675](https://github.com/adap/flower/pull/1675), " -"[#1676](https://github.com/adap/flower/pull/1676), " -"[#1693](https://github.com/adap/flower/pull/1693), " -"[#1662](https://github.com/adap/flower/pull/1662), " -"[#1794](https://github.com/adap/flower/pull/1794))" +"Instead of separately starting a SuperLink and a `ServerApp` for " +"simulation, simulations must now be started using the single `flower-" +"simulation` command." msgstr "" -"**改进（试验性）驱动程序应用程序接口** ([#1663](https://github.com/adap/flower/pull/1663)," -" [#1666](https://github.com/adap/flower/pull/1666), " -"[#1667](https://github.com/adap/flower/pull/1667), " -"[#1664](https://github.com/adap/flower/pull/1664), " -"[#1675](https://github.com/adap/flower/pull/1675), " -"[#1676](https://github.com/adap/flower/pull/1676), " -"[#1693](https://github.com/adap/flower/pull/1693), " -"[#1662](https://github.com/adap/flower/pull/1662), " -"[#1794](https://github.com/adap/flower/pull/1794))" -#: ../../source/ref-changelog.md:324 +#: ../../source/ref-changelog.md:99 +#, fuzzy msgid "" -"The Driver API is still an experimental feature, but this release " -"introduces some major upgrades. One of the main improvements is the " -"introduction of an SQLite database to store server state on disk (instead" -" of in-memory). Another improvement is that tasks (instructions or " -"results) that have been delivered will now be deleted. This greatly " -"improves the memory efficiency of a long-running Flower server." +"**Merge** `--grpc-rere` **and** `--rest` **SuperLink options** " +"([#3527](https://github.com/adap/flower/pull/3527))" msgstr "" -"驱动程序应用程序接口（Driver API）仍是一项试验性功能，但这一版本引入了一些重大升级。主要改进之一是引入了 SQLite " -"数据库，将服务器状态存储在磁盘上（而不是内存中）。另一项改进是，已交付的任务（指令或结果）现在将被删除。这大大提高了长期运行的 Flower " -"服务器的内存效率。" +"**重新命名** `rnd` ** to** `server_round` " +"([#1321](https://github.com/adap/flower/pull/1321))" -#: ../../source/ref-changelog.md:326 +#: ../../source/ref-changelog.md:101 msgid "" -"**Fix spilling issues related to Ray during simulations** " -"([#1698](https://github.com/adap/flower/pull/1698))" -msgstr "**修复模拟过程中与Ray有关的溢出问题** ([#1698](https://github.com/adap/flower/pull/1698))" +"To simplify the usage of `flower-superlink`, previously separate sets of " +"CLI options for gRPC and REST were merged into one unified set of " +"options. Consult the [Flower CLI reference " +"documentation](https://flower.ai/docs/framework/ref-api-cli.html) for " +"details." +msgstr "" -#: ../../source/ref-changelog.md:328 -msgid "" -"While running long simulations, `ray` was sometimes spilling huge amounts" -" of data that would make the training unable to continue. This is now " -"fixed! 🎉" -msgstr "在运行长时间模拟时，`ray` 有时会溢出大量数据，导致训练无法继续。现在这个问题已经解决！🎉" +#: ../../source/ref-changelog.md:103 +#, fuzzy +msgid "v1.8.0 (2024-04-03)" +msgstr "v1.3.0 (2023-02-06)" -#: ../../source/ref-changelog.md:330 +#: ../../source/ref-changelog.md:109 +#, fuzzy msgid "" -"**Add new example using** `TabNet` **and Flower** " -"([#1725](https://github.com/adap/flower/pull/1725))" +"`Adam Narozniak`, `Charles Beauville`, `Daniel J. Beutel`, `Daniel Nata " +"Nugraha`, `Danny`, `Gustavo Bertoli`, `Heng Pan`, `Ikko Eltociear " +"Ashimine`, `Jack Cook`, `Javier`, `Raj Parekh`, `Robert Steiner`, " +"`Sebastian van der Voort`, `Taner Topal`, `Yan Gao`, `mohammadnaseri`, " +"`tabdar-khan` " msgstr "" -"** 添加使用** `TabNet` ** 的新示例** " -"([#1725](https://github.com/adap/flower/pull/1725))" +"`Adam Narozniak`, `Anass Anhari`, `Charles Beauville`, `Dana-Farber`, " +"`Daniel J. Beutel`, `Daniel Nata Nugraha`, `Edoardo Gabrielli`, `Gustavo " +"Bertoli`, `Heng Pan`, `Javier`, `Mahdi`, `Steven Hé (Sīchàng)`, `Taner " +"Topal`, `achiverram28`, `danielnugraha`, `eunchung`, `ruthgal` " -#: ../../source/ref-changelog.md:332 -msgid "" -"TabNet is a powerful and flexible framework for training machine learning" -" models on tabular data. We now have a federated example using Flower: " -"[quickstart-tabnet](https://github.com/adap/flower/tree/main/examples" -"/quickstart-tabnet)." +#: ../../source/ref-changelog.md:113 +#, fuzzy +msgid "" +"**Introduce Flower Next high-level API (stable)** " +"([#3002](https://github.com/adap/flower/pull/3002), " +"[#2934](https://github.com/adap/flower/pull/2934), " +"[#2958](https://github.com/adap/flower/pull/2958), " +"[#3173](https://github.com/adap/flower/pull/3173), " +"[#3174](https://github.com/adap/flower/pull/3174), " +"[#2923](https://github.com/adap/flower/pull/2923), " +"[#2691](https://github.com/adap/flower/pull/2691), " +"[#3079](https://github.com/adap/flower/pull/3079), " +"[#2961](https://github.com/adap/flower/pull/2961), " +"[#2924](https://github.com/adap/flower/pull/2924), " +"[#3166](https://github.com/adap/flower/pull/3166), " +"[#3031](https://github.com/adap/flower/pull/3031), " +"[#3057](https://github.com/adap/flower/pull/3057), " +"[#3000](https://github.com/adap/flower/pull/3000), " +"[#3113](https://github.com/adap/flower/pull/3113), " +"[#2957](https://github.com/adap/flower/pull/2957), " +"[#3183](https://github.com/adap/flower/pull/3183), " +"[#3180](https://github.com/adap/flower/pull/3180), " +"[#3035](https://github.com/adap/flower/pull/3035), " +"[#3189](https://github.com/adap/flower/pull/3189), " +"[#3185](https://github.com/adap/flower/pull/3185), " +"[#3190](https://github.com/adap/flower/pull/3190), " +"[#3191](https://github.com/adap/flower/pull/3191), " +"[#3195](https://github.com/adap/flower/pull/3195), " +"[#3197](https://github.com/adap/flower/pull/3197))" +msgstr "" +"**介绍 Flower Next 高级应用程序接口（稳定版）** " +"([#3002](https://github.com/adap/flower/pull/3002), " +"[#2934](https://github.com/adap/flower/pull/2934), " +"[#2958](https://github.com/adap/flower/pull/2958), " +"[#3173](https://github.com/adap/flower/pull/3173), " +"[#3174](https://github.com/adap/flower/pull/3174), " +"[#2923](https://github.com/adap/flower/pull/2923), " +"[#2691](https://github.com/adap/flower/pull/2691), " +"[#3079](https://github.com/adap/flower/pull/3079), " +"[#2961](https://github.com/adap/flower/pull/2961), " +"[#2924](https://github.com/adap/flower/pull/2924), " +"[#3166](https://github.com/adap/flower/pull/3166), " +"[#3031](https://github.com/adap/flower/pull/3031), " +"[#3057](https://github.com/adap/flower/pull/3057), " +"[#3000](https://github.com/adap/flower/pull/3000), " +"[#3113](https://github.com/adap/flower/pull/3113), " +"[#2957](https://github.com/adap/flower/pull/2957), " +"[#3183](https://github.com/adap/flower/pull/3183), " +"[#3180](https://github.com/adap/flower/pull/3180), " +"[#3035](https://github.com/adap/flower/pull/3035), " +"[#3189](https://github.com/adap/flower/pull/3189), " +"[#3185](https://github.com/adap/flower/pull/3185), " +"[#3190](https://github.com/adap/flower/pull/3190), " +"[#3191](https://github.com/adap/flower/pull/3191), " +"[#3195](https://github.com/adap/flower/pull/3195), " +"[#3197](https://github.com/adap/flower/pull/3197))" + +#: ../../source/ref-changelog.md:115 +#, fuzzy +msgid "" +"The Flower Next high-level API is stable! Flower Next is the future of " +"Flower - all new features (like Flower Mods) will be built on top of it. " +"You can start to migrate your existing projects to Flower Next by using " +"`ServerApp` and `ClientApp` (check out `quickstart-pytorch` or " +"`quickstart-tensorflow`, a detailed migration guide will follow shortly)." +" Flower Next allows you to run multiple projects concurrently (we call " +"this multi-run) and execute the same project in either simulation " +"environments or deployment environments without having to change a single" +" line of code. The best part? It's fully compatible with existing Flower " +"projects that use `Strategy`, `NumPyClient` & co." +msgstr "" +"Flower Next 高级应用程序接口已经稳定！Flower Next 是 Flower 的未来 - 所有新功能（如 Flower " +"Mods）都将构建在它之上。您可以使用 `ServerApp` 和 `ClientApp` 开始将现有项目迁移到 Flower Next（请查看 " +"`quickstart-pytorch` 或 `quickstart-tensorflow` ，详细的迁移指南将在不久后发布）。Flower " +"Next 允许您同时运行多个项目（我们称之为多重运行），并在模拟环境或部署环境中执行同一项目，而无需更改任何代码。最棒的是什么？它与使用 " +"`Strategy`、`NumPyClient` 等的现有 Flower 项目完全兼容。" + +#: ../../source/ref-changelog.md:117 +#, fuzzy +msgid "" +"**Introduce Flower Next low-level API (preview)** " +"([#3062](https://github.com/adap/flower/pull/3062), " +"[#3034](https://github.com/adap/flower/pull/3034), " +"[#3069](https://github.com/adap/flower/pull/3069))" msgstr "" -"TabNet 是一个强大而灵活的框架，用于在表格数据上训练机器学习模型。我们现在有一个使用 Flower 的联邦示例：[quickstart-" -"tabnet](https://github.com/adap/flower/tree/main/examples/quickstart-" -"tabnet)。" +"** 统一客户端应用程序接口** ([#2303](https://github.com/adap/flower/pull/2303), " +"[#2390](https://github.com/adap/flower/pull/2390), " +"[#2493](https://github.com/adap/flower/pull/2493))" -#: ../../source/ref-changelog.md:334 +#: ../../source/ref-changelog.md:119 +#, fuzzy msgid "" -"**Add new how-to guide for monitoring simulations** " -"([#1649](https://github.com/adap/flower/pull/1649))" -msgstr "** 添加新的模拟监控指南** ([#1649](https://github.com/adap/flower/pull/1649))" +"In addition to the Flower Next *high-level* API that uses `Strategy`, " +"`NumPyClient` & co, Flower 1.8 also comes with a preview version of the " +"new Flower Next *low-level* API. The low-level API allows for granular " +"control of every aspect of the learning process by sending/receiving " +"individual messages to/from client nodes. The new `ServerApp` supports " +"registering a custom `main` function that allows writing custom training " +"loops for methods like async FL, cyclic training, or federated analytics." +" The new `ClientApp` supports registering `train`, `evaluate` and `query`" +" functions that can access the raw message received from the `ServerApp`." +" New abstractions like `RecordSet`, `Message` and `Context` further " +"enable sending multiple models, multiple sets of config values and " +"metrics, stateful computations on the client node and implementations of " +"custom SMPC protocols, to name just a few." +msgstr "" +"除了使用 \"Strategy\"、\"NumPyClient \"等的 Flower Next 高级应用程序接口外，Flower 1.8 " +"还提供了新的 Flower Next " +"低级应用程序接口的预览版。低级应用程序接口允许通过向/从客户端节点发送/接收单个消息，对学习过程的各个方面进行细粒度控制。新的 " +"\"ServerApp \"支持注册一个自定义的 \"main \"函数，允许为异步FL、循环训练或联合分析等方法编写自定义训练循环。新的 " +"\"ClientApp \"支持注册 \"训练\"、\"评估 \"和 \"查询 \"函数，这些函数可以访问从 \"ServerApp " +"\"接收到的原始信息。新的抽象（如 \"RecordSet\"、\"Message \"和 " +"\"Context\"）进一步支持发送多个模型、多套配置值和指标、客户端节点上的有状态计算以及自定义 SMPC 协议的实现等。" -#: ../../source/ref-changelog.md:336 +#: ../../source/ref-changelog.md:121 +#, fuzzy msgid "" -"We now have a documentation guide to help users monitor their performance" -" during simulations." -msgstr "我们现在有一份文档指南，可帮助用户在模拟过程中监控其性能。" +"**Introduce Flower Mods (preview)** " +"([#3054](https://github.com/adap/flower/pull/3054), " +"[#2911](https://github.com/adap/flower/pull/2911), " +"[#3083](https://github.com/adap/flower/pull/3083))" +msgstr "" +"**引入新的模拟引擎** ([#1969](https://github.com/adap/flower/pull/1969), " +"[#2221](https://github.com/adap/flower/pull/2221), " +"[#2248](https://github.com/adap/flower/pull/2248))" -#: ../../source/ref-changelog.md:338 +#: ../../source/ref-changelog.md:123 +#, fuzzy msgid "" -"**Add training metrics to** `History` **object during simulations** " -"([#1696](https://github.com/adap/flower/pull/1696))" +"Flower Modifiers (we call them Mods) can intercept messages and analyze, " +"edit or handle them directly. Mods can be used to develop pluggable " +"modules that work across different projects. Flower 1.8 already includes " +"mods to log the size of a message, the number of parameters sent over the" +" network, differential privacy with fixed clipping and adaptive clipping," +" local differential privacy and secure aggregation protocols SecAgg and " +"SecAgg+. The Flower Mods API is released as a preview, but researchers " +"can already use it to experiment with arbirtrary SMPC protocols." msgstr "" -"**在模拟过程中为***`历史`***对象添加训练指标*** " -"([#1696](https://github.com/adap/flower/pull/1696))" +"Flower Modifiers（我们称之为 " +"Mods）可以拦截信息，并直接对其进行分析、编辑或处理。修改器可用于开发可在不同项目中使用的可插拔模块。Flower 1.8 " +"已经包含了记录信息大小、通过网络发送的参数数量、固定剪切和自适应剪切的差分隐私、本地差分隐私以及安全聚合协议 SecAgg 和 SecAgg+ 的" +" Mods。Flower Mods API 作为预览版发布，但研究人员已经可以用它来试验任意的 SMPC 协议。" -#: ../../source/ref-changelog.md:340 +#: ../../source/ref-changelog.md:125 +#, fuzzy msgid "" -"The `fit_metrics_aggregation_fn` can be used to aggregate training " -"metrics, but previous releases did not save the results in the `History` " -"object. This is now the case!" +"**Fine-tune LLMs with LLM FlowerTune** " +"([#3029](https://github.com/adap/flower/pull/3029), " +"[#3089](https://github.com/adap/flower/pull/3089), " +"[#3092](https://github.com/adap/flower/pull/3092), " +"[#3100](https://github.com/adap/flower/pull/3100), " +"[#3114](https://github.com/adap/flower/pull/3114), " +"[#3162](https://github.com/adap/flower/pull/3162), " +"[#3172](https://github.com/adap/flower/pull/3172))" msgstr "" -"`fit_metrics_aggregation_fn`可用于汇总训练指标，但以前的版本不会将结果保存在 \"History " -"\"对象中。现在可以了！" +"**改进教程** ([#1468](https://github.com/adap/flower/pull/1468), " +"[#1470](https://github.com/adap/flower/pull/1470), " +"[#1472](https://github.com/adap/flower/pull/1472), " +"[#1473](https://github.com/adap/flower/pull/1473), " +"[#1474](https://github.com/adap/flower/pull/1474), " +"[#1475](https://github.com/adap/flower/pull/1475)))" -#: ../../source/ref-changelog.md:342 +#: ../../source/ref-changelog.md:127 +#, fuzzy msgid "" -"**General improvements** " -"([#1659](https://github.com/adap/flower/pull/1659), " -"[#1646](https://github.com/adap/flower/pull/1646), " -"[#1647](https://github.com/adap/flower/pull/1647), " -"[#1471](https://github.com/adap/flower/pull/1471), " -"[#1648](https://github.com/adap/flower/pull/1648), " -"[#1651](https://github.com/adap/flower/pull/1651), " -"[#1652](https://github.com/adap/flower/pull/1652), " -"[#1653](https://github.com/adap/flower/pull/1653), " -"[#1659](https://github.com/adap/flower/pull/1659), " -"[#1665](https://github.com/adap/flower/pull/1665), " -"[#1670](https://github.com/adap/flower/pull/1670), " -"[#1672](https://github.com/adap/flower/pull/1672), " -"[#1677](https://github.com/adap/flower/pull/1677), " -"[#1684](https://github.com/adap/flower/pull/1684), " -"[#1683](https://github.com/adap/flower/pull/1683), " -"[#1686](https://github.com/adap/flower/pull/1686), " -"[#1682](https://github.com/adap/flower/pull/1682), " -"[#1685](https://github.com/adap/flower/pull/1685), " -"[#1692](https://github.com/adap/flower/pull/1692), " -"[#1705](https://github.com/adap/flower/pull/1705), " -"[#1708](https://github.com/adap/flower/pull/1708), " -"[#1711](https://github.com/adap/flower/pull/1711), " -"[#1713](https://github.com/adap/flower/pull/1713), " -"[#1714](https://github.com/adap/flower/pull/1714), " -"[#1718](https://github.com/adap/flower/pull/1718), " -"[#1716](https://github.com/adap/flower/pull/1716), " -"[#1723](https://github.com/adap/flower/pull/1723), " -"[#1735](https://github.com/adap/flower/pull/1735), " -"[#1678](https://github.com/adap/flower/pull/1678), " -"[#1750](https://github.com/adap/flower/pull/1750), " -"[#1753](https://github.com/adap/flower/pull/1753), " -"[#1736](https://github.com/adap/flower/pull/1736), " -"[#1766](https://github.com/adap/flower/pull/1766), " -"[#1760](https://github.com/adap/flower/pull/1760), " -"[#1775](https://github.com/adap/flower/pull/1775), " -"[#1776](https://github.com/adap/flower/pull/1776), " -"[#1777](https://github.com/adap/flower/pull/1777), " -"[#1779](https://github.com/adap/flower/pull/1779), " -"[#1784](https://github.com/adap/flower/pull/1784), " -"[#1773](https://github.com/adap/flower/pull/1773), " -"[#1755](https://github.com/adap/flower/pull/1755), " -"[#1789](https://github.com/adap/flower/pull/1789), " -"[#1788](https://github.com/adap/flower/pull/1788), " -"[#1798](https://github.com/adap/flower/pull/1798), " -"[#1799](https://github.com/adap/flower/pull/1799), " -"[#1739](https://github.com/adap/flower/pull/1739), " -"[#1800](https://github.com/adap/flower/pull/1800), " -"[#1804](https://github.com/adap/flower/pull/1804), " -"[#1805](https://github.com/adap/flower/pull/1805))" +"We are introducing LLM FlowerTune, an introductory example that " +"demonstrates federated LLM fine-tuning of pre-trained Llama2 models on " +"the Alpaca-GPT4 dataset. The example is built to be easily adapted to use" +" different models and/or datasets. Read our blog post [LLM FlowerTune: " +"Federated LLM Fine-tuning with Flower](https://flower.ai/blog/2024-03-14" +"-llm-flowertune-federated-llm-finetuning-with-flower/) for more details." msgstr "" -"**普通改进** ([#1659](https://github.com/adap/flower/pull/1659), " -"[#1646](https://github.com/adap/flower/pull/1646), " -"[#1647](https://github.com/adap/flower/pull/1647), " -"[#1471](https://github.com/adap/flower/pull/1471), " -"[#1648](https://github.com/adap/flower/pull/1648), " -"[#1651](https://github.com/adap/flower/pull/1651), " -"[#1652](https://github.com/adap/flower/pull/1652), " -"[#1653](https://github.com/adap/flower/pull/1653), " -"[#1659](https://github.com/adap/flower/pull/1659), " -"[#1665](https://github.com/adap/flower/pull/1665), " -"[#1670](https://github.com/adap/flower/pull/1670), " -"[#1672](https://github.com/adap/flower/pull/1672), " -"[#1677](https://github.com/adap/flower/pull/1677), " -"[#1684](https://github.com/adap/flower/pull/1684), " -"[#1683](https://github.com/adap/flower/pull/1683), " -"[#1686](https://github.com/adap/flower/pull/1686), " -"[#1682](https://github.com/adap/flower/pull/1682), " -"[#1685](https://github.com/adap/flower/pull/1685), " -"[#1692](https://github.com/adap/flower/pull/1692), " -"[#1705](https://github.com/adap/flower/pull/1705), " -"[#1708](https://github.com/adap/flower/pull/1708), " -"[#1711](https://github.com/adap/flower/pull/1711), " -"[#1713](https://github.com/adap/flower/pull/1713), " -"[#1714](https://github.com/adap/flower/pull/1714), " -"[#1718](https://github.com/adap/flower/pull/1718), " -"[#1716](https://github.com/adap/flower/pull/1716), " -"[#1723](https://github.com/adap/flower/pull/1723), " -"[#1735](https://github.com/adap/flower/pull/1735), " -"[#1678](https://github.com/adap/flower/pull/1678), " -"[#1750](https://github.com/adap/flower/pull/1750), " -"[#1753](https://github.com/adap/flower/pull/1753), " -"[#1736](https://github.com/adap/flower/pull/1736), " -"[#1766](https://github.com/adap/flower/pull/1766), " -"[#1760](https://github.com/adap/flower/pull/1760), " -"[#1775](https://github.com/adap/flower/pull/1775), " -"[#1776](https://github.com/adap/flower/pull/1776), " -"[#1777](https://github.com/adap/flower/pull/1777), " -"[#1779](https://github.com/adap/flower/pull/1779), " -"[#1784](https://github.com/adap/flower/pull/1784), " -"[#1773](https://github.com/adap/flower/pull/1773), " -"[#1755](https://github.com/adap/flower/pull/1755), " -"[#1789](https://github.com/adap/flower/pull/1789), " -"[#1788](https://github.com/adap/flower/pull/1788), " -"[#1798](https://github.com/adap/flower/pull/1798), " -"[#1799](https://github.com/adap/flower/pull/1799), " -"[#1739](https://github.com/adap/flower/pull/1739), " -"[#1800](https://github.com/adap/flower/pull/1800), " -"[#1804](https://github.com/adap/flower/pull/1804), " -"[#1805](https://github.com/adap/flower/pull/1805))" - -#: ../../source/ref-changelog.md:350 -msgid "v1.3.0 (2023-02-06)" -msgstr "v1.3.0 (2023-02-06)" +"我们将介绍 LLM FlowerTune，这是一个介绍性示例，演示了在 Alpaca-GPT4 数据集上对预先训练好的 Llama2 模型进行联合" +" LLM 微调。该示例可轻松调整以使用不同的模型和/或数据集。请阅读我们的博文 [LLM FlowerTune: Federated LLM " +"Fine-tuning with Flower](https://flower.ai/blog/2024-03-14-llm-" +"flowertune-federated-llm-finetuning-with-flower/) 了解更多详情。" -#: ../../source/ref-changelog.md:356 +#: ../../source/ref-changelog.md:129 +#, fuzzy msgid "" -"`Adam Narozniak`, `Alexander Viala Bellander`, `Charles Beauville`, " -"`Daniel J. Beutel`, `JDRanpariya`, `Lennart Behme`, `Taner Topal`" +"**Introduce built-in Differential Privacy (preview)** " +"([#2798](https://github.com/adap/flower/pull/2798), " +"[#2959](https://github.com/adap/flower/pull/2959), " +"[#3038](https://github.com/adap/flower/pull/3038), " +"[#3147](https://github.com/adap/flower/pull/3147), " +"[#2909](https://github.com/adap/flower/pull/2909), " +"[#2893](https://github.com/adap/flower/pull/2893), " +"[#2892](https://github.com/adap/flower/pull/2892), " +"[#3039](https://github.com/adap/flower/pull/3039), " +"[#3074](https://github.com/adap/flower/pull/3074))" msgstr "" -"`Adam Narozniak`, `Alexander Viala Bellander`, `Charles Beauville`, " -"`Daniel J. Beutel`, `JDRanpariya`, `Lennart Behme`, `Taner Topal`" +"** 支持 SSL 的服务器和客户端** ([#842](https://github.com/adap/flower/pull/842), " +"[#844](https://github.com/adap/flower/pull/844), " +"[#845](https://github.com/adap/flower/pull/845), " +"[#847](https://github.com/adap/flower/pull/847), " +"[#993](https://github.com/adap/flower/pull/993), " +"[#994](https://github.com/adap/flower/pull/994))" -#: ../../source/ref-changelog.md:360 +#: ../../source/ref-changelog.md:131 +#, fuzzy msgid "" -"**Add support for** `workload_id` **and** `group_id` **in Driver API** " -"([#1595](https://github.com/adap/flower/pull/1595))" +"Built-in Differential Privacy is here! Flower supports both central and " +"local differential privacy (DP). Central DP can be configured with either" +" fixed or adaptive clipping. The clipping can happen either on the " +"server-side or the client-side. Local DP does both clipping and noising " +"on the client-side. A new documentation page [explains Differential " +"Privacy approaches](https://flower.ai/docs/framework/explanation-" +"differential-privacy.html) and a new how-to guide describes [how to use " +"the new Differential Privacy components](https://flower.ai/docs/framework" +"/how-to-use-differential-privacy.html) in Flower." msgstr "" -"**在驱动程序应用程序接口中添加对** `workload_id` **和** `group_id` **的支持** " -"([#1595](https://github.com/adap/flower/pull/1595))" +"内置差分保密功能！Flower 支持中央和本地差分保密 (DP)。中央差分隐私可配置为固定或自适应剪切。剪切可以发生在服务器端或客户端。本地 DP" +" 在客户端进行剪切和噪声处理。新的文档页面[解释差分隐私方法](https://flower.ai/docs/framework" +"/explanation-differential-privacy.html) " +"和新的操作指南[如何使用新的差分隐私组件](https://flower.ai/docs/framework/how-to-use-" +"differential-privacy.html) 介绍了 Flower 的使用方法。" -#: ../../source/ref-changelog.md:362 +#: ../../source/ref-changelog.md:133 +#, fuzzy msgid "" -"The (experimental) Driver API now supports a `workload_id` that can be " -"used to identify which workload a task belongs to. It also supports a new" -" `group_id` that can be used, for example, to indicate the current " -"training round. Both the `workload_id` and `group_id` enable client nodes" -" to decide whether they want to handle a task or not." +"**Introduce built-in Secure Aggregation (preview)** " +"([#3120](https://github.com/adap/flower/pull/3120), " +"[#3110](https://github.com/adap/flower/pull/3110), " +"[#3108](https://github.com/adap/flower/pull/3108))" msgstr "" -"驱动程序 API（试验性）现在支持 `workload_id`，可用于识别任务所属的工作量。它还支持新的 " -"`group_id`，例如，可用于指示当前的训练轮次。通过 `workload_id` 和 `group_id` " -"客户端节点可以决定是否要处理某个任务。" +"**引入新的模拟引擎** ([#1969](https://github.com/adap/flower/pull/1969), " +"[#2221](https://github.com/adap/flower/pull/2221), " +"[#2248](https://github.com/adap/flower/pull/2248))" -#: ../../source/ref-changelog.md:364 +#: ../../source/ref-changelog.md:135 +#, fuzzy msgid "" -"**Make Driver API and Fleet API address configurable** " -"([#1637](https://github.com/adap/flower/pull/1637))" +"Built-in Secure Aggregation is here! Flower now supports different secure" +" aggregation protocols out-of-the-box. The best part? You can add secure " +"aggregation to your Flower projects with only a few lines of code. In " +"this initial release, we inlcude support for SecAgg and SecAgg+, but more" +" protocols will be implemented shortly. We'll also add detailed docs that" +" explain secure aggregation and how to use it in Flower. You can already " +"check out the new code example that shows how to use Flower to easily " +"combine Federated Learning, Differential Privacy and Secure Aggregation " +"in the same project." msgstr "" -"**使Driver API 和Fleet " -"API地址可配置**（[#1637](https://github.com/adap/flower/pull/1637)）" +"内置安全聚合功能！Flower 现在支持不同的安全聚合协议。最棒的是什么？只需几行代码，您就可以将安全聚合添加到 Flower " +"项目中。在这个初始版本中，我们包含了对 SecAgg 和 SecAgg+ " +"的支持，但更多协议将很快实现。我们还将添加详细的文档，解释安全聚合以及如何在 Flower 中使用它。您可以查看新的代码示例，了解如何使用 " +"Flower 在同一项目中轻松结合联合学习、差分隐私和安全聚合。" -#: ../../source/ref-changelog.md:366 +#: ../../source/ref-changelog.md:137 +#, fuzzy msgid "" -"The (experimental) long-running Flower server (Driver API and Fleet API) " -"can now configure the server address of both Driver API (via `--driver-" -"api-address`) and Fleet API (via `--fleet-api-address`) when starting:" +"**Introduce** `flwr` **CLI (preview)** " +"([#2942](https://github.com/adap/flower/pull/2942), " +"[#3055](https://github.com/adap/flower/pull/3055), " +"[#3111](https://github.com/adap/flower/pull/3111), " +"[#3130](https://github.com/adap/flower/pull/3130), " +"[#3136](https://github.com/adap/flower/pull/3136), " +"[#3094](https://github.com/adap/flower/pull/3094), " +"[#3059](https://github.com/adap/flower/pull/3059), " +"[#3049](https://github.com/adap/flower/pull/3049), " +"[#3142](https://github.com/adap/flower/pull/3142))" msgstr "" -"长期运行的 Flower 服务器（Driver API 和 Fleet API）现在可以在启动时配置 Driver API（通过 " -"`--driver-api-address`）和 Fleet API（通过 `-fleet-api-address`）的服务器地址：" +"**普通改进**（[#1872](https://github.com/adap/flower/pull/1872), " +"[#1866](https://github.com/adap/flower/pull/1866), " +"[#1884](https://github.com/adap/flower/pull/1884), " +"[#1837](https://github.com/adap/flower/pull/1837), " +"[#1477](https://github.com/adap/flower/pull/1477), " +"[#2171](https://github.com/adap/flower/pull/2171))" -#: ../../source/ref-changelog.md:368 +#: ../../source/ref-changelog.md:139 #, fuzzy msgid "" -"`flower-server --driver-api-address \"0.0.0.0:8081\" --fleet-api-address " -"\"0.0.0.0:8086\"`" -msgstr "" -"`flower-server --driver-api-address \"0.0.0.0:8081\" --fleet-api-address " -"\"0.0.0.0:8086\"`" - -#: ../../source/ref-changelog.md:370 -msgid "Both IPv4 and IPv6 addresses are supported." -msgstr "支持 IPv4 和 IPv6 地址。" +"A new `flwr` CLI command allows creating new Flower projects (`flwr new`)" +" and then running them using the Simulation Engine (`flwr run`)." +msgstr "新的 `flwr` CLI 命令允许创建新的 Flower 项目（`flwr new`），然后使用仿真引擎运行它们（`flwr run`）。" -#: ../../source/ref-changelog.md:372 +#: ../../source/ref-changelog.md:141 +#, fuzzy msgid "" -"**Add new example of Federated Learning using fastai and Flower** " -"([#1598](https://github.com/adap/flower/pull/1598))" +"**Introduce Flower Next Simulation Engine** " +"([#3024](https://github.com/adap/flower/pull/3024), " +"[#3061](https://github.com/adap/flower/pull/3061), " +"[#2997](https://github.com/adap/flower/pull/2997), " +"[#2783](https://github.com/adap/flower/pull/2783), " +"[#3184](https://github.com/adap/flower/pull/3184), " +"[#3075](https://github.com/adap/flower/pull/3075), " +"[#3047](https://github.com/adap/flower/pull/3047), " +"[#2998](https://github.com/adap/flower/pull/2998), " +"[#3009](https://github.com/adap/flower/pull/3009), " +"[#3008](https://github.com/adap/flower/pull/3008))" msgstr "" -"** 添加使用 fastai 和 Flower 进行联邦学习的新示例** " -"([#1598](https://github.com/adap/flower/pull/1598))" +"**引入（试验性）REST API** ([#1594](https://github.com/adap/flower/pull/1594), " +"[#1690](https://github.com/adap/flower/pull/1690), " +"[#1695](https://github.com/adap/flower/pull/1695), " +"[#1712](https://github.com/adap/flower/pull/1712), " +"[#1802](https://github.com/adap/flower/pull/1802), " +"[#1770](https://github.com/adap/flower/pull/1770), " +"[#1733](https://github.com/adap/flower/pull/1733))" -#: ../../source/ref-changelog.md:374 +#: ../../source/ref-changelog.md:143 +#, fuzzy msgid "" -"A new code example (`quickstart-fastai`) demonstrates federated learning " -"with [fastai](https://www.fast.ai/) and Flower. You can find it here: " -"[quickstart-fastai](https://github.com/adap/flower/tree/main/examples" -"/quickstart-fastai)." +"The Flower Simulation Engine can now run Flower Next projects. For " +"notebook environments, there's also a new `run_simulation` function that " +"can run `ServerApp` and `ClientApp`." msgstr "" -"一个新的代码示例（`quickstart-fastai`）演示了使用 [fastai](https://www.fast.ai/) 和 " -"Flower 的联邦学习。您可以在这里找到它： [quickstart-" -"fastai](https://github.com/adap/flower/tree/main/examples/quickstart-" -"fastai)。" +"Flower 模拟引擎现在可以运行 Flower Next 项目。对于笔记本环境，还有一个新的 `run_simulation` 函数，可以运行 " +"`ServerApp` 和 `ClientApp`。" -#: ../../source/ref-changelog.md:376 +#: ../../source/ref-changelog.md:145 +#, fuzzy msgid "" -"**Make Android example compatible with** `flwr >= 1.0.0` **and the latest" -" versions of Android** " -"([#1603](https://github.com/adap/flower/pull/1603))" -msgstr "" -"**使安卓示例兼容** `flwr >= 1.0.0` **和最新版本的安卓** " -"([#1603](https://github.com/adap/flower/pull/1603))" +"**Handle SuperNode connection errors** " +"([#2969](https://github.com/adap/flower/pull/2969))" +msgstr "** 添加一个新的 gRPC 选项**（[#2197](https://github.com/adap/flower/pull/2197)）" -#: ../../source/ref-changelog.md:378 +#: ../../source/ref-changelog.md:147 +#, fuzzy msgid "" -"The Android code example has received a substantial update: the project " -"is compatible with Flower 1.0 (and later), the UI received a full " -"refresh, and the project is updated to be compatible with newer Android " -"tooling." +"A SuperNode will now try to reconnect indefinitely to the SuperLink in " +"case of connection errors. The arguments `--max-retries` and `--max-wait-" +"time` can now be passed to the `flower-client-app` command. `--max-" +"retries` will define the number of tentatives the client should make " +"before it gives up trying to reconnect to the SuperLink, and, `--max-" +"wait-time` defines the time before the SuperNode gives up trying to " +"reconnect to the SuperLink." msgstr "" -"Android 代码示例已进行了大幅更新：项目兼容 Flower 1.0（及更高版本），用户界面已全面刷新，项目已更新为兼容较新的 Android" -" 工具。" +"如果出现连接错误，超级节点现在会尝试无限期地重新连接超级链接。现在可以向 `flower-client-app` 命令传递参数 `-ax-" +"retries` 和 `-max-wait-time`。最大重试次数 \"将定义客户端在放弃重新连接超级链接之前的重试次数，而 \"最大等待时间 " +"\"则定义超级节点放弃重新连接超级链接之前的等待时间。" -#: ../../source/ref-changelog.md:380 +#: ../../source/ref-changelog.md:149 +#, fuzzy msgid "" -"**Add new `FedProx` strategy** " -"([#1619](https://github.com/adap/flower/pull/1619))" -msgstr "**添加新的`FedProx`策略** （[#1619](https://github.com/adap/flower/pull/1619)）" +"**General updates to Flower Baselines** " +"([#2904](https://github.com/adap/flower/pull/2904), " +"[#2482](https://github.com/adap/flower/pull/2482), " +"[#2985](https://github.com/adap/flower/pull/2985), " +"[#2968](https://github.com/adap/flower/pull/2968))" +msgstr "" +"**引入新的 Flower Baseline： FedProx MNIST** " +"（[#1513](https://github.com/adap/flower/pull/1513), " +"[#1680](https://github.com/adap/flower/pull/1680), " +"[#1681](https://github.com/adap/flower/pull/1681), " +"[#1679](https://github.com/adap/flower/pull/1679)" -#: ../../source/ref-changelog.md:382 +#: ../../source/ref-changelog.md:151 +#, fuzzy msgid "" -"This " -"[strategy](https://github.com/adap/flower/blob/main/src/py/flwr/server/strategy/fedprox.py)" -" is almost identical to " -"[`FedAvg`](https://github.com/adap/flower/blob/main/src/py/flwr/server/strategy/fedavg.py)," -" but helps users replicate what is described in this " -"[paper](https://arxiv.org/abs/1812.06127). It essentially adds a " -"parameter called `proximal_mu` to regularize the local models with " -"respect to the global models." +"There's a new [FedStar](https://flower.ai/docs/baselines/fedstar.html) " +"baseline. Several other baselined have been updated as well." msgstr "" -"该[策略](https://github.com/adap/flower/blob/main/src/py/flwr/server/strategy/fedprox.py)与[`FedAvg`](https://github.com/adap/flower/blob/main/src/py/flwr/server/strategy/fedavg.py)几乎相同，但可以帮助用户复现本[论文](https://arxiv.org/abs/1812.06127)中的描述。它的本质是添加一个名为" -" `proximal_mu`的参数，使局部模型与全局模型正则化。" +"有一条新的 [FedStar](https://flower.ai/docs/baselines/fedstar.html) " +"基准线。其他几条基准线也已更新。" -#: ../../source/ref-changelog.md:384 +#: ../../source/ref-changelog.md:153 +#, fuzzy msgid "" -"**Add new metrics to telemetry events** " -"([#1640](https://github.com/adap/flower/pull/1640))" -msgstr "**为遥测事件添加新指标**（[#1640](https://github.com/adap/flower/pull/1640)）" +"**Improve documentation and translations** " +"([#3050](https://github.com/adap/flower/pull/3050), " +"[#3044](https://github.com/adap/flower/pull/3044), " +"[#3043](https://github.com/adap/flower/pull/3043), " +"[#2986](https://github.com/adap/flower/pull/2986), " +"[#3041](https://github.com/adap/flower/pull/3041), " +"[#3046](https://github.com/adap/flower/pull/3046), " +"[#3042](https://github.com/adap/flower/pull/3042), " +"[#2978](https://github.com/adap/flower/pull/2978), " +"[#2952](https://github.com/adap/flower/pull/2952), " +"[#3167](https://github.com/adap/flower/pull/3167), " +"[#2953](https://github.com/adap/flower/pull/2953), " +"[#3045](https://github.com/adap/flower/pull/3045), " +"[#2654](https://github.com/adap/flower/pull/2654), " +"[#3082](https://github.com/adap/flower/pull/3082), " +"[#2990](https://github.com/adap/flower/pull/2990), " +"[#2989](https://github.com/adap/flower/pull/2989))" +msgstr "" +"**改进文件和翻译** ([#3050](https://github.com/adap/flower/pull/3050), " +"[#3044](https://github.com/adap/flower/pull/3044), " +"[#3043](https://github.com/adap/flower/pull/3043), " +"[#2986](https://github.com/adap/flower/pull/2986), " +"[#3041](https://github.com/adap/flower/pull/3041), " +"[#3046](https://github.com/adap/flower/pull/3046), " +"[#3042](https://github.com/adap/flower/pull/3042), " +"[#2978](https://github.com/adap/flower/pull/2978), " +"[#2952](https://github.com/adap/flower/pull/2952), " +"[#3167](https://github.com/adap/flower/pull/3167), " +"[#2953](https://github.com/adap/flower/pull/2953), " +"[#3045](https://github.com/adap/flower/pull/3045), " +"[#2654](https://github.com/adap/flower/pull/2654), " +"[#3082](https://github.com/adap/flower/pull/3082), " +"[#2990](https://github.com/adap/flower/pull/2990), " +"[#2989](https://github.com/adap/flower/pull/2989))" -#: ../../source/ref-changelog.md:386 +#: ../../source/ref-changelog.md:155 +#, fuzzy msgid "" -"An updated event structure allows, for example, the clustering of events " -"within the same workload." -msgstr "例如，更新后的事件结构可以将同一工作负载中的事件集中在一起。" +"As usual, we merged many smaller and larger improvements to the " +"documentation. A special thank you goes to [Sebastian van der " +"Voort](https://github.com/svdvoort) for landing a big documentation PR!" +msgstr "" +"像往常一样，我们合并了许多对文档的较大和较小的改进。特别要感谢 [Sebastian van der " +"Voort](https://github.com/svdvoort)，他为我们带来了一份重要的文档 PR！" -#: ../../source/ref-changelog.md:388 +#: ../../source/ref-changelog.md:157 +#, fuzzy msgid "" -"**Add new custom strategy tutorial section** " -"[#1623](https://github.com/adap/flower/pull/1623)" -msgstr "**添加新的自定义策略教程部分** [#1623](https://github.com/adap/flower/pull/1623)" +"**General updates to Flower Examples** " +"([3134](https://github.com/adap/flower/pull/3134), " +"[2996](https://github.com/adap/flower/pull/2996), " +"[2930](https://github.com/adap/flower/pull/2930), " +"[2967](https://github.com/adap/flower/pull/2967), " +"[2467](https://github.com/adap/flower/pull/2467), " +"[2910](https://github.com/adap/flower/pull/2910), " +"[#2918](https://github.com/adap/flower/pull/2918), " +"[#2773](https://github.com/adap/flower/pull/2773), " +"[#3063](https://github.com/adap/flower/pull/3063), " +"[#3116](https://github.com/adap/flower/pull/3116), " +"[#3117](https://github.com/adap/flower/pull/3117))" +msgstr "" +"** 更新文档** ([#1494](https://github.com/adap/flower/pull/1494), " +"[#1496](https://github.com/adap/flower/pull/1496), " +"[#1500](https://github.com/adap/flower/pull/1500), " +"[#1503](https://github.com/adap/flower/pull/1503), " +"[#1505](https://github.com/adap/flower/pull/1505), " +"[#1524](https://github.com/adap/flower/pull/1524), " +"[#1518](https://github.com/adap/flower/pull/1518), " +"[#1519](https://github.com/adap/flower/pull/1519), " +"[#1515](https://github.com/adap/flower/pull/1515))" -#: ../../source/ref-changelog.md:390 +#: ../../source/ref-changelog.md:159 +#, fuzzy msgid "" -"The Flower tutorial now has a new section that covers implementing a " -"custom strategy from scratch: [Open in " -"Colab](https://colab.research.google.com/github/adap/flower/blob/main/doc/source" -"/tutorial-build-a-strategy-from-scratch-pytorch.ipynb)" +"Two new examples show federated training of a Vision Transformer (ViT) " +"and federated learning in a medical context using the popular MONAI " +"library. `quickstart-pytorch` and `quickstart-tensorflow` demonstrate the" +" new Flower Next `ServerApp` and `ClientApp`. Many other examples " +"received considerable updates as well." msgstr "" -"Flower 教程新增了一个章节，介绍如何从零开始实施自定义策略： [在 Colab " -"中打开](https://colab.research.google.com/github/adap/flower/blob/main/doc/source" -"/tutorial-build-a-strategy-from-scratch-pytorch.ipynb)" +"两个新示例展示了视觉转换器（ViT）的联合训练，以及使用流行的 MONAI 库在医疗环境中进行的联合学习。quickstart-pytorch " +"\"和 \"quickstart-tensorflow \"展示了新的 Flower Next \"ServerApp \"和 " +"\"ClientApp\"。许多其他示例也得到了大量更新。" -#: ../../source/ref-changelog.md:392 +#: ../../source/ref-changelog.md:161 +#, fuzzy msgid "" -"**Add new custom serialization tutorial section** " -"([#1622](https://github.com/adap/flower/pull/1622))" -msgstr "** 添加新的自定义序列化教程部分** ([#1622](https://github.com/adap/flower/pull/1622))" +"**General improvements** " +"([#3171](https://github.com/adap/flower/pull/3171), " +"[3099](https://github.com/adap/flower/pull/3099), " +"[3003](https://github.com/adap/flower/pull/3003), " +"[3145](https://github.com/adap/flower/pull/3145), " +"[3017](https://github.com/adap/flower/pull/3017), " +"[3085](https://github.com/adap/flower/pull/3085), " +"[3012](https://github.com/adap/flower/pull/3012), " +"[3119](https://github.com/adap/flower/pull/3119), " +"[2991](https://github.com/adap/flower/pull/2991), " +"[2970](https://github.com/adap/flower/pull/2970), " +"[2980](https://github.com/adap/flower/pull/2980), " +"[3086](https://github.com/adap/flower/pull/3086), " +"[2932](https://github.com/adap/flower/pull/2932), " +"[2928](https://github.com/adap/flower/pull/2928), " +"[2941](https://github.com/adap/flower/pull/2941), " +"[2933](https://github.com/adap/flower/pull/2933), " +"[3181](https://github.com/adap/flower/pull/3181), " +"[2973](https://github.com/adap/flower/pull/2973), " +"[2992](https://github.com/adap/flower/pull/2992), " +"[2915](https://github.com/adap/flower/pull/2915), " +"[3040](https://github.com/adap/flower/pull/3040), " +"[3022](https://github.com/adap/flower/pull/3022), " +"[3032](https://github.com/adap/flower/pull/3032), " +"[2902](https://github.com/adap/flower/pull/2902), " +"[2931](https://github.com/adap/flower/pull/2931), " +"[3005](https://github.com/adap/flower/pull/3005), " +"[3132](https://github.com/adap/flower/pull/3132), " +"[3115](https://github.com/adap/flower/pull/3115), " +"[2944](https://github.com/adap/flower/pull/2944), " +"[3064](https://github.com/adap/flower/pull/3064), " +"[3106](https://github.com/adap/flower/pull/3106), " +"[2974](https://github.com/adap/flower/pull/2974), " +"[3178](https://github.com/adap/flower/pull/3178), " +"[2993](https://github.com/adap/flower/pull/2993), " +"[3186](https://github.com/adap/flower/pull/3186), " +"[3091](https://github.com/adap/flower/pull/3091), " +"[3125](https://github.com/adap/flower/pull/3125), " +"[3093](https://github.com/adap/flower/pull/3093), " +"[3013](https://github.com/adap/flower/pull/3013), " +"[3033](https://github.com/adap/flower/pull/3033), " +"[3133](https://github.com/adap/flower/pull/3133), " +"[3068](https://github.com/adap/flower/pull/3068), " +"[2916](https://github.com/adap/flower/pull/2916), " +"[2975](https://github.com/adap/flower/pull/2975), " +"[2984](https://github.com/adap/flower/pull/2984), " +"[2846](https://github.com/adap/flower/pull/2846), " +"[3077](https://github.com/adap/flower/pull/3077), " +"[3143](https://github.com/adap/flower/pull/3143), " +"[2921](https://github.com/adap/flower/pull/2921), " +"[3101](https://github.com/adap/flower/pull/3101), " +"[2927](https://github.com/adap/flower/pull/2927), " +"[2995](https://github.com/adap/flower/pull/2995), " +"[2972](https://github.com/adap/flower/pull/2972), " +"[2912](https://github.com/adap/flower/pull/2912), " +"[3065](https://github.com/adap/flower/pull/3065), " +"[3028](https://github.com/adap/flower/pull/3028), " +"[2922](https://github.com/adap/flower/pull/2922), " +"[2982](https://github.com/adap/flower/pull/2982), " +"[2914](https://github.com/adap/flower/pull/2914), " +"[3179](https://github.com/adap/flower/pull/3179), " +"[3080](https://github.com/adap/flower/pull/3080), " +"[2994](https://github.com/adap/flower/pull/2994), " +"[3187](https://github.com/adap/flower/pull/3187), " +"[2926](https://github.com/adap/flower/pull/2926), " +"[3018](https://github.com/adap/flower/pull/3018), " +"[3144](https://github.com/adap/flower/pull/3144), " +"[3011](https://github.com/adap/flower/pull/3011), " +"[#3152](https://github.com/adap/flower/pull/3152), " +"[#2836](https://github.com/adap/flower/pull/2836), " +"[#2929](https://github.com/adap/flower/pull/2929), " +"[#2943](https://github.com/adap/flower/pull/2943), " +"[#2955](https://github.com/adap/flower/pull/2955), " +"[#2954](https://github.com/adap/flower/pull/2954))" +msgstr "" +"**一般改进**([#3171](https://github.com/adap/flower/pull/3171), " +"[3099](https://github.com/adap/flower/pull/3099), " +"[3003](https://github.com/adap/flower/pull/3003), " 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+"[3022](https://github.com/adap/flower/pull/3022), " +"[3032](https://github.com/adap/flower/pull/3032), " +"[2902](https://github.com/adap/flower/pull/2902), " +"[2931](https://github.com/adap/flower/pull/2931), " +"[3005](https://github.com/adap/flower/pull/3005), " +"[3132](https://github.com/adap/flower/pull/3132), " +"[3115](https://github.com/adap/flower/pull/3115), " +"[2944](https://github.com/adap/flower/pull/2944), " +"[3064](https://github.com/adap/flower/pull/3064), " +"[3106](https://github.com/adap/flower/pull/3106), " +"[2974](https://github.com/adap/flower/pull/2974), " +"[3178](https://github.com/adap/flower/pull/3178), " +"[2993](https://github.com/adap/flower/pull/2993), " +"[3186](https://github.com/adap/flower/pull/3186), " +"[3091](https://github.com/adap/flower/pull/3091), " +"[3125](https://github.com/adap/flower/pull/3125), " +"[3093](https://github.com/adap/flower/pull/3093), " +"[3013](https://github.com/adap/flower/pull/3013), " +"[3033](https://github.com/adap/flower/pull/3033), " +"[3133](https://github.com/adap/flower/pull/3133), " +"[3068](https://github.com/adap/flower/pull/3068), " +"[2916](https://github.com/adap/flower/pull/2916), " +"[2975](https://github.com/adap/flower/pull/2975), " +"[2984](https://github.com/adap/flower/pull/2984), " +"[2846](https://github.com/adap/flower/pull/2846), " +"[3077](https://github.com/adap/flower/pull/3077), " +"[3143](https://github.com/adap/flower/pull/3143), " +"[2921](https://github.com/adap/flower/pull/2921), " +"[3101](https://github.com/adap/flower/pull/3101), " +"[2927](https://github.com/adap/flower/pull/2927), " +"[2995](https://github.com/adap/flower/pull/2995), " +"[2972](https://github.com/adap/flower/pull/2972), " +"[2912](https://github.com/adap/flower/pull/2912), " +"[3065](https://github.com/adap/flower/pull/3065), " +"[3028](https://github.com/adap/flower/pull/3028), " +"[2922](https://github.com/adap/flower/pull/2922), " +"[2982](https://github.com/adap/flower/pull/2982), " +"[2914](https://github.com/adap/flower/pull/2914), " +"[3179](https://github.com/adap/flower/pull/3179), " +"[3080](https://github.com/adap/flower/pull/3080), " +"[2994](https://github.com/adap/flower/pull/2994), " +"[3187](https://github.com/adap/flower/pull/3187), " +"[2926](https://github.com/adap/flower/pull/2926), " +"[3018](https://github.com/adap/flower/pull/3018), " +"[3144](https://github.com/adap/flower/pull/3144), " +"[3011](https://github.com/adap/flower/pull/3011), " +"[#3152](https://github.com/adap/flower/pull/3152), " +"[#2836](https://github.com/adap/flower/pull/2836), " +"[#2929](https://github.com/adap/flower/pull/2929), " +"[#2943](https://github.com/adap/flower/pull/2943), " +"[#2955](https://github.com/adap/flower/pull/2955), " +"[#2954](https://github.com/adap/flower/pull/2954))" + +#: ../../source/ref-changelog.md:165 ../../source/ref-changelog.md:442 +#: ../../source/ref-changelog.md:506 ../../source/ref-changelog.md:564 +#: ../../source/ref-changelog.md:633 ../../source/ref-changelog.md:695 +msgid "None" +msgstr "无" -#: ../../source/ref-changelog.md:394 +#: ../../source/ref-changelog.md:167 +#, fuzzy +msgid "v1.7.0 (2024-02-05)" +msgstr "v1.3.0 (2023-02-06)" + +#: ../../source/ref-changelog.md:173 +#, fuzzy msgid "" -"The Flower tutorial now has a new section that covers custom " -"serialization: [Open in " -"Colab](https://colab.research.google.com/github/adap/flower/blob/main/doc/source" -"/tutorial-customize-the-client-pytorch.ipynb)" +"`Aasheesh Singh`, `Adam Narozniak`, `Aml Hassan Esmil`, `Charles " +"Beauville`, `Daniel J. Beutel`, `Daniel Nata Nugraha`, `Edoardo " +"Gabrielli`, `Gustavo Bertoli`, `HelinLin`, `Heng Pan`, `Javier`, `M S " +"Chaitanya Kumar`, `Mohammad Naseri`, `Nikos Vlachakis`, `Pritam Neog`, " +"`Robert Kuska`, `Robert Steiner`, `Taner Topal`, `Yahia Salaheldin " +"Shaaban`, `Yan Gao`, `Yasar Abbas` " msgstr "" -"Flower 教程现在新增了一个章节，介绍自定义序列化： [在 Colab " -"中打开](https://colab.research.google.com/github/adap/flower/blob/main/doc/source" -"/tutorial-customize-the-client-pytorch.ipynb)" +"`Adam Narozniak`, `Anass Anhari`, `Charles Beauville`, `Dana-Farber`, " +"`Daniel J. Beutel`, `Daniel Nata Nugraha`, `Edoardo Gabrielli`, `Gustavo " +"Bertoli`, `Heng Pan`, `Javier`, `Mahdi`, `Steven Hé (Sīchàng)`, `Taner " +"Topal`, `achiverram28`, `danielnugraha`, `eunchung`, `ruthgal` " -#: ../../source/ref-changelog.md:396 +#: ../../source/ref-changelog.md:177 +#, fuzzy msgid "" -"**General improvements** " -"([#1638](https://github.com/adap/flower/pull/1638), " -"[#1634](https://github.com/adap/flower/pull/1634), " -"[#1636](https://github.com/adap/flower/pull/1636), " -"[#1635](https://github.com/adap/flower/pull/1635), " -"[#1633](https://github.com/adap/flower/pull/1633), " -"[#1632](https://github.com/adap/flower/pull/1632), " -"[#1631](https://github.com/adap/flower/pull/1631), " -"[#1630](https://github.com/adap/flower/pull/1630), " -"[#1627](https://github.com/adap/flower/pull/1627), " -"[#1593](https://github.com/adap/flower/pull/1593), " -"[#1616](https://github.com/adap/flower/pull/1616), " -"[#1615](https://github.com/adap/flower/pull/1615), " -"[#1607](https://github.com/adap/flower/pull/1607), " -"[#1609](https://github.com/adap/flower/pull/1609), " -"[#1608](https://github.com/adap/flower/pull/1608), " -"[#1603](https://github.com/adap/flower/pull/1603), " -"[#1590](https://github.com/adap/flower/pull/1590), " -"[#1580](https://github.com/adap/flower/pull/1580), " -"[#1599](https://github.com/adap/flower/pull/1599), " -"[#1600](https://github.com/adap/flower/pull/1600), " -"[#1601](https://github.com/adap/flower/pull/1601), " -"[#1597](https://github.com/adap/flower/pull/1597), " -"[#1595](https://github.com/adap/flower/pull/1595), " -"[#1591](https://github.com/adap/flower/pull/1591), " -"[#1588](https://github.com/adap/flower/pull/1588), " -"[#1589](https://github.com/adap/flower/pull/1589), " -"[#1587](https://github.com/adap/flower/pull/1587), " -"[#1573](https://github.com/adap/flower/pull/1573), " -"[#1581](https://github.com/adap/flower/pull/1581), " -"[#1578](https://github.com/adap/flower/pull/1578), " -"[#1574](https://github.com/adap/flower/pull/1574), " -"[#1572](https://github.com/adap/flower/pull/1572), " -"[#1586](https://github.com/adap/flower/pull/1586))" +"**Introduce stateful clients (experimental)** " +"([#2770](https://github.com/adap/flower/pull/2770), " +"[#2686](https://github.com/adap/flower/pull/2686), " +"[#2696](https://github.com/adap/flower/pull/2696), " +"[#2643](https://github.com/adap/flower/pull/2643), " +"[#2769](https://github.com/adap/flower/pull/2769))" msgstr "" -"**普通改进** ([#1638](https://github.com/adap/flower/pull/1638), " -"[#1634](https://github.com/adap/flower/pull/1634), " -"[#1636](https://github.com/adap/flower/pull/1636), " -"[#1635](https://github.com/adap/flower/pull/1635), " -"[#1633](https://github.com/adap/flower/pull/1633), " -"[#1632](https://github.com/adap/flower/pull/1632), " -"[#1631](https://github.com/adap/flower/pull/1631), " -"[#1630](https://github.com/adap/flower/pull/1630), " -"[#1627](https://github. com/adap/flower/pull/1627), " -"[#1593](https://github.com/adap/flower/pull/1593), " -"[#1616](https://github.com/adap/flower/pull/1616), " -"[#1615](https://github.com/adap/flower/pull/1615), " -"[#1607](https://github.com/adap/flower/pull/1607), " -"[#1609](https://github.com/adap/flower/pull/1609), " -"[#1608](https://github.com/adap/flower/pull/1608), " -"[#1603](https://github.com/adap/flower/pull/1603), " -"[#1590](https://github. com/adap/flower/pull/1590), " -"[#1580](https://github.com/adap/flower/pull/1580), " -"[#1599](https://github.com/adap/flower/pull/1599), " -"[#1600](https://github.com/adap/flower/pull/1600), " -"[#1601](https://github.com/adap/flower/pull/1601), " -"[#1597](https://github.com/adap/flower/pull/1597), " -"[#1595](https://github.com/adap/flower/pull/1595), " -"[#1591](https://github.com/adap/flower/pull/1591), " -"[#1588](https://github. com/adap/flower/pull/1588), " -"[#1589](https://github.com/adap/flower/pull/1589), " -"[#1587](https://github.com/adap/flower/pull/1587), " -"[#1573](https://github.com/adap/flower/pull/1573), " -"[#1581](https://github.com/adap/flower/pull/1581), " -"[#1578](https://github.com/adap/flower/pull/1578), " -"[#1574](https://github.com/adap/flower/pull/1574), " -"[#1572](https://github.com/adap/flower/pull/1572), " -"[#1586](https://github.com/adap/flower/pull/1586))" +"** baselines的普通更新** ([#2301](https://github.com/adap/flower/pull/2301), " +"[#2305](https://github.com/adap/flower/pull/2305), " +"[#2307](https://github.com/adap/flower/pull/2307), " +"[#2327](https://github.com/adap/flower/pull/2327), " +"[#2435](https://github.com/adap/flower/pull/2435))" -#: ../../source/ref-changelog.md:400 +#: ../../source/ref-changelog.md:179 +#, fuzzy msgid "" -"**Updated documentation** " -"([#1629](https://github.com/adap/flower/pull/1629), " -"[#1628](https://github.com/adap/flower/pull/1628), " -"[#1620](https://github.com/adap/flower/pull/1620), " -"[#1618](https://github.com/adap/flower/pull/1618), " -"[#1617](https://github.com/adap/flower/pull/1617), " -"[#1613](https://github.com/adap/flower/pull/1613), " -"[#1614](https://github.com/adap/flower/pull/1614))" +"Subclasses of `Client` and `NumPyClient` can now store local state that " +"remains on the client. Let's start with the highlight first: this new " +"feature is compatible with both simulated clients (via " +"`start_simulation`) and networked clients (via `start_client`). It's also" +" the first preview of new abstractions like `Context` and `RecordSet`. " +"Clients can access state of type `RecordSet` via `state: RecordSet = " +"self.context.state`. Changes to this `RecordSet` are preserved across " +"different rounds of execution to enable stateful computations in a " +"unified way across simulation and deployment." msgstr "" -"** 更新文档** ([#1629](https://github.com/adap/flower/pull/1629), " -"[#1628](https://github.com/adap/flower/pull/1628), " -"[#1620](https://github.com/adap/flower/pull/1620), " -"[#1618](https://github.com/adap/flower/pull/1618), " -"[#1617](https://github.com/adap/flower/pull/1617), " -"[#1613](https://github.com/adap/flower/pull/1613), " -"[#1614](https://github.com/adap/flower/pull/1614)))" +"客户端 \"和 \"NumPyClient \"的子类现在可以存储保留在客户端上的本地状态。让我们先从亮点开始：这一新功能与模拟客户端（通过 " +"`start_simulation`）和网络客户端（通过 `start_client`）兼容。这也是 `Context` 和 " +"`RecordSet` 等新抽象的首次预览。客户端可以通过 `state.RecordSet` 访问 `RecordSet` 类型的状态： " +"RecordSet = self.context.state`。对该 `RecordSet` " +"的更改会在不同轮执行中保留，以便在模拟和部署中以统一的方式进行有状态计算。" -#: ../../source/ref-changelog.md:402 ../../source/ref-changelog.md:469 +#: ../../source/ref-changelog.md:181 +#, fuzzy msgid "" -"As usual, the documentation has improved quite a bit. It is another step " -"in our effort to make the Flower documentation the best documentation of " -"any project. Stay tuned and as always, feel free to provide feedback!" -msgstr "和往常一样，我们的文档有了很大的改进。这是我们努力使 Flower 文档成为所有项目中最好文档的又一步骤。请继续关注，并随时提供反馈意见！" +"**Improve performance** " +"([#2293](https://github.com/adap/flower/pull/2293))" +msgstr "**改进示例笔记** ([#2005](https://github.com/adap/flower/pull/2005))" -#: ../../source/ref-changelog.md:408 -msgid "v1.2.0 (2023-01-13)" -msgstr "v1.2.0 (2023-01-13)" - -#: ../../source/ref-changelog.md:414 +#: ../../source/ref-changelog.md:183 +#, fuzzy msgid "" -"`Adam Narozniak`, `Charles Beauville`, `Daniel J. Beutel`, `Edoardo`, `L." -" Jiang`, `Ragy`, `Taner Topal`, `dannymcy`" +"Flower is faster than ever. All `FedAvg`-derived strategies now use in-" +"place aggregation to reduce memory consumption. The Flower client " +"serialization/deserialization has been rewritten from the ground up, " +"which results in significant speedups, especially when the client-side " +"training time is short." msgstr "" -"`Adam Narozniak`, `Charles Beauville`, `Daniel J. Beutel`, `Edoardo`, `L." -" Jiang`, `Ragy`, `Taner Topal`, `dannymcy`" +"Flower 的速度比以往更快。所有源于 `FedAvg` 的策略现在都使用就地聚合，以减少内存消耗。Flower " +"客户端序列化/解序列化已从头开始重写，从而显著提高了速度，尤其是在客户端训练时间较短的情况下。" -#: ../../source/ref-changelog.md:418 +#: ../../source/ref-changelog.md:185 +#, fuzzy msgid "" -"**Introduce new Flower Baseline: FedAvg MNIST** " -"([#1497](https://github.com/adap/flower/pull/1497), " -"[#1552](https://github.com/adap/flower/pull/1552))" +"**Support Federated Learning with Apple MLX and Flower** " +"([#2693](https://github.com/adap/flower/pull/2693))" msgstr "" -"**引入新的 Flower Baseline： FedAvg MNIST** " -"([#1497](https://github.com/adap/flower/pull/1497), " -"[#1552](https://github.com/adap/flower/pull/1552))" +"** 添加使用 fastai 和 Flower 进行联邦学习的新示例** " +"([#1598](https://github.com/adap/flower/pull/1598))" -#: ../../source/ref-changelog.md:420 +#: ../../source/ref-changelog.md:187 +#, fuzzy msgid "" -"Over the coming weeks, we will be releasing a number of new reference " -"implementations useful especially to FL newcomers. They will typically " -"revisit well known papers from the literature, and be suitable for " -"integration in your own application or for experimentation, in order to " -"deepen your knowledge of FL in general. Today's release is the first in " -"this series. [Read more.](https://flower.ai/blog/2023-01-12-fl-starter-" -"pack-fedavg-mnist-cnn/)" +"Flower has official support for federated learning using [Apple " +"MLX](https://ml-explore.github.io/mlx) via the new `quickstart-mlx` code " +"example." msgstr "" -"在未来几周内，我们将发布一些新的参考，特别是对 FL " -"新手有用的方法。它们通常会重温文献中的知名论文，适合集成到您自己的应用程序中或用于实验，以加深您对 FL " -"的总体了解。今天发布的是该系列中的第一篇。[阅读全文](https://flower.ai/blog/2023-01-12-fl-starter-" -"pack-fedavg-mnist-cnn/)" +"通过新的 `quickstart-mlx` 代码示例，Flower 正式支持使用 [Apple MLX](https://ml-" +"explore.github.io/mlx)的联合学习。" -#: ../../source/ref-changelog.md:422 +#: ../../source/ref-changelog.md:189 +#, fuzzy msgid "" -"**Improve GPU support in simulations** " -"([#1555](https://github.com/adap/flower/pull/1555))" -msgstr "**改进模拟中的 GPU 支持**（[#1555](https://github.com/adap/flower/pull/1555)）" +"**Introduce new XGBoost cyclic strategy** " +"([#2666](https://github.com/adap/flower/pull/2666), " +"[#2668](https://github.com/adap/flower/pull/2668))" +msgstr "" +"**介绍 iOS SDK（预览版）** ([#1621](https://github.com/adap/flower/pull/1621), " +"[#1764](https://github.com/adap/flower/pull/1764))" -#: ../../source/ref-changelog.md:424 +#: ../../source/ref-changelog.md:191 +#, fuzzy msgid "" -"The Ray-based Virtual Client Engine (`start_simulation`) has been updated" -" to improve GPU support. The update includes some of the hard-earned " -"lessons from scaling simulations in GPU cluster environments. New " -"defaults make running GPU-based simulations substantially more robust." +"A new strategy called `FedXgbCyclic` supports a client-by-client style of" +" training (often called cyclic). The `xgboost-comprehensive` code example" +" shows how to use it in a full project. In addition to that, `xgboost-" +"comprehensive` now also supports simulation mode. With this, Flower " +"offers best-in-class XGBoost support." msgstr "" -"基于 Ray 的虚拟客户端引擎 (`start_simulation`)已更新，以改进对 GPU 的支持。此次更新包含了在 GPU " -"集群环境中扩展模拟的一些经验教训。新的默认设置使基于 GPU 的模拟运行更加稳健。" +"名为 `FedXgbCyclic` 的新策略支持逐个客户端的训练风格（通常称为循环）。xgboost-comprehensive " +"\"代码示例展示了如何在一个完整的项目中使用它。除此之外，`xgboost-comprehensive` 现在还支持模拟模式。由此，Flower " +"提供了同类最佳的 XGBoost 支持。" -#: ../../source/ref-changelog.md:426 +#: ../../source/ref-changelog.md:193 +#, fuzzy msgid "" -"**Improve GPU support in Jupyter Notebook tutorials** " -"([#1527](https://github.com/adap/flower/pull/1527), " -"[#1558](https://github.com/adap/flower/pull/1558))" -msgstr "" -"**改进 Jupyter Notebook 教程中的 GPU 支持** " -"([#1527](https://github.com/adap/flower/pull/1527), " -"[#1558](https://github.com/adap/flower/pull/1558))" +"**Support Python 3.11** " +"([#2394](https://github.com/adap/flower/pull/2394))" +msgstr "** 支持 Python 3.10** ([#1320](https://github.com/adap/flower/pull/1320))" -#: ../../source/ref-changelog.md:428 +#: ../../source/ref-changelog.md:195 +#, fuzzy msgid "" -"Some users reported that Jupyter Notebooks have not always been easy to " -"use on GPU instances. We listened and made improvements to all of our " -"Jupyter notebooks! Check out the updated notebooks here:" -msgstr "" -"一些用户报告说，在 GPU 实例上使用 Jupyter 笔记本并不是很方便。我们听取了他们的意见，并对所有 Jupyter " -"笔记本进行了改进！点击这里查看更新后的笔记本：" +"Framework tests now run on Python 3.8, 3.9, 3.10, and 3.11. This will " +"ensure better support for users using more recent Python versions." +msgstr "框架测试现在可在 Python 3.8、3.9、3.10 和 3.11 上运行。这将确保为使用最新 Python 版本的用户提供更好的支持。" -#: ../../source/ref-changelog.md:430 +#: ../../source/ref-changelog.md:197 +#, fuzzy msgid "" -"[An Introduction to Federated Learning](https://flower.ai/docs/framework" -"/tutorial-get-started-with-flower-pytorch.html)" +"**Update gRPC and ProtoBuf dependencies** " +"([#2814](https://github.com/adap/flower/pull/2814))" msgstr "" -"[联邦学习简介](https://flower.ai/docs/framework/tutorial-get-started-with-" -"flower-pytorch.html)" +"**更新 REST API 以支持创建和删除节点** " +"([#2283](https://github.com/adap/flower/pull/2283))" -#: ../../source/ref-changelog.md:431 +#: ../../source/ref-changelog.md:199 +#, fuzzy msgid "" -"[Strategies in Federated Learning](https://flower.ai/docs/framework" -"/tutorial-use-a-federated-learning-strategy-pytorch.html)" -msgstr "" -"[联邦学习策略](https://flower.ai/docs/framework/tutorial-use-a-federated-" -"learning-strategy-pytorch.html)" +"The `grpcio` and `protobuf` dependencies were updated to their latest " +"versions for improved security and performance." +msgstr "为提高安全性和性能，\"grpcio \"和 \"protobuf \"依赖项已更新至最新版本。" -#: ../../source/ref-changelog.md:432 +#: ../../source/ref-changelog.md:201 +#, fuzzy msgid "" -"[Building a Strategy](https://flower.ai/docs/framework/tutorial-build-a" -"-strategy-from-scratch-pytorch.html)" +"**Introduce Docker image for Flower server** " +"([#2700](https://github.com/adap/flower/pull/2700), " +"[#2688](https://github.com/adap/flower/pull/2688), " +"[#2705](https://github.com/adap/flower/pull/2705), " +"[#2695](https://github.com/adap/flower/pull/2695), " +"[#2747](https://github.com/adap/flower/pull/2747), " +"[#2746](https://github.com/adap/flower/pull/2746), " +"[#2680](https://github.com/adap/flower/pull/2680), " +"[#2682](https://github.com/adap/flower/pull/2682), " +"[#2701](https://github.com/adap/flower/pull/2701))" msgstr "" -"[制定策略](https://flower.ai/docs/framework/tutorial-build-a-strategy-from-" -"scratch-pytorch.html)" +"** 支持 SSL 的服务器和客户端** ([#842](https://github.com/adap/flower/pull/842), " +"[#844](https://github.com/adap/flower/pull/844), " +"[#845](https://github.com/adap/flower/pull/845), " +"[#847](https://github.com/adap/flower/pull/847), " +"[#993](https://github.com/adap/flower/pull/993), " +"[#994](https://github.com/adap/flower/pull/994))" -#: ../../source/ref-changelog.md:433 +#: ../../source/ref-changelog.md:203 +#, fuzzy msgid "" -"[Client and NumPyClient](https://flower.ai/docs/framework/tutorial-" -"customize-the-client-pytorch.html)" +"The Flower server can now be run using an official Docker image. A new " +"how-to guide explains [how to run Flower using " +"Docker](https://flower.ai/docs/framework/how-to-run-flower-using-" +"docker.html). An official Flower client Docker image will follow." msgstr "" -"[客户端和 NumPyClient](https://flower.ai/docs/framework/tutorial-customize-" -"the-client-pytorch.html)" +"现在可以使用官方 Docker 映像运行 Flower 服务器了。新的操作指南介绍了 [如何使用 Docker 运行 " +"Flower](https://flower.ai/docs/framework/how-to-run-flower-using-" +"docker.html)。Flower 客户端 Docker 官方镜像将随后发布。" -#: ../../source/ref-changelog.md:435 +#: ../../source/ref-changelog.md:205 +#, fuzzy msgid "" -"**Introduce optional telemetry** " -"([#1533](https://github.com/adap/flower/pull/1533), " -"[#1544](https://github.com/adap/flower/pull/1544), " -"[#1584](https://github.com/adap/flower/pull/1584))" +"**Introduce** `flower-via-docker-compose` **example** " +"([#2626](https://github.com/adap/flower/pull/2626))" msgstr "" -"**引入可选遥测**（[#1533](https://github.com/adap/flower/pull/1533), " -"[#1544](https://github.com/adap/flower/pull/1544), " -"[#1584](https://github.com/adap/flower/pull/1584)" +"**介绍Flower Android SDK** " +"([#2131](https://github.com/adap/flower/pull/2131))" -#: ../../source/ref-changelog.md:437 +#: ../../source/ref-changelog.md:207 +#, fuzzy msgid "" -"After a [request for " -"feedback](https://github.com/adap/flower/issues/1534) from the community," -" the Flower open-source project introduces optional collection of " -"*anonymous* usage metrics to make well-informed decisions to improve " -"Flower. Doing this enables the Flower team to understand how Flower is " -"used and what challenges users might face." -msgstr "" -"在社区发出[反馈请求](https://github.com/adap/flower/issues/1534)之后，Flower " -"开放源码项目引入了可选的*匿名*使用指标收集，以便在充分知情的情况下做出改进 Flower 的决定。这样做能让 Flower 团队了解 " -"Flower 的使用情况以及用户可能面临的挑战。" +"**Introduce** `quickstart-sklearn-tabular` **example** " +"([#2719](https://github.com/adap/flower/pull/2719))" +msgstr "**引入 start_driver**（[#1697](https://github.com/adap/flower/pull/1697)）" -#: ../../source/ref-changelog.md:439 +#: ../../source/ref-changelog.md:209 +#, fuzzy msgid "" -"**Flower is a friendly framework for collaborative AI and data science.**" -" Staying true to this statement, Flower makes it easy to disable " -"telemetry for users who do not want to share anonymous usage metrics. " -"[Read more.](https://flower.ai/docs/telemetry.html)." -msgstr "" -"**Flower 是一个用于协作式人工智能和数据科学的友好框架。** Flower " -"遵循这一声明，让不想分享匿名使用指标的用户可以轻松禁用遥测技术。[阅读全文](https://flower.ai/docs/telemetry.html)。" +"**Introduce** `custom-metrics` **example** " +"([#1958](https://github.com/adap/flower/pull/1958))" +msgstr "**引入 start_driver**（[#1697](https://github.com/adap/flower/pull/1697)）" -#: ../../source/ref-changelog.md:441 +#: ../../source/ref-changelog.md:211 +#, fuzzy msgid "" -"**Introduce (experimental) Driver API** " -"([#1520](https://github.com/adap/flower/pull/1520), " -"[#1525](https://github.com/adap/flower/pull/1525), " -"[#1545](https://github.com/adap/flower/pull/1545), " -"[#1546](https://github.com/adap/flower/pull/1546), " -"[#1550](https://github.com/adap/flower/pull/1550), " -"[#1551](https://github.com/adap/flower/pull/1551), " -"[#1567](https://github.com/adap/flower/pull/1567))" +"**Update code examples to use Flower Datasets** " +"([#2450](https://github.com/adap/flower/pull/2450), " +"[#2456](https://github.com/adap/flower/pull/2456), " +"[#2318](https://github.com/adap/flower/pull/2318), " +"[#2712](https://github.com/adap/flower/pull/2712))" msgstr "" -"**引入（试验性）Driver API** ([#1520](https://github.com/adap/flower/pull/1520)," -" [#1525](https://github.com/adap/flower/pull/1525), " -"[#1545](https://github.com/adap/flower/pull/1545), " -"[#1546](https://github.com/adap/flower/pull/1546), " -"[#1550](https://github.com/adap/flower/pull/1550), " -"[#1551](https://github.com/adap/flower/pull/1551), " -"[#1567](https://github.com/adap/flower/pull/1567))" +"更新开发人员工具（[#1231](https://github.com/adap/flower/pull/1231), " +"[#1276](https://github.com/adap/flower/pull/1276), " +"[#1301](https://github.com/adap/flower/pull/1301), " +"[#1310](https://github.com/adap/flower/pull/1310)" -#: ../../source/ref-changelog.md:443 +#: ../../source/ref-changelog.md:213 +#, fuzzy msgid "" -"Flower now has a new (experimental) Driver API which will enable fully " -"programmable, async, and multi-tenant Federated Learning and Federated " -"Analytics applications. Phew, that's a lot! Going forward, the Driver API" -" will be the abstraction that many upcoming features will be built on - " -"and you can start building those things now, too." -msgstr "" -"Flower 现在有了一个新的（试验性的）驱动程序应用程序接口（Driver " -"API），它将支持完全可编程、异步和多租户的联邦学习（Federated Learning）和联邦分析（Federated " -"Analytics）应用程序。展望未来，Driver API 将成为许多即将推出的功能的抽象基础，您现在就可以开始构建这些功能。" +"Several code examples were updated to use [Flower " +"Datasets](https://flower.ai/docs/datasets/)." +msgstr "更新了多个代码示例，以使用 [Flower Datasets](https://flower.ai/docs/datasets/) 。" -#: ../../source/ref-changelog.md:445 +#: ../../source/ref-changelog.md:215 +#, fuzzy msgid "" -"The Driver API also enables a new execution mode in which the server runs" -" indefinitely. Multiple individual workloads can run concurrently and " -"start and stop their execution independent of the server. This is " -"especially useful for users who want to deploy Flower in production." +"**General updates to Flower Examples** " +"([#2381](https://github.com/adap/flower/pull/2381), " +"[#2805](https://github.com/adap/flower/pull/2805), " +"[#2782](https://github.com/adap/flower/pull/2782), " +"[#2806](https://github.com/adap/flower/pull/2806), " +"[#2829](https://github.com/adap/flower/pull/2829), " +"[#2825](https://github.com/adap/flower/pull/2825), " +"[#2816](https://github.com/adap/flower/pull/2816), " +"[#2726](https://github.com/adap/flower/pull/2726), " +"[#2659](https://github.com/adap/flower/pull/2659), " +"[#2655](https://github.com/adap/flower/pull/2655))" msgstr "" -"驱动程序应用程序接口还支持一种新的执行模式，在这种模式下，服务器可无限期运行。多个单独的工作负载可以同时运行，并独立于服务器启动和停止执行。这对于希望在生产中部署" -" Flower 的用户来说尤其有用。" +"**改进（试验性）驱动程序应用程序接口** ([#1663](https://github.com/adap/flower/pull/1663)," +" [#1666](https://github.com/adap/flower/pull/1666), " +"[#1667](https://github.com/adap/flower/pull/1667), " +"[#1664](https://github.com/adap/flower/pull/1664), " +"[#1675](https://github.com/adap/flower/pull/1675), " +"[#1676](https://github.com/adap/flower/pull/1676), " +"[#1693](https://github.com/adap/flower/pull/1693), " +"[#1662](https://github.com/adap/flower/pull/1662), " +"[#1794](https://github.com/adap/flower/pull/1794))" -#: ../../source/ref-changelog.md:447 -msgid "" -"To learn more, check out the `mt-pytorch` code example. We look forward " -"to you feedback!" -msgstr "要了解更多信息，请查看 `mt-pytorch` 代码示例。我们期待您的反馈！" +#: ../../source/ref-changelog.md:217 +#, fuzzy +msgid "Many Flower code examples received substantial updates." +msgstr "许多 \"Flower \"代码示例得到了大幅更新。" -#: ../../source/ref-changelog.md:449 -msgid "" -"Please note: *The Driver API is still experimental and will likely change" -" significantly over time.*" -msgstr "请注意：Driver API仍处于试验阶段，随着时间的推移可能会发生重大变化。*" +#: ../../source/ref-changelog.md:219 ../../source/ref-changelog.md:312 +msgid "**Update Flower Baselines**" +msgstr "**更新 Flower Baselines**" -#: ../../source/ref-changelog.md:451 +#: ../../source/ref-changelog.md:221 +#, fuzzy msgid "" -"**Add new Federated Analytics with Pandas example** " -"([#1469](https://github.com/adap/flower/pull/1469), " -"[#1535](https://github.com/adap/flower/pull/1535))" +"HFedXGBoost ([#2226](https://github.com/adap/flower/pull/2226), " +"[#2771](https://github.com/adap/flower/pull/2771))" msgstr "" -"** 添加新的使用 Pandas " -"的联邦分析示例**（[#1469](https://github.com/adap/flower/pull/1469), " -"[#1535](https://github.com/adap/flower/pull/1535)" - -#: ../../source/ref-changelog.md:453 -msgid "" -"A new code example (`quickstart-pandas`) demonstrates federated analytics" -" with Pandas and Flower. You can find it here: [quickstart-" -"pandas](https://github.com/adap/flower/tree/main/examples/quickstart-" -"pandas)." -msgstr "" -"新代码示例（`quickstart-pandas`）演示了使用 Pandas 和 Flower 进行联邦分析。您可以在此处找到它： " -"[quickstart-pandas](https://github.com/adap/flower/tree/main/examples" -"/quickstart-pandas)。" - -#: ../../source/ref-changelog.md:455 -msgid "" -"**Add new strategies: Krum and MultiKrum** " -"([#1481](https://github.com/adap/flower/pull/1481))" -msgstr "" -"**添加新策略： Krum 和 MultiKrum** " -"([#1481](https://github.com/adap/flower/pull/1481))" +"FedBN ([#2608](https://github.com/adap/flower/pull/2608), " +"[#2615](https://github.com/adap/flower/pull/2615))" -#: ../../source/ref-changelog.md:457 -msgid "" -"Edoardo, a computer science student at the Sapienza University of Rome, " -"contributed a new `Krum` strategy that enables users to easily use Krum " -"and MultiKrum in their workloads." -msgstr "" -"罗马萨皮恩扎大学（Sapienza University）计算机科学专业的学生埃多尔多（Edoardo）提出了一种新的 \"Krum " -"\"策略，使用户能够在其工作负载中轻松使用 Krum 和 MultiKrum。" +#: ../../source/ref-changelog.md:222 +#, fuzzy +msgid "FedVSSL ([#2412](https://github.com/adap/flower/pull/2412))" +msgstr "FjORD [#2431](https://github.com/adap/flower/pull/2431)" -#: ../../source/ref-changelog.md:459 -msgid "" -"**Update C++ example to be compatible with Flower v1.2.0** " -"([#1495](https://github.com/adap/flower/pull/1495))" -msgstr "" -"** 更新 C++ 示例，与 Flower v1.2.0 兼容** " -"([#1495](https://github.com/adap/flower/pull/1495))" +#: ../../source/ref-changelog.md:223 +#, fuzzy +msgid "FedNova ([#2179](https://github.com/adap/flower/pull/2179))" +msgstr "FjORD [#2431](https://github.com/adap/flower/pull/2431)" -#: ../../source/ref-changelog.md:461 -msgid "" -"The C++ code example has received a substantial update to make it " -"compatible with the latest version of Flower." -msgstr "为了与最新版本的 Flower 兼容，C++ 示例代码进行了大幅更新。" +#: ../../source/ref-changelog.md:224 +#, fuzzy +msgid "HeteroFL ([#2439](https://github.com/adap/flower/pull/2439))" +msgstr "FedMeta [#2438](https://github.com/adap/flower/pull/2438)" -#: ../../source/ref-changelog.md:463 -msgid "" -"**General improvements** " -"([#1491](https://github.com/adap/flower/pull/1491), " -"[#1504](https://github.com/adap/flower/pull/1504), " -"[#1506](https://github.com/adap/flower/pull/1506), " -"[#1514](https://github.com/adap/flower/pull/1514), " -"[#1522](https://github.com/adap/flower/pull/1522), " -"[#1523](https://github.com/adap/flower/pull/1523), " -"[#1526](https://github.com/adap/flower/pull/1526), " -"[#1528](https://github.com/adap/flower/pull/1528), " -"[#1547](https://github.com/adap/flower/pull/1547), " -"[#1549](https://github.com/adap/flower/pull/1549), " -"[#1560](https://github.com/adap/flower/pull/1560), " -"[#1564](https://github.com/adap/flower/pull/1564), " -"[#1566](https://github.com/adap/flower/pull/1566))" -msgstr "" -"**普通改进** ([#1491](https://github.com/adap/flower/pull/1491), " -"[#1504](https://github.com/adap/flower/pull/1504), " -"[#1506](https://github.com/adap/flower/pull/1506), " -"[#1514](https://github.com/adap/flower/pull/1514), " -"[#1522](https://github.com/adap/flower/pull/1522), " -"[#1523](https://github.com/adap/flower/pull/1523), " -"[#1526](https://github. com/adap/flower/pull/1526), " -"[#1528](https://github.com/adap/flower/pull/1528), " -"[#1547](https://github.com/adap/flower/pull/1547), " -"[#1549](https://github.com/adap/flower/pull/1549), " -"[#1560](https://github.com/adap/flower/pull/1560), " -"[#1564](https://github.com/adap/flower/pull/1564), " -"[#1566](https://github.com/adap/flower/pull/1566))" +#: ../../source/ref-changelog.md:225 +#, fuzzy +msgid "FedAvgM ([#2246](https://github.com/adap/flower/pull/2246))" +msgstr "FedPer [#2266](https://github.com/adap/flower/pull/2266)" -#: ../../source/ref-changelog.md:467 -msgid "" -"**Updated documentation** " -"([#1494](https://github.com/adap/flower/pull/1494), " -"[#1496](https://github.com/adap/flower/pull/1496), " -"[#1500](https://github.com/adap/flower/pull/1500), " -"[#1503](https://github.com/adap/flower/pull/1503), " -"[#1505](https://github.com/adap/flower/pull/1505), " -"[#1524](https://github.com/adap/flower/pull/1524), " -"[#1518](https://github.com/adap/flower/pull/1518), " -"[#1519](https://github.com/adap/flower/pull/1519), " -"[#1515](https://github.com/adap/flower/pull/1515))" -msgstr "" -"** 更新文档** ([#1494](https://github.com/adap/flower/pull/1494), " -"[#1496](https://github.com/adap/flower/pull/1496), " -"[#1500](https://github.com/adap/flower/pull/1500), " -"[#1503](https://github.com/adap/flower/pull/1503), " -"[#1505](https://github.com/adap/flower/pull/1505), " -"[#1524](https://github.com/adap/flower/pull/1524), " -"[#1518](https://github.com/adap/flower/pull/1518), " -"[#1519](https://github.com/adap/flower/pull/1519), " -"[#1515](https://github.com/adap/flower/pull/1515))" +#: ../../source/ref-changelog.md:226 +#, fuzzy +msgid "FedPara ([#2722](https://github.com/adap/flower/pull/2722))" +msgstr "FedPer [#2266](https://github.com/adap/flower/pull/2266)" -#: ../../source/ref-changelog.md:471 +#: ../../source/ref-changelog.md:228 +#, fuzzy msgid "" -"One highlight is the new [first time contributor " -"guide](https://flower.ai/docs/first-time-contributors.html): if you've " -"never contributed on GitHub before, this is the perfect place to start!" +"**Improve documentation** " +"([#2674](https://github.com/adap/flower/pull/2674), " +"[#2480](https://github.com/adap/flower/pull/2480), " +"[#2826](https://github.com/adap/flower/pull/2826), " +"[#2727](https://github.com/adap/flower/pull/2727), " +"[#2761](https://github.com/adap/flower/pull/2761), " +"[#2900](https://github.com/adap/flower/pull/2900))" msgstr "" -"其中一个亮点是新的[首次贡献者指南](https://flower.ai/docs/first-time-" -"contributors.html)：如果你以前从未在 GitHub 上做过贡献，这将是一个完美的开始！" - -#: ../../source/ref-changelog.md:477 -msgid "v1.1.0 (2022-10-31)" -msgstr "v1.1.0 (2022-10-31)" - -#: ../../source/ref-changelog.md:481 -msgid "" -"We would like to give our **special thanks** to all the contributors who " -"made the new version of Flower possible (in `git shortlog` order):" -msgstr "在此，我们向所有促成 Flower 新版本的贡献者致以**特别的谢意（按 \"git shortlog \"顺序排列）：" +"** 更新文档** ([#1629](https://github.com/adap/flower/pull/1629), " +"[#1628](https://github.com/adap/flower/pull/1628), " +"[#1620](https://github.com/adap/flower/pull/1620), " +"[#1618](https://github.com/adap/flower/pull/1618), " +"[#1617](https://github.com/adap/flower/pull/1617), " +"[#1613](https://github.com/adap/flower/pull/1613), " +"[#1614](https://github.com/adap/flower/pull/1614)))" -#: ../../source/ref-changelog.md:483 +#: ../../source/ref-changelog.md:230 +#, fuzzy msgid "" -"`Akis Linardos`, `Christopher S`, `Daniel J. Beutel`, `George`, `Jan " -"Schlicht`, `Mohammad Fares`, `Pedro Porto Buarque de Gusmão`, `Philipp " -"Wiesner`, `Rob Luke`, `Taner Topal`, `VasundharaAgarwal`, " -"`danielnugraha`, `edogab33`" +"**Improved testing and development infrastructure** " +"([#2797](https://github.com/adap/flower/pull/2797), " +"[#2676](https://github.com/adap/flower/pull/2676), " +"[#2644](https://github.com/adap/flower/pull/2644), " +"[#2656](https://github.com/adap/flower/pull/2656), " +"[#2848](https://github.com/adap/flower/pull/2848), " +"[#2675](https://github.com/adap/flower/pull/2675), " +"[#2735](https://github.com/adap/flower/pull/2735), " +"[#2767](https://github.com/adap/flower/pull/2767), " +"[#2732](https://github.com/adap/flower/pull/2732), " +"[#2744](https://github.com/adap/flower/pull/2744), " +"[#2681](https://github.com/adap/flower/pull/2681), " +"[#2699](https://github.com/adap/flower/pull/2699), " +"[#2745](https://github.com/adap/flower/pull/2745), " +"[#2734](https://github.com/adap/flower/pull/2734), " +"[#2731](https://github.com/adap/flower/pull/2731), " +"[#2652](https://github.com/adap/flower/pull/2652), " +"[#2720](https://github.com/adap/flower/pull/2720), " +"[#2721](https://github.com/adap/flower/pull/2721), " +"[#2717](https://github.com/adap/flower/pull/2717), " +"[#2864](https://github.com/adap/flower/pull/2864), " +"[#2694](https://github.com/adap/flower/pull/2694), " +"[#2709](https://github.com/adap/flower/pull/2709), " +"[#2658](https://github.com/adap/flower/pull/2658), " +"[#2796](https://github.com/adap/flower/pull/2796), " +"[#2692](https://github.com/adap/flower/pull/2692), " +"[#2657](https://github.com/adap/flower/pull/2657), " +"[#2813](https://github.com/adap/flower/pull/2813), " +"[#2661](https://github.com/adap/flower/pull/2661), " +"[#2398](https://github.com/adap/flower/pull/2398))" msgstr "" -"`Akis Linardos`, `Christopher S`, `Daniel J. Beutel`, `George`, `Jan " -"Schlicht`, `Mohammad Fares`, `Pedro Porto Buarque de Gusmão`, `Philipp " -"Wiesner`, `Rob Luke`, `Taner Topal`, `VasundharaAgarwal`, " -"`danielnugraha`, `edogab33`" +"**改进测试和开发基础设施** ([#2797](https://github.com/adap/flower/pull/2797), " +"[#2676](https://github.com/adap/flower/pull/2676), " +"[#2644](https://github.com/adap/flower/pull/2644), " +"[#2656](https://github.com/adap/flower/pull/2656), " +"[#2848](https://github.com/adap/flower/pull/2848), " +"[#2675](https://github.com/adap/flower/pull/2675), " +"[#2735](https://github.com/adap/flower/pull/2735), " +"[#2767](https://github.com/adap/flower/pull/2767), " +"[#2732](https://github.com/adap/flower/pull/2732), " +"[#2744](https://github.com/adap/flower/pull/2744), " +"[#2681](https://github.com/adap/flower/pull/2681), " +"[#2699](https://github.com/adap/flower/pull/2699), " +"[#2745](https://github.com/adap/flower/pull/2745), " +"[#2734](https://github.com/adap/flower/pull/2734), " +"[#2731](https://github.com/adap/flower/pull/2731), " +"[#2652](https://github.com/adap/flower/pull/2652), " +"[#2720](https://github.com/adap/flower/pull/2720), " +"[#2721](https://github.com/adap/flower/pull/2721), " +"[#2717](https://github.com/adap/flower/pull/2717), " +"[#2864](https://github.com/adap/flower/pull/2864), " +"[#2694](https://github.com/adap/flower/pull/2694), " +"[#2709](https://github.com/adap/flower/pull/2709), " +"[#2658](https://github.com/adap/flower/pull/2658), " +"[#2796](https://github.com/adap/flower/pull/2796), " +"[#2692](https://github.com/adap/flower/pull/2692), " +"[#2657](https://github.com/adap/flower/pull/2657), " +"[#2813](https://github.com/adap/flower/pull/2813), " +"[#2661](https://github.com/adap/flower/pull/2661), " +"[#2398](https://github.com/adap/flower/pull/2398))" -#: ../../source/ref-changelog.md:487 +#: ../../source/ref-changelog.md:232 +#, fuzzy msgid "" -"**Introduce Differential Privacy wrappers (preview)** " -"([#1357](https://github.com/adap/flower/pull/1357), " -"[#1460](https://github.com/adap/flower/pull/1460))" -msgstr "" -"**引入差分隐私包装器（预览）** ([#1357](https://github.com/adap/flower/pull/1357), " -"[#1460](https://github.com/adap/flower/pull/1460))" +"The Flower testing and development infrastructure has received " +"substantial updates. This makes Flower 1.7 the most tested release ever." +msgstr "Flower 测试和开发基础架构已得到大幅更新。这使得 Flower 1.7 成为有史以来经过最多测试的版本。" -#: ../../source/ref-changelog.md:489 +#: ../../source/ref-changelog.md:234 +#, fuzzy msgid "" -"The first (experimental) preview of pluggable Differential Privacy " -"wrappers enables easy configuration and usage of differential privacy " -"(DP). The pluggable DP wrappers enable framework-agnostic **and** " -"strategy-agnostic usage of both client-side DP and server-side DP. Head " -"over to the Flower docs, a new explainer goes into more detail." +"**Update dependencies** " +"([#2753](https://github.com/adap/flower/pull/2753), " +"[#2651](https://github.com/adap/flower/pull/2651), " +"[#2739](https://github.com/adap/flower/pull/2739), " +"[#2837](https://github.com/adap/flower/pull/2837), " +"[#2788](https://github.com/adap/flower/pull/2788), " +"[#2811](https://github.com/adap/flower/pull/2811), " +"[#2774](https://github.com/adap/flower/pull/2774), " +"[#2790](https://github.com/adap/flower/pull/2790), " +"[#2751](https://github.com/adap/flower/pull/2751), " +"[#2850](https://github.com/adap/flower/pull/2850), " +"[#2812](https://github.com/adap/flower/pull/2812), " +"[#2872](https://github.com/adap/flower/pull/2872), " +"[#2736](https://github.com/adap/flower/pull/2736), " +"[#2756](https://github.com/adap/flower/pull/2756), " +"[#2857](https://github.com/adap/flower/pull/2857), " +"[#2757](https://github.com/adap/flower/pull/2757), " +"[#2810](https://github.com/adap/flower/pull/2810), " +"[#2740](https://github.com/adap/flower/pull/2740), " +"[#2789](https://github.com/adap/flower/pull/2789))" msgstr "" -"可插拔差分隐私封装器的首个（实验性）预览版可轻松配置和使用差分隐私（DP）。可插拔的差分隐私封装器可实现客户端差分隐私和服务器端差分隐私的框架无关**以及**策略无关的使用。请访问" -" Flower 文档，新的解释器会提供更多细节。" +"**更新Example** ([#1772](https://github.com/adap/flower/pull/1772), " +"[#1873](https://github.com/adap/flower/pull/1873), " +"[#1981](https://github.com/adap/flower/pull/1981), " +"[#1988](https://github.com/adap/flower/pull/1988), " +"[#1984](https://github.com/adap/flower/pull/1984), " +"[#1982](https://github.com/adap/flower/pull/1982), " +"[#2112](https://github.com/adap/flower/pull/2112), " +"[#2144](https://github.com/adap/flower/pull/2144), " +"[#2174](https://github.com/adap/flower/pull/2174), " +"[#2225](https://github.com/adap/flower/pull/2225), " +"[#2183](https://github.com/adap/flower/pull/2183))" -#: ../../source/ref-changelog.md:491 +#: ../../source/ref-changelog.md:236 +#, fuzzy msgid "" -"**New iOS CoreML code example** " -"([#1289](https://github.com/adap/flower/pull/1289))" -msgstr "**新的 iOS CoreML 代码示例**（[#1289](https://github.com/adap/flower/pull/1289)）" +"**General improvements** " +"([#2803](https://github.com/adap/flower/pull/2803), " +"[#2847](https://github.com/adap/flower/pull/2847), " +"[#2877](https://github.com/adap/flower/pull/2877), " +"[#2690](https://github.com/adap/flower/pull/2690), " +"[#2889](https://github.com/adap/flower/pull/2889), " +"[#2874](https://github.com/adap/flower/pull/2874), " +"[#2819](https://github.com/adap/flower/pull/2819), " +"[#2689](https://github.com/adap/flower/pull/2689), " +"[#2457](https://github.com/adap/flower/pull/2457), " +"[#2870](https://github.com/adap/flower/pull/2870), " +"[#2669](https://github.com/adap/flower/pull/2669), " +"[#2876](https://github.com/adap/flower/pull/2876), " +"[#2885](https://github.com/adap/flower/pull/2885), " +"[#2858](https://github.com/adap/flower/pull/2858), " +"[#2867](https://github.com/adap/flower/pull/2867), " +"[#2351](https://github.com/adap/flower/pull/2351), " +"[#2886](https://github.com/adap/flower/pull/2886), " +"[#2860](https://github.com/adap/flower/pull/2860), " +"[#2828](https://github.com/adap/flower/pull/2828), " +"[#2869](https://github.com/adap/flower/pull/2869), " +"[#2875](https://github.com/adap/flower/pull/2875), " +"[#2733](https://github.com/adap/flower/pull/2733), " +"[#2488](https://github.com/adap/flower/pull/2488), " +"[#2646](https://github.com/adap/flower/pull/2646), " +"[#2879](https://github.com/adap/flower/pull/2879), " +"[#2821](https://github.com/adap/flower/pull/2821), " +"[#2855](https://github.com/adap/flower/pull/2855), " +"[#2800](https://github.com/adap/flower/pull/2800), " +"[#2807](https://github.com/adap/flower/pull/2807), " +"[#2801](https://github.com/adap/flower/pull/2801), " +"[#2804](https://github.com/adap/flower/pull/2804), " +"[#2851](https://github.com/adap/flower/pull/2851), " +"[#2787](https://github.com/adap/flower/pull/2787), " +"[#2852](https://github.com/adap/flower/pull/2852), " +"[#2672](https://github.com/adap/flower/pull/2672), " +"[#2759](https://github.com/adap/flower/pull/2759))" +msgstr "" +"**一般改进** ([#2803](https://github.com/adap/flower/pull/2803), " +"[#2847](https://github.com/adap/flower/pull/2847), " +"[#2877](https://github.com/adap/flower/pull/2877), " +"[#2690](https://github.com/adap/flower/pull/2690), " +"[#2889](https://github.com/adap/flower/pull/2889), " +"[#2874](https://github.com/adap/flower/pull/2874), " +"[#2819](https://github.com/adap/flower/pull/2819), " +"[#2689](https://github.com/adap/flower/pull/2689), " +"[#2457](https://github.com/adap/flower/pull/2457), " +"[#2870](https://github.com/adap/flower/pull/2870), " +"[#2669](https://github.com/adap/flower/pull/2669), " +"[#2876](https://github.com/adap/flower/pull/2876), " +"[#2885](https://github.com/adap/flower/pull/2885), " +"[#2858](https://github.com/adap/flower/pull/2858), " +"[#2867](https://github.com/adap/flower/pull/2867), " +"[#2351](https://github.com/adap/flower/pull/2351), " +"[#2886](https://github.com/adap/flower/pull/2886), " +"[#2860](https://github.com/adap/flower/pull/2860), " +"[#2828](https://github.com/adap/flower/pull/2828), " +"[#2869](https://github.com/adap/flower/pull/2869), " +"[#2875](https://github.com/adap/flower/pull/2875), " +"[#2733](https://github.com/adap/flower/pull/2733), " +"[#2488](https://github.com/adap/flower/pull/2488), " +"[#2646](https://github.com/adap/flower/pull/2646), " +"[#2879](https://github.com/adap/flower/pull/2879), " +"[#2821](https://github.com/adap/flower/pull/2821), " +"[#2855](https://github.com/adap/flower/pull/2855), " +"[#2800](https://github.com/adap/flower/pull/2800), " +"[#2807](https://github.com/adap/flower/pull/2807), " +"[#2801](https://github.com/adap/flower/pull/2801), " +"[#2804](https://github.com/adap/flower/pull/2804), " +"[#2851](https://github.com/adap/flower/pull/2851), " +"[#2787](https://github.com/adap/flower/pull/2787), " +"[#2852](https://github.com/adap/flower/pull/2852), " +"[#2672](https://github.com/adap/flower/pull/2672), " +"[#2759](https://github.com/adap/flower/pull/2759))" -#: ../../source/ref-changelog.md:493 +#: ../../source/ref-changelog.md:240 +#, fuzzy msgid "" -"Flower goes iOS! A massive new code example shows how Flower clients can " -"be built for iOS. The code example contains both Flower iOS SDK " -"components that can be used for many tasks, and one task example running " -"on CoreML." +"**Deprecate** `start_numpy_client` " +"([#2563](https://github.com/adap/flower/pull/2563), " +"[#2718](https://github.com/adap/flower/pull/2718))" msgstr "" -"Flower 进入 iOS！大量新代码示例展示了如何为 iOS 构建 Flower 客户端。该代码示例包含可用于多种任务的 Flower iOS " -"SDK 组件，以及在 CoreML 上运行的一个任务示例。" +"TAMUNA ([#2254](https://github.com/adap/flower/pull/2254), " +"[#2508](https://github.com/adap/flower/pull/2508))" -#: ../../source/ref-changelog.md:495 +#: ../../source/ref-changelog.md:242 +#, fuzzy msgid "" -"**New FedMedian strategy** " -"([#1461](https://github.com/adap/flower/pull/1461))" -msgstr "**新的联邦医疗策略** ([#1461](https://github.com/adap/flower/pull/1461))" +"Until now, clients of type `NumPyClient` needed to be started via " +"`start_numpy_client`. In our efforts to consolidate framework APIs, we " +"have introduced changes, and now all client types should start via " +"`start_client`. To continue using `NumPyClient` clients, you simply need " +"to first call the `.to_client()` method and then pass returned `Client` " +"object to `start_client`. The examples and the documentation have been " +"updated accordingly." +msgstr "" +"到目前为止，\"NumPyClient \"类型的客户端需要通过 \"start_numpy_client \"启动。为了整合框架 " +"API，我们引入了一些变化，现在所有客户端类型都应通过 `start_client` 启动。要继续使用 `NumPyClient` " +"客户端，只需首先调用 `.to_client()` 方法，然后将返回的 `Client` 对象传递给 " +"`start_client`。示例和文档已相应更新。" -#: ../../source/ref-changelog.md:497 +#: ../../source/ref-changelog.md:244 +#, fuzzy msgid "" -"The new `FedMedian` strategy implements Federated Median (FedMedian) by " -"[Yin et al., 2018](https://arxiv.org/pdf/1803.01498v1.pdf)." -msgstr "" -"新的 \"FedMedian \"战略实现了[Yin " -"等人，2018]的联邦中值（FedMedian）(https://arxiv.org/pdf/1803.01498v1.pdf)。" +"**Deprecate legacy DP wrappers** " +"([#2749](https://github.com/adap/flower/pull/2749))" +msgstr "**移除过时的 KerasClient**（[#857](https://github.com/adap/flower/pull/857)）" -#: ../../source/ref-changelog.md:499 +#: ../../source/ref-changelog.md:246 +#, fuzzy msgid "" -"**Log** `Client` **exceptions in Virtual Client Engine** " -"([#1493](https://github.com/adap/flower/pull/1493))" -msgstr "**虚拟客户端引擎中的**日志**`客户端`**异常（[#1493](https://github.com/adap/flower/pull/1493)）" +"Legacy DP wrapper classes are deprecated, but still functional. This is " +"in preparation for an all-new pluggable version of differential privacy " +"support in Flower." +msgstr "传统的 DP 封装类已废弃，但仍可正常使用。这是为 Flower 中的全新可插拔差分隐私支持版本做准备。" -#: ../../source/ref-changelog.md:501 +#: ../../source/ref-changelog.md:248 +#, fuzzy msgid "" -"All `Client` exceptions happening in the VCE are now logged by default " -"and not just exposed to the configured `Strategy` (via the `failures` " -"argument)." -msgstr "VCE 中发生的所有 \"客户端 \"异常现在都会被默认记录下来，而不只是暴露给配置的 `Strategy`（通过 `failures`参数）。" +"**Make optional arg** `--callable` **in** `flower-client` **a required " +"positional arg** ([#2673](https://github.com/adap/flower/pull/2673))" +msgstr "" +"**从** `start_client` 中移除** `rest` **实验参数 " +"([#2324](https://github.com/adap/flower/pull/2324))" -#: ../../source/ref-changelog.md:503 +#: ../../source/ref-changelog.md:250 +#, fuzzy msgid "" -"**Improve Virtual Client Engine internals** " -"([#1401](https://github.com/adap/flower/pull/1401), " -"[#1453](https://github.com/adap/flower/pull/1453))" -msgstr "**改进虚拟客户端引擎内部**（[#1401](https://github.com/adap/flower/pull/1401)、[#1453](https://github.com/adap/flower/pull/1453)）" +"**Rename** `certificates` **to** `root_certificates` **in** `Driver` " +"([#2890](https://github.com/adap/flower/pull/2890))" +msgstr "" +"**重新命名** `rnd` ** to** `server_round` " +"([#1321](https://github.com/adap/flower/pull/1321))" -#: ../../source/ref-changelog.md:505 +#: ../../source/ref-changelog.md:252 +#, fuzzy msgid "" -"Some internals of the Virtual Client Engine have been revamped. The VCE " -"now uses Ray 2.0 under the hood, the value type of the `client_resources`" -" dictionary changed to `float` to allow fractions of resources to be " -"allocated." +"**Drop experimental** `Task` **fields** " +"([#2866](https://github.com/adap/flower/pull/2866), " +"[#2865](https://github.com/adap/flower/pull/2865))" msgstr "" -"虚拟客户端引擎的部分内部结构已进行了修改。VCE 现在使用 Ray 2.0，\"client_resources \"字典的值类型改为 " -"\"float\"，以允许分配分数资源。" +"FedBN ([#2608](https://github.com/adap/flower/pull/2608), " +"[#2615](https://github.com/adap/flower/pull/2615))" -#: ../../source/ref-changelog.md:507 +#: ../../source/ref-changelog.md:254 +#, fuzzy msgid "" -"**Support optional** `Client`**/**`NumPyClient` **methods in Virtual " -"Client Engine**" -msgstr "**支持虚拟客户端引擎中的可选** `Client`**/**`NumPyClient` **方法**" +"Experimental fields `sa`, `legacy_server_message` and " +"`legacy_client_message` were removed from `Task` message. The removed " +"fields are superseded by the new `RecordSet` abstraction." +msgstr "" +"从 `Task` 消息中删除了试验性字段 `sa`、 `legacy_server_message` 和 " +"`legacy_client_message`。删除的字段已被新的 `RecordSet` 抽象所取代。" -#: ../../source/ref-changelog.md:509 +#: ../../source/ref-changelog.md:256 +#, fuzzy msgid "" -"The Virtual Client Engine now has full support for optional `Client` (and" -" `NumPyClient`) methods." -msgstr "虚拟客户端引擎现在完全支持可选的 `Client`（和 `NumPyClient`）方法。" +"**Retire MXNet examples** " +"([#2724](https://github.com/adap/flower/pull/2724))" +msgstr "**新的 scikit-learn 代码示例** ([#748](https://github.com/adap/flower/pull/748))" -#: ../../source/ref-changelog.md:511 +#: ../../source/ref-changelog.md:258 +#, fuzzy msgid "" -"**Provide type information to packages using** `flwr` " -"([#1377](https://github.com/adap/flower/pull/1377))" +"The development of the MXNet fremework has ended and the project is now " +"[archived on GitHub](https://github.com/apache/mxnet). Existing MXNet " +"examples won't receive updates." msgstr "" -"**使用** `flwr`向软件包提供类型信息 " -"([#1377](https://github.com/adap/flower/pull/1377))" +"MXNet fremework 的开发工作已经结束，该项目现已[归档于 " +"GitHub](https://github.com/apache/mxnet)。现有的 MXNet 示例不会收到更新。" + +#: ../../source/ref-changelog.md:260 +#, fuzzy +msgid "v1.6.0 (2023-11-28)" +msgstr "v1.4.0 (2023-04-21)" -#: ../../source/ref-changelog.md:513 +#: ../../source/ref-changelog.md:266 +#, fuzzy msgid "" -"The package `flwr` is now bundled with a `py.typed` file indicating that " -"the package is typed. This enables typing support for projects or " -"packages that use `flwr` by enabling them to improve their code using " -"static type checkers like `mypy`." +"`Aashish Kolluri`, `Adam Narozniak`, `Alessio Mora`, `Barathwaja S`, " +"`Charles Beauville`, `Daniel J. Beutel`, `Daniel Nata Nugraha`, `Gabriel " +"Mota`, `Heng Pan`, `Ivan Agarský`, `JS.KIM`, `Javier`, `Marius Schlegel`," +" `Navin Chandra`, `Nic Lane`, `Peterpan828`, `Qinbin Li`, `Shaz-hash`, " +"`Steve Laskaridis`, `Taner Topal`, `William Lindskog`, `Yan Gao`, " +"`cnxdeveloper`, `k3nfalt` " msgstr "" -"软件包 `flwr` 现在捆绑了一个 `py.typed` 文件，表明该软件包是类型化的。这样，使用 `flwr` 的项目或软件包就可以使用 " -"`mypy` 等静态类型检查器改进代码，从而获得类型支持。" +"`Aashish Kolluri`, `Adam Narozniak`, `Alessio Mora`, `Barathwaja S`, " +"`Charles Beauville`, `Daniel J. Beutel`, `Daniel Nata Nugraha`, `Gabriel " +"Mota`, `Heng Pan`, `Ivan Agarský`, `JS.KIM`, `Javier`, `Marius Schlegel`," +" `Navin Chandra`, `Nic Lane`, `Peterpan828`, `Qinbin Li`, `Shaz-hash`, " +"`Steve Laskaridis`, `Taner Topal`, `William Lindskog`, `Yan Gao`, " +"`cnxdeveloper`, `k3nfalt` " -#: ../../source/ref-changelog.md:515 +#: ../../source/ref-changelog.md:270 msgid "" -"**Updated code example** " -"([#1344](https://github.com/adap/flower/pull/1344), " -"[#1347](https://github.com/adap/flower/pull/1347))" +"**Add experimental support for Python 3.12** " +"([#2565](https://github.com/adap/flower/pull/2565))" msgstr "" -"** 更新代码示例** ([#1344](https://github.com/adap/flower/pull/1344), " -"[#1347](https://github.com/adap/flower/pull/1347))" +"** 增加对 Python 3.12 的实验支持** " +"([#2565](https://github.com/adap/flower/pull/2565))" -#: ../../source/ref-changelog.md:517 +#: ../../source/ref-changelog.md:272 +#, fuzzy msgid "" -"The code examples covering scikit-learn and PyTorch Lightning have been " -"updated to work with the latest version of Flower." -msgstr "涵盖 scikit-learn 和 PyTorch Lightning 的代码示例已更新，以便与最新版本的 Flower 配合使用。" +"**Add new XGBoost examples** " +"([#2612](https://github.com/adap/flower/pull/2612), " +"[#2554](https://github.com/adap/flower/pull/2554), " +"[#2617](https://github.com/adap/flower/pull/2617), " +"[#2618](https://github.com/adap/flower/pull/2618), " +"[#2619](https://github.com/adap/flower/pull/2619), " +"[#2567](https://github.com/adap/flower/pull/2567))" +msgstr "" +"**引入（试验性）Driver API** ([#1520](https://github.com/adap/flower/pull/1520)," +" [#1525](https://github.com/adap/flower/pull/1525), " +"[#1545](https://github.com/adap/flower/pull/1545), " +"[#1546](https://github.com/adap/flower/pull/1546), " +"[#1550](https://github.com/adap/flower/pull/1550), " +"[#1551](https://github.com/adap/flower/pull/1551), " +"[#1567](https://github.com/adap/flower/pull/1567))" -#: ../../source/ref-changelog.md:519 +#: ../../source/ref-changelog.md:274 +#, fuzzy msgid "" -"**Updated documentation** " -"([#1355](https://github.com/adap/flower/pull/1355), " -"[#1558](https://github.com/adap/flower/pull/1558), " -"[#1379](https://github.com/adap/flower/pull/1379), " -"[#1380](https://github.com/adap/flower/pull/1380), " -"[#1381](https://github.com/adap/flower/pull/1381), " -"[#1332](https://github.com/adap/flower/pull/1332), " -"[#1391](https://github.com/adap/flower/pull/1391), " -"[#1403](https://github.com/adap/flower/pull/1403), " -"[#1364](https://github.com/adap/flower/pull/1364), " -"[#1409](https://github.com/adap/flower/pull/1409), " -"[#1419](https://github.com/adap/flower/pull/1419), " -"[#1444](https://github.com/adap/flower/pull/1444), " -"[#1448](https://github.com/adap/flower/pull/1448), " -"[#1417](https://github.com/adap/flower/pull/1417), " -"[#1449](https://github.com/adap/flower/pull/1449), " -"[#1465](https://github.com/adap/flower/pull/1465), " -"[#1467](https://github.com/adap/flower/pull/1467))" -msgstr "" -"**更新文档** ([#1355](https://github.com/adap/flower/pull/1355), " -"[#1558](https://github.com/adap/flower/pull/1558), " -"[#1379](https://github.com/adap/flower/pull/1379), " -"[#1380](https://github.com/adap/flower/pull/1380), " -"[#1381](https://github.com/adap/flower/pull/1381), " -"[#1332](https://github.com/adap/flower/pull/1332), " -"[#1391](https://github.com/adap/flower/pull/1391), " -"[#1403](https://github.com/adap/flower/pull/1403), " -"[#1364](https://github. com/adap/flower/pull/1364), " -"[#1409](https://github.com/adap/flower/pull/1409), " -"[#1419](https://github.com/adap/flower/pull/1419), " -"[#1444](https://github.com/adap/flower/pull/1444), " -"[#1448](https://github.com/adap/flower/pull/1448), " -"[#1417](https://github.com/adap/flower/pull/1417), " -"[#1449](https://github.com/adap/flower/pull/1449), " -"[#1465](https://github.com/adap/flower/pull/1465), " -"[#1467](https://github.com/adap/flower/pull/1467))" - -#: ../../source/ref-changelog.md:521 -msgid "" -"There have been so many documentation updates that it doesn't even make " -"sense to list them individually." -msgstr "文档更新的数量之多，甚至没有必要逐一列出。" +"We have added a new `xgboost-quickstart` example alongside a new " +"`xgboost-comprehensive` example that goes more in-depth." +msgstr "" +"我们添加了一个新的 \"xgboost-quickstart \"示例和一个新的 \"xgboost-comprehensive " +"\"示例，后者更加深入。" -#: ../../source/ref-changelog.md:523 +#: ../../source/ref-changelog.md:276 +#, fuzzy msgid "" -"**Restructured documentation** " -"([#1387](https://github.com/adap/flower/pull/1387))" -msgstr "**重构文档**（[#1387](https://github.com/adap/flower/pull/1387)）" +"**Add Vertical FL example** " +"([#2598](https://github.com/adap/flower/pull/2598))" +msgstr "**新的 iOS CoreML 代码示例**（[#1289](https://github.com/adap/flower/pull/1289)）" -#: ../../source/ref-changelog.md:525 +#: ../../source/ref-changelog.md:278 +#, fuzzy msgid "" -"The documentation has been restructured to make it easier to navigate. " -"This is just the first step in a larger effort to make the Flower " -"documentation the best documentation of any project ever. Stay tuned!" -msgstr "我们对文档进行了重组，使其更易于浏览。这只是让 Flower 文档成为所有项目中最好文档的第一步。敬请期待！" +"We had many questions about Vertical Federated Learning using Flower, so " +"we decided to add an simple example for it on the [Titanic " +"dataset](https://www.kaggle.com/competitions/titanic/data) alongside a " +"tutorial (in the README)." +msgstr "" +"我们收到了许多关于使用 Flower 进行垂直联合学习的问题，因此我们决定在 [Titanic " +"数据集](https://www.kaggle.com/competitions/titanic/data) 上添加一个简单的示例，并附上教程（在" +" README 中）。" -#: ../../source/ref-changelog.md:527 +#: ../../source/ref-changelog.md:280 msgid "" -"**Open in Colab button** " -"([#1389](https://github.com/adap/flower/pull/1389))" -msgstr "**在 Colab 中打开按钮** ([#1389](https://github.com/adap/flower/pull/1389))" +"**Support custom** `ClientManager` **in** `start_driver()` " +"([#2292](https://github.com/adap/flower/pull/2292))" +msgstr "**在***`start_driver()`中支持自定义***`ClientManager([#2292](https://github.com/adap/flower/pull/2292))" -#: ../../source/ref-changelog.md:529 +#: ../../source/ref-changelog.md:282 msgid "" -"The four parts of the Flower Federated Learning Tutorial now come with a " -"new `Open in Colab` button. No need to install anything on your local " -"machine, you can now use and learn about Flower in your browser, it's " -"only a single click away." +"**Update REST API to support create and delete nodes** " +"([#2283](https://github.com/adap/flower/pull/2283))" msgstr "" -"Flower 联邦学习教程的四个部分现在都带有一个新的 \"在 Colab 中打开 " -"\"按钮。现在，您无需在本地计算机上安装任何软件，只需点击一下，就可以在浏览器中使用和学习 Flower。" +"**更新 REST API 以支持创建和删除节点** " +"([#2283](https://github.com/adap/flower/pull/2283))" -#: ../../source/ref-changelog.md:531 +#: ../../source/ref-changelog.md:284 +#, fuzzy msgid "" -"**Improved tutorial** ([#1468](https://github.com/adap/flower/pull/1468)," -" [#1470](https://github.com/adap/flower/pull/1470), " -"[#1472](https://github.com/adap/flower/pull/1472), " -"[#1473](https://github.com/adap/flower/pull/1473), " -"[#1474](https://github.com/adap/flower/pull/1474), " -"[#1475](https://github.com/adap/flower/pull/1475))" +"**Update the Android SDK** " +"([#2187](https://github.com/adap/flower/pull/2187))" msgstr "" -"**改进教程** ([#1468](https://github.com/adap/flower/pull/1468), " -"[#1470](https://github.com/adap/flower/pull/1470), " -"[#1472](https://github.com/adap/flower/pull/1472), " -"[#1473](https://github.com/adap/flower/pull/1473), " -"[#1474](https://github.com/adap/flower/pull/1474), " -"[#1475](https://github.com/adap/flower/pull/1475)))" +"**介绍Flower Android SDK** " +"([#2131](https://github.com/adap/flower/pull/2131))" + +#: ../../source/ref-changelog.md:286 +#, fuzzy +msgid "Add gRPC request-response capability to the Android SDK." +msgstr "为 C++ SDK 添加 gRPC 请求-响应功能。" -#: ../../source/ref-changelog.md:533 +#: ../../source/ref-changelog.md:288 +#, fuzzy msgid "" -"The Flower Federated Learning Tutorial has two brand-new parts covering " -"custom strategies (still WIP) and the distinction between `Client` and " -"`NumPyClient`. The existing parts one and two have also been improved " -"(many small changes and fixes)." +"**Update the C++ SDK** " +"([#2537](https://github.com/adap/flower/pull/2537), " +"[#2528](https://github.com/adap/flower/pull/2528), " +"[#2523](https://github.com/adap/flower/pull/2523), " +"[#2522](https://github.com/adap/flower/pull/2522))" msgstr "" -"Flower 联邦学习教程有两个全新的部分，涉及自定义策略（仍处于 WIP 阶段）和 `Client` 与 `NumPyClient` " -"之间的区别。现有的第一和第二部分也得到了改进（许多小改动和修正）。" - -#: ../../source/ref-changelog.md:539 -msgid "v1.0.0 (2022-07-28)" -msgstr "v1.0.0 (2022-07-28)" - -#: ../../source/ref-changelog.md:541 -msgid "Highlights" -msgstr "亮点" - -#: ../../source/ref-changelog.md:543 -msgid "Stable **Virtual Client Engine** (accessible via `start_simulation`)" -msgstr "稳定的**虚拟客户端引擎**（可通过`start_simulation`访问）" - -#: ../../source/ref-changelog.md:544 -msgid "All `Client`/`NumPyClient` methods are now optional" -msgstr "所有 `Client`/`NumPyClient` 方法现在都是可选的了" - -#: ../../source/ref-changelog.md:545 -msgid "Configurable `get_parameters`" -msgstr "可配置的`get_parameters`" +"** 更新 C++ SDK** ([#2537](https://github/com/adap/flower/pull/2537), " +"[#2528](https://github/com/adap/flower/pull/2528), " +"[#2523](https://github.com/adap/flower/pull/2523), " +"[#2522](https://github.com/adap/flower/pull/2522))" -#: ../../source/ref-changelog.md:546 -msgid "" -"Tons of small API cleanups resulting in a more coherent developer " -"experience" -msgstr "对大量小型应用程序接口进行了清理，使开发人员的体验更加一致" +#: ../../source/ref-changelog.md:290 +msgid "Add gRPC request-response capability to the C++ SDK." +msgstr "为 C++ SDK 添加 gRPC 请求-响应功能。" -#: ../../source/ref-changelog.md:550 +#: ../../source/ref-changelog.md:292 +#, fuzzy msgid "" -"We would like to give our **special thanks** to all the contributors who " -"made Flower 1.0 possible (in reverse [GitHub " -"Contributors](https://github.com/adap/flower/graphs/contributors) order):" +"**Make HTTPS the new default** " +"([#2591](https://github.com/adap/flower/pull/2591), " +"[#2636](https://github.com/adap/flower/pull/2636))" msgstr "" -"在此，我们谨向所有促成 Flower 1.0 的贡献者致以**特别的谢意（按[GitHub " -"贡献者](https://github.com/adap/flower/graphs/contributors) 倒序排列）：" +"Baselines文档（[#2290](https://github.com/adap/flower/pull/2290), " +"[#2400](https://github.com/adap/flower/pull/2400)" -#: ../../source/ref-changelog.md:552 +#: ../../source/ref-changelog.md:294 +#, fuzzy msgid "" -"[@rtaiello](https://github.com/rtaiello), " -"[@g-pichler](https://github.com/g-pichler), [@rob-" -"luke](https://github.com/rob-luke), [@andreea-zaharia](https://github.com" -"/andreea-zaharia), [@kinshukdua](https://github.com/kinshukdua), " -"[@nfnt](https://github.com/nfnt), " -"[@tatiana-s](https://github.com/tatiana-s), " -"[@TParcollet](https://github.com/TParcollet), " -"[@vballoli](https://github.com/vballoli), " -"[@negedng](https://github.com/negedng), " -"[@RISHIKESHAVAN](https://github.com/RISHIKESHAVAN), " -"[@hei411](https://github.com/hei411), " -"[@SebastianSpeitel](https://github.com/SebastianSpeitel), " -"[@AmitChaulwar](https://github.com/AmitChaulwar), " -"[@Rubiel1](https://github.com/Rubiel1), [@FANTOME-PAN](https://github.com" -"/FANTOME-PAN), [@Rono-BC](https://github.com/Rono-BC), " -"[@lbhm](https://github.com/lbhm), " -"[@sishtiaq](https://github.com/sishtiaq), " -"[@remde](https://github.com/remde), [@Jueun-Park](https://github.com" -"/Jueun-Park), [@architjen](https://github.com/architjen), " -"[@PratikGarai](https://github.com/PratikGarai), " -"[@mrinaald](https://github.com/mrinaald), " -"[@zliel](https://github.com/zliel), " -"[@MeiruiJiang](https://github.com/MeiruiJiang), " -"[@sancarlim](https://github.com/sancarlim), " -"[@gubertoli](https://github.com/gubertoli), " -"[@Vingt100](https://github.com/Vingt100), " -"[@MakGulati](https://github.com/MakGulati), " -"[@cozek](https://github.com/cozek), " -"[@jafermarq](https://github.com/jafermarq), " -"[@sisco0](https://github.com/sisco0), " -"[@akhilmathurs](https://github.com/akhilmathurs), " -"[@CanTuerk](https://github.com/CanTuerk), " -"[@mariaboerner1987](https://github.com/mariaboerner1987), " -"[@pedropgusmao](https://github.com/pedropgusmao), " -"[@tanertopal](https://github.com/tanertopal), " -"[@danieljanes](https://github.com/danieljanes)." +"Flower is moving to HTTPS by default. The new `flower-server` requires " +"passing `--certificates`, but users can enable `--insecure` to use HTTP " +"for prototyping. The same applies to `flower-client`, which can either " +"use user-provided credentials or gRPC-bundled certificates to connect to " +"an HTTPS-enabled server or requires opt-out via passing `--insecure` to " +"enable insecure HTTP connections." msgstr "" -"[@rtaiello](https://github.com/rtaiello), " -"[@g-pichler](https://github.com/g-pichler), [@rob-" -"luke](https://github.com/rob-luke), [@andreea-zaharia](https://github.com" -"/andreea-zaharia), [@kinshukdua](https://github.com/kinshukdua), " -"[@nfnt](https://github.com/nfnt), " -"[@tatiana-s](https://github.com/tatiana-s), " -"[@TParcollet](https://github.com/TParcollet), " -"[@vballoli](https://github.com/vballoli), " -"[@negedng](https://github.com/negedng), " -"[@RISHIKESHAVAN](https://github.com/RISHIKESHAVAN), " -"[@hei411](https://github.com/hei411), " -"[@SebastianSpeitel](https://github.com/SebastianSpeitel), " -"[@AmitChaulwar](https://github.com/AmitChaulwar), " -"[@Rubiel1](https://github.com/Rubiel1), [@FANTOME-PAN](https://github.com" -"/FANTOME-PAN), [@Rono-BC](https://github.com/Rono-BC), " -"[@lbhm](https://github.com/lbhm), " -"[@sishtiaq](https://github.com/sishtiaq), " -"[@remde](https://github.com/remde), [@Jueun-Park](https://github.com" -"/Jueun-Park), [@architjen](https://github.com/architjen), " -"[@PratikGarai](https://github.com/PratikGarai), " -"[@mrinaald](https://github.com/mrinaald), " -"[@zliel](https://github.com/zliel), " -"[@MeiruiJiang](https://github.com/MeiruiJiang), " -"[@sancarlim](https://github.com/sancarlim), " -"[@gubertoli](https://github.com/gubertoli), " -"[@Vingt100](https://github.com/Vingt100), " -"[@MakGulati](https://github.com/MakGulati), " -"[@cozek](https://github.com/cozek), " -"[@jafermarq](https://github.com/jafermarq), " -"[@sisco0](https://github.com/sisco0), " -"[@akhilmathurs](https://github.com/akhilmathurs), " -"[@CanTuerk](https://github.com/CanTuerk), " -"[@mariaboerner1987](https://github.com/mariaboerner1987), " -"[@pedropgusmao](https://github.com/pedropgusmao), " -"[@tanertopal](https://github.com/tanertopal), " -"[@danieljanes](https://github.com/danieljanes)." +"Flower 默认使用 HTTPS。新的 \"flower-server \"需要通过\"--证书\"，但用户可以启用\"--不安全 \"来使用 " +"HTTP 进行原型开发。这同样适用于 `flower-client`，它可以使用用户提供的凭证或 gRPC 绑定证书连接到支持 HTTPS " +"的服务器，也可以通过传递 `--insecure`来启用不安全的 HTTP 连接。" -#: ../../source/ref-changelog.md:556 +#: ../../source/ref-changelog.md:296 +#, fuzzy msgid "" -"**All arguments must be passed as keyword arguments** " -"([#1338](https://github.com/adap/flower/pull/1338))" -msgstr "** 所有参数必须作为关键字参数传递** ([#1338](https://github.com/adap/flower/pull/1338))" +"For backward compatibility, `start_client()` and `start_numpy_client()` " +"will still start in insecure mode by default. In a future release, " +"insecure connections will require user opt-in by passing `insecure=True`." +msgstr "" +"为了向后兼容，`start_client()` 和 `start_numpy_client()` " +"默认仍以不安全模式启动。在未来的版本中，不安全连接将需要用户通过传递 `insecure=True` 进行选择。" -#: ../../source/ref-changelog.md:558 +#: ../../source/ref-changelog.md:298 msgid "" -"Pass all arguments as keyword arguments, positional arguments are not " -"longer supported. Code that uses positional arguments (e.g., " -"`start_client(\"127.0.0.1:8080\", FlowerClient())`) must add the keyword " -"for each positional argument (e.g., " -"`start_client(server_address=\"127.0.0.1:8080\", " -"client=FlowerClient())`)." +"**Unify client API** ([#2303](https://github.com/adap/flower/pull/2303), " +"[#2390](https://github.com/adap/flower/pull/2390), " +"[#2493](https://github.com/adap/flower/pull/2493))" msgstr "" -"以关键字参数传递所有参数，不再支持位置参数。使用位置参数的代码（例如，`start_client(\"127.0.0.1:8080\", " -"FlowerClient())`）必须为每个位置参数添加关键字（例如，`start_client(server_address=\"127.0.0.1:8080\"," -" client=FlowerClient())`）。" +"** 统一客户端应用程序接口** ([#2303](https://github.com/adap/flower/pull/2303), " +"[#2390](https://github.com/adap/flower/pull/2390), " +"[#2493](https://github.com/adap/flower/pull/2493))" -#: ../../source/ref-changelog.md:560 +#: ../../source/ref-changelog.md:300 +#, fuzzy msgid "" -"**Introduce configuration object** `ServerConfig` **in** `start_server` " -"**and** `start_simulation` " -"([#1317](https://github.com/adap/flower/pull/1317))" +"Using the `client_fn`, Flower clients can interchangeably run as " +"standalone processes (i.e. via `start_client`) or in simulation (i.e. via" +" `start_simulation`) without requiring changes to how the client class is" +" defined and instantiated. The `to_client()` function is introduced to " +"convert a `NumPyClient` to a `Client`." msgstr "" -"**在*** `start_server` ***和*** `start_simulation` 中引入配置对象*** " -"`ServerConfig` ([#1317](https://github.com/adap/flower/pull/1317))" +"使用 `client_fn`，Flower 客户端可以作为独立进程（即通过 `start_client`）或在模拟中（即通过 " +"`start_simulation`）交替运行，而无需更改客户端类的定义和实例化方式。调用 `start_numpy_client` 现已过时。" -#: ../../source/ref-changelog.md:562 +#: ../../source/ref-changelog.md:302 msgid "" -"Instead of a config dictionary `{\"num_rounds\": 3, \"round_timeout\": " -"600.0}`, `start_server` and `start_simulation` now expect a configuration" -" object of type `flwr.server.ServerConfig`. `ServerConfig` takes the same" -" arguments that as the previous config dict, but it makes writing type-" -"safe code easier and the default parameters values more transparent." +"**Add new** `Bulyan` **strategy** " +"([#1817](https://github.com/adap/flower/pull/1817), " +"[#1891](https://github.com/adap/flower/pull/1891))" msgstr "" -"并非配置字典`{\"num_rounds\"： 3, \"round_timeout\"： 600.0}`, `start_server`和 " -"`start_simulation`现在用一个类型为 " -"`flwr.server.ServerConfig`的配置对象。`ServerConfig`接收的参数与之前的 config dict " -"相同，但它使编写类型安全代码变得更容易，默认参数值也更加透明。" - -#: ../../source/ref-changelog.md:564 -msgid "" -"**Rename built-in strategy parameters for clarity** " -"([#1334](https://github.com/adap/flower/pull/1334))" -msgstr "**重新命名内置策略参数，使其更加清晰** ([#1334](https://github.com/adap/flower/pull/1334))" +"**添加新**\"Bulyan " +"\"**策略**（[#1817](https://github.com/adap/flower/pull/1817), " +"[#1891](https://github.com/adap/flower/pull/1891)" -#: ../../source/ref-changelog.md:566 +#: ../../source/ref-changelog.md:304 msgid "" -"The following built-in strategy parameters were renamed to improve " -"readability and consistency with other API's:" -msgstr "以下内置策略参数已重新命名，以提高可读性并与其他 API 保持一致：" - -#: ../../source/ref-changelog.md:568 -msgid "`fraction_eval` --> `fraction_evaluate`" -msgstr "`fraction_eval` --> `fraction_evaluate`" - -#: ../../source/ref-changelog.md:569 -msgid "`min_eval_clients` --> `min_evaluate_clients`" -msgstr "`min_eval_clients` --> `min_evaluate_clients`" - -#: ../../source/ref-changelog.md:570 -msgid "`eval_fn` --> `evaluate_fn`" -msgstr "`eval_fn` --> `evaluate_fn`" +"The new `Bulyan` strategy implements Bulyan by [El Mhamdi et al., " +"2018](https://arxiv.org/abs/1802.07927)" +msgstr "新的 \"Bulyan\"策略通过[El Mhamdi 等人，2018]（https://arxiv.org/abs/1802.07927）实现" -#: ../../source/ref-changelog.md:572 +#: ../../source/ref-changelog.md:306 +#, fuzzy msgid "" -"**Update default arguments of built-in strategies** " -"([#1278](https://github.com/adap/flower/pull/1278))" -msgstr "**更新内置策略的默认参数** ([#1278](https://github.com/adap/flower/pull/1278))" +"**Add new** `XGB Bagging` **strategy** " +"([#2611](https://github.com/adap/flower/pull/2611))" +msgstr "**添加新的`FedProx`策略** （[#1619](https://github.com/adap/flower/pull/1619)）" -#: ../../source/ref-changelog.md:574 +#: ../../source/ref-changelog.md:308 ../../source/ref-changelog.md:310 +#, fuzzy msgid "" -"All built-in strategies now use `fraction_fit=1.0` and " -"`fraction_evaluate=1.0`, which means they select *all* currently " -"available clients for training and evaluation. Projects that relied on " -"the previous default values can get the previous behaviour by " -"initializing the strategy in the following way:" +"**Introduce `WorkloadState`** " +"([#2564](https://github.com/adap/flower/pull/2564), " +"[#2632](https://github.com/adap/flower/pull/2632))" msgstr "" -"所有内置策略现在都使用 \"fraction_fit=1.0 \"和 " -"\"fraction_evaluate=1.0\"，这意味着它们会选择*所有*当前可用的客户端进行训练和评估。依赖以前默认值的项目可以通过以下方式初始化策略，获得以前的行为：" - -#: ../../source/ref-changelog.md:576 -msgid "`strategy = FedAvg(fraction_fit=0.1, fraction_evaluate=0.1)`" -msgstr "`strategy = FedAvg(fraction_fit=0.1, fraction_evaluate=0.1)`" +"**新的内置策略**（[#828](https://github.com/adap/flower/pull/828) " +"[#822](https://github.com/adap/flower/pull/822)" -#: ../../source/ref-changelog.md:578 +#: ../../source/ref-changelog.md:314 msgid "" -"**Add** `server_round` **to** `Strategy.evaluate` " -"([#1334](https://github.com/adap/flower/pull/1334))" +"FedProx ([#2210](https://github.com/adap/flower/pull/2210), " +"[#2286](https://github.com/adap/flower/pull/2286), " +"[#2509](https://github.com/adap/flower/pull/2509))" msgstr "" -"**添加*** `server_round` ***到*** `Strategy.evaluate` " -"([#1334](https://github.com/adap/flower/pull/1334))" - -#: ../../source/ref-changelog.md:580 -msgid "" -"The `Strategy` method `evaluate` now receives the current round of " -"federated learning/evaluation as the first parameter." -msgstr "`Strategy`的`evaluate` 方法现在会接收当前一轮联邦学习/评估作为第一个参数。" +"FedProx ([#2210](https://github.com/adap/flower/pull/2210), " +"[#2286](https://github.com/adap/flower/pull/2286), " +"[#2509](https://github.com/adap/flower/pull/2509))" -#: ../../source/ref-changelog.md:582 +#: ../../source/ref-changelog.md:316 msgid "" -"**Add** `server_round` **and** `config` **parameters to** `evaluate_fn` " -"([#1334](https://github.com/adap/flower/pull/1334))" +"Baselines Docs ([#2290](https://github.com/adap/flower/pull/2290), " +"[#2400](https://github.com/adap/flower/pull/2400))" msgstr "" -"**将*** `server_round` **和*** `config` **参数添加到*** `evaluate_fn` " -"([#1334](https://github.com/adap/flower/pull/1334))" +"Baselines文档（[#2290](https://github.com/adap/flower/pull/2290), " +"[#2400](https://github.com/adap/flower/pull/2400)" -#: ../../source/ref-changelog.md:584 +#: ../../source/ref-changelog.md:318 msgid "" -"The `evaluate_fn` passed to built-in strategies like `FedAvg` now takes " -"three parameters: (1) The current round of federated learning/evaluation " -"(`server_round`), (2) the model parameters to evaluate (`parameters`), " -"and (3) a config dictionary (`config`)." +"FedMLB ([#2340](https://github.com/adap/flower/pull/2340), " +"[#2507](https://github.com/adap/flower/pull/2507))" msgstr "" -"传递给内置策略（如 `FedAvg`）的 `evaluate_fn` 现在需要三个参数：(1) 当前一轮联邦学习/评估 " -"(`server_round`)，(2) 要评估的模型参数 (`parameters`)，(3) 配置字典 (`config`)。" +"FedMLB ([#2340](https://github.com/adap/flower/pull/2340), " +"[#2507](https://github.com/adap/flower/pull/2507))" -#: ../../source/ref-changelog.md:586 +#: ../../source/ref-changelog.md:320 msgid "" -"**Rename** `rnd` **to** `server_round` " -"([#1321](https://github.com/adap/flower/pull/1321))" +"TAMUNA ([#2254](https://github.com/adap/flower/pull/2254), " +"[#2508](https://github.com/adap/flower/pull/2508))" msgstr "" -"**重新命名** `rnd` ** to** `server_round` " -"([#1321](https://github.com/adap/flower/pull/1321))" +"TAMUNA ([#2254](https://github.com/adap/flower/pull/2254), " +"[#2508](https://github.com/adap/flower/pull/2508))" -#: ../../source/ref-changelog.md:588 -msgid "" -"Several Flower methods and functions (`evaluate_fn`, `configure_fit`, " -"`aggregate_fit`, `configure_evaluate`, `aggregate_evaluate`) receive the " -"current round of federated learning/evaluation as their first parameter. " -"To improve reaability and avoid confusion with *random*, this parameter " -"has been renamed from `rnd` to `server_round`." -msgstr "" -"几个 Flower " -"方法和函数（`evaluate_fn`、`configure_fit`、`aggregate_fit`、`configure_evaluate`、`aggregate_evaluate`）的第一个参数是当前一轮的联邦学习/评估。为提高可重复性并避免与" -" *random* 混淆，该参数已从 `rnd` 更名为 `server_round`。" +#: ../../source/ref-changelog.md:322 +msgid "FedMeta [#2438](https://github.com/adap/flower/pull/2438)" +msgstr "FedMeta [#2438](https://github.com/adap/flower/pull/2438)" -#: ../../source/ref-changelog.md:590 -msgid "" -"**Move** `flwr.dataset` **to** `flwr_baselines` " -"([#1273](https://github.com/adap/flower/pull/1273))" -msgstr "" -"**移动*** `flwr.dataset` **到*** `flwr_baselines` " -"([#1273](https://github.com/adap/flower/pull/1273))" +#: ../../source/ref-changelog.md:324 +msgid "FjORD [#2431](https://github.com/adap/flower/pull/2431)" +msgstr "FjORD [#2431](https://github.com/adap/flower/pull/2431)" -#: ../../source/ref-changelog.md:592 -msgid "The experimental package `flwr.dataset` was migrated to Flower Baselines." -msgstr "实验软件包 `flwr.dataset` 已迁移至 Flower Baselines。" +#: ../../source/ref-changelog.md:326 +msgid "MOON [#2421](https://github.com/adap/flower/pull/2421)" +msgstr "MOON [#2421](https://github.com/adap/flower/pull/2421)" -#: ../../source/ref-changelog.md:594 -msgid "" -"**Remove experimental strategies** " -"([#1280](https://github.com/adap/flower/pull/1280))" -msgstr "**删除实验策略** ([#1280](https://github.com/adap/flower/pull/1280))" +#: ../../source/ref-changelog.md:328 +msgid "DepthFL [#2295](https://github.com/adap/flower/pull/2295)" +msgstr "DepthFL [#2295](https://github.com/adap/flower/pull/2295)" -#: ../../source/ref-changelog.md:596 -msgid "" -"Remove unmaintained experimental strategies (`FastAndSlow`, `FedFSv0`, " -"`FedFSv1`)." -msgstr "移除未维护的试验性策略（`FastAndSlow`、`FedFSv0`、`FedFSv1`）。" +#: ../../source/ref-changelog.md:330 +msgid "FedPer [#2266](https://github.com/adap/flower/pull/2266)" +msgstr "FedPer [#2266](https://github.com/adap/flower/pull/2266)" -#: ../../source/ref-changelog.md:598 -msgid "" -"**Rename** `Weights` **to** `NDArrays` " -"([#1258](https://github.com/adap/flower/pull/1258), " -"[#1259](https://github.com/adap/flower/pull/1259))" -msgstr "" -"**重新命名** `Weights` **到** `NDArrays` " -"([#1258](https://github.com/adap/flower/pull/1258), " -"[#1259](https://github.com/adap/flower/pull/1259))" +#: ../../source/ref-changelog.md:332 +msgid "FedWav2vec [#2551](https://github.com/adap/flower/pull/2551)" +msgstr "FedWav2vec [#2551](https://github.com/adap/flower/pull/2551)" -#: ../../source/ref-changelog.md:600 -msgid "" -"`flwr.common.Weights` was renamed to `flwr.common.NDArrays` to better " -"capture what this type is all about." -msgstr "flwr.common.Weights \"更名为 \"flwr.common.NDArrays\"，以更好地反映该类型的含义。" +#: ../../source/ref-changelog.md:334 +msgid "niid-Bench [#2428](https://github.com/adap/flower/pull/2428)" +msgstr "niid-Bench [#2428](https://github.com/adap/flower/pull/2428)" -#: ../../source/ref-changelog.md:602 +#: ../../source/ref-changelog.md:336 msgid "" -"**Remove antiquated** `force_final_distributed_eval` **from** " -"`start_server` ([#1258](https://github.com/adap/flower/pull/1258), " -"[#1259](https://github.com/adap/flower/pull/1259))" +"FedBN ([#2608](https://github.com/adap/flower/pull/2608), " +"[#2615](https://github.com/adap/flower/pull/2615))" msgstr "" -"**从** `start_server` 中移除过时的** `force_final_distributed_eval` " -"([#1258](https://github.com/adap/flower/pull/1258), " -"[#1259](https://github.com/adap/flower/pull/1259))" +"FedBN ([#2608](https://github.com/adap/flower/pull/2608), " +"[#2615](https://github.com/adap/flower/pull/2615))" -#: ../../source/ref-changelog.md:604 +#: ../../source/ref-changelog.md:338 +#, fuzzy msgid "" -"The `start_server` parameter `force_final_distributed_eval` has long been" -" a historic artefact, in this release it is finally gone for good." +"**General updates to Flower Examples** " +"([#2384](https://github.com/adap/flower/pull/2384), " +"[#2425](https://github.com/adap/flower/pull/2425), " +"[#2526](https://github.com/adap/flower/pull/2526), " +"[#2302](https://github.com/adap/flower/pull/2302), " +"[#2545](https://github.com/adap/flower/pull/2545))" msgstr "" -"start_server \"参数 \"force_final_distributed_eval " -"\"长期以来一直是个历史遗留问题，在此版本中终于永远消失了。" +"** 更新 C++ SDK** ([#2537](https://github/com/adap/flower/pull/2537), " +"[#2528](https://github/com/adap/flower/pull/2528), " +"[#2523](https://github.com/adap/flower/pull/2523), " +"[#2522](https://github.com/adap/flower/pull/2522))" -#: ../../source/ref-changelog.md:606 +#: ../../source/ref-changelog.md:340 +#, fuzzy msgid "" -"**Make** `get_parameters` **configurable** " -"([#1242](https://github.com/adap/flower/pull/1242))" +"**General updates to Flower Baselines** " +"([#2301](https://github.com/adap/flower/pull/2301), " +"[#2305](https://github.com/adap/flower/pull/2305), " +"[#2307](https://github.com/adap/flower/pull/2307), " +"[#2327](https://github.com/adap/flower/pull/2327), " +"[#2435](https://github.com/adap/flower/pull/2435), " +"[#2462](https://github.com/adap/flower/pull/2462), " +"[#2463](https://github.com/adap/flower/pull/2463), " +"[#2461](https://github.com/adap/flower/pull/2461), " +"[#2469](https://github.com/adap/flower/pull/2469), " +"[#2466](https://github.com/adap/flower/pull/2466), " +"[#2471](https://github.com/adap/flower/pull/2471), " +"[#2472](https://github.com/adap/flower/pull/2472), " +"[#2470](https://github.com/adap/flower/pull/2470))" msgstr "" -"**使** `get_parameters` **可配置** " -"([#1242](https://github.com/adap/flower/pull/1242))" +"**普通改进**（[#2309](https://github.com/adap/flower/pull/2309), " +"[#2310](https://github.com/adap/flower/pull/2310), " +"[2313](https://github.com/adap/flower/pull/2313), " +"[#2316](https://github.com/adap/flower/pull/2316), " +"[2317](https://github.com/adap/flower/pull/2317),[#2349](https://github.com/adap/flower/pull/2349)," +" [#2360](https://github.com/adap/flower/pull/2360), " +"[#2402](https://github.com/adap/flower/pull/2402), " +"[#2446](https://github.com/adap/flower/pull/2446) " +"[#2561](https://github.com/adap/flower/pull/2561))" -#: ../../source/ref-changelog.md:608 +#: ../../source/ref-changelog.md:342 +#, fuzzy msgid "" -"The `get_parameters` method now accepts a configuration dictionary, just " -"like `get_properties`, `fit`, and `evaluate`." +"**General updates to the simulation engine** " +"([#2331](https://github.com/adap/flower/pull/2331), " +"[#2447](https://github.com/adap/flower/pull/2447), " +"[#2448](https://github.com/adap/flower/pull/2448), " +"[#2294](https://github.com/adap/flower/pull/2294))" msgstr "" -"现在，\"get_parameters \"方法与 \"get_properties\"、\"fit \"和 \"evaluate " -"\"一样，都接受配置字典。" +"**模拟引擎的普通更新** ([#2331](https://github.com/adap/flower/pull/2331), " +"[#2447](https://github.com/adap/flower/pull/2447), " +"[#2448](https://github.com/adap/flower/pull/2448))" -#: ../../source/ref-changelog.md:610 +#: ../../source/ref-changelog.md:344 +#, fuzzy msgid "" -"**Replace** `num_rounds` **in** `start_simulation` **with new** `config` " -"**parameter** ([#1281](https://github.com/adap/flower/pull/1281))" +"**General updates to Flower SDKs** " +"([#2288](https://github.com/adap/flower/pull/2288), " +"[#2429](https://github.com/adap/flower/pull/2429), " +"[#2555](https://github.com/adap/flower/pull/2555), " +"[#2543](https://github.com/adap/flower/pull/2543), " +"[#2544](https://github.com/adap/flower/pull/2544), " +"[#2597](https://github.com/adap/flower/pull/2597), " +"[#2623](https://github.com/adap/flower/pull/2623))" msgstr "" -"**用新的** `config` 参数** 替换** `num_rounds` ** in** `start_simulation` ** " -"([#1281](https://github.com/adap/flower/pull/1281))" +"**改进教程** ([#1468](https://github.com/adap/flower/pull/1468), " +"[#1470](https://github.com/adap/flower/pull/1470), " +"[#1472](https://github.com/adap/flower/pull/1472), " +"[#1473](https://github.com/adap/flower/pull/1473), " +"[#1474](https://github.com/adap/flower/pull/1474), " +"[#1475](https://github.com/adap/flower/pull/1475)))" -#: ../../source/ref-changelog.md:612 +#: ../../source/ref-changelog.md:346 +#, fuzzy msgid "" -"The `start_simulation` function now accepts a configuration dictionary " -"`config` instead of the `num_rounds` integer. This improves the " -"consistency between `start_simulation` and `start_server` and makes " -"transitioning between the two easier." +"**General improvements** " +"([#2309](https://github.com/adap/flower/pull/2309), " +"[#2310](https://github.com/adap/flower/pull/2310), " +"[#2313](https://github.com/adap/flower/pull/2313), " +"[#2316](https://github.com/adap/flower/pull/2316), " +"[#2317](https://github.com/adap/flower/pull/2317), " +"[#2349](https://github.com/adap/flower/pull/2349), " +"[#2360](https://github.com/adap/flower/pull/2360), " +"[#2402](https://github.com/adap/flower/pull/2402), " +"[#2446](https://github.com/adap/flower/pull/2446), " +"[#2561](https://github.com/adap/flower/pull/2561), " +"[#2273](https://github.com/adap/flower/pull/2273), " +"[#2267](https://github.com/adap/flower/pull/2267), " +"[#2274](https://github.com/adap/flower/pull/2274), " +"[#2275](https://github.com/adap/flower/pull/2275), " +"[#2432](https://github.com/adap/flower/pull/2432), " +"[#2251](https://github.com/adap/flower/pull/2251), " +"[#2321](https://github.com/adap/flower/pull/2321), " +"[#1936](https://github.com/adap/flower/pull/1936), " +"[#2408](https://github.com/adap/flower/pull/2408), " +"[#2413](https://github.com/adap/flower/pull/2413), " +"[#2401](https://github.com/adap/flower/pull/2401), " +"[#2531](https://github.com/adap/flower/pull/2531), " +"[#2534](https://github.com/adap/flower/pull/2534), " +"[#2535](https://github.com/adap/flower/pull/2535), " +"[#2521](https://github.com/adap/flower/pull/2521), " +"[#2553](https://github.com/adap/flower/pull/2553), " +"[#2596](https://github.com/adap/flower/pull/2596))" msgstr "" -"现在，`start_simulation`（开始模拟）` 函数接受配置字典 `config` 而不是 `num_rounds` 整数。这改进了 " -"`start_simulation` 和 `start_server` 之间的一致性，并使两者之间的转换更容易。" - -#: ../../source/ref-changelog.md:616 -msgid "" -"**Support Python 3.10** " -"([#1320](https://github.com/adap/flower/pull/1320))" -msgstr "** 支持 Python 3.10** ([#1320](https://github.com/adap/flower/pull/1320))" - -#: ../../source/ref-changelog.md:618 -msgid "" -"The previous Flower release introduced experimental support for Python " -"3.10, this release declares Python 3.10 support as stable." -msgstr "上一个 Flower 版本引入了对 Python 3.10 的实验支持，而本版本则宣布对 Python 3.10 的支持为稳定支持。" +"**一般改进** ([#2309](https://github.com/adap/flower/pull/2309), " +"[#2310](https://github.com/adap/flower/pull/2310), " +"[#2313](https://github.com/adap/flower/pull/2313), " +"[#2316](https://github.com/adap/flower/pull/2316), " +"[#2317](https://github.com/adap/flower/pull/2317), " +"[#2349](https://github.com/adap/flower/pull/2349), " +"[#2360](https://github.com/adap/flower/pull/2360), " +"[#2402](https://github.com/adap/flower/pull/2402), " +"[#2446](https://github.com/adap/flower/pull/2446), " +"[#2561](https://github.com/adap/flower/pull/2561), " +"[#2273](https://github.com/adap/flower/pull/2273), " +"[#2267](https://github.com/adap/flower/pull/2267), " +"[#2274](https://github.com/adap/flower/pull/2274), " +"[#2275](https://github.com/adap/flower/pull/2275), " +"[#2432](https://github.com/adap/flower/pull/2432), " +"[#2251](https://github.com/adap/flower/pull/2251), " +"[#2321](https://github.com/adap/flower/pull/2321), " +"[#1936](https://github.com/adap/flower/pull/1936), " +"[#2408](https://github.com/adap/flower/pull/2408), " +"[#2413](https://github.com/adap/flower/pull/2413), " +"[#2401](https://github.com/adap/flower/pull/2401), " +"[#2531](https://github.com/adap/flower/pull/2531), " +"[#2534](https://github.com/adap/flower/pull/2534), " +"[#2535](https://github.com/adap/flower/pull/2535), " +"[#2521](https://github.com/adap/flower/pull/2521), " +"[#2553](https://github.com/adap/flower/pull/2553), " +"[#2596](https://github.com/adap/flower/pull/2596))" -#: ../../source/ref-changelog.md:620 -msgid "" -"**Make all** `Client` **and** `NumPyClient` **methods optional** " -"([#1260](https://github.com/adap/flower/pull/1260), " -"[#1277](https://github.com/adap/flower/pull/1277))" -msgstr "" -"**使所有** `Client` **和** `NumPyClient` **方法成为可选** " -"([#1260](https://github.com/adap/flower/pull/1260), " -"[#1277](https://github.com/adap/flower/pull/1277))" +#: ../../source/ref-changelog.md:348 ../../source/ref-changelog.md:438 +#: ../../source/ref-changelog.md:502 ../../source/ref-changelog.md:556 +#: ../../source/ref-changelog.md:623 +msgid "Flower received many improvements under the hood, too many to list here." +msgstr "Flower 进行了许多改进，这里就不一一列举了。" -#: ../../source/ref-changelog.md:622 +#: ../../source/ref-changelog.md:352 msgid "" -"The `Client`/`NumPyClient` methods `get_properties`, `get_parameters`, " -"`fit`, and `evaluate` are all optional. This enables writing clients that" -" implement, for example, only `fit`, but no other method. No need to " -"implement `evaluate` when using centralized evaluation!" +"**Remove support for Python 3.7** " +"([#2280](https://github.com/adap/flower/pull/2280), " +"[#2299](https://github.com/adap/flower/pull/2299), " +"[#2304](https://github.com/adap/flower/pull/2304), " +"[#2306](https://github.com/adap/flower/pull/2306), " +"[#2355](https://github.com/adap/flower/pull/2355), " +"[#2356](https://github.com/adap/flower/pull/2356))" msgstr "" -"`Client`/`NumPyClient`的 \"get_properties\"、\"get_parameters\"、\"fit \"和 " -"\"evaluate \"方法都是可选的。这样就可以编写只实现 `fit` 而不实现其他方法的客户端。使用集中评估时，无需实现 " -"`evaluate`！" +"**移除对 Python 3.7 的支持** " +"([#2280](https://github.com/adap/flower/pull/2280), " +"[#2299](https://github.com/adap/flower/pull/2299), " +"[#2304](https://github.com/adap/flower/pull/2304), " +"[#2306](https://github.com/adap/flower/pull/2306), " +"[#2355](https://github.com/adap/flower/pull/2355), " +"[#2356](https://github.com/adap/flower/pull/2356))" -#: ../../source/ref-changelog.md:624 +#: ../../source/ref-changelog.md:354 msgid "" -"**Enable passing a** `Server` **instance to** `start_simulation` " -"([#1281](https://github.com/adap/flower/pull/1281))" -msgstr "" -"**启用向** `start_simulation` 传递** `Server` 实例 " -"([#1281](https://github.com/adap/flower/pull/1281))" +"Python 3.7 support was deprecated in Flower 1.5, and this release removes" +" support. Flower now requires Python 3.8." +msgstr "在 Flower 1.5 中，Python 3.7 支持已被弃用，本版本将删除该支持。Flower 现在需要 Python 3.8。" -#: ../../source/ref-changelog.md:626 +#: ../../source/ref-changelog.md:356 msgid "" -"Similar to `start_server`, `start_simulation` now accepts a full `Server`" -" instance. This enables users to heavily customize the execution of " -"eperiments and opens the door to running, for example, async FL using the" -" Virtual Client Engine." +"**Remove experimental argument** `rest` **from** `start_client` " +"([#2324](https://github.com/adap/flower/pull/2324))" msgstr "" -"与 `start_server` 类似，`start_simulation` 现在也接受一个完整的 `Server` " -"实例。这使得用户可以对实验的执行进行大量自定义，并为使用虚拟客户端引擎运行异步 FL 等打开了大门。" +"**从** `start_client` 中移除** `rest` **实验参数 " +"([#2324](https://github.com/adap/flower/pull/2324))" -#: ../../source/ref-changelog.md:628 +#: ../../source/ref-changelog.md:358 msgid "" -"**Update code examples** " -"([#1291](https://github.com/adap/flower/pull/1291), " -"[#1286](https://github.com/adap/flower/pull/1286), " -"[#1282](https://github.com/adap/flower/pull/1282))" +"The (still experimental) argument `rest` was removed from `start_client` " +"and `start_numpy_client`. Use `transport=\"rest\"` to opt into the " +"experimental REST API instead." msgstr "" -"**更新代码示例** ([#1291](https://github.com/adap/flower/pull/1291), " -"[#1286](https://github.com/adap/flower/pull/1286), " -"[#1282](https://github.com/adap/flower/pull/1282))" - -#: ../../source/ref-changelog.md:630 -msgid "" -"Many code examples received small or even large maintenance updates, " -"among them are" -msgstr "许多代码示例都进行了小规模甚至大规模的维护更新，其中包括" - -#: ../../source/ref-changelog.md:632 -msgid "`scikit-learn`" -msgstr "`scikit-learn`" - -#: ../../source/ref-changelog.md:633 -msgid "`simulation_pytorch`" -msgstr "`simulation_pytorch`" - -#: ../../source/ref-changelog.md:634 -msgid "`quickstart_pytorch`" -msgstr "`quickstart_pytorch`" - -#: ../../source/ref-changelog.md:635 -msgid "`quickstart_simulation`" -msgstr "`quickstart_simulation`" - -#: ../../source/ref-changelog.md:636 -msgid "`quickstart_tensorflow`" -msgstr "`quickstart_tensorflow`" +"删除了 `start_client` 和 `start_numpy_client` 中的参数 `rest`（仍属试验性质）。请使用 " +"`transport=\"rest\"` 来选择使用试验性 REST API。" -#: ../../source/ref-changelog.md:637 -msgid "`advanced_tensorflow`" -msgstr "`advanced_tensorflow`" +#: ../../source/ref-changelog.md:360 +msgid "v1.5.0 (2023-08-31)" +msgstr "v1.5.0 (2023-08-31)" -#: ../../source/ref-changelog.md:639 +#: ../../source/ref-changelog.md:366 msgid "" -"**Remove the obsolete simulation example** " -"([#1328](https://github.com/adap/flower/pull/1328))" -msgstr "**删除过时的模拟示例** ([#1328](https://github.com/adap/flower/pull/1328))" +"`Adam Narozniak`, `Anass Anhari`, `Charles Beauville`, `Dana-Farber`, " +"`Daniel J. Beutel`, `Daniel Nata Nugraha`, `Edoardo Gabrielli`, `Gustavo " +"Bertoli`, `Heng Pan`, `Javier`, `Mahdi`, `Steven Hé (Sīchàng)`, `Taner " +"Topal`, `achiverram28`, `danielnugraha`, `eunchung`, `ruthgal` " +msgstr "" +"`Adam Narozniak`, `Anass Anhari`, `Charles Beauville`, `Dana-Farber`, " +"`Daniel J. Beutel`, `Daniel Nata Nugraha`, `Edoardo Gabrielli`, `Gustavo " +"Bertoli`, `Heng Pan`, `Javier`, `Mahdi`, `Steven Hé (Sīchàng)`, `Taner " +"Topal`, `achiverram28`, `danielnugraha`, `eunchung`, `ruthgal` " -#: ../../source/ref-changelog.md:641 +#: ../../source/ref-changelog.md:370 msgid "" -"Removes the obsolete `simulation` example and renames " -"`quickstart_simulation` to `simulation_tensorflow` so it fits withs the " -"naming of `simulation_pytorch`" +"**Introduce new simulation engine** " +"([#1969](https://github.com/adap/flower/pull/1969), " +"[#2221](https://github.com/adap/flower/pull/2221), " +"[#2248](https://github.com/adap/flower/pull/2248))" msgstr "" -"删除过时的 \"simulation \"示例，并将 \"quickstart_simulation \"重命名为 " -"\"simulation_tensorflow\"，使其与 \"simulation_pytorch \"的命名一致" +"**引入新的模拟引擎** ([#1969](https://github.com/adap/flower/pull/1969), " +"[#2221](https://github.com/adap/flower/pull/2221), " +"[#2248](https://github.com/adap/flower/pull/2248))" -#: ../../source/ref-changelog.md:643 +#: ../../source/ref-changelog.md:372 msgid "" -"**Update documentation** " -"([#1223](https://github.com/adap/flower/pull/1223), " -"[#1209](https://github.com/adap/flower/pull/1209), " -"[#1251](https://github.com/adap/flower/pull/1251), " -"[#1257](https://github.com/adap/flower/pull/1257), " -"[#1267](https://github.com/adap/flower/pull/1267), " -"[#1268](https://github.com/adap/flower/pull/1268), " -"[#1300](https://github.com/adap/flower/pull/1300), " -"[#1304](https://github.com/adap/flower/pull/1304), " -"[#1305](https://github.com/adap/flower/pull/1305), " -"[#1307](https://github.com/adap/flower/pull/1307))" +"The new simulation engine has been rewritten from the ground up, yet it " +"remains fully backwards compatible. It offers much improved stability and" +" memory handling, especially when working with GPUs. Simulations " +"transparently adapt to different settings to scale simulation in CPU-" +"only, CPU+GPU, multi-GPU, or multi-node multi-GPU environments." msgstr "" -"**更新文档** ([#1223](https://github.com/adap/flower/pull/1223), " -"[#1209](https://github.com/adap/flower/pull/1209), " -"[#1251](https://github.com/adap/flower/pull/1251), " -"[#1257](https://github.com/adap/flower/pull/1257), " -"[#1267](https://github.com/adap/flower/pull/1267), " -"[#1268](https://github.com/adap/flower/pull/1268), " -"[#1300](https://github.com/adap/flower/pull/1300), " -"[#1304](https://github.com/adap/flower/pull/1304), " -"[#1305](https://github.com/adap/flower/pull/1305), " -"[#1307](https://github.com/adap/flower/pull/1307))" +"新的模拟引擎从头开始重新编写，但仍完全向后兼容。它的稳定性和内存处理能力大大提高，尤其是在使用 GPU 时。仿真可透明地适应不同的设置，以在仅 " +"CPU、CPU+GPU、多 GPU 或多节点多 GPU 环境中扩展模拟。" -#: ../../source/ref-changelog.md:645 +#: ../../source/ref-changelog.md:374 msgid "" -"One substantial documentation update fixes multiple smaller rendering " -"issues, makes titles more succinct to improve navigation, removes a " -"deprecated library, updates documentation dependencies, includes the " -"`flwr.common` module in the API reference, includes support for markdown-" -"based documentation, migrates the changelog from `.rst` to `.md`, and " -"fixes a number of smaller details!" +"Comprehensive documentation includes a new [how-to run " +"simulations](https://flower.ai/docs/framework/how-to-run-" +"simulations.html) guide, new [simulation-" +"pytorch](https://flower.ai/docs/examples/simulation-pytorch.html) and " +"[simulation-tensorflow](https://flower.ai/docs/examples/simulation-" +"tensorflow.html) notebooks, and a new [YouTube tutorial " +"series](https://www.youtube.com/watch?v=cRebUIGB5RU&list=PLNG4feLHqCWlnj8a_E1A_n5zr2-8pafTB)." msgstr "" -"其中一个实质性的文档更新修复了多个较小的渲染问题，使标题更加简洁以改善导航，删除了一个已废弃的库，更新了文档依赖关系，在 API 参考中包含了 " -"`flwr.common` 模块，包含了对基于 markdown 的文档的支持，将更新日志从 `.rst` 移植到了 " -"`.md`，并修复了一些较小的细节！" - -#: ../../source/ref-changelog.md:647 ../../source/ref-changelog.md:702 -#: ../../source/ref-changelog.md:771 ../../source/ref-changelog.md:810 -msgid "**Minor updates**" -msgstr "**小规模更新**" +"综合文档包括新的[how-to run simulations](https://flower.ai/docs/framework/how-to-" +"run-simulations.html) guide, new [simulation-" +"pytorch](https://flower.ai/docs/examples/simulation-pytorch.html) and " +"[simulation-tensorflow](https://flower.ai/docs/examples/simulation-" +"tensorflow.html) notebooks, and a new [YouTube tutorial " +"series](https://www.youtube.com/watch?v=cRebUIGB5RU&list=PLNG4feLHqCWlnj8a_E1A_n5zr2-8pafTB)。" -#: ../../source/ref-changelog.md:649 +#: ../../source/ref-changelog.md:376 msgid "" -"Add round number to fit and evaluate log messages " -"([#1266](https://github.com/adap/flower/pull/1266))" -msgstr "添加四舍五入数字，以适应和评估日志信息（[#1266](https://github.com/adap/flower/pull/1266)）" +"**Restructure Flower Docs** " +"([#1824](https://github.com/adap/flower/pull/1824), " +"[#1865](https://github.com/adap/flower/pull/1865), " +"[#1884](https://github.com/adap/flower/pull/1884), " +"[#1887](https://github.com/adap/flower/pull/1887), " +"[#1919](https://github.com/adap/flower/pull/1919), " +"[#1922](https://github.com/adap/flower/pull/1922), " +"[#1920](https://github.com/adap/flower/pull/1920), " +"[#1923](https://github.com/adap/flower/pull/1923), " +"[#1924](https://github.com/adap/flower/pull/1924), " +"[#1962](https://github.com/adap/flower/pull/1962), " +"[#2006](https://github.com/adap/flower/pull/2006), " +"[#2133](https://github.com/adap/flower/pull/2133), " +"[#2203](https://github.com/adap/flower/pull/2203), " +"[#2215](https://github.com/adap/flower/pull/2215), " +"[#2122](https://github.com/adap/flower/pull/2122), " +"[#2223](https://github.com/adap/flower/pull/2223), " +"[#2219](https://github.com/adap/flower/pull/2219), " +"[#2232](https://github.com/adap/flower/pull/2232), " +"[#2233](https://github.com/adap/flower/pull/2233), " +"[#2234](https://github.com/adap/flower/pull/2234), " +"[#2235](https://github.com/adap/flower/pull/2235), " +"[#2237](https://github.com/adap/flower/pull/2237), " +"[#2238](https://github.com/adap/flower/pull/2238), " +"[#2242](https://github.com/adap/flower/pull/2242), " +"[#2231](https://github.com/adap/flower/pull/2231), " +"[#2243](https://github.com/adap/flower/pull/2243), " +"[#2227](https://github.com/adap/flower/pull/2227))" +msgstr "" +"**重构 Flower 文档** ([#1824](https://github.com/adap/flower/pull/1824), " +"[#1865](https://github.com/adap/flower/pull/1865), " +"[#1884](https://github.com/adap/flower/pull/1884), " +"[#1887](https://github.com/adap/flower/pull/1887), " +"[#1919](https://github.com/adap/flower/pull/1919), " +"[#1922](https://github.com/adap/flower/pull/1922), " +"[#1920](https://github.com/adap/flower/pull/1920), " +"[#1923](https://github.com/adap/flower/pull/1923), " +"[#1924](https://github.com/adap/flower/pull/1924), " +"[#1962](https://github.com/adap/flower/pull/1962), " +"[#2006](https://github.com/adap/flower/pull/2006), " +"[#2133](https://github.com/adap/flower/pull/2133), " +"[#2203](https://github.com/adap/flower/pull/2203), " +"[#2215](https://github.com/adap/flower/pull/2215), " +"[#2122](https://github.com/adap/flower/pull/2122), " +"[#2223](https://github.com/adap/flower/pull/2223), " +"[#2219](https://github.com/adap/flower/pull/2219), " +"[#2232](https://github.com/adap/flower/pull/2232), " +"[#2233](https://github.com/adap/flower/pull/2233), " +"[#2234](https://github.com/adap/flower/pull/2234), " +"[#2235](https://github.com/adap/flower/pull/2235), " +"[#2237](https://github.com/adap/flower/pull/2237), " +"[#2238](https://github.com/adap/flower/pull/2238), " +"[#2242](https://github.com/adap/flower/pull/2242), " +"[#2231](https://github.com/adap/flower/pull/2231), " +"[#2243](https://github.com/adap/flower/pull/2243), " +"[#2227](https://github.com/adap/flower/pull/2227))" -#: ../../source/ref-changelog.md:650 +#: ../../source/ref-changelog.md:378 +#, fuzzy msgid "" -"Add secure gRPC connection to the `advanced_tensorflow` code example " -"([#847](https://github.com/adap/flower/pull/847))" +"Much effort went into a completely restructured Flower docs experience. " +"The documentation on [flower.ai/docs](https://flower.ai/docs) is now " +"divided into Flower Framework, Flower Baselines, Flower Android SDK, " +"Flower iOS SDK, and code example projects." msgstr "" -"为 `advanced_tensorflow` 代码示例添加安全 gRPC 连接 " -"([#847](https://github.com/adap/flower/pull/847))" +"Flower 文档体验的全面重构耗费了大量精力。现在，[flower.ai/docs](flower.ai/docs)上的文档分为 Flower " +"Framework、Flower Baselines、Flower Android SDK、Flower iOS SDK 和代码示例项目。" -#: ../../source/ref-changelog.md:651 +#: ../../source/ref-changelog.md:380 msgid "" -"Update developer tooling " -"([#1231](https://github.com/adap/flower/pull/1231), " -"[#1276](https://github.com/adap/flower/pull/1276), " -"[#1301](https://github.com/adap/flower/pull/1301), " -"[#1310](https://github.com/adap/flower/pull/1310))" +"**Introduce Flower Swift SDK** " +"([#1858](https://github.com/adap/flower/pull/1858), " +"[#1897](https://github.com/adap/flower/pull/1897))" msgstr "" -"更新开发人员工具（[#1231](https://github.com/adap/flower/pull/1231), " -"[#1276](https://github.com/adap/flower/pull/1276), " -"[#1301](https://github.com/adap/flower/pull/1301), " -"[#1310](https://github.com/adap/flower/pull/1310)" +"**介绍 Flower Swift SDK** " +"([#1858](https://github.com/adap/flower/pull/1858), " +"[#1897](https://github.com/adap/flower/pull/1897))" -#: ../../source/ref-changelog.md:652 +#: ../../source/ref-changelog.md:382 msgid "" -"Rename ProtoBuf messages to improve consistency " -"([#1214](https://github.com/adap/flower/pull/1214), " -"[#1258](https://github.com/adap/flower/pull/1258), " -"[#1259](https://github.com/adap/flower/pull/1259))" +"This is the first preview release of the Flower Swift SDK. Flower support" +" on iOS is improving, and alongside the Swift SDK and code example, there" +" is now also an iOS quickstart tutorial." msgstr "" -"重命名 ProtoBuf 消息以提高一致性（[#1214](https://github.com/adap/flower/pull/1214), " -"[#1258](https://github.com/adap/flower/pull/1258), " -"[#1259](https://github.com/adap/flower/pull/1259)" +"这是 Flower Swift SDK 的首个预览版。Flower 对 iOS 的支持正在不断改进，除了 Swift SDK " +"和代码示例外，现在还有 iOS 快速入门教程。" -#: ../../source/ref-changelog.md:654 -msgid "v0.19.0 (2022-05-18)" -msgstr "v0.19.0 (2022-05-18)" +#: ../../source/ref-changelog.md:384 +msgid "" +"**Introduce Flower Android SDK** " +"([#2131](https://github.com/adap/flower/pull/2131))" +msgstr "" +"**介绍Flower Android SDK** " +"([#2131](https://github.com/adap/flower/pull/2131))" -#: ../../source/ref-changelog.md:658 +#: ../../source/ref-changelog.md:386 msgid "" -"**Flower Baselines (preview): FedOpt, FedBN, FedAvgM** " -"([#919](https://github.com/adap/flower/pull/919), " -"[#1127](https://github.com/adap/flower/pull/1127), " -"[#914](https://github.com/adap/flower/pull/914))" +"This is the first preview release of the Flower Kotlin SDK. Flower " +"support on Android is improving, and alongside the Kotlin SDK and code " +"example, there is now also an Android quickstart tutorial." msgstr "" -"**Flower Baselines（预览）： FedOpt、FedBN、FedAvgM** " -"([#919](https://github.com/adap/flower/pull/919), " -"[#1127](https://github.com/adap/flower/pull/1127), " -"[#914](https://github.com/adap/flower/pull/914))" +"这是 Flower Kotlin SDK 的首个预览版。Flower 对 Android 的支持正在不断改进，除了 Kotlin SDK " +"和代码示例，现在还有 Android 快速入门教程。" -#: ../../source/ref-changelog.md:660 -#, fuzzy +#: ../../source/ref-changelog.md:388 msgid "" -"The first preview release of Flower Baselines has arrived! We're " -"kickstarting Flower Baselines with implementations of FedOpt (FedYogi, " -"FedAdam, FedAdagrad), FedBN, and FedAvgM. Check the documentation on how " -"to use [Flower Baselines](https://flower.ai/docs/using-baselines.html). " -"With this first preview release we're also inviting the community to " -"[contribute their own baselines](https://flower.ai/docs/baselines/how-to-" -"contribute-baselines.html)." +"**Introduce new end-to-end testing infrastructure** " +"([#1842](https://github.com/adap/flower/pull/1842), " +"[#2071](https://github.com/adap/flower/pull/2071), " +"[#2072](https://github.com/adap/flower/pull/2072), " +"[#2068](https://github.com/adap/flower/pull/2068), " +"[#2067](https://github.com/adap/flower/pull/2067), " +"[#2069](https://github.com/adap/flower/pull/2069), " +"[#2073](https://github.com/adap/flower/pull/2073), " +"[#2070](https://github.com/adap/flower/pull/2070), " +"[#2074](https://github.com/adap/flower/pull/2074), " +"[#2082](https://github.com/adap/flower/pull/2082), " +"[#2084](https://github.com/adap/flower/pull/2084), " +"[#2093](https://github.com/adap/flower/pull/2093), " +"[#2109](https://github.com/adap/flower/pull/2109), " +"[#2095](https://github.com/adap/flower/pull/2095), " +"[#2140](https://github.com/adap/flower/pull/2140), " +"[#2137](https://github.com/adap/flower/pull/2137), " +"[#2165](https://github.com/adap/flower/pull/2165))" msgstr "" -"Flower Baselines 的第一个预览版已经发布！我们通过实现 " -"FedOpt（FedYogi、FedAdam、FedAdagrad）、FedBN 和 FedAvgM 来启动 Flower " -"Baselines。请查阅文档了解如何使用 [Flower Baselines](https://flower.ai/docs/using-" -"baselines.html)。在首次发布预览版时，我们还邀请社区成员[贡献自己的Baselines](https://flower.ai/docs" -"/contributing-baselines.html)。" +"*介绍新的端到端测试** ([#1842](https://github.com/adap/flower/pull/1842), " +"[#2071](https://github.com/adap/flower/pull/2071), " +"[#2072](https://github.com/adap/flower/pull/2072), " +"[#2068](https://github.com/adap/flower/pull/2068), " +"[#2067](https://github.com/adap/flower/pull/2067), " +"[#2069](https://github.com/adap/flower/pull/2069), " +"[#2073](https://github.com/adap/flower/pull/2073), " +"[#2070](https://github.com/adap/flower/pull/2070), " +"[#2074](https://github.com/adap/flower/pull/2074), " +"[#2082](https://github.com/adap/flower/pull/2082), " +"[#2084](https://github.com/adap/flower/pull/2084), " +"[#2093](https://github.com/adap/flower/pull/2093), " +"[#2109](https://github.com/adap/flower/pull/2109), " +"[#2095](https://github.com/adap/flower/pull/2095), " +"[#2140](https://github.com/adap/flower/pull/2140), " +"[#2137](https://github.com/adap/flower/pull/2137), " +"[#2165](https://github.com/adap/flower/pull/2165))" -#: ../../source/ref-changelog.md:662 +#: ../../source/ref-changelog.md:390 msgid "" -"**C++ client SDK (preview) and code example** " -"([#1111](https://github.com/adap/flower/pull/1111))" -msgstr "**C++客户端SDK（预览版）和代码示例**（[#1111](https://github.com/adap/flower/pull/1111)）" +"A new testing infrastructure ensures that new changes stay compatible " +"with existing framework integrations or strategies." +msgstr "新的测试设施可确保新的变更与现有的框架集成或策略保持兼容。" + +#: ../../source/ref-changelog.md:392 +msgid "**Deprecate Python 3.7**" +msgstr "** 过时的 Python 3.7**" -#: ../../source/ref-changelog.md:664 +#: ../../source/ref-changelog.md:394 msgid "" -"Preview support for Flower clients written in C++. The C++ preview " -"includes a Flower client SDK and a quickstart code example that " -"demonstrates a simple C++ client using the SDK." -msgstr "" -"预览版支持用 C++ 编写的 Flower 客户端。C++ 预览版包括一个 Flower 客户端 SDK 和一个快速入门代码示例，使用 SDK " -"演示了一个简单的 C++ 客户端。" +"Since Python 3.7 reached its end of life (EOL) on 2023-06-27, support for" +" Python 3.7 is now deprecated and will be removed in an upcoming release." +msgstr "由于 Python 3.7 已于 2023-06-27 弃用 (EOL)，对 Python 3.7 的支持现已废弃，并将在即将发布的版本中移除。" -#: ../../source/ref-changelog.md:666 +#: ../../source/ref-changelog.md:396 msgid "" -"**Add experimental support for Python 3.10 and Python 3.11** " -"([#1135](https://github.com/adap/flower/pull/1135))" +"**Add new** `FedTrimmedAvg` **strategy** " +"([#1769](https://github.com/adap/flower/pull/1769), " +"[#1853](https://github.com/adap/flower/pull/1853))" msgstr "" -"** 增加对 Python 3.10 和 Python 3.11 的实验支持** " -"([#1135](https://github.com/adap/flower/pull/1135))" +"**添加新的**`FedTrimmedAvg`**策略**（[#1769](https://github.com/adap/flower/pull/1769)," +" [#1853](https://github.com/adap/flower/pull/1853)" -#: ../../source/ref-changelog.md:668 +#: ../../source/ref-changelog.md:398 msgid "" -"Python 3.10 is the latest stable release of Python and Python 3.11 is due" -" to be released in October. This Flower release adds experimental support" -" for both Python versions." +"The new `FedTrimmedAvg` strategy implements Trimmed Mean by [Dong Yin, " +"2018](https://arxiv.org/abs/1803.01498)." msgstr "" -"Python 3.10 是 Python 的最新稳定版本，Python 3.11 将于 10 月份发布。Flower 版本增加了对这两个 " -"Python 版本的实验支持。" +"新的 \"FedTrimmedAvg \"策略实现了[Dong Yin, " +"2018]（https://arxiv.org/abs/1803.01498）的 \"Trimmed Mean\"。" -#: ../../source/ref-changelog.md:670 +#: ../../source/ref-changelog.md:400 msgid "" -"**Aggregate custom metrics through user-provided functions** " -"([#1144](https://github.com/adap/flower/pull/1144))" -msgstr "**通过用户提供的函数聚合自定义指标**（[#1144](https://github.com/adap/flower/pull/1144)）" +"**Introduce start_driver** " +"([#1697](https://github.com/adap/flower/pull/1697))" +msgstr "**引入 start_driver**（[#1697](https://github.com/adap/flower/pull/1697)）" -#: ../../source/ref-changelog.md:672 +#: ../../source/ref-changelog.md:402 msgid "" -"Custom metrics (e.g., `accuracy`) can now be aggregated without having to" -" customize the strategy. Built-in strategies support two new arguments, " -"`fit_metrics_aggregation_fn` and `evaluate_metrics_aggregation_fn`, that " -"allow passing custom metric aggregation functions." +"In addition to `start_server` and using the raw Driver API, there is a " +"new `start_driver` function that allows for running `start_server` " +"scripts as a Flower driver with only a single-line code change. Check out" +" the `mt-pytorch` code example to see a working example using " +"`start_driver`." msgstr "" -"现在无需定制策略即可聚合自定义度量（如`准确度`）。内置策略支持两个新参数：`fit_metrics_aggregation_fn` " -"和`evaluate_metrics_aggregation_fn`，允许传递自定义度量聚合函数。" +"除了 `start_server` 和使用原始驱动 API 之外，还有一个新的 `start_driver` 函数，只需修改一行代码，就能将 " +"`start_server` 脚本作为 Flower 驱动程序运行。请查看 `mt-pytorch` 代码示例，了解使用 " +"`start_driver` 的工作示例。" -#: ../../source/ref-changelog.md:674 +#: ../../source/ref-changelog.md:404 msgid "" -"**User-configurable round timeout** " -"([#1162](https://github.com/adap/flower/pull/1162))" -msgstr "**用户可配置的回合超时**（[#1162](https://github.com/adap/flower/pull/1162)）" +"**Add parameter aggregation to** `mt-pytorch` **code example** " +"([#1785](https://github.com/adap/flower/pull/1785))" +msgstr "" +"为 `mt-pytorch` **代码示例**添加参数聚合 " +"([#1785](https://github.com/adap/flower/pull/1785))" -#: ../../source/ref-changelog.md:676 +#: ../../source/ref-changelog.md:406 msgid "" -"A new configuration value allows the round timeout to be set for " -"`start_server` and `start_simulation`. If the `config` dictionary " -"contains a `round_timeout` key (with a `float` value in seconds), the " -"server will wait *at least* `round_timeout` seconds before it closes the " -"connection." +"The `mt-pytorch` example shows how to aggregate parameters when writing a" +" driver script. The included `driver.py` and `server.py` have been " +"aligned to demonstrate both the low-level way and the high-level way of " +"building server-side logic." msgstr "" -"新的配置值允许为 `start_server` 和 `start_simulation` 设置回合超时。如果 `config` 字典中包含一个 " -"`round_timeout` 键（以秒为单位的 `float`值），服务器将至少等待 ** `round_timeout` 秒后才关闭连接。" +"`mt-pytorch`示例展示了如何在编写驱动程序脚本时聚合参数。附带的 `driver.py` 和 `server.py` " +"已经进行了调整，以演示构建服务器端逻辑的低级方法和高级方法。" -#: ../../source/ref-changelog.md:678 +#: ../../source/ref-changelog.md:408 msgid "" -"**Enable both federated evaluation and centralized evaluation to be used " -"at the same time in all built-in strategies** " -"([#1091](https://github.com/adap/flower/pull/1091))" -msgstr "" -"**允许在所有内置策略中同时使用联邦评价和集中评估** " -"([#1091](https://github.com/adap/flower/pull/1091))" - -#: ../../source/ref-changelog.md:680 -msgid "" -"Built-in strategies can now perform both federated evaluation (i.e., " -"client-side) and centralized evaluation (i.e., server-side) in the same " -"round. Federated evaluation can be disabled by setting `fraction_eval` to" -" `0.0`." +"**Migrate experimental REST API to Starlette** " +"([2171](https://github.com/adap/flower/pull/2171))" msgstr "" -"内置策略现在可以在同一轮中同时执行联邦评估（即客户端）和集中评估（即服务器端）。可以通过将 `fraction_eval` 设置为 " -"`0.0`来禁用联邦评估。" +"**将实验性 REST API 移植到 Starlette** " +"([2171](https://github.com/adap/flower/pull/2171))" -#: ../../source/ref-changelog.md:682 +#: ../../source/ref-changelog.md:410 msgid "" -"**Two new Jupyter Notebook tutorials** " -"([#1141](https://github.com/adap/flower/pull/1141))" +"The (experimental) REST API used to be implemented in " +"[FastAPI](https://fastapi.tiangolo.com/), but it has now been migrated to" +" use [Starlette](https://www.starlette.io/) directly." msgstr "" -"**两本新的 Jupyter Notebook 教程** " -"([#1141](https://github.com/adap/flower/pull/1141))" +"REST API（试验性）曾在 [FastAPI](https://fastapi.tiangolo.com/) 中实现，但现在已迁移到直接使用 " +"[Starlette](https://www.starlette.io/) 。" -#: ../../source/ref-changelog.md:684 +#: ../../source/ref-changelog.md:412 msgid "" -"Two Jupyter Notebook tutorials (compatible with Google Colab) explain " -"basic and intermediate Flower features:" -msgstr "两本 Jupyter Notebook 教程（与 Google Colab 兼容）介绍了 Flower 的基本和中级功能：" +"Please note: The REST request-response API is still experimental and will" +" likely change significantly over time." +msgstr "请注意：REST 请求-响应 API 仍处于试验阶段，随着时间的推移可能会发生重大变化。" -#: ../../source/ref-changelog.md:686 +#: ../../source/ref-changelog.md:414 msgid "" -"*An Introduction to Federated Learning*: [Open in " -"Colab](https://colab.research.google.com/github/adap/flower/blob/main/tutorials/Flower-1" -"-Intro-to-FL-PyTorch.ipynb)" +"**Introduce experimental gRPC request-response API** " +"([#1867](https://github.com/adap/flower/pull/1867), " +"[#1901](https://github.com/adap/flower/pull/1901))" msgstr "" -"*联邦学习简介*： [在 Colab " -"中打开](https://colab.research.google.com/github/adap/flower/blob/main/tutorials/Flower-1" -"-Intro-to-FL-PyTorch.ipynb)" +"**引入实验性 gRPC 请求-响应 API** " +"（[#1867](https://github.com/adap/flower/pull/1867), " +"[#1901](https://github.com/adap/flower/pull/1901)" -#: ../../source/ref-changelog.md:688 +#: ../../source/ref-changelog.md:416 msgid "" -"*Using Strategies in Federated Learning*: [Open in " -"Colab](https://colab.research.google.com/github/adap/flower/blob/main/tutorials/Flower-2" -"-Strategies-in-FL-PyTorch.ipynb)" +"In addition to the existing gRPC API (based on bidirectional streaming) " +"and the experimental REST API, there is now a new gRPC API that uses a " +"request-response model to communicate with client nodes." msgstr "" -"*在联邦学习中使用策略*： [在 Colab " -"中打开](https://colab.research.google.com/github/adap/flower/blob/main/tutorials/Flower-2" -"-Strategies-in-FL-PyTorch.ipynb)" +"除了现有的 gRPC 应用程序接口（基于双向流）和试验性 REST 应用程序接口外，现在还有一个新的 gRPC " +"应用程序接口，它使用请求-响应模型与客户端节点通信。" -#: ../../source/ref-changelog.md:690 +#: ../../source/ref-changelog.md:418 msgid "" -"**New FedAvgM strategy (Federated Averaging with Server Momentum)** " -"([#1076](https://github.com/adap/flower/pull/1076))" -msgstr "" -"**新的 FedAvgM 策略（带服务器动量的联邦平均）** " -"([#1076](https://github.com/adap/flower/pull/1076))" +"Please note: The gRPC request-response API is still experimental and will" +" likely change significantly over time." +msgstr "请注意：gRPC 请求-响应 API 仍处于试验阶段，随着时间的推移可能会发生重大变化。" -#: ../../source/ref-changelog.md:692 +#: ../../source/ref-changelog.md:420 msgid "" -"The new `FedAvgM` strategy implements Federated Averaging with Server " -"Momentum \\[Hsu et al., 2019\\]." -msgstr "新的 \"FedAvgM \"策略实现了带服务器动量的联邦平均[Hsu et al., 2019\\]." +"**Replace the experimental** `start_client(rest=True)` **with the new** " +"`start_client(transport=\"rest\")` " +"([#1880](https://github.com/adap/flower/pull/1880))" +msgstr "" +"**用新的** `start_client(transport=\"rest\")` 替换实验性** " +"`start_client(rest=True)` " +"([#1880](https://github.com/adap/flower/pull/1880))" -#: ../../source/ref-changelog.md:694 +#: ../../source/ref-changelog.md:422 msgid "" -"**New advanced PyTorch code example** " -"([#1007](https://github.com/adap/flower/pull/1007))" -msgstr "**新的 PyTorch 高级代码示例** ([#1007](https://github.com/adap/flower/pull/1007))" +"The (experimental) `start_client` argument `rest` was deprecated in " +"favour of a new argument `transport`. `start_client(transport=\"rest\")` " +"will yield the same behaviour as `start_client(rest=True)` did before. " +"All code should migrate to the new argument `transport`. The deprecated " +"argument `rest` will be removed in a future release." +msgstr "" +"已废弃（试验性的）`start_client`参数`rest`，改用新参数`transport`。`start_client(transport=\"rest\")`将产生与以前的`start_client(rest=True)`相同的行为。所有代码都应迁移到新参数" +" `transport`。过时的参数 `rest` 将在今后的版本中删除。" -#: ../../source/ref-changelog.md:696 +#: ../../source/ref-changelog.md:424 msgid "" -"A new code example (`advanced_pytorch`) demonstrates advanced Flower " -"concepts with PyTorch." -msgstr "新代码示例 (`advanced_pytorch`) 演示了 PyTorch 的高级 Flower 概念。" +"**Add a new gRPC option** " +"([#2197](https://github.com/adap/flower/pull/2197))" +msgstr "** 添加一个新的 gRPC 选项**（[#2197](https://github.com/adap/flower/pull/2197)）" -#: ../../source/ref-changelog.md:698 +#: ../../source/ref-changelog.md:426 msgid "" -"**New JAX code example** " -"([#906](https://github.com/adap/flower/pull/906), " -"[#1143](https://github.com/adap/flower/pull/1143))" +"We now start a gRPC server with the `grpc.keepalive_permit_without_calls`" +" option set to 0 by default. This prevents the clients from sending " +"keepalive pings when there is no outstanding stream." msgstr "" -"**新的 JAX 代码示例**（[#906](https://github.com/adap/flower/pull/906), " -"[#1143](https://github.com/adap/flower/pull/1143)" +"现在我们启动一个 gRPC 服务器，并将 `grpc.keepalive_permit_without_calls` 选项默认设置为 " +"0。这将防止客户端在没有未处理数据流时发送 keepalive pings。" -#: ../../source/ref-changelog.md:700 +#: ../../source/ref-changelog.md:428 msgid "" -"A new code example (`jax_from_centralized_to_federated`) shows federated " -"learning with JAX and Flower." -msgstr "新代码示例（`jax_from_centralized_to_federated`）展示了使用 JAX 和 Flower 的联邦学习。" +"**Improve example notebooks** " +"([#2005](https://github.com/adap/flower/pull/2005))" +msgstr "**改进示例笔记** ([#2005](https://github.com/adap/flower/pull/2005))" -#: ../../source/ref-changelog.md:704 -msgid "" -"New option to keep Ray running if Ray was already initialized in " -"`start_simulation` ([#1177](https://github.com/adap/flower/pull/1177))" -msgstr "" -"新增选项，用于在 \"start_simulation\"（开始模拟）中已初始化 Ray 的情况下保持 Ray " -"运行（[#1177](https://github.com/adap/flower/pull/1177)）" +#: ../../source/ref-changelog.md:430 +msgid "There's a new 30min Federated Learning PyTorch tutorial!" +msgstr "有一个新的 30 分钟的联邦学习 PyTorch 教程！" -#: ../../source/ref-changelog.md:705 +#: ../../source/ref-changelog.md:432 msgid "" -"Add support for custom `ClientManager` as a `start_simulation` parameter " -"([#1171](https://github.com/adap/flower/pull/1171))" +"**Example updates** ([#1772](https://github.com/adap/flower/pull/1772), " +"[#1873](https://github.com/adap/flower/pull/1873), " +"[#1981](https://github.com/adap/flower/pull/1981), " +"[#1988](https://github.com/adap/flower/pull/1988), " +"[#1984](https://github.com/adap/flower/pull/1984), " +"[#1982](https://github.com/adap/flower/pull/1982), " +"[#2112](https://github.com/adap/flower/pull/2112), " +"[#2144](https://github.com/adap/flower/pull/2144), " +"[#2174](https://github.com/adap/flower/pull/2174), " +"[#2225](https://github.com/adap/flower/pull/2225), " +"[#2183](https://github.com/adap/flower/pull/2183))" msgstr "" -"添加对自定义 \"客户端管理器 \"作为 \"start_simulation " -"\"参数的支持（[#1171](https://github.com/adap/flower/pull/1171)）" +"**更新Example** ([#1772](https://github.com/adap/flower/pull/1772), " +"[#1873](https://github.com/adap/flower/pull/1873), " +"[#1981](https://github.com/adap/flower/pull/1981), " +"[#1988](https://github.com/adap/flower/pull/1988), " +"[#1984](https://github.com/adap/flower/pull/1984), " +"[#1982](https://github.com/adap/flower/pull/1982), " +"[#2112](https://github.com/adap/flower/pull/2112), " +"[#2144](https://github.com/adap/flower/pull/2144), " +"[#2174](https://github.com/adap/flower/pull/2174), " +"[#2225](https://github.com/adap/flower/pull/2225), " +"[#2183](https://github.com/adap/flower/pull/2183))" -#: ../../source/ref-changelog.md:706 +#: ../../source/ref-changelog.md:434 msgid "" -"New documentation for [implementing " -"strategies](https://flower.ai/docs/framework/how-to-implement-" -"strategies.html) ([#1097](https://github.com/adap/flower/pull/1097), " -"[#1175](https://github.com/adap/flower/pull/1175))" +"Many examples have received significant updates, including simplified " +"advanced-tensorflow and advanced-pytorch examples, improved macOS " +"compatibility of TensorFlow examples, and code examples for simulation. A" +" major upgrade is that all code examples now have a `requirements.txt` " +"(in addition to `pyproject.toml`)." msgstr "" -"[实施战略](https://flower.ai/docs/framework/how-to-implement-strategies.html)" -" 的新文件（[#1097](https://github.com/adap/flower/pull/1097), " -"[#1175](https://github.com/adap/flower/pull/1175)" - -#: ../../source/ref-changelog.md:707 -msgid "" -"New mobile-friendly documentation theme " -"([#1174](https://github.com/adap/flower/pull/1174))" -msgstr "新的移动友好型文档主题 ([#1174](https://github.com/adap/flower/pull/1174))" +"许多示例都进行了重大更新，包括简化了 advanced-tensorflow 和 advanced-pytorch 示例，改进了 " +"TensorFlow 示例的 macOS 兼容性，以及模拟代码示例。一项重大升级是所有代码示例现在都有了 " +"\"requirements.txt\"（除 \"pyproject.toml \"外）。" -#: ../../source/ref-changelog.md:708 +#: ../../source/ref-changelog.md:436 msgid "" -"Limit version range for (optional) `ray` dependency to include only " -"compatible releases (`>=1.9.2,<1.12.0`) " -"([#1205](https://github.com/adap/flower/pull/1205))" +"**General improvements** " +"([#1872](https://github.com/adap/flower/pull/1872), " +"[#1866](https://github.com/adap/flower/pull/1866), " +"[#1884](https://github.com/adap/flower/pull/1884), " +"[#1837](https://github.com/adap/flower/pull/1837), " +"[#1477](https://github.com/adap/flower/pull/1477), " +"[#2171](https://github.com/adap/flower/pull/2171))" msgstr "" -"限制（可选）`ray`依赖的版本范围，使其仅包含兼容版本（`>=1.9.2,<1.12.0`） " -"([#1205](https://github.com/adap/flower/pull/1205))" +"**普通改进**（[#1872](https://github.com/adap/flower/pull/1872), " +"[#1866](https://github.com/adap/flower/pull/1866), " +"[#1884](https://github.com/adap/flower/pull/1884), " +"[#1837](https://github.com/adap/flower/pull/1837), " +"[#1477](https://github.com/adap/flower/pull/1477), " +"[#2171](https://github.com/adap/flower/pull/2171))" -#: ../../source/ref-changelog.md:712 -msgid "" -"**Remove deprecated support for Python 3.6** " -"([#871](https://github.com/adap/flower/pull/871))" -msgstr "**删除对 Python 3.6 的过时支持** ([#871](https://github.com/adap/flower/pull/871))" +#: ../../source/ref-changelog.md:444 +msgid "v1.4.0 (2023-04-21)" +msgstr "v1.4.0 (2023-04-21)" -#: ../../source/ref-changelog.md:713 +#: ../../source/ref-changelog.md:450 msgid "" -"**Remove deprecated KerasClient** " -"([#857](https://github.com/adap/flower/pull/857))" -msgstr "**移除过时的 KerasClient**（[#857](https://github.com/adap/flower/pull/857)）" +"`Adam Narozniak`, `Alexander Viala Bellander`, `Charles Beauville`, " +"`Chenyang Ma (Danny)`, `Daniel J. Beutel`, `Edoardo`, `Gautam Jajoo`, " +"`Iacob-Alexandru-Andrei`, `JDRanpariya`, `Jean Charle Yaacoub`, `Kunal " +"Sarkhel`, `L. Jiang`, `Lennart Behme`, `Max Kapsecker`, `Michał`, `Nic " +"Lane`, `Nikolaos Episkopos`, `Ragy`, `Saurav Maheshkar`, `Semo Yang`, " +"`Steve Laskaridis`, `Steven Hé (Sīchàng)`, `Taner Topal`" +msgstr "" +"`Adam Narozniak`, `Alexander Viala Bellander`, `Charles Beauville`, " +"`Chenyang Ma (Danny)`, `Daniel J. Beutel`, `Edoardo`, `Gautam Jajoo`, " +"`Iacob-Alexandru-Andrei`, `JDRanpariya`, `Jean Charle Yaacoub`, `Kunal " +"Sarkhel`, `L. Jiang`, `Lennart Behme`, `Max Kapsecker`, `Michał`, `Nic " +"Lane`, `Nikolaos Episkopos`, `Ragy`, `Saurav Maheshkar`, `Semo Yang`, " +"`Steve Laskaridis`, `Steven Hé (Sīchàng)`, `Taner Topal`" -#: ../../source/ref-changelog.md:714 +#: ../../source/ref-changelog.md:454 msgid "" -"**Remove deprecated no-op extra installs** " -"([#973](https://github.com/adap/flower/pull/973))" -msgstr "**移除过时的不操作额外安装** ([#973](https://github.com/adap/flower/pull/973))" +"**Introduce support for XGBoost (**`FedXgbNnAvg` **strategy and " +"example)** ([#1694](https://github.com/adap/flower/pull/1694), " +"[#1709](https://github.com/adap/flower/pull/1709), " +"[#1715](https://github.com/adap/flower/pull/1715), " +"[#1717](https://github.com/adap/flower/pull/1717), " +"[#1763](https://github.com/adap/flower/pull/1763), " +"[#1795](https://github.com/adap/flower/pull/1795))" +msgstr "" +"**引入对XGBoost的支持（**`FedXgbNnAvg` **策略和示例）** " +"([#1694](https://github.com/adap/flower/pull/1694), " +"[#1709](https://github.com/adap/flower/pull/1709), " +"[#1715](https://github.com/adap/flower/pull/1715), " +"[#1717](https://github.com/adap/flower/pull/1717), " +"[#1763](https://github.com/adap/flower/pull/1763), " +"[#1795](https://github.com/adap/flower/pull/1795))" -#: ../../source/ref-changelog.md:715 +#: ../../source/ref-changelog.md:456 msgid "" -"**Remove deprecated proto fields from** `FitRes` **and** `EvaluateRes` " -"([#869](https://github.com/adap/flower/pull/869))" +"XGBoost is a tree-based ensemble machine learning algorithm that uses " +"gradient boosting to improve model accuracy. We added a new `FedXgbNnAvg`" +" " +"[strategy](https://github.com/adap/flower/tree/main/src/py/flwr/server/strategy/fedxgb_nn_avg.py)," +" and a [code example](https://github.com/adap/flower/tree/main/examples" +"/xgboost-quickstart) that demonstrates the usage of this new strategy in " +"an XGBoost project." msgstr "" -"**从** `FitRes` **和** `EvaluateRes` 中移除已废弃的 proto 字段 " -"([#869](https://github.com/adap/flower/pull/869))" +"XGBoost 是一种基于树的集合机器学习算法，它使用梯度提升来提高模型的准确性。我们添加了一个新的 " +"\"FedXgbNnAvg\"[策略](https://github.com/adap/flower/tree/main/src/py/flwr/server/strategy/fedxgb_nn_avg.py)和一个[代码示例](https://github.com/adap/flower/tree/main/examples" +"/xgboost-quickstart)，演示如何在 XGBoost 项目中使用这个新策略。" -#: ../../source/ref-changelog.md:716 +#: ../../source/ref-changelog.md:458 msgid "" -"**Remove deprecated QffedAvg strategy (replaced by QFedAvg)** " -"([#1107](https://github.com/adap/flower/pull/1107))" +"**Introduce iOS SDK (preview)** " +"([#1621](https://github.com/adap/flower/pull/1621), " +"[#1764](https://github.com/adap/flower/pull/1764))" msgstr "" -"**移除过时的 QffedAvg 策略（由 QFedAvg 取代）** " -"([#1107](https://github.com/adap/flower/pull/1107))" +"**介绍 iOS SDK（预览版）** ([#1621](https://github.com/adap/flower/pull/1621), " +"[#1764](https://github.com/adap/flower/pull/1764))" -#: ../../source/ref-changelog.md:717 +#: ../../source/ref-changelog.md:460 msgid "" -"**Remove deprecated DefaultStrategy strategy** " -"([#1142](https://github.com/adap/flower/pull/1142))" -msgstr "" -"**删除过时的 DefaultStrategy 策略** " -"([#1142](https://github.com/adap/flower/pull/1142))" +"This is a major update for anyone wanting to implement Federated Learning" +" on iOS mobile devices. We now have a swift iOS SDK present under " +"[src/swift/flwr](https://github.com/adap/flower/tree/main/src/swift/flwr)" +" that will facilitate greatly the app creating process. To showcase its " +"use, the [iOS " +"example](https://github.com/adap/flower/tree/main/examples/ios) has also " +"been updated!" +msgstr "" +"对于想要在 iOS 移动设备上实施联邦学习的人来说，这是一次重大更新。现在，我们在 " +"[src/swift/flwr](https://github.com/adap/flower/tree/main/src/swift/flwr)" +" 下提供了一个迅捷的 iOS SDK，这将大大方便应用程序的创建过程。为了展示其使用情况，我们还更新了 [iOS " +"示例](https://github.com/adap/flower/tree/main/examples/ios)！" -#: ../../source/ref-changelog.md:718 +#: ../../source/ref-changelog.md:462 msgid "" -"**Remove deprecated support for eval_fn accuracy return value** " -"([#1142](https://github.com/adap/flower/pull/1142))" +"**Introduce new \"What is Federated Learning?\" tutorial** " +"([#1657](https://github.com/adap/flower/pull/1657), " +"[#1721](https://github.com/adap/flower/pull/1721))" msgstr "" -"**删除已过时的对 eval_fn 返回值准确性的支持** " -"([#1142](https://github.com/adap/flower/pull/1142))" +"**引入新的 " +"\"什么是联邦学习？\"教程**（[#1657](https://github.com/adap/flower/pull/1657), " +"[#1721](https://github.com/adap/flower/pull/1721)" -#: ../../source/ref-changelog.md:719 +#: ../../source/ref-changelog.md:464 msgid "" -"**Remove deprecated support for passing initial parameters as NumPy " -"ndarrays** ([#1142](https://github.com/adap/flower/pull/1142))" +"A new [entry-level tutorial](https://flower.ai/docs/framework/tutorial-" +"what-is-federated-learning.html) in our documentation explains the basics" +" of Fedetated Learning. It enables anyone who's unfamiliar with Federated" +" Learning to start their journey with Flower. Forward it to anyone who's " +"interested in Federated Learning!" msgstr "" -"**移除对以 NumPy ndarrays 传递初始参数的过时支持** " -"([#1142](https://github.com/adap/flower/pull/1142))" - -#: ../../source/ref-changelog.md:721 -msgid "v0.18.0 (2022-02-28)" -msgstr "v0.18.0 (2022-02-28)" +"我们的文档中新增了一个[入门级教程](https://flower.ai/docs/framework/tutorial-what-is-" +"federated-learning.html)，解释了联邦学习的基础知识。它让任何不熟悉联邦学习的人都能开始 Flower " +"之旅。请转发给对联邦学习感兴趣的人！" -#: ../../source/ref-changelog.md:725 +#: ../../source/ref-changelog.md:466 msgid "" -"**Improved Virtual Client Engine compatibility with Jupyter Notebook / " -"Google Colab** ([#866](https://github.com/adap/flower/pull/866), " -"[#872](https://github.com/adap/flower/pull/872), " -"[#833](https://github.com/adap/flower/pull/833), " -"[#1036](https://github.com/adap/flower/pull/1036))" +"**Introduce new Flower Baseline: FedProx MNIST** " +"([#1513](https://github.com/adap/flower/pull/1513), " +"[#1680](https://github.com/adap/flower/pull/1680), " +"[#1681](https://github.com/adap/flower/pull/1681), " +"[#1679](https://github.com/adap/flower/pull/1679))" msgstr "" -"**改进了虚拟客户端引擎与 Jupyter Notebook / Google Colab 的兼容性** " -"([#866](https://github.com/adap/flower/pull/866), " -"[#872](https://github.com/adap/flower/pull/872), " -"[#833](https://github.com/adap/flower/pull/833), " -"[#1036](https://github.com/adap/flower/pull/1036))" +"**引入新的 Flower Baseline： FedProx MNIST** " +"（[#1513](https://github.com/adap/flower/pull/1513), " +"[#1680](https://github.com/adap/flower/pull/1680), " +"[#1681](https://github.com/adap/flower/pull/1681), " +"[#1679](https://github.com/adap/flower/pull/1679)" -#: ../../source/ref-changelog.md:727 +#: ../../source/ref-changelog.md:468 msgid "" -"Simulations (using the Virtual Client Engine through `start_simulation`) " -"now work more smoothly on Jupyter Notebooks (incl. Google Colab) after " -"installing Flower with the `simulation` extra (`pip install " -"flwr[simulation]`)." +"This new baseline replicates the MNIST+CNN task from the paper [Federated" +" Optimization in Heterogeneous Networks (Li et al., " +"2018)](https://arxiv.org/abs/1812.06127). It uses the `FedProx` strategy," +" which aims at making convergence more robust in heterogeneous settings." msgstr "" -"通过 `start_simulation` 在 Jupyter 笔记本（包括 Google Colab）上安装 Flower 并附加 " -"`simulation` (`pip install flwr[simulation]`)后，模拟（通过 `start_simulation` " -"使用虚拟客户端引擎）现在可以更流畅地运行。" +"这条新Baseline复现了论文[Federated Optimization in Heterogeneous Networks (Li et " +"al., 2018)](https://arxiv.org/abs/1812.06127)中的 MNIST+CNN 任务。它使用 " +"\"FedProx \"策略，旨在使收敛在异构环境中更加稳健。" -#: ../../source/ref-changelog.md:729 +#: ../../source/ref-changelog.md:470 msgid "" -"**New Jupyter Notebook code example** " -"([#833](https://github.com/adap/flower/pull/833))" +"**Introduce new Flower Baseline: FedAvg FEMNIST** " +"([#1655](https://github.com/adap/flower/pull/1655))" msgstr "" -"**新的 Jupyter Notebook 代码示例** " -"([#833](https://github.com/adap/flower/pull/833))" +"**引入新的 Flower Baseline： FedAvg FEMNIST** " +"([#1655](https://github.com/adap/flower/pull/1655))" -#: ../../source/ref-changelog.md:731 +#: ../../source/ref-changelog.md:472 msgid "" -"A new code example (`quickstart_simulation`) demonstrates Flower " -"simulations using the Virtual Client Engine through Jupyter Notebook " -"(incl. Google Colab)." +"This new baseline replicates an experiment evaluating the performance of " +"the FedAvg algorithm on the FEMNIST dataset from the paper [LEAF: A " +"Benchmark for Federated Settings (Caldas et al., " +"2018)](https://arxiv.org/abs/1812.01097)." msgstr "" -"新代码示例（`quickstart_simulation`）通过 Jupyter Notebook（包括 Google " -"Colab）演示了使用虚拟客户端引擎进行 Flower 模拟。" +"这一新Baseline复现了论文[LEAF: A Benchmark for Federated Settings（Caldas 等人，2018 " +"年）](https://arxiv.org/abs/1812.01097)中评估 FedAvg 算法在 FEMNIST 数据集上性能的实验。" -#: ../../source/ref-changelog.md:733 +#: ../../source/ref-changelog.md:474 msgid "" -"**Client properties (feature preview)** " -"([#795](https://github.com/adap/flower/pull/795))" -msgstr "**客户端属性（功能预览）** ([#795](https://github.com/adap/flower/pull/795))" +"**Introduce (experimental) REST API** " +"([#1594](https://github.com/adap/flower/pull/1594), " +"[#1690](https://github.com/adap/flower/pull/1690), " +"[#1695](https://github.com/adap/flower/pull/1695), " +"[#1712](https://github.com/adap/flower/pull/1712), " +"[#1802](https://github.com/adap/flower/pull/1802), " +"[#1770](https://github.com/adap/flower/pull/1770), " +"[#1733](https://github.com/adap/flower/pull/1733))" +msgstr "" +"**引入（试验性）REST API** ([#1594](https://github.com/adap/flower/pull/1594), " +"[#1690](https://github.com/adap/flower/pull/1690), " +"[#1695](https://github.com/adap/flower/pull/1695), " +"[#1712](https://github.com/adap/flower/pull/1712), " +"[#1802](https://github.com/adap/flower/pull/1802), " +"[#1770](https://github.com/adap/flower/pull/1770), " +"[#1733](https://github.com/adap/flower/pull/1733))" -#: ../../source/ref-changelog.md:735 +#: ../../source/ref-changelog.md:476 msgid "" -"Clients can implement a new method `get_properties` to enable server-side" -" strategies to query client properties." -msgstr "客户端可以实现一个新方法 `get_properties`，以启用服务器端策略来查询客户端属性。" +"A new REST API has been introduced as an alternative to the gRPC-based " +"communication stack. In this initial version, the REST API only supports " +"anonymous clients." +msgstr "作为基于 gRPC 的通信栈的替代方案，我们引入了新的 REST API。在初始版本中，REST API 仅支持匿名客户端。" -#: ../../source/ref-changelog.md:737 +#: ../../source/ref-changelog.md:478 msgid "" -"**Experimental Android support with TFLite** " -"([#865](https://github.com/adap/flower/pull/865))" -msgstr "** 使用 TFLite 实验性支持安卓系统** ([#865](https://github.com/adap/flower/pull/865))" +"Please note: The REST API is still experimental and will likely change " +"significantly over time." +msgstr "请注意：REST API 仍处于试验阶段，随着时间的推移可能会发生重大变化。" -#: ../../source/ref-changelog.md:739 +#: ../../source/ref-changelog.md:480 msgid "" -"Android support has finally arrived in `main`! Flower is both client-" -"agnostic and framework-agnostic by design. One can integrate arbitrary " -"client platforms and with this release, using Flower on Android has " -"become a lot easier." +"**Improve the (experimental) Driver API** " +"([#1663](https://github.com/adap/flower/pull/1663), " +"[#1666](https://github.com/adap/flower/pull/1666), " +"[#1667](https://github.com/adap/flower/pull/1667), " +"[#1664](https://github.com/adap/flower/pull/1664), " +"[#1675](https://github.com/adap/flower/pull/1675), " +"[#1676](https://github.com/adap/flower/pull/1676), " +"[#1693](https://github.com/adap/flower/pull/1693), " +"[#1662](https://github.com/adap/flower/pull/1662), " +"[#1794](https://github.com/adap/flower/pull/1794))" msgstr "" -"`main`终于支持 Android 了！Flower 的设计与客户端和框架无关。我们可以集成任意客户端平台，有了这个版本，在安卓系统上使用 " -"Flower 就变得更容易了。" +"**改进（试验性）驱动程序应用程序接口** ([#1663](https://github.com/adap/flower/pull/1663)," +" [#1666](https://github.com/adap/flower/pull/1666), " +"[#1667](https://github.com/adap/flower/pull/1667), " +"[#1664](https://github.com/adap/flower/pull/1664), " +"[#1675](https://github.com/adap/flower/pull/1675), " +"[#1676](https://github.com/adap/flower/pull/1676), " +"[#1693](https://github.com/adap/flower/pull/1693), " +"[#1662](https://github.com/adap/flower/pull/1662), " +"[#1794](https://github.com/adap/flower/pull/1794))" -#: ../../source/ref-changelog.md:741 +#: ../../source/ref-changelog.md:482 msgid "" -"The example uses TFLite on the client side, along with a new " -"`FedAvgAndroid` strategy. The Android client and `FedAvgAndroid` are " -"still experimental, but they are a first step towards a fully-fledged " -"Android SDK and a unified `FedAvg` implementation that integrated the new" -" functionality from `FedAvgAndroid`." +"The Driver API is still an experimental feature, but this release " +"introduces some major upgrades. One of the main improvements is the " +"introduction of an SQLite database to store server state on disk (instead" +" of in-memory). Another improvement is that tasks (instructions or " +"results) that have been delivered will now be deleted. This greatly " +"improves the memory efficiency of a long-running Flower server." msgstr "" -"该示例在客户端使用了 TFLite 以及新的 `FedAvgAndroid`策略。Android 客户端和 " -"`FedAvgAndroid`仍处于试验阶段，但这是向成熟的 Android SDK 和集成了 `FedAvgAndroid`新功能的统一 " -"`FedAvg`实现迈出的第一步。" +"驱动程序应用程序接口（Driver API）仍是一项试验性功能，但这一版本引入了一些重大升级。主要改进之一是引入了 SQLite " +"数据库，将服务器状态存储在磁盘上（而不是内存中）。另一项改进是，已交付的任务（指令或结果）现在将被删除。这大大提高了长期运行的 Flower " +"服务器的内存效率。" -#: ../../source/ref-changelog.md:743 +#: ../../source/ref-changelog.md:484 msgid "" -"**Make gRPC keepalive time user-configurable and decrease default " -"keepalive time** ([#1069](https://github.com/adap/flower/pull/1069))" -msgstr "" -"**使 gRPC 保持连接时间可由用户配置，并缩短默认保持连接时间** " -"([#1069](https://github.com/adap/flower/pull/1069))" +"**Fix spilling issues related to Ray during simulations** " +"([#1698](https://github.com/adap/flower/pull/1698))" +msgstr "**修复模拟过程中与Ray有关的溢出问题** ([#1698](https://github.com/adap/flower/pull/1698))" -#: ../../source/ref-changelog.md:745 +#: ../../source/ref-changelog.md:486 msgid "" -"The default gRPC keepalive time has been reduced to increase the " -"compatibility of Flower with more cloud environments (for example, " -"Microsoft Azure). Users can configure the keepalive time to customize the" -" gRPC stack based on specific requirements." -msgstr "" -"为提高 Flower 与更多云环境（如 Microsoft Azure）的兼容性，缩短了默认 gRPC 保持时间。用户可以根据具体要求配置 " -"keepalive 时间，自定义 gRPC 堆栈。" +"While running long simulations, `ray` was sometimes spilling huge amounts" +" of data that would make the training unable to continue. This is now " +"fixed! 🎉" +msgstr "在运行长时间模拟时，`ray` 有时会溢出大量数据，导致训练无法继续。现在这个问题已经解决！🎉" -#: ../../source/ref-changelog.md:747 +#: ../../source/ref-changelog.md:488 msgid "" -"**New differential privacy example using Opacus and PyTorch** " -"([#805](https://github.com/adap/flower/pull/805))" +"**Add new example using** `TabNet` **and Flower** " +"([#1725](https://github.com/adap/flower/pull/1725))" msgstr "" -"**使用 Opacus 和 PyTorch 的新差分隐私示例** " -"([#805](https://github.com/adap/flower/pull/805))" - -#: ../../source/ref-changelog.md:749 -msgid "" -"A new code example (`opacus`) demonstrates differentially-private " -"federated learning with Opacus, PyTorch, and Flower." -msgstr "一个新的代码示例（\"opacus\"）演示了使用 Opacus、PyTorch 和 Flower 进行差分隐私的联邦学习。" +"** 添加使用** `TabNet` ** 的新示例** " +"([#1725](https://github.com/adap/flower/pull/1725))" -#: ../../source/ref-changelog.md:751 +#: ../../source/ref-changelog.md:490 msgid "" -"**New Hugging Face Transformers code example** " -"([#863](https://github.com/adap/flower/pull/863))" +"TabNet is a powerful and flexible framework for training machine learning" +" models on tabular data. We now have a federated example using Flower: " +"[quickstart-tabnet](https://github.com/adap/flower/tree/main/examples" +"/quickstart-tabnet)." msgstr "" -"**新的Hugging Face Transformers代码示例** " -"([#863](https://github.com/adap/flower/pull/863))" +"TabNet 是一个强大而灵活的框架，用于在表格数据上训练机器学习模型。我们现在有一个使用 Flower 的联邦示例：[quickstart-" +"tabnet](https://github.com/adap/flower/tree/main/examples/quickstart-" +"tabnet)。" -#: ../../source/ref-changelog.md:753 +#: ../../source/ref-changelog.md:492 msgid "" -"A new code example (`quickstart_huggingface`) demonstrates usage of " -"Hugging Face Transformers with Flower." -msgstr "新的代码示例(`quickstart_huggingface`)证明了结合Flower和Hugging Face Transformers的实用性。" +"**Add new how-to guide for monitoring simulations** " +"([#1649](https://github.com/adap/flower/pull/1649))" +msgstr "** 添加新的模拟监控指南** ([#1649](https://github.com/adap/flower/pull/1649))" -#: ../../source/ref-changelog.md:755 +#: ../../source/ref-changelog.md:494 msgid "" -"**New MLCube code example** " -"([#779](https://github.com/adap/flower/pull/779), " -"[#1034](https://github.com/adap/flower/pull/1034), " -"[#1065](https://github.com/adap/flower/pull/1065), " -"[#1090](https://github.com/adap/flower/pull/1090))" -msgstr "" -"**新的 MLCube 代码示例** ([#779](https://github.com/adap/flower/pull/779), " -"[#1034](https://github.com/adap/flower/pull/1034), " -"[#1065](https://github.com/adap/flower/pull/1065), " -"[#1090](https://github.com/adap/flower/pull/1090))" +"We now have a documentation guide to help users monitor their performance" +" during simulations." +msgstr "我们现在有一份文档指南，可帮助用户在模拟过程中监控其性能。" -#: ../../source/ref-changelog.md:757 +#: ../../source/ref-changelog.md:496 msgid "" -"A new code example (`quickstart_mlcube`) demonstrates usage of MLCube " -"with Flower." -msgstr "新代码示例（\"quickstart_mlcube\"）演示了 MLCube 与 Flower 的用法。" +"**Add training metrics to** `History` **object during simulations** " +"([#1696](https://github.com/adap/flower/pull/1696))" +msgstr "" +"**在模拟过程中为***`历史`***对象添加训练指标*** " +"([#1696](https://github.com/adap/flower/pull/1696))" -#: ../../source/ref-changelog.md:759 +#: ../../source/ref-changelog.md:498 msgid "" -"**SSL-enabled server and client** " -"([#842](https://github.com/adap/flower/pull/842), " -"[#844](https://github.com/adap/flower/pull/844), " -"[#845](https://github.com/adap/flower/pull/845), " -"[#847](https://github.com/adap/flower/pull/847), " -"[#993](https://github.com/adap/flower/pull/993), " -"[#994](https://github.com/adap/flower/pull/994))" +"The `fit_metrics_aggregation_fn` can be used to aggregate training " +"metrics, but previous releases did not save the results in the `History` " +"object. This is now the case!" msgstr "" -"** 支持 SSL 的服务器和客户端** ([#842](https://github.com/adap/flower/pull/842), " -"[#844](https://github.com/adap/flower/pull/844), " -"[#845](https://github.com/adap/flower/pull/845), " -"[#847](https://github.com/adap/flower/pull/847), " -"[#993](https://github.com/adap/flower/pull/993), " -"[#994](https://github.com/adap/flower/pull/994))" +"`fit_metrics_aggregation_fn`可用于汇总训练指标，但以前的版本不会将结果保存在 \"History " +"\"对象中。现在可以了！" -#: ../../source/ref-changelog.md:761 +#: ../../source/ref-changelog.md:500 msgid "" -"SSL enables secure encrypted connections between clients and servers. " -"This release open-sources the Flower secure gRPC implementation to make " -"encrypted communication channels accessible to all Flower users." -msgstr "SSL 可实现客户端与服务器之间的安全加密连接。该版本开源了 Flower 安全 gRPC 实现，使所有 Flower 用户都能访问加密通信通道。" - -#: ../../source/ref-changelog.md:763 -msgid "" -"**Updated** `FedAdam` **and** `FedYogi` **strategies** " -"([#885](https://github.com/adap/flower/pull/885), " -"[#895](https://github.com/adap/flower/pull/895))" +"**General improvements** " +"([#1659](https://github.com/adap/flower/pull/1659), " +"[#1646](https://github.com/adap/flower/pull/1646), " +"[#1647](https://github.com/adap/flower/pull/1647), " +"[#1471](https://github.com/adap/flower/pull/1471), " +"[#1648](https://github.com/adap/flower/pull/1648), " +"[#1651](https://github.com/adap/flower/pull/1651), " +"[#1652](https://github.com/adap/flower/pull/1652), " +"[#1653](https://github.com/adap/flower/pull/1653), " +"[#1659](https://github.com/adap/flower/pull/1659), " +"[#1665](https://github.com/adap/flower/pull/1665), " +"[#1670](https://github.com/adap/flower/pull/1670), " +"[#1672](https://github.com/adap/flower/pull/1672), " +"[#1677](https://github.com/adap/flower/pull/1677), " +"[#1684](https://github.com/adap/flower/pull/1684), " +"[#1683](https://github.com/adap/flower/pull/1683), " +"[#1686](https://github.com/adap/flower/pull/1686), " +"[#1682](https://github.com/adap/flower/pull/1682), " +"[#1685](https://github.com/adap/flower/pull/1685), " +"[#1692](https://github.com/adap/flower/pull/1692), " +"[#1705](https://github.com/adap/flower/pull/1705), " +"[#1708](https://github.com/adap/flower/pull/1708), " +"[#1711](https://github.com/adap/flower/pull/1711), " +"[#1713](https://github.com/adap/flower/pull/1713), " +"[#1714](https://github.com/adap/flower/pull/1714), " +"[#1718](https://github.com/adap/flower/pull/1718), " +"[#1716](https://github.com/adap/flower/pull/1716), " +"[#1723](https://github.com/adap/flower/pull/1723), " +"[#1735](https://github.com/adap/flower/pull/1735), " +"[#1678](https://github.com/adap/flower/pull/1678), " +"[#1750](https://github.com/adap/flower/pull/1750), " +"[#1753](https://github.com/adap/flower/pull/1753), " +"[#1736](https://github.com/adap/flower/pull/1736), " +"[#1766](https://github.com/adap/flower/pull/1766), " +"[#1760](https://github.com/adap/flower/pull/1760), " +"[#1775](https://github.com/adap/flower/pull/1775), " +"[#1776](https://github.com/adap/flower/pull/1776), " +"[#1777](https://github.com/adap/flower/pull/1777), " +"[#1779](https://github.com/adap/flower/pull/1779), " +"[#1784](https://github.com/adap/flower/pull/1784), " +"[#1773](https://github.com/adap/flower/pull/1773), " +"[#1755](https://github.com/adap/flower/pull/1755), " +"[#1789](https://github.com/adap/flower/pull/1789), " +"[#1788](https://github.com/adap/flower/pull/1788), " +"[#1798](https://github.com/adap/flower/pull/1798), " +"[#1799](https://github.com/adap/flower/pull/1799), " +"[#1739](https://github.com/adap/flower/pull/1739), " +"[#1800](https://github.com/adap/flower/pull/1800), " +"[#1804](https://github.com/adap/flower/pull/1804), " +"[#1805](https://github.com/adap/flower/pull/1805))" msgstr "" -"**更新**`FedAdam`**和**`FedYogi`**战略** " -"([#885](https://github.com/adap/flower/pull/885), " -"[#895](https://github.com/adap/flower/pull/895))" +"**普通改进** ([#1659](https://github.com/adap/flower/pull/1659), " +"[#1646](https://github.com/adap/flower/pull/1646), " +"[#1647](https://github.com/adap/flower/pull/1647), " +"[#1471](https://github.com/adap/flower/pull/1471), " +"[#1648](https://github.com/adap/flower/pull/1648), " +"[#1651](https://github.com/adap/flower/pull/1651), " +"[#1652](https://github.com/adap/flower/pull/1652), " +"[#1653](https://github.com/adap/flower/pull/1653), " +"[#1659](https://github.com/adap/flower/pull/1659), " +"[#1665](https://github.com/adap/flower/pull/1665), " +"[#1670](https://github.com/adap/flower/pull/1670), " +"[#1672](https://github.com/adap/flower/pull/1672), " +"[#1677](https://github.com/adap/flower/pull/1677), " +"[#1684](https://github.com/adap/flower/pull/1684), " +"[#1683](https://github.com/adap/flower/pull/1683), " +"[#1686](https://github.com/adap/flower/pull/1686), " +"[#1682](https://github.com/adap/flower/pull/1682), " +"[#1685](https://github.com/adap/flower/pull/1685), " +"[#1692](https://github.com/adap/flower/pull/1692), " +"[#1705](https://github.com/adap/flower/pull/1705), " +"[#1708](https://github.com/adap/flower/pull/1708), " +"[#1711](https://github.com/adap/flower/pull/1711), " +"[#1713](https://github.com/adap/flower/pull/1713), " +"[#1714](https://github.com/adap/flower/pull/1714), " +"[#1718](https://github.com/adap/flower/pull/1718), " +"[#1716](https://github.com/adap/flower/pull/1716), " +"[#1723](https://github.com/adap/flower/pull/1723), " +"[#1735](https://github.com/adap/flower/pull/1735), " +"[#1678](https://github.com/adap/flower/pull/1678), " +"[#1750](https://github.com/adap/flower/pull/1750), " +"[#1753](https://github.com/adap/flower/pull/1753), " +"[#1736](https://github.com/adap/flower/pull/1736), " +"[#1766](https://github.com/adap/flower/pull/1766), " +"[#1760](https://github.com/adap/flower/pull/1760), " +"[#1775](https://github.com/adap/flower/pull/1775), " +"[#1776](https://github.com/adap/flower/pull/1776), " +"[#1777](https://github.com/adap/flower/pull/1777), " +"[#1779](https://github.com/adap/flower/pull/1779), " +"[#1784](https://github.com/adap/flower/pull/1784), " +"[#1773](https://github.com/adap/flower/pull/1773), " +"[#1755](https://github.com/adap/flower/pull/1755), " +"[#1789](https://github.com/adap/flower/pull/1789), " +"[#1788](https://github.com/adap/flower/pull/1788), " +"[#1798](https://github.com/adap/flower/pull/1798), " +"[#1799](https://github.com/adap/flower/pull/1799), " +"[#1739](https://github.com/adap/flower/pull/1739), " +"[#1800](https://github.com/adap/flower/pull/1800), " +"[#1804](https://github.com/adap/flower/pull/1804), " +"[#1805](https://github.com/adap/flower/pull/1805))" -#: ../../source/ref-changelog.md:765 -msgid "" -"`FedAdam` and `FedAdam` match the latest version of the Adaptive " -"Federated Optimization paper." -msgstr "FedAdam \"和 \"FedAdam \"与最新版本的 \"自适应联邦优化 \"论文相匹配。" +#: ../../source/ref-changelog.md:508 +msgid "v1.3.0 (2023-02-06)" +msgstr "v1.3.0 (2023-02-06)" -#: ../../source/ref-changelog.md:767 +#: ../../source/ref-changelog.md:514 msgid "" -"**Initialize** `start_simulation` **with a list of client IDs** " -"([#860](https://github.com/adap/flower/pull/860))" +"`Adam Narozniak`, `Alexander Viala Bellander`, `Charles Beauville`, " +"`Daniel J. Beutel`, `JDRanpariya`, `Lennart Behme`, `Taner Topal`" msgstr "" -"**初始化** `start_simulation` **使用客户端 ID 列表** " -"([#860](https://github.com/adap/flower/pull/860))" +"`Adam Narozniak`, `Alexander Viala Bellander`, `Charles Beauville`, " +"`Daniel J. Beutel`, `JDRanpariya`, `Lennart Behme`, `Taner Topal`" -#: ../../source/ref-changelog.md:769 +#: ../../source/ref-changelog.md:518 msgid "" -"`start_simulation` can now be called with a list of client IDs " -"(`clients_ids`, type: `List[str]`). Those IDs will be passed to the " -"`client_fn` whenever a client needs to be initialized, which can make it " -"easier to load data partitions that are not accessible through `int` " -"identifiers." +"**Add support for** `workload_id` **and** `group_id` **in Driver API** " +"([#1595](https://github.com/adap/flower/pull/1595))" msgstr "" -"现在可以使用客户端 ID 列表（`clients_ids`，类型：`List[str]`）调用 " -"`start_simulation`。每当需要初始化客户端时，这些 ID 就会被传递到 `client_fn` 中，这样就能更轻松地加载无法通过 " -"`int` 标识符访问的数据分区。" +"**在驱动程序应用程序接口中添加对** `workload_id` **和** `group_id` **的支持** " +"([#1595](https://github.com/adap/flower/pull/1595))" -#: ../../source/ref-changelog.md:773 +#: ../../source/ref-changelog.md:520 msgid "" -"Update `num_examples` calculation in PyTorch code examples in " -"([#909](https://github.com/adap/flower/pull/909))" +"The (experimental) Driver API now supports a `workload_id` that can be " +"used to identify which workload a task belongs to. It also supports a new" +" `group_id` that can be used, for example, to indicate the current " +"training round. Both the `workload_id` and `group_id` enable client nodes" +" to decide whether they want to handle a task or not." msgstr "" -"更新 PyTorch 代码示例中的 \"num_examples \"计算 " -"([#909](https://github.com/adap/flower/pull/909))" +"驱动程序 API（试验性）现在支持 `workload_id`，可用于识别任务所属的工作量。它还支持新的 " +"`group_id`，例如，可用于指示当前的训练轮次。通过 `workload_id` 和 `group_id` " +"客户端节点可以决定是否要处理某个任务。" -#: ../../source/ref-changelog.md:774 +#: ../../source/ref-changelog.md:522 msgid "" -"Expose Flower version through `flwr.__version__` " -"([#952](https://github.com/adap/flower/pull/952))" +"**Make Driver API and Fleet API address configurable** " +"([#1637](https://github.com/adap/flower/pull/1637))" msgstr "" -"通过 `flwr.__version__` 公开 Flower 版本 " -"([#952](https://github.com/adap/flower/pull/952))" +"**使Driver API 和Fleet " +"API地址可配置**（[#1637](https://github.com/adap/flower/pull/1637)）" -#: ../../source/ref-changelog.md:775 +#: ../../source/ref-changelog.md:524 msgid "" -"`start_server` in `app.py` now returns a `History` object containing " -"metrics from training ([#974](https://github.com/adap/flower/pull/974))" +"The (experimental) long-running Flower server (Driver API and Fleet API) " +"can now configure the server address of both Driver API (via `--driver-" +"api-address`) and Fleet API (via `--fleet-api-address`) when starting:" msgstr "" -"`app.py`中的 `start_server`现在会返回一个 `History` " -"对象，其中包含训练中的指标（[#974](https://github.com/adap/flower/pull/974)）" +"长期运行的 Flower 服务器（Driver API 和 Fleet API）现在可以在启动时配置 Driver API（通过 " +"`--driver-api-address`）和 Fleet API（通过 `-fleet-api-address`）的服务器地址：" -#: ../../source/ref-changelog.md:776 +#: ../../source/ref-changelog.md:526 +#, fuzzy msgid "" -"Make `max_workers` (used by `ThreadPoolExecutor`) configurable " -"([#978](https://github.com/adap/flower/pull/978))" +"`flower-server --driver-api-address \"0.0.0.0:8081\" --fleet-api-address " +"\"0.0.0.0:8086\"`" msgstr "" -"使 `max_workers`（由 " -"`ThreadPoolExecutor`使用）可配置（[#978](https://github.com/adap/flower/pull/978)）" +"`flower-server --driver-api-address \"0.0.0.0:8081\" --fleet-api-address " +"\"0.0.0.0:8086\"`" -#: ../../source/ref-changelog.md:777 -msgid "" -"Increase sleep time after server start to three seconds in all code " -"examples ([#1086](https://github.com/adap/flower/pull/1086))" -msgstr "在所有代码示例中，将服务器启动后的休眠时间延长至三秒（[#1086](https://github.com/adap/flower/pull/1086)）" +#: ../../source/ref-changelog.md:528 +msgid "Both IPv4 and IPv6 addresses are supported." +msgstr "支持 IPv4 和 IPv6 地址。" -#: ../../source/ref-changelog.md:778 +#: ../../source/ref-changelog.md:530 msgid "" -"Added a new FAQ section to the documentation " -"([#948](https://github.com/adap/flower/pull/948))" -msgstr "在文档中添加了新的常见问题部分 ([#948](https://github.com/adap/flower/pull/948))" +"**Add new example of Federated Learning using fastai and Flower** " +"([#1598](https://github.com/adap/flower/pull/1598))" +msgstr "" +"** 添加使用 fastai 和 Flower 进行联邦学习的新示例** " +"([#1598](https://github.com/adap/flower/pull/1598))" -#: ../../source/ref-changelog.md:779 +#: ../../source/ref-changelog.md:532 msgid "" -"And many more under-the-hood changes, library updates, documentation " -"changes, and tooling improvements!" -msgstr "还有更多底层更改、库更新、文档更改和工具改进！" +"A new code example (`quickstart-fastai`) demonstrates federated learning " +"with [fastai](https://www.fast.ai/) and Flower. You can find it here: " +"[quickstart-fastai](https://github.com/adap/flower/tree/main/examples" +"/quickstart-fastai)." +msgstr "" +"一个新的代码示例（`quickstart-fastai`）演示了使用 [fastai](https://www.fast.ai/) 和 " +"Flower 的联邦学习。您可以在这里找到它： [quickstart-" +"fastai](https://github.com/adap/flower/tree/main/examples/quickstart-" +"fastai)。" -#: ../../source/ref-changelog.md:783 +#: ../../source/ref-changelog.md:534 msgid "" -"**Removed** `flwr_example` **and** `flwr_experimental` **from release " -"build** ([#869](https://github.com/adap/flower/pull/869))" +"**Make Android example compatible with** `flwr >= 1.0.0` **and the latest" +" versions of Android** " +"([#1603](https://github.com/adap/flower/pull/1603))" msgstr "" -"**从发布版中删除**`flwr_example`**和**`flwr_experimental`** " -"([#869](https://github.com/adap/flower/pull/869))" +"**使安卓示例兼容** `flwr >= 1.0.0` **和最新版本的安卓** " +"([#1603](https://github.com/adap/flower/pull/1603))" -#: ../../source/ref-changelog.md:785 +#: ../../source/ref-changelog.md:536 msgid "" -"The packages `flwr_example` and `flwr_experimental` have been deprecated " -"since Flower 0.12.0 and they are not longer included in Flower release " -"builds. The associated extras (`baseline`, `examples-pytorch`, `examples-" -"tensorflow`, `http-logger`, `ops`) are now no-op and will be removed in " -"an upcoming release." +"The Android code example has received a substantial update: the project " +"is compatible with Flower 1.0 (and later), the UI received a full " +"refresh, and the project is updated to be compatible with newer Android " +"tooling." msgstr "" -"自 Flower 0.12.0 起，软件包 `flwr_example` 和 `flwr_experimental` 已被弃用，它们不再包含在 " -"Flower 的发布版本中。相关的额外包（`baseline`, `examples-pytorch`, `examples-" -"tensorflow`, `http-logger`, `ops`）现在已不再使用，并将在即将发布的版本中移除。" +"Android 代码示例已进行了大幅更新：项目兼容 Flower 1.0（及更高版本），用户界面已全面刷新，项目已更新为兼容较新的 Android" +" 工具。" -#: ../../source/ref-changelog.md:787 -msgid "v0.17.0 (2021-09-24)" -msgstr "v0.17.0 (2021-09-24)" +#: ../../source/ref-changelog.md:538 +msgid "" +"**Add new `FedProx` strategy** " +"([#1619](https://github.com/adap/flower/pull/1619))" +msgstr "**添加新的`FedProx`策略** （[#1619](https://github.com/adap/flower/pull/1619)）" -#: ../../source/ref-changelog.md:791 +#: ../../source/ref-changelog.md:540 msgid "" -"**Experimental virtual client engine** " -"([#781](https://github.com/adap/flower/pull/781) " -"[#790](https://github.com/adap/flower/pull/790) " -"[#791](https://github.com/adap/flower/pull/791))" +"This " +"[strategy](https://github.com/adap/flower/blob/main/src/py/flwr/server/strategy/fedprox.py)" +" is almost identical to " +"[`FedAvg`](https://github.com/adap/flower/blob/main/src/py/flwr/server/strategy/fedavg.py)," +" but helps users replicate what is described in this " +"[paper](https://arxiv.org/abs/1812.06127). It essentially adds a " +"parameter called `proximal_mu` to regularize the local models with " +"respect to the global models." msgstr "" -"**实验性虚拟客户端引擎** ([#781](https://github.com/adap/flower/pull/781) " -"[#790](https://github.com/adap/flower/pull/790) " -"[#791](https://github.com/adap/flower/pull/791))" +"该[策略](https://github.com/adap/flower/blob/main/src/py/flwr/server/strategy/fedprox.py)与[`FedAvg`](https://github.com/adap/flower/blob/main/src/py/flwr/server/strategy/fedavg.py)几乎相同，但可以帮助用户复现本[论文](https://arxiv.org/abs/1812.06127)中的描述。它的本质是添加一个名为" +" `proximal_mu`的参数，使局部模型与全局模型正则化。" -#: ../../source/ref-changelog.md:793 +#: ../../source/ref-changelog.md:542 msgid "" -"One of Flower's goals is to enable research at scale. This release " -"enables a first (experimental) peek at a major new feature, codenamed the" -" virtual client engine. Virtual clients enable simulations that scale to " -"a (very) large number of clients on a single machine or compute cluster. " -"The easiest way to test the new functionality is to look at the two new " -"code examples called `quickstart_simulation` and `simulation_pytorch`." -msgstr "" -"Flower 的目标之一是实现大规模研究。这一版本首次（试验性地）展示了代号为 \"虚拟客户端引擎 " -"\"的重要新功能。虚拟客户端可以在单台机器或计算集群上对大量客户端进行模拟。测试新功能的最简单方法是查看名为 " -"\"quickstart_simulation \"和 \"simulation_pytorch \"的两个新代码示例。" +"**Add new metrics to telemetry events** " +"([#1640](https://github.com/adap/flower/pull/1640))" +msgstr "**为遥测事件添加新指标**（[#1640](https://github.com/adap/flower/pull/1640)）" -#: ../../source/ref-changelog.md:795 +#: ../../source/ref-changelog.md:544 msgid "" -"The feature is still experimental, so there's no stability guarantee for " -"the API. It's also not quite ready for prime time and comes with a few " -"known caveats. However, those who are curious are encouraged to try it " -"out and share their thoughts." -msgstr "" -"该功能仍处于试验阶段，因此无法保证 API " -"的稳定性。此外，它还没有完全准备好进入黄金时间，并有一些已知的注意事项。不过，我们鼓励好奇的用户尝试使用并分享他们的想法。" +"An updated event structure allows, for example, the clustering of events " +"within the same workload." +msgstr "例如，更新后的事件结构可以将同一工作负载中的事件集中在一起。" -#: ../../source/ref-changelog.md:797 +#: ../../source/ref-changelog.md:546 msgid "" -"**New built-in strategies** " -"([#828](https://github.com/adap/flower/pull/828) " -"[#822](https://github.com/adap/flower/pull/822))" -msgstr "" -"**新的内置策略**（[#828](https://github.com/adap/flower/pull/828) " -"[#822](https://github.com/adap/flower/pull/822)" +"**Add new custom strategy tutorial section** " +"[#1623](https://github.com/adap/flower/pull/1623)" +msgstr "**添加新的自定义策略教程部分** [#1623](https://github.com/adap/flower/pull/1623)" -#: ../../source/ref-changelog.md:799 +#: ../../source/ref-changelog.md:548 msgid "" -"FedYogi - Federated learning strategy using Yogi on server-side. " -"Implementation based on https://arxiv.org/abs/2003.00295" -msgstr "FedYogi - 在服务器端使用 Yogi 的联邦学习策略。基于 https://arxiv.org/abs/2003.00295 实现" +"The Flower tutorial now has a new section that covers implementing a " +"custom strategy from scratch: [Open in " +"Colab](https://colab.research.google.com/github/adap/flower/blob/main/doc/source" +"/tutorial-build-a-strategy-from-scratch-pytorch.ipynb)" +msgstr "" +"Flower 教程新增了一个章节，介绍如何从零开始实施自定义策略： [在 Colab " +"中打开](https://colab.research.google.com/github/adap/flower/blob/main/doc/source" +"/tutorial-build-a-strategy-from-scratch-pytorch.ipynb)" -#: ../../source/ref-changelog.md:800 +#: ../../source/ref-changelog.md:550 msgid "" -"FedAdam - Federated learning strategy using Adam on server-side. " -"Implementation based on https://arxiv.org/abs/2003.00295" -msgstr "FedAdam - 在服务器端使用 Adam 的联邦学习策略。基于 https://arxiv.org/abs/2003.00295 实现" +"**Add new custom serialization tutorial section** " +"([#1622](https://github.com/adap/flower/pull/1622))" +msgstr "** 添加新的自定义序列化教程部分** ([#1622](https://github.com/adap/flower/pull/1622))" -#: ../../source/ref-changelog.md:802 +#: ../../source/ref-changelog.md:552 msgid "" -"**New PyTorch Lightning code example** " -"([#617](https://github.com/adap/flower/pull/617))" +"The Flower tutorial now has a new section that covers custom " +"serialization: [Open in " +"Colab](https://colab.research.google.com/github/adap/flower/blob/main/doc/source" +"/tutorial-customize-the-client-pytorch.ipynb)" msgstr "" -"**新的 PyTorch Lightning 代码示例** " -"([#617](https://github.com/adap/flower/pull/617))" +"Flower 教程现在新增了一个章节，介绍自定义序列化： [在 Colab " +"中打开](https://colab.research.google.com/github/adap/flower/blob/main/doc/source" +"/tutorial-customize-the-client-pytorch.ipynb)" -#: ../../source/ref-changelog.md:804 +#: ../../source/ref-changelog.md:554 msgid "" -"**New Variational Auto-Encoder code example** " -"([#752](https://github.com/adap/flower/pull/752))" -msgstr "**新的变分自动编码器代码示例** ([#752](https://github.com/adap/flower/pull/752))" +"**General improvements** " +"([#1638](https://github.com/adap/flower/pull/1638), " +"[#1634](https://github.com/adap/flower/pull/1634), " +"[#1636](https://github.com/adap/flower/pull/1636), " +"[#1635](https://github.com/adap/flower/pull/1635), " +"[#1633](https://github.com/adap/flower/pull/1633), " +"[#1632](https://github.com/adap/flower/pull/1632), " +"[#1631](https://github.com/adap/flower/pull/1631), " +"[#1630](https://github.com/adap/flower/pull/1630), " +"[#1627](https://github.com/adap/flower/pull/1627), " +"[#1593](https://github.com/adap/flower/pull/1593), " +"[#1616](https://github.com/adap/flower/pull/1616), " +"[#1615](https://github.com/adap/flower/pull/1615), " +"[#1607](https://github.com/adap/flower/pull/1607), " +"[#1609](https://github.com/adap/flower/pull/1609), " +"[#1608](https://github.com/adap/flower/pull/1608), " +"[#1603](https://github.com/adap/flower/pull/1603), " +"[#1590](https://github.com/adap/flower/pull/1590), " +"[#1580](https://github.com/adap/flower/pull/1580), " +"[#1599](https://github.com/adap/flower/pull/1599), " +"[#1600](https://github.com/adap/flower/pull/1600), " +"[#1601](https://github.com/adap/flower/pull/1601), " +"[#1597](https://github.com/adap/flower/pull/1597), " +"[#1595](https://github.com/adap/flower/pull/1595), " +"[#1591](https://github.com/adap/flower/pull/1591), " +"[#1588](https://github.com/adap/flower/pull/1588), " +"[#1589](https://github.com/adap/flower/pull/1589), " +"[#1587](https://github.com/adap/flower/pull/1587), " +"[#1573](https://github.com/adap/flower/pull/1573), " +"[#1581](https://github.com/adap/flower/pull/1581), " +"[#1578](https://github.com/adap/flower/pull/1578), " +"[#1574](https://github.com/adap/flower/pull/1574), " +"[#1572](https://github.com/adap/flower/pull/1572), " +"[#1586](https://github.com/adap/flower/pull/1586))" +msgstr "" +"**普通改进** ([#1638](https://github.com/adap/flower/pull/1638), " +"[#1634](https://github.com/adap/flower/pull/1634), " +"[#1636](https://github.com/adap/flower/pull/1636), " +"[#1635](https://github.com/adap/flower/pull/1635), " +"[#1633](https://github.com/adap/flower/pull/1633), " +"[#1632](https://github.com/adap/flower/pull/1632), " +"[#1631](https://github.com/adap/flower/pull/1631), " +"[#1630](https://github.com/adap/flower/pull/1630), " +"[#1627](https://github. com/adap/flower/pull/1627), " +"[#1593](https://github.com/adap/flower/pull/1593), " +"[#1616](https://github.com/adap/flower/pull/1616), " +"[#1615](https://github.com/adap/flower/pull/1615), " +"[#1607](https://github.com/adap/flower/pull/1607), " +"[#1609](https://github.com/adap/flower/pull/1609), " +"[#1608](https://github.com/adap/flower/pull/1608), " +"[#1603](https://github.com/adap/flower/pull/1603), " +"[#1590](https://github. com/adap/flower/pull/1590), " +"[#1580](https://github.com/adap/flower/pull/1580), " +"[#1599](https://github.com/adap/flower/pull/1599), " +"[#1600](https://github.com/adap/flower/pull/1600), " +"[#1601](https://github.com/adap/flower/pull/1601), " +"[#1597](https://github.com/adap/flower/pull/1597), " +"[#1595](https://github.com/adap/flower/pull/1595), " +"[#1591](https://github.com/adap/flower/pull/1591), " +"[#1588](https://github. com/adap/flower/pull/1588), " +"[#1589](https://github.com/adap/flower/pull/1589), " +"[#1587](https://github.com/adap/flower/pull/1587), " +"[#1573](https://github.com/adap/flower/pull/1573), " +"[#1581](https://github.com/adap/flower/pull/1581), " +"[#1578](https://github.com/adap/flower/pull/1578), " +"[#1574](https://github.com/adap/flower/pull/1574), " +"[#1572](https://github.com/adap/flower/pull/1572), " +"[#1586](https://github.com/adap/flower/pull/1586))" -#: ../../source/ref-changelog.md:806 +#: ../../source/ref-changelog.md:558 msgid "" -"**New scikit-learn code example** " -"([#748](https://github.com/adap/flower/pull/748))" -msgstr "**新的 scikit-learn 代码示例** ([#748](https://github.com/adap/flower/pull/748))" +"**Updated documentation** " +"([#1629](https://github.com/adap/flower/pull/1629), " +"[#1628](https://github.com/adap/flower/pull/1628), " +"[#1620](https://github.com/adap/flower/pull/1620), " +"[#1618](https://github.com/adap/flower/pull/1618), " +"[#1617](https://github.com/adap/flower/pull/1617), " +"[#1613](https://github.com/adap/flower/pull/1613), " +"[#1614](https://github.com/adap/flower/pull/1614))" +msgstr "" +"** 更新文档** ([#1629](https://github.com/adap/flower/pull/1629), " +"[#1628](https://github.com/adap/flower/pull/1628), " +"[#1620](https://github.com/adap/flower/pull/1620), " +"[#1618](https://github.com/adap/flower/pull/1618), " +"[#1617](https://github.com/adap/flower/pull/1617), " +"[#1613](https://github.com/adap/flower/pull/1613), " +"[#1614](https://github.com/adap/flower/pull/1614)))" -#: ../../source/ref-changelog.md:808 +#: ../../source/ref-changelog.md:560 ../../source/ref-changelog.md:627 msgid "" -"**New experimental TensorBoard strategy** " -"([#789](https://github.com/adap/flower/pull/789))" -msgstr "**新的实验性 TensorBoard 策略**（[#789](https://github.com/adap/flower/pull/789)）" +"As usual, the documentation has improved quite a bit. It is another step " +"in our effort to make the Flower documentation the best documentation of " +"any project. Stay tuned and as always, feel free to provide feedback!" +msgstr "和往常一样，我们的文档有了很大的改进。这是我们努力使 Flower 文档成为所有项目中最好文档的又一步骤。请继续关注，并随时提供反馈意见！" -#: ../../source/ref-changelog.md:812 -msgid "" -"Improved advanced TensorFlow code example " -"([#769](https://github.com/adap/flower/pull/769))" -msgstr "改进的高级 TensorFlow 代码示例（[#769](https://github.com/adap/flower/pull/769)" +#: ../../source/ref-changelog.md:566 +msgid "v1.2.0 (2023-01-13)" +msgstr "v1.2.0 (2023-01-13)" -#: ../../source/ref-changelog.md:813 +#: ../../source/ref-changelog.md:572 msgid "" -"Warning when `min_available_clients` is misconfigured " -"([#830](https://github.com/adap/flower/pull/830))" +"`Adam Narozniak`, `Charles Beauville`, `Daniel J. Beutel`, `Edoardo`, `L." +" Jiang`, `Ragy`, `Taner Topal`, `dannymcy`" msgstr "" -"当 `min_available_clients` 配置错误时发出警告 " -"([#830](https://github.com/adap/flower/pull/830))" +"`Adam Narozniak`, `Charles Beauville`, `Daniel J. Beutel`, `Edoardo`, `L." +" Jiang`, `Ragy`, `Taner Topal`, `dannymcy`" -#: ../../source/ref-changelog.md:814 +#: ../../source/ref-changelog.md:576 msgid "" -"Improved gRPC server docs " -"([#841](https://github.com/adap/flower/pull/841))" -msgstr "改进了 gRPC 服务器文档（[#841](https://github.com/adap/flower/pull/841)）" +"**Introduce new Flower Baseline: FedAvg MNIST** " +"([#1497](https://github.com/adap/flower/pull/1497), " +"[#1552](https://github.com/adap/flower/pull/1552))" +msgstr "" +"**引入新的 Flower Baseline： FedAvg MNIST** " +"([#1497](https://github.com/adap/flower/pull/1497), " +"[#1552](https://github.com/adap/flower/pull/1552))" -#: ../../source/ref-changelog.md:815 +#: ../../source/ref-changelog.md:578 msgid "" -"Improved error message in `NumPyClient` " -"([#851](https://github.com/adap/flower/pull/851))" -msgstr "改进了 `NumPyClient` 中的错误信息 ([#851](https://github.com/adap/flower/pull/851))" +"Over the coming weeks, we will be releasing a number of new reference " +"implementations useful especially to FL newcomers. They will typically " +"revisit well known papers from the literature, and be suitable for " +"integration in your own application or for experimentation, in order to " +"deepen your knowledge of FL in general. Today's release is the first in " +"this series. [Read more.](https://flower.ai/blog/2023-01-12-fl-starter-" +"pack-fedavg-mnist-cnn/)" +msgstr "" +"在未来几周内，我们将发布一些新的参考，特别是对 FL " +"新手有用的方法。它们通常会重温文献中的知名论文，适合集成到您自己的应用程序中或用于实验，以加深您对 FL " +"的总体了解。今天发布的是该系列中的第一篇。[阅读全文](https://flower.ai/blog/2023-01-12-fl-starter-" +"pack-fedavg-mnist-cnn/)" -#: ../../source/ref-changelog.md:816 +#: ../../source/ref-changelog.md:580 msgid "" -"Improved PyTorch quickstart code example " -"([#852](https://github.com/adap/flower/pull/852))" -msgstr "改进的 PyTorch 快速启动代码示例 ([#852](https://github.com/adap/flower/pull/852))" +"**Improve GPU support in simulations** " +"([#1555](https://github.com/adap/flower/pull/1555))" +msgstr "**改进模拟中的 GPU 支持**（[#1555](https://github.com/adap/flower/pull/1555)）" -#: ../../source/ref-changelog.md:820 +#: ../../source/ref-changelog.md:582 msgid "" -"**Disabled final distributed evaluation** " -"([#800](https://github.com/adap/flower/pull/800))" -msgstr "**禁用最终分布式评价** ([#800](https://github.com/adap/flower/pull/800))" +"The Ray-based Virtual Client Engine (`start_simulation`) has been updated" +" to improve GPU support. The update includes some of the hard-earned " +"lessons from scaling simulations in GPU cluster environments. New " +"defaults make running GPU-based simulations substantially more robust." +msgstr "" +"基于 Ray 的虚拟客户端引擎 (`start_simulation`)已更新，以改进对 GPU 的支持。此次更新包含了在 GPU " +"集群环境中扩展模拟的一些经验教训。新的默认设置使基于 GPU 的模拟运行更加稳健。" -#: ../../source/ref-changelog.md:822 +#: ../../source/ref-changelog.md:584 msgid "" -"Prior behaviour was to perform a final round of distributed evaluation on" -" all connected clients, which is often not required (e.g., when using " -"server-side evaluation). The prior behaviour can be enabled by passing " -"`force_final_distributed_eval=True` to `start_server`." +"**Improve GPU support in Jupyter Notebook tutorials** " +"([#1527](https://github.com/adap/flower/pull/1527), " +"[#1558](https://github.com/adap/flower/pull/1558))" msgstr "" -"之前的行为是在所有连接的客户端上执行最后一轮分布式评估，而这通常是不需要的（例如，在使用服务器端评估时）。可以通过向 `start_server`" -" 传递 `force_final_distributed_eval=True` 来启用之前的行为。" +"**改进 Jupyter Notebook 教程中的 GPU 支持** " +"([#1527](https://github.com/adap/flower/pull/1527), " +"[#1558](https://github.com/adap/flower/pull/1558))" -#: ../../source/ref-changelog.md:824 +#: ../../source/ref-changelog.md:586 msgid "" -"**Renamed q-FedAvg strategy** " -"([#802](https://github.com/adap/flower/pull/802))" -msgstr "**更名为 q-FedAvg 策略** ([#802](https://github.com/adap/flower/pull/802))" +"Some users reported that Jupyter Notebooks have not always been easy to " +"use on GPU instances. We listened and made improvements to all of our " +"Jupyter notebooks! Check out the updated notebooks here:" +msgstr "" +"一些用户报告说，在 GPU 实例上使用 Jupyter 笔记本并不是很方便。我们听取了他们的意见，并对所有 Jupyter " +"笔记本进行了改进！点击这里查看更新后的笔记本：" -#: ../../source/ref-changelog.md:826 +#: ../../source/ref-changelog.md:588 msgid "" -"The strategy named `QffedAvg` was renamed to `QFedAvg` to better reflect " -"the notation given in the original paper (q-FFL is the optimization " -"objective, q-FedAvg is the proposed solver). Note the original (now " -"deprecated) `QffedAvg` class is still available for compatibility reasons" -" (it will be removed in a future release)." +"[An Introduction to Federated Learning](https://flower.ai/docs/framework" +"/tutorial-get-started-with-flower-pytorch.html)" msgstr "" -"名为 `QffedAvg` 的策略已更名为 `QFedAvg`，以更好地反映原始论文中给出的符号（q-FFL 是优化目标，q-FedAvg " -"是建议的求解器）。请注意，出于兼容性原因，原始（现已废弃）的 `QffedAvg` 类仍然可用（它将在未来的版本中移除）。" +"[联邦学习简介](https://flower.ai/docs/framework/tutorial-get-started-with-" +"flower-pytorch.html)" -#: ../../source/ref-changelog.md:828 +#: ../../source/ref-changelog.md:589 msgid "" -"**Deprecated and renamed code example** `simulation_pytorch` **to** " -"`simulation_pytorch_legacy` " -"([#791](https://github.com/adap/flower/pull/791))" +"[Strategies in Federated Learning](https://flower.ai/docs/framework" +"/tutorial-use-a-federated-learning-strategy-pytorch.html)" msgstr "" -"**删除并重命名代码示例**`simulation_pytorch`**为**`simulation_pytorch_legacy` " -"([#791](https://github.com/adap/flower/pull/791))" +"[联邦学习策略](https://flower.ai/docs/framework/tutorial-use-a-federated-" +"learning-strategy-pytorch.html)" -#: ../../source/ref-changelog.md:830 +#: ../../source/ref-changelog.md:590 msgid "" -"This example has been replaced by a new example. The new example is based" -" on the experimental virtual client engine, which will become the new " -"default way of doing most types of large-scale simulations in Flower. The" -" existing example was kept for reference purposes, but it might be " -"removed in the future." +"[Building a Strategy](https://flower.ai/docs/framework/tutorial-build-a" +"-strategy-from-scratch-pytorch.html)" msgstr "" -"该示例已被新示例取代。新示例基于试验性虚拟客户端引擎，它将成为在 Flower " -"中进行大多数类型大规模模拟的新的默认方式。现有示例将作为参考保留，但将来可能会删除。" - -#: ../../source/ref-changelog.md:832 -msgid "v0.16.0 (2021-05-11)" -msgstr "v0.16.0 (2021-05-11)" - -#: ../../source/ref-changelog.md:836 -msgid "" -"**New built-in strategies** " -"([#549](https://github.com/adap/flower/pull/549))" -msgstr "**新的内置策略** ([#549](https://github.com/adap/flower/pull/549))" - -#: ../../source/ref-changelog.md:838 -msgid "(abstract) FedOpt" -msgstr "（摘要） FedOpt" - -#: ../../source/ref-changelog.md:841 -msgid "" -"**Custom metrics for server and strategies** " -"([#717](https://github.com/adap/flower/pull/717))" -msgstr "**服务器和策略的自定义指标** ([#717](https://github.com/adap/flower/pull/717))" +"[制定策略](https://flower.ai/docs/framework/tutorial-build-a-strategy-from-" +"scratch-pytorch.html)" -#: ../../source/ref-changelog.md:843 +#: ../../source/ref-changelog.md:591 msgid "" -"The Flower server is now fully task-agnostic, all remaining instances of " -"task-specific metrics (such as `accuracy`) have been replaced by custom " -"metrics dictionaries. Flower 0.15 introduced the capability to pass a " -"dictionary containing custom metrics from client to server. As of this " -"release, custom metrics replace task-specific metrics on the server." +"[Client and NumPyClient](https://flower.ai/docs/framework/tutorial-" +"customize-the-client-pytorch.html)" msgstr "" -"Flower 服务器现在完全与任务无关，所有剩余的任务特定度量（如 \"准确度\"）都已被自定义度量字典取代。Flower 0.15 " -"引入了从客户端向服务器传递包含自定义指标的字典的功能。从本版本开始，自定义指标将取代服务器上的特定任务指标。" +"[客户端和 NumPyClient](https://flower.ai/docs/framework/tutorial-customize-" +"the-client-pytorch.html)" -#: ../../source/ref-changelog.md:845 +#: ../../source/ref-changelog.md:593 msgid "" -"Custom metric dictionaries are now used in two user-facing APIs: they are" -" returned from Strategy methods `aggregate_fit`/`aggregate_evaluate` and " -"they enable evaluation functions passed to built-in strategies (via " -"`eval_fn`) to return more than two evaluation metrics. Strategies can " -"even return *aggregated* metrics dictionaries for the server to keep " -"track of." +"**Introduce optional telemetry** " +"([#1533](https://github.com/adap/flower/pull/1533), " +"[#1544](https://github.com/adap/flower/pull/1544), " +"[#1584](https://github.com/adap/flower/pull/1584))" msgstr "" -"自定义度量字典现在可在两个面向用户的 API 中使用：它们可从策略方法 `aggregate_fit`/`aggregate_evaluate` " -"返回，还可使传递给内置策略（通过 `eval_fn`）的评估函数返回两个以上的评估度量。策略甚至可以返回 *aggregated* " -"指标字典，以便服务器跟踪。" +"**引入可选遥测**（[#1533](https://github.com/adap/flower/pull/1533), " +"[#1544](https://github.com/adap/flower/pull/1544), " +"[#1584](https://github.com/adap/flower/pull/1584)" -#: ../../source/ref-changelog.md:847 +#: ../../source/ref-changelog.md:595 msgid "" -"Strategy implementations should migrate their `aggregate_fit` and " -"`aggregate_evaluate` methods to the new return type (e.g., by simply " -"returning an empty `{}`), server-side evaluation functions should migrate" -" from `return loss, accuracy` to `return loss, {\"accuracy\": accuracy}`." +"After a [request for " +"feedback](https://github.com/adap/flower/issues/1534) from the community," +" the Flower open-source project introduces optional collection of " +"*anonymous* usage metrics to make well-informed decisions to improve " +"Flower. Doing this enables the Flower team to understand how Flower is " +"used and what challenges users might face." msgstr "" -"Strategy 实现应将其 `aggregate_fit` 和 `aggregate_evaluate` " -"方法迁移到新的返回类型（例如，只需返回空的 `{}`），服务器端评估函数应从 `return loss, accuracy` 迁移到 " -"`return loss, {\"accuracy\": accuracy}`。" - -#: ../../source/ref-changelog.md:849 -msgid "" -"Flower 0.15-style return types are deprecated (but still supported), " -"compatibility will be removed in a future release." -msgstr "Flower 0.15 风格的返回类型已被弃用（但仍受支持），兼容性将在未来的版本中移除。" +"在社区发出[反馈请求](https://github.com/adap/flower/issues/1534)之后，Flower " +"开放源码项目引入了可选的*匿名*使用指标收集，以便在充分知情的情况下做出改进 Flower 的决定。这样做能让 Flower 团队了解 " +"Flower 的使用情况以及用户可能面临的挑战。" -#: ../../source/ref-changelog.md:851 +#: ../../source/ref-changelog.md:597 msgid "" -"**Migration warnings for deprecated functionality** " -"([#690](https://github.com/adap/flower/pull/690))" -msgstr "** 过时功能的迁移警告** ([#690](https://github.com/adap/flower/pull/690))" +"**Flower is a friendly framework for collaborative AI and data science.**" +" Staying true to this statement, Flower makes it easy to disable " +"telemetry for users who do not want to share anonymous usage metrics. " +"[Read more.](https://flower.ai/docs/telemetry.html)." +msgstr "" +"**Flower 是一个用于协作式人工智能和数据科学的友好框架。** Flower " +"遵循这一声明，让不想分享匿名使用指标的用户可以轻松禁用遥测技术。[阅读全文](https://flower.ai/docs/telemetry.html)。" -#: ../../source/ref-changelog.md:853 +#: ../../source/ref-changelog.md:599 msgid "" -"Earlier versions of Flower were often migrated to new APIs, while " -"maintaining compatibility with legacy APIs. This release introduces " -"detailed warning messages if usage of deprecated APIs is detected. The " -"new warning messages often provide details on how to migrate to more " -"recent APIs, thus easing the transition from one release to another." +"**Introduce (experimental) Driver API** " +"([#1520](https://github.com/adap/flower/pull/1520), " +"[#1525](https://github.com/adap/flower/pull/1525), " +"[#1545](https://github.com/adap/flower/pull/1545), " +"[#1546](https://github.com/adap/flower/pull/1546), " +"[#1550](https://github.com/adap/flower/pull/1550), " +"[#1551](https://github.com/adap/flower/pull/1551), " +"[#1567](https://github.com/adap/flower/pull/1567))" msgstr "" -"Flower 早期版本通常会迁移到新的应用程序接口，同时保持与旧版应用程序接口的兼容。如果检测到使用了过时的 " -"API，本版本将引入详细的警告信息。新的警告信息通常会详细说明如何迁移到更新的 API，从而简化从一个版本到另一个版本的过渡。" +"**引入（试验性）Driver API** ([#1520](https://github.com/adap/flower/pull/1520)," +" [#1525](https://github.com/adap/flower/pull/1525), " +"[#1545](https://github.com/adap/flower/pull/1545), " +"[#1546](https://github.com/adap/flower/pull/1546), " +"[#1550](https://github.com/adap/flower/pull/1550), " +"[#1551](https://github.com/adap/flower/pull/1551), " +"[#1567](https://github.com/adap/flower/pull/1567))" -#: ../../source/ref-changelog.md:855 +#: ../../source/ref-changelog.md:601 msgid "" -"Improved docs and docstrings " -"([#691](https://github.com/adap/flower/pull/691) " -"[#692](https://github.com/adap/flower/pull/692) " -"[#713](https://github.com/adap/flower/pull/713))" +"Flower now has a new (experimental) Driver API which will enable fully " +"programmable, async, and multi-tenant Federated Learning and Federated " +"Analytics applications. Phew, that's a lot! Going forward, the Driver API" +" will be the abstraction that many upcoming features will be built on - " +"and you can start building those things now, too." msgstr "" -"改进了文档和文档说明 ([#691](https://github.com/adap/flower/pull/691) " -"[#692](https://github.com/adap/flower/pull/692) " -"[#713](https://github.com/adap/flower/pull/713))" - -#: ../../source/ref-changelog.md:857 -msgid "MXNet example and documentation" -msgstr "MXNet 示例和文档" +"Flower 现在有了一个新的（试验性的）驱动程序应用程序接口（Driver " +"API），它将支持完全可编程、异步和多租户的联邦学习（Federated Learning）和联邦分析（Federated " +"Analytics）应用程序。展望未来，Driver API 将成为许多即将推出的功能的抽象基础，您现在就可以开始构建这些功能。" -#: ../../source/ref-changelog.md:859 +#: ../../source/ref-changelog.md:603 msgid "" -"FedBN implementation in example PyTorch: From Centralized To Federated " -"([#696](https://github.com/adap/flower/pull/696) " -"[#702](https://github.com/adap/flower/pull/702) " -"[#705](https://github.com/adap/flower/pull/705))" +"The Driver API also enables a new execution mode in which the server runs" +" indefinitely. Multiple individual workloads can run concurrently and " +"start and stop their execution independent of the server. This is " +"especially useful for users who want to deploy Flower in production." msgstr "" -"PyTorch 示例中的 FedBN 实现： 从集中到联邦 " -"([#696](https://github.com/adap/flower/pull/696) " -"[#702](https://github.com/adap/flower/pull/702) " -"[#705](https://github.com/adap/flower/pull/705))" +"驱动程序应用程序接口还支持一种新的执行模式，在这种模式下，服务器可无限期运行。多个单独的工作负载可以同时运行，并独立于服务器启动和停止执行。这对于希望在生产中部署" +" Flower 的用户来说尤其有用。" -#: ../../source/ref-changelog.md:863 +#: ../../source/ref-changelog.md:605 msgid "" -"**Serialization-agnostic server** " -"([#721](https://github.com/adap/flower/pull/721))" -msgstr "**序列化无关服务器** ([#721](https://github.com/adap/flower/pull/721))" +"To learn more, check out the `mt-pytorch` code example. We look forward " +"to you feedback!" +msgstr "要了解更多信息，请查看 `mt-pytorch` 代码示例。我们期待您的反馈！" -#: ../../source/ref-changelog.md:865 +#: ../../source/ref-changelog.md:607 msgid "" -"The Flower server is now fully serialization-agnostic. Prior usage of " -"class `Weights` (which represents parameters as deserialized NumPy " -"ndarrays) was replaced by class `Parameters` (e.g., in `Strategy`). " -"`Parameters` objects are fully serialization-agnostic and represents " -"parameters as byte arrays, the `tensor_type` attributes indicates how " -"these byte arrays should be interpreted (e.g., for " -"serialization/deserialization)." -msgstr "" -"Flower 服务器现在完全不依赖序列化。之前使用的 `Weights` 类（以反序列化的 NumPy ndarrays 表示参数）已被 " -"`Parameters` 类取代（例如在 `Strategy`中）。参数 " -"\"对象与序列化完全无关，它以字节数组的形式表示参数，\"tensor_type \"属性表示如何解释这些字节数组（例如，用于序列化/反序列化）。" +"Please note: *The Driver API is still experimental and will likely change" +" significantly over time.*" +msgstr "请注意：Driver API仍处于试验阶段，随着时间的推移可能会发生重大变化。*" -#: ../../source/ref-changelog.md:867 +#: ../../source/ref-changelog.md:609 msgid "" -"Built-in strategies implement this approach by handling serialization and" -" deserialization to/from `Weights` internally. Custom/3rd-party Strategy " -"implementations should update to the slightly changed Strategy method " -"definitions. Strategy authors can consult PR " -"[#721](https://github.com/adap/flower/pull/721) to see how strategies can" -" easily migrate to the new format." +"**Add new Federated Analytics with Pandas example** " +"([#1469](https://github.com/adap/flower/pull/1469), " +"[#1535](https://github.com/adap/flower/pull/1535))" msgstr "" -"内置策略通过在内部处理序列化和反序列化到/从`Weights`来实现这种方法。自定义/第三方策略实现应更新为稍有改动的策略方法定义。策略作者可查阅" -" PR [#721](https://github.com/adap/flower/pull/721) 以了解如何将策略轻松迁移到新格式。" +"** 添加新的使用 Pandas " +"的联邦分析示例**（[#1469](https://github.com/adap/flower/pull/1469), " +"[#1535](https://github.com/adap/flower/pull/1535)" -#: ../../source/ref-changelog.md:869 +#: ../../source/ref-changelog.md:611 msgid "" -"Deprecated `flwr.server.Server.evaluate`, use " -"`flwr.server.Server.evaluate_round` instead " -"([#717](https://github.com/adap/flower/pull/717))" +"A new code example (`quickstart-pandas`) demonstrates federated analytics" +" with Pandas and Flower. You can find it here: [quickstart-" +"pandas](https://github.com/adap/flower/tree/main/examples/quickstart-" +"pandas)." msgstr "" -"已弃用 `flwr.server.Server.evaluate`，改用 " -"`flwr.server.Server.evaluate_round`（[#717](https://github.com/adap/flower/pull/717)" - -#: ../../source/ref-changelog.md:871 -msgid "v0.15.0 (2021-03-12)" -msgstr "v0.15.0 (2021-03-12)" +"新代码示例（`quickstart-pandas`）演示了使用 Pandas 和 Flower 进行联邦分析。您可以在此处找到它： " +"[quickstart-pandas](https://github.com/adap/flower/tree/main/examples" +"/quickstart-pandas)。" -#: ../../source/ref-changelog.md:875 +#: ../../source/ref-changelog.md:613 msgid "" -"**Server-side parameter initialization** " -"([#658](https://github.com/adap/flower/pull/658))" -msgstr "**服务器端参数初始化** ([#658](https://github.com/adap/flower/pull/658))" +"**Add new strategies: Krum and MultiKrum** " +"([#1481](https://github.com/adap/flower/pull/1481))" +msgstr "" +"**添加新策略： Krum 和 MultiKrum** " +"([#1481](https://github.com/adap/flower/pull/1481))" -#: ../../source/ref-changelog.md:877 +#: ../../source/ref-changelog.md:615 msgid "" -"Model parameters can now be initialized on the server-side. Server-side " -"parameter initialization works via a new `Strategy` method called " -"`initialize_parameters`." +"Edoardo, a computer science student at the Sapienza University of Rome, " +"contributed a new `Krum` strategy that enables users to easily use Krum " +"and MultiKrum in their workloads." msgstr "" -"现在可以在服务器端初始化模型参数。服务器端参数初始化通过名为 \"initialize_parameters \"的新 \"Strategy " -"\"方法进行。" +"罗马萨皮恩扎大学（Sapienza University）计算机科学专业的学生埃多尔多（Edoardo）提出了一种新的 \"Krum " +"\"策略，使用户能够在其工作负载中轻松使用 Krum 和 MultiKrum。" -#: ../../source/ref-changelog.md:879 +#: ../../source/ref-changelog.md:617 msgid "" -"Built-in strategies support a new constructor argument called " -"`initial_parameters` to set the initial parameters. Built-in strategies " -"will provide these initial parameters to the server on startup and then " -"delete them to free the memory afterwards." +"**Update C++ example to be compatible with Flower v1.2.0** " +"([#1495](https://github.com/adap/flower/pull/1495))" msgstr "" -"内置策略支持名为 \"initial_parameters " -"\"的新构造函数参数，用于设置初始参数。内置策略会在启动时向服务器提供这些初始参数，然后删除它们以释放内存。" +"** 更新 C++ 示例，与 Flower v1.2.0 兼容** " +"([#1495](https://github.com/adap/flower/pull/1495))" -#: ../../source/ref-changelog.md:898 +#: ../../source/ref-changelog.md:619 msgid "" -"If no initial parameters are provided to the strategy, the server will " -"continue to use the current behaviour (namely, it will ask one of the " -"connected clients for its parameters and use these as the initial global " -"parameters)." -msgstr "如果没有向策略提供初始参数，服务器将继续使用当前行为（即向其中一个已连接的客户端询问参数，并将这些参数用作初始全局参数）。" - -#: ../../source/ref-changelog.md:900 -msgid "Deprecations" -msgstr "停用" +"The C++ code example has received a substantial update to make it " +"compatible with the latest version of Flower." +msgstr "为了与最新版本的 Flower 兼容，C++ 示例代码进行了大幅更新。" -#: ../../source/ref-changelog.md:902 +#: ../../source/ref-changelog.md:621 msgid "" -"Deprecate `flwr.server.strategy.DefaultStrategy` (migrate to " -"`flwr.server.strategy.FedAvg`, which is equivalent)" +"**General improvements** " +"([#1491](https://github.com/adap/flower/pull/1491), " +"[#1504](https://github.com/adap/flower/pull/1504), " +"[#1506](https://github.com/adap/flower/pull/1506), " +"[#1514](https://github.com/adap/flower/pull/1514), " +"[#1522](https://github.com/adap/flower/pull/1522), " +"[#1523](https://github.com/adap/flower/pull/1523), " +"[#1526](https://github.com/adap/flower/pull/1526), " +"[#1528](https://github.com/adap/flower/pull/1528), " +"[#1547](https://github.com/adap/flower/pull/1547), " +"[#1549](https://github.com/adap/flower/pull/1549), " +"[#1560](https://github.com/adap/flower/pull/1560), " +"[#1564](https://github.com/adap/flower/pull/1564), " +"[#1566](https://github.com/adap/flower/pull/1566))" msgstr "" -"停用 `flwr.server.strategy.DefaultStrategy`（迁移到等价的 " -"`flwr.server.strategy.FedAvg`）" - -#: ../../source/ref-changelog.md:904 -msgid "v0.14.0 (2021-02-18)" -msgstr "v0.14.0 (2021-02-18)" +"**普通改进** ([#1491](https://github.com/adap/flower/pull/1491), " +"[#1504](https://github.com/adap/flower/pull/1504), " +"[#1506](https://github.com/adap/flower/pull/1506), " +"[#1514](https://github.com/adap/flower/pull/1514), " +"[#1522](https://github.com/adap/flower/pull/1522), " +"[#1523](https://github.com/adap/flower/pull/1523), " +"[#1526](https://github. com/adap/flower/pull/1526), " +"[#1528](https://github.com/adap/flower/pull/1528), " +"[#1547](https://github.com/adap/flower/pull/1547), " +"[#1549](https://github.com/adap/flower/pull/1549), " +"[#1560](https://github.com/adap/flower/pull/1560), " +"[#1564](https://github.com/adap/flower/pull/1564), " +"[#1566](https://github.com/adap/flower/pull/1566))" -#: ../../source/ref-changelog.md:908 +#: ../../source/ref-changelog.md:625 msgid "" -"**Generalized** `Client.fit` **and** `Client.evaluate` **return values** " -"([#610](https://github.com/adap/flower/pull/610) " -"[#572](https://github.com/adap/flower/pull/572) " -"[#633](https://github.com/adap/flower/pull/633))" +"**Updated documentation** " +"([#1494](https://github.com/adap/flower/pull/1494), " +"[#1496](https://github.com/adap/flower/pull/1496), " +"[#1500](https://github.com/adap/flower/pull/1500), " +"[#1503](https://github.com/adap/flower/pull/1503), " +"[#1505](https://github.com/adap/flower/pull/1505), " +"[#1524](https://github.com/adap/flower/pull/1524), " +"[#1518](https://github.com/adap/flower/pull/1518), " +"[#1519](https://github.com/adap/flower/pull/1519), " +"[#1515](https://github.com/adap/flower/pull/1515))" msgstr "" -"**通用** `Client.fit` **和** `Client.evaluate` **返回值** " -"([#610](https://github.com/adap/flower/pull/610) " -"[#572](https://github.com/adap/flower/pull/572) " -"[#633](https://github.com/adap/flower/pull/633))" +"** 更新文档** ([#1494](https://github.com/adap/flower/pull/1494), " +"[#1496](https://github.com/adap/flower/pull/1496), " +"[#1500](https://github.com/adap/flower/pull/1500), " +"[#1503](https://github.com/adap/flower/pull/1503), " +"[#1505](https://github.com/adap/flower/pull/1505), " +"[#1524](https://github.com/adap/flower/pull/1524), " +"[#1518](https://github.com/adap/flower/pull/1518), " +"[#1519](https://github.com/adap/flower/pull/1519), " +"[#1515](https://github.com/adap/flower/pull/1515))" -#: ../../source/ref-changelog.md:910 +#: ../../source/ref-changelog.md:629 msgid "" -"Clients can now return an additional dictionary mapping `str` keys to " -"values of the following types: `bool`, `bytes`, `float`, `int`, `str`. " -"This means one can return almost arbitrary values from `fit`/`evaluate` " -"and make use of them on the server side!" +"One highlight is the new [first time contributor " +"guide](https://flower.ai/docs/first-time-contributors.html): if you've " +"never contributed on GitHub before, this is the perfect place to start!" msgstr "" -"客户端现在可以返回一个额外的字典，将 `str` 键映射为以下类型的值： " -"bool`、`bytes`、`float`、`int`、`str`。这意味着我们可以从 `fit`/`evaluate` " -"返回几乎任意的值，并在服务器端使用它们！" +"其中一个亮点是新的[首次贡献者指南](https://flower.ai/docs/first-time-" +"contributors.html)：如果你以前从未在 GitHub 上做过贡献，这将是一个完美的开始！" -#: ../../source/ref-changelog.md:912 +#: ../../source/ref-changelog.md:635 +msgid "v1.1.0 (2022-10-31)" +msgstr "v1.1.0 (2022-10-31)" + +#: ../../source/ref-changelog.md:639 msgid "" -"This improvement also allowed for more consistent return types between " -"`fit` and `evaluate`: `evaluate` should now return a tuple `(float, int, " -"dict)` representing the loss, number of examples, and a dictionary " -"holding arbitrary problem-specific values like accuracy." -msgstr "" -"这一改进还使 `fit` 和 `evaluate` 之间的返回类型更加一致：`evaluate` 现在应返回一个元组`(float, int, " -"dict)`，代表损失、示例数和一个包含特定问题任意值（如准确度）的字典。" +"We would like to give our **special thanks** to all the contributors who " +"made the new version of Flower possible (in `git shortlog` order):" +msgstr "在此，我们向所有促成 Flower 新版本的贡献者致以**特别的谢意（按 \"git shortlog \"顺序排列）：" -#: ../../source/ref-changelog.md:914 +#: ../../source/ref-changelog.md:641 msgid "" -"In case you wondered: this feature is compatible with existing projects, " -"the additional dictionary return value is optional. New code should " -"however migrate to the new return types to be compatible with upcoming " -"Flower releases (`fit`: `List[np.ndarray], int, Dict[str, Scalar]`, " -"`evaluate`: `float, int, Dict[str, Scalar]`). See the example below for " -"details." +"`Akis Linardos`, `Christopher S`, `Daniel J. Beutel`, `George`, `Jan " +"Schlicht`, `Mohammad Fares`, `Pedro Porto Buarque de Gusmão`, `Philipp " +"Wiesner`, `Rob Luke`, `Taner Topal`, `VasundharaAgarwal`, " +"`danielnugraha`, `edogab33`" msgstr "" -"如果你想知道：此功能与现有项目兼容，额外的字典返回值是可选的。不过，新代码应迁移到新的返回类型，以便与即将发布的 Flower " -"版本兼容（`fit`: `List[np.ndarray], int, Dict[str, Scalar]`，`evaluate`: " -"`float, int, Dict[str, Scalar]`）。详见下面的示例。" +"`Akis Linardos`, `Christopher S`, `Daniel J. Beutel`, `George`, `Jan " +"Schlicht`, `Mohammad Fares`, `Pedro Porto Buarque de Gusmão`, `Philipp " +"Wiesner`, `Rob Luke`, `Taner Topal`, `VasundharaAgarwal`, " +"`danielnugraha`, `edogab33`" -#: ../../source/ref-changelog.md:916 +#: ../../source/ref-changelog.md:645 msgid "" -"*Code example:* note the additional dictionary return values in both " -"`FlwrClient.fit` and `FlwrClient.evaluate`:" -msgstr "*代码示例：* 注意 `FlwrClient.fit` 和 `FlwrClient.evaluate` 中的附加字典返回值：" +"**Introduce Differential Privacy wrappers (preview)** " +"([#1357](https://github.com/adap/flower/pull/1357), " +"[#1460](https://github.com/adap/flower/pull/1460))" +msgstr "" +"**引入差分隐私包装器（预览）** ([#1357](https://github.com/adap/flower/pull/1357), " +"[#1460](https://github.com/adap/flower/pull/1460))" -#: ../../source/ref-changelog.md:931 +#: ../../source/ref-changelog.md:647 msgid "" -"**Generalized** `config` **argument in** `Client.fit` **and** " -"`Client.evaluate` ([#595](https://github.com/adap/flower/pull/595))" +"The first (experimental) preview of pluggable Differential Privacy " +"wrappers enables easy configuration and usage of differential privacy " +"(DP). The pluggable DP wrappers enable framework-agnostic **and** " +"strategy-agnostic usage of both client-side DP and server-side DP. Head " +"over to the Flower docs, a new explainer goes into more detail." msgstr "" -"**在**`Client.fit` " -"**和**`Client.evaluate`中泛化**`config`参数（[#595](https://github.com/adap/flower/pull/595)）" +"可插拔差分隐私封装器的首个（实验性）预览版可轻松配置和使用差分隐私（DP）。可插拔的差分隐私封装器可实现客户端差分隐私和服务器端差分隐私的框架无关**以及**策略无关的使用。请访问" +" Flower 文档，新的解释器会提供更多细节。" -#: ../../source/ref-changelog.md:933 +#: ../../source/ref-changelog.md:649 msgid "" -"The `config` argument used to be of type `Dict[str, str]`, which means " -"that dictionary values were expected to be strings. The new release " -"generalizes this to enable values of the following types: `bool`, " -"`bytes`, `float`, `int`, `str`." -msgstr "" -"`config`参数曾是 \"字典[str, str]\"类型，这意味着字典值应是字符串。新版本将其扩展为以下类型的值： " -"bool`、`bytes`、`float`、`int`、`str`。" +"**New iOS CoreML code example** " +"([#1289](https://github.com/adap/flower/pull/1289))" +msgstr "**新的 iOS CoreML 代码示例**（[#1289](https://github.com/adap/flower/pull/1289)）" -#: ../../source/ref-changelog.md:935 +#: ../../source/ref-changelog.md:651 msgid "" -"This means one can now pass almost arbitrary values to `fit`/`evaluate` " -"using the `config` dictionary. Yay, no more `str(epochs)` on the server-" -"side and `int(config[\"epochs\"])` on the client side!" +"Flower goes iOS! A massive new code example shows how Flower clients can " +"be built for iOS. The code example contains both Flower iOS SDK " +"components that can be used for many tasks, and one task example running " +"on CoreML." msgstr "" -"这意味着现在可以使用 `config` 字典向 `fit`/`evaluate` 传递几乎任意的值。耶，服务器端不再需要 " -"`str(epochs)`，客户端不再需要 `int(config[\"epochs\"])`！" +"Flower 进入 iOS！大量新代码示例展示了如何为 iOS 构建 Flower 客户端。该代码示例包含可用于多种任务的 Flower iOS " +"SDK 组件，以及在 CoreML 上运行的一个任务示例。" -#: ../../source/ref-changelog.md:937 +#: ../../source/ref-changelog.md:653 msgid "" -"*Code example:* note that the `config` dictionary now contains non-`str` " -"values in both `Client.fit` and `Client.evaluate`:" -msgstr "*代码示例：* 注意 `config` 字典现在在 `Client.fit` 和 `Client.evaluate` 中都包含非 `str` 值：" - -#: ../../source/ref-changelog.md:954 -msgid "v0.13.0 (2021-01-08)" -msgstr "v0.13.0 (2021-01-08)" +"**New FedMedian strategy** " +"([#1461](https://github.com/adap/flower/pull/1461))" +msgstr "**新的联邦医疗策略** ([#1461](https://github.com/adap/flower/pull/1461))" -#: ../../source/ref-changelog.md:958 +#: ../../source/ref-changelog.md:655 msgid "" -"New example: PyTorch From Centralized To Federated " -"([#549](https://github.com/adap/flower/pull/549))" -msgstr "新示例： PyTorch 从集中到联邦 ([#549](https://github.com/adap/flower/pull/549))" - -#: ../../source/ref-changelog.md:959 -msgid "Improved documentation" -msgstr "改进文档" - -#: ../../source/ref-changelog.md:960 -msgid "New documentation theme ([#551](https://github.com/adap/flower/pull/551))" -msgstr "新文档主题 ([#551](https://github.com/adap/flower/pull/551))" - -#: ../../source/ref-changelog.md:961 -msgid "New API reference ([#554](https://github.com/adap/flower/pull/554))" -msgstr "新的 API 参考 ([#554](https://github.com/adap/flower/pull/554))" +"The new `FedMedian` strategy implements Federated Median (FedMedian) by " +"[Yin et al., 2018](https://arxiv.org/pdf/1803.01498v1.pdf)." +msgstr "" +"新的 \"FedMedian \"战略实现了[Yin " +"等人，2018]的联邦中值（FedMedian）(https://arxiv.org/pdf/1803.01498v1.pdf)。" -#: ../../source/ref-changelog.md:962 +#: ../../source/ref-changelog.md:657 msgid "" -"Updated examples documentation " -"([#549](https://github.com/adap/flower/pull/549))" -msgstr "更新了示例文档 ([#549](https://github.com/adap/flower/pull/549))" +"**Log** `Client` **exceptions in Virtual Client Engine** " +"([#1493](https://github.com/adap/flower/pull/1493))" +msgstr "**虚拟客户端引擎中的**日志**`客户端`**异常（[#1493](https://github.com/adap/flower/pull/1493)）" -#: ../../source/ref-changelog.md:963 +#: ../../source/ref-changelog.md:659 msgid "" -"Removed obsolete documentation " -"([#548](https://github.com/adap/flower/pull/548))" -msgstr "删除了过时的文档 ([#548](https://github.com/adap/flower/pull/548))" +"All `Client` exceptions happening in the VCE are now logged by default " +"and not just exposed to the configured `Strategy` (via the `failures` " +"argument)." +msgstr "VCE 中发生的所有 \"客户端 \"异常现在都会被默认记录下来，而不只是暴露给配置的 `Strategy`（通过 `failures`参数）。" -#: ../../source/ref-changelog.md:965 -msgid "Bugfix:" -msgstr "错误修正：" +#: ../../source/ref-changelog.md:661 +msgid "" +"**Improve Virtual Client Engine internals** " +"([#1401](https://github.com/adap/flower/pull/1401), " +"[#1453](https://github.com/adap/flower/pull/1453))" +msgstr "**改进虚拟客户端引擎内部**（[#1401](https://github.com/adap/flower/pull/1401)、[#1453](https://github.com/adap/flower/pull/1453)）" -#: ../../source/ref-changelog.md:967 +#: ../../source/ref-changelog.md:663 msgid "" -"`Server.fit` does not disconnect clients when finished, disconnecting the" -" clients is now handled in `flwr.server.start_server` " -"([#553](https://github.com/adap/flower/pull/553) " -"[#540](https://github.com/adap/flower/issues/540))." +"Some internals of the Virtual Client Engine have been revamped. The VCE " +"now uses Ray 2.0 under the hood, the value type of the `client_resources`" +" dictionary changed to `float` to allow fractions of resources to be " +"allocated." msgstr "" -"Server.fit \"完成后不会断开客户端连接，现在断开客户端连接是在 \"flwr.server.start_server " -"\"中处理的（[#553](https://github.com/adap/flower/pull/553) " -"[#540](https://github.com/adap/flower/issues/540)）。" - -#: ../../source/ref-changelog.md:969 -msgid "v0.12.0 (2020-12-07)" -msgstr "v0.12.0 (2020-12-07)" +"虚拟客户端引擎的部分内部结构已进行了修改。VCE 现在使用 Ray 2.0，\"client_resources \"字典的值类型改为 " +"\"float\"，以允许分配分数资源。" -#: ../../source/ref-changelog.md:971 ../../source/ref-changelog.md:987 -msgid "Important changes:" -msgstr "重要变更：" +#: ../../source/ref-changelog.md:665 +msgid "" +"**Support optional** `Client`**/**`NumPyClient` **methods in Virtual " +"Client Engine**" +msgstr "**支持虚拟客户端引擎中的可选** `Client`**/**`NumPyClient` **方法**" -#: ../../source/ref-changelog.md:973 +#: ../../source/ref-changelog.md:667 msgid "" -"Added an example for embedded devices " -"([#507](https://github.com/adap/flower/pull/507))" -msgstr "添加了嵌入式设备示例 ([#507](https://github.com/adap/flower/pull/507))" +"The Virtual Client Engine now has full support for optional `Client` (and" +" `NumPyClient`) methods." +msgstr "虚拟客户端引擎现在完全支持可选的 `Client`（和 `NumPyClient`）方法。" -#: ../../source/ref-changelog.md:974 +#: ../../source/ref-changelog.md:669 msgid "" -"Added a new NumPyClient (in addition to the existing KerasClient) " -"([#504](https://github.com/adap/flower/pull/504) " -"[#508](https://github.com/adap/flower/pull/508))" +"**Provide type information to packages using** `flwr` " +"([#1377](https://github.com/adap/flower/pull/1377))" msgstr "" -"添加了一个新的 NumPyClient（除现有的 KerasClient " -"之外）（[#504](https://github.com/adap/flower/pull/504) " -"[#508](https://github.com/adap/flower/pull/508)" +"**使用** `flwr`向软件包提供类型信息 " +"([#1377](https://github.com/adap/flower/pull/1377))" -#: ../../source/ref-changelog.md:975 +#: ../../source/ref-changelog.md:671 msgid "" -"Deprecated `flwr_example` package and started to migrate examples into " -"the top-level `examples` directory " -"([#494](https://github.com/adap/flower/pull/494) " -"[#512](https://github.com/adap/flower/pull/512))" +"The package `flwr` is now bundled with a `py.typed` file indicating that " +"the package is typed. This enables typing support for projects or " +"packages that use `flwr` by enabling them to improve their code using " +"static type checkers like `mypy`." msgstr "" -"弃用 `flwr_example` 软件包，并开始将示例迁移到顶层的 `examples` 目录 " -"([#494](https://github.com/adap/flower/pull/494) " -"[#512](https://github.com/adap/flower/pull/512))" +"软件包 `flwr` 现在捆绑了一个 `py.typed` 文件，表明该软件包是类型化的。这样，使用 `flwr` 的项目或软件包就可以使用 " +"`mypy` 等静态类型检查器改进代码，从而获得类型支持。" -#: ../../source/ref-changelog.md:977 -msgid "v0.11.0 (2020-11-30)" -msgstr "v0.11.0 (2020-11-30)" - -#: ../../source/ref-changelog.md:979 -msgid "Incompatible changes:" -msgstr "不兼容的更改：" - -#: ../../source/ref-changelog.md:981 -msgid "" -"Renamed strategy methods " -"([#486](https://github.com/adap/flower/pull/486)) to unify the naming of " -"Flower's public APIs. Other public methods/functions (e.g., every method " -"in `Client`, but also `Strategy.evaluate`) do not use the `on_` prefix, " -"which is why we're removing it from the four methods in Strategy. To " -"migrate rename the following `Strategy` methods accordingly:" -msgstr "" -"重命名了策略方法（[#486](https://github.com/adap/flower/pull/486)），以统一 Flower公共 " -"API 的命名。其他公共方法/函数（例如 `Client` 中的每个方法，以及 `Strategy.evaluate`）不使用 `on_` " -"前缀，这就是我们从 Strategy 中的四个方法中移除它的原因。迁移时，请相应地重命名以下 `Strategy` 方法：" - -#: ../../source/ref-changelog.md:982 -msgid "`on_configure_evaluate` => `configure_evaluate`" -msgstr "`on_configure_evaluate` => `configure_evaluate`" - -#: ../../source/ref-changelog.md:983 -msgid "`on_aggregate_evaluate` => `aggregate_evaluate`" -msgstr "`on_aggregate_evaluate` => `aggregate_evaluate`" - -#: ../../source/ref-changelog.md:984 -msgid "`on_configure_fit` => `configure_fit`" -msgstr "`on_configure_fit` => `configure_fit`" - -#: ../../source/ref-changelog.md:985 -msgid "`on_aggregate_fit` => `aggregate_fit`" -msgstr "`on_aggregate_fit` => `aggregate_fit`" - -#: ../../source/ref-changelog.md:989 +#: ../../source/ref-changelog.md:673 msgid "" -"Deprecated `DefaultStrategy` " -"([#479](https://github.com/adap/flower/pull/479)). To migrate use " -"`FedAvg` instead." +"**Updated code example** " +"([#1344](https://github.com/adap/flower/pull/1344), " +"[#1347](https://github.com/adap/flower/pull/1347))" msgstr "" -"已废弃的 `DefaultStrategy` ([#479](https://github.com/adap/flower/pull/479)) " -"。迁移时请使用 `FedAvg`。" - -#: ../../source/ref-changelog.md:990 -msgid "" -"Simplified examples and baselines " -"([#484](https://github.com/adap/flower/pull/484))." -msgstr "简化示例和baselines（[#484](https://github.com/adap/flower/pull/484)）。" +"** 更新代码示例** ([#1344](https://github.com/adap/flower/pull/1344), " +"[#1347](https://github.com/adap/flower/pull/1347))" -#: ../../source/ref-changelog.md:991 +#: ../../source/ref-changelog.md:675 msgid "" -"Removed presently unused `on_conclude_round` from strategy interface " -"([#483](https://github.com/adap/flower/pull/483))." -msgstr "" -"删除了策略界面中目前未使用的 " -"\"on_conclude_round\"（[#483](https://github.com/adap/flower/pull/483)）。" +"The code examples covering scikit-learn and PyTorch Lightning have been " +"updated to work with the latest version of Flower." +msgstr "涵盖 scikit-learn 和 PyTorch Lightning 的代码示例已更新，以便与最新版本的 Flower 配合使用。" -#: ../../source/ref-changelog.md:992 +#: ../../source/ref-changelog.md:677 msgid "" -"Set minimal Python version to 3.6.1 instead of 3.6.9 " -"([#471](https://github.com/adap/flower/pull/471))." +"**Updated documentation** " +"([#1355](https://github.com/adap/flower/pull/1355), " +"[#1558](https://github.com/adap/flower/pull/1558), " +"[#1379](https://github.com/adap/flower/pull/1379), " +"[#1380](https://github.com/adap/flower/pull/1380), " +"[#1381](https://github.com/adap/flower/pull/1381), " +"[#1332](https://github.com/adap/flower/pull/1332), " +"[#1391](https://github.com/adap/flower/pull/1391), " +"[#1403](https://github.com/adap/flower/pull/1403), " +"[#1364](https://github.com/adap/flower/pull/1364), " +"[#1409](https://github.com/adap/flower/pull/1409), " +"[#1419](https://github.com/adap/flower/pull/1419), " +"[#1444](https://github.com/adap/flower/pull/1444), " +"[#1448](https://github.com/adap/flower/pull/1448), " +"[#1417](https://github.com/adap/flower/pull/1417), " +"[#1449](https://github.com/adap/flower/pull/1449), " +"[#1465](https://github.com/adap/flower/pull/1465), " +"[#1467](https://github.com/adap/flower/pull/1467))" msgstr "" -"将最小 Python 版本设为 3.6.1，而不是 3.6.9 " -"([#471](https://github.com/adap/flower/pull/471))." +"**更新文档** ([#1355](https://github.com/adap/flower/pull/1355), " +"[#1558](https://github.com/adap/flower/pull/1558), " +"[#1379](https://github.com/adap/flower/pull/1379), " +"[#1380](https://github.com/adap/flower/pull/1380), " +"[#1381](https://github.com/adap/flower/pull/1381), " +"[#1332](https://github.com/adap/flower/pull/1332), " +"[#1391](https://github.com/adap/flower/pull/1391), " +"[#1403](https://github.com/adap/flower/pull/1403), " +"[#1364](https://github. com/adap/flower/pull/1364), " +"[#1409](https://github.com/adap/flower/pull/1409), " +"[#1419](https://github.com/adap/flower/pull/1419), " +"[#1444](https://github.com/adap/flower/pull/1444), " +"[#1448](https://github.com/adap/flower/pull/1448), " +"[#1417](https://github.com/adap/flower/pull/1417), " +"[#1449](https://github.com/adap/flower/pull/1449), " +"[#1465](https://github.com/adap/flower/pull/1465), " +"[#1467](https://github.com/adap/flower/pull/1467))" -#: ../../source/ref-changelog.md:993 +#: ../../source/ref-changelog.md:679 msgid "" -"Improved `Strategy` docstrings " -"([#470](https://github.com/adap/flower/pull/470))." -msgstr "" -"改进了 `Strategy` " -"docstrings（[#470](https://github.com/adap/flower/pull/470)）。" - -#: ../../source/ref-example-projects.rst:2 -msgid "Example projects" -msgstr "项目实例" +"There have been so many documentation updates that it doesn't even make " +"sense to list them individually." +msgstr "文档更新的数量之多，甚至没有必要逐一列出。" -#: ../../source/ref-example-projects.rst:4 +#: ../../source/ref-changelog.md:681 msgid "" -"Flower comes with a number of usage examples. The examples demonstrate " -"how Flower can be used to federate different kinds of existing machine " -"learning pipelines, usually leveraging popular machine learning " -"frameworks such as `PyTorch `_ or `TensorFlow " -"`_." -msgstr "" -"Flower 附带了许多使用示例。这些示例演示了如何使用 Flower 联邦不同类型的现有机器学习形式，通常是利用流行的机器学习框架，如 " -"`PyTorch `_ 或 `TensorFlow " -"`_。" +"**Restructured documentation** " +"([#1387](https://github.com/adap/flower/pull/1387))" +msgstr "**重构文档**（[#1387](https://github.com/adap/flower/pull/1387)）" -#: ../../source/ref-example-projects.rst:10 -#, fuzzy +#: ../../source/ref-changelog.md:683 msgid "" -"The following examples are available as standalone projects. Quickstart " -"TensorFlow/Keras ---------------------------" -msgstr "以下示例可作为独立项目使用。" +"The documentation has been restructured to make it easier to navigate. " +"This is just the first step in a larger effort to make the Flower " +"documentation the best documentation of any project ever. Stay tuned!" +msgstr "我们对文档进行了重组，使其更易于浏览。这只是让 Flower 文档成为所有项目中最好文档的第一步。敬请期待！" -#: ../../source/ref-example-projects.rst:14 +#: ../../source/ref-changelog.md:685 msgid "" -"The TensorFlow/Keras quickstart example shows CIFAR-10 image " -"classification with MobileNetV2:" -msgstr "TensorFlow/Keras 快速入门示例展示了使用 MobileNetV2 进行的 CIFAR-10 图像分类：" +"**Open in Colab button** " +"([#1389](https://github.com/adap/flower/pull/1389))" +msgstr "**在 Colab 中打开按钮** ([#1389](https://github.com/adap/flower/pull/1389))" -#: ../../source/ref-example-projects.rst:17 +#: ../../source/ref-changelog.md:687 msgid "" -"`Quickstart TensorFlow (Code) " -"`_" -msgstr "" -"`TensorFlow快速入门 (代码) `_" - -#: ../../source/ref-example-projects.rst:18 -#, fuzzy -msgid ":doc:`Quickstart TensorFlow (Tutorial) `" +"The four parts of the Flower Federated Learning Tutorial now come with a " +"new `Open in Colab` button. No need to install anything on your local " +"machine, you can now use and learn about Flower in your browser, it's " +"only a single click away." msgstr "" -"`TensorFlow快速入门 (教程) `_" +"Flower 联邦学习教程的四个部分现在都带有一个新的 \"在 Colab 中打开 " +"\"按钮。现在，您无需在本地计算机上安装任何软件，只需点击一下，就可以在浏览器中使用和学习 Flower。" -#: ../../source/ref-example-projects.rst:19 +#: ../../source/ref-changelog.md:689 msgid "" -"`Quickstart TensorFlow (Blog Post) `_" +"**Improved tutorial** ([#1468](https://github.com/adap/flower/pull/1468)," +" [#1470](https://github.com/adap/flower/pull/1470), " +"[#1472](https://github.com/adap/flower/pull/1472), " +"[#1473](https://github.com/adap/flower/pull/1473), " +"[#1474](https://github.com/adap/flower/pull/1474), " +"[#1475](https://github.com/adap/flower/pull/1475))" msgstr "" -"`TensorFlow快速入门 (博客) `_" - -#: ../../source/ref-example-projects.rst:23 -#: ../../source/tutorial-quickstart-pytorch.rst:5 -msgid "Quickstart PyTorch" -msgstr "PyTorch快速入门" - -#: ../../source/ref-example-projects.rst:25 -msgid "" -"The PyTorch quickstart example shows CIFAR-10 image classification with a" -" simple Convolutional Neural Network:" -msgstr "PyTorch 快速入门范例展示了使用简单卷积神经网络进行 CIFAR-10 图像分类的情况：" +"**改进教程** ([#1468](https://github.com/adap/flower/pull/1468), " +"[#1470](https://github.com/adap/flower/pull/1470), " +"[#1472](https://github.com/adap/flower/pull/1472), " +"[#1473](https://github.com/adap/flower/pull/1473), " +"[#1474](https://github.com/adap/flower/pull/1474), " +"[#1475](https://github.com/adap/flower/pull/1475)))" -#: ../../source/ref-example-projects.rst:28 +#: ../../source/ref-changelog.md:691 msgid "" -"`Quickstart PyTorch (Code) " -"`_" -msgstr "" -"`PyTorch快速入门 (代码) `_" - -#: ../../source/ref-example-projects.rst:29 -#, fuzzy -msgid ":doc:`Quickstart PyTorch (Tutorial) `" +"The Flower Federated Learning Tutorial has two brand-new parts covering " +"custom strategies (still WIP) and the distinction between `Client` and " +"`NumPyClient`. The existing parts one and two have also been improved " +"(many small changes and fixes)." msgstr "" -"`PyTorch快速入门 (教程) `_" +"Flower 联邦学习教程有两个全新的部分，涉及自定义策略（仍处于 WIP 阶段）和 `Client` 与 `NumPyClient` " +"之间的区别。现有的第一和第二部分也得到了改进（许多小改动和修正）。" -#: ../../source/ref-example-projects.rst:33 -msgid "PyTorch: From Centralized To Federated" -msgstr "PyTorch： 从集中式到联邦式" +#: ../../source/ref-changelog.md:697 +msgid "v1.0.0 (2022-07-28)" +msgstr "v1.0.0 (2022-07-28)" -#: ../../source/ref-example-projects.rst:35 -msgid "" -"This example shows how a regular PyTorch project can be federated using " -"Flower:" -msgstr "本例展示了如何使用 Flower 联邦化一个普通的 PyTorch 项目：" +#: ../../source/ref-changelog.md:699 +msgid "Highlights" +msgstr "亮点" -#: ../../source/ref-example-projects.rst:37 -msgid "" -"`PyTorch: From Centralized To Federated (Code) " -"`_" -msgstr "" -"PyTorch： 从集中式到联邦式（代码） `_" +#: ../../source/ref-changelog.md:701 +msgid "Stable **Virtual Client Engine** (accessible via `start_simulation`)" +msgstr "稳定的**虚拟客户端引擎**（可通过`start_simulation`访问）" -#: ../../source/ref-example-projects.rst:38 -#, fuzzy -msgid "" -":doc:`PyTorch: From Centralized To Federated (Tutorial) `" -msgstr "" -"PyTorch： 从集中式到联邦式（教程） `_" +#: ../../source/ref-changelog.md:702 +msgid "All `Client`/`NumPyClient` methods are now optional" +msgstr "所有 `Client`/`NumPyClient` 方法现在都是可选的了" -#: ../../source/ref-example-projects.rst:42 -msgid "Federated Learning on Raspberry Pi and Nvidia Jetson" -msgstr "树莓派和 Nvidia Jetson 上的联邦学习" +#: ../../source/ref-changelog.md:703 +msgid "Configurable `get_parameters`" +msgstr "可配置的`get_parameters`" -#: ../../source/ref-example-projects.rst:44 +#: ../../source/ref-changelog.md:704 msgid "" -"This example shows how Flower can be used to build a federated learning " -"system that run across Raspberry Pi and Nvidia Jetson:" -msgstr "本示例展示了如何利用 Flower 建立一个跨 Raspberry Pi 和 Nvidia Jetson 运行的联邦学习系统：" +"Tons of small API cleanups resulting in a more coherent developer " +"experience" +msgstr "对大量小型应用程序接口进行了清理，使开发人员的体验更加一致" -#: ../../source/ref-example-projects.rst:46 +#: ../../source/ref-changelog.md:708 msgid "" -"`Federated Learning on Raspberry Pi and Nvidia Jetson (Code) " -"`_" +"We would like to give our **special thanks** to all the contributors who " +"made Flower 1.0 possible (in reverse [GitHub " +"Contributors](https://github.com/adap/flower/graphs/contributors) order):" msgstr "" -"Raspberry Pi 和 Nvidia Jetson 上的联邦学习（代码） " -"`_" +"在此，我们谨向所有促成 Flower 1.0 的贡献者致以**特别的谢意（按[GitHub " +"贡献者](https://github.com/adap/flower/graphs/contributors) 倒序排列）：" -#: ../../source/ref-example-projects.rst:47 +#: ../../source/ref-changelog.md:710 msgid "" -"`Federated Learning on Raspberry Pi and Nvidia Jetson (Blog Post) " -"`_" +"[@rtaiello](https://github.com/rtaiello), " +"[@g-pichler](https://github.com/g-pichler), [@rob-" +"luke](https://github.com/rob-luke), [@andreea-zaharia](https://github.com" +"/andreea-zaharia), [@kinshukdua](https://github.com/kinshukdua), " +"[@nfnt](https://github.com/nfnt), " +"[@tatiana-s](https://github.com/tatiana-s), " +"[@TParcollet](https://github.com/TParcollet), " +"[@vballoli](https://github.com/vballoli), " +"[@negedng](https://github.com/negedng), " +"[@RISHIKESHAVAN](https://github.com/RISHIKESHAVAN), " +"[@hei411](https://github.com/hei411), " +"[@SebastianSpeitel](https://github.com/SebastianSpeitel), " +"[@AmitChaulwar](https://github.com/AmitChaulwar), " +"[@Rubiel1](https://github.com/Rubiel1), [@FANTOME-PAN](https://github.com" +"/FANTOME-PAN), [@Rono-BC](https://github.com/Rono-BC), " +"[@lbhm](https://github.com/lbhm), " +"[@sishtiaq](https://github.com/sishtiaq), " +"[@remde](https://github.com/remde), [@Jueun-Park](https://github.com" +"/Jueun-Park), [@architjen](https://github.com/architjen), " +"[@PratikGarai](https://github.com/PratikGarai), " +"[@mrinaald](https://github.com/mrinaald), " +"[@zliel](https://github.com/zliel), " +"[@MeiruiJiang](https://github.com/MeiruiJiang), " +"[@sancarlim](https://github.com/sancarlim), " +"[@gubertoli](https://github.com/gubertoli), " +"[@Vingt100](https://github.com/Vingt100), " +"[@MakGulati](https://github.com/MakGulati), " +"[@cozek](https://github.com/cozek), " +"[@jafermarq](https://github.com/jafermarq), " +"[@sisco0](https://github.com/sisco0), " +"[@akhilmathurs](https://github.com/akhilmathurs), " +"[@CanTuerk](https://github.com/CanTuerk), " +"[@mariaboerner1987](https://github.com/mariaboerner1987), " +"[@pedropgusmao](https://github.com/pedropgusmao), " +"[@tanertopal](https://github.com/tanertopal), " +"[@danieljanes](https://github.com/danieljanes)." msgstr "" -"Raspberry Pi和 Nvidia Jetson 上的联邦学习（博客） " -"`_" - -#: ../../source/ref-faq.rst:4 -msgid "" -"This page collects answers to commonly asked questions about Federated " -"Learning with Flower." -msgstr "本页收集了有关 \"Flower 联邦学习 \"常见问题的答案。" - -#: ../../source/ref-faq.rst -#, fuzzy -msgid ":fa:`eye,mr-1` Can Flower run on Jupyter Notebooks / Google Colab?" -msgstr ":fa:`eye,mr-1` Flower 可以在 Juptyter Notebooks / Google Colab 上运行吗？" +"[@rtaiello](https://github.com/rtaiello), " +"[@g-pichler](https://github.com/g-pichler), [@rob-" +"luke](https://github.com/rob-luke), [@andreea-zaharia](https://github.com" +"/andreea-zaharia), [@kinshukdua](https://github.com/kinshukdua), " +"[@nfnt](https://github.com/nfnt), " +"[@tatiana-s](https://github.com/tatiana-s), " +"[@TParcollet](https://github.com/TParcollet), " +"[@vballoli](https://github.com/vballoli), " +"[@negedng](https://github.com/negedng), " +"[@RISHIKESHAVAN](https://github.com/RISHIKESHAVAN), " +"[@hei411](https://github.com/hei411), " +"[@SebastianSpeitel](https://github.com/SebastianSpeitel), " +"[@AmitChaulwar](https://github.com/AmitChaulwar), " +"[@Rubiel1](https://github.com/Rubiel1), [@FANTOME-PAN](https://github.com" +"/FANTOME-PAN), [@Rono-BC](https://github.com/Rono-BC), " +"[@lbhm](https://github.com/lbhm), " +"[@sishtiaq](https://github.com/sishtiaq), " +"[@remde](https://github.com/remde), [@Jueun-Park](https://github.com" +"/Jueun-Park), [@architjen](https://github.com/architjen), " +"[@PratikGarai](https://github.com/PratikGarai), " +"[@mrinaald](https://github.com/mrinaald), " +"[@zliel](https://github.com/zliel), " +"[@MeiruiJiang](https://github.com/MeiruiJiang), " +"[@sancarlim](https://github.com/sancarlim), " +"[@gubertoli](https://github.com/gubertoli), " +"[@Vingt100](https://github.com/Vingt100), " +"[@MakGulati](https://github.com/MakGulati), " +"[@cozek](https://github.com/cozek), " +"[@jafermarq](https://github.com/jafermarq), " +"[@sisco0](https://github.com/sisco0), " +"[@akhilmathurs](https://github.com/akhilmathurs), " +"[@CanTuerk](https://github.com/CanTuerk), " +"[@mariaboerner1987](https://github.com/mariaboerner1987), " +"[@pedropgusmao](https://github.com/pedropgusmao), " +"[@tanertopal](https://github.com/tanertopal), " +"[@danieljanes](https://github.com/danieljanes)." -#: ../../source/ref-faq.rst:8 +#: ../../source/ref-changelog.md:714 msgid "" -"Yes, it can! Flower even comes with a few under-the-hood optimizations to" -" make it work even better on Colab. Here's a quickstart example:" -msgstr "是的，它可以！Flower 甚至还进行了一些底层优化，使其在 Colab 上运行得更好。下面是一个快速启动示例：" +"**All arguments must be passed as keyword arguments** " +"([#1338](https://github.com/adap/flower/pull/1338))" +msgstr "** 所有参数必须作为关键字参数传递** ([#1338](https://github.com/adap/flower/pull/1338))" -#: ../../source/ref-faq.rst:10 +#: ../../source/ref-changelog.md:716 msgid "" -"`Flower simulation PyTorch " -"`_" +"Pass all arguments as keyword arguments, positional arguments are not " +"longer supported. Code that uses positional arguments (e.g., " +"`start_client(\"127.0.0.1:8080\", FlowerClient())`) must add the keyword " +"for each positional argument (e.g., " +"`start_client(server_address=\"127.0.0.1:8080\", " +"client=FlowerClient())`)." msgstr "" -"`Flower 模拟 PyTorch " -"`_" +"以关键字参数传递所有参数，不再支持位置参数。使用位置参数的代码（例如，`start_client(\"127.0.0.1:8080\", " +"FlowerClient())`）必须为每个位置参数添加关键字（例如，`start_client(server_address=\"127.0.0.1:8080\"," +" client=FlowerClient())`）。" -#: ../../source/ref-faq.rst:11 +#: ../../source/ref-changelog.md:718 msgid "" -"`Flower simulation TensorFlow/Keras " -"`_" +"**Introduce configuration object** `ServerConfig` **in** `start_server` " +"**and** `start_simulation` " +"([#1317](https://github.com/adap/flower/pull/1317))" msgstr "" -"`Flower模拟TensorFlow/Keras " -"`_" - -#: ../../source/ref-faq.rst -msgid ":fa:`eye,mr-1` How can I run Federated Learning on a Raspberry Pi?" -msgstr ":fa:`eye,mr-1` 如何在 Raspberry Pi 上运行联邦学习？" +"**在*** `start_server` ***和*** `start_simulation` 中引入配置对象*** " +"`ServerConfig` ([#1317](https://github.com/adap/flower/pull/1317))" -#: ../../source/ref-faq.rst:15 +#: ../../source/ref-changelog.md:720 msgid "" -"Find the `blog post about federated learning on embedded device here " -"`_" -" and the corresponding `GitHub code example " -"`_." +"Instead of a config dictionary `{\"num_rounds\": 3, \"round_timeout\": " +"600.0}`, `start_server` and `start_simulation` now expect a configuration" +" object of type `flwr.server.ServerConfig`. `ServerConfig` takes the same" +" arguments that as the previous config dict, but it makes writing type-" +"safe code easier and the default parameters values more transparent." msgstr "" -"请点击此处查看有关嵌入式设备联邦学习的 " -"\"博文\"`_和相应的" -" \"GitHub 代码示例\"`_。" - -#: ../../source/ref-faq.rst -msgid ":fa:`eye,mr-1` Does Flower support federated learning on Android devices?" -msgstr ":fa:`eye,mr-1` Flower 是否支持安卓设备上的联邦学习？" +"并非配置字典`{\"num_rounds\"： 3, \"round_timeout\"： 600.0}`, `start_server`和 " +"`start_simulation`现在用一个类型为 " +"`flwr.server.ServerConfig`的配置对象。`ServerConfig`接收的参数与之前的 config dict " +"相同，但它使编写类型安全代码变得更容易，默认参数值也更加透明。" -#: ../../source/ref-faq.rst:19 +#: ../../source/ref-changelog.md:722 msgid "" -"Yes, it does. Please take a look at our `blog post " -"`_ or check out the code examples:" -msgstr "" -"是的，确实如此。请查看我们的 \"博客文章 `_\" 或查看代码示例：" +"**Rename built-in strategy parameters for clarity** " +"([#1334](https://github.com/adap/flower/pull/1334))" +msgstr "**重新命名内置策略参数，使其更加清晰** ([#1334](https://github.com/adap/flower/pull/1334))" -#: ../../source/ref-faq.rst:21 +#: ../../source/ref-changelog.md:724 msgid "" -"`Android Kotlin example `_" -msgstr "`Android Kotlin 示例 `_" +"The following built-in strategy parameters were renamed to improve " +"readability and consistency with other API's:" +msgstr "以下内置策略参数已重新命名，以提高可读性并与其他 API 保持一致：" -#: ../../source/ref-faq.rst:22 -msgid "`Android Java example `_" -msgstr "Android Java 示例 `_" +#: ../../source/ref-changelog.md:726 +msgid "`fraction_eval` --> `fraction_evaluate`" +msgstr "`fraction_eval` --> `fraction_evaluate`" -#: ../../source/ref-faq.rst -msgid ":fa:`eye,mr-1` Can I combine federated learning with blockchain?" -msgstr ":fa:`eye,mr-1` 我可以将联邦学习与区块链结合起来吗？" +#: ../../source/ref-changelog.md:727 +msgid "`min_eval_clients` --> `min_evaluate_clients`" +msgstr "`min_eval_clients` --> `min_evaluate_clients`" -#: ../../source/ref-faq.rst:26 +#: ../../source/ref-changelog.md:728 +msgid "`eval_fn` --> `evaluate_fn`" +msgstr "`eval_fn` --> `evaluate_fn`" + +#: ../../source/ref-changelog.md:730 msgid "" -"Yes, of course. A list of available examples using Flower within a " -"blockchain environment is available here:" -msgstr "当然可以。有关在区块链环境中使用 Flower 的可用示例列表，请点击此处：" +"**Update default arguments of built-in strategies** " +"([#1278](https://github.com/adap/flower/pull/1278))" +msgstr "**更新内置策略的默认参数** ([#1278](https://github.com/adap/flower/pull/1278))" -#: ../../source/ref-faq.rst:28 +#: ../../source/ref-changelog.md:732 msgid "" -"`Flower meets Nevermined GitHub Repository `_." +"All built-in strategies now use `fraction_fit=1.0` and " +"`fraction_evaluate=1.0`, which means they select *all* currently " +"available clients for training and evaluation. Projects that relied on " +"the previous default values can get the previous behaviour by " +"initializing the strategy in the following way:" msgstr "" -"`Flower meets Nevermined GitHub Repository `_." +"所有内置策略现在都使用 \"fraction_fit=1.0 \"和 " +"\"fraction_evaluate=1.0\"，这意味着它们会选择*所有*当前可用的客户端进行训练和评估。依赖以前默认值的项目可以通过以下方式初始化策略，获得以前的行为：" -#: ../../source/ref-faq.rst:29 +#: ../../source/ref-changelog.md:734 +msgid "`strategy = FedAvg(fraction_fit=0.1, fraction_evaluate=0.1)`" +msgstr "`strategy = FedAvg(fraction_fit=0.1, fraction_evaluate=0.1)`" + +#: ../../source/ref-changelog.md:736 msgid "" -"`Flower meets Nevermined YouTube video " -"`_." +"**Add** `server_round` **to** `Strategy.evaluate` " +"([#1334](https://github.com/adap/flower/pull/1334))" msgstr "" -"`Flower meets Nevermined YouTube 视频 " -"`_." +"**添加*** `server_round` ***到*** `Strategy.evaluate` " +"([#1334](https://github.com/adap/flower/pull/1334))" -#: ../../source/ref-faq.rst:30 -#, fuzzy +#: ../../source/ref-changelog.md:738 msgid "" -"`Flower meets KOSMoS `_." -msgstr "" -"`Flower meets KOSMoS `_." +"The `Strategy` method `evaluate` now receives the current round of " +"federated learning/evaluation as the first parameter." +msgstr "`Strategy`的`evaluate` 方法现在会接收当前一轮联邦学习/评估作为第一个参数。" -#: ../../source/ref-faq.rst:31 +#: ../../source/ref-changelog.md:740 msgid "" -"`Flower meets Talan blog post `_ ." +"**Add** `server_round` **and** `config` **parameters to** `evaluate_fn` " +"([#1334](https://github.com/adap/flower/pull/1334))" msgstr "" -"`Flower meets Talan博文 `_ 。" +"**将*** `server_round` **和*** `config` **参数添加到*** `evaluate_fn` " +"([#1334](https://github.com/adap/flower/pull/1334))" -#: ../../source/ref-faq.rst:32 +#: ../../source/ref-changelog.md:742 msgid "" -"`Flower meets Talan GitHub Repository " -"`_ ." +"The `evaluate_fn` passed to built-in strategies like `FedAvg` now takes " +"three parameters: (1) The current round of federated learning/evaluation " +"(`server_round`), (2) the model parameters to evaluate (`parameters`), " +"and (3) a config dictionary (`config`)." msgstr "" -"`Flower meets Talan GitHub Repository " -"`_ ." +"传递给内置策略（如 `FedAvg`）的 `evaluate_fn` 现在需要三个参数：(1) 当前一轮联邦学习/评估 " +"(`server_round`)，(2) 要评估的模型参数 (`parameters`)，(3) 配置字典 (`config`)。" -#: ../../source/ref-telemetry.md:1 -msgid "Telemetry" -msgstr "遥测功能" +#: ../../source/ref-changelog.md:744 +msgid "" +"**Rename** `rnd` **to** `server_round` " +"([#1321](https://github.com/adap/flower/pull/1321))" +msgstr "" +"**重新命名** `rnd` ** to** `server_round` " +"([#1321](https://github.com/adap/flower/pull/1321))" -#: ../../source/ref-telemetry.md:3 +#: ../../source/ref-changelog.md:746 msgid "" -"The Flower open-source project collects **anonymous** usage metrics to " -"make well-informed decisions to improve Flower. Doing this enables the " -"Flower team to understand how Flower is used and what challenges users " -"might face." +"Several Flower methods and functions (`evaluate_fn`, `configure_fit`, " +"`aggregate_fit`, `configure_evaluate`, `aggregate_evaluate`) receive the " +"current round of federated learning/evaluation as their first parameter. " +"To improve reaability and avoid confusion with *random*, this parameter " +"has been renamed from `rnd` to `server_round`." msgstr "" -"Flower 开源项目收集**匿名**使用指标，以便在充分知情的情况下做出改进 Flower 的决定。这样做能让 Flower 团队了解 " -"Flower 的使用情况以及用户可能面临的挑战。" +"几个 Flower " +"方法和函数（`evaluate_fn`、`configure_fit`、`aggregate_fit`、`configure_evaluate`、`aggregate_evaluate`）的第一个参数是当前一轮的联邦学习/评估。为提高可重复性并避免与" +" *random* 混淆，该参数已从 `rnd` 更名为 `server_round`。" -#: ../../source/ref-telemetry.md:5 +#: ../../source/ref-changelog.md:748 msgid "" -"**Flower is a friendly framework for collaborative AI and data science.**" -" Staying true to this statement, Flower makes it easy to disable " -"telemetry for users that do not want to share anonymous usage metrics." -msgstr "**Flower 是一个用于协作式人工智能和数据科学的友好框架。** Flower 遵循这一声明，让不想分享匿名使用指标的用户可以轻松禁用遥测技术。" - -#: ../../source/ref-telemetry.md:7 -msgid "Principles" -msgstr "原则" +"**Move** `flwr.dataset` **to** `flwr_baselines` " +"([#1273](https://github.com/adap/flower/pull/1273))" +msgstr "" +"**移动*** `flwr.dataset` **到*** `flwr_baselines` " +"([#1273](https://github.com/adap/flower/pull/1273))" -#: ../../source/ref-telemetry.md:9 -msgid "We follow strong principles guarding anonymous usage metrics collection:" -msgstr "我们遵循严格的匿名使用指标收集原则：" +#: ../../source/ref-changelog.md:750 +msgid "The experimental package `flwr.dataset` was migrated to Flower Baselines." +msgstr "实验软件包 `flwr.dataset` 已迁移至 Flower Baselines。" -#: ../../source/ref-telemetry.md:11 +#: ../../source/ref-changelog.md:752 msgid "" -"**Optional:** You will always be able to disable telemetry; read on to " -"learn “[How to opt-out](#how-to-opt-out)”." -msgstr "**可选：** 您始终可以禁用遥测功能；请继续阅读\"[如何退出](#how-to-opt-out)\"。" +"**Remove experimental strategies** " +"([#1280](https://github.com/adap/flower/pull/1280))" +msgstr "**删除实验策略** ([#1280](https://github.com/adap/flower/pull/1280))" -#: ../../source/ref-telemetry.md:12 +#: ../../source/ref-changelog.md:754 msgid "" -"**Anonymous:** The reported usage metrics are anonymous and do not " -"contain any personally identifiable information (PII). See “[Collected " -"metrics](#collected-metrics)” to understand what metrics are being " -"reported." -msgstr "" -"**匿名：** 报告的使用指标是匿名的，不包含任何个人身份信息 (PII)。请参阅\"[收集的指标](#collected-metrics) " -"\"了解报告的指标。" +"Remove unmaintained experimental strategies (`FastAndSlow`, `FedFSv0`, " +"`FedFSv1`)." +msgstr "移除未维护的试验性策略（`FastAndSlow`、`FedFSv0`、`FedFSv1`）。" -#: ../../source/ref-telemetry.md:13 +#: ../../source/ref-changelog.md:756 msgid "" -"**Transparent:** You can easily inspect what anonymous metrics are being " -"reported; see the section “[How to inspect what is being reported](#how-" -"to-inspect-what-is-being-reported)”" +"**Rename** `Weights` **to** `NDArrays` " +"([#1258](https://github.com/adap/flower/pull/1258), " +"[#1259](https://github.com/adap/flower/pull/1259))" msgstr "" -"**透明：** 您可以轻松查看正在报告的匿名指标；请参阅\"[如何查看正在报告的指标]（#how-to-inspect-what-is-" -"being-reported）\"部分" +"**重新命名** `Weights` **到** `NDArrays` " +"([#1258](https://github.com/adap/flower/pull/1258), " +"[#1259](https://github.com/adap/flower/pull/1259))" -#: ../../source/ref-telemetry.md:14 +#: ../../source/ref-changelog.md:758 msgid "" -"**Open for feedback:** You can always reach out to us if you have " -"feedback; see the section “[How to contact us](#how-to-contact-us)” for " -"details." -msgstr "**欢迎反馈：** 如果您有反馈意见，可以随时联系我们；详情请参见\"[如何联系我们](#how-to-contact-us) \"部分。" - -#: ../../source/ref-telemetry.md:16 -msgid "How to opt-out" -msgstr "如何退出" +"`flwr.common.Weights` was renamed to `flwr.common.NDArrays` to better " +"capture what this type is all about." +msgstr "flwr.common.Weights \"更名为 \"flwr.common.NDArrays\"，以更好地反映该类型的含义。" -#: ../../source/ref-telemetry.md:18 +#: ../../source/ref-changelog.md:760 msgid "" -"When Flower starts, it will check for an environment variable called " -"`FLWR_TELEMETRY_ENABLED`. Telemetry can easily be disabled by setting " -"`FLWR_TELEMETRY_ENABLED=0`. Assuming you are starting a Flower server or " -"client, simply do so by prepending your command as in:" +"**Remove antiquated** `force_final_distributed_eval` **from** " +"`start_server` ([#1258](https://github.com/adap/flower/pull/1258), " +"[#1259](https://github.com/adap/flower/pull/1259))" msgstr "" -"Flower 启动时，会检查环境变量 `FLWR_TELEMETRY_ENABLED` 是否存在。通过设置 " -"`FLWR_TELEMETRY_ENABLED=0` 可以轻松禁用遥测功能。假设你启动的是 Flower " -"服务器或客户端，只需在命令前添加以下内容即可：" +"**从** `start_server` 中移除过时的** `force_final_distributed_eval` " +"([#1258](https://github.com/adap/flower/pull/1258), " +"[#1259](https://github.com/adap/flower/pull/1259))" -#: ../../source/ref-telemetry.md:24 +#: ../../source/ref-changelog.md:762 msgid "" -"Alternatively, you can export `FLWR_TELEMETRY_ENABLED=0` in, for example," -" `.bashrc` (or whatever configuration file applies to your environment) " -"to disable Flower telemetry permanently." +"The `start_server` parameter `force_final_distributed_eval` has long been" +" a historic artefact, in this release it is finally gone for good." msgstr "" -"或者，你也可以在 `.bashrc`（或任何适用于你的环境的配置文件）中导出 `FLWR_TELEMETRY_ENABLED=0` 来永久禁用 " -"Flower telemetry。" - -#: ../../source/ref-telemetry.md:26 -msgid "Collected metrics" -msgstr "收集的指标" - -#: ../../source/ref-telemetry.md:28 -msgid "Flower telemetry collects the following metrics:" -msgstr "Flower 遥测技术收集以下指标：" +"start_server \"参数 \"force_final_distributed_eval " +"\"长期以来一直是个历史遗留问题，在此版本中终于永远消失了。" -#: ../../source/ref-telemetry.md:30 +#: ../../source/ref-changelog.md:764 msgid "" -"**Flower version.** Understand which versions of Flower are currently " -"being used. This helps us to decide whether we should invest effort into " -"releasing a patch version for an older version of Flower or instead use " -"the bandwidth to build new features." -msgstr "**了解目前使用的 Flower 版本。这有助于我们决定是否应该投入精力为旧版本的 Flower 发布补丁版本，还是利用带宽来构建新功能。" +"**Make** `get_parameters` **configurable** " +"([#1242](https://github.com/adap/flower/pull/1242))" +msgstr "" +"**使** `get_parameters` **可配置** " +"([#1242](https://github.com/adap/flower/pull/1242))" -#: ../../source/ref-telemetry.md:32 +#: ../../source/ref-changelog.md:766 msgid "" -"**Operating system.** Enables us to answer questions such as: *Should we " -"create more guides for Linux, macOS, or Windows?*" -msgstr "**操作系统**使我们能够回答以下问题： *我们应该为 Linux、macOS 还是 Windows 创建更多指南？*" +"The `get_parameters` method now accepts a configuration dictionary, just " +"like `get_properties`, `fit`, and `evaluate`." +msgstr "" +"现在，\"get_parameters \"方法与 \"get_properties\"、\"fit \"和 \"evaluate " +"\"一样，都接受配置字典。" -#: ../../source/ref-telemetry.md:34 +#: ../../source/ref-changelog.md:768 msgid "" -"**Python version.** Knowing the Python version helps us, for example, to " -"decide whether we should invest effort into supporting old versions of " -"Python or stop supporting them and start taking advantage of new Python " -"features." -msgstr "**例如，了解 Python 版本有助于我们决定是否应该投入精力支持旧版本的 Python，还是停止支持这些版本并开始利用新的 Python 功能。" +"**Replace** `num_rounds` **in** `start_simulation` **with new** `config` " +"**parameter** ([#1281](https://github.com/adap/flower/pull/1281))" +msgstr "" +"**用新的** `config` 参数** 替换** `num_rounds` ** in** `start_simulation` ** " +"([#1281](https://github.com/adap/flower/pull/1281))" -#: ../../source/ref-telemetry.md:36 +#: ../../source/ref-changelog.md:770 msgid "" -"**Hardware properties.** Understanding the hardware environment that " -"Flower is being used in helps to decide whether we should, for example, " -"put more effort into supporting low-resource environments." -msgstr "**硬件属性** 了解 Flower 的硬件使用环境，有助于决定我们是否应在支持低资源环境等方面投入更多精力。" +"The `start_simulation` function now accepts a configuration dictionary " +"`config` instead of the `num_rounds` integer. This improves the " +"consistency between `start_simulation` and `start_server` and makes " +"transitioning between the two easier." +msgstr "" +"现在，`start_simulation`（开始模拟）` 函数接受配置字典 `config` 而不是 `num_rounds` 整数。这改进了 " +"`start_simulation` 和 `start_server` 之间的一致性，并使两者之间的转换更容易。" -#: ../../source/ref-telemetry.md:38 +#: ../../source/ref-changelog.md:774 msgid "" -"**Execution mode.** Knowing what execution mode Flower starts in enables " -"us to understand how heavily certain features are being used and better " -"prioritize based on that." -msgstr "** 执行模式** 了解 Flower 的启动执行模式，能让我们了解某些功能的使用率，并据此更好地确定优先级。" +"**Support Python 3.10** " +"([#1320](https://github.com/adap/flower/pull/1320))" +msgstr "** 支持 Python 3.10** ([#1320](https://github.com/adap/flower/pull/1320))" -#: ../../source/ref-telemetry.md:40 +#: ../../source/ref-changelog.md:776 msgid "" -"**Cluster.** Flower telemetry assigns a random in-memory cluster ID each " -"time a Flower workload starts. This allows us to understand which device " -"types not only start Flower workloads but also successfully complete " -"them." -msgstr "" -"**每次 Flower 工作负载启动时，Flower 遥测都会随机分配一个内存集群 ID。这样，我们就能了解哪些设备类型不仅启动了 Flower " -"工作负载，而且还成功完成了它们。" +"The previous Flower release introduced experimental support for Python " +"3.10, this release declares Python 3.10 support as stable." +msgstr "上一个 Flower 版本引入了对 Python 3.10 的实验支持，而本版本则宣布对 Python 3.10 的支持为稳定支持。" -#: ../../source/ref-telemetry.md:42 +#: ../../source/ref-changelog.md:778 msgid "" -"**Source.** Flower telemetry tries to store a random source ID in " -"`~/.flwr/source` the first time a telemetry event is generated. The " -"source ID is important to identify whether an issue is recurring or " -"whether an issue is triggered by multiple clusters running concurrently " -"(which often happens in simulation). For example, if a device runs " -"multiple workloads at the same time, and this results in an issue, then, " -"in order to reproduce the issue, multiple workloads must be started at " -"the same time." +"**Make all** `Client` **and** `NumPyClient` **methods optional** " +"([#1260](https://github.com/adap/flower/pull/1260), " +"[#1277](https://github.com/adap/flower/pull/1277))" msgstr "" -"**Source.** Flower 遥测会在第一次生成遥测事件时，尝试在 `~/.flwr/source` 中存储一个随机源 ID。源 ID " -"对于识别问题是否反复出现或问题是否由多个集群同时运行触发（这在模拟中经常发生）非常重要。例如，如果设备同时运行多个工作负载并导致问题，那么为了重现问题，必须同时启动多个工作负载。" +"**使所有** `Client` **和** `NumPyClient` **方法成为可选** " +"([#1260](https://github.com/adap/flower/pull/1260), " +"[#1277](https://github.com/adap/flower/pull/1277))" -#: ../../source/ref-telemetry.md:44 +#: ../../source/ref-changelog.md:780 msgid "" -"You may delete the source ID at any time. If you wish for all events " -"logged under a specific source ID to be deleted, you can send a deletion " -"request mentioning the source ID to `telemetry@flower.ai`. All events " -"related to that source ID will then be permanently deleted." +"The `Client`/`NumPyClient` methods `get_properties`, `get_parameters`, " +"`fit`, and `evaluate` are all optional. This enables writing clients that" +" implement, for example, only `fit`, but no other method. No need to " +"implement `evaluate` when using centralized evaluation!" msgstr "" -"您可以随时删除源 ID。如果您希望删除特定源 ID 下记录的所有事件，可以向 `telemetry@flower.ai` 发送删除请求，并提及该源" -" ID。届时，与该源 ID 相关的所有事件都将被永久删除。" +"`Client`/`NumPyClient`的 \"get_properties\"、\"get_parameters\"、\"fit \"和 " +"\"evaluate \"方法都是可选的。这样就可以编写只实现 `fit` 而不实现其他方法的客户端。使用集中评估时，无需实现 " +"`evaluate`！" -#: ../../source/ref-telemetry.md:46 +#: ../../source/ref-changelog.md:782 msgid "" -"We will not collect any personally identifiable information. If you think" -" any of the metrics collected could be misused in any way, please [get in" -" touch with us](#how-to-contact-us). We will update this page to reflect " -"any changes to the metrics collected and publish changes in the " -"changelog." +"**Enable passing a** `Server` **instance to** `start_simulation` " +"([#1281](https://github.com/adap/flower/pull/1281))" msgstr "" -"我们不会收集任何个人身份信息。如果您认为所收集的任何指标可能以任何方式被滥用，请[与我们联系]（#how-to-contact-" -"us）。我们将更新本页面，以反映对所收集指标的任何更改，并在更新日志中公布更改内容。" +"**启用向** `start_simulation` 传递** `Server` 实例 " +"([#1281](https://github.com/adap/flower/pull/1281))" -#: ../../source/ref-telemetry.md:48 +#: ../../source/ref-changelog.md:784 msgid "" -"If you think other metrics would be helpful for us to better guide our " -"decisions, please let us know! We will carefully review them; if we are " -"confident that they do not compromise user privacy, we may add them." -msgstr "如果您认为其他指标有助于我们更好地指导决策，请告诉我们！我们将仔细审查这些指标；如果我们确信它们不会损害用户隐私，我们可能会添加这些指标。" - -#: ../../source/ref-telemetry.md:50 -msgid "How to inspect what is being reported" -msgstr "如何检查报告中的内容" +"Similar to `start_server`, `start_simulation` now accepts a full `Server`" +" instance. This enables users to heavily customize the execution of " +"eperiments and opens the door to running, for example, async FL using the" +" Virtual Client Engine." +msgstr "" +"与 `start_server` 类似，`start_simulation` 现在也接受一个完整的 `Server` " +"实例。这使得用户可以对实验的执行进行大量自定义，并为使用虚拟客户端引擎运行异步 FL 等打开了大门。" -#: ../../source/ref-telemetry.md:52 +#: ../../source/ref-changelog.md:786 msgid "" -"We wanted to make it very easy for you to inspect what anonymous usage " -"metrics are reported. You can view all the reported telemetry information" -" by setting the environment variable `FLWR_TELEMETRY_LOGGING=1`. Logging " -"is disabled by default. You may use logging independently from " -"`FLWR_TELEMETRY_ENABLED` so that you can inspect the telemetry feature " -"without sending any metrics." +"**Update code examples** " +"([#1291](https://github.com/adap/flower/pull/1291), " +"[#1286](https://github.com/adap/flower/pull/1286), " +"[#1282](https://github.com/adap/flower/pull/1282))" msgstr "" -"我们希望能让您轻松查看所报告的匿名使用指标。通过设置环境变量 `FLWR_TELEMETRY_LOGGING=1` " -"可以查看所有报告的遥测信息。日志记录默认为禁用。您可以不使用 `FLWR_TELEMETRY_ENABLED` " -"而单独使用日志记录，这样就可以在不发送任何指标的情况下检查遥测功能。" +"**更新代码示例** ([#1291](https://github.com/adap/flower/pull/1291), " +"[#1286](https://github.com/adap/flower/pull/1286), " +"[#1282](https://github.com/adap/flower/pull/1282))" -#: ../../source/ref-telemetry.md:58 +#: ../../source/ref-changelog.md:788 msgid "" -"The inspect Flower telemetry without sending any anonymous usage metrics," -" use both environment variables:" -msgstr "在不发送任何匿名使用指标的情况下检查 Flower 遥测，可使用这两个环境变量：" +"Many code examples received small or even large maintenance updates, " +"among them are" +msgstr "许多代码示例都进行了小规模甚至大规模的维护更新，其中包括" -#: ../../source/ref-telemetry.md:64 -msgid "How to contact us" -msgstr "如何联系我们" +#: ../../source/ref-changelog.md:790 +msgid "`scikit-learn`" +msgstr "`scikit-learn`" -#: ../../source/ref-telemetry.md:66 -msgid "" -"We want to hear from you. If you have any feedback or ideas on how to " -"improve the way we handle anonymous usage metrics, reach out to us via " -"[Slack](https://flower.ai/join-slack/) (channel `#telemetry`) or email " -"(`telemetry@flower.ai`)." -msgstr "" -"我们希望听到您的意见。如果您对如何改进我们处理匿名使用指标的方式有任何反馈或想法，请通过 [Slack](https://flower.ai" -"/join-slack/) （频道 `#telemetry`）或电子邮件 (`telemetry@flower.ai`)与我们联系。" +#: ../../source/ref-changelog.md:791 +msgid "`simulation_pytorch`" +msgstr "`simulation_pytorch`" -#: ../../source/tutorial-quickstart-android.rst:-1 -msgid "" -"Read this Federated Learning quickstart tutorial for creating an Android " -"app using Flower." -msgstr "阅读本联邦学习快速入门教程，了解如何使用 Flower 创建 Android 应用程序。" +#: ../../source/ref-changelog.md:792 +msgid "`quickstart_pytorch`" +msgstr "`quickstart_pytorch`" -#: ../../source/tutorial-quickstart-android.rst:5 -msgid "Quickstart Android" -msgstr "快速入门 Android" +#: ../../source/ref-changelog.md:793 +msgid "`quickstart_simulation`" +msgstr "`quickstart_simulation`" -#: ../../source/tutorial-quickstart-android.rst:10 -msgid "" -"Let's build a federated learning system using TFLite and Flower on " -"Android!" -msgstr "让我们在 Android 上使用 TFLite 和 Flower 构建一个联邦学习系统！" +#: ../../source/ref-changelog.md:794 +msgid "`quickstart_tensorflow`" +msgstr "`quickstart_tensorflow`" -#: ../../source/tutorial-quickstart-android.rst:12 -msgid "" -"Please refer to the `full code example " -"`_ to learn " -"more." -msgstr "" -"请参阅`完整代码示例 " -"`_了解更多信息。" +#: ../../source/ref-changelog.md:795 +msgid "`advanced_tensorflow`" +msgstr "`advanced_tensorflow`" -#: ../../source/tutorial-quickstart-fastai.rst:-1 +#: ../../source/ref-changelog.md:797 msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with FastAI to train a vision model on CIFAR-10." -msgstr "查看此联邦学习快速入门教程，了解如何使用 Flower 和 FastAI 在 CIFAR-10 上训练视觉模型。" - -#: ../../source/tutorial-quickstart-fastai.rst:5 -msgid "Quickstart fastai" -msgstr "快速入门 fastai" +"**Remove the obsolete simulation example** " +"([#1328](https://github.com/adap/flower/pull/1328))" +msgstr "**删除过时的模拟示例** ([#1328](https://github.com/adap/flower/pull/1328))" -#: ../../source/tutorial-quickstart-fastai.rst:10 -msgid "Let's build a federated learning system using fastai and Flower!" -msgstr "让我们用 fastai 和 Flower 建立一个联邦学习系统！" +#: ../../source/ref-changelog.md:799 +msgid "" +"Removes the obsolete `simulation` example and renames " +"`quickstart_simulation` to `simulation_tensorflow` so it fits withs the " +"naming of `simulation_pytorch`" +msgstr "" +"删除过时的 \"simulation \"示例，并将 \"quickstart_simulation \"重命名为 " +"\"simulation_tensorflow\"，使其与 \"simulation_pytorch \"的命名一致" -#: ../../source/tutorial-quickstart-fastai.rst:12 +#: ../../source/ref-changelog.md:801 msgid "" -"Please refer to the `full code example " -"`_ " -"to learn more." +"**Update documentation** " +"([#1223](https://github.com/adap/flower/pull/1223), " +"[#1209](https://github.com/adap/flower/pull/1209), " +"[#1251](https://github.com/adap/flower/pull/1251), " +"[#1257](https://github.com/adap/flower/pull/1257), " +"[#1267](https://github.com/adap/flower/pull/1267), " +"[#1268](https://github.com/adap/flower/pull/1268), " +"[#1300](https://github.com/adap/flower/pull/1300), " +"[#1304](https://github.com/adap/flower/pull/1304), " +"[#1305](https://github.com/adap/flower/pull/1305), " +"[#1307](https://github.com/adap/flower/pull/1307))" msgstr "" -"请参阅 `完整代码示例 `_了解更多信息。" +"**更新文档** ([#1223](https://github.com/adap/flower/pull/1223), " +"[#1209](https://github.com/adap/flower/pull/1209), " +"[#1251](https://github.com/adap/flower/pull/1251), " +"[#1257](https://github.com/adap/flower/pull/1257), " +"[#1267](https://github.com/adap/flower/pull/1267), " +"[#1268](https://github.com/adap/flower/pull/1268), " +"[#1300](https://github.com/adap/flower/pull/1300), " +"[#1304](https://github.com/adap/flower/pull/1304), " +"[#1305](https://github.com/adap/flower/pull/1305), " +"[#1307](https://github.com/adap/flower/pull/1307))" -#: ../../source/tutorial-quickstart-huggingface.rst:-1 +#: ../../source/ref-changelog.md:803 msgid "" -"Check out this Federating Learning quickstart tutorial for using Flower " -"with HuggingFace Transformers in order to fine-tune an LLM." -msgstr "查看此联邦学习 快速入门教程，了解如何使用 Flower 和 HuggingFace Transformers 来微调 LLM。" +"One substantial documentation update fixes multiple smaller rendering " +"issues, makes titles more succinct to improve navigation, removes a " +"deprecated library, updates documentation dependencies, includes the " +"`flwr.common` module in the API reference, includes support for markdown-" +"based documentation, migrates the changelog from `.rst` to `.md`, and " +"fixes a number of smaller details!" +msgstr "" +"其中一个实质性的文档更新修复了多个较小的渲染问题，使标题更加简洁以改善导航，删除了一个已废弃的库，更新了文档依赖关系，在 API 参考中包含了 " +"`flwr.common` 模块，包含了对基于 markdown 的文档的支持，将更新日志从 `.rst` 移植到了 " +"`.md`，并修复了一些较小的细节！" -#: ../../source/tutorial-quickstart-huggingface.rst:5 -msgid "Quickstart 🤗 Transformers" -msgstr "🤗 Transformers快速入门" +#: ../../source/ref-changelog.md:805 ../../source/ref-changelog.md:860 +#: ../../source/ref-changelog.md:929 ../../source/ref-changelog.md:968 +msgid "**Minor updates**" +msgstr "**小规模更新**" -#: ../../source/tutorial-quickstart-huggingface.rst:10 +#: ../../source/ref-changelog.md:807 msgid "" -"Let's build a federated learning system using Hugging Face Transformers " -"and Flower!" -msgstr "让我们用Hugging Face Transformers和Flower来构建一个联邦学习系统！" +"Add round number to fit and evaluate log messages " +"([#1266](https://github.com/adap/flower/pull/1266))" +msgstr "添加四舍五入数字，以适应和评估日志信息（[#1266](https://github.com/adap/flower/pull/1266)）" -#: ../../source/tutorial-quickstart-huggingface.rst:12 +#: ../../source/ref-changelog.md:808 msgid "" -"We will leverage Hugging Face to federate the training of language models" -" over multiple clients using Flower. More specifically, we will fine-tune" -" a pre-trained Transformer model (distilBERT) for sequence classification" -" over a dataset of IMDB ratings. The end goal is to detect if a movie " -"rating is positive or negative." +"Add secure gRPC connection to the `advanced_tensorflow` code example " +"([#847](https://github.com/adap/flower/pull/847))" msgstr "" -"我们将利用Hugging Face技术，使用 Flower 在多个客户端上联邦训练语言模型。更具体地说，我们将对预先训练好的 " -"Transformer 模型（distilBERT）进行微调，以便在 IMDB 评分数据集上进行序列分类。最终目标是检测电影评分是正面还是负面。" - -#: ../../source/tutorial-quickstart-huggingface.rst:18 -msgid "Dependencies" -msgstr "依赖关系" +"为 `advanced_tensorflow` 代码示例添加安全 gRPC 连接 " +"([#847](https://github.com/adap/flower/pull/847))" -#: ../../source/tutorial-quickstart-huggingface.rst:20 +#: ../../source/ref-changelog.md:809 msgid "" -"To follow along this tutorial you will need to install the following " -"packages: :code:`datasets`, :code:`evaluate`, :code:`flwr`, " -":code:`torch`, and :code:`transformers`. This can be done using " -":code:`pip`:" +"Update developer tooling " +"([#1231](https://github.com/adap/flower/pull/1231), " +"[#1276](https://github.com/adap/flower/pull/1276), " +"[#1301](https://github.com/adap/flower/pull/1301), " +"[#1310](https://github.com/adap/flower/pull/1310))" msgstr "" -"要学习本教程，您需要安装以下软件包： :code:`datasets`、 :code:`evaluate`、 :code:`flwr`、 " -":code:`torch`和 :code:`transformers`。这可以通过 :code:`pip` 来完成：" - -#: ../../source/tutorial-quickstart-huggingface.rst:30 -msgid "Standard Hugging Face workflow" -msgstr "标准Hugging Face工作流程" - -#: ../../source/tutorial-quickstart-huggingface.rst:33 -msgid "Handling the data" -msgstr "处理数据" +"更新开发人员工具（[#1231](https://github.com/adap/flower/pull/1231), " +"[#1276](https://github.com/adap/flower/pull/1276), " +"[#1301](https://github.com/adap/flower/pull/1301), " +"[#1310](https://github.com/adap/flower/pull/1310)" -#: ../../source/tutorial-quickstart-huggingface.rst:35 +#: ../../source/ref-changelog.md:810 msgid "" -"To fetch the IMDB dataset, we will use Hugging Face's :code:`datasets` " -"library. We then need to tokenize the data and create :code:`PyTorch` " -"dataloaders, this is all done in the :code:`load_data` function:" +"Rename ProtoBuf messages to improve consistency " +"([#1214](https://github.com/adap/flower/pull/1214), " +"[#1258](https://github.com/adap/flower/pull/1258), " +"[#1259](https://github.com/adap/flower/pull/1259))" msgstr "" -"为了获取 IMDB 数据集，我们将使用 Hugging Face 的 :code:`datasets` 库。然后，我们需要对数据进行标记化，并创建" -" :code:`PyTorch` 数据加载器，这些都将在 :code:`load_data` 函数中完成：" +"重命名 ProtoBuf 消息以提高一致性（[#1214](https://github.com/adap/flower/pull/1214), " +"[#1258](https://github.com/adap/flower/pull/1258), " +"[#1259](https://github.com/adap/flower/pull/1259)" -#: ../../source/tutorial-quickstart-huggingface.rst:81 -msgid "Training and testing the model" -msgstr "训练和测试模型" +#: ../../source/ref-changelog.md:812 +msgid "v0.19.0 (2022-05-18)" +msgstr "v0.19.0 (2022-05-18)" -#: ../../source/tutorial-quickstart-huggingface.rst:83 +#: ../../source/ref-changelog.md:816 msgid "" -"Once we have a way of creating our trainloader and testloader, we can " -"take care of the training and testing. This is very similar to any " -":code:`PyTorch` training or testing loop:" +"**Flower Baselines (preview): FedOpt, FedBN, FedAvgM** " +"([#919](https://github.com/adap/flower/pull/919), " +"[#1127](https://github.com/adap/flower/pull/1127), " +"[#914](https://github.com/adap/flower/pull/914))" msgstr "" -"有了创建 trainloader 和 testloader 的方法后，我们就可以进行训练和测试了。这与任何 :code:`PyTorch` " -"训练或测试循环都非常相似：" - -#: ../../source/tutorial-quickstart-huggingface.rst:121 -msgid "Creating the model itself" -msgstr "创建模型本身" +"**Flower Baselines（预览）： FedOpt、FedBN、FedAvgM** " +"([#919](https://github.com/adap/flower/pull/919), " +"[#1127](https://github.com/adap/flower/pull/1127), " +"[#914](https://github.com/adap/flower/pull/914))" -#: ../../source/tutorial-quickstart-huggingface.rst:123 +#: ../../source/ref-changelog.md:818 +#, fuzzy msgid "" -"To create the model itself, we will just load the pre-trained distillBERT" -" model using Hugging Face’s :code:`AutoModelForSequenceClassification` :" +"The first preview release of Flower Baselines has arrived! We're " +"kickstarting Flower Baselines with implementations of FedOpt (FedYogi, " +"FedAdam, FedAdagrad), FedBN, and FedAvgM. Check the documentation on how " +"to use [Flower Baselines](https://flower.ai/docs/using-baselines.html). " +"With this first preview release we're also inviting the community to " +"[contribute their own baselines](https://flower.ai/docs/baselines/how-to-" +"contribute-baselines.html)." msgstr "" -"要创建模型本身，我们只需使用 Hugging Face 的 :code:`AutoModelForSequenceClassification` " -"加载预训练的 distillBERT 模型：" - -#: ../../source/tutorial-quickstart-huggingface.rst:136 -msgid "Federating the example" -msgstr "将示例联邦化" +"Flower Baselines 的第一个预览版已经发布！我们通过实现 " +"FedOpt（FedYogi、FedAdam、FedAdagrad）、FedBN 和 FedAvgM 来启动 Flower " +"Baselines。请查阅文档了解如何使用 [Flower Baselines](https://flower.ai/docs/using-" +"baselines.html)。在首次发布预览版时，我们还邀请社区成员[贡献自己的Baselines](https://flower.ai/docs" +"/contributing-baselines.html)。" -#: ../../source/tutorial-quickstart-huggingface.rst:139 -msgid "Creating the IMDBClient" -msgstr "创建 IMDBClient" +#: ../../source/ref-changelog.md:820 +msgid "" +"**C++ client SDK (preview) and code example** " +"([#1111](https://github.com/adap/flower/pull/1111))" +msgstr "**C++客户端SDK（预览版）和代码示例**（[#1111](https://github.com/adap/flower/pull/1111)）" -#: ../../source/tutorial-quickstart-huggingface.rst:141 +#: ../../source/ref-changelog.md:822 msgid "" -"To federate our example to multiple clients, we first need to write our " -"Flower client class (inheriting from :code:`flwr.client.NumPyClient`). " -"This is very easy, as our model is a standard :code:`PyTorch` model:" +"Preview support for Flower clients written in C++. The C++ preview " +"includes a Flower client SDK and a quickstart code example that " +"demonstrates a simple C++ client using the SDK." msgstr "" -"要将我们的示例联邦到多个客户端，我们首先需要编写 Flower 客户端类（继承自 " -":code:`flwr.client.NumPyClient`）。这很容易，因为我们的模型是一个标准的 :code:`PyTorch` 模型：" +"预览版支持用 C++ 编写的 Flower 客户端。C++ 预览版包括一个 Flower 客户端 SDK 和一个快速入门代码示例，使用 SDK " +"演示了一个简单的 C++ 客户端。" -#: ../../source/tutorial-quickstart-huggingface.rst:169 +#: ../../source/ref-changelog.md:824 msgid "" -"The :code:`get_parameters` function lets the server get the client's " -"parameters. Inversely, the :code:`set_parameters` function allows the " -"server to send its parameters to the client. Finally, the :code:`fit` " -"function trains the model locally for the client, and the " -":code:`evaluate` function tests the model locally and returns the " -"relevant metrics." +"**Add experimental support for Python 3.10 and Python 3.11** " +"([#1135](https://github.com/adap/flower/pull/1135))" msgstr "" -":code:`get_parameters` " -"函数允许服务器获取客户端的参数。相反，:code:`set_parameters`函数允许服务器将其参数发送给客户端。最后，:code:`fit`函数在本地为客户端训练模型，:code:`evaluate`函数在本地测试模型并返回相关指标。" - -#: ../../source/tutorial-quickstart-huggingface.rst:175 -msgid "Starting the server" -msgstr "启动服务器" +"** 增加对 Python 3.10 和 Python 3.11 的实验支持** " +"([#1135](https://github.com/adap/flower/pull/1135))" -#: ../../source/tutorial-quickstart-huggingface.rst:177 +#: ../../source/ref-changelog.md:826 msgid "" -"Now that we have a way to instantiate clients, we need to create our " -"server in order to aggregate the results. Using Flower, this can be done " -"very easily by first choosing a strategy (here, we are using " -":code:`FedAvg`, which will define the global weights as the average of " -"all the clients' weights at each round) and then using the " -":code:`flwr.server.start_server` function:" +"Python 3.10 is the latest stable release of Python and Python 3.11 is due" +" to be released in October. This Flower release adds experimental support" +" for both Python versions." msgstr "" -"现在我们有了实例化客户端的方法，我们需要创建服务器，以便汇总结果。使用 Flower，首先选择一个策略（这里我们使用 " -":code:`FedAvg`，它将把全局模型参数定义为每轮所有客户端模型参数的平均值），然后使用 " -":code:`flwr.server.start_server`函数，就可以非常轻松地完成这项工作：" +"Python 3.10 是 Python 的最新稳定版本，Python 3.11 将于 10 月份发布。Flower 版本增加了对这两个 " +"Python 版本的实验支持。" -#: ../../source/tutorial-quickstart-huggingface.rst:205 +#: ../../source/ref-changelog.md:828 msgid "" -"The :code:`weighted_average` function is there to provide a way to " -"aggregate the metrics distributed amongst the clients (basically this " -"allows us to display a nice average accuracy and loss for every round)." -msgstr "" -"使用 :code:`weighted_average` " -"函数是为了提供一种方法来汇总分布在客户端的指标（基本上，这可以让我们显示每一轮的平均精度和损失值）。" - -#: ../../source/tutorial-quickstart-huggingface.rst:209 -msgid "Putting everything together" -msgstr "把所有东西放在一起" +"**Aggregate custom metrics through user-provided functions** " +"([#1144](https://github.com/adap/flower/pull/1144))" +msgstr "**通过用户提供的函数聚合自定义指标**（[#1144](https://github.com/adap/flower/pull/1144)）" -#: ../../source/tutorial-quickstart-huggingface.rst:211 -msgid "We can now start client instances using:" -msgstr "现在我们可以使用：" +#: ../../source/ref-changelog.md:830 +msgid "" +"Custom metrics (e.g., `accuracy`) can now be aggregated without having to" +" customize the strategy. Built-in strategies support two new arguments, " +"`fit_metrics_aggregation_fn` and `evaluate_metrics_aggregation_fn`, that " +"allow passing custom metric aggregation functions." +msgstr "" +"现在无需定制策略即可聚合自定义度量（如`准确度`）。内置策略支持两个新参数：`fit_metrics_aggregation_fn` " +"和`evaluate_metrics_aggregation_fn`，允许传递自定义度量聚合函数。" -#: ../../source/tutorial-quickstart-huggingface.rst:221 +#: ../../source/ref-changelog.md:832 msgid "" -"And they will be able to connect to the server and start the federated " -"training." -msgstr "他们就能连接到服务器，开始联邦训练。" +"**User-configurable round timeout** " +"([#1162](https://github.com/adap/flower/pull/1162))" +msgstr "**用户可配置的回合超时**（[#1162](https://github.com/adap/flower/pull/1162)）" -#: ../../source/tutorial-quickstart-huggingface.rst:223 -#, fuzzy +#: ../../source/ref-changelog.md:834 msgid "" -"If you want to check out everything put together, you should check out " -"the `full code example `_ ." +"A new configuration value allows the round timeout to be set for " +"`start_server` and `start_simulation`. If the `config` dictionary " +"contains a `round_timeout` key (with a `float` value in seconds), the " +"server will wait *at least* `round_timeout` seconds before it closes the " +"connection." msgstr "" -"如果您想查看所有内容，请查看完整的代码示例： [https://github.com/adap/flower/tree/main/examples" -"/quickstart-" -"huggingface](https://github.com/adap/flower/tree/main/examples" -"/quickstart-huggingface)." +"新的配置值允许为 `start_server` 和 `start_simulation` 设置回合超时。如果 `config` 字典中包含一个 " +"`round_timeout` 键（以秒为单位的 `float`值），服务器将至少等待 ** `round_timeout` 秒后才关闭连接。" -#: ../../source/tutorial-quickstart-huggingface.rst:226 +#: ../../source/ref-changelog.md:836 msgid "" -"Of course, this is a very basic example, and a lot can be added or " -"modified, it was just to showcase how simply we could federate a Hugging " -"Face workflow using Flower." -msgstr "当然，这只是一个非常基本的示例，还可以添加或修改很多内容，只是为了展示我们可以如何简单地使用 Flower 联合Hugging Face的工作流程。" +"**Enable both federated evaluation and centralized evaluation to be used " +"at the same time in all built-in strategies** " +"([#1091](https://github.com/adap/flower/pull/1091))" +msgstr "" +"**允许在所有内置策略中同时使用联邦评价和集中评估** " +"([#1091](https://github.com/adap/flower/pull/1091))" -#: ../../source/tutorial-quickstart-huggingface.rst:229 +#: ../../source/ref-changelog.md:838 msgid "" -"Note that in this example we used :code:`PyTorch`, but we could have very" -" well used :code:`TensorFlow`." -msgstr "请注意，在本例中我们使用了 :code:`PyTorch`，但也完全可以使用 :code:`TensorFlow`。" +"Built-in strategies can now perform both federated evaluation (i.e., " +"client-side) and centralized evaluation (i.e., server-side) in the same " +"round. Federated evaluation can be disabled by setting `fraction_eval` to" +" `0.0`." +msgstr "" +"内置策略现在可以在同一轮中同时执行联邦评估（即客户端）和集中评估（即服务器端）。可以通过将 `fraction_eval` 设置为 " +"`0.0`来禁用联邦评估。" -#: ../../source/tutorial-quickstart-ios.rst:-1 +#: ../../source/ref-changelog.md:840 msgid "" -"Read this Federated Learning quickstart tutorial for creating an iOS app " -"using Flower to train a neural network on MNIST." -msgstr "阅读本联邦学习快速入门教程，了解如何使用 Flower 创建 iOS 应用程序，并在 MNIST 上训练神经网络。" - -#: ../../source/tutorial-quickstart-ios.rst:5 -msgid "Quickstart iOS" -msgstr "快速入门 iOS" +"**Two new Jupyter Notebook tutorials** " +"([#1141](https://github.com/adap/flower/pull/1141))" +msgstr "" +"**两本新的 Jupyter Notebook 教程** " +"([#1141](https://github.com/adap/flower/pull/1141))" -#: ../../source/tutorial-quickstart-ios.rst:10 +#: ../../source/ref-changelog.md:842 msgid "" -"In this tutorial we will learn how to train a Neural Network on MNIST " -"using Flower and CoreML on iOS devices." -msgstr "在本教程中，我们将学习如何在 iOS 设备上使用 Flower 和 CoreML 在 MNIST 上训练神经网络。" +"Two Jupyter Notebook tutorials (compatible with Google Colab) explain " +"basic and intermediate Flower features:" +msgstr "两本 Jupyter Notebook 教程（与 Google Colab 兼容）介绍了 Flower 的基本和中级功能：" -#: ../../source/tutorial-quickstart-ios.rst:12 -#, fuzzy +#: ../../source/ref-changelog.md:844 msgid "" -"First of all, for running the Flower Python server, it is recommended to " -"create a virtual environment and run everything within a :doc:`virtualenv" -" `. For the Flower client " -"implementation in iOS, it is recommended to use Xcode as our IDE." +"*An Introduction to Federated Learning*: [Open in " +"Colab](https://colab.research.google.com/github/adap/flower/blob/main/tutorials/Flower-1" +"-Intro-to-FL-PyTorch.ipynb)" msgstr "" -"首先，为了运行 Flower Python 服务器，建议创建一个虚拟环境，并在 `virtualenv " -"`_ 中运行一切。对于在 iOS 中实现 " -"Flower 客户端，建议使用 Xcode 作为我们的集成开发环境。" +"*联邦学习简介*： [在 Colab " +"中打开](https://colab.research.google.com/github/adap/flower/blob/main/tutorials/Flower-1" +"-Intro-to-FL-PyTorch.ipynb)" -#: ../../source/tutorial-quickstart-ios.rst:15 +#: ../../source/ref-changelog.md:846 msgid "" -"Our example consists of one Python *server* and two iPhone *clients* that" -" all have the same model." -msgstr "我们的示例包括一个 Python *服务器*和两个 iPhone *客户端*，它们都具有相同的模型。" +"*Using Strategies in Federated Learning*: [Open in " +"Colab](https://colab.research.google.com/github/adap/flower/blob/main/tutorials/Flower-2" +"-Strategies-in-FL-PyTorch.ipynb)" +msgstr "" +"*在联邦学习中使用策略*： [在 Colab " +"中打开](https://colab.research.google.com/github/adap/flower/blob/main/tutorials/Flower-2" +"-Strategies-in-FL-PyTorch.ipynb)" -#: ../../source/tutorial-quickstart-ios.rst:17 +#: ../../source/ref-changelog.md:848 msgid "" -"*Clients* are responsible for generating individual weight updates for " -"the model based on their local datasets. These updates are then sent to " -"the *server* which will aggregate them to produce a better model. " -"Finally, the *server* sends this improved version of the model back to " -"each *client*. A complete cycle of weight updates is called a *round*." -msgstr "*客户端*负责根据其本地数据集为模型生成独立的模型参数。然后，这些参数更新会被发送到*服务器*，由*服务器*汇总后生成一个更好的模型。最后，*服务器*将改进后的模型发送回每个*客户端*。一个完整的参数更新周期称为一*轮*。" +"**New FedAvgM strategy (Federated Averaging with Server Momentum)** " +"([#1076](https://github.com/adap/flower/pull/1076))" +msgstr "" +"**新的 FedAvgM 策略（带服务器动量的联邦平均）** " +"([#1076](https://github.com/adap/flower/pull/1076))" -#: ../../source/tutorial-quickstart-ios.rst:21 +#: ../../source/ref-changelog.md:850 msgid "" -"Now that we have a rough idea of what is going on, let's get started to " -"setup our Flower server environment. We first need to install Flower. You" -" can do this by using pip:" -msgstr "现在我们已经有了一个大致的概念，让我们开始设置 Flower 服务器环境吧。首先，我们需要安装 Flower。你可以使用 pip 来安装：" - -#: ../../source/tutorial-quickstart-ios.rst:27 -msgid "Or Poetry:" -msgstr "或者Poetry：" +"The new `FedAvgM` strategy implements Federated Averaging with Server " +"Momentum \\[Hsu et al., 2019\\]." +msgstr "新的 \"FedAvgM \"策略实现了带服务器动量的联邦平均[Hsu et al., 2019\\]." -#: ../../source/tutorial-quickstart-ios.rst:34 -#: ../../source/tutorial-quickstart-mxnet.rst:36 -#: ../../source/tutorial-quickstart-pytorch.rst:37 -#: ../../source/tutorial-quickstart-scikitlearn.rst:40 -#: ../../source/tutorial-quickstart-tensorflow.rst:29 -#: ../../source/tutorial-quickstart-xgboost.rst:55 -msgid "Flower Client" -msgstr "Flower 客户端" +#: ../../source/ref-changelog.md:852 +msgid "" +"**New advanced PyTorch code example** " +"([#1007](https://github.com/adap/flower/pull/1007))" +msgstr "**新的 PyTorch 高级代码示例** ([#1007](https://github.com/adap/flower/pull/1007))" -#: ../../source/tutorial-quickstart-ios.rst:36 +#: ../../source/ref-changelog.md:854 msgid "" -"Now that we have all our dependencies installed, let's run a simple " -"distributed training using CoreML as our local training pipeline and " -"MNIST as our dataset. For simplicity reasons we will use the complete " -"Flower client with CoreML, that has been implemented and stored inside " -"the Swift SDK. The client implementation can be seen below:" -msgstr "" -"现在我们已经安装了所有依赖项，让我们使用 CoreML 作为本地训练框架和 MNIST " -"作为数据集，运行一个简单的分布式训练。为了简单起见，我们将使用 CoreML 的完整 Flower 客户端，该客户端已在 Swift SDK " -"中实现并存储。客户端实现如下：" +"A new code example (`advanced_pytorch`) demonstrates advanced Flower " +"concepts with PyTorch." +msgstr "新代码示例 (`advanced_pytorch`) 演示了 PyTorch 的高级 Flower 概念。" -#: ../../source/tutorial-quickstart-ios.rst:72 +#: ../../source/ref-changelog.md:856 msgid "" -"Let's create a new application project in Xcode and add :code:`flwr` as a" -" dependency in your project. For our application, we will store the logic" -" of our app in :code:`FLiOSModel.swift` and the UI elements in " -":code:`ContentView.swift`. We will focus more on :code:`FLiOSModel.swift`" -" in this quickstart. Please refer to the `full code example " -"`_ to learn more " -"about the app." +"**New JAX code example** " +"([#906](https://github.com/adap/flower/pull/906), " +"[#1143](https://github.com/adap/flower/pull/1143))" msgstr "" -"让我们在 Xcode 中创建一个新的应用程序项目，并在项目中添加 :code:`flwr` 作为依赖关系。对于我们的应用程序，我们将在 " -":code:`FLiOSModel.swift` 中存储应用程序的逻辑，在 :code:`ContentView.swift` 中存储 UI " -"元素。在本快速入门中，我们将更多地关注 :code:`FLiOSModel.swift`。请参阅 `完整代码示例 " -"`_ 以了解更多有关应用程序的信息。" - -#: ../../source/tutorial-quickstart-ios.rst:75 -msgid "Import Flower and CoreML related packages in :code:`FLiOSModel.swift`:" -msgstr "在 :code:`FLiOSModel.swift` 中导入 Flower 和 CoreML 相关软件包：" +"**新的 JAX 代码示例**（[#906](https://github.com/adap/flower/pull/906), " +"[#1143](https://github.com/adap/flower/pull/1143)" -#: ../../source/tutorial-quickstart-ios.rst:83 +#: ../../source/ref-changelog.md:858 msgid "" -"Then add the mlmodel to the project simply by drag-and-drop, the mlmodel " -"will be bundled inside the application during deployment to your iOS " -"device. We need to pass the url to access mlmodel and run CoreML machine " -"learning processes, it can be retrieved by calling the function " -":code:`Bundle.main.url`. For the MNIST dataset, we need to preprocess it " -"into :code:`MLBatchProvider` object. The preprocessing is done inside " -":code:`DataLoader.swift`." -msgstr "" -"然后通过拖放将 mlmodel 添加到项目中，在部署到 iOS 设备时，mlmodel 将被捆绑到应用程序中。我们需要传递 url 以访问 " -"mlmodel 并运行 CoreML 机器学习进程，可通过调用函数 :code:`Bundle.main.url` 获取。对于 MNIST " -"数据集，我们需要将其预处理为 :code:`MLBatchProvider` 对象。预处理在 :code:`DataLoader.swift` " -"中完成。" +"A new code example (`jax_from_centralized_to_federated`) shows federated " +"learning with JAX and Flower." +msgstr "新代码示例（`jax_from_centralized_to_federated`）展示了使用 JAX 和 Flower 的联邦学习。" -#: ../../source/tutorial-quickstart-ios.rst:99 -#, fuzzy +#: ../../source/ref-changelog.md:862 msgid "" -"Since CoreML does not allow the model parameters to be seen before " -"training, and accessing the model parameters during or after the training" -" can only be done by specifying the layer name, we need to know this " -"information beforehand, through looking at the model specification, which" -" are written as proto files. The implementation can be seen in " -":code:`MLModelInspect`." +"New option to keep Ray running if Ray was already initialized in " +"`start_simulation` ([#1177](https://github.com/adap/flower/pull/1177))" msgstr "" -"由于 CoreML 不允许在训练前查看模型参数，而在训练过程中或训练后访问模型参数只能通过指定层名来完成，因此我们需要事先通过查看模型规范（写成 " -"proto 文件）来了解这些信息。具体实现可参见 :code:`MLModelInspect`。" +"新增选项，用于在 \"start_simulation\"（开始模拟）中已初始化 Ray 的情况下保持 Ray " +"运行（[#1177](https://github.com/adap/flower/pull/1177)）" -#: ../../source/tutorial-quickstart-ios.rst:102 -#, fuzzy +#: ../../source/ref-changelog.md:863 msgid "" -"After we have all of the necessary information, let's create our Flower " -"client." -msgstr "获得所有必要信息后，让我们创建 Flower 客户端。" +"Add support for custom `ClientManager` as a `start_simulation` parameter " +"([#1171](https://github.com/adap/flower/pull/1171))" +msgstr "" +"添加对自定义 \"客户端管理器 \"作为 \"start_simulation " +"\"参数的支持（[#1171](https://github.com/adap/flower/pull/1171)）" -#: ../../source/tutorial-quickstart-ios.rst:117 +#: ../../source/ref-changelog.md:864 msgid "" -"Then start the Flower gRPC client and start communicating to the server " -"by passing our Flower client to the function :code:`startFlwrGRPC`." -msgstr "然后启动 Flower gRPC 客户端，并通过将 Flower 客户端传递给函数 :code:`startFlwrGRPC` 来开始与服务器通信。" - -#: ../../source/tutorial-quickstart-ios.rst:124 -msgid "" -"That's it for the client. We only have to implement :code:`Client` or " -"call the provided :code:`MLFlwrClient` and call :code:`startFlwrGRPC()`. " -"The attribute :code:`hostname` and :code:`port` tells the client which " -"server to connect to. This can be done by entering the hostname and port " -"in the application before clicking the start button to start the " -"federated learning process." +"New documentation for [implementing " +"strategies](https://flower.ai/docs/framework/how-to-implement-" +"strategies.html) ([#1097](https://github.com/adap/flower/pull/1097), " +"[#1175](https://github.com/adap/flower/pull/1175))" msgstr "" -"这就是客户端。我们只需实现 :code:`Client` 或调用提供的 :code:`MLFlwrClient` 并调用 " -":code:`startFlwrGRPC()`。属性 :code:`hostname` 和 :code:`port` " -"会告诉客户端要连接到哪个服务器。这可以通过在应用程序中输入主机名和端口来实现，然后再点击开始按钮启动联邦学习进程。" +"[实施战略](https://flower.ai/docs/framework/how-to-implement-strategies.html)" +" 的新文件（[#1097](https://github.com/adap/flower/pull/1097), " +"[#1175](https://github.com/adap/flower/pull/1175)" -#: ../../source/tutorial-quickstart-ios.rst:129 -#: ../../source/tutorial-quickstart-mxnet.rst:226 -#: ../../source/tutorial-quickstart-pytorch.rst:203 -#: ../../source/tutorial-quickstart-scikitlearn.rst:157 -#: ../../source/tutorial-quickstart-tensorflow.rst:98 -#: ../../source/tutorial-quickstart-xgboost.rst:309 -msgid "Flower Server" -msgstr "Flower 服务器" +#: ../../source/ref-changelog.md:865 +msgid "" +"New mobile-friendly documentation theme " +"([#1174](https://github.com/adap/flower/pull/1174))" +msgstr "新的移动友好型文档主题 ([#1174](https://github.com/adap/flower/pull/1174))" -#: ../../source/tutorial-quickstart-ios.rst:131 -#: ../../source/tutorial-quickstart-mxnet.rst:228 -#: ../../source/tutorial-quickstart-pytorch.rst:205 -#: ../../source/tutorial-quickstart-tensorflow.rst:100 +#: ../../source/ref-changelog.md:866 msgid "" -"For simple workloads we can start a Flower server and leave all the " -"configuration possibilities at their default values. In a file named " -":code:`server.py`, import Flower and start the server:" +"Limit version range for (optional) `ray` dependency to include only " +"compatible releases (`>=1.9.2,<1.12.0`) " +"([#1205](https://github.com/adap/flower/pull/1205))" msgstr "" -"对于简单的工作负载，我们可以启动 Flower 服务器，并将所有配置选项保留为默认值。在名为 :code:`server.py` 的文件中，导入 " -"Flower 并启动服务器：" +"限制（可选）`ray`依赖的版本范围，使其仅包含兼容版本（`>=1.9.2,<1.12.0`） " +"([#1205](https://github.com/adap/flower/pull/1205))" -#: ../../source/tutorial-quickstart-ios.rst:142 -#: ../../source/tutorial-quickstart-mxnet.rst:239 -#: ../../source/tutorial-quickstart-pytorch.rst:216 -#: ../../source/tutorial-quickstart-scikitlearn.rst:215 -#: ../../source/tutorial-quickstart-tensorflow.rst:112 -msgid "Train the model, federated!" -msgstr "联邦训练模型！" +#: ../../source/ref-changelog.md:870 +msgid "" +"**Remove deprecated support for Python 3.6** " +"([#871](https://github.com/adap/flower/pull/871))" +msgstr "**删除对 Python 3.6 的过时支持** ([#871](https://github.com/adap/flower/pull/871))" -#: ../../source/tutorial-quickstart-ios.rst:144 -#: ../../source/tutorial-quickstart-pytorch.rst:218 -#: ../../source/tutorial-quickstart-tensorflow.rst:114 -#: ../../source/tutorial-quickstart-xgboost.rst:525 +#: ../../source/ref-changelog.md:871 msgid "" -"With both client and server ready, we can now run everything and see " -"federated learning in action. FL systems usually have a server and " -"multiple clients. We therefore have to start the server first:" -msgstr "客户端和服务器都已准备就绪，我们现在可以运行一切，看看联邦学习的实际效果。FL 系统通常有一个服务器和多个客户端。因此，我们必须先启动服务器：" +"**Remove deprecated KerasClient** " +"([#857](https://github.com/adap/flower/pull/857))" +msgstr "**移除过时的 KerasClient**（[#857](https://github.com/adap/flower/pull/857)）" -#: ../../source/tutorial-quickstart-ios.rst:152 +#: ../../source/ref-changelog.md:872 msgid "" -"Once the server is running we can start the clients in different " -"terminals. Build and run the client through your Xcode, one through Xcode" -" Simulator and the other by deploying it to your iPhone. To see more " -"about how to deploy your app to iPhone or Simulator visit `here " -"`_." +"**Remove deprecated no-op extra installs** " +"([#973](https://github.com/adap/flower/pull/973))" +msgstr "**移除过时的不操作额外安装** ([#973](https://github.com/adap/flower/pull/973))" + +#: ../../source/ref-changelog.md:873 +msgid "" +"**Remove deprecated proto fields from** `FitRes` **and** `EvaluateRes` " +"([#869](https://github.com/adap/flower/pull/869))" msgstr "" -"服务器运行后，我们就可以在不同的终端启动客户端。通过 Xcode 构建并运行客户端，一个通过 Xcode 模拟器，另一个通过部署到 " -"iPhone。要了解更多有关如何将应用程序部署到 iPhone 或模拟器的信息，请访问 `此处 " -"`_。" +"**从** `FitRes` **和** `EvaluateRes` 中移除已废弃的 proto 字段 " +"([#869](https://github.com/adap/flower/pull/869))" -#: ../../source/tutorial-quickstart-ios.rst:156 +#: ../../source/ref-changelog.md:874 msgid "" -"Congratulations! You've successfully built and run your first federated " -"learning system in your ios device. The full `source code " -"`_ for this " -"example can be found in :code:`examples/ios`." +"**Remove deprecated QffedAvg strategy (replaced by QFedAvg)** " +"([#1107](https://github.com/adap/flower/pull/1107))" msgstr "" -"恭喜您！ 您已经成功地在 ios 设备中构建并运行了第一个联邦学习系统。本示例的`完整源代码 " -"`_ 可在 " -":code:`examples/ios` 中找到。" +"**移除过时的 QffedAvg 策略（由 QFedAvg 取代）** " +"([#1107](https://github.com/adap/flower/pull/1107))" -#: ../../source/tutorial-quickstart-jax.rst:-1 +#: ../../source/ref-changelog.md:875 msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with Jax to train a linear regression model on a scikit-learn dataset." -msgstr "查看此联邦学习快速入门教程，了解如何使用 Flower 和 Jax 在 scikit-learn 数据集上训练线性回归模型。" +"**Remove deprecated DefaultStrategy strategy** " +"([#1142](https://github.com/adap/flower/pull/1142))" +msgstr "" +"**删除过时的 DefaultStrategy 策略** " +"([#1142](https://github.com/adap/flower/pull/1142))" -#: ../../source/tutorial-quickstart-jax.rst:5 -msgid "Quickstart JAX" -msgstr "快速入门 JAX" +#: ../../source/ref-changelog.md:876 +msgid "" +"**Remove deprecated support for eval_fn accuracy return value** " +"([#1142](https://github.com/adap/flower/pull/1142))" +msgstr "" +"**删除已过时的对 eval_fn 返回值准确性的支持** " +"([#1142](https://github.com/adap/flower/pull/1142))" -#: ../../source/tutorial-quickstart-mxnet.rst:-1 +#: ../../source/ref-changelog.md:877 msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with MXNet to train a Sequential model on MNIST." -msgstr "查看此联邦学习 快速入门教程，了解如何使用 Flower 和 MXNet 在 MNIST 上训练序列模型。" +"**Remove deprecated support for passing initial parameters as NumPy " +"ndarrays** ([#1142](https://github.com/adap/flower/pull/1142))" +msgstr "" +"**移除对以 NumPy ndarrays 传递初始参数的过时支持** " +"([#1142](https://github.com/adap/flower/pull/1142))" -#: ../../source/tutorial-quickstart-mxnet.rst:5 -msgid "Quickstart MXNet" -msgstr "快速入门 MXNet" +#: ../../source/ref-changelog.md:879 +msgid "v0.18.0 (2022-02-28)" +msgstr "v0.18.0 (2022-02-28)" -#: ../../source/tutorial-quickstart-mxnet.rst:7 +#: ../../source/ref-changelog.md:883 msgid "" -"MXNet is no longer maintained and has been moved into `Attic " -"`_. As a result, we would " -"encourage you to use other ML frameworks alongside Flower, for example, " -"PyTorch. This tutorial might be removed in future versions of Flower." +"**Improved Virtual Client Engine compatibility with Jupyter Notebook / " +"Google Colab** ([#866](https://github.com/adap/flower/pull/866), " +"[#872](https://github.com/adap/flower/pull/872), " +"[#833](https://github.com/adap/flower/pull/833), " +"[#1036](https://github.com/adap/flower/pull/1036))" msgstr "" +"**改进了虚拟客户端引擎与 Jupyter Notebook / Google Colab 的兼容性** " +"([#866](https://github.com/adap/flower/pull/866), " +"[#872](https://github.com/adap/flower/pull/872), " +"[#833](https://github.com/adap/flower/pull/833), " +"[#1036](https://github.com/adap/flower/pull/1036))" -#: ../../source/tutorial-quickstart-mxnet.rst:12 +#: ../../source/ref-changelog.md:885 msgid "" -"In this tutorial, we will learn how to train a :code:`Sequential` model " -"on MNIST using Flower and MXNet." -msgstr "在本教程中，我们将学习如何使用 Flower 和 MXNet 在 MNIST 上训练 :code:`Sequential` 模型。" +"Simulations (using the Virtual Client Engine through `start_simulation`) " +"now work more smoothly on Jupyter Notebooks (incl. Google Colab) after " +"installing Flower with the `simulation` extra (`pip install " +"flwr[simulation]`)." +msgstr "" +"通过 `start_simulation` 在 Jupyter 笔记本（包括 Google Colab）上安装 Flower 并附加 " +"`simulation` (`pip install flwr[simulation]`)后，模拟（通过 `start_simulation` " +"使用虚拟客户端引擎）现在可以更流畅地运行。" -#: ../../source/tutorial-quickstart-mxnet.rst:14 -#: ../../source/tutorial-quickstart-scikitlearn.rst:12 -#, fuzzy +#: ../../source/ref-changelog.md:887 msgid "" -"It is recommended to create a virtual environment and run everything " -"within this :doc:`virtualenv `." +"**New Jupyter Notebook code example** " +"([#833](https://github.com/adap/flower/pull/833))" msgstr "" -"建议创建一个虚拟环境，并在此 `virtualenv `_ 中运行所有内容。" +"**新的 Jupyter Notebook 代码示例** " +"([#833](https://github.com/adap/flower/pull/833))" -#: ../../source/tutorial-quickstart-mxnet.rst:16 -#: ../../source/tutorial-quickstart-pytorch.rst:17 -#: ../../source/tutorial-quickstart-scikitlearn.rst:14 +#: ../../source/ref-changelog.md:889 msgid "" -"Our example consists of one *server* and two *clients* all having the " -"same model." -msgstr "我们的例子包括一个*服务器*和两个*客户端*，它们都有相同的模型。" +"A new code example (`quickstart_simulation`) demonstrates Flower " +"simulations using the Virtual Client Engine through Jupyter Notebook " +"(incl. Google Colab)." +msgstr "" +"新代码示例（`quickstart_simulation`）通过 Jupyter Notebook（包括 Google " +"Colab）演示了使用虚拟客户端引擎进行 Flower 模拟。" -#: ../../source/tutorial-quickstart-mxnet.rst:18 -#: ../../source/tutorial-quickstart-scikitlearn.rst:16 +#: ../../source/ref-changelog.md:891 msgid "" -"*Clients* are responsible for generating individual model parameter " -"updates for the model based on their local datasets. These updates are " -"then sent to the *server* which will aggregate them to produce an updated" -" global model. Finally, the *server* sends this improved version of the " -"model back to each *client*. A complete cycle of parameters updates is " -"called a *round*." -msgstr "*客户端*负责根据其本地数据集为模型生成单独的模型参数更新。然后，这些参数更新将被发送到*服务器*，由*服务器*汇总后生成一个更新的全局模型。最后，*服务器*将这一改进版模型发回给每个*客户端*。一个完整的参数更新周期称为一*轮*。" +"**Client properties (feature preview)** " +"([#795](https://github.com/adap/flower/pull/795))" +msgstr "**客户端属性（功能预览）** ([#795](https://github.com/adap/flower/pull/795))" -#: ../../source/tutorial-quickstart-mxnet.rst:22 -#: ../../source/tutorial-quickstart-scikitlearn.rst:20 +#: ../../source/ref-changelog.md:893 msgid "" -"Now that we have a rough idea of what is going on, let's get started. We " -"first need to install Flower. You can do this by running:" -msgstr "现在，我们已经有了一个大致的概念，让我们开始吧。首先，我们需要安装 Flower。运行：" +"Clients can implement a new method `get_properties` to enable server-side" +" strategies to query client properties." +msgstr "客户端可以实现一个新方法 `get_properties`，以启用服务器端策略来查询客户端属性。" -#: ../../source/tutorial-quickstart-mxnet.rst:28 -msgid "Since we want to use MXNet, let's go ahead and install it:" -msgstr "既然我们要使用 MXNet，那就继续安装吧：" +#: ../../source/ref-changelog.md:895 +msgid "" +"**Experimental Android support with TFLite** " +"([#865](https://github.com/adap/flower/pull/865))" +msgstr "** 使用 TFLite 实验性支持安卓系统** ([#865](https://github.com/adap/flower/pull/865))" -#: ../../source/tutorial-quickstart-mxnet.rst:38 +#: ../../source/ref-changelog.md:897 msgid "" -"Now that we have all our dependencies installed, let's run a simple " -"distributed training with two clients and one server. Our training " -"procedure and network architecture are based on MXNet´s `Hand-written " -"Digit Recognition tutorial " -"`_." +"Android support has finally arrived in `main`! Flower is both client-" +"agnostic and framework-agnostic by design. One can integrate arbitrary " +"client platforms and with this release, using Flower on Android has " +"become a lot easier." msgstr "" -"现在，我们已经安装了所有依赖项，让我们用两个客户端和一个服务器来运行一个简单的分布式训练。我们的训练程序和网络架构基于 MXNet 的 " -"`手写数字识别教程 " -"`_\"。" +"`main`终于支持 Android 了！Flower 的设计与客户端和框架无关。我们可以集成任意客户端平台，有了这个版本，在安卓系统上使用 " +"Flower 就变得更容易了。" -#: ../../source/tutorial-quickstart-mxnet.rst:40 +#: ../../source/ref-changelog.md:899 msgid "" -"In a file called :code:`client.py`, import Flower and MXNet related " -"packages:" -msgstr "在名为 :code:`client.py` 的文件中，导入 Flower 和 MXNet 相关软件包：" - -#: ../../source/tutorial-quickstart-mxnet.rst:55 -msgid "In addition, define the device allocation in MXNet with:" -msgstr "此外，还可以在 MXNet 中定义设备分配：" +"The example uses TFLite on the client side, along with a new " +"`FedAvgAndroid` strategy. The Android client and `FedAvgAndroid` are " +"still experimental, but they are a first step towards a fully-fledged " +"Android SDK and a unified `FedAvg` implementation that integrated the new" +" functionality from `FedAvgAndroid`." +msgstr "" +"该示例在客户端使用了 TFLite 以及新的 `FedAvgAndroid`策略。Android 客户端和 " +"`FedAvgAndroid`仍处于试验阶段，但这是向成熟的 Android SDK 和集成了 `FedAvgAndroid`新功能的统一 " +"`FedAvg`实现迈出的第一步。" -#: ../../source/tutorial-quickstart-mxnet.rst:61 +#: ../../source/ref-changelog.md:901 msgid "" -"We use MXNet to load MNIST, a popular image classification dataset of " -"handwritten digits for machine learning. The MXNet utility " -":code:`mx.test_utils.get_mnist()` downloads the training and test data." +"**Make gRPC keepalive time user-configurable and decrease default " +"keepalive time** ([#1069](https://github.com/adap/flower/pull/1069))" msgstr "" -"我们使用 MXNet 加载 MNIST，这是一个用于机器学习的流行手写数字图像分类数据集。MXNet 工具 " -":code:`mx.test_utils.get_mnist()` 会下载训练和测试数据。" +"**使 gRPC 保持连接时间可由用户配置，并缩短默认保持连接时间** " +"([#1069](https://github.com/adap/flower/pull/1069))" -#: ../../source/tutorial-quickstart-mxnet.rst:75 +#: ../../source/ref-changelog.md:903 msgid "" -"Define the training and loss with MXNet. We train the model by looping " -"over the dataset, measure the corresponding loss, and optimize it." -msgstr "用 MXNet 定义训练和损失值。我们在数据集上循环训练模型，测量相应的损失值，并对其进行优化。" +"The default gRPC keepalive time has been reduced to increase the " +"compatibility of Flower with more cloud environments (for example, " +"Microsoft Azure). Users can configure the keepalive time to customize the" +" gRPC stack based on specific requirements." +msgstr "" +"为提高 Flower 与更多云环境（如 Microsoft Azure）的兼容性，缩短了默认 gRPC 保持时间。用户可以根据具体要求配置 " +"keepalive 时间，自定义 gRPC 堆栈。" -#: ../../source/tutorial-quickstart-mxnet.rst:113 +#: ../../source/ref-changelog.md:905 msgid "" -"Next, we define the validation of our machine learning model. We loop " -"over the test set and measure both loss and accuracy on the test set." -msgstr "接下来，我们定义机器学习模型的验证。我们在测试集上循环，测量测试集上的损失值和准确率。" +"**New differential privacy example using Opacus and PyTorch** " +"([#805](https://github.com/adap/flower/pull/805))" +msgstr "" +"**使用 Opacus 和 PyTorch 的新差分隐私示例** " +"([#805](https://github.com/adap/flower/pull/805))" -#: ../../source/tutorial-quickstart-mxnet.rst:137 +#: ../../source/ref-changelog.md:907 msgid "" -"After defining the training and testing of a MXNet machine learning " -"model, we use these functions to implement a Flower client." -msgstr "在定义了 MXNet 机器学习模型的训练和测试后，我们使用这些函数实现了 Flower 客户端。" - -#: ../../source/tutorial-quickstart-mxnet.rst:139 -msgid "Our Flower clients will use a simple :code:`Sequential` model:" -msgstr "我们的 Flower 客户端将使用简单的 :code:`Sequential` 模型：" +"A new code example (`opacus`) demonstrates differentially-private " +"federated learning with Opacus, PyTorch, and Flower." +msgstr "一个新的代码示例（\"opacus\"）演示了使用 Opacus、PyTorch 和 Flower 进行差分隐私的联邦学习。" -#: ../../source/tutorial-quickstart-mxnet.rst:158 +#: ../../source/ref-changelog.md:909 msgid "" -"After loading the dataset with :code:`load_data()` we perform one forward" -" propagation to initialize the model and model parameters with " -":code:`model(init)`. Next, we implement a Flower client." +"**New Hugging Face Transformers code example** " +"([#863](https://github.com/adap/flower/pull/863))" msgstr "" -"使用 :code:`load_data()` 加载数据集后，我们会执行一次前向传播，使用 :code:`model(init)` " -"初始化模型和模型参数。接下来，我们实现一个 Flower 客户端。" +"**新的Hugging Face Transformers代码示例** " +"([#863](https://github.com/adap/flower/pull/863))" -#: ../../source/tutorial-quickstart-mxnet.rst:160 -#: ../../source/tutorial-quickstart-pytorch.rst:144 -#: ../../source/tutorial-quickstart-tensorflow.rst:54 +#: ../../source/ref-changelog.md:911 msgid "" -"The Flower server interacts with clients through an interface called " -":code:`Client`. When the server selects a particular client for training," -" it sends training instructions over the network. The client receives " -"those instructions and calls one of the :code:`Client` methods to run " -"your code (i.e., to train the neural network we defined earlier)." -msgstr "" -"Flower 服务器通过一个名为 :code:`Client` " -"的接口与客户端交互。当服务器选择一个特定的客户端进行训练时，它会通过网络发送训练指令。客户端接收到这些指令后，会调用 :code:`Client`" -" 方法之一来运行您的代码（即训练我们之前定义的神经网络）。" +"A new code example (`quickstart_huggingface`) demonstrates usage of " +"Hugging Face Transformers with Flower." +msgstr "新的代码示例(`quickstart_huggingface`)证明了结合Flower和Hugging Face Transformers的实用性。" -#: ../../source/tutorial-quickstart-mxnet.rst:166 +#: ../../source/ref-changelog.md:913 msgid "" -"Flower provides a convenience class called :code:`NumPyClient` which " -"makes it easier to implement the :code:`Client` interface when your " -"workload uses MXNet. Implementing :code:`NumPyClient` usually means " -"defining the following methods (:code:`set_parameters` is optional " -"though):" +"**New MLCube code example** " +"([#779](https://github.com/adap/flower/pull/779), " +"[#1034](https://github.com/adap/flower/pull/1034), " +"[#1065](https://github.com/adap/flower/pull/1065), " +"[#1090](https://github.com/adap/flower/pull/1090))" msgstr "" -"Flower 提供了一个名为 :code:`NumPyClient` 的便捷类，当您的工作负载使用 MXNet 时，它可以让您更轻松地实现 " -":code:`Client` 接口。实现 :code:`NumPyClient` " -"通常意味着定义以下方法（:code:`set_parameters` 是可选的）：" - -#: ../../source/tutorial-quickstart-mxnet.rst:172 -#: ../../source/tutorial-quickstart-pytorch.rst:156 -#: ../../source/tutorial-quickstart-scikitlearn.rst:109 -msgid "return the model weight as a list of NumPy ndarrays" -msgstr "以 NumPy ndarrays 列表形式返回模型参数" - -#: ../../source/tutorial-quickstart-mxnet.rst:173 -#: ../../source/tutorial-quickstart-pytorch.rst:157 -#: ../../source/tutorial-quickstart-scikitlearn.rst:111 -msgid ":code:`set_parameters` (optional)" -msgstr ":code:`set_parameters` （可选）" - -#: ../../source/tutorial-quickstart-mxnet.rst:174 -#: ../../source/tutorial-quickstart-pytorch.rst:158 -#: ../../source/tutorial-quickstart-scikitlearn.rst:111 -msgid "" -"update the local model weights with the parameters received from the " -"server" -msgstr "用从服务器接收到的参数更新本地模型参数" - -#: ../../source/tutorial-quickstart-mxnet.rst:176 -#: ../../source/tutorial-quickstart-pytorch.rst:160 -#: ../../source/tutorial-quickstart-scikitlearn.rst:114 -msgid "set the local model weights" -msgstr "设置本地模型参数" - -#: ../../source/tutorial-quickstart-mxnet.rst:177 -#: ../../source/tutorial-quickstart-pytorch.rst:161 -#: ../../source/tutorial-quickstart-scikitlearn.rst:115 -msgid "train the local model" -msgstr "训练本地模型" - -#: ../../source/tutorial-quickstart-mxnet.rst:178 -#: ../../source/tutorial-quickstart-pytorch.rst:162 -#: ../../source/tutorial-quickstart-scikitlearn.rst:116 -msgid "receive the updated local model weights" -msgstr "接收更新的本地模型参数" - -#: ../../source/tutorial-quickstart-mxnet.rst:180 -#: ../../source/tutorial-quickstart-pytorch.rst:164 -#: ../../source/tutorial-quickstart-scikitlearn.rst:118 -msgid "test the local model" -msgstr "测试本地模型" - -#: ../../source/tutorial-quickstart-mxnet.rst:182 -msgid "They can be implemented in the following way:" -msgstr "它们可以通过以下方式实现：" +"**新的 MLCube 代码示例** ([#779](https://github.com/adap/flower/pull/779), " +"[#1034](https://github.com/adap/flower/pull/1034), " +"[#1065](https://github.com/adap/flower/pull/1065), " +"[#1090](https://github.com/adap/flower/pull/1090))" -#: ../../source/tutorial-quickstart-mxnet.rst:212 +#: ../../source/ref-changelog.md:915 msgid "" -"We can now create an instance of our class :code:`MNISTClient` and add " -"one line to actually run this client:" -msgstr "现在我们可以创建一个 :code:`MNISTClient` 类的实例，并添加一行来实际运行该客户端：" +"A new code example (`quickstart_mlcube`) demonstrates usage of MLCube " +"with Flower." +msgstr "新代码示例（\"quickstart_mlcube\"）演示了 MLCube 与 Flower 的用法。" -#: ../../source/tutorial-quickstart-mxnet.rst:219 +#: ../../source/ref-changelog.md:917 msgid "" -"That's it for the client. We only have to implement :code:`Client` or " -":code:`NumPyClient` and call :code:`fl.client.start_client()` or " -":code:`fl.client.start_numpy_client()`. The string " -":code:`\"0.0.0.0:8080\"` tells the client which server to connect to. In " -"our case we can run the server and the client on the same machine, " -"therefore we use :code:`\"0.0.0.0:8080\"`. If we run a truly federated " -"workload with the server and clients running on different machines, all " -"that needs to change is the :code:`server_address` we pass to the client." +"**SSL-enabled server and client** " +"([#842](https://github.com/adap/flower/pull/842), " +"[#844](https://github.com/adap/flower/pull/844), " +"[#845](https://github.com/adap/flower/pull/845), " +"[#847](https://github.com/adap/flower/pull/847), " +"[#993](https://github.com/adap/flower/pull/993), " +"[#994](https://github.com/adap/flower/pull/994))" msgstr "" -"这就是客户端。我们只需实现 :code:`Client` 或 :code:`NumPyClient` 并调用 " -":code:`fl.client.start_client()` 或 " -":code:`fl.client.start_numpy_client()`。字符串 " -":code:`\"0.0.0.0:8080\"`会告诉客户端要连接的服务器。在本例中，我们可以在同一台机器上运行服务器和客户端，因此我们使用 " -":code:`\"0.0.0.0:8080\"`。如果我们运行的是真正的联邦工作负载，服务器和客户端运行在不同的机器上，那么需要改变的只是传递给客户端的" -" :code:`server_address`。" +"** 支持 SSL 的服务器和客户端** ([#842](https://github.com/adap/flower/pull/842), " +"[#844](https://github.com/adap/flower/pull/844), " +"[#845](https://github.com/adap/flower/pull/845), " +"[#847](https://github.com/adap/flower/pull/847), " +"[#993](https://github.com/adap/flower/pull/993), " +"[#994](https://github.com/adap/flower/pull/994))" -#: ../../source/tutorial-quickstart-mxnet.rst:241 +#: ../../source/ref-changelog.md:919 msgid "" -"With both client and server ready, we can now run everything and see " -"federated learning in action. Federated learning systems usually have a " -"server and multiple clients. We therefore have to start the server first:" -msgstr "客户端和服务器都准备就绪后，我们现在就可以运行一切，看看联邦学习的运行情况。联邦学习系统通常有一个服务器和多个客户端。因此，我们必须先启动服务器：" +"SSL enables secure encrypted connections between clients and servers. " +"This release open-sources the Flower secure gRPC implementation to make " +"encrypted communication channels accessible to all Flower users." +msgstr "SSL 可实现客户端与服务器之间的安全加密连接。该版本开源了 Flower 安全 gRPC 实现，使所有 Flower 用户都能访问加密通信通道。" -#: ../../source/tutorial-quickstart-mxnet.rst:249 -#: ../../source/tutorial-quickstart-pytorch.rst:226 -#: ../../source/tutorial-quickstart-scikitlearn.rst:224 -#: ../../source/tutorial-quickstart-tensorflow.rst:122 -#: ../../source/tutorial-quickstart-xgboost.rst:533 +#: ../../source/ref-changelog.md:921 msgid "" -"Once the server is running we can start the clients in different " -"terminals. Open a new terminal and start the first client:" -msgstr "服务器运行后，我们就可以在不同终端启动客户端了。打开一个新终端，启动第一个客户端：" - -#: ../../source/tutorial-quickstart-mxnet.rst:256 -#: ../../source/tutorial-quickstart-pytorch.rst:233 -#: ../../source/tutorial-quickstart-scikitlearn.rst:231 -#: ../../source/tutorial-quickstart-tensorflow.rst:129 -#: ../../source/tutorial-quickstart-xgboost.rst:540 -msgid "Open another terminal and start the second client:" -msgstr "打开另一台终端，启动第二个客户端：" +"**Updated** `FedAdam` **and** `FedYogi` **strategies** " +"([#885](https://github.com/adap/flower/pull/885), " +"[#895](https://github.com/adap/flower/pull/895))" +msgstr "" +"**更新**`FedAdam`**和**`FedYogi`**战略** " +"([#885](https://github.com/adap/flower/pull/885), " +"[#895](https://github.com/adap/flower/pull/895))" -#: ../../source/tutorial-quickstart-mxnet.rst:262 -#: ../../source/tutorial-quickstart-pytorch.rst:239 -#: ../../source/tutorial-quickstart-scikitlearn.rst:237 -#: ../../source/tutorial-quickstart-xgboost.rst:546 +#: ../../source/ref-changelog.md:923 msgid "" -"Each client will have its own dataset. You should now see how the " -"training does in the very first terminal (the one that started the " -"server):" -msgstr "每个客户端都有自己的数据集。现在你应该看到第一个终端（启动服务器的终端）的训练效果了：" +"`FedAdam` and `FedAdam` match the latest version of the Adaptive " +"Federated Optimization paper." +msgstr "FedAdam \"和 \"FedAdam \"与最新版本的 \"自适应联邦优化 \"论文相匹配。" -#: ../../source/tutorial-quickstart-mxnet.rst:294 +#: ../../source/ref-changelog.md:925 msgid "" -"Congratulations! You've successfully built and run your first federated " -"learning system. The full `source code " -"`_ for this example can be found in :code:`examples" -"/quickstart-mxnet`." +"**Initialize** `start_simulation` **with a list of client IDs** " +"([#860](https://github.com/adap/flower/pull/860))" msgstr "" -"恭喜您！您已经成功构建并运行了第一个联邦学习系统。本示例的`完整源代码 " -"`_ 可在 :code:`examples/quickstart-mxnet` 中找到。" +"**初始化** `start_simulation` **使用客户端 ID 列表** " +"([#860](https://github.com/adap/flower/pull/860))" -#: ../../source/tutorial-quickstart-pandas.rst:-1 +#: ../../source/ref-changelog.md:927 msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with Pandas to perform Federated Analytics." -msgstr "查看此联邦学习快速入门教程，了解如何使用 Flower 和 Pandas 执行联邦分析。" - -#: ../../source/tutorial-quickstart-pandas.rst:5 -msgid "Quickstart Pandas" -msgstr "快速入门Pandas" - -#: ../../source/tutorial-quickstart-pandas.rst:10 -msgid "Let's build a federated analytics system using Pandas and Flower!" -msgstr "让我们使用 Pandas 和 Flower 建立一个联邦分析系统！" +"`start_simulation` can now be called with a list of client IDs " +"(`clients_ids`, type: `List[str]`). Those IDs will be passed to the " +"`client_fn` whenever a client needs to be initialized, which can make it " +"easier to load data partitions that are not accessible through `int` " +"identifiers." +msgstr "" +"现在可以使用客户端 ID 列表（`clients_ids`，类型：`List[str]`）调用 " +"`start_simulation`。每当需要初始化客户端时，这些 ID 就会被传递到 `client_fn` 中，这样就能更轻松地加载无法通过 " +"`int` 标识符访问的数据分区。" -#: ../../source/tutorial-quickstart-pandas.rst:12 +#: ../../source/ref-changelog.md:931 msgid "" -"Please refer to the `full code example " -"`_ " -"to learn more." +"Update `num_examples` calculation in PyTorch code examples in " +"([#909](https://github.com/adap/flower/pull/909))" msgstr "" -"请参阅 `完整代码示例 `_\" 了解更多信息。" +"更新 PyTorch 代码示例中的 \"num_examples \"计算 " +"([#909](https://github.com/adap/flower/pull/909))" -#: ../../source/tutorial-quickstart-pytorch.rst:-1 +#: ../../source/ref-changelog.md:932 msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with PyTorch to train a CNN model on MNIST." -msgstr "查看此联邦学习快速入门教程，了解如何使用 Flower 和 PyTorch 在 MNIST 上训练 CNN 模型。" +"Expose Flower version through `flwr.__version__` " +"([#952](https://github.com/adap/flower/pull/952))" +msgstr "" +"通过 `flwr.__version__` 公开 Flower 版本 " +"([#952](https://github.com/adap/flower/pull/952))" -#: ../../source/tutorial-quickstart-pytorch.rst:13 +#: ../../source/ref-changelog.md:933 msgid "" -"In this tutorial we will learn how to train a Convolutional Neural " -"Network on CIFAR10 using Flower and PyTorch." -msgstr "在本教程中，我们将学习如何使用 Flower 和 PyTorch 在 CIFAR10 上训练卷积神经网络。" +"`start_server` in `app.py` now returns a `History` object containing " +"metrics from training ([#974](https://github.com/adap/flower/pull/974))" +msgstr "" +"`app.py`中的 `start_server`现在会返回一个 `History` " +"对象，其中包含训练中的指标（[#974](https://github.com/adap/flower/pull/974)）" -#: ../../source/tutorial-quickstart-pytorch.rst:15 -#: ../../source/tutorial-quickstart-xgboost.rst:39 -#, fuzzy +#: ../../source/ref-changelog.md:934 msgid "" -"First of all, it is recommended to create a virtual environment and run " -"everything within a :doc:`virtualenv `." +"Make `max_workers` (used by `ThreadPoolExecutor`) configurable " +"([#978](https://github.com/adap/flower/pull/978))" msgstr "" -"首先，建议创建一个虚拟环境，并在 `virtualenv `_ 中运行一切。" +"使 `max_workers`（由 " +"`ThreadPoolExecutor`使用）可配置（[#978](https://github.com/adap/flower/pull/978)）" -#: ../../source/tutorial-quickstart-pytorch.rst:19 +#: ../../source/ref-changelog.md:935 msgid "" -"*Clients* are responsible for generating individual weight-updates for " -"the model based on their local datasets. These updates are then sent to " -"the *server* which will aggregate them to produce a better model. " -"Finally, the *server* sends this improved version of the model back to " -"each *client*. A complete cycle of weight updates is called a *round*." -msgstr "*客户端*负责在其本地数据集上更新模型参数。然后，这些参数会被发送到*服务器*，由*服务器*聚合后生成一个更好的模型。最后，*服务器*将改进后的模型发送回每个*客户端*。一个完整的模型参数更新周期称为一*轮*。" +"Increase sleep time after server start to three seconds in all code " +"examples ([#1086](https://github.com/adap/flower/pull/1086))" +msgstr "在所有代码示例中，将服务器启动后的休眠时间延长至三秒（[#1086](https://github.com/adap/flower/pull/1086)）" -#: ../../source/tutorial-quickstart-pytorch.rst:23 +#: ../../source/ref-changelog.md:936 msgid "" -"Now that we have a rough idea of what is going on, let's get started. We " -"first need to install Flower. You can do this by running :" -msgstr "现在，我们已经有了一个大致的概念了，那就让我们开始吧。首先，我们需要安装 Flower。可以通过运行 ：" +"Added a new FAQ section to the documentation " +"([#948](https://github.com/adap/flower/pull/948))" +msgstr "在文档中添加了新的常见问题部分 ([#948](https://github.com/adap/flower/pull/948))" -#: ../../source/tutorial-quickstart-pytorch.rst:29 +#: ../../source/ref-changelog.md:937 msgid "" -"Since we want to use PyTorch to solve a computer vision task, let's go " -"ahead and install PyTorch and the **torchvision** library:" -msgstr "既然我们想用 PyTorch 解决计算机视觉任务，那就继续安装 PyTorch 和 **torchvision** 库吧：" +"And many more under-the-hood changes, library updates, documentation " +"changes, and tooling improvements!" +msgstr "还有更多底层更改、库更新、文档更改和工具改进！" -#: ../../source/tutorial-quickstart-pytorch.rst:39 +#: ../../source/ref-changelog.md:941 msgid "" -"Now that we have all our dependencies installed, let's run a simple " -"distributed training with two clients and one server. Our training " -"procedure and network architecture are based on PyTorch's `Deep Learning " -"with PyTorch " -"`_." +"**Removed** `flwr_example` **and** `flwr_experimental` **from release " +"build** ([#869](https://github.com/adap/flower/pull/869))" msgstr "" -"现在我们已经安装了所有的依赖项，让我们用两个客户端和一个服务器来运行一个简单的分布式训练。我们的训练过程和网络架构基于 PyTorch " -"的《Deep Learning with PyTorch " -"`_》。" +"**从发布版中删除**`flwr_example`**和**`flwr_experimental`** " +"([#869](https://github.com/adap/flower/pull/869))" -#: ../../source/tutorial-quickstart-pytorch.rst:41 +#: ../../source/ref-changelog.md:943 msgid "" -"In a file called :code:`client.py`, import Flower and PyTorch related " -"packages:" -msgstr "在名为 :code:`client.py` 的文件中，导入 Flower 和 PyTorch 相关软件包：" +"The packages `flwr_example` and `flwr_experimental` have been deprecated " +"since Flower 0.12.0 and they are not longer included in Flower release " +"builds. The associated extras (`baseline`, `examples-pytorch`, `examples-" +"tensorflow`, `http-logger`, `ops`) are now no-op and will be removed in " +"an upcoming release." +msgstr "" +"自 Flower 0.12.0 起，软件包 `flwr_example` 和 `flwr_experimental` 已被弃用，它们不再包含在 " +"Flower 的发布版本中。相关的额外包（`baseline`, `examples-pytorch`, `examples-" +"tensorflow`, `http-logger`, `ops`）现在已不再使用，并将在即将发布的版本中移除。" -#: ../../source/tutorial-quickstart-pytorch.rst:56 -msgid "In addition, we define the device allocation in PyTorch with:" -msgstr "此外，我们还在 PyTorch 中定义了设备分配：" +#: ../../source/ref-changelog.md:945 +msgid "v0.17.0 (2021-09-24)" +msgstr "v0.17.0 (2021-09-24)" -#: ../../source/tutorial-quickstart-pytorch.rst:62 +#: ../../source/ref-changelog.md:949 msgid "" -"We use PyTorch to load CIFAR10, a popular colored image classification " -"dataset for machine learning. The PyTorch :code:`DataLoader()` downloads " -"the training and test data that are then normalized." +"**Experimental virtual client engine** " +"([#781](https://github.com/adap/flower/pull/781) " +"[#790](https://github.com/adap/flower/pull/790) " +"[#791](https://github.com/adap/flower/pull/791))" msgstr "" -"我们使用 PyTorch 来加载 CIFAR10，这是一个用于机器学习的流行彩色图像分类数据集。PyTorch " -":code:`DataLoader()`下载训练数据和测试数据，然后进行归一化处理。" +"**实验性虚拟客户端引擎** ([#781](https://github.com/adap/flower/pull/781) " +"[#790](https://github.com/adap/flower/pull/790) " +"[#791](https://github.com/adap/flower/pull/791))" -#: ../../source/tutorial-quickstart-pytorch.rst:78 +#: ../../source/ref-changelog.md:951 msgid "" -"Define the loss and optimizer with PyTorch. The training of the dataset " -"is done by looping over the dataset, measure the corresponding loss and " -"optimize it." -msgstr "使用 PyTorch 定义损失和优化器。数据集的训练是通过循环数据集、测量相应的损失值并对其进行优化来完成的。" +"One of Flower's goals is to enable research at scale. This release " +"enables a first (experimental) peek at a major new feature, codenamed the" +" virtual client engine. Virtual clients enable simulations that scale to " +"a (very) large number of clients on a single machine or compute cluster. " +"The easiest way to test the new functionality is to look at the two new " +"code examples called `quickstart_simulation` and `simulation_pytorch`." +msgstr "" +"Flower 的目标之一是实现大规模研究。这一版本首次（试验性地）展示了代号为 \"虚拟客户端引擎 " +"\"的重要新功能。虚拟客户端可以在单台机器或计算集群上对大量客户端进行模拟。测试新功能的最简单方法是查看名为 " +"\"quickstart_simulation \"和 \"simulation_pytorch \"的两个新代码示例。" -#: ../../source/tutorial-quickstart-pytorch.rst:94 +#: ../../source/ref-changelog.md:953 msgid "" -"Define then the validation of the machine learning network. We loop over" -" the test set and measure the loss and accuracy of the test set." -msgstr "然后定义机器学习网络的验证。我们在测试集上循环，计算测试集的损失值和准确率。" - -#: ../../source/tutorial-quickstart-pytorch.rst:113 +"The feature is still experimental, so there's no stability guarantee for " +"the API. It's also not quite ready for prime time and comes with a few " +"known caveats. However, those who are curious are encouraged to try it " +"out and share their thoughts." +msgstr "" +"该功能仍处于试验阶段，因此无法保证 API " +"的稳定性。此外，它还没有完全准备好进入黄金时间，并有一些已知的注意事项。不过，我们鼓励好奇的用户尝试使用并分享他们的想法。" + +#: ../../source/ref-changelog.md:955 msgid "" -"After defining the training and testing of a PyTorch machine learning " -"model, we use the functions for the Flower clients." -msgstr "在定义了 PyTorch 机器学习模型的训练和测试之后，我们将这些功能用于 Flower 客户端。" +"**New built-in strategies** " +"([#828](https://github.com/adap/flower/pull/828) " +"[#822](https://github.com/adap/flower/pull/822))" +msgstr "" +"**新的内置策略**（[#828](https://github.com/adap/flower/pull/828) " +"[#822](https://github.com/adap/flower/pull/822)" -#: ../../source/tutorial-quickstart-pytorch.rst:115 +#: ../../source/ref-changelog.md:957 msgid "" -"The Flower clients will use a simple CNN adapted from 'PyTorch: A 60 " -"Minute Blitz':" -msgstr "Flower 客户端将使用一个简单的从“PyTorch： 60 分钟突击\"改编的CNN：" +"FedYogi - Federated learning strategy using Yogi on server-side. " +"Implementation based on https://arxiv.org/abs/2003.00295" +msgstr "FedYogi - 在服务器端使用 Yogi 的联邦学习策略。基于 https://arxiv.org/abs/2003.00295 实现" -#: ../../source/tutorial-quickstart-pytorch.rst:142 +#: ../../source/ref-changelog.md:958 msgid "" -"After loading the data set with :code:`load_data()` we define the Flower " -"interface." -msgstr "使用 :code:`load_data()` 加载数据集后，我们定义了 Flower 接口。" +"FedAdam - Federated learning strategy using Adam on server-side. " +"Implementation based on https://arxiv.org/abs/2003.00295" +msgstr "FedAdam - 在服务器端使用 Adam 的联邦学习策略。基于 https://arxiv.org/abs/2003.00295 实现" -#: ../../source/tutorial-quickstart-pytorch.rst:150 +#: ../../source/ref-changelog.md:960 msgid "" -"Flower provides a convenience class called :code:`NumPyClient` which " -"makes it easier to implement the :code:`Client` interface when your " -"workload uses PyTorch. Implementing :code:`NumPyClient` usually means " -"defining the following methods (:code:`set_parameters` is optional " -"though):" +"**New PyTorch Lightning code example** " +"([#617](https://github.com/adap/flower/pull/617))" msgstr "" -"Flower 提供了一个名为 :code:`NumPyClient` 的便捷类，当您的工作负载使用 PyTorch 时，它使 " -":code:`Client` 接口的实现变得更容易。实现 :code:`NumPyClient` " -"通常意味着定义以下方法（:code:`set_parameters` 是可选的）：" - -#: ../../source/tutorial-quickstart-pytorch.rst:166 -msgid "which can be implemented in the following way:" -msgstr "可以通过以下方式实现：" +"**新的 PyTorch Lightning 代码示例** " +"([#617](https://github.com/adap/flower/pull/617))" -#: ../../source/tutorial-quickstart-pytorch.rst:189 -#: ../../source/tutorial-quickstart-tensorflow.rst:82 +#: ../../source/ref-changelog.md:962 msgid "" -"We can now create an instance of our class :code:`CifarClient` and add " -"one line to actually run this client:" -msgstr "现在我们可以创建一个 :code:`CifarClient` 类的实例，并添加一行来实际运行该客户端：" +"**New Variational Auto-Encoder code example** " +"([#752](https://github.com/adap/flower/pull/752))" +msgstr "**新的变分自动编码器代码示例** ([#752](https://github.com/adap/flower/pull/752))" -#: ../../source/tutorial-quickstart-pytorch.rst:196 -#: ../../source/tutorial-quickstart-tensorflow.rst:90 -#, fuzzy +#: ../../source/ref-changelog.md:964 msgid "" -"That's it for the client. We only have to implement :code:`Client` or " -":code:`NumPyClient` and call :code:`fl.client.start_client()`. If you " -"implement a client of type :code:`NumPyClient` you'll need to first call " -"its :code:`to_client()` method. The string :code:`\"[::]:8080\"` tells " -"the client which server to connect to. In our case we can run the server " -"and the client on the same machine, therefore we use " -":code:`\"[::]:8080\"`. If we run a truly federated workload with the " -"server and clients running on different machines, all that needs to " -"change is the :code:`server_address` we point the client at." -msgstr "" -"这就是客户端。我们只需实现 :code:`Client` 或 :code:`NumPyClient` 并调用 " -":code:`fl.client.start_client()` 或 " -":code:`fl.client.start_numpy_client()`。字符串 " -":code:`\"[::]:8080\"`会告诉客户端要连接的服务器。在本例中，我们可以在同一台机器上运行服务器和客户端，因此使用 " -":code:`\"[::]:8080\"。如果我们运行的是真正的联邦工作负载，服务器和客户端运行在不同的机器上，那么需要改变的只是客户端指向的 " -":code:`server_address`。" +"**New scikit-learn code example** " +"([#748](https://github.com/adap/flower/pull/748))" +msgstr "**新的 scikit-learn 代码示例** ([#748](https://github.com/adap/flower/pull/748))" -#: ../../source/tutorial-quickstart-pytorch.rst:271 +#: ../../source/ref-changelog.md:966 msgid "" -"Congratulations! You've successfully built and run your first federated " -"learning system. The full `source code " -"`_ for this example can be found in :code:`examples" -"/quickstart-pytorch`." -msgstr "" -"恭喜您！您已经成功构建并运行了第一个联邦学习系统。本示例的`完整源代码 " -"`_ 可以在 :code:`examples/quickstart-pytorch` 中找到。" +"**New experimental TensorBoard strategy** " +"([#789](https://github.com/adap/flower/pull/789))" +msgstr "**新的实验性 TensorBoard 策略**（[#789](https://github.com/adap/flower/pull/789)）" -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:-1 +#: ../../source/ref-changelog.md:970 msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with PyTorch Lightning to train an Auto Encoder model on MNIST." -msgstr "查看此联邦学习快速入门教程，了解如何使用 Flower 和 PyTorch Lightning 在 MNIST 上训练自动编码器模型。" +"Improved advanced TensorFlow code example " +"([#769](https://github.com/adap/flower/pull/769))" +msgstr "改进的高级 TensorFlow 代码示例（[#769](https://github.com/adap/flower/pull/769)" -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:5 -msgid "Quickstart PyTorch Lightning" -msgstr "快速入门 PyTorch Lightning" +#: ../../source/ref-changelog.md:971 +msgid "" +"Warning when `min_available_clients` is misconfigured " +"([#830](https://github.com/adap/flower/pull/830))" +msgstr "" +"当 `min_available_clients` 配置错误时发出警告 " +"([#830](https://github.com/adap/flower/pull/830))" -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:10 +#: ../../source/ref-changelog.md:972 msgid "" -"Let's build a horizontal federated learning system using PyTorch " -"Lightning and Flower!" -msgstr "让我们使用 PyTorch Lightning 和 Flower 构建一个水平联邦学习系统！" +"Improved gRPC server docs " +"([#841](https://github.com/adap/flower/pull/841))" +msgstr "改进了 gRPC 服务器文档（[#841](https://github.com/adap/flower/pull/841)）" -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:12 +#: ../../source/ref-changelog.md:973 msgid "" -"Please refer to the `full code example " -"`_ to learn more." -msgstr "" -"请参阅 `完整代码示例 `_ 了解更多信息。" +"Improved error message in `NumPyClient` " +"([#851](https://github.com/adap/flower/pull/851))" +msgstr "改进了 `NumPyClient` 中的错误信息 ([#851](https://github.com/adap/flower/pull/851))" -#: ../../source/tutorial-quickstart-scikitlearn.rst:-1 +#: ../../source/ref-changelog.md:974 msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with scikit-learn to train a linear regression model." -msgstr "查看此联邦学习快速入门教程，了解如何使用 Flower 和 scikit-learn 训练线性回归模型。" +"Improved PyTorch quickstart code example " +"([#852](https://github.com/adap/flower/pull/852))" +msgstr "改进的 PyTorch 快速启动代码示例 ([#852](https://github.com/adap/flower/pull/852))" -#: ../../source/tutorial-quickstart-scikitlearn.rst:5 -msgid "Quickstart scikit-learn" -msgstr "scikit-learn快速入门" +#: ../../source/ref-changelog.md:978 +msgid "" +"**Disabled final distributed evaluation** " +"([#800](https://github.com/adap/flower/pull/800))" +msgstr "**禁用最终分布式评价** ([#800](https://github.com/adap/flower/pull/800))" -#: ../../source/tutorial-quickstart-scikitlearn.rst:10 +#: ../../source/ref-changelog.md:980 msgid "" -"In this tutorial, we will learn how to train a :code:`Logistic " -"Regression` model on MNIST using Flower and scikit-learn." +"Prior behaviour was to perform a final round of distributed evaluation on" +" all connected clients, which is often not required (e.g., when using " +"server-side evaluation). The prior behaviour can be enabled by passing " +"`force_final_distributed_eval=True` to `start_server`." msgstr "" -"在本教程中，我们将学习如何使用 Flower 和 scikit-learn 在 MNIST 上训练一个 :code:`Logistic " -"Regression` 模型。" - -#: ../../source/tutorial-quickstart-scikitlearn.rst:26 -#, fuzzy -msgid "Since we want to use scikit-learn, let's go ahead and install it:" -msgstr "既然我们要使用 scikt-learn，那就继续安装吧：" +"之前的行为是在所有连接的客户端上执行最后一轮分布式评估，而这通常是不需要的（例如，在使用服务器端评估时）。可以通过向 `start_server`" +" 传递 `force_final_distributed_eval=True` 来启用之前的行为。" -#: ../../source/tutorial-quickstart-scikitlearn.rst:32 -msgid "Or simply install all dependencies using Poetry:" -msgstr "或者直接使用 Poetry 安装所有依赖项：" +#: ../../source/ref-changelog.md:982 +msgid "" +"**Renamed q-FedAvg strategy** " +"([#802](https://github.com/adap/flower/pull/802))" +msgstr "**更名为 q-FedAvg 策略** ([#802](https://github.com/adap/flower/pull/802))" -#: ../../source/tutorial-quickstart-scikitlearn.rst:42 +#: ../../source/ref-changelog.md:984 msgid "" -"Now that we have all our dependencies installed, let's run a simple " -"distributed training with two clients and one server. However, before " -"setting up the client and server, we will define all functionalities that" -" we need for our federated learning setup within :code:`utils.py`. The " -":code:`utils.py` contains different functions defining all the machine " -"learning basics:" +"The strategy named `QffedAvg` was renamed to `QFedAvg` to better reflect " +"the notation given in the original paper (q-FFL is the optimization " +"objective, q-FedAvg is the proposed solver). Note the original (now " +"deprecated) `QffedAvg` class is still available for compatibility reasons" +" (it will be removed in a future release)." msgstr "" -"现在我们已经安装了所有的依赖项，让我们用两个客户端和一个服务器来运行一个简单的分布式训练。不过，在设置客户端和服务器之前，我们将在 " -":code:`utils.py` 中定义联邦学习设置所需的所有功能。:code:`utils.py`包含定义所有机器学习基础知识的不同函数：" - -#: ../../source/tutorial-quickstart-scikitlearn.rst:45 -msgid ":code:`get_model_parameters()`" -msgstr ":code:`get_model_parameters()`" - -#: ../../source/tutorial-quickstart-scikitlearn.rst:46 -msgid "Returns the parameters of a :code:`sklearn` LogisticRegression model" -msgstr "返回 :code:`sklearn` LogisticRegression 模型的参数" - -#: ../../source/tutorial-quickstart-scikitlearn.rst:47 -msgid ":code:`set_model_params()`" -msgstr ":code:`set_model_params()`" - -#: ../../source/tutorial-quickstart-scikitlearn.rst:48 -msgid "Sets the parameters of a :code:`sklean` LogisticRegression model" -msgstr "设置:code:`sklean`的LogisticRegression模型的参数" - -#: ../../source/tutorial-quickstart-scikitlearn.rst:49 -msgid ":code:`set_initial_params()`" -msgstr ":code:`set_initial_params()`" - -#: ../../source/tutorial-quickstart-scikitlearn.rst:50 -msgid "Initializes the model parameters that the Flower server will ask for" -msgstr "初始化 Flower 服务器将要求的模型参数" - -#: ../../source/tutorial-quickstart-scikitlearn.rst:51 -msgid ":code:`load_mnist()`" -msgstr ":code:`load_mnist()`" +"名为 `QffedAvg` 的策略已更名为 `QFedAvg`，以更好地反映原始论文中给出的符号（q-FFL 是优化目标，q-FedAvg " +"是建议的求解器）。请注意，出于兼容性原因，原始（现已废弃）的 `QffedAvg` 类仍然可用（它将在未来的版本中移除）。" -#: ../../source/tutorial-quickstart-scikitlearn.rst:52 -msgid "Loads the MNIST dataset using OpenML" -msgstr "使用 OpenML 加载 MNIST 数据集" +#: ../../source/ref-changelog.md:986 +msgid "" +"**Deprecated and renamed code example** `simulation_pytorch` **to** " +"`simulation_pytorch_legacy` " +"([#791](https://github.com/adap/flower/pull/791))" +msgstr "" +"**删除并重命名代码示例**`simulation_pytorch`**为**`simulation_pytorch_legacy` " +"([#791](https://github.com/adap/flower/pull/791))" -#: ../../source/tutorial-quickstart-scikitlearn.rst:53 -msgid ":code:`shuffle()`" -msgstr ":code:`shuffle()`" +#: ../../source/ref-changelog.md:988 +msgid "" +"This example has been replaced by a new example. The new example is based" +" on the experimental virtual client engine, which will become the new " +"default way of doing most types of large-scale simulations in Flower. The" +" existing example was kept for reference purposes, but it might be " +"removed in the future." +msgstr "" +"该示例已被新示例取代。新示例基于试验性虚拟客户端引擎，它将成为在 Flower " +"中进行大多数类型大规模模拟的新的默认方式。现有示例将作为参考保留，但将来可能会删除。" -#: ../../source/tutorial-quickstart-scikitlearn.rst:54 -msgid "Shuffles data and its label" -msgstr "对数据及其标签进行洗牌" +#: ../../source/ref-changelog.md:990 +msgid "v0.16.0 (2021-05-11)" +msgstr "v0.16.0 (2021-05-11)" -#: ../../source/tutorial-quickstart-scikitlearn.rst:56 -msgid ":code:`partition()`" -msgstr ":code:`partition()`" +#: ../../source/ref-changelog.md:994 +msgid "" +"**New built-in strategies** " +"([#549](https://github.com/adap/flower/pull/549))" +msgstr "**新的内置策略** ([#549](https://github.com/adap/flower/pull/549))" -#: ../../source/tutorial-quickstart-scikitlearn.rst:56 -msgid "Splits datasets into a number of partitions" -msgstr "将数据集分割成多个分区" +#: ../../source/ref-changelog.md:996 +msgid "(abstract) FedOpt" +msgstr "（摘要） FedOpt" -#: ../../source/tutorial-quickstart-scikitlearn.rst:58 +#: ../../source/ref-changelog.md:999 msgid "" -"Please check out :code:`utils.py` `here " -"`_ for more details. The pre-defined functions are used in" -" the :code:`client.py` and imported. The :code:`client.py` also requires " -"to import several packages such as Flower and scikit-learn:" -msgstr "" -"更多详情请查看 :code:`utils.py`` 这里 " -"`_。在 :code:`client.py` 中使用并导入了预定义函数。:code:`client.py` " -"还需要导入几个软件包，如 Flower 和 scikit-learn：" +"**Custom metrics for server and strategies** " +"([#717](https://github.com/adap/flower/pull/717))" +msgstr "**服务器和策略的自定义指标** ([#717](https://github.com/adap/flower/pull/717))" -#: ../../source/tutorial-quickstart-scikitlearn.rst:73 -#, fuzzy +#: ../../source/ref-changelog.md:1001 msgid "" -"We load the MNIST dataset from `OpenML " -"`_, a popular " -"image classification dataset of handwritten digits for machine learning. " -"The utility :code:`utils.load_mnist()` downloads the training and test " -"data. The training set is split afterwards into 10 partitions with " -":code:`utils.partition()`." +"The Flower server is now fully task-agnostic, all remaining instances of " +"task-specific metrics (such as `accuracy`) have been replaced by custom " +"metrics dictionaries. Flower 0.15 introduced the capability to pass a " +"dictionary containing custom metrics from client to server. As of this " +"release, custom metrics replace task-specific metrics on the server." msgstr "" -"我们从 `OpenML `_ 中加载 MNIST " -"数据集，这是一个用于机器学习的流行手写数字图像分类数据集。实用程序 :code:`utils.load_mnist()` " -"下载训练和测试数据。然后使用 :code:`utils.partition()`将训练集分割成 10 个分区。" +"Flower 服务器现在完全与任务无关，所有剩余的任务特定度量（如 \"准确度\"）都已被自定义度量字典取代。Flower 0.15 " +"引入了从客户端向服务器传递包含自定义指标的字典的功能。从本版本开始，自定义指标将取代服务器上的特定任务指标。" -#: ../../source/tutorial-quickstart-scikitlearn.rst:85 +#: ../../source/ref-changelog.md:1003 msgid "" -"Next, the logistic regression model is defined and initialized with " -":code:`utils.set_initial_params()`." -msgstr "接下来，使用 :code:`utils.set_initial_params()` 对逻辑回归模型进行定义和初始化。" - -#: ../../source/tutorial-quickstart-scikitlearn.rst:97 -msgid "" -"The Flower server interacts with clients through an interface called " -":code:`Client`. When the server selects a particular client for training," -" it sends training instructions over the network. The client receives " -"those instructions and calls one of the :code:`Client` methods to run " -"your code (i.e., to fit the logistic regression we defined earlier)." +"Custom metric dictionaries are now used in two user-facing APIs: they are" +" returned from Strategy methods `aggregate_fit`/`aggregate_evaluate` and " +"they enable evaluation functions passed to built-in strategies (via " +"`eval_fn`) to return more than two evaluation metrics. Strategies can " +"even return *aggregated* metrics dictionaries for the server to keep " +"track of." msgstr "" -"Flower 服务器通过一个名为 :code:`Client` " -"的接口与客户端交互。当服务器选择一个特定的客户端进行训练时，它会通过网络发送训练指令。客户端接收到这些指令后，会调用 :code:`Client`" -" 方法之一来运行您的代码（即拟合我们之前定义的逻辑回归）。" +"自定义度量字典现在可在两个面向用户的 API 中使用：它们可从策略方法 `aggregate_fit`/`aggregate_evaluate` " +"返回，还可使传递给内置策略（通过 `eval_fn`）的评估函数返回两个以上的评估度量。策略甚至可以返回 *aggregated* " +"指标字典，以便服务器跟踪。" -#: ../../source/tutorial-quickstart-scikitlearn.rst:103 +#: ../../source/ref-changelog.md:1005 msgid "" -"Flower provides a convenience class called :code:`NumPyClient` which " -"makes it easier to implement the :code:`Client` interface when your " -"workload uses scikit-learn. Implementing :code:`NumPyClient` usually " -"means defining the following methods (:code:`set_parameters` is optional " -"though):" +"Strategy implementations should migrate their `aggregate_fit` and " +"`aggregate_evaluate` methods to the new return type (e.g., by simply " +"returning an empty `{}`), server-side evaluation functions should migrate" +" from `return loss, accuracy` to `return loss, {\"accuracy\": accuracy}`." msgstr "" -"Flower 提供了一个名为 :code:`NumPyClient` 的便捷类，当你的工作负载使用 scikit-learn " -"时，它可以让你更容易地实现 :code:`Client` 接口。实现 :code:`NumPyClient` " -"通常意味着定义以下方法（:code:`set_parameters` 是可选的）：" - -#: ../../source/tutorial-quickstart-scikitlearn.rst:112 -msgid "is directly imported with :code:`utils.set_model_params()`" -msgstr "直接导入 :code:`utils.set_model_params()`" +"Strategy 实现应将其 `aggregate_fit` 和 `aggregate_evaluate` " +"方法迁移到新的返回类型（例如，只需返回空的 `{}`），服务器端评估函数应从 `return loss, accuracy` 迁移到 " +"`return loss, {\"accuracy\": accuracy}`。" -#: ../../source/tutorial-quickstart-scikitlearn.rst:120 -msgid "The methods can be implemented in the following way:" -msgstr "这些方法可以通过以下方式实现：" +#: ../../source/ref-changelog.md:1007 +msgid "" +"Flower 0.15-style return types are deprecated (but still supported), " +"compatibility will be removed in a future release." +msgstr "Flower 0.15 风格的返回类型已被弃用（但仍受支持），兼容性将在未来的版本中移除。" -#: ../../source/tutorial-quickstart-scikitlearn.rst:143 +#: ../../source/ref-changelog.md:1009 msgid "" -"We can now create an instance of our class :code:`MnistClient` and add " -"one line to actually run this client:" -msgstr "现在我们可以创建一个 :code:`MnistClient` 类的实例，并添加一行来实际运行该客户端：" +"**Migration warnings for deprecated functionality** " +"([#690](https://github.com/adap/flower/pull/690))" +msgstr "** 过时功能的迁移警告** ([#690](https://github.com/adap/flower/pull/690))" -#: ../../source/tutorial-quickstart-scikitlearn.rst:150 -#, fuzzy +#: ../../source/ref-changelog.md:1011 msgid "" -"That's it for the client. We only have to implement :code:`Client` or " -":code:`NumPyClient` and call :code:`fl.client.start_client()`. If you " -"implement a client of type :code:`NumPyClient` you'll need to first call " -"its :code:`to_client()` method. The string :code:`\"0.0.0.0:8080\"` tells" -" the client which server to connect to. In our case we can run the server" -" and the client on the same machine, therefore we use " -":code:`\"0.0.0.0:8080\"`. If we run a truly federated workload with the " -"server and clients running on different machines, all that needs to " -"change is the :code:`server_address` we pass to the client." +"Earlier versions of Flower were often migrated to new APIs, while " +"maintaining compatibility with legacy APIs. This release introduces " +"detailed warning messages if usage of deprecated APIs is detected. The " +"new warning messages often provide details on how to migrate to more " +"recent APIs, thus easing the transition from one release to another." msgstr "" -"这就是客户端。我们只需实现 :code:`Client` 或 :code:`NumPyClient` 并调用 " -":code:`fl.client.start_client()` 或 " -":code:`fl.client.start_numpy_client()`。字符串 " -":code:`\"0.0.0.0:8080\"`会告诉客户端要连接的服务器。在本例中，我们可以在同一台机器上运行服务器和客户端，因此我们使用 " -":code:`\"0.0.0.0:8080\"`。如果我们运行的是真正的联邦工作负载，服务器和客户端运行在不同的机器上，那么需要改变的只是传递给客户端的" -" :code:`server_address`。" +"Flower 早期版本通常会迁移到新的应用程序接口，同时保持与旧版应用程序接口的兼容。如果检测到使用了过时的 " +"API，本版本将引入详细的警告信息。新的警告信息通常会详细说明如何迁移到更新的 API，从而简化从一个版本到另一个版本的过渡。" -#: ../../source/tutorial-quickstart-scikitlearn.rst:159 +#: ../../source/ref-changelog.md:1013 msgid "" -"The following Flower server is a little bit more advanced and returns an " -"evaluation function for the server-side evaluation. First, we import " -"again all required libraries such as Flower and scikit-learn." +"Improved docs and docstrings " +"([#691](https://github.com/adap/flower/pull/691) " +"[#692](https://github.com/adap/flower/pull/692) " +"[#713](https://github.com/adap/flower/pull/713))" msgstr "" -"下面的 Flower 服务器更先进一些，会返回一个用于服务器端评估的评估函数。首先，我们再次导入所有需要的库，如 Flower 和 scikit-" -"learn。" +"改进了文档和文档说明 ([#691](https://github.com/adap/flower/pull/691) " +"[#692](https://github.com/adap/flower/pull/692) " +"[#713](https://github.com/adap/flower/pull/713))" -#: ../../source/tutorial-quickstart-scikitlearn.rst:162 -msgid ":code:`server.py`, import Flower and start the server:" -msgstr ":code:`server.py`, 导入 Flower 并启动服务器：" +#: ../../source/ref-changelog.md:1015 +msgid "MXNet example and documentation" +msgstr "MXNet 示例和文档" -#: ../../source/tutorial-quickstart-scikitlearn.rst:173 +#: ../../source/ref-changelog.md:1017 msgid "" -"The number of federated learning rounds is set in :code:`fit_round()` and" -" the evaluation is defined in :code:`get_evaluate_fn()`. The evaluation " -"function is called after each federated learning round and gives you " -"information about loss and accuracy." +"FedBN implementation in example PyTorch: From Centralized To Federated " +"([#696](https://github.com/adap/flower/pull/696) " +"[#702](https://github.com/adap/flower/pull/702) " +"[#705](https://github.com/adap/flower/pull/705))" msgstr "" -"联邦学习轮数在 :code:`fit_round()` 中设置，评估在 :code:`get_evaluate_fn()` " -"中定义。每轮联邦学习后都会调用评估函数，并提供有关损失值和准确率的信息。" +"PyTorch 示例中的 FedBN 实现： 从集中到联邦 " +"([#696](https://github.com/adap/flower/pull/696) " +"[#702](https://github.com/adap/flower/pull/702) " +"[#705](https://github.com/adap/flower/pull/705))" -#: ../../source/tutorial-quickstart-scikitlearn.rst:198 +#: ../../source/ref-changelog.md:1021 msgid "" -"The :code:`main` contains the server-side parameter initialization " -":code:`utils.set_initial_params()` as well as the aggregation strategy " -":code:`fl.server.strategy:FedAvg()`. The strategy is the default one, " -"federated averaging (or FedAvg), with two clients and evaluation after " -"each federated learning round. The server can be started with the command" -" :code:`fl.server.start_server(server_address=\"0.0.0.0:8080\", " -"strategy=strategy, config=fl.server.ServerConfig(num_rounds=3))`." -msgstr "" -":code:`main`包含服务器端参数初始化:code:`utils.set_initial_params()`以及聚合策略 " -":code:`fl.server.strategy:FedAvg()`。该策略是默认的联邦平均（或 " -"FedAvg）策略，有两个客户端，在每轮联邦学习后进行评估。可以使用 " -":code:`fl.server.start_server(server_address=\"0.0.0.0:8080\", " -"strategy=strategy, config=fl.server.ServerConfig(num_rounds=3))` 命令启动服务器。" +"**Serialization-agnostic server** " +"([#721](https://github.com/adap/flower/pull/721))" +msgstr "**序列化无关服务器** ([#721](https://github.com/adap/flower/pull/721))" -#: ../../source/tutorial-quickstart-scikitlearn.rst:217 +#: ../../source/ref-changelog.md:1023 msgid "" -"With both client and server ready, we can now run everything and see " -"federated learning in action. Federated learning systems usually have a " -"server and multiple clients. We, therefore, have to start the server " -"first:" -msgstr "客户端和服务器都准备就绪后，我们现在就可以运行一切，看看联邦学习的运行情况。联邦学习系统通常有一个服务器和多个客户端。因此，我们必须先启动服务器：" +"The Flower server is now fully serialization-agnostic. Prior usage of " +"class `Weights` (which represents parameters as deserialized NumPy " +"ndarrays) was replaced by class `Parameters` (e.g., in `Strategy`). " +"`Parameters` objects are fully serialization-agnostic and represents " +"parameters as byte arrays, the `tensor_type` attributes indicates how " +"these byte arrays should be interpreted (e.g., for " +"serialization/deserialization)." +msgstr "" +"Flower 服务器现在完全不依赖序列化。之前使用的 `Weights` 类（以反序列化的 NumPy ndarrays 表示参数）已被 " +"`Parameters` 类取代（例如在 `Strategy`中）。参数 " +"\"对象与序列化完全无关，它以字节数组的形式表示参数，\"tensor_type \"属性表示如何解释这些字节数组（例如，用于序列化/反序列化）。" -#: ../../source/tutorial-quickstart-scikitlearn.rst:271 +#: ../../source/ref-changelog.md:1025 msgid "" -"Congratulations! You've successfully built and run your first federated " -"learning system. The full `source code " -"`_ for this example can be found in :code:`examples/sklearn-logreg-" -"mnist`." +"Built-in strategies implement this approach by handling serialization and" +" deserialization to/from `Weights` internally. Custom/3rd-party Strategy " +"implementations should update to the slightly changed Strategy method " +"definitions. Strategy authors can consult PR " +"[#721](https://github.com/adap/flower/pull/721) to see how strategies can" +" easily migrate to the new format." msgstr "" -"恭喜您！您已经成功构建并运行了第一个联邦学习系统。本示例的`完整源代码 " -"`_ 可以在 :code:`examples/sklearn-logreg-mnist` 中找到。" +"内置策略通过在内部处理序列化和反序列化到/从`Weights`来实现这种方法。自定义/第三方策略实现应更新为稍有改动的策略方法定义。策略作者可查阅" +" PR [#721](https://github.com/adap/flower/pull/721) 以了解如何将策略轻松迁移到新格式。" -#: ../../source/tutorial-quickstart-tensorflow.rst:-1 +#: ../../source/ref-changelog.md:1027 msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with TensorFlow to train a MobilNetV2 model on CIFAR-10." -msgstr "查看此联邦学习快速入门教程，了解如何使用 Flower 和 TensorFlow 在 CIFAR-10 上训练 MobilNetV2 模型。" - -#: ../../source/tutorial-quickstart-tensorflow.rst:5 -msgid "Quickstart TensorFlow" -msgstr "快速入门 TensorFlow" - -#: ../../source/tutorial-quickstart-tensorflow.rst:13 -msgid "Let's build a federated learning system in less than 20 lines of code!" -msgstr "让我们用不到 20 行代码构建一个联邦学习系统！" +"Deprecated `flwr.server.Server.evaluate`, use " +"`flwr.server.Server.evaluate_round` instead " +"([#717](https://github.com/adap/flower/pull/717))" +msgstr "" +"已弃用 `flwr.server.Server.evaluate`，改用 " +"`flwr.server.Server.evaluate_round`（[#717](https://github.com/adap/flower/pull/717)" -#: ../../source/tutorial-quickstart-tensorflow.rst:15 -msgid "Before Flower can be imported we have to install it:" -msgstr "在导入 Flower 之前，我们必须先安装它：" +#: ../../source/ref-changelog.md:1029 +msgid "v0.15.0 (2021-03-12)" +msgstr "v0.15.0 (2021-03-12)" -#: ../../source/tutorial-quickstart-tensorflow.rst:21 +#: ../../source/ref-changelog.md:1033 msgid "" -"Since we want to use the Keras API of TensorFlow (TF), we have to install" -" TF as well:" -msgstr "由于我们要使用 TensorFlow (TF) 的 Keras API，因此还必须安装 TF：" +"**Server-side parameter initialization** " +"([#658](https://github.com/adap/flower/pull/658))" +msgstr "**服务器端参数初始化** ([#658](https://github.com/adap/flower/pull/658))" -#: ../../source/tutorial-quickstart-tensorflow.rst:31 -msgid "Next, in a file called :code:`client.py`, import Flower and TensorFlow:" -msgstr "接下来，在名为 :code:`client.py` 的文件中导入 Flower 和 TensorFlow：" +#: ../../source/ref-changelog.md:1035 +msgid "" +"Model parameters can now be initialized on the server-side. Server-side " +"parameter initialization works via a new `Strategy` method called " +"`initialize_parameters`." +msgstr "" +"现在可以在服务器端初始化模型参数。服务器端参数初始化通过名为 \"initialize_parameters \"的新 \"Strategy " +"\"方法进行。" -#: ../../source/tutorial-quickstart-tensorflow.rst:38 +#: ../../source/ref-changelog.md:1037 msgid "" -"We use the Keras utilities of TF to load CIFAR10, a popular colored image" -" classification dataset for machine learning. The call to " -":code:`tf.keras.datasets.cifar10.load_data()` downloads CIFAR10, caches " -"it locally, and then returns the entire training and test set as NumPy " -"ndarrays." +"Built-in strategies support a new constructor argument called " +"`initial_parameters` to set the initial parameters. Built-in strategies " +"will provide these initial parameters to the server on startup and then " +"delete them to free the memory afterwards." msgstr "" -"我们使用 TF 的 Keras 实用程序加载 CIFAR10，这是一个用于机器学习的流行彩色图像分类数据集。调用 " -":code:`tf.keras.datasets.cifar10.load_data()` 会下载 CIFAR10，将其缓存到本地，然后以 " -"NumPy ndarrays 的形式返回整个训练集和测试集。" +"内置策略支持名为 \"initial_parameters " +"\"的新构造函数参数，用于设置初始参数。内置策略会在启动时向服务器提供这些初始参数，然后删除它们以释放内存。" -#: ../../source/tutorial-quickstart-tensorflow.rst:47 +#: ../../source/ref-changelog.md:1056 msgid "" -"Next, we need a model. For the purpose of this tutorial, we use " -"MobilNetV2 with 10 output classes:" -msgstr "接下来，我们需要一个模型。在本教程中，我们使用带有 10 个输出类的 MobilNetV2：" +"If no initial parameters are provided to the strategy, the server will " +"continue to use the current behaviour (namely, it will ask one of the " +"connected clients for its parameters and use these as the initial global " +"parameters)." +msgstr "如果没有向策略提供初始参数，服务器将继续使用当前行为（即向其中一个已连接的客户端询问参数，并将这些参数用作初始全局参数）。" -#: ../../source/tutorial-quickstart-tensorflow.rst:60 +#: ../../source/ref-changelog.md:1060 msgid "" -"Flower provides a convenience class called :code:`NumPyClient` which " -"makes it easier to implement the :code:`Client` interface when your " -"workload uses Keras. The :code:`NumPyClient` interface defines three " -"methods which can be implemented in the following way:" +"Deprecate `flwr.server.strategy.DefaultStrategy` (migrate to " +"`flwr.server.strategy.FedAvg`, which is equivalent)" msgstr "" -"Flower 提供了一个名为 :code:`NumPyClient` 的便捷类，当您的工作负载使用 Keras 时，该类可以更轻松地实现 " -":code:`Client` 接口。:code:`NumPyClient` 接口定义了三个方法，可以通过以下方式实现：" - -#: ../../source/tutorial-quickstart-tensorflow.rst:135 -msgid "Each client will have its own dataset." -msgstr "每个客户都有自己的数据集。" +"停用 `flwr.server.strategy.DefaultStrategy`（迁移到等价的 " +"`flwr.server.strategy.FedAvg`）" -#: ../../source/tutorial-quickstart-tensorflow.rst:137 -msgid "" -"You should now see how the training does in the very first terminal (the " -"one that started the server):" -msgstr "现在你应该能在第一个终端（启动服务器的终端）看到训练的效果了：" +#: ../../source/ref-changelog.md:1062 +msgid "v0.14.0 (2021-02-18)" +msgstr "v0.14.0 (2021-02-18)" -#: ../../source/tutorial-quickstart-tensorflow.rst:169 +#: ../../source/ref-changelog.md:1066 msgid "" -"Congratulations! You've successfully built and run your first federated " -"learning system. The full `source code " -"`_ for this can be found in :code:`examples" -"/quickstart-tensorflow/client.py`." +"**Generalized** `Client.fit` **and** `Client.evaluate` **return values** " +"([#610](https://github.com/adap/flower/pull/610) " +"[#572](https://github.com/adap/flower/pull/572) " +"[#633](https://github.com/adap/flower/pull/633))" msgstr "" -"恭喜您！您已经成功构建并运行了第一个联邦学习系统。`完整的源代码 " -"`_ 可以在 :code:`examples/quickstart-" -"tensorflow/client.py` 中找到。" +"**通用** `Client.fit` **和** `Client.evaluate` **返回值** " +"([#610](https://github.com/adap/flower/pull/610) " +"[#572](https://github.com/adap/flower/pull/572) " +"[#633](https://github.com/adap/flower/pull/633))" -#: ../../source/tutorial-quickstart-xgboost.rst:-1 +#: ../../source/ref-changelog.md:1068 msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with XGBoost to train classification models on trees." -msgstr "查看此联邦学习 快速入门教程，了解如何使用 Flower 和 XGBoost 上训练分类模型。" - -#: ../../source/tutorial-quickstart-xgboost.rst:5 -msgid "Quickstart XGBoost" -msgstr "XGBoost快速入门" - -#: ../../source/tutorial-quickstart-xgboost.rst:14 -msgid "Federated XGBoost" -msgstr "联邦化 XGBoost" +"Clients can now return an additional dictionary mapping `str` keys to " +"values of the following types: `bool`, `bytes`, `float`, `int`, `str`. " +"This means one can return almost arbitrary values from `fit`/`evaluate` " +"and make use of them on the server side!" +msgstr "" +"客户端现在可以返回一个额外的字典，将 `str` 键映射为以下类型的值： " +"bool`、`bytes`、`float`、`int`、`str`。这意味着我们可以从 `fit`/`evaluate` " +"返回几乎任意的值，并在服务器端使用它们！" -#: ../../source/tutorial-quickstart-xgboost.rst:16 +#: ../../source/ref-changelog.md:1070 msgid "" -"EXtreme Gradient Boosting (**XGBoost**) is a robust and efficient " -"implementation of gradient-boosted decision tree (**GBDT**), that " -"maximises the computational boundaries for boosted tree methods. It's " -"primarily designed to enhance both the performance and computational " -"speed of machine learning models. In XGBoost, trees are constructed " -"concurrently, unlike the sequential approach taken by GBDT." +"This improvement also allowed for more consistent return types between " +"`fit` and `evaluate`: `evaluate` should now return a tuple `(float, int, " +"dict)` representing the loss, number of examples, and a dictionary " +"holding arbitrary problem-specific values like accuracy." msgstr "" -"EXtreme Gradient " -"Boosting（**XGBoost**）是梯度提升决策树（**GBDT**）的一种稳健而高效的实现方法，能最大限度地提高提升树方法的计算边界。它主要用于提高机器学习模型的性能和计算速度。在" -" XGBoost 中，决策树是并发构建的，与 GBDT 采用的顺序方法不同。" +"这一改进还使 `fit` 和 `evaluate` 之间的返回类型更加一致：`evaluate` 现在应返回一个元组`(float, int, " +"dict)`，代表损失、示例数和一个包含特定问题任意值（如准确度）的字典。" -#: ../../source/tutorial-quickstart-xgboost.rst:20 +#: ../../source/ref-changelog.md:1072 msgid "" -"Often, for tabular data on medium-sized datasets with fewer than 10k " -"training examples, XGBoost surpasses the results of deep learning " -"techniques." -msgstr "对于训练示例少于 10k 的中型数据集上的表格数据，XGBoost 的结果往往超过深度学习技术。" - -#: ../../source/tutorial-quickstart-xgboost.rst:23 -msgid "Why federated XGBoost?" -msgstr "为什么选择联邦 XGBoost？" +"In case you wondered: this feature is compatible with existing projects, " +"the additional dictionary return value is optional. New code should " +"however migrate to the new return types to be compatible with upcoming " +"Flower releases (`fit`: `List[np.ndarray], int, Dict[str, Scalar]`, " +"`evaluate`: `float, int, Dict[str, Scalar]`). See the example below for " +"details." +msgstr "" +"如果你想知道：此功能与现有项目兼容，额外的字典返回值是可选的。不过，新代码应迁移到新的返回类型，以便与即将发布的 Flower " +"版本兼容（`fit`: `List[np.ndarray], int, Dict[str, Scalar]`，`evaluate`: " +"`float, int, Dict[str, Scalar]`）。详见下面的示例。" -#: ../../source/tutorial-quickstart-xgboost.rst:25 +#: ../../source/ref-changelog.md:1074 msgid "" -"Indeed, as the demand for data privacy and decentralized learning grows, " -"there's an increasing requirement to implement federated XGBoost systems " -"for specialised applications, like survival analysis and financial fraud " -"detection." -msgstr "事实上，随着对数据隐私和分散学习的需求不断增长，越来越多的专业应用（如生存分析和金融欺诈检测）需要实施联邦 XGBoost 系统。" +"*Code example:* note the additional dictionary return values in both " +"`FlwrClient.fit` and `FlwrClient.evaluate`:" +msgstr "*代码示例：* 注意 `FlwrClient.fit` 和 `FlwrClient.evaluate` 中的附加字典返回值：" -#: ../../source/tutorial-quickstart-xgboost.rst:27 +#: ../../source/ref-changelog.md:1089 msgid "" -"Federated learning ensures that raw data remains on the local device, " -"making it an attractive approach for sensitive domains where data " -"security and privacy are paramount. Given the robustness and efficiency " -"of XGBoost, combining it with federated learning offers a promising " -"solution for these specific challenges." +"**Generalized** `config` **argument in** `Client.fit` **and** " +"`Client.evaluate` ([#595](https://github.com/adap/flower/pull/595))" msgstr "" -"联邦学习可确保原始数据保留在本地设备上，因此对于数据安全和隐私至关重要的敏感领域来说，这是一种极具吸引力的方法。鉴于 XGBoost " -"的稳健性和高效性，将其与联邦学习相结合为应对这些特定挑战提供了一种前景广阔的解决方案。" +"**在**`Client.fit` " +"**和**`Client.evaluate`中泛化**`config`参数（[#595](https://github.com/adap/flower/pull/595)）" -#: ../../source/tutorial-quickstart-xgboost.rst:30 +#: ../../source/ref-changelog.md:1091 msgid "" -"In this tutorial we will learn how to train a federated XGBoost model on " -"HIGGS dataset using Flower and :code:`xgboost` package. We use a simple " -"example (`full code xgboost-quickstart " -"`_)" -" with two *clients* and one *server* to demonstrate how federated XGBoost" -" works, and then we dive into a more complex example (`full code xgboost-" -"comprehensive `_) to run various experiments." +"The `config` argument used to be of type `Dict[str, str]`, which means " +"that dictionary values were expected to be strings. The new release " +"generalizes this to enable values of the following types: `bool`, " +"`bytes`, `float`, `int`, `str`." msgstr "" -"在本教程中，我们将学习如何使用 Flower 和 :code:`xgboost` 软件包在 HIGGS 数据集上训练联邦 XGBoost " -"模型。我们将使用一个包含两个 * 客户端* 和一个 * 服务器* 的简单示例 (`完整代码 xgboost-quickstart " -"`_)来演示联邦 XGBoost 如何工作，然后我们将深入到一个更复杂的示例 (`完整代码 xgboost-" -"comprehensive `_)，以运行各种实验。" - -#: ../../source/tutorial-quickstart-xgboost.rst:37 -msgid "Environment Setup" -msgstr "环境设定" +"`config`参数曾是 \"字典[str, str]\"类型，这意味着字典值应是字符串。新版本将其扩展为以下类型的值： " +"bool`、`bytes`、`float`、`int`、`str`。" -#: ../../source/tutorial-quickstart-xgboost.rst:41 +#: ../../source/ref-changelog.md:1093 msgid "" -"We first need to install Flower and Flower Datasets. You can do this by " -"running :" -msgstr "我们首先需要安装 Flower 和 Flower Datasets。您可以通过运行 ：" +"This means one can now pass almost arbitrary values to `fit`/`evaluate` " +"using the `config` dictionary. Yay, no more `str(epochs)` on the server-" +"side and `int(config[\"epochs\"])` on the client side!" +msgstr "" +"这意味着现在可以使用 `config` 字典向 `fit`/`evaluate` 传递几乎任意的值。耶，服务器端不再需要 " +"`str(epochs)`，客户端不再需要 `int(config[\"epochs\"])`！" -#: ../../source/tutorial-quickstart-xgboost.rst:47 +#: ../../source/ref-changelog.md:1095 msgid "" -"Since we want to use :code:`xgboost` package to build up XGBoost trees, " -"let's go ahead and install :code:`xgboost`:" -msgstr "既然我们要使用 :code:`xgboost` 软件包来构建 XGBoost 树，那就继续安装 :code:`xgboost`：" +"*Code example:* note that the `config` dictionary now contains non-`str` " +"values in both `Client.fit` and `Client.evaluate`:" +msgstr "*代码示例：* 注意 `config` 字典现在在 `Client.fit` 和 `Client.evaluate` 中都包含非 `str` 值：" -#: ../../source/tutorial-quickstart-xgboost.rst:57 -msgid "" -"*Clients* are responsible for generating individual weight-updates for " -"the model based on their local datasets. Now that we have all our " -"dependencies installed, let's run a simple distributed training with two " -"clients and one server." -msgstr "*客户端*负责根据其本地数据集为模型生成单独的模型参数更新。现在我们已经安装了所有的依赖项，让我们用两个客户端和一个服务器来运行一个简单的分布式训练。" +#: ../../source/ref-changelog.md:1112 +msgid "v0.13.0 (2021-01-08)" +msgstr "v0.13.0 (2021-01-08)" -#: ../../source/tutorial-quickstart-xgboost.rst:60 +#: ../../source/ref-changelog.md:1116 msgid "" -"In a file called :code:`client.py`, import xgboost, Flower, Flower " -"Datasets and other related functions:" -msgstr "在名为 :code:`client.py` 的文件中，导入 xgboost、Flower、Flower Datasets 和其他相关函数：" +"New example: PyTorch From Centralized To Federated " +"([#549](https://github.com/adap/flower/pull/549))" +msgstr "新示例： PyTorch 从集中到联邦 ([#549](https://github.com/adap/flower/pull/549))" -#: ../../source/tutorial-quickstart-xgboost.rst:87 -msgid "Dataset partition and hyper-parameter selection" -msgstr "数据集划分和超参数选择" +#: ../../source/ref-changelog.md:1117 +msgid "Improved documentation" +msgstr "改进文档" -#: ../../source/tutorial-quickstart-xgboost.rst:89 -msgid "" -"Prior to local training, we require loading the HIGGS dataset from Flower" -" Datasets and conduct data partitioning for FL:" -msgstr "在本地训练之前，我们需要从 Flower Datasets 加载 HIGGS 数据集，并对 FL 进行数据分区：" +#: ../../source/ref-changelog.md:1118 +msgid "New documentation theme ([#551](https://github.com/adap/flower/pull/551))" +msgstr "新文档主题 ([#551](https://github.com/adap/flower/pull/551))" -#: ../../source/tutorial-quickstart-xgboost.rst:102 -msgid "" -"In this example, we split the dataset into two partitions with uniform " -"distribution (:code:`IidPartitioner(num_partitions=2)`). Then, we load " -"the partition for the given client based on :code:`node_id`:" -msgstr "" -"在此示例中，我们将数据集分割成两个均匀分布的分区（:code:`IidPartitioner(num_partitions=2)`）。然后，我们根据" -" :code:`node_id` 为给定客户端加载分区：" +#: ../../source/ref-changelog.md:1119 +msgid "New API reference ([#554](https://github.com/adap/flower/pull/554))" +msgstr "新的 API 参考 ([#554](https://github.com/adap/flower/pull/554))" -#: ../../source/tutorial-quickstart-xgboost.rst:121 +#: ../../source/ref-changelog.md:1120 msgid "" -"After that, we do train/test splitting on the given partition (client's " -"local data), and transform data format for :code:`xgboost` package." -msgstr "然后，我们在给定的分区（客户端的本地数据）上进行训练/测试分割，并为 :code:`xgboost` 软件包转换数据格式。" +"Updated examples documentation " +"([#549](https://github.com/adap/flower/pull/549))" +msgstr "更新了示例文档 ([#549](https://github.com/adap/flower/pull/549))" -#: ../../source/tutorial-quickstart-xgboost.rst:134 +#: ../../source/ref-changelog.md:1121 msgid "" -"The functions of :code:`train_test_split` and " -":code:`transform_dataset_to_dmatrix` are defined as below:" -msgstr ":code:`train_test_split` 和 :code:`transform_dataset_too_dmatrix` 的函数定义如下：" +"Removed obsolete documentation " +"([#548](https://github.com/adap/flower/pull/548))" +msgstr "删除了过时的文档 ([#548](https://github.com/adap/flower/pull/548))" -#: ../../source/tutorial-quickstart-xgboost.rst:158 -msgid "Finally, we define the hyper-parameters used for XGBoost training." -msgstr "最后，我们定义了用于 XGBoost 训练的超参数。" +#: ../../source/ref-changelog.md:1123 +msgid "Bugfix:" +msgstr "错误修正：" -#: ../../source/tutorial-quickstart-xgboost.rst:174 +#: ../../source/ref-changelog.md:1125 msgid "" -"The :code:`num_local_round` represents the number of iterations for local" -" tree boost. We use CPU for the training in default. One can shift it to " -"GPU by setting :code:`tree_method` to :code:`gpu_hist`. We use AUC as " -"evaluation metric." +"`Server.fit` does not disconnect clients when finished, disconnecting the" +" clients is now handled in `flwr.server.start_server` " +"([#553](https://github.com/adap/flower/pull/553) " +"[#540](https://github.com/adap/flower/issues/540))." msgstr "" -"代码:`num_local_round`表示本地树的迭代次数。我们默认使用 CPU 进行训练。可以通过将 :code:`tree_method` " -"设置为 :code:`gpu_hist`，将其转换为 GPU。我们使用 AUC 作为评估指标。" +"Server.fit \"完成后不会断开客户端连接，现在断开客户端连接是在 \"flwr.server.start_server " +"\"中处理的（[#553](https://github.com/adap/flower/pull/553) " +"[#540](https://github.com/adap/flower/issues/540)）。" -#: ../../source/tutorial-quickstart-xgboost.rst:181 -msgid "Flower client definition for XGBoost" -msgstr "用于 XGBoost 的 Flower 客户端定义" +#: ../../source/ref-changelog.md:1127 +msgid "v0.12.0 (2020-12-07)" +msgstr "v0.12.0 (2020-12-07)" -#: ../../source/tutorial-quickstart-xgboost.rst:183 +#: ../../source/ref-changelog.md:1129 ../../source/ref-changelog.md:1145 +msgid "Important changes:" +msgstr "重要变更：" + +#: ../../source/ref-changelog.md:1131 msgid "" -"After loading the dataset we define the Flower client. We follow the " -"general rule to define :code:`XgbClient` class inherited from " -":code:`fl.client.Client`." -msgstr "" -"加载数据集后，我们定义 Flower 客户端。我们按照一般规则定义从 :code:`fl.client.Client` 继承而来的 " -":code:`XgbClient` 类。" +"Added an example for embedded devices " +"([#507](https://github.com/adap/flower/pull/507))" +msgstr "添加了嵌入式设备示例 ([#507](https://github.com/adap/flower/pull/507))" -#: ../../source/tutorial-quickstart-xgboost.rst:193 +#: ../../source/ref-changelog.md:1132 msgid "" -"The :code:`self.bst` is used to keep the Booster objects that remain " -"consistent across rounds, allowing them to store predictions from trees " -"integrated in earlier rounds and maintain other essential data structures" -" for training." +"Added a new NumPyClient (in addition to the existing KerasClient) " +"([#504](https://github.com/adap/flower/pull/504) " +"[#508](https://github.com/adap/flower/pull/508))" msgstr "" -"代码:`self.bst`用于保存在各轮中保持一致的 Booster " -"对象，使其能够存储在前几轮中集成的树的预测结果，并维护其他用于训练的重要数据结构。" +"添加了一个新的 NumPyClient（除现有的 KerasClient " +"之外）（[#504](https://github.com/adap/flower/pull/504) " +"[#508](https://github.com/adap/flower/pull/508)" -#: ../../source/tutorial-quickstart-xgboost.rst:196 +#: ../../source/ref-changelog.md:1133 msgid "" -"Then, we override :code:`get_parameters`, :code:`fit` and " -":code:`evaluate` methods insides :code:`XgbClient` class as follows." +"Deprecated `flwr_example` package and started to migrate examples into " +"the top-level `examples` directory " +"([#494](https://github.com/adap/flower/pull/494) " +"[#512](https://github.com/adap/flower/pull/512))" msgstr "" -"然后，我们在 :code:`XgbClient` 类中重写 :code:`get_parameters`、:code:`fit` 和 " -":code:`evaluate` 方法如下。" +"弃用 `flwr_example` 软件包，并开始将示例迁移到顶层的 `examples` 目录 " +"([#494](https://github.com/adap/flower/pull/494) " +"[#512](https://github.com/adap/flower/pull/512))" -#: ../../source/tutorial-quickstart-xgboost.rst:210 +#: ../../source/ref-changelog.md:1135 +msgid "v0.11.0 (2020-11-30)" +msgstr "v0.11.0 (2020-11-30)" + +#: ../../source/ref-changelog.md:1137 +msgid "Incompatible changes:" +msgstr "不兼容的更改：" + +#: ../../source/ref-changelog.md:1139 msgid "" -"Unlike neural network training, XGBoost trees are not started from a " -"specified random weights. In this case, we do not use " -":code:`get_parameters` and :code:`set_parameters` to initialise model " -"parameters for XGBoost. As a result, let's return an empty tensor in " -":code:`get_parameters` when it is called by the server at the first " -"round." +"Renamed strategy methods " +"([#486](https://github.com/adap/flower/pull/486)) to unify the naming of " +"Flower's public APIs. Other public methods/functions (e.g., every method " +"in `Client`, but also `Strategy.evaluate`) do not use the `on_` prefix, " +"which is why we're removing it from the four methods in Strategy. To " +"migrate rename the following `Strategy` methods accordingly:" msgstr "" -"与神经网络训练不同，XGBoost 树不是从指定的随机参数开始的。在这种情况下，我们不使用 :code:`get_parameters` 和 " -":code:`set_parameters` 来初始化 XGBoost 的模型参数。因此，当服务器在第一轮调用 " -":code:`get_parameters` 时，让我们在 :code:`get_parameters` 中返回一个空张量。" +"重命名了策略方法（[#486](https://github.com/adap/flower/pull/486)），以统一 Flower公共 " +"API 的命名。其他公共方法/函数（例如 `Client` 中的每个方法，以及 `Strategy.evaluate`）不使用 `on_` " +"前缀，这就是我们从 Strategy 中的四个方法中移除它的原因。迁移时，请相应地重命名以下 `Strategy` 方法：" -#: ../../source/tutorial-quickstart-xgboost.rst:251 -msgid "" -"In :code:`fit`, at the first round, we call :code:`xgb.train()` to build " -"up the first set of trees. the returned Booster object and config are " -"stored in :code:`self.bst` and :code:`self.config`, respectively. From " -"the second round, we load the global model sent from server to " -":code:`self.bst`, and then update model weights on local training data " -"with function :code:`local_boost` as follows:" -msgstr "" -"在 :code:`fit`中，第一轮我们调用 :code:`xgb.train()`来建立第一组树，返回的 Booster 对象和 config " -"分别存储在 :code:`self.bst` 和 :code:`self.config` 中。从第二轮开始，我们将服务器发送的全局模型加载到 " -":code:`self.bst`，然后使用函数 :code:`local_boost`更新本地训练数据的模型权重，如下所示：" +#: ../../source/ref-changelog.md:1140 +msgid "`on_configure_evaluate` => `configure_evaluate`" +msgstr "`on_configure_evaluate` => `configure_evaluate`" -#: ../../source/tutorial-quickstart-xgboost.rst:269 -msgid "" -"Given :code:`num_local_round`, we update trees by calling " -":code:`self.bst.update` method. After training, the last " -":code:`N=num_local_round` trees will be extracted to send to the server." -msgstr "" -"给定 :code:`num_local_round`，我们通过调用 " -":code:`self.bst.update`方法更新树。训练结束后，我们将提取最后一个 :code:`N=num_local_round` " -"树并发送给服务器。" +#: ../../source/ref-changelog.md:1141 +msgid "`on_aggregate_evaluate` => `aggregate_evaluate`" +msgstr "`on_aggregate_evaluate` => `aggregate_evaluate`" -#: ../../source/tutorial-quickstart-xgboost.rst:291 +#: ../../source/ref-changelog.md:1142 +msgid "`on_configure_fit` => `configure_fit`" +msgstr "`on_configure_fit` => `configure_fit`" + +#: ../../source/ref-changelog.md:1143 +msgid "`on_aggregate_fit` => `aggregate_fit`" +msgstr "`on_aggregate_fit` => `aggregate_fit`" + +#: ../../source/ref-changelog.md:1147 msgid "" -"In :code:`evaluate`, we call :code:`self.bst.eval_set` function to " -"conduct evaluation on valid set. The AUC value will be returned." -msgstr "在 :code:`evaluate`中，我们调用 :code:`self.bst.eval_set`函数对有效集合进行评估。将返回 AUC 值。" +"Deprecated `DefaultStrategy` " +"([#479](https://github.com/adap/flower/pull/479)). To migrate use " +"`FedAvg` instead." +msgstr "" +"已废弃的 `DefaultStrategy` ([#479](https://github.com/adap/flower/pull/479)) " +"。迁移时请使用 `FedAvg`。" -#: ../../source/tutorial-quickstart-xgboost.rst:294 +#: ../../source/ref-changelog.md:1148 msgid "" -"Now, we can create an instance of our class :code:`XgbClient` and add one" -" line to actually run this client:" -msgstr "现在，我们可以创建一个 :code:`XgbClient` 类的实例，并添加一行来实际运行该客户端：" +"Simplified examples and baselines " +"([#484](https://github.com/adap/flower/pull/484))." +msgstr "简化示例和baselines（[#484](https://github.com/adap/flower/pull/484)）。" -#: ../../source/tutorial-quickstart-xgboost.rst:300 +#: ../../source/ref-changelog.md:1149 msgid "" -"That's it for the client. We only have to implement :code:`Client`and " -"call :code:`fl.client.start_client()`. The string :code:`\"[::]:8080\"` " -"tells the client which server to connect to. In our case we can run the " -"server and the client on the same machine, therefore we use " -":code:`\"[::]:8080\"`. If we run a truly federated workload with the " -"server and clients running on different machines, all that needs to " -"change is the :code:`server_address` we point the client at." +"Removed presently unused `on_conclude_round` from strategy interface " +"([#483](https://github.com/adap/flower/pull/483))." msgstr "" -"这就是客户端。我们只需实现 :code:`客户端`并调用 :code:`fl.client.start_client()`。字符串 " -":code:`\"[::]:8080\"`会告诉客户端要连接的服务器。在本例中，我们可以在同一台机器上运行服务器和客户端，因此我们使用 " -":code:`\"[::]:8080\"`。如果我们运行的是真正的联邦工作负载，服务器和客户端运行在不同的机器上，那么需要改变的只是客户端指向的 " -":code:`server_address`。" +"删除了策略界面中目前未使用的 " +"\"on_conclude_round\"（[#483](https://github.com/adap/flower/pull/483)）。" -#: ../../source/tutorial-quickstart-xgboost.rst:311 +#: ../../source/ref-changelog.md:1150 msgid "" -"These updates are then sent to the *server* which will aggregate them to " -"produce a better model. Finally, the *server* sends this improved version" -" of the model back to each *client* to finish a complete FL round." +"Set minimal Python version to 3.6.1 instead of 3.6.9 " +"([#471](https://github.com/adap/flower/pull/471))." msgstr "" -"然后，这些更新会被发送到*服务器*，由*服务器*聚合后生成一个更好的模型。最后，*服务器*将这个改进版的模型发回给每个*客户端*，以完成一轮完整的" -" FL。" +"将最小 Python 版本设为 3.6.1，而不是 3.6.9 " +"([#471](https://github.com/adap/flower/pull/471))." -#: ../../source/tutorial-quickstart-xgboost.rst:314 +#: ../../source/ref-changelog.md:1151 msgid "" -"In a file named :code:`server.py`, import Flower and FedXgbBagging from " -":code:`flwr.server.strategy`." +"Improved `Strategy` docstrings " +"([#470](https://github.com/adap/flower/pull/470))." msgstr "" -"在名为 :code:`server.py` 的文件中，从 :code:`flwr.server.strategy` 导入 Flower 和 " -"FedXgbBagging。" +"改进了 `Strategy` " +"docstrings（[#470](https://github.com/adap/flower/pull/470)）。" -#: ../../source/tutorial-quickstart-xgboost.rst:316 -msgid "We first define a strategy for XGBoost bagging aggregation." -msgstr "我们首先定义了 XGBoost bagging聚合策略。" +#: ../../source/ref-example-projects.rst:2 +msgid "Example projects" +msgstr "项目实例" -#: ../../source/tutorial-quickstart-xgboost.rst:339 +#: ../../source/ref-example-projects.rst:4 msgid "" -"We use two clients for this example. An " -":code:`evaluate_metrics_aggregation` function is defined to collect and " -"wighted average the AUC values from clients." +"Flower comes with a number of usage examples. The examples demonstrate " +"how Flower can be used to federate different kinds of existing machine " +"learning pipelines, usually leveraging popular machine learning " +"frameworks such as `PyTorch `_ or `TensorFlow " +"`_." msgstr "" -"本示例使用两个客户端。我们定义了一个 :code:`evaluate_metrics_aggregation` 函数，用于收集客户机的 AUC " -"值并求取平均值。" - -#: ../../source/tutorial-quickstart-xgboost.rst:342 -msgid "Then, we start the server:" -msgstr "然后，我们启动服务器：" - -#: ../../source/tutorial-quickstart-xgboost.rst:354 -msgid "Tree-based bagging aggregation" -msgstr "基于树的bagging聚合" +"Flower 附带了许多使用示例。这些示例演示了如何使用 Flower 联邦不同类型的现有机器学习形式，通常是利用流行的机器学习框架，如 " +"`PyTorch `_ 或 `TensorFlow " +"`_。" -#: ../../source/tutorial-quickstart-xgboost.rst:356 +#: ../../source/ref-example-projects.rst:10 +#, fuzzy msgid "" -"You must be curious about how bagging aggregation works. Let's look into " -"the details." -msgstr "您一定很好奇bagging聚合是如何工作的。让我们来详细了解一下。" +"The following examples are available as standalone projects. Quickstart " +"TensorFlow/Keras ---------------------------" +msgstr "以下示例可作为独立项目使用。" -#: ../../source/tutorial-quickstart-xgboost.rst:358 +#: ../../source/ref-example-projects.rst:14 msgid "" -"In file :code:`flwr.server.strategy.fedxgb_bagging.py`, we define " -":code:`FedXgbBagging` inherited from :code:`flwr.server.strategy.FedAvg`." -" Then, we override the :code:`aggregate_fit`, :code:`aggregate_evaluate` " -"and :code:`evaluate` methods as follows:" -msgstr "" -"在文件 :code:`flwr.server.strategy.fedxgb_bagging.py`中，我们定义了从 " -":code:`flwr.server.strategy.FedAvg`继承的 :code:`FedXgbBagging`。然后，我们覆盖 " -":code:`aggregate_fit`、:code:`aggregate_evaluate` 和 :code:`evaluate` 方法如下：" +"The TensorFlow/Keras quickstart example shows CIFAR-10 image " +"classification with MobileNetV2:" +msgstr "TensorFlow/Keras 快速入门示例展示了使用 MobileNetV2 进行的 CIFAR-10 图像分类：" -#: ../../source/tutorial-quickstart-xgboost.rst:454 +#: ../../source/ref-example-projects.rst:17 msgid "" -"In :code:`aggregate_fit`, we sequentially aggregate the clients' XGBoost " -"trees by calling :code:`aggregate()` function:" +"`Quickstart TensorFlow (Code) " +"`_" msgstr "" -"在 :code:`aggregate_fit` 中，我们通过调用 :code:`aggregate()` 函数，按顺序聚合客户端的 XGBoost" -" 树：" +"`TensorFlow快速入门 (代码) `_" -#: ../../source/tutorial-quickstart-xgboost.rst:513 -msgid "" -"In this function, we first fetch the number of trees and the number of " -"parallel trees for the current and previous model by calling " -":code:`_get_tree_nums`. Then, the fetched information will be aggregated." -" After that, the trees (containing model weights) are aggregated to " -"generate a new tree model." +#: ../../source/ref-example-projects.rst:18 +#, fuzzy +msgid ":doc:`Quickstart TensorFlow (Tutorial) `" msgstr "" -"在该函数中，我们首先通过调用 :code:`_get_tree_nums` " -"获取当前模型和上一个模型的树数和并行树数。然后，对获取的信息进行聚合。然后，聚合树（包含模型参数）生成新的树模型。" +"`TensorFlow快速入门 (教程) `_" -#: ../../source/tutorial-quickstart-xgboost.rst:518 +#: ../../source/ref-example-projects.rst:19 msgid "" -"After traversal of all clients' models, a new global model is generated, " -"followed by the serialisation, and sending back to each client." -msgstr "在遍历所有客户端的模型后，会生成一个新的全局模型，然后进行序列化，并发回给每个客户端。" +"`Quickstart TensorFlow (Blog Post) `_" +msgstr "" +"`TensorFlow快速入门 (博客) `_" -#: ../../source/tutorial-quickstart-xgboost.rst:523 -msgid "Launch Federated XGBoost!" -msgstr "启动联邦 XGBoost！" +#: ../../source/ref-example-projects.rst:23 +#: ../../source/tutorial-quickstart-pytorch.rst:5 +msgid "Quickstart PyTorch" +msgstr "PyTorch快速入门" -#: ../../source/tutorial-quickstart-xgboost.rst:585 +#: ../../source/ref-example-projects.rst:25 msgid "" -"Congratulations! You've successfully built and run your first federated " -"XGBoost system. The AUC values can be checked in " -":code:`metrics_distributed`. One can see that the average AUC increases " -"over FL rounds." -msgstr "" -"恭喜您！您已成功构建并运行了第一个联邦 XGBoost 系统。可以在 :code:`metrics_distributed` 中查看 AUC " -"值。我们可以看到，平均 AUC 随 FL 轮数的增加而增加。" +"The PyTorch quickstart example shows CIFAR-10 image classification with a" +" simple Convolutional Neural Network:" +msgstr "PyTorch 快速入门范例展示了使用简单卷积神经网络进行 CIFAR-10 图像分类的情况：" -#: ../../source/tutorial-quickstart-xgboost.rst:590 +#: ../../source/ref-example-projects.rst:28 msgid "" -"The full `source code `_ for this example can be found in :code:`examples" -"/xgboost-quickstart`." +"`Quickstart PyTorch (Code) " +"`_" msgstr "" -"此示例的`完整源代码 `_ 可在 :code:`examples/xgboost-quickstart` 中找到。" - -#: ../../source/tutorial-quickstart-xgboost.rst:594 -msgid "Comprehensive Federated XGBoost" -msgstr "综合的联邦 XGBoost" +"`PyTorch快速入门 (代码) `_" -#: ../../source/tutorial-quickstart-xgboost.rst:596 +#: ../../source/ref-example-projects.rst:29 #, fuzzy -msgid "" -"Now that you have known how federated XGBoost work with Flower, it's time" -" to run some more comprehensive experiments by customising the " -"experimental settings. In the xgboost-comprehensive example (`full code " -"`_), we provide more options to define various experimental" -" setups, including aggregation strategies, data partitioning and " -"centralised/distributed evaluation. We also support :doc:`Flower " -"simulation ` making it easy to simulate large " -"client cohorts in a resource-aware manner. Let's take a look!" +msgid ":doc:`Quickstart PyTorch (Tutorial) `" msgstr "" -"既然您已经知道联合 XGBoost 如何与 Flower 协同工作，那么现在就该通过自定义实验设置来运行一些更综合的实验了。在 xgboost-" -"comprehensive 示例 (`完整代码 " -"`_)中，我们提供了更多选项来定义各种实验设置，包括数据分区和集中/分布式评估。让我们一起来看看！" +"`PyTorch快速入门 (教程) `_" -#: ../../source/tutorial-quickstart-xgboost.rst:603 -#, fuzzy -msgid "Cyclic training" -msgstr "集中式训练" +#: ../../source/ref-example-projects.rst:33 +msgid "PyTorch: From Centralized To Federated" +msgstr "PyTorch： 从集中式到联邦式" -#: ../../source/tutorial-quickstart-xgboost.rst:605 +#: ../../source/ref-example-projects.rst:35 msgid "" -"In addition to bagging aggregation, we offer a cyclic training scheme, " -"which performs FL in a client-by-client fashion. Instead of aggregating " -"multiple clients, there is only one single client participating in the " -"training per round in the cyclic training scenario. The trained local " -"XGBoost trees will be passed to the next client as an initialised model " -"for next round's boosting." -msgstr "" +"This example shows how a regular PyTorch project can be federated using " +"Flower:" +msgstr "本例展示了如何使用 Flower 联邦化一个普通的 PyTorch 项目：" -#: ../../source/tutorial-quickstart-xgboost.rst:609 +#: ../../source/ref-example-projects.rst:37 msgid "" -"To do this, we first customise a :code:`ClientManager` in " -":code:`server_utils.py`:" +"`PyTorch: From Centralized To Federated (Code) " +"`_" msgstr "" +"PyTorch： 从集中式到联邦式（代码） `_" -#: ../../source/tutorial-quickstart-xgboost.rst:649 +#: ../../source/ref-example-projects.rst:38 +#, fuzzy msgid "" -"The customised :code:`ClientManager` samples all available clients in " -"each FL round based on the order of connection to the server. Then, we " -"define a new strategy :code:`FedXgbCyclic` in " -":code:`flwr.server.strategy.fedxgb_cyclic.py`, in order to sequentially " -"select only one client in given round and pass the received model to next" -" client." +":doc:`PyTorch: From Centralized To Federated (Tutorial) `" msgstr "" +"PyTorch： 从集中式到联邦式（教程） `_" -#: ../../source/tutorial-quickstart-xgboost.rst:690 +#: ../../source/ref-example-projects.rst:42 +msgid "Federated Learning on Raspberry Pi and Nvidia Jetson" +msgstr "树莓派和 Nvidia Jetson 上的联邦学习" + +#: ../../source/ref-example-projects.rst:44 msgid "" -"Unlike the original :code:`FedAvg`, we don't perform aggregation here. " -"Instead, we just make a copy of the received client model as global model" -" by overriding :code:`aggregate_fit`." -msgstr "" +"This example shows how Flower can be used to build a federated learning " +"system that run across Raspberry Pi and Nvidia Jetson:" +msgstr "本示例展示了如何利用 Flower 建立一个跨 Raspberry Pi 和 Nvidia Jetson 运行的联邦学习系统：" -#: ../../source/tutorial-quickstart-xgboost.rst:693 +#: ../../source/ref-example-projects.rst:46 msgid "" -"Also, the customised :code:`configure_fit` and :code:`configure_evaluate`" -" methods ensure the clients to be sequentially selected given FL round:" +"`Federated Learning on Raspberry Pi and Nvidia Jetson (Code) " +"`_" msgstr "" +"Raspberry Pi 和 Nvidia Jetson 上的联邦学习（代码） " +"`_" -#: ../../source/tutorial-quickstart-xgboost.rst:757 -msgid "Customised data partitioning" -msgstr "定制数据分区" - -#: ../../source/tutorial-quickstart-xgboost.rst:759 +#: ../../source/ref-example-projects.rst:47 msgid "" -"In :code:`dataset.py`, we have a function :code:`instantiate_partitioner`" -" to instantiate the data partitioner based on the given " -":code:`num_partitions` and :code:`partitioner_type`. Currently, we " -"provide four supported partitioner type to simulate the uniformity/non-" -"uniformity in data quantity (uniform, linear, square, exponential)." +"`Federated Learning on Raspberry Pi and Nvidia Jetson (Blog Post) " +"`_" msgstr "" -"在 :code:`dataset.py` 中，我们有一个函数 :code:`instantiate_partitioner` 来根据给定的 " -":code:`num_partitions` 和 :code:`partitioner_type` " -"来实例化数据分区器。目前，我们提供四种支持的分区器类型（均匀、线性、正方形、指数）来模拟数据量的均匀性/非均匀性。" +"Raspberry Pi和 Nvidia Jetson 上的联邦学习（博客） " +"`_" -#: ../../source/tutorial-quickstart-xgboost.rst:790 -msgid "Customised centralised/distributed evaluation" -msgstr "定制的集中/分布式评估" +#: ../../source/ref-faq.rst:4 +msgid "" +"This page collects answers to commonly asked questions about Federated " +"Learning with Flower." +msgstr "本页收集了有关 \"Flower 联邦学习 \"常见问题的答案。" -#: ../../source/tutorial-quickstart-xgboost.rst:792 +#: ../../source/ref-faq.rst #, fuzzy +msgid ":fa:`eye,mr-1` Can Flower run on Jupyter Notebooks / Google Colab?" +msgstr ":fa:`eye,mr-1` Flower 可以在 Juptyter Notebooks / Google Colab 上运行吗？" + +#: ../../source/ref-faq.rst:8 msgid "" -"To facilitate centralised evaluation, we define a function in " -":code:`server_utils.py`:" -msgstr "为便于集中评估，我们在 :code:`server.py` 中定义了一个函数：" +"Yes, it can! Flower even comes with a few under-the-hood optimizations to" +" make it work even better on Colab. Here's a quickstart example:" +msgstr "是的，它可以！Flower 甚至还进行了一些底层优化，使其在 Colab 上运行得更好。下面是一个快速启动示例：" -#: ../../source/tutorial-quickstart-xgboost.rst:824 +#: ../../source/ref-faq.rst:10 msgid "" -"This function returns a evaluation function which instantiates a " -":code:`Booster` object and loads the global model weights to it. The " -"evaluation is conducted by calling :code:`eval_set()` method, and the " -"tested AUC value is reported." +"`Flower simulation PyTorch " +"`_" msgstr "" -"此函数返回一个评估函数，该函数实例化一个 :code:`Booster` 对象，并向其加载全局模型参数。评估通过调用 " -":code:`eval_set()` 方法进行，并报告测试的 AUC 值。" +"`Flower 模拟 PyTorch " +"`_" -#: ../../source/tutorial-quickstart-xgboost.rst:827 -#, fuzzy +#: ../../source/ref-faq.rst:11 msgid "" -"As for distributed evaluation on the clients, it's same as the quick-" -"start example by overriding the :code:`evaluate()` method insides the " -":code:`XgbClient` class in :code:`client_utils.py`." +"`Flower simulation TensorFlow/Keras " +"`_" msgstr "" -"至于客户端上的分布式评估，与快速启动示例相同，通过覆盖 :code:`client.py` 中 :code:`XgbClient` 类内部的 " -":code:`evaluate()` 方法。" +"`Flower模拟TensorFlow/Keras " +"`_" -#: ../../source/tutorial-quickstart-xgboost.rst:831 -#, fuzzy -msgid "Flower simulation" -msgstr "运行模拟" +#: ../../source/ref-faq.rst +msgid ":fa:`eye,mr-1` How can I run Federated Learning on a Raspberry Pi?" +msgstr ":fa:`eye,mr-1` 如何在 Raspberry Pi 上运行联邦学习？" -#: ../../source/tutorial-quickstart-xgboost.rst:832 +#: ../../source/ref-faq.rst:15 msgid "" -"We also provide an example code (:code:`sim.py`) to use the simulation " -"capabilities of Flower to simulate federated XGBoost training on either a" -" single machine or a cluster of machines." +"Find the `blog post about federated learning on embedded device here " +"`_" +" and the corresponding `GitHub code example " +"`_." msgstr "" +"请点击此处查看有关嵌入式设备联邦学习的 " +"\"博文\"`_和相应的" +" \"GitHub 代码示例\"`_。" -#: ../../source/tutorial-quickstart-xgboost.rst:866 +#: ../../source/ref-faq.rst +msgid ":fa:`eye,mr-1` Does Flower support federated learning on Android devices?" +msgstr ":fa:`eye,mr-1` Flower 是否支持安卓设备上的联邦学习？" + +#: ../../source/ref-faq.rst:19 msgid "" -"After importing all required packages, we define a :code:`main()` " -"function to perform the simulation process:" +"Yes, it does. Please take a look at our `blog post " +"`_ or check out the code examples:" msgstr "" +"是的，确实如此。请查看我们的 \"博客文章 `_\" 或查看代码示例：" -#: ../../source/tutorial-quickstart-xgboost.rst:921 +#: ../../source/ref-faq.rst:21 msgid "" -"We first load the dataset and perform data partitioning, and the pre-" -"processed data is stored in a :code:`list`. After the simulation begins, " -"the clients won't need to pre-process their partitions again." -msgstr "" +"`Android Kotlin example `_" +msgstr "`Android Kotlin 示例 `_" -#: ../../source/tutorial-quickstart-xgboost.rst:924 -msgid "Then, we define the strategies and other hyper-parameters:" -msgstr "" +#: ../../source/ref-faq.rst:22 +msgid "`Android Java example `_" +msgstr "Android Java 示例 `_" -#: ../../source/tutorial-quickstart-xgboost.rst:975 +#: ../../source/ref-faq.rst +msgid ":fa:`eye,mr-1` Can I combine federated learning with blockchain?" +msgstr ":fa:`eye,mr-1` 我可以将联邦学习与区块链结合起来吗？" + +#: ../../source/ref-faq.rst:26 msgid "" -"After that, we start the simulation by calling " -":code:`fl.simulation.start_simulation`:" -msgstr "" +"Yes, of course. A list of available examples using Flower within a " +"blockchain environment is available here:" +msgstr "当然可以。有关在区块链环境中使用 Flower 的可用示例列表，请点击此处：" -#: ../../source/tutorial-quickstart-xgboost.rst:995 +#: ../../source/ref-faq.rst:28 msgid "" -"One of key parameters for :code:`start_simulation` is :code:`client_fn` " -"which returns a function to construct a client. We define it as follows:" +"`Flower meets Nevermined GitHub Repository `_." msgstr "" +"`Flower meets Nevermined GitHub Repository `_." -#: ../../source/tutorial-quickstart-xgboost.rst:1038 -msgid "Arguments parser" -msgstr "参数解析器" - -#: ../../source/tutorial-quickstart-xgboost.rst:1040 -#, fuzzy +#: ../../source/ref-faq.rst:29 msgid "" -"In :code:`utils.py`, we define the arguments parsers for clients, server " -"and simulation, allowing users to specify different experimental " -"settings. Let's first see the sever side:" -msgstr "在 :code:`utils.py` 中，我们定义了客户端和服务器端的参数解析器，允许用户指定不同的实验设置。让我们先看看服务器端：" +"`Flower meets Nevermined YouTube video " +"`_." +msgstr "" +"`Flower meets Nevermined YouTube 视频 " +"`_." -#: ../../source/tutorial-quickstart-xgboost.rst:1086 +#: ../../source/ref-faq.rst:30 #, fuzzy msgid "" -"This allows user to specify training strategies / the number of total " -"clients / FL rounds / participating clients / clients for evaluation, and" -" evaluation fashion. Note that with :code:`--centralised-eval`, the sever" -" will do centralised evaluation and all functionalities for client " -"evaluation will be disabled." +"`Flower meets KOSMoS `_." msgstr "" -"这允许用户指定总客户数/FL 轮数/参与客户数/评估客户数以及评估方式。请注意，如果使用 :code:`--centralised-" -"eval`，服务器将进行集中评估，客户端评估的所有功能将被禁用。" - -#: ../../source/tutorial-quickstart-xgboost.rst:1090 -msgid "Then, the argument parser on client side:" -msgstr "然后是客户端的参数解析器：" +"`Flower meets KOSMoS `_." -#: ../../source/tutorial-quickstart-xgboost.rst:1144 -#, fuzzy +#: ../../source/ref-faq.rst:31 msgid "" -"This defines various options for client data partitioning. Besides, " -"clients also have an option to conduct evaluation on centralised test set" -" by setting :code:`--centralised-eval`, as well as an option to perform " -"scaled learning rate based on the number of clients by setting :code" -":`--scaled-lr`." -msgstr "这定义了客户端数据分区的各种选项。此外，通过设置 :code:`-centralised-eval`，客户端还可以选择在集中测试集上进行评估。" - -#: ../../source/tutorial-quickstart-xgboost.rst:1148 -msgid "We also have an argument parser for simulation:" +"`Flower meets Talan blog post `_ ." msgstr "" +"`Flower meets Talan博文 `_ 。" -#: ../../source/tutorial-quickstart-xgboost.rst:1226 -msgid "This integrates all arguments for both client and server sides." +#: ../../source/ref-faq.rst:32 +msgid "" +"`Flower meets Talan GitHub Repository " +"`_ ." msgstr "" +"`Flower meets Talan GitHub Repository " +"`_ ." -#: ../../source/tutorial-quickstart-xgboost.rst:1229 -msgid "Example commands" -msgstr "命令示例" +#: ../../source/ref-telemetry.md:1 +msgid "Telemetry" +msgstr "遥测功能" -#: ../../source/tutorial-quickstart-xgboost.rst:1231 -#, fuzzy +#: ../../source/ref-telemetry.md:3 msgid "" -"To run a centralised evaluated experiment with bagging strategy on 5 " -"clients with exponential distribution for 50 rounds, we first start the " -"server as below:" -msgstr "为了在 5 个客户端上进行 50 轮指数分布的集中评估实验，我们首先启动服务器，如下所示：" - -#: ../../source/tutorial-quickstart-xgboost.rst:1238 -msgid "Then, on each client terminal, we start the clients:" -msgstr "然后，我们在每个客户终端上启动客户机：" - -#: ../../source/tutorial-quickstart-xgboost.rst:1244 -msgid "To run the same experiment with Flower simulation:" +"The Flower open-source project collects **anonymous** usage metrics to " +"make well-informed decisions to improve Flower. Doing this enables the " +"Flower team to understand how Flower is used and what challenges users " +"might face." msgstr "" +"Flower 开源项目收集**匿名**使用指标，以便在充分知情的情况下做出改进 Flower 的决定。这样做能让 Flower 团队了解 " +"Flower 的使用情况以及用户可能面临的挑战。" -#: ../../source/tutorial-quickstart-xgboost.rst:1250 -#, fuzzy +#: ../../source/ref-telemetry.md:5 msgid "" -"The full `code `_ for this comprehensive example can be found in" -" :code:`examples/xgboost-comprehensive`." -msgstr "" -"此综合示例的全部`源代码 `_ 可在 :code:`examples/xgboost-comprehensive` 中找到。" - -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:9 -msgid "Build a strategy from scratch" -msgstr "从零开始制定策略" +"**Flower is a friendly framework for collaborative AI and data science.**" +" Staying true to this statement, Flower makes it easy to disable " +"telemetry for users that do not want to share anonymous usage metrics." +msgstr "**Flower 是一个用于协作式人工智能和数据科学的友好框架。** Flower 遵循这一声明，让不想分享匿名使用指标的用户可以轻松禁用遥测技术。" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:11 +#: ../../source/ref-telemetry.md:7 +msgid "Principles" +msgstr "原则" + +#: ../../source/ref-telemetry.md:9 +msgid "We follow strong principles guarding anonymous usage metrics collection:" +msgstr "我们遵循严格的匿名使用指标收集原则：" + +#: ../../source/ref-telemetry.md:11 msgid "" -"Welcome to the third part of the Flower federated learning tutorial. In " -"previous parts of this tutorial, we introduced federated learning with " -"PyTorch and Flower (`part 1 `__) and we learned how strategies " -"can be used to customize the execution on both the server and the clients" -" (`part 2 `__)." -msgstr "" -"欢迎来到 Flower 联邦学习教程的第三部分。在本教程的前几部分，我们介绍了 PyTorch 和 Flower 的联邦学习（`part 1 " -"`__），并学习了如何使用策略来定制服务器和客户端的执行（`part 2 " -"`__）。" +"**Optional:** You will always be able to disable telemetry; read on to " +"learn “[How to opt-out](#how-to-opt-out)”." +msgstr "**可选：** 您始终可以禁用遥测功能；请继续阅读\"[如何退出](#how-to-opt-out)\"。" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:13 +#: ../../source/ref-telemetry.md:12 msgid "" -"In this notebook, we'll continue to customize the federated learning " -"system we built previously by creating a custom version of FedAvg (again," -" using `Flower `__ and `PyTorch " -"`__)." +"**Anonymous:** The reported usage metrics are anonymous and do not " +"contain any personally identifiable information (PII). See “[Collected " +"metrics](#collected-metrics)” to understand what metrics are being " +"reported." msgstr "" -"在本笔记中，我们将通过创建 FedAvg 的自定义版本（再次使用 `Flower `__ 和 " -"`PyTorch `__），继续定制我们之前构建的联邦学习系统。" +"**匿名：** 报告的使用指标是匿名的，不包含任何个人身份信息 (PII)。请参阅\"[收集的指标](#collected-metrics) " +"\"了解报告的指标。" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:15 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:16 -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:15 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:15 +#: ../../source/ref-telemetry.md:13 msgid "" -"`Star Flower on GitHub `__ ⭐️ and join " -"the Flower community on Slack to connect, ask questions, and get help: " -"`Join Slack `__ 🌼 We'd love to hear from " -"you in the ``#introductions`` channel! And if anything is unclear, head " -"over to the ``#questions`` channel." +"**Transparent:** You can easily inspect what anonymous metrics are being " +"reported; see the section “[How to inspect what is being reported](#how-" +"to-inspect-what-is-being-reported)”" msgstr "" -"`Star Flower on GitHub `__ ⭐️ 并加入 Slack " -"上的 Flower 社区，进行交流、提问并获得帮助： 加入 Slack `__ 🌼 " -"我们希望在 ``#introductions`` 频道听到您的声音！如果有任何不清楚的地方，请访问 ``#questions`` 频道。" - -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:17 -msgid "Let's build a new ``Strategy`` from scratch!" -msgstr "让我们从头开始构建一个新的``Strategy``！" - -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:29 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:29 -msgid "Preparation" -msgstr "准备工作" +"**透明：** 您可以轻松查看正在报告的匿名指标；请参阅\"[如何查看正在报告的指标]（#how-to-inspect-what-is-" +"being-reported）\"部分" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:31 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:32 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:31 +#: ../../source/ref-telemetry.md:14 msgid "" -"Before we begin with the actual code, let's make sure that we have " -"everything we need." -msgstr "在开始实际代码之前，让我们先确保我们已经准备好了所需的一切。" - -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:43 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:44 -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:43 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:43 -msgid "Installing dependencies" -msgstr "安装依赖项" - -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:45 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:46 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:45 -msgid "First, we install the necessary packages:" -msgstr "首先，我们安装必要的软件包：" +"**Open for feedback:** You can always reach out to us if you have " +"feedback; see the section “[How to contact us](#how-to-contact-us)” for " +"details." +msgstr "**欢迎反馈：** 如果您有反馈意见，可以随时联系我们；详情请参见\"[如何联系我们](#how-to-contact-us) \"部分。" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:65 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:66 -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:65 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:65 -msgid "" -"Now that we have all dependencies installed, we can import everything we " -"need for this tutorial:" -msgstr "现在我们已经安装了所有依赖项，可以导入本教程所需的所有内容：" +#: ../../source/ref-telemetry.md:16 +msgid "How to opt-out" +msgstr "如何退出" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:101 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:102 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:101 +#: ../../source/ref-telemetry.md:18 msgid "" -"It is possible to switch to a runtime that has GPU acceleration enabled " -"(on Google Colab: ``Runtime > Change runtime type > Hardware acclerator: " -"GPU > Save``). Note, however, that Google Colab is not always able to " -"offer GPU acceleration. If you see an error related to GPU availability " -"in one of the following sections, consider switching back to CPU-based " -"execution by setting ``DEVICE = torch.device(\"cpu\")``. If the runtime " -"has GPU acceleration enabled, you should see the output ``Training on " -"cuda``, otherwise it'll say ``Training on cpu``." +"When Flower starts, it will check for an environment variable called " +"`FLWR_TELEMETRY_ENABLED`. Telemetry can easily be disabled by setting " +"`FLWR_TELEMETRY_ENABLED=0`. Assuming you are starting a Flower server or " +"client, simply do so by prepending your command as in:" msgstr "" -"可以切换到已启用 GPU 加速的运行时（在 Google Colab 上： 运行时 > 更改运行时类型 > 硬件加速： GPU > " -"保存``）。但请注意，Google Colab 并非总能提供 GPU 加速。如果在以下部分中看到与 GPU 可用性相关的错误，请考虑通过设置 " -"``DEVICE = torch.device(\"cpu\")`` 切回基于 CPU 的执行。如果运行时已启用 GPU " -"加速，你应该会看到输出``Training on cuda``，否则会显示``Training on cpu``。" - -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:114 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:115 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:114 -msgid "Data loading" -msgstr "数据加载" +"Flower 启动时，会检查环境变量 `FLWR_TELEMETRY_ENABLED` 是否存在。通过设置 " +"`FLWR_TELEMETRY_ENABLED=0` 可以轻松禁用遥测功能。假设你启动的是 Flower " +"服务器或客户端，只需在命令前添加以下内容即可：" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:116 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:116 +#: ../../source/ref-telemetry.md:24 msgid "" -"Let's now load the CIFAR-10 training and test set, partition them into " -"ten smaller datasets (each split into training and validation set), and " -"wrap everything in their own ``DataLoader``. We introduce a new parameter" -" ``num_clients`` which allows us to call ``load_datasets`` with different" -" numbers of clients." +"Alternatively, you can export `FLWR_TELEMETRY_ENABLED=0` in, for example," +" `.bashrc` (or whatever configuration file applies to your environment) " +"to disable Flower telemetry permanently." msgstr "" -"现在，让我们加载 CIFAR-10 训练集和测试集，将它们分割成 10 " -"个较小的数据集（每个数据集又分为训练集和验证集），并将所有数据都封装在各自的 ``DataLoader`` 中。我们引入了一个新参数 " -"``num_clients``，它允许我们使用不同数量的客户端调用 ``load_datasets``。" +"或者，你也可以在 `.bashrc`（或任何适用于你的环境的配置文件）中导出 `FLWR_TELEMETRY_ENABLED=0` 来永久禁用 " +"Flower telemetry。" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:167 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:168 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:167 -msgid "Model training/evaluation" -msgstr "模型培训/评估" +#: ../../source/ref-telemetry.md:26 +msgid "Collected metrics" +msgstr "收集的指标" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:169 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:170 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:169 +#: ../../source/ref-telemetry.md:28 +msgid "Flower telemetry collects the following metrics:" +msgstr "Flower 遥测技术收集以下指标：" + +#: ../../source/ref-telemetry.md:30 msgid "" -"Let's continue with the usual model definition (including " -"``set_parameters`` and ``get_parameters``), training and test functions:" -msgstr "让我们继续使用常见的模型定义（包括 `set_parameters` 和 `get_parameters`）、训练和测试函数：" +"**Flower version.** Understand which versions of Flower are currently " +"being used. This helps us to decide whether we should invest effort into " +"releasing a patch version for an older version of Flower or instead use " +"the bandwidth to build new features." +msgstr "**了解目前使用的 Flower 版本。这有助于我们决定是否应该投入精力为旧版本的 Flower 发布补丁版本，还是利用带宽来构建新功能。" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:258 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:258 -msgid "Flower client" -msgstr "Flower 客户端" +#: ../../source/ref-telemetry.md:32 +msgid "" +"**Operating system.** Enables us to answer questions such as: *Should we " +"create more guides for Linux, macOS, or Windows?*" +msgstr "**操作系统**使我们能够回答以下问题： *我们应该为 Linux、macOS 还是 Windows 创建更多指南？*" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:260 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:260 +#: ../../source/ref-telemetry.md:34 msgid "" -"To implement the Flower client, we (again) create a subclass of " -"``flwr.client.NumPyClient`` and implement the three methods " -"``get_parameters``, ``fit``, and ``evaluate``. Here, we also pass the " -"``cid`` to the client and use it log additional details:" -msgstr "" -"为了实现 Flower 客户端，我们（再次）创建了 ``flwr.client.NumPyClient`` 的子类，并实现了 " -"``get_parameters``、``fit`` 和 ``evaluate``三个方法。在这里，我们还将 ``cid`` " -"传递给客户端，并使用它记录其他详细信息：" +"**Python version.** Knowing the Python version helps us, for example, to " +"decide whether we should invest effort into supporting old versions of " +"Python or stop supporting them and start taking advantage of new Python " +"features." +msgstr "**例如，了解 Python 版本有助于我们决定是否应该投入精力支持旧版本的 Python，还是停止支持这些版本并开始利用新的 Python 功能。" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:308 -msgid "Let's test what we have so far before we continue:" -msgstr "在继续之前，让我们先测试一下我们目前掌握的情况：" +#: ../../source/ref-telemetry.md:36 +msgid "" +"**Hardware properties.** Understanding the hardware environment that " +"Flower is being used in helps to decide whether we should, for example, " +"put more effort into supporting low-resource environments." +msgstr "**硬件属性** 了解 Flower 的硬件使用环境，有助于决定我们是否应在支持低资源环境等方面投入更多精力。" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:339 -msgid "Build a Strategy from scratch" -msgstr "从零开始构建策略" +#: ../../source/ref-telemetry.md:38 +msgid "" +"**Execution mode.** Knowing what execution mode Flower starts in enables " +"us to understand how heavily certain features are being used and better " +"prioritize based on that." +msgstr "** 执行模式** 了解 Flower 的启动执行模式，能让我们了解某些功能的使用率，并据此更好地确定优先级。" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:341 +#: ../../source/ref-telemetry.md:40 msgid "" -"Let’s overwrite the ``configure_fit`` method such that it passes a higher" -" learning rate (potentially also other hyperparameters) to the optimizer " -"of a fraction of the clients. We will keep the sampling of the clients as" -" it is in ``FedAvg`` and then change the configuration dictionary (one of" -" the ``FitIns`` attributes)." +"**Cluster.** Flower telemetry assigns a random in-memory cluster ID each " +"time a Flower workload starts. This allows us to understand which device " +"types not only start Flower workloads but also successfully complete " +"them." msgstr "" -"让我们重写 ``configure_fit`` 方法，使其向一部分客户的优化器传递更高的学习率（可能还有其他超参数）。我们将保持 " -"``FedAvg`` 中的客户端采样，然后更改配置字典（``FitIns`` 属性之一）。" +"**每次 Flower 工作负载启动时，Flower 遥测都会随机分配一个内存集群 ID。这样，我们就能了解哪些设备类型不仅启动了 Flower " +"工作负载，而且还成功完成了它们。" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:507 +#: ../../source/ref-telemetry.md:42 msgid "" -"The only thing left is to use the newly created custom Strategy " -"``FedCustom`` when starting the experiment:" -msgstr "剩下的唯一工作就是在启动实验时使用新创建的自定义策略 ``FedCustom`` ：" +"**Source.** Flower telemetry tries to store a random source ID in " +"`~/.flwr/source` the first time a telemetry event is generated. The " +"source ID is important to identify whether an issue is recurring or " +"whether an issue is triggered by multiple clusters running concurrently " +"(which often happens in simulation). For example, if a device runs " +"multiple workloads at the same time, and this results in an issue, then, " +"in order to reproduce the issue, multiple workloads must be started at " +"the same time." +msgstr "" +"**Source.** Flower 遥测会在第一次生成遥测事件时，尝试在 `~/.flwr/source` 中存储一个随机源 ID。源 ID " +"对于识别问题是否反复出现或问题是否由多个集群同时运行触发（这在模拟中经常发生）非常重要。例如，如果设备同时运行多个工作负载并导致问题，那么为了重现问题，必须同时启动多个工作负载。" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:534 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:932 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:697 -msgid "Recap" -msgstr "回顾" - -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:536 +#: ../../source/ref-telemetry.md:44 msgid "" -"In this notebook, we’ve seen how to implement a custom strategy. A custom" -" strategy enables granular control over client node configuration, result" -" aggregation, and more. To define a custom strategy, you only have to " -"overwrite the abstract methods of the (abstract) base class ``Strategy``." -" To make custom strategies even more powerful, you can pass custom " -"functions to the constructor of your new class (``__init__``) and then " -"call these functions whenever needed." +"You may delete the source ID at any time. If you wish for all events " +"logged under a specific source ID to be deleted, you can send a deletion " +"request mentioning the source ID to `telemetry@flower.ai`. All events " +"related to that source ID will then be permanently deleted." msgstr "" -"在本笔记中，我们了解了如何实施自定义策略。自定义策略可以对客户端节点配置、结果聚合等进行细粒度控制。要定义自定义策略，只需覆盖（抽象）基类 " -"``Strategy`` " -"的抽象方法即可。为使自定义策略更加强大，您可以将自定义函数传递给新类的构造函数（`__init__``），然后在需要时调用这些函数。" +"您可以随时删除源 ID。如果您希望删除特定源 ID 下记录的所有事件，可以向 `telemetry@flower.ai` 发送删除请求，并提及该源" +" ID。届时，与该源 ID 相关的所有事件都将被永久删除。" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:550 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:948 -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:729 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:715 -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:369 +#: ../../source/ref-telemetry.md:46 msgid "" -"Before you continue, make sure to join the Flower community on Slack: " -"`Join Slack `__" +"We will not collect any personally identifiable information. If you think" +" any of the metrics collected could be misused in any way, please [get in" +" touch with us](#how-to-contact-us). We will update this page to reflect " +"any changes to the metrics collected and publish changes in the " +"changelog." msgstr "" -"在继续之前，请务必加入 Slack 上的 Flower 社区：`Join Slack `__" +"我们不会收集任何个人身份信息。如果您认为所收集的任何指标可能以任何方式被滥用，请[与我们联系]（#how-to-contact-" +"us）。我们将更新本页面，以反映对所收集指标的任何更改，并在更新日志中公布更改内容。" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:552 -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:950 -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:731 -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:717 -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:371 +#: ../../source/ref-telemetry.md:48 msgid "" -"There's a dedicated ``#questions`` channel if you need help, but we'd " -"also love to hear who you are in ``#introductions``!" -msgstr "如果您需要帮助，我们有专门的 ``#questions`` 频道，但我们也很乐意在 ``#introductions`` 中了解您是谁！" +"If you think other metrics would be helpful for us to better guide our " +"decisions, please let us know! We will carefully review them; if we are " +"confident that they do not compromise user privacy, we may add them." +msgstr "如果您认为其他指标有助于我们更好地指导决策，请告诉我们！我们将仔细审查这些指标；如果我们确信它们不会损害用户隐私，我们可能会添加这些指标。" -#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:554 +#: ../../source/ref-telemetry.md:50 +msgid "How to inspect what is being reported" +msgstr "如何检查报告中的内容" + +#: ../../source/ref-telemetry.md:52 msgid "" -"The `Flower Federated Learning Tutorial - Part 4 " -"`__ introduces ``Client``, the flexible API underlying " -"``NumPyClient``." +"We wanted to make it very easy for you to inspect what anonymous usage " +"metrics are reported. You can view all the reported telemetry information" +" by setting the environment variable `FLWR_TELEMETRY_LOGGING=1`. Logging " +"is disabled by default. You may use logging independently from " +"`FLWR_TELEMETRY_ENABLED` so that you can inspect the telemetry feature " +"without sending any metrics." msgstr "" -"Flower联邦学习教程 - 第4部分 `__ 介绍了``Client``，它是``NumPyClient``底层的灵活应用程序接口。" - -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:9 -msgid "Customize the client" -msgstr "自定义客户端" +"我们希望能让您轻松查看所报告的匿名使用指标。通过设置环境变量 `FLWR_TELEMETRY_LOGGING=1` " +"可以查看所有报告的遥测信息。日志记录默认为禁用。您可以不使用 `FLWR_TELEMETRY_ENABLED` " +"而单独使用日志记录，这样就可以在不发送任何指标的情况下检查遥测功能。" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:11 +#: ../../source/ref-telemetry.md:58 msgid "" -"Welcome to the fourth part of the Flower federated learning tutorial. In " -"the previous parts of this tutorial, we introduced federated learning " -"with PyTorch and Flower (`part 1 `__), we learned how " -"strategies can be used to customize the execution on both the server and " -"the clients (`part 2 `__), and we built our own " -"custom strategy from scratch (`part 3 `__)." -msgstr "" -"欢迎来到 Flower 联邦学习教程的第四部分。在本教程的前几部分中，我们介绍了 PyTorch 和 Flower 的联邦学习（`part 1 " -"`__），了解了如何使用策略来定制服务器和客户端的执行（`part 2 " -"`__），并从头开始构建了我们自己的定制策略（`part 3 " -"`__）。" +"The inspect Flower telemetry without sending any anonymous usage metrics," +" use both environment variables:" +msgstr "在不发送任何匿名使用指标的情况下检查 Flower 遥测，可使用这两个环境变量：" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:14 +#: ../../source/ref-telemetry.md:64 +msgid "How to contact us" +msgstr "如何联系我们" + +#: ../../source/ref-telemetry.md:66 msgid "" -"In this notebook, we revisit ``NumPyClient`` and introduce a new " -"baseclass for building clients, simply named ``Client``. In previous " -"parts of this tutorial, we've based our client on ``NumPyClient``, a " -"convenience class which makes it easy to work with machine learning " -"libraries that have good NumPy interoperability. With ``Client``, we gain" -" a lot of flexibility that we didn't have before, but we'll also have to " -"do a few things the we didn't have to do before." +"We want to hear from you. If you have any feedback or ideas on how to " +"improve the way we handle anonymous usage metrics, reach out to us via " +"[Slack](https://flower.ai/join-slack/) (channel `#telemetry`) or email " +"(`telemetry@flower.ai`)." msgstr "" -"在本笔记中，我们将重温 ``NumPyClient`` 并引入一个用于构建客户端的新基类，简单命名为 " -"``Client``。在本教程的前几部分中，我们的客户端基于``NumPyClient``，这是一个便捷类，可以让我们轻松地与具有良好 NumPy" -" 互操作性的机器学习库协同工作。有了 ``Client``，我们获得了很多以前没有的灵活性，但我们也必须做一些以前不需要做的事情。" +"我们希望听到您的意见。如果您对如何改进我们处理匿名使用指标的方式有任何反馈或想法，请通过 [Slack](https://flower.ai" +"/join-slack/) （频道 `#telemetry`）或电子邮件 (`telemetry@flower.ai`)与我们联系。" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:18 +#: ../../source/tutorial-quickstart-android.rst:-1 msgid "" -"Let's go deeper and see what it takes to move from ``NumPyClient`` to " -"``Client``!" -msgstr "让我们深入了解一下从 ``NumPyClient`` 到 ``Client`` 的过程！" +"Read this Federated Learning quickstart tutorial for creating an Android " +"app using Flower." +msgstr "阅读本联邦学习快速入门教程，了解如何使用 Flower 创建 Android 应用程序。" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:30 -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:29 -msgid "Step 0: Preparation" -msgstr "步骤 0：准备工作" +#: ../../source/tutorial-quickstart-android.rst:5 +msgid "Quickstart Android" +msgstr "快速入门 Android" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:117 +#: ../../source/tutorial-quickstart-android.rst:10 msgid "" -"Let's now load the CIFAR-10 training and test set, partition them into " -"ten smaller datasets (each split into training and validation set), and " -"wrap everything in their own ``DataLoader``." -msgstr "" -"现在，让我们加载 CIFAR-10 训练集和测试集，将它们分割成十个较小的数据集（每个数据集又分为训练集和验证集），并将所有数据都封装在各自的 " -"``DataLoader`` 中。" - -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:259 -msgid "Step 1: Revisiting NumPyClient" -msgstr "步骤 1：重温 NumPyClient" +"Let's build a federated learning system using TFLite and Flower on " +"Android!" +msgstr "让我们在 Android 上使用 TFLite 和 Flower 构建一个联邦学习系统！" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:261 +#: ../../source/tutorial-quickstart-android.rst:12 msgid "" -"So far, we've implemented our client by subclassing " -"``flwr.client.NumPyClient``. The three methods we implemented are " -"``get_parameters``, ``fit``, and ``evaluate``. Finally, we wrap the " -"creation of instances of this class in a function called ``client_fn``:" +"Please refer to the `full code example " +"`_ to learn " +"more." msgstr "" -"到目前为止，我们通过子类化 ``flwr.client.NumPyClient`` " -"实现了我们的客户端。我们实现了三个方法：``get_parameters``, ``fit`, 和``evaluate``。最后，我们用一个名为 " -"``client_fn`` 的函数来创建该类的实例：" +"请参阅`完整代码示例 " +"`_了解更多信息。" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:309 +#: ../../source/tutorial-quickstart-fastai.rst:-1 msgid "" -"We've seen this before, there's nothing new so far. The only *tiny* " -"difference compared to the previous notebook is naming, we've changed " -"``FlowerClient`` to ``FlowerNumPyClient`` and ``client_fn`` to " -"``numpyclient_fn``. Let's run it to see the output we get:" -msgstr "" -"我们以前见过这种情况，目前没有什么新东西。与之前的笔记相比，唯一*小*的不同是命名，我们把 ``FlowerClient`` 改成了 " -"``FlowerNumPyClient``，把 `client_fn` 改成了 ``numpyclient_fn``。让我们运行它看看输出结果：" +"Check out this Federated Learning quickstart tutorial for using Flower " +"with FastAI to train a vision model on CIFAR-10." +msgstr "查看此联邦学习快速入门教程，了解如何使用 Flower 和 FastAI 在 CIFAR-10 上训练视觉模型。" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:339 -msgid "" -"This works as expected, two clients are training for three rounds of " -"federated learning." -msgstr "结果不出所料，两个客户端正在进行三轮联邦学习训练。" +#: ../../source/tutorial-quickstart-fastai.rst:5 +msgid "Quickstart fastai" +msgstr "快速入门 fastai" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:341 -msgid "" -"Let's dive a little bit deeper and discuss how Flower executes this " -"simulation. Whenever a client is selected to do some work, " -"``start_simulation`` calls the function ``numpyclient_fn`` to create an " -"instance of our ``FlowerNumPyClient`` (along with loading the model and " -"the data)." -msgstr "" -"让我们再深入一点，讨论一下 Flower 是如何执行模拟的。每当一个客户端被选中进行工作时，`start_simulation`` 就会调用函数 " -"`numpyclient_fn` 来创建我们的 ``FlowerNumPyClient`` 实例（同时加载模型和数据）。" +#: ../../source/tutorial-quickstart-fastai.rst:10 +msgid "Let's build a federated learning system using fastai and Flower!" +msgstr "让我们用 fastai 和 Flower 建立一个联邦学习系统！" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:343 +#: ../../source/tutorial-quickstart-fastai.rst:12 msgid "" -"But here's the perhaps surprising part: Flower doesn't actually use the " -"``FlowerNumPyClient`` object directly. Instead, it wraps the object to " -"makes it look like a subclass of ``flwr.client.Client``, not " -"``flwr.client.NumPyClient``. In fact, the Flower core framework doesn't " -"know how to handle ``NumPyClient``'s, it only knows how to handle " -"``Client``'s. ``NumPyClient`` is just a convenience abstraction built on " -"top of ``Client``." +"Please refer to the `full code example " +"`_ " +"to learn more." msgstr "" -"但令人惊讶的部分也许就在这里： Flower 实际上并不直接使用 ``FlowerNumPyClient`` " -"对象。相反，它封装了该对象，使其看起来像 ``flwr.client.Client`` 的子类，而不是 " -"``flwr.client.NumPyClient``。事实上，Flower 核心框架不知道如何处理 " -"``NumPyClient``，它只知道如何处理 ``Client``。``NumPyClient`` " -"只是建立在``Client``之上的便捷抽象类。" +"请参阅 `完整代码示例 `_了解更多信息。" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:345 +#: ../../source/tutorial-quickstart-huggingface.rst:-1 msgid "" -"Instead of building on top of ``NumPyClient``, we can directly build on " -"top of ``Client``." -msgstr "与其在 ``NumPyClient`` 上构建，我们可以直接在 ``Client`` 上构建。" +"Check out this Federating Learning quickstart tutorial for using Flower " +"with HuggingFace Transformers in order to fine-tune an LLM." +msgstr "查看此联邦学习 快速入门教程，了解如何使用 Flower 和 HuggingFace Transformers 来微调 LLM。" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:357 -msgid "Step 2: Moving from ``NumPyClient`` to ``Client``" -msgstr "步骤 2：从 ``NumPyClient`` 移至 ``Client``" +#: ../../source/tutorial-quickstart-huggingface.rst:5 +msgid "Quickstart 🤗 Transformers" +msgstr "🤗 Transformers快速入门" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:359 +#: ../../source/tutorial-quickstart-huggingface.rst:10 msgid "" -"Let's try to do the same thing using ``Client`` instead of " -"``NumPyClient``." -msgstr "让我们尝试使用 ``Client`` 代替 ``NumPyClient`` 做同样的事情。" +"Let's build a federated learning system using Hugging Face Transformers " +"and Flower!" +msgstr "让我们用Hugging Face Transformers和Flower来构建一个联邦学习系统！" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:465 +#: ../../source/tutorial-quickstart-huggingface.rst:12 msgid "" -"Before we discuss the code in more detail, let's try to run it! Gotta " -"make sure our new ``Client``-based client works, right?" -msgstr "在详细讨论代码之前，让我们试着运行它！必须确保我们基于 ``Client`` 的新客户端能正常运行，对吗？" +"We will leverage Hugging Face to federate the training of language models" +" over multiple clients using Flower. More specifically, we will fine-tune" +" a pre-trained Transformer model (distilBERT) for sequence classification" +" over a dataset of IMDB ratings. The end goal is to detect if a movie " +"rating is positive or negative." +msgstr "" +"我们将利用Hugging Face技术，使用 Flower 在多个客户端上联邦训练语言模型。更具体地说，我们将对预先训练好的 " +"Transformer 模型（distilBERT）进行微调，以便在 IMDB 评分数据集上进行序列分类。最终目标是检测电影评分是正面还是负面。" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:490 +#: ../../source/tutorial-quickstart-huggingface.rst:18 +msgid "Dependencies" +msgstr "依赖关系" + +#: ../../source/tutorial-quickstart-huggingface.rst:20 msgid "" -"That's it, we're now using ``Client``. It probably looks similar to what " -"we've done with ``NumPyClient``. So what's the difference?" -msgstr "就是这样，我们现在开始使用 ``Client``。它看起来可能与我们使用 ``NumPyClient`` 所做的类似。那么有什么不同呢？" +"To follow along this tutorial you will need to install the following " +"packages: :code:`datasets`, :code:`evaluate`, :code:`flwr`, " +":code:`torch`, and :code:`transformers`. This can be done using " +":code:`pip`:" +msgstr "" +"要学习本教程，您需要安装以下软件包： :code:`datasets`、 :code:`evaluate`、 :code:`flwr`、 " +":code:`torch`和 :code:`transformers`。这可以通过 :code:`pip` 来完成：" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:492 +#: ../../source/tutorial-quickstart-huggingface.rst:30 +msgid "Standard Hugging Face workflow" +msgstr "标准Hugging Face工作流程" + +#: ../../source/tutorial-quickstart-huggingface.rst:33 +msgid "Handling the data" +msgstr "处理数据" + +#: ../../source/tutorial-quickstart-huggingface.rst:35 msgid "" -"First of all, it's more code. But why? The difference comes from the fact" -" that ``Client`` expects us to take care of parameter serialization and " -"deserialization. For Flower to be able to send parameters over the " -"network, it eventually needs to turn these parameters into ``bytes``. " -"Turning parameters (e.g., NumPy ``ndarray``'s) into raw bytes is called " -"serialization. Turning raw bytes into something more useful (like NumPy " -"``ndarray``'s) is called deserialization. Flower needs to do both: it " -"needs to serialize parameters on the server-side and send them to the " -"client, the client needs to deserialize them to use them for local " -"training, and then serialize the updated parameters again to send them " -"back to the server, which (finally!) deserializes them again in order to " -"aggregate them with the updates received from other clients." +"To fetch the IMDB dataset, we will use Hugging Face's :code:`datasets` " +"library. We then need to tokenize the data and create :code:`PyTorch` " +"dataloaders, this is all done in the :code:`load_data` function:" msgstr "" -"首先，它的代码更多。但为什么呢？区别在于 ``Client`` 希望我们处理参数的序列化和反序列化。Flower " -"要想通过网络发送参数，最终需要将这些参数转化为 ``字节``。把参数（例如 NumPy 的 ``ndarray`` " -"参数）变成原始字节叫做序列化。将原始字节转换成更有用的东西（如 NumPy ``ndarray`）称为反序列化。Flower " -"需要同时做这两件事：它需要在服务器端序列化参数并将其发送到客户端，客户端需要反序列化参数以便将其用于本地训练，然后再次序列化更新后的参数并将其发送回服务器，服务器（最后）再次反序列化参数以便将其与从其他客户端接收到的更新汇总在一起。" +"为了获取 IMDB 数据集，我们将使用 Hugging Face 的 :code:`datasets` 库。然后，我们需要对数据进行标记化，并创建" +" :code:`PyTorch` 数据加载器，这些都将在 :code:`load_data` 函数中完成：" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:495 +#: ../../source/tutorial-quickstart-huggingface.rst:81 +msgid "Training and testing the model" +msgstr "训练和测试模型" + +#: ../../source/tutorial-quickstart-huggingface.rst:83 msgid "" -"The only *real* difference between Client and NumPyClient is that " -"NumPyClient takes care of serialization and deserialization for you. It " -"can do so because it expects you to return parameters as NumPy ndarray's," -" and it knows how to handle these. This makes working with machine " -"learning libraries that have good NumPy support (most of them) a breeze." +"Once we have a way of creating our trainloader and testloader, we can " +"take care of the training and testing. This is very similar to any " +":code:`PyTorch` training or testing loop:" msgstr "" -"Client 与 NumPyClient 之间的唯一**真正区别在于，NumPyClient " -"会为你处理序列化和反序列化。NumPyClient之所以能做到这一点，是因为它预计你会以NumPy " -"ndarray的形式返回参数，而且它知道如何处理这些参数。这使得与具有良好 NumPy 支持的大多数机器学习库一起工作变得轻而易举。" +"有了创建 trainloader 和 testloader 的方法后，我们就可以进行训练和测试了。这与任何 :code:`PyTorch` " +"训练或测试循环都非常相似：" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:497 +#: ../../source/tutorial-quickstart-huggingface.rst:121 +msgid "Creating the model itself" +msgstr "创建模型本身" + +#: ../../source/tutorial-quickstart-huggingface.rst:123 msgid "" -"In terms of API, there's one major difference: all methods in Client take" -" exactly one argument (e.g., ``FitIns`` in ``Client.fit``) and return " -"exactly one value (e.g., ``FitRes`` in ``Client.fit``). The methods in " -"``NumPyClient`` on the other hand have multiple arguments (e.g., " -"``parameters`` and ``config`` in ``NumPyClient.fit``) and multiple return" -" values (e.g., ``parameters``, ``num_example``, and ``metrics`` in " -"``NumPyClient.fit``) if there are multiple things to handle. These " -"``*Ins`` and ``*Res`` objects in ``Client`` wrap all the individual " -"values you're used to from ``NumPyClient``." +"To create the model itself, we will just load the pre-trained distillBERT" +" model using Hugging Face’s :code:`AutoModelForSequenceClassification` :" msgstr "" -"在 API 方面，有一个主要区别：Client 中的所有方法都只接受一个参数（例如，``Client.fit`` 中的 " -"``FitIns``），并只返回一个值（例如，``Client.fit`` 中的 " -"``FitRes``）。另一方面，``NumPyClient``中的方法有多个参数（例如，``NumPyClient.fit``中的``parameters``和``config``）和多个返回值（例如，``NumPyClient.fit``中的``parameters``、``num_example``和``metrics``）。在" -" ``Client`` 中的这些 ``*Ins`` 和 ``*Res`` 对象封装了你在 ``NumPyClient`` 中习惯使用的所有单个值。" +"要创建模型本身，我们只需使用 Hugging Face 的 :code:`AutoModelForSequenceClassification` " +"加载预训练的 distillBERT 模型：" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:510 -msgid "Step 3: Custom serialization" -msgstr "步骤 3：自定义序列化" +#: ../../source/tutorial-quickstart-huggingface.rst:136 +msgid "Federating the example" +msgstr "将示例联邦化" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:512 -msgid "" -"Here we will explore how to implement custom serialization with a simple " -"example." -msgstr "下面我们将通过一个简单的示例来探讨如何实现自定义序列化。" +#: ../../source/tutorial-quickstart-huggingface.rst:139 +msgid "Creating the IMDBClient" +msgstr "创建 IMDBClient" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:514 +#: ../../source/tutorial-quickstart-huggingface.rst:141 msgid "" -"But first what is serialization? Serialization is just the process of " -"converting an object into raw bytes, and equally as important, " -"deserialization is the process of converting raw bytes back into an " -"object. This is very useful for network communication. Indeed, without " -"serialization, you could not just a Python object through the internet." +"To federate our example to multiple clients, we first need to write our " +"Flower client class (inheriting from :code:`flwr.client.NumPyClient`). " +"This is very easy, as our model is a standard :code:`PyTorch` model:" msgstr "" -"首先，什么是序列化？序列化只是将对象转换为原始字节的过程，同样重要的是，反序列化是将原始字节转换回对象的过程。这对网络通信非常有用。事实上，如果没有序列化，你就无法通过互联网传输一个" -" Python 对象。" - -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:516 -msgid "" -"Federated Learning relies heavily on internet communication for training " -"by sending Python objects back and forth between the clients and the " -"server. This means that serialization is an essential part of Federated " -"Learning." -msgstr "通过在客户端和服务器之间来回发送 Python 对象，联合学习在很大程度上依赖于互联网通信进行训练。这意味着序列化是联邦学习的重要组成部分。" +"要将我们的示例联邦到多个客户端，我们首先需要编写 Flower 客户端类（继承自 " +":code:`flwr.client.NumPyClient`）。这很容易，因为我们的模型是一个标准的 :code:`PyTorch` 模型：" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:518 +#: ../../source/tutorial-quickstart-huggingface.rst:169 msgid "" -"In the following section, we will write a basic example where instead of " -"sending a serialized version of our ``ndarray``\\ s containing our " -"parameters, we will first convert the ``ndarray`` into sparse matrices, " -"before sending them. This technique can be used to save bandwidth, as in " -"certain cases where the weights of a model are sparse (containing many 0 " -"entries), converting them to a sparse matrix can greatly improve their " -"bytesize." +"The :code:`get_parameters` function lets the server get the client's " +"parameters. Inversely, the :code:`set_parameters` function allows the " +"server to send its parameters to the client. Finally, the :code:`fit` " +"function trains the model locally for the client, and the " +":code:`evaluate` function tests the model locally and returns the " +"relevant metrics." msgstr "" -"在下面的章节中，我们将编写一个基本示例，在发送包含参数的 ``ndarray`` 前，我们将首先把 ``ndarray`` " -"转换为稀疏矩阵，而不是发送序列化版本。这种技术可以用来节省带宽，因为在某些情况下，模型的参数是稀疏的（包含许多 0 " -"条目），将它们转换成稀疏矩阵可以大大提高它们的字节数。" +":code:`get_parameters` " +"函数允许服务器获取客户端的参数。相反，:code:`set_parameters`函数允许服务器将其参数发送给客户端。最后，:code:`fit`函数在本地为客户端训练模型，:code:`evaluate`函数在本地测试模型并返回相关指标。" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:521 -msgid "Our custom serialization/deserialization functions" -msgstr "我们的定制序列化/反序列化功能" +#: ../../source/tutorial-quickstart-huggingface.rst:175 +msgid "Starting the server" +msgstr "启动服务器" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:523 +#: ../../source/tutorial-quickstart-huggingface.rst:177 msgid "" -"This is where the real serialization/deserialization will happen, " -"especially in ``ndarray_to_sparse_bytes`` for serialization and " -"``sparse_bytes_to_ndarray`` for deserialization." +"Now that we have a way to instantiate clients, we need to create our " +"server in order to aggregate the results. Using Flower, this can be done " +"very easily by first choosing a strategy (here, we are using " +":code:`FedAvg`, which will define the global weights as the average of " +"all the clients' weights at each round) and then using the " +":code:`flwr.server.start_server` function:" msgstr "" -"这才是真正的序列化/反序列化，尤其是在用于序列化的 ``ndarray_too_sparse_bytes`` 和用于反序列化的 " -"``sparse_bytes_too_ndarray`` 中。" +"现在我们有了实例化客户端的方法，我们需要创建服务器，以便汇总结果。使用 Flower，首先选择一个策略（这里我们使用 " +":code:`FedAvg`，它将把全局模型参数定义为每轮所有客户端模型参数的平均值），然后使用 " +":code:`flwr.server.start_server`函数，就可以非常轻松地完成这项工作：" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:525 +#: ../../source/tutorial-quickstart-huggingface.rst:205 msgid "" -"Note that we imported the ``scipy.sparse`` library in order to convert " -"our arrays." -msgstr "请注意，为了转换数组，我们导入了 ``scipy.sparse`` 库。" +"The :code:`weighted_average` function is there to provide a way to " +"aggregate the metrics distributed amongst the clients (basically this " +"allows us to display a nice average accuracy and loss for every round)." +msgstr "" +"使用 :code:`weighted_average` " +"函数是为了提供一种方法来汇总分布在客户端的指标（基本上，这可以让我们显示每一轮的平均精度和损失值）。" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:613 -msgid "Client-side" -msgstr "客户端" +#: ../../source/tutorial-quickstart-huggingface.rst:209 +msgid "Putting everything together" +msgstr "把所有东西放在一起" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:615 -msgid "" -"To be able to serialize our ``ndarray``\\ s into sparse parameters, we " -"will just have to call our custom functions in our " -"``flwr.client.Client``." -msgstr "为了能够将我们的 ``ndarray`` 序列化为稀疏参数，我们只需在 ``flwr.client.Client`` 中调用我们的自定义函数。" +#: ../../source/tutorial-quickstart-huggingface.rst:211 +msgid "We can now start client instances using:" +msgstr "现在我们可以使用：" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:617 +#: ../../source/tutorial-quickstart-huggingface.rst:221 msgid "" -"Indeed, in ``get_parameters`` we need to serialize the parameters we got " -"from our network using our custom ``ndarrays_to_sparse_parameters`` " -"defined above." -msgstr "" -"事实上，在 `get_parameters` 中，我们需要使用上文定义的自定义 `ndarrays_too_sparse_parameters` " -"序列化从网络中获取的参数。" +"And they will be able to connect to the server and start the federated " +"training." +msgstr "他们就能连接到服务器，开始联邦训练。" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:619 +#: ../../source/tutorial-quickstart-huggingface.rst:223 +#, fuzzy msgid "" -"In ``fit``, we first need to deserialize the parameters coming from the " -"server using our custom ``sparse_parameters_to_ndarrays`` and then we " -"need to serialize our local results with " -"``ndarrays_to_sparse_parameters``." +"If you want to check out everything put together, you should check out " +"the `full code example `_ ." msgstr "" -"在 ``fit`` 中，我们首先需要使用自定义的 ``sparse_parameters_to_ndarrays`` " -"反序列化来自服务器的参数，然后使用 ``ndarrays_to_sparse_parameters`` 序列化本地结果。" +"如果您想查看所有内容，请查看完整的代码示例： [https://github.com/adap/flower/tree/main/examples" +"/quickstart-" +"huggingface](https://github.com/adap/flower/tree/main/examples" +"/quickstart-huggingface)." -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:621 +#: ../../source/tutorial-quickstart-huggingface.rst:226 msgid "" -"In ``evaluate``, we will only need to deserialize the global parameters " -"with our custom function." -msgstr "在 ``evaluate`` 中，我们只需要用自定义函数反序列化全局参数。" +"Of course, this is a very basic example, and a lot can be added or " +"modified, it was just to showcase how simply we could federate a Hugging " +"Face workflow using Flower." +msgstr "当然，这只是一个非常基本的示例，还可以添加或修改很多内容，只是为了展示我们可以如何简单地使用 Flower 联合Hugging Face的工作流程。" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:725 -msgid "Server-side" -msgstr "服务器端" +#: ../../source/tutorial-quickstart-huggingface.rst:229 +msgid "" +"Note that in this example we used :code:`PyTorch`, but we could have very" +" well used :code:`TensorFlow`." +msgstr "请注意，在本例中我们使用了 :code:`PyTorch`，但也完全可以使用 :code:`TensorFlow`。" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:727 +#: ../../source/tutorial-quickstart-ios.rst:-1 msgid "" -"For this example, we will just use ``FedAvg`` as a strategy. To change " -"the serialization and deserialization here, we only need to reimplement " -"the ``evaluate`` and ``aggregate_fit`` functions of ``FedAvg``. The other" -" functions of the strategy will be inherited from the super class " -"``FedAvg``." -msgstr "" -"在本例中，我们将只使用 ``FedAvg`` 作为策略。要改变这里的序列化和反序列化，我们只需重新实现 ``FedAvg`` 的 " -"``evaluate`` 和 ``aggregate_fit`` 函数。策略的其他函数将从超类 ``FedAvg`` 继承。" +"Read this Federated Learning quickstart tutorial for creating an iOS app " +"using Flower to train a neural network on MNIST." +msgstr "阅读本联邦学习快速入门教程，了解如何使用 Flower 创建 iOS 应用程序，并在 MNIST 上训练神经网络。" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:729 -msgid "As you can see only one line as change in ``evaluate``:" -msgstr "正如你所看到的，``evaluate``中只修改了一行：" +#: ../../source/tutorial-quickstart-ios.rst:5 +msgid "Quickstart iOS" +msgstr "快速入门 iOS" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:735 +#: ../../source/tutorial-quickstart-ios.rst:10 msgid "" -"And for ``aggregate_fit``, we will first deserialize every result we " -"received:" -msgstr "而对于 ``aggregate_fit``，我们将首先反序列化收到的每个结果：" - -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:744 -msgid "And then serialize the aggregated result:" -msgstr "然后将汇总结果序列化：" - -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:903 -msgid "We can now run our custom serialization example!" -msgstr "现在我们可以运行自定义序列化示例！" +"In this tutorial we will learn how to train a Neural Network on MNIST " +"using Flower and CoreML on iOS devices." +msgstr "在本教程中，我们将学习如何在 iOS 设备上使用 Flower 和 CoreML 在 MNIST 上训练神经网络。" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:934 +#: ../../source/tutorial-quickstart-ios.rst:12 +#, fuzzy msgid "" -"In this part of the tutorial, we've seen how we can build clients by " -"subclassing either ``NumPyClient`` or ``Client``. ``NumPyClient`` is a " -"convenience abstraction that makes it easier to work with machine " -"learning libraries that have good NumPy interoperability. ``Client`` is a" -" more flexible abstraction that allows us to do things that are not " -"possible in ``NumPyClient``. In order to do so, it requires us to handle " -"parameter serialization and deserialization ourselves." +"First of all, for running the Flower Python server, it is recommended to " +"create a virtual environment and run everything within a :doc:`virtualenv" +" `. For the Flower client " +"implementation in iOS, it is recommended to use Xcode as our IDE." msgstr "" -"在本部分教程中，我们已经了解了如何通过子类化 ``NumPyClient`` 或 ``Client`` 来构建客户端。NumPyClient " -"\"是一个便捷的抽象类，可以让我们更容易地与具有良好NumPy互操作性的机器学习库一起工作。``Client``是一个更灵活的抽象类，允许我们做一些在`NumPyClient``中做不到的事情。为此，它要求我们自己处理参数序列化和反序列化。" +"首先，为了运行 Flower Python 服务器，建议创建一个虚拟环境，并在 `virtualenv " +"`_ 中运行一切。对于在 iOS 中实现 " +"Flower 客户端，建议使用 Xcode 作为我们的集成开发环境。" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:952 +#: ../../source/tutorial-quickstart-ios.rst:15 msgid "" -"This is the final part of the Flower tutorial (for now!), " -"congratulations! You're now well equipped to understand the rest of the " -"documentation. There are many topics we didn't cover in the tutorial, we " -"recommend the following resources:" -msgstr "这暂时是 Flower 教程的最后一部分，恭喜您！您现在已经具备了理解其余文档的能力。本教程还有许多内容没有涉及，我们推荐您参考以下资源：" - -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:954 -msgid "`Read Flower Docs `__" -msgstr "阅读Flower文档 `__" +"Our example consists of one Python *server* and two iPhone *clients* that" +" all have the same model." +msgstr "我们的示例包括一个 Python *服务器*和两个 iPhone *客户端*，它们都具有相同的模型。" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:955 +#: ../../source/tutorial-quickstart-ios.rst:17 msgid "" -"`Check out Flower Code Examples " -"`__" -msgstr "查看 Flower 代码示例 `__" +"*Clients* are responsible for generating individual weight updates for " +"the model based on their local datasets. These updates are then sent to " +"the *server* which will aggregate them to produce a better model. " +"Finally, the *server* sends this improved version of the model back to " +"each *client*. A complete cycle of weight updates is called a *round*." +msgstr "*客户端*负责根据其本地数据集为模型生成独立的模型参数。然后，这些参数更新会被发送到*服务器*，由*服务器*汇总后生成一个更好的模型。最后，*服务器*将改进后的模型发送回每个*客户端*。一个完整的参数更新周期称为一*轮*。" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:956 +#: ../../source/tutorial-quickstart-ios.rst:21 msgid "" -"`Use Flower Baselines for your research " -"`__" -msgstr "使用 \"Flower Baselines \"进行研究 `__" +"Now that we have a rough idea of what is going on, let's get started to " +"setup our Flower server environment. We first need to install Flower. You" +" can do this by using pip:" +msgstr "现在我们已经有了一个大致的概念，让我们开始设置 Flower 服务器环境吧。首先，我们需要安装 Flower。你可以使用 pip 来安装：" -#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:957 -msgid "" -"`Watch Flower Summit 2023 videos `__" -msgstr "观看 2023 年Flower峰会视频 `__" +#: ../../source/tutorial-quickstart-ios.rst:27 +msgid "Or Poetry:" +msgstr "或者Poetry：" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:9 -msgid "Get started with Flower" -msgstr "开始使用Flower" +#: ../../source/tutorial-quickstart-ios.rst:34 +#: ../../source/tutorial-quickstart-pytorch.rst:37 +#: ../../source/tutorial-quickstart-scikitlearn.rst:40 +#: ../../source/tutorial-quickstart-tensorflow.rst:29 +#: ../../source/tutorial-quickstart-xgboost.rst:55 +msgid "Flower Client" +msgstr "Flower 客户端" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:11 -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:11 -msgid "Welcome to the Flower federated learning tutorial!" -msgstr "欢迎阅读Flower联邦学习教程！" +#: ../../source/tutorial-quickstart-ios.rst:36 +msgid "" +"Now that we have all our dependencies installed, let's run a simple " +"distributed training using CoreML as our local training pipeline and " +"MNIST as our dataset. For simplicity reasons we will use the complete " +"Flower client with CoreML, that has been implemented and stored inside " +"the Swift SDK. The client implementation can be seen below:" +msgstr "" +"现在我们已经安装了所有依赖项，让我们使用 CoreML 作为本地训练框架和 MNIST " +"作为数据集，运行一个简单的分布式训练。为了简单起见，我们将使用 CoreML 的完整 Flower 客户端，该客户端已在 Swift SDK " +"中实现并存储。客户端实现如下：" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:13 -#, fuzzy +#: ../../source/tutorial-quickstart-ios.rst:72 msgid "" -"In this notebook, we'll build a federated learning system using Flower, " -"`Flower Datasets `__ and PyTorch. In " -"part 1, we use PyTorch for the model training pipeline and data loading. " -"In part 2, we continue to federate the PyTorch-based pipeline using " -"Flower." +"Let's create a new application project in Xcode and add :code:`flwr` as a" +" dependency in your project. For our application, we will store the logic" +" of our app in :code:`FLiOSModel.swift` and the UI elements in " +":code:`ContentView.swift`. We will focus more on :code:`FLiOSModel.swift`" +" in this quickstart. Please refer to the `full code example " +"`_ to learn more " +"about the app." msgstr "" -"在本笔记中，我们将使用 Flower 和 PyTorch 构建一个联邦学习系统。在第一部分中，我们使用 PyTorch " -"进行模型训练和数据加载。在第二部分中，我们将继续使用 Flower 联邦化基于 PyTorch 的框架。" +"让我们在 Xcode 中创建一个新的应用程序项目，并在项目中添加 :code:`flwr` 作为依赖关系。对于我们的应用程序，我们将在 " +":code:`FLiOSModel.swift` 中存储应用程序的逻辑，在 :code:`ContentView.swift` 中存储 UI " +"元素。在本快速入门中，我们将更多地关注 :code:`FLiOSModel.swift`。请参阅 `完整代码示例 " +"`_ 以了解更多有关应用程序的信息。" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:17 -msgid "Let's get stated!" -msgstr "让我们开始吧！" +#: ../../source/tutorial-quickstart-ios.rst:75 +msgid "Import Flower and CoreML related packages in :code:`FLiOSModel.swift`:" +msgstr "在 :code:`FLiOSModel.swift` 中导入 Flower 和 CoreML 相关软件包：" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:31 +#: ../../source/tutorial-quickstart-ios.rst:83 msgid "" -"Before we begin with any actual code, let's make sure that we have " -"everything we need." -msgstr "在开始编写实际代码之前，让我们先确保我们已经准备好了所需的一切。" +"Then add the mlmodel to the project simply by drag-and-drop, the mlmodel " +"will be bundled inside the application during deployment to your iOS " +"device. We need to pass the url to access mlmodel and run CoreML machine " +"learning processes, it can be retrieved by calling the function " +":code:`Bundle.main.url`. For the MNIST dataset, we need to preprocess it " +"into :code:`MLBatchProvider` object. The preprocessing is done inside " +":code:`DataLoader.swift`." +msgstr "" +"然后通过拖放将 mlmodel 添加到项目中，在部署到 iOS 设备时，mlmodel 将被捆绑到应用程序中。我们需要传递 url 以访问 " +"mlmodel 并运行 CoreML 机器学习进程，可通过调用函数 :code:`Bundle.main.url` 获取。对于 MNIST " +"数据集，我们需要将其预处理为 :code:`MLBatchProvider` 对象。预处理在 :code:`DataLoader.swift` " +"中完成。" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:45 +#: ../../source/tutorial-quickstart-ios.rst:99 #, fuzzy msgid "" -"Next, we install the necessary packages for PyTorch (``torch`` and " -"``torchvision``), Flower Datasets (``flwr-datasets``) and Flower " -"(``flwr``):" -msgstr "接下来，我们为 PyTorch（`torch`` 和`torchvision``）和 Flower（`flwr`）安装必要的软件包：" +"Since CoreML does not allow the model parameters to be seen before " +"training, and accessing the model parameters during or after the training" +" can only be done by specifying the layer name, we need to know this " +"information beforehand, through looking at the model specification, which" +" are written as proto files. The implementation can be seen in " +":code:`MLModelInspect`." +msgstr "" +"由于 CoreML 不允许在训练前查看模型参数，而在训练过程中或训练后访问模型参数只能通过指定层名来完成，因此我们需要事先通过查看模型规范（写成 " +"proto 文件）来了解这些信息。具体实现可参见 :code:`MLModelInspect`。" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:105 +#: ../../source/tutorial-quickstart-ios.rst:102 #, fuzzy msgid "" -"It is possible to switch to a runtime that has GPU acceleration enabled " -"(on Google Colab: ``Runtime > Change runtime type > Hardware accelerator:" -" GPU > Save``). Note, however, that Google Colab is not always able to " -"offer GPU acceleration. If you see an error related to GPU availability " -"in one of the following sections, consider switching back to CPU-based " -"execution by setting ``DEVICE = torch.device(\"cpu\")``. If the runtime " -"has GPU acceleration enabled, you should see the output ``Training on " -"cuda``, otherwise it'll say ``Training on cpu``." -msgstr "" -"可以切换到已启用 GPU 加速的运行时（在 Google Colab 上： 运行时 > 更改运行时类型 > 硬件加速： GPU > " -"保存``）。但请注意，Google Colab 并非总能提供 GPU 加速。如果在以下部分中看到与 GPU 可用性相关的错误，请考虑通过设置 " -"``DEVICE = torch.device(\"cpu\")`` 切回基于 CPU 的执行。如果运行时已启用 GPU " -"加速，你应该会看到输出``Training on cuda``，否则会显示``Training on cpu``。" +"After we have all of the necessary information, let's create our Flower " +"client." +msgstr "获得所有必要信息后，让我们创建 Flower 客户端。" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:118 -msgid "Loading the data" -msgstr "加载数据" +#: ../../source/tutorial-quickstart-ios.rst:117 +msgid "" +"Then start the Flower gRPC client and start communicating to the server " +"by passing our Flower client to the function :code:`startFlwrGRPC`." +msgstr "然后启动 Flower gRPC 客户端，并通过将 Flower 客户端传递给函数 :code:`startFlwrGRPC` 来开始与服务器通信。" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:120 -#, fuzzy +#: ../../source/tutorial-quickstart-ios.rst:124 msgid "" -"Federated learning can be applied to many different types of tasks across" -" different domains. In this tutorial, we introduce federated learning by " -"training a simple convolutional neural network (CNN) on the popular " -"CIFAR-10 dataset. CIFAR-10 can be used to train image classifiers that " -"distinguish between images from ten different classes: 'airplane', " -"'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', and " -"'truck'." +"That's it for the client. We only have to implement :code:`Client` or " +"call the provided :code:`MLFlwrClient` and call :code:`startFlwrGRPC()`. " +"The attribute :code:`hostname` and :code:`port` tells the client which " +"server to connect to. This can be done by entering the hostname and port " +"in the application before clicking the start button to start the " +"federated learning process." msgstr "" -"联邦学习可应用于不同领域的多种不同类型任务。在本教程中，我们将通过在流行的 CIFAR-10 数据集上训练一个简单的卷积神经网络 (CNN) " -"来介绍联合学习。CIFAR-10 可用于训练图像分类器，以区分来自十个不同类别的图像：" +"这就是客户端。我们只需实现 :code:`Client` 或调用提供的 :code:`MLFlwrClient` 并调用 " +":code:`startFlwrGRPC()`。属性 :code:`hostname` 和 :code:`port` " +"会告诉客户端要连接到哪个服务器。这可以通过在应用程序中输入主机名和端口来实现，然后再点击开始按钮启动联邦学习进程。" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:131 +#: ../../source/tutorial-quickstart-ios.rst:129 +#: ../../source/tutorial-quickstart-pytorch.rst:203 +#: ../../source/tutorial-quickstart-scikitlearn.rst:167 +#: ../../source/tutorial-quickstart-tensorflow.rst:98 +#: ../../source/tutorial-quickstart-xgboost.rst:309 +msgid "Flower Server" +msgstr "Flower 服务器" + +#: ../../source/tutorial-quickstart-ios.rst:131 +#: ../../source/tutorial-quickstart-pytorch.rst:205 +#: ../../source/tutorial-quickstart-tensorflow.rst:100 msgid "" -"We simulate having multiple datasets from multiple organizations (also " -"called the \"cross-silo\" setting in federated learning) by splitting the" -" original CIFAR-10 dataset into multiple partitions. Each partition will " -"represent the data from a single organization. We're doing this purely " -"for experimentation purposes, in the real world there's no need for data " -"splitting because each organization already has their own data (so the " -"data is naturally partitioned)." +"For simple workloads we can start a Flower server and leave all the " +"configuration possibilities at their default values. In a file named " +":code:`server.py`, import Flower and start the server:" msgstr "" -"我们通过将原始 CIFAR-10 数据集拆分成多个分区来模拟来自多个组织的多个数据集（也称为联邦学习中的 \"跨分区 " -"\"设置）。每个分区代表一个组织的数据。我们这样做纯粹是为了实验目的，在现实世界中不需要拆分数据，因为每个组织都已经有了自己的数据（所以数据是自然分区的）。" +"对于简单的工作负载，我们可以启动 Flower 服务器，并将所有配置选项保留为默认值。在名为 :code:`server.py` 的文件中，导入 " +"Flower 并启动服务器：" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:133 -#, fuzzy -msgid "" -"Each organization will act as a client in the federated learning system. " -"So having ten organizations participate in a federation means having ten " -"clients connected to the federated learning server." -msgstr "每个组织都将充当联邦学习系统中的客户端。因此，有十个组织参与联邦学习，就意味着有十个客户端连接到联邦学习服务器：" +#: ../../source/tutorial-quickstart-ios.rst:142 +#: ../../source/tutorial-quickstart-pytorch.rst:216 +#: ../../source/tutorial-quickstart-scikitlearn.rst:230 +#: ../../source/tutorial-quickstart-tensorflow.rst:112 +msgid "Train the model, federated!" +msgstr "联邦训练模型！" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:144 +#: ../../source/tutorial-quickstart-ios.rst:144 +#: ../../source/tutorial-quickstart-pytorch.rst:218 +#: ../../source/tutorial-quickstart-tensorflow.rst:114 +#: ../../source/tutorial-quickstart-xgboost.rst:525 msgid "" -"Let's now create the Federated Dataset abstraction that from ``flwr-" -"datasets`` that partitions the CIFAR-10. We will create small training " -"and test set for each edge device and wrap each of them into a PyTorch " -"``DataLoader``:" -msgstr "" +"With both client and server ready, we can now run everything and see " +"federated learning in action. FL systems usually have a server and " +"multiple clients. We therefore have to start the server first:" +msgstr "客户端和服务器都已准备就绪，我们现在可以运行一切，看看联邦学习的实际效果。FL 系统通常有一个服务器和多个客户端。因此，我们必须先启动服务器：" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:198 +#: ../../source/tutorial-quickstart-ios.rst:152 msgid "" -"We now have a list of ten training sets and ten validation sets " -"(``trainloaders`` and ``valloaders``) representing the data of ten " -"different organizations. Each ``trainloader``/``valloader`` pair contains" -" 4500 training examples and 500 validation examples. There's also a " -"single ``testloader`` (we did not split the test set). Again, this is " -"only necessary for building research or educational systems, actual " -"federated learning systems have their data naturally distributed across " -"multiple partitions." +"Once the server is running we can start the clients in different " +"terminals. Build and run the client through your Xcode, one through Xcode" +" Simulator and the other by deploying it to your iPhone. To see more " +"about how to deploy your app to iPhone or Simulator visit `here " +"`_." msgstr "" -"现在，我们有一个包含十个训练集和十个验证集（`trainloaders`` 和`valloaders``）的列表，代表十个不同组织的数据。每对 " -"``trainloader``/``valloader`` 都包含 4500 个训练示例和 500 个验证数据。还有一个单独的 " -"``测试加载器``（我们没有拆分测试集）。同样，这只有在构建研究或教育系统时才有必要，实际的联邦学习系统的数据自然分布在多个分区中。" +"服务器运行后，我们就可以在不同的终端启动客户端。通过 Xcode 构建并运行客户端，一个通过 Xcode 模拟器，另一个通过部署到 " +"iPhone。要了解更多有关如何将应用程序部署到 iPhone 或模拟器的信息，请访问 `此处 " +"`_。" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:201 +#: ../../source/tutorial-quickstart-ios.rst:156 msgid "" -"Let's take a look at the first batch of images and labels in the first " -"training set (i.e., ``trainloaders[0]``) before we move on:" -msgstr "在继续之前，让我们先看看第一个训练集中的第一批图像和标签（即 ``trainloaders[0]``）：" +"Congratulations! You've successfully built and run your first federated " +"learning system in your ios device. The full `source code " +"`_ for this " +"example can be found in :code:`examples/ios`." +msgstr "" +"恭喜您！ 您已经成功地在 ios 设备中构建并运行了第一个联邦学习系统。本示例的`完整源代码 " +"`_ 可在 " +":code:`examples/ios` 中找到。" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:240 +#: ../../source/tutorial-quickstart-jax.rst:-1 msgid "" -"The output above shows a random batch of images from the first " -"``trainloader`` in our list of ten ``trainloaders``. It also prints the " -"labels associated with each image (i.e., one of the ten possible labels " -"we've seen above). If you run the cell again, you should see another " -"batch of images." -msgstr "" -"上面的输出显示了来自十个 \"trainloader \"列表中第一个 \"trainloader " -"\"的随机图像。它还打印了与每幅图像相关的标签（即我们上面看到的十个可能标签之一）。如果您再次运行该单元，应该会看到另一批图像。" +"Check out this Federated Learning quickstart tutorial for using Flower " +"with Jax to train a linear regression model on a scikit-learn dataset." +msgstr "查看此联邦学习快速入门教程，了解如何使用 Flower 和 Jax 在 scikit-learn 数据集上训练线性回归模型。" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:252 -msgid "Step 1: Centralized Training with PyTorch" -msgstr "步骤 1：使用 PyTorch 进行集中训练" +#: ../../source/tutorial-quickstart-jax.rst:5 +msgid "Quickstart JAX" +msgstr "快速入门 JAX" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:263 +#: ../../source/tutorial-quickstart-pandas.rst:-1 msgid "" -"Next, we're going to use PyTorch to define a simple convolutional neural " -"network. This introduction assumes basic familiarity with PyTorch, so it " -"doesn't cover the PyTorch-related aspects in full detail. If you want to " -"dive deeper into PyTorch, we recommend `DEEP LEARNING WITH PYTORCH: A 60 " -"MINUTE BLITZ " -"`__." -msgstr "" -"接下来，我们将使用 PyTorch 来定义一个简单的卷积神经网络。本介绍假定您对 PyTorch 有基本的了解，因此不会详细介绍与 PyTorch" -" 相关的内容。如果你想更深入地了解 PyTorch，我们推荐你阅读 `DEEP LEARNING WITH PYTORCH： a 60 " -"minute blitz " -"`__。" +"Check out this Federated Learning quickstart tutorial for using Flower " +"with Pandas to perform Federated Analytics." +msgstr "查看此联邦学习快速入门教程，了解如何使用 Flower 和 Pandas 执行联邦分析。" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:275 -msgid "Defining the model" -msgstr "定义模型" +#: ../../source/tutorial-quickstart-pandas.rst:5 +msgid "Quickstart Pandas" +msgstr "快速入门Pandas" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:277 +#: ../../source/tutorial-quickstart-pandas.rst:10 +msgid "Let's build a federated analytics system using Pandas and Flower!" +msgstr "让我们使用 Pandas 和 Flower 建立一个联邦分析系统！" + +#: ../../source/tutorial-quickstart-pandas.rst:12 msgid "" -"We use the simple CNN described in the `PyTorch tutorial " -"`__:" +"Please refer to the `full code example " +"`_ " +"to learn more." msgstr "" -"我们使用` PyTorch 教程 " -"`__ 中描述的简单 CNN：" - -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:314 -msgid "Let's continue with the usual training and test functions:" -msgstr "让我们继续进行常规的训练和测试功能：" +"请参阅 `完整代码示例 `_\" 了解更多信息。" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:374 -msgid "Training the model" -msgstr "训练模型" +#: ../../source/tutorial-quickstart-pytorch.rst:-1 +msgid "" +"Check out this Federated Learning quickstart tutorial for using Flower " +"with PyTorch to train a CNN model on MNIST." +msgstr "查看此联邦学习快速入门教程，了解如何使用 Flower 和 PyTorch 在 MNIST 上训练 CNN 模型。" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:376 +#: ../../source/tutorial-quickstart-pytorch.rst:13 msgid "" -"We now have all the basic building blocks we need: a dataset, a model, a " -"training function, and a test function. Let's put them together to train " -"the model on the dataset of one of our organizations " -"(``trainloaders[0]``). This simulates the reality of most machine " -"learning projects today: each organization has their own data and trains " -"models only on this internal data:" -msgstr "现在我们拥有了所需的所有基本构件：数据集、模型、训练函数和测试函数。让我们把它们放在一起，在我们其中一个组织的数据集（``trainloaders[0]``）上训练模型。这模拟了当今大多数机器学习项目的实际情况：每个组织都有自己的数据，并且只在这些内部数据上训练模型：" +"In this tutorial we will learn how to train a Convolutional Neural " +"Network on CIFAR10 using Flower and PyTorch." +msgstr "在本教程中，我们将学习如何使用 Flower 和 PyTorch 在 CIFAR10 上训练卷积神经网络。" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:406 +#: ../../source/tutorial-quickstart-pytorch.rst:15 +#: ../../source/tutorial-quickstart-xgboost.rst:39 +#, fuzzy msgid "" -"Training the simple CNN on our CIFAR-10 split for 5 epochs should result " -"in a test set accuracy of about 41%, which is not good, but at the same " -"time, it doesn't really matter for the purposes of this tutorial. The " -"intent was just to show a simplistic centralized training pipeline that " -"sets the stage for what comes next - federated learning!" +"First of all, it is recommended to create a virtual environment and run " +"everything within a :doc:`virtualenv `." msgstr "" -"在我们的 CIFAR-10 分片上对简单 CNN 进行 5 个遍历的训练后，测试集的准确率应为 " -"41%，这并不理想，但同时对本教程而言也并不重要。我们只是想展示一个简单的集中式训练流程，为接下来的联邦学习做好铺垫！" +"首先，建议创建一个虚拟环境，并在 `virtualenv `_ 中运行一切。" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:418 -msgid "Step 2: Federated Learning with Flower" -msgstr "步骤 2：使用 Flower 联邦学习" +#: ../../source/tutorial-quickstart-pytorch.rst:17 +#: ../../source/tutorial-quickstart-scikitlearn.rst:14 +msgid "" +"Our example consists of one *server* and two *clients* all having the " +"same model." +msgstr "我们的例子包括一个*服务器*和两个*客户端*，它们都有相同的模型。" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:420 +#: ../../source/tutorial-quickstart-pytorch.rst:19 msgid "" -"Step 1 demonstrated a simple centralized training pipeline. All data was " -"in one place (i.e., a single ``trainloader`` and a single ``valloader``)." -" Next, we'll simulate a situation where we have multiple datasets in " -"multiple organizations and where we train a model over these " -"organizations using federated learning." -msgstr "" -"步骤 1 演示了一个简单的集中式训练流程。所有数据都在一个地方（即一个 \"trainloader \"和一个 " -"\"valloader\"）。接下来，我们将模拟在多个组织中拥有多个数据集的情况，并使用联邦学习在这些组织中训练一个模型。" +"*Clients* are responsible for generating individual weight-updates for " +"the model based on their local datasets. These updates are then sent to " +"the *server* which will aggregate them to produce a better model. " +"Finally, the *server* sends this improved version of the model back to " +"each *client*. A complete cycle of weight updates is called a *round*." +msgstr "*客户端*负责在其本地数据集上更新模型参数。然后，这些参数会被发送到*服务器*，由*服务器*聚合后生成一个更好的模型。最后，*服务器*将改进后的模型发送回每个*客户端*。一个完整的模型参数更新周期称为一*轮*。" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:432 -msgid "Updating model parameters" -msgstr "更新模型参数" +#: ../../source/tutorial-quickstart-pytorch.rst:23 +msgid "" +"Now that we have a rough idea of what is going on, let's get started. We " +"first need to install Flower. You can do this by running :" +msgstr "现在，我们已经有了一个大致的概念了，那就让我们开始吧。首先，我们需要安装 Flower。可以通过运行 ：" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:434 +#: ../../source/tutorial-quickstart-pytorch.rst:29 msgid "" -"In federated learning, the server sends the global model parameters to " -"the client, and the client updates the local model with the parameters " -"received from the server. It then trains the model on the local data " -"(which changes the model parameters locally) and sends the " -"updated/changed model parameters back to the server (or, alternatively, " -"it sends just the gradients back to the server, not the full model " -"parameters)." -msgstr "在联邦学习中，服务器将全局模型参数发送给客户端，客户端根据从服务器接收到的参数更新本地模型。然后，客户端根据本地数据对模型进行训练（在本地更改模型参数），并将更新/更改后的模型参数发回服务器（或者，客户端只将梯度参数发回服务器，而不是全部模型参数）。" +"Since we want to use PyTorch to solve a computer vision task, let's go " +"ahead and install PyTorch and the **torchvision** library:" +msgstr "既然我们想用 PyTorch 解决计算机视觉任务，那就继续安装 PyTorch 和 **torchvision** 库吧：" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:436 +#: ../../source/tutorial-quickstart-pytorch.rst:39 msgid "" -"We need two helper functions to update the local model with parameters " -"received from the server and to get the updated model parameters from the" -" local model: ``set_parameters`` and ``get_parameters``. The following " -"two functions do just that for the PyTorch model above." +"Now that we have all our dependencies installed, let's run a simple " +"distributed training with two clients and one server. Our training " +"procedure and network architecture are based on PyTorch's `Deep Learning " +"with PyTorch " +"`_." msgstr "" -"我们需要两个辅助函数，用从服务器接收到的参数更新本地模型，并从本地模型获取更新后的模型参数：`` " -"set_parameters```和`get_parameters``。下面两个函数就是为上面的 PyTorch 模型做这些工作的。" +"现在我们已经安装了所有的依赖项，让我们用两个客户端和一个服务器来运行一个简单的分布式训练。我们的训练过程和网络架构基于 PyTorch " +"的《Deep Learning with PyTorch " +"`_》。" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:438 +#: ../../source/tutorial-quickstart-pytorch.rst:41 msgid "" -"The details of how this works are not really important here (feel free to" -" consult the PyTorch documentation if you want to learn more). In " -"essence, we use ``state_dict`` to access PyTorch model parameter tensors." -" The parameter tensors are then converted to/from a list of NumPy " -"ndarray's (which Flower knows how to serialize/deserialize):" -msgstr "" -"在这里，如何工作的细节并不重要（如果你想了解更多，请随时查阅 PyTorch 文档）。本质上，我们使用 ``state_dict`` 访问 " -"PyTorch 模型参数张量。然后，参数张量会被转换成/转换成 NumPy ndarray 列表（Flower 知道如何序列化/反序列化）：" +"In a file called :code:`client.py`, import Flower and PyTorch related " +"packages:" +msgstr "在名为 :code:`client.py` 的文件中，导入 Flower 和 PyTorch 相关软件包：" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:466 -msgid "Implementing a Flower client" -msgstr "实现 Flower 客户端" +#: ../../source/tutorial-quickstart-pytorch.rst:56 +msgid "In addition, we define the device allocation in PyTorch with:" +msgstr "此外，我们还在 PyTorch 中定义了设备分配：" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:468 +#: ../../source/tutorial-quickstart-pytorch.rst:62 msgid "" -"With that out of the way, let's move on to the interesting part. " -"Federated learning systems consist of a server and multiple clients. In " -"Flower, we create clients by implementing subclasses of " -"``flwr.client.Client`` or ``flwr.client.NumPyClient``. We use " -"``NumPyClient`` in this tutorial because it is easier to implement and " -"requires us to write less boilerplate." +"We use PyTorch to load CIFAR10, a popular colored image classification " +"dataset for machine learning. The PyTorch :code:`DataLoader()` downloads " +"the training and test data that are then normalized." msgstr "" -"说完这些，让我们进入有趣的部分。联邦学习系统由一个服务器和多个客户端组成。在 Flower 中，我们通过实现 " -"``flwr.client.Client`` 或 ``flwr.client.NumPyClient`` " -"的子类来创建客户端。在本教程中，我们使用``NumPyClient``，因为它更容易实现，需要我们编写的模板也更少。" +"我们使用 PyTorch 来加载 CIFAR10，这是一个用于机器学习的流行彩色图像分类数据集。PyTorch " +":code:`DataLoader()`下载训练数据和测试数据，然后进行归一化处理。" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:470 +#: ../../source/tutorial-quickstart-pytorch.rst:78 msgid "" -"To implement the Flower client, we create a subclass of " -"``flwr.client.NumPyClient`` and implement the three methods " -"``get_parameters``, ``fit``, and ``evaluate``:" -msgstr "" -"为实现 Flower 客户端，我们创建了 ``flwr.client.NumPyClient`` 的子类，并实现了 " -"``get_parameters``、``fit`` 和``evaluate`` 三个方法：" - -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:472 -msgid "``get_parameters``: Return the current local model parameters" -msgstr "``get_parameters``： 返回当前本地模型参数" +"Define the loss and optimizer with PyTorch. The training of the dataset " +"is done by looping over the dataset, measure the corresponding loss and " +"optimize it." +msgstr "使用 PyTorch 定义损失和优化器。数据集的训练是通过循环数据集、测量相应的损失值并对其进行优化来完成的。" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:473 +#: ../../source/tutorial-quickstart-pytorch.rst:94 msgid "" -"``fit``: Receive model parameters from the server, train the model " -"parameters on the local data, and return the (updated) model parameters " -"to the server" -msgstr "``fit``： 从服务器接收模型参数，在本地数据上训练模型参数，并将（更新的）模型参数返回服务器" +"Define then the validation of the machine learning network. We loop over" +" the test set and measure the loss and accuracy of the test set." +msgstr "然后定义机器学习网络的验证。我们在测试集上循环，计算测试集的损失值和准确率。" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:474 +#: ../../source/tutorial-quickstart-pytorch.rst:113 msgid "" -"``evaluate``: Receive model parameters from the server, evaluate the " -"model parameters on the local data, and return the evaluation result to " -"the server" -msgstr "``evaluate ``： 从服务器接收模型参数，在本地数据上评估模型参数，并将评估结果返回服务器" +"After defining the training and testing of a PyTorch machine learning " +"model, we use the functions for the Flower clients." +msgstr "在定义了 PyTorch 机器学习模型的训练和测试之后，我们将这些功能用于 Flower 客户端。" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:476 +#: ../../source/tutorial-quickstart-pytorch.rst:115 msgid "" -"We mentioned that our clients will use the previously defined PyTorch " -"components for model training and evaluation. Let's see a simple Flower " -"client implementation that brings everything together:" -msgstr "" -"我们提到，我们的客户端将使用之前定义的 PyTorch 组件进行模型训练和评估。让我们来看看一个简单的 Flower " -"客户端实现，它将一切都整合在一起：" +"The Flower clients will use a simple CNN adapted from 'PyTorch: A 60 " +"Minute Blitz':" +msgstr "Flower 客户端将使用一个简单的从“PyTorch： 60 分钟突击\"改编的CNN：" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:513 +#: ../../source/tutorial-quickstart-pytorch.rst:142 msgid "" -"Our class ``FlowerClient`` defines how local training/evaluation will be " -"performed and allows Flower to call the local training/evaluation through" -" ``fit`` and ``evaluate``. Each instance of ``FlowerClient`` represents a" -" *single client* in our federated learning system. Federated learning " -"systems have multiple clients (otherwise, there's not much to federate), " -"so each client will be represented by its own instance of " -"``FlowerClient``. If we have, for example, three clients in our workload," -" then we'd have three instances of ``FlowerClient``. Flower calls " -"``FlowerClient.fit`` on the respective instance when the server selects a" -" particular client for training (and ``FlowerClient.evaluate`` for " -"evaluation)." -msgstr "" -"我们的类 ``FlowerClient`` 定义了本地训练/评估的执行方式，并允许 Flower 通过 ``fit`` 和 " -"``evaluate`` 调用本地训练/评估。每个 ``FlowerClient`` " -"实例都代表联邦学习系统中的*单个客户端*。联邦学习系统有多个客户端（否则就没有什么可联邦的），因此每个客户端都将由自己的 " -"``FlowerClient`` 实例来代表。例如，如果我们的工作负载中有三个客户端，那么我们就会有三个 ``FlowerClient`` " -"实例。当服务器选择特定客户端进行训练时，Flower 会调用相应实例上的 ``FlowerClient.fit`` （评估时调用 " -"``FlowerClient.evaluate``）。" - -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:517 -msgid "Using the Virtual Client Engine" -msgstr "使用虚拟客户端引擎" +"After loading the data set with :code:`load_data()` we define the Flower " +"interface." +msgstr "使用 :code:`load_data()` 加载数据集后，我们定义了 Flower 接口。" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:519 +#: ../../source/tutorial-quickstart-pytorch.rst:144 +#: ../../source/tutorial-quickstart-tensorflow.rst:54 msgid "" -"In this notebook, we want to simulate a federated learning system with 10" -" clients on a single machine. This means that the server and all 10 " -"clients will live on a single machine and share resources such as CPU, " -"GPU, and memory. Having 10 clients would mean having 10 instances of " -"``FlowerClient`` in memory. Doing this on a single machine can quickly " -"exhaust the available memory resources, even if only a subset of these " -"clients participates in a single round of federated learning." +"The Flower server interacts with clients through an interface called " +":code:`Client`. When the server selects a particular client for training," +" it sends training instructions over the network. The client receives " +"those instructions and calls one of the :code:`Client` methods to run " +"your code (i.e., to train the neural network we defined earlier)." msgstr "" -"在本笔记中，我们要模拟一个联邦学习系统，在一台机器上有 10 个客户端。这意味着服务器和所有 10 个客户端都将位于一台机器上，并共享 " -"CPU、GPU 和内存等资源。有 10 个客户端就意味着内存中有 10 个 ``FlowerClient`` " -"实例。在单台机器上这样做会很快耗尽可用的内存资源，即使这些客户端中只有一个子集参与了一轮联邦学习。" +"Flower 服务器通过一个名为 :code:`Client` " +"的接口与客户端交互。当服务器选择一个特定的客户端进行训练时，它会通过网络发送训练指令。客户端接收到这些指令后，会调用 :code:`Client`" +" 方法之一来运行您的代码（即训练我们之前定义的神经网络）。" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:521 +#: ../../source/tutorial-quickstart-pytorch.rst:150 msgid "" -"In addition to the regular capabilities where server and clients run on " -"multiple machines, Flower, therefore, provides special simulation " -"capabilities that create ``FlowerClient`` instances only when they are " -"actually necessary for training or evaluation. To enable the Flower " -"framework to create clients when necessary, we need to implement a " -"function called ``client_fn`` that creates a ``FlowerClient`` instance on" -" demand. Flower calls ``client_fn`` whenever it needs an instance of one " -"particular client to call ``fit`` or ``evaluate`` (those instances are " -"usually discarded after use, so they should not keep any local state). " -"Clients are identified by a client ID, or short ``cid``. The ``cid`` can " -"be used, for example, to load different local data partitions for " -"different clients, as can be seen below:" +"Flower provides a convenience class called :code:`NumPyClient` which " +"makes it easier to implement the :code:`Client` interface when your " +"workload uses PyTorch. Implementing :code:`NumPyClient` usually means " +"defining the following methods (:code:`set_parameters` is optional " +"though):" msgstr "" -"除了服务器和客户端在多台机器上运行的常规功能外，Flower 还提供了特殊的模拟功能，即只有在训练或评估实际需要时才创建 " -"``FlowerClient`` 实例。为了让 Flower 框架能在必要时创建客户端，我们需要实现一个名为 ``client_fn`` " -"的函数，它能按需创建一个 ``FlowerClient`` 实例。每当 Flower 需要一个特定的客户端实例来调用 ``fit`` 或 " -"``evaluate`` 时，它就会调用 " -"``client_fn``（这些实例在使用后通常会被丢弃，因此它们不应保留任何本地状态）。客户端由一个客户端 ID 或简短的 ``cid`` " -"标识。例如，可以使用 ``cid`` 为不同的客户端加载不同的本地数据分区，如下所示：" - -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:556 -msgid "Starting the training" -msgstr "开始训练" +"Flower 提供了一个名为 :code:`NumPyClient` 的便捷类，当您的工作负载使用 PyTorch 时，它使 " +":code:`Client` 接口的实现变得更容易。实现 :code:`NumPyClient` " +"通常意味着定义以下方法（:code:`set_parameters` 是可选的）：" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:558 -msgid "" -"We now have the class ``FlowerClient`` which defines client-side " -"training/evaluation and ``client_fn`` which allows Flower to create " -"``FlowerClient`` instances whenever it needs to call ``fit`` or " -"``evaluate`` on one particular client. The last step is to start the " -"actual simulation using ``flwr.simulation.start_simulation``." -msgstr "" -"现在我们有了定义客户端训练/评估的类 ``FlowerClient`` 和允许 Flower 在需要调用某个客户端的 ``fit` 或 " -"``evaluate` 时创建 ``FlowerClient`` 实例的 ``client_fn` 类。最后一步是使用 " -"``flwr.simulation.start_simulation`` 启动实际模拟。" +#: ../../source/tutorial-quickstart-pytorch.rst:156 +#: ../../source/tutorial-quickstart-scikitlearn.rst:119 +msgid "return the model weight as a list of NumPy ndarrays" +msgstr "以 NumPy ndarrays 列表形式返回模型参数" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:560 -msgid "" -"The function ``start_simulation`` accepts a number of arguments, amongst " -"them the ``client_fn`` used to create ``FlowerClient`` instances, the " -"number of clients to simulate (``num_clients``), the number of federated " -"learning rounds (``num_rounds``), and the strategy. The strategy " -"encapsulates the federated learning approach/algorithm, for example, " -"*Federated Averaging* (FedAvg)." -msgstr "" -"函数 ``start_simulation`` 接受许多参数，其中包括用于创建 ``FlowerClient`` 实例的 " -"``client_fn``、要模拟的客户端数量（``num_clients``）、联邦学习轮数（``num_rounds``）和策略。策略封装了联邦学习方法/算法，例如*联邦平均*" -" (FedAvg)。" +#: ../../source/tutorial-quickstart-pytorch.rst:157 +#: ../../source/tutorial-quickstart-scikitlearn.rst:121 +msgid ":code:`set_parameters` (optional)" +msgstr ":code:`set_parameters` （可选）" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:562 +#: ../../source/tutorial-quickstart-pytorch.rst:158 +#: ../../source/tutorial-quickstart-scikitlearn.rst:121 msgid "" -"Flower has a number of built-in strategies, but we can also use our own " -"strategy implementations to customize nearly all aspects of the federated" -" learning approach. For this example, we use the built-in ``FedAvg`` " -"implementation and customize it using a few basic parameters. The last " -"step is the actual call to ``start_simulation`` which - you guessed it - " -"starts the simulation:" -msgstr "" -"Flower 有许多内置策略，但我们也可以使用自己的策略实现来定制联邦学习方法的几乎所有方面。在本例中，我们使用内置的 ``FedAvg`` " -"实现，并使用一些基本参数对其进行定制。最后一步是实际调用 ``start_simulation``开始模拟：" +"update the local model weights with the parameters received from the " +"server" +msgstr "用从服务器接收到的参数更新本地模型参数" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:608 -msgid "Behind the scenes" -msgstr "幕后" +#: ../../source/tutorial-quickstart-pytorch.rst:160 +#: ../../source/tutorial-quickstart-scikitlearn.rst:124 +msgid "set the local model weights" +msgstr "设置本地模型参数" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:610 -msgid "So how does this work? How does Flower execute this simulation?" -msgstr "那么它是如何工作的呢？Flower 如何进行模拟？" +#: ../../source/tutorial-quickstart-pytorch.rst:161 +#: ../../source/tutorial-quickstart-scikitlearn.rst:125 +msgid "train the local model" +msgstr "训练本地模型" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:612 -#, python-format -msgid "" -"When we call ``start_simulation``, we tell Flower that there are 10 " -"clients (``num_clients=10``). Flower then goes ahead an asks the " -"``FedAvg`` strategy to select clients. ``FedAvg`` knows that it should " -"select 100% of the available clients (``fraction_fit=1.0``), so it goes " -"ahead and selects 10 random clients (i.e., 100% of 10)." -msgstr "" -"当我们调用 ``start_simulation`` 时，我们会告诉 Flower 有 10 " -"个客户（`num_clients=10``）。然后，Flower 会要求 ``FedAvg`` 策略选择客户。``FedAvg`` 知道它应该选择" -" 100%的可用客户（``fraction_fit=1.0``），所以它会随机选择 10 个客户（即 10 的 100%）。" +#: ../../source/tutorial-quickstart-pytorch.rst:162 +#: ../../source/tutorial-quickstart-scikitlearn.rst:126 +msgid "receive the updated local model weights" +msgstr "接收更新的本地模型参数" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:614 -msgid "" -"Flower then asks the selected 10 clients to train the model. When the " -"server receives the model parameter updates from the clients, it hands " -"those updates over to the strategy (*FedAvg*) for aggregation. The " -"strategy aggregates those updates and returns the new global model, which" -" then gets used in the next round of federated learning." -msgstr "" -"然后，Flower 会要求选定的 10 " -"个客户端对模型进行训练。服务器收到客户端的模型参数更新后，会将这些更新交给策略（*FedAvg*）进行聚合。策略会聚合这些更新并返回新的全局模型，然后将其用于下一轮联邦学习。" +#: ../../source/tutorial-quickstart-pytorch.rst:164 +#: ../../source/tutorial-quickstart-scikitlearn.rst:128 +msgid "test the local model" +msgstr "测试本地模型" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:626 -msgid "Where's the accuracy?" -msgstr "准确度在哪里找？" +#: ../../source/tutorial-quickstart-pytorch.rst:166 +msgid "which can be implemented in the following way:" +msgstr "可以通过以下方式实现：" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:628 +#: ../../source/tutorial-quickstart-pytorch.rst:189 +#: ../../source/tutorial-quickstart-tensorflow.rst:82 msgid "" -"You may have noticed that all metrics except for ``losses_distributed`` " -"are empty. Where did the ``{\"accuracy\": float(accuracy)}`` go?" -msgstr "" -"您可能已经注意到，除了 ``losses_distributed`` 以外，所有指标都是空的。{\"准确度\": " -"float(准确度)}``去哪儿了？" +"We can now create an instance of our class :code:`CifarClient` and add " +"one line to actually run this client:" +msgstr "现在我们可以创建一个 :code:`CifarClient` 类的实例，并添加一行来实际运行该客户端：" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:630 +#: ../../source/tutorial-quickstart-pytorch.rst:196 +#: ../../source/tutorial-quickstart-tensorflow.rst:90 +#, fuzzy msgid "" -"Flower can automatically aggregate losses returned by individual clients," -" but it cannot do the same for metrics in the generic metrics dictionary " -"(the one with the ``accuracy`` key). Metrics dictionaries can contain " -"very different kinds of metrics and even key/value pairs that are not " -"metrics at all, so the framework does not (and can not) know how to " -"handle these automatically." +"That's it for the client. We only have to implement :code:`Client` or " +":code:`NumPyClient` and call :code:`fl.client.start_client()`. If you " +"implement a client of type :code:`NumPyClient` you'll need to first call " +"its :code:`to_client()` method. The string :code:`\"[::]:8080\"` tells " +"the client which server to connect to. In our case we can run the server " +"and the client on the same machine, therefore we use " +":code:`\"[::]:8080\"`. If we run a truly federated workload with the " +"server and clients running on different machines, all that needs to " +"change is the :code:`server_address` we point the client at." msgstr "" -"Flower 可以自动汇总单个客户端返回的损失值，但无法对通用度量字典中的度量进行同样的处理（即带有 \"准确度 " -"\"键的度量字典）。度量值字典可以包含非常不同种类的度量值，甚至包含根本不是度量值的键/值对，因此框架不知道（也无法知道）如何自动处理这些度量值。" +"这就是客户端。我们只需实现 :code:`Client` 或 :code:`NumPyClient` 并调用 " +":code:`fl.client.start_client()` 或 " +":code:`fl.client.start_numpy_client()`。字符串 " +":code:`\"[::]:8080\"`会告诉客户端要连接的服务器。在本例中，我们可以在同一台机器上运行服务器和客户端，因此使用 " +":code:`\"[::]:8080\"。如果我们运行的是真正的联邦工作负载，服务器和客户端运行在不同的机器上，那么需要改变的只是客户端指向的 " +":code:`server_address`。" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:632 +#: ../../source/tutorial-quickstart-pytorch.rst:226 +#: ../../source/tutorial-quickstart-scikitlearn.rst:239 +#: ../../source/tutorial-quickstart-tensorflow.rst:122 +#: ../../source/tutorial-quickstart-xgboost.rst:533 msgid "" -"As users, we need to tell the framework how to handle/aggregate these " -"custom metrics, and we do so by passing metric aggregation functions to " -"the strategy. The strategy will then call these functions whenever it " -"receives fit or evaluate metrics from clients. The two possible functions" -" are ``fit_metrics_aggregation_fn`` and " -"``evaluate_metrics_aggregation_fn``." -msgstr "" -"作为用户，我们需要告诉框架如何处理/聚合这些自定义指标，为此，我们将指标聚合函数传递给策略。然后，只要从客户端接收到拟合或评估指标，策略就会调用这些函数。两个可能的函数是" -" ``fit_metrics_aggregation_fn`` 和 ``evaluate_metrics_aggregation_fn``。" +"Once the server is running we can start the clients in different " +"terminals. Open a new terminal and start the first client:" +msgstr "服务器运行后，我们就可以在不同终端启动客户端了。打开一个新终端，启动第一个客户端：" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:634 +#: ../../source/tutorial-quickstart-pytorch.rst:233 +#: ../../source/tutorial-quickstart-scikitlearn.rst:246 +#: ../../source/tutorial-quickstart-tensorflow.rst:129 +#: ../../source/tutorial-quickstart-xgboost.rst:540 +msgid "Open another terminal and start the second client:" +msgstr "打开另一台终端，启动第二个客户端：" + +#: ../../source/tutorial-quickstart-pytorch.rst:239 +#: ../../source/tutorial-quickstart-scikitlearn.rst:252 +#: ../../source/tutorial-quickstart-xgboost.rst:546 msgid "" -"Let's create a simple weighted averaging function to aggregate the " -"``accuracy`` metric we return from ``evaluate``:" -msgstr "让我们创建一个简单的加权平均函数来汇总从 ``evaluate`` 返回的 ``accuracy`` 指标：" +"Each client will have its own dataset. You should now see how the " +"training does in the very first terminal (the one that started the " +"server):" +msgstr "每个客户端都有自己的数据集。现在你应该看到第一个终端（启动服务器的终端）的训练效果了：" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:660 +#: ../../source/tutorial-quickstart-pytorch.rst:271 msgid "" -"The only thing left to do is to tell the strategy to call this function " -"whenever it receives evaluation metric dictionaries from the clients:" -msgstr "剩下要做的就是告诉策略，每当它从客户端接收到评估度量字典时，都要调用这个函数：" - -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:697 -msgid "" -"We now have a full system that performs federated training and federated " -"evaluation. It uses the ``weighted_average`` function to aggregate custom" -" evaluation metrics and calculates a single ``accuracy`` metric across " -"all clients on the server side." +"Congratulations! You've successfully built and run your first federated " +"learning system. The full `source code " +"`_ for this example can be found in :code:`examples" +"/quickstart-pytorch`." msgstr "" -"我们现在有了一个完整的系统，可以执行联邦训练和联邦评估。它使用 ``weighted_average`` " -"函数汇总自定义评估指标，并在服务器端计算所有客户端的单一 ``accuracy`` 指标。" +"恭喜您！您已经成功构建并运行了第一个联邦学习系统。本示例的`完整源代码 " +"`_ 可以在 :code:`examples/quickstart-pytorch` 中找到。" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:699 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:-1 msgid "" -"The other two categories of metrics (``losses_centralized`` and " -"``metrics_centralized``) are still empty because they only apply when " -"centralized evaluation is being used. Part two of the Flower tutorial " -"will cover centralized evaluation." -msgstr "" -"其他两类指标（`losses_centralized`` 和 " -"`metrics_centralized`）仍然是空的，因为它们只适用于集中评估。Flower 教程的第二部分将介绍集中式评估。" +"Check out this Federated Learning quickstart tutorial for using Flower " +"with PyTorch Lightning to train an Auto Encoder model on MNIST." +msgstr "查看此联邦学习快速入门教程，了解如何使用 Flower 和 PyTorch Lightning 在 MNIST 上训练自动编码器模型。" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:711 -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:351 -msgid "Final remarks" -msgstr "结束语" +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:5 +msgid "Quickstart PyTorch Lightning" +msgstr "快速入门 PyTorch Lightning" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:713 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:10 msgid "" -"Congratulations, you just trained a convolutional neural network, " -"federated over 10 clients! With that, you understand the basics of " -"federated learning with Flower. The same approach you've seen can be used" -" with other machine learning frameworks (not just PyTorch) and tasks (not" -" just CIFAR-10 images classification), for example NLP with Hugging Face " -"Transformers or speech with SpeechBrain." -msgstr "" -"恭喜您，你刚刚训练了一个由 10 个客户端组成的卷积神经网络！这样，你就了解了使用 Flower " -"进行联邦学习的基础知识。你所看到的方法同样适用于其他机器学习框架（不只是 PyTorch）和任务（不只是 CIFAR-10 图像分类），例如使用 " -"Hugging Face Transformers 的 NLP 或使用 SpeechBrain 的语音。" +"Let's build a horizontal federated learning system using PyTorch " +"Lightning and Flower!" +msgstr "让我们使用 PyTorch Lightning 和 Flower 构建一个水平联邦学习系统！" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:715 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:12 msgid "" -"In the next notebook, we're going to cover some more advanced concepts. " -"Want to customize your strategy? Initialize parameters on the server " -"side? Or evaluate the aggregated model on the server side? We'll cover " -"all this and more in the next tutorial." -msgstr "在下一个笔记中，我们将介绍一些更先进的概念。想定制你的策略吗？在服务器端初始化参数？或者在服务器端评估聚合模型？我们将在下一个教程中介绍所有这些内容以及更多。" +"Please refer to the `full code example " +"`_ to learn more." +msgstr "" +"请参阅 `完整代码示例 `_ 了解更多信息。" -#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:733 +#: ../../source/tutorial-quickstart-scikitlearn.rst:-1 msgid "" -"The `Flower Federated Learning Tutorial - Part 2 " -"`__ goes into more depth about strategies and all " -"the advanced things you can build with them." -msgstr "" -"`Flower 联邦学习教程 - 第 2 部分 `__ 更深入地介绍了策略以及可以使用策略构建的所有高级功能。" +"Check out this Federated Learning quickstart tutorial for using Flower " +"with scikit-learn to train a linear regression model." +msgstr "查看此联邦学习快速入门教程，了解如何使用 Flower 和 scikit-learn 训练线性回归模型。" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:9 -msgid "Use a federated learning strategy" -msgstr "使用联邦学习策略" +#: ../../source/tutorial-quickstart-scikitlearn.rst:5 +msgid "Quickstart scikit-learn" +msgstr "scikit-learn快速入门" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:11 +#: ../../source/tutorial-quickstart-scikitlearn.rst:10 msgid "" -"Welcome to the next part of the federated learning tutorial. In previous " -"parts of this tutorial, we introduced federated learning with PyTorch and" -" Flower (`part 1 `__)." +"In this tutorial, we will learn how to train a :code:`Logistic " +"Regression` model on MNIST using Flower and scikit-learn." msgstr "" -"欢迎来到联邦学习教程的下一部分。在本教程的前几部分，我们介绍了使用 PyTorch 和 Flower 进行联邦学习（`第 1 部分 " -"`___）。" +"在本教程中，我们将学习如何使用 Flower 和 scikit-learn 在 MNIST 上训练一个 :code:`Logistic " +"Regression` 模型。" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:13 +#: ../../source/tutorial-quickstart-scikitlearn.rst:12 +#, fuzzy msgid "" -"In this notebook, we'll begin to customize the federated learning system " -"we built in the introductory notebook (again, using `Flower " -"`__ and `PyTorch `__)." +"It is recommended to create a virtual environment and run everything " +"within this :doc:`virtualenv `." msgstr "" -"在本笔记中，我们将开始定制在入门笔记中构建的联邦学习系统（再次使用 `Flower `__ 和 " -"`PyTorch `__）。" +"建议创建一个虚拟环境，并在此 `virtualenv `_ 中运行所有内容。" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:17 -msgid "Let's move beyond FedAvg with Flower strategies!" -msgstr "让我们超越 FedAvg，采用Flower策略！" +#: ../../source/tutorial-quickstart-scikitlearn.rst:16 +msgid "" +"*Clients* are responsible for generating individual model parameter " +"updates for the model based on their local datasets. These updates are " +"then sent to the *server* which will aggregate them to produce an updated" +" global model. Finally, the *server* sends this improved version of the " +"model back to each *client*. A complete cycle of parameters updates is " +"called a *round*." +msgstr "*客户端*负责根据其本地数据集为模型生成单独的模型参数更新。然后，这些参数更新将被发送到*服务器*，由*服务器*汇总后生成一个更新的全局模型。最后，*服务器*将这一改进版模型发回给每个*客户端*。一个完整的参数更新周期称为一*轮*。" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:309 -msgid "Strategy customization" -msgstr "策略定制" +#: ../../source/tutorial-quickstart-scikitlearn.rst:20 +msgid "" +"Now that we have a rough idea of what is going on, let's get started. We " +"first need to install Flower. You can do this by running:" +msgstr "现在，我们已经有了一个大致的概念，让我们开始吧。首先，我们需要安装 Flower。运行：" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:311 +#: ../../source/tutorial-quickstart-scikitlearn.rst:26 +#, fuzzy +msgid "Since we want to use scikit-learn, let's go ahead and install it:" +msgstr "既然我们要使用 scikt-learn，那就继续安装吧：" + +#: ../../source/tutorial-quickstart-scikitlearn.rst:32 +msgid "Or simply install all dependencies using Poetry:" +msgstr "或者直接使用 Poetry 安装所有依赖项：" + +#: ../../source/tutorial-quickstart-scikitlearn.rst:42 msgid "" -"So far, everything should look familiar if you've worked through the " -"introductory notebook. With that, we're ready to introduce a number of " -"new features." -msgstr "到目前为止，如果您已经阅读过入门笔记本，那么一切都应该很熟悉了。接下来，我们将介绍一些新功能。" +"Now that we have all our dependencies installed, let's run a simple " +"distributed training with two clients and one server. However, before " +"setting up the client and server, we will define all functionalities that" +" we need for our federated learning setup within :code:`utils.py`. The " +":code:`utils.py` contains different functions defining all the machine " +"learning basics:" +msgstr "" +"现在我们已经安装了所有的依赖项，让我们用两个客户端和一个服务器来运行一个简单的分布式训练。不过，在设置客户端和服务器之前，我们将在 " +":code:`utils.py` 中定义联邦学习设置所需的所有功能。:code:`utils.py`包含定义所有机器学习基础知识的不同函数：" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:323 -msgid "Server-side parameter **initialization**" -msgstr "服务器端参数 **初始化**" +#: ../../source/tutorial-quickstart-scikitlearn.rst:45 +msgid ":code:`get_model_parameters()`" +msgstr ":code:`get_model_parameters()`" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:325 +#: ../../source/tutorial-quickstart-scikitlearn.rst:46 +msgid "Returns the parameters of a :code:`sklearn` LogisticRegression model" +msgstr "返回 :code:`sklearn` LogisticRegression 模型的参数" + +#: ../../source/tutorial-quickstart-scikitlearn.rst:47 +msgid ":code:`set_model_params()`" +msgstr ":code:`set_model_params()`" + +#: ../../source/tutorial-quickstart-scikitlearn.rst:48 +#, fuzzy +msgid "Sets the parameters of a :code:`sklearn` LogisticRegression model" +msgstr "设置:code:`sklean`的LogisticRegression模型的参数" + +#: ../../source/tutorial-quickstart-scikitlearn.rst:50 +msgid ":code:`set_initial_params()`" +msgstr ":code:`set_initial_params()`" + +#: ../../source/tutorial-quickstart-scikitlearn.rst:50 +msgid "Initializes the model parameters that the Flower server will ask for" +msgstr "初始化 Flower 服务器将要求的模型参数" + +#: ../../source/tutorial-quickstart-scikitlearn.rst:52 msgid "" -"Flower, by default, initializes the global model by asking one random " -"client for the initial parameters. In many cases, we want more control " -"over parameter initialization though. Flower therefore allows you to " -"directly pass the initial parameters to the Strategy:" +"Please check out :code:`utils.py` `here " +"`_ for more details. The pre-defined functions are used in" +" the :code:`client.py` and imported. The :code:`client.py` also requires " +"to import several packages such as Flower and scikit-learn:" msgstr "" -"默认情况下，Flower 会通过向一个随机客户端询问初始参数来初始化全局模型。但在许多情况下，我们需要对参数初始化进行更多控制。因此，Flower" -" 允许您直接将初始参数传递给策略：" +"更多详情请查看 :code:`utils.py`` 这里 " +"`_。在 :code:`client.py` 中使用并导入了预定义函数。:code:`client.py` " +"还需要导入几个软件包，如 Flower 和 scikit-learn：" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:370 +#: ../../source/tutorial-quickstart-scikitlearn.rst:67 +#, fuzzy msgid "" -"Passing ``initial_parameters`` to the ``FedAvg`` strategy prevents Flower" -" from asking one of the clients for the initial parameters. If we look " -"closely, we can see that the logs do not show any calls to the " -"``FlowerClient.get_parameters`` method." +"Prior to local training, we need to load the MNIST dataset, a popular " +"image classification dataset of handwritten digits for machine learning, " +"and partition the dataset for FL. This can be conveniently achieved using" +" `Flower Datasets `_. The " +":code:`FederatedDataset.load_partition()` method loads the partitioned " +"training set for each partition ID defined in the :code:`--partition-id` " +"argument." msgstr "" -"向 ``FedAvg`` 策略传递 ``initial_parameters`` 可以防止 Flower " -"向其中一个客户端询问初始参数。如果我们仔细观察，就会发现日志中没有显示对 ``FlowerClient.get_parameters`` " -"方法的任何调用。" +"在本地训练之前，我们需要加载 MNIST 数据集（一个用于机器学习的流行手写数字图像分类数据集），并对数据集进行 FL 分区。使用 " +"\"Flower Datasets " +"`_\"可以方便地实现这一点。:code:`FederatedDataset.load_partition()`" +" 方法为 :code:`--partition-id` 参数中定义的每个分区 ID 加载分区训练集。" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:382 -msgid "Starting with a customized strategy" -msgstr "从定制战略开始" +#: ../../source/tutorial-quickstart-scikitlearn.rst:95 +msgid "" +"Next, the logistic regression model is defined and initialized with " +":code:`utils.set_initial_params()`." +msgstr "接下来，使用 :code:`utils.set_initial_params()` 对逻辑回归模型进行定义和初始化。" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:384 +#: ../../source/tutorial-quickstart-scikitlearn.rst:107 msgid "" -"We've seen the function ``start_simulation`` before. It accepts a number " -"of arguments, amongst them the ``client_fn`` used to create " -"``FlowerClient`` instances, the number of clients to simulate " -"``num_clients``, the number of rounds ``num_rounds``, and the strategy." +"The Flower server interacts with clients through an interface called " +":code:`Client`. When the server selects a particular client for training," +" it sends training instructions over the network. The client receives " +"those instructions and calls one of the :code:`Client` methods to run " +"your code (i.e., to fit the logistic regression we defined earlier)." msgstr "" -"我们以前见过函数 ``start_simulation``。它接受许多参数，其中包括用于创建 ``FlowerClient`` 实例的 " -"``client_fn``、要模拟的客户数量 ``num_clients``、回合数 ``num_rounds``和策略。" +"Flower 服务器通过一个名为 :code:`Client` " +"的接口与客户端交互。当服务器选择一个特定的客户端进行训练时，它会通过网络发送训练指令。客户端接收到这些指令后，会调用 :code:`Client`" +" 方法之一来运行您的代码（即拟合我们之前定义的逻辑回归）。" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:386 +#: ../../source/tutorial-quickstart-scikitlearn.rst:113 msgid "" -"The strategy encapsulates the federated learning approach/algorithm, for " -"example, ``FedAvg`` or ``FedAdagrad``. Let's try to use a different " -"strategy this time:" -msgstr "该策略封装了联邦学习方法/算法，例如`FedAvg``或`FedAdagrad``。这次让我们尝试使用不同的策略：" +"Flower provides a convenience class called :code:`NumPyClient` which " +"makes it easier to implement the :code:`Client` interface when your " +"workload uses scikit-learn. Implementing :code:`NumPyClient` usually " +"means defining the following methods (:code:`set_parameters` is optional " +"though):" +msgstr "" +"Flower 提供了一个名为 :code:`NumPyClient` 的便捷类，当你的工作负载使用 scikit-learn " +"时，它可以让你更容易地实现 :code:`Client` 接口。实现 :code:`NumPyClient` " +"通常意味着定义以下方法（:code:`set_parameters` 是可选的）：" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:424 -msgid "Server-side parameter **evaluation**" -msgstr "服务器端参数**评估**" +#: ../../source/tutorial-quickstart-scikitlearn.rst:122 +msgid "is directly imported with :code:`utils.set_model_params()`" +msgstr "直接导入 :code:`utils.set_model_params()`" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:426 -msgid "" -"Flower can evaluate the aggregated model on the server-side or on the " -"client-side. Client-side and server-side evaluation are similar in some " -"ways, but different in others." -msgstr "Flower 可以在服务器端或客户端评估聚合模型。客户端和服务器端评估在某些方面相似，但也有不同之处。" +#: ../../source/tutorial-quickstart-scikitlearn.rst:130 +msgid "The methods can be implemented in the following way:" +msgstr "这些方法可以通过以下方式实现：" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:428 +#: ../../source/tutorial-quickstart-scikitlearn.rst:153 msgid "" -"**Centralized Evaluation** (or *server-side evaluation*) is conceptually " -"simple: it works the same way that evaluation in centralized machine " -"learning does. If there is a server-side dataset that can be used for " -"evaluation purposes, then that's great. We can evaluate the newly " -"aggregated model after each round of training without having to send the " -"model to clients. We're also fortunate in the sense that our entire " -"evaluation dataset is available at all times." -msgstr "**集中评估**（或*服务器端评估*）在概念上很简单：它的工作方式与集中式机器学习中的评估方式相同。如果有一个服务器端数据集可用于评估目的，那就太好了。我们可以在每一轮训练后对新聚合的模型进行评估，而无需将模型发送给客户端。我们也很幸运，因为我们的整个评估数据集随时可用。" +"We can now create an instance of our class :code:`MnistClient` and add " +"one line to actually run this client:" +msgstr "现在我们可以创建一个 :code:`MnistClient` 类的实例，并添加一行来实际运行该客户端：" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:430 +#: ../../source/tutorial-quickstart-scikitlearn.rst:160 +#, fuzzy msgid "" -"**Federated Evaluation** (or *client-side evaluation*) is more complex, " -"but also more powerful: it doesn't require a centralized dataset and " -"allows us to evaluate models over a larger set of data, which often " -"yields more realistic evaluation results. In fact, many scenarios require" -" us to use **Federated Evaluation** if we want to get representative " -"evaluation results at all. But this power comes at a cost: once we start " -"to evaluate on the client side, we should be aware that our evaluation " -"dataset can change over consecutive rounds of learning if those clients " -"are not always available. Moreover, the dataset held by each client can " -"also change over consecutive rounds. This can lead to evaluation results " -"that are not stable, so even if we would not change the model, we'd see " -"our evaluation results fluctuate over consecutive rounds." -msgstr "**联邦评估**（或*客户端评估*）更为复杂，但也更为强大：它不需要集中的数据集，允许我们在更大的数据集上对模型进行评估，这通常会产生更真实的评估结果。事实上，如果我们想得到有代表性的评估结果，很多情况下都需要使用**联邦评估**。但是，这种能力是有代价的：一旦我们开始在客户端进行评估，我们就应该意识到，如果这些客户端并不总是可用，我们的评估数据集可能会在连续几轮学习中发生变化。此外，每个客户端所拥有的数据集也可能在连续几轮学习中发生变化。这可能会导致评估结果不稳定，因此即使我们不改变模型，也会看到评估结果在连续几轮中波动。" +"That's it for the client. We only have to implement :code:`Client` or " +":code:`NumPyClient` and call :code:`fl.client.start_client()`. If you " +"implement a client of type :code:`NumPyClient` you'll need to first call " +"its :code:`to_client()` method. The string :code:`\"0.0.0.0:8080\"` tells" +" the client which server to connect to. In our case we can run the server" +" and the client on the same machine, therefore we use " +":code:`\"0.0.0.0:8080\"`. If we run a truly federated workload with the " +"server and clients running on different machines, all that needs to " +"change is the :code:`server_address` we pass to the client." +msgstr "" +"这就是客户端。我们只需实现 :code:`Client` 或 :code:`NumPyClient` 并调用 " +":code:`fl.client.start_client()` 或 " +":code:`fl.client.start_numpy_client()`。字符串 " +":code:`\"0.0.0.0:8080\"`会告诉客户端要连接的服务器。在本例中，我们可以在同一台机器上运行服务器和客户端，因此我们使用 " +":code:`\"0.0.0.0:8080\"`。如果我们运行的是真正的联邦工作负载，服务器和客户端运行在不同的机器上，那么需要改变的只是传递给客户端的" +" :code:`server_address`。" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:433 +#: ../../source/tutorial-quickstart-scikitlearn.rst:169 msgid "" -"We've seen how federated evaluation works on the client side (i.e., by " -"implementing the ``evaluate`` method in ``FlowerClient``). Now let's see " -"how we can evaluate aggregated model parameters on the server-side:" +"The following Flower server is a little bit more advanced and returns an " +"evaluation function for the server-side evaluation. First, we import " +"again all required libraries such as Flower and scikit-learn." msgstr "" -"我们已经了解了联邦评估如何在客户端工作（即通过在 ``FlowerClient`` 中实现 ``evaluate`` " -"方法）。现在让我们看看如何在服务器端评估聚合模型参数：" +"下面的 Flower 服务器更先进一些，会返回一个用于服务器端评估的评估函数。首先，我们再次导入所有需要的库，如 Flower 和 scikit-" +"learn。" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:490 -msgid "Sending/receiving arbitrary values to/from clients" -msgstr "向/从客户端发送/接收任意值" +#: ../../source/tutorial-quickstart-scikitlearn.rst:172 +msgid ":code:`server.py`, import Flower and start the server:" +msgstr ":code:`server.py`, 导入 Flower 并启动服务器：" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:492 +#: ../../source/tutorial-quickstart-scikitlearn.rst:185 +#, fuzzy msgid "" -"In some situations, we want to configure client-side execution (training," -" evaluation) from the server-side. One example for that is the server " -"asking the clients to train for a certain number of local epochs. Flower " -"provides a way to send configuration values from the server to the " -"clients using a dictionary. Let's look at an example where the clients " -"receive values from the server through the ``config`` parameter in " -"``fit`` (``config`` is also available in ``evaluate``). The ``fit`` " -"method receives the configuration dictionary through the ``config`` " -"parameter and can then read values from this dictionary. In this example," -" it reads ``server_round`` and ``local_epochs`` and uses those values to " -"improve the logging and configure the number of local training epochs:" +"The number of federated learning rounds is set in :code:`fit_round()` and" +" the evaluation is defined in :code:`get_evaluate_fn()`. The evaluation " +"function is called after each federated learning round and gives you " +"information about loss and accuracy. Note that we also make use of Flower" +" Datasets here to load the test split of the MNIST dataset for server-" +"side evaluation." msgstr "" -"在某些情况下，我们希望从服务器端配置客户端的执行（训练、评估）。其中一个例子就是服务器要求客户端训练一定数量的本地遍历。Flower " -"提供了一种使用字典从服务器向客户端发送配置值的方法。让我们来看一个例子：客户端通过 ``fit`` 中的 ``config`` " -"参数从服务器接收配置值（``evaluate`` 中也有 ``config`` 参数）。``fit`` 方法通过 ``config`` " -"参数接收配置字典，然后从字典中读取值。在本例中，它读取了 ``server_round`` 和 " -"``local_epochs``，并使用这些值来改进日志记录和配置本地训练遍历的数量：" +"联邦学习轮数在 :code:`fit_round()` 中设置，评估在 :code:`get_evaluate_fn()` " +"中定义。每轮联邦学习后都会调用评估函数，并提供有关损失值和准确率的信息。" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:546 +#: ../../source/tutorial-quickstart-scikitlearn.rst:213 msgid "" -"So how can we send this config dictionary from server to clients? The " -"built-in Flower Strategies provide way to do this, and it works similarly" -" to the way server-side evaluation works. We provide a function to the " -"strategy, and the strategy calls this function for every round of " -"federated learning:" +"The :code:`main` contains the server-side parameter initialization " +":code:`utils.set_initial_params()` as well as the aggregation strategy " +":code:`fl.server.strategy:FedAvg()`. The strategy is the default one, " +"federated averaging (or FedAvg), with two clients and evaluation after " +"each federated learning round. The server can be started with the command" +" :code:`fl.server.start_server(server_address=\"0.0.0.0:8080\", " +"strategy=strategy, config=fl.server.ServerConfig(num_rounds=3))`." msgstr "" -"那么，如何将配置字典从服务器发送到客户端呢？内置的 \"Flower策略\"（Flower " -"Strategies）提供了这样的方法，其工作原理与服务器端评估的工作原理类似。我们为策略提供一个函数，策略会在每一轮联邦学习中调用这个函数：" +":code:`main`包含服务器端参数初始化:code:`utils.set_initial_params()`以及聚合策略 " +":code:`fl.server.strategy:FedAvg()`。该策略是默认的联邦平均（或 " +"FedAvg）策略，有两个客户端，在每轮联邦学习后进行评估。可以使用 " +":code:`fl.server.start_server(server_address=\"0.0.0.0:8080\", " +"strategy=strategy, config=fl.server.ServerConfig(num_rounds=3))` 命令启动服务器。" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:576 +#: ../../source/tutorial-quickstart-scikitlearn.rst:232 msgid "" -"Next, we'll just pass this function to the FedAvg strategy before " -"starting the simulation:" -msgstr "接下来，我们只需在开始模拟前将此函数传递给 FedAvg 策略即可：" +"With both client and server ready, we can now run everything and see " +"federated learning in action. Federated learning systems usually have a " +"server and multiple clients. We, therefore, have to start the server " +"first:" +msgstr "客户端和服务器都准备就绪后，我们现在就可以运行一切，看看联邦学习的运行情况。联邦学习系统通常有一个服务器和多个客户端。因此，我们必须先启动服务器：" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:613 +#: ../../source/tutorial-quickstart-scikitlearn.rst:286 msgid "" -"As we can see, the client logs now include the current round of federated" -" learning (which they read from the ``config`` dictionary). We can also " -"configure local training to run for one epoch during the first and second" -" round of federated learning, and then for two epochs during the third " -"round." +"Congratulations! You've successfully built and run your first federated " +"learning system. The full `source code " +"`_ for this example can be found in :code:`examples/sklearn-logreg-" +"mnist`." msgstr "" -"我们可以看到，客户端日志现在包含了当前一轮的联邦学习（从 ``config`` " -"字典中读取）。我们还可以将本地训练配置为在第一轮和第二轮联邦学习期间运行一个遍历，然后在第三轮联邦学习期间运行两个遍历。" +"恭喜您！您已经成功构建并运行了第一个联邦学习系统。本示例的`完整源代码 " +"`_ 可以在 :code:`examples/sklearn-logreg-mnist` 中找到。" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:615 +#: ../../source/tutorial-quickstart-tensorflow.rst:-1 msgid "" -"Clients can also return arbitrary values to the server. To do so, they " -"return a dictionary from ``fit`` and/or ``evaluate``. We have seen and " -"used this concept throughout this notebook without mentioning it " -"explicitly: our ``FlowerClient`` returns a dictionary containing a custom" -" key/value pair as the third return value in ``evaluate``." -msgstr "" -"客户端还可以向服务器返回任意值。为此，它们会从 ``fit`` 和/或 ``evaluate`` " -"返回一个字典。我们在本笔记中看到并使用了这一概念，但并未明确提及：我们的 ``FlowerClient`` 返回一个包含自定义键/值对的字典，作为" -" ``evaluate`` 中的第三个返回值。" +"Check out this Federated Learning quickstart tutorial for using Flower " +"with TensorFlow to train a MobilNetV2 model on CIFAR-10." +msgstr "查看此联邦学习快速入门教程，了解如何使用 Flower 和 TensorFlow 在 CIFAR-10 上训练 MobilNetV2 模型。" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:627 -msgid "Scaling federated learning" -msgstr "扩大联邦学习的规模" +#: ../../source/tutorial-quickstart-tensorflow.rst:5 +msgid "Quickstart TensorFlow" +msgstr "快速入门 TensorFlow" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:629 +#: ../../source/tutorial-quickstart-tensorflow.rst:13 +msgid "Let's build a federated learning system in less than 20 lines of code!" +msgstr "让我们用不到 20 行代码构建一个联邦学习系统！" + +#: ../../source/tutorial-quickstart-tensorflow.rst:15 +msgid "Before Flower can be imported we have to install it:" +msgstr "在导入 Flower 之前，我们必须先安装它：" + +#: ../../source/tutorial-quickstart-tensorflow.rst:21 msgid "" -"As a last step in this notebook, let's see how we can use Flower to " -"experiment with a large number of clients." -msgstr "作为本笔记的最后一步，让我们看看如何使用 Flower 对大量客户端进行实验。" +"Since we want to use the Keras API of TensorFlow (TF), we have to install" +" TF as well:" +msgstr "由于我们要使用 TensorFlow (TF) 的 Keras API，因此还必须安装 TF：" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:651 -#, python-format +#: ../../source/tutorial-quickstart-tensorflow.rst:31 +msgid "Next, in a file called :code:`client.py`, import Flower and TensorFlow:" +msgstr "接下来，在名为 :code:`client.py` 的文件中导入 Flower 和 TensorFlow：" + +#: ../../source/tutorial-quickstart-tensorflow.rst:38 msgid "" -"We now have 1000 partitions, each holding 45 training and 5 validation " -"examples. Given that the number of training examples on each client is " -"quite small, we should probably train the model a bit longer, so we " -"configure the clients to perform 3 local training epochs. We should also " -"adjust the fraction of clients selected for training during each round " -"(we don't want all 1000 clients participating in every round), so we " -"adjust ``fraction_fit`` to ``0.05``, which means that only 5% of " -"available clients (so 50 clients) will be selected for training each " -"round:" +"We use the Keras utilities of TF to load CIFAR10, a popular colored image" +" classification dataset for machine learning. The call to " +":code:`tf.keras.datasets.cifar10.load_data()` downloads CIFAR10, caches " +"it locally, and then returns the entire training and test set as NumPy " +"ndarrays." msgstr "" -"现在我们有 1000 个分区，每个分区有 45 个训练数据和 5 " -"个验证数据。鉴于每个客户端上的训练示例数量较少，我们可能需要对模型进行更长时间的训练，因此我们将客户端配置为执行 3 " -"个本地训练遍历。我们还应该调整每轮训练中被选中的客户端的比例（我们不希望每轮训练都有 1000 个客户端参与），因此我们将 " -"``fraction_fit`` 调整为 ``0.05``，这意味着每轮训练只选中 5%的可用客户端（即 50 个客户端）：" +"我们使用 TF 的 Keras 实用程序加载 CIFAR10，这是一个用于机器学习的流行彩色图像分类数据集。调用 " +":code:`tf.keras.datasets.cifar10.load_data()` 会下载 CIFAR10，将其缓存到本地，然后以 " +"NumPy ndarrays 的形式返回整个训练集和测试集。" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:699 +#: ../../source/tutorial-quickstart-tensorflow.rst:47 msgid "" -"In this notebook, we've seen how we can gradually enhance our system by " -"customizing the strategy, initializing parameters on the server side, " -"choosing a different strategy, and evaluating models on the server-side. " -"That's quite a bit of flexibility with so little code, right?" -msgstr "在本笔记中，我们看到了如何通过自定义策略、在服务器端初始化参数、选择不同的策略以及在服务器端评估模型来逐步增强我们的系统。用这么少的代码就能实现这么大的灵活性，不是吗？" +"Next, we need a model. For the purpose of this tutorial, we use " +"MobilNetV2 with 10 output classes:" +msgstr "接下来，我们需要一个模型。在本教程中，我们使用带有 10 个输出类的 MobilNetV2：" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:701 +#: ../../source/tutorial-quickstart-tensorflow.rst:60 msgid "" -"In the later sections, we've seen how we can communicate arbitrary values" -" between server and clients to fully customize client-side execution. " -"With that capability, we built a large-scale Federated Learning " -"simulation using the Flower Virtual Client Engine and ran an experiment " -"involving 1000 clients in the same workload - all in a Jupyter Notebook!" +"Flower provides a convenience class called :code:`NumPyClient` which " +"makes it easier to implement the :code:`Client` interface when your " +"workload uses Keras. The :code:`NumPyClient` interface defines three " +"methods which can be implemented in the following way:" msgstr "" -"在后面的章节中，我们将看到如何在服务器和客户端之间传递任意值，以完全自定义客户端执行。有了这种能力，我们使用 Flower " -"虚拟客户端引擎构建了一个大规模的联邦学习模拟，并在 Jupyter Notebook 中进行了一次实验，在相同的工作负载中运行了 1000 " -"个客户端！" +"Flower 提供了一个名为 :code:`NumPyClient` 的便捷类，当您的工作负载使用 Keras 时，该类可以更轻松地实现 " +":code:`Client` 接口。:code:`NumPyClient` 接口定义了三个方法，可以通过以下方式实现：" -#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:719 -msgid "" -"The `Flower Federated Learning Tutorial - Part 3 " -"`__ shows how to build a fully custom ``Strategy`` from " -"scratch." -msgstr "" -"`Flower 联邦学习教程 - 第 3 部分 `__ 展示了如何从头开始构建完全自定义的 \"策略\"。" +#: ../../source/tutorial-quickstart-tensorflow.rst:135 +msgid "Each client will have its own dataset." +msgstr "每个客户都有自己的数据集。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:9 -msgid "What is Federated Learning?" -msgstr "什么是联邦学习？" +#: ../../source/tutorial-quickstart-tensorflow.rst:137 +msgid "" +"You should now see how the training does in the very first terminal (the " +"one that started the server):" +msgstr "现在你应该能在第一个终端（启动服务器的终端）看到训练的效果了：" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:13 +#: ../../source/tutorial-quickstart-tensorflow.rst:169 msgid "" -"In this tutorial, you will learn what federated learning is, build your " -"first system in Flower, and gradually extend it. If you work through all " -"parts of the tutorial, you will be able to build advanced federated " -"learning systems that approach the current state of the art in the field." +"Congratulations! You've successfully built and run your first federated " +"learning system. The full `source code " +"`_ for this can be found in :code:`examples" +"/quickstart-tensorflow/client.py`." msgstr "" -"在本教程中，你将了解什么是联邦学习，用 Flower " -"搭建第一个系统，并逐步对其进行扩展。如果你能完成本教程的所有部分，你就能构建高级的联邦学习系统，从而接近该领域当前的技术水平。" +"恭喜您！您已经成功构建并运行了第一个联邦学习系统。`完整的源代码 " +"`_ 可以在 :code:`examples/quickstart-" +"tensorflow/client.py` 中找到。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:15 +#: ../../source/tutorial-quickstart-xgboost.rst:-1 msgid "" -"🧑‍🏫 This tutorial starts at zero and expects no familiarity with " -"federated learning. Only a basic understanding of data science and Python" -" programming is assumed." -msgstr "🧑‍🏫 本教程从零开始，不要求熟悉联邦学习。仅假定对数据科学和 Python 编程有基本了解。" +"Check out this Federated Learning quickstart tutorial for using Flower " +"with XGBoost to train classification models on trees." +msgstr "查看此联邦学习 快速入门教程，了解如何使用 Flower 和 XGBoost 上训练分类模型。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:17 +#: ../../source/tutorial-quickstart-xgboost.rst:5 +msgid "Quickstart XGBoost" +msgstr "XGBoost快速入门" + +#: ../../source/tutorial-quickstart-xgboost.rst:14 +msgid "Federated XGBoost" +msgstr "联邦化 XGBoost" + +#: ../../source/tutorial-quickstart-xgboost.rst:16 msgid "" -"`Star Flower on GitHub `__ ⭐️ and join " -"the open-source Flower community on Slack to connect, ask questions, and " -"get help: `Join Slack `__ 🌼 We'd love to " -"hear from you in the ``#introductions`` channel! And if anything is " -"unclear, head over to the ``#questions`` channel." +"EXtreme Gradient Boosting (**XGBoost**) is a robust and efficient " +"implementation of gradient-boosted decision tree (**GBDT**), that " +"maximises the computational boundaries for boosted tree methods. It's " +"primarily designed to enhance both the performance and computational " +"speed of machine learning models. In XGBoost, trees are constructed " +"concurrently, unlike the sequential approach taken by GBDT." msgstr "" -"`Star Flower on GitHub `__ ⭐️ 并加入 Slack " -"上的开源 Flower 社区，进行交流、提问并获得帮助： 加入 Slack `__ 🌼" -" 我们希望在 ``#introductions`` 频道听到您的声音！如果有任何不清楚的地方，请访问 ``#questions`` 频道。" +"EXtreme Gradient " +"Boosting（**XGBoost**）是梯度提升决策树（**GBDT**）的一种稳健而高效的实现方法，能最大限度地提高提升树方法的计算边界。它主要用于提高机器学习模型的性能和计算速度。在" +" XGBoost 中，决策树是并发构建的，与 GBDT 采用的顺序方法不同。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:19 -msgid "Let's get started!" -msgstr "让我们开始吧！" +#: ../../source/tutorial-quickstart-xgboost.rst:20 +msgid "" +"Often, for tabular data on medium-sized datasets with fewer than 10k " +"training examples, XGBoost surpasses the results of deep learning " +"techniques." +msgstr "对于训练示例少于 10k 的中型数据集上的表格数据，XGBoost 的结果往往超过深度学习技术。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:31 -msgid "Classic machine learning" -msgstr "经典机器学习" +#: ../../source/tutorial-quickstart-xgboost.rst:23 +msgid "Why federated XGBoost?" +msgstr "为什么选择联邦 XGBoost？" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:33 +#: ../../source/tutorial-quickstart-xgboost.rst:25 msgid "" -"Before we begin to discuss federated learning, let us quickly recap how " -"most machine learning works today." -msgstr "在开始讨论联邦学习之前，让我们先快速回顾一下目前大多数机器学习的工作原理。" +"Indeed, as the demand for data privacy and decentralized learning grows, " +"there's an increasing requirement to implement federated XGBoost systems " +"for specialised applications, like survival analysis and financial fraud " +"detection." +msgstr "事实上，随着对数据隐私和分散学习的需求不断增长，越来越多的专业应用（如生存分析和金融欺诈检测）需要实施联邦 XGBoost 系统。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:35 +#: ../../source/tutorial-quickstart-xgboost.rst:27 msgid "" -"In machine learning, we have a model, and we have data. The model could " -"be a neural network (as depicted here), or something else, like classical" -" linear regression." -msgstr "在机器学习中，我们有一个模型和数据。模型可以是一个神经网络（如图所示），也可以是其他东西，比如经典的线性回归。" - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:41 -msgid "|2b5c62c529f6416f840c594cce062fbb|" +"Federated learning ensures that raw data remains on the local device, " +"making it an attractive approach for sensitive domains where data " +"security and privacy are paramount. Given the robustness and efficiency " +"of XGBoost, combining it with federated learning offers a promising " +"solution for these specific challenges." msgstr "" +"联邦学习可确保原始数据保留在本地设备上，因此对于数据安全和隐私至关重要的敏感领域来说，这是一种极具吸引力的方法。鉴于 XGBoost " +"的稳健性和高效性，将其与联邦学习相结合为应对这些特定挑战提供了一种前景广阔的解决方案。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:109 -msgid "Model and data" -msgstr "模型和数据" - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:47 +#: ../../source/tutorial-quickstart-xgboost.rst:30 msgid "" -"We train the model using the data to perform a useful task. A task could " -"be to detect objects in images, transcribe an audio recording, or play a " -"game like Go." -msgstr "我们使用数据来训练模型，以完成一项有用的任务。任务可以是检测图像中的物体、转录音频或玩围棋等游戏。" - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:53 -msgid "|90b334680cb7467d9a04d39b8e8dca9f|" +"In this tutorial we will learn how to train a federated XGBoost model on " +"HIGGS dataset using Flower and :code:`xgboost` package. We use a simple " +"example (`full code xgboost-quickstart " +"`_)" +" with two *clients* and one *server* to demonstrate how federated XGBoost" +" works, and then we dive into a more complex example (`full code xgboost-" +"comprehensive `_) to run various experiments." msgstr "" +"在本教程中，我们将学习如何使用 Flower 和 :code:`xgboost` 软件包在 HIGGS 数据集上训练联邦 XGBoost " +"模型。我们将使用一个包含两个 * 客户端* 和一个 * 服务器* 的简单示例 (`完整代码 xgboost-quickstart " +"`_)来演示联邦 XGBoost 如何工作，然后我们将深入到一个更复杂的示例 (`完整代码 xgboost-" +"comprehensive `_)，以运行各种实验。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:111 -msgid "Train model using data" -msgstr "使用数据训练模型" +#: ../../source/tutorial-quickstart-xgboost.rst:37 +msgid "Environment Setup" +msgstr "环境设定" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:59 +#: ../../source/tutorial-quickstart-xgboost.rst:41 msgid "" -"Now, in practice, the training data we work with doesn't originate on the" -" machine we train the model on. It gets created somewhere else." -msgstr "实际上，我们使用的训练数据并不来自我们训练模型的机器。它是在其他地方创建的。" +"We first need to install Flower and Flower Datasets. You can do this by " +"running :" +msgstr "我们首先需要安装 Flower 和 Flower Datasets。您可以通过运行 ：" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:61 +#: ../../source/tutorial-quickstart-xgboost.rst:47 msgid "" -"It originates on a smartphone by the user interacting with an app, a car " -"collecting sensor data, a laptop receiving input via the keyboard, or a " -"smart speaker listening to someone trying to sing a song." -msgstr "它源于智能手机上用户与应用程序的交互、汽车上传感器数据的收集、笔记本电脑上键盘输入的接收，或者智能扬声器上某人试着唱的歌。" - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:67 -msgid "|65764ceee89f4335bfd93fd0b115e831|" -msgstr "" - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:113 -msgid "Data on a phone" -msgstr "手机上的数据" +"Since we want to use :code:`xgboost` package to build up XGBoost trees, " +"let's go ahead and install :code:`xgboost`:" +msgstr "既然我们要使用 :code:`xgboost` 软件包来构建 XGBoost 树，那就继续安装 :code:`xgboost`：" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:73 +#: ../../source/tutorial-quickstart-xgboost.rst:57 msgid "" -"What's also important to mention, this \"somewhere else\" is usually not " -"just one place, it's many places. It could be several devices all running" -" the same app. But it could also be several organizations, all generating" -" data for the same task." -msgstr "" -"值得一提的是，这个 \"其他地方 " -"\"通常不只是一个地方，而是很多地方。它可能是多个运行同一应用程序的设备。但也可能是多个组织，都在为同一任务生成数据。" +"*Clients* are responsible for generating individual weight-updates for " +"the model based on their local datasets. Now that we have all our " +"dependencies installed, let's run a simple distributed training with two " +"clients and one server." +msgstr "*客户端*负责根据其本地数据集为模型生成单独的模型参数更新。现在我们已经安装了所有的依赖项，让我们用两个客户端和一个服务器来运行一个简单的分布式训练。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:79 -msgid "|d97319ec28bb407ea0ab9705e38f3bcf|" -msgstr "" +#: ../../source/tutorial-quickstart-xgboost.rst:60 +msgid "" +"In a file called :code:`client.py`, import xgboost, Flower, Flower " +"Datasets and other related functions:" +msgstr "在名为 :code:`client.py` 的文件中，导入 xgboost、Flower、Flower Datasets 和其他相关函数：" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:115 -msgid "Data is on many devices" -msgstr "数据存在于多种设备中" +#: ../../source/tutorial-quickstart-xgboost.rst:87 +msgid "Dataset partition and hyper-parameter selection" +msgstr "数据集划分和超参数选择" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:85 +#: ../../source/tutorial-quickstart-xgboost.rst:89 msgid "" -"So to use machine learning, or any kind of data analysis, the approach " -"that has been used in the past was to collect all data on a central " -"server. This server can be somewhere in a data center, or somewhere in " -"the cloud." -msgstr "因此，要使用机器学习或任何类型的数据分析，过去使用的方法是在中央服务器上收集所有数据。这个服务器可以在数据中心的某个地方，也可以在云端的某个地方。" +"Prior to local training, we require loading the HIGGS dataset from Flower" +" Datasets and conduct data partitioning for FL:" +msgstr "在本地训练之前，我们需要从 Flower Datasets 加载 HIGGS 数据集，并对 FL 进行数据分区：" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:91 -msgid "|11e95ac83a8548d8b3505b4663187d07|" +#: ../../source/tutorial-quickstart-xgboost.rst:102 +msgid "" +"In this example, we split the dataset into two partitions with uniform " +"distribution (:code:`IidPartitioner(num_partitions=2)`). Then, we load " +"the partition for the given client based on :code:`node_id`:" msgstr "" +"在此示例中，我们将数据集分割成两个均匀分布的分区（:code:`IidPartitioner(num_partitions=2)`）。然后，我们根据" +" :code:`node_id` 为给定客户端加载分区：" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:117 -msgid "Central data collection" -msgstr "集中数据收集" - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:97 +#: ../../source/tutorial-quickstart-xgboost.rst:121 msgid "" -"Once all the data is collected in one place, we can finally use machine " -"learning algorithms to train our model on the data. This is the machine " -"learning approach that we've basically always relied on." -msgstr "一旦所有数据都收集到一处，我们最终就可以使用机器学习算法在数据上训练我们的模型。这就是我们基本上一直依赖的机器学习方法。" - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:103 -msgid "|1dab2f3a23674abc8a6731f20fa10730|" -msgstr "" +"After that, we do train/test splitting on the given partition (client's " +"local data), and transform data format for :code:`xgboost` package." +msgstr "然后，我们在给定的分区（客户端的本地数据）上进行训练/测试分割，并为 :code:`xgboost` 软件包转换数据格式。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:119 -msgid "Central model training" -msgstr "集中模型训练" +#: ../../source/tutorial-quickstart-xgboost.rst:134 +msgid "" +"The functions of :code:`train_test_split` and " +":code:`transform_dataset_to_dmatrix` are defined as below:" +msgstr ":code:`train_test_split` 和 :code:`transform_dataset_too_dmatrix` 的函数定义如下：" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:130 -msgid "Challenges of classical machine learning" -msgstr "经典机器学习面临的挑战" +#: ../../source/tutorial-quickstart-xgboost.rst:158 +msgid "Finally, we define the hyper-parameters used for XGBoost training." +msgstr "最后，我们定义了用于 XGBoost 训练的超参数。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:132 +#: ../../source/tutorial-quickstart-xgboost.rst:174 msgid "" -"The classic machine learning approach we've just seen can be used in some" -" cases. Great examples include categorizing holiday photos, or analyzing " -"web traffic. Cases, where all the data is naturally available on a " -"centralized server." -msgstr "我们刚刚看到的经典机器学习方法可以在某些情况下使用。很好的例子包括对假日照片进行分类或分析网络流量。在这些案例中，所有数据自然都可以在中央服务器上获得。" - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:138 -msgid "|7f0ee162da38450788493a21627306f7|" +"The :code:`num_local_round` represents the number of iterations for local" +" tree boost. We use CPU for the training in default. One can shift it to " +"GPU by setting :code:`tree_method` to :code:`gpu_hist`. We use AUC as " +"evaluation metric." msgstr "" +"代码:`num_local_round`表示本地树的迭代次数。我们默认使用 CPU 进行训练。可以通过将 :code:`tree_method` " +"设置为 :code:`gpu_hist`，将其转换为 GPU。我们使用 AUC 作为评估指标。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:173 -msgid "Centralized possible" -msgstr "可集中管理" +#: ../../source/tutorial-quickstart-xgboost.rst:181 +msgid "Flower client definition for XGBoost" +msgstr "用于 XGBoost 的 Flower 客户端定义" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:144 +#: ../../source/tutorial-quickstart-xgboost.rst:183 msgid "" -"But the approach can not be used in many other cases. Cases, where the " -"data is not available on a centralized server, or cases where the data " -"available on one server is not enough to train a good model." -msgstr "但这种方法并不适用于许多其他情况。例如，集中服务器上没有数据，或者一台服务器上的数据不足以训练出一个好的模型。" - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:150 -msgid "|296a1fb72c514b23b3d8905ff0ff98c6|" +"After loading the dataset we define the Flower client. We follow the " +"general rule to define :code:`XgbClient` class inherited from " +":code:`fl.client.Client`." msgstr "" +"加载数据集后，我们定义 Flower 客户端。我们按照一般规则定义从 :code:`fl.client.Client` 继承而来的 " +":code:`XgbClient` 类。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:175 -msgid "Centralized impossible" -msgstr "无法集中" - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:156 +#: ../../source/tutorial-quickstart-xgboost.rst:193 msgid "" -"There are many reasons why the classic centralized machine learning " -"approach does not work for a large number of highly important real-world " -"use cases. Those reasons include:" -msgstr "传统的集中式机器学习方法无法满足现实世界中大量极为重要的使用案例，原因有很多。这些原因包括：" +"The :code:`self.bst` is used to keep the Booster objects that remain " +"consistent across rounds, allowing them to store predictions from trees " +"integrated in earlier rounds and maintain other essential data structures" +" for training." +msgstr "" +"代码:`self.bst`用于保存在各轮中保持一致的 Booster " +"对象，使其能够存储在前几轮中集成的树的预测结果，并维护其他用于训练的重要数据结构。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:158 +#: ../../source/tutorial-quickstart-xgboost.rst:196 msgid "" -"**Regulations**: GDPR (Europe), CCPA (California), PIPEDA (Canada), LGPD " -"(Brazil), PDPL (Argentina), KVKK (Turkey), POPI (South Africa), FSS " -"(Russia), CDPR (China), PDPB (India), PIPA (Korea), APPI (Japan), PDP " -"(Indonesia), PDPA (Singapore), APP (Australia), and other regulations " -"protect sensitive data from being moved. In fact, those regulations " -"sometimes even prevent single organizations from combining their own " -"users' data for artificial intelligence training because those users live" -" in different parts of the world, and their data is governed by different" -" data protection regulations." +"Then, we override :code:`get_parameters`, :code:`fit` and " +":code:`evaluate` methods insides :code:`XgbClient` class as follows." msgstr "" -"**法规**： " -"GDPR（欧洲）、CCPA（加利福尼亚）、PIPEDA（加拿大）、LGPD（巴西）、PDPL（阿根廷）、KVKK（土耳其）、POPI（南非）、FSS（俄罗斯）、CDPR（中国）、PDPB（印度）、PIPA（韩国）、APPI（日本）、PDP（印度尼西亚）、PDPA（新加坡）、APP（澳大利亚）等法规保护敏感数据不被移动。事实上，这些法规有时甚至会阻止单个组织将自己的用户数据用于人工智能培训，因为这些用户生活在世界不同地区，他们的数据受不同的数据保护法规管辖。" +"然后，我们在 :code:`XgbClient` 类中重写 :code:`get_parameters`、:code:`fit` 和 " +":code:`evaluate` 方法如下。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:160 +#: ../../source/tutorial-quickstart-xgboost.rst:210 msgid "" -"**User preference**: In addition to regulation, there are use cases where" -" users just expect that no data leaves their device, ever. If you type " -"your passwords and credit card info into the digital keyboard of your " -"phone, you don't expect those passwords to end up on the server of the " -"company that developed that keyboard, do you? In fact, that use case was " -"the reason federated learning was invented in the first place." +"Unlike neural network training, XGBoost trees are not started from a " +"specified random weights. In this case, we do not use " +":code:`get_parameters` and :code:`set_parameters` to initialise model " +"parameters for XGBoost. As a result, let's return an empty tensor in " +":code:`get_parameters` when it is called by the server at the first " +"round." msgstr "" -"**用户偏好**： " -"除了法规之外，在一些使用案例中，用户只是希望数据永远不会离开他们的设备。如果你在手机的数字键盘上输入密码和信用卡信息，你不会希望这些密码最终出现在开发该键盘的公司的服务器上吧？事实上，这种用例正是联邦学习发明的初衷。" +"与神经网络训练不同，XGBoost 树不是从指定的随机参数开始的。在这种情况下，我们不使用 :code:`get_parameters` 和 " +":code:`set_parameters` 来初始化 XGBoost 的模型参数。因此，当服务器在第一轮调用 " +":code:`get_parameters` 时，让我们在 :code:`get_parameters` 中返回一个空张量。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:161 +#: ../../source/tutorial-quickstart-xgboost.rst:251 msgid "" -"**Data volume**: Some sensors, like cameras, produce such a high data " -"volume that it is neither feasible nor economic to collect all the data " -"(due to, for example, bandwidth or communication efficiency). Think about" -" a national rail service with hundreds of train stations across the " -"country. If each of these train stations is outfitted with a number of " -"security cameras, the volume of raw on-device data they produce requires " -"incredibly powerful and exceedingly expensive infrastructure to process " -"and store. And most of the data isn't even useful." +"In :code:`fit`, at the first round, we call :code:`xgb.train()` to build " +"up the first set of trees. the returned Booster object and config are " +"stored in :code:`self.bst` and :code:`self.config`, respectively. From " +"the second round, we load the global model sent from server to " +":code:`self.bst`, and then update model weights on local training data " +"with function :code:`local_boost` as follows:" msgstr "" -"**数据量**： " -"有些传感器（如摄像头）产生的数据量很大，收集所有数据既不可行，也不经济（例如，由于带宽或通信效率的原因）。试想一下全国铁路服务，全国有数百个火车站。如果每个火车站都安装了许多安全摄像头，那么它们所产生的大量原始设备数据就需要功能强大且极其昂贵的基础设施来处理和存储。而大部分数据甚至都是无用的。" +"在 :code:`fit`中，第一轮我们调用 :code:`xgb.train()`来建立第一组树，返回的 Booster 对象和 config " +"分别存储在 :code:`self.bst` 和 :code:`self.config` 中。从第二轮开始，我们将服务器发送的全局模型加载到 " +":code:`self.bst`，然后使用函数 :code:`local_boost`更新本地训练数据的模型权重，如下所示：" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:164 -msgid "Examples where centralized machine learning does not work include:" -msgstr "集中式机器学习不起作用的例子包括：" +#: ../../source/tutorial-quickstart-xgboost.rst:269 +msgid "" +"Given :code:`num_local_round`, we update trees by calling " +":code:`self.bst.update` method. After training, the last " +":code:`N=num_local_round` trees will be extracted to send to the server." +msgstr "" +"给定 :code:`num_local_round`，我们通过调用 " +":code:`self.bst.update`方法更新树。训练结束后，我们将提取最后一个 :code:`N=num_local_round` " +"树并发送给服务器。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:166 +#: ../../source/tutorial-quickstart-xgboost.rst:291 msgid "" -"Sensitive healthcare records from multiple hospitals to train cancer " -"detection models" -msgstr "用多家医院的敏感医疗记录训练癌症检测模型" +"In :code:`evaluate`, we call :code:`self.bst.eval_set` function to " +"conduct evaluation on valid set. The AUC value will be returned." +msgstr "在 :code:`evaluate`中，我们调用 :code:`self.bst.eval_set`函数对有效集合进行评估。将返回 AUC 值。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:167 +#: ../../source/tutorial-quickstart-xgboost.rst:294 msgid "" -"Financial information from different organizations to detect financial " -"fraud" -msgstr "不同组织的财务信息，以侦查财务欺诈行为" +"Now, we can create an instance of our class :code:`XgbClient` and add one" +" line to actually run this client:" +msgstr "现在，我们可以创建一个 :code:`XgbClient` 类的实例，并添加一行来实际运行该客户端：" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:168 -msgid "Location data from your electric car to make better range prediction" -msgstr "通过电动汽车的定位数据更好地预测续航里程" +#: ../../source/tutorial-quickstart-xgboost.rst:300 +msgid "" +"That's it for the client. We only have to implement :code:`Client`and " +"call :code:`fl.client.start_client()`. The string :code:`\"[::]:8080\"` " +"tells the client which server to connect to. In our case we can run the " +"server and the client on the same machine, therefore we use " +":code:`\"[::]:8080\"`. If we run a truly federated workload with the " +"server and clients running on different machines, all that needs to " +"change is the :code:`server_address` we point the client at." +msgstr "" +"这就是客户端。我们只需实现 :code:`客户端`并调用 :code:`fl.client.start_client()`。字符串 " +":code:`\"[::]:8080\"`会告诉客户端要连接的服务器。在本例中，我们可以在同一台机器上运行服务器和客户端，因此我们使用 " +":code:`\"[::]:8080\"`。如果我们运行的是真正的联邦工作负载，服务器和客户端运行在不同的机器上，那么需要改变的只是客户端指向的 " +":code:`server_address`。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:169 -msgid "End-to-end encrypted messages to train better auto-complete models" -msgstr "端到端加密信息可训练出更好的自动完成模型" +#: ../../source/tutorial-quickstart-xgboost.rst:311 +msgid "" +"These updates are then sent to the *server* which will aggregate them to " +"produce a better model. Finally, the *server* sends this improved version" +" of the model back to each *client* to finish a complete FL round." +msgstr "" +"然后，这些更新会被发送到*服务器*，由*服务器*聚合后生成一个更好的模型。最后，*服务器*将这个改进版的模型发回给每个*客户端*，以完成一轮完整的" +" FL。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:171 +#: ../../source/tutorial-quickstart-xgboost.rst:314 msgid "" -"The popularity of privacy-enhancing systems like the `Brave " -"`__ browser or the `Signal `__ " -"messenger shows that users care about privacy. In fact, they choose the " -"privacy-enhancing version over other alternatives, if such an alternative" -" exists. But what can we do to apply machine learning and data science to" -" these cases to utilize private data? After all, these are all areas that" -" would benefit significantly from recent advances in AI." +"In a file named :code:`server.py`, import Flower and FedXgbBagging from " +":code:`flwr.server.strategy`." msgstr "" -"像 `Brave `__浏览器或 `Signal " -"`__信息管理器这样的隐私增强系统的流行表明，用户关心隐私。事实上，他们会选择隐私性更好的产品。但是，我们能做些什么来将机器学习和数据科学应用到这些情况中，以利用隐私数据呢？毕竟，这些领域都将从人工智能的最新进展中受益匪浅。" +"在名为 :code:`server.py` 的文件中，从 :code:`flwr.server.strategy` 导入 Flower 和 " +"FedXgbBagging。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:186 -msgid "Federated learning" -msgstr "联邦学习" +#: ../../source/tutorial-quickstart-xgboost.rst:316 +msgid "We first define a strategy for XGBoost bagging aggregation." +msgstr "我们首先定义了 XGBoost bagging聚合策略。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:188 +#: ../../source/tutorial-quickstart-xgboost.rst:339 msgid "" -"Federated learning simply reverses this approach. It enables machine " -"learning on distributed data by moving the training to the data, instead " -"of moving the data to the training. Here's the single-sentence " -"explanation:" -msgstr "联邦学习简单地颠覆了这种方法。它通过将训练转移到数据上，而不是将数据转移到训练上，在分布式数据上实现机器学习。下面是一句话的解释：" +"We use two clients for this example. An " +":code:`evaluate_metrics_aggregation` function is defined to collect and " +"wighted average the AUC values from clients." +msgstr "" +"本示例使用两个客户端。我们定义了一个 :code:`evaluate_metrics_aggregation` 函数，用于收集客户机的 AUC " +"值并求取平均值。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:190 -msgid "Central machine learning: move the data to the computation" -msgstr "集中式机器学习：将数据转移到计算中心" +#: ../../source/tutorial-quickstart-xgboost.rst:342 +msgid "Then, we start the server:" +msgstr "然后，我们启动服务器：" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:191 -msgid "Federated (machine) learning: move the computation to the data" -msgstr "联邦式（机器）学习：将计算转移到数据上" +#: ../../source/tutorial-quickstart-xgboost.rst:354 +msgid "Tree-based bagging aggregation" +msgstr "基于树的bagging聚合" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:193 +#: ../../source/tutorial-quickstart-xgboost.rst:356 msgid "" -"By doing so, it enables us to use machine learning (and other data " -"science approaches) in areas where it wasn't possible before. We can now " -"train excellent medical AI models by enabling different hospitals to work" -" together. We can solve financial fraud by training AI models on the data" -" of different financial institutions. We can build novel privacy-" -"enhancing applications (such as secure messaging) that have better built-" -"in AI than their non-privacy-enhancing alternatives. And those are just a" -" few of the examples that come to mind. As we deploy federated learning, " -"we discover more and more areas that can suddenly be reinvented because " -"they now have access to vast amounts of previously inaccessible data." -msgstr "这样，我们就能在以前不可能的领域使用机器学习（和其他数据科学方法）。现在，我们可以通过让不同的医院协同工作来训练优秀的医疗人工智能模型。我们可以通过在不同金融机构的数据上训练人工智能模型来解决金融欺诈问题。我们可以构建新颖的隐私增强型应用（如安全信息），其内置的人工智能比非隐私增强型应用更好。以上只是我想到的几个例子。随着联邦学习的部署，我们会发现越来越多的领域可以突然重获新生，因为它们现在可以访问大量以前无法访问的数据。" +"You must be curious about how bagging aggregation works. Let's look into " +"the details." +msgstr "您一定很好奇bagging聚合是如何工作的。让我们来详细了解一下。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:196 +#: ../../source/tutorial-quickstart-xgboost.rst:358 msgid "" -"So how does federated learning work, exactly? Let's start with an " -"intuitive explanation." -msgstr "那么，联邦学习究竟是如何运作的呢？让我们从直观的解释开始。" - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:199 -msgid "Federated learning in five steps" -msgstr "联邦学习的五个步骤" - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:202 -msgid "Step 0: Initialize global model" -msgstr "步骤 0：初始化全局模型" +"In file :code:`flwr.server.strategy.fedxgb_bagging.py`, we define " +":code:`FedXgbBagging` inherited from :code:`flwr.server.strategy.FedAvg`." +" Then, we override the :code:`aggregate_fit`, :code:`aggregate_evaluate` " +"and :code:`evaluate` methods as follows:" +msgstr "" +"在文件 :code:`flwr.server.strategy.fedxgb_bagging.py`中，我们定义了从 " +":code:`flwr.server.strategy.FedAvg`继承的 :code:`FedXgbBagging`。然后，我们覆盖 " +":code:`aggregate_fit`、:code:`aggregate_evaluate` 和 :code:`evaluate` 方法如下：" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:204 +#: ../../source/tutorial-quickstart-xgboost.rst:454 msgid "" -"We start by initializing the model on the server. This is exactly the " -"same in classic centralized learning: we initialize the model parameters," -" either randomly or from a previously saved checkpoint." -msgstr "我们首先在服务器上初始化模型。这与经典的集中式学习完全相同：我们随机或从先前保存的检查点初始化模型参数。" - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:210 -msgid "|5b1408eec0d746cdb91162a9107b6089|" +"In :code:`aggregate_fit`, we sequentially aggregate the clients' XGBoost " +"trees by calling :code:`aggregate()` function:" msgstr "" +"在 :code:`aggregate_fit` 中，我们通过调用 :code:`aggregate()` 函数，按顺序聚合客户端的 XGBoost" +" 树：" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:307 -msgid "Initialize global model" -msgstr "初始化全局模型" - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:217 +#: ../../source/tutorial-quickstart-xgboost.rst:513 msgid "" -"Step 1: Send model to a number of connected organizations/devices (client" -" nodes)" -msgstr "第 1 步：将模型发送到多个连接的组织/设备（客户节点）" +"In this function, we first fetch the number of trees and the number of " +"parallel trees for the current and previous model by calling " +":code:`_get_tree_nums`. Then, the fetched information will be aggregated." +" After that, the trees (containing model weights) are aggregated to " +"generate a new tree model." +msgstr "" +"在该函数中，我们首先通过调用 :code:`_get_tree_nums` " +"获取当前模型和上一个模型的树数和并行树数。然后，对获取的信息进行聚合。然后，聚合树（包含模型参数）生成新的树模型。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:219 +#: ../../source/tutorial-quickstart-xgboost.rst:518 msgid "" -"Next, we send the parameters of the global model to the connected client " -"nodes (think: edge devices like smartphones or servers belonging to " -"organizations). This is to ensure that each participating node starts " -"their local training using the same model parameters. We often use only a" -" few of the connected nodes instead of all nodes. The reason for this is " -"that selecting more and more client nodes has diminishing returns." -msgstr "接下来，我们会将全局模型的参数发送到连接的客户端节点（如智能手机等边缘设备或企业的服务器）。这是为了确保每个参与节点都使用相同的模型参数开始本地训练。我们通常只使用几个连接节点，而不是所有节点。这样做的原因是，选择越来越多的客户端节点会导致收益递减。" +"After traversal of all clients' models, a new global model is generated, " +"followed by the serialisation, and sending back to each client." +msgstr "在遍历所有客户端的模型后，会生成一个新的全局模型，然后进行序列化，并发回给每个客户端。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:225 -msgid "|aef19f4b122c4e8d9f4c57f99bcd5dd2|" -msgstr "" +#: ../../source/tutorial-quickstart-xgboost.rst:523 +msgid "Launch Federated XGBoost!" +msgstr "启动联邦 XGBoost！" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:309 -msgid "Send global model" -msgstr "发送全局模型" +#: ../../source/tutorial-quickstart-xgboost.rst:585 +msgid "" +"Congratulations! You've successfully built and run your first federated " +"XGBoost system. The AUC values can be checked in " +":code:`metrics_distributed`. One can see that the average AUC increases " +"over FL rounds." +msgstr "" +"恭喜您！您已成功构建并运行了第一个联邦 XGBoost 系统。可以在 :code:`metrics_distributed` 中查看 AUC " +"值。我们可以看到，平均 AUC 随 FL 轮数的增加而增加。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:232 +#: ../../source/tutorial-quickstart-xgboost.rst:590 msgid "" -"Step 2: Train model locally on the data of each organization/device " -"(client node)" -msgstr "步骤 2：在本地对每个机构/设备（客户端节点）的数据进行模型训练" - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:234 -msgid "" -"Now that all (selected) client nodes have the latest version of the " -"global model parameters, they start the local training. They use their " -"own local dataset to train their own local model. They don't train the " -"model until full convergence, but they only train for a little while. " -"This could be as little as one epoch on the local data, or even just a " -"few steps (mini-batches)." +"The full `source code `_ for this example can be found in :code:`examples" +"/xgboost-quickstart`." msgstr "" -"现在，所有（选定的）客户端节点都有了最新版本的全局模型参数，它们开始进行本地训练。它们使用自己的本地数据集来训练自己的本地模型。它们不会一直训练到模型完全收敛为止，而只是训练一小段时间。这可能只是本地数据上的一个遍历，甚至只是几个步骤" -"（mini-batches）。" +"此示例的`完整源代码 `_ 可在 :code:`examples/xgboost-quickstart` 中找到。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:240 -msgid "|2881a86d8fc54ba29d96b29fc2819f4a|" -msgstr "" +#: ../../source/tutorial-quickstart-xgboost.rst:594 +msgid "Comprehensive Federated XGBoost" +msgstr "综合的联邦 XGBoost" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:311 -msgid "Train on local data" -msgstr "根据本地数据进行训练" +#: ../../source/tutorial-quickstart-xgboost.rst:596 +#, fuzzy +msgid "" +"Now that you have known how federated XGBoost work with Flower, it's time" +" to run some more comprehensive experiments by customising the " +"experimental settings. In the xgboost-comprehensive example (`full code " +"`_), we provide more options to define various experimental" +" setups, including aggregation strategies, data partitioning and " +"centralised/distributed evaluation. We also support :doc:`Flower " +"simulation ` making it easy to simulate large " +"client cohorts in a resource-aware manner. Let's take a look!" +msgstr "" +"既然您已经知道联合 XGBoost 如何与 Flower 协同工作，那么现在就该通过自定义实验设置来运行一些更综合的实验了。在 xgboost-" +"comprehensive 示例 (`完整代码 " +"`_)中，我们提供了更多选项来定义各种实验设置，包括数据分区和集中/分布式评估。让我们一起来看看！" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:247 -msgid "Step 3: Return model updates back to the server" -msgstr "步骤 3：将模型参数更新返回服务器" +#: ../../source/tutorial-quickstart-xgboost.rst:603 +#, fuzzy +msgid "Cyclic training" +msgstr "集中式训练" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:249 +#: ../../source/tutorial-quickstart-xgboost.rst:605 +#, fuzzy msgid "" -"After local training, each client node has a slightly different version " -"of the model parameters they originally received. The parameters are all " -"different because each client node has different examples in its local " -"dataset. The client nodes then send those model updates back to the " -"server. The model updates they send can either be the full model " -"parameters or just the gradients that were accumulated during local " -"training." -msgstr "经过本地训练后，每个客户节点最初收到的模型参数都会略有不同。参数之所以不同，是因为每个客户端节点的本地数据集中都有不同的数据。然后，客户端节点将这些模型更新发回服务器。它们发送的模型更新既可以是完整的模型参数，也可以只是本地训练过程中积累的梯度。" - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:255 -msgid "|ec1fe880237247e0975f52766775ab84|" +"In addition to bagging aggregation, we offer a cyclic training scheme, " +"which performs FL in a client-by-client fashion. Instead of aggregating " +"multiple clients, there is only one single client participating in the " +"training per round in the cyclic training scenario. The trained local " +"XGBoost trees will be passed to the next client as an initialised model " +"for next round's boosting." msgstr "" +"除了袋式聚合，我们还提供了一种循环训练方案，它以逐个客户端的方式执行 " +"FL。在循环训练方案中，每轮只有一个客户端参与训练，而不是多个客户端聚合在一起。训练好的本地 XGBoost " +"树将传递给下一个客户端，作为下一轮提升的初始化模型。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:313 -msgid "Send model updates" -msgstr "发送模型参数更新" - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:262 -msgid "Step 4: Aggregate model updates into a new global model" -msgstr "步骤 4：将模型更新聚合到新的全局模型中" +#: ../../source/tutorial-quickstart-xgboost.rst:609 +#, fuzzy +msgid "" +"To do this, we first customise a :code:`ClientManager` in " +":code:`server_utils.py`:" +msgstr "为此，我们首先要在 :code:`server_utils.py` 中自定义一个 :code:`ClientManager`：" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:264 +#: ../../source/tutorial-quickstart-xgboost.rst:649 +#, fuzzy msgid "" -"The server receives model updates from the selected client nodes. If it " -"selected 100 client nodes, it now has 100 slightly different versions of " -"the original global model, each trained on the local data of one client. " -"But didn't we want to have one model that contains the learnings from the" -" data of all 100 client nodes?" +"The customised :code:`ClientManager` samples all available clients in " +"each FL round based on the order of connection to the server. Then, we " +"define a new strategy :code:`FedXgbCyclic` in " +":code:`flwr.server.strategy.fedxgb_cyclic.py`, in order to sequentially " +"select only one client in given round and pass the received model to next" +" client." msgstr "" -"服务器从选定的客户端节点接收模型更新。如果服务器选择了 100 个客户端节点，那么它现在就拥有 100 " -"个略有不同的原始全局模型版本，每个版本都是根据一个客户端的本地数据训练出来的。难道我们不希望有一个包含所有 100 个客户节点数据的模型吗？" +"定制的 :code:`ClientManager` 会根据连接服务器的顺序，在每轮 FL 中对所有可用客户端进行采样。然后，我们在 " +":code:`flwr.server.strategy.fedxgb_cyclic.py`\"中定义了一个新策略 " +":code:`FedXgbCyclic`，以便在给定回合中按顺序只选择一个客户端，并将接收到的模型传递给下一个客户端。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:266 +#: ../../source/tutorial-quickstart-xgboost.rst:690 +#, fuzzy msgid "" -"In order to get one single model, we have to combine all the model " -"updates we received from the client nodes. This process is called " -"*aggregation*, and there are many different ways to do it. The most basic" -" way to do it is called *Federated Averaging* (`McMahan et al., 2016 " -"`__), often abbreviated as *FedAvg*. " -"*FedAvg* takes the 100 model updates and, as the name suggests, averages " -"them. To be more precise, it takes the *weighted average* of the model " -"updates, weighted by the number of examples each client used for " -"training. The weighting is important to make sure that each data example " -"has the same \"influence\" on the resulting global model. If one client " -"has 10 examples, and another client has 100 examples, then - without " -"weighting - each of the 10 examples would influence the global model ten " -"times as much as each of the 100 examples." +"Unlike the original :code:`FedAvg`, we don't perform aggregation here. " +"Instead, we just make a copy of the received client model as global model" +" by overriding :code:`aggregate_fit`." msgstr "" -"为了得到一个单一的模型，我们必须将从客户端节点收到的所有模型更新合并起来。这个过程称为*聚合*，有许多不同的方法。最基本的方法称为 " -"*Federated Averaging* (`McMahan等人，2016 " -"`__)，通常缩写为*FedAvg*。*FedAvg* 可以把100 " -"个模型更新进行平均。更准确地说，它取的是模型更新的*加权平均值*，根据每个客户端用于训练的数据数量进行加权。加权对于确保每个数据示例对生成的全局模型具有相同的" -" \"影响 \"非常重要。如果一个客户端有 10 个数据点，而另一个客户有 100 个数据点，那么在不加权的情况下，10 个示例对全局模型的影响是" -" 100 个示例的 10 倍。" +"与最初的 :code:`FedAvg` 不同，我们在这里不执行聚合。相反，我们只是通过覆盖 :code:`aggregate_fit` " +"将接收到的客户端模型复制为全局模型。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:273 -msgid "|9fdf048ed58d4467b2718cdf4aaf1ec3|" +#: ../../source/tutorial-quickstart-xgboost.rst:693 +#, fuzzy +msgid "" +"Also, the customised :code:`configure_fit` and :code:`configure_evaluate`" +" methods ensure the clients to be sequentially selected given FL round:" msgstr "" +"此外，定制的 :code:`configure_fit` 和 :code:`configure_evaluate` 方法可确保在 FL " +"轮中按顺序选择客户：" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:315 -msgid "Aggregate model updates" -msgstr "聚合模型参数更新" - -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:280 -msgid "Step 5: Repeat steps 1 to 4 until the model converges" -msgstr "步骤 5：重复步骤 1 至 4，直至模型收敛" +#: ../../source/tutorial-quickstart-xgboost.rst:757 +msgid "Customised data partitioning" +msgstr "定制数据分区" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:282 +#: ../../source/tutorial-quickstart-xgboost.rst:759 msgid "" -"Steps 1 to 4 are what we call a single round of federated learning. The " -"global model parameters get sent to the participating client nodes (step " -"1), the client nodes train on their local data (step 2), they send their " -"updated models to the server (step 3), and the server then aggregates the" -" model updates to get a new version of the global model (step 4)." +"In :code:`dataset.py`, we have a function :code:`instantiate_partitioner`" +" to instantiate the data partitioner based on the given " +":code:`num_partitions` and :code:`partitioner_type`. Currently, we " +"provide four supported partitioner type to simulate the uniformity/non-" +"uniformity in data quantity (uniform, linear, square, exponential)." msgstr "" -"步骤 1 至 4 就是我们所说的单轮联邦学习。全局模型参数被发送到参与的客户端节点（第 1 步），客户端节点对其本地数据进行训练（第 2 " -"步），然后将更新后的模型发送到服务器（第 3 步），服务器汇总模型更新，得到新版本的全局模型（第 4 步）。" +"在 :code:`dataset.py` 中，我们有一个函数 :code:`instantiate_partitioner` 来根据给定的 " +":code:`num_partitions` 和 :code:`partitioner_type` " +"来实例化数据分区器。目前，我们提供四种支持的分区器类型（均匀、线性、正方形、指数）来模拟数据量的均匀性/非均匀性。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:284 +#: ../../source/tutorial-quickstart-xgboost.rst:790 +msgid "Customised centralised/distributed evaluation" +msgstr "定制的集中/分布式评估" + +#: ../../source/tutorial-quickstart-xgboost.rst:792 +#, fuzzy msgid "" -"During a single round, each client node that participates in that " -"iteration only trains for a little while. This means that after the " -"aggregation step (step 4), we have a model that has been trained on all " -"the data of all participating client nodes, but only for a little while. " -"We then have to repeat this training process over and over again to " -"eventually arrive at a fully trained model that performs well across the " -"data of all client nodes." -msgstr "" -"在一轮迭代中，每个参与迭代的客户节点只训练一小段时间。这意味着，在聚合步骤（步骤 " -"4）之后，我们的模型已经在所有参与的客户节点的所有数据上训练过了，但只训练了一小会儿。然后，我们必须一次又一次地重复这一训练过程，最终得到一个经过全面训练的模型，该模型在所有客户节点的数据中都表现良好。" +"To facilitate centralised evaluation, we define a function in " +":code:`server_utils.py`:" +msgstr "为便于集中评估，我们在 :code:`server.py` 中定义了一个函数：" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:289 +#: ../../source/tutorial-quickstart-xgboost.rst:824 msgid "" -"Congratulations, you now understand the basics of federated learning. " -"There's a lot more to discuss, of course, but that was federated learning" -" in a nutshell. In later parts of this tutorial, we will go into more " -"detail. Interesting questions include: How can we select the best client " -"nodes that should participate in the next round? What's the best way to " -"aggregate model updates? How can we handle failing client nodes " -"(stragglers)?" +"This function returns a evaluation function which instantiates a " +":code:`Booster` object and loads the global model weights to it. The " +"evaluation is conducted by calling :code:`eval_set()` method, and the " +"tested AUC value is reported." msgstr "" -"恭喜您，现在您已经了解了联邦学习的基础知识。当然，要讨论的内容还有很多，但这只是联邦学习的一个缩影。在本教程的后半部分，我们将进行更详细的介绍。有趣的问题包括" -" 我们如何选择最好的客户端节点参与下一轮学习？聚合模型更新的最佳方法是什么？如何处理失败的客户端节点（落伍者）？" +"此函数返回一个评估函数，该函数实例化一个 :code:`Booster` 对象，并向其加载全局模型参数。评估通过调用 " +":code:`eval_set()` 方法进行，并报告测试的 AUC 值。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:294 +#: ../../source/tutorial-quickstart-xgboost.rst:827 +#, fuzzy msgid "" -"Just like we can train a model on the decentralized data of different " -"client nodes, we can also evaluate the model on that data to receive " -"valuable metrics. This is called federated evaluation, sometimes " -"abbreviated as FE. In fact, federated evaluation is an integral part of " -"most federated learning systems." +"As for distributed evaluation on the clients, it's same as the quick-" +"start example by overriding the :code:`evaluate()` method insides the " +":code:`XgbClient` class in :code:`client_utils.py`." msgstr "" -"就像我们可以在不同客户节点的分散数据上训练一个模型一样，我们也可以在这些数据上对模型进行评估，以获得有价值的指标。这就是所谓的联邦评估，有时简称为" -" FE。事实上，联邦评估是大多数联邦学习系统不可或缺的一部分。" +"至于客户端上的分布式评估，与快速启动示例相同，通过覆盖 :code:`client.py` 中 :code:`XgbClient` 类内部的 " +":code:`evaluate()` 方法。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:297 -msgid "Federated analytics" -msgstr "联邦分析" +#: ../../source/tutorial-quickstart-xgboost.rst:831 +#, fuzzy +msgid "Flower simulation" +msgstr "运行模拟" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:299 +#: ../../source/tutorial-quickstart-xgboost.rst:832 +#, fuzzy msgid "" -"In many cases, machine learning isn't necessary to derive value from " -"data. Data analysis can yield valuable insights, but again, there's often" -" not enough data to get a clear answer. What's the average age at which " -"people develop a certain type of health condition? Federated analytics " -"enables such queries over multiple client nodes. It is usually used in " -"conjunction with other privacy-enhancing technologies like secure " -"aggregation to prevent the server from seeing the results submitted by " -"individual client nodes." -msgstr "在很多情况下，机器学习并不是从数据中获取价值的必要条件。数据分析可以产生有价值的见解，但同样，往往没有足够的数据来获得明确的答案。人们患某种健康疾病的平均年龄是多少？联邦分析可以通过多个客户端节点进行此类查询。它通常与安全聚合等其他隐私增强技术结合使用，以防止服务器看到单个客户端节点提交的结果。" +"We also provide an example code (:code:`sim.py`) to use the simulation " +"capabilities of Flower to simulate federated XGBoost training on either a" +" single machine or a cluster of machines." +msgstr "我们还提供了一个示例代码（:code:`sim.py`），用于使用 Flower 的模拟功能在单台机器或机器集群上模拟联合 XGBoost 训练。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:305 +#: ../../source/tutorial-quickstart-xgboost.rst:866 +#, fuzzy msgid "" -"Differential privacy (DP) is often mentioned in the context of Federated " -"Learning. It is a privacy-preserving method used when analyzing and " -"sharing statistical data, ensuring the privacy of individual " -"participants. DP achieves this by adding statistical noise to the model " -"updates, ensuring any individual participants’ information cannot be " -"distinguished or re-identified. This technique can be considered an " -"optimization that provides a quantifiable privacy protection measure." -msgstr "" -"差分隐私（DP）经常在联邦学习中被提及。这是一种在分析和共享统计数据时使用的隐私保护方法，可确保单个参与者的隐私。DP " -"通过在模型更新中添加统计噪声来实现这一目的，确保任何个体参与者的信息都无法被区分或重新识别。这种技术可被视为一种优化，提供了一种可量化的隐私保护措施。" +"After importing all required packages, we define a :code:`main()` " +"function to perform the simulation process:" +msgstr "导入所有需要的软件包后，我们定义了一个 :code:`main()` 函数来执行模拟程序：" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:326 -msgid "Flower" -msgstr "Flower" +#: ../../source/tutorial-quickstart-xgboost.rst:921 +#, fuzzy +msgid "" +"We first load the dataset and perform data partitioning, and the pre-" +"processed data is stored in a :code:`list`. After the simulation begins, " +"the clients won't need to pre-process their partitions again." +msgstr "我们首先加载数据集并执行数据分区，预处理后的数据存储在 :code:`list` 中。模拟开始后，客户端就不需要再预处理分区了。" + +#: ../../source/tutorial-quickstart-xgboost.rst:924 +#, fuzzy +msgid "Then, we define the strategies and other hyper-parameters:" +msgstr "然后，我们定义策略和其他超参数：" + +#: ../../source/tutorial-quickstart-xgboost.rst:975 +#, fuzzy +msgid "" +"After that, we start the simulation by calling " +":code:`fl.simulation.start_simulation`:" +msgstr "然后，我们调用 :code:`fl.simulation.start_simulation` 开始模拟：" + +#: ../../source/tutorial-quickstart-xgboost.rst:995 +#, fuzzy +msgid "" +"One of key parameters for :code:`start_simulation` is :code:`client_fn` " +"which returns a function to construct a client. We define it as follows:" +msgstr "" +":code:`start_simulation` 的一个关键参数是 " +":code:`client_fn`，它返回一个用于构建客户端的函数。我们将其定义如下：" + +#: ../../source/tutorial-quickstart-xgboost.rst:1038 +msgid "Arguments parser" +msgstr "参数解析器" + +#: ../../source/tutorial-quickstart-xgboost.rst:1040 +#, fuzzy +msgid "" +"In :code:`utils.py`, we define the arguments parsers for clients, server " +"and simulation, allowing users to specify different experimental " +"settings. Let's first see the sever side:" +msgstr "在 :code:`utils.py` 中，我们定义了客户端和服务器端的参数解析器，允许用户指定不同的实验设置。让我们先看看服务器端：" + +#: ../../source/tutorial-quickstart-xgboost.rst:1086 +#, fuzzy +msgid "" +"This allows user to specify training strategies / the number of total " +"clients / FL rounds / participating clients / clients for evaluation, and" +" evaluation fashion. Note that with :code:`--centralised-eval`, the sever" +" will do centralised evaluation and all functionalities for client " +"evaluation will be disabled." +msgstr "" +"这允许用户指定总客户数/FL 轮数/参与客户数/评估客户数以及评估方式。请注意，如果使用 :code:`--centralised-" +"eval`，服务器将进行集中评估，客户端评估的所有功能将被禁用。" + +#: ../../source/tutorial-quickstart-xgboost.rst:1090 +msgid "Then, the argument parser on client side:" +msgstr "然后是客户端的参数解析器：" + +#: ../../source/tutorial-quickstart-xgboost.rst:1144 +#, fuzzy +msgid "" +"This defines various options for client data partitioning. Besides, " +"clients also have an option to conduct evaluation on centralised test set" +" by setting :code:`--centralised-eval`, as well as an option to perform " +"scaled learning rate based on the number of clients by setting :code" +":`--scaled-lr`." +msgstr "这定义了客户端数据分区的各种选项。此外，通过设置 :code:`-centralised-eval`，客户端还可以选择在集中测试集上进行评估。" + +#: ../../source/tutorial-quickstart-xgboost.rst:1148 +#, fuzzy +msgid "We also have an argument parser for simulation:" +msgstr "我们还有一个用于模拟的参数解析器：" + +#: ../../source/tutorial-quickstart-xgboost.rst:1226 +#, fuzzy +msgid "This integrates all arguments for both client and server sides." +msgstr "这整合了客户端和服务器端的所有参数。" + +#: ../../source/tutorial-quickstart-xgboost.rst:1229 +msgid "Example commands" +msgstr "命令示例" + +#: ../../source/tutorial-quickstart-xgboost.rst:1231 +#, fuzzy +msgid "" +"To run a centralised evaluated experiment with bagging strategy on 5 " +"clients with exponential distribution for 50 rounds, we first start the " +"server as below:" +msgstr "为了在 5 个客户端上进行 50 轮指数分布的集中评估实验，我们首先启动服务器，如下所示：" + +#: ../../source/tutorial-quickstart-xgboost.rst:1238 +msgid "Then, on each client terminal, we start the clients:" +msgstr "然后，我们在每个客户终端上启动客户机：" + +#: ../../source/tutorial-quickstart-xgboost.rst:1244 +#, fuzzy +msgid "To run the same experiment with Flower simulation:" +msgstr "运行与 Flower 模拟相同的实验：" + +#: ../../source/tutorial-quickstart-xgboost.rst:1250 +#, fuzzy +msgid "" +"The full `code `_ for this comprehensive example can be found in" +" :code:`examples/xgboost-comprehensive`." +msgstr "" +"此综合示例的全部`源代码 `_ 可在 :code:`examples/xgboost-comprehensive` 中找到。" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:9 +msgid "Build a strategy from scratch" +msgstr "从零开始制定策略" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:11 +msgid "" +"Welcome to the third part of the Flower federated learning tutorial. In " +"previous parts of this tutorial, we introduced federated learning with " +"PyTorch and Flower (`part 1 `__) and we learned how strategies " +"can be used to customize the execution on both the server and the clients" +" (`part 2 `__)." +msgstr "" +"欢迎来到 Flower 联邦学习教程的第三部分。在本教程的前几部分，我们介绍了 PyTorch 和 Flower 的联邦学习（`part 1 " +"`__），并学习了如何使用策略来定制服务器和客户端的执行（`part 2 " +"`__）。" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:13 +msgid "" +"In this notebook, we'll continue to customize the federated learning " +"system we built previously by creating a custom version of FedAvg (again," +" using `Flower `__ and `PyTorch " +"`__)." +msgstr "" +"在本笔记中，我们将通过创建 FedAvg 的自定义版本（再次使用 `Flower `__ 和 " +"`PyTorch `__），继续定制我们之前构建的联邦学习系统。" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:15 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:16 +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:15 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:15 +msgid "" +"`Star Flower on GitHub `__ ⭐️ and join " +"the Flower community on Slack to connect, ask questions, and get help: " +"`Join Slack `__ 🌼 We'd love to hear from " +"you in the ``#introductions`` channel! And if anything is unclear, head " +"over to the ``#questions`` channel." +msgstr "" +"`Star Flower on GitHub `__ ⭐️ 并加入 Slack " +"上的 Flower 社区，进行交流、提问并获得帮助： 加入 Slack `__ 🌼 " +"我们希望在 ``#introductions`` 频道听到您的声音！如果有任何不清楚的地方，请访问 ``#questions`` 频道。" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:17 +msgid "Let's build a new ``Strategy`` from scratch!" +msgstr "让我们从头开始构建一个新的``Strategy``！" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:29 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:29 +msgid "Preparation" +msgstr "准备工作" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:31 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:32 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:31 +msgid "" +"Before we begin with the actual code, let's make sure that we have " +"everything we need." +msgstr "在开始实际代码之前，让我们先确保我们已经准备好了所需的一切。" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:43 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:44 +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:43 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:43 +msgid "Installing dependencies" +msgstr "安装依赖项" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:45 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:46 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:45 +msgid "First, we install the necessary packages:" +msgstr "首先，我们安装必要的软件包：" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:65 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:66 +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:65 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:65 +msgid "" +"Now that we have all dependencies installed, we can import everything we " +"need for this tutorial:" +msgstr "现在我们已经安装了所有依赖项，可以导入本教程所需的所有内容：" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:101 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:102 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:101 +msgid "" +"It is possible to switch to a runtime that has GPU acceleration enabled " +"(on Google Colab: ``Runtime > Change runtime type > Hardware acclerator: " +"GPU > Save``). Note, however, that Google Colab is not always able to " +"offer GPU acceleration. If you see an error related to GPU availability " +"in one of the following sections, consider switching back to CPU-based " +"execution by setting ``DEVICE = torch.device(\"cpu\")``. If the runtime " +"has GPU acceleration enabled, you should see the output ``Training on " +"cuda``, otherwise it'll say ``Training on cpu``." +msgstr "" +"可以切换到已启用 GPU 加速的运行时（在 Google Colab 上： 运行时 > 更改运行时类型 > 硬件加速： GPU > " +"保存``）。但请注意，Google Colab 并非总能提供 GPU 加速。如果在以下部分中看到与 GPU 可用性相关的错误，请考虑通过设置 " +"``DEVICE = torch.device(\"cpu\")`` 切回基于 CPU 的执行。如果运行时已启用 GPU " +"加速，你应该会看到输出``Training on cuda``，否则会显示``Training on cpu``。" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:114 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:115 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:114 +msgid "Data loading" +msgstr "数据加载" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:116 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:116 +msgid "" +"Let's now load the CIFAR-10 training and test set, partition them into " +"ten smaller datasets (each split into training and validation set), and " +"wrap everything in their own ``DataLoader``. We introduce a new parameter" +" ``num_clients`` which allows us to call ``load_datasets`` with different" +" numbers of clients." +msgstr "" +"现在，让我们加载 CIFAR-10 训练集和测试集，将它们分割成 10 " +"个较小的数据集（每个数据集又分为训练集和验证集），并将所有数据都封装在各自的 ``DataLoader`` 中。我们引入了一个新参数 " +"``num_clients``，它允许我们使用不同数量的客户端调用 ``load_datasets``。" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:167 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:168 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:167 +msgid "Model training/evaluation" +msgstr "模型培训/评估" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:169 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:170 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:169 +msgid "" +"Let's continue with the usual model definition (including " +"``set_parameters`` and ``get_parameters``), training and test functions:" +msgstr "让我们继续使用常见的模型定义（包括 `set_parameters` 和 `get_parameters`）、训练和测试函数：" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:258 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:258 +msgid "Flower client" +msgstr "Flower 客户端" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:260 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:260 +msgid "" +"To implement the Flower client, we (again) create a subclass of " +"``flwr.client.NumPyClient`` and implement the three methods " +"``get_parameters``, ``fit``, and ``evaluate``. Here, we also pass the " +"``cid`` to the client and use it log additional details:" +msgstr "" +"为了实现 Flower 客户端，我们（再次）创建了 ``flwr.client.NumPyClient`` 的子类，并实现了 " +"``get_parameters``、``fit`` 和 ``evaluate``三个方法。在这里，我们还将 ``cid`` " +"传递给客户端，并使用它记录其他详细信息：" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:308 +msgid "Let's test what we have so far before we continue:" +msgstr "在继续之前，让我们先测试一下我们目前掌握的情况：" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:339 +msgid "Build a Strategy from scratch" +msgstr "从零开始构建策略" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:341 +msgid "" +"Let’s overwrite the ``configure_fit`` method such that it passes a higher" +" learning rate (potentially also other hyperparameters) to the optimizer " +"of a fraction of the clients. We will keep the sampling of the clients as" +" it is in ``FedAvg`` and then change the configuration dictionary (one of" +" the ``FitIns`` attributes)." +msgstr "" +"让我们重写 ``configure_fit`` 方法，使其向一部分客户的优化器传递更高的学习率（可能还有其他超参数）。我们将保持 " +"``FedAvg`` 中的客户端采样，然后更改配置字典（``FitIns`` 属性之一）。" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:507 +msgid "" +"The only thing left is to use the newly created custom Strategy " +"``FedCustom`` when starting the experiment:" +msgstr "剩下的唯一工作就是在启动实验时使用新创建的自定义策略 ``FedCustom`` ：" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:534 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:932 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:697 +msgid "Recap" +msgstr "回顾" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:536 +msgid "" +"In this notebook, we’ve seen how to implement a custom strategy. A custom" +" strategy enables granular control over client node configuration, result" +" aggregation, and more. To define a custom strategy, you only have to " +"overwrite the abstract methods of the (abstract) base class ``Strategy``." +" To make custom strategies even more powerful, you can pass custom " +"functions to the constructor of your new class (``__init__``) and then " +"call these functions whenever needed." +msgstr "" +"在本笔记中，我们了解了如何实施自定义策略。自定义策略可以对客户端节点配置、结果聚合等进行细粒度控制。要定义自定义策略，只需覆盖（抽象）基类 " +"``Strategy`` " +"的抽象方法即可。为使自定义策略更加强大，您可以将自定义函数传递给新类的构造函数（`__init__``），然后在需要时调用这些函数。" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:550 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:948 +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:729 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:715 +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:369 +msgid "" +"Before you continue, make sure to join the Flower community on Slack: " +"`Join Slack `__" +msgstr "" +"在继续之前，请务必加入 Slack 上的 Flower 社区：`Join Slack `__" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:552 +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:950 +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:731 +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:717 +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:371 +msgid "" +"There's a dedicated ``#questions`` channel if you need help, but we'd " +"also love to hear who you are in ``#introductions``!" +msgstr "如果您需要帮助，我们有专门的 ``#questions`` 频道，但我们也很乐意在 ``#introductions`` 中了解您是谁！" + +#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:554 +msgid "" +"The `Flower Federated Learning Tutorial - Part 4 " +"`__ introduces ``Client``, the flexible API underlying " +"``NumPyClient``." +msgstr "" +"Flower联邦学习教程 - 第4部分 `__ 介绍了``Client``，它是``NumPyClient``底层的灵活应用程序接口。" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:9 +msgid "Customize the client" +msgstr "自定义客户端" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:11 +msgid "" +"Welcome to the fourth part of the Flower federated learning tutorial. In " +"the previous parts of this tutorial, we introduced federated learning " +"with PyTorch and Flower (`part 1 `__), we learned how " +"strategies can be used to customize the execution on both the server and " +"the clients (`part 2 `__), and we built our own " +"custom strategy from scratch (`part 3 `__)." +msgstr "" +"欢迎来到 Flower 联邦学习教程的第四部分。在本教程的前几部分中，我们介绍了 PyTorch 和 Flower 的联邦学习（`part 1 " +"`__），了解了如何使用策略来定制服务器和客户端的执行（`part 2 " +"`__），并从头开始构建了我们自己的定制策略（`part 3 " +"`__）。" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:14 +msgid "" +"In this notebook, we revisit ``NumPyClient`` and introduce a new " +"baseclass for building clients, simply named ``Client``. In previous " +"parts of this tutorial, we've based our client on ``NumPyClient``, a " +"convenience class which makes it easy to work with machine learning " +"libraries that have good NumPy interoperability. With ``Client``, we gain" +" a lot of flexibility that we didn't have before, but we'll also have to " +"do a few things the we didn't have to do before." +msgstr "" +"在本笔记中，我们将重温 ``NumPyClient`` 并引入一个用于构建客户端的新基类，简单命名为 " +"``Client``。在本教程的前几部分中，我们的客户端基于``NumPyClient``，这是一个便捷类，可以让我们轻松地与具有良好 NumPy" +" 互操作性的机器学习库协同工作。有了 ``Client``，我们获得了很多以前没有的灵活性，但我们也必须做一些以前不需要做的事情。" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:18 +msgid "" +"Let's go deeper and see what it takes to move from ``NumPyClient`` to " +"``Client``!" +msgstr "让我们深入了解一下从 ``NumPyClient`` 到 ``Client`` 的过程！" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:30 +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:29 +msgid "Step 0: Preparation" +msgstr "步骤 0：准备工作" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:117 +msgid "" +"Let's now load the CIFAR-10 training and test set, partition them into " +"ten smaller datasets (each split into training and validation set), and " +"wrap everything in their own ``DataLoader``." +msgstr "" +"现在，让我们加载 CIFAR-10 训练集和测试集，将它们分割成十个较小的数据集（每个数据集又分为训练集和验证集），并将所有数据都封装在各自的 " +"``DataLoader`` 中。" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:259 +msgid "Step 1: Revisiting NumPyClient" +msgstr "步骤 1：重温 NumPyClient" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:261 +msgid "" +"So far, we've implemented our client by subclassing " +"``flwr.client.NumPyClient``. The three methods we implemented are " +"``get_parameters``, ``fit``, and ``evaluate``. Finally, we wrap the " +"creation of instances of this class in a function called ``client_fn``:" +msgstr "" +"到目前为止，我们通过子类化 ``flwr.client.NumPyClient`` " +"实现了我们的客户端。我们实现了三个方法：``get_parameters``, ``fit`, 和``evaluate``。最后，我们用一个名为 " +"``client_fn`` 的函数来创建该类的实例：" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:309 +msgid "" +"We've seen this before, there's nothing new so far. The only *tiny* " +"difference compared to the previous notebook is naming, we've changed " +"``FlowerClient`` to ``FlowerNumPyClient`` and ``client_fn`` to " +"``numpyclient_fn``. Let's run it to see the output we get:" +msgstr "" +"我们以前见过这种情况，目前没有什么新东西。与之前的笔记相比，唯一*小*的不同是命名，我们把 ``FlowerClient`` 改成了 " +"``FlowerNumPyClient``，把 `client_fn` 改成了 ``numpyclient_fn``。让我们运行它看看输出结果：" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:339 +msgid "" +"This works as expected, two clients are training for three rounds of " +"federated learning." +msgstr "结果不出所料，两个客户端正在进行三轮联邦学习训练。" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:341 +msgid "" +"Let's dive a little bit deeper and discuss how Flower executes this " +"simulation. Whenever a client is selected to do some work, " +"``start_simulation`` calls the function ``numpyclient_fn`` to create an " +"instance of our ``FlowerNumPyClient`` (along with loading the model and " +"the data)." +msgstr "" +"让我们再深入一点，讨论一下 Flower 是如何执行模拟的。每当一个客户端被选中进行工作时，`start_simulation`` 就会调用函数 " +"`numpyclient_fn` 来创建我们的 ``FlowerNumPyClient`` 实例（同时加载模型和数据）。" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:343 +msgid "" +"But here's the perhaps surprising part: Flower doesn't actually use the " +"``FlowerNumPyClient`` object directly. Instead, it wraps the object to " +"makes it look like a subclass of ``flwr.client.Client``, not " +"``flwr.client.NumPyClient``. In fact, the Flower core framework doesn't " +"know how to handle ``NumPyClient``'s, it only knows how to handle " +"``Client``'s. ``NumPyClient`` is just a convenience abstraction built on " +"top of ``Client``." +msgstr "" +"但令人惊讶的部分也许就在这里： Flower 实际上并不直接使用 ``FlowerNumPyClient`` " +"对象。相反，它封装了该对象，使其看起来像 ``flwr.client.Client`` 的子类，而不是 " +"``flwr.client.NumPyClient``。事实上，Flower 核心框架不知道如何处理 " +"``NumPyClient``，它只知道如何处理 ``Client``。``NumPyClient`` " +"只是建立在``Client``之上的便捷抽象类。" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:345 +msgid "" +"Instead of building on top of ``NumPyClient``, we can directly build on " +"top of ``Client``." +msgstr "与其在 ``NumPyClient`` 上构建，我们可以直接在 ``Client`` 上构建。" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:357 +msgid "Step 2: Moving from ``NumPyClient`` to ``Client``" +msgstr "步骤 2：从 ``NumPyClient`` 移至 ``Client``" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:359 +msgid "" +"Let's try to do the same thing using ``Client`` instead of " +"``NumPyClient``." +msgstr "让我们尝试使用 ``Client`` 代替 ``NumPyClient`` 做同样的事情。" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:465 +msgid "" +"Before we discuss the code in more detail, let's try to run it! Gotta " +"make sure our new ``Client``-based client works, right?" +msgstr "在详细讨论代码之前，让我们试着运行它！必须确保我们基于 ``Client`` 的新客户端能正常运行，对吗？" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:490 +msgid "" +"That's it, we're now using ``Client``. It probably looks similar to what " +"we've done with ``NumPyClient``. So what's the difference?" +msgstr "就是这样，我们现在开始使用 ``Client``。它看起来可能与我们使用 ``NumPyClient`` 所做的类似。那么有什么不同呢？" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:492 +msgid "" +"First of all, it's more code. But why? The difference comes from the fact" +" that ``Client`` expects us to take care of parameter serialization and " +"deserialization. For Flower to be able to send parameters over the " +"network, it eventually needs to turn these parameters into ``bytes``. " +"Turning parameters (e.g., NumPy ``ndarray``'s) into raw bytes is called " +"serialization. Turning raw bytes into something more useful (like NumPy " +"``ndarray``'s) is called deserialization. Flower needs to do both: it " +"needs to serialize parameters on the server-side and send them to the " +"client, the client needs to deserialize them to use them for local " +"training, and then serialize the updated parameters again to send them " +"back to the server, which (finally!) deserializes them again in order to " +"aggregate them with the updates received from other clients." +msgstr "" +"首先，它的代码更多。但为什么呢？区别在于 ``Client`` 希望我们处理参数的序列化和反序列化。Flower " +"要想通过网络发送参数，最终需要将这些参数转化为 ``字节``。把参数（例如 NumPy 的 ``ndarray`` " +"参数）变成原始字节叫做序列化。将原始字节转换成更有用的东西（如 NumPy ``ndarray`）称为反序列化。Flower " +"需要同时做这两件事：它需要在服务器端序列化参数并将其发送到客户端，客户端需要反序列化参数以便将其用于本地训练，然后再次序列化更新后的参数并将其发送回服务器，服务器（最后）再次反序列化参数以便将其与从其他客户端接收到的更新汇总在一起。" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:495 +msgid "" +"The only *real* difference between Client and NumPyClient is that " +"NumPyClient takes care of serialization and deserialization for you. It " +"can do so because it expects you to return parameters as NumPy ndarray's," +" and it knows how to handle these. This makes working with machine " +"learning libraries that have good NumPy support (most of them) a breeze." +msgstr "" +"Client 与 NumPyClient 之间的唯一**真正区别在于，NumPyClient " +"会为你处理序列化和反序列化。NumPyClient之所以能做到这一点，是因为它预计你会以NumPy " +"ndarray的形式返回参数，而且它知道如何处理这些参数。这使得与具有良好 NumPy 支持的大多数机器学习库一起工作变得轻而易举。" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:497 +msgid "" +"In terms of API, there's one major difference: all methods in Client take" +" exactly one argument (e.g., ``FitIns`` in ``Client.fit``) and return " +"exactly one value (e.g., ``FitRes`` in ``Client.fit``). The methods in " +"``NumPyClient`` on the other hand have multiple arguments (e.g., " +"``parameters`` and ``config`` in ``NumPyClient.fit``) and multiple return" +" values (e.g., ``parameters``, ``num_example``, and ``metrics`` in " +"``NumPyClient.fit``) if there are multiple things to handle. These " +"``*Ins`` and ``*Res`` objects in ``Client`` wrap all the individual " +"values you're used to from ``NumPyClient``." +msgstr "" +"在 API 方面，有一个主要区别：Client 中的所有方法都只接受一个参数（例如，``Client.fit`` 中的 " +"``FitIns``），并只返回一个值（例如，``Client.fit`` 中的 " +"``FitRes``）。另一方面，``NumPyClient``中的方法有多个参数（例如，``NumPyClient.fit``中的``parameters``和``config``）和多个返回值（例如，``NumPyClient.fit``中的``parameters``、``num_example``和``metrics``）。在" +" ``Client`` 中的这些 ``*Ins`` 和 ``*Res`` 对象封装了你在 ``NumPyClient`` 中习惯使用的所有单个值。" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:510 +msgid "Step 3: Custom serialization" +msgstr "步骤 3：自定义序列化" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:512 +msgid "" +"Here we will explore how to implement custom serialization with a simple " +"example." +msgstr "下面我们将通过一个简单的示例来探讨如何实现自定义序列化。" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:514 +msgid "" +"But first what is serialization? Serialization is just the process of " +"converting an object into raw bytes, and equally as important, " +"deserialization is the process of converting raw bytes back into an " +"object. This is very useful for network communication. Indeed, without " +"serialization, you could not just a Python object through the internet." +msgstr "" +"首先，什么是序列化？序列化只是将对象转换为原始字节的过程，同样重要的是，反序列化是将原始字节转换回对象的过程。这对网络通信非常有用。事实上，如果没有序列化，你就无法通过互联网传输一个" +" Python 对象。" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:516 +msgid "" +"Federated Learning relies heavily on internet communication for training " +"by sending Python objects back and forth between the clients and the " +"server. This means that serialization is an essential part of Federated " +"Learning." +msgstr "通过在客户端和服务器之间来回发送 Python 对象，联合学习在很大程度上依赖于互联网通信进行训练。这意味着序列化是联邦学习的重要组成部分。" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:518 +msgid "" +"In the following section, we will write a basic example where instead of " +"sending a serialized version of our ``ndarray``\\ s containing our " +"parameters, we will first convert the ``ndarray`` into sparse matrices, " +"before sending them. This technique can be used to save bandwidth, as in " +"certain cases where the weights of a model are sparse (containing many 0 " +"entries), converting them to a sparse matrix can greatly improve their " +"bytesize." +msgstr "" +"在下面的章节中，我们将编写一个基本示例，在发送包含参数的 ``ndarray`` 前，我们将首先把 ``ndarray`` " +"转换为稀疏矩阵，而不是发送序列化版本。这种技术可以用来节省带宽，因为在某些情况下，模型的参数是稀疏的（包含许多 0 " +"条目），将它们转换成稀疏矩阵可以大大提高它们的字节数。" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:521 +msgid "Our custom serialization/deserialization functions" +msgstr "我们的定制序列化/反序列化功能" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:523 +msgid "" +"This is where the real serialization/deserialization will happen, " +"especially in ``ndarray_to_sparse_bytes`` for serialization and " +"``sparse_bytes_to_ndarray`` for deserialization." +msgstr "" +"这才是真正的序列化/反序列化，尤其是在用于序列化的 ``ndarray_too_sparse_bytes`` 和用于反序列化的 " +"``sparse_bytes_too_ndarray`` 中。" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:525 +msgid "" +"Note that we imported the ``scipy.sparse`` library in order to convert " +"our arrays." +msgstr "请注意，为了转换数组，我们导入了 ``scipy.sparse`` 库。" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:613 +msgid "Client-side" +msgstr "客户端" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:615 +msgid "" +"To be able to serialize our ``ndarray``\\ s into sparse parameters, we " +"will just have to call our custom functions in our " +"``flwr.client.Client``." +msgstr "为了能够将我们的 ``ndarray`` 序列化为稀疏参数，我们只需在 ``flwr.client.Client`` 中调用我们的自定义函数。" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:617 +msgid "" +"Indeed, in ``get_parameters`` we need to serialize the parameters we got " +"from our network using our custom ``ndarrays_to_sparse_parameters`` " +"defined above." +msgstr "" +"事实上，在 `get_parameters` 中，我们需要使用上文定义的自定义 `ndarrays_too_sparse_parameters` " +"序列化从网络中获取的参数。" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:619 +msgid "" +"In ``fit``, we first need to deserialize the parameters coming from the " +"server using our custom ``sparse_parameters_to_ndarrays`` and then we " +"need to serialize our local results with " +"``ndarrays_to_sparse_parameters``." +msgstr "" +"在 ``fit`` 中，我们首先需要使用自定义的 ``sparse_parameters_to_ndarrays`` " +"反序列化来自服务器的参数，然后使用 ``ndarrays_to_sparse_parameters`` 序列化本地结果。" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:621 +msgid "" +"In ``evaluate``, we will only need to deserialize the global parameters " +"with our custom function." +msgstr "在 ``evaluate`` 中，我们只需要用自定义函数反序列化全局参数。" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:725 +msgid "Server-side" +msgstr "服务器端" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:727 +msgid "" +"For this example, we will just use ``FedAvg`` as a strategy. To change " +"the serialization and deserialization here, we only need to reimplement " +"the ``evaluate`` and ``aggregate_fit`` functions of ``FedAvg``. The other" +" functions of the strategy will be inherited from the super class " +"``FedAvg``." +msgstr "" +"在本例中，我们将只使用 ``FedAvg`` 作为策略。要改变这里的序列化和反序列化，我们只需重新实现 ``FedAvg`` 的 " +"``evaluate`` 和 ``aggregate_fit`` 函数。策略的其他函数将从超类 ``FedAvg`` 继承。" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:729 +msgid "As you can see only one line as change in ``evaluate``:" +msgstr "正如你所看到的，``evaluate``中只修改了一行：" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:735 +msgid "" +"And for ``aggregate_fit``, we will first deserialize every result we " +"received:" +msgstr "而对于 ``aggregate_fit``，我们将首先反序列化收到的每个结果：" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:744 +msgid "And then serialize the aggregated result:" +msgstr "然后将汇总结果序列化：" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:903 +msgid "We can now run our custom serialization example!" +msgstr "现在我们可以运行自定义序列化示例！" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:934 +msgid "" +"In this part of the tutorial, we've seen how we can build clients by " +"subclassing either ``NumPyClient`` or ``Client``. ``NumPyClient`` is a " +"convenience abstraction that makes it easier to work with machine " +"learning libraries that have good NumPy interoperability. ``Client`` is a" +" more flexible abstraction that allows us to do things that are not " +"possible in ``NumPyClient``. In order to do so, it requires us to handle " +"parameter serialization and deserialization ourselves." +msgstr "" +"在本部分教程中，我们已经了解了如何通过子类化 ``NumPyClient`` 或 ``Client`` 来构建客户端。NumPyClient " +"\"是一个便捷的抽象类，可以让我们更容易地与具有良好NumPy互操作性的机器学习库一起工作。``Client``是一个更灵活的抽象类，允许我们做一些在`NumPyClient``中做不到的事情。为此，它要求我们自己处理参数序列化和反序列化。" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:952 +msgid "" +"This is the final part of the Flower tutorial (for now!), " +"congratulations! You're now well equipped to understand the rest of the " +"documentation. There are many topics we didn't cover in the tutorial, we " +"recommend the following resources:" +msgstr "这暂时是 Flower 教程的最后一部分，恭喜您！您现在已经具备了理解其余文档的能力。本教程还有许多内容没有涉及，我们推荐您参考以下资源：" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:954 +msgid "`Read Flower Docs `__" +msgstr "阅读Flower文档 `__" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:955 +msgid "" +"`Check out Flower Code Examples " +"`__" +msgstr "查看 Flower 代码示例 `__" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:956 +msgid "" +"`Use Flower Baselines for your research " +"`__" +msgstr "使用 \"Flower Baselines \"进行研究 `__" + +#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:957 +msgid "" +"`Watch Flower Summit 2023 videos `__" +msgstr "观看 2023 年Flower峰会视频 `__" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:9 +msgid "Get started with Flower" +msgstr "开始使用Flower" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:11 +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:11 +msgid "Welcome to the Flower federated learning tutorial!" +msgstr "欢迎阅读Flower联邦学习教程！" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:13 +#, fuzzy +msgid "" +"In this notebook, we'll build a federated learning system using Flower, " +"`Flower Datasets `__ and PyTorch. In " +"part 1, we use PyTorch for the model training pipeline and data loading. " +"In part 2, we continue to federate the PyTorch-based pipeline using " +"Flower." +msgstr "" +"在本笔记中，我们将使用 Flower 和 PyTorch 构建一个联邦学习系统。在第一部分中，我们使用 PyTorch " +"进行模型训练和数据加载。在第二部分中，我们将继续使用 Flower 联邦化基于 PyTorch 的框架。" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:17 +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:19 +msgid "Let's get started!" +msgstr "让我们开始吧！" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:31 +msgid "" +"Before we begin with any actual code, let's make sure that we have " +"everything we need." +msgstr "在开始编写实际代码之前，让我们先确保我们已经准备好了所需的一切。" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:45 +#, fuzzy +msgid "" +"Next, we install the necessary packages for PyTorch (``torch`` and " +"``torchvision``), Flower Datasets (``flwr-datasets``) and Flower " +"(``flwr``):" +msgstr "接下来，我们为 PyTorch（`torch`` 和`torchvision``）和 Flower（`flwr`）安装必要的软件包：" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:105 +#, fuzzy +msgid "" +"It is possible to switch to a runtime that has GPU acceleration enabled " +"(on Google Colab: ``Runtime > Change runtime type > Hardware accelerator:" +" GPU > Save``). Note, however, that Google Colab is not always able to " +"offer GPU acceleration. If you see an error related to GPU availability " +"in one of the following sections, consider switching back to CPU-based " +"execution by setting ``DEVICE = torch.device(\"cpu\")``. If the runtime " +"has GPU acceleration enabled, you should see the output ``Training on " +"cuda``, otherwise it'll say ``Training on cpu``." +msgstr "" +"可以切换到已启用 GPU 加速的运行时（在 Google Colab 上： 运行时 > 更改运行时类型 > 硬件加速： GPU > " +"保存``）。但请注意，Google Colab 并非总能提供 GPU 加速。如果在以下部分中看到与 GPU 可用性相关的错误，请考虑通过设置 " +"``DEVICE = torch.device(\"cpu\")`` 切回基于 CPU 的执行。如果运行时已启用 GPU " +"加速，你应该会看到输出``Training on cuda``，否则会显示``Training on cpu``。" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:118 +msgid "Loading the data" +msgstr "加载数据" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:120 +#, fuzzy +msgid "" +"Federated learning can be applied to many different types of tasks across" +" different domains. In this tutorial, we introduce federated learning by " +"training a simple convolutional neural network (CNN) on the popular " +"CIFAR-10 dataset. CIFAR-10 can be used to train image classifiers that " +"distinguish between images from ten different classes: 'airplane', " +"'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', and " +"'truck'." +msgstr "" +"联邦学习可应用于不同领域的多种不同类型任务。在本教程中，我们将通过在流行的 CIFAR-10 数据集上训练一个简单的卷积神经网络 (CNN) " +"来介绍联合学习。CIFAR-10 可用于训练图像分类器，以区分来自十个不同类别的图像：" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:131 +msgid "" +"We simulate having multiple datasets from multiple organizations (also " +"called the \"cross-silo\" setting in federated learning) by splitting the" +" original CIFAR-10 dataset into multiple partitions. Each partition will " +"represent the data from a single organization. We're doing this purely " +"for experimentation purposes, in the real world there's no need for data " +"splitting because each organization already has their own data (so the " +"data is naturally partitioned)." +msgstr "" +"我们通过将原始 CIFAR-10 数据集拆分成多个分区来模拟来自多个组织的多个数据集（也称为联邦学习中的 \"跨分区 " +"\"设置）。每个分区代表一个组织的数据。我们这样做纯粹是为了实验目的，在现实世界中不需要拆分数据，因为每个组织都已经有了自己的数据（所以数据是自然分区的）。" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:133 +#, fuzzy +msgid "" +"Each organization will act as a client in the federated learning system. " +"So having ten organizations participate in a federation means having ten " +"clients connected to the federated learning server." +msgstr "每个组织都将充当联邦学习系统中的客户端。因此，有十个组织参与联邦学习，就意味着有十个客户端连接到联邦学习服务器：" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:144 +#, fuzzy +msgid "" +"Let's now create the Federated Dataset abstraction that from ``flwr-" +"datasets`` that partitions the CIFAR-10. We will create small training " +"and test set for each edge device and wrap each of them into a PyTorch " +"``DataLoader``:" +msgstr "" +"现在，让我们从 ``flwr-datasets`` 中创建 Federated Dataset 抽象，以分割 " +"CIFAR-10。我们将为每个边缘设备创建小型训练集和测试集，并将它们分别封装到 PyTorch ``DataLoader`` 中：" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:198 +#, fuzzy +msgid "" +"We now have a list of ten training sets and ten validation sets " +"(``trainloaders`` and ``valloaders``) representing the data of ten " +"different organizations. Each ``trainloader``/``valloader`` pair contains" +" 4000 training examples and 1000 validation examples. There's also a " +"single ``testloader`` (we did not split the test set). Again, this is " +"only necessary for building research or educational systems, actual " +"federated learning systems have their data naturally distributed across " +"multiple partitions." +msgstr "" +"现在，我们有一个包含十个训练集和十个验证集（`trainloaders`` 和`valloaders``）的列表，代表十个不同组织的数据。每对 " +"``trainloader``/``valloader`` 都包含 4500 个训练示例和 500 个验证数据。还有一个单独的 " +"``测试加载器``（我们没有拆分测试集）。同样，这只有在构建研究或教育系统时才有必要，实际的联邦学习系统的数据自然分布在多个分区中。" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:201 +msgid "" +"Let's take a look at the first batch of images and labels in the first " +"training set (i.e., ``trainloaders[0]``) before we move on:" +msgstr "在继续之前，让我们先看看第一个训练集中的第一批图像和标签（即 ``trainloaders[0]``）：" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:240 +msgid "" +"The output above shows a random batch of images from the first " +"``trainloader`` in our list of ten ``trainloaders``. It also prints the " +"labels associated with each image (i.e., one of the ten possible labels " +"we've seen above). If you run the cell again, you should see another " +"batch of images." +msgstr "" +"上面的输出显示了来自十个 \"trainloader \"列表中第一个 \"trainloader " +"\"的随机图像。它还打印了与每幅图像相关的标签（即我们上面看到的十个可能标签之一）。如果您再次运行该单元，应该会看到另一批图像。" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:252 +msgid "Step 1: Centralized Training with PyTorch" +msgstr "步骤 1：使用 PyTorch 进行集中训练" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:263 +msgid "" +"Next, we're going to use PyTorch to define a simple convolutional neural " +"network. This introduction assumes basic familiarity with PyTorch, so it " +"doesn't cover the PyTorch-related aspects in full detail. If you want to " +"dive deeper into PyTorch, we recommend `DEEP LEARNING WITH PYTORCH: A 60 " +"MINUTE BLITZ " +"`__." +msgstr "" +"接下来，我们将使用 PyTorch 来定义一个简单的卷积神经网络。本介绍假定您对 PyTorch 有基本的了解，因此不会详细介绍与 PyTorch" +" 相关的内容。如果你想更深入地了解 PyTorch，我们推荐你阅读 `DEEP LEARNING WITH PYTORCH： a 60 " +"minute blitz " +"`__。" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:275 +msgid "Defining the model" +msgstr "定义模型" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:277 +msgid "" +"We use the simple CNN described in the `PyTorch tutorial " +"`__:" +msgstr "" +"我们使用` PyTorch 教程 " +"`__ 中描述的简单 CNN：" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:314 +msgid "Let's continue with the usual training and test functions:" +msgstr "让我们继续进行常规的训练和测试功能：" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:374 +msgid "Training the model" +msgstr "训练模型" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:376 +msgid "" +"We now have all the basic building blocks we need: a dataset, a model, a " +"training function, and a test function. Let's put them together to train " +"the model on the dataset of one of our organizations " +"(``trainloaders[0]``). This simulates the reality of most machine " +"learning projects today: each organization has their own data and trains " +"models only on this internal data:" +msgstr "现在我们拥有了所需的所有基本构件：数据集、模型、训练函数和测试函数。让我们把它们放在一起，在我们其中一个组织的数据集（``trainloaders[0]``）上训练模型。这模拟了当今大多数机器学习项目的实际情况：每个组织都有自己的数据，并且只在这些内部数据上训练模型：" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:406 +msgid "" +"Training the simple CNN on our CIFAR-10 split for 5 epochs should result " +"in a test set accuracy of about 41%, which is not good, but at the same " +"time, it doesn't really matter for the purposes of this tutorial. The " +"intent was just to show a simplistic centralized training pipeline that " +"sets the stage for what comes next - federated learning!" +msgstr "" +"在我们的 CIFAR-10 分片上对简单 CNN 进行 5 个遍历的训练后，测试集的准确率应为 " +"41%，这并不理想，但同时对本教程而言也并不重要。我们只是想展示一个简单的集中式训练流程，为接下来的联邦学习做好铺垫！" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:418 +msgid "Step 2: Federated Learning with Flower" +msgstr "步骤 2：使用 Flower 联邦学习" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:420 +msgid "" +"Step 1 demonstrated a simple centralized training pipeline. All data was " +"in one place (i.e., a single ``trainloader`` and a single ``valloader``)." +" Next, we'll simulate a situation where we have multiple datasets in " +"multiple organizations and where we train a model over these " +"organizations using federated learning." +msgstr "" +"步骤 1 演示了一个简单的集中式训练流程。所有数据都在一个地方（即一个 \"trainloader \"和一个 " +"\"valloader\"）。接下来，我们将模拟在多个组织中拥有多个数据集的情况，并使用联邦学习在这些组织中训练一个模型。" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:432 +msgid "Updating model parameters" +msgstr "更新模型参数" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:434 +msgid "" +"In federated learning, the server sends the global model parameters to " +"the client, and the client updates the local model with the parameters " +"received from the server. It then trains the model on the local data " +"(which changes the model parameters locally) and sends the " +"updated/changed model parameters back to the server (or, alternatively, " +"it sends just the gradients back to the server, not the full model " +"parameters)." +msgstr "在联邦学习中，服务器将全局模型参数发送给客户端，客户端根据从服务器接收到的参数更新本地模型。然后，客户端根据本地数据对模型进行训练（在本地更改模型参数），并将更新/更改后的模型参数发回服务器（或者，客户端只将梯度参数发回服务器，而不是全部模型参数）。" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:436 +msgid "" +"We need two helper functions to update the local model with parameters " +"received from the server and to get the updated model parameters from the" +" local model: ``set_parameters`` and ``get_parameters``. The following " +"two functions do just that for the PyTorch model above." +msgstr "" +"我们需要两个辅助函数，用从服务器接收到的参数更新本地模型，并从本地模型获取更新后的模型参数：`` " +"set_parameters```和`get_parameters``。下面两个函数就是为上面的 PyTorch 模型做这些工作的。" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:438 +msgid "" +"The details of how this works are not really important here (feel free to" +" consult the PyTorch documentation if you want to learn more). In " +"essence, we use ``state_dict`` to access PyTorch model parameter tensors." +" The parameter tensors are then converted to/from a list of NumPy " +"ndarray's (which Flower knows how to serialize/deserialize):" +msgstr "" +"在这里，如何工作的细节并不重要（如果你想了解更多，请随时查阅 PyTorch 文档）。本质上，我们使用 ``state_dict`` 访问 " +"PyTorch 模型参数张量。然后，参数张量会被转换成/转换成 NumPy ndarray 列表（Flower 知道如何序列化/反序列化）：" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:466 +msgid "Implementing a Flower client" +msgstr "实现 Flower 客户端" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:468 +msgid "" +"With that out of the way, let's move on to the interesting part. " +"Federated learning systems consist of a server and multiple clients. In " +"Flower, we create clients by implementing subclasses of " +"``flwr.client.Client`` or ``flwr.client.NumPyClient``. We use " +"``NumPyClient`` in this tutorial because it is easier to implement and " +"requires us to write less boilerplate." +msgstr "" +"说完这些，让我们进入有趣的部分。联邦学习系统由一个服务器和多个客户端组成。在 Flower 中，我们通过实现 " +"``flwr.client.Client`` 或 ``flwr.client.NumPyClient`` " +"的子类来创建客户端。在本教程中，我们使用``NumPyClient``，因为它更容易实现，需要我们编写的模板也更少。" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:470 +msgid "" +"To implement the Flower client, we create a subclass of " +"``flwr.client.NumPyClient`` and implement the three methods " +"``get_parameters``, ``fit``, and ``evaluate``:" +msgstr "" +"为实现 Flower 客户端，我们创建了 ``flwr.client.NumPyClient`` 的子类，并实现了 " +"``get_parameters``、``fit`` 和``evaluate`` 三个方法：" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:472 +msgid "``get_parameters``: Return the current local model parameters" +msgstr "``get_parameters``： 返回当前本地模型参数" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:473 +msgid "" +"``fit``: Receive model parameters from the server, train the model " +"parameters on the local data, and return the (updated) model parameters " +"to the server" +msgstr "``fit``： 从服务器接收模型参数，在本地数据上训练模型参数，并将（更新的）模型参数返回服务器" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:474 +msgid "" +"``evaluate``: Receive model parameters from the server, evaluate the " +"model parameters on the local data, and return the evaluation result to " +"the server" +msgstr "``evaluate ``： 从服务器接收模型参数，在本地数据上评估模型参数，并将评估结果返回服务器" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:476 +msgid "" +"We mentioned that our clients will use the previously defined PyTorch " +"components for model training and evaluation. Let's see a simple Flower " +"client implementation that brings everything together:" +msgstr "" +"我们提到，我们的客户端将使用之前定义的 PyTorch 组件进行模型训练和评估。让我们来看看一个简单的 Flower " +"客户端实现，它将一切都整合在一起：" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:513 +msgid "" +"Our class ``FlowerClient`` defines how local training/evaluation will be " +"performed and allows Flower to call the local training/evaluation through" +" ``fit`` and ``evaluate``. Each instance of ``FlowerClient`` represents a" +" *single client* in our federated learning system. Federated learning " +"systems have multiple clients (otherwise, there's not much to federate), " +"so each client will be represented by its own instance of " +"``FlowerClient``. If we have, for example, three clients in our workload," +" then we'd have three instances of ``FlowerClient``. Flower calls " +"``FlowerClient.fit`` on the respective instance when the server selects a" +" particular client for training (and ``FlowerClient.evaluate`` for " +"evaluation)." +msgstr "" +"我们的类 ``FlowerClient`` 定义了本地训练/评估的执行方式，并允许 Flower 通过 ``fit`` 和 " +"``evaluate`` 调用本地训练/评估。每个 ``FlowerClient`` " +"实例都代表联邦学习系统中的*单个客户端*。联邦学习系统有多个客户端（否则就没有什么可联邦的），因此每个客户端都将由自己的 " +"``FlowerClient`` 实例来代表。例如，如果我们的工作负载中有三个客户端，那么我们就会有三个 ``FlowerClient`` " +"实例。当服务器选择特定客户端进行训练时，Flower 会调用相应实例上的 ``FlowerClient.fit`` （评估时调用 " +"``FlowerClient.evaluate``）。" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:517 +msgid "Using the Virtual Client Engine" +msgstr "使用虚拟客户端引擎" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:519 +msgid "" +"In this notebook, we want to simulate a federated learning system with 10" +" clients on a single machine. This means that the server and all 10 " +"clients will live on a single machine and share resources such as CPU, " +"GPU, and memory. Having 10 clients would mean having 10 instances of " +"``FlowerClient`` in memory. Doing this on a single machine can quickly " +"exhaust the available memory resources, even if only a subset of these " +"clients participates in a single round of federated learning." +msgstr "" +"在本笔记中，我们要模拟一个联邦学习系统，在一台机器上有 10 个客户端。这意味着服务器和所有 10 个客户端都将位于一台机器上，并共享 " +"CPU、GPU 和内存等资源。有 10 个客户端就意味着内存中有 10 个 ``FlowerClient`` " +"实例。在单台机器上这样做会很快耗尽可用的内存资源，即使这些客户端中只有一个子集参与了一轮联邦学习。" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:521 +msgid "" +"In addition to the regular capabilities where server and clients run on " +"multiple machines, Flower, therefore, provides special simulation " +"capabilities that create ``FlowerClient`` instances only when they are " +"actually necessary for training or evaluation. To enable the Flower " +"framework to create clients when necessary, we need to implement a " +"function called ``client_fn`` that creates a ``FlowerClient`` instance on" +" demand. Flower calls ``client_fn`` whenever it needs an instance of one " +"particular client to call ``fit`` or ``evaluate`` (those instances are " +"usually discarded after use, so they should not keep any local state). " +"Clients are identified by a client ID, or short ``cid``. The ``cid`` can " +"be used, for example, to load different local data partitions for " +"different clients, as can be seen below:" +msgstr "" +"除了服务器和客户端在多台机器上运行的常规功能外，Flower 还提供了特殊的模拟功能，即只有在训练或评估实际需要时才创建 " +"``FlowerClient`` 实例。为了让 Flower 框架能在必要时创建客户端，我们需要实现一个名为 ``client_fn`` " +"的函数，它能按需创建一个 ``FlowerClient`` 实例。每当 Flower 需要一个特定的客户端实例来调用 ``fit`` 或 " +"``evaluate`` 时，它就会调用 " +"``client_fn``（这些实例在使用后通常会被丢弃，因此它们不应保留任何本地状态）。客户端由一个客户端 ID 或简短的 ``cid`` " +"标识。例如，可以使用 ``cid`` 为不同的客户端加载不同的本地数据分区，如下所示：" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:556 +msgid "Starting the training" +msgstr "开始训练" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:558 +msgid "" +"We now have the class ``FlowerClient`` which defines client-side " +"training/evaluation and ``client_fn`` which allows Flower to create " +"``FlowerClient`` instances whenever it needs to call ``fit`` or " +"``evaluate`` on one particular client. The last step is to start the " +"actual simulation using ``flwr.simulation.start_simulation``." +msgstr "" +"现在我们有了定义客户端训练/评估的类 ``FlowerClient`` 和允许 Flower 在需要调用某个客户端的 ``fit` 或 " +"``evaluate` 时创建 ``FlowerClient`` 实例的 ``client_fn` 类。最后一步是使用 " +"``flwr.simulation.start_simulation`` 启动实际模拟。" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:560 +msgid "" +"The function ``start_simulation`` accepts a number of arguments, amongst " +"them the ``client_fn`` used to create ``FlowerClient`` instances, the " +"number of clients to simulate (``num_clients``), the number of federated " +"learning rounds (``num_rounds``), and the strategy. The strategy " +"encapsulates the federated learning approach/algorithm, for example, " +"*Federated Averaging* (FedAvg)." +msgstr "" +"函数 ``start_simulation`` 接受许多参数，其中包括用于创建 ``FlowerClient`` 实例的 " +"``client_fn``、要模拟的客户端数量（``num_clients``）、联邦学习轮数（``num_rounds``）和策略。策略封装了联邦学习方法/算法，例如*联邦平均*" +" (FedAvg)。" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:562 +msgid "" +"Flower has a number of built-in strategies, but we can also use our own " +"strategy implementations to customize nearly all aspects of the federated" +" learning approach. For this example, we use the built-in ``FedAvg`` " +"implementation and customize it using a few basic parameters. The last " +"step is the actual call to ``start_simulation`` which - you guessed it - " +"starts the simulation:" +msgstr "" +"Flower 有许多内置策略，但我们也可以使用自己的策略实现来定制联邦学习方法的几乎所有方面。在本例中，我们使用内置的 ``FedAvg`` " +"实现，并使用一些基本参数对其进行定制。最后一步是实际调用 ``start_simulation``开始模拟：" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:608 +msgid "Behind the scenes" +msgstr "幕后" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:610 +msgid "So how does this work? How does Flower execute this simulation?" +msgstr "那么它是如何工作的呢？Flower 如何进行模拟？" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:612 +#, python-format +msgid "" +"When we call ``start_simulation``, we tell Flower that there are 10 " +"clients (``num_clients=10``). Flower then goes ahead an asks the " +"``FedAvg`` strategy to select clients. ``FedAvg`` knows that it should " +"select 100% of the available clients (``fraction_fit=1.0``), so it goes " +"ahead and selects 10 random clients (i.e., 100% of 10)." +msgstr "" +"当我们调用 ``start_simulation`` 时，我们会告诉 Flower 有 10 " +"个客户（`num_clients=10``）。然后，Flower 会要求 ``FedAvg`` 策略选择客户。``FedAvg`` 知道它应该选择" +" 100%的可用客户（``fraction_fit=1.0``），所以它会随机选择 10 个客户（即 10 的 100%）。" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:614 +msgid "" +"Flower then asks the selected 10 clients to train the model. When the " +"server receives the model parameter updates from the clients, it hands " +"those updates over to the strategy (*FedAvg*) for aggregation. The " +"strategy aggregates those updates and returns the new global model, which" +" then gets used in the next round of federated learning." +msgstr "" +"然后，Flower 会要求选定的 10 " +"个客户端对模型进行训练。服务器收到客户端的模型参数更新后，会将这些更新交给策略（*FedAvg*）进行聚合。策略会聚合这些更新并返回新的全局模型，然后将其用于下一轮联邦学习。" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:626 +msgid "Where's the accuracy?" +msgstr "准确度在哪里找？" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:628 +msgid "" +"You may have noticed that all metrics except for ``losses_distributed`` " +"are empty. Where did the ``{\"accuracy\": float(accuracy)}`` go?" +msgstr "" +"您可能已经注意到，除了 ``losses_distributed`` 以外，所有指标都是空的。{\"准确度\": " +"float(准确度)}``去哪儿了？" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:630 +msgid "" +"Flower can automatically aggregate losses returned by individual clients," +" but it cannot do the same for metrics in the generic metrics dictionary " +"(the one with the ``accuracy`` key). Metrics dictionaries can contain " +"very different kinds of metrics and even key/value pairs that are not " +"metrics at all, so the framework does not (and can not) know how to " +"handle these automatically." +msgstr "" +"Flower 可以自动汇总单个客户端返回的损失值，但无法对通用度量字典中的度量进行同样的处理（即带有 \"准确度 " +"\"键的度量字典）。度量值字典可以包含非常不同种类的度量值，甚至包含根本不是度量值的键/值对，因此框架不知道（也无法知道）如何自动处理这些度量值。" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:632 +msgid "" +"As users, we need to tell the framework how to handle/aggregate these " +"custom metrics, and we do so by passing metric aggregation functions to " +"the strategy. The strategy will then call these functions whenever it " +"receives fit or evaluate metrics from clients. The two possible functions" +" are ``fit_metrics_aggregation_fn`` and " +"``evaluate_metrics_aggregation_fn``." +msgstr "" +"作为用户，我们需要告诉框架如何处理/聚合这些自定义指标，为此，我们将指标聚合函数传递给策略。然后，只要从客户端接收到拟合或评估指标，策略就会调用这些函数。两个可能的函数是" +" ``fit_metrics_aggregation_fn`` 和 ``evaluate_metrics_aggregation_fn``。" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:634 +msgid "" +"Let's create a simple weighted averaging function to aggregate the " +"``accuracy`` metric we return from ``evaluate``:" +msgstr "让我们创建一个简单的加权平均函数来汇总从 ``evaluate`` 返回的 ``accuracy`` 指标：" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:660 +msgid "" +"The only thing left to do is to tell the strategy to call this function " +"whenever it receives evaluation metric dictionaries from the clients:" +msgstr "剩下要做的就是告诉策略，每当它从客户端接收到评估度量字典时，都要调用这个函数：" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:697 +msgid "" +"We now have a full system that performs federated training and federated " +"evaluation. It uses the ``weighted_average`` function to aggregate custom" +" evaluation metrics and calculates a single ``accuracy`` metric across " +"all clients on the server side." +msgstr "" +"我们现在有了一个完整的系统，可以执行联邦训练和联邦评估。它使用 ``weighted_average`` " +"函数汇总自定义评估指标，并在服务器端计算所有客户端的单一 ``accuracy`` 指标。" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:699 +msgid "" +"The other two categories of metrics (``losses_centralized`` and " +"``metrics_centralized``) are still empty because they only apply when " +"centralized evaluation is being used. Part two of the Flower tutorial " +"will cover centralized evaluation." +msgstr "" +"其他两类指标（`losses_centralized`` 和 " +"`metrics_centralized`）仍然是空的，因为它们只适用于集中评估。Flower 教程的第二部分将介绍集中式评估。" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:711 +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:351 +msgid "Final remarks" +msgstr "结束语" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:713 +msgid "" +"Congratulations, you just trained a convolutional neural network, " +"federated over 10 clients! With that, you understand the basics of " +"federated learning with Flower. The same approach you've seen can be used" +" with other machine learning frameworks (not just PyTorch) and tasks (not" +" just CIFAR-10 images classification), for example NLP with Hugging Face " +"Transformers or speech with SpeechBrain." +msgstr "" +"恭喜您，你刚刚训练了一个由 10 个客户端组成的卷积神经网络！这样，你就了解了使用 Flower " +"进行联邦学习的基础知识。你所看到的方法同样适用于其他机器学习框架（不只是 PyTorch）和任务（不只是 CIFAR-10 图像分类），例如使用 " +"Hugging Face Transformers 的 NLP 或使用 SpeechBrain 的语音。" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:715 +msgid "" +"In the next notebook, we're going to cover some more advanced concepts. " +"Want to customize your strategy? Initialize parameters on the server " +"side? Or evaluate the aggregated model on the server side? We'll cover " +"all this and more in the next tutorial." +msgstr "在下一个笔记中，我们将介绍一些更先进的概念。想定制你的策略吗？在服务器端初始化参数？或者在服务器端评估聚合模型？我们将在下一个教程中介绍所有这些内容以及更多。" + +#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:733 +msgid "" +"The `Flower Federated Learning Tutorial - Part 2 " +"`__ goes into more depth about strategies and all " +"the advanced things you can build with them." +msgstr "" +"`Flower 联邦学习教程 - 第 2 部分 `__ 更深入地介绍了策略以及可以使用策略构建的所有高级功能。" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:9 +msgid "Use a federated learning strategy" +msgstr "使用联邦学习策略" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:11 +msgid "" +"Welcome to the next part of the federated learning tutorial. In previous " +"parts of this tutorial, we introduced federated learning with PyTorch and" +" Flower (`part 1 `__)." +msgstr "" +"欢迎来到联邦学习教程的下一部分。在本教程的前几部分，我们介绍了使用 PyTorch 和 Flower 进行联邦学习（`第 1 部分 " +"`___）。" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:13 +msgid "" +"In this notebook, we'll begin to customize the federated learning system " +"we built in the introductory notebook (again, using `Flower " +"`__ and `PyTorch `__)." +msgstr "" +"在本笔记中，我们将开始定制在入门笔记中构建的联邦学习系统（再次使用 `Flower `__ 和 " +"`PyTorch `__）。" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:17 +msgid "Let's move beyond FedAvg with Flower strategies!" +msgstr "让我们超越 FedAvg，采用Flower策略！" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:309 +msgid "Strategy customization" +msgstr "策略定制" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:311 +msgid "" +"So far, everything should look familiar if you've worked through the " +"introductory notebook. With that, we're ready to introduce a number of " +"new features." +msgstr "到目前为止，如果您已经阅读过入门笔记本，那么一切都应该很熟悉了。接下来，我们将介绍一些新功能。" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:323 +msgid "Server-side parameter **initialization**" +msgstr "服务器端参数 **初始化**" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:325 +msgid "" +"Flower, by default, initializes the global model by asking one random " +"client for the initial parameters. In many cases, we want more control " +"over parameter initialization though. Flower therefore allows you to " +"directly pass the initial parameters to the Strategy:" +msgstr "" +"默认情况下，Flower 会通过向一个随机客户端询问初始参数来初始化全局模型。但在许多情况下，我们需要对参数初始化进行更多控制。因此，Flower" +" 允许您直接将初始参数传递给策略：" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:370 +msgid "" +"Passing ``initial_parameters`` to the ``FedAvg`` strategy prevents Flower" +" from asking one of the clients for the initial parameters. If we look " +"closely, we can see that the logs do not show any calls to the " +"``FlowerClient.get_parameters`` method." +msgstr "" +"向 ``FedAvg`` 策略传递 ``initial_parameters`` 可以防止 Flower " +"向其中一个客户端询问初始参数。如果我们仔细观察，就会发现日志中没有显示对 ``FlowerClient.get_parameters`` " +"方法的任何调用。" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:382 +msgid "Starting with a customized strategy" +msgstr "从定制战略开始" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:384 +msgid "" +"We've seen the function ``start_simulation`` before. It accepts a number " +"of arguments, amongst them the ``client_fn`` used to create " +"``FlowerClient`` instances, the number of clients to simulate " +"``num_clients``, the number of rounds ``num_rounds``, and the strategy." +msgstr "" +"我们以前见过函数 ``start_simulation``。它接受许多参数，其中包括用于创建 ``FlowerClient`` 实例的 " +"``client_fn``、要模拟的客户数量 ``num_clients``、回合数 ``num_rounds``和策略。" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:386 +msgid "" +"The strategy encapsulates the federated learning approach/algorithm, for " +"example, ``FedAvg`` or ``FedAdagrad``. Let's try to use a different " +"strategy this time:" +msgstr "该策略封装了联邦学习方法/算法，例如`FedAvg``或`FedAdagrad``。这次让我们尝试使用不同的策略：" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:424 +msgid "Server-side parameter **evaluation**" +msgstr "服务器端参数**评估**" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:426 +msgid "" +"Flower can evaluate the aggregated model on the server-side or on the " +"client-side. Client-side and server-side evaluation are similar in some " +"ways, but different in others." +msgstr "Flower 可以在服务器端或客户端评估聚合模型。客户端和服务器端评估在某些方面相似，但也有不同之处。" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:428 +msgid "" +"**Centralized Evaluation** (or *server-side evaluation*) is conceptually " +"simple: it works the same way that evaluation in centralized machine " +"learning does. If there is a server-side dataset that can be used for " +"evaluation purposes, then that's great. We can evaluate the newly " +"aggregated model after each round of training without having to send the " +"model to clients. We're also fortunate in the sense that our entire " +"evaluation dataset is available at all times." +msgstr "**集中评估**（或*服务器端评估*）在概念上很简单：它的工作方式与集中式机器学习中的评估方式相同。如果有一个服务器端数据集可用于评估目的，那就太好了。我们可以在每一轮训练后对新聚合的模型进行评估，而无需将模型发送给客户端。我们也很幸运，因为我们的整个评估数据集随时可用。" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:430 +msgid "" +"**Federated Evaluation** (or *client-side evaluation*) is more complex, " +"but also more powerful: it doesn't require a centralized dataset and " +"allows us to evaluate models over a larger set of data, which often " +"yields more realistic evaluation results. In fact, many scenarios require" +" us to use **Federated Evaluation** if we want to get representative " +"evaluation results at all. But this power comes at a cost: once we start " +"to evaluate on the client side, we should be aware that our evaluation " +"dataset can change over consecutive rounds of learning if those clients " +"are not always available. Moreover, the dataset held by each client can " +"also change over consecutive rounds. This can lead to evaluation results " +"that are not stable, so even if we would not change the model, we'd see " +"our evaluation results fluctuate over consecutive rounds." +msgstr "**联邦评估**（或*客户端评估*）更为复杂，但也更为强大：它不需要集中的数据集，允许我们在更大的数据集上对模型进行评估，这通常会产生更真实的评估结果。事实上，如果我们想得到有代表性的评估结果，很多情况下都需要使用**联邦评估**。但是，这种能力是有代价的：一旦我们开始在客户端进行评估，我们就应该意识到，如果这些客户端并不总是可用，我们的评估数据集可能会在连续几轮学习中发生变化。此外，每个客户端所拥有的数据集也可能在连续几轮学习中发生变化。这可能会导致评估结果不稳定，因此即使我们不改变模型，也会看到评估结果在连续几轮中波动。" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:433 +msgid "" +"We've seen how federated evaluation works on the client side (i.e., by " +"implementing the ``evaluate`` method in ``FlowerClient``). Now let's see " +"how we can evaluate aggregated model parameters on the server-side:" +msgstr "" +"我们已经了解了联邦评估如何在客户端工作（即通过在 ``FlowerClient`` 中实现 ``evaluate`` " +"方法）。现在让我们看看如何在服务器端评估聚合模型参数：" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:490 +msgid "Sending/receiving arbitrary values to/from clients" +msgstr "向/从客户端发送/接收任意值" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:492 +msgid "" +"In some situations, we want to configure client-side execution (training," +" evaluation) from the server-side. One example for that is the server " +"asking the clients to train for a certain number of local epochs. Flower " +"provides a way to send configuration values from the server to the " +"clients using a dictionary. Let's look at an example where the clients " +"receive values from the server through the ``config`` parameter in " +"``fit`` (``config`` is also available in ``evaluate``). The ``fit`` " +"method receives the configuration dictionary through the ``config`` " +"parameter and can then read values from this dictionary. In this example," +" it reads ``server_round`` and ``local_epochs`` and uses those values to " +"improve the logging and configure the number of local training epochs:" +msgstr "" +"在某些情况下，我们希望从服务器端配置客户端的执行（训练、评估）。其中一个例子就是服务器要求客户端训练一定数量的本地遍历。Flower " +"提供了一种使用字典从服务器向客户端发送配置值的方法。让我们来看一个例子：客户端通过 ``fit`` 中的 ``config`` " +"参数从服务器接收配置值（``evaluate`` 中也有 ``config`` 参数）。``fit`` 方法通过 ``config`` " +"参数接收配置字典，然后从字典中读取值。在本例中，它读取了 ``server_round`` 和 " +"``local_epochs``，并使用这些值来改进日志记录和配置本地训练遍历的数量：" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:546 +msgid "" +"So how can we send this config dictionary from server to clients? The " +"built-in Flower Strategies provide way to do this, and it works similarly" +" to the way server-side evaluation works. We provide a function to the " +"strategy, and the strategy calls this function for every round of " +"federated learning:" +msgstr "" +"那么，如何将配置字典从服务器发送到客户端呢？内置的 \"Flower策略\"（Flower " +"Strategies）提供了这样的方法，其工作原理与服务器端评估的工作原理类似。我们为策略提供一个函数，策略会在每一轮联邦学习中调用这个函数：" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:576 +msgid "" +"Next, we'll just pass this function to the FedAvg strategy before " +"starting the simulation:" +msgstr "接下来，我们只需在开始模拟前将此函数传递给 FedAvg 策略即可：" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:613 +msgid "" +"As we can see, the client logs now include the current round of federated" +" learning (which they read from the ``config`` dictionary). We can also " +"configure local training to run for one epoch during the first and second" +" round of federated learning, and then for two epochs during the third " +"round." +msgstr "" +"我们可以看到，客户端日志现在包含了当前一轮的联邦学习（从 ``config`` " +"字典中读取）。我们还可以将本地训练配置为在第一轮和第二轮联邦学习期间运行一个遍历，然后在第三轮联邦学习期间运行两个遍历。" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:615 +msgid "" +"Clients can also return arbitrary values to the server. To do so, they " +"return a dictionary from ``fit`` and/or ``evaluate``. We have seen and " +"used this concept throughout this notebook without mentioning it " +"explicitly: our ``FlowerClient`` returns a dictionary containing a custom" +" key/value pair as the third return value in ``evaluate``." +msgstr "" +"客户端还可以向服务器返回任意值。为此，它们会从 ``fit`` 和/或 ``evaluate`` " +"返回一个字典。我们在本笔记中看到并使用了这一概念，但并未明确提及：我们的 ``FlowerClient`` 返回一个包含自定义键/值对的字典，作为" +" ``evaluate`` 中的第三个返回值。" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:627 +msgid "Scaling federated learning" +msgstr "扩大联邦学习的规模" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:629 +msgid "" +"As a last step in this notebook, let's see how we can use Flower to " +"experiment with a large number of clients." +msgstr "作为本笔记的最后一步，让我们看看如何使用 Flower 对大量客户端进行实验。" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:651 +#, python-format +msgid "" +"We now have 1000 partitions, each holding 45 training and 5 validation " +"examples. Given that the number of training examples on each client is " +"quite small, we should probably train the model a bit longer, so we " +"configure the clients to perform 3 local training epochs. We should also " +"adjust the fraction of clients selected for training during each round " +"(we don't want all 1000 clients participating in every round), so we " +"adjust ``fraction_fit`` to ``0.05``, which means that only 5% of " +"available clients (so 50 clients) will be selected for training each " +"round:" +msgstr "" +"现在我们有 1000 个分区，每个分区有 45 个训练数据和 5 " +"个验证数据。鉴于每个客户端上的训练示例数量较少，我们可能需要对模型进行更长时间的训练，因此我们将客户端配置为执行 3 " +"个本地训练遍历。我们还应该调整每轮训练中被选中的客户端的比例（我们不希望每轮训练都有 1000 个客户端参与），因此我们将 " +"``fraction_fit`` 调整为 ``0.05``，这意味着每轮训练只选中 5%的可用客户端（即 50 个客户端）：" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:699 +msgid "" +"In this notebook, we've seen how we can gradually enhance our system by " +"customizing the strategy, initializing parameters on the server side, " +"choosing a different strategy, and evaluating models on the server-side. " +"That's quite a bit of flexibility with so little code, right?" +msgstr "在本笔记中，我们看到了如何通过自定义策略、在服务器端初始化参数、选择不同的策略以及在服务器端评估模型来逐步增强我们的系统。用这么少的代码就能实现这么大的灵活性，不是吗？" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:701 +msgid "" +"In the later sections, we've seen how we can communicate arbitrary values" +" between server and clients to fully customize client-side execution. " +"With that capability, we built a large-scale Federated Learning " +"simulation using the Flower Virtual Client Engine and ran an experiment " +"involving 1000 clients in the same workload - all in a Jupyter Notebook!" +msgstr "" +"在后面的章节中，我们将看到如何在服务器和客户端之间传递任意值，以完全自定义客户端执行。有了这种能力，我们使用 Flower " +"虚拟客户端引擎构建了一个大规模的联邦学习模拟，并在 Jupyter Notebook 中进行了一次实验，在相同的工作负载中运行了 1000 " +"个客户端！" + +#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:719 +msgid "" +"The `Flower Federated Learning Tutorial - Part 3 " +"`__ shows how to build a fully custom ``Strategy`` from " +"scratch." +msgstr "" +"`Flower 联邦学习教程 - 第 3 部分 `__ 展示了如何从头开始构建完全自定义的 \"策略\"。" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:9 +msgid "What is Federated Learning?" +msgstr "什么是联邦学习？" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:13 +msgid "" +"In this tutorial, you will learn what federated learning is, build your " +"first system in Flower, and gradually extend it. If you work through all " +"parts of the tutorial, you will be able to build advanced federated " +"learning systems that approach the current state of the art in the field." +msgstr "" +"在本教程中，你将了解什么是联邦学习，用 Flower " +"搭建第一个系统，并逐步对其进行扩展。如果你能完成本教程的所有部分，你就能构建高级的联邦学习系统，从而接近该领域当前的技术水平。" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:15 +msgid "" +"🧑‍🏫 This tutorial starts at zero and expects no familiarity with " +"federated learning. Only a basic understanding of data science and Python" +" programming is assumed." +msgstr "🧑‍🏫 本教程从零开始，不要求熟悉联邦学习。仅假定对数据科学和 Python 编程有基本了解。" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:17 +msgid "" +"`Star Flower on GitHub `__ ⭐️ and join " +"the open-source Flower community on Slack to connect, ask questions, and " +"get help: `Join Slack `__ 🌼 We'd love to " +"hear from you in the ``#introductions`` channel! And if anything is " +"unclear, head over to the ``#questions`` channel." +msgstr "" +"`Star Flower on GitHub `__ ⭐️ 并加入 Slack " +"上的开源 Flower 社区，进行交流、提问并获得帮助： 加入 Slack `__ 🌼" +" 我们希望在 ``#introductions`` 频道听到您的声音！如果有任何不清楚的地方，请访问 ``#questions`` 频道。" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:31 +msgid "Classic machine learning" +msgstr "经典机器学习" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:33 +msgid "" +"Before we begin to discuss federated learning, let us quickly recap how " +"most machine learning works today." +msgstr "在开始讨论联邦学习之前，让我们先快速回顾一下目前大多数机器学习的工作原理。" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:35 +msgid "" +"In machine learning, we have a model, and we have data. The model could " +"be a neural network (as depicted here), or something else, like classical" +" linear regression." +msgstr "在机器学习中，我们有一个模型和数据。模型可以是一个神经网络（如图所示），也可以是其他东西，比如经典的线性回归。" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:41 +msgid "|93b02017c78049bbbd5ae456dcb2c91b|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:109 +msgid "Model and data" +msgstr "模型和数据" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:47 +msgid "" +"We train the model using the data to perform a useful task. A task could " +"be to detect objects in images, transcribe an audio recording, or play a " +"game like Go." +msgstr "我们使用数据来训练模型，以完成一项有用的任务。任务可以是检测图像中的物体、转录音频或玩围棋等游戏。" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:53 +msgid "|01471150fd5144c080a176b43e92a3ff|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:111 +msgid "Train model using data" +msgstr "使用数据训练模型" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:59 +msgid "" +"Now, in practice, the training data we work with doesn't originate on the" +" machine we train the model on. It gets created somewhere else." +msgstr "实际上，我们使用的训练数据并不来自我们训练模型的机器。它是在其他地方创建的。" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:61 +msgid "" +"It originates on a smartphone by the user interacting with an app, a car " +"collecting sensor data, a laptop receiving input via the keyboard, or a " +"smart speaker listening to someone trying to sing a song." +msgstr "它源于智能手机上用户与应用程序的交互、汽车上传感器数据的收集、笔记本电脑上键盘输入的接收，或者智能扬声器上某人试着唱的歌。" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:67 +msgid "|9bc21c7dbd17444a8f070c60786e3484|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:113 +msgid "Data on a phone" +msgstr "手机上的数据" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:73 +msgid "" +"What's also important to mention, this \"somewhere else\" is usually not " +"just one place, it's many places. It could be several devices all running" +" the same app. But it could also be several organizations, all generating" +" data for the same task." +msgstr "" +"值得一提的是，这个 \"其他地方 " +"\"通常不只是一个地方，而是很多地方。它可能是多个运行同一应用程序的设备。但也可能是多个组织，都在为同一任务生成数据。" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:79 +msgid "|3047bbce54b34099ae559963d0420d79|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:115 +msgid "Data is on many devices" +msgstr "数据存在于多种设备中" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:85 +msgid "" +"So to use machine learning, or any kind of data analysis, the approach " +"that has been used in the past was to collect all data on a central " +"server. This server can be somewhere in a data center, or somewhere in " +"the cloud." +msgstr "因此，要使用机器学习或任何类型的数据分析，过去使用的方法是在中央服务器上收集所有数据。这个服务器可以在数据中心的某个地方，也可以在云端的某个地方。" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:91 +msgid "|e9f8ce948593444fb838d2f354c7ec5d|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:117 +msgid "Central data collection" +msgstr "集中数据收集" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:97 +msgid "" +"Once all the data is collected in one place, we can finally use machine " +"learning algorithms to train our model on the data. This is the machine " +"learning approach that we've basically always relied on." +msgstr "一旦所有数据都收集到一处，我们最终就可以使用机器学习算法在数据上训练我们的模型。这就是我们基本上一直依赖的机器学习方法。" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:103 +msgid "|c24c1478b30e4f74839208628a842d1e|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:119 +msgid "Central model training" +msgstr "集中模型训练" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:130 +msgid "Challenges of classical machine learning" +msgstr "经典机器学习面临的挑战" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:132 +msgid "" +"The classic machine learning approach we've just seen can be used in some" +" cases. Great examples include categorizing holiday photos, or analyzing " +"web traffic. Cases, where all the data is naturally available on a " +"centralized server." +msgstr "我们刚刚看到的经典机器学习方法可以在某些情况下使用。很好的例子包括对假日照片进行分类或分析网络流量。在这些案例中，所有数据自然都可以在中央服务器上获得。" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:138 +msgid "|1b3613d7a58847b59e1d3180802dbc09|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:173 +msgid "Centralized possible" +msgstr "可集中管理" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:144 +msgid "" +"But the approach can not be used in many other cases. Cases, where the " +"data is not available on a centralized server, or cases where the data " +"available on one server is not enough to train a good model." +msgstr "但这种方法并不适用于许多其他情况。例如，集中服务器上没有数据，或者一台服务器上的数据不足以训练出一个好的模型。" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:150 +msgid "|9980b5213db547d0b8024a50992b9e3f|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:175 +msgid "Centralized impossible" +msgstr "无法集中" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:156 +msgid "" +"There are many reasons why the classic centralized machine learning " +"approach does not work for a large number of highly important real-world " +"use cases. Those reasons include:" +msgstr "传统的集中式机器学习方法无法满足现实世界中大量极为重要的使用案例，原因有很多。这些原因包括：" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:158 +msgid "" +"**Regulations**: GDPR (Europe), CCPA (California), PIPEDA (Canada), LGPD " +"(Brazil), PDPL (Argentina), KVKK (Turkey), POPI (South Africa), FSS " +"(Russia), CDPR (China), PDPB (India), PIPA (Korea), APPI (Japan), PDP " +"(Indonesia), PDPA (Singapore), APP (Australia), and other regulations " +"protect sensitive data from being moved. In fact, those regulations " +"sometimes even prevent single organizations from combining their own " +"users' data for artificial intelligence training because those users live" +" in different parts of the world, and their data is governed by different" +" data protection regulations." +msgstr "" +"**法规**： " +"GDPR（欧洲）、CCPA（加利福尼亚）、PIPEDA（加拿大）、LGPD（巴西）、PDPL（阿根廷）、KVKK（土耳其）、POPI（南非）、FSS（俄罗斯）、CDPR（中国）、PDPB（印度）、PIPA（韩国）、APPI（日本）、PDP（印度尼西亚）、PDPA（新加坡）、APP（澳大利亚）等法规保护敏感数据不被移动。事实上，这些法规有时甚至会阻止单个组织将自己的用户数据用于人工智能培训，因为这些用户生活在世界不同地区，他们的数据受不同的数据保护法规管辖。" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:160 +msgid "" +"**User preference**: In addition to regulation, there are use cases where" +" users just expect that no data leaves their device, ever. If you type " +"your passwords and credit card info into the digital keyboard of your " +"phone, you don't expect those passwords to end up on the server of the " +"company that developed that keyboard, do you? In fact, that use case was " +"the reason federated learning was invented in the first place." +msgstr "" +"**用户偏好**： " +"除了法规之外，在一些使用案例中，用户只是希望数据永远不会离开他们的设备。如果你在手机的数字键盘上输入密码和信用卡信息，你不会希望这些密码最终出现在开发该键盘的公司的服务器上吧？事实上，这种用例正是联邦学习发明的初衷。" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:161 +msgid "" +"**Data volume**: Some sensors, like cameras, produce such a high data " +"volume that it is neither feasible nor economic to collect all the data " +"(due to, for example, bandwidth or communication efficiency). Think about" +" a national rail service with hundreds of train stations across the " +"country. If each of these train stations is outfitted with a number of " +"security cameras, the volume of raw on-device data they produce requires " +"incredibly powerful and exceedingly expensive infrastructure to process " +"and store. And most of the data isn't even useful." +msgstr "" +"**数据量**： " +"有些传感器（如摄像头）产生的数据量很大，收集所有数据既不可行，也不经济（例如，由于带宽或通信效率的原因）。试想一下全国铁路服务，全国有数百个火车站。如果每个火车站都安装了许多安全摄像头，那么它们所产生的大量原始设备数据就需要功能强大且极其昂贵的基础设施来处理和存储。而大部分数据甚至都是无用的。" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:164 +msgid "Examples where centralized machine learning does not work include:" +msgstr "集中式机器学习不起作用的例子包括：" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:166 +msgid "" +"Sensitive healthcare records from multiple hospitals to train cancer " +"detection models" +msgstr "用多家医院的敏感医疗记录训练癌症检测模型" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:167 +msgid "" +"Financial information from different organizations to detect financial " +"fraud" +msgstr "不同组织的财务信息，以侦查财务欺诈行为" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:168 +msgid "Location data from your electric car to make better range prediction" +msgstr "通过电动汽车的定位数据更好地预测续航里程" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:169 +msgid "End-to-end encrypted messages to train better auto-complete models" +msgstr "端到端加密信息可训练出更好的自动完成模型" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:171 +msgid "" +"The popularity of privacy-enhancing systems like the `Brave " +"`__ browser or the `Signal `__ " +"messenger shows that users care about privacy. In fact, they choose the " +"privacy-enhancing version over other alternatives, if such an alternative" +" exists. But what can we do to apply machine learning and data science to" +" these cases to utilize private data? After all, these are all areas that" +" would benefit significantly from recent advances in AI." +msgstr "" +"像 `Brave `__浏览器或 `Signal " +"`__信息管理器这样的隐私增强系统的流行表明，用户关心隐私。事实上，他们会选择隐私性更好的产品。但是，我们能做些什么来将机器学习和数据科学应用到这些情况中，以利用隐私数据呢？毕竟，这些领域都将从人工智能的最新进展中受益匪浅。" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:186 +msgid "Federated learning" +msgstr "联邦学习" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:188 +msgid "" +"Federated learning simply reverses this approach. It enables machine " +"learning on distributed data by moving the training to the data, instead " +"of moving the data to the training. Here's the single-sentence " +"explanation:" +msgstr "联邦学习简单地颠覆了这种方法。它通过将训练转移到数据上，而不是将数据转移到训练上，在分布式数据上实现机器学习。下面是一句话的解释：" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:190 +msgid "Central machine learning: move the data to the computation" +msgstr "集中式机器学习：将数据转移到计算中心" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:191 +msgid "Federated (machine) learning: move the computation to the data" +msgstr "联邦式（机器）学习：将计算转移到数据上" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:193 +msgid "" +"By doing so, it enables us to use machine learning (and other data " +"science approaches) in areas where it wasn't possible before. We can now " +"train excellent medical AI models by enabling different hospitals to work" +" together. We can solve financial fraud by training AI models on the data" +" of different financial institutions. We can build novel privacy-" +"enhancing applications (such as secure messaging) that have better built-" +"in AI than their non-privacy-enhancing alternatives. And those are just a" +" few of the examples that come to mind. As we deploy federated learning, " +"we discover more and more areas that can suddenly be reinvented because " +"they now have access to vast amounts of previously inaccessible data." +msgstr "这样，我们就能在以前不可能的领域使用机器学习（和其他数据科学方法）。现在，我们可以通过让不同的医院协同工作来训练优秀的医疗人工智能模型。我们可以通过在不同金融机构的数据上训练人工智能模型来解决金融欺诈问题。我们可以构建新颖的隐私增强型应用（如安全信息），其内置的人工智能比非隐私增强型应用更好。以上只是我想到的几个例子。随着联邦学习的部署，我们会发现越来越多的领域可以突然重获新生，因为它们现在可以访问大量以前无法访问的数据。" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:196 +msgid "" +"So how does federated learning work, exactly? Let's start with an " +"intuitive explanation." +msgstr "那么，联邦学习究竟是如何运作的呢？让我们从直观的解释开始。" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:199 +msgid "Federated learning in five steps" +msgstr "联邦学习的五个步骤" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:202 +msgid "Step 0: Initialize global model" +msgstr "步骤 0：初始化全局模型" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:204 +msgid "" +"We start by initializing the model on the server. This is exactly the " +"same in classic centralized learning: we initialize the model parameters," +" either randomly or from a previously saved checkpoint." +msgstr "我们首先在服务器上初始化模型。这与经典的集中式学习完全相同：我们随机或从先前保存的检查点初始化模型参数。" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:210 +msgid "|c7afb4c92d154bfaa5e8cb9a150e17f1|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:307 +msgid "Initialize global model" +msgstr "初始化全局模型" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:217 +msgid "" +"Step 1: Send model to a number of connected organizations/devices (client" +" nodes)" +msgstr "第 1 步：将模型发送到多个连接的组织/设备（客户节点）" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:219 +msgid "" +"Next, we send the parameters of the global model to the connected client " +"nodes (think: edge devices like smartphones or servers belonging to " +"organizations). This is to ensure that each participating node starts " +"their local training using the same model parameters. We often use only a" +" few of the connected nodes instead of all nodes. The reason for this is " +"that selecting more and more client nodes has diminishing returns." +msgstr "接下来，我们会将全局模型的参数发送到连接的客户端节点（如智能手机等边缘设备或企业的服务器）。这是为了确保每个参与节点都使用相同的模型参数开始本地训练。我们通常只使用几个连接节点，而不是所有节点。这样做的原因是，选择越来越多的客户端节点会导致收益递减。" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:225 +msgid "|032eb6fed6924ac387b9f13854919196|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:309 +msgid "Send global model" +msgstr "发送全局模型" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:232 +msgid "" +"Step 2: Train model locally on the data of each organization/device " +"(client node)" +msgstr "步骤 2：在本地对每个机构/设备（客户端节点）的数据进行模型训练" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:234 +msgid "" +"Now that all (selected) client nodes have the latest version of the " +"global model parameters, they start the local training. They use their " +"own local dataset to train their own local model. They don't train the " +"model until full convergence, but they only train for a little while. " +"This could be as little as one epoch on the local data, or even just a " +"few steps (mini-batches)." +msgstr "" +"现在，所有（选定的）客户端节点都有了最新版本的全局模型参数，它们开始进行本地训练。它们使用自己的本地数据集来训练自己的本地模型。它们不会一直训练到模型完全收敛为止，而只是训练一小段时间。这可能只是本地数据上的一个遍历，甚至只是几个步骤" +"（mini-batches）。" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:240 +msgid "|fbf225add7fd4df5a9bf25a95597d954|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:311 +msgid "Train on local data" +msgstr "根据本地数据进行训练" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:247 +msgid "Step 3: Return model updates back to the server" +msgstr "步骤 3：将模型参数更新返回服务器" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:249 +msgid "" +"After local training, each client node has a slightly different version " +"of the model parameters they originally received. The parameters are all " +"different because each client node has different examples in its local " +"dataset. The client nodes then send those model updates back to the " +"server. The model updates they send can either be the full model " +"parameters or just the gradients that were accumulated during local " +"training." +msgstr "经过本地训练后，每个客户节点最初收到的模型参数都会略有不同。参数之所以不同，是因为每个客户端节点的本地数据集中都有不同的数据。然后，客户端节点将这些模型更新发回服务器。它们发送的模型更新既可以是完整的模型参数，也可以只是本地训练过程中积累的梯度。" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:255 +msgid "|7efbe3d29d8349b89594e8947e910525|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:313 +msgid "Send model updates" +msgstr "发送模型参数更新" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:262 +msgid "Step 4: Aggregate model updates into a new global model" +msgstr "步骤 4：将模型更新聚合到新的全局模型中" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:264 +msgid "" +"The server receives model updates from the selected client nodes. If it " +"selected 100 client nodes, it now has 100 slightly different versions of " +"the original global model, each trained on the local data of one client. " +"But didn't we want to have one model that contains the learnings from the" +" data of all 100 client nodes?" +msgstr "" +"服务器从选定的客户端节点接收模型更新。如果服务器选择了 100 个客户端节点，那么它现在就拥有 100 " +"个略有不同的原始全局模型版本，每个版本都是根据一个客户端的本地数据训练出来的。难道我们不希望有一个包含所有 100 个客户节点数据的模型吗？" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:266 +msgid "" +"In order to get one single model, we have to combine all the model " +"updates we received from the client nodes. This process is called " +"*aggregation*, and there are many different ways to do it. The most basic" +" way to do it is called *Federated Averaging* (`McMahan et al., 2016 " +"`__), often abbreviated as *FedAvg*. " +"*FedAvg* takes the 100 model updates and, as the name suggests, averages " +"them. To be more precise, it takes the *weighted average* of the model " +"updates, weighted by the number of examples each client used for " +"training. The weighting is important to make sure that each data example " +"has the same \"influence\" on the resulting global model. If one client " +"has 10 examples, and another client has 100 examples, then - without " +"weighting - each of the 10 examples would influence the global model ten " +"times as much as each of the 100 examples." +msgstr "" +"为了得到一个单一的模型，我们必须将从客户端节点收到的所有模型更新合并起来。这个过程称为*聚合*，有许多不同的方法。最基本的方法称为 " +"*Federated Averaging* (`McMahan等人，2016 " +"`__)，通常缩写为*FedAvg*。*FedAvg* 可以把100 " +"个模型更新进行平均。更准确地说，它取的是模型更新的*加权平均值*，根据每个客户端用于训练的数据数量进行加权。加权对于确保每个数据示例对生成的全局模型具有相同的" +" \"影响 \"非常重要。如果一个客户端有 10 个数据点，而另一个客户有 100 个数据点，那么在不加权的情况下，10 个示例对全局模型的影响是" +" 100 个示例的 10 倍。" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:273 +msgid "|329fb3c04c744eda83bb51fa444c2266|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:315 +msgid "Aggregate model updates" +msgstr "聚合模型参数更新" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:280 +msgid "Step 5: Repeat steps 1 to 4 until the model converges" +msgstr "步骤 5：重复步骤 1 至 4，直至模型收敛" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:282 +msgid "" +"Steps 1 to 4 are what we call a single round of federated learning. The " +"global model parameters get sent to the participating client nodes (step " +"1), the client nodes train on their local data (step 2), they send their " +"updated models to the server (step 3), and the server then aggregates the" +" model updates to get a new version of the global model (step 4)." +msgstr "" +"步骤 1 至 4 就是我们所说的单轮联邦学习。全局模型参数被发送到参与的客户端节点（第 1 步），客户端节点对其本地数据进行训练（第 2 " +"步），然后将更新后的模型发送到服务器（第 3 步），服务器汇总模型更新，得到新版本的全局模型（第 4 步）。" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:284 +msgid "" +"During a single round, each client node that participates in that " +"iteration only trains for a little while. This means that after the " +"aggregation step (step 4), we have a model that has been trained on all " +"the data of all participating client nodes, but only for a little while. " +"We then have to repeat this training process over and over again to " +"eventually arrive at a fully trained model that performs well across the " +"data of all client nodes." +msgstr "" +"在一轮迭代中，每个参与迭代的客户节点只训练一小段时间。这意味着，在聚合步骤（步骤 " +"4）之后，我们的模型已经在所有参与的客户节点的所有数据上训练过了，但只训练了一小会儿。然后，我们必须一次又一次地重复这一训练过程，最终得到一个经过全面训练的模型，该模型在所有客户节点的数据中都表现良好。" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:289 +msgid "" +"Congratulations, you now understand the basics of federated learning. " +"There's a lot more to discuss, of course, but that was federated learning" +" in a nutshell. In later parts of this tutorial, we will go into more " +"detail. Interesting questions include: How can we select the best client " +"nodes that should participate in the next round? What's the best way to " +"aggregate model updates? How can we handle failing client nodes " +"(stragglers)?" +msgstr "" +"恭喜您，现在您已经了解了联邦学习的基础知识。当然，要讨论的内容还有很多，但这只是联邦学习的一个缩影。在本教程的后半部分，我们将进行更详细的介绍。有趣的问题包括" +" 我们如何选择最好的客户端节点参与下一轮学习？聚合模型更新的最佳方法是什么？如何处理失败的客户端节点（落伍者）？" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:294 +msgid "" +"Just like we can train a model on the decentralized data of different " +"client nodes, we can also evaluate the model on that data to receive " +"valuable metrics. This is called federated evaluation, sometimes " +"abbreviated as FE. In fact, federated evaluation is an integral part of " +"most federated learning systems." +msgstr "" +"就像我们可以在不同客户节点的分散数据上训练一个模型一样，我们也可以在这些数据上对模型进行评估，以获得有价值的指标。这就是所谓的联邦评估，有时简称为" +" FE。事实上，联邦评估是大多数联邦学习系统不可或缺的一部分。" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:297 +msgid "Federated analytics" +msgstr "联邦分析" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:299 +msgid "" +"In many cases, machine learning isn't necessary to derive value from " +"data. Data analysis can yield valuable insights, but again, there's often" +" not enough data to get a clear answer. What's the average age at which " +"people develop a certain type of health condition? Federated analytics " +"enables such queries over multiple client nodes. It is usually used in " +"conjunction with other privacy-enhancing technologies like secure " +"aggregation to prevent the server from seeing the results submitted by " +"individual client nodes." +msgstr "在很多情况下，机器学习并不是从数据中获取价值的必要条件。数据分析可以产生有价值的见解，但同样，往往没有足够的数据来获得明确的答案。人们患某种健康疾病的平均年龄是多少？联邦分析可以通过多个客户端节点进行此类查询。它通常与安全聚合等其他隐私增强技术结合使用，以防止服务器看到单个客户端节点提交的结果。" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:305 +msgid "" +"Differential privacy (DP) is often mentioned in the context of Federated " +"Learning. It is a privacy-preserving method used when analyzing and " +"sharing statistical data, ensuring the privacy of individual " +"participants. DP achieves this by adding statistical noise to the model " +"updates, ensuring any individual participants’ information cannot be " +"distinguished or re-identified. This technique can be considered an " +"optimization that provides a quantifiable privacy protection measure." +msgstr "" +"差分隐私（DP）经常在联邦学习中被提及。这是一种在分析和共享统计数据时使用的隐私保护方法，可确保单个参与者的隐私。DP " +"通过在模型更新中添加统计噪声来实现这一目的，确保任何个体参与者的信息都无法被区分或重新识别。这种技术可被视为一种优化，提供了一种可量化的隐私保护措施。" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:326 +msgid "Flower" +msgstr "Flower" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:328 +msgid "" +"Federated learning, federated evaluation, and federated analytics require" +" infrastructure to move machine learning models back and forth, train and" +" evaluate them on local data, and then aggregate the updated models. " +"Flower provides the infrastructure to do exactly that in an easy, " +"scalable, and secure way. In short, Flower presents a unified approach to" +" federated learning, analytics, and evaluation. It allows the user to " +"federate any workload, any ML framework, and any programming language." +msgstr "" +"联邦学习、联邦评估和联邦分析需要基础框架来来回移动机器学习模型，在本地数据上对其进行训练和评估，然后汇总更新的模型。Flower " +"提供的基础架构正是以简单、可扩展和安全的方式实现这些目标的。简而言之，Flower " +"为联邦学习、分析和评估提供了一种统一的方法。它允许用户联邦化任何工作负载、任何 ML 框架和任何编程语言。" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:334 +msgid "|c00bf2750bc24d229737a0fe1395f0fc|" +msgstr "" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:340 +msgid "" +"Flower federated learning server and client nodes (car, scooter, personal" +" computer, roomba, and phone)" +msgstr "Flower联邦学习服务器和客户端节点（汽车、滑板车、个人电脑、roomba 和电话）" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:353 +msgid "" +"Congratulations, you just learned the basics of federated learning and " +"how it relates to the classic (centralized) machine learning!" +msgstr "恭喜您，您刚刚了解了联邦学习的基础知识，以及它与传统（集中式）机器学习的关系！" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:355 +msgid "" +"In the next part of this tutorial, we are going to build a first " +"federated learning system with Flower." +msgstr "在本教程的下一部分，我们将用 Flower 建立第一个联邦学习系统。" + +#: ../../source/tutorial-series-what-is-federated-learning.ipynb:373 +msgid "" +"The `Flower Federated Learning Tutorial - Part 1 " +"`__ shows how to build a simple federated learning system " +"with PyTorch and Flower." +msgstr "" +"`Flower 联邦学习教程 - 第 1 部分 `__ 展示了如何使用 PyTorch 和 Flower " +"构建一个简单的联邦学习系统。" + +#~ msgid "Before the release" +#~ msgstr "发布前" + +#~ msgid "" +#~ "Update the changelog (``changelog.md``) with" +#~ " all relevant changes that happened " +#~ "after the last release. If the " +#~ "last release was tagged ``v1.2.0``, you" +#~ " can use the following URL to " +#~ "see all commits that got merged " +#~ "into ``main`` since then:" +#~ msgstr "" +#~ "更新更新日志 (``changelog.md``)，加入上次发布后发生的所有相关变更。如果上次发布的版本被标记为 " +#~ "``v1.2.0``，则可以使用以下 URL 查看此后合并到 ``main`` 的所有提交：" + +#~ msgid "" +#~ "`GitHub: Compare v1.2.0...main " +#~ "`_" +#~ msgstr "" +#~ "`GitHub: Compare v1.2.0...main " +#~ "`_" + +#~ msgid "" +#~ "Thank the authors who contributed since" +#~ " the last release. This can be " +#~ "done by running the ``./dev/add-" +#~ "shortlog.sh`` convenience script (it can " +#~ "be ran multiple times and will " +#~ "update the names in the list if" +#~ " new contributors were added in the" +#~ " meantime)." +#~ msgstr "" +#~ "感谢自上次发布以来做出贡献的作者。可以通过运行 ``./dev/add-shortlog.sh`` " +#~ "方便脚本来完成（可以多次运行，如果在此期间有新的贡献者加入，则会更新列表中的名字）。" + +#~ msgid "" +#~ "Update the ``changelog.md`` section header " +#~ "``Unreleased`` to contain the version " +#~ "number and date for the release " +#~ "you are building. Create a pull " +#~ "request with the change." +#~ msgstr "" +#~ "更新 ``changelog.md`` 部分的标题 ``Unreleased`` " +#~ "以包含你正在构建的版本的版本号和日期。创建一个包含更改的拉取请求。" + +#~ msgid "" +#~ "Second, create a virtual environment " +#~ "(and activate it). If you chose to" +#~ " use :code:`pyenv` (with the :code" +#~ ":`pyenv-virtualenv` plugin) and already " +#~ "have it installed , you can use" +#~ " the following convenience script (by " +#~ "default it will use :code:`Python " +#~ "3.8.17`, but you can change it by" +#~ " providing a specific :code:``)::" +#~ msgstr "" +#~ "其次，创建虚拟环境（并激活它）。如果您选择使用 :code:`pyenv`（使用 :code:`pyenv-" +#~ "virtualenv`插件），并且已经安装了该插件，则可以使用下面的便捷脚本（默认情况下使用 " +#~ ":code:`Python3.8.17`，但您可以通过提供特定的 :code:`<版本>`来更改）：：" + +#~ msgid "flwr (Python API reference)" +#~ msgstr "flwr（Python API 参考）" + +#~ msgid "..." +#~ msgstr "..." + +#~ msgid "Starting a client with an insecure server connection:" +#~ msgstr "使用不安全的服务器连接启动客户端：" + +#~ msgid "server.strategy.FedAvg" +#~ msgstr "server.strategy.FedAvg" + +#~ msgid "server.strategy.FedAvgM" +#~ msgstr "server.strategy.FedAvgM" + +#~ msgid "Configurable FedAvg with Momentum strategy implementation." +#~ msgstr "可配置的 FedAvg 动量策略实施。" + +#~ msgid "Fraction of clients used during training. Defaults to 0.1." +#~ msgstr "训练期间使用客户的比例。默认为 0.1。" + +#~ msgid "Fraction of clients used during validation. Defaults to 0.1." +#~ msgstr "验证过程中使用的客户端比例。默认为 0.1。" + +#~ msgid "server.strategy.FedMedian" +#~ msgstr "server.strategy.FedMedian" + +#~ msgid "server.strategy.QFedAvg" +#~ msgstr "server.strategy.QFedAvg" + +#~ msgid "server.strategy.FedOpt" +#~ msgstr "server.strategy.FedOpt" + +#~ msgid "Configurable FedAdagrad strategy implementation." +#~ msgstr "可配置的 FedAdagrad 策略实施。" + +#~ msgid "Federated Optim strategy interface." +#~ msgstr "Federated Optim 策略界面。" + +#~ msgid "server.strategy.FedProx" +#~ msgstr "server.strategy.FedProx" + +#~ msgid "Configurable FedProx strategy implementation." +#~ msgstr "可配置的 FedProx 策略实施。" + +#~ msgid "server.strategy.FedAdagrad" +#~ msgstr "server.strategy.FedAdagrad" + +#~ msgid "Paper: https://arxiv.org/abs/2003.00295" +#~ msgstr "论文： https://arxiv.org/abs/2003.00295" + +#~ msgid "Federated learning strategy using Adagrad on server-side." +#~ msgstr "在服务器端使用 Adagrad 的联邦学习策略。" + +#~ msgid "server.strategy.FedAdam" +#~ msgstr "server.strategy.FedAdam" + +#~ msgid "server.strategy.FedYogi" +#~ msgstr "server.strategy.FedYogi" + +#~ msgid "Adaptive Federated Optimization using Yogi." +#~ msgstr "使用 Yogi 的自适应联合优化。" + +#~ msgid "Federated learning strategy using Yogi on server-side." +#~ msgstr "在服务器端使用 Yogi 的联邦学习策略。" + +#~ msgid "Paper: https://arxiv.org/abs/1803.01498" +#~ msgstr "论文：https://arxiv.org/abs/1803.01498" + +#~ msgid "server.strategy.Krum" +#~ msgstr "server.strategy.Krum" + +#~ msgid "Configurable Krum strategy implementation." +#~ msgstr "可配置的 Krum 策略实施。" + +#~ msgid "server.strategy.Bulyan" +#~ msgstr "server.strategy.Bulyan" + +#~ msgid "Bulyan strategy implementation." +#~ msgstr "Bulyan策略的实施。" + +#~ msgid "server.strategy.FedXgbNnAvg" +#~ msgstr "server.strategy.FedXgbNnAvg" + +#~ msgid "Federated XGBoost [Ma et al., 2023] strategy." +#~ msgstr "Federated XGBoost [Ma 等人，2023] 策略。" + +#~ msgid "server.strategy.DPFedAvgAdaptive" +#~ msgstr "server.strategy.DPFedAvgAdaptive" + +#~ msgid "" +#~ "**Fix the incorrect return types of " +#~ "Strategy** " +#~ "([#2432](https://github.com/adap/flower/pull/2432/files))" +#~ msgstr "" +#~ "**修复策略的错误返回类型** " +#~ "([#2432](https://github.com/adap/flower/pull/2432/files))" + +#~ msgid "" +#~ "The types of the return values in" +#~ " the docstrings in two methods " +#~ "(`aggregate_fit` and `aggregate_evaluate`) now " +#~ "match the hint types in the code." +#~ msgstr "" +#~ "两个方法（\"aggregate_fit \"和 " +#~ "\"aggregate_evaluate\"）的文档说明中的返回值类型现在与代码中的提示类型一致。" + +#~ msgid "" +#~ "**Update Flower Examples** " +#~ "([#2384](https://github.com/adap/flower/pull/2384),[#2425](https://github.com/adap/flower/pull/2425)," +#~ " [#2526](https://github.com/adap/flower/pull/2526))" +#~ msgstr "" +#~ "** 更新 Flower Examples** " +#~ "([#2384](https://github.com/adap/flower/pull/2384),[#2425](https://github.com/adap/flower/pull/2425)," +#~ " [#2526](https://github.com/adap/flower/pull/2526))" + +#~ msgid "" +#~ "That's it for the client. We only" +#~ " have to implement :code:`Client` or " +#~ ":code:`NumPyClient` and call " +#~ ":code:`fl.client.start_client()`. The string " +#~ ":code:`\"0.0.0.0:8080\"` tells the client " +#~ "which server to connect to. In our" +#~ " case we can run the server and" +#~ " the client on the same machine, " +#~ "therefore we use :code:`\"0.0.0.0:8080\"`. If" +#~ " we run a truly federated workload" +#~ " with the server and clients running" +#~ " on different machines, all that " +#~ "needs to change is the " +#~ ":code:`server_address` we pass to the " +#~ "client." +#~ msgstr "" +#~ "对于客户端就需要做这么多。我们仅需要实现 " +#~ ":code:`Client`或者:code:`NumPyClient`然后调用:code:`fl.client.start_client()`。字符串" +#~ " :code:`\"0.0.0.0:8080\"` " +#~ "告诉客户端要连接到哪个服务器。在我们的例子中，我们可以在同一台机器上运行服务器和客户端，因此我们使用:code:`\"0.0.0.0:8080\"`。如果我们运行真正联邦学习的工作负载，服务器和客户端在不同的机器上运行，则需要更改的只是我们传递给客户端的" +#~ " server_address 。" + +#~ msgid "" +#~ "That's it for the client. We only" +#~ " have to implement :code:`Client` or " +#~ ":code:`NumPyClient` and call " +#~ ":code:`fl.client.start_client()`. The string " +#~ ":code:`\"[::]:8080\"` tells the client which" +#~ " server to connect to. In our " +#~ "case we can run the server and " +#~ "the client on the same machine, " +#~ "therefore we use :code:`\"[::]:8080\"`. If " +#~ "we run a truly federated workload " +#~ "with the server and clients running " +#~ "on different machines, all that needs" +#~ " to change is the :code:`server_address`" +#~ " we point the client at." +#~ msgstr "" +#~ "对于客户来说就是这样了。我们只需实现 :code:`Client` 或 " +#~ ":code:`NumPyClient` 并调用:code:`fl.client.start_client()` " +#~ "即可。字符串 :code:`\"[::]:8080\"` " +#~ "告诉客户端要连接到哪个服务器。在我们的例子中，我们可以在同一台机器上运行服务器和客户端，因此我们使用 " +#~ ":code:`\"[::]:8080\"`。如果我们运行真正联邦的工作负载，服务器和客户端运行在不同的机器上，则需要更改的只是我们指向客户端的" +#~ " server_address 。" + +#~ msgid "" +#~ "Let's now load the CIFAR-10 training " +#~ "and test set, partition them into " +#~ "ten smaller datasets (each split into" +#~ " training and validation set), and " +#~ "wrap the resulting partitions by " +#~ "creating a PyTorch ``DataLoader`` for " +#~ "each of them:" +#~ msgstr "" +#~ "现在，让我们加载 CIFAR-10 训练集和测试集，将它们分割成 10 " +#~ "个较小的数据集（每个数据集又分为训练集和验证集），并通过为每个数据集创建 PyTorch " +#~ "``DataLoader`` 来包装由此产生的分割集：" + +#~ msgid "|e1dd4b4129b040bea23a894266227080|" +#~ msgstr "|e1dd4b4129b040bea23a894266227080|" + +#~ msgid "|c0d4cc6a442948dca8da40d2440068d9|" +#~ msgstr "|c0d4cc6a442948dca8da40d2440068d9|" + +#~ msgid "|174e1e4fa1f149a19bfbc8bc1126f46a|" +#~ msgstr "|174e1e4fa1f149a19bfbc8bc1126f46a|" + +#~ msgid "|4e021a3dc08249d2a89daa3ab03c2714|" +#~ msgstr "|4e021a3dc08249d2a89daa3ab03c2714|" + +#~ msgid "|e74a1d5ce7eb49688651f2167a59065b|" +#~ msgstr "|e74a1d5ce7eb49688651f2167a59065b|" + +#~ msgid "|eb29ec4c7aef4e93976795ed72df647e|" +#~ msgstr "|eb29ec4c7aef4e93976795ed72df647e|" + +#~ msgid "|c2f699d8ac484f5081721a6f1511f70d|" +#~ msgstr "|c2f699d8ac484f5081721a6f1511f70d|" + +#~ msgid "|cf42accdacbf4e5eb4fa0503108ba7a7|" +#~ msgstr "|cf42accdacbf4e5eb4fa0503108ba7a7|" + +#~ msgid "|5ec8356bc2564fa09178b1ceed5beccc|" +#~ msgstr "|5ec8356bc2564fa09178b1ceed5beccc|" + +#~ msgid "|7c9329e97bd0430bad335ab605a897a7|" +#~ msgstr "|7c9329e97bd0430bad335ab605a897a7|" + +#~ msgid "|88002bbce1094ba1a83c9151df18f707|" +#~ msgstr "|88002bbce1094ba1a83c9151df18f707|" + +#~ msgid "|391766aee87c482c834c93f7c22225e2|" +#~ msgstr "|391766aee87c482c834c93f7c22225e2|" + +#~ msgid "|93b9a15bd27f4e91b40f642c253dfaac|" +#~ msgstr "|93b9a15bd27f4e91b40f642c253dfaac|" + +#~ msgid "|a23d9638f96342ef9d25209951e2d564|" +#~ msgstr "|a23d9638f96342ef9d25209951e2d564|" + +#~ msgid "Upload the whl (e.g., ``flwr-1.6.0-py3-none-any.whl``)" +#~ msgstr "上传 whl（例如 ``flwr-1.6.0-py3-none-any.whl``)" + +#~ msgid "" +#~ "Change ``!pip install -q 'flwr[simulation]'" +#~ " torch torchvision matplotlib`` to ``!pip" +#~ " install -q 'flwr-1.6.0-py3-none-" +#~ "any.whl[simulation]' torch torchvision matplotlib``" +#~ msgstr "" +#~ "将``!pip install -q 'flwr[simulation]' torch" +#~ " torchvision matplotlib``更改为``!pip install -q " +#~ "'flwr-1.6.0-py3-none-any.whl[simulation]' torch " +#~ "torch torchvision matplotlib``" + +#~ msgid "" +#~ "All that's left to do it to " +#~ "define a function that loads both " +#~ "model and data, creates a " +#~ ":code:`CifarClient`, and starts this client." +#~ " You load your data and model " +#~ "by using :code:`cifar.py`. Start " +#~ ":code:`CifarClient` with the function " +#~ ":code:`fl.client.start_numpy_client()` by pointing " +#~ "it at the same IP address we " +#~ "used in :code:`server.py`:" +#~ msgstr "" +#~ "剩下要做的就是定义一个加载模型和数据的函数，创建一个 :code:`CifarClient` 并启动该客户端。使用" +#~ " :code:`cifar.py` 加载数据和模型。使用函数 " +#~ ":code:`fl.client.start_numpy_client()` 启动 " +#~ ":code:`CifarClient`，将其指向我们在 :code:`server.py` 中使用的相同 " +#~ "IP 地址：" + +#~ msgid "" +#~ "The :code:`VirtualClientEngine` schedules, launches" +#~ " and manages `virtual` clients. These " +#~ "clients are identical to `non-virtual`" +#~ " clients (i.e. the ones you launch" +#~ " via the command `flwr.client.start_numpy_client" +#~ " `_)" +#~ " in the sense that they can be" +#~ " configure by creating a class " +#~ "inheriting, for example, from " +#~ "`flwr.client.NumPyClient `_ and therefore " +#~ "behave in an identical way. In " +#~ "addition to that, clients managed by " +#~ "the :code:`VirtualClientEngine` are:" +#~ msgstr "" +#~ "代码:`VirtualClientEngine`调度、启动和管理`虚拟`客户端。这些客户端与 \"非虚拟 " +#~ "\"客户端（即通过命令 `flwr.client.start_numpy_client `_启动的客户端）完全相同，它们可以通过创建一个继承自 \"flwr.client.NumPyClient " +#~ "`_\" " +#~ "的类来配置，因此行为方式也完全相同。除此之外，由 :code:`VirtualClientEngine` " +#~ "管理的客户端还包括：" + +#~ msgid "Example: Walk-Through PyTorch & MNIST" +#~ msgstr "实例： PyTorch 和 MNIST 的演练" + +#~ msgid "" +#~ "In this tutorial we will learn, " +#~ "how to train a Convolutional Neural " +#~ "Network on MNIST using Flower and " +#~ "PyTorch." +#~ msgstr "在本教程中，我们将学习如何使用 Flower 和 PyTorch 在 MNIST 上训练卷积神经网络。" + +#~ msgid "" +#~ "Since we want to use PyTorch to" +#~ " solve a computer vision task, let's" +#~ " go ahead an install PyTorch and " +#~ "the **torchvision** library:" +#~ msgstr "我们想用 PyTorch 来做计算机视觉任务，需要先安装 PyTorch 和 **torchvision** 库：" + +#~ msgid "Ready... Set... Train!" +#~ msgstr "准备...设置...训练!" + +#~ msgid "" +#~ "Now that we have all our " +#~ "dependencies installed, let's run a " +#~ "simple distributed training with two " +#~ "clients and one server. Our training " +#~ "procedure and network architecture are " +#~ "based on PyTorch's `Basic MNIST Example" +#~ " `_. " +#~ "This will allow you see how easy" +#~ " it is to wrap your code with" +#~ " Flower and begin training in a " +#~ "federated way. We provide you with " +#~ "two helper scripts, namely *run-" +#~ "server.sh*, and *run-clients.sh*. Don't " +#~ "be afraid to look inside, they are" +#~ " simple enough =)." +#~ msgstr "" +#~ "现在我们已经安装了所有的依赖包，让我们用两个客户端和一个服务器来运行一个简单的分布式训练。我们的训练过程和网络架构基于 " +#~ "PyTorch 的 `Basic MNIST Example " +#~ "`_。您会发现用 " +#~ "Flower 来封装您的代码并进行联邦学习训练是多么容易。我们为您提供了两个辅助脚本，即 *run-" +#~ "server.sh* 和 *run-clients.sh*。别害怕，它们很简单 =)。" + +#~ msgid "" +#~ "Go ahead and launch on a terminal" +#~ " the *run-server.sh* script first as" +#~ " follows:" +#~ msgstr "首先在终端上启动 *run-server.sh* 脚本，如下所示：" + +#~ msgid "Now that the server is up and running, go ahead and launch the clients." +#~ msgstr "现在服务器已经启动并运行，请继续启动客户端。" + +#~ msgid "" +#~ "Et voilà! You should be seeing the" +#~ " training procedure and, after a few" +#~ " iterations, the test accuracy for " +#~ "each client." +#~ msgstr "然后就可以了！您应该能看到训练过程，以及经过几次反复后，每个客户端的测试准确率。" + +#~ msgid "Now, let's see what is really happening inside." +#~ msgstr "现在，让我们看看里面到底发生了什么。" + +#~ msgid "" +#~ "Inside the server helper script *run-" +#~ "server.sh* you will find the following" +#~ " code that basically runs the " +#~ ":code:`server.py`" +#~ msgstr "在服务器辅助脚本 *run-server.sh* 中，你可以找到以下代码，这些代码基本上都是运行 :code:`server.py` 的代码" + +#~ msgid "" +#~ "We can go a bit deeper and " +#~ "see that :code:`server.py` simply launches " +#~ "a server that will coordinate three " +#~ "rounds of training. Flower Servers are" +#~ " very customizable, but for simple " +#~ "workloads, we can start a server " +#~ "using the :ref:`start_server ` function and leave " +#~ "all the configuration possibilities at " +#~ "their default values, as seen below." +#~ msgstr "" +#~ "我们可以再深入一点，:code:`server.py` 只是启动了一个服务器，该服务器将协调三轮训练。Flower " +#~ "服务器是非常容易修改的，但对于简单的工作，我们可以使用 :ref:`start_server `函数启动服务器，并将所有可能的配置保留为默认值，如下所示。" + +#~ msgid "" +#~ "Next, let's take a look at the " +#~ "*run-clients.sh* file. You will see " +#~ "that it contains the main loop " +#~ "that starts a set of *clients*." +#~ msgstr "接下来，让我们看看 *run-clients.sh* 文件。您会看到它包含了用来启动多个 *客户端* 的代码。" + +#~ msgid "" +#~ "**cid**: is the client ID. It is" +#~ " an integer that uniquely identifies " +#~ "client identifier." +#~ msgstr "**cid**：是客户 ID。它是一个整数，可唯一标识客户标识符。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:328 -msgid "" -"Federated learning, federated evaluation, and federated analytics require" -" infrastructure to move machine learning models back and forth, train and" -" evaluate them on local data, and then aggregate the updated models. " -"Flower provides the infrastructure to do exactly that in an easy, " -"scalable, and secure way. In short, Flower presents a unified approach to" -" federated learning, analytics, and evaluation. It allows the user to " -"federate any workload, any ML framework, and any programming language." -msgstr "" -"联邦学习、联邦评估和联邦分析需要基础框架来来回移动机器学习模型，在本地数据上对其进行训练和评估，然后汇总更新的模型。Flower " -"提供的基础架构正是以简单、可扩展和安全的方式实现这些目标的。简而言之，Flower " -"为联邦学习、分析和评估提供了一种统一的方法。它允许用户联邦化任何工作负载、任何 ML 框架和任何编程语言。" +#~ msgid "**sever_address**: String that identifies IP and port of the server." +#~ msgstr "**sever_address**： 标识服务器 IP 和端口的字符串。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:334 -msgid "|ff726bc5505e432388ee2fdd6ef420b9|" -msgstr "" +#~ msgid "" +#~ "**nb_clients**: This defines the number " +#~ "of clients being created. This piece " +#~ "of information is not required by " +#~ "the client, but it helps us " +#~ "partition the original MNIST dataset to" +#~ " make sure that every client is " +#~ "working on unique subsets of both " +#~ "*training* and *test* sets." +#~ msgstr "" +#~ "**nb_clients**： 这定义了正在创建的客户端数量。客户端并不需要这一信息，但它有助于我们对原始 " +#~ "MNIST 数据集进行划分，以确保每个客户端都在 *training* 和 *test*" +#~ " 数据集上有独立的数据。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:340 -msgid "" -"Flower federated learning server and client nodes (car, scooter, personal" -" computer, roomba, and phone)" -msgstr "Flower联邦学习服务器和客户端节点（汽车、滑板车、个人电脑、roomba 和电话）" +#~ msgid "" +#~ "Again, we can go deeper and look" +#~ " inside :code:`flwr_example/quickstart-" +#~ "pytorch/client.py`. After going through the" +#~ " argument parsing code at the " +#~ "beginning of our :code:`main` function, " +#~ "you will find a call to " +#~ ":code:`mnist.load_data`. This function is " +#~ "responsible for partitioning the original " +#~ "MNIST datasets (*training* and *test*) " +#~ "and returning a :code:`torch.utils.data.DataLoader`" +#~ " s for each of them. We then" +#~ " instantiate a :code:`PytorchMNISTClient` object" +#~ " with our client ID, our DataLoaders," +#~ " the number of epochs in each " +#~ "round, and which device we want to" +#~ " use for training (CPU or GPU)." +#~ msgstr "" +#~ "我们可以深入看一下 :code:`flwr_example/quickstart-" +#~ "pytorch/client.py`。查看 :code:`main` 函数开头的参数解析代码后，你会发现一个对" +#~ " :code:`mnist.load_data` 的调用。该函数负责分割原始 MNIST " +#~ "数据集（*training* 和 *test*），并为每个数据集返回一个 " +#~ ":code:`torch.utils.data.DataLoader` 。然后，我们实例化一个 " +#~ ":code:`PytorchMNISTClient` 对象，其中包含我们的客户端 ID、 " +#~ "DataLoader、每一轮中的遍历数，以及我们希望用于训练的设备（CPU 或 GPU）。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:353 -msgid "" -"Congratulations, you just learned the basics of federated learning and " -"how it relates to the classic (centralized) machine learning!" -msgstr "恭喜您，您刚刚了解了联邦学习的基础知识，以及它与传统（集中式）机器学习的关系！" +#~ msgid "" +#~ "The :code:`PytorchMNISTClient` object when " +#~ "finally passed to :code:`fl.client.start_client` " +#~ "along with the server's address as " +#~ "the training process begins." +#~ msgstr "" +#~ "当训练过程开始时，:code:`PytorchMNISTClient` 对象会连同服务器地址一起传递给 " +#~ ":code:`fl.client.start_client`。" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:355 -msgid "" -"In the next part of this tutorial, we are going to build a first " -"federated learning system with Flower." -msgstr "在本教程的下一部分，我们将用 Flower 建立第一个联邦学习系统。" +#~ msgid "A Closer Look" +#~ msgstr "仔细看一下" -#: ../../source/tutorial-series-what-is-federated-learning.ipynb:373 -msgid "" -"The `Flower Federated Learning Tutorial - Part 1 " -"`__ shows how to build a simple federated learning system " -"with PyTorch and Flower." -msgstr "" -"`Flower 联邦学习教程 - 第 1 部分 `__ 展示了如何使用 PyTorch 和 Flower " -"构建一个简单的联邦学习系统。" +#~ msgid "" +#~ "Now, let's look closely into the " +#~ ":code:`PytorchMNISTClient` inside :code:`flwr_example" +#~ ".quickstart-pytorch.mnist` and see what it" +#~ " is doing:" +#~ msgstr "" +#~ "现在，让我们仔细研究一下 :code:`flwr_example.quickstart-pytorch.mnist`" +#~ " 中的 :code:`PytorchMNISTClient`，看看它在做什么：" -#~ msgid "Before the release" -#~ msgstr "发布前" +#~ msgid "" +#~ "The first thing to notice is that" +#~ " :code:`PytorchMNISTClient` instantiates a CNN" +#~ " model inside its constructor" +#~ msgstr "首先要注意的是 :code:`PytorchMNISTClient` 在其构造函数中实例化了一个 CNN 模型" #~ msgid "" -#~ "Update the changelog (``changelog.md``) with" -#~ " all relevant changes that happened " -#~ "after the last release. If the " -#~ "last release was tagged ``v1.2.0``, you" -#~ " can use the following URL to " -#~ "see all commits that got merged " -#~ "into ``main`` since then:" +#~ "The code for the CNN is available" +#~ " under :code:`quickstart-pytorch.mnist` and " +#~ "it is reproduced below. It is the" +#~ " same network found in `Basic MNIST" +#~ " Example " +#~ "`_." #~ msgstr "" -#~ "更新更新日志 (``changelog.md``)，加入上次发布后发生的所有相关变更。如果上次发布的版本被标记为 " -#~ "``v1.2.0``，则可以使用以下 URL 查看此后合并到 ``main`` 的所有提交：" +#~ "CNN 的代码可在 :code:`quickstart-pytorch.mnist` " +#~ "下找到，现复制如下。它与 `Basic MNIST Example " +#~ "`_中的网络相同。" #~ msgid "" -#~ "`GitHub: Compare v1.2.0...main " -#~ "`_" +#~ "The second thing to notice is that" +#~ " :code:`PytorchMNISTClient` class inherits from" +#~ " the :code:`fl.client.Client`, and hence it" +#~ " must implement the following methods:" #~ msgstr "" -#~ "`GitHub: Compare v1.2.0...main " -#~ "`_" +#~ "第二件要注意的事是 :code:`PytorchMNISTClient` 类继承自 " +#~ ":code:`fl.client.Client`，因此它必须实现以下方法：" #~ msgid "" -#~ "Thank the authors who contributed since" -#~ " the last release. This can be " -#~ "done by running the ``./dev/add-" -#~ "shortlog.sh`` convenience script (it can " -#~ "be ran multiple times and will " -#~ "update the names in the list if" -#~ " new contributors were added in the" -#~ " meantime)." +#~ "When comparing the abstract class to " +#~ "its derived class :code:`PytorchMNISTClient` " +#~ "you will notice that :code:`fit` calls" +#~ " a :code:`train` function and that " +#~ ":code:`evaluate` calls a :code:`test`: " +#~ "function." #~ msgstr "" -#~ "感谢自上次发布以来做出贡献的作者。可以通过运行 ``./dev/add-shortlog.sh`` " -#~ "方便脚本来完成（可以多次运行，如果在此期间有新的贡献者加入，则会更新列表中的名字）。" +#~ "将抽象类与其派生类 :code:`PytorchMNISTClient` 进行比较时，您会发现 " +#~ ":code:`fit` 调用了一个 :code:`train` 函数，而 " +#~ ":code:`evaluate` 则调用了一个 :code:`test`: 函数。" #~ msgid "" -#~ "Update the ``changelog.md`` section header " -#~ "``Unreleased`` to contain the version " -#~ "number and date for the release " -#~ "you are building. Create a pull " -#~ "request with the change." +#~ "These functions can both be found " +#~ "inside the same :code:`quickstart-" +#~ "pytorch.mnist` module:" +#~ msgstr "这些函数都可以在同一个 :code:`quickstart-pytorch.mnist` 模块中找到：" + +#~ msgid "" +#~ "Observe that these functions encapsulate " +#~ "regular training and test loops and " +#~ "provide :code:`fit` and :code:`evaluate` with" +#~ " final statistics for each round. You" +#~ " could substitute them with your " +#~ "custom train and test loops and " +#~ "change the network architecture, and the" +#~ " entire example would still work " +#~ "flawlessly. As a matter of fact, " +#~ "why not try and modify the code" +#~ " to an example of your liking?" #~ msgstr "" -#~ "更新 ``changelog.md`` 部分的标题 ``Unreleased`` " -#~ "以包含你正在构建的版本的版本号和日期。创建一个包含更改的拉取请求。" +#~ "请注意，这些函数封装了常规的训练和测试循环，并为 :code:`fit` 和 " +#~ ":code:`evaluate` " +#~ "提供了每轮的最终统计数据。您可以用自定义的训练和测试循环来替代它们，并改变网络结构，整个示例仍然可以完美运行。事实上，为什么不按照自己的喜好修改代码呢？" + +#~ msgid "Give It a Try" +#~ msgstr "试试看" #~ msgid "" -#~ "Second, create a virtual environment " -#~ "(and activate it). If you chose to" -#~ " use :code:`pyenv` (with the :code" -#~ ":`pyenv-virtualenv` plugin) and already " -#~ "have it installed , you can use" -#~ " the following convenience script (by " -#~ "default it will use :code:`Python " -#~ "3.8.17`, but you can change it by" -#~ " providing a specific :code:``)::" +#~ "Looking through the quickstart code " +#~ "description above will have given a " +#~ "good understanding of how *clients* and" +#~ " *servers* work in Flower, how to " +#~ "run a simple experiment, and the " +#~ "internals of a client wrapper. Here " +#~ "are a few things you could try " +#~ "on your own and get more " +#~ "experience with Flower:" #~ msgstr "" -#~ "其次，创建虚拟环境（并激活它）。如果您选择使用 :code:`pyenv`（使用 :code:`pyenv-" -#~ "virtualenv`插件），并且已经安装了该插件，则可以使用下面的便捷脚本（默认情况下使用 " -#~ ":code:`Python3.8.17`，但您可以通过提供特定的 :code:`<版本>`来更改）：：" +#~ "通过上面的快速入门代码描述，你将对 Flower " +#~ "中*客户端*和*服务器*的工作方式、如何运行一个简单的实验以及客户端封装器的内部结构有一个很好的了解。您可以自己尝试以下内容，以获得更多使用" +#~ " Flower 的经验：" -#~ msgid "flwr (Python API reference)" -#~ msgstr "flwr（Python API 参考）" +#~ msgid "" +#~ "Try and change :code:`PytorchMNISTClient` so" +#~ " it can accept different architectures." +#~ msgstr "尝试修改 :code:`PytorchMNISTClient`，使其可以接受不同的架构。" -#~ msgid "..." -#~ msgstr "..." +#~ msgid "" +#~ "Modify the :code:`train` function so " +#~ "that it accepts different optimizers" +#~ msgstr "修改 :code:`train` 函数，使其接受不同的优化器" -#~ msgid "Starting a client with an insecure server connection:" -#~ msgstr "使用不安全的服务器连接启动客户端：" +#~ msgid "" +#~ "Modify the :code:`test` function so that" +#~ " it proves not only the top-1 " +#~ "(regular accuracy) but also the top-5" +#~ " accuracy?" +#~ msgstr "修改 :code:`test` 函数，使其不仅能输出前 1 名（常规精确度），还能证明前 5 名的精确度？" -#~ msgid "server.strategy.FedAvg" -#~ msgstr "server.strategy.FedAvg" +#~ msgid "" +#~ "Go larger! Try to adapt the code" +#~ " to larger images and datasets. Why" +#~ " not try training on ImageNet with" +#~ " a ResNet-50?" +#~ msgstr "让我们尝试让代码适应更大的图像和数据集。为什么不尝试使用 ResNet-50 在 ImageNet 上进行训练呢？" -#~ msgid "server.strategy.FedAvgM" -#~ msgstr "server.strategy.FedAvgM" +#~ msgid "You are ready now. Enjoy learning in a federated way!" +#~ msgstr "您现在已经准备就绪。尽情享受联邦学习的乐趣吧！" -#~ msgid "Configurable FedAvg with Momentum strategy implementation." -#~ msgstr "可配置的 FedAvg 动量策略实施。" +#~ msgid "Differential privacy" +#~ msgstr "差别隐私" -#~ msgid "Fraction of clients used during training. Defaults to 0.1." -#~ msgstr "训练期间使用客户的比例。默认为 0.1。" +#~ msgid "" +#~ "Flower provides differential privacy (DP) " +#~ "wrapper classes for the easy integration" +#~ " of the central DP guarantees " +#~ "provided by DP-FedAvg into training " +#~ "pipelines defined in any of the " +#~ "various ML frameworks that Flower is " +#~ "compatible with." +#~ msgstr "" +#~ "Flower 提供了差分隐私 (DP) 封装类，可将 DP-FedAvg " +#~ "提供的核心 DP 轻松集成到 Flower 兼容的各种 ML " +#~ "框架中定义的训练模式中。" -#~ msgid "Fraction of clients used during validation. Defaults to 0.1." -#~ msgstr "验证过程中使用的客户端比例。默认为 0.1。" +#~ msgid "" +#~ "Please note that these components are" +#~ " still experimental; the correct " +#~ "configuration of DP for a specific " +#~ "task is still an unsolved problem." +#~ msgstr "请注意，这些组件仍处于试验阶段，如何为特定任务正确配置 DP 仍是一个尚未解决的问题。" -#~ msgid "server.strategy.FedMedian" -#~ msgstr "server.strategy.FedMedian" +#~ msgid "" +#~ "The name DP-FedAvg is misleading " +#~ "since it can be applied on top " +#~ "of any FL algorithm that conforms " +#~ "to the general structure prescribed by" +#~ " the FedOpt family of algorithms." +#~ msgstr "DP-FedAvg 这个名称容易引起误解，因为它可以应用于任何符合 FedOpt 系列算法规定的一般结构的 FL 算法之上。" -#~ msgid "server.strategy.QFedAvg" -#~ msgstr "server.strategy.QFedAvg" +#~ msgid "DP-FedAvg" +#~ msgstr "DP-FedAvg" -#~ msgid "server.strategy.FedOpt" -#~ msgstr "server.strategy.FedOpt" +#~ msgid "" +#~ "DP-FedAvg, originally proposed by " +#~ "McMahan et al. [mcmahan]_ and extended" +#~ " by Andrew et al. [andrew]_, is " +#~ "essentially FedAvg with the following " +#~ "modifications." +#~ msgstr "DP-FedAvg 最初由McMahan等人提出，并由Andrew等人加以扩展。" -#~ msgid "Configurable FedAdagrad strategy implementation." -#~ msgstr "可配置的 FedAdagrad 策略实施。" +#~ msgid "" +#~ "**Clipping** : The influence of each " +#~ "client's update is bounded by clipping" +#~ " it. This is achieved by enforcing" +#~ " a cap on the L2 norm of " +#~ "the update, scaling it down if " +#~ "needed." +#~ msgstr "**裁剪** ： 裁剪会影响到每个客户端的模型参数。具体做法是对参数的 L2 准则设置上限，必要时将其缩减。" -#~ msgid "Federated Optim strategy interface." -#~ msgstr "Federated Optim 策略界面。" +#~ msgid "" +#~ "**Noising** : Gaussian noise, calibrated " +#~ "to the clipping threshold, is added " +#~ "to the average computed at the " +#~ "server." +#~ msgstr "**噪声** ： 在服务器计算出的平均值中加入高斯噪声，该噪声根据剪切阈值进行校准。" -#~ msgid "server.strategy.FedProx" -#~ msgstr "server.strategy.FedProx" +#~ msgid "" +#~ "The distribution of the update norm " +#~ "has been shown to vary from " +#~ "task-to-task and to evolve as " +#~ "training progresses. This variability is " +#~ "crucial in understanding its impact on" +#~ " differential privacy guarantees, emphasizing " +#~ "the need for an adaptive approach " +#~ "[andrew]_ that continuously adjusts the " +#~ "clipping threshold to track a " +#~ "prespecified quantile of the update norm" +#~ " distribution." +#~ msgstr "事实证明，参数更新准则的分布会随着任务的不同而变化，并随着训练的进展而演变。因此，我们采用了一种自适应方法，该方法会不断调整剪切阈值，以跟踪参数更新准则分布的预设量化值。" -#~ msgid "Configurable FedProx strategy implementation." -#~ msgstr "可配置的 FedProx 策略实施。" +#~ msgid "Simplifying Assumptions" +#~ msgstr "简化假设" -#~ msgid "server.strategy.FedAdagrad" -#~ msgstr "server.strategy.FedAdagrad" +#~ msgid "" +#~ "We make (and attempt to enforce) a" +#~ " number of assumptions that must be" +#~ " satisfied to ensure that the " +#~ "training process actually realizes the " +#~ ":math:`(\\epsilon, \\delta)` guarantees the " +#~ "user has in mind when configuring " +#~ "the setup." +#~ msgstr "" +#~ "我们提出（并试图执行）了一系列必须满足的假设，以确保训练过程真正实现用户在配置设置时所定的 " +#~ ":math:`(\\epsilon,\\delta)` 。" -#~ msgid "Paper: https://arxiv.org/abs/2003.00295" -#~ msgstr "论文： https://arxiv.org/abs/2003.00295" +#~ msgid "" +#~ "**Fixed-size subsampling** :Fixed-size " +#~ "subsamples of the clients must be " +#~ "taken at each round, as opposed to" +#~ " variable-sized Poisson subsamples." +#~ msgstr "** 固定大小的子样本** :与可变大小的泊松分布子样本相比，每轮必须抽取固定大小的客户端子样本。" -#~ msgid "Federated learning strategy using Adagrad on server-side." -#~ msgstr "在服务器端使用 Adagrad 的联邦学习策略。" +#~ msgid "" +#~ "**Unweighted averaging** : The contributions" +#~ " from all the clients must weighted" +#~ " equally in the aggregate to " +#~ "eliminate the requirement for the server" +#~ " to know in advance the sum of" +#~ " the weights of all clients available" +#~ " for selection." +#~ msgstr "**非加权平均**： 所有客户端的贡献必须加权相等，这样服务器就不需要事先知道所有客户的权重总和。" -#~ msgid "server.strategy.FedAdam" -#~ msgstr "server.strategy.FedAdam" +#~ msgid "" +#~ "**No client failures** : The set " +#~ "of available clients must stay constant" +#~ " across all rounds of training. In" +#~ " other words, clients cannot drop out" +#~ " or fail." +#~ msgstr "**没有失败的客户端** ： 在各轮训练中，可用客户端的数量必须保持不变。换句话说，客户端不能退出或失败。" -#~ msgid "server.strategy.FedYogi" -#~ msgstr "server.strategy.FedYogi" +#~ msgid "" +#~ "The first two are useful for " +#~ "eliminating a multitude of complications " +#~ "associated with calibrating the noise to" +#~ " the clipping threshold, while the " +#~ "third one is required to comply " +#~ "with the assumptions of the privacy " +#~ "analysis." +#~ msgstr "前两种方法有助于消除将噪声校准为削波阈值所带来的诸多复杂问题，而第三种方法则需要符合隐私分析的假设。" -#~ msgid "Adaptive Federated Optimization using Yogi." -#~ msgstr "使用 Yogi 的自适应联合优化。" +#~ msgid "" +#~ "These restrictions are in line with " +#~ "constraints imposed by Andrew et al. " +#~ "[andrew]_." +#~ msgstr "这些限制与 Andrew 等人所施加的限制一致。" -#~ msgid "Federated learning strategy using Yogi on server-side." -#~ msgstr "在服务器端使用 Yogi 的联邦学习策略。" +#~ msgid "Customizable Responsibility for Noise injection" +#~ msgstr "可定制的噪声注入" -#~ msgid "Paper: https://arxiv.org/abs/1803.01498" -#~ msgstr "论文：https://arxiv.org/abs/1803.01498" +#~ msgid "" +#~ "In contrast to other implementations " +#~ "where the addition of noise is " +#~ "performed at the server, you can " +#~ "configure the site of noise injection" +#~ " to better match your threat model." +#~ " We provide users with the " +#~ "flexibility to set up the training " +#~ "such that each client independently adds" +#~ " a small amount of noise to the" +#~ " clipped update, with the result that" +#~ " simply aggregating the noisy updates " +#~ "is equivalent to the explicit addition" +#~ " of noise to the non-noisy " +#~ "aggregate at the server." +#~ msgstr "与其他在服务器上添加噪声的实现方法不同，您可以配置噪声注入的位置，以便更好地匹配您的威胁模型。我们为用户提供了设置训练的灵活性，使每个客户端都能独立地为剪切参数更新添加少量噪声，这样，只需聚合噪声更新，就相当于在服务器上为非噪声聚合添加噪声了。" -#~ msgid "server.strategy.Krum" -#~ msgstr "server.strategy.Krum" +#~ msgid "" +#~ "To be precise, if we let :math:`m`" +#~ " be the number of clients sampled " +#~ "each round and :math:`\\sigma_\\Delta` be " +#~ "the scale of the total Gaussian " +#~ "noise that needs to be added to" +#~ " the sum of the model updates, " +#~ "we can use simple maths to show" +#~ " that this is equivalent to each " +#~ "client adding noise with scale " +#~ ":math:`\\sigma_\\Delta/\\sqrt{m}`." +#~ msgstr "" +#~ "准确地说，我们假设每轮采样的客户端数量为:math:`m`，:math:`\\sigma_\\Delta` " +#~ "为需要添加到模型更新总和中的总高斯噪声的规模，我们就可以用简单的数学方法证明了，这相当于每个客户端都添加了规模为 " +#~ ":math:`\\sigma_\\Delta/\\sqrt{m}` 的噪声。" -#~ msgid "Configurable Krum strategy implementation." -#~ msgstr "可配置的 Krum 策略实施。" +#~ msgid "Wrapper-based approach" +#~ msgstr "基于封装的方法" -#~ msgid "server.strategy.Bulyan" -#~ msgstr "server.strategy.Bulyan" +#~ msgid "" +#~ "Introducing DP to an existing workload" +#~ " can be thought of as adding an" +#~ " extra layer of security around it." +#~ " This inspired us to provide the " +#~ "additional server and client-side logic" +#~ " needed to make the training process" +#~ " differentially private as wrappers for " +#~ "instances of the :code:`Strategy` and " +#~ ":code:`NumPyClient` abstract classes respectively." +#~ " This wrapper-based approach has the" +#~ " advantage of being easily composable " +#~ "with other wrappers that someone might" +#~ " contribute to the Flower library in" +#~ " the future, e.g., for secure " +#~ "aggregation. Using Inheritance instead can " +#~ "be tedious because that would require" +#~ " the creation of new sub- classes " +#~ "every time a new class implementing " +#~ ":code:`Strategy` or :code:`NumPyClient` is " +#~ "defined." +#~ msgstr "" +#~ "在现有工作负载中引入 DP " +#~ "可以被认为是在其周围增加了一层额外的安全性。受此启发，我们提供了额外的服务器端和客户端逻辑，分别作为 " +#~ ":code:`Strategy` 和 :code:`NumPyClient` " +#~ "抽象类实例的封装器，使训练过程具有不同的隐私性。这种基于封装器的方法的优点是可以很容易地与将来有人贡献给 Flower " +#~ "的其他封装器（例如用于安全聚合的封装器）进行组合。使用继承可能会比较繁琐，因为每次定义实现 :code:`Strategy`" +#~ " 或 :code:`NumPyClient` 的新类时，都需要创建新的子类。" -#~ msgid "Bulyan strategy implementation." -#~ msgstr "Bulyan策略的实施。" +#~ msgid "" +#~ "The first version of our solution " +#~ "was to define a decorator whose " +#~ "constructor accepted, among other things, " +#~ "a boolean-valued variable indicating " +#~ "whether adaptive clipping was to be " +#~ "enabled or not. We quickly realized " +#~ "that this would clutter its " +#~ ":code:`__init__()` function with variables " +#~ "corresponding to hyperparameters of adaptive" +#~ " clipping that would remain unused " +#~ "when it was disabled. A cleaner " +#~ "implementation could be achieved by " +#~ "splitting the functionality into two " +#~ "decorators, :code:`DPFedAvgFixed` and " +#~ ":code:`DPFedAvgAdaptive`, with the latter sub-" +#~ " classing the former. The constructors " +#~ "for both classes accept a boolean " +#~ "parameter :code:`server_side_noising`, which, as " +#~ "the name suggests, determines where " +#~ "noising is to be performed." +#~ msgstr "" +#~ "我们的第一版解决方案是定义一个装饰器，其构造函数接受一个布尔值变量，表示是否启用自适应剪裁。我们很快意识到，这样会使其 " +#~ ":code:`__init__()` " +#~ "函数中与自适应裁剪超参数相对应的变量变得杂乱无章，而这些变量在自适应裁剪被禁用时将保持未使用状态。要实现更简洁的功能，可以将该功能拆分为两个装饰器，即" +#~ " :code:`DPFedAvgFixed` 和 " +#~ ":code:`DPFedAvgAdaptive`，后者是前者的子类。这两个类的构造函数都接受一个布尔参数 " +#~ ":code:`server_side_noising`，顾名思义，它决定了在哪里加噪声。" -#~ msgid "server.strategy.FedXgbNnAvg" -#~ msgstr "server.strategy.FedXgbNnAvg" +#~ msgid "" +#~ "The server-side capabilities required " +#~ "for the original version of DP-" +#~ "FedAvg, i.e., the one which performed" +#~ " fixed clipping, can be completely " +#~ "captured with the help of wrapper " +#~ "logic for just the following two " +#~ "methods of the :code:`Strategy` abstract " +#~ "class." +#~ msgstr "" +#~ "只需对 :code:`Strategy` 抽象类的以下两个方法进行封装，就能完全捕获 DP-" +#~ "FedAvg 原始版本（即执行固定剪裁的版本）所需的服务器端功能。" + +#~ msgid "" +#~ ":code:`configure_fit()` : The config " +#~ "dictionary being sent by the wrapped " +#~ ":code:`Strategy` to each client needs to" +#~ " be augmented with an additional " +#~ "value equal to the clipping threshold" +#~ " (keyed under :code:`dpfedavg_clip_norm`) and," +#~ " if :code:`server_side_noising=true`, another one" +#~ " equal to the scale of the " +#~ "Gaussian noise that needs to be " +#~ "added at the client (keyed under " +#~ ":code:`dpfedavg_noise_stddev`). This entails " +#~ "*post*-processing of the results returned " +#~ "by the wrappee's implementation of " +#~ ":code:`configure_fit()`." +#~ msgstr "" +#~ ":code:`configure_fit()` ：由封装的 :code:`Strategy` " +#~ "发送到每个客户端的配置字典需要使用等于裁剪阈值的附加值（在 :code:`dpfedavg_clip_norm` " +#~ "下键入）进行扩充。并且，如果 " +#~ "server_side_noising=true，则另一个值等于需要在客户端添加的高斯噪声的大小（在 " +#~ "dpfedavg_noise_stddev 下键入）。这需要对封装后的configure_fit() " +#~ "所返回的结果进行后处理。" -#~ msgid "Federated XGBoost [Ma et al., 2023] strategy." -#~ msgstr "Federated XGBoost [Ma 等人，2023] 策略。" +#~ msgid "" +#~ ":code:`aggregate_fit()`: We check whether any" +#~ " of the sampled clients dropped out" +#~ " or failed to upload an update " +#~ "before the round timed out. In " +#~ "that case, we need to abort the" +#~ " current round, discarding any successful" +#~ " updates that were received, and move" +#~ " on to the next one. On the " +#~ "other hand, if all clients responded " +#~ "successfully, we must force the " +#~ "averaging of the updates to happen " +#~ "in an unweighted manner by intercepting" +#~ " the :code:`parameters` field of " +#~ ":code:`FitRes` for each received update " +#~ "and setting it to 1. Furthermore, " +#~ "if :code:`server_side_noising=true`, each update " +#~ "is perturbed with an amount of " +#~ "noise equal to what it would have" +#~ " been subjected to had client-side" +#~ " noising being enabled. This entails " +#~ "*pre*-processing of the arguments to " +#~ "this method before passing them on " +#~ "to the wrappee's implementation of " +#~ ":code:`aggregate_fit()`." +#~ msgstr "" +#~ ":code:`aggregate_fit()`： " +#~ "我们会检查是否有任何客户端在本轮超时前退出或未能上传参数更新。在这种情况下，我们需要中止当前一轮，丢弃已收到的所有参数更新，然后继续下一轮。另一方面，如果所有客户端都成功响应，我们就必须通过拦截" +#~ " :code:`FitRes` 的 :code:`parameters` 字段并将其设置为 " +#~ "1，强制以不加权的方式平均更新。此外，如果 " +#~ ":code:`server_side_noising=true`，每次更新都会受到一定量的噪声扰动，其扰动量相当于启用客户端噪声时的扰动量。" +#~ " 这就需要在将本方法的参数传递给封装的 :code:`aggregate_fit()` " +#~ "之前，对参数进行*预*处理。" -#~ msgid "server.strategy.DPFedAvgAdaptive" -#~ msgstr "server.strategy.DPFedAvgAdaptive" +#~ msgid "" +#~ "We can't directly change the aggregation" +#~ " function of the wrapped strategy to" +#~ " force it to add noise to the" +#~ " aggregate, hence we simulate client-" +#~ "side noising to implement server-side" +#~ " noising." +#~ msgstr "我们无法直接改变封装策略的聚合函数，迫使它在聚合中添加噪声，因此我们模拟客户端噪声来实现服务器端噪声。" #~ msgid "" -#~ "**Fix the incorrect return types of " -#~ "Strategy** " -#~ "([#2432](https://github.com/adap/flower/pull/2432/files))" +#~ "These changes have been put together " +#~ "into a class called :code:`DPFedAvgFixed`, " +#~ "whose constructor accepts the strategy " +#~ "being decorated, the clipping threshold " +#~ "and the number of clients sampled " +#~ "every round as compulsory arguments. The" +#~ " user is expected to specify the " +#~ "clipping threshold since the order of" +#~ " magnitude of the update norms is " +#~ "highly dependent on the model being " +#~ "trained and providing a default value" +#~ " would be misleading. The number of" +#~ " clients sampled at every round is" +#~ " required to calculate the amount of" +#~ " noise that must be added to " +#~ "each individual update, either by the" +#~ " server or the clients." #~ msgstr "" -#~ "**修复策略的错误返回类型** " -#~ "([#2432](https://github.com/adap/flower/pull/2432/files))" +#~ "这些变化被整合到一个名为 :code:`DPFedAvgFixed` " +#~ "的类中，其构造函数接受被装饰的策略、剪切阈值和每轮采样的客户数作为必选参数。用户需要指定剪切阈值，因为参数更新规范的数量级在很大程度上取决于正在训练的模型，提供默认值会产生误导。每轮采样的客户端数量是计算服务器或客户在每次参数更新时添加的噪音量所必需的。" #~ msgid "" -#~ "The types of the return values in" -#~ " the docstrings in two methods " -#~ "(`aggregate_fit` and `aggregate_evaluate`) now " -#~ "match the hint types in the code." +#~ "The additional functionality required to " +#~ "facilitate adaptive clipping has been " +#~ "provided in :code:`DPFedAvgAdaptive`, a " +#~ "subclass of :code:`DPFedAvgFixed`. It " +#~ "overrides the above-mentioned methods to" +#~ " do the following." #~ msgstr "" -#~ "两个方法（\"aggregate_fit \"和 " -#~ "\"aggregate_evaluate\"）的文档说明中的返回值类型现在与代码中的提示类型一致。" +#~ "自适应剪裁所需的附加功能在 :code:`DPFedAvgAdaptive` 中提供，其是 " +#~ ":code:`DPFedAvgFixed` 的子类。它重写了上述方法，以实现以下功能。" #~ msgid "" -#~ "**Update Flower Examples** " -#~ "([#2384](https://github.com/adap/flower/pull/2384),[#2425](https://github.com/adap/flower/pull/2425)," -#~ " [#2526](https://github.com/adap/flower/pull/2526))" +#~ ":code:`configure_fit()` : It intercepts the" +#~ " config dict returned by " +#~ ":code:`super.configure_fit()` to add the " +#~ "key-value pair " +#~ ":code:`dpfedavg_adaptive_clip_enabled:True` to it, " +#~ "which the client interprets as an " +#~ "instruction to include an indicator bit" +#~ " (1 if update norm <= clipping " +#~ "threshold, 0 otherwise) in the results" +#~ " returned by it." #~ msgstr "" -#~ "** 更新 Flower Examples** " -#~ "([#2384](https://github.com/adap/flower/pull/2384),[#2425](https://github.com/adap/flower/pull/2425)," -#~ " [#2526](https://github.com/adap/flower/pull/2526))" +#~ ":code:`configure_fit()`：它截取由 :code:`super.configure_fit()` " +#~ "返回的 config 字典，并在其中添加键-值对 " +#~ ":code:`dpfedavg_adaptive_clip_enabled:True\"，客户端将其解释为在返回结果中包含一个指示位（如果参数更新范式" +#~ " <= 剪裁阈值，则为 1，否则为 0）的指令。" #~ msgid "" -#~ "That's it for the client. We only" -#~ " have to implement :code:`Client` or " -#~ ":code:`NumPyClient` and call " -#~ ":code:`fl.client.start_client()`. The string " -#~ ":code:`\"0.0.0.0:8080\"` tells the client " -#~ "which server to connect to. In our" -#~ " case we can run the server and" -#~ " the client on the same machine, " -#~ "therefore we use :code:`\"0.0.0.0:8080\"`. If" -#~ " we run a truly federated workload" -#~ " with the server and clients running" -#~ " on different machines, all that " -#~ "needs to change is the " -#~ ":code:`server_address` we pass to the " -#~ "client." -#~ msgstr "" -#~ "对于客户端就需要做这么多。我们仅需要实现 " -#~ ":code:`Client`或者:code:`NumPyClient`然后调用:code:`fl.client.start_client()`。字符串" -#~ " :code:`\"0.0.0.0:8080\"` " -#~ "告诉客户端要连接到哪个服务器。在我们的例子中，我们可以在同一台机器上运行服务器和客户端，因此我们使用:code:`\"0.0.0.0:8080\"`。如果我们运行真正联邦学习的工作负载，服务器和客户端在不同的机器上运行，则需要更改的只是我们传递给客户端的" -#~ " server_address 。" +#~ ":code:`aggregate_fit()` : It follows a " +#~ "call to :code:`super.aggregate_fit()` with one" +#~ " to :code:`__update_clip_norm__()`, a procedure" +#~ " which adjusts the clipping threshold " +#~ "on the basis of the indicator bits" +#~ " received from the sampled clients." +#~ msgstr ":code:`aggregate_fit()`：在调用:code:`super.aggregate_fit()`后，再调用:code:`__update_clip_norm__()`，该过程根据从采样客户端接收到的指示位调整裁剪阈值。" #~ msgid "" -#~ "That's it for the client. We only" -#~ " have to implement :code:`Client` or " -#~ ":code:`NumPyClient` and call " -#~ ":code:`fl.client.start_client()`. The string " -#~ ":code:`\"[::]:8080\"` tells the client which" -#~ " server to connect to. In our " -#~ "case we can run the server and " -#~ "the client on the same machine, " -#~ "therefore we use :code:`\"[::]:8080\"`. If " -#~ "we run a truly federated workload " -#~ "with the server and clients running " -#~ "on different machines, all that needs" -#~ " to change is the :code:`server_address`" -#~ " we point the client at." +#~ "The client-side capabilities required " +#~ "can be completely captured through " +#~ "wrapper logic for just the :code:`fit()`" +#~ " method of the :code:`NumPyClient` abstract" +#~ " class. To be precise, we need " +#~ "to *post-process* the update computed" +#~ " by the wrapped client to clip " +#~ "it, if necessary, to the threshold " +#~ "value supplied by the server as " +#~ "part of the config dictionary. In " +#~ "addition to this, it may need to" +#~ " perform some extra work if either" +#~ " (or both) of the following keys " +#~ "are also present in the dict." #~ msgstr "" -#~ "对于客户来说就是这样了。我们只需实现 :code:`Client` 或 " -#~ ":code:`NumPyClient` 并调用:code:`fl.client.start_client()` " -#~ "即可。字符串 :code:`\"[::]:8080\"` " -#~ "告诉客户端要连接到哪个服务器。在我们的例子中，我们可以在同一台机器上运行服务器和客户端，因此我们使用 " -#~ ":code:`\"[::]:8080\"`。如果我们运行真正联邦的工作负载，服务器和客户端运行在不同的机器上，则需要更改的只是我们指向客户端的" -#~ " server_address 。" +#~ "客户端所需的功能完全可以通过 :code:`NumPyClient` 抽象类的 " +#~ ":code:`fit()` " +#~ "方法的封装逻辑来实现。准确地说，我们需要对封装客户端计算的参数更新进行处理，以便在必要时将其剪切到服务器作为配置字典的一部分提供的阈值。除此之外，如果配置字典中还存在以下任一（或两个）键，客户端可能还需要执行一些额外的工作。" #~ msgid "" -#~ "Let's now load the CIFAR-10 training " -#~ "and test set, partition them into " -#~ "ten smaller datasets (each split into" -#~ " training and validation set), and " -#~ "wrap the resulting partitions by " -#~ "creating a PyTorch ``DataLoader`` for " -#~ "each of them:" +#~ ":code:`dpfedavg_noise_stddev` : Generate and " +#~ "add the specified amount of noise " +#~ "to the clipped update." +#~ msgstr "code:`dpfedavg_noise_stddev`：生成并在剪切参数更新中添加指定数量的噪声。" + +#~ msgid "" +#~ ":code:`dpfedavg_adaptive_clip_enabled` : Augment the" +#~ " metrics dict in the :code:`FitRes` " +#~ "object being returned to the server " +#~ "with an indicator bit, calculated as " +#~ "described earlier." #~ msgstr "" -#~ "现在，让我们加载 CIFAR-10 训练集和测试集，将它们分割成 10 " -#~ "个较小的数据集（每个数据集又分为训练集和验证集），并通过为每个数据集创建 PyTorch " -#~ "``DataLoader`` 来包装由此产生的分割集：" +#~ ":code:`dpfedavg_adaptive_clip_enabled`：在返回给服务器的 :code:`FitRes`" +#~ " 对象中的度量值字典中增加一个指标位，计算方法如前所述。" -#~ msgid "|e1dd4b4129b040bea23a894266227080|" -#~ msgstr "|e1dd4b4129b040bea23a894266227080|" +#~ msgid "Performing the :math:`(\\epsilon, \\delta)` analysis" +#~ msgstr "进行 :math:`(epsilon, \\delta)` 分析" -#~ msgid "|c0d4cc6a442948dca8da40d2440068d9|" -#~ msgstr "|c0d4cc6a442948dca8da40d2440068d9|" +#~ msgid "" +#~ "Assume you have trained for :math:`n`" +#~ " rounds with sampling fraction :math:`q`" +#~ " and noise multiplier :math:`z`. In " +#~ "order to calculate the :math:`\\epsilon` " +#~ "value this would result in for a" +#~ " particular :math:`\\delta`, the following " +#~ "script may be used." +#~ msgstr "" +#~ "假设您已经训练了 :math:`n` 轮，采样比例为 :math:`q`，噪声乘数为 " +#~ ":math:`z`。为了计算特定 :math:`\\delta` 的 :math:`epsilon`" +#~ " 值，可以使用下面的脚本。" -#~ msgid "|174e1e4fa1f149a19bfbc8bc1126f46a|" -#~ msgstr "|174e1e4fa1f149a19bfbc8bc1126f46a|" +#~ msgid "Flower driver SDK." +#~ msgstr "Flower 服务器。" -#~ msgid "|4e021a3dc08249d2a89daa3ab03c2714|" -#~ msgstr "|4e021a3dc08249d2a89daa3ab03c2714|" +#~ msgid "driver" +#~ msgstr "服务器" -#~ msgid "|e74a1d5ce7eb49688651f2167a59065b|" -#~ msgstr "|e74a1d5ce7eb49688651f2167a59065b|" +#~ msgid "Get task results." +#~ msgstr "汇总训练结果。" -#~ msgid "|eb29ec4c7aef4e93976795ed72df647e|" -#~ msgstr "|eb29ec4c7aef4e93976795ed72df647e|" +#~ msgid "Request for run ID." +#~ msgstr "Flower 基线申请" -#~ msgid "|c2f699d8ac484f5081721a6f1511f70d|" -#~ msgstr "|c2f699d8ac484f5081721a6f1511f70d|" +#~ msgid "Get client IDs." +#~ msgstr "返回客户端（本身）。" -#~ msgid "|cf42accdacbf4e5eb4fa0503108ba7a7|" -#~ msgstr "|cf42accdacbf4e5eb4fa0503108ba7a7|" +#~ msgid "" +#~ "Flower usage examples used to be " +#~ "bundled with Flower in a package " +#~ "called ``flwr_example``. We are migrating " +#~ "those examples to standalone projects to" +#~ " make them easier to use. All " +#~ "new examples are based in the " +#~ "directory `examples " +#~ "`_." +#~ msgstr "" +#~ "Flower 的使用示例曾与 Flower 捆绑在一个名为 ``flwr_example``" +#~ " 的软件包中。我们正在将这些示例迁移到独立项目中，以使它们更易于使用。所有新示例都位于目录 `examples " +#~ "`_。" + +#~ msgid "Quickstart TensorFlow/Keras" +#~ msgstr "快速入门 TensorFlow/Keras" -#~ msgid "|5ec8356bc2564fa09178b1ceed5beccc|" -#~ msgstr "|5ec8356bc2564fa09178b1ceed5beccc|" +#~ msgid "Legacy Examples (`flwr_example`)" +#~ msgstr "传统示例 (`flwr_example`)" -#~ msgid "|7c9329e97bd0430bad335ab605a897a7|" -#~ msgstr "|7c9329e97bd0430bad335ab605a897a7|" +#~ msgid "" +#~ "The useage examples in `flwr_example` " +#~ "are deprecated and will be removed " +#~ "in the future. New examples are " +#~ "provided as standalone projects in " +#~ "`examples `_." +#~ msgstr "" +#~ "在 `flwr_example` 中的使用示例已被弃用，今后将被移除。新示例将作为独立项目在 " +#~ "`examples `_" +#~ " 中提供。" -#~ msgid "|88002bbce1094ba1a83c9151df18f707|" -#~ msgstr "|88002bbce1094ba1a83c9151df18f707|" +#~ msgid "Extra Dependencies" +#~ msgstr "额外依赖" -#~ msgid "|391766aee87c482c834c93f7c22225e2|" -#~ msgstr "|391766aee87c482c834c93f7c22225e2|" +#~ msgid "" +#~ "The core Flower framework keeps a " +#~ "minimal set of dependencies. The " +#~ "examples demonstrate Flower in the " +#~ "context of different machine learning " +#~ "frameworks, so additional dependencies need" +#~ " to be installed before an example" +#~ " can be run." +#~ msgstr "" +#~ "Flower 核心框架只保留了最低限度的依赖项。这些示例在不同机器学习框架的背景下演示了 " +#~ "Flower，因此在运行示例之前需要安装额外的依赖项。" -#~ msgid "|93b9a15bd27f4e91b40f642c253dfaac|" -#~ msgstr "|93b9a15bd27f4e91b40f642c253dfaac|" +#~ msgid "For PyTorch examples::" +#~ msgstr "PyTorch 示例：：" -#~ msgid "|a23d9638f96342ef9d25209951e2d564|" -#~ msgstr "|a23d9638f96342ef9d25209951e2d564|" +#~ msgid "For TensorFlow examples::" +#~ msgstr "TensorFlow 示例：：" -#~ msgid "Upload the whl (e.g., ``flwr-1.6.0-py3-none-any.whl``)" -#~ msgstr "上传 whl（例如 ``flwr-1.6.0-py3-none-any.whl``)" +#~ msgid "For both PyTorch and TensorFlow examples::" +#~ msgstr "PyTorch 和 TensorFlow 示例：：" #~ msgid "" -#~ "Change ``!pip install -q 'flwr[simulation]'" -#~ " torch torchvision matplotlib`` to ``!pip" -#~ " install -q 'flwr-1.6.0-py3-none-" -#~ "any.whl[simulation]' torch torchvision matplotlib``" +#~ "Please consult :code:`pyproject.toml` for a" +#~ " full list of possible extras " +#~ "(section :code:`[tool.poetry.extras]`)." #~ msgstr "" -#~ "将``!pip install -q 'flwr[simulation]' torch" -#~ " torchvision matplotlib``更改为``!pip install -q " -#~ "'flwr-1.6.0-py3-none-any.whl[simulation]' torch " -#~ "torch torchvision matplotlib``" +#~ "请参阅 :code:`pyproject.toml`，了解可能的 extras 的完整列表（章节 " +#~ ":code:`[tool.poems.extras]`）。" -#~ msgid "" -#~ "All that's left to do it to " -#~ "define a function that loads both " -#~ "model and data, creates a " -#~ ":code:`CifarClient`, and starts this client." -#~ " You load your data and model " -#~ "by using :code:`cifar.py`. Start " -#~ ":code:`CifarClient` with the function " -#~ ":code:`fl.client.start_numpy_client()` by pointing " -#~ "it at the same IP address we " -#~ "used in :code:`server.py`:" -#~ msgstr "" -#~ "剩下要做的就是定义一个加载模型和数据的函数，创建一个 :code:`CifarClient` 并启动该客户端。使用" -#~ " :code:`cifar.py` 加载数据和模型。使用函数 " -#~ ":code:`fl.client.start_numpy_client()` 启动 " -#~ ":code:`CifarClient`，将其指向我们在 :code:`server.py` 中使用的相同 " -#~ "IP 地址：" +#~ msgid "PyTorch Examples" +#~ msgstr "PyTorch 示例" #~ msgid "" -#~ "The :code:`VirtualClientEngine` schedules, launches" -#~ " and manages `virtual` clients. These " -#~ "clients are identical to `non-virtual`" -#~ " clients (i.e. the ones you launch" -#~ " via the command `flwr.client.start_numpy_client" -#~ " `_)" -#~ " in the sense that they can be" -#~ " configure by creating a class " -#~ "inheriting, for example, from " -#~ "`flwr.client.NumPyClient `_ and therefore " -#~ "behave in an identical way. In " -#~ "addition to that, clients managed by " -#~ "the :code:`VirtualClientEngine` are:" -#~ msgstr "" -#~ "代码:`VirtualClientEngine`调度、启动和管理`虚拟`客户端。这些客户端与 \"非虚拟 " -#~ "\"客户端（即通过命令 `flwr.client.start_numpy_client `_启动的客户端）完全相同，它们可以通过创建一个继承自 \"flwr.client.NumPyClient " -#~ "`_\" " -#~ "的类来配置，因此行为方式也完全相同。除此之外，由 :code:`VirtualClientEngine` " -#~ "管理的客户端还包括：" +#~ "Our PyTorch examples are based on " +#~ "PyTorch 1.7. They should work with " +#~ "other releases as well. So far, we" +#~ " provide the following examples." +#~ msgstr "我们的 PyTorch 示例基于 PyTorch 1.7。它们应该也能在其他版本中使用。到目前为止，我们提供了以下示例。" -#~ msgid "" -#~ "Please follow the first section on " -#~ "`Run Flower using Docker " -#~ "`_ which covers this" -#~ " step in more detail." -#~ msgstr "" +#~ msgid "CIFAR-10 Image Classification" +#~ msgstr "CIFAR-10 图像分类" #~ msgid "" -#~ "If the section is completely empty " -#~ "(without any token) or non-existant, " -#~ "the changelog will just contain the " -#~ "title of the PR for the changelog" -#~ " entry, without any description." +#~ "`CIFAR-10 and CIFAR-100 " +#~ "`_ are " +#~ "popular RGB image datasets. The Flower" +#~ " CIFAR-10 example uses PyTorch to " +#~ "train a simple CNN classifier in a" +#~ " federated learning setup with two " +#~ "clients." #~ msgstr "" +#~ "CIFAR-10 和 CIFAR-100 " +#~ "``_ 是流行的 RGB" +#~ " 图像数据集。Flower CIFAR-10 示例使用 PyTorch " +#~ "在有两个客户端的联邦学习设置中训练一个简单的 CNN 分类器。" -#~ msgid "Example: Walk-Through PyTorch & MNIST" -#~ msgstr "实例： PyTorch 和 MNIST 的演练" +#~ msgid "First, start a Flower server:" +#~ msgstr "首先，启动 Flower 服务器：" -#~ msgid "" -#~ "In this tutorial we will learn, " -#~ "how to train a Convolutional Neural " -#~ "Network on MNIST using Flower and " -#~ "PyTorch." -#~ msgstr "在本教程中，我们将学习如何使用 Flower 和 PyTorch 在 MNIST 上训练卷积神经网络。" +#~ msgid "$ ./src/py/flwr_example/pytorch_cifar/run-server.sh" +#~ msgstr "$ ./src/py/flwr_example/pytorch_cifar/run-server.sh" -#~ msgid "" -#~ "Since we want to use PyTorch to" -#~ " solve a computer vision task, let's" -#~ " go ahead an install PyTorch and " -#~ "the **torchvision** library:" -#~ msgstr "我们想用 PyTorch 来做计算机视觉任务，需要先安装 PyTorch 和 **torchvision** 库：" +#~ msgid "Then, start the two clients in a new terminal window:" +#~ msgstr "然后，在新的终端窗口中启动两个客户端：" -#~ msgid "Ready... Set... Train!" -#~ msgstr "准备...设置...训练!" +#~ msgid "$ ./src/py/flwr_example/pytorch_cifar/run-clients.sh" +#~ msgstr "$ ./src/py/flwr_example/pytorch_cifar/run-clients.sh" + +#~ msgid "For more details, see :code:`src/py/flwr_example/pytorch_cifar`." +#~ msgstr "更多详情，请参阅 :code:`src/py/flwr_example/pytorch_cifar`。" + +#~ msgid "ImageNet-2012 Image Classification" +#~ msgstr "ImageNet-2012 图像分类" #~ msgid "" -#~ "Now that we have all our " -#~ "dependencies installed, let's run a " -#~ "simple distributed training with two " -#~ "clients and one server. Our training " -#~ "procedure and network architecture are " -#~ "based on PyTorch's `Basic MNIST Example" -#~ " `_. " -#~ "This will allow you see how easy" -#~ " it is to wrap your code with" -#~ " Flower and begin training in a " -#~ "federated way. We provide you with " -#~ "two helper scripts, namely *run-" -#~ "server.sh*, and *run-clients.sh*. Don't " -#~ "be afraid to look inside, they are" -#~ " simple enough =)." +#~ "`ImageNet-2012 `_ is " +#~ "one of the major computer vision " +#~ "datasets. The Flower ImageNet example " +#~ "uses PyTorch to train a ResNet-18 " +#~ "classifier in a federated learning setup" +#~ " with ten clients." #~ msgstr "" -#~ "现在我们已经安装了所有的依赖包，让我们用两个客户端和一个服务器来运行一个简单的分布式训练。我们的训练过程和网络架构基于 " -#~ "PyTorch 的 `Basic MNIST Example " -#~ "`_。您会发现用 " -#~ "Flower 来封装您的代码并进行联邦学习训练是多么容易。我们为您提供了两个辅助脚本，即 *run-" -#~ "server.sh* 和 *run-clients.sh*。别害怕，它们很简单 =)。" +#~ "ImageNet-2012 `_ " +#~ "是主要的计算机视觉数据集之一。Flower ImageNet 示例使用 PyTorch " +#~ "在有十个客户端的联邦学习设置中训练 ResNet-18 分类器。" -#~ msgid "" -#~ "Go ahead and launch on a terminal" -#~ " the *run-server.sh* script first as" -#~ " follows:" -#~ msgstr "首先在终端上启动 *run-server.sh* 脚本，如下所示：" +#~ msgid "$ ./src/py/flwr_example/pytorch_imagenet/run-server.sh" +#~ msgstr "$ ./src/py/flwr_example/pytorch_imagenet/run-server.sh" -#~ msgid "Now that the server is up and running, go ahead and launch the clients." -#~ msgstr "现在服务器已经启动并运行，请继续启动客户端。" +#~ msgid "$ ./src/py/flwr_example/pytorch_imagenet/run-clients.sh" +#~ msgstr "$ ./src/py/flwr_example/pytorch_imagenet/run-clients.sh" -#~ msgid "" -#~ "Et voilà! You should be seeing the" -#~ " training procedure and, after a few" -#~ " iterations, the test accuracy for " -#~ "each client." -#~ msgstr "然后就可以了！您应该能看到训练过程，以及经过几次反复后，每个客户端的测试准确率。" +#~ msgid "For more details, see :code:`src/py/flwr_example/pytorch_imagenet`." +#~ msgstr "更多详情，请参阅 :code:`src/py/flwr_example/pytorch_imagenet`。" -#~ msgid "Now, let's see what is really happening inside." -#~ msgstr "现在，让我们看看里面到底发生了什么。" +#~ msgid "TensorFlow Examples" +#~ msgstr "TensorFlow 示例" #~ msgid "" -#~ "Inside the server helper script *run-" -#~ "server.sh* you will find the following" -#~ " code that basically runs the " -#~ ":code:`server.py`" -#~ msgstr "在服务器辅助脚本 *run-server.sh* 中，你可以找到以下代码，这些代码基本上都是运行 :code:`server.py` 的代码" +#~ "Our TensorFlow examples are based on " +#~ "TensorFlow 2.0 or newer. So far, " +#~ "we provide the following examples." +#~ msgstr "我们的 TensorFlow 示例基于 TensorFlow 2.0 或更新版本。到目前为止，我们提供了以下示例。" + +#~ msgid "Fashion-MNIST Image Classification" +#~ msgstr "Fashion-MNIST 图像分类" #~ msgid "" -#~ "We can go a bit deeper and " -#~ "see that :code:`server.py` simply launches " -#~ "a server that will coordinate three " -#~ "rounds of training. Flower Servers are" -#~ " very customizable, but for simple " -#~ "workloads, we can start a server " -#~ "using the :ref:`start_server ` function and leave " -#~ "all the configuration possibilities at " -#~ "their default values, as seen below." +#~ "`Fashion-MNIST `_ is often used as " +#~ "the \"Hello, world!\" of machine " +#~ "learning. We follow this tradition and" +#~ " provide an example which samples " +#~ "random local datasets from Fashion-MNIST" +#~ " and trains a simple image " +#~ "classification model over those partitions." #~ msgstr "" -#~ "我们可以再深入一点，:code:`server.py` 只是启动了一个服务器，该服务器将协调三轮训练。Flower " -#~ "服务器是非常容易修改的，但对于简单的工作，我们可以使用 :ref:`start_server `函数启动服务器，并将所有可能的配置保留为默认值，如下所示。" +#~ "`Fashion-MNIST `_ 经常被用作机器学习的 \"你好，世界！\"。我们遵循这一传统" +#~ "，提供了一个从Fashion-MNIST 中随机抽样本地数据集的示例，并在这些分区上训练一个简单的图像分类模型。" + +#~ msgid "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-server.sh" +#~ msgstr "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-server.sh" + +#~ msgid "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-clients.sh" +#~ msgstr "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-clients.sh" #~ msgid "" -#~ "Next, let's take a look at the " -#~ "*run-clients.sh* file. You will see " -#~ "that it contains the main loop " -#~ "that starts a set of *clients*." -#~ msgstr "接下来，让我们看看 *run-clients.sh* 文件。您会看到它包含了用来启动多个 *客户端* 的代码。" +#~ "For more details, see " +#~ ":code:`src/py/flwr_example/tensorflow_fashion_mnist`." +#~ msgstr "更多详情，请参阅 :code:`src/py/flwr_example/tensorflow_fashion_mnist`。" -#~ msgid "" -#~ "**cid**: is the client ID. It is" -#~ " an integer that uniquely identifies " -#~ "client identifier." -#~ msgstr "**cid**：是客户 ID。它是一个整数，可唯一标识客户标识符。" +#~ msgid "``BASE_IMAGE_TAG``" +#~ msgstr "基本图像标签" -#~ msgid "**sever_address**: String that identifies IP and port of the server." -#~ msgstr "**sever_address**： 标识服务器 IP 和端口的字符串。" +#~ msgid "The image tag of the base image." +#~ msgstr "基础图像的图像标记。" #~ msgid "" -#~ "**nb_clients**: This defines the number " -#~ "of clients being created. This piece " -#~ "of information is not required by " -#~ "the client, but it helps us " -#~ "partition the original MNIST dataset to" -#~ " make sure that every client is " -#~ "working on unique subsets of both " -#~ "*training* and *test* sets." +#~ "It is important to follow the " +#~ "instructions described in comments. For " +#~ "instance, in order to not break " +#~ "how our changelog system works, you " +#~ "should read the information above the" +#~ " ``Changelog entry`` section carefully. You" +#~ " can also checkout some examples and" +#~ " details in the :ref:`changelogentry` " +#~ "appendix." #~ msgstr "" -#~ "**nb_clients**： 这定义了正在创建的客户端数量。客户端并不需要这一信息，但它有助于我们对原始 " -#~ "MNIST 数据集进行划分，以确保每个客户端都在 *training* 和 *test*" -#~ " 数据集上有独立的数据。" +#~ "请务必遵守注释中的说明。例如，为了不破坏我们的更新日志系统，你应该仔细阅读\"`更新日志条目``\"部分上面的信息。您还可以查看 " +#~ ":ref:`changelogentry` 附录中的一些示例和细节。" -#~ msgid "" -#~ "Again, we can go deeper and look" -#~ " inside :code:`flwr_example/quickstart-" -#~ "pytorch/client.py`. After going through the" -#~ " argument parsing code at the " -#~ "beginning of our :code:`main` function, " -#~ "you will find a call to " -#~ ":code:`mnist.load_data`. This function is " -#~ "responsible for partitioning the original " -#~ "MNIST datasets (*training* and *test*) " -#~ "and returning a :code:`torch.utils.data.DataLoader`" -#~ " s for each of them. We then" -#~ " instantiate a :code:`PytorchMNISTClient` object" -#~ " with our client ID, our DataLoaders," -#~ " the number of epochs in each " -#~ "round, and which device we want to" -#~ " use for training (CPU or GPU)." -#~ msgstr "" -#~ "我们可以深入看一下 :code:`flwr_example/quickstart-" -#~ "pytorch/client.py`。查看 :code:`main` 函数开头的参数解析代码后，你会发现一个对" -#~ " :code:`mnist.load_data` 的调用。该函数负责分割原始 MNIST " -#~ "数据集（*training* 和 *test*），并为每个数据集返回一个 " -#~ ":code:`torch.utils.data.DataLoader` 。然后，我们实例化一个 " -#~ ":code:`PytorchMNISTClient` 对象，其中包含我们的客户端 ID、 " -#~ "DataLoader、每一轮中的遍历数，以及我们希望用于训练的设备（CPU 或 GPU）。" +#~ msgid "Open a PR (as shown above)" +#~ msgstr "打开 PR（如上图所示）" -#~ msgid "" -#~ "The :code:`PytorchMNISTClient` object when " -#~ "finally passed to :code:`fl.client.start_client` " -#~ "along with the server's address as " -#~ "the training process begins." -#~ msgstr "" -#~ "当训练过程开始时，:code:`PytorchMNISTClient` 对象会连同服务器地址一起传递给 " -#~ ":code:`fl.client.start_client`。" +#~ msgid "How to write a good PR title" +#~ msgstr "如何撰写好的公关标题" -#~ msgid "A Closer Look" -#~ msgstr "仔细看一下" +#~ msgid "" +#~ "A well-crafted PR title helps team" +#~ " members quickly understand the purpose " +#~ "and scope of the changes being " +#~ "proposed. Here's a guide to help " +#~ "you write a good GitHub PR title:" +#~ msgstr "一个精心撰写的公关标题能帮助团队成员迅速了解所提修改的目的和范围。以下指南可帮助您撰写一个好的 GitHub PR 标题：" #~ msgid "" -#~ "Now, let's look closely into the " -#~ ":code:`PytorchMNISTClient` inside :code:`flwr_example" -#~ ".quickstart-pytorch.mnist` and see what it" -#~ " is doing:" +#~ "1. Be Clear and Concise: Provide a" +#~ " clear summary of the changes in " +#~ "a concise manner. 1. Use Actionable " +#~ "Verbs: Start with verbs like \"Add,\"" +#~ " \"Update,\" or \"Fix\" to indicate " +#~ "the purpose. 1. Include Relevant " +#~ "Information: Mention the affected feature " +#~ "or module for context. 1. Keep it" +#~ " Short: Avoid lengthy titles for easy" +#~ " readability. 1. Use Proper Capitalization" +#~ " and Punctuation: Follow grammar rules " +#~ "for clarity." #~ msgstr "" -#~ "现在，让我们仔细研究一下 :code:`flwr_example.quickstart-pytorch.mnist`" -#~ " 中的 :code:`PytorchMNISTClient`，看看它在做什么：" +#~ "1. 简明扼要： 以简明扼要的方式清楚地概述变化。1. 使用可操作的动词： 使用 " +#~ "\"添加\"、\"更新 \"或 \"修复 \"等动词来表明目的。1. 包含相关信息： " +#~ "提及受影响的功能或模块以了解上下文。1. 简短：避免冗长的标题，以方便阅读。1. 使用正确的大小写和标点符号：" +#~ " 遵守语法规则，以确保清晰。" #~ msgid "" -#~ "The first thing to notice is that" -#~ " :code:`PytorchMNISTClient` instantiates a CNN" -#~ " model inside its constructor" -#~ msgstr "首先要注意的是 :code:`PytorchMNISTClient` 在其构造函数中实例化了一个 CNN 模型" +#~ "Let's start with a few examples " +#~ "for titles that should be avoided " +#~ "because they do not provide meaningful" +#~ " information:" +#~ msgstr "让我们先举例说明几个应该避免使用的标题，因为它们不能提供有意义的信息：" + +#~ msgid "Implement Algorithm" +#~ msgstr "执行算法" + +#~ msgid "Add my_new_file.py to codebase" +#~ msgstr "在代码库中添加 my_new_file.py" + +#~ msgid "Improve code in module" +#~ msgstr "改进模块中的代码" + +#~ msgid "Change SomeModule" +#~ msgstr "更改 SomeModule" #~ msgid "" -#~ "The code for the CNN is available" -#~ " under :code:`quickstart-pytorch.mnist` and " -#~ "it is reproduced below. It is the" -#~ " same network found in `Basic MNIST" -#~ " Example " -#~ "`_." -#~ msgstr "" -#~ "CNN 的代码可在 :code:`quickstart-pytorch.mnist` " -#~ "下找到，现复制如下。它与 `Basic MNIST Example " -#~ "`_中的网络相同。" +#~ "Here are a few positive examples " +#~ "which provide helpful information without " +#~ "repeating how they do it, as that" +#~ " is already visible in the \"Files" +#~ " changed\" section of the PR:" +#~ msgstr "这里有几个正面的例子，提供了有用的信息，但没有重复他们是如何做的，因为在 PR 的 \"已更改文件 \"部分已经可以看到：" + +#~ msgid "Update docs banner to mention Flower Summit 2023" +#~ msgstr "更新文件横幅，提及 2023 年 Flower 峰会" + +#~ msgid "Remove unnecessary XGBoost dependency" +#~ msgstr "移除不必要的 XGBoost 依赖性" + +#~ msgid "Remove redundant attributes in strategies subclassing FedAvg" +#~ msgstr "删除 FedAvg 子类化策略中的多余属性" #~ msgid "" -#~ "The second thing to notice is that" -#~ " :code:`PytorchMNISTClient` class inherits from" -#~ " the :code:`fl.client.Client`, and hence it" -#~ " must implement the following methods:" -#~ msgstr "" -#~ "第二件要注意的事是 :code:`PytorchMNISTClient` 类继承自 " -#~ ":code:`fl.client.Client`，因此它必须实现以下方法：" +#~ "Add CI job to deploy the staging" +#~ " system when the ``main`` branch " +#~ "changes" +#~ msgstr "添加 CI 作业，以便在 \"主 \"分支发生变化时部署暂存系统" #~ msgid "" -#~ "When comparing the abstract class to " -#~ "its derived class :code:`PytorchMNISTClient` " -#~ "you will notice that :code:`fit` calls" -#~ " a :code:`train` function and that " -#~ ":code:`evaluate` calls a :code:`test`: " -#~ "function." -#~ msgstr "" -#~ "将抽象类与其派生类 :code:`PytorchMNISTClient` 进行比较时，您会发现 " -#~ ":code:`fit` 调用了一个 :code:`train` 函数，而 " -#~ ":code:`evaluate` 则调用了一个 :code:`test`: 函数。" +#~ "Add new amazing library which will " +#~ "be used to improve the simulation " +#~ "engine" +#~ msgstr "添加新的惊人库，用于改进模拟引擎" + +#~ msgid "Changelog entry" +#~ msgstr "更新日志" #~ msgid "" -#~ "These functions can both be found " -#~ "inside the same :code:`quickstart-" -#~ "pytorch.mnist` module:" -#~ msgstr "这些函数都可以在同一个 :code:`quickstart-pytorch.mnist` 模块中找到：" +#~ "When opening a new PR, inside its" +#~ " description, there should be a " +#~ "``Changelog entry`` header." +#~ msgstr "打开一个新 PR 时，在其描述中应有一个 ``Changelog entry`` 标头。" #~ msgid "" -#~ "Observe that these functions encapsulate " -#~ "regular training and test loops and " -#~ "provide :code:`fit` and :code:`evaluate` with" -#~ " final statistics for each round. You" -#~ " could substitute them with your " -#~ "custom train and test loops and " -#~ "change the network architecture, and the" -#~ " entire example would still work " -#~ "flawlessly. As a matter of fact, " -#~ "why not try and modify the code" -#~ " to an example of your liking?" -#~ msgstr "" -#~ "请注意，这些函数封装了常规的训练和测试循环，并为 :code:`fit` 和 " -#~ ":code:`evaluate` " -#~ "提供了每轮的最终统计数据。您可以用自定义的训练和测试循环来替代它们，并改变网络结构，整个示例仍然可以完美运行。事实上，为什么不按照自己的喜好修改代码呢？" +#~ "Above this header you should see " +#~ "the following comment that explains how" +#~ " to write your changelog entry:" +#~ msgstr "在页眉上方，你会看到以下注释，说明如何编写更新日志条目：" -#~ msgid "Give It a Try" -#~ msgstr "试试看" +#~ msgid "" +#~ "Inside the following 'Changelog entry' " +#~ "section, you should put the description" +#~ " of your changes that will be " +#~ "added to the changelog alongside your" +#~ " PR title." +#~ msgstr "在下面的 \"更新日志条目 \"部分中，您应该在 PR 标题旁边写上将添加到更新日志中的更改描述。" #~ msgid "" -#~ "Looking through the quickstart code " -#~ "description above will have given a " -#~ "good understanding of how *clients* and" -#~ " *servers* work in Flower, how to " -#~ "run a simple experiment, and the " -#~ "internals of a client wrapper. Here " -#~ "are a few things you could try " -#~ "on your own and get more " -#~ "experience with Flower:" -#~ msgstr "" -#~ "通过上面的快速入门代码描述，你将对 Flower " -#~ "中*客户端*和*服务器*的工作方式、如何运行一个简单的实验以及客户端封装器的内部结构有一个很好的了解。您可以自己尝试以下内容，以获得更多使用" -#~ " Flower 的经验：" +#~ "If the section is completely empty " +#~ "(without any token) or non-existent, " +#~ "the changelog will just contain the " +#~ "title of the PR for the changelog" +#~ " entry, without any description." +#~ msgstr "如果该部分完全为空（没有任何标记）或不存在，更新日志将只包含更新日志条目的 PR 标题，而不包含任何描述。" #~ msgid "" -#~ "Try and change :code:`PytorchMNISTClient` so" -#~ " it can accept different architectures." -#~ msgstr "尝试修改 :code:`PytorchMNISTClient`，使其可以接受不同的架构。" +#~ "If the section contains some text " +#~ "other than tokens, it will use it" +#~ " to add a description to the " +#~ "change." +#~ msgstr "如果该部分包含标记以外的文本，它将使用这些文本为更改添加说明。" #~ msgid "" -#~ "Modify the :code:`train` function so " -#~ "that it accepts different optimizers" -#~ msgstr "修改 :code:`train` 函数，使其接受不同的优化器" +#~ "If the section contains one of the" +#~ " following tokens it will ignore any" +#~ " other text and put the PR " +#~ "under the corresponding section of the" +#~ " changelog:" +#~ msgstr "如果该部分包含以下标记之一，它将忽略任何其他文本，并将 PR 放在更新日志的相应部分下：" + +#~ msgid " is for classifying a PR as a general improvement." +#~ msgstr " 用于将 PR 划分为一般改进。" + +#~ msgid " is to not add the PR to the changelog" +#~ msgstr "表示不将 PR 添加到更新日志中" + +#~ msgid " is to add a general baselines change to the PR" +#~ msgstr " 是指在 PR 中添加一般基线更改" + +#~ msgid " is to add a general examples change to the PR" +#~ msgstr " 是在 PR 中添加对一般示例的修改" + +#~ msgid " is to add a general sdk change to the PR" +#~ msgstr " 是指在 PR 中添加一般的 sdk 更改" + +#~ msgid " is to add a general simulations change to the PR" +#~ msgstr "（模拟）是在 PR 中添加一般模拟变更" + +#~ msgid "Note that only one token should be used." +#~ msgstr "请注意，只能使用一个标记。" #~ msgid "" -#~ "Modify the :code:`test` function so that" -#~ " it proves not only the top-1 " -#~ "(regular accuracy) but also the top-5" -#~ " accuracy?" -#~ msgstr "修改 :code:`test` 函数，使其不仅能输出前 1 名（常规精确度），还能证明前 5 名的精确度？" +#~ "Its content must have a specific " +#~ "format. We will break down what " +#~ "each possibility does:" +#~ msgstr "其内容必须有特定的格式。我们将分析每种可能性的作用：" #~ msgid "" -#~ "Go larger! Try to adapt the code" -#~ " to larger images and datasets. Why" -#~ " not try training on ImageNet with" -#~ " a ResNet-50?" -#~ msgstr "让我们尝试让代码适应更大的图像和数据集。为什么不尝试使用 ResNet-50 在 ImageNet 上进行训练呢？" +#~ "If the ``### Changelog entry`` section" +#~ " contains nothing or doesn't exist, " +#~ "the following text will be added " +#~ "to the changelog::" +#~ msgstr "如果 ``#### Changelog entry`` 部分不包含任何内容或不存在，则会在更新日志中添加以下文本：：" -#~ msgid "You are ready now. Enjoy learning in a federated way!" -#~ msgstr "您现在已经准备就绪。尽情享受联邦学习的乐趣吧！" +#~ msgid "" +#~ "If the ``### Changelog entry`` section" +#~ " contains a description (and no " +#~ "token), the following text will be " +#~ "added to the changelog::" +#~ msgstr "如果 ``#### Changelog entry`` 部分包含描述（但没有标记），则会在更新日志中添加以下文本：：" -#~ msgid "Differential privacy" -#~ msgstr "差别隐私" +#~ msgid "" +#~ "If the ``### Changelog entry`` section" +#~ " contains ````, nothing will change" +#~ " in the changelog." +#~ msgstr "如果 ``#### Changelog entry`` 部分包含 ````，更新日志中将不会有任何更改。" #~ msgid "" -#~ "Flower provides differential privacy (DP) " -#~ "wrapper classes for the easy integration" -#~ " of the central DP guarantees " -#~ "provided by DP-FedAvg into training " -#~ "pipelines defined in any of the " -#~ "various ML frameworks that Flower is " -#~ "compatible with." -#~ msgstr "" -#~ "Flower 提供了差分隐私 (DP) 封装类，可将 DP-FedAvg " -#~ "提供的核心 DP 轻松集成到 Flower 兼容的各种 ML " -#~ "框架中定义的训练模式中。" +#~ "If the ``### Changelog entry`` section" +#~ " contains ````, the following text" +#~ " will be added to the changelog::" +#~ msgstr "如果 ``### Changelog entry`` 部分包含 ````，则会在更新日志中添加以下文本：：" #~ msgid "" -#~ "Please note that these components are" -#~ " still experimental; the correct " -#~ "configuration of DP for a specific " -#~ "task is still an unsolved problem." -#~ msgstr "请注意，这些组件仍处于试验阶段，如何为特定任务正确配置 DP 仍是一个尚未解决的问题。" +#~ "If the ``### Changelog entry`` section" +#~ " contains ````, the following " +#~ "text will be added to the " +#~ "changelog::" +#~ msgstr "如果``### 更新日志条目``部分包含``<基准线>``，则会在更新日志中添加以下文本：：" #~ msgid "" -#~ "The name DP-FedAvg is misleading " -#~ "since it can be applied on top " -#~ "of any FL algorithm that conforms " -#~ "to the general structure prescribed by" -#~ " the FedOpt family of algorithms." -#~ msgstr "DP-FedAvg 这个名称容易引起误解，因为它可以应用于任何符合 FedOpt 系列算法规定的一般结构的 FL 算法之上。" +#~ "If the ``### Changelog entry`` section" +#~ " contains ````, the following " +#~ "text will be added to the " +#~ "changelog::" +#~ msgstr "如果``### 更新日志条目``部分包含``<示例>``，则会在更新日志中添加以下文本：：" -#~ msgid "DP-FedAvg" -#~ msgstr "DP-FedAvg" +#~ msgid "" +#~ "If the ``### Changelog entry`` section" +#~ " contains ````, the following text " +#~ "will be added to the changelog::" +#~ msgstr "如果``### 更新日志条目``部分包含````，则会在更新日志中添加以下文本：：" #~ msgid "" -#~ "DP-FedAvg, originally proposed by " -#~ "McMahan et al. [mcmahan]_ and extended" -#~ " by Andrew et al. [andrew]_, is " -#~ "essentially FedAvg with the following " -#~ "modifications." -#~ msgstr "DP-FedAvg 最初由McMahan等人提出，并由Andrew等人加以扩展。" +#~ "If the ``### Changelog entry`` section" +#~ " contains ````, the following " +#~ "text will be added to the " +#~ "changelog::" +#~ msgstr "如果 ``### Changelog entry`` 部分包含 ````，则会在更新日志中添加以下文本：：" #~ msgid "" -#~ "**Clipping** : The influence of each " -#~ "client's update is bounded by clipping" -#~ " it. This is achieved by enforcing" -#~ " a cap on the L2 norm of " -#~ "the update, scaling it down if " -#~ "needed." -#~ msgstr "**裁剪** ： 裁剪会影响到每个客户端的模型参数。具体做法是对参数的 L2 准则设置上限，必要时将其缩减。" +#~ "Note that only one token must be" +#~ " provided, otherwise, only the first " +#~ "action (in the order listed above), " +#~ "will be performed." +#~ msgstr "请注意，必须只提供一个标记，否则将只执行第一个操作（按上述顺序）。" + +#~ msgid "Example: MXNet - Run MXNet Federated" +#~ msgstr "示例： MXNet - 运行联邦式 MXNet" #~ msgid "" -#~ "**Noising** : Gaussian noise, calibrated " -#~ "to the clipping threshold, is added " -#~ "to the average computed at the " -#~ "server." -#~ msgstr "**噪声** ： 在服务器计算出的平均值中加入高斯噪声，该噪声根据剪切阈值进行校准。" +#~ "This tutorial will show you how to" +#~ " use Flower to build a federated " +#~ "version of an existing MXNet workload." +#~ " We are using MXNet to train a" +#~ " Sequential model on the MNIST " +#~ "dataset. We will structure the example" +#~ " similar to our `PyTorch - From " +#~ "Centralized To Federated " +#~ "`_ walkthrough. " +#~ "MXNet and PyTorch are very similar " +#~ "and a very good comparison between " +#~ "MXNet and PyTorch is given `here " +#~ "`_. First, " +#~ "we build a centralized training approach" +#~ " based on the `Handwritten Digit " +#~ "Recognition " +#~ "`_" +#~ " tutorial. Then, we build upon the" +#~ " centralized training code to run the" +#~ " training in a federated fashion." +#~ msgstr "" +#~ "本教程将向您展示如何使用 Flower 构建现有 MXNet 的联学习版本。我们将使用" +#~ " MXNet 在 MNIST 数据集上训练一个序列模型。另外，我们将采用与我们的 " +#~ "`PyTorch - 从集中式到联邦式 " +#~ "`_ 教程类似的示例结构。MXNet" +#~ " 和 PyTorch 非常相似，参考 `此处 " +#~ "`_对 MXNet " +#~ "和 PyTorch 进行了详细的比较。首先，我们根据 `手写数字识别 " +#~ "`" +#~ " 教程 建立了集中式训练方法。然后，我们在集中式训练代码的基础上，以联邦方式运行训练。" #~ msgid "" -#~ "The distribution of the update norm " -#~ "has been shown to vary from " -#~ "task-to-task and to evolve as " -#~ "training progresses. This variability is " -#~ "crucial in understanding its impact on" -#~ " differential privacy guarantees, emphasizing " -#~ "the need for an adaptive approach " -#~ "[andrew]_ that continuously adjusts the " -#~ "clipping threshold to track a " -#~ "prespecified quantile of the update norm" -#~ " distribution." -#~ msgstr "事实证明，参数更新准则的分布会随着任务的不同而变化，并随着训练的进展而演变。因此，我们采用了一种自适应方法，该方法会不断调整剪切阈值，以跟踪参数更新准则分布的预设量化值。" +#~ "Before we start setting up our " +#~ "MXNet example, we install the " +#~ ":code:`mxnet` and :code:`flwr` packages:" +#~ msgstr "在开始设置 MXNet 示例之前，我们先安装 :code:`mxnet` 和 :code:`flwr` 软件包：" -#~ msgid "Simplifying Assumptions" -#~ msgstr "简化假设" +#~ msgid "MNIST Training with MXNet" +#~ msgstr "使用 MXNet 进行 MNIST 训练" #~ msgid "" -#~ "We make (and attempt to enforce) a" -#~ " number of assumptions that must be" -#~ " satisfied to ensure that the " -#~ "training process actually realizes the " -#~ ":math:`(\\epsilon, \\delta)` guarantees the " -#~ "user has in mind when configuring " -#~ "the setup." +#~ "We begin with a brief description " +#~ "of the centralized training code based" +#~ " on a :code:`Sequential` model. If " +#~ "you want a more in-depth " +#~ "explanation of what's going on then " +#~ "have a look at the official `MXNet" +#~ " tutorial " +#~ "`_." #~ msgstr "" -#~ "我们提出（并试图执行）了一系列必须满足的假设，以确保训练过程真正实现用户在配置设置时所定的 " -#~ ":math:`(\\epsilon,\\delta)` 。" +#~ "首先，我们将简要介绍基于 :code:`Sequential` " +#~ "模型的集中式训练代码。如果您想获得更深入的解释，请参阅官方的 `MXNet教程 " +#~ "`_。" #~ msgid "" -#~ "**Fixed-size subsampling** :Fixed-size " -#~ "subsamples of the clients must be " -#~ "taken at each round, as opposed to" -#~ " variable-sized Poisson subsamples." -#~ msgstr "** 固定大小的子样本** :与可变大小的泊松分布子样本相比，每轮必须抽取固定大小的客户端子样本。" +#~ "Let's create a new file " +#~ "called:code:`mxnet_mnist.py` with all the " +#~ "components required for a traditional " +#~ "(centralized) MNIST training. First, the " +#~ "MXNet package :code:`mxnet` needs to be" +#~ " imported. You can see that we " +#~ "do not yet import the :code:`flwr` " +#~ "package for federated learning. This " +#~ "will be done later." +#~ msgstr "" +#~ "让我们创建一个名为:code:`mxnet_mnist.py`的新文件，其中包含传统（集中式）MNIST " +#~ "训练所需的所有组件。首先，需要导入 MXNet 包 " +#~ ":code:`mxnet`。您可以看到，我们尚未导入用于联合学习的 :code:`flwr` 包，这将在稍后完成。" #~ msgid "" -#~ "**Unweighted averaging** : The contributions" -#~ " from all the clients must weighted" -#~ " equally in the aggregate to " -#~ "eliminate the requirement for the server" -#~ " to know in advance the sum of" -#~ " the weights of all clients available" -#~ " for selection." -#~ msgstr "**非加权平均**： 所有客户端的贡献必须加权相等，这样服务器就不需要事先知道所有客户的权重总和。" +#~ "The :code:`load_data()` function loads the " +#~ "MNIST training and test sets." +#~ msgstr ":code:`load_data()` 函数加载 MNIST 训练集和测试集。" #~ msgid "" -#~ "**No client failures** : The set " -#~ "of available clients must stay constant" -#~ " across all rounds of training. In" -#~ " other words, clients cannot drop out" -#~ " or fail." -#~ msgstr "**没有失败的客户端** ： 在各轮训练中，可用客户端的数量必须保持不变。换句话说，客户端不能退出或失败。" +#~ "As already mentioned, we will use " +#~ "the MNIST dataset for this machine " +#~ "learning workload. The model architecture " +#~ "(a very simple :code:`Sequential` model) " +#~ "is defined in :code:`model()`." +#~ msgstr "" +#~ "如前所述，我们将使用 MNIST 数据集进行机器学习。模型架构（一个非常简单的 " +#~ ":code:`Sequential` 模型）在 :code:`model()` 中定义。" #~ msgid "" -#~ "The first two are useful for " -#~ "eliminating a multitude of complications " -#~ "associated with calibrating the noise to" -#~ " the clipping threshold, while the " -#~ "third one is required to comply " -#~ "with the assumptions of the privacy " -#~ "analysis." -#~ msgstr "前两种方法有助于消除将噪声校准为削波阈值所带来的诸多复杂问题，而第三种方法则需要符合隐私分析的假设。" +#~ "We now need to define the training" +#~ " (function :code:`train()`) which loops " +#~ "over the training set and measures " +#~ "the loss for each batch of " +#~ "training examples." +#~ msgstr "现在，我们需要定义训练函数（ :code:`train()`），该函数在训练集上循环训练，并计算每批训练示例的损失值。" #~ msgid "" -#~ "These restrictions are in line with " -#~ "constraints imposed by Andrew et al. " -#~ "[andrew]_." -#~ msgstr "这些限制与 Andrew 等人所施加的限制一致。" - -#~ msgid "Customizable Responsibility for Noise injection" -#~ msgstr "可定制的噪声注入" +#~ "The evaluation of the model is " +#~ "defined in function :code:`test()`. The " +#~ "function loops over all test samples " +#~ "and measures the loss and accuracy " +#~ "of the model based on the test " +#~ "dataset." +#~ msgstr "模型的评估在函数 :code:`test()` 中定义。该函数循环遍历所有测试样本，并根据测试数据集计算模型的损失值和准确度。" #~ msgid "" -#~ "In contrast to other implementations " -#~ "where the addition of noise is " -#~ "performed at the server, you can " -#~ "configure the site of noise injection" -#~ " to better match your threat model." -#~ " We provide users with the " -#~ "flexibility to set up the training " -#~ "such that each client independently adds" -#~ " a small amount of noise to the" -#~ " clipped update, with the result that" -#~ " simply aggregating the noisy updates " -#~ "is equivalent to the explicit addition" -#~ " of noise to the non-noisy " -#~ "aggregate at the server." -#~ msgstr "与其他在服务器上添加噪声的实现方法不同，您可以配置噪声注入的位置，以便更好地匹配您的威胁模型。我们为用户提供了设置训练的灵活性，使每个客户端都能独立地为剪切参数更新添加少量噪声，这样，只需聚合噪声更新，就相当于在服务器上为非噪声聚合添加噪声了。" +#~ "Having defined the data loading, model" +#~ " architecture, training, and evaluation we" +#~ " can put everything together and " +#~ "train our model on MNIST. Note " +#~ "that the GPU/CPU device for the " +#~ "training and testing is defined within" +#~ " the :code:`ctx` (context)." +#~ msgstr "" +#~ "在定义了数据加载、模型架构、训练和评估之后，我们就可以把所有放在一起，在 MNIST " +#~ "上训练我们的模型了。请注意，用于训练和测试的 GPU/CPU 设备是在 :code:`ctx`中定义的。" + +#~ msgid "You can now run your (centralized) MXNet machine learning workload:" +#~ msgstr "现在，您可以运行（集中式）MXNet 机器学习工作：" #~ msgid "" -#~ "To be precise, if we let :math:`m`" -#~ " be the number of clients sampled " -#~ "each round and :math:`\\sigma_\\Delta` be " -#~ "the scale of the total Gaussian " -#~ "noise that needs to be added to" -#~ " the sum of the model updates, " -#~ "we can use simple maths to show" -#~ " that this is equivalent to each " -#~ "client adding noise with scale " -#~ ":math:`\\sigma_\\Delta/\\sqrt{m}`." +#~ "So far this should all look fairly" +#~ " familiar if you've used MXNet (or" +#~ " even PyTorch) before. Let's take the" +#~ " next step and use what we've " +#~ "built to create a simple federated " +#~ "learning system consisting of one server" +#~ " and two clients." #~ msgstr "" -#~ "准确地说，我们假设每轮采样的客户端数量为:math:`m`，:math:`\\sigma_\\Delta` " -#~ "为需要添加到模型更新总和中的总高斯噪声的规模，我们就可以用简单的数学方法证明了，这相当于每个客户端都添加了规模为 " -#~ ":math:`\\sigma_\\Delta/\\sqrt{m}` 的噪声。" +#~ "到目前为止，如果你以前使用过 MXNet（甚至 " +#~ "PyTorch），这一切看起来应该相当熟悉。下一步，让我们利用已构建的内容创建一个简单联邦学习系统（由一个服务器和两个客户端组成）。" -#~ msgid "Wrapper-based approach" -#~ msgstr "基于封装的方法" +#~ msgid "MXNet meets Flower" +#~ msgstr "MXNet 结合 Flower" #~ msgid "" -#~ "Introducing DP to an existing workload" -#~ " can be thought of as adding an" -#~ " extra layer of security around it." -#~ " This inspired us to provide the " -#~ "additional server and client-side logic" -#~ " needed to make the training process" -#~ " differentially private as wrappers for " -#~ "instances of the :code:`Strategy` and " -#~ ":code:`NumPyClient` abstract classes respectively." -#~ " This wrapper-based approach has the" -#~ " advantage of being easily composable " -#~ "with other wrappers that someone might" -#~ " contribute to the Flower library in" -#~ " the future, e.g., for secure " -#~ "aggregation. Using Inheritance instead can " -#~ "be tedious because that would require" -#~ " the creation of new sub- classes " -#~ "every time a new class implementing " -#~ ":code:`Strategy` or :code:`NumPyClient` is " -#~ "defined." +#~ "So far, it was not easily possible" +#~ " to use MXNet workloads for federated" +#~ " learning because federated learning is " +#~ "not supported in MXNet. Since Flower " +#~ "is fully agnostic towards the underlying" +#~ " machine learning framework, it can " +#~ "be used to federated arbitrary machine" +#~ " learning workloads. This section will " +#~ "show you how Flower can be used" +#~ " to federate our centralized MXNet " +#~ "workload." #~ msgstr "" -#~ "在现有工作负载中引入 DP " -#~ "可以被认为是在其周围增加了一层额外的安全性。受此启发，我们提供了额外的服务器端和客户端逻辑，分别作为 " -#~ ":code:`Strategy` 和 :code:`NumPyClient` " -#~ "抽象类实例的封装器，使训练过程具有不同的隐私性。这种基于封装器的方法的优点是可以很容易地与将来有人贡献给 Flower " -#~ "的其他封装器（例如用于安全聚合的封装器）进行组合。使用继承可能会比较繁琐，因为每次定义实现 :code:`Strategy`" -#~ " 或 :code:`NumPyClient` 的新类时，都需要创建新的子类。" +#~ "由于 MXNet 目前不支持联邦学习，因此无法轻松地直接将 MXNet " +#~ "用于联邦学习之中。Flower 与底层机器学习框架完全无关，因此它可用于任意联邦式机器学习工作。本节将向你展示如何使用 " +#~ "Flower 将我们的集中式 MXNet 改为联邦式训练。" #~ msgid "" -#~ "The first version of our solution " -#~ "was to define a decorator whose " -#~ "constructor accepted, among other things, " -#~ "a boolean-valued variable indicating " -#~ "whether adaptive clipping was to be " -#~ "enabled or not. We quickly realized " -#~ "that this would clutter its " -#~ ":code:`__init__()` function with variables " -#~ "corresponding to hyperparameters of adaptive" -#~ " clipping that would remain unused " -#~ "when it was disabled. A cleaner " -#~ "implementation could be achieved by " -#~ "splitting the functionality into two " -#~ "decorators, :code:`DPFedAvgFixed` and " -#~ ":code:`DPFedAvgAdaptive`, with the latter sub-" -#~ " classing the former. The constructors " -#~ "for both classes accept a boolean " -#~ "parameter :code:`server_side_noising`, which, as " -#~ "the name suggests, determines where " -#~ "noising is to be performed." +#~ "The concept to federate an existing " +#~ "workload is always the same and " +#~ "easy to understand. We have to " +#~ "start a *server* and then use the" +#~ " code in :code:`mxnet_mnist.py` for the " +#~ "*clients* that are connected to the " +#~ "*server*. The *server* sends model " +#~ "parameters to the clients. The *clients*" +#~ " run the training and update the " +#~ "parameters. The updated parameters are " +#~ "sent back to the *server* which " +#~ "averages all received parameter updates. " +#~ "This describes one round of the " +#~ "federated learning process and we repeat" +#~ " this for multiple rounds." #~ msgstr "" -#~ "我们的第一版解决方案是定义一个装饰器，其构造函数接受一个布尔值变量，表示是否启用自适应剪裁。我们很快意识到，这样会使其 " -#~ ":code:`__init__()` " -#~ "函数中与自适应裁剪超参数相对应的变量变得杂乱无章，而这些变量在自适应裁剪被禁用时将保持未使用状态。要实现更简洁的功能，可以将该功能拆分为两个装饰器，即" -#~ " :code:`DPFedAvgFixed` 和 " -#~ ":code:`DPFedAvgAdaptive`，后者是前者的子类。这两个类的构造函数都接受一个布尔参数 " -#~ ":code:`server_side_noising`，顾名思义，它决定了在哪里加噪声。" +#~ "将现有模型框架联邦化的概念始终是相同的，也很容易理解。我们必须启动一个*服务器*，然后对连接到*服务器*的*客户端*使用 " +#~ ":code:`mxnet_mnist.py`中的代码。*服务器*向客户端发送模型参数，然后*客户端*运行训练并更新参数。更新后的参数被发回*服务器*，然后会对所有收到的参数更新进行平均聚合。以上描述的是一轮联邦学习过程，我们将重复进行多轮学习。" #~ msgid "" -#~ "The server-side capabilities required " -#~ "for the original version of DP-" -#~ "FedAvg, i.e., the one which performed" -#~ " fixed clipping, can be completely " -#~ "captured with the help of wrapper " -#~ "logic for just the following two " -#~ "methods of the :code:`Strategy` abstract " -#~ "class." +#~ "Finally, we will define our *client* " +#~ "logic in :code:`client.py` and build " +#~ "upon the previously defined MXNet " +#~ "training in :code:`mxnet_mnist.py`. Our " +#~ "*client* needs to import :code:`flwr`, " +#~ "but also :code:`mxnet` to update the " +#~ "parameters on our MXNet model:" #~ msgstr "" -#~ "只需对 :code:`Strategy` 抽象类的以下两个方法进行封装，就能完全捕获 DP-" -#~ "FedAvg 原始版本（即执行固定剪裁的版本）所需的服务器端功能。" +#~ "最后，我们将在 :code:`client.py` 中定义我们的 *client* " +#~ "逻辑，并以之前在 :code:`mxnet_mnist.py` 中定义的 MXNet " +#~ "训练为基础。我们的 *client* 不仅需要导入 :code:`flwr`，还需要导入 " +#~ ":code:`mxnet`，以更新 MXNet 模型的参数：" #~ msgid "" -#~ ":code:`configure_fit()` : The config " -#~ "dictionary being sent by the wrapped " -#~ ":code:`Strategy` to each client needs to" -#~ " be augmented with an additional " -#~ "value equal to the clipping threshold" -#~ " (keyed under :code:`dpfedavg_clip_norm`) and," -#~ " if :code:`server_side_noising=true`, another one" -#~ " equal to the scale of the " -#~ "Gaussian noise that needs to be " -#~ "added at the client (keyed under " -#~ ":code:`dpfedavg_noise_stddev`). This entails " -#~ "*post*-processing of the results returned " -#~ "by the wrappee's implementation of " -#~ ":code:`configure_fit()`." +#~ "Implementing a Flower *client* basically " +#~ "means implementing a subclass of either" +#~ " :code:`flwr.client.Client` or " +#~ ":code:`flwr.client.NumPyClient`. Our implementation " +#~ "will be based on " +#~ ":code:`flwr.client.NumPyClient` and we'll call " +#~ "it :code:`MNISTClient`. :code:`NumPyClient` is " +#~ "slightly easier to implement than " +#~ ":code:`Client` if you use a framework" +#~ " with good NumPy interoperability (like " +#~ "PyTorch or MXNet) because it avoids " +#~ "some of the boilerplate that would " +#~ "otherwise be necessary. :code:`MNISTClient` " +#~ "needs to implement four methods, two " +#~ "methods for getting/setting model parameters," +#~ " one method for training the model," +#~ " and one method for testing the " +#~ "model:" #~ msgstr "" -#~ ":code:`configure_fit()` ：由封装的 :code:`Strategy` " -#~ "发送到每个客户端的配置字典需要使用等于裁剪阈值的附加值（在 :code:`dpfedavg_clip_norm` " -#~ "下键入）进行扩充。并且，如果 " -#~ "server_side_noising=true，则另一个值等于需要在客户端添加的高斯噪声的大小（在 " -#~ "dpfedavg_noise_stddev 下键入）。这需要对封装后的configure_fit() " -#~ "所返回的结果进行后处理。" +#~ "实现 Flower *client*基本上意味着要实现 " +#~ ":code:`flwr.client.Client` 或 " +#~ ":code:`flwr.client.NumPyClient` 的子类。我们的代码实现将基于 " +#~ ":code:`flwr.client.NumPyClient`，并将其命名为 " +#~ ":code:`MNISTClient`。如果使用具有良好 NumPy 互操作性的框架（如 PyTorch" +#~ " 或 MXNet），:code:`NumPyClient` 比 " +#~ ":code:`Client`更容易实现，因为它避免了一些不必要的操作。:code:`MNISTClient` " +#~ "需要实现四个方法，两个用于获取/设置模型参数，一个用于训练模型，一个用于测试模型：" + +#~ msgid "transform MXNet :code:`NDArray`'s to NumPy :code:`ndarray`'s" +#~ msgstr "将 MXNet :code:`NDArray` 转换为 NumPy :code:`ndarray`" #~ msgid "" -#~ ":code:`aggregate_fit()`: We check whether any" -#~ " of the sampled clients dropped out" -#~ " or failed to upload an update " -#~ "before the round timed out. In " -#~ "that case, we need to abort the" -#~ " current round, discarding any successful" -#~ " updates that were received, and move" -#~ " on to the next one. On the " -#~ "other hand, if all clients responded " -#~ "successfully, we must force the " -#~ "averaging of the updates to happen " -#~ "in an unweighted manner by intercepting" -#~ " the :code:`parameters` field of " -#~ ":code:`FitRes` for each received update " -#~ "and setting it to 1. Furthermore, " -#~ "if :code:`server_side_noising=true`, each update " -#~ "is perturbed with an amount of " -#~ "noise equal to what it would have" -#~ " been subjected to had client-side" -#~ " noising being enabled. This entails " -#~ "*pre*-processing of the arguments to " -#~ "this method before passing them on " -#~ "to the wrappee's implementation of " -#~ ":code:`aggregate_fit()`." +#~ "The challenging part is to transform " +#~ "the MXNet parameters from :code:`NDArray` " +#~ "to :code:`NumPy Arrays` to make it " +#~ "readable for Flower." #~ msgstr "" -#~ ":code:`aggregate_fit()`： " -#~ "我们会检查是否有任何客户端在本轮超时前退出或未能上传参数更新。在这种情况下，我们需要中止当前一轮，丢弃已收到的所有参数更新，然后继续下一轮。另一方面，如果所有客户端都成功响应，我们就必须通过拦截" -#~ " :code:`FitRes` 的 :code:`parameters` 字段并将其设置为 " -#~ "1，强制以不加权的方式平均更新。此外，如果 " -#~ ":code:`server_side_noising=true`，每次更新都会受到一定量的噪声扰动，其扰动量相当于启用客户端噪声时的扰动量。" -#~ " 这就需要在将本方法的参数传递给封装的 :code:`aggregate_fit()` " -#~ "之前，对参数进行*预*处理。" +#~ "具有挑战性的部分是将 MXNet 参数从 :code:`NDArray` 转换为 " +#~ ":code:`NumPy Arrays` 以便 Flower 可以读取。" #~ msgid "" -#~ "We can't directly change the aggregation" -#~ " function of the wrapped strategy to" -#~ " force it to add noise to the" -#~ " aggregate, hence we simulate client-" -#~ "side noising to implement server-side" -#~ " noising." -#~ msgstr "我们无法直接改变封装策略的聚合函数，迫使它在聚合中添加噪声，因此我们模拟客户端噪声来实现服务器端噪声。" +#~ "The two :code:`NumPyClient` methods " +#~ ":code:`fit` and :code:`evaluate` make use " +#~ "of the functions :code:`train()` and " +#~ ":code:`test()` previously defined in " +#~ ":code:`mxnet_mnist.py`. So what we really " +#~ "do here is we tell Flower through" +#~ " our :code:`NumPyClient` subclass which of" +#~ " our already defined functions to " +#~ "call for training and evaluation. We " +#~ "included type annotations to give you" +#~ " a better understanding of the data" +#~ " types that get passed around." +#~ msgstr "" +#~ "这两个 :code:`NumPyClient` 方法 :code:`fit` 和 " +#~ ":code:`evaluate` 使用了之前在 :code:`mxnet_mnist.py` " +#~ "中定义的函数 :code:`train()` 和 :code:`test()`。因此，我们要做的就是通过" +#~ " :code:`NumPyClient` 子类告知 Flower " +#~ "在训练和评估时要调用哪些已定义的函数。我们加入了类型注解，以便让您更好地理解传递的数据类型。" #~ msgid "" -#~ "These changes have been put together " -#~ "into a class called :code:`DPFedAvgFixed`, " -#~ "whose constructor accepts the strategy " -#~ "being decorated, the clipping threshold " -#~ "and the number of clients sampled " -#~ "every round as compulsory arguments. The" -#~ " user is expected to specify the " -#~ "clipping threshold since the order of" -#~ " magnitude of the update norms is " -#~ "highly dependent on the model being " -#~ "trained and providing a default value" -#~ " would be misleading. The number of" -#~ " clients sampled at every round is" -#~ " required to calculate the amount of" -#~ " noise that must be added to " -#~ "each individual update, either by the" -#~ " server or the clients." +#~ "Having defined data loading, model " +#~ "architecture, training, and evaluation we " +#~ "can put everything together and train" +#~ " our :code:`Sequential` model on MNIST." #~ msgstr "" -#~ "这些变化被整合到一个名为 :code:`DPFedAvgFixed` " -#~ "的类中，其构造函数接受被装饰的策略、剪切阈值和每轮采样的客户数作为必选参数。用户需要指定剪切阈值，因为参数更新规范的数量级在很大程度上取决于正在训练的模型，提供默认值会产生误导。每轮采样的客户端数量是计算服务器或客户在每次参数更新时添加的噪音量所必需的。" +#~ "在定义了数据加载、模型架构、训练和评估之后，我们就可以将所有内容整合在一起，在 MNIST 上训练我们的 " +#~ ":code:`Sequential` 模型。" #~ msgid "" -#~ "The additional functionality required to " -#~ "facilitate adaptive clipping has been " -#~ "provided in :code:`DPFedAvgAdaptive`, a " -#~ "subclass of :code:`DPFedAvgFixed`. It " -#~ "overrides the above-mentioned methods to" -#~ " do the following." +#~ "in each window (make sure that the" +#~ " server is still running before you" +#~ " do so) and see your MXNet " +#~ "project run federated learning across " +#~ "two clients. Congratulations!" +#~ msgstr "确保服务器仍在运行后，然后就能在每个窗口中看到 MXNet 项目在两个客户端上运行联邦学习了。祝贺!" + +#~ msgid "" +#~ "The full source code for this " +#~ "example: `MXNet: From Centralized To " +#~ "Federated (Code) " +#~ "`_. Our " +#~ "example is of course somewhat over-" +#~ "simplified because both clients load the" +#~ " exact same dataset, which isn't " +#~ "realistic. You're now prepared to " +#~ "explore this topic further. How about" +#~ " using a CNN or using a " +#~ "different dataset? How about adding more" +#~ " clients?" #~ msgstr "" -#~ "自适应剪裁所需的附加功能在 :code:`DPFedAvgAdaptive` 中提供，其是 " -#~ ":code:`DPFedAvgFixed` 的子类。它重写了上述方法，以实现以下功能。" +#~ "此示例的完整源代码在：\"MXNet： From Centralized To " +#~ "Federated (Code) " +#~ "`_。当然，我们的示例有些过于简单，因为两个客户端都加载了完全相同的数据集，这并不真实。现在您已经准备好进一步探讨了。使用" +#~ " CNN 或使用不同的数据集会如何？添加更多客户端会如何？" + +#~ msgid "with the following command sequence:" +#~ msgstr "使用以下命令序列：" #~ msgid "" -#~ ":code:`configure_fit()` : It intercepts the" -#~ " config dict returned by " -#~ ":code:`super.configure_fit()` to add the " -#~ "key-value pair " -#~ ":code:`dpfedavg_adaptive_clip_enabled:True` to it, " -#~ "which the client interprets as an " -#~ "instruction to include an indicator bit" -#~ " (1 if update norm <= clipping " -#~ "threshold, 0 otherwise) in the results" -#~ " returned by it." +#~ "In case you are a researcher you" +#~ " might be just fine using the " +#~ "self-signed certificates generated using " +#~ "the scripts which are part of this" +#~ " guide." +#~ msgstr "如果你是一名研究人员，使用本指南中的脚本生成的自签名证书就可以了。" + +#~ msgid "" +#~ "We are now going to show how " +#~ "to write a sever which uses the" +#~ " previously generated scripts." +#~ msgstr "现在，我们将展示如何编写一个使用先前生成的脚本的服务器。" + +#~ msgid "" +#~ "When providing certificates, the server " +#~ "expects a tuple of three certificates." +#~ " :code:`Path` can be used to easily" +#~ " read the contents of those files " +#~ "into byte strings, which is the " +#~ "data type :code:`start_server` expects." #~ msgstr "" -#~ ":code:`configure_fit()`：它截取由 :code:`super.configure_fit()` " -#~ "返回的 config 字典，并在其中添加键-值对 " -#~ ":code:`dpfedavg_adaptive_clip_enabled:True\"，客户端将其解释为在返回结果中包含一个指示位（如果参数更新范式" -#~ " <= 剪裁阈值，则为 1，否则为 0）的指令。" +#~ "在提供证书时，服务器希望得到由三个证书组成的元组。 :code:`Path` " +#~ "可用于轻松地将这些文件的内容读取为字节字符串，这就是 :code:`start_server` 期望的数据类型。" + +#~ msgid "Flower server" +#~ msgstr "Flower 服务器" + +#~ msgid "flower-driver-api" +#~ msgstr "flower-driver-api" + +#~ msgid "flower-fleet-api" +#~ msgstr "flower-fleet-api" #~ msgid "" -#~ ":code:`aggregate_fit()` : It follows a " -#~ "call to :code:`super.aggregate_fit()` with one" -#~ " to :code:`__update_clip_norm__()`, a procedure" -#~ " which adjusts the clipping threshold " -#~ "on the basis of the indicator bits" -#~ " received from the sampled clients." -#~ msgstr ":code:`aggregate_fit()`：在调用:code:`super.aggregate_fit()`后，再调用:code:`__update_clip_norm__()`，该过程根据从采样客户端接收到的指示位调整裁剪阈值。" +#~ ":py:obj:`start_driver `\\ " +#~ "\\(\\*\\[\\, server\\_address\\, server\\, ...\\]\\)" +#~ msgstr "" +#~ ":py:obj:`start_driver `\\ " +#~ "\\(\\*\\[\\, server\\_address\\, server\\, ...\\]\\)" + +#~ msgid "Start a Flower Driver API server." +#~ msgstr "启动基于 Ray 的Flower模拟服务器。" #~ msgid "" -#~ "The client-side capabilities required " -#~ "can be completely captured through " -#~ "wrapper logic for just the :code:`fit()`" -#~ " method of the :code:`NumPyClient` abstract" -#~ " class. To be precise, we need " -#~ "to *post-process* the update computed" -#~ " by the wrapped client to clip " -#~ "it, if necessary, to the threshold " -#~ "value supplied by the server as " -#~ "part of the config dictionary. In " -#~ "addition to this, it may need to" -#~ " perform some extra work if either" -#~ " (or both) of the following keys " -#~ "are also present in the dict." +#~ ":py:obj:`Driver `\\ " +#~ "\\(\\[driver\\_service\\_address\\, ...\\]\\)" #~ msgstr "" -#~ "客户端所需的功能完全可以通过 :code:`NumPyClient` 抽象类的 " -#~ ":code:`fit()` " -#~ "方法的封装逻辑来实现。准确地说，我们需要对封装客户端计算的参数更新进行处理，以便在必要时将其剪切到服务器作为配置字典的一部分提供的阈值。除此之外，如果配置字典中还存在以下任一（或两个）键，客户端可能还需要执行一些额外的工作。" +#~ "Flower 1.0: ``start_server(..., " +#~ "config=flwr.server.ServerConfig(num_rounds=3, " +#~ "round_timeout=600.0), ...)``" + +#~ msgid "`Driver` class provides an interface to the Driver API." +#~ msgstr "`Driver` 类为驱动程序 API 提供了一个接口。" + +#~ msgid "" +#~ "The IPv4 or IPv6 address of the" +#~ " Driver API server. Defaults to " +#~ "`\"[::]:9091\"`." +#~ msgstr "服务器的 IPv4 或 IPv6 地址。默认为 `\"[::]:8080\"。" + +#~ msgid ":py:obj:`close `\\ \\(\\)" +#~ msgstr "server.strategy.Strategy" + +#~ msgid "Disconnect from the SuperLink if connected." +#~ msgstr "如果已连接，请断开与超级链接的连接。" + +#~ msgid "start\\_driver" +#~ msgstr "启动客户端" + +#~ msgid "" +#~ "The IPv4 or IPv6 address of the" +#~ " Driver API server. Defaults to " +#~ "`\"[::]:8080\"`." +#~ msgstr "服务器的 IPv4 或 IPv6 地址。默认为 `\"[::]:8080\"。" #~ msgid "" -#~ ":code:`dpfedavg_noise_stddev` : Generate and " -#~ "add the specified amount of noise " -#~ "to the clipped update." -#~ msgstr "code:`dpfedavg_noise_stddev`：生成并在剪切参数更新中添加指定数量的噪声。" +#~ "A server implementation, either " +#~ "`flwr.server.Server` or a subclass thereof." +#~ " If no instance is provided, then " +#~ "`start_driver` will create one." +#~ msgstr "服务器实现，可以是 `flwr.server.Server` 或其子类。如果没有提供实例，`start_server` 将创建一个。" #~ msgid "" -#~ ":code:`dpfedavg_adaptive_clip_enabled` : Augment the" -#~ " metrics dict in the :code:`FitRes` " -#~ "object being returned to the server " -#~ "with an indicator bit, calculated as " -#~ "described earlier." +#~ "An implementation of the class " +#~ "`flwr.server.ClientManager`. If no implementation" +#~ " is provided, then `start_driver` will " +#~ "use `flwr.server.SimpleClientManager`." #~ msgstr "" -#~ ":code:`dpfedavg_adaptive_clip_enabled`：在返回给服务器的 :code:`FitRes`" -#~ " 对象中的度量值字典中增加一个指标位，计算方法如前所述。" +#~ "抽象基类 `flwr.server.ClientManager` " +#~ "的实现。如果没有提供实现，`start_server` 将使用 " +#~ "`flwr.server.client_manager.SimpleClientManager`。" -#~ msgid "Performing the :math:`(\\epsilon, \\delta)` analysis" -#~ msgstr "进行 :math:`(epsilon, \\delta)` 分析" +#~ msgid "The Driver object to use." +#~ msgstr "要使用的驱动程序对象。" + +#~ msgid "Starting a driver that connects to an insecure server:" +#~ msgstr "启动不安全的服务器：" + +#~ msgid "Starting a driver that connects to an SSL-enabled server:" +#~ msgstr "启动支持 SSL 的服务器：" #~ msgid "" -#~ "Assume you have trained for :math:`n`" -#~ " rounds with sampling fraction :math:`q`" -#~ " and noise multiplier :math:`z`. In " -#~ "order to calculate the :math:`\\epsilon` " -#~ "value this would result in for a" -#~ " particular :math:`\\delta`, the following " -#~ "script may be used." +#~ ":py:obj:`run_simulation_from_cli " +#~ "`\\ \\(\\)" #~ msgstr "" -#~ "假设您已经训练了 :math:`n` 轮，采样比例为 :math:`q`，噪声乘数为 " -#~ ":math:`z`。为了计算特定 :math:`\\delta` 的 :math:`epsilon`" -#~ " 值，可以使用下面的脚本。" -#~ msgid "Enjoy building more robust and flexible ``ClientApp``s with mods!" +#~ msgid "Run Simulation Engine from the CLI." #~ msgstr "" +#~ msgid "run\\_simulation\\_from\\_cli" +#~ msgstr "运行模拟" + #~ msgid "" -#~ ":py:obj:`ClientApp `\\ " -#~ "\\(client\\_fn\\[\\, mods\\]\\)" -#~ msgstr "" +#~ "Check out this Federated Learning " +#~ "quickstart tutorial for using Flower " +#~ "with MXNet to train a Sequential " +#~ "model on MNIST." +#~ msgstr "查看此联邦学习 快速入门教程，了解如何使用 Flower 和 MXNet 在 MNIST 上训练序列模型。" + +#~ msgid "Quickstart MXNet" +#~ msgstr "快速入门 MXNet" -#~ msgid ":py:obj:`flwr.server.driver `\\" +#~ msgid "" +#~ "MXNet is no longer maintained and " +#~ "has been moved into `Attic " +#~ "`_. As a " +#~ "result, we would encourage you to " +#~ "use other ML frameworks alongside " +#~ "Flower, for example, PyTorch. This " +#~ "tutorial might be removed in future " +#~ "versions of Flower." #~ msgstr "" -#~ msgid "Flower driver SDK." -#~ msgstr "Flower 服务器。" +#~ msgid "" +#~ "In this tutorial, we will learn " +#~ "how to train a :code:`Sequential` model" +#~ " on MNIST using Flower and MXNet." +#~ msgstr "在本教程中，我们将学习如何使用 Flower 和 MXNet 在 MNIST 上训练 :code:`Sequential` 模型。" -#~ msgid "driver" -#~ msgstr "服务器" +#~ msgid "Since we want to use MXNet, let's go ahead and install it:" +#~ msgstr "既然我们要使用 MXNet，那就继续安装吧：" #~ msgid "" -#~ ":py:obj:`start_driver `\\ " -#~ "\\(\\*\\[\\, server\\_address\\, server\\, ...\\]\\)" +#~ "Now that we have all our " +#~ "dependencies installed, let's run a " +#~ "simple distributed training with two " +#~ "clients and one server. Our training " +#~ "procedure and network architecture are " +#~ "based on MXNet´s `Hand-written Digit " +#~ "Recognition tutorial " +#~ "`_." #~ msgstr "" +#~ "现在，我们已经安装了所有依赖项，让我们用两个客户端和一个服务器来运行一个简单的分布式训练。我们的训练程序和网络架构基于 " +#~ "MXNet 的 `手写数字识别教程 " +#~ "`_\"。" #~ msgid "" -#~ ":py:obj:`Driver `\\ " -#~ "\\(\\[driver\\_service\\_address\\, ...\\]\\)" -#~ msgstr "" +#~ "In a file called :code:`client.py`, " +#~ "import Flower and MXNet related " +#~ "packages:" +#~ msgstr "在名为 :code:`client.py` 的文件中，导入 Flower 和 MXNet 相关软件包：" + +#~ msgid "In addition, define the device allocation in MXNet with:" +#~ msgstr "此外，还可以在 MXNet 中定义设备分配：" #~ msgid "" -#~ ":py:obj:`GrpcDriver `\\ " -#~ "\\(\\[driver\\_service\\_address\\, ...\\]\\)" +#~ "We use MXNet to load MNIST, a " +#~ "popular image classification dataset of " +#~ "handwritten digits for machine learning. " +#~ "The MXNet utility :code:`mx.test_utils.get_mnist()`" +#~ " downloads the training and test " +#~ "data." #~ msgstr "" +#~ "我们使用 MXNet 加载 MNIST，这是一个用于机器学习的流行手写数字图像分类数据集。MXNet" +#~ " 工具 :code:`mx.test_utils.get_mnist()` 会下载训练和测试数据。" -#~ msgid "`GrpcDriver` provides access to the gRPC Driver API/service." +#~ msgid "" +#~ "Define the training and loss with " +#~ "MXNet. We train the model by " +#~ "looping over the dataset, measure the" +#~ " corresponding loss, and optimize it." +#~ msgstr "用 MXNet 定义训练和损失值。我们在数据集上循环训练模型，测量相应的损失值，并对其进行优化。" + +#~ msgid "" +#~ "Next, we define the validation of " +#~ "our machine learning model. We loop " +#~ "over the test set and measure both" +#~ " loss and accuracy on the test " +#~ "set." +#~ msgstr "接下来，我们定义机器学习模型的验证。我们在测试集上循环，测量测试集上的损失值和准确率。" + +#~ msgid "" +#~ "After defining the training and testing" +#~ " of a MXNet machine learning model," +#~ " we use these functions to implement" +#~ " a Flower client." +#~ msgstr "在定义了 MXNet 机器学习模型的训练和测试后，我们使用这些函数实现了 Flower 客户端。" + +#~ msgid "Our Flower clients will use a simple :code:`Sequential` model:" +#~ msgstr "我们的 Flower 客户端将使用简单的 :code:`Sequential` 模型：" + +#~ msgid "" +#~ "After loading the dataset with " +#~ ":code:`load_data()` we perform one forward " +#~ "propagation to initialize the model and" +#~ " model parameters with :code:`model(init)`. " +#~ "Next, we implement a Flower client." #~ msgstr "" +#~ "使用 :code:`load_data()` 加载数据集后，我们会执行一次前向传播，使用 " +#~ ":code:`model(init)` 初始化模型和模型参数。接下来，我们实现一个 Flower " +#~ "客户端。" -#~ msgid ":py:obj:`get_nodes `\\ \\(\\)" +#~ msgid "" +#~ "Flower provides a convenience class " +#~ "called :code:`NumPyClient` which makes it " +#~ "easier to implement the :code:`Client` " +#~ "interface when your workload uses MXNet." +#~ " Implementing :code:`NumPyClient` usually means" +#~ " defining the following methods " +#~ "(:code:`set_parameters` is optional though):" #~ msgstr "" +#~ "Flower 提供了一个名为 :code:`NumPyClient` 的便捷类，当您的工作负载使用" +#~ " MXNet 时，它可以让您更轻松地实现 :code:`Client` 接口。实现 " +#~ ":code:`NumPyClient` 通常意味着定义以下方法（:code:`set_parameters` " +#~ "是可选的）：" + +#~ msgid "They can be implemented in the following way:" +#~ msgstr "它们可以通过以下方式实现：" + +#~ msgid "" +#~ "We can now create an instance of" +#~ " our class :code:`MNISTClient` and add " +#~ "one line to actually run this " +#~ "client:" +#~ msgstr "现在我们可以创建一个 :code:`MNISTClient` 类的实例，并添加一行来实际运行该客户端：" #~ msgid "" -#~ ":py:obj:`pull_task_res " -#~ "`\\ \\(task\\_ids\\)" +#~ "That's it for the client. We only" +#~ " have to implement :code:`Client` or " +#~ ":code:`NumPyClient` and call " +#~ ":code:`fl.client.start_client()` or " +#~ ":code:`fl.client.start_numpy_client()`. The string " +#~ ":code:`\"0.0.0.0:8080\"` tells the client " +#~ "which server to connect to. In our" +#~ " case we can run the server and" +#~ " the client on the same machine, " +#~ "therefore we use :code:`\"0.0.0.0:8080\"`. If" +#~ " we run a truly federated workload" +#~ " with the server and clients running" +#~ " on different machines, all that " +#~ "needs to change is the " +#~ ":code:`server_address` we pass to the " +#~ "client." #~ msgstr "" +#~ "这就是客户端。我们只需实现 :code:`Client` 或 :code:`NumPyClient`" +#~ " 并调用 :code:`fl.client.start_client()` 或 " +#~ ":code:`fl.client.start_numpy_client()`。字符串 " +#~ ":code:`\"0.0.0.0:8080\"`会告诉客户端要连接的服务器。在本例中，我们可以在同一台机器上运行服务器和客户端，因此我们使用" +#~ " " +#~ ":code:`\"0.0.0.0:8080\"`。如果我们运行的是真正的联邦工作负载，服务器和客户端运行在不同的机器上，那么需要改变的只是传递给客户端的" +#~ " :code:`server_address`。" -#~ msgid "Get task results." -#~ msgstr "汇总训练结果。" +#~ msgid "" +#~ "With both client and server ready, " +#~ "we can now run everything and see" +#~ " federated learning in action. Federated" +#~ " learning systems usually have a " +#~ "server and multiple clients. We " +#~ "therefore have to start the server " +#~ "first:" +#~ msgstr "客户端和服务器都准备就绪后，我们现在就可以运行一切，看看联邦学习的运行情况。联邦学习系统通常有一个服务器和多个客户端。因此，我们必须先启动服务器：" #~ msgid "" -#~ ":py:obj:`push_task_ins " -#~ "`\\ " -#~ "\\(task\\_ins\\_list\\)" +#~ "Congratulations! You've successfully built and" +#~ " run your first federated learning " +#~ "system. The full `source code " +#~ "`_ for this example can " +#~ "be found in :code:`examples/quickstart-mxnet`." #~ msgstr "" +#~ "恭喜您！您已经成功构建并运行了第一个联邦学习系统。本示例的`完整源代码 " +#~ "`_ 可在 :code:`examples/quickstart-" +#~ "mxnet` 中找到。" + +#~ msgid ":code:`load_mnist()`" +#~ msgstr ":code:`load_mnist()`" + +#~ msgid "Loads the MNIST dataset using OpenML" +#~ msgstr "使用 OpenML 加载 MNIST 数据集" + +#~ msgid ":code:`shuffle()`" +#~ msgstr ":code:`shuffle()`" -#~ msgid "Schedule tasks." +#~ msgid "Shuffles data and its label" +#~ msgstr "对数据及其标签进行洗牌" + +#~ msgid ":code:`partition()`" +#~ msgstr ":code:`partition()`" + +#~ msgid "Splits datasets into a number of partitions" +#~ msgstr "将数据集分割成多个分区" + +#~ msgid "" +#~ "We load the MNIST dataset from " +#~ "`OpenML " +#~ "`_, a" +#~ " popular image classification dataset of" +#~ " handwritten digits for machine learning." +#~ " The utility :code:`utils.load_mnist()` downloads" +#~ " the training and test data. The " +#~ "training set is split afterwards into" +#~ " 10 partitions with :code:`utils.partition()`." #~ msgstr "" +#~ "我们从 `OpenML `_ 中加载 " +#~ "MNIST 数据集，这是一个用于机器学习的流行手写数字图像分类数据集。实用程序 " +#~ ":code:`utils.load_mnist()` 下载训练和测试数据。然后使用 " +#~ ":code:`utils.partition()`将训练集分割成 10 个分区。" -#~ msgid "GrpcDriver" +#~ msgid "Let's get stated!" +#~ msgstr "让我们开始吧！" + +#~ msgid "|2b5c62c529f6416f840c594cce062fbb|" #~ msgstr "" -#~ msgid ":py:obj:`connect `\\ \\(\\)" +#~ msgid "|90b334680cb7467d9a04d39b8e8dca9f|" #~ msgstr "" -#~ msgid "Connect to the Driver API." +#~ msgid "|65764ceee89f4335bfd93fd0b115e831|" #~ msgstr "" -#~ msgid "" -#~ ":py:obj:`create_run " -#~ "`\\ \\(req\\)" +#~ msgid "|d97319ec28bb407ea0ab9705e38f3bcf|" #~ msgstr "" -#~ msgid "Request for run ID." -#~ msgstr "Flower 基线申请" +#~ msgid "|11e95ac83a8548d8b3505b4663187d07|" +#~ msgstr "" -#~ msgid "" -#~ ":py:obj:`disconnect " -#~ "`\\ \\(\\)" +#~ msgid "|1dab2f3a23674abc8a6731f20fa10730|" #~ msgstr "" -#~ msgid "Disconnect from the Driver API." +#~ msgid "|7f0ee162da38450788493a21627306f7|" #~ msgstr "" -#~ msgid "" -#~ ":py:obj:`get_nodes `\\" -#~ " \\(req\\)" +#~ msgid "|296a1fb72c514b23b3d8905ff0ff98c6|" #~ msgstr "" -#~ msgid "Get client IDs." -#~ msgstr "返回客户端（本身）。" +#~ msgid "|5b1408eec0d746cdb91162a9107b6089|" +#~ msgstr "" -#~ msgid "" -#~ ":py:obj:`pull_task_res " -#~ "`\\ \\(req\\)" +#~ msgid "|aef19f4b122c4e8d9f4c57f99bcd5dd2|" #~ msgstr "" -#~ msgid "" -#~ ":py:obj:`push_task_ins " -#~ "`\\ \\(req\\)" +#~ msgid "|2881a86d8fc54ba29d96b29fc2819f4a|" #~ msgstr "" -#~ msgid "" -#~ "Flower usage examples used to be " -#~ "bundled with Flower in a package " -#~ "called ``flwr_example``. We are migrating " -#~ "those examples to standalone projects to" -#~ " make them easier to use. All " -#~ "new examples are based in the " -#~ "directory `examples " -#~ "`_." +#~ msgid "|ec1fe880237247e0975f52766775ab84|" #~ msgstr "" -#~ "Flower 的使用示例曾与 Flower 捆绑在一个名为 ``flwr_example``" -#~ " 的软件包中。我们正在将这些示例迁移到独立项目中，以使它们更易于使用。所有新示例都位于目录 `examples " -#~ "`_。" -#~ msgid "Quickstart TensorFlow/Keras" -#~ msgstr "快速入门 TensorFlow/Keras" +#~ msgid "|9fdf048ed58d4467b2718cdf4aaf1ec3|" +#~ msgstr "" -#~ msgid "Legacy Examples (`flwr_example`)" -#~ msgstr "传统示例 (`flwr_example`)" +#~ msgid "|ff726bc5505e432388ee2fdd6ef420b9|" +#~ msgstr "" #~ msgid "" -#~ "The useage examples in `flwr_example` " -#~ "are deprecated and will be removed " -#~ "in the future. New examples are " -#~ "provided as standalone projects in " -#~ "`examples `_." +#~ "Currently, Flower provides two images, a" +#~ " ``base`` image and a ``superlink`` " +#~ "image. The base image, as the name" +#~ " suggests, contains basic dependencies that" +#~ " the SuperLink needs. This includes " +#~ "system dependencies, Python and Python " +#~ "tools. The SuperLink image is based " +#~ "on the base image, but it " +#~ "additionally installs the SuperLink using " +#~ "``pip``." #~ msgstr "" -#~ "在 `flwr_example` 中的使用示例已被弃用，今后将被移除。新示例将作为独立项目在 " -#~ "`examples `_" -#~ " 中提供。" +#~ "目前，Flower " +#~ "提供两个镜像，一个基础镜像和一个服务器镜像。不久还将推出客户端镜像。基础镜像，顾名思义，包含服务器和客户端都需要的基本依赖项。其中包括系统依赖项、Python" +#~ " 和 Python 工具。服务器镜像基于基础镜像，但它会使用 ``pip`` 额外安装" +#~ " Flower 服务器。" -#~ msgid "Extra Dependencies" -#~ msgstr "额外依赖" +#~ msgid "``3.11``" +#~ msgstr "``1.0.0rc1``" -#~ msgid "" -#~ "The core Flower framework keeps a " -#~ "minimal set of dependencies. The " -#~ "examples demonstrate Flower in the " -#~ "context of different machine learning " -#~ "frameworks, so additional dependencies need" -#~ " to be installed before an example" -#~ " can be run." -#~ msgstr "" -#~ "Flower 核心框架只保留了最低限度的依赖项。这些示例在不同机器学习框架的背景下演示了 " -#~ "Flower，因此在运行示例之前需要安装额外的依赖项。" +#~ msgid "Defaults to ``22.04``." +#~ msgstr "默认为 ``22.04``。" -#~ msgid "For PyTorch examples::" -#~ msgstr "PyTorch 示例：：" +#~ msgid "Building the SuperLink image" +#~ msgstr "启动服务器" -#~ msgid "For TensorFlow examples::" -#~ msgstr "TensorFlow 示例：：" +#~ msgid "Defaults to ``flwr/base``." +#~ msgstr "默认为 ``flwr/server``。" -#~ msgid "For both PyTorch and TensorFlow examples::" -#~ msgstr "PyTorch 和 TensorFlow 示例：：" +#~ msgid "The Python version of the base image." +#~ msgstr "基础镜像的存储库名称。" -#~ msgid "" -#~ "Please consult :code:`pyproject.toml` for a" -#~ " full list of possible extras " -#~ "(section :code:`[tool.poetry.extras]`)." -#~ msgstr "" -#~ "请参阅 :code:`pyproject.toml`，了解可能的 extras 的完整列表（章节 " -#~ ":code:`[tool.poems.extras]`）。" +#~ msgid "Defaults to ``py3.11``." +#~ msgstr "默认为 ``22.04``。" -#~ msgid "PyTorch Examples" -#~ msgstr "PyTorch 示例" +#~ msgid "Defaults to ``ubuntu22.04``." +#~ msgstr "默认为 ``py3.11-ubuntu22.04``。" + +#~ msgid "Defaults to ``flwr``." +#~ msgstr "默认为 ``flwr/server``。" #~ msgid "" -#~ "Our PyTorch examples are based on " -#~ "PyTorch 1.7. They should work with " -#~ "other releases as well. So far, we" -#~ " provide the following examples." -#~ msgstr "我们的 PyTorch 示例基于 PyTorch 1.7。它们应该也能在其他版本中使用。到目前为止，我们提供了以下示例。" +#~ "The name of image is ``flwr_superlink``" +#~ " and the tag ``0.1.0``. Remember that" +#~ " the build arguments as well as " +#~ "the name and tag can be adapted" +#~ " to your needs. These values serve" +#~ " as examples only." +#~ msgstr "图像名称为 ``flwr_server``，标记为 ``0.1.0``。请记住，编译参数以及名称和标记都可以根据需要进行调整。这些值仅供参考。" -#~ msgid "CIFAR-10 Image Classification" -#~ msgstr "CIFAR-10 图像分类" +#~ msgid "Creating New Messages" +#~ msgstr "创建新信息" #~ msgid "" -#~ "`CIFAR-10 and CIFAR-100 " -#~ "`_ are " -#~ "popular RGB image datasets. The Flower" -#~ " CIFAR-10 example uses PyTorch to " -#~ "train a simple CNN classifier in a" -#~ " federated learning setup with two " -#~ "clients." -#~ msgstr "" -#~ "CIFAR-10 和 CIFAR-100 " -#~ "``_ 是流行的 RGB" -#~ " 图像数据集。Flower CIFAR-10 示例使用 PyTorch " -#~ "在有两个客户端的联邦学习设置中训练一个简单的 CNN 分类器。" - -#~ msgid "First, start a Flower server:" -#~ msgstr "首先，启动 Flower 服务器：" +#~ "This is a simple guide for " +#~ "creating a new type of message " +#~ "between the server and clients in " +#~ "Flower." +#~ msgstr "这是一个如何用Flower在服务器和客户端之间创建新类型的信息的简要指导。" -#~ msgid "$ ./src/py/flwr_example/pytorch_cifar/run-server.sh" -#~ msgstr "$ ./src/py/flwr_example/pytorch_cifar/run-server.sh" +#~ msgid "" +#~ "Let's suppose we have the following " +#~ "example functions in :code:`server.py` and " +#~ ":code:`numpy_client.py`..." +#~ msgstr "假设我们在脚本code:`server.py`和code:`numpy_client.py`中有以下的示例函数..." -#~ msgid "Then, start the two clients in a new terminal window:" -#~ msgstr "然后，在新的终端窗口中启动两个客户端：" +#~ msgid "Server's side:" +#~ msgstr "在服务器端：" -#~ msgid "$ ./src/py/flwr_example/pytorch_cifar/run-clients.sh" -#~ msgstr "$ ./src/py/flwr_example/pytorch_cifar/run-clients.sh" +#~ msgid "Client's side:" +#~ msgstr "在客户端：" -#~ msgid "For more details, see :code:`src/py/flwr_example/pytorch_cifar`." -#~ msgstr "更多详情，请参阅 :code:`src/py/flwr_example/pytorch_cifar`。" +#~ msgid "" +#~ "Let's now see what we need to " +#~ "implement in order to get this " +#~ "simple function between the server and" +#~ " client to work!" +#~ msgstr "现在让我们来看看，为了让服务器和客户端之间的这个简单的函数正常工作，我们需要实现哪些功能！" -#~ msgid "ImageNet-2012 Image Classification" -#~ msgstr "ImageNet-2012 图像分类" +#~ msgid "Message Types for Protocol Buffers" +#~ msgstr "协议缓冲区的信息类型" #~ msgid "" -#~ "`ImageNet-2012 `_ is " -#~ "one of the major computer vision " -#~ "datasets. The Flower ImageNet example " -#~ "uses PyTorch to train a ResNet-18 " -#~ "classifier in a federated learning setup" -#~ " with ten clients." +#~ "The first thing we need to do " +#~ "is to define a message type for" +#~ " the RPC system in :code:`transport.proto`." +#~ " Note that we have to do it " +#~ "for both the request and response " +#~ "messages. For more details on the " +#~ "syntax of proto3, please see the " +#~ "`official documentation `_." #~ msgstr "" -#~ "ImageNet-2012 `_ " -#~ "是主要的计算机视觉数据集之一。Flower ImageNet 示例使用 PyTorch " -#~ "在有十个客户端的联邦学习设置中训练 ResNet-18 分类器。" +#~ "我们需要做的第一件事是在脚本code:`transport.proto`中定义 RPC " +#~ "系统的消息类型。请注意，我们必须对请求信息和响应信息都这样做。有关 proto3 语法的更多详情，请参阅官方文档" +#~ " `_。" -#~ msgid "$ ./src/py/flwr_example/pytorch_imagenet/run-server.sh" -#~ msgstr "$ ./src/py/flwr_example/pytorch_imagenet/run-server.sh" +#~ msgid "Within the :code:`ServerMessage` block:" +#~ msgstr "在 :code:`ServerMessage` 代码块中：" -#~ msgid "$ ./src/py/flwr_example/pytorch_imagenet/run-clients.sh" -#~ msgstr "$ ./src/py/flwr_example/pytorch_imagenet/run-clients.sh" +#~ msgid "Within the ClientMessage block:" +#~ msgstr "在 ClientMessage 代码块中：" -#~ msgid "For more details, see :code:`src/py/flwr_example/pytorch_imagenet`." -#~ msgstr "更多详情，请参阅 :code:`src/py/flwr_example/pytorch_imagenet`。" +#~ msgid "" +#~ "Make sure to also add a field " +#~ "of the newly created message type " +#~ "in :code:`oneof msg`." +#~ msgstr "确保在 :code:`oneof msg` 中也添加一个新创建的消息类型字段。" -#~ msgid "TensorFlow Examples" -#~ msgstr "TensorFlow 示例" +#~ msgid "Once that is done, we will compile the file with:" +#~ msgstr "完成后，我们将使用：" -#~ msgid "" -#~ "Our TensorFlow examples are based on " -#~ "TensorFlow 2.0 or newer. So far, " -#~ "we provide the following examples." -#~ msgstr "我们的 TensorFlow 示例基于 TensorFlow 2.0 或更新版本。到目前为止，我们提供了以下示例。" +#~ msgid "If it compiles successfully, you should see the following message:" +#~ msgstr "如果编译成功，你应该会看到以下信息：" -#~ msgid "Fashion-MNIST Image Classification" -#~ msgstr "Fashion-MNIST 图像分类" +#~ msgid "Serialization and Deserialization Functions" +#~ msgstr "序列化和反序列化函数" #~ msgid "" -#~ "`Fashion-MNIST `_ is often used as " -#~ "the \"Hello, world!\" of machine " -#~ "learning. We follow this tradition and" -#~ " provide an example which samples " -#~ "random local datasets from Fashion-MNIST" -#~ " and trains a simple image " -#~ "classification model over those partitions." +#~ "Our next step is to add functions" +#~ " to serialize and deserialize Python " +#~ "datatypes to or from our defined " +#~ "RPC message types. You should add " +#~ "these functions in :code:`serde.py`." #~ msgstr "" -#~ "`Fashion-MNIST `_ 经常被用作机器学习的 \"你好，世界！\"。我们遵循这一传统" -#~ "，提供了一个从Fashion-MNIST 中随机抽样本地数据集的示例，并在这些分区上训练一个简单的图像分类模型。" +#~ "下一步是添加函数，以便将 Python 数据类型序列化和反序列化为我们定义的 RPC " +#~ "消息类型或从我们定义的 RPC 消息类型反序列化和反序列化 Python 数据类型。您应该在" +#~ " :code:`serde.py` 中添加这些函数。" -#~ msgid "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-server.sh" -#~ msgstr "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-server.sh" +#~ msgid "The four functions:" +#~ msgstr "四种函数：" -#~ msgid "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-clients.sh" -#~ msgstr "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-clients.sh" +#~ msgid "Sending the Message from the Server" +#~ msgstr "从服务器发送信息" #~ msgid "" -#~ "For more details, see " -#~ ":code:`src/py/flwr_example/tensorflow_fashion_mnist`." -#~ msgstr "更多详情，请参阅 :code:`src/py/flwr_example/tensorflow_fashion_mnist`。" +#~ "Now write the request function in " +#~ "your Client Proxy class (e.g., " +#~ ":code:`grpc_client_proxy.py`) using the serde " +#~ "functions you just created:" +#~ msgstr "现在，在客户端代理类（例如 :code:`grpc_client_proxy.py`）中使用刚才创建的 serde 函数编写请求函数：" + +#~ msgid "Receiving the Message by the Client" +#~ msgstr "由客户端接收信息" #~ msgid "" -#~ "MXNet is no longer maintained and " -#~ "has been moved into `Attic " -#~ "`_. As a " -#~ "result, we would encourage you to " -#~ "use other ML frameworks alongise Flower," -#~ " for example, PyTorch. This tutorial " -#~ "might be removed in future versions " -#~ "of Flower." +#~ "Last step! Modify the code in " +#~ ":code:`message_handler.py` to check the field" +#~ " of your message and call the " +#~ ":code:`example_response` function. Remember to " +#~ "use the serde functions!" #~ msgstr "" +#~ "最后一步 修改 :code:`message_handler.py` 中的代码，检查信息的字段并调用" +#~ " :code:`example_response` 函数。记住使用 serde 函数！" -#~ msgid "|31e4b1afa87c4b968327bbeafbf184d4|" -#~ msgstr "" +#~ msgid "Within the handle function:" +#~ msgstr "在句柄函数内：" -#~ msgid "|c9d935b4284e4c389a33d86b33e07c0a|" -#~ msgstr "" +#~ msgid "And add a new function:" +#~ msgstr "并增加一个新函数：" -#~ msgid "|00727b5faffb468f84dd1b03ded88638|" -#~ msgstr "" +#~ msgid "Hopefully, when you run your program you will get the intended result!" +#~ msgstr "希望您在运行程序时能得到预期的结果！" -#~ msgid "|daf0cf0ff4c24fd29439af78416cf47b|" -#~ msgstr "" +#~ msgid ":py:obj:`run_driver_api `\\ \\(\\)" +#~ msgstr ":py:obj:`run_driver_api `\\ \\(\\)" -#~ msgid "|9f093007080d471d94ca90d3e9fde9b6|" -#~ msgstr "" +#~ msgid "Run Flower server (Driver API)." +#~ msgstr "flower-driver-api" -#~ msgid "|46a26e6150e0479fbd3dfd655f36eb13|" -#~ msgstr "" +#~ msgid ":py:obj:`run_fleet_api `\\ \\(\\)" +#~ msgstr ":py:obj:`run_fleet_api `\\ \\(\\)" -#~ msgid "|3daba297595c4c7fb845d90404a6179a|" -#~ msgstr "" +#~ msgid "Run Flower server (Fleet API)." +#~ msgstr "Flower 服务器。" -#~ msgid "|5769874fa9c4455b80b2efda850d39d7|" -#~ msgstr "" +#~ msgid "Unreleased" +#~ msgstr "尚未发布" -#~ msgid "|ba47ffb421814b0f8f9fa5719093d839|" -#~ msgstr "" +#~ msgid "|d8bf04f23d9b46d8a23cc6f4887d7873|" +#~ msgstr "|d8bf04f23d9b46d8a23cc6f4887d7873|" -#~ msgid "|aeac5bf79cbf497082e979834717e01b|" -#~ msgstr "" +#~ msgid "|5aa1711387d74d0f8b9c499e1a51627e|" +#~ msgstr "|5aa1711387d74d0f8b9c499e1a51627e|" -#~ msgid "|ce27ed4bbe95459dba016afc42486ba2|" -#~ msgstr "" +#~ msgid "|2bc8e069228d4873804061ff4a95048c|" +#~ msgstr "|2bc8e069228d4873804061ff4a95048c|" -#~ msgid "|ae94a7f71dda443cbec2385751427d41|" -#~ msgstr "" +#~ msgid "|c258488766324dc9a6807f0e7c4fd5f4|" +#~ msgstr "|c258488766324dc9a6807f0e7c4fd5f4|" -#~ msgid "|e61fce4d43d243e7bb08bdde97d81ce6|" -#~ msgstr "" +#~ msgid "|d5f962c3f4ec48529efda980868c14b0|" +#~ msgstr "|d5f962c3f4ec48529efda980868c14b0|" -#~ msgid "|08cb60859b07461588fe44e55810b050|" -#~ msgstr "" +#~ msgid "|a5eccea18d4c43a68b54b65043cabef8|" +#~ msgstr "|a5eccea18d4c43a68b54b65043cabef8|" + +#~ msgid "|f17662f7df2d42f68cac70a1fdeda8a7|" +#~ msgstr "|f17662f7df2d42f68cac70a1fdeda8a7|" + +#~ msgid "|241fc906441a4f038c625a19d30d01b2|" +#~ msgstr "|241fc906441a4f038c625a19d30d01b2|" + +#~ msgid "|0aa5aa05810b44b6a835cecce28f3137|" +#~ msgstr "|0aa5aa05810b44b6a835cecce28f3137|" + +#~ msgid "|c742940dd4bf4de09d8d0d5e8d179638|" +#~ msgstr "|c742940dd4bf4de09d8d0d5e8d179638|" + +#~ msgid "|1f169ab4601a47e1a226f1628f4ebddb|" +#~ msgstr "|1f169ab4601a47e1a226f1628f4ebddb|" + +#~ msgid "|12cfa9cde14440ecb8c8f6c1d7185bec|" +#~ msgstr "|12cfa9cde14440ecb8c8f6c1d7185bec|" + +#~ msgid "|72939caf6e294b0986fee6dde96614d7|" +#~ msgstr "|72939caf6e294b0986fee6dde96614d7|" + +#~ msgid "|83a8daee45da4a98b8d6f24ae098fc50|" +#~ msgstr "|83a8daee45da4a98b8d6f24ae098fc50|" diff --git a/doc/locales/zh_Hans/LC_MESSAGES/sphinx.po b/doc/locales/zh_Hans/LC_MESSAGES/sphinx.po index 98f300b872e3..6c786f9ed8ac 100644 --- a/doc/locales/zh_Hans/LC_MESSAGES/sphinx.po +++ b/doc/locales/zh_Hans/LC_MESSAGES/sphinx.po @@ -8,7 +8,7 @@ msgid "" msgstr "" "Project-Id-Version: Flower main\n" "Report-Msgid-Bugs-To: \n" -"POT-Creation-Date: 2023-11-23 18:31+0100\n" +"POT-Creation-Date: 2024-05-28 11:47+0200\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_Hans\n" @@ -17,7 +17,7 @@ msgstr "" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" -"Generated-By: Babel 2.13.1\n" +"Generated-By: Babel 2.15.0\n" #: ../../source/_templates/base.html:18 msgid "About these documents" @@ -28,6 +28,7 @@ msgid "Index" msgstr "" #: ../../source/_templates/base.html:24 +#: ../../source/_templates/sidebar/search.html:2 msgid "Search" msgstr "" @@ -35,3 +36,7 @@ msgstr "" msgid "Copyright" msgstr "" +#: ../../source/_templates/sidebar/search.html:2 +msgid "Type / to search" +msgstr "" + diff --git a/doc/source/_static/docker-ci-release.png b/doc/source/_static/docker-ci-release.png new file mode 100644 index 000000000000..6ec97ce9fb06 Binary files /dev/null and b/doc/source/_static/docker-ci-release.png differ diff --git a/doc/source/conf.py b/doc/source/conf.py index 1c53a827dcf5..1452f9c9a7b0 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -86,7 +86,7 @@ author = "The Flower Authors" # The full version, including alpha/beta/rc tags -release = "1.9.0" +release = "1.10.0" # -- General configuration --------------------------------------------------- @@ -108,6 +108,7 @@ "sphinxcontrib.youtube", "sphinx_reredirects", "nbsphinx", + "sphinx_click", ] # Generate .rst files @@ -248,6 +249,8 @@ def find_test_modules(package_path): "quickstart-mxnet": "index.html", "tutorial-quickstart-mxnet": "index.html", "example-mxnet-walk-through": "index.html", + "ref-api/flwr.simulation.run_simulation_from_cli": "index.html", + "contributor-how-to-create-new-messages": "index.html", } # -- Options for HTML output ------------------------------------------------- diff --git a/doc/source/contributor-how-to-build-docker-images.rst b/doc/source/contributor-how-to-build-docker-images.rst index b97ee2c434ce..4c178f439a07 100644 --- a/doc/source/contributor-how-to-build-docker-images.rst +++ b/doc/source/contributor-how-to-build-docker-images.rst @@ -2,9 +2,10 @@ How to build Docker Flower images locally ========================================= Flower provides pre-made docker images on `Docker Hub `_ -that include all necessary dependencies for running the SuperLink. You can also build your own custom -docker images from scratch with a different version of Python or Ubuntu if that is what you need. -In this guide, we will explain what images exist and how to build them locally. +that include all necessary dependencies for running the SuperLink, SuperNode or ServerApp. +You can also build your own custom docker images from scratch with a different version of Python +or Linux distribution (Ubuntu/Alpine) if that is what you need. In this guide, we will explain what +images exist and how to build them locally. Before we can start, we need to meet a few prerequisites in our local development environment. @@ -20,19 +21,15 @@ Before we can start, we need to meet a few prerequisites in our local developmen :doc:`Run Flower using Docker ` which covers this step in more detail. -Currently, Flower provides two images, a ``base`` image and a ``superlink`` image. The base image, -as the name suggests, contains basic dependencies that the SuperLink needs. -This includes system dependencies, Python and Python tools. The SuperLink image is -based on the base image, but it additionally installs the SuperLink using ``pip``. The build instructions that assemble the images are located in the respective Dockerfiles. You can find them in the subdirectories of ``src/docker``. -Both, base and SuperLink image are configured via build arguments. Through build arguments, we can make -our build more flexible. For example, in the base image, we can specify the version of Python to -install using the ``PYTHON_VERSION`` build argument. Some of the build arguments have default -values, others must be specified when building the image. All available build arguments for each -image are listed in one of the tables below. +Flower Docker images are configured via build arguments. Through build arguments, we can make the +creation of images more flexible. For example, in the base image, we can specify the version of +Python to install using the ``PYTHON_VERSION`` build argument. Some of the build arguments have +default values, others must be specified when building the image. All available build arguments for +each image are listed in one of the tables below. Building the base image ----------------------- @@ -45,10 +42,18 @@ Building the base image - Description - Required - Example + * - ``DISTRO`` + - The Linux distribution to use as the base image. + - No + - ``ubuntu`` + * - ``DISTRO_VERSION`` + - Version of the Linux distribution. + - No + - ``22.04`` * - ``PYTHON_VERSION`` - Version of ``python`` to be installed. - - Yes - - ``3.11`` + - No + - ``3.11`` or ``3.11.1`` * - ``PIP_VERSION`` - Version of ``pip`` to be installed. - Yes @@ -57,18 +62,25 @@ Building the base image - Version of ``setuptools`` to be installed. - Yes - ``69.0.2`` - * - ``UBUNTU_VERSION`` - - Version of the official Ubuntu Docker image. - - Defaults to ``22.04``. - - + * - ``FLWR_VERSION`` + - Version of Flower to be installed. + - Yes + - ``1.9.0`` + * - ``FLWR_PACKAGE`` + - The Flower package to be installed. + - No + - ``flwr`` or ``flwr-nightly`` + -The following example creates a base image with Python 3.11.0, pip 23.0.1 and setuptools 69.0.2: +The following example creates a base Ubuntu/Alpine image with Python 3.11.0, pip 23.0.1, +setuptools 69.0.2 and Flower 1.9.0: .. code-block:: bash - $ cd src/docker/base/ubuntu + $ cd src/docker/base/ $ docker build \ --build-arg PYTHON_VERSION=3.11.0 \ + --build-arg FLWR_VERSION=1.9.0 \ --build-arg PIP_VERSION=23.0.1 \ --build-arg SETUPTOOLS_VERSION=69.0.2 \ -t flwr_base:0.1.0 . @@ -76,8 +88,8 @@ The following example creates a base image with Python 3.11.0, pip 23.0.1 and se The name of image is ``flwr_base`` and the tag ``0.1.0``. Remember that the build arguments as well as the name and tag can be adapted to your needs. These values serve as examples only. -Building the SuperLink image ----------------------------- +Building the SuperLink/SuperNode or ServerApp image +--------------------------------------------------- .. list-table:: :widths: 25 45 15 15 @@ -89,50 +101,33 @@ Building the SuperLink image - Example * - ``BASE_REPOSITORY`` - The repository name of the base image. - - Defaults to ``flwr/base``. - - - * - ``PYTHON_VERSION`` - - The Python version of the base image. - - Defaults to ``py3.11``. - - - * - ``UBUNTU_VERSION`` - - The Ubuntu version of the base image. - - Defaults to ``ubuntu22.04``. - - - * - ``FLWR_PACKAGE`` - - The PyPI package to install. - - Defaults to ``flwr``. - - - * - ``FLWR_VERSION`` - - Version of Flower to be installed. + - No + - ``flwr/base`` + * - ``BASE_IMAGE`` + - The Tag of the Flower base image. - Yes - - ``1.8.0`` - + - ``1.9.0-py3.10-ubuntu22.04`` -The following example creates a SuperLink image with the official Flower base image -py3.11-ubuntu22.04 and Flower 1.8.0: +The following example creates a SuperLink/SuperNode or ServerApp image with the official Flower +base image: .. code-block:: bash - $ cd src/docker/superlink/ + $ cd src/docker// $ docker build \ - --build-arg FLWR_VERSION=1.8.0 \ + --build-arg BASE_IMAGE=-py- \ -t flwr_superlink:0.1.0 . -The name of image is ``flwr_superlink`` and the tag ``0.1.0``. Remember that the build arguments as -well as the name and tag can be adapted to your needs. These values serve as examples only. If you want to use your own base image instead of the official Flower base image, all you need to do -is set the ``BASE_REPOSITORY``, ``PYTHON_VERSION`` and ``UBUNTU_VERSION`` build arguments. +is set the ``BASE_REPOSITORY`` build argument. .. code-block:: bash $ cd src/docker/superlink/ $ docker build \ --build-arg BASE_REPOSITORY=flwr_base \ - --build-arg PYTHON_VERSION=3.11 \ - --build-arg UBUNTU_VERSION=ubuntu22.04 \ - --build-arg FLWR_VERSION=1.8.0 \ + --build-arg BASE_IMAGE=0.1.0 -t flwr_superlink:0.1.0 . After creating the image, we can test whether the image is working: diff --git a/doc/source/contributor-how-to-create-new-messages.rst b/doc/source/contributor-how-to-create-new-messages.rst deleted file mode 100644 index 3f1849bdce47..000000000000 --- a/doc/source/contributor-how-to-create-new-messages.rst +++ /dev/null @@ -1,153 +0,0 @@ -Creating New Messages -===================== - -This is a simple guide for creating a new type of message between the server and clients in Flower. - -Let's suppose we have the following example functions in :code:`server.py` and :code:`numpy_client.py`... - -Server's side: - -.. code-block:: python - - def example_request(self, client: ClientProxy) -> Tuple[str, int]: - question = "Could you find the sum of the list, Bob?" - l = [1, 2, 3] - return client.request(question, l) - -Client's side: - -.. code-block:: python - - def example_response(self, question: str, l: List[int]) -> Tuple[str, int]: - response = "Here you go Alice!" - answer = sum(question) - return response, answer - -Let's now see what we need to implement in order to get this simple function between the server and client to work! - - -Message Types for Protocol Buffers ----------------------------------- - -The first thing we need to do is to define a message type for the RPC system in :code:`transport.proto`. -Note that we have to do it for both the request and response messages. For more details on the syntax of proto3, please see the `official documentation `_. - -Within the :code:`ServerMessage` block: - -.. code-block:: proto - - message ExampleIns{ - string question=1; - repeated int64 l=2; - } - oneof msg { - ReconnectIns reconnect_ins = 1; - GetPropertiesIns get_properties_ins = 2; - GetParametersIns get_parameters_ins = 3; - FitIns fit_ins = 4; - EvaluateIns evaluate_ins = 5; - ExampleIns example_ins = 6; - } - -Within the ClientMessage block: - -.. code-block:: proto - - message ExampleRes{ - string response = 1; - int64 answer = 2; - } - - oneof msg { - DisconnectRes disconnect_res = 1; - GetPropertiesRes get_properties_res = 2; - GetParametersRes get_parameters_res = 3; - FitRes fit_res = 4; - EvaluateRes evaluate_res = 5; - ExampleRes examples_res = 6; - } - -Make sure to also add a field of the newly created message type in :code:`oneof msg`. - -Once that is done, we will compile the file with: - -.. code-block:: shell - - $ python -m flwr_tool.protoc - -If it compiles successfully, you should see the following message: - -.. code-block:: shell - - Writing mypy to flwr/proto/transport_pb2.pyi - Writing mypy to flwr/proto/transport_pb2_grpc.pyi - - -Serialization and Deserialization Functions --------------------------------------------- - -Our next step is to add functions to serialize and deserialize Python datatypes to or from our defined RPC message types. You should add these functions in :code:`serde.py`. - -The four functions: - -.. code-block:: python - - def example_msg_to_proto(question: str, l: List[int]) -> ServerMessage.ExampleIns: - return ServerMessage.ExampleIns(question=question, l=l) - - - def example_msg_from_proto(msg: ServerMessage.ExampleIns) -> Tuple[str, List[int]]: - return msg.question, msg.l - - - def example_res_to_proto(response: str, answer: int) -> ClientMessage.ExampleRes: - return ClientMessage.ExampleRes(response=response, answer=answer) - - - def example_res_from_proto(res: ClientMessage.ExampleRes) -> Tuple[str, int]: - return res.response, res.answer - - -Sending the Message from the Server ------------------------------------ - -Now write the request function in your Client Proxy class (e.g., :code:`grpc_client_proxy.py`) using the serde functions you just created: - -.. code-block:: python - - def request(self, question: str, l: List[int]) -> Tuple[str, int]: - request_msg = serde.example_msg_to_proto(question, l) - client_msg: ClientMessage = self.bridge.request( - ServerMessage(example_ins=request_msg) - ) - response, answer = serde.example_res_from_proto(client_msg.examples_res) - return response, answer - - -Receiving the Message by the Client ------------------------------------ - -Last step! Modify the code in :code:`message_handler.py` to check the field of your message and call the :code:`example_response` function. Remember to use the serde functions! - -Within the handle function: - -.. code-block:: python - - if server_msg.HasField("example_ins"): - return _example_response(client, server_msg.example_ins), 0, True - -And add a new function: - -.. code-block:: python - - def _example_response(client: Client, msg: ServerMessage.ExampleIns) -> ClientMessage: - question,l = serde.evaluate_ins_from_proto(msg) - response, answer = client.example_response(question,l) - example_res = serde.example_res_to_proto(response,answer) - return ClientMessage(examples_res=example_res) - -Hopefully, when you run your program you will get the intended result! - -.. code-block:: shell - - ('Here you go Alice!', 6) diff --git a/doc/source/contributor-how-to-release-flower.rst b/doc/source/contributor-how-to-release-flower.rst index 4853d87bc4c1..fc4c2d436b05 100644 --- a/doc/source/contributor-how-to-release-flower.rst +++ b/doc/source/contributor-how-to-release-flower.rst @@ -12,6 +12,24 @@ The version number of a release is stated in ``pyproject.toml``. To release a ne 2. Once the changelog has been updated with all the changes, run ``./dev/prepare-release-changelog.sh v``, where ```` is the version stated in ``pyproject.toml`` (notice the ``v`` added before it). This will replace the ``Unreleased`` header of the changelog by the version and current date, and it will add a thanking message for the contributors. Open a pull request with those changes. 3. Once the pull request is merged, tag the release commit with the version number as soon as the PR is merged: ``git tag v`` (notice the ``v`` added before the version number), then ``git push --tags``. This will create a draft release on GitHub containing the correct artifacts and the relevant part of the changelog. 4. Check the draft release on GitHub, and if everything is good, publish it. +5. Trigger the CI for building the Docker images. + +To trigger the workflow, a collaborator must create a ``workflow_dispatch`` event in the +GitHub CI. This can be done either through the UI or via the GitHub CLI. The event requires only one +input, the Flower version, to be released. + +**Via the UI** + +1. Go to the ``Build docker images`` workflow `page `_. +2. Click on the ``Run workflow`` button and type the new version of Flower in the ``Version of Flower`` input field. +3. Click on the **green** ``Run workflow`` button. + +.. image:: _static/docker-ci-release.png + +**Via the GitHub CI** + +1. Make sure you are logged in via ``gh auth login`` and that the current working directory is the root of the Flower repository. +2. Trigger the workflow via ``gh workflow run docker-images.yml -f flwr-version=``. After the release ----------------- diff --git a/doc/source/contributor-tutorial-get-started-as-a-contributor.rst b/doc/source/contributor-tutorial-get-started-as-a-contributor.rst index 43f9739987ac..d7d647996a3d 100644 --- a/doc/source/contributor-tutorial-get-started-as-a-contributor.rst +++ b/doc/source/contributor-tutorial-get-started-as-a-contributor.rst @@ -17,8 +17,8 @@ supports `PEP 517 `_. Developer Machine Setup ----------------------- -Preliminarities -~~~~~~~~~~~~~~~ +Preliminaries +~~~~~~~~~~~~~ Some system-wide dependencies are needed. For macOS diff --git a/doc/source/how-to-authenticate-supernodes.rst b/doc/source/how-to-authenticate-supernodes.rst index 472ba64a8310..73987261b29f 100644 --- a/doc/source/how-to-authenticate-supernodes.rst +++ b/doc/source/how-to-authenticate-supernodes.rst @@ -9,7 +9,7 @@ Flower node authentication works similar to how GitHub SSH authentication works: * Shared secret is used to compute the HMAC value of the message sent from SuperNode to SuperLink as a token * SuperLink verifies the token -We recommend you to check out the complete `code example `_ demonstrating federated learning with Flower in an authenticated setting. +We recommend you to check out the complete `code example `_ demonstrating federated learning with Flower in an authenticated setting. .. note:: This guide covers a preview feature that might change in future versions of Flower. @@ -28,16 +28,20 @@ Use the following terminal command to start a Flower :code:`SuperNode` that has .. code-block:: bash flower-superlink - --certificates certificates/ca.crt certificates/server.pem certificates/server.key - --require-client-authentication ./keys/client_public_keys.csv ./keys/server_credentials ./keys/server_credentials.pub + --ssl-ca-certfile certificates/ca.crt + --ssl-certfile certificates/server.pem + --ssl-keyfile certificates/server.key + --auth-list-public-keys keys/client_public_keys.csv + --auth-superlink-private-key keys/server_credentials + --auth-superlink-public-key keys/server_credentials.pub -Let's break down the :code:`--require-client-authentication` flag: +Let's break down the authentication flags: -1. The first argument is a path to a CSV file storing all known node public keys. You need to store all known node public keys that are allowed to participate in a federation in one CSV file (:code:`.csv`). +1. The first flag :code:`--auth-list-public-keys` expects a path to a CSV file storing all known node public keys. You need to store all known node public keys that are allowed to participate in a federation in one CSV file (:code:`.csv`). A valid CSV file storing known node public keys should list the keys in OpenSSH format, separated by commas and without any comments. For an example, refer to our code sample, which contains a CSV file with two known node public keys. -2. The second and third arguments are paths to the server's private and public keys. For development purposes, you can generate a private and public key pair using :code:`ssh-keygen -t ecdsa -b 384`. +2. The second and third flags :code:`--auth-superlink-private-key` and :code:`--auth-superlink-public-key` expect paths to the server's private and public keys. For development purposes, you can generate a private and public key pair using :code:`ssh-keygen -t ecdsa -b 384`. .. note:: In Flower 1.9, there is no support for dynamically removing, editing, or adding known node public keys to the SuperLink. @@ -55,10 +59,11 @@ Use the following terminal command to start an authenticated :code:`SuperNode`: flower-client-app client:app --root-certificates certificates/ca.crt - --server 127.0.0.1:9092 - --authentication-keys ./keys/client_credentials ./keys/client_credentials.pub + --superlink 127.0.0.1:9092 + --auth-supernode-private-key keys/client_credentials + --auth-supernode-public-key keys/client_credentials.pub -The :code:`--authentication-keys` flag expects two arguments: a path to the node's private key file and a path to the node's public key file. For development purposes, you can generate a private and public key pair using :code:`ssh-keygen -t ecdsa -b 384`. +The :code:`--auth-supernode-private-key` flag expects a path to the node's private key file and the :code:`--auth-supernode-public-key` flag expects a path to the node's public key file. For development purposes, you can generate a private and public key pair using :code:`ssh-keygen -t ecdsa -b 384`. Security notice diff --git a/doc/source/how-to-enable-ssl-connections.rst b/doc/source/how-to-enable-ssl-connections.rst index 5c762fe1169a..1828f4ed3258 100644 --- a/doc/source/how-to-enable-ssl-connections.rst +++ b/doc/source/how-to-enable-ssl-connections.rst @@ -42,7 +42,10 @@ Use the following terminal command to start a sever (SuperLink) that uses the pr .. code-block:: bash - flower-superlink --certificates certificates/ca.crt certificates/server.pem certificates/server.key + flower-superlink + --ssl-ca-certfile certificates/ca.crt + --ssl-certfile certificates/server.pem + --ssl-keyfile certificates/server.key When providing certificates, the server expects a tuple of three certificates paths: CA certificate, server certificate and server private key. @@ -56,7 +59,7 @@ Use the following terminal command to start a client (SuperNode) that uses the p flower-client-app client:app --root-certificates certificates/ca.crt - --server 127.0.0.1:9092 + --superlink 127.0.0.1:9092 When setting :code:`root_certificates`, the client expects a file path to PEM-encoded root certificates. diff --git a/doc/source/how-to-install-flower.rst b/doc/source/how-to-install-flower.rst index 964b23125c0b..b00e2ae803ab 100644 --- a/doc/source/how-to-install-flower.rst +++ b/doc/source/how-to-install-flower.rst @@ -20,7 +20,7 @@ Stable releases are available on `PyPI `_:: For simulations that use the Virtual Client Engine, ``flwr`` should be installed with the ``simulation`` extra:: - python -m pip install flwr[simulation] + python -m pip install "flwr[simulation]" Using conda (or mamba) @@ -48,7 +48,7 @@ Verify installation The following command can be used to verify if Flower was successfully installed. If everything worked, it should print the version of Flower to the command line:: python -c "import flwr;print(flwr.__version__)" - 1.8.0 + 1.9.0 Advanced installation options diff --git a/doc/source/how-to-run-flower-using-docker.rst b/doc/source/how-to-run-flower-using-docker.rst index 9b1f16bba610..7d9ec883960a 100644 --- a/doc/source/how-to-run-flower-using-docker.rst +++ b/doc/source/how-to-run-flower-using-docker.rst @@ -2,7 +2,8 @@ Run Flower using Docker ======================= The simplest way to get started with Flower is by using the pre-made Docker images, which you can -find on `Docker Hub `__. +find on `Docker Hub `__. Supported architectures include ``amd64`` +and ``arm64v8``. Before you start, make sure that the Docker daemon is running: @@ -37,10 +38,10 @@ If you're looking to try out Flower, you can use the following command: .. code-block:: bash - $ docker run --rm -p 9091:9091 -p 9092:9092 flwr/superlink:1.8.0 --insecure + $ docker run --rm -p 9091:9091 -p 9092:9092 flwr/superlink:1.9.0 --insecure -The command pulls the Docker image with the tag ``1.8.0`` from Docker Hub. The tag specifies -the Flower version. In this case, Flower 1.8.0. The ``--rm`` flag tells Docker to remove the +The command pulls the Docker image with the tag ``1.9.0`` from Docker Hub. The tag specifies +the Flower version. In this case, Flower 1.9.0. The ``--rm`` flag tells Docker to remove the container after it exits. .. note:: @@ -65,25 +66,33 @@ You can use ``--help`` to view all available flags that the SuperLink supports: .. code-block:: bash - $ docker run --rm flwr/superlink:1.8.0 --help + $ docker run --rm flwr/superlink:1.9.0 --help Mounting a volume to store the state on the host system ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ If you want to persist the state of the SuperLink on your host system, all you need to do is specify -a path where you want to save the file on your host system and a name for the database file. In the -example below, we tell Docker via the flag ``--volume`` to mount the user's home directory -(``~/`` on your host) into the ``/app/`` directory of the container. Furthermore, we use the -flag ``--database`` to specify the name of the database file. +a directory where you want to save the file on your host system and a name for the database file. By +default, the SuperLink container runs with a non-root user called ``app`` with the user ID +``49999``. It is recommended to create new directory and change the user ID of the directory to +``49999`` to ensure the mounted directory has the proper permissions. If you later want to delete +the directory, you can change the user ID back to the current user ID by running +``sudo chown -R $USER:$(id -gn) state``. + +In the example below, we create a new directory, change the user ID and tell Docker via the flag +``--volume`` to mount the local ``state`` directory into the ``/app/state`` directory of the +container. Furthermore, we use the flag ``--database`` to specify the name of the database file. .. code-block:: bash + $ mkdir state + $ sudo chown -R 49999:49999 state $ docker run --rm \ - -p 9091:9091 -p 9092:9092 --volume ~/:/app/ flwr/superlink:1.8.0 \ + -p 9091:9091 -p 9092:9092 --volume ./state/:/app/state flwr/superlink:1.9.0 \ --insecure \ --database state.db -As soon as the SuperLink starts, the file ``state.db`` is created in the user's home directory on +As soon as the SuperLink starts, the file ``state.db`` is created in the ``state`` directory on your host system. If the file already exists, the SuperLink tries to restore the state from the file. To start the SuperLink with an empty database, simply remove the ``state.db`` file. @@ -99,15 +108,27 @@ PEM-encoded certificate chain. page contains a section that will guide you through the process. Assuming all files we need are in the local ``certificates`` directory, we can use the flag -``--volume`` to mount the local directory into the ``/app/`` directory of the container. This allows -the SuperLink to access the files within the container. Finally, we pass the names of the -certificates to the SuperLink with the ``--certificates`` flag. +``--volume`` to mount the local directory into the ``/app/certificates/`` directory of the container. +This allows the SuperLink to access the files within the container. The ``ro`` stands for +``read-only``. Docker volumes default to ``read-write``; that option tells Docker to make the volume +``read-only`` instead. Finally, we pass the names of the certificates and key file to the SuperLink +with the ``--ssl-ca-certfile``, ``--ssl-certfile`` and ``--ssl-keyfile`` flag. .. code-block:: bash $ docker run --rm \ - -p 9091:9091 -p 9092:9092 --volume ./certificates/:/app/ flwr/superlink:1.8.0 \ - --certificates ca.crt server.pem server.key + -p 9091:9091 -p 9092:9092 \ + --volume ./certificates/:/app/certificates/:ro flwr/superlink:1.9.0 \ + --ssl-ca-certfile certificates/ca.crt \ + --ssl-certfile certificates/server.pem \ + --ssl-keyfile certificates/server.key + +.. note:: + + Because Flower containers, by default, run with a non-root user ``app``, the mounted files and + directories must have the proper permissions for the user ID ``49999``. For example, to change the + user ID of all files in the ``certificates/`` directory, you can run + ``sudo chown -R 49999:49999 certificates/*``. Flower SuperNode ---------------- @@ -115,14 +136,6 @@ Flower SuperNode The SuperNode Docker image comes with a pre-installed version of Flower and serves as a base for building your own SuperNode image. -.. important:: - - The SuperNode Docker image currently works only with the 1.9.0-nightly release. A stable version - will be available when Flower 1.9.0 (stable) gets released (ETA: May). A SuperNode nightly image - must be paired with the corresponding SuperLink and ServerApp nightly images released on the same - day. To ensure the versions are in sync, using the concrete tag, e.g., ``1.9.0.dev20240501`` - instead of ``nightly`` is recommended. - We will use the ``quickstart-pytorch`` example, which you can find in the Flower repository, to illustrate how you can dockerize your ClientApp. @@ -165,9 +178,9 @@ is located. In the file, we list all the dependencies that the ClientApp require .. code-block:: - flwr-datasets[vision]>=0.0.2,<1.0.0 - torch==2.1.1 - torchvision==0.16.1 + flwr-datasets[vision]>=0.1.0,<1.0.0 + torch==2.2.1 + torchvision==0.17.1 tqdm==4.66.3 .. important:: @@ -183,17 +196,17 @@ The ``Dockerfile.supernode`` contains the instructions that assemble the SuperNo .. code-block:: dockerfile - FROM flwr/supernode:nightly + FROM flwr/supernode:1.9.0 WORKDIR /app COPY requirements.txt . - RUN python -m pip install -U --no-cache-dir -r requirements.txt && pyenv rehash + RUN python -m pip install -U --no-cache-dir -r requirements.txt COPY client.py ./ ENTRYPOINT ["flower-client-app", "client:app"] -In the first two lines, we instruct Docker to use the SuperNode image tagged ``nightly`` as a base +In the first two lines, we instruct Docker to use the SuperNode image tagged ``1.9.0`` as a base image and set our working directory to ``/app``. The following instructions will now be executed in the ``/app`` directory. Next, we install the ClientApp dependencies by copying the ``requirements.txt`` file into the image and run ``pip install``. In the last two lines, @@ -222,9 +235,9 @@ Now that we have built the SuperNode image, we can finally run it. .. code-block:: bash - $ docker run --rm flwr_supernode:0.0.1 client:app \ + $ docker run --rm flwr_supernode:0.0.1 \ --insecure \ - --server 192.168.1.100:9092 + --superlink 192.168.1.100:9092 Let's break down each part of this command: @@ -240,7 +253,7 @@ Let's break down each part of this command: `SSL `__ when deploying to a production environment. -* | ``--server 192.168.1.100:9092``: This option specifies the address of the SuperLinks Fleet +* | ``--superlink 192.168.1.100:9092``: This option specifies the address of the SuperLinks Fleet | API to connect to. Remember to update it with your SuperLink IP. .. note:: @@ -254,7 +267,7 @@ To see all available flags that the SuperNode supports, run: .. code-block:: bash - $ docker run --rm flwr/supernode:nightly --help + $ docker run --rm flwr/supernode:1.9.0 --help Enabling SSL for secure connections ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -263,13 +276,14 @@ To enable SSL, we will need to mount a PEM-encoded root certificate into your Su Assuming the certificate already exists locally, we can use the flag ``--volume`` to mount the local certificate into the container's ``/app/`` directory. This allows the SuperNode to access the -certificate within the container. Use the ``--certificates`` flag when starting the container. +certificate within the container. Use the ``--root-certificates`` flag when starting the container. .. code-block:: bash - $ docker run --rm --volume ./ca.crt:/app/ca.crt flwr_supernode:0.0.1 client:app \ - --server 192.168.1.100:9092 \ - --certificates ca.crt + + $ docker run --rm --volume ./ca.crt:/app/ca.crt flwr_supernode:0.0.1 \ + --superlink 192.168.1.100:9092 \ + --root-certificates ca.crt Flower ServerApp ---------------- @@ -303,14 +317,14 @@ The ``Dockerfile.serverapp`` contains the instructions that assemble the ServerA .. code-block:: dockerfile - FROM flwr/serverapp:1.8.0 + FROM flwr/serverapp:1.9.0 WORKDIR /app COPY server.py ./ ENTRYPOINT ["flower-server-app", "server:app"] -In the first two lines, we instruct Docker to use the ServerApp image tagged ``1.8.0`` as a base +In the first two lines, we instruct Docker to use the ServerApp image tagged ``1.9.0`` as a base image and set our working directory to ``/app``. The following instructions will now be executed in the ``/app`` directory. In the last two lines, we copy the ``server.py`` module into the image and set the entry point to ``flower-server-app`` with the argument ``server:app``. @@ -340,7 +354,7 @@ Now that we have built the ServerApp image, we can finally run it. $ docker run --rm flwr_serverapp:0.0.1 \ --insecure \ - --server 192.168.1.100:9091 + --superlink 192.168.1.100:9091 Let's break down each part of this command: @@ -356,7 +370,7 @@ Let's break down each part of this command: `SSL `__ when deploying to a production environment. -* | ``--server 192.168.1.100:9091``: This option specifies the address of the SuperLinks Driver +* | ``--superlink 192.168.1.100:9091``: This option specifies the address of the SuperLinks Driver | API to connect to. Remember to update it with your SuperLink IP. .. note:: @@ -369,7 +383,7 @@ To see all available flags that the ServerApp supports, run: .. code-block:: bash - $ docker run --rm flwr/serverapp:1.8.0 --help + $ docker run --rm flwr/serverapp:1.9.0 --help Enabling SSL for secure connections ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -378,23 +392,68 @@ To enable SSL, we will need to mount a PEM-encoded root certificate into your Se Assuming the certificate already exists locally, we can use the flag ``--volume`` to mount the local certificate into the container's ``/app/`` directory. This allows the ServerApp to access the -certificate within the container. Use the ``--certificates`` flag when starting the container. +certificate within the container. Use the ``--root-certificates`` flags when starting the container. .. code-block:: bash - $ docker run --rm --volume ./ca.crt:/app/ca.crt flwr_serverapp:0.0.1 client:app \ - --server 192.168.1.100:9091 \ - --certificates ca.crt + $ docker run --rm --volume ./ca.crt:/app/ca.crt flwr_serverapp:0.0.1 \ + --superlink 192.168.1.100:9091 \ + --root-certificates ca.crt Advanced Docker options ----------------------- +Run with root user privileges +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Flower Docker images, by default, run with a non-root user (username/groupname: ``app``, +UID/GID: ``49999``). Using root user is not recommended unless it is necessary for specific +tasks during the build process. Always make sure to run the container as a non-root user in +production to maintain security best practices. + +**Run a container with root user privileges** + +Run the Docker image with the ``-u`` flag and specify ``root`` as the username: + +.. code-block:: bash + + $ docker run --rm -u root flwr/superlink:1.9.0 + +This command will run the Docker container with root user privileges. + +**Run the build process with root user privileges** + +If you want to switch to the root user during the build process of the Docker image to install +missing system dependencies, you can use the ``USER root`` directive within your Dockerfile. + +.. code-block:: dockerfile + + FROM flwr/supernode:1.9.0 + + # Switch to root user + USER root + + # Install missing dependencies (requires root access) + RUN apt-get update && apt-get install -y + + # Switch back to non-root user app + USER app + + # Continue with your Docker image build process + ... + Using a different Flower version ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ If you want to use a different version of Flower, for example Flower nightly, you can do so by -changing the tag. All available versions are on -`Docker Hub `__. +changing the tag. All available versions are on `Docker Hub `__. + +.. important:: + + When using Flower nightly, the SuperLink nightly image must be paired with the corresponding + SuperNode and ServerApp nightly images released on the same day. To ensure the versions are + in sync, using the concrete tag, e.g., ``1.10.0.dev20240610`` instead of ``nightly`` is + recommended. Pinning a Docker image to a specific version ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -404,19 +463,19 @@ updates of system dependencies that should not change the functionality of Flowe want to ensure that you always use the same image, you can specify the hash of the image instead of the tag. -The following command returns the current image hash referenced by the ``superlink:1.8.0`` tag: +The following command returns the current image hash referenced by the ``superlink:1.9.0`` tag: .. code-block:: bash - $ docker inspect --format='{{index .RepoDigests 0}}' flwr/superlink:1.8.0 - flwr/superlink@sha256:1b855d1fa4e344e4d95db99793f2bb35d8c63f6a1decdd736863bfe4bb0fe46c + $ docker inspect --format='{{index .RepoDigests 0}}' flwr/superlink:1.9.0 + flwr/superlink@sha256:985c24b2b337ab7f15a554fde9d860cede95079bcaa244fda8f12c0805e34c7d Next, we can pin the hash when running a new SuperLink container: .. code-block:: bash $ docker run \ - --rm flwr/superlink@sha256:1b855d1fa4e344e4d95db99793f2bb35d8c63f6a1decdd736863bfe4bb0fe46c \ + --rm flwr/superlink@sha256:985c24b2b337ab7f15a554fde9d860cede95079bcaa244fda8f12c0805e34c7d \ --insecure Setting environment variables @@ -427,4 +486,4 @@ To set a variable inside a Docker container, you can use the ``-e = .. code-block:: bash $ docker run -e FLWR_TELEMETRY_ENABLED=0 \ - --rm flwr/superlink:1.8.0 --insecure + --rm flwr/superlink:1.9.0 --insecure diff --git a/doc/source/how-to-upgrade-to-flower-next.rst b/doc/source/how-to-upgrade-to-flower-next.rst index 8c8f3c3f8fd7..a17756247566 100644 --- a/doc/source/how-to-upgrade-to-flower-next.rst +++ b/doc/source/how-to-upgrade-to-flower-next.rst @@ -22,21 +22,22 @@ Let's dive in! .. |startclient_link| replace:: ``start_client()`` .. |startserver_link| replace:: ``start_server()`` .. |startsim_link| replace:: ``start_simulation()`` -.. |runsimcli_link| replace:: ``flower-simulation`` .. |runsim_link| replace:: ``run_simulation()`` .. |flowernext_superlink_link| replace:: ``flower-superlink`` .. |flowernext_clientapp_link| replace:: ``flower-client-app`` .. |flowernext_serverapp_link| replace:: ``flower-server-app`` +.. |flower_simulation_link| replace:: ``flower-simulation`` .. _clientapp_link: ref-api/flwr.client.ClientApp.html .. _serverapp_link: ref-api/flwr.server.ServerApp.html .. _startclient_link: ref-api/flwr.client.start_client.html .. _startserver_link: ref-api/flwr.server.start_server.html .. _startsim_link: ref-api/flwr.simulation.start_simulation.html -.. _runsimcli_link: ref-api/flwr.simulation.run_simulation_from_cli.html .. _runsim_link: ref-api/flwr.simulation.run_simulation.html .. _flowernext_superlink_link: ref-api-cli.html#flower-superlink .. _flowernext_clientapp_link: ref-api-cli.html#flower-client-app .. _flowernext_serverapp_link: ref-api-cli.html#flower-server-app +.. _flower_simulation_link: ref-api-cli.html#flower-simulation + Install update -------------- @@ -170,31 +171,31 @@ Deployment # In yet another terminal window, run the ServerApp (this starts the actual training run) $ flower-server-app server:app --insecure -- Here's another example to start with HTTPS. Use the ``--certificates`` command line - argument to pass paths to (CA certificate, server certificate, and server private key). +- Here's another example to start with HTTPS. Use the ``--ssl-ca-certfile``, ``--ssl-certfile``, and ``--ssl-keyfile`` command line + options to pass paths to (CA certificate, server certificate, and server private key). .. code-block:: bash # Start a secure Superlink - $ flower-superlink --certificates \ - \ - \ - + $ flower-superlink \ + --ssl-ca-certfile \ + --ssl-certfile \ + --ssl-keyfile # In a new terminal window, start a long-running secure SuperNode $ flower-client-app client:app \ --root-certificates \ - --server 127.0.0.1:9092 + --superlink 127.0.0.1:9092 # In another terminal window, start another long-running secure SuperNode (at least 2 SuperNodes are required) $ flower-client-app client:app \ --root-certificates \ - --server 127.0.0.1:9092 + --superlink 127.0.0.1:9092 # In yet another terminal window, run the ServerApp (this starts the actual training run) $ flower-server-app server:app \ --root-certificates \ - --server 127.0.0.1:9091 + --superlink 127.0.0.1:9091 Simulation in CLI ~~~~~~~~~~~~~~~~~ @@ -228,7 +229,7 @@ Simulation in CLI ... ) -- Run |runsimcli_link|_ in CLI and point to the ``server_app`` / ``client_app`` object in the +- Run |flower_simulation_link|_ in CLI and point to the ``server_app`` / ``client_app`` object in the code instead of executing the Python script. Here's an example (assuming the ``server_app`` and ``client_app`` objects are in a ``sim.py`` module): diff --git a/doc/source/how-to-use-differential-privacy.rst b/doc/source/how-to-use-differential-privacy.rst index c8901bd906cc..5d4fa3dca1a4 100644 --- a/doc/source/how-to-use-differential-privacy.rst +++ b/doc/source/how-to-use-differential-privacy.rst @@ -9,7 +9,7 @@ This guide explains how you can utilize differential privacy in the Flower frame Central Differential Privacy ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -This approach consists of two seprate phases: clipping of the updates and adding noise to the aggregated model. +This approach consists of two separate phases: clipping of the updates and adding noise to the aggregated model. For the clipping phase, Flower framework has made it possible to decide whether to perform clipping on the server side or the client side. - **Server-side Clipping**: This approach has the advantage of the server enforcing uniform clipping across all clients' updates and reducing the communication overhead for clipping values. However, it also has the disadvantage of increasing the computational load on the server due to the need to perform the clipping operation for all clients. diff --git a/doc/source/how-to-use-strategies.rst b/doc/source/how-to-use-strategies.rst index d0e2cd63a091..8ac120124951 100644 --- a/doc/source/how-to-use-strategies.rst +++ b/doc/source/how-to-use-strategies.rst @@ -72,7 +72,7 @@ It must return a dictionary of arbitrary configuration values :code:`client.fit ) fl.server.start_server(config=fl.server.ServerConfig(num_rounds=3), strategy=strategy) -The :code:`on_fit_config_fn` can be used to pass arbitrary configuration values from server to client, and poetentially change these values each round, for example, to adjust the learning rate. +The :code:`on_fit_config_fn` can be used to pass arbitrary configuration values from server to client, and potentially change these values each round, for example, to adjust the learning rate. The client will receive the dictionary returned by the :code:`on_fit_config_fn` in its own :code:`client.fit()` function. Similar to :code:`on_fit_config_fn`, there is also :code:`on_evaluate_config_fn` to customize the configuration sent to :code:`client.evaluate()` diff --git a/doc/source/index.rst b/doc/source/index.rst index df41d9d4ccb0..f62c5ebf4786 100644 --- a/doc/source/index.rst +++ b/doc/source/index.rst @@ -164,7 +164,6 @@ The Flower community welcomes contributions. The following docs are intended to contributor-how-to-install-development-versions contributor-how-to-set-up-a-virtual-env contributor-how-to-develop-in-vscode-dev-containers - contributor-how-to-create-new-messages contributor-how-to-write-documentation contributor-how-to-release-flower contributor-how-to-contribute-translations diff --git a/doc/source/ref-api-cli.rst b/doc/source/ref-api-cli.rst index 296c2219a065..4397ae056941 100644 --- a/doc/source/ref-api-cli.rst +++ b/doc/source/ref-api-cli.rst @@ -1,6 +1,15 @@ Flower CLI reference ==================== +.. _flwr-apiref: + +flwr CLI +~~~~~~~~ + +.. click:: flwr.cli.app:typer_click_object + :prog: flwr + :nested: full + .. _flower-simulation-apiref: flower-simulation diff --git a/doc/source/ref-changelog.md b/doc/source/ref-changelog.md index c742b8cd9cbe..58fc8b4f69b1 100644 --- a/doc/source/ref-changelog.md +++ b/doc/source/ref-changelog.md @@ -1,12 +1,104 @@ # Changelog -## Unreleased +## v1.9.0 (2024-06-10) + +### Thanks to our contributors + +We would like to give our special thanks to all the contributors who made the new version of Flower possible (in `git shortlog` order): + +`Adam Narozniak`, `Charles Beauville`, `Chong Shen Ng`, `Daniel J. Beutel`, `Daniel Nata Nugraha`, `Heng Pan`, `Javier`, `Mahdi Beitollahi`, `Robert Steiner`, `Taner Topal`, `Yan Gao`, `bapic`, `mohammadnaseri` ### What's new? +- **Introduce built-in authentication (preview)** ([#2946](https://github.com/adap/flower/pull/2946), [#3388](https://github.com/adap/flower/pull/3388), [#2948](https://github.com/adap/flower/pull/2948), [#2917](https://github.com/adap/flower/pull/2917), [#3386](https://github.com/adap/flower/pull/3386), [#3308](https://github.com/adap/flower/pull/3308), [#3001](https://github.com/adap/flower/pull/3001), [#3409](https://github.com/adap/flower/pull/3409), [#2999](https://github.com/adap/flower/pull/2999), [#2979](https://github.com/adap/flower/pull/2979), [#3389](https://github.com/adap/flower/pull/3389), [#3503](https://github.com/adap/flower/pull/3503), [#3366](https://github.com/adap/flower/pull/3366), [#3357](https://github.com/adap/flower/pull/3357)) + + Flower 1.9 introduces the first build-in version of client node authentication. In previous releases, users often wrote glue code to connect Flower to external authentication systems. With this release, the SuperLink can authenticate SuperNodes using a built-in authentication system. A new [how-to guide](https://flower.ai/docs/framework/how-to-authenticate-supernodes.html) and a new [code example](https://github.com/adap/flower/tree/main/examples/flower-authentication) help you to get started. + + This is the first preview release of the Flower-native authentication system. Many additional features are on the roadmap for upcoming Flower releases - stay tuned. + +- **Introduce end-to-end Docker support** ([#3483](https://github.com/adap/flower/pull/3483), [#3266](https://github.com/adap/flower/pull/3266), [#3390](https://github.com/adap/flower/pull/3390), [#3283](https://github.com/adap/flower/pull/3283), [#3285](https://github.com/adap/flower/pull/3285), [#3391](https://github.com/adap/flower/pull/3391), [#3403](https://github.com/adap/flower/pull/3403), [#3458](https://github.com/adap/flower/pull/3458), [#3533](https://github.com/adap/flower/pull/3533), [#3453](https://github.com/adap/flower/pull/3453), [#3486](https://github.com/adap/flower/pull/3486), [#3290](https://github.com/adap/flower/pull/3290)) + + Full Flower Next Docker support is here! With the release of Flower 1.9, Flower provides stable Docker images for the Flower SuperLink, the Flower SuperNode, and the Flower `ServerApp`. This set of images enables you to run all Flower components in Docker. Check out the new [how-to guide](https://flower.ai/docs/framework/how-to-run-flower-using-docker.html) to get stated. + +- **Re-architect Flower Next simulation engine** ([#3307](https://github.com/adap/flower/pull/3307), [#3355](https://github.com/adap/flower/pull/3355), [#3272](https://github.com/adap/flower/pull/3272), [#3273](https://github.com/adap/flower/pull/3273), [#3417](https://github.com/adap/flower/pull/3417), [#3281](https://github.com/adap/flower/pull/3281), [#3343](https://github.com/adap/flower/pull/3343), [#3326](https://github.com/adap/flower/pull/3326)) + + Flower Next simulations now use a new in-memory `Driver` that improves the reliability of simulations, especially in notebook environments. This is a significant step towards a complete overhaul of the Flower Next simulation architecture. + +- **Upgrade simulation engine** ([#3354](https://github.com/adap/flower/pull/3354), [#3378](https://github.com/adap/flower/pull/3378), [#3262](https://github.com/adap/flower/pull/3262), [#3435](https://github.com/adap/flower/pull/3435), [#3501](https://github.com/adap/flower/pull/3501), [#3482](https://github.com/adap/flower/pull/3482), [#3494](https://github.com/adap/flower/pull/3494)) + + The Flower Next simulation engine comes with improved and configurable logging. The Ray-based simulation backend in Flower 1.9 was updated to use Ray 2.10. + +- **Introduce FedPFT baseline** ([#3268](https://github.com/adap/flower/pull/3268)) + + FedPFT allows you to perform one-shot Federated Learning by leveraging widely available foundational models, dramatically reducing communication costs while delivering high performing models. This is work led by Mahdi Beitollahi from Huawei Noah's Ark Lab (Montreal, Canada). Read all the details in their paper: "Parametric Feature Transfer: One-shot Federated Learning with Foundation Models" ([arxiv](https://arxiv.org/abs/2402.01862)) + +- **Launch additional** `flwr new` **templates for Apple MLX, Hugging Face Transformers, scikit-learn and TensorFlow** ([#3291](https://github.com/adap/flower/pull/3291), [#3139](https://github.com/adap/flower/pull/3139), [#3284](https://github.com/adap/flower/pull/3284), [#3251](https://github.com/adap/flower/pull/3251), [#3376](https://github.com/adap/flower/pull/3376), [#3287](https://github.com/adap/flower/pull/3287)) + + The `flwr` CLI's `flwr new` command is starting to become everone's favorite way of creating new Flower projects. This release introduces additional `flwr new` templates for Apple MLX, Hugging Face Transformers, scikit-learn and TensorFlow. In addition to that, existing templates also received updates. + +- **Refine** `RecordSet` **API** ([#3209](https://github.com/adap/flower/pull/3209), [#3331](https://github.com/adap/flower/pull/3331), [#3334](https://github.com/adap/flower/pull/3334), [#3335](https://github.com/adap/flower/pull/3335), [#3375](https://github.com/adap/flower/pull/3375), [#3368](https://github.com/adap/flower/pull/3368)) + + `RecordSet` is part of the Flower Next low-level API preview release. In Flower 1.9, `RecordSet` received a number of usability improvements that make it easier to build `RecordSet`-based `ServerApp`s and `ClientApp`s. + +- **Beautify logging** ([#3379](https://github.com/adap/flower/pull/3379), [#3430](https://github.com/adap/flower/pull/3430), [#3461](https://github.com/adap/flower/pull/3461), [#3360](https://github.com/adap/flower/pull/3360), [#3433](https://github.com/adap/flower/pull/3433)) + + Logs received a substantial update. Not only are logs now much nicer to look at, but they are also more configurable. + +- **Improve reliability** ([#3564](https://github.com/adap/flower/pull/3564), [#3561](https://github.com/adap/flower/pull/3561), [#3566](https://github.com/adap/flower/pull/3566), [#3462](https://github.com/adap/flower/pull/3462), [#3225](https://github.com/adap/flower/pull/3225), [#3514](https://github.com/adap/flower/pull/3514), [#3535](https://github.com/adap/flower/pull/3535), [#3372](https://github.com/adap/flower/pull/3372)) + + Flower 1.9 includes reliability improvements across many parts of the system. One example is a much improved SuperNode shutdown procedure. + +- **Update Swift and C++ SDKs** ([#3321](https://github.com/adap/flower/pull/3321), [#2763](https://github.com/adap/flower/pull/2763)) + + In the C++ SDK, communication-related code is now separate from main client logic. A new abstract class `Communicator` has been introduced alongside a gRPC implementation of it. + +- **Improve testing, tooling and CI/CD infrastructure** ([#3294](https://github.com/adap/flower/pull/3294), [#3282](https://github.com/adap/flower/pull/3282), [#3311](https://github.com/adap/flower/pull/3311), [#2878](https://github.com/adap/flower/pull/2878), [#3333](https://github.com/adap/flower/pull/3333), [#3255](https://github.com/adap/flower/pull/3255), [#3349](https://github.com/adap/flower/pull/3349), [#3400](https://github.com/adap/flower/pull/3400), [#3401](https://github.com/adap/flower/pull/3401), [#3399](https://github.com/adap/flower/pull/3399), [#3346](https://github.com/adap/flower/pull/3346), [#3398](https://github.com/adap/flower/pull/3398), [#3397](https://github.com/adap/flower/pull/3397), [#3347](https://github.com/adap/flower/pull/3347), [#3502](https://github.com/adap/flower/pull/3502), [#3387](https://github.com/adap/flower/pull/3387), [#3542](https://github.com/adap/flower/pull/3542), [#3396](https://github.com/adap/flower/pull/3396), [#3496](https://github.com/adap/flower/pull/3496), [#3465](https://github.com/adap/flower/pull/3465), [#3473](https://github.com/adap/flower/pull/3473), [#3484](https://github.com/adap/flower/pull/3484), [#3521](https://github.com/adap/flower/pull/3521), [#3363](https://github.com/adap/flower/pull/3363), [#3497](https://github.com/adap/flower/pull/3497), [#3464](https://github.com/adap/flower/pull/3464), [#3495](https://github.com/adap/flower/pull/3495), [#3478](https://github.com/adap/flower/pull/3478), [#3271](https://github.com/adap/flower/pull/3271)) + + As always, the Flower tooling, testing, and CI/CD infrastructure has received many updates. + +- **Improve documentation** ([#3530](https://github.com/adap/flower/pull/3530), [#3539](https://github.com/adap/flower/pull/3539), [#3425](https://github.com/adap/flower/pull/3425), [#3520](https://github.com/adap/flower/pull/3520), [#3286](https://github.com/adap/flower/pull/3286), [#3516](https://github.com/adap/flower/pull/3516), [#3523](https://github.com/adap/flower/pull/3523), [#3545](https://github.com/adap/flower/pull/3545), [#3498](https://github.com/adap/flower/pull/3498), [#3439](https://github.com/adap/flower/pull/3439), [#3440](https://github.com/adap/flower/pull/3440), [#3382](https://github.com/adap/flower/pull/3382), [#3559](https://github.com/adap/flower/pull/3559), [#3432](https://github.com/adap/flower/pull/3432), [#3278](https://github.com/adap/flower/pull/3278), [#3371](https://github.com/adap/flower/pull/3371), [#3519](https://github.com/adap/flower/pull/3519), [#3267](https://github.com/adap/flower/pull/3267), [#3204](https://github.com/adap/flower/pull/3204), [#3274](https://github.com/adap/flower/pull/3274)) + + As always, the Flower documentation has received many updates. Notable new pages include: + + - [How-to upgrate to Flower Next (Flower Next migration guide)](https://flower.ai/docs/framework/how-to-upgrade-to-flower-next.html) + + - [How-to run Flower using Docker](https://flower.ai/docs/framework/how-to-run-flower-using-docker.html) + + - [Flower Mods reference](https://flower.ai/docs/framework/ref-api/flwr.client.mod.html#module-flwr.client.mod) + +- **General updates to Flower Examples** ([#3205](https://github.com/adap/flower/pull/3205), [#3226](https://github.com/adap/flower/pull/3226), [#3211](https://github.com/adap/flower/pull/3211), [#3252](https://github.com/adap/flower/pull/3252), [#3427](https://github.com/adap/flower/pull/3427), [#3410](https://github.com/adap/flower/pull/3410), [#3426](https://github.com/adap/flower/pull/3426), [#3228](https://github.com/adap/flower/pull/3228), [#3342](https://github.com/adap/flower/pull/3342), [#3200](https://github.com/adap/flower/pull/3200), [#3202](https://github.com/adap/flower/pull/3202), [#3394](https://github.com/adap/flower/pull/3394), [#3488](https://github.com/adap/flower/pull/3488), [#3329](https://github.com/adap/flower/pull/3329), [#3526](https://github.com/adap/flower/pull/3526), [#3392](https://github.com/adap/flower/pull/3392), [#3474](https://github.com/adap/flower/pull/3474), [#3269](https://github.com/adap/flower/pull/3269)) + + As always, Flower code examples have received many updates. + +- **General improvements** ([#3532](https://github.com/adap/flower/pull/3532), [#3318](https://github.com/adap/flower/pull/3318), [#3565](https://github.com/adap/flower/pull/3565), [#3296](https://github.com/adap/flower/pull/3296), [#3305](https://github.com/adap/flower/pull/3305), [#3246](https://github.com/adap/flower/pull/3246), [#3224](https://github.com/adap/flower/pull/3224), [#3475](https://github.com/adap/flower/pull/3475), [#3297](https://github.com/adap/flower/pull/3297), [#3317](https://github.com/adap/flower/pull/3317), [#3429](https://github.com/adap/flower/pull/3429), [#3196](https://github.com/adap/flower/pull/3196), [#3534](https://github.com/adap/flower/pull/3534), [#3240](https://github.com/adap/flower/pull/3240), [#3365](https://github.com/adap/flower/pull/3365), [#3407](https://github.com/adap/flower/pull/3407), [#3563](https://github.com/adap/flower/pull/3563), [#3344](https://github.com/adap/flower/pull/3344), [#3330](https://github.com/adap/flower/pull/3330), [#3436](https://github.com/adap/flower/pull/3436), [#3300](https://github.com/adap/flower/pull/3300), [#3327](https://github.com/adap/flower/pull/3327), [#3254](https://github.com/adap/flower/pull/3254), [#3253](https://github.com/adap/flower/pull/3253), [#3419](https://github.com/adap/flower/pull/3419), [#3289](https://github.com/adap/flower/pull/3289), [#3208](https://github.com/adap/flower/pull/3208), [#3245](https://github.com/adap/flower/pull/3245), [#3319](https://github.com/adap/flower/pull/3319), [#3203](https://github.com/adap/flower/pull/3203), [#3423](https://github.com/adap/flower/pull/3423), [#3352](https://github.com/adap/flower/pull/3352), [#3292](https://github.com/adap/flower/pull/3292), [#3261](https://github.com/adap/flower/pull/3261)) + +### Deprecations + +- **Deprecate Python 3.8 support** + + Python 3.8 will stop receiving security fixes in [October 2024](https://devguide.python.org/versions/). Support for Python 3.8 is now deprecated and will be removed in an upcoming release. + +- **Deprecate (experimental)** `flower-driver-api` **and** `flower-fleet-api` ([#3416](https://github.com/adap/flower/pull/3416), [#3420](https://github.com/adap/flower/pull/3420)) + + Flower 1.9 deprecates the two (experimental) commands `flower-driver-api` and `flower-fleet-api`. Both commands will be removed in an upcoming release. Use `flower-superlink` instead. + +- **Deprecate** `--server` **in favor of** `--superlink` ([#3518](https://github.com/adap/flower/pull/3518)) + + The commands `flower-server-app` and `flower-client-app` should use `--superlink` instead of the now deprecated `--server`. Support for `--server` will be removed in a future release. + ### Incompatible changes -None +- **Replace** `flower-superlink` **CLI option** `--certificates` **with** `--ssl-ca-certfile` **,** `--ssl-certfile` **and** `--ssl-keyfile` ([#3512](https://github.com/adap/flower/pull/3512), [#3408](https://github.com/adap/flower/pull/3408)) + + SSL-related `flower-superlink` CLI arguments were restructured in an incompatible way. Instead of passing a single `--certificates` flag with three values, you now need to pass three flags (`--ssl-ca-certfile`, `--ssl-certfile` and `--ssl-keyfile`) with one value each. Check out the [SSL connections](https://flower.ai/docs/framework/how-to-enable-ssl-connections.html) documentation page for details. + +- **Remove SuperLink** `--vce` **option** ([#3513](https://github.com/adap/flower/pull/3513)) + + Instead of separately starting a SuperLink and a `ServerApp` for simulation, simulations must now be started using the single `flower-simulation` command. + +- **Merge** `--grpc-rere` **and** `--rest` **SuperLink options** ([#3527](https://github.com/adap/flower/pull/3527)) + + To simplify the usage of `flower-superlink`, previously separate sets of CLI options for gRPC and REST were merged into one unified set of options. Consult the [Flower CLI reference documentation](https://flower.ai/docs/framework/ref-api-cli.html) for details. ## v1.8.0 (2024-04-03) diff --git a/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb b/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb index c9d38b417a92..d8e6e58fafab 100644 --- a/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb +++ b/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb @@ -160,7 +160,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We now have a list of ten training sets and ten validation sets (`trainloaders` and `valloaders`) representing the data of ten different organizations. Each `trainloader`/`valloader` pair contains 4500 training examples and 500 validation examples. There's also a single `testloader` (we did not split the test set). Again, this is only necessary for building research or educational systems, actual federated learning systems have their data naturally distributed across multiple partitions.\n", + "We now have a list of ten training sets and ten validation sets (`trainloaders` and `valloaders`) representing the data of ten different organizations. Each `trainloader`/`valloader` pair contains 4000 training examples and 1000 validation examples. There's also a single `testloader` (we did not split the test set). Again, this is only necessary for building research or educational systems, actual federated learning systems have their data naturally distributed across multiple partitions.\n", "\n", "Let's take a look at the first batch of images and labels in the first training set (i.e., `trainloaders[0]`) before we move on:" ] diff --git a/e2e/bare-client-auth/client.py b/e2e/bare-client-auth/client.py index a56ba5eca552..e82f17088bd9 100644 --- a/e2e/bare-client-auth/client.py +++ b/e2e/bare-client-auth/client.py @@ -1,11 +1,11 @@ -import flwr as fl import numpy as np -from pathlib import Path +import flwr as fl model_params = np.array([1]) objective = 5 + # Define Flower client class FlowerClient(fl.client.NumPyClient): def get_parameters(self, config): @@ -13,18 +13,20 @@ def get_parameters(self, config): def fit(self, parameters, config): model_params = parameters - model_params = [param * (objective/np.mean(param)) for param in model_params] + model_params = [param * (objective / np.mean(param)) for param in model_params] return model_params, 1, {} def evaluate(self, parameters, config): model_params = parameters - loss = min(np.abs(1 - np.mean(model_params)/objective), 1) + loss = min(np.abs(1 - np.mean(model_params) / objective), 1) accuracy = 1 - loss return loss, 1, {"accuracy": accuracy} + def client_fn(cid): return FlowerClient().to_client() + app = fl.client.ClientApp( client_fn=client_fn, ) diff --git a/e2e/bare-client-auth/pyproject.toml b/e2e/bare-client-auth/pyproject.toml index 693fec815474..839f0779cc01 100644 --- a/e2e/bare-client-auth/pyproject.toml +++ b/e2e/bare-client-auth/pyproject.toml @@ -1,13 +1,20 @@ [build-system] -requires = ["poetry-core>=1.4.0"] -build-backend = "poetry.core.masonry.api" +requires = ["hatchling"] +build-backend = "hatchling.build" -[tool.poetry] +[project] name = "bare_client_auth_test" version = "0.1.0" description = "Client-auth-enabled bare Federated Learning test with Flower" -authors = ["The Flower Authors "] +authors = [ + { name = "The Flower Authors", email = "hello@flower.ai" }, +] +dependencies = [ + "flwr @ {root:parent:parent:uri}", +] -[tool.poetry.dependencies] -python = "^3.8" -flwr = { path = "../../", develop = true } +[tool.hatch.build.targets.wheel] +packages = ["."] + +[tool.hatch.metadata] +allow-direct-references = true diff --git a/e2e/bare-client-auth/server.py b/e2e/bare-client-auth/server.py index 7e4f96e15fd9..e10d5ebc5760 100644 --- a/e2e/bare-client-auth/server.py +++ b/e2e/bare-client-auth/server.py @@ -1,6 +1,7 @@ -import flwr as fl from pathlib import Path +import flwr as fl + app = fl.server.ServerApp() diff --git a/e2e/bare-https/client.py b/e2e/bare-https/client.py index b4570b36512d..8f5c1412fd01 100644 --- a/e2e/bare-https/client.py +++ b/e2e/bare-https/client.py @@ -1,11 +1,13 @@ -import flwr as fl -import numpy as np from pathlib import Path +import numpy as np + +import flwr as fl model_params = np.array([1]) objective = 5 + # Define Flower client class FlowerClient(fl.client.NumPyClient): def get_parameters(self, config): @@ -13,18 +15,20 @@ def get_parameters(self, config): def fit(self, parameters, config): model_params = parameters - model_params = [param * (objective/np.mean(param)) for param in model_params] + model_params = [param * (objective / np.mean(param)) for param in model_params] return model_params, 1, {} def evaluate(self, parameters, config): model_params = parameters - loss = min(np.abs(1 - np.mean(model_params)/objective), 1) + loss = min(np.abs(1 - np.mean(model_params) / objective), 1) accuracy = 1 - loss return loss, 1, {"accuracy": accuracy} + def client_fn(cid): return FlowerClient().to_client() + app = fl.client.ClientApp( client_fn=client_fn, ) @@ -32,7 +36,7 @@ def client_fn(cid): if __name__ == "__main__": # Start Flower client fl.client.start_client( - server_address="127.0.0.1:8080", + server_address="127.0.0.1:8080", client=FlowerClient().to_client(), root_certificates=Path("certificates/ca.crt").read_bytes(), insecure=False, diff --git a/e2e/bare-https/pyproject.toml b/e2e/bare-https/pyproject.toml index 3afb7b57a084..de8aa92cbd02 100644 --- a/e2e/bare-https/pyproject.toml +++ b/e2e/bare-https/pyproject.toml @@ -1,13 +1,20 @@ [build-system] -requires = ["poetry-core>=1.4.0"] -build-backend = "poetry.core.masonry.api" +requires = ["hatchling"] +build-backend = "hatchling.build" -[tool.poetry] +[project] name = "bare_https_test" version = "0.1.0" description = "HTTPS-enabled bare Federated Learning test with Flower" -authors = ["The Flower Authors "] +authors = [ + { name = "The Flower Authors", email = "hello@flower.ai" }, +] +dependencies = [ + "flwr @ {root:parent:parent:uri}", +] -[tool.poetry.dependencies] -python = "^3.8" -flwr = { path = "../../", develop = true } +[tool.hatch.build.targets.wheel] +packages = ["."] + +[tool.hatch.metadata] +allow-direct-references = true diff --git a/e2e/bare-https/server.py b/e2e/bare-https/server.py index d85c0623e92c..e10d5ebc5760 100644 --- a/e2e/bare-https/server.py +++ b/e2e/bare-https/server.py @@ -1,6 +1,6 @@ -import flwr as fl from pathlib import Path +import flwr as fl app = fl.server.ServerApp() diff --git a/e2e/bare/client.py b/e2e/bare/client.py index 5f8642e27675..402d775ac3a9 100644 --- a/e2e/bare/client.py +++ b/e2e/bare/client.py @@ -1,8 +1,8 @@ from datetime import datetime -import flwr as fl import numpy as np +import flwr as fl from flwr.common import ConfigsRecord SUBSET_SIZE = 1000 diff --git a/e2e/bare/pyproject.toml b/e2e/bare/pyproject.toml index 45ce7ea333af..ba8c1b2b2276 100644 --- a/e2e/bare/pyproject.toml +++ b/e2e/bare/pyproject.toml @@ -1,13 +1,20 @@ [build-system] -requires = ["poetry-core>=1.4.0"] -build-backend = "poetry.core.masonry.api" +requires = ["hatchling"] +build-backend = "hatchling.build" -[tool.poetry] +[project] name = "bare_test" version = "0.1.0" description = "Bare Federated Learning test with Flower" -authors = ["The Flower Authors "] +authors = [ + { name = "The Flower Authors", email = "hello@flower.ai" }, +] +dependencies = [ + "flwr[simulation,rest] @ {root:parent:parent:uri}", +] -[tool.poetry.dependencies] -python = "^3.8" -flwr = { path = "../../", develop = true, extras = ["simulation", "rest"] } +[tool.hatch.build.targets.wheel] +packages = ["."] + +[tool.hatch.metadata] +allow-direct-references = true diff --git a/e2e/bare/simulation.py b/e2e/bare/simulation.py index 3a90d90a0ae0..25868eb8e33f 100644 --- a/e2e/bare/simulation.py +++ b/e2e/bare/simulation.py @@ -1,11 +1,12 @@ from typing import List, Tuple + import numpy as np +from client import client_fn import flwr as fl from flwr.common import Metrics -from client import client_fn -STATE_VAR = 'timestamp' +STATE_VAR = "timestamp" # Define metric aggregation function @@ -14,18 +15,22 @@ def record_state_metrics(metrics: List[Tuple[int, Metrics]]) -> Metrics: states = [] for _, m in metrics: # split string and covert timestamps to float - states.append([float(tt) for tt in m[STATE_VAR].split(',')]) + states.append([float(tt) for tt in m[STATE_VAR].split(",")]) for client_state in states: if len(client_state) == 1: continue deltas = np.diff(client_state) - assert np.all(deltas > 0), f"Timestamps are not monotonically increasing: {client_state}" + assert np.all( + deltas > 0 + ), f"Timestamps are not monotonically increasing: {client_state}" return {STATE_VAR: states} -strategy = fl.server.strategy.FedAvg(evaluate_metrics_aggregation_fn=record_state_metrics) +strategy = fl.server.strategy.FedAvg( + evaluate_metrics_aggregation_fn=record_state_metrics +) hist = fl.simulation.start_simulation( client_fn=client_fn, @@ -34,8 +39,13 @@ def record_state_metrics(metrics: List[Tuple[int, Metrics]]) -> Metrics: strategy=strategy, ) -assert hist.losses_distributed[-1][1] == 0 or (hist.losses_distributed[0][1] / hist.losses_distributed[-1][1]) >= 0.98 +assert ( + hist.losses_distributed[-1][1] == 0 + or (hist.losses_distributed[0][1] / hist.losses_distributed[-1][1]) >= 0.98 +) # The checks in record_state_metrics don't do anythinng if client's state has a single entry state_metrics_last_round = hist.metrics_distributed[STATE_VAR][-1] -assert len(state_metrics_last_round[1][0]) == 2*state_metrics_last_round[0], f"There should be twice as many entries in the client state as rounds" +assert ( + len(state_metrics_last_round[1][0]) == 2 * state_metrics_last_round[0] +), "There should be twice as many entries in the client state as rounds" diff --git a/e2e/docker/client.py b/e2e/docker/client.py index cea752ea5777..8451b810416b 100644 --- a/e2e/docker/client.py +++ b/e2e/docker/client.py @@ -1,14 +1,14 @@ import warnings from collections import OrderedDict -from flwr.client import NumPyClient, ClientApp -from flwr_datasets import FederatedDataset import torch import torch.nn as nn import torch.nn.functional as F +from flwr_datasets import FederatedDataset from torch.utils.data import DataLoader, Subset from torchvision.transforms import Compose, Normalize, ToTensor +from flwr.client import ClientApp, NumPyClient # ############################################################################# # 1. Regular PyTorch pipeline: nn.Module, train, test, and DataLoader @@ -19,6 +19,7 @@ SUBSET_SIZE = 1_000 + class Net(nn.Module): """Model (simple CNN adapted from 'PyTorch: A 60 Minute Blitz')""" diff --git a/e2e/docker/compose.yaml b/e2e/docker/compose.yaml index 073ca9f60a57..c31bc81692f2 100644 --- a/e2e/docker/compose.yaml +++ b/e2e/docker/compose.yaml @@ -19,7 +19,7 @@ services: resources: limits: cpus: '2' - command: [ "--insecure", "--server", "superlink:9092" ] + command: [ "--insecure", "--superlink", "superlink:9092" ] depends_on: - superlink @@ -27,7 +27,7 @@ services: serverapp: build: dockerfile: serverapp.Dockerfile - command: [ "--insecure", "--server", "superlink:9091" ] + command: [ "--insecure", "--superlink", "superlink:9091" ] # enforce dependency for graceful execution depends_on: - superlink diff --git a/e2e/docker/server.py b/e2e/docker/server.py index cb3490b75dca..61825f0a5df3 100644 --- a/e2e/docker/server.py +++ b/e2e/docker/server.py @@ -1,8 +1,8 @@ from typing import List, Tuple +from flwr.common import Metrics from flwr.server import ServerApp, ServerConfig from flwr.server.strategy import FedAvg -from flwr.common import Metrics # Define metric aggregation function diff --git a/e2e/docker/supernode.Dockerfile b/e2e/docker/supernode.Dockerfile index 2770315a1b54..c94c571cebb5 100644 --- a/e2e/docker/supernode.Dockerfile +++ b/e2e/docker/supernode.Dockerfile @@ -2,8 +2,7 @@ FROM flwr/supernode:nightly WORKDIR /app COPY pyproject.toml ./ -RUN python -m pip install -U --no-cache-dir . \ - && pyenv rehash +RUN python -m pip install -U --no-cache-dir . COPY client.py ./ ENTRYPOINT [ "flower-client-app", "client:app" ] diff --git a/e2e/fastai/client.py b/e2e/fastai/client.py index c4bfb89c2dde..1d98a1134941 100644 --- a/e2e/fastai/client.py +++ b/e2e/fastai/client.py @@ -1,11 +1,11 @@ import warnings from collections import OrderedDict -import flwr as fl import numpy as np import torch from fastai.vision.all import * +import flwr as fl warnings.filterwarnings("ignore", category=UserWarning) @@ -17,7 +17,7 @@ path, valid_pct=0.5, train="training", valid="testing", num_workers=0 ) -subset_size = 100 # Or whatever +subset_size = 100 # Or whatever selected_train = np.random.choice(dls.train_ds.items, subset_size, replace=False) selected_valid = np.random.choice(dls.valid_ds.items, subset_size, replace=False) # Swap in the subset for the whole thing (Note: this mutates dls, so re-initialize before full training!) diff --git a/e2e/fastai/pyproject.toml b/e2e/fastai/pyproject.toml index feed31f6d202..53d3b7e7baf1 100644 --- a/e2e/fastai/pyproject.toml +++ b/e2e/fastai/pyproject.toml @@ -1,15 +1,22 @@ [build-system] -requires = ["poetry-core>=1.4.0"] -build-backend = "poetry.core.masonry.api" +requires = ["hatchling"] +build-backend = "hatchling.build" -[tool.poetry] +[project] name = "quickstart-fastai" version = "0.1.0" description = "Fastai Federated Learning E2E test with Flower" -authors = ["The Flower Authors "] +authors = [ + { name = "The Flower Authors", email = "hello@flower.ai" }, +] +dependencies = [ + "flwr[simulation] @ {root:parent:parent:uri}", + "fastai>=2.7.12,<3.0.0", + "torch>=2.0.0,!=2.0.1,<2.1.0", +] -[tool.poetry.dependencies] -python = ">=3.8,<3.10" -flwr = { path = "../../", develop = true, extras = ["simulation"] } -fastai = "^2.7.12" -torch = ">=2.0.0, !=2.0.1, < 2.1.0" +[tool.hatch.build.targets.wheel] +packages = ["."] + +[tool.hatch.metadata] +allow-direct-references = true diff --git a/e2e/fastai/simulation.py b/e2e/fastai/simulation.py index 5f0e5334bd08..bf05a77cf32a 100644 --- a/e2e/fastai/simulation.py +++ b/e2e/fastai/simulation.py @@ -1,11 +1,14 @@ -import flwr as fl - from client import client_fn +import flwr as fl + hist = fl.simulation.start_simulation( client_fn=client_fn, num_clients=2, config=fl.server.ServerConfig(num_rounds=3), ) -assert hist.losses_distributed[-1][1] == 0 or (hist.losses_distributed[0][1] / hist.losses_distributed[-1][1]) >= 0.98 +assert ( + hist.losses_distributed[-1][1] == 0 + or (hist.losses_distributed[0][1] / hist.losses_distributed[-1][1]) >= 0.98 +) diff --git a/e2e/jax/client.py b/e2e/jax/client.py index a4e4d1f55117..347a005d923a 100644 --- a/e2e/jax/client.py +++ b/e2e/jax/client.py @@ -1,15 +1,12 @@ """Flower client example using JAX for linear regression.""" +from typing import Dict, List, Tuple -from typing import Dict, List, Tuple, Callable - -import flwr as fl -import numpy as np import jax -import jax.numpy as jnp - import jax_training +import numpy as np +import flwr as fl # Load data and determine model shape train_x, train_y, test_x, test_y = jax_training.load_data() @@ -50,13 +47,17 @@ def evaluate( ) return float(loss), num_examples, {"loss": float(loss)} + def client_fn(cid): return FlowerClient().to_client() + app = fl.client.ClientApp( client_fn=client_fn, ) if __name__ == "__main__": # Start Flower client - fl.client.start_client(server_address="127.0.0.1:8080", client=FlowerClient().to_client()) + fl.client.start_client( + server_address="127.0.0.1:8080", client=FlowerClient().to_client() + ) diff --git a/e2e/jax/jax_training.py b/e2e/jax/jax_training.py index 2b523a08516e..f57db75d5963 100644 --- a/e2e/jax/jax_training.py +++ b/e2e/jax/jax_training.py @@ -7,13 +7,13 @@ please read the JAX documentation or the mentioned tutorial. """ +from typing import Callable, Dict, List, Tuple -from typing import Dict, List, Tuple, Callable import jax import jax.numpy as jnp +import numpy as np from sklearn.datasets import make_regression from sklearn.model_selection import train_test_split -import numpy as np key = jax.random.PRNGKey(0) diff --git a/e2e/jax/pyproject.toml b/e2e/jax/pyproject.toml index 9a4af5dee59a..bb024ba14d23 100644 --- a/e2e/jax/pyproject.toml +++ b/e2e/jax/pyproject.toml @@ -1,17 +1,24 @@ -[tool.poetry] +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[project] name = "jax_example" version = "0.1.0" description = "JAX example training a linear regression model with federated learning" -authors = ["The Flower Authors "] +authors = [ + { name = "The Flower Authors", email = "hello@flower.ai" }, +] +dependencies = [ + "flwr[simulation] @ {root:parent:parent:uri}", + "jax==0.4.13", + "jaxlib==0.4.13", + "scikit-learn>=1.1.1,<2.0.0", + "numpy>=1.21.4,<2.0.0", +] -[tool.poetry.dependencies] -python = "^3.8" -flwr = { path = "../../", develop = true, extras = ["simulation"] } -jax = "==0.4.13" -jaxlib = "==0.4.13" -scikit-learn = "^1.1.1" -numpy = "^1.21.4" +[tool.hatch.build.targets.wheel] +packages = ["."] -[build-system] -requires = ["poetry-core>=1.4.0"] -build-backend = "poetry.core.masonry.api" +[tool.hatch.metadata] +allow-direct-references = true diff --git a/e2e/jax/simulation.py b/e2e/jax/simulation.py index 5f0e5334bd08..bf05a77cf32a 100644 --- a/e2e/jax/simulation.py +++ b/e2e/jax/simulation.py @@ -1,11 +1,14 @@ -import flwr as fl - from client import client_fn +import flwr as fl + hist = fl.simulation.start_simulation( client_fn=client_fn, num_clients=2, config=fl.server.ServerConfig(num_rounds=3), ) -assert hist.losses_distributed[-1][1] == 0 or (hist.losses_distributed[0][1] / hist.losses_distributed[-1][1]) >= 0.98 +assert ( + hist.losses_distributed[-1][1] == 0 + or (hist.losses_distributed[0][1] / hist.losses_distributed[-1][1]) >= 0.98 +) diff --git a/e2e/opacus/client.py b/e2e/opacus/client.py index 00437a31233c..c9ebe319063a 100644 --- a/e2e/opacus/client.py +++ b/e2e/opacus/client.py @@ -11,7 +11,6 @@ import flwr as fl - # Define parameters. PARAMS = { "batch_size": 32, @@ -57,9 +56,7 @@ def train(net, trainloader, privacy_engine, optimizer, epochs): loss = criterion(net(images), labels) loss.backward() optimizer.step() - epsilon = privacy_engine.get_epsilon( - delta=PRIVACY_PARAMS["target_delta"] - ) + epsilon = privacy_engine.get_epsilon(delta=PRIVACY_PARAMS["target_delta"]) return epsilon @@ -76,22 +73,27 @@ def test(net, testloader): accuracy = correct / len(testloader.dataset) return loss, accuracy + def load_data(): transform = transforms.Compose( [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))] ) data = CIFAR10("./data", train=True, download=True, transform=transform) - split = math.floor(len(data)* 0.01 * PARAMS["train_split"]) + split = math.floor(len(data) * 0.01 * PARAMS["train_split"]) trainset = torch.utils.data.Subset(data, list(range(0, split))) - testset = torch.utils.data.Subset(data, list(range(split, math.floor(len(data) * 0.01)))) + testset = torch.utils.data.Subset( + data, list(range(split, math.floor(len(data) * 0.01))) + ) trainloader = DataLoader(trainset, PARAMS["batch_size"]) testloader = DataLoader(testset, PARAMS["batch_size"]) sample_rate = PARAMS["batch_size"] / len(trainset) return trainloader, testloader, sample_rate + model = Net() trainloader, testloader, sample_rate = load_data() + # Define Flower client. class FlowerClient(fl.client.NumPyClient): def __init__(self, model) -> None: @@ -118,7 +120,11 @@ def set_parameters(self, parameters): def fit(self, parameters, config): self.set_parameters(parameters) epsilon = train( - self.model, self.trainloader, self.privacy_engine, self.optimizer, PARAMS["local_epochs"] + self.model, + self.trainloader, + self.privacy_engine, + self.optimizer, + PARAMS["local_epochs"], ) print(f"epsilon = {epsilon:.2f}") return ( @@ -137,12 +143,12 @@ def client_fn(cid): model = Net() return FlowerClient(model).to_client() + app = fl.client.ClientApp( client_fn=client_fn, ) if __name__ == "__main__": fl.client.start_client( - server_address="127.0.0.1:8080", - client=FlowerClient(model).to_client() + server_address="127.0.0.1:8080", client=FlowerClient(model).to_client() ) diff --git a/e2e/opacus/pyproject.toml b/e2e/opacus/pyproject.toml index ab4a727cc00b..cee9fc1914cf 100644 --- a/e2e/opacus/pyproject.toml +++ b/e2e/opacus/pyproject.toml @@ -1,16 +1,23 @@ [build-system] -requires = ["poetry-core>=1.4.0"] -build-backend = "poetry.core.masonry.api" +requires = ["hatchling"] +build-backend = "hatchling.build" -[tool.poetry] +[project] name = "opacus_e2e" version = "0.1.0" description = "Opacus E2E testing" -authors = ["The Flower Authors "] +authors = [ + { name = "The Flower Authors", email = "hello@flower.ai" }, +] +dependencies = [ + "flwr[simulation] @ {root:parent:parent:uri}", + "opacus>=1.4.0,<2.0.0", + "torch>=1.13.1,<3.0.0", + "torchvision>=0.14.0,<2.0.0", +] -[tool.poetry.dependencies] -python = "^3.8" -flwr = { path = "../../", develop = true, extras = ["simulation"] } -opacus = "^1.4.0" -torch = "^1.13.1" -torchvision = "^0.14.0" +[tool.hatch.build.targets.wheel] +packages = ["."] + +[tool.hatch.metadata] +allow-direct-references = true diff --git a/e2e/opacus/simulation.py b/e2e/opacus/simulation.py index 5f0e5334bd08..bf05a77cf32a 100644 --- a/e2e/opacus/simulation.py +++ b/e2e/opacus/simulation.py @@ -1,11 +1,14 @@ -import flwr as fl - from client import client_fn +import flwr as fl + hist = fl.simulation.start_simulation( client_fn=client_fn, num_clients=2, config=fl.server.ServerConfig(num_rounds=3), ) -assert hist.losses_distributed[-1][1] == 0 or (hist.losses_distributed[0][1] / hist.losses_distributed[-1][1]) >= 0.98 +assert ( + hist.losses_distributed[-1][1] == 0 + or (hist.losses_distributed[0][1] / hist.losses_distributed[-1][1]) >= 0.98 +) diff --git a/e2e/pandas/client.py b/e2e/pandas/client.py index 0ecd75df3ae8..19e15f5a3b11 100644 --- a/e2e/pandas/client.py +++ b/e2e/pandas/client.py @@ -5,7 +5,6 @@ import flwr as fl - df = pd.read_csv("./data/client.csv") column_names = ["sepal length (cm)", "sepal width (cm)"] @@ -32,9 +31,11 @@ def fit( {}, ) + def client_fn(cid): return FlowerClient().to_client() + app = fl.client.ClientApp( client_fn=client_fn, ) diff --git a/e2e/pandas/pyproject.toml b/e2e/pandas/pyproject.toml index 416dfeec3460..f8f8488a7006 100644 --- a/e2e/pandas/pyproject.toml +++ b/e2e/pandas/pyproject.toml @@ -1,17 +1,26 @@ [build-system] -requires = ["poetry-core>=1.4.0"] -build-backend = "poetry.core.masonry.api" +requires = ["hatchling"] +build-backend = "hatchling.build" -[tool.poetry] +[project] name = "quickstart-pandas" version = "0.1.0" -description = "Pandas Federated Analytics Quickstart with Flower" -authors = ["Ragy Haddad "] -maintainers = ["The Flower Authors "] +description = "Pandas E2E test with Flower" +authors = [ + { name = "Ragy Haddad", email = "ragy202@gmail.com" }, +] +maintainers = [ + { name = "The Flower Authors", email = "hello@flower.ai" }, +] +dependencies = [ + "flwr[simulation] @ {root:parent:parent:uri}", + "numpy>=1.21.0,<2.0.0", + "pandas>=2.0.0,<3.0.0", + "scikit-learn>=1.1.1,<2.0.0", +] -[tool.poetry.dependencies] -python = "^3.8" -flwr = { path = "../../", develop = true, extras = ["simulation"] } -numpy = "^1.21.0" -pandas = "^2.0.0" -scikit-learn = "^1.1.1" +[tool.hatch.build.targets.wheel] +packages = ["."] + +[tool.hatch.metadata] +allow-direct-references = true diff --git a/e2e/pandas/server.py b/e2e/pandas/server.py index 4c69ab3881d2..ef0e92a11ea2 100644 --- a/e2e/pandas/server.py +++ b/e2e/pandas/server.py @@ -1,7 +1,7 @@ -import flwr as fl - from strategy import FedAnalytics +import flwr as fl + app = fl.server.ServerApp() diff --git a/e2e/pandas/simulation.py b/e2e/pandas/simulation.py index b548b5ebb760..8160fb744229 100644 --- a/e2e/pandas/simulation.py +++ b/e2e/pandas/simulation.py @@ -1,8 +1,8 @@ -import flwr as fl - from client import client_fn from strategy import FedAnalytics +import flwr as fl + hist = fl.simulation.start_simulation( client_fn=client_fn, num_clients=2, diff --git a/e2e/pytorch-lightning/client.py b/e2e/pytorch-lightning/client.py index fde550e31c08..fdd55b3dc344 100644 --- a/e2e/pytorch-lightning/client.py +++ b/e2e/pytorch-lightning/client.py @@ -1,9 +1,11 @@ -import flwr as fl +from collections import OrderedDict + import mnist import pytorch_lightning as pl -from collections import OrderedDict import torch +import flwr as fl + class FlowerClient(fl.client.NumPyClient): def __init__(self, model, train_loader, val_loader, test_loader): @@ -48,6 +50,7 @@ def _set_parameters(model, parameters): state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict}) model.load_state_dict(state_dict, strict=True) + def client_fn(cid): model = mnist.LitAutoEncoder() train_loader, val_loader, test_loader = mnist.load_data() @@ -55,10 +58,12 @@ def client_fn(cid): # Flower client return FlowerClient(model, train_loader, val_loader, test_loader).to_client() + app = fl.client.ClientApp( client_fn=client_fn, ) + def main() -> None: # Model and data model = mnist.LitAutoEncoder() diff --git a/e2e/pytorch-lightning/mnist.py b/e2e/pytorch-lightning/mnist.py index 462087739474..b23efc50d1e4 100644 --- a/e2e/pytorch-lightning/mnist.py +++ b/e2e/pytorch-lightning/mnist.py @@ -3,14 +3,13 @@ Source: pytorchlightning.ai (2021/02/04) """ - +import pytorch_lightning as pl import torch from torch import nn from torch.nn import functional as F from torch.utils.data import DataLoader, Subset, random_split from torchvision import transforms from torchvision.datasets import MNIST -import pytorch_lightning as pl class LitAutoEncoder(pl.LightningModule): @@ -62,14 +61,18 @@ def _evaluate(self, batch, stage=None): def load_data(): # Training / validation set - trainset = MNIST("./data", train=True, download=True, transform=transforms.ToTensor()) + trainset = MNIST( + "./data", train=True, download=True, transform=transforms.ToTensor() + ) trainset = Subset(trainset, range(1000)) mnist_train, mnist_val = random_split(trainset, [800, 200]) train_loader = DataLoader(mnist_train, batch_size=32, shuffle=True, num_workers=0) val_loader = DataLoader(mnist_val, batch_size=32, shuffle=False, num_workers=0) # Test set - testset = MNIST("./data", train=False, download=True, transform=transforms.ToTensor()) + testset = MNIST( + "./data", train=False, download=True, transform=transforms.ToTensor() + ) testset = Subset(testset, range(10)) test_loader = DataLoader(testset, batch_size=32, shuffle=False, num_workers=0) @@ -78,7 +81,6 @@ def load_data(): def main() -> None: """Centralized training.""" - # Load data train_loader, val_loader, test_loader = load_data() diff --git a/e2e/pytorch-lightning/pyproject.toml b/e2e/pytorch-lightning/pyproject.toml index 90d659813c28..8706ef098d8b 100644 --- a/e2e/pytorch-lightning/pyproject.toml +++ b/e2e/pytorch-lightning/pyproject.toml @@ -1,15 +1,22 @@ [build-system] -requires = ["poetry-core>=1.4.0"] -build-backend = "poetry.masonry.api" +requires = ["hatchling"] +build-backend = "hatchling.build" -[tool.poetry] -name = "quickstart-pytorch-lightning" +[project] +name = "quickstart-pytorch-lightning-test" version = "0.1.0" description = "Federated Learning E2E test with Flower and PyTorch Lightning" -authors = ["The Flower Authors "] +authors = [ + { name = "The Flower Authors", email = "hello@flower.ai" }, +] +dependencies = [ + "flwr[simulation] @ {root:parent:parent:uri}", + "pytorch-lightning==2.2.4", + "torchvision==0.14.1", +] -[tool.poetry.dependencies] -python = "^3.8" -flwr = { path = "../../", develop = true, extras = ["simulation"] } -pytorch-lightning = "2.2.4" -torchvision = "0.14.1" +[tool.hatch.build.targets.wheel] +packages = ["."] + +[tool.hatch.metadata] +allow-direct-references = true diff --git a/e2e/pytorch-lightning/simulation.py b/e2e/pytorch-lightning/simulation.py index 5f0e5334bd08..bf05a77cf32a 100644 --- a/e2e/pytorch-lightning/simulation.py +++ b/e2e/pytorch-lightning/simulation.py @@ -1,11 +1,14 @@ -import flwr as fl - from client import client_fn +import flwr as fl + hist = fl.simulation.start_simulation( client_fn=client_fn, num_clients=2, config=fl.server.ServerConfig(num_rounds=3), ) -assert hist.losses_distributed[-1][1] == 0 or (hist.losses_distributed[0][1] / hist.losses_distributed[-1][1]) >= 0.98 +assert ( + hist.losses_distributed[-1][1] == 0 + or (hist.losses_distributed[0][1] / hist.losses_distributed[-1][1]) >= 0.98 +) diff --git a/e2e/pytorch/client.py b/e2e/pytorch/client.py index 1fd07763148e..dbfbfed1ffa7 100644 --- a/e2e/pytorch/client.py +++ b/e2e/pytorch/client.py @@ -20,7 +20,7 @@ warnings.filterwarnings("ignore", category=UserWarning) DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") SUBSET_SIZE = 1000 -STATE_VAR = 'timestamp' +STATE_VAR = "timestamp" class Net(nn.Module): @@ -92,7 +92,7 @@ def load_data(): class FlowerClient(fl.client.NumPyClient): def get_parameters(self, config): return [val.cpu().numpy() for _, val in net.state_dict().items()] - + def _record_timestamp_to_state(self): """Record timestamp to client's state.""" t_stamp = datetime.now().timestamp() @@ -101,31 +101,45 @@ def _record_timestamp_to_state(self): value = self.context.state.configs_records[STATE_VAR][STATE_VAR] # type: ignore value += f",{t_stamp}" - self.context.state.configs_records[STATE_VAR] = ConfigsRecord({STATE_VAR: value}) - + self.context.state.configs_records[STATE_VAR] = ConfigsRecord( + {STATE_VAR: value} + ) + def _retrieve_timestamp_from_state(self): return self.context.state.configs_records[STATE_VAR][STATE_VAR] + def fit(self, parameters, config): set_parameters(net, parameters) train(net, trainloader, epochs=1) self._record_timestamp_to_state() - return self.get_parameters(config={}), len(trainloader.dataset), {STATE_VAR: self._retrieve_timestamp_from_state()} + return ( + self.get_parameters(config={}), + len(trainloader.dataset), + {STATE_VAR: self._retrieve_timestamp_from_state()}, + ) def evaluate(self, parameters, config): set_parameters(net, parameters) loss, accuracy = test(net, testloader) self._record_timestamp_to_state() - return loss, len(testloader.dataset), {"accuracy": accuracy, STATE_VAR: self._retrieve_timestamp_from_state()} + return ( + loss, + len(testloader.dataset), + {"accuracy": accuracy, STATE_VAR: self._retrieve_timestamp_from_state()}, + ) + def set_parameters(model, parameters): params_dict = zip(model.state_dict().keys(), parameters) state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict}) model.load_state_dict(state_dict, strict=True) - return + return + def client_fn(cid): return FlowerClient().to_client() + app = fl.client.ClientApp( client_fn=client_fn, ) diff --git a/e2e/pytorch/pyproject.toml b/e2e/pytorch/pyproject.toml index e538f1437df6..8c59c43d50df 100644 --- a/e2e/pytorch/pyproject.toml +++ b/e2e/pytorch/pyproject.toml @@ -1,16 +1,23 @@ [build-system] -requires = ["poetry-core>=1.4.0"] -build-backend = "poetry.core.masonry.api" +requires = ["hatchling"] +build-backend = "hatchling.build" -[tool.poetry] -name = "quickstart-pytorch" +[project] +name = "pytorch_e2e" version = "0.1.0" -description = "PyTorch Federated Learning Quickstart with Flower" -authors = ["The Flower Authors "] +description = "PyTorch Federated Learning E2E test with Flower" +authors = [ + { name = "The Flower Authors", email = "hello@flower.ai" }, +] +dependencies = [ + "flwr[simulation] @ {root:parent:parent:uri}", + "torch>=1.12.0,<2.0.0", + "torchvision>=0.14.1,<0.15.0", + "tqdm>=4.63.0,<5.0.0", +] -[tool.poetry.dependencies] -python = "^3.8" -flwr = { path = "../../", develop = true, extras = ["simulation"] } -torch = "^1.12.0" -torchvision = "^0.14.1" -tqdm = "^4.63.0" +[tool.hatch.build.targets.wheel] +packages = ["."] + +[tool.hatch.metadata] +allow-direct-references = true diff --git a/e2e/pytorch/simulation.py b/e2e/pytorch/simulation.py index a4c8d4642be2..25868eb8e33f 100644 --- a/e2e/pytorch/simulation.py +++ b/e2e/pytorch/simulation.py @@ -1,12 +1,12 @@ from typing import List, Tuple + import numpy as np +from client import client_fn import flwr as fl from flwr.common import Metrics - -from client import client_fn -STATE_VAR = 'timestamp' +STATE_VAR = "timestamp" # Define metric aggregation function @@ -15,18 +15,22 @@ def record_state_metrics(metrics: List[Tuple[int, Metrics]]) -> Metrics: states = [] for _, m in metrics: # split string and covert timestamps to float - states.append([float(tt) for tt in m[STATE_VAR].split(',')]) + states.append([float(tt) for tt in m[STATE_VAR].split(",")]) for client_state in states: if len(client_state) == 1: continue deltas = np.diff(client_state) - assert np.all(deltas > 0), f"Timestamps are not monotonically increasing: {client_state}" + assert np.all( + deltas > 0 + ), f"Timestamps are not monotonically increasing: {client_state}" return {STATE_VAR: states} -strategy = fl.server.strategy.FedAvg(evaluate_metrics_aggregation_fn=record_state_metrics) +strategy = fl.server.strategy.FedAvg( + evaluate_metrics_aggregation_fn=record_state_metrics +) hist = fl.simulation.start_simulation( client_fn=client_fn, @@ -35,8 +39,13 @@ def record_state_metrics(metrics: List[Tuple[int, Metrics]]) -> Metrics: strategy=strategy, ) -assert hist.losses_distributed[-1][1] == 0 or (hist.losses_distributed[0][1] / hist.losses_distributed[-1][1]) >= 0.98 +assert ( + hist.losses_distributed[-1][1] == 0 + or (hist.losses_distributed[0][1] / hist.losses_distributed[-1][1]) >= 0.98 +) # The checks in record_state_metrics don't do anythinng if client's state has a single entry state_metrics_last_round = hist.metrics_distributed[STATE_VAR][-1] -assert len(state_metrics_last_round[1][0]) == 2*state_metrics_last_round[0], f"There should be twice as many entries in the client state as rounds" +assert ( + len(state_metrics_last_round[1][0]) == 2 * state_metrics_last_round[0] +), "There should be twice as many entries in the client state as rounds" diff --git a/e2e/pytorch/simulation_next.py b/e2e/pytorch/simulation_next.py new file mode 100644 index 000000000000..ba1719dfb75b --- /dev/null +++ b/e2e/pytorch/simulation_next.py @@ -0,0 +1,14 @@ +from client import app as client_app + +import flwr as fl + +# Define ServerAppp +server_app = fl.server.ServerApp( + config=fl.server.ServerConfig(num_rounds=3), +) + + +# Run with FlowerNext +fl.simulation.run_simulation( + server_app=server_app, client_app=client_app, num_supernodes=2 +) diff --git a/e2e/scikit-learn/client.py b/e2e/scikit-learn/client.py index e073d3cb2748..b0691e75a79d 100644 --- a/e2e/scikit-learn/client.py +++ b/e2e/scikit-learn/client.py @@ -1,11 +1,11 @@ import warnings -import flwr as fl -import numpy as np +import numpy as np +import utils from sklearn.linear_model import LogisticRegression from sklearn.metrics import log_loss -import utils +import flwr as fl # Load MNIST dataset from https://www.openml.org/d/554 (X_train, y_train), (X_test, y_test) = utils.load_mnist() @@ -24,6 +24,7 @@ # Setting initial parameters, akin to model.compile for keras models utils.set_initial_params(model) + # Define Flower client class FlowerClient(fl.client.NumPyClient): def get_parameters(self, config): # type: ignore @@ -42,14 +43,18 @@ def evaluate(self, parameters, config): # type: ignore loss = log_loss(y_test, model.predict_proba(X_test)) accuracy = model.score(X_test, y_test) return loss, len(X_test), {"accuracy": accuracy} - + + def client_fn(cid): return FlowerClient().to_client() + app = fl.client.ClientApp( client_fn=client_fn, ) if __name__ == "__main__": # Start Flower client - fl.client.start_client(server_address="0.0.0.0:8080", client=FlowerClient().to_client()) + fl.client.start_client( + server_address="0.0.0.0:8080", client=FlowerClient().to_client() + ) diff --git a/e2e/scikit-learn/pyproject.toml b/e2e/scikit-learn/pyproject.toml index 50c07d31add7..caba2324d44f 100644 --- a/e2e/scikit-learn/pyproject.toml +++ b/e2e/scikit-learn/pyproject.toml @@ -1,18 +1,23 @@ [build-system] -requires = ["poetry-core>=1.4.0"] -build-backend = "poetry.core.masonry.api" +requires = ["hatchling"] +build-backend = "hatchling.build" -[tool.poetry] -name = "sklearn-mnist" +[project] +name = "sklearn-mnist-test" version = "0.1.0" -description = "Federated learning with scikit-learn and Flower" +description = "Federated learning E2E test with scikit-learn and Flower" authors = [ - "The Flower Authors ", - "Kaushik Amar Das ", + { name = "The Flower Authors", email = "hello@flower.ai" }, + { name = "Kaushik Amar Das", email = "kaushik.das@iiitg.ac.in"}, ] +dependencies = [ + "flwr[simulation,rest] @ {root:parent:parent:uri}", + "scikit-learn>=1.1.1,<2.0.0", + "openml>=0.14.0,<0.15.0" +] + +[tool.hatch.build.targets.wheel] +packages = ["."] -[tool.poetry.dependencies] -python = "^3.8" -flwr = { path = "../../", develop = true, extras = ["simulation"] } -scikit-learn = "^1.1.1" -openml = "^0.14.0" +[tool.hatch.metadata] +allow-direct-references = true diff --git a/e2e/scikit-learn/simulation.py b/e2e/scikit-learn/simulation.py index 5f0e5334bd08..bf05a77cf32a 100644 --- a/e2e/scikit-learn/simulation.py +++ b/e2e/scikit-learn/simulation.py @@ -1,11 +1,14 @@ -import flwr as fl - from client import client_fn +import flwr as fl + hist = fl.simulation.start_simulation( client_fn=client_fn, num_clients=2, config=fl.server.ServerConfig(num_rounds=3), ) -assert hist.losses_distributed[-1][1] == 0 or (hist.losses_distributed[0][1] / hist.losses_distributed[-1][1]) >= 0.98 +assert ( + hist.losses_distributed[-1][1] == 0 + or (hist.losses_distributed[0][1] / hist.losses_distributed[-1][1]) >= 0.98 +) diff --git a/e2e/scikit-learn/utils.py b/e2e/scikit-learn/utils.py index 2b8dcf8655ee..b7b202f7b760 100644 --- a/e2e/scikit-learn/utils.py +++ b/e2e/scikit-learn/utils.py @@ -1,7 +1,8 @@ -from typing import Tuple, Union, List +from typing import List, Tuple, Union + import numpy as np -from sklearn.linear_model import LogisticRegression import openml +from sklearn.linear_model import LogisticRegression XY = Tuple[np.ndarray, np.ndarray] Dataset = Tuple[XY, XY] @@ -34,12 +35,11 @@ def set_model_params( def set_initial_params(model: LogisticRegression): - """Sets initial parameters as zeros Required since model params are - uninitialized until model.fit is called. + """Sets initial parameters as zeros Required since model params are uninitialized + until model.fit is called. - But server asks for initial parameters from clients at launch. Refer - to sklearn.linear_model.LogisticRegression documentation for more - information. + But server asks for initial parameters from clients at launch. Refer to + sklearn.linear_model.LogisticRegression documentation for more information. """ n_classes = 10 # MNIST has 10 classes n_features = 784 # Number of features in dataset diff --git a/e2e/server.py b/e2e/server.py index 3b37ac4244e9..c678cd0a2446 100644 --- a/e2e/server.py +++ b/e2e/server.py @@ -1,6 +1,5 @@ import numpy as np - import flwr as fl STATE_VAR = "timestamp" @@ -77,4 +76,4 @@ def main(driver, context): state_metrics_last_round = hist.metrics_distributed[STATE_VAR][-1] assert ( len(state_metrics_last_round[1][0]) == 2 * state_metrics_last_round[0] - ), f"There should be twice as many entries in the client state as rounds" + ), "There should be twice as many entries in the client state as rounds" diff --git a/e2e/strategies/client.py b/e2e/strategies/client.py index 3b49f770dc6b..505340e013a5 100644 --- a/e2e/strategies/client.py +++ b/e2e/strategies/client.py @@ -1,19 +1,23 @@ import os -import flwr as fl import tensorflow as tf +import flwr as fl + SUBSET_SIZE = 1000 # Make TensorFlow log less verbose os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" + def get_model(): - model = tf.keras.models.Sequential([ - tf.keras.layers.Flatten(input_shape=(28, 28)), - tf.keras.layers.Dense(128, activation='relu'), - tf.keras.layers.Dense(10) - ]) + model = tf.keras.models.Sequential( + [ + tf.keras.layers.Flatten(input_shape=(28, 28)), + tf.keras.layers.Dense(128, activation="relu"), + tf.keras.layers.Dense(10), + ] + ) model.compile( optimizer=tf.keras.optimizers.Adam(0.001), loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), @@ -21,6 +25,7 @@ def get_model(): ) return model + model = get_model() (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data() x_train, y_train = x_train[:SUBSET_SIZE], y_train[:SUBSET_SIZE] @@ -54,4 +59,6 @@ def client_fn(cid): if __name__ == "__main__": # Start Flower client - fl.client.start_client(server_address="127.0.0.1:8080", client=FlowerClient().to_client()) + fl.client.start_client( + server_address="127.0.0.1:8080", client=FlowerClient().to_client() + ) diff --git a/e2e/tabnet/client.py b/e2e/tabnet/client.py index 0290ba4629de..1a7ecfd68f73 100644 --- a/e2e/tabnet/client.py +++ b/e2e/tabnet/client.py @@ -1,8 +1,10 @@ import os -import flwr as fl + +import tabnet import tensorflow as tf import tensorflow_datasets as tfds -import tabnet + +import flwr as fl train_size = 125 BATCH_SIZE = 50 @@ -81,10 +83,13 @@ def evaluate(self, parameters, config): def client_fn(cid): return FlowerClient().to_client() + app = fl.client.ClientApp( client_fn=client_fn, ) if __name__ == "__main__": # Start Flower client - fl.client.start_client(server_address="127.0.0.1:8080", client=FlowerClient().to_client()) + fl.client.start_client( + server_address="127.0.0.1:8080", client=FlowerClient().to_client() + ) diff --git a/e2e/tabnet/pyproject.toml b/e2e/tabnet/pyproject.toml index b1abf382a24a..99379ddb607e 100644 --- a/e2e/tabnet/pyproject.toml +++ b/e2e/tabnet/pyproject.toml @@ -1,18 +1,25 @@ [build-system] -requires = ["poetry-core>=1.4.0"] -build-backend = "poetry.core.masonry.api" +requires = ["hatchling"] +build-backend = "hatchling.build" -[tool.poetry] -name = "quickstart-tabnet" +[project] +name = "quickstart-tabnet-test" version = "0.1.0" -description = "Tabnet Federated Learning Quickstart with Flower" -authors = ["The Flower Authors "] +description = "Tabnet Federated Learning E2E test with Flower" +authors = [ + { name = "The Flower Authors", email = "hello@flower.ai" }, +] +dependencies = [ + "flwr[simulation] @ {root:parent:parent:uri}", + "tensorflow-cpu>=2.9.1,!=2.11.1; platform_machine == \"x86_64\"", + "tensorflow-macos>=2.9.1,!=2.11.1; sys_platform == \"darwin\" and platform_machine == \"arm64\"", + "tensorflow_datasets==4.9.2", + "tensorflow-io-gcs-filesystem<0.35.0", + "tabnet==0.1.6", +] -[tool.poetry.dependencies] -python = ">=3.8,<3.11" -flwr = { path = "../../", develop = true, extras = ["simulation"] } -tensorflow-cpu = { version = ">=2.9.1,<2.11.1 || >2.11.1", markers = "platform_machine == \"x86_64\"" } -tensorflow-macos = { version = ">=2.9.1,<2.11.1 || >2.11.1", markers = "sys_platform == \"darwin\" and platform_machine == \"arm64\"" } -tensorflow_datasets = "4.9.2" -tensorflow-io-gcs-filesystem = "<0.35.0" -tabnet = "0.1.6" +[tool.hatch.build.targets.wheel] +packages = ["."] + +[tool.hatch.metadata] +allow-direct-references = true diff --git a/e2e/tabnet/simulation.py b/e2e/tabnet/simulation.py index 5f0e5334bd08..bf05a77cf32a 100644 --- a/e2e/tabnet/simulation.py +++ b/e2e/tabnet/simulation.py @@ -1,11 +1,14 @@ -import flwr as fl - from client import client_fn +import flwr as fl + hist = fl.simulation.start_simulation( client_fn=client_fn, num_clients=2, config=fl.server.ServerConfig(num_rounds=3), ) -assert hist.losses_distributed[-1][1] == 0 or (hist.losses_distributed[0][1] / hist.losses_distributed[-1][1]) >= 0.98 +assert ( + hist.losses_distributed[-1][1] == 0 + or (hist.losses_distributed[0][1] / hist.losses_distributed[-1][1]) >= 0.98 +) diff --git a/e2e/tensorflow/client.py b/e2e/tensorflow/client.py index 10ee91136241..779be0c3746d 100644 --- a/e2e/tensorflow/client.py +++ b/e2e/tensorflow/client.py @@ -1,8 +1,9 @@ import os -import flwr as fl import tensorflow as tf +import flwr as fl + SUBSET_SIZE = 1000 # Make TensorFlow log less verbose @@ -31,13 +32,17 @@ def evaluate(self, parameters, config): loss, accuracy = model.evaluate(x_test, y_test) return loss, len(x_test), {"accuracy": accuracy} + def client_fn(cid): return FlowerClient().to_client() + app = fl.client.ClientApp( client_fn=client_fn, ) if __name__ == "__main__": # Start Flower client - fl.client.start_client(server_address="127.0.0.1:8080", client=FlowerClient().to_client()) + fl.client.start_client( + server_address="127.0.0.1:8080", client=FlowerClient().to_client() + ) diff --git a/e2e/tensorflow/pyproject.toml b/e2e/tensorflow/pyproject.toml index a7dbfe2305db..4b035873223c 100644 --- a/e2e/tensorflow/pyproject.toml +++ b/e2e/tensorflow/pyproject.toml @@ -1,15 +1,22 @@ [build-system] -requires = ["poetry-core>=1.4.0"] -build-backend = "poetry.core.masonry.api" +requires = ["hatchling"] +build-backend = "hatchling.build" -[tool.poetry] -name = "quickstart-tensorflow" +[project] +name = "quickstart-tensorflow-test" version = "0.1.0" -description = "Keras Federated Learning Quickstart with Flower" -authors = ["The Flower Authors "] +description = "Keras Federated Learning E2E test with Flower" +authors = [ + { name = "The Flower Authors", email = "hello@flower.ai" }, +] +dependencies = [ + "flwr[simulation] @ {root:parent:parent:uri}", + "tensorflow-cpu>=2.9.1,!=2.11.1", + "tensorflow-io-gcs-filesystem<0.35.0", +] -[tool.poetry.dependencies] -python = ">=3.8,<3.11" -flwr = { path = "../../", develop = true, extras = ["simulation"] } -tensorflow-cpu = "^2.9.1, !=2.11.1" -tensorflow-io-gcs-filesystem = "<0.35.0" +[tool.hatch.build.targets.wheel] +packages = ["."] + +[tool.hatch.metadata] +allow-direct-references = true diff --git a/e2e/tensorflow/simulation.py b/e2e/tensorflow/simulation.py index 5f0e5334bd08..bf05a77cf32a 100644 --- a/e2e/tensorflow/simulation.py +++ b/e2e/tensorflow/simulation.py @@ -1,11 +1,14 @@ -import flwr as fl - from client import client_fn +import flwr as fl + hist = fl.simulation.start_simulation( client_fn=client_fn, num_clients=2, config=fl.server.ServerConfig(num_rounds=3), ) -assert hist.losses_distributed[-1][1] == 0 or (hist.losses_distributed[0][1] / hist.losses_distributed[-1][1]) >= 0.98 +assert ( + hist.losses_distributed[-1][1] == 0 + or (hist.losses_distributed[0][1] / hist.losses_distributed[-1][1]) >= 0.98 +) diff --git a/e2e/tensorflow/simulation_next.py b/e2e/tensorflow/simulation_next.py new file mode 100644 index 000000000000..ba1719dfb75b --- /dev/null +++ b/e2e/tensorflow/simulation_next.py @@ -0,0 +1,14 @@ +from client import app as client_app + +import flwr as fl + +# Define ServerAppp +server_app = fl.server.ServerApp( + config=fl.server.ServerConfig(num_rounds=3), +) + + +# Run with FlowerNext +fl.simulation.run_simulation( + server_app=server_app, client_app=client_app, num_supernodes=2 +) diff --git a/e2e/test_driver.sh b/e2e/test_driver.sh index 6d7f6ec864fd..e177863bab78 100755 --- a/e2e/test_driver.sh +++ b/e2e/test_driver.sh @@ -9,7 +9,7 @@ case "$1" in ;; bare-https) ./generate.sh - server_arg="--certificates certificates/ca.crt certificates/server.pem certificates/server.key" + server_arg="--ssl-ca-certfile certificates/ca.crt --ssl-certfile certificates/server.pem --ssl-keyfile certificates/server.key" client_arg="--root-certificates certificates/ca.crt" server_dir="./" ;; @@ -22,7 +22,8 @@ esac case "$2" in rest) - rest_arg="--rest" + rest_arg_superlink="--fleet-api-type rest" + rest_arg_supernode="--rest" server_address="http://localhost:9093" server_app_address="127.0.0.1:9091" db_arg="--database :flwr-in-memory-state:" @@ -31,7 +32,8 @@ case "$2" in client_auth_2="" ;; sqlite) - rest_arg="" + rest_arg_superlink="" + rest_arg_supernode="" server_address="127.0.0.1:9092" server_app_address="127.0.0.1:9091" db_arg="--database $(date +%s).db" @@ -41,18 +43,20 @@ case "$2" in ;; client-auth) ./generate.sh - rest_arg="" + rest_arg_superlink="" + rest_arg_supernode="" server_address="127.0.0.1:9092" server_app_address="127.0.0.1:9091" db_arg="--database :flwr-in-memory-state:" - server_arg="--certificates certificates/ca.crt certificates/server.pem certificates/server.key" + server_arg="--ssl-ca-certfile certificates/ca.crt --ssl-certfile certificates/server.pem --ssl-keyfile certificates/server.key" client_arg="--root-certificates certificates/ca.crt" - server_auth="--require-client-authentication keys/client_public_keys.csv keys/server_credentials keys/server_credentials.pub" - client_auth_1="--authentication-keys keys/client_credentials_1 keys/client_credentials_1.pub" - client_auth_2="--authentication-keys keys/client_credentials_2 keys/client_credentials_2.pub" + server_auth="--auth-list-public-keys keys/client_public_keys.csv --auth-superlink-private-key keys/server_credentials --auth-superlink-public-key keys/server_credentials.pub" + client_auth_1="--auth-supernode-private-key keys/client_credentials_1 --auth-supernode-public-key keys/client_credentials_1.pub" + client_auth_2="--auth-supernode-private-key keys/client_credentials_2 --auth-supernode-public-key keys/client_credentials_2.pub" ;; *) - rest_arg="" + rest_arg_superlink="" + rest_arg_supernode="" server_address="127.0.0.1:9092" server_app_address="127.0.0.1:9091" db_arg="--database :flwr-in-memory-state:" @@ -62,19 +66,19 @@ case "$2" in ;; esac -timeout 2m flower-superlink $server_arg $db_arg $rest_arg $server_auth & +timeout 2m flower-superlink $server_arg $db_arg $rest_arg_superlink $server_auth & sl_pid=$! sleep 3 -timeout 2m flower-client-app client:app $client_arg $rest_arg --server $server_address $client_auth_1 & +timeout 2m flower-client-app client:app $client_arg $rest_arg_supernode --superlink $server_address $client_auth_1 & cl1_pid=$! sleep 3 -timeout 2m flower-client-app client:app $client_arg $rest_arg --server $server_address $client_auth_2 & +timeout 2m flower-client-app client:app $client_arg $rest_arg_supernode --superlink $server_address $client_auth_2 & cl2_pid=$! sleep 3 -timeout 2m flower-server-app server:app $client_arg --dir $server_dir --server $server_app_address & +timeout 2m flower-server-app server:app $client_arg --dir $server_dir --superlink $server_app_address & pid=$! wait $pid @@ -84,4 +88,3 @@ if [[ "$res" = "0" ]]; then echo "Training worked correctly"; kill $cl1_pid; kill $cl2_pid; kill $sl_pid; else echo "Training had an issue" && exit 1; fi - diff --git a/examples/doc/source/conf.py b/examples/doc/source/conf.py index b9c18fba2e18..47847a0fd767 100644 --- a/examples/doc/source/conf.py +++ b/examples/doc/source/conf.py @@ -29,7 +29,7 @@ author = "The Flower Authors" # The full version, including alpha/beta/rc tags -release = "1.9.0" +release = "1.10.0" # -- General configuration --------------------------------------------------- diff --git a/examples/dp-sgd-mnist/README.md b/examples/dp-sgd-mnist/README.md deleted file mode 100644 index fcf602306c90..000000000000 --- a/examples/dp-sgd-mnist/README.md +++ /dev/null @@ -1,105 +0,0 @@ -# Flower Example Using Tensorflow/Keras and Tensorflow Privacy - -This example of Flower trains a federeated learning system where clients are free to choose -between non-private and private optimizers. Specifically, clients can choose to train Keras models using the standard SGD optimizer or __Differentially Private__ SGD (DPSGD) from [Tensorflow Privacy](https://github.com/tensorflow/privacy). For this task we use the MNIST dataset which is split artificially among clients. This causes the dataset to be i.i.d. The clients using DPSGD track the amount of privacy spent and display it at the end of the training. - -This example is adapted from https://github.com/tensorflow/privacy/blob/master/tutorials/mnist_dpsgd_tutorial_keras.py - -## Project Setup - -Start by cloning the example project. We prepared a single-line command that you can copy into your shell which will checkout the example for you: - -```shell -git clone --depth=1 https://github.com/adap/flower.git && mv flower/examples/dp-sgd-mnist . && rm -rf flower && cd dp-sgd-mnist -``` - -This will create a new directory called `dp-sgd-mnist` containing the following files: - -```shell --- pyproject.toml --- requirements.txt --- client.py --- server.py --- common.py --- README.md -``` - -### Installing Dependencies - -Project dependencies (such as `tensorflow` and `tensorflow-privacy`) are defined in `pyproject.toml` and `requirements.txt`. We recommend [Poetry](https://python-poetry.org/docs/) to install those dependencies and manage your virtual environment ([Poetry installation](https://python-poetry.org/docs/#installation)) or [pip](https://pip.pypa.io/en/latest/development/), but feel free to use a different way of installing dependencies and managing virtual environments if you have other preferences. - -#### Poetry - -```shell -poetry install -poetry shell -``` - -Poetry will install all your dependencies in a newly created virtual environment. To verify that everything works correctly you can run the following command: - -```shell -poetry run python3 -c "import flwr" -``` - -If you don't see any errors you're good to go! - -#### pip - -Write the command below in your terminal to install the dependencies according to the configuration file requirements.txt. - -```shell -pip install -r requirements.txt -``` - -## Run Federated Learning with TensorFlow/Keras/Tensorflow-Privacy and Flower - -Afterwards you are ready to start the Flower server as well as the clients. You can simply start the server in a terminal as follows: - -```shell -# terminal 1 -poetry run python3 server.py -``` - -Now you are ready to start the Flower clients which will participate in the learning. To do so simply open two more terminals and run the following command in each: - -```shell -# terminal 2 -poetry run python3 client.py --partition 0 -``` - -```shell -# terminal 3 -# We will set the second client to use `dpsgd` -poetry run python3 client.py --partition 1 --dpsgd True -``` - -Alternatively you can run all of it in one shell as follows: - -```shell -poetry run python3 server.py & -poetry run python3 client.py --partition 0 & -poetry run python3 client.py --partition 1 --dpsgd True -``` - -It should be noted that when starting more than 2 clients, the total number of clients you intend to run and the data partition the client is expected to use must be specified. This is because the `num_clients` is used to split the dataset. - -For example, in case of 3 clients - -```shell -poetry run python3 server.py --num-clients 3 & -poetry run python3 client.py --num-clients 3 --partition 0 --dpsgd True & -poetry run python3 client.py --num-clients 3 --partition 1 & -poetry run python3 client.py --num-clients 3 --partition 2 --dpsgd True -``` - -Additional training parameters for the client and server can be referenced by passing `--help` to either script. - -Other things to note is that when all clients are running `dpsgd`, either train for more rounds or increase the local epochs to achieve optimal performance. You shall need to carefully tune the hyperparameters to your specific setup. - -```shell -poetry run python3 server.py --num-clients 3 --num-rounds 20 -``` - -```shell -poetry run python3 client.py --num-clients 3 --partition 1 --local-epochs 4 --dpsgd True -``` diff --git a/examples/dp-sgd-mnist/client.py b/examples/dp-sgd-mnist/client.py deleted file mode 100644 index cffe0e241645..000000000000 --- a/examples/dp-sgd-mnist/client.py +++ /dev/null @@ -1,162 +0,0 @@ -import argparse -import os - -import tensorflow as tf -from tensorflow_privacy.privacy.optimizers.dp_optimizer_keras_vectorized import ( - VectorizedDPKerasSGDOptimizer, -) - -import flwr as fl - -import common - - -# Make TensorFlow logs less verbose -os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" - -# global for tracking privacy -PRIVACY_LOSS = 0 - - -# Define Flower client -class MnistClient(fl.client.NumPyClient): - def __init__(self, model, x_train, y_train, x_test, y_test, args): - self.model = model - self.x_train, self.y_train = x_train, y_train - self.x_test, self.y_test = x_test, y_test - self.batch_size = args.batch_size - self.local_epochs = args.local_epochs - self.dpsgd = args.dpsgd - - if args.dpsgd: - self.noise_multiplier = args.noise_multiplier - if args.batch_size % args.microbatches != 0: - raise ValueError( - "Number of microbatches should divide evenly batch_size" - ) - optimizer = VectorizedDPKerasSGDOptimizer( - l2_norm_clip=args.l2_norm_clip, - noise_multiplier=args.noise_multiplier, - num_microbatches=args.microbatches, - learning_rate=args.learning_rate, - ) - # Compute vector of per-example loss rather than its mean over a minibatch. - loss = tf.keras.losses.CategoricalCrossentropy( - from_logits=True, reduction=tf.losses.Reduction.NONE - ) - else: - optimizer = tf.keras.optimizers.SGD(learning_rate=args.learning_rate) - loss = tf.keras.losses.CategoricalCrossentropy(from_logits=True) - - # Compile model with Keras - model.compile(optimizer=optimizer, loss=loss, metrics=["accuracy"]) - - def get_parameters(self, config): - """Get parameters of the local model.""" - raise Exception("Not implemented (server-side parameter initialization)") - - def fit(self, parameters, config): - """Train parameters on the locally held training set.""" - # Update local model parameters - global PRIVACY_LOSS - if self.dpsgd: - privacy_spent = common.compute_epsilon( - self.local_epochs, - len(self.x_train), - self.batch_size, - self.noise_multiplier, - ) - PRIVACY_LOSS += privacy_spent - - self.model.set_weights(parameters) - # Train the model - self.model.fit( - self.x_train, - self.y_train, - epochs=self.local_epochs, - batch_size=self.batch_size, - ) - - return self.model.get_weights(), len(self.x_train), {} - - def evaluate(self, parameters, config): - """Evaluate parameters on the locally held test set.""" - - # Update local model with global parameters - self.model.set_weights(parameters) - - # Evaluate global model parameters on the local test data and return results - loss, accuracy = self.model.evaluate(self.x_test, self.y_test) - num_examples_test = len(self.x_test) - return loss, num_examples_test, {"accuracy": accuracy} - - -def main(args) -> None: - # Load Keras model - model = common.create_cnn_model() - - # Load a subset of MNIST to simulate the local data partition - (x_train, y_train), (x_test, y_test) = common.load(args.num_clients)[args.partition] - - # drop samples to form exact batches for dpsgd - # this is necessary since dpsgd is sensitive to uneven batches - # due to microbatching - if args.dpsgd and x_train.shape[0] % args.batch_size != 0: - drop_num = x_train.shape[0] % args.batch_size - x_train = x_train[:-drop_num] - y_train = y_train[:-drop_num] - - # Start Flower client - client = MnistClient(model, x_train, y_train, x_test, y_test, args) - fl.client.start_numpy_client(server_address="127.0.0.1:8080", client=client) - if args.dpsgd: - print("Privacy Loss: ", PRIVACY_LOSS) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Flower Client") - parser.add_argument( - "--num-clients", - default=2, - type=int, - help="Total number of fl participants, requied to get correct partition", - ) - parser.add_argument( - "--partition", - type=int, - required=True, - help="Data Partion to train on. Must be less than number of clients", - ) - parser.add_argument( - "--local-epochs", - default=1, - type=int, - help="Total number of local epochs to train", - ) - parser.add_argument("--batch-size", default=32, type=int, help="Batch size") - parser.add_argument( - "--learning-rate", default=0.15, type=float, help="Learning rate for training" - ) - # DPSGD specific arguments - parser.add_argument( - "--dpsgd", - default=False, - type=bool, - help="If True, train with DP-SGD. If False, " "train with vanilla SGD.", - ) - parser.add_argument("--l2-norm-clip", default=1.0, type=float, help="Clipping norm") - parser.add_argument( - "--noise-multiplier", - default=1.1, - type=float, - help="Ratio of the standard deviation to the clipping norm", - ) - parser.add_argument( - "--microbatches", - default=32, - type=int, - help="Number of microbatches " "(must evenly divide batch_size)", - ) - args = parser.parse_args() - - main(args) diff --git a/examples/dp-sgd-mnist/common.py b/examples/dp-sgd-mnist/common.py deleted file mode 100644 index fbb2f6374203..000000000000 --- a/examples/dp-sgd-mnist/common.py +++ /dev/null @@ -1,103 +0,0 @@ -from typing import List, Tuple - -import numpy as np -import tensorflow as tf - -from tensorflow_privacy.privacy.analysis.rdp_accountant import compute_rdp -from tensorflow_privacy.privacy.analysis.rdp_accountant import get_privacy_spent - - -XY = Tuple[np.ndarray, np.ndarray] -XYList = List[XY] -PartitionedDataset = List[Tuple[XY, XY]] - - -def compute_epsilon( - epochs: int, num_train_examples: int, batch_size: int, noise_multiplier: float -) -> float: - """Computes epsilon value for given hyperparameters. - - Based on - github.com/tensorflow/privacy/blob/master/tutorials/mnist_dpsgd_tutorial_keras.py - """ - if noise_multiplier == 0.0: - return float("inf") - steps = epochs * num_train_examples // batch_size - orders = [1 + x / 10.0 for x in range(1, 100)] + list(range(12, 64)) - sampling_probability = batch_size / num_train_examples - rdp = compute_rdp( - q=sampling_probability, - noise_multiplier=noise_multiplier, - steps=steps, - orders=orders, - ) - # Delta is set to approximate 1 / (number of training points). - return get_privacy_spent(orders, rdp, target_delta=1 / num_train_examples)[0] - - -def create_cnn_model() -> tf.keras.Model: - """Returns a sequential keras CNN Model.""" - return tf.keras.Sequential( - [ - tf.keras.layers.Conv2D( - 16, - 8, - strides=2, - padding="same", - activation="relu", - input_shape=(28, 28, 1), - ), - tf.keras.layers.MaxPool2D(2, 1), - tf.keras.layers.Conv2D( - 32, 4, strides=2, padding="valid", activation="relu" - ), - tf.keras.layers.MaxPool2D(2, 1), - tf.keras.layers.Flatten(), - tf.keras.layers.Dense(32, activation="relu"), - tf.keras.layers.Dense(10), - ] - ) - - -def shuffle(X: np.ndarray, y: np.ndarray) -> XY: - """Shuffle X and y.""" - rng = np.random.default_rng() - idx = rng.permutation(len(X)) - return X[idx], y[idx] - - -def partition(X: np.ndarray, y: np.ndarray, num_partitions: int) -> XYList: - """Split X and y into a number of partitions.""" - return list( - zip(np.array_split(X, num_partitions), np.array_split(y, num_partitions)) - ) - - -def preprocess(X: np.ndarray, y: np.ndarray) -> XY: - """Basic preprocessing for MNIST dataset.""" - X = np.array(X, dtype=np.float32) / 255 - X = X.reshape((X.shape[0], 28, 28, 1)) - - y = np.array(y, dtype=np.int32) - y = tf.keras.utils.to_categorical(y, num_classes=10) - - return X, y - - -def create_partitions(source_dataset: XY, num_partitions: int) -> XYList: - """Create partitioned version of a source dataset.""" - X, y = source_dataset - X, y = shuffle(X, y) - X, y = preprocess(X, y) - xy_partitions = partition(X, y, num_partitions) - return xy_partitions - - -def load( - num_partitions: int, -) -> PartitionedDataset: - """Create partitioned version of MNIST.""" - xy_train, xy_test = tf.keras.datasets.mnist.load_data() - xy_train_partitions = create_partitions(xy_train, num_partitions) - xy_test_partitions = create_partitions(xy_test, num_partitions) - return list(zip(xy_train_partitions, xy_test_partitions)) diff --git a/examples/dp-sgd-mnist/pyproject.toml b/examples/dp-sgd-mnist/pyproject.toml deleted file mode 100644 index 161952fd2aa4..000000000000 --- a/examples/dp-sgd-mnist/pyproject.toml +++ /dev/null @@ -1,20 +0,0 @@ -[build-system] -requires = ["poetry-core>=1.4.0"] -build-backend = "poetry.core.masonry.api" - -[tool.poetry] -name = "dp-sgd-mnist" -version = "0.1.0" -description = "Federated training with Tensorflow Privacy" -authors = [ - "The Flower Authors ", - "Kaushik Amar Das ", -] - -[tool.poetry.dependencies] -python = ">=3.8,<3.11" -# flwr = { path = "../../", develop = true } # Development -flwr = ">=1.0,<2.0" -tensorflow-cpu = { version = ">=2.9.1,<2.11.1 || >2.11.1", markers = "platform_machine == \"x86_64\"" } -tensorflow-macos = { version = ">=2.9.1,<2.11.1 || >2.11.1", markers = "sys_platform == \"darwin\" and platform_machine == \"arm64\"" } -tensorflow-privacy = "0.8.10" diff --git a/examples/dp-sgd-mnist/requirements.txt b/examples/dp-sgd-mnist/requirements.txt deleted file mode 100644 index bd5478de342b..000000000000 --- a/examples/dp-sgd-mnist/requirements.txt +++ /dev/null @@ -1,4 +0,0 @@ -flwr>=1.0, <2.0 -tensorflow-macos>=2.9.1, != 2.11.1 ; sys_platform == "darwin" and platform_machine == "arm64" -tensorflow-cpu>=2.9.1, != 2.11.1 ; platform_machine == "x86_64" -tensorflow-privacy==0.8.10 diff --git a/examples/dp-sgd-mnist/server.py b/examples/dp-sgd-mnist/server.py deleted file mode 100644 index 5f6f7163834e..000000000000 --- a/examples/dp-sgd-mnist/server.py +++ /dev/null @@ -1,56 +0,0 @@ -import argparse -import os - -import tensorflow as tf - -import flwr as fl - -import common - -# Make TensorFlow logs less verbose -os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" - - -def get_evaluate_fn(model): - """Return an evaluation function for server-side evaluation.""" - - # Load test data here to avoid the overhead of doing it in `evaluate` itself - _, test = tf.keras.datasets.mnist.load_data() - test_data, test_labels = test - - # preprocessing - test_data, test_labels = common.preprocess(test_data, test_labels) - - # The `evaluate` function will be called after every round - def evaluate(weights: fl.common.NDArrays): - model.set_weights(weights) # Update model with the latest parameters - loss, accuracy = model.evaluate(test_data, test_labels) - return loss, {"accuracy": accuracy} - - return evaluate - - -def main(args) -> None: - model = common.create_cnn_model() - loss = tf.keras.losses.CategoricalCrossentropy(from_logits=True) - model.compile("sgd", loss=loss, metrics=["accuracy"]) - strategy = fl.server.strategy.FedAvg( - fraction_fit=args.fraction_fit, - min_available_clients=args.num_clients, - evaluate_fn=get_evaluate_fn(model), - initial_parameters=fl.common.ndarrays_to_parameters(model.get_weights()), - ) - fl.server.start_server( - server_address="0.0.0.0:8080", - strategy=strategy, - config=fl.server.ServerConfig(num_rounds=args.num_rounds), - ) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Server Script") - parser.add_argument("--num-clients", default=2, type=int) - parser.add_argument("--num-rounds", default=1, type=int) - parser.add_argument("--fraction-fit", default=1.0, type=float) - args = parser.parse_args() - main(args) diff --git a/examples/fl-tabular/README.md b/examples/fl-tabular/README.md new file mode 100644 index 000000000000..58afd1080b70 --- /dev/null +++ b/examples/fl-tabular/README.md @@ -0,0 +1,39 @@ +# Flower Example on Adult Census Income Tabular Dataset + +This code exemplifies a federated learning setup using the Flower framework on the ["Adult Census Income"](https://huggingface.co/datasets/scikit-learn/adult-census-income) tabular dataset. The "Adult Census Income" dataset contains demographic information such as age, education, occupation, etc., with the target attribute being income level (\<=50K or >50K). The dataset is partitioned into subsets, simulating a federated environment with 5 clients, each holding a distinct portion of the data. Categorical variables are one-hot encoded, and the data is split into training and testing sets. Federated learning is conducted using the FedAvg strategy for 5 rounds. + +This example uses [Flower Datasets](https://flower.ai/docs/datasets/) to download, partition and preprocess the dataset. + +## Environments Setup + +Start by cloning the example. We prepared a single-line command that you can copy into your shell which will checkout the example for you: + +```shell +git clone --depth=1 https://github.com/adap/flower.git && mv flower/examples/fl-tabular . && rm -rf flower && cd fl-tabular +``` + +This will create a new directory called `fl-tabular` containing the following files: + +```shell +-- pyproject.toml +-- client.py +-- server.py +-- task.py +-- README.md +``` + +### Installing dependencies + +Project dependencies are defined in `pyproject.toml`. Install them with: + +```shell +pip install . +``` + +## Running Code + +### Federated Using Flower Simulation + +```bash +flower-simulation --server-app server:app --client-app client:app --num-supernodes 5 +``` diff --git a/examples/fl-tabular/client.py b/examples/fl-tabular/client.py new file mode 100644 index 000000000000..228183f4edc4 --- /dev/null +++ b/examples/fl-tabular/client.py @@ -0,0 +1,38 @@ +from flwr.client import Client, ClientApp, NumPyClient +from flwr_datasets import FederatedDataset +from task import set_weights, get_weights, train, evaluate, IncomeClassifier, load_data + +NUMBER_OF_CLIENTS = 5 + + +class FlowerClient(NumPyClient): + def __init__(self, net, trainloader, testloader): + self.net = net + self.trainloader = trainloader + self.testloader = testloader + + def fit(self, parameters, config): + set_weights(self.net, parameters) + train(self.net, self.trainloader) + return get_weights(self.net), len(self.trainloader), {} + + def evaluate(self, parameters, config): + set_weights(self.net, parameters) + loss, accuracy = evaluate(self.net, self.testloader) + return loss, len(self.testloader), {"accuracy": accuracy} + + +def get_client_fn(dataset: FederatedDataset): + def client_fn(cid: str) -> Client: + train_loader, test_loader = load_data(partition_id=int(cid), fds=dataset) + net = IncomeClassifier(14) + return FlowerClient(net, train_loader, test_loader).to_client() + + return client_fn + + +fds = FederatedDataset( + dataset="scikit-learn/adult-census-income", + partitioners={"train": NUMBER_OF_CLIENTS}, +) +app = ClientApp(client_fn=get_client_fn(fds)) diff --git a/examples/fl-tabular/pyproject.toml b/examples/fl-tabular/pyproject.toml new file mode 100644 index 000000000000..21498f73a4f3 --- /dev/null +++ b/examples/fl-tabular/pyproject.toml @@ -0,0 +1,20 @@ +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[project] +name = "fl-tabular" +version = "0.1.0" +description = "Adult Census Income Tabular Dataset and Federated Learning in Flower" +authors = [ + { name = "The Flower Authors", email = "hello@flower.ai" }, +] +dependencies = [ + "flwr[simulation]>=1.9.0,<2.0", + "flwr-datasets>=0.1.0,<1.0.0", + "torch==2.1.1", + "scikit-learn==1.5.0", +] + +[tool.hatch.build.targets.wheel] +packages = ["."] diff --git a/examples/fl-tabular/server.py b/examples/fl-tabular/server.py new file mode 100644 index 000000000000..376726f832f7 --- /dev/null +++ b/examples/fl-tabular/server.py @@ -0,0 +1,24 @@ +from flwr.common import ndarrays_to_parameters +from flwr.server import ServerApp, ServerConfig +from flwr.server.strategy import FedAvg +from task import IncomeClassifier, get_weights + +net = IncomeClassifier(input_dim=14) +params = ndarrays_to_parameters(get_weights(net)) + + +def weighted_average(metrics): + accuracies = [num_examples * m["accuracy"] for num_examples, m in metrics] + examples = [num_examples for num_examples, _ in metrics] + + return {"accuracy": sum(accuracies) / sum(examples)} + + +strategy = FedAvg( + initial_parameters=params, + evaluate_metrics_aggregation_fn=weighted_average, +) +app = ServerApp( + strategy=strategy, + config=ServerConfig(num_rounds=5), +) diff --git a/examples/fl-tabular/task.py b/examples/fl-tabular/task.py new file mode 100644 index 000000000000..b07365c733d6 --- /dev/null +++ b/examples/fl-tabular/task.py @@ -0,0 +1,108 @@ +import torch +import torch.nn as nn +import torch.optim as optim +from torch.utils.data import TensorDataset, DataLoader +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler, OrdinalEncoder +from sklearn.compose import ColumnTransformer +from sklearn.pipeline import Pipeline +from collections import OrderedDict +from flwr_datasets import FederatedDataset + + +def load_data(partition_id: int, fds: FederatedDataset): + dataset = fds.load_partition(partition_id, "train").with_format("pandas")[:] + + dataset.dropna(inplace=True) + + categorical_cols = dataset.select_dtypes(include=["object"]).columns + ordinal_encoder = OrdinalEncoder() + dataset[categorical_cols] = ordinal_encoder.fit_transform(dataset[categorical_cols]) + + X = dataset.drop("income", axis=1) + y = dataset["income"] + + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=0.2, random_state=42 + ) + + numeric_features = X.select_dtypes(include=["float64", "int64"]).columns + numeric_transformer = Pipeline(steps=[("scaler", StandardScaler())]) + + preprocessor = ColumnTransformer( + transformers=[("num", numeric_transformer, numeric_features)] + ) + + X_train = preprocessor.fit_transform(X_train) + X_test = preprocessor.transform(X_test) + + X_train_tensor = torch.tensor(X_train, dtype=torch.float32) + X_test_tensor = torch.tensor(X_test, dtype=torch.float32) + y_train_tensor = torch.tensor(y_train.values, dtype=torch.float32).view(-1, 1) + y_test_tensor = torch.tensor(y_test.values, dtype=torch.float32).view(-1, 1) + + train_dataset = TensorDataset(X_train_tensor, y_train_tensor) + test_dataset = TensorDataset(X_test_tensor, y_test_tensor) + train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True) + test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False) + + return train_loader, test_loader + + +class IncomeClassifier(nn.Module): + def __init__(self, input_dim: int): + super(IncomeClassifier, self).__init__() + self.layer1 = nn.Linear(input_dim, 128) + self.layer2 = nn.Linear(128, 64) + self.output = nn.Linear(64, 1) + self.relu = nn.ReLU() + self.sigmoid = nn.Sigmoid() + + def forward(self, x): + x = self.relu(self.layer1(x)) + x = self.relu(self.layer2(x)) + x = self.sigmoid(self.output(x)) + return x + + +def train(model, train_loader, num_epochs=1): + criterion = nn.BCELoss() + optimizer = optim.Adam(model.parameters(), lr=0.001) + model.train() + for epoch in range(num_epochs): + for X_batch, y_batch in train_loader: + optimizer.zero_grad() + outputs = model(X_batch) + loss = criterion(outputs, y_batch) + loss.backward() + optimizer.step() + + +def evaluate(model, test_loader): + model.eval() + criterion = nn.BCELoss() + loss = 0.0 + correct = 0 + total = 0 + with torch.no_grad(): + for X_batch, y_batch in test_loader: + outputs = model(X_batch) + batch_loss = criterion(outputs, y_batch) + loss += batch_loss.item() + predicted = (outputs > 0.5).float() + total += y_batch.size(0) + correct += (predicted == y_batch).sum().item() + accuracy = correct / total + loss = loss / len(test_loader) + return loss, accuracy + + +def set_weights(net, parameters): + params_dict = zip(net.state_dict().keys(), parameters) + state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict}) + net.load_state_dict(state_dict, strict=True) + + +def get_weights(net): + ndarrays = [val.cpu().numpy() for _, val in net.state_dict().items()] + return ndarrays diff --git a/examples/flower-client-authentication/README.md b/examples/flower-authentication/README.md similarity index 70% rename from examples/flower-client-authentication/README.md rename to examples/flower-authentication/README.md index 7c724fc26f64..589270e621c9 100644 --- a/examples/flower-client-authentication/README.md +++ b/examples/flower-authentication/README.md @@ -1,19 +1,19 @@ -# Flower Client Authentication with PyTorch 🧪 +# Flower Authentication with PyTorch 🧪 > 🧪 = This example covers experimental features that might change in future versions of Flower > Please consult the regular PyTorch code examples ([quickstart](https://github.com/adap/flower/tree/main/examples/quickstart-pytorch), [advanced](https://github.com/adap/flower/tree/main/examples/advanced-pytorch)) to learn how to use Flower with PyTorch. -The following steps describe how to start a long-running Flower server (SuperLink) and a long-running Flower client (SuperNode) with client authentication enabled. +The following steps describe how to start a long-running Flower server (SuperLink) and a long-running Flower client (SuperNode) with authentication enabled. ## Project Setup Start by cloning the example project. We prepared a single-line command that you can copy into your shell which will checkout the example for you: ```shell -git clone --depth=1 https://github.com/adap/flower.git _tmp && mv _tmp/examples/flower-client-authentication . && rm -rf _tmp && cd flower-client-authentication +git clone --depth=1 https://github.com/adap/flower.git _tmp && mv _tmp/examples/flower-authentication . && rm -rf _tmp && cd flower-authentication ``` -This will create a new directory called `flower-client-authentication` with the following project structure: +This will create a new directory called `flower-authentication` with the following project structure: ```bash $ tree . @@ -62,26 +62,30 @@ The script also generates a CSV file that includes each of the generated (client ## Start the long-running Flower server (SuperLink) -To start a long-running Flower server and enable client authentication is very easy; all you need to do is type -`--require-client-authentication` followed by the path to the known `client_public_keys.csv`, server's private key -`server_credentials`, and server's public key `server_credentials.pub`. Notice that you can only enable client -authentication with a secure TLS connection. +To start a long-running Flower server (SuperLink) and enable authentication is very easy; all you need to do is type +`--auth-list-public-keys` containing file path to the known `client_public_keys.csv`, `--auth-superlink-private-key` +containing file path to the SuperLink's private key `server_credentials`, and `--auth-superlink-public-key` containing file path to the SuperLink's public key `server_credentials.pub`. Notice that you can only enable authentication with a secure TLS connection. ```bash flower-superlink \ - --certificates certificates/ca.crt certificates/server.pem certificates/server.key \ - --require-client-authentication keys/client_public_keys.csv keys/server_credentials keys/server_credentials.pub + --ssl-ca-certfile certificates/ca.crt \ + --ssl-certfile certificates/server.pem \ + --ssl-keyfile certificates/server.key \ + --auth-list-public-keys keys/client_public_keys.csv \ + --auth-superlink-private-key keys/server_credentials \ + --auth-superlink-public-key keys/server_credentials.pub ``` ## Start the long-running Flower client (SuperNode) -In a new terminal window, start the first long-running Flower client: +In a new terminal window, start the first long-running Flower client (SuperNode): ```bash flower-client-app client:app \ --root-certificates certificates/ca.crt \ --server 127.0.0.1:9092 \ - --authentication-keys keys/client_credentials_1 keys/client_credentials_1.pub + --auth-supernode-private-key keys/client_credentials_1 \ + --auth-supernode-public-key keys/client_credentials_1.pub ``` In yet another new terminal window, start the second long-running Flower client: @@ -90,7 +94,8 @@ In yet another new terminal window, start the second long-running Flower client: flower-client-app client:app \ --root-certificates certificates/ca.crt \ --server 127.0.0.1:9092 \ - --authentication-keys keys/client_credentials_2 keys/client_credentials_2.pub + --auth-supernode-private-key keys/client_credentials_2 \ + --auth-supernode-public-key keys/client_credentials_2.pub ``` If you generated more than 2 client credentials, you can add more clients by opening new terminal windows and running the command diff --git a/examples/flower-client-authentication/certificate.conf b/examples/flower-authentication/certificate.conf similarity index 100% rename from examples/flower-client-authentication/certificate.conf rename to examples/flower-authentication/certificate.conf diff --git a/examples/flower-client-authentication/client.py b/examples/flower-authentication/client.py similarity index 100% rename from examples/flower-client-authentication/client.py rename to examples/flower-authentication/client.py diff --git a/examples/flower-client-authentication/generate.sh b/examples/flower-authentication/generate.sh old mode 100644 new mode 100755 similarity index 100% rename from examples/flower-client-authentication/generate.sh rename to examples/flower-authentication/generate.sh diff --git a/examples/flower-client-authentication/pyproject.toml b/examples/flower-authentication/pyproject.toml similarity index 100% rename from examples/flower-client-authentication/pyproject.toml rename to examples/flower-authentication/pyproject.toml diff --git a/examples/flower-client-authentication/server.py b/examples/flower-authentication/server.py similarity index 100% rename from examples/flower-client-authentication/server.py rename to examples/flower-authentication/server.py diff --git a/examples/flower-client-authentication/task.py b/examples/flower-authentication/task.py similarity index 100% rename from examples/flower-client-authentication/task.py rename to examples/flower-authentication/task.py diff --git a/examples/tensorflow-privacy/README.md b/examples/tensorflow-privacy/README.md new file mode 100644 index 000000000000..a1f1be00f6b0 --- /dev/null +++ b/examples/tensorflow-privacy/README.md @@ -0,0 +1,60 @@ +# Training with Sample-Level Differential Privacy using TensorFlow-Privacy Engine + +In this example, we demonstrate how to train a model with sample-level differential privacy (DP) using Flower. We employ TensorFlow and integrate the tensorflow-privacy Engine to achieve sample-level differential privacy. This setup ensures robust privacy guarantees during the client training phase. + +For more information about DP in Flower please refer to the [tutorial](https://flower.ai/docs/framework/how-to-use-differential-privacy.html). For additional information about tensorflow-privacy, visit the official [website](https://www.tensorflow.org/responsible_ai/privacy/guide). + +## Environments Setup + +Start by cloning the example. We prepared a single-line command that you can copy into your shell which will checkout the example for you: + +```shell +git clone --depth=1 https://github.com/adap/flower.git && mv flower/examples/tensorflow-privacy . && rm -rf flower && cd tensorflow-privacy +``` + +This will create a new directory called `tensorflow-privacy` containing the following files: + +```shell +-- pyproject.toml +-- client.py +-- server.py +-- README.md +``` + +### Installing dependencies + +Project dependencies are defined in `pyproject.toml`. Install them with: + +```shell +pip install . +``` + +## Run Flower with tensorflow-privacy and TensorFlow + +### 1. Start the long-running Flower server (SuperLink) + +```bash +flower-superlink --insecure +``` + +### 2. Start the long-running Flower clients (SuperNodes) + +Start 2 Flower `SuperNodes` in 2 separate terminal windows, using: + +```bash +flower-client-app client:appA --insecure +``` + +```bash +flower-client-app client:appB --insecure +``` + +tensorflow-privacy hyperparameters can be passed for each client in `ClientApp` instantiation (in `client.py`). In this example, `noise_multiplier=1.5` and `noise_multiplier=1` are used for the first and second client respectively. + +### 3. Run the Flower App + +With both the long-running server (SuperLink) and two clients (SuperNode) up and running, we can now run the actual Flower App: + +```bash +flower-server-app server:app --insecure +``` diff --git a/examples/tensorflow-privacy/client.py b/examples/tensorflow-privacy/client.py new file mode 100644 index 000000000000..4aec85da014a --- /dev/null +++ b/examples/tensorflow-privacy/client.py @@ -0,0 +1,150 @@ +import argparse +import os +from flwr.client import ClientApp, NumPyClient +import tensorflow as tf +from flwr_datasets import FederatedDataset +import tensorflow_privacy + +from tensorflow_privacy.privacy.analysis.compute_dp_sgd_privacy_lib import ( + compute_dp_sgd_privacy_statement, +) + +# Make TensorFlow log less verbose +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" + + +def load_data(partition_id, batch_size): + fds = FederatedDataset(dataset="mnist", partitioners={"train": 2}) + partition = fds.load_partition(partition_id, "train") + partition.set_format("numpy") + + # Divide data on each node: 80% train, 20% test + partition = partition.train_test_split(test_size=0.2, seed=42) + x_train, y_train = partition["train"]["image"] / 255.0, partition["train"]["label"] + x_test, y_test = partition["test"]["image"] / 255.0, partition["test"]["label"] + + # Adjust the size of the training dataset to make it evenly divisible by the batch size + remainder = len(x_train) % batch_size + if remainder != 0: + x_train = x_train[:-remainder] + y_train = y_train[:-remainder] + + return (x_train, y_train), (x_test, y_test) + + +class FlowerClient(NumPyClient): + def __init__( + self, + model, + train_data, + test_data, + l2_norm_clip, + noise_multiplier, + num_microbatches, + learning_rate, + batch_size, + ) -> None: + super().__init__() + self.model = model + self.x_train, self.y_train = train_data + self.x_test, self.y_test = test_data + self.noise_multiplier = noise_multiplier + self.l2_norm_clip = l2_norm_clip + self.num_microbatches = num_microbatches + self.learning_rate = learning_rate + self.batch_size = batch_size + if self.batch_size % self.num_microbatches != 0: + raise ValueError( + f"Batch size {self.batch_size} is not divisible by the number of microbatches {self.num_microbatches}" + ) + + self.optimizer = tensorflow_privacy.DPKerasSGDOptimizer( + l2_norm_clip=l2_norm_clip, + noise_multiplier=noise_multiplier, + num_microbatches=num_microbatches, + learning_rate=learning_rate, + ) + loss = tf.keras.losses.SparseCategoricalCrossentropy( + reduction=tf.losses.Reduction.NONE + ) + self.model.compile(optimizer=self.optimizer, loss=loss, metrics=["accuracy"]) + + def get_parameters(self, config): + return self.model.get_weights() + + def fit(self, parameters, config): + self.model.set_weights(parameters) + + self.model.fit( + self.x_train, + self.y_train, + epochs=1, + batch_size=self.batch_size, + ) + + compute_dp_sgd_privacy_statement( + number_of_examples=self.x_train.shape[0], + batch_size=self.batch_size, + num_epochs=1, + noise_multiplier=self.noise_multiplier, + delta=1e-5, + ) + + return self.model.get_weights(), len(self.x_train), {} + + def evaluate(self, parameters, config): + self.model.set_weights(parameters) + self.model.compile( + optimizer=self.optimizer, + loss="sparse_categorical_crossentropy", + metrics=["accuracy"], + ) + loss, accuracy = self.model.evaluate(self.x_test, self.y_test) + return loss, len(self.x_test), {"accuracy": accuracy} + + +def client_fn_parameterized( + partition_id, + noise_multiplier, + l2_norm_clip=1.0, + num_microbatches=64, + learning_rate=0.01, + batch_size=64, +): + def client_fn(cid: str): + model = tf.keras.Sequential( + [ + tf.keras.layers.InputLayer(input_shape=(28, 28, 1)), + tf.keras.layers.Conv2D(32, kernel_size=(3, 3), activation="relu"), + tf.keras.layers.MaxPooling2D(pool_size=(2, 2)), + tf.keras.layers.Conv2D(64, kernel_size=(3, 3), activation="relu"), + tf.keras.layers.MaxPooling2D(pool_size=(2, 2)), + tf.keras.layers.Flatten(), + tf.keras.layers.Dense(128, activation="relu"), + tf.keras.layers.Dense(10, activation="softmax"), + ] + ) + train_data, test_data = load_data( + partition_id=partition_id, batch_size=batch_size + ) + return FlowerClient( + model, + train_data, + test_data, + noise_multiplier, + l2_norm_clip, + num_microbatches, + learning_rate, + batch_size, + ).to_client() + + return client_fn + + +appA = ClientApp( + client_fn=client_fn_parameterized(partition_id=0, noise_multiplier=1.0), +) + +appB = ClientApp( + client_fn=client_fn_parameterized(partition_id=1, noise_multiplier=1.5), +) diff --git a/examples/tensorflow-privacy/pyproject.toml b/examples/tensorflow-privacy/pyproject.toml new file mode 100644 index 000000000000..884ba3b5f07b --- /dev/null +++ b/examples/tensorflow-privacy/pyproject.toml @@ -0,0 +1,22 @@ +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[project] +name = "tensorflow-privacy-fl" +version = "0.1.0" +description = "Sample-level Differential Privacy with Tensorflow-Privacy in Flower" +authors = [ + { name = "The Flower Authors", email = "hello@flower.ai" }, +] +dependencies = [ + "flwr>=1.8.0,<2.0", + "flwr-datasets[vision]>=0.1.0,<1.0.0", + "tensorflow-estimator~=2.4", + "tensorflow-probability~=0.22.0", + "tensorflow>=2.4.0,<=2.15.0", + "tensorflow-privacy == 0.9.0" +] + +[tool.hatch.build.targets.wheel] +packages = ["."] diff --git a/examples/tensorflow-privacy/server.py b/examples/tensorflow-privacy/server.py new file mode 100644 index 000000000000..1e399fa7e833 --- /dev/null +++ b/examples/tensorflow-privacy/server.py @@ -0,0 +1,22 @@ +from typing import List, Tuple + +from flwr.server import ServerApp, ServerConfig +from flwr.server.strategy import FedAvg +from flwr.common import Metrics + + +def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics: + accuracies = [num_examples * m["accuracy"] for num_examples, m in metrics] + examples = [num_examples for num_examples, _ in metrics] + + return {"accuracy": sum(accuracies) / sum(examples)} + + +strategy = FedAvg(evaluate_metrics_aggregation_fn=weighted_average) + +config = ServerConfig(num_rounds=3) + +app = ServerApp( + config=config, + strategy=strategy, +) diff --git a/examples/vertical-fl/README.md b/examples/vertical-fl/README.md index d8c599d617c4..ba8228a059f9 100644 --- a/examples/vertical-fl/README.md +++ b/examples/vertical-fl/README.md @@ -459,7 +459,7 @@ evaluate function are bogus, as they won't be used on the server side. The `client_fn` we will use in our `start_simulation` function to generate our 3 clients will be very basic: -```pyhton3 +```python3 partitions, label = get_partitions_and_label() def client_fn(cid): diff --git a/examples/xgboost-quickstart/README.md b/examples/xgboost-quickstart/README.md index b196520d37e6..713b6eab8bac 100644 --- a/examples/xgboost-quickstart/README.md +++ b/examples/xgboost-quickstart/README.md @@ -21,37 +21,26 @@ This will create a new directory called `xgboost-quickstart` containing the foll -- server.py <- Defines the server-side logic -- client.py <- Defines the client-side logic -- run.sh <- Commands to run experiments --- pyproject.toml <- Example dependencies (if you use Poetry) --- requirements.txt <- Example dependencies +-- pyproject.toml <- Example dependencies ``` ### Installing Dependencies -Project dependencies (such as `xgboost` and `flwr`) are defined in `pyproject.toml` and `requirements.txt`. We recommend [Poetry](https://python-poetry.org/docs/) to install those dependencies and manage your virtual environment ([Poetry installation](https://python-poetry.org/docs/#installation)) or [pip](https://pip.pypa.io/en/latest/development/), but feel free to use a different way of installing dependencies and managing virtual environments if you have other preferences. - -#### Poetry +Project dependencies (such as `xgboost` and `flwr`) are defined in `pyproject.toml`. You can install the dependencies by invoking `pip`: ```shell -poetry install -poetry shell +# From a new python environment, run: +pip install . ``` -Poetry will install all your dependencies in a newly created virtual environment. To verify that everything works correctly you can run the following command: +Then, to verify that everything works correctly you can run the following command: ```shell -poetry run python3 -c "import flwr" +python3 -c "import flwr" ``` If you don't see any errors you're good to go! -#### pip - -Write the command below in your terminal to install the dependencies according to the configuration file requirements.txt. - -```shell -pip install -r requirements.txt -``` - ## Run Federated Learning with XGBoost and Flower Afterwards you are ready to start the Flower server as well as the clients. diff --git a/examples/xgboost-quickstart/client.py b/examples/xgboost-quickstart/client.py index 6ac23ae15148..5a4d88bb7e43 100644 --- a/examples/xgboost-quickstart/client.py +++ b/examples/xgboost-quickstart/client.py @@ -92,9 +92,21 @@ def transform_dataset_to_dmatrix(data: Union[Dataset, DatasetDict]) -> xgb.core. # Define Flower client class XgbClient(fl.client.Client): - def __init__(self): - self.bst = None - self.config = None + def __init__( + self, + train_dmatrix, + valid_dmatrix, + num_train, + num_val, + num_local_round, + params, + ): + self.train_dmatrix = train_dmatrix + self.valid_dmatrix = valid_dmatrix + self.num_train = num_train + self.num_val = num_val + self.num_local_round = num_local_round + self.params = params def get_parameters(self, ins: GetParametersIns) -> GetParametersRes: _ = (self, ins) @@ -106,41 +118,41 @@ def get_parameters(self, ins: GetParametersIns) -> GetParametersRes: parameters=Parameters(tensor_type="", tensors=[]), ) - def _local_boost(self): + def _local_boost(self, bst_input): # Update trees based on local training data. - for i in range(num_local_round): - self.bst.update(train_dmatrix, self.bst.num_boosted_rounds()) + for i in range(self.num_local_round): + bst_input.update(self.train_dmatrix, bst_input.num_boosted_rounds()) - # Extract the last N=num_local_round trees for sever aggregation - bst = self.bst[ - self.bst.num_boosted_rounds() - - num_local_round : self.bst.num_boosted_rounds() + # Bagging: extract the last N=num_local_round trees for sever aggregation + bst = bst_input[ + bst_input.num_boosted_rounds() + - self.num_local_round : bst_input.num_boosted_rounds() ] return bst def fit(self, ins: FitIns) -> FitRes: - if not self.bst: + global_round = int(ins.config["global_round"]) + if global_round == 1: # First round local training - log(INFO, "Start training at round 1") bst = xgb.train( - params, - train_dmatrix, - num_boost_round=num_local_round, - evals=[(valid_dmatrix, "validate"), (train_dmatrix, "train")], + self.params, + self.train_dmatrix, + num_boost_round=self.num_local_round, + evals=[(self.valid_dmatrix, "validate"), (self.train_dmatrix, "train")], ) - self.config = bst.save_config() - self.bst = bst else: + bst = xgb.Booster(params=self.params) for item in ins.parameters.tensors: global_model = bytearray(item) # Load global model into booster - self.bst.load_model(global_model) - self.bst.load_config(self.config) + bst.load_model(global_model) - bst = self._local_boost() + # Local training + bst = self._local_boost(bst) + # Save model local_model = bst.save_raw("json") local_model_bytes = bytes(local_model) @@ -150,27 +162,47 @@ def fit(self, ins: FitIns) -> FitRes: message="OK", ), parameters=Parameters(tensor_type="", tensors=[local_model_bytes]), - num_examples=num_train, + num_examples=self.num_train, metrics={}, ) def evaluate(self, ins: EvaluateIns) -> EvaluateRes: - eval_results = self.bst.eval_set( - evals=[(valid_dmatrix, "valid")], - iteration=self.bst.num_boosted_rounds() - 1, + # Load global model + bst = xgb.Booster(params=self.params) + for para in ins.parameters.tensors: + para_b = bytearray(para) + bst.load_model(para_b) + + # Run evaluation + eval_results = bst.eval_set( + evals=[(self.valid_dmatrix, "valid")], + iteration=bst.num_boosted_rounds() - 1, ) auc = round(float(eval_results.split("\t")[1].split(":")[1]), 4) + global_round = ins.config["global_round"] + log(INFO, f"AUC = {auc} at round {global_round}") + return EvaluateRes( status=Status( code=Code.OK, message="OK", ), loss=0.0, - num_examples=num_val, + num_examples=self.num_val, metrics={"AUC": auc}, ) # Start Flower client -fl.client.start_client(server_address="127.0.0.1:8080", client=XgbClient().to_client()) +fl.client.start_client( + server_address="127.0.0.1:8080", + client=XgbClient( + train_dmatrix, + valid_dmatrix, + num_train, + num_val, + num_local_round, + params, + ).to_client(), +) diff --git a/examples/xgboost-quickstart/pyproject.toml b/examples/xgboost-quickstart/pyproject.toml index c16542ea7ffe..f1e451fe779a 100644 --- a/examples/xgboost-quickstart/pyproject.toml +++ b/examples/xgboost-quickstart/pyproject.toml @@ -1,15 +1,19 @@ [build-system] -requires = ["poetry-core>=1.4.0"] -build-backend = "poetry.core.masonry.api" +requires = ["hatchling"] +build-backend = "hatchling.build" -[tool.poetry] -name = "xgboost-quickstart" +[project] +name = "quickstart-xgboost" version = "0.1.0" -description = "Federated XGBoost with Flower (quickstart)" -authors = ["The Flower Authors "] +description = "XGBoost Federated Learning Quickstart with Flower" +authors = [ + { name = "The Flower Authors", email = "hello@flower.ai" }, +] +dependencies = [ + "flwr>=1.8.0,<2.0", + "flwr-datasets>=0.1.0,<1.0.0", + "xgboost>=2.0.0,<3.0.0", +] -[tool.poetry.dependencies] -python = ">=3.8,<3.11" -flwr = ">=1.7.0,<2.0" -flwr-datasets = ">=0.0.1,<1.0.0" -xgboost = ">=2.0.0,<3.0.0" +[tool.hatch.build.targets.wheel] +packages = ["."] diff --git a/examples/xgboost-quickstart/requirements.txt b/examples/xgboost-quickstart/requirements.txt deleted file mode 100644 index c6949e0651c5..000000000000 --- a/examples/xgboost-quickstart/requirements.txt +++ /dev/null @@ -1,3 +0,0 @@ -flwr>=1.7.0, <2.0 -flwr-datasets>=0.0.1, <1.0.0 -xgboost>=2.0.0, <3.0.0 diff --git a/examples/xgboost-quickstart/server.py b/examples/xgboost-quickstart/server.py index b45a375ce94f..e9239fde696c 100644 --- a/examples/xgboost-quickstart/server.py +++ b/examples/xgboost-quickstart/server.py @@ -1,3 +1,4 @@ +from typing import Dict import flwr as fl from flwr.server.strategy import FedXgbBagging @@ -19,6 +20,14 @@ def evaluate_metrics_aggregation(eval_metrics): return metrics_aggregated +def config_func(rnd: int) -> Dict[str, str]: + """Return a configuration with global epochs.""" + config = { + "global_round": str(rnd), + } + return config + + # Define strategy strategy = FedXgbBagging( fraction_fit=(float(num_clients_per_round) / pool_size), @@ -27,6 +36,8 @@ def evaluate_metrics_aggregation(eval_metrics): min_evaluate_clients=num_evaluate_clients, fraction_evaluate=1.0, evaluate_metrics_aggregation_fn=evaluate_metrics_aggregation, + on_evaluate_config_fn=config_func, + on_fit_config_fn=config_func, ) # Start Flower server diff --git a/pyproject.toml b/pyproject.toml index 57ed4450cc52..dbab703c7671 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api" [tool.poetry] name = "flwr" -version = "1.9.0" +version = "1.10.0" description = "Flower: A Friendly Federated Learning Framework" license = "Apache-2.0" authors = ["The Flower Authors "] @@ -53,9 +53,8 @@ exclude = [ [tool.poetry.scripts] flwr = "flwr.cli.app:app" -flower-driver-api = "flwr.server:run_driver_api" -flower-fleet-api = "flwr.server:run_fleet_api" flower-superlink = "flwr.server:run_superlink" +flower-superexec = "flwr.superexec:run_superexec" flower-supernode = "flwr.client:run_supernode" flower-client-app = "flwr.client:run_client_app" flower-server-app = "flwr.server:run_server_app" @@ -74,15 +73,14 @@ typer = { version = "^0.9.0", extras=["all"] } tomli = "^2.0.1" pathspec = "^0.12.1" # Optional dependencies (Simulation Engine) -ray = { version = "==2.6.3", optional = true, python = ">=3.8,<3.12" } +ray = { version = "==2.10.0", optional = true, python = ">=3.8,<3.12" } # Optional dependencies (REST transport layer) requests = { version = "^2.31.0", optional = true } starlette = { version = "^0.31.0", optional = true } uvicorn = { version = "^0.23.0", extras = ["standard"], optional = true } -setuptools = "69.5.1" [tool.poetry.extras] -simulation = ["ray", "pydantic"] +simulation = ["ray"] rest = ["requests", "starlette", "uvicorn"] [tool.poetry.group.dev.dependencies] @@ -106,7 +104,8 @@ jupyterlab = "==4.0.12" rope = "==1.11.0" semver = "==3.0.2" sphinx = "==6.2.1" -sphinx-intl = "==2.1.0" +sphinx-intl = "==2.2.0" +sphinx-click = "==5.1.0" myst-parser = "==1.0.0" sphinx-design = "==0.5.0" sphinx-copybutton = "==0.5.2" @@ -114,7 +113,7 @@ sphinxcontrib-mermaid = "==0.9.2" sphinxcontrib-youtube = "==1.4.1" furo = "==2023.9.10" sphinx-reredirects = "==0.1.3" -nbsphinx = "==0.9.3" +nbsphinx = "==0.9.4" nbstripout = "==0.6.1" ruff = "==0.1.9" sphinx-argparse = "==0.4.0" diff --git a/src/docker/base/alpine/Dockerfile b/src/docker/base/alpine/Dockerfile index 4d7c95721dc3..04864b525e2e 100644 --- a/src/docker/base/alpine/Dockerfile +++ b/src/docker/base/alpine/Dockerfile @@ -34,10 +34,10 @@ RUN apk add --no-cache \ g++ \ libffi-dev \ # create virtual env - && python -m venv /app/venv + && python -m venv /python/venv # Make sure we use the virtualenv -ENV PATH=/app/venv/bin:$PATH +ENV PATH=/python/venv/bin:$PATH # Install specific version of pip, setuptools and flwr ARG PIP_VERSION @@ -59,12 +59,14 @@ RUN apk add --no-cache \ --no-create-home \ --disabled-password \ --gecos "" \ - --uid 49999 app + --uid 49999 app \ + && mkdir -p /app \ + && chown -R app:app /app -COPY --from=compile --chown=app:app /app/venv /app/venv +COPY --from=compile --chown=app:app /python/venv /python/venv # Make sure we use the virtualenv -ENV PATH=/app/venv/bin:$PATH \ +ENV PATH=/python/venv/bin:$PATH \ # Send stdout and stderr stream directly to the terminal. Ensures that no # output is retained in a buffer if the application crashes. PYTHONUNBUFFERED=1 \ diff --git a/src/docker/base/ubuntu/Dockerfile b/src/docker/base/ubuntu/Dockerfile index 75152d00f8d6..4aeddc3f8d8d 100644 --- a/src/docker/base/ubuntu/Dockerfile +++ b/src/docker/base/ubuntu/Dockerfile @@ -54,7 +54,7 @@ ENV DEBIAN_FRONTEND=noninteractive RUN apt-get update \ && apt-get -y --no-install-recommends install \ - libsqlite3-dev \ + libsqlite3-0 \ && rm -rf /var/lib/apt/lists/* COPY --from=python /usr/local/bin/python /usr/local/bin/python @@ -74,8 +74,8 @@ ENV PATH=/usr/local/bin/python/bin:$PATH \ # Use a virtual environment to ensure that Python packages are installed in the same location # regardless of whether the subsequent image build is run with the app or the root user -RUN python -m venv /app/venv -ENV PATH=/app/venv/bin:$PATH +RUN python -m venv /python/venv +ENV PATH=/python/venv/bin:$PATH ARG PIP_VERSION ARG SETUPTOOLS_VERSION @@ -92,6 +92,8 @@ RUN adduser \ --disabled-password \ --gecos "" \ --uid 49999 app \ + && mkdir -p /app \ + && chown -R app:app /python \ && chown -R app:app /app WORKDIR /app diff --git a/src/docker/serverapp/Dockerfile b/src/docker/serverapp/Dockerfile index 22b464a3838c..08eceacc4557 100644 --- a/src/docker/serverapp/Dockerfile +++ b/src/docker/serverapp/Dockerfile @@ -14,19 +14,7 @@ # ============================================================================== ARG BASE_REPOSITORY=flwr/base -ARG PYTHON_VERSION=3.11 -ARG UBUNTU_VERSION=ubuntu22.04 -FROM $BASE_REPOSITORY:py${PYTHON_VERSION}-${UBUNTU_VERSION} +ARG BASE_IMAGE +FROM $BASE_REPOSITORY:$BASE_IMAGE -ARG FLWR_PACKAGE=flwr -ARG FLWR_VERSION -RUN python -m pip install -U --no-cache-dir \ - ${FLWR_PACKAGE}==${FLWR_VERSION} && \ - # Without pyenv rehash the executable cannot be found. - # pyenv rehash is usually called via the shell by adding - # `pyenv init -` in the shell profile, but that doesn't work - # well in docker - pyenv rehash - -WORKDIR /app ENTRYPOINT ["flower-server-app"] diff --git a/src/docker/superlink/Dockerfile b/src/docker/superlink/Dockerfile index acf06f66f2fb..31c0906dfd22 100644 --- a/src/docker/superlink/Dockerfile +++ b/src/docker/superlink/Dockerfile @@ -14,19 +14,7 @@ # ============================================================================== ARG BASE_REPOSITORY=flwr/base -ARG PYTHON_VERSION=3.11 -ARG UBUNTU_VERSION=ubuntu22.04 -FROM $BASE_REPOSITORY:py${PYTHON_VERSION}-${UBUNTU_VERSION} +ARG BASE_IMAGE +FROM $BASE_REPOSITORY:$BASE_IMAGE -ARG FLWR_PACKAGE=flwr -ARG FLWR_VERSION -RUN python -m pip install -U --no-cache-dir \ - ${FLWR_PACKAGE}==${FLWR_VERSION} && \ - # Without pyenv rehash the executable cannot be found. - # pyenv rehash is usually called via the shell by adding - # `pyenv init -` in the shell profile, but that doesn't work - # well in docker - pyenv rehash - -WORKDIR /app ENTRYPOINT ["flower-superlink"] diff --git a/src/docker/supernode/Dockerfile b/src/docker/supernode/Dockerfile index 8117dcc295df..8dce1c389a5b 100644 --- a/src/docker/supernode/Dockerfile +++ b/src/docker/supernode/Dockerfile @@ -14,15 +14,7 @@ # ============================================================================== ARG BASE_REPOSITORY=flwr/base -ARG PYTHON_VERSION=3.11 -ARG UBUNTU_VERSION=ubuntu22.04 -FROM $BASE_REPOSITORY:py${PYTHON_VERSION}-${UBUNTU_VERSION} +ARG BASE_IMAGE +FROM $BASE_REPOSITORY:$BASE_IMAGE -ARG FLWR_PACKAGE=flwr -ARG FLWR_VERSION -RUN python -m pip install -U --no-cache-dir \ - ${FLWR_PACKAGE}==${FLWR_VERSION} && \ - pyenv rehash - -WORKDIR /app ENTRYPOINT ["flower-client-app"] diff --git a/src/proto/flwr/proto/driver.proto b/src/proto/flwr/proto/driver.proto index 54e6b6b41b68..edbd5d91bb5b 100644 --- a/src/proto/flwr/proto/driver.proto +++ b/src/proto/flwr/proto/driver.proto @@ -19,6 +19,7 @@ package flwr.proto; import "flwr/proto/node.proto"; import "flwr/proto/task.proto"; +import "flwr/proto/run.proto"; service Driver { // Request run_id @@ -32,6 +33,9 @@ service Driver { // Get task results rpc PullTaskRes(PullTaskResRequest) returns (PullTaskResResponse) {} + + // Get run details + rpc GetRun(GetRunRequest) returns (GetRunResponse) {} } // CreateRun diff --git a/src/proto/flwr/proto/exec.proto b/src/proto/flwr/proto/exec.proto new file mode 100644 index 000000000000..8e5f53b02ca8 --- /dev/null +++ b/src/proto/flwr/proto/exec.proto @@ -0,0 +1,31 @@ +// Copyright 2024 Flower Labs GmbH. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ============================================================================== + +syntax = "proto3"; + +package flwr.proto; + +service Exec { + // Start run upon request + rpc StartRun(StartRunRequest) returns (StartRunResponse) {} + + // Start log stream upon request + rpc StreamLogs(StreamLogsRequest) returns (stream StreamLogsResponse) {} +} + +message StartRunRequest { bytes fab_file = 1; } +message StartRunResponse { sint64 run_id = 1; } +message StreamLogsRequest { sint64 run_id = 1; } +message StreamLogsResponse { string log_output = 1; } diff --git a/src/proto/flwr/proto/fab.proto b/src/proto/flwr/proto/fab.proto new file mode 100644 index 000000000000..1e796a59c82a --- /dev/null +++ b/src/proto/flwr/proto/fab.proto @@ -0,0 +1,15 @@ +syntax = "proto3"; + +package flwr.proto; + +message Fab { + // This field is the hash of the data field. It is used to identify the data. + // The hash is calculated using the SHA-256 algorithm and is represented as a + // hex string (sha256hex). + string hash = 1; + // This field contains the fab file contents a one bytes blob. + bytes content = 2; +} + +message GetFabRequest { string hash = 1; } +message GetFabResponse { Fab fab = 1; } diff --git a/src/proto/flwr/proto/fleet.proto b/src/proto/flwr/proto/fleet.proto index df6b5843023d..24f60bb3d825 100644 --- a/src/proto/flwr/proto/fleet.proto +++ b/src/proto/flwr/proto/fleet.proto @@ -19,6 +19,7 @@ package flwr.proto; import "flwr/proto/node.proto"; import "flwr/proto/task.proto"; +import "flwr/proto/run.proto"; service Fleet { rpc CreateNode(CreateNodeRequest) returns (CreateNodeResponse) {} @@ -70,13 +71,4 @@ message PushTaskResResponse { map results = 2; } -// GetRun messages -message Run { - sint64 run_id = 1; - string fab_id = 2; - string fab_version = 3; -} -message GetRunRequest { sint64 run_id = 1; } -message GetRunResponse { Run run = 1; } - message Reconnect { uint64 reconnect = 1; } diff --git a/src/proto/flwr/proto/grpcadapter.proto b/src/proto/flwr/proto/grpcadapter.proto new file mode 100644 index 000000000000..acf9a9d3d94f --- /dev/null +++ b/src/proto/flwr/proto/grpcadapter.proto @@ -0,0 +1,28 @@ +// Copyright 2024 Flower Labs GmbH. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ============================================================================== + +syntax = "proto3"; + +package flwr.proto; + +service GrpcAdapter { + rpc SendReceive(MessageContainer) returns (MessageContainer) {} +} + +message MessageContainer { + map metadata = 1; + string grpc_message_name = 2; + bytes grpc_message_content = 3; +} diff --git a/src/proto/flwr/proto/run.proto b/src/proto/flwr/proto/run.proto new file mode 100644 index 000000000000..76a7fd91532f --- /dev/null +++ b/src/proto/flwr/proto/run.proto @@ -0,0 +1,26 @@ +// Copyright 2024 Flower Labs GmbH. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// ============================================================================== + +syntax = "proto3"; + +package flwr.proto; + +message Run { + sint64 run_id = 1; + string fab_id = 2; + string fab_version = 3; +} +message GetRunRequest { sint64 run_id = 1; } +message GetRunResponse { Run run = 1; } diff --git a/src/py/flwr/cli/app.py b/src/py/flwr/cli/app.py index e1417f1267ac..d1b270026cd7 100644 --- a/src/py/flwr/cli/app.py +++ b/src/py/flwr/cli/app.py @@ -15,9 +15,11 @@ """Flower command line interface.""" import typer +from typer.main import get_command from .build import build from .example import example +from .install import install from .new import new from .run import run @@ -34,6 +36,9 @@ app.command()(example) app.command()(run) app.command()(build) +app.command()(install) + +typer_click_object = get_command(app) if __name__ == "__main__": app() diff --git a/src/py/flwr/cli/build.py b/src/py/flwr/cli/build.py index 37753e5b57b1..4a9b54f9223f 100644 --- a/src/py/flwr/cli/build.py +++ b/src/py/flwr/cli/build.py @@ -14,7 +14,6 @@ # ============================================================================== """Flower command line interface `build` command.""" -import hashlib import os import zipfile from pathlib import Path @@ -24,8 +23,8 @@ import typer from typing_extensions import Annotated -from .config_utils import load_and_validate_with_defaults -from .utils import is_valid_project_name +from .config_utils import load_and_validate +from .utils import get_sha256_hash, is_valid_project_name # pylint: disable=too-many-locals @@ -34,16 +33,12 @@ def build( Optional[Path], typer.Option(help="The Flower project directory to bundle into a FAB"), ] = None, -) -> None: +) -> str: """Build a Flower project into a Flower App Bundle (FAB). - You can run `flwr build` without any argument to bundle the current directory: - - `flwr build` - - You can also build a specific directory: - - `flwr build --directory ./projects/flower-hello-world` + You can run ``flwr build`` without any arguments to bundle the current directory, + or you can use ``--directory`` to build a specific directory: + ``flwr build --directory ./projects/flower-hello-world``. """ if directory is None: directory = Path.cwd() @@ -67,9 +62,7 @@ def build( ) raise typer.Exit(code=1) - conf, errors, warnings = load_and_validate_with_defaults( - directory / "pyproject.toml" - ) + conf, errors, warnings = load_and_validate(directory / "pyproject.toml") if conf is None: typer.secho( "Project configuration could not be loaded.\npyproject.toml is invalid:\n" @@ -117,7 +110,7 @@ def build( fab_file.write(file_path, archive_path) # Calculate file info - sha256_hash = _get_sha256_hash(file_path) + sha256_hash = get_sha256_hash(file_path) file_size_bits = os.path.getsize(file_path) * 8 # size in bits list_file_content += f"{archive_path},{sha256_hash},{file_size_bits}\n" @@ -128,17 +121,7 @@ def build( f"🎊 Successfully built {fab_filename}.", fg=typer.colors.GREEN, bold=True ) - -def _get_sha256_hash(file_path: Path) -> str: - """Calculate the SHA-256 hash of a file.""" - sha256 = hashlib.sha256() - with open(file_path, "rb") as f: - while True: - data = f.read(65536) # Read in 64kB blocks - if not data: - break - sha256.update(data) - return sha256.hexdigest() + return fab_filename def _load_gitignore(directory: Path) -> pathspec.PathSpec: diff --git a/src/py/flwr/cli/config_utils.py b/src/py/flwr/cli/config_utils.py index bca35a51dde5..d06a1d6dba96 100644 --- a/src/py/flwr/cli/config_utils.py +++ b/src/py/flwr/cli/config_utils.py @@ -14,16 +14,59 @@ # ============================================================================== """Utility to validate the `pyproject.toml` file.""" +import zipfile +from io import BytesIO from pathlib import Path -from typing import Any, Dict, List, Optional, Tuple +from typing import IO, Any, Dict, List, Optional, Tuple, Union import tomli from flwr.common import object_ref -def load_and_validate_with_defaults( +def get_fab_metadata(fab_file: Union[Path, bytes]) -> Tuple[str, str]: + """Extract the fab_id and the fab_version from a FAB file or path. + + Parameters + ---------- + fab_file : Union[Path, bytes] + The Flower App Bundle file to validate and extract the metadata from. + It can either be a path to the file or the file itself as bytes. + + Returns + ------- + Tuple[str, str] + The `fab_version` and `fab_id` of the given Flower App Bundle. + """ + fab_file_archive: Union[Path, IO[bytes]] + if isinstance(fab_file, bytes): + fab_file_archive = BytesIO(fab_file) + elif isinstance(fab_file, Path): + fab_file_archive = fab_file + else: + raise ValueError("fab_file must be either a Path or bytes") + + with zipfile.ZipFile(fab_file_archive, "r") as zipf: + with zipf.open("pyproject.toml") as file: + toml_content = file.read().decode("utf-8") + + conf = load_from_string(toml_content) + if conf is None: + raise ValueError("Invalid TOML content in pyproject.toml") + + is_valid, errors, _ = validate(conf, check_module=False) + if not is_valid: + raise ValueError(errors) + + return ( + conf["project"]["version"], + f"{conf['flower']['publisher']}/{conf['project']['name']}", + ) + + +def load_and_validate( path: Optional[Path] = None, + check_module: bool = True, ) -> Tuple[Optional[Dict[str, Any]], List[str], List[str]]: """Load and validate pyproject.toml as dict. @@ -42,19 +85,11 @@ def load_and_validate_with_defaults( ] return (None, errors, []) - is_valid, errors, warnings = validate(config) + is_valid, errors, warnings = validate(config, check_module) if not is_valid: return (None, errors, warnings) - # Apply defaults - defaults = { - "flower": { - "engine": {"name": "simulation", "simulation": {"supernode": {"num": 2}}} - } - } - config = apply_defaults(config, defaults) - return (config, errors, warnings) @@ -70,8 +105,7 @@ def load(path: Optional[Path] = None) -> Optional[Dict[str, Any]]: return None with toml_path.open(encoding="utf-8") as toml_file: - data = tomli.loads(toml_file.read()) - return data + return load_from_string(toml_file.read()) # pylint: disable=too-many-branches @@ -110,7 +144,9 @@ def validate_fields(config: Dict[str, Any]) -> Tuple[bool, List[str], List[str]] return len(errors) == 0, errors, warnings -def validate(config: Dict[str, Any]) -> Tuple[bool, List[str], List[str]]: +def validate( + config: Dict[str, Any], check_module: bool = True +) -> Tuple[bool, List[str], List[str]]: """Validate pyproject.toml.""" is_valid, errors, warnings = validate_fields(config) @@ -118,12 +154,16 @@ def validate(config: Dict[str, Any]) -> Tuple[bool, List[str], List[str]]: return False, errors, warnings # Validate serverapp - is_valid, reason = object_ref.validate(config["flower"]["components"]["serverapp"]) + is_valid, reason = object_ref.validate( + config["flower"]["components"]["serverapp"], check_module + ) if not is_valid and isinstance(reason, str): return False, [reason], [] # Validate clientapp - is_valid, reason = object_ref.validate(config["flower"]["components"]["clientapp"]) + is_valid, reason = object_ref.validate( + config["flower"]["components"]["clientapp"], check_module + ) if not is_valid and isinstance(reason, str): return False, [reason], [] @@ -131,15 +171,10 @@ def validate(config: Dict[str, Any]) -> Tuple[bool, List[str], List[str]]: return True, [], [] -def apply_defaults( - config: Dict[str, Any], - defaults: Dict[str, Any], -) -> Dict[str, Any]: - """Apply defaults to config.""" - for key in defaults: - if key in config: - if isinstance(config[key], dict) and isinstance(defaults[key], dict): - apply_defaults(config[key], defaults[key]) - else: - config[key] = defaults[key] - return config +def load_from_string(toml_content: str) -> Optional[Dict[str, Any]]: + """Load TOML content from a string and return as dict.""" + try: + data = tomli.loads(toml_content) + return data + except tomli.TOMLDecodeError: + return None diff --git a/src/py/flwr/cli/config_utils_test.py b/src/py/flwr/cli/config_utils_test.py index b47206249dfc..b24425cd08f4 100644 --- a/src/py/flwr/cli/config_utils_test.py +++ b/src/py/flwr/cli/config_utils_test.py @@ -39,7 +39,7 @@ def test_load_pyproject_toml_load_from_cwd(tmp_path: Path) -> None: ] license = {text = "Apache License (2.0)"} dependencies = [ - "flwr[simulation]>=1.8.0,<2.0", + "flwr[simulation]>=1.9.0,<2.0", "numpy>=1.21.0", ] @@ -64,7 +64,7 @@ def test_load_pyproject_toml_load_from_cwd(tmp_path: Path) -> None: "description": "", "authors": [{"email": "hello@flower.ai", "name": "The Flower Authors"}], "license": {"text": "Apache License (2.0)"}, - "dependencies": ["flwr[simulation]>=1.8.0,<2.0", "numpy>=1.21.0"], + "dependencies": ["flwr[simulation]>=1.9.0,<2.0", "numpy>=1.21.0"], }, "flower": { "publisher": "flwrlabs", @@ -114,7 +114,7 @@ def test_load_pyproject_toml_from_path(tmp_path: Path) -> None: ] license = {text = "Apache License (2.0)"} dependencies = [ - "flwr[simulation]>=1.8.0,<2.0", + "flwr[simulation]>=1.9.0,<2.0", "numpy>=1.21.0", ] @@ -139,7 +139,7 @@ def test_load_pyproject_toml_from_path(tmp_path: Path) -> None: "description": "", "authors": [{"email": "hello@flower.ai", "name": "The Flower Authors"}], "license": {"text": "Apache License (2.0)"}, - "dependencies": ["flwr[simulation]>=1.8.0,<2.0", "numpy>=1.21.0"], + "dependencies": ["flwr[simulation]>=1.9.0,<2.0", "numpy>=1.21.0"], }, "flower": { "publisher": "flwrlabs", diff --git a/src/py/flwr/cli/install.py b/src/py/flwr/cli/install.py new file mode 100644 index 000000000000..de9227bee450 --- /dev/null +++ b/src/py/flwr/cli/install.py @@ -0,0 +1,211 @@ +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Flower command line interface `install` command.""" + + +import shutil +import tempfile +import zipfile +from io import BytesIO +from pathlib import Path +from typing import IO, Optional, Union + +import typer +from typing_extensions import Annotated + +from flwr.common.config import get_flwr_dir + +from .config_utils import load_and_validate +from .utils import get_sha256_hash + + +def install( + source: Annotated[ + Optional[Path], + typer.Argument(metavar="source", help="The source FAB file to install."), + ] = None, + flwr_dir: Annotated[ + Optional[Path], + typer.Option(help="The desired install path."), + ] = None, +) -> None: + """Install a Flower App Bundle. + + It can be ran with a single FAB file argument: + + ``flwr install ./target_project.fab`` + + The target install directory can be specified with ``--flwr-dir``: + + ``flwr install ./target_project.fab --flwr-dir ./docs/flwr`` + + This will install ``target_project`` to ``./docs/flwr/``. By default, + ``flwr-dir`` is equal to: + + - ``$FLWR_HOME/`` if ``$FLWR_HOME`` is defined + - ``$XDG_DATA_HOME/.flwr/`` if ``$XDG_DATA_HOME`` is defined + - ``$HOME/.flwr/`` in all other cases + """ + if source is None: + source = Path(typer.prompt("Enter the source FAB file")) + + source = source.resolve() + if not source.exists() or not source.is_file(): + typer.secho( + f"❌ The source {source} does not exist or is not a file.", + fg=typer.colors.RED, + bold=True, + ) + raise typer.Exit(code=1) + + if source.suffix != ".fab": + typer.secho( + f"❌ The source {source} is not a `.fab` file.", + fg=typer.colors.RED, + bold=True, + ) + raise typer.Exit(code=1) + + install_from_fab(source, flwr_dir) + + +def install_from_fab( + fab_file: Union[Path, bytes], + flwr_dir: Optional[Path], + skip_prompt: bool = False, +) -> Path: + """Install from a FAB file after extracting and validating.""" + fab_file_archive: Union[Path, IO[bytes]] + fab_name: Optional[str] + if isinstance(fab_file, bytes): + fab_file_archive = BytesIO(fab_file) + fab_name = None + elif isinstance(fab_file, Path): + fab_file_archive = fab_file + fab_name = fab_file.stem + else: + raise ValueError("fab_file must be either a Path or bytes") + + with tempfile.TemporaryDirectory() as tmpdir: + with zipfile.ZipFile(fab_file_archive, "r") as zipf: + zipf.extractall(tmpdir) + tmpdir_path = Path(tmpdir) + info_dir = tmpdir_path / ".info" + if not info_dir.exists(): + typer.secho( + "❌ FAB file has incorrect format.", + fg=typer.colors.RED, + bold=True, + ) + raise typer.Exit(code=1) + + content_file = info_dir / "CONTENT" + + if not content_file.exists() or not _verify_hashes( + content_file.read_text(), tmpdir_path + ): + typer.secho( + "❌ File hashes couldn't be verified.", + fg=typer.colors.RED, + bold=True, + ) + raise typer.Exit(code=1) + + shutil.rmtree(info_dir) + + installed_path = validate_and_install( + tmpdir_path, fab_name, flwr_dir, skip_prompt + ) + + return installed_path + + +def validate_and_install( + project_dir: Path, + fab_name: Optional[str], + flwr_dir: Optional[Path], + skip_prompt: bool = False, +) -> Path: + """Validate TOML files and install the project to the desired directory.""" + config, _, _ = load_and_validate(project_dir / "pyproject.toml", check_module=False) + + if config is None: + typer.secho( + "❌ Invalid config inside FAB file.", + fg=typer.colors.RED, + bold=True, + ) + raise typer.Exit(code=1) + + publisher = config["flower"]["publisher"] + project_name = config["project"]["name"] + version = config["project"]["version"] + + if ( + fab_name + and fab_name != f"{publisher}.{project_name}.{version.replace('.', '-')}" + ): + typer.secho( + "❌ FAB file has incorrect name. The file name must follow the format " + "`...fab`.", + fg=typer.colors.RED, + bold=True, + ) + raise typer.Exit(code=1) + + install_dir: Path = ( + (get_flwr_dir() if not flwr_dir else flwr_dir) + / "apps" + / publisher + / project_name + / version + ) + if install_dir.exists() and not skip_prompt: + if not typer.confirm( + typer.style( + f"\n💬 {project_name} version {version} is already installed, " + "do you want to reinstall it?", + fg=typer.colors.MAGENTA, + bold=True, + ) + ): + return install_dir + + install_dir.mkdir(parents=True, exist_ok=True) + + # Move contents from source directory + for item in project_dir.iterdir(): + if item.is_dir(): + shutil.copytree(item, install_dir / item.name, dirs_exist_ok=True) + else: + shutil.copy2(item, install_dir / item.name) + + typer.secho( + f"🎊 Successfully installed {project_name} to {install_dir}.", + fg=typer.colors.GREEN, + bold=True, + ) + + return install_dir + + +def _verify_hashes(list_content: str, tmpdir: Path) -> bool: + """Verify file hashes based on the LIST content.""" + for line in list_content.strip().split("\n"): + rel_path, hash_expected, _ = line.split(",") + file_path = tmpdir / rel_path + if not file_path.exists() or get_sha256_hash(file_path) != hash_expected: + return False + return True diff --git a/src/py/flwr/cli/new/new.py b/src/py/flwr/cli/new/new.py index 9bbc016de1a8..9367cf6c9ffb 100644 --- a/src/py/flwr/cli/new/new.py +++ b/src/py/flwr/cli/new/new.py @@ -41,6 +41,16 @@ class MlFramework(str, Enum): HUGGINGFACE = "HF" MLX = "MLX" SKLEARN = "sklearn" + FLOWERTUNE = "FlowerTune" + + +class LlmChallengeName(str, Enum): + """Available LLM challenges.""" + + GENERALNLP = "GeneralNLP" + FINANCE = "Finance" + MEDICAL = "Medical" + CODE = "Code" class TemplateNotFound(Exception): @@ -81,6 +91,7 @@ def render_and_create(file_path: str, template: str, context: Dict[str, str]) -> create_file(file_path, content) +# pylint: disable=too-many-locals,too-many-branches,too-many-statements def new( project_name: Annotated[ Optional[str], @@ -125,6 +136,19 @@ def new( framework_str = framework_str.lower() + if framework_str == "flowertune": + llm_challenge_value = prompt_options( + "Please select LLM challenge by typing in the number", + sorted([challenge.value for challenge in LlmChallengeName]), + ) + selected_value = [ + name + for name, value in vars(LlmChallengeName).items() + if value == llm_challenge_value + ] + llm_challenge_str = selected_value[0] + llm_challenge_str = llm_challenge_str.lower() + print( typer.style( f"\n🔨 Creating Flower project {project_name}...", @@ -139,33 +163,6 @@ def new( import_name = package_name.replace("-", "_") project_dir = os.path.join(cwd, package_name) - # List of files to render - files = { - ".gitignore": {"template": "app/.gitignore.tpl"}, - "README.md": {"template": "app/README.md.tpl"}, - "pyproject.toml": {"template": f"app/pyproject.{framework_str}.toml.tpl"}, - f"{import_name}/__init__.py": {"template": "app/code/__init__.py.tpl"}, - f"{import_name}/server.py": { - "template": f"app/code/server.{framework_str}.py.tpl" - }, - f"{import_name}/client.py": { - "template": f"app/code/client.{framework_str}.py.tpl" - }, - } - - # Depending on the framework, generate task.py file - frameworks_with_tasks = [ - MlFramework.PYTORCH.value.lower(), - MlFramework.JAX.value.lower(), - MlFramework.HUGGINGFACE.value.lower(), - MlFramework.MLX.value.lower(), - MlFramework.TENSORFLOW.value.lower(), - ] - if framework_str in frameworks_with_tasks: - files[f"{import_name}/task.py"] = { - "template": f"app/code/task.{framework_str}.py.tpl" - } - context = { "project_name": project_name, "package_name": package_name, @@ -173,6 +170,85 @@ def new( "username": username, } + # List of files to render + if framework_str == "flowertune": + files = { + ".gitignore": {"template": "app/.gitignore.tpl"}, + "pyproject.toml": {"template": f"app/pyproject.{framework_str}.toml.tpl"}, + "README.md": {"template": f"app/README.{framework_str}.md.tpl"}, + f"{import_name}/__init__.py": {"template": "app/code/__init__.py.tpl"}, + f"{import_name}/server.py": { + "template": "app/code/flwr_tune/server.py.tpl" + }, + f"{import_name}/client.py": { + "template": "app/code/flwr_tune/client.py.tpl" + }, + f"{import_name}/app.py": {"template": "app/code/flwr_tune/app.py.tpl"}, + f"{import_name}/models.py": { + "template": "app/code/flwr_tune/models.py.tpl" + }, + f"{import_name}/dataset.py": { + "template": "app/code/flwr_tune/dataset.py.tpl" + }, + f"{import_name}/conf/config.yaml": { + "template": "app/code/flwr_tune/config.yaml.tpl" + }, + f"{import_name}/conf/static_config.yaml": { + "template": "app/code/flwr_tune/static_config.yaml.tpl" + }, + } + + # Challenge specific context + fraction_fit = "0.2" if llm_challenge_str == "code" else "0.1" + if llm_challenge_str == "generalnlp": + challenge_name = "General NLP" + num_clients = "20" + dataset_name = "vicgalle/alpaca-gpt4" + elif llm_challenge_str == "finance": + challenge_name = "Finance" + num_clients = "50" + dataset_name = "FinGPT/fingpt-sentiment-train" + elif llm_challenge_str == "medical": + challenge_name = "Medical" + num_clients = "20" + dataset_name = "medalpaca/medical_meadow_medical_flashcards" + else: + challenge_name = "Code" + num_clients = "10" + dataset_name = "lucasmccabe-lmi/CodeAlpaca-20k" + + context["llm_challenge_str"] = llm_challenge_str + context["fraction_fit"] = fraction_fit + context["challenge_name"] = challenge_name + context["num_clients"] = num_clients + context["dataset_name"] = dataset_name + else: + files = { + ".gitignore": {"template": "app/.gitignore.tpl"}, + "README.md": {"template": "app/README.md.tpl"}, + "pyproject.toml": {"template": f"app/pyproject.{framework_str}.toml.tpl"}, + f"{import_name}/__init__.py": {"template": "app/code/__init__.py.tpl"}, + f"{import_name}/server.py": { + "template": f"app/code/server.{framework_str}.py.tpl" + }, + f"{import_name}/client.py": { + "template": f"app/code/client.{framework_str}.py.tpl" + }, + } + + # Depending on the framework, generate task.py file + frameworks_with_tasks = [ + MlFramework.PYTORCH.value.lower(), + MlFramework.JAX.value.lower(), + MlFramework.HUGGINGFACE.value.lower(), + MlFramework.MLX.value.lower(), + MlFramework.TENSORFLOW.value.lower(), + ] + if framework_str in frameworks_with_tasks: + files[f"{import_name}/task.py"] = { + "template": f"app/code/task.{framework_str}.py.tpl" + } + for file_path, value in files.items(): render_and_create( file_path=os.path.join(project_dir, file_path), @@ -190,7 +266,7 @@ def new( ) print( typer.style( - f" cd {project_name}\n" + " pip install -e .\n flwr run\n", + f" cd {package_name}\n" + " pip install -e .\n flwr run\n", fg=typer.colors.BRIGHT_CYAN, bold=True, ) diff --git a/src/py/flwr/cli/new/templates/app/README.flowertune.md.tpl b/src/py/flwr/cli/new/templates/app/README.flowertune.md.tpl new file mode 100644 index 000000000000..2b59937e4130 --- /dev/null +++ b/src/py/flwr/cli/new/templates/app/README.flowertune.md.tpl @@ -0,0 +1,56 @@ +# FlowerTune LLM on $challenge_name Dataset + +This directory conducts federated instruction tuning with a pretrained [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.3) model on a [$challenge_name dataset](https://huggingface.co/datasets/$dataset_name). +We use [Flower Datasets](https://flower.dev/docs/datasets/) to download, partition and preprocess the dataset. +Flower's Simulation Engine is used to simulate the LLM fine-tuning process in federated way, +which allows users to perform the training on a single GPU. + + +## Methodology + +This baseline performs federated LLM fine-tuning with [LoRA](https://arxiv.org/pdf/2106.09685) using the [🤗PEFT](https://huggingface.co/docs/peft/en/index) library. +The clients' models are aggregated with FedAvg strategy. +This provides a baseline performance for the leaderboard of $challenge_name challenge. + + +## Environments setup + +Project dependencies are defined in `pyproject.toml`. Install them in an activated Python environment with: + +```shell +pip install -e . +``` + +## Experimental setup + +The dataset is partitioned into $num_clients shards with IID fashion serving as clients. +We randomly sample $fraction_fit clients to be available for each round, +and the federated fine-tuning lasts for `200` rounds. +All settings are defined in `$project_name/conf/static_config.yaml`, which is not allowed to be modified for fair competition if you plan to participated in the [LLM leaderboard](https://flower.ai/benchmarks/llm-leaderboard). + + +## Running the challenge + +First make sure that you have got the access to [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.3) model with your Hugging-Face account. You can request access directly from the Hugging-Face website. +Then, follow the instruction [here](https://huggingface.co/docs/huggingface_hub/en/quick-start#login-command) to log in your account. Note you only need to complete this stage once in your development machine: + +```bash +huggingface-cli login +``` + +Run the challenge with default config values. +The configs are in `$project_name/conf/config.yaml` and `$project_name/conf/static_config.yaml`, and are loaded automatically. + +```bash +flwr run +``` + +## VRAM consumption + +We use Mistral-7B model with 4-bit quantization as default. The estimated VRAM consumption per client for each challenge is shown below: + +| Challenges | GeneralNLP | Finance | Medical | Code | +| :--------: | :--------: | :--------: | :--------: | :--------: | +| VRAM | ~25.50 GB | ~17.30 GB | ~22.80 GB | ~17.40 GB | + +You can adjust the CPU/GPU resources you assign to each of the clients based on your device, which is specified with `flower.engine.simulation` in `pyproject.toml`. diff --git a/src/py/flwr/cli/new/templates/app/code/flwr_tune/__init__.py b/src/py/flwr/cli/new/templates/app/code/flwr_tune/__init__.py new file mode 100644 index 000000000000..6d886f216bc9 --- /dev/null +++ b/src/py/flwr/cli/new/templates/app/code/flwr_tune/__init__.py @@ -0,0 +1,15 @@ +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Flower CLI `new` command app / code / flwr_tune templates.""" diff --git a/src/py/flwr/cli/new/templates/app/code/flwr_tune/app.py.tpl b/src/py/flwr/cli/new/templates/app/code/flwr_tune/app.py.tpl new file mode 100644 index 000000000000..ecb87bd71e3f --- /dev/null +++ b/src/py/flwr/cli/new/templates/app/code/flwr_tune/app.py.tpl @@ -0,0 +1,86 @@ +"""$project_name: A Flower / FlowerTune app.""" + +import os +import warnings +from datetime import datetime + +from flwr_datasets import FederatedDataset +from hydra import compose, initialize +from hydra.utils import instantiate + +from flwr.client import ClientApp +from flwr.common import ndarrays_to_parameters +from flwr.server import ServerApp, ServerConfig + +from $import_name.client import gen_client_fn, get_parameters +from $import_name.dataset import get_tokenizer_and_data_collator_and_propt_formatting +from $import_name.models import get_model +from $import_name.server import fit_weighted_average, get_evaluate_fn, get_on_fit_config + +# Avoid warnings +warnings.filterwarnings("ignore", category=UserWarning) +os.environ["TOKENIZERS_PARALLELISM"] = "true" +os.environ["RAY_DISABLE_DOCKER_CPU_WARNING"] = "1" + +# Initialise regular config +with initialize(config_path="conf", version_base="1.1"): + cfg = compose(config_name="config") + +# Initialise static config +with initialize(config_path="conf", version_base="1.1"): + cfg_static = compose(config_name="static_config") + +cfg.train.num_rounds = cfg_static.num_rounds + +# Create output directory given current timestamp +current_time = datetime.now() +folder_name = current_time.strftime("%Y-%m-%d_%H-%M-%S") +save_path = os.path.join(os.getcwd(), f"results/{folder_name}") +os.makedirs(save_path, exist_ok=True) + +# Partition dataset and get dataloaders +partitioner = instantiate(cfg_static.partitioner) +fds = FederatedDataset( + dataset=cfg_static.dataset.name, partitioners={"train": partitioner} +) +( + tokenizer, + data_collator, + formatting_prompts_func, +) = get_tokenizer_and_data_collator_and_propt_formatting(cfg.model.name) + +# ClientApp for Flower Next +client = ClientApp( + client_fn=gen_client_fn( + fds, + tokenizer, + formatting_prompts_func, + data_collator, + cfg.model, + cfg.train, + save_path, + ), +) + +# Get initial model weights +init_model = get_model(cfg.model) +init_model_parameters = get_parameters(init_model) +init_model_parameters = ndarrays_to_parameters(init_model_parameters) + +# Instantiate strategy according to config. Here we pass other arguments +# that are only defined at runtime. +strategy = instantiate( + cfg.strategy, + on_fit_config_fn=get_on_fit_config(), + fit_metrics_aggregation_fn=fit_weighted_average, + initial_parameters=init_model_parameters, + evaluate_fn=get_evaluate_fn( + cfg.model, cfg.train.save_every_round, cfg_static.num_rounds, save_path + ), +) + +# ServerApp for Flower Next +server = ServerApp( + config=ServerConfig(num_rounds=cfg_static.num_rounds), + strategy=strategy, +) diff --git a/src/py/flwr/cli/new/templates/app/code/flwr_tune/client.py.tpl b/src/py/flwr/cli/new/templates/app/code/flwr_tune/client.py.tpl new file mode 100644 index 000000000000..c0d5842964fd --- /dev/null +++ b/src/py/flwr/cli/new/templates/app/code/flwr_tune/client.py.tpl @@ -0,0 +1,124 @@ +"""$project_name: A Flower / FlowerTune app.""" + +from collections import OrderedDict +from typing import Callable, Dict, Tuple + +import torch +from omegaconf import DictConfig +from peft import get_peft_model_state_dict, set_peft_model_state_dict +from transformers import TrainingArguments +from trl import SFTTrainer + +from flwr.client import NumPyClient +from flwr.common.typing import NDArrays, Scalar +from $import_name.dataset import reformat +from $import_name.models import cosine_annealing, get_model + + +# pylint: disable=too-many-arguments +# pylint: disable=too-many-instance-attributes +class FlowerClient(NumPyClient): + """Standard Flower client for CNN training.""" + + def __init__( + self, + model_cfg: DictConfig, + train_cfg: DictConfig, + trainset, + tokenizer, + formatting_prompts_func, + data_collator, + save_path, + ): # pylint: disable=too-many-arguments + self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + self.train_cfg = train_cfg + self.training_argumnets = TrainingArguments(**train_cfg.training_arguments) + self.tokenizer = tokenizer + self.formatting_prompts_func = formatting_prompts_func + self.data_collator = data_collator + self.save_path = save_path + + # instantiate model + self.model = get_model(model_cfg) + + self.trainset = trainset + + def fit( + self, parameters: NDArrays, config: Dict[str, Scalar] + ) -> Tuple[NDArrays, int, Dict]: + """Implement distributed fit function for a given client.""" + set_parameters(self.model, parameters) + + new_lr = cosine_annealing( + int(config["current_round"]), + self.train_cfg.num_rounds, + self.train_cfg.learning_rate_max, + self.train_cfg.learning_rate_min, + ) + + self.training_argumnets.learning_rate = new_lr + self.training_argumnets.output_dir = self.save_path + + # Construct trainer + trainer = SFTTrainer( + model=self.model, + tokenizer=self.tokenizer, + args=self.training_argumnets, + max_seq_length=self.train_cfg.seq_length, + train_dataset=self.trainset, + formatting_func=self.formatting_prompts_func, + data_collator=self.data_collator, + ) + + # Do local training + results = trainer.train() + + return ( + get_parameters(self.model), + len(self.trainset), + {"train_loss": results.training_loss}, + ) + + +def set_parameters(model, parameters: NDArrays) -> None: + """Change the parameters of the model using the given ones.""" + peft_state_dict_keys = get_peft_model_state_dict(model).keys() + params_dict = zip(peft_state_dict_keys, parameters) + state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict}) + set_peft_model_state_dict(model, state_dict) + + +def get_parameters(model) -> NDArrays: + """Return the parameters of the current net.""" + state_dict = get_peft_model_state_dict(model) + return [val.cpu().numpy() for _, val in state_dict.items()] + + +def gen_client_fn( + fds, + tokenizer, + formatting_prompts_func, + data_collator, + model_cfg: DictConfig, + train_cfg: DictConfig, + save_path: str, +) -> Callable[[str], FlowerClient]: # pylint: disable=too-many-arguments + """Generate the client function that creates the Flower Clients.""" + + def client_fn(cid: str) -> FlowerClient: + """Create a Flower client representing a single organization.""" + # Let's get the partition corresponding to the i-th client + client_trainset = fds.load_partition(int(cid), "train") + client_trainset = reformat(client_trainset, llm_task="$llm_challenge_str") + + return FlowerClient( + model_cfg, + train_cfg, + client_trainset, + tokenizer, + formatting_prompts_func, + data_collator, + save_path, + ).to_client() + + return client_fn diff --git a/src/py/flwr/cli/new/templates/app/code/flwr_tune/config.yaml.tpl b/src/py/flwr/cli/new/templates/app/code/flwr_tune/config.yaml.tpl new file mode 100644 index 000000000000..9f700dd5b8da --- /dev/null +++ b/src/py/flwr/cli/new/templates/app/code/flwr_tune/config.yaml.tpl @@ -0,0 +1,34 @@ +# Federated Instruction Tuning +--- +model: + name: "mistralai/Mistral-7B-v0.3" + quantization: 4 # 8 or 4 if you want to do quantization with BitsAndBytes + gradient_checkpointing: True + lora: + peft_lora_r: 32 + peft_lora_alpha: 64 + +train: + num_rounds: null + save_every_round: 5 + learning_rate_max: 5e-5 + learning_rate_min: 1e-6 + seq_length: 512 + training_arguments: + output_dir: null # to be set by hydra + learning_rate: null # to be set by the client + per_device_train_batch_size: 16 + gradient_accumulation_steps: 1 + logging_steps: 10 + num_train_epochs: 3 + max_steps: 10 + report_to: null + save_steps: 1000 + save_total_limit: 10 + gradient_checkpointing: True + lr_scheduler_type: "constant" + +strategy: + _target_: flwr.server.strategy.FedAvg + fraction_fit: $fraction_fit + fraction_evaluate: 0.0 # no client evaluation diff --git a/src/py/flwr/cli/new/templates/app/code/flwr_tune/dataset.py.tpl b/src/py/flwr/cli/new/templates/app/code/flwr_tune/dataset.py.tpl new file mode 100644 index 000000000000..1b3691d7cf3c --- /dev/null +++ b/src/py/flwr/cli/new/templates/app/code/flwr_tune/dataset.py.tpl @@ -0,0 +1,57 @@ +"""$project_name: A Flower / FlowerTune app.""" + +from transformers import AutoTokenizer +from trl import DataCollatorForCompletionOnlyLM + + +def formatting_prompts_func(example): + """Construct prompts.""" + output_texts = [] + # Constructing a standard Alpaca + # (https://github.com/tatsu-lab/stanford_alpaca#data-release) prompt + mssg = ( + "Below is an instruction that describes a task. " + "Write a response that appropriately completes the request." + ) + for i in range(len(example["instruction"])): + text = ( + f"{mssg}\n### Instruction:\n{example['instruction'][i]}\n" + f"### Response: {example['response'][i]}" + ) + output_texts.append(text) + return output_texts + + +def get_tokenizer_and_data_collator_and_propt_formatting(model_name: str): + """Get tokenizer, data_collator and prompt formatting.""" + # From: https://huggingface.co/docs/trl/en/sft_trainer + tokenizer = AutoTokenizer.from_pretrained( + model_name, use_fast=True, padding_side="right" + ) + tokenizer.pad_token = tokenizer.eos_token + response_template_with_context = "\n### Response:" # alpaca response tag + response_template_ids = tokenizer.encode( + response_template_with_context, add_special_tokens=False + )[2:] + data_collator = DataCollatorForCompletionOnlyLM( + response_template_ids, tokenizer=tokenizer + ) + + return tokenizer, data_collator, formatting_prompts_func + + +def formatting(dataset): + """Format dataset.""" + dataset["instruction"] = dataset["instruction"] + " " + dataset["input"] + return dataset + + +def reformat(dataset, llm_task): + """Reformat datasets.""" + dataset = dataset.rename_column("output", "response") + if llm_task == "finance" or llm_task == "code": + dataset = dataset.map(formatting, remove_columns=["input"]) + if llm_task == "medical": + dataset = dataset.remove_columns(["instruction"]) + dataset = dataset.rename_column("input", "instruction") + return dataset diff --git a/src/py/flwr/cli/new/templates/app/code/flwr_tune/models.py.tpl b/src/py/flwr/cli/new/templates/app/code/flwr_tune/models.py.tpl new file mode 100644 index 000000000000..a2794f35518c --- /dev/null +++ b/src/py/flwr/cli/new/templates/app/code/flwr_tune/models.py.tpl @@ -0,0 +1,59 @@ +"""$project_name: A Flower / FlowerTune app.""" + +import math + +import torch +from omegaconf import DictConfig +from peft import LoraConfig, get_peft_model +from peft.utils import prepare_model_for_kbit_training +from transformers import AutoModelForCausalLM, BitsAndBytesConfig + + +def cosine_annealing( + current_round: int, + total_round: int, + lrate_max: float = 0.001, + lrate_min: float = 0.0, +) -> float: + """Implement cosine annealing learning rate schedule.""" + cos_inner = math.pi * current_round / total_round + return lrate_min + 0.5 * (lrate_max - lrate_min) * (1 + math.cos(cos_inner)) + + +def get_model(model_cfg: DictConfig): + """Load model with appropriate quantization config and other optimizations. + + Please refer to this example for `peft + BitsAndBytes`: + https://github.com/huggingface/peft/blob/main/examples/fp4_finetuning/finetune_fp4_opt_bnb_peft.py + """ + if model_cfg.quantization == 4: + quantization_config = BitsAndBytesConfig(load_in_4bit=True) + elif model_cfg.quantization == 8: + quantization_config = BitsAndBytesConfig(load_in_8bit=True) + else: + raise ValueError( + f"Use 4-bit or 8-bit quantization. You passed: {model_cfg.quantization}/" + ) + + model = AutoModelForCausalLM.from_pretrained( + model_cfg.name, + quantization_config=quantization_config, + torch_dtype=torch.bfloat16, + low_cpu_mem_usage=True, + ) + + model = prepare_model_for_kbit_training( + model, use_gradient_checkpointing=model_cfg.gradient_checkpointing + ) + + peft_config = LoraConfig( + r=model_cfg.lora.peft_lora_r, + lora_alpha=model_cfg.lora.peft_lora_alpha, + lora_dropout=0.075, + task_type="CAUSAL_LM", + ) + + if model_cfg.gradient_checkpointing: + model.config.use_cache = False + + return get_peft_model(model, peft_config) diff --git a/src/py/flwr/cli/new/templates/app/code/flwr_tune/server.py.tpl b/src/py/flwr/cli/new/templates/app/code/flwr_tune/server.py.tpl new file mode 100644 index 000000000000..19223148bca5 --- /dev/null +++ b/src/py/flwr/cli/new/templates/app/code/flwr_tune/server.py.tpl @@ -0,0 +1,48 @@ +"""$project_name: A Flower / FlowerTune app.""" + +from $import_name.client import set_parameters +from $import_name.models import get_model + + +# Get function that will be executed by the strategy's evaluate() method +# Here we use it to save global model checkpoints +def get_evaluate_fn(model_cfg, save_every_round, total_round, save_path): + """Return an evaluation function for saving global model.""" + + def evaluate(server_round: int, parameters, config): + # Save model + if server_round != 0 and ( + server_round == total_round or server_round % save_every_round == 0 + ): + # Init model + model = get_model(model_cfg) + set_parameters(model, parameters) + + model.save_pretrained(f"{save_path}/peft_{server_round}") + + return 0.0, {} + + return evaluate + + +def get_on_fit_config(): + """ + Return a function that will be used to construct the config + that the client's fit() method will receive. + """ + + def fit_config_fn(server_round: int): + fit_config = {"current_round": server_round} + return fit_config + + return fit_config_fn + + +def fit_weighted_average(metrics): + """Aggregate (federated) evaluation metrics.""" + # Multiply accuracy of each client by number of examples used + losses = [num_examples * m["train_loss"] for num_examples, m in metrics] + examples = [num_examples for num_examples, _ in metrics] + + # Aggregate and return custom metric (weighted average) + return {"train_loss": sum(losses) / sum(examples)} diff --git a/src/py/flwr/cli/new/templates/app/code/flwr_tune/static_config.yaml.tpl b/src/py/flwr/cli/new/templates/app/code/flwr_tune/static_config.yaml.tpl new file mode 100644 index 000000000000..a8a4039fc831 --- /dev/null +++ b/src/py/flwr/cli/new/templates/app/code/flwr_tune/static_config.yaml.tpl @@ -0,0 +1,11 @@ +# Federated Instruction Tuning (static) +--- +dataset: + name: $dataset_name + +# FL experimental settings +num_clients: $num_clients # total number of clients +num_rounds: 200 +partitioner: + _target_: flwr_datasets.partitioner.IidPartitioner + num_partitions: $num_clients diff --git a/src/py/flwr/cli/new/templates/app/pyproject.flowertune.toml.tpl b/src/py/flwr/cli/new/templates/app/pyproject.flowertune.toml.tpl new file mode 100644 index 000000000000..2ed6bd36fd89 --- /dev/null +++ b/src/py/flwr/cli/new/templates/app/pyproject.flowertune.toml.tpl @@ -0,0 +1,42 @@ +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[project] +name = "$package_name" +version = "1.0.0" +description = "" +authors = [ + { name = "The Flower Authors", email = "hello@flower.ai" }, +] +license = { text = "Apache License (2.0)" } +dependencies = [ + "flwr[simulation]>=1.9.0,<2.0", + "flwr-datasets>=0.1.0,<1.0.0", + "hydra-core==1.3.2", + "trl==0.8.1", + "bitsandbytes==0.43.0", + "scipy==1.13.0", + "peft==0.6.2", + "transformers==4.39.3", + "sentencepiece==0.2.0", +] + +[tool.hatch.build.targets.wheel] +packages = ["."] + +[flower] +publisher = "$username" + +[flower.components] +serverapp = "$import_name.app:server" +clientapp = "$import_name.app:client" + +[flower.engine] +name = "simulation" + +[flower.engine.simulation.supernode] +num = $num_clients + +[flower.engine.simulation] +backend_config = { client_resources = { num_cpus = 8, num_gpus = 1.0 } } diff --git a/src/py/flwr/cli/new/templates/app/pyproject.hf.toml.tpl b/src/py/flwr/cli/new/templates/app/pyproject.hf.toml.tpl index 3bd980b2340e..71004f3421cd 100644 --- a/src/py/flwr/cli/new/templates/app/pyproject.hf.toml.tpl +++ b/src/py/flwr/cli/new/templates/app/pyproject.hf.toml.tpl @@ -11,7 +11,7 @@ authors = [ ] license = { text = "Apache License (2.0)" } dependencies = [ - "flwr[simulation]>=1.8.0,<2.0", + "flwr[simulation]>=1.9.0,<2.0", "flwr-datasets>=0.0.2,<1.0.0", "torch==2.2.1", "transformers>=4.30.0,<5.0" @@ -29,3 +29,9 @@ publisher = "$username" [flower.components] serverapp = "$import_name.server:app" clientapp = "$import_name.client:app" + +[flower.engine] +name = "simulation" + +[flower.engine.simulation.supernode] +num = 2 diff --git a/src/py/flwr/cli/new/templates/app/pyproject.jax.toml.tpl b/src/py/flwr/cli/new/templates/app/pyproject.jax.toml.tpl index 1d32cfd77481..c5463e08b92c 100644 --- a/src/py/flwr/cli/new/templates/app/pyproject.jax.toml.tpl +++ b/src/py/flwr/cli/new/templates/app/pyproject.jax.toml.tpl @@ -11,7 +11,7 @@ authors = [ ] license = {text = "Apache License (2.0)"} dependencies = [ - "flwr[simulation]>=1.8.0,<2.0", + "flwr[simulation]>=1.9.0,<2.0", "jax==0.4.26", "jaxlib==0.4.26", "scikit-learn==1.4.2", diff --git a/src/py/flwr/cli/new/templates/app/pyproject.mlx.toml.tpl b/src/py/flwr/cli/new/templates/app/pyproject.mlx.toml.tpl index 0f56dc7eacc5..a850135a1fc5 100644 --- a/src/py/flwr/cli/new/templates/app/pyproject.mlx.toml.tpl +++ b/src/py/flwr/cli/new/templates/app/pyproject.mlx.toml.tpl @@ -11,7 +11,7 @@ authors = [ ] license = { text = "Apache License (2.0)" } dependencies = [ - "flwr[simulation]>=1.8.0,<2.0", + "flwr[simulation]>=1.9.0,<2.0", "flwr-datasets[vision]>=0.0.2,<1.0.0", "mlx==0.10.0", "numpy==1.24.4", @@ -26,3 +26,9 @@ publisher = "$username" [flower.components] serverapp = "$import_name.server:app" clientapp = "$import_name.client:app" + +[flower.engine] +name = "simulation" + +[flower.engine.simulation.supernode] +num = 2 diff --git a/src/py/flwr/cli/new/templates/app/pyproject.numpy.toml.tpl b/src/py/flwr/cli/new/templates/app/pyproject.numpy.toml.tpl index bbf8463054f4..d49015eb567f 100644 --- a/src/py/flwr/cli/new/templates/app/pyproject.numpy.toml.tpl +++ b/src/py/flwr/cli/new/templates/app/pyproject.numpy.toml.tpl @@ -11,7 +11,7 @@ authors = [ ] license = { text = "Apache License (2.0)" } dependencies = [ - "flwr[simulation]>=1.8.0,<2.0", + "flwr[simulation]>=1.9.0,<2.0", "numpy>=1.21.0", ] @@ -24,3 +24,9 @@ publisher = "$username" [flower.components] serverapp = "$import_name.server:app" clientapp = "$import_name.client:app" + +[flower.engine] +name = "simulation" + +[flower.engine.simulation.supernode] +num = 2 diff --git a/src/py/flwr/cli/new/templates/app/pyproject.pytorch.toml.tpl b/src/py/flwr/cli/new/templates/app/pyproject.pytorch.toml.tpl index a41ce1a6a4c6..b56c0041b96c 100644 --- a/src/py/flwr/cli/new/templates/app/pyproject.pytorch.toml.tpl +++ b/src/py/flwr/cli/new/templates/app/pyproject.pytorch.toml.tpl @@ -11,7 +11,7 @@ authors = [ ] license = { text = "Apache License (2.0)" } dependencies = [ - "flwr[simulation]>=1.8.0,<2.0", + "flwr[simulation]>=1.9.0,<2.0", "flwr-datasets[vision]>=0.0.2,<1.0.0", "torch==2.2.1", "torchvision==0.17.1", @@ -26,3 +26,9 @@ publisher = "$username" [flower.components] serverapp = "$import_name.server:app" clientapp = "$import_name.client:app" + +[flower.engine] +name = "simulation" + +[flower.engine.simulation.supernode] +num = 2 diff --git a/src/py/flwr/cli/new/templates/app/pyproject.sklearn.toml.tpl b/src/py/flwr/cli/new/templates/app/pyproject.sklearn.toml.tpl index 25645f0cde1a..6f914ae659b1 100644 --- a/src/py/flwr/cli/new/templates/app/pyproject.sklearn.toml.tpl +++ b/src/py/flwr/cli/new/templates/app/pyproject.sklearn.toml.tpl @@ -11,7 +11,7 @@ authors = [ ] license = { text = "Apache License (2.0)" } dependencies = [ - "flwr[simulation]>=1.8.0,<2.0", + "flwr[simulation]>=1.9.0,<2.0", "flwr-datasets[vision]>=0.0.2,<1.0.0", "scikit-learn>=1.1.1", ] @@ -25,3 +25,9 @@ publisher = "$username" [flower.components] serverapp = "$import_name.server:app" clientapp = "$import_name.client:app" + +[flower.engine] +name = "simulation" + +[flower.engine.simulation.supernode] +num = 2 diff --git a/src/py/flwr/cli/new/templates/app/pyproject.tensorflow.toml.tpl b/src/py/flwr/cli/new/templates/app/pyproject.tensorflow.toml.tpl index 3968e3aa327b..4ecd16143dcc 100644 --- a/src/py/flwr/cli/new/templates/app/pyproject.tensorflow.toml.tpl +++ b/src/py/flwr/cli/new/templates/app/pyproject.tensorflow.toml.tpl @@ -11,7 +11,7 @@ authors = [ ] license = { text = "Apache License (2.0)" } dependencies = [ - "flwr[simulation]>=1.8.0,<2.0", + "flwr[simulation]>=1.9.0,<2.0", "flwr-datasets[vision]>=0.0.2,<1.0.0", "tensorflow>=2.11.1", ] @@ -25,3 +25,9 @@ publisher = "$username" [flower.components] serverapp = "$import_name.server:app" clientapp = "$import_name.client:app" + +[flower.engine] +name = "simulation" + +[flower.engine.simulation.supernode] +num = 2 diff --git a/src/py/flwr/cli/run/run.py b/src/py/flwr/cli/run/run.py index 9c50c8cb1980..4c95a4041c05 100644 --- a/src/py/flwr/cli/run/run.py +++ b/src/py/flwr/cli/run/run.py @@ -15,22 +15,57 @@ """Flower command line interface `run` command.""" import sys +from enum import Enum +from logging import DEBUG +from typing import Optional import typer +from typing_extensions import Annotated from flwr.cli import config_utils +from flwr.common.constant import SUPEREXEC_DEFAULT_ADDRESS +from flwr.common.grpc import GRPC_MAX_MESSAGE_LENGTH, create_channel +from flwr.common.logger import log +from flwr.proto.exec_pb2 import StartRunRequest # pylint: disable=E0611 +from flwr.proto.exec_pb2_grpc import ExecStub from flwr.simulation.run_simulation import _run_simulation -def run() -> None: +class Engine(str, Enum): + """Enum defining the engine to run on.""" + + SIMULATION = "simulation" + + +# pylint: disable-next=too-many-locals +def run( + engine: Annotated[ + Optional[Engine], + typer.Option( + case_sensitive=False, + help="The engine to run FL with (currently only simulation is supported).", + ), + ] = None, + use_superexec: Annotated[ + bool, + typer.Option( + case_sensitive=False, help="Use this flag to use the new SuperExec API" + ), + ] = False, +) -> None: """Run Flower project.""" + if use_superexec: + _start_superexec_run() + return + typer.secho("Loading project configuration... ", fg=typer.colors.BLUE) - config, errors, warnings = config_utils.load_and_validate_with_defaults() + config, errors, warnings = config_utils.load_and_validate() if config is None: typer.secho( - "Project configuration could not be loaded.\npyproject.toml is invalid:\n" + "Project configuration could not be loaded.\n" + "pyproject.toml is invalid:\n" + "\n".join([f"- {line}" for line in errors]), fg=typer.colors.RED, bold=True, @@ -49,16 +84,22 @@ def run() -> None: server_app_ref = config["flower"]["components"]["serverapp"] client_app_ref = config["flower"]["components"]["clientapp"] - engine = config["flower"]["engine"]["name"] - if engine == "simulation": + if engine is None: + engine = config["flower"]["engine"]["name"] + + if engine == Engine.SIMULATION: num_supernodes = config["flower"]["engine"]["simulation"]["supernode"]["num"] + backend_config = config["flower"]["engine"]["simulation"].get( + "backend_config", None + ) typer.secho("Starting run... ", fg=typer.colors.BLUE) _run_simulation( server_app_attr=server_app_ref, client_app_attr=client_app_ref, num_supernodes=num_supernodes, + backend_config=backend_config, ) else: typer.secho( @@ -66,3 +107,22 @@ def run() -> None: fg=typer.colors.RED, bold=True, ) + + +def _start_superexec_run() -> None: + def on_channel_state_change(channel_connectivity: str) -> None: + """Log channel connectivity.""" + log(DEBUG, channel_connectivity) + + channel = create_channel( + server_address=SUPEREXEC_DEFAULT_ADDRESS, + insecure=True, + root_certificates=None, + max_message_length=GRPC_MAX_MESSAGE_LENGTH, + interceptors=None, + ) + channel.subscribe(on_channel_state_change) + stub = ExecStub(channel) + + req = StartRunRequest() + stub.StartRun(req) diff --git a/src/py/flwr/cli/utils.py b/src/py/flwr/cli/utils.py index 6460b770b184..2f5a8831fa7c 100644 --- a/src/py/flwr/cli/utils.py +++ b/src/py/flwr/cli/utils.py @@ -14,7 +14,9 @@ # ============================================================================== """Flower command line interface utils.""" +import hashlib import re +from pathlib import Path from typing import Callable, List, Optional, cast import typer @@ -122,3 +124,15 @@ def sanitize_project_name(name: str) -> str: sanitized_name = sanitized_name[1:] return sanitized_name + + +def get_sha256_hash(file_path: Path) -> str: + """Calculate the SHA-256 hash of a file.""" + sha256 = hashlib.sha256() + with open(file_path, "rb") as f: + while True: + data = f.read(65536) # Read in 64kB blocks + if not data: + break + sha256.update(data) + return sha256.hexdigest() diff --git a/src/py/flwr/client/__init__.py b/src/py/flwr/client/__init__.py index fd8647dbaf2e..58fd94448586 100644 --- a/src/py/flwr/client/__init__.py +++ b/src/py/flwr/client/__init__.py @@ -29,6 +29,7 @@ "ClientApp", "ClientFn", "NumPyClient", + "mod", "run_client_app", "run_supernode", "start_client", diff --git a/src/py/flwr/client/app.py b/src/py/flwr/client/app.py index d7c05d8afbb2..1226a0d7bc21 100644 --- a/src/py/flwr/client/app.py +++ b/src/py/flwr/client/app.py @@ -14,10 +14,12 @@ # ============================================================================== """Flower client app.""" +import signal import sys import time +from dataclasses import dataclass from logging import DEBUG, ERROR, INFO, WARN -from typing import Callable, ContextManager, Optional, Tuple, Type, Union +from typing import Callable, ContextManager, Dict, Optional, Tuple, Type, Union from cryptography.hazmat.primitives.asymmetric import ec from grpc import RpcError @@ -29,6 +31,7 @@ from flwr.common.address import parse_address from flwr.common.constant import ( MISSING_EXTRA_REST, + TRANSPORT_TYPE_GRPC_ADAPTER, TRANSPORT_TYPE_GRPC_BIDI, TRANSPORT_TYPE_GRPC_RERE, TRANSPORT_TYPE_REST, @@ -37,8 +40,9 @@ ) from flwr.common.logger import log, warn_deprecated_feature from flwr.common.message import Error -from flwr.common.retry_invoker import RetryInvoker, exponential +from flwr.common.retry_invoker import RetryInvoker, RetryState, exponential +from .grpc_adapter_client.connection import grpc_adapter from .grpc_client.connection import grpc_connection from .grpc_rere_client.connection import grpc_request_response from .message_handler.message_handler import handle_control_message @@ -175,7 +179,7 @@ class `flwr.client.Client` (default: None) def _start_client_internal( *, server_address: str, - load_client_app_fn: Optional[Callable[[], ClientApp]] = None, + load_client_app_fn: Optional[Callable[[str, str], ClientApp]] = None, client_fn: Optional[ClientFn] = None, client: Optional[Client] = None, grpc_max_message_length: int = GRPC_MAX_MESSAGE_LENGTH, @@ -250,7 +254,7 @@ def single_client_factory( client_fn = single_client_factory - def _load_client_app() -> ClientApp: + def _load_client_app(_1: str, _2: str) -> ClientApp: return ClientApp(client_fn=client_fn) load_client_app_fn = _load_client_app @@ -263,6 +267,29 @@ def _load_client_app() -> ClientApp: transport, server_address ) + app_state_tracker = _AppStateTracker() + + def _on_sucess(retry_state: RetryState) -> None: + app_state_tracker.is_connected = True + if retry_state.tries > 1: + log( + INFO, + "Connection successful after %.2f seconds and %s tries.", + retry_state.elapsed_time, + retry_state.tries, + ) + + def _on_backoff(retry_state: RetryState) -> None: + app_state_tracker.is_connected = False + if retry_state.tries == 1: + log(WARN, "Connection attempt failed, retrying...") + else: + log( + DEBUG, + "Connection attempt failed, retrying in %.2f seconds", + retry_state.actual_wait, + ) + retry_invoker = RetryInvoker( wait_gen_factory=exponential, recoverable_exceptions=connection_error_type, @@ -278,30 +305,15 @@ def _load_client_app() -> ClientApp: if retry_state.tries > 1 else None ), - on_success=lambda retry_state: ( - log( - INFO, - "Connection successful after %.2f seconds and %s tries.", - retry_state.elapsed_time, - retry_state.tries, - ) - if retry_state.tries > 1 - else None - ), - on_backoff=lambda retry_state: ( - log(WARN, "Connection attempt failed, retrying...") - if retry_state.tries == 1 - else log( - DEBUG, - "Connection attempt failed, retrying in %.2f seconds", - retry_state.actual_wait, - ) - ), + on_success=_on_sucess, + on_backoff=_on_backoff, ) node_state = NodeState() + # run_id -> (fab_id, fab_version) + run_info: Dict[int, Tuple[str, str]] = {} - while True: + while not app_state_tracker.interrupt: sleep_duration: int = 0 with connection( address, @@ -311,106 +323,125 @@ def _load_client_app() -> ClientApp: root_certificates, authentication_keys, ) as conn: - # pylint: disable-next=W0612 receive, send, create_node, delete_node, get_run = conn # Register node if create_node is not None: create_node() # pylint: disable=not-callable - while True: - # Receive - message = receive() - if message is None: - time.sleep(3) # Wait for 3s before asking again - continue - - log(INFO, "") - if len(message.metadata.group_id) > 0: + app_state_tracker.register_signal_handler() + while not app_state_tracker.interrupt: + try: + # Receive + message = receive() + if message is None: + time.sleep(3) # Wait for 3s before asking again + continue + + log(INFO, "") + if len(message.metadata.group_id) > 0: + log( + INFO, + "[RUN %s, ROUND %s]", + message.metadata.run_id, + message.metadata.group_id, + ) log( INFO, - "[RUN %s, ROUND %s]", - message.metadata.run_id, - message.metadata.group_id, + "Received: %s message %s", + message.metadata.message_type, + message.metadata.message_id, ) - log( - INFO, - "Received: %s message %s", - message.metadata.message_type, - message.metadata.message_id, - ) - - # Handle control message - out_message, sleep_duration = handle_control_message(message) - if out_message: - send(out_message) - break - - # Register context for this run - node_state.register_context(run_id=message.metadata.run_id) - - # Retrieve context for this run - context = node_state.retrieve_context(run_id=message.metadata.run_id) - # Create an error reply message that will never be used to prevent - # the used-before-assignment linting error - reply_message = message.create_error_reply( - error=Error(code=ErrorCode.UNKNOWN, reason="Unknown") - ) + # Handle control message + out_message, sleep_duration = handle_control_message(message) + if out_message: + send(out_message) + break + + # Get run info + run_id = message.metadata.run_id + if run_id not in run_info: + if get_run is not None: + run_info[run_id] = get_run(run_id) + # If get_run is None, i.e., in grpc-bidi mode + else: + run_info[run_id] = ("", "") + + # Register context for this run + node_state.register_context(run_id=run_id) + + # Retrieve context for this run + context = node_state.retrieve_context(run_id=run_id) + + # Create an error reply message that will never be used to prevent + # the used-before-assignment linting error + reply_message = message.create_error_reply( + error=Error(code=ErrorCode.UNKNOWN, reason="Unknown") + ) - # Handle app loading and task message - try: - # Load ClientApp instance - client_app: ClientApp = load_client_app_fn() - - # Execute ClientApp - reply_message = client_app(message=message, context=context) - except Exception as ex: # pylint: disable=broad-exception-caught - - # Legacy grpc-bidi - if transport in ["grpc-bidi", None]: - log(ERROR, "Client raised an exception.", exc_info=ex) - # Raise exception, crash process - raise ex - - # Don't update/change NodeState - - e_code = ErrorCode.CLIENT_APP_RAISED_EXCEPTION - # Reason example: ":<'division by zero'>" - reason = str(type(ex)) + ":<'" + str(ex) + "'>" - exc_entity = "ClientApp" - if isinstance(ex, LoadClientAppError): - reason = ( - "An exception was raised when attempting to load " - "`ClientApp`" + # Handle app loading and task message + try: + # Load ClientApp instance + client_app: ClientApp = load_client_app_fn(*run_info[run_id]) + + # Execute ClientApp + reply_message = client_app(message=message, context=context) + except Exception as ex: # pylint: disable=broad-exception-caught + + # Legacy grpc-bidi + if transport in ["grpc-bidi", None]: + log(ERROR, "Client raised an exception.", exc_info=ex) + # Raise exception, crash process + raise ex + + # Don't update/change NodeState + + e_code = ErrorCode.CLIENT_APP_RAISED_EXCEPTION + # Ex fmt: ":<'division by zero'>" + reason = str(type(ex)) + ":<'" + str(ex) + "'>" + exc_entity = "ClientApp" + if isinstance(ex, LoadClientAppError): + reason = ( + "An exception was raised when attempting to load " + "`ClientApp`" + ) + e_code = ErrorCode.LOAD_CLIENT_APP_EXCEPTION + exc_entity = "SuperNode" + + if not app_state_tracker.interrupt: + log( + ERROR, "%s raised an exception", exc_entity, exc_info=ex + ) + + # Create error message + reply_message = message.create_error_reply( + error=Error(code=e_code, reason=reason) + ) + else: + # No exception, update node state + node_state.update_context( + run_id=run_id, + context=context, ) - e_code = ErrorCode.LOAD_CLIENT_APP_EXCEPTION - exc_entity = "SuperNode" - - log(ERROR, "%s raised an exception", exc_entity, exc_info=ex) - # Create error message - reply_message = message.create_error_reply( - error=Error(code=e_code, reason=reason) - ) - else: - # No exception, update node state - node_state.update_context( - run_id=message.metadata.run_id, - context=context, - ) + # Send + send(reply_message) + log(INFO, "Sent reply") - # Send - send(reply_message) - log(INFO, "Sent reply") + except StopIteration: + sleep_duration = 0 + break # Unregister node - if delete_node is not None: + if delete_node is not None and app_state_tracker.is_connected: delete_node() # pylint: disable=not-callable if sleep_duration == 0: log(INFO, "Disconnect and shut down") + del app_state_tracker break + # Sleep and reconnect afterwards log( INFO, @@ -571,6 +602,8 @@ def _init_connection(transport: Optional[str], server_address: str) -> Tuple[ connection, error_type = http_request_response, RequestsConnectionError elif transport == TRANSPORT_TYPE_GRPC_RERE: connection, error_type = grpc_request_response, RpcError + elif transport == TRANSPORT_TYPE_GRPC_ADAPTER: + connection, error_type = grpc_adapter, RpcError elif transport == TRANSPORT_TYPE_GRPC_BIDI: connection, error_type = grpc_connection, RpcError else: @@ -579,3 +612,20 @@ def _init_connection(transport: Optional[str], server_address: str) -> Tuple[ ) return connection, address, error_type + + +@dataclass +class _AppStateTracker: + interrupt: bool = False + is_connected: bool = False + + def register_signal_handler(self) -> None: + """Register handlers for exit signals.""" + + def signal_handler(sig, frame): # type: ignore + # pylint: disable=unused-argument + self.interrupt = True + raise StopIteration from None + + signal.signal(signal.SIGINT, signal_handler) + signal.signal(signal.SIGTERM, signal_handler) diff --git a/src/py/flwr/client/app_test.py b/src/py/flwr/client/app_test.py index 56d6308a0fe2..74ade03f973a 100644 --- a/src/py/flwr/client/app_test.py +++ b/src/py/flwr/client/app_test.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2022 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/client/client_app.py b/src/py/flwr/client/client_app.py index c9d337700147..2e810f6560f2 100644 --- a/src/py/flwr/client/client_app.py +++ b/src/py/flwr/client/client_app.py @@ -1,4 +1,4 @@ -# Copyright 2023 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -221,7 +221,7 @@ def _registration_error(fn_name: str) -> ValueError: >>> def client_fn(cid) -> Client: >>> return FlowerClient().to_client() >>> - >>> app = ClientApp() + >>> app = ClientApp( >>> client_fn=client_fn, >>> ) diff --git a/src/py/flwr/client/client_test.py b/src/py/flwr/client/client_test.py index 373c676e5edc..343b6cf093b2 100644 --- a/src/py/flwr/client/client_test.py +++ b/src/py/flwr/client/client_test.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2022 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/client/dpfedavg_numpy_client.py b/src/py/flwr/client/dpfedavg_numpy_client.py index ab31a289d29b..c592d10936d5 100644 --- a/src/py/flwr/client/dpfedavg_numpy_client.py +++ b/src/py/flwr/client/dpfedavg_numpy_client.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2022 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/client/grpc_adapter_client/__init__.py b/src/py/flwr/client/grpc_adapter_client/__init__.py new file mode 100644 index 000000000000..5900e2dc2d06 --- /dev/null +++ b/src/py/flwr/client/grpc_adapter_client/__init__.py @@ -0,0 +1,15 @@ +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Client-side part of the GrpcAdapter transport layer.""" diff --git a/src/py/flwr/client/grpc_adapter_client/connection.py b/src/py/flwr/client/grpc_adapter_client/connection.py new file mode 100644 index 000000000000..e4e32b3accd0 --- /dev/null +++ b/src/py/flwr/client/grpc_adapter_client/connection.py @@ -0,0 +1,94 @@ +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Contextmanager for a GrpcAdapter channel to the Flower server.""" + + +from contextlib import contextmanager +from logging import ERROR +from typing import Callable, Iterator, Optional, Tuple, Union + +from cryptography.hazmat.primitives.asymmetric import ec + +from flwr.client.grpc_rere_client.connection import grpc_request_response +from flwr.client.grpc_rere_client.grpc_adapter import GrpcAdapter +from flwr.common import GRPC_MAX_MESSAGE_LENGTH +from flwr.common.logger import log +from flwr.common.message import Message +from flwr.common.retry_invoker import RetryInvoker + + +@contextmanager +def grpc_adapter( # pylint: disable=R0913 + server_address: str, + insecure: bool, + retry_invoker: RetryInvoker, + max_message_length: int = GRPC_MAX_MESSAGE_LENGTH, # pylint: disable=W0613 + root_certificates: Optional[Union[bytes, str]] = None, + authentication_keys: Optional[ # pylint: disable=unused-argument + Tuple[ec.EllipticCurvePrivateKey, ec.EllipticCurvePublicKey] + ] = None, +) -> Iterator[ + Tuple[ + Callable[[], Optional[Message]], + Callable[[Message], None], + Optional[Callable[[], None]], + Optional[Callable[[], None]], + Optional[Callable[[int], Tuple[str, str]]], + ] +]: + """Primitives for request/response-based interaction with a server via GrpcAdapter. + + Parameters + ---------- + server_address : str + The IPv6 address of the server with `http://` or `https://`. + If the Flower server runs on the same machine + on port 8080, then `server_address` would be `"http://[::]:8080"`. + insecure : bool + Starts an insecure gRPC connection when True. Enables HTTPS connection + when False, using system certificates if `root_certificates` is None. + retry_invoker: RetryInvoker + `RetryInvoker` object that will try to reconnect the client to the server + after gRPC errors. If None, the client will only try to + reconnect once after a failure. + max_message_length : int + Ignored, only present to preserve API-compatibility. + root_certificates : Optional[Union[bytes, str]] (default: None) + Path of the root certificate. If provided, a secure + connection using the certificates will be established to an SSL-enabled + Flower server. Bytes won't work for the REST API. + authentication_keys : Optional[Tuple[PrivateKey, PublicKey]] (default: None) + Client authentication is not supported for this transport type. + + Returns + ------- + receive : Callable + send : Callable + create_node : Optional[Callable] + delete_node : Optional[Callable] + get_run : Optional[Callable] + """ + if authentication_keys is not None: + log(ERROR, "Client authentication is not supported for this transport type.") + with grpc_request_response( + server_address=server_address, + insecure=insecure, + retry_invoker=retry_invoker, + max_message_length=max_message_length, + root_certificates=root_certificates, + authentication_keys=None, # Authentication is not supported + adapter_cls=GrpcAdapter, + ) as conn: + yield conn diff --git a/src/py/flwr/client/grpc_client/connection.py b/src/py/flwr/client/grpc_client/connection.py index 6e5227cf5e5f..8c049861c672 100644 --- a/src/py/flwr/client/grpc_client/connection.py +++ b/src/py/flwr/client/grpc_client/connection.py @@ -17,7 +17,7 @@ import uuid from contextlib import contextmanager -from logging import DEBUG +from logging import DEBUG, ERROR from pathlib import Path from queue import Queue from typing import Callable, Iterator, Optional, Tuple, Union, cast @@ -101,6 +101,8 @@ def grpc_connection( # pylint: disable=R0913, R0915 The PEM-encoded root certificates as a byte string or a path string. If provided, a secure connection using the certificates will be established to an SSL-enabled Flower server. + authentication_keys : Optional[Tuple[PrivateKey, PublicKey]] (default: None) + Client authentication is not supported for this transport type. Returns ------- @@ -123,6 +125,8 @@ def grpc_connection( # pylint: disable=R0913, R0915 """ if isinstance(root_certificates, str): root_certificates = Path(root_certificates).read_bytes() + if authentication_keys is not None: + log(ERROR, "Client authentication is not supported for this transport type.") channel = create_channel( server_address=server_address, diff --git a/src/py/flwr/client/grpc_rere_client/__init__.py b/src/py/flwr/client/grpc_rere_client/__init__.py index 93903e725776..e7c9408c0047 100644 --- a/src/py/flwr/client/grpc_rere_client/__init__.py +++ b/src/py/flwr/client/grpc_rere_client/__init__.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2023 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/client/grpc_rere_client/client_interceptor.py b/src/py/flwr/client/grpc_rere_client/client_interceptor.py index 8bc55878971d..d2dded8a73d9 100644 --- a/src/py/flwr/client/grpc_rere_client/client_interceptor.py +++ b/src/py/flwr/client/grpc_rere_client/client_interceptor.py @@ -31,11 +31,11 @@ from flwr.proto.fleet_pb2 import ( # pylint: disable=E0611 CreateNodeRequest, DeleteNodeRequest, - GetRunRequest, PingRequest, PullTaskInsRequest, PushTaskResRequest, ) +from flwr.proto.run_pb2 import GetRunRequest # pylint: disable=E0611 _PUBLIC_KEY_HEADER = "public-key" _AUTH_TOKEN_HEADER = "auth-token" diff --git a/src/py/flwr/client/grpc_rere_client/client_interceptor_test.py b/src/py/flwr/client/grpc_rere_client/client_interceptor_test.py index 487361a06026..9607dac5679e 100644 --- a/src/py/flwr/client/grpc_rere_client/client_interceptor_test.py +++ b/src/py/flwr/client/grpc_rere_client/client_interceptor_test.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -41,13 +41,12 @@ CreateNodeResponse, DeleteNodeRequest, DeleteNodeResponse, - GetRunRequest, - GetRunResponse, PullTaskInsRequest, PullTaskInsResponse, PushTaskResRequest, PushTaskResResponse, ) +from flwr.proto.run_pb2 import GetRunRequest, GetRunResponse # pylint: disable=E0611 from .client_interceptor import _AUTH_TOKEN_HEADER, _PUBLIC_KEY_HEADER, Request diff --git a/src/py/flwr/client/grpc_rere_client/connection.py b/src/py/flwr/client/grpc_rere_client/connection.py index 3778fd4061f9..34dc0e417383 100644 --- a/src/py/flwr/client/grpc_rere_client/connection.py +++ b/src/py/flwr/client/grpc_rere_client/connection.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2023 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -21,7 +21,7 @@ from copy import copy from logging import DEBUG, ERROR from pathlib import Path -from typing import Callable, Iterator, Optional, Sequence, Tuple, Union, cast +from typing import Callable, Iterator, Optional, Sequence, Tuple, Type, Union, cast import grpc from cryptography.hazmat.primitives.asymmetric import ec @@ -44,8 +44,6 @@ from flwr.proto.fleet_pb2 import ( # pylint: disable=E0611 CreateNodeRequest, DeleteNodeRequest, - GetRunRequest, - GetRunResponse, PingRequest, PingResponse, PullTaskInsRequest, @@ -53,9 +51,11 @@ ) from flwr.proto.fleet_pb2_grpc import FleetStub # pylint: disable=E0611 from flwr.proto.node_pb2 import Node # pylint: disable=E0611 +from flwr.proto.run_pb2 import GetRunRequest, GetRunResponse # pylint: disable=E0611 from flwr.proto.task_pb2 import TaskIns # pylint: disable=E0611 from .client_interceptor import AuthenticateClientInterceptor +from .grpc_adapter import GrpcAdapter def on_channel_state_change(channel_connectivity: str) -> None: @@ -73,6 +73,7 @@ def grpc_request_response( # pylint: disable=R0913, R0914, R0915 authentication_keys: Optional[ Tuple[ec.EllipticCurvePrivateKey, ec.EllipticCurvePublicKey] ] = None, + adapter_cls: Optional[Union[Type[FleetStub], Type[GrpcAdapter]]] = None, ) -> Iterator[ Tuple[ Callable[[], Optional[Message]], @@ -106,6 +107,11 @@ def grpc_request_response( # pylint: disable=R0913, R0914, R0915 Path of the root certificate. If provided, a secure connection using the certificates will be established to an SSL-enabled Flower server. Bytes won't work for the REST API. + authentication_keys : Optional[Tuple[PrivateKey, PublicKey]] (default: None) + Tuple containing the elliptic curve private key and public key for + authentication from the cryptography library. + Source: https://cryptography.io/en/latest/hazmat/primitives/asymmetric/ec/ + Used to establish an authenticated connection with the server. Returns ------- @@ -113,6 +119,7 @@ def grpc_request_response( # pylint: disable=R0913, R0914, R0915 send : Callable create_node : Optional[Callable] delete_node : Optional[Callable] + get_run : Optional[Callable] """ if isinstance(root_certificates, str): root_certificates = Path(root_certificates).read_bytes() @@ -133,7 +140,9 @@ def grpc_request_response( # pylint: disable=R0913, R0914, R0915 channel.subscribe(on_channel_state_change) # Shared variables for inner functions - stub = FleetStub(channel) + if adapter_cls is None: + adapter_cls = FleetStub + stub = adapter_cls(channel) metadata: Optional[Metadata] = None node: Optional[Node] = None ping_thread: Optional[threading.Thread] = None @@ -190,8 +199,6 @@ def delete_node() -> None: # Stop the ping-loop thread ping_stop_event.set() - if ping_thread is not None: - ping_thread.join() # Call FleetAPI delete_node_request = DeleteNodeRequest(node=node) diff --git a/src/py/flwr/client/grpc_rere_client/grpc_adapter.py b/src/py/flwr/client/grpc_rere_client/grpc_adapter.py new file mode 100644 index 000000000000..77c3d601020d --- /dev/null +++ b/src/py/flwr/client/grpc_rere_client/grpc_adapter.py @@ -0,0 +1,133 @@ +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""GrpcAdapter implementation.""" + + +import sys +from logging import DEBUG +from typing import Any, Type, TypeVar, cast + +import grpc +from google.protobuf.message import Message as GrpcMessage + +from flwr.common import log +from flwr.common.constant import ( + GRPC_ADAPTER_METADATA_FLOWER_VERSION_KEY, + GRPC_ADAPTER_METADATA_SHOULD_EXIT_KEY, +) +from flwr.common.version import package_version +from flwr.proto.fleet_pb2 import ( # pylint: disable=E0611 + CreateNodeRequest, + CreateNodeResponse, + DeleteNodeRequest, + DeleteNodeResponse, + PingRequest, + PingResponse, + PullTaskInsRequest, + PullTaskInsResponse, + PushTaskResRequest, + PushTaskResResponse, +) +from flwr.proto.grpcadapter_pb2 import MessageContainer # pylint: disable=E0611 +from flwr.proto.grpcadapter_pb2_grpc import GrpcAdapterStub +from flwr.proto.run_pb2 import GetRunRequest, GetRunResponse # pylint: disable=E0611 + +T = TypeVar("T", bound=GrpcMessage) + + +class GrpcAdapter: + """Adapter class to send and receive gRPC messages via the ``GrpcAdapterStub``. + + This class utilizes the ``GrpcAdapterStub`` to send and receive gRPC messages + which are defined and used by the Fleet API, as defined in ``fleet.proto``. + """ + + def __init__(self, channel: grpc.Channel) -> None: + self.stub = GrpcAdapterStub(channel) + + def _send_and_receive( + self, request: GrpcMessage, response_type: Type[T], **kwargs: Any + ) -> T: + # Serialize request + container_req = MessageContainer( + metadata={GRPC_ADAPTER_METADATA_FLOWER_VERSION_KEY: package_version}, + grpc_message_name=request.__class__.__qualname__, + grpc_message_content=request.SerializeToString(), + ) + + # Send via the stub + container_res = cast( + MessageContainer, self.stub.SendReceive(container_req, **kwargs) + ) + + # Handle control message + should_exit = ( + container_res.metadata.get(GRPC_ADAPTER_METADATA_SHOULD_EXIT_KEY, "false") + == "true" + ) + if should_exit: + log( + DEBUG, + 'Received shutdown signal: exit flag is set to ``"true"``. Exiting...', + ) + sys.exit(0) + + # Check the grpc_message_name of the response + if container_res.grpc_message_name != response_type.__qualname__: + raise ValueError( + f"Invalid grpc_message_name. Expected {response_type.__qualname__}" + f", but got {container_res.grpc_message_name}." + ) + + # Deserialize response + response = response_type() + response.ParseFromString(container_res.grpc_message_content) + return response + + def CreateNode( # pylint: disable=C0103 + self, request: CreateNodeRequest, **kwargs: Any + ) -> CreateNodeResponse: + """.""" + return self._send_and_receive(request, CreateNodeResponse, **kwargs) + + def DeleteNode( # pylint: disable=C0103 + self, request: DeleteNodeRequest, **kwargs: Any + ) -> DeleteNodeResponse: + """.""" + return self._send_and_receive(request, DeleteNodeResponse, **kwargs) + + def Ping( # pylint: disable=C0103 + self, request: PingRequest, **kwargs: Any + ) -> PingResponse: + """.""" + return self._send_and_receive(request, PingResponse, **kwargs) + + def PullTaskIns( # pylint: disable=C0103 + self, request: PullTaskInsRequest, **kwargs: Any + ) -> PullTaskInsResponse: + """.""" + return self._send_and_receive(request, PullTaskInsResponse, **kwargs) + + def PushTaskRes( # pylint: disable=C0103 + self, request: PushTaskResRequest, **kwargs: Any + ) -> PushTaskResResponse: + """.""" + return self._send_and_receive(request, PushTaskResResponse, **kwargs) + + def GetRun( # pylint: disable=C0103 + self, request: GetRunRequest, **kwargs: Any + ) -> GetRunResponse: + """.""" + return self._send_and_receive(request, GetRunResponse, **kwargs) diff --git a/src/py/flwr/client/grpc_rere_client/grpc_adapter_test.py b/src/py/flwr/client/grpc_rere_client/grpc_adapter_test.py new file mode 100644 index 000000000000..e62111e084bc --- /dev/null +++ b/src/py/flwr/client/grpc_rere_client/grpc_adapter_test.py @@ -0,0 +1,38 @@ +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the GrpcAdapter class.""" + + +import inspect + +from flwr.proto.fleet_pb2_grpc import FleetServicer + +from .grpc_adapter import GrpcAdapter + + +def test_grpc_adapter_methods() -> None: + """Test if GrpcAdapter implements all required methods.""" + # Prepare + methods = { + name for name, ref in inspect.getmembers(GrpcAdapter) if inspect.isfunction(ref) + } + expected_methods = { + name + for name, ref in inspect.getmembers(FleetServicer) + if inspect.isfunction(ref) + } + + # Assert + assert expected_methods.issubset(methods) diff --git a/src/py/flwr/client/message_handler/__init__.py b/src/py/flwr/client/message_handler/__init__.py index 653563963de5..a345b4af3ef2 100644 --- a/src/py/flwr/client/message_handler/__init__.py +++ b/src/py/flwr/client/message_handler/__init__.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2022 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/client/message_handler/message_handler.py b/src/py/flwr/client/message_handler/message_handler.py index e5acbe0cc9d0..68326852970f 100644 --- a/src/py/flwr/client/message_handler/message_handler.py +++ b/src/py/flwr/client/message_handler/message_handler.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2022 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/client/message_handler/message_handler_test.py b/src/py/flwr/client/message_handler/message_handler_test.py index 8a2db1804e4a..40907942513d 100644 --- a/src/py/flwr/client/message_handler/message_handler_test.py +++ b/src/py/flwr/client/message_handler/message_handler_test.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2022 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/client/mod/__init__.py b/src/py/flwr/client/mod/__init__.py index 1cd79fa944fe..35d1fa81805c 100644 --- a/src/py/flwr/client/mod/__init__.py +++ b/src/py/flwr/client/mod/__init__.py @@ -1,4 +1,4 @@ -# Copyright 2023 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Mods.""" +"""Flower Built-in Mods.""" from .centraldp_mods import adaptiveclipping_mod, fixedclipping_mod @@ -22,12 +22,12 @@ from .utils import make_ffn __all__ = [ + "LocalDpMod", "adaptiveclipping_mod", "fixedclipping_mod", - "LocalDpMod", "make_ffn", - "secagg_mod", - "secaggplus_mod", "message_size_mod", "parameters_size_mod", + "secagg_mod", + "secaggplus_mod", ] diff --git a/src/py/flwr/client/mod/secure_aggregation/__init__.py b/src/py/flwr/client/mod/secure_aggregation/__init__.py index 8892d8c03935..a64bc89e62c9 100644 --- a/src/py/flwr/client/mod/secure_aggregation/__init__.py +++ b/src/py/flwr/client/mod/secure_aggregation/__init__.py @@ -1,4 +1,4 @@ -# Copyright 2023 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/client/mod/utils.py b/src/py/flwr/client/mod/utils.py index 4c3c32944f01..c8fb21379783 100644 --- a/src/py/flwr/client/mod/utils.py +++ b/src/py/flwr/client/mod/utils.py @@ -1,4 +1,4 @@ -# Copyright 2023 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/client/mod/utils_test.py b/src/py/flwr/client/mod/utils_test.py index 4676a2c02c4b..035e41639b10 100644 --- a/src/py/flwr/client/mod/utils_test.py +++ b/src/py/flwr/client/mod/utils_test.py @@ -1,4 +1,4 @@ -# Copyright 2023 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/client/numpy_client_test.py b/src/py/flwr/client/numpy_client_test.py index 526098798e45..06a0deafe2c9 100644 --- a/src/py/flwr/client/numpy_client_test.py +++ b/src/py/flwr/client/numpy_client_test.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2022 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/client/rest_client/__init__.py b/src/py/flwr/client/rest_client/__init__.py index c3485483ad35..a24d822a6d75 100644 --- a/src/py/flwr/client/rest_client/__init__.py +++ b/src/py/flwr/client/rest_client/__init__.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2023 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/client/rest_client/connection.py b/src/py/flwr/client/rest_client/connection.py index da8fbd351ab1..db5bd7eb6770 100644 --- a/src/py/flwr/client/rest_client/connection.py +++ b/src/py/flwr/client/rest_client/connection.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2023 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -46,8 +46,6 @@ CreateNodeResponse, DeleteNodeRequest, DeleteNodeResponse, - GetRunRequest, - GetRunResponse, PingRequest, PingResponse, PullTaskInsRequest, @@ -56,6 +54,7 @@ PushTaskResResponse, ) from flwr.proto.node_pb2 import Node # pylint: disable=E0611 +from flwr.proto.run_pb2 import GetRunRequest, GetRunResponse # pylint: disable=E0611 from flwr.proto.task_pb2 import TaskIns # pylint: disable=E0611 try: @@ -118,10 +117,16 @@ def http_request_response( # pylint: disable=,R0913, R0914, R0915 Path of the root certificate. If provided, a secure connection using the certificates will be established to an SSL-enabled Flower server. Bytes won't work for the REST API. + authentication_keys : Optional[Tuple[PrivateKey, PublicKey]] (default: None) + Client authentication is not supported for this transport type. Returns ------- - receive, send : Callable, Callable + receive : Callable + send : Callable + create_node : Optional[Callable] + delete_node : Optional[Callable] + get_run : Optional[Callable] """ log( WARN, @@ -146,6 +151,8 @@ def http_request_response( # pylint: disable=,R0913, R0914, R0915 "For the REST API, the root certificates " "must be provided as a string path to the client.", ) + if authentication_keys is not None: + log(ERROR, "Client authentication is not supported for this transport type.") # Shared variables for inner functions metadata: Optional[Metadata] = None diff --git a/src/py/flwr/client/supernode/app.py b/src/py/flwr/client/supernode/app.py index e46ed43cc676..c9a16edeaf15 100644 --- a/src/py/flwr/client/supernode/app.py +++ b/src/py/flwr/client/supernode/app.py @@ -20,6 +20,7 @@ from pathlib import Path from typing import Callable, Optional, Tuple +from cryptography.exceptions import UnsupportedAlgorithm from cryptography.hazmat.primitives.asymmetric import ec from cryptography.hazmat.primitives.serialization import ( load_ssh_private_key, @@ -28,15 +29,20 @@ from flwr.client.client_app import ClientApp, LoadClientAppError from flwr.common import EventType, event +from flwr.common.config import get_flwr_dir, get_project_config, get_project_dir +from flwr.common.constant import ( + TRANSPORT_TYPE_GRPC_ADAPTER, + TRANSPORT_TYPE_GRPC_RERE, + TRANSPORT_TYPE_REST, +) from flwr.common.exit_handlers import register_exit_handlers -from flwr.common.logger import log +from flwr.common.logger import log, warn_deprecated_feature from flwr.common.object_ref import load_app, validate -from flwr.common.secure_aggregation.crypto.symmetric_encryption import ( - ssh_types_to_elliptic_curve, -) from ..app import _start_client_internal +ADDRESS_FLEET_API_GRPC_RERE = "0.0.0.0:9092" + def run_supernode() -> None: """Run Flower SuperNode.""" @@ -44,11 +50,23 @@ def run_supernode() -> None: event(EventType.RUN_SUPERNODE_ENTER) - _ = _parse_args_run_supernode().parse_args() + args = _parse_args_run_supernode().parse_args() + + _warn_deprecated_server_arg(args) + + root_certificates = _get_certificates(args) + load_fn = _get_load_client_app_fn(args, multi_app=True) + authentication_keys = _try_setup_client_authentication(args) - log( - DEBUG, - "Flower SuperNode starting...", + _start_client_internal( + server_address=args.superlink, + load_client_app_fn=load_fn, + transport=args.transport, + root_certificates=root_certificates, + insecure=args.insecure, + authentication_keys=authentication_keys, + max_retries=args.max_retries, + max_wait_time=args.max_wait_time, ) # Graceful shutdown @@ -65,19 +83,16 @@ def run_client_app() -> None: args = _parse_args_run_client_app().parse_args() + _warn_deprecated_server_arg(args) + root_certificates = _get_certificates(args) - log( - DEBUG, - "Flower will load ClientApp `%s`", - getattr(args, "client-app"), - ) - load_fn = _get_load_client_app_fn(args) + load_fn = _get_load_client_app_fn(args, multi_app=False) authentication_keys = _try_setup_client_authentication(args) _start_client_internal( - server_address=args.server, + server_address=args.superlink, load_client_app_fn=load_fn, - transport="rest" if args.rest else "grpc-rere", + transport=args.transport, root_certificates=root_certificates, insecure=args.insecure, authentication_keys=authentication_keys, @@ -87,6 +102,26 @@ def run_client_app() -> None: register_exit_handlers(event_type=EventType.RUN_CLIENT_APP_LEAVE) +def _warn_deprecated_server_arg(args: argparse.Namespace) -> None: + """Warn about the deprecated argument `--server`.""" + if args.server != ADDRESS_FLEET_API_GRPC_RERE: + warn = "Passing flag --server is deprecated. Use --superlink instead." + warn_deprecated_feature(warn) + + if args.superlink != ADDRESS_FLEET_API_GRPC_RERE: + # if `--superlink` also passed, then + # warn user that this argument overrides what was passed with `--server` + log( + WARN, + "Both `--server` and `--superlink` were passed. " + "`--server` will be ignored. Connecting to the Superlink Fleet API " + "at %s.", + args.superlink, + ) + else: + args.superlink = args.server + + def _get_certificates(args: argparse.Namespace) -> Optional[bytes]: """Load certificates if specified in args.""" # Obtain certificates @@ -102,7 +137,7 @@ def _get_certificates(args: argparse.Namespace) -> Optional[bytes]: WARN, "Option `--insecure` was set. " "Starting insecure HTTP client connected to %s.", - args.server, + args.superlink, ) root_certificates = None else: @@ -116,31 +151,95 @@ def _get_certificates(args: argparse.Namespace) -> Optional[bytes]: DEBUG, "Starting secure HTTPS client connected to %s " "with the following certificates: %s.", - args.server, + args.superlink, cert_path, ) return root_certificates def _get_load_client_app_fn( - args: argparse.Namespace, -) -> Callable[[], ClientApp]: - """Get the load_client_app_fn function.""" - client_app_dir = args.dir - if client_app_dir is not None: - sys.path.insert(0, client_app_dir) + args: argparse.Namespace, multi_app: bool +) -> Callable[[str, str], ClientApp]: + """Get the load_client_app_fn function. + + If `multi_app` is True, this function loads the specified ClientApp + based on `fab_id` and `fab_version`. If `fab_id` is empty, a default + ClientApp will be loaded. + + If `multi_app` is False, it ignores `fab_id` and `fab_version` and + loads a default ClientApp. + """ + # Find the Flower directory containing Flower Apps (only for multi-app) + flwr_dir = Path("") + if "flwr_dir" in args: + if args.flwr_dir is None: + flwr_dir = get_flwr_dir() + else: + flwr_dir = Path(args.flwr_dir).absolute() - app_ref: str = getattr(args, "client-app") - valid, error_msg = validate(app_ref) - if not valid and error_msg: - raise LoadClientAppError(error_msg) from None + sys.path.insert(0, str(flwr_dir.absolute())) - def _load() -> ClientApp: - client_app = load_app(app_ref, LoadClientAppError) + default_app_ref: str = getattr(args, "client-app") + + if not multi_app: + log( + DEBUG, + "Flower SuperNode will load and validate ClientApp `%s`", + getattr(args, "client-app"), + ) + valid, error_msg = validate(default_app_ref) + if not valid and error_msg: + raise LoadClientAppError(error_msg) from None + + def _load(fab_id: str, fab_version: str) -> ClientApp: + # If multi-app feature is disabled + if not multi_app: + # Get sys path to be inserted + sys_path = Path(args.dir).absolute() + + # Set app reference + client_app_ref = default_app_ref + # If multi-app feature is enabled but the fab id is not specified + elif fab_id == "": + if default_app_ref == "": + raise LoadClientAppError( + "Invalid FAB ID: The FAB ID is empty.", + ) from None + + log(WARN, "FAB ID is not provided; the default ClientApp will be loaded.") + # Get sys path to be inserted + sys_path = Path(args.dir).absolute() + + # Set app reference + client_app_ref = default_app_ref + # If multi-app feature is enabled + else: + try: + project_dir = get_project_dir(fab_id, fab_version, flwr_dir) + config = get_project_config(project_dir) + except Exception as e: + raise LoadClientAppError("Failed to load ClientApp") from e + + # Get sys path to be inserted + sys_path = Path(project_dir).absolute() + + # Set app reference + client_app_ref = config["flower"]["components"]["clientapp"] + + # Set sys.path + sys.path.insert(0, str(sys_path)) + + # Load ClientApp + log( + DEBUG, + "Loading ClientApp `%s`", + client_app_ref, + ) + client_app = load_app(client_app_ref, LoadClientAppError, sys_path) if not isinstance(client_app, ClientApp): raise LoadClientAppError( - f"Attribute {app_ref} is not of type {ClientApp}", + f"Attribute {client_app_ref} is not of type {ClientApp}", ) from None return client_app @@ -168,7 +267,7 @@ def _parse_args_run_supernode() -> argparse.ArgumentParser: "--flwr-dir", default=None, help="""The path containing installed Flower Apps. - By default, this value isequal to: + By default, this value is equal to: - `$FLWR_HOME/` if `$FLWR_HOME` is defined - `$XDG_DATA_HOME/.flwr/` if `$XDG_DATA_HOME` is defined @@ -201,9 +300,27 @@ def _parse_args_common(parser: argparse.ArgumentParser) -> None: help="Run the client without HTTPS. By default, the client runs with " "HTTPS enabled. Use this flag only if you understand the risks.", ) - parser.add_argument( + ex_group = parser.add_mutually_exclusive_group() + ex_group.add_argument( + "--grpc-rere", + action="store_const", + dest="transport", + const=TRANSPORT_TYPE_GRPC_RERE, + default=TRANSPORT_TYPE_GRPC_RERE, + help="Use grpc-rere as a transport layer for the client.", + ) + ex_group.add_argument( + "--grpc-adapter", + action="store_const", + dest="transport", + const=TRANSPORT_TYPE_GRPC_ADAPTER, + help="Use grpc-adapter as a transport layer for the client.", + ) + ex_group.add_argument( "--rest", - action="store_true", + action="store_const", + dest="transport", + const=TRANSPORT_TYPE_REST, help="Use REST as a transport layer for the client.", ) parser.add_argument( @@ -215,9 +332,14 @@ def _parse_args_common(parser: argparse.ArgumentParser) -> None: ) parser.add_argument( "--server", - default="0.0.0.0:9092", + default=ADDRESS_FLEET_API_GRPC_RERE, help="Server address", ) + parser.add_argument( + "--superlink", + default=ADDRESS_FLEET_API_GRPC_RERE, + help="SuperLink Fleet API (gRPC-rere) address (IPv4, IPv6, or a domain name)", + ) parser.add_argument( "--max-retries", type=int, @@ -242,40 +364,60 @@ def _parse_args_common(parser: argparse.ArgumentParser) -> None: " Default: current working directory.", ) parser.add_argument( - "--authentication-keys", - nargs=2, - metavar=("CLIENT_PRIVATE_KEY", "CLIENT_PUBLIC_KEY"), + "--auth-supernode-private-key", + type=str, + help="The SuperNode's private key (as a path str) to enable authentication.", + ) + parser.add_argument( + "--auth-supernode-public-key", type=str, - help="Provide two file paths: (1) the client's private " - "key file, and (2) the client's public key file.", + help="The SuperNode's public key (as a path str) to enable authentication.", ) def _try_setup_client_authentication( args: argparse.Namespace, ) -> Optional[Tuple[ec.EllipticCurvePrivateKey, ec.EllipticCurvePublicKey]]: - if not args.authentication_keys: + if not args.auth_supernode_private_key and not args.auth_supernode_public_key: return None - ssh_private_key = load_ssh_private_key( - Path(args.authentication_keys[0]).read_bytes(), - None, - ) - ssh_public_key = load_ssh_public_key(Path(args.authentication_keys[1]).read_bytes()) + if not args.auth_supernode_private_key or not args.auth_supernode_public_key: + sys.exit( + "Authentication requires file paths to both " + "'--auth-supernode-private-key' and '--auth-supernode-public-key'" + "to be provided (providing only one of them is not sufficient)." + ) + + try: + ssh_private_key = load_ssh_private_key( + Path(args.auth_supernode_private_key).read_bytes(), + None, + ) + if not isinstance(ssh_private_key, ec.EllipticCurvePrivateKey): + raise ValueError() + except (ValueError, UnsupportedAlgorithm): + sys.exit( + "Error: Unable to parse the private key file in " + "'--auth-supernode-private-key'. Authentication requires elliptic " + "curve private and public key pair. Please ensure that the file " + "path points to a valid private key file and try again." + ) try: - client_private_key, client_public_key = ssh_types_to_elliptic_curve( - ssh_private_key, ssh_public_key + ssh_public_key = load_ssh_public_key( + Path(args.auth_supernode_public_key).read_bytes() ) - except TypeError: + if not isinstance(ssh_public_key, ec.EllipticCurvePublicKey): + raise ValueError() + except (ValueError, UnsupportedAlgorithm): sys.exit( - "The file paths provided could not be read as a private and public " - "key pair. Client authentication requires an elliptic curve public and " - "private key pair. Please provide the file paths containing elliptic " - "curve private and public keys to '--authentication-keys'." + "Error: Unable to parse the public key file in " + "'--auth-supernode-public-key'. Authentication requires elliptic " + "curve private and public key pair. Please ensure that the file " + "path points to a valid public key file and try again." ) return ( - client_private_key, - client_public_key, + ssh_private_key, + ssh_public_key, ) diff --git a/src/py/flwr/common/__init__.py b/src/py/flwr/common/__init__.py index 2fb98c82dd6f..bbdf48425e0a 100644 --- a/src/py/flwr/common/__init__.py +++ b/src/py/flwr/common/__init__.py @@ -63,43 +63,34 @@ __all__ = [ "Array", - "array_from_numpy", - "bytes_to_ndarray", "ClientMessage", "Code", "Config", "ConfigsRecord", - "configure", "Context", + "DEFAULT_TTL", "DisconnectRes", + "Error", "EvaluateIns", "EvaluateRes", - "event", "EventType", "FitIns", "FitRes", - "Error", + "GRPC_MAX_MESSAGE_LENGTH", "GetParametersIns", "GetParametersRes", "GetPropertiesIns", "GetPropertiesRes", - "GRPC_MAX_MESSAGE_LENGTH", - "log", "Message", "MessageType", "MessageTypeLegacy", - "DEFAULT_TTL", "Metadata", "Metrics", "MetricsAggregationFn", "MetricsRecord", - "ndarray_to_bytes", - "now", "NDArray", "NDArrays", - "ndarrays_to_parameters", "Parameters", - "parameters_to_ndarrays", "ParametersRecord", "Properties", "ReconnectIns", @@ -107,4 +98,13 @@ "Scalar", "ServerMessage", "Status", + "array_from_numpy", + "bytes_to_ndarray", + "configure", + "event", + "log", + "ndarray_to_bytes", + "ndarrays_to_parameters", + "now", + "parameters_to_ndarrays", ] diff --git a/src/py/flwr/common/address.py b/src/py/flwr/common/address.py index 71b6d684597f..1c6481b80a74 100644 --- a/src/py/flwr/common/address.py +++ b/src/py/flwr/common/address.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2023 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/common/address_test.py b/src/py/flwr/common/address_test.py index 420b89871d69..d5901ed640b1 100644 --- a/src/py/flwr/common/address_test.py +++ b/src/py/flwr/common/address_test.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2023 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/common/config.py b/src/py/flwr/common/config.py new file mode 100644 index 000000000000..20de00a6fba9 --- /dev/null +++ b/src/py/flwr/common/config.py @@ -0,0 +1,73 @@ +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Provide functions for managing global Flower config.""" + +import os +from pathlib import Path +from typing import Any, Dict, Optional, Union + +import tomli + +from flwr.cli.config_utils import validate_fields +from flwr.common.constant import APP_DIR, FAB_CONFIG_FILE, FLWR_HOME + + +def get_flwr_dir(provided_path: Optional[str] = None) -> Path: + """Return the Flower home directory based on env variables.""" + if provided_path is None or not Path(provided_path).is_dir(): + return Path( + os.getenv( + FLWR_HOME, + f"{os.getenv('XDG_DATA_HOME', os.getenv('HOME'))}/.flwr", + ) + ) + return Path(provided_path).absolute() + + +def get_project_dir( + fab_id: str, fab_version: str, flwr_dir: Optional[Union[str, Path]] = None +) -> Path: + """Return the project directory based on the given fab_id and fab_version.""" + # Check the fab_id + if fab_id.count("/") != 1: + raise ValueError( + f"Invalid FAB ID: {fab_id}", + ) + publisher, project_name = fab_id.split("/") + if flwr_dir is None: + flwr_dir = get_flwr_dir() + return Path(flwr_dir) / APP_DIR / publisher / project_name / fab_version + + +def get_project_config(project_dir: Union[str, Path]) -> Dict[str, Any]: + """Return pyproject.toml in the given project directory.""" + # Load pyproject.toml file + toml_path = Path(project_dir) / FAB_CONFIG_FILE + if not toml_path.is_file(): + raise FileNotFoundError( + f"Cannot find {FAB_CONFIG_FILE} in {project_dir}", + ) + with toml_path.open(encoding="utf-8") as toml_file: + config = tomli.loads(toml_file.read()) + + # Validate pyproject.toml fields + is_valid, errors, _ = validate_fields(config) + if not is_valid: + error_msg = "\n".join([f" - {error}" for error in errors]) + raise ValueError( + f"Invalid {FAB_CONFIG_FILE}:\n{error_msg}", + ) + + return config diff --git a/src/py/flwr/common/constant.py b/src/py/flwr/common/constant.py index b6d39b6e8932..ce29b3edb30e 100644 --- a/src/py/flwr/common/constant.py +++ b/src/py/flwr/common/constant.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2023 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -27,6 +27,7 @@ TRANSPORT_TYPE_GRPC_BIDI = "grpc-bidi" TRANSPORT_TYPE_GRPC_RERE = "grpc-rere" +TRANSPORT_TYPE_GRPC_ADAPTER = "grpc-adapter" TRANSPORT_TYPE_REST = "rest" TRANSPORT_TYPE_VCE = "vce" TRANSPORT_TYPES = [ @@ -36,6 +37,8 @@ TRANSPORT_TYPE_VCE, ] +SUPEREXEC_DEFAULT_ADDRESS = "0.0.0.0:9093" + # Constants for ping PING_DEFAULT_INTERVAL = 30 PING_CALL_TIMEOUT = 5 @@ -43,6 +46,18 @@ PING_RANDOM_RANGE = (-0.1, 0.1) PING_MAX_INTERVAL = 1e300 +GRPC_ADAPTER_METADATA_FLOWER_VERSION_KEY = "flower-version" +GRPC_ADAPTER_METADATA_SHOULD_EXIT_KEY = "should-exit" + +# Constants for FAB +APP_DIR = "apps" +FAB_CONFIG_FILE = "pyproject.toml" +FLWR_HOME = "FLWR_HOME" + + +GRPC_ADAPTER_METADATA_FLOWER_VERSION_KEY = "flower-version" +GRPC_ADAPTER_METADATA_SHOULD_EXIT_KEY = "should-exit" + class MessageType: """Message type.""" diff --git a/src/py/flwr/common/date.py b/src/py/flwr/common/date.py index f47ad5470106..7f30f5e0591a 100644 --- a/src/py/flwr/common/date.py +++ b/src/py/flwr/common/date.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2023 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/common/dp.py b/src/py/flwr/common/dp.py index 83a72b8ce749..527805c8ef42 100644 --- a/src/py/flwr/common/dp.py +++ b/src/py/flwr/common/dp.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2022 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/common/grpc.py b/src/py/flwr/common/grpc.py index ead0329ca79c..ec8fe823a7eb 100644 --- a/src/py/flwr/common/grpc.py +++ b/src/py/flwr/common/grpc.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2022 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/common/message_test.py b/src/py/flwr/common/message_test.py index 19f8aeb1eb63..daee57896903 100644 --- a/src/py/flwr/common/message_test.py +++ b/src/py/flwr/common/message_test.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/common/object_ref.py b/src/py/flwr/common/object_ref.py index 4660f07e24a4..ac52be160c2e 100644 --- a/src/py/flwr/common/object_ref.py +++ b/src/py/flwr/common/object_ref.py @@ -17,8 +17,13 @@ import ast import importlib +import sys from importlib.util import find_spec -from typing import Any, Optional, Tuple, Type +from logging import WARN +from pathlib import Path +from typing import Any, Optional, Tuple, Type, Union + +from .logger import log OBJECT_REF_HELP_STR = """ \n\nThe object reference string should have the form :. Valid @@ -30,6 +35,7 @@ def validate( module_attribute_str: str, + check_module: bool = True, ) -> Tuple[bool, Optional[str]]: """Validate object reference. @@ -56,15 +62,18 @@ def validate( f"Missing attribute in {module_attribute_str}{OBJECT_REF_HELP_STR}", ) - # Load module - module = find_spec(module_str) - if module and module.origin: - if not _find_attribute_in_module(module.origin, attributes_str): - return ( - False, - f"Unable to find attribute {attributes_str} in module {module_str}" - f"{OBJECT_REF_HELP_STR}", - ) + if check_module: + # Load module + module = find_spec(module_str) + if module and module.origin: + if not _find_attribute_in_module(module.origin, attributes_str): + return ( + False, + f"Unable to find attribute {attributes_str} in module {module_str}" + f"{OBJECT_REF_HELP_STR}", + ) + return (True, None) + else: return (True, None) return ( @@ -73,9 +82,10 @@ def validate( ) -def load_app( +def load_app( # pylint: disable= too-many-branches module_attribute_str: str, error_type: Type[Exception], + project_dir: Optional[Union[str, Path]] = None, ) -> Any: """Return the object specified in a module attribute string. @@ -91,11 +101,39 @@ def load_app( module_str, _, attributes_str = module_attribute_str.partition(":") try: - module = importlib.import_module(module_str) - except ModuleNotFoundError: + if module_str not in sys.modules: + module = importlib.import_module(module_str) + # Hack: `tabnet` does not work with `importlib.reload` + elif "tabnet" in sys.modules: + log( + WARN, + "Cannot reload module `%s` from disk due to compatibility issues " + "with the `tabnet` library. The module will be loaded from the " + "cache instead. If you experience issues, consider restarting " + "the application.", + module_str, + ) + module = sys.modules[module_str] + else: + module = sys.modules[module_str] + if project_dir is None: + path: Optional[str] = getattr(module, "__file__", None) + if path is not None: + project_dir = str(Path(path).parent) + else: + project_dir = str(Path(project_dir).absolute()) + + # Reload cached modules in the project directory + if project_dir is not None: + for m in list(sys.modules.values()): + path = getattr(m, "__file__", None) + if path is not None and path.startswith(project_dir): + importlib.reload(m) + + except ModuleNotFoundError as err: raise error_type( f"Unable to load module {module_str}{OBJECT_REF_HELP_STR}", - ) from None + ) from err # Recursively load attribute attribute = module diff --git a/src/py/flwr/common/record/__init__.py b/src/py/flwr/common/record/__init__.py index 60bc54b8552a..88eef5f7aea1 100644 --- a/src/py/flwr/common/record/__init__.py +++ b/src/py/flwr/common/record/__init__.py @@ -22,9 +22,9 @@ __all__ = [ "Array", - "array_from_numpy", "ConfigsRecord", "MetricsRecord", "ParametersRecord", "RecordSet", + "array_from_numpy", ] diff --git a/src/py/flwr/common/recordset_compat.py b/src/py/flwr/common/recordset_compat.py index 394ea1353bab..1b0bf52d8277 100644 --- a/src/py/flwr/common/recordset_compat.py +++ b/src/py/flwr/common/recordset_compat.py @@ -35,6 +35,8 @@ Status, ) +EMPTY_TENSOR_KEY = "_empty" + def parametersrecord_to_parameters( record: ParametersRecord, keep_input: bool @@ -59,7 +61,8 @@ def parametersrecord_to_parameters( parameters = Parameters(tensors=[], tensor_type="") for key in list(record.keys()): - parameters.tensors.append(record[key].data) + if key != EMPTY_TENSOR_KEY: + parameters.tensors.append(record[key].data) if not parameters.tensor_type: # Setting from first array in record. Recall the warning in the docstrings @@ -103,6 +106,10 @@ def parameters_to_parametersrecord( data=tensor, dtype="", stype=tensor_type, shape=[] ) + if num_arrays == 0: + ordered_dict[EMPTY_TENSOR_KEY] = Array( + data=b"", dtype="", stype=tensor_type, shape=[] + ) return ParametersRecord(ordered_dict, keep_input=keep_input) diff --git a/src/py/flwr/common/recordset_compat_test.py b/src/py/flwr/common/recordset_compat_test.py index 288326dc9e83..e0ac7f216af9 100644 --- a/src/py/flwr/common/recordset_compat_test.py +++ b/src/py/flwr/common/recordset_compat_test.py @@ -74,6 +74,11 @@ def _get_valid_fitins() -> FitIns: return FitIns(parameters=ndarrays_to_parameters(arrays), config={"a": 1.0, "b": 0}) +def _get_valid_fitins_with_empty_ndarrays() -> FitIns: + pp = ndarrays_to_parameters([]) + return FitIns(parameters=pp, config={"a": 1.0, "b": 0}) + + def _get_valid_fitres() -> FitRes: """Returnn Valid parameters but potentially invalid config.""" arrays = get_ndarrays() @@ -138,23 +143,29 @@ def _get_valid_getpropertiesres() -> GetPropertiesRes: @pytest.mark.parametrize( - "keep_input, validate_freed_fn", + "keep_input, validate_freed_fn, fn", [ ( False, lambda x, x_copy, y: len(x.parameters.tensors) == 0 and x_copy == y, + _get_valid_fitins, ), # check tensors were freed + (True, lambda x, x_copy, y: x == y, _get_valid_fitins), ( - True, - lambda x, x_copy, y: x == y, - ), + False, + lambda x, x_copy, y: len(x.parameters.tensors) == 0 and x_copy == y, + _get_valid_fitins_with_empty_ndarrays, + ), # check tensors were freed + (True, lambda x, x_copy, y: x == y, _get_valid_fitins_with_empty_ndarrays), ], ) def test_fitins_to_recordset_and_back( - keep_input: bool, validate_freed_fn: Callable[[FitIns, FitIns, FitIns], bool] + keep_input: bool, + validate_freed_fn: Callable[[FitIns, FitIns, FitIns], bool], + fn: Callable[[], FitIns], ) -> None: """Test conversion FitIns --> RecordSet --> FitIns.""" - fitins = _get_valid_fitins() + fitins = fn() fitins_copy = deepcopy(fitins) diff --git a/src/py/flwr/common/secure_aggregation/__init__.py b/src/py/flwr/common/secure_aggregation/__init__.py index b4e0acc0c148..77e1ea3842d7 100644 --- a/src/py/flwr/common/secure_aggregation/__init__.py +++ b/src/py/flwr/common/secure_aggregation/__init__.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2023 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/common/secure_aggregation/crypto/__init__.py b/src/py/flwr/common/secure_aggregation/crypto/__init__.py index 2cb34493f7d0..3788dbc0ca15 100644 --- a/src/py/flwr/common/secure_aggregation/crypto/__init__.py +++ b/src/py/flwr/common/secure_aggregation/crypto/__init__.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2023 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/common/secure_aggregation/crypto/shamir.py b/src/py/flwr/common/secure_aggregation/crypto/shamir.py index e56e21b89371..688bfa2153ea 100644 --- a/src/py/flwr/common/secure_aggregation/crypto/shamir.py +++ b/src/py/flwr/common/secure_aggregation/crypto/shamir.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2023 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/common/secure_aggregation/crypto/symmetric_encryption.py b/src/py/flwr/common/secure_aggregation/crypto/symmetric_encryption.py index 9856b8b706f9..59ca84d604b8 100644 --- a/src/py/flwr/common/secure_aggregation/crypto/symmetric_encryption.py +++ b/src/py/flwr/common/secure_aggregation/crypto/symmetric_encryption.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2023 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -117,18 +117,3 @@ def verify_hmac(key: bytes, message: bytes, hmac_value: bytes) -> bool: return True except InvalidSignature: return False - - -def ssh_types_to_elliptic_curve( - private_key: serialization.SSHPrivateKeyTypes, - public_key: serialization.SSHPublicKeyTypes, -) -> Tuple[ec.EllipticCurvePrivateKey, ec.EllipticCurvePublicKey]: - """Cast SSH key types to elliptic curve.""" - if isinstance(private_key, ec.EllipticCurvePrivateKey) and isinstance( - public_key, ec.EllipticCurvePublicKey - ): - return (private_key, public_key) - - raise TypeError( - "The provided key is not an EllipticCurvePrivateKey or EllipticCurvePublicKey" - ) diff --git a/src/py/flwr/common/secure_aggregation/ndarrays_arithmetic.py b/src/py/flwr/common/secure_aggregation/ndarrays_arithmetic.py index e926a9531bea..207c15b61518 100644 --- a/src/py/flwr/common/secure_aggregation/ndarrays_arithmetic.py +++ b/src/py/flwr/common/secure_aggregation/ndarrays_arithmetic.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2023 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/common/secure_aggregation/quantization.py b/src/py/flwr/common/secure_aggregation/quantization.py index 56c25e2bd59c..7946276b6a4f 100644 --- a/src/py/flwr/common/secure_aggregation/quantization.py +++ b/src/py/flwr/common/secure_aggregation/quantization.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2023 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/common/secure_aggregation/secaggplus_constants.py b/src/py/flwr/common/secure_aggregation/secaggplus_constants.py index 8a15908c13c5..545507eb44ed 100644 --- a/src/py/flwr/common/secure_aggregation/secaggplus_constants.py +++ b/src/py/flwr/common/secure_aggregation/secaggplus_constants.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2023 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/common/secure_aggregation/secaggplus_utils.py b/src/py/flwr/common/secure_aggregation/secaggplus_utils.py index c373573477b9..cf6ac3bfb003 100644 --- a/src/py/flwr/common/secure_aggregation/secaggplus_utils.py +++ b/src/py/flwr/common/secure_aggregation/secaggplus_utils.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2023 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/common/serde_test.py b/src/py/flwr/common/serde_test.py index f9969426fc36..afb11b6956f2 100644 --- a/src/py/flwr/common/serde_test.py +++ b/src/py/flwr/common/serde_test.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2021 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/common/telemetry.py b/src/py/flwr/common/telemetry.py index 41fe1508e652..eeb255e8d6eb 100644 --- a/src/py/flwr/common/telemetry.py +++ b/src/py/flwr/common/telemetry.py @@ -164,6 +164,10 @@ def _generate_next_value_(name: str, start: int, count: int, last_values: List[A RUN_SUPERNODE_ENTER = auto() RUN_SUPERNODE_LEAVE = auto() + # SuperExec + RUN_SUPEREXEC_ENTER = auto() + RUN_SUPEREXEC_LEAVE = auto() + # Use the ThreadPoolExecutor with max_workers=1 to have a queue # and also ensure that telemetry calls are not blocking. diff --git a/src/py/flwr/common/typing.py b/src/py/flwr/common/typing.py index d6b2ec9b158c..f51830955679 100644 --- a/src/py/flwr/common/typing.py +++ b/src/py/flwr/common/typing.py @@ -185,3 +185,12 @@ class ClientMessage: get_parameters_res: Optional[GetParametersRes] = None fit_res: Optional[FitRes] = None evaluate_res: Optional[EvaluateRes] = None + + +@dataclass +class Run: + """Run details.""" + + run_id: int + fab_id: str + fab_version: str diff --git a/src/py/flwr/common/version.py b/src/py/flwr/common/version.py index 6808c66606b1..ac13f70d8a88 100644 --- a/src/py/flwr/common/version.py +++ b/src/py/flwr/common/version.py @@ -1,3 +1,17 @@ +# Copyright 2023 Flower Labs GmbH. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== """Flower package version helper.""" import importlib.metadata as importlib_metadata diff --git a/src/py/flwr/proto/driver_pb2.py b/src/py/flwr/proto/driver_pb2.py index b0caae58ff6f..a2458b445563 100644 --- a/src/py/flwr/proto/driver_pb2.py +++ b/src/py/flwr/proto/driver_pb2.py @@ -14,31 +14,32 @@ from flwr.proto import node_pb2 as flwr_dot_proto_dot_node__pb2 from flwr.proto import task_pb2 as flwr_dot_proto_dot_task__pb2 +from flwr.proto import run_pb2 as flwr_dot_proto_dot_run__pb2 -DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x17\x66lwr/proto/driver.proto\x12\nflwr.proto\x1a\x15\x66lwr/proto/node.proto\x1a\x15\x66lwr/proto/task.proto\"7\n\x10\x43reateRunRequest\x12\x0e\n\x06\x66\x61\x62_id\x18\x01 \x01(\t\x12\x13\n\x0b\x66\x61\x62_version\x18\x02 \x01(\t\"#\n\x11\x43reateRunResponse\x12\x0e\n\x06run_id\x18\x01 \x01(\x12\"!\n\x0fGetNodesRequest\x12\x0e\n\x06run_id\x18\x01 \x01(\x12\"3\n\x10GetNodesResponse\x12\x1f\n\x05nodes\x18\x01 \x03(\x0b\x32\x10.flwr.proto.Node\"@\n\x12PushTaskInsRequest\x12*\n\rtask_ins_list\x18\x01 \x03(\x0b\x32\x13.flwr.proto.TaskIns\"\'\n\x13PushTaskInsResponse\x12\x10\n\x08task_ids\x18\x02 \x03(\t\"F\n\x12PullTaskResRequest\x12\x1e\n\x04node\x18\x01 \x01(\x0b\x32\x10.flwr.proto.Node\x12\x10\n\x08task_ids\x18\x02 \x03(\t\"A\n\x13PullTaskResResponse\x12*\n\rtask_res_list\x18\x01 \x03(\x0b\x32\x13.flwr.proto.TaskRes2\xc1\x02\n\x06\x44river\x12J\n\tCreateRun\x12\x1c.flwr.proto.CreateRunRequest\x1a\x1d.flwr.proto.CreateRunResponse\"\x00\x12G\n\x08GetNodes\x12\x1b.flwr.proto.GetNodesRequest\x1a\x1c.flwr.proto.GetNodesResponse\"\x00\x12P\n\x0bPushTaskIns\x12\x1e.flwr.proto.PushTaskInsRequest\x1a\x1f.flwr.proto.PushTaskInsResponse\"\x00\x12P\n\x0bPullTaskRes\x12\x1e.flwr.proto.PullTaskResRequest\x1a\x1f.flwr.proto.PullTaskResResponse\"\x00\x62\x06proto3') +DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x17\x66lwr/proto/driver.proto\x12\nflwr.proto\x1a\x15\x66lwr/proto/node.proto\x1a\x15\x66lwr/proto/task.proto\x1a\x14\x66lwr/proto/run.proto\"7\n\x10\x43reateRunRequest\x12\x0e\n\x06\x66\x61\x62_id\x18\x01 \x01(\t\x12\x13\n\x0b\x66\x61\x62_version\x18\x02 \x01(\t\"#\n\x11\x43reateRunResponse\x12\x0e\n\x06run_id\x18\x01 \x01(\x12\"!\n\x0fGetNodesRequest\x12\x0e\n\x06run_id\x18\x01 \x01(\x12\"3\n\x10GetNodesResponse\x12\x1f\n\x05nodes\x18\x01 \x03(\x0b\x32\x10.flwr.proto.Node\"@\n\x12PushTaskInsRequest\x12*\n\rtask_ins_list\x18\x01 \x03(\x0b\x32\x13.flwr.proto.TaskIns\"\'\n\x13PushTaskInsResponse\x12\x10\n\x08task_ids\x18\x02 \x03(\t\"F\n\x12PullTaskResRequest\x12\x1e\n\x04node\x18\x01 \x01(\x0b\x32\x10.flwr.proto.Node\x12\x10\n\x08task_ids\x18\x02 \x03(\t\"A\n\x13PullTaskResResponse\x12*\n\rtask_res_list\x18\x01 \x03(\x0b\x32\x13.flwr.proto.TaskRes2\x84\x03\n\x06\x44river\x12J\n\tCreateRun\x12\x1c.flwr.proto.CreateRunRequest\x1a\x1d.flwr.proto.CreateRunResponse\"\x00\x12G\n\x08GetNodes\x12\x1b.flwr.proto.GetNodesRequest\x1a\x1c.flwr.proto.GetNodesResponse\"\x00\x12P\n\x0bPushTaskIns\x12\x1e.flwr.proto.PushTaskInsRequest\x1a\x1f.flwr.proto.PushTaskInsResponse\"\x00\x12P\n\x0bPullTaskRes\x12\x1e.flwr.proto.PullTaskResRequest\x1a\x1f.flwr.proto.PullTaskResResponse\"\x00\x12\x41\n\x06GetRun\x12\x19.flwr.proto.GetRunRequest\x1a\x1a.flwr.proto.GetRunResponse\"\x00\x62\x06proto3') _globals = globals() _builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) _builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'flwr.proto.driver_pb2', _globals) if _descriptor._USE_C_DESCRIPTORS == False: DESCRIPTOR._options = None - _globals['_CREATERUNREQUEST']._serialized_start=85 - _globals['_CREATERUNREQUEST']._serialized_end=140 - _globals['_CREATERUNRESPONSE']._serialized_start=142 - _globals['_CREATERUNRESPONSE']._serialized_end=177 - _globals['_GETNODESREQUEST']._serialized_start=179 - _globals['_GETNODESREQUEST']._serialized_end=212 - _globals['_GETNODESRESPONSE']._serialized_start=214 - _globals['_GETNODESRESPONSE']._serialized_end=265 - _globals['_PUSHTASKINSREQUEST']._serialized_start=267 - _globals['_PUSHTASKINSREQUEST']._serialized_end=331 - _globals['_PUSHTASKINSRESPONSE']._serialized_start=333 - _globals['_PUSHTASKINSRESPONSE']._serialized_end=372 - _globals['_PULLTASKRESREQUEST']._serialized_start=374 - _globals['_PULLTASKRESREQUEST']._serialized_end=444 - _globals['_PULLTASKRESRESPONSE']._serialized_start=446 - _globals['_PULLTASKRESRESPONSE']._serialized_end=511 - _globals['_DRIVER']._serialized_start=514 - _globals['_DRIVER']._serialized_end=835 + _globals['_CREATERUNREQUEST']._serialized_start=107 + _globals['_CREATERUNREQUEST']._serialized_end=162 + _globals['_CREATERUNRESPONSE']._serialized_start=164 + _globals['_CREATERUNRESPONSE']._serialized_end=199 + _globals['_GETNODESREQUEST']._serialized_start=201 + _globals['_GETNODESREQUEST']._serialized_end=234 + _globals['_GETNODESRESPONSE']._serialized_start=236 + _globals['_GETNODESRESPONSE']._serialized_end=287 + _globals['_PUSHTASKINSREQUEST']._serialized_start=289 + _globals['_PUSHTASKINSREQUEST']._serialized_end=353 + _globals['_PUSHTASKINSRESPONSE']._serialized_start=355 + _globals['_PUSHTASKINSRESPONSE']._serialized_end=394 + _globals['_PULLTASKRESREQUEST']._serialized_start=396 + _globals['_PULLTASKRESREQUEST']._serialized_end=466 + _globals['_PULLTASKRESRESPONSE']._serialized_start=468 + _globals['_PULLTASKRESRESPONSE']._serialized_end=533 + _globals['_DRIVER']._serialized_start=536 + _globals['_DRIVER']._serialized_end=924 # @@protoc_insertion_point(module_scope) diff --git a/src/py/flwr/proto/driver_pb2_grpc.py b/src/py/flwr/proto/driver_pb2_grpc.py index ac6815023ebd..2cd3ebe62a63 100644 --- a/src/py/flwr/proto/driver_pb2_grpc.py +++ b/src/py/flwr/proto/driver_pb2_grpc.py @@ -3,6 +3,7 @@ import grpc from flwr.proto import driver_pb2 as flwr_dot_proto_dot_driver__pb2 +from flwr.proto import run_pb2 as flwr_dot_proto_dot_run__pb2 class DriverStub(object): @@ -34,6 +35,11 @@ def __init__(self, channel): request_serializer=flwr_dot_proto_dot_driver__pb2.PullTaskResRequest.SerializeToString, response_deserializer=flwr_dot_proto_dot_driver__pb2.PullTaskResResponse.FromString, ) + self.GetRun = channel.unary_unary( + '/flwr.proto.Driver/GetRun', + request_serializer=flwr_dot_proto_dot_run__pb2.GetRunRequest.SerializeToString, + response_deserializer=flwr_dot_proto_dot_run__pb2.GetRunResponse.FromString, + ) class DriverServicer(object): @@ -67,6 +73,13 @@ def PullTaskRes(self, request, context): context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') + def GetRun(self, request, context): + """Get run details + """ + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + def add_DriverServicer_to_server(servicer, server): rpc_method_handlers = { @@ -90,6 +103,11 @@ def add_DriverServicer_to_server(servicer, server): request_deserializer=flwr_dot_proto_dot_driver__pb2.PullTaskResRequest.FromString, response_serializer=flwr_dot_proto_dot_driver__pb2.PullTaskResResponse.SerializeToString, ), + 'GetRun': grpc.unary_unary_rpc_method_handler( + servicer.GetRun, + request_deserializer=flwr_dot_proto_dot_run__pb2.GetRunRequest.FromString, + response_serializer=flwr_dot_proto_dot_run__pb2.GetRunResponse.SerializeToString, + ), } generic_handler = grpc.method_handlers_generic_handler( 'flwr.proto.Driver', rpc_method_handlers) @@ -167,3 +185,20 @@ def PullTaskRes(request, flwr_dot_proto_dot_driver__pb2.PullTaskResResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) + + @staticmethod + def GetRun(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary(request, target, '/flwr.proto.Driver/GetRun', + flwr_dot_proto_dot_run__pb2.GetRunRequest.SerializeToString, + flwr_dot_proto_dot_run__pb2.GetRunResponse.FromString, + options, channel_credentials, + insecure, call_credentials, compression, wait_for_ready, timeout, metadata) diff --git a/src/py/flwr/proto/driver_pb2_grpc.pyi b/src/py/flwr/proto/driver_pb2_grpc.pyi index 43cf45f39b25..4ff09db588ca 100644 --- a/src/py/flwr/proto/driver_pb2_grpc.pyi +++ b/src/py/flwr/proto/driver_pb2_grpc.pyi @@ -4,6 +4,7 @@ isort:skip_file """ import abc import flwr.proto.driver_pb2 +import flwr.proto.run_pb2 import grpc class DriverStub: @@ -28,6 +29,11 @@ class DriverStub: flwr.proto.driver_pb2.PullTaskResResponse] """Get task results""" + GetRun: grpc.UnaryUnaryMultiCallable[ + flwr.proto.run_pb2.GetRunRequest, + flwr.proto.run_pb2.GetRunResponse] + """Get run details""" + class DriverServicer(metaclass=abc.ABCMeta): @abc.abstractmethod @@ -62,5 +68,13 @@ class DriverServicer(metaclass=abc.ABCMeta): """Get task results""" pass + @abc.abstractmethod + def GetRun(self, + request: flwr.proto.run_pb2.GetRunRequest, + context: grpc.ServicerContext, + ) -> flwr.proto.run_pb2.GetRunResponse: + """Get run details""" + pass + def add_DriverServicer_to_server(servicer: DriverServicer, server: grpc.Server) -> None: ... diff --git a/src/py/flwr/proto/exec_pb2.py b/src/py/flwr/proto/exec_pb2.py new file mode 100644 index 000000000000..7b037a9454c0 --- /dev/null +++ b/src/py/flwr/proto/exec_pb2.py @@ -0,0 +1,34 @@ +# -*- coding: utf-8 -*- +# Generated by the protocol buffer compiler. DO NOT EDIT! +# source: flwr/proto/exec.proto +# Protobuf Python Version: 4.25.0 +"""Generated protocol buffer code.""" +from google.protobuf import descriptor as _descriptor +from google.protobuf import descriptor_pool as _descriptor_pool +from google.protobuf import symbol_database as _symbol_database +from google.protobuf.internal import builder as _builder +# @@protoc_insertion_point(imports) + +_sym_db = _symbol_database.Default() + + + + +DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x15\x66lwr/proto/exec.proto\x12\nflwr.proto\"#\n\x0fStartRunRequest\x12\x10\n\x08\x66\x61\x62_file\x18\x01 \x01(\x0c\"\"\n\x10StartRunResponse\x12\x0e\n\x06run_id\x18\x01 \x01(\x12\"#\n\x11StreamLogsRequest\x12\x0e\n\x06run_id\x18\x01 \x01(\x12\"(\n\x12StreamLogsResponse\x12\x12\n\nlog_output\x18\x01 \x01(\t2\xa0\x01\n\x04\x45xec\x12G\n\x08StartRun\x12\x1b.flwr.proto.StartRunRequest\x1a\x1c.flwr.proto.StartRunResponse\"\x00\x12O\n\nStreamLogs\x12\x1d.flwr.proto.StreamLogsRequest\x1a\x1e.flwr.proto.StreamLogsResponse\"\x00\x30\x01\x62\x06proto3') + +_globals = globals() +_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) +_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'flwr.proto.exec_pb2', _globals) +if _descriptor._USE_C_DESCRIPTORS == False: + DESCRIPTOR._options = None + _globals['_STARTRUNREQUEST']._serialized_start=37 + _globals['_STARTRUNREQUEST']._serialized_end=72 + _globals['_STARTRUNRESPONSE']._serialized_start=74 + _globals['_STARTRUNRESPONSE']._serialized_end=108 + _globals['_STREAMLOGSREQUEST']._serialized_start=110 + _globals['_STREAMLOGSREQUEST']._serialized_end=145 + _globals['_STREAMLOGSRESPONSE']._serialized_start=147 + _globals['_STREAMLOGSRESPONSE']._serialized_end=187 + _globals['_EXEC']._serialized_start=190 + _globals['_EXEC']._serialized_end=350 +# @@protoc_insertion_point(module_scope) diff --git a/src/py/flwr/proto/exec_pb2.pyi b/src/py/flwr/proto/exec_pb2.pyi new file mode 100644 index 000000000000..466812808da8 --- /dev/null +++ b/src/py/flwr/proto/exec_pb2.pyi @@ -0,0 +1,55 @@ +""" +@generated by mypy-protobuf. Do not edit manually! +isort:skip_file +""" +import builtins +import google.protobuf.descriptor +import google.protobuf.message +import typing +import typing_extensions + +DESCRIPTOR: google.protobuf.descriptor.FileDescriptor + +class StartRunRequest(google.protobuf.message.Message): + DESCRIPTOR: google.protobuf.descriptor.Descriptor + FAB_FILE_FIELD_NUMBER: builtins.int + fab_file: builtins.bytes + def __init__(self, + *, + fab_file: builtins.bytes = ..., + ) -> None: ... + def ClearField(self, field_name: typing_extensions.Literal["fab_file",b"fab_file"]) -> None: ... +global___StartRunRequest = StartRunRequest + +class StartRunResponse(google.protobuf.message.Message): + DESCRIPTOR: google.protobuf.descriptor.Descriptor + RUN_ID_FIELD_NUMBER: builtins.int + run_id: builtins.int + def __init__(self, + *, + run_id: builtins.int = ..., + ) -> None: ... + def ClearField(self, field_name: typing_extensions.Literal["run_id",b"run_id"]) -> None: ... +global___StartRunResponse = StartRunResponse + +class StreamLogsRequest(google.protobuf.message.Message): + DESCRIPTOR: google.protobuf.descriptor.Descriptor + RUN_ID_FIELD_NUMBER: builtins.int + run_id: builtins.int + def __init__(self, + *, + run_id: builtins.int = ..., + ) -> None: ... + def ClearField(self, field_name: typing_extensions.Literal["run_id",b"run_id"]) -> None: ... +global___StreamLogsRequest = StreamLogsRequest + +class StreamLogsResponse(google.protobuf.message.Message): + DESCRIPTOR: google.protobuf.descriptor.Descriptor + LOG_OUTPUT_FIELD_NUMBER: builtins.int + log_output: typing.Text + def __init__(self, + *, + log_output: typing.Text = ..., + ) -> None: ... + def ClearField(self, field_name: typing_extensions.Literal["log_output",b"log_output"]) -> None: ... +global___StreamLogsResponse = StreamLogsResponse diff --git a/src/py/flwr/proto/exec_pb2_grpc.py b/src/py/flwr/proto/exec_pb2_grpc.py new file mode 100644 index 000000000000..8cf4ce52a300 --- /dev/null +++ b/src/py/flwr/proto/exec_pb2_grpc.py @@ -0,0 +1,101 @@ +# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT! +"""Client and server classes corresponding to protobuf-defined services.""" +import grpc + +from flwr.proto import exec_pb2 as flwr_dot_proto_dot_exec__pb2 + + +class ExecStub(object): + """Missing associated documentation comment in .proto file.""" + + def __init__(self, channel): + """Constructor. + + Args: + channel: A grpc.Channel. + """ + self.StartRun = channel.unary_unary( + '/flwr.proto.Exec/StartRun', + request_serializer=flwr_dot_proto_dot_exec__pb2.StartRunRequest.SerializeToString, + response_deserializer=flwr_dot_proto_dot_exec__pb2.StartRunResponse.FromString, + ) + self.StreamLogs = channel.unary_stream( + '/flwr.proto.Exec/StreamLogs', + request_serializer=flwr_dot_proto_dot_exec__pb2.StreamLogsRequest.SerializeToString, + response_deserializer=flwr_dot_proto_dot_exec__pb2.StreamLogsResponse.FromString, + ) + + +class ExecServicer(object): + """Missing associated documentation comment in .proto file.""" + + def StartRun(self, request, context): + """Start run upon request + """ + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def StreamLogs(self, request, context): + """Start log stream upon request + """ + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + +def add_ExecServicer_to_server(servicer, server): + rpc_method_handlers = { + 'StartRun': grpc.unary_unary_rpc_method_handler( + servicer.StartRun, + request_deserializer=flwr_dot_proto_dot_exec__pb2.StartRunRequest.FromString, + response_serializer=flwr_dot_proto_dot_exec__pb2.StartRunResponse.SerializeToString, + ), + 'StreamLogs': grpc.unary_stream_rpc_method_handler( + servicer.StreamLogs, + request_deserializer=flwr_dot_proto_dot_exec__pb2.StreamLogsRequest.FromString, + response_serializer=flwr_dot_proto_dot_exec__pb2.StreamLogsResponse.SerializeToString, + ), + } + generic_handler = grpc.method_handlers_generic_handler( + 'flwr.proto.Exec', rpc_method_handlers) + server.add_generic_rpc_handlers((generic_handler,)) + + + # This class is part of an EXPERIMENTAL API. +class Exec(object): + """Missing associated documentation comment in .proto file.""" + + @staticmethod + def StartRun(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary(request, target, '/flwr.proto.Exec/StartRun', + flwr_dot_proto_dot_exec__pb2.StartRunRequest.SerializeToString, + flwr_dot_proto_dot_exec__pb2.StartRunResponse.FromString, + options, channel_credentials, + insecure, call_credentials, compression, wait_for_ready, timeout, metadata) + + @staticmethod + def StreamLogs(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_stream(request, target, '/flwr.proto.Exec/StreamLogs', + flwr_dot_proto_dot_exec__pb2.StreamLogsRequest.SerializeToString, + flwr_dot_proto_dot_exec__pb2.StreamLogsResponse.FromString, + options, channel_credentials, + insecure, call_credentials, compression, wait_for_ready, timeout, metadata) diff --git a/src/py/flwr/proto/exec_pb2_grpc.pyi b/src/py/flwr/proto/exec_pb2_grpc.pyi new file mode 100644 index 000000000000..20da3a53f4a8 --- /dev/null +++ b/src/py/flwr/proto/exec_pb2_grpc.pyi @@ -0,0 +1,41 @@ +""" +@generated by mypy-protobuf. Do not edit manually! +isort:skip_file +""" +import abc +import flwr.proto.exec_pb2 +import grpc +import typing + +class ExecStub: + def __init__(self, channel: grpc.Channel) -> None: ... + StartRun: grpc.UnaryUnaryMultiCallable[ + flwr.proto.exec_pb2.StartRunRequest, + flwr.proto.exec_pb2.StartRunResponse] + """Start run upon request""" + + StreamLogs: grpc.UnaryStreamMultiCallable[ + flwr.proto.exec_pb2.StreamLogsRequest, + flwr.proto.exec_pb2.StreamLogsResponse] + """Start log stream upon request""" + + +class ExecServicer(metaclass=abc.ABCMeta): + @abc.abstractmethod + def StartRun(self, + request: flwr.proto.exec_pb2.StartRunRequest, + context: grpc.ServicerContext, + ) -> flwr.proto.exec_pb2.StartRunResponse: + """Start run upon request""" + pass + + @abc.abstractmethod + def StreamLogs(self, + request: flwr.proto.exec_pb2.StreamLogsRequest, + context: grpc.ServicerContext, + ) -> typing.Iterator[flwr.proto.exec_pb2.StreamLogsResponse]: + """Start log stream upon request""" + pass + + +def add_ExecServicer_to_server(servicer: ExecServicer, server: grpc.Server) -> None: ... diff --git a/src/py/flwr/proto/fab_pb2.py b/src/py/flwr/proto/fab_pb2.py new file mode 100644 index 000000000000..c146a1635597 --- /dev/null +++ b/src/py/flwr/proto/fab_pb2.py @@ -0,0 +1,30 @@ +# -*- coding: utf-8 -*- +# Generated by the protocol buffer compiler. DO NOT EDIT! +# source: flwr/proto/fab.proto +# Protobuf Python Version: 4.25.0 +"""Generated protocol buffer code.""" +from google.protobuf import descriptor as _descriptor +from google.protobuf import descriptor_pool as _descriptor_pool +from google.protobuf import symbol_database as _symbol_database +from google.protobuf.internal import builder as _builder +# @@protoc_insertion_point(imports) + +_sym_db = _symbol_database.Default() + + + + +DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x14\x66lwr/proto/fab.proto\x12\nflwr.proto\"$\n\x03\x46\x61\x62\x12\x0c\n\x04hash\x18\x01 \x01(\t\x12\x0f\n\x07\x63ontent\x18\x02 \x01(\x0c\"\x1d\n\rGetFabRequest\x12\x0c\n\x04hash\x18\x01 \x01(\t\".\n\x0eGetFabResponse\x12\x1c\n\x03\x66\x61\x62\x18\x01 \x01(\x0b\x32\x0f.flwr.proto.Fabb\x06proto3') + +_globals = globals() +_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) +_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'flwr.proto.fab_pb2', _globals) +if _descriptor._USE_C_DESCRIPTORS == False: + DESCRIPTOR._options = None + _globals['_FAB']._serialized_start=36 + _globals['_FAB']._serialized_end=72 + _globals['_GETFABREQUEST']._serialized_start=74 + _globals['_GETFABREQUEST']._serialized_end=103 + _globals['_GETFABRESPONSE']._serialized_start=105 + _globals['_GETFABRESPONSE']._serialized_end=151 +# @@protoc_insertion_point(module_scope) diff --git a/src/py/flwr/proto/fab_pb2.pyi b/src/py/flwr/proto/fab_pb2.pyi new file mode 100644 index 000000000000..dafc217d0ce2 --- /dev/null +++ b/src/py/flwr/proto/fab_pb2.pyi @@ -0,0 +1,56 @@ +""" +@generated by mypy-protobuf. Do not edit manually! +isort:skip_file +""" +import builtins +import google.protobuf.descriptor +import google.protobuf.message +import typing +import typing_extensions + +DESCRIPTOR: google.protobuf.descriptor.FileDescriptor + +class Fab(google.protobuf.message.Message): + DESCRIPTOR: google.protobuf.descriptor.Descriptor + HASH_FIELD_NUMBER: builtins.int + CONTENT_FIELD_NUMBER: builtins.int + hash: typing.Text + """This field is the hash of the data field. It is used to identify the data. + The hash is calculated using the SHA-256 algorithm and is represented as a + hex string (sha256hex). + """ + + content: builtins.bytes + """This field contains the fab file contents a one bytes blob.""" + + def __init__(self, + *, + hash: typing.Text = ..., + content: builtins.bytes = ..., + ) -> None: ... + def ClearField(self, field_name: typing_extensions.Literal["content",b"content","hash",b"hash"]) -> None: ... +global___Fab = Fab + +class GetFabRequest(google.protobuf.message.Message): + DESCRIPTOR: google.protobuf.descriptor.Descriptor + HASH_FIELD_NUMBER: builtins.int + hash: typing.Text + def __init__(self, + *, + hash: typing.Text = ..., + ) -> None: ... + def ClearField(self, field_name: typing_extensions.Literal["hash",b"hash"]) -> None: ... +global___GetFabRequest = GetFabRequest + +class GetFabResponse(google.protobuf.message.Message): + DESCRIPTOR: google.protobuf.descriptor.Descriptor + FAB_FIELD_NUMBER: builtins.int + @property + def fab(self) -> global___Fab: ... + def __init__(self, + *, + fab: typing.Optional[global___Fab] = ..., + ) -> None: ... + def HasField(self, field_name: typing_extensions.Literal["fab",b"fab"]) -> builtins.bool: ... + def ClearField(self, field_name: typing_extensions.Literal["fab",b"fab"]) -> None: ... +global___GetFabResponse = GetFabResponse diff --git a/src/py/flwr/proto/fab_pb2_grpc.py b/src/py/flwr/proto/fab_pb2_grpc.py new file mode 100644 index 000000000000..2daafffebfc8 --- /dev/null +++ b/src/py/flwr/proto/fab_pb2_grpc.py @@ -0,0 +1,4 @@ +# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT! +"""Client and server classes corresponding to protobuf-defined services.""" +import grpc + diff --git a/src/py/flwr/proto/fab_pb2_grpc.pyi b/src/py/flwr/proto/fab_pb2_grpc.pyi new file mode 100644 index 000000000000..f3a5a087ef5d --- /dev/null +++ b/src/py/flwr/proto/fab_pb2_grpc.pyi @@ -0,0 +1,4 @@ +""" +@generated by mypy-protobuf. Do not edit manually! +isort:skip_file +""" diff --git a/src/py/flwr/proto/fleet_pb2.py b/src/py/flwr/proto/fleet_pb2.py index 42f3292d910d..9763b71fed2f 100644 --- a/src/py/flwr/proto/fleet_pb2.py +++ b/src/py/flwr/proto/fleet_pb2.py @@ -14,9 +14,10 @@ from flwr.proto import node_pb2 as flwr_dot_proto_dot_node__pb2 from flwr.proto import task_pb2 as flwr_dot_proto_dot_task__pb2 +from flwr.proto import run_pb2 as flwr_dot_proto_dot_run__pb2 -DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x16\x66lwr/proto/fleet.proto\x12\nflwr.proto\x1a\x15\x66lwr/proto/node.proto\x1a\x15\x66lwr/proto/task.proto\"*\n\x11\x43reateNodeRequest\x12\x15\n\rping_interval\x18\x01 \x01(\x01\"4\n\x12\x43reateNodeResponse\x12\x1e\n\x04node\x18\x01 \x01(\x0b\x32\x10.flwr.proto.Node\"3\n\x11\x44\x65leteNodeRequest\x12\x1e\n\x04node\x18\x01 \x01(\x0b\x32\x10.flwr.proto.Node\"\x14\n\x12\x44\x65leteNodeResponse\"D\n\x0bPingRequest\x12\x1e\n\x04node\x18\x01 \x01(\x0b\x32\x10.flwr.proto.Node\x12\x15\n\rping_interval\x18\x02 \x01(\x01\"\x1f\n\x0cPingResponse\x12\x0f\n\x07success\x18\x01 \x01(\x08\"F\n\x12PullTaskInsRequest\x12\x1e\n\x04node\x18\x01 \x01(\x0b\x32\x10.flwr.proto.Node\x12\x10\n\x08task_ids\x18\x02 \x03(\t\"k\n\x13PullTaskInsResponse\x12(\n\treconnect\x18\x01 \x01(\x0b\x32\x15.flwr.proto.Reconnect\x12*\n\rtask_ins_list\x18\x02 \x03(\x0b\x32\x13.flwr.proto.TaskIns\"@\n\x12PushTaskResRequest\x12*\n\rtask_res_list\x18\x01 \x03(\x0b\x32\x13.flwr.proto.TaskRes\"\xae\x01\n\x13PushTaskResResponse\x12(\n\treconnect\x18\x01 \x01(\x0b\x32\x15.flwr.proto.Reconnect\x12=\n\x07results\x18\x02 \x03(\x0b\x32,.flwr.proto.PushTaskResResponse.ResultsEntry\x1a.\n\x0cResultsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\r:\x02\x38\x01\":\n\x03Run\x12\x0e\n\x06run_id\x18\x01 \x01(\x12\x12\x0e\n\x06\x66\x61\x62_id\x18\x02 \x01(\t\x12\x13\n\x0b\x66\x61\x62_version\x18\x03 \x01(\t\"\x1f\n\rGetRunRequest\x12\x0e\n\x06run_id\x18\x01 \x01(\x12\".\n\x0eGetRunResponse\x12\x1c\n\x03run\x18\x01 \x01(\x0b\x32\x0f.flwr.proto.Run\"\x1e\n\tReconnect\x12\x11\n\treconnect\x18\x01 \x01(\x04\x32\xc9\x03\n\x05\x46leet\x12M\n\nCreateNode\x12\x1d.flwr.proto.CreateNodeRequest\x1a\x1e.flwr.proto.CreateNodeResponse\"\x00\x12M\n\nDeleteNode\x12\x1d.flwr.proto.DeleteNodeRequest\x1a\x1e.flwr.proto.DeleteNodeResponse\"\x00\x12;\n\x04Ping\x12\x17.flwr.proto.PingRequest\x1a\x18.flwr.proto.PingResponse\"\x00\x12P\n\x0bPullTaskIns\x12\x1e.flwr.proto.PullTaskInsRequest\x1a\x1f.flwr.proto.PullTaskInsResponse\"\x00\x12P\n\x0bPushTaskRes\x12\x1e.flwr.proto.PushTaskResRequest\x1a\x1f.flwr.proto.PushTaskResResponse\"\x00\x12\x41\n\x06GetRun\x12\x19.flwr.proto.GetRunRequest\x1a\x1a.flwr.proto.GetRunResponse\"\x00\x62\x06proto3') +DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x16\x66lwr/proto/fleet.proto\x12\nflwr.proto\x1a\x15\x66lwr/proto/node.proto\x1a\x15\x66lwr/proto/task.proto\x1a\x14\x66lwr/proto/run.proto\"*\n\x11\x43reateNodeRequest\x12\x15\n\rping_interval\x18\x01 \x01(\x01\"4\n\x12\x43reateNodeResponse\x12\x1e\n\x04node\x18\x01 \x01(\x0b\x32\x10.flwr.proto.Node\"3\n\x11\x44\x65leteNodeRequest\x12\x1e\n\x04node\x18\x01 \x01(\x0b\x32\x10.flwr.proto.Node\"\x14\n\x12\x44\x65leteNodeResponse\"D\n\x0bPingRequest\x12\x1e\n\x04node\x18\x01 \x01(\x0b\x32\x10.flwr.proto.Node\x12\x15\n\rping_interval\x18\x02 \x01(\x01\"\x1f\n\x0cPingResponse\x12\x0f\n\x07success\x18\x01 \x01(\x08\"F\n\x12PullTaskInsRequest\x12\x1e\n\x04node\x18\x01 \x01(\x0b\x32\x10.flwr.proto.Node\x12\x10\n\x08task_ids\x18\x02 \x03(\t\"k\n\x13PullTaskInsResponse\x12(\n\treconnect\x18\x01 \x01(\x0b\x32\x15.flwr.proto.Reconnect\x12*\n\rtask_ins_list\x18\x02 \x03(\x0b\x32\x13.flwr.proto.TaskIns\"@\n\x12PushTaskResRequest\x12*\n\rtask_res_list\x18\x01 \x03(\x0b\x32\x13.flwr.proto.TaskRes\"\xae\x01\n\x13PushTaskResResponse\x12(\n\treconnect\x18\x01 \x01(\x0b\x32\x15.flwr.proto.Reconnect\x12=\n\x07results\x18\x02 \x03(\x0b\x32,.flwr.proto.PushTaskResResponse.ResultsEntry\x1a.\n\x0cResultsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\r:\x02\x38\x01\"\x1e\n\tReconnect\x12\x11\n\treconnect\x18\x01 \x01(\x04\x32\xc9\x03\n\x05\x46leet\x12M\n\nCreateNode\x12\x1d.flwr.proto.CreateNodeRequest\x1a\x1e.flwr.proto.CreateNodeResponse\"\x00\x12M\n\nDeleteNode\x12\x1d.flwr.proto.DeleteNodeRequest\x1a\x1e.flwr.proto.DeleteNodeResponse\"\x00\x12;\n\x04Ping\x12\x17.flwr.proto.PingRequest\x1a\x18.flwr.proto.PingResponse\"\x00\x12P\n\x0bPullTaskIns\x12\x1e.flwr.proto.PullTaskInsRequest\x1a\x1f.flwr.proto.PullTaskInsResponse\"\x00\x12P\n\x0bPushTaskRes\x12\x1e.flwr.proto.PushTaskResRequest\x1a\x1f.flwr.proto.PushTaskResResponse\"\x00\x12\x41\n\x06GetRun\x12\x19.flwr.proto.GetRunRequest\x1a\x1a.flwr.proto.GetRunResponse\"\x00\x62\x06proto3') _globals = globals() _builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) @@ -25,36 +26,30 @@ DESCRIPTOR._options = None _globals['_PUSHTASKRESRESPONSE_RESULTSENTRY']._options = None _globals['_PUSHTASKRESRESPONSE_RESULTSENTRY']._serialized_options = b'8\001' - _globals['_CREATENODEREQUEST']._serialized_start=84 - _globals['_CREATENODEREQUEST']._serialized_end=126 - _globals['_CREATENODERESPONSE']._serialized_start=128 - _globals['_CREATENODERESPONSE']._serialized_end=180 - _globals['_DELETENODEREQUEST']._serialized_start=182 - _globals['_DELETENODEREQUEST']._serialized_end=233 - _globals['_DELETENODERESPONSE']._serialized_start=235 - _globals['_DELETENODERESPONSE']._serialized_end=255 - _globals['_PINGREQUEST']._serialized_start=257 - _globals['_PINGREQUEST']._serialized_end=325 - _globals['_PINGRESPONSE']._serialized_start=327 - _globals['_PINGRESPONSE']._serialized_end=358 - _globals['_PULLTASKINSREQUEST']._serialized_start=360 - _globals['_PULLTASKINSREQUEST']._serialized_end=430 - _globals['_PULLTASKINSRESPONSE']._serialized_start=432 - _globals['_PULLTASKINSRESPONSE']._serialized_end=539 - _globals['_PUSHTASKRESREQUEST']._serialized_start=541 - _globals['_PUSHTASKRESREQUEST']._serialized_end=605 - _globals['_PUSHTASKRESRESPONSE']._serialized_start=608 - _globals['_PUSHTASKRESRESPONSE']._serialized_end=782 - _globals['_PUSHTASKRESRESPONSE_RESULTSENTRY']._serialized_start=736 - _globals['_PUSHTASKRESRESPONSE_RESULTSENTRY']._serialized_end=782 - _globals['_RUN']._serialized_start=784 - _globals['_RUN']._serialized_end=842 - _globals['_GETRUNREQUEST']._serialized_start=844 - _globals['_GETRUNREQUEST']._serialized_end=875 - _globals['_GETRUNRESPONSE']._serialized_start=877 - _globals['_GETRUNRESPONSE']._serialized_end=923 - _globals['_RECONNECT']._serialized_start=925 - _globals['_RECONNECT']._serialized_end=955 - _globals['_FLEET']._serialized_start=958 - _globals['_FLEET']._serialized_end=1415 + _globals['_CREATENODEREQUEST']._serialized_start=106 + _globals['_CREATENODEREQUEST']._serialized_end=148 + _globals['_CREATENODERESPONSE']._serialized_start=150 + _globals['_CREATENODERESPONSE']._serialized_end=202 + _globals['_DELETENODEREQUEST']._serialized_start=204 + _globals['_DELETENODEREQUEST']._serialized_end=255 + _globals['_DELETENODERESPONSE']._serialized_start=257 + _globals['_DELETENODERESPONSE']._serialized_end=277 + _globals['_PINGREQUEST']._serialized_start=279 + _globals['_PINGREQUEST']._serialized_end=347 + _globals['_PINGRESPONSE']._serialized_start=349 + _globals['_PINGRESPONSE']._serialized_end=380 + _globals['_PULLTASKINSREQUEST']._serialized_start=382 + _globals['_PULLTASKINSREQUEST']._serialized_end=452 + _globals['_PULLTASKINSRESPONSE']._serialized_start=454 + _globals['_PULLTASKINSRESPONSE']._serialized_end=561 + _globals['_PUSHTASKRESREQUEST']._serialized_start=563 + _globals['_PUSHTASKRESREQUEST']._serialized_end=627 + _globals['_PUSHTASKRESRESPONSE']._serialized_start=630 + _globals['_PUSHTASKRESRESPONSE']._serialized_end=804 + _globals['_PUSHTASKRESRESPONSE_RESULTSENTRY']._serialized_start=758 + _globals['_PUSHTASKRESRESPONSE_RESULTSENTRY']._serialized_end=804 + _globals['_RECONNECT']._serialized_start=806 + _globals['_RECONNECT']._serialized_end=836 + _globals['_FLEET']._serialized_start=839 + _globals['_FLEET']._serialized_end=1296 # @@protoc_insertion_point(module_scope) diff --git a/src/py/flwr/proto/fleet_pb2.pyi b/src/py/flwr/proto/fleet_pb2.pyi index a6f38b703e76..5989f45c5c60 100644 --- a/src/py/flwr/proto/fleet_pb2.pyi +++ b/src/py/flwr/proto/fleet_pb2.pyi @@ -164,48 +164,6 @@ class PushTaskResResponse(google.protobuf.message.Message): def ClearField(self, field_name: typing_extensions.Literal["reconnect",b"reconnect","results",b"results"]) -> None: ... global___PushTaskResResponse = PushTaskResResponse -class Run(google.protobuf.message.Message): - """GetRun messages""" - DESCRIPTOR: google.protobuf.descriptor.Descriptor - RUN_ID_FIELD_NUMBER: builtins.int - FAB_ID_FIELD_NUMBER: builtins.int - FAB_VERSION_FIELD_NUMBER: builtins.int - run_id: builtins.int - fab_id: typing.Text - fab_version: typing.Text - def __init__(self, - *, - run_id: builtins.int = ..., - fab_id: typing.Text = ..., - fab_version: typing.Text = ..., - ) -> None: ... - def ClearField(self, field_name: typing_extensions.Literal["fab_id",b"fab_id","fab_version",b"fab_version","run_id",b"run_id"]) -> None: ... -global___Run = Run - -class GetRunRequest(google.protobuf.message.Message): - DESCRIPTOR: google.protobuf.descriptor.Descriptor - RUN_ID_FIELD_NUMBER: builtins.int - run_id: builtins.int - def __init__(self, - *, - run_id: builtins.int = ..., - ) -> None: ... - def ClearField(self, field_name: typing_extensions.Literal["run_id",b"run_id"]) -> None: ... -global___GetRunRequest = GetRunRequest - -class GetRunResponse(google.protobuf.message.Message): - DESCRIPTOR: google.protobuf.descriptor.Descriptor - RUN_FIELD_NUMBER: builtins.int - @property - def run(self) -> global___Run: ... - def __init__(self, - *, - run: typing.Optional[global___Run] = ..., - ) -> None: ... - def HasField(self, field_name: typing_extensions.Literal["run",b"run"]) -> builtins.bool: ... - def ClearField(self, field_name: typing_extensions.Literal["run",b"run"]) -> None: ... -global___GetRunResponse = GetRunResponse - class Reconnect(google.protobuf.message.Message): DESCRIPTOR: google.protobuf.descriptor.Descriptor RECONNECT_FIELD_NUMBER: builtins.int diff --git a/src/py/flwr/proto/fleet_pb2_grpc.py b/src/py/flwr/proto/fleet_pb2_grpc.py index 16757eaed381..e0b0fbc50460 100644 --- a/src/py/flwr/proto/fleet_pb2_grpc.py +++ b/src/py/flwr/proto/fleet_pb2_grpc.py @@ -3,6 +3,7 @@ import grpc from flwr.proto import fleet_pb2 as flwr_dot_proto_dot_fleet__pb2 +from flwr.proto import run_pb2 as flwr_dot_proto_dot_run__pb2 class FleetStub(object): @@ -41,8 +42,8 @@ def __init__(self, channel): ) self.GetRun = channel.unary_unary( '/flwr.proto.Fleet/GetRun', - request_serializer=flwr_dot_proto_dot_fleet__pb2.GetRunRequest.SerializeToString, - response_deserializer=flwr_dot_proto_dot_fleet__pb2.GetRunResponse.FromString, + request_serializer=flwr_dot_proto_dot_run__pb2.GetRunRequest.SerializeToString, + response_deserializer=flwr_dot_proto_dot_run__pb2.GetRunResponse.FromString, ) @@ -121,8 +122,8 @@ def add_FleetServicer_to_server(servicer, server): ), 'GetRun': grpc.unary_unary_rpc_method_handler( servicer.GetRun, - request_deserializer=flwr_dot_proto_dot_fleet__pb2.GetRunRequest.FromString, - response_serializer=flwr_dot_proto_dot_fleet__pb2.GetRunResponse.SerializeToString, + request_deserializer=flwr_dot_proto_dot_run__pb2.GetRunRequest.FromString, + response_serializer=flwr_dot_proto_dot_run__pb2.GetRunResponse.SerializeToString, ), } generic_handler = grpc.method_handlers_generic_handler( @@ -231,7 +232,7 @@ def GetRun(request, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/flwr.proto.Fleet/GetRun', - flwr_dot_proto_dot_fleet__pb2.GetRunRequest.SerializeToString, - flwr_dot_proto_dot_fleet__pb2.GetRunResponse.FromString, + flwr_dot_proto_dot_run__pb2.GetRunRequest.SerializeToString, + flwr_dot_proto_dot_run__pb2.GetRunResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) diff --git a/src/py/flwr/proto/fleet_pb2_grpc.pyi b/src/py/flwr/proto/fleet_pb2_grpc.pyi index f275cd149d69..1c0ab862d45c 100644 --- a/src/py/flwr/proto/fleet_pb2_grpc.pyi +++ b/src/py/flwr/proto/fleet_pb2_grpc.pyi @@ -4,6 +4,7 @@ isort:skip_file """ import abc import flwr.proto.fleet_pb2 +import flwr.proto.run_pb2 import grpc class FleetStub: @@ -37,8 +38,8 @@ class FleetStub: """ GetRun: grpc.UnaryUnaryMultiCallable[ - flwr.proto.fleet_pb2.GetRunRequest, - flwr.proto.fleet_pb2.GetRunResponse] + flwr.proto.run_pb2.GetRunRequest, + flwr.proto.run_pb2.GetRunResponse] class FleetServicer(metaclass=abc.ABCMeta): @@ -84,9 +85,9 @@ class FleetServicer(metaclass=abc.ABCMeta): @abc.abstractmethod def GetRun(self, - request: flwr.proto.fleet_pb2.GetRunRequest, + request: flwr.proto.run_pb2.GetRunRequest, context: grpc.ServicerContext, - ) -> flwr.proto.fleet_pb2.GetRunResponse: ... + ) -> flwr.proto.run_pb2.GetRunResponse: ... def add_FleetServicer_to_server(servicer: FleetServicer, server: grpc.Server) -> None: ... diff --git a/src/py/flwr/proto/grpcadapter_pb2.py b/src/py/flwr/proto/grpcadapter_pb2.py new file mode 100644 index 000000000000..2eff4bb78e47 --- /dev/null +++ b/src/py/flwr/proto/grpcadapter_pb2.py @@ -0,0 +1,32 @@ +# -*- coding: utf-8 -*- +# Generated by the protocol buffer compiler. DO NOT EDIT! +# source: flwr/proto/grpcadapter.proto +# Protobuf Python Version: 4.25.0 +"""Generated protocol buffer code.""" +from google.protobuf import descriptor as _descriptor +from google.protobuf import descriptor_pool as _descriptor_pool +from google.protobuf import symbol_database as _symbol_database +from google.protobuf.internal import builder as _builder +# @@protoc_insertion_point(imports) + +_sym_db = _symbol_database.Default() + + + + +DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x1c\x66lwr/proto/grpcadapter.proto\x12\nflwr.proto\"\xba\x01\n\x10MessageContainer\x12<\n\x08metadata\x18\x01 \x03(\x0b\x32*.flwr.proto.MessageContainer.MetadataEntry\x12\x19\n\x11grpc_message_name\x18\x02 \x01(\t\x12\x1c\n\x14grpc_message_content\x18\x03 \x01(\x0c\x1a/\n\rMetadataEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t:\x02\x38\x01\x32Z\n\x0bGrpcAdapter\x12K\n\x0bSendReceive\x12\x1c.flwr.proto.MessageContainer\x1a\x1c.flwr.proto.MessageContainer\"\x00\x62\x06proto3') + +_globals = globals() +_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) +_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'flwr.proto.grpcadapter_pb2', _globals) +if _descriptor._USE_C_DESCRIPTORS == False: + DESCRIPTOR._options = None + _globals['_MESSAGECONTAINER_METADATAENTRY']._options = None + _globals['_MESSAGECONTAINER_METADATAENTRY']._serialized_options = b'8\001' + _globals['_MESSAGECONTAINER']._serialized_start=45 + _globals['_MESSAGECONTAINER']._serialized_end=231 + _globals['_MESSAGECONTAINER_METADATAENTRY']._serialized_start=184 + _globals['_MESSAGECONTAINER_METADATAENTRY']._serialized_end=231 + _globals['_GRPCADAPTER']._serialized_start=233 + _globals['_GRPCADAPTER']._serialized_end=323 +# @@protoc_insertion_point(module_scope) diff --git a/src/py/flwr/proto/grpcadapter_pb2.pyi b/src/py/flwr/proto/grpcadapter_pb2.pyi new file mode 100644 index 000000000000..35889b30d2b6 --- /dev/null +++ b/src/py/flwr/proto/grpcadapter_pb2.pyi @@ -0,0 +1,43 @@ +""" +@generated by mypy-protobuf. Do not edit manually! +isort:skip_file +""" +import builtins +import google.protobuf.descriptor +import google.protobuf.internal.containers +import google.protobuf.message +import typing +import typing_extensions + +DESCRIPTOR: google.protobuf.descriptor.FileDescriptor + +class MessageContainer(google.protobuf.message.Message): + DESCRIPTOR: google.protobuf.descriptor.Descriptor + class MetadataEntry(google.protobuf.message.Message): + DESCRIPTOR: google.protobuf.descriptor.Descriptor + KEY_FIELD_NUMBER: builtins.int + VALUE_FIELD_NUMBER: builtins.int + key: typing.Text + value: typing.Text + def __init__(self, + *, + key: typing.Text = ..., + value: typing.Text = ..., + ) -> None: ... + def ClearField(self, field_name: typing_extensions.Literal["key",b"key","value",b"value"]) -> None: ... + + METADATA_FIELD_NUMBER: builtins.int + GRPC_MESSAGE_NAME_FIELD_NUMBER: builtins.int + GRPC_MESSAGE_CONTENT_FIELD_NUMBER: builtins.int + @property + def metadata(self) -> google.protobuf.internal.containers.ScalarMap[typing.Text, typing.Text]: ... + grpc_message_name: typing.Text + grpc_message_content: builtins.bytes + def __init__(self, + *, + metadata: typing.Optional[typing.Mapping[typing.Text, typing.Text]] = ..., + grpc_message_name: typing.Text = ..., + grpc_message_content: builtins.bytes = ..., + ) -> None: ... + def ClearField(self, field_name: typing_extensions.Literal["grpc_message_content",b"grpc_message_content","grpc_message_name",b"grpc_message_name","metadata",b"metadata"]) -> None: ... +global___MessageContainer = MessageContainer diff --git a/src/py/flwr/proto/grpcadapter_pb2_grpc.py b/src/py/flwr/proto/grpcadapter_pb2_grpc.py new file mode 100644 index 000000000000..831f99d7b237 --- /dev/null +++ b/src/py/flwr/proto/grpcadapter_pb2_grpc.py @@ -0,0 +1,66 @@ +# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT! +"""Client and server classes corresponding to protobuf-defined services.""" +import grpc + +from flwr.proto import grpcadapter_pb2 as flwr_dot_proto_dot_grpcadapter__pb2 + + +class GrpcAdapterStub(object): + """Missing associated documentation comment in .proto file.""" + + def __init__(self, channel): + """Constructor. + + Args: + channel: A grpc.Channel. + """ + self.SendReceive = channel.unary_unary( + '/flwr.proto.GrpcAdapter/SendReceive', + request_serializer=flwr_dot_proto_dot_grpcadapter__pb2.MessageContainer.SerializeToString, + response_deserializer=flwr_dot_proto_dot_grpcadapter__pb2.MessageContainer.FromString, + ) + + +class GrpcAdapterServicer(object): + """Missing associated documentation comment in .proto file.""" + + def SendReceive(self, request, context): + """Missing associated documentation comment in .proto file.""" + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + +def add_GrpcAdapterServicer_to_server(servicer, server): + rpc_method_handlers = { + 'SendReceive': grpc.unary_unary_rpc_method_handler( + servicer.SendReceive, + request_deserializer=flwr_dot_proto_dot_grpcadapter__pb2.MessageContainer.FromString, + response_serializer=flwr_dot_proto_dot_grpcadapter__pb2.MessageContainer.SerializeToString, + ), + } + generic_handler = grpc.method_handlers_generic_handler( + 'flwr.proto.GrpcAdapter', rpc_method_handlers) + server.add_generic_rpc_handlers((generic_handler,)) + + + # This class is part of an EXPERIMENTAL API. +class GrpcAdapter(object): + """Missing associated documentation comment in .proto file.""" + + @staticmethod + def SendReceive(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary(request, target, '/flwr.proto.GrpcAdapter/SendReceive', + flwr_dot_proto_dot_grpcadapter__pb2.MessageContainer.SerializeToString, + flwr_dot_proto_dot_grpcadapter__pb2.MessageContainer.FromString, + options, channel_credentials, + insecure, call_credentials, compression, wait_for_ready, timeout, metadata) diff --git a/src/py/flwr/proto/grpcadapter_pb2_grpc.pyi b/src/py/flwr/proto/grpcadapter_pb2_grpc.pyi new file mode 100644 index 000000000000..640f983e6e04 --- /dev/null +++ b/src/py/flwr/proto/grpcadapter_pb2_grpc.pyi @@ -0,0 +1,24 @@ +""" +@generated by mypy-protobuf. Do not edit manually! +isort:skip_file +""" +import abc +import flwr.proto.grpcadapter_pb2 +import grpc + +class GrpcAdapterStub: + def __init__(self, channel: grpc.Channel) -> None: ... + SendReceive: grpc.UnaryUnaryMultiCallable[ + flwr.proto.grpcadapter_pb2.MessageContainer, + flwr.proto.grpcadapter_pb2.MessageContainer] + + +class GrpcAdapterServicer(metaclass=abc.ABCMeta): + @abc.abstractmethod + def SendReceive(self, + request: flwr.proto.grpcadapter_pb2.MessageContainer, + context: grpc.ServicerContext, + ) -> flwr.proto.grpcadapter_pb2.MessageContainer: ... + + +def add_GrpcAdapterServicer_to_server(servicer: GrpcAdapterServicer, server: grpc.Server) -> None: ... diff --git a/src/py/flwr/proto/run_pb2.py b/src/py/flwr/proto/run_pb2.py new file mode 100644 index 000000000000..13f06e7169aa --- /dev/null +++ b/src/py/flwr/proto/run_pb2.py @@ -0,0 +1,30 @@ +# -*- coding: utf-8 -*- +# Generated by the protocol buffer compiler. DO NOT EDIT! +# source: flwr/proto/run.proto +# Protobuf Python Version: 4.25.0 +"""Generated protocol buffer code.""" +from google.protobuf import descriptor as _descriptor +from google.protobuf import descriptor_pool as _descriptor_pool +from google.protobuf import symbol_database as _symbol_database +from google.protobuf.internal import builder as _builder +# @@protoc_insertion_point(imports) + +_sym_db = _symbol_database.Default() + + + + +DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x14\x66lwr/proto/run.proto\x12\nflwr.proto\":\n\x03Run\x12\x0e\n\x06run_id\x18\x01 \x01(\x12\x12\x0e\n\x06\x66\x61\x62_id\x18\x02 \x01(\t\x12\x13\n\x0b\x66\x61\x62_version\x18\x03 \x01(\t\"\x1f\n\rGetRunRequest\x12\x0e\n\x06run_id\x18\x01 \x01(\x12\".\n\x0eGetRunResponse\x12\x1c\n\x03run\x18\x01 \x01(\x0b\x32\x0f.flwr.proto.Runb\x06proto3') + +_globals = globals() +_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) +_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'flwr.proto.run_pb2', _globals) +if _descriptor._USE_C_DESCRIPTORS == False: + DESCRIPTOR._options = None + _globals['_RUN']._serialized_start=36 + _globals['_RUN']._serialized_end=94 + _globals['_GETRUNREQUEST']._serialized_start=96 + _globals['_GETRUNREQUEST']._serialized_end=127 + _globals['_GETRUNRESPONSE']._serialized_start=129 + _globals['_GETRUNRESPONSE']._serialized_end=175 +# @@protoc_insertion_point(module_scope) diff --git a/src/py/flwr/proto/run_pb2.pyi b/src/py/flwr/proto/run_pb2.pyi new file mode 100644 index 000000000000..401d27855a41 --- /dev/null +++ b/src/py/flwr/proto/run_pb2.pyi @@ -0,0 +1,52 @@ +""" +@generated by mypy-protobuf. Do not edit manually! +isort:skip_file +""" +import builtins +import google.protobuf.descriptor +import google.protobuf.message +import typing +import typing_extensions + +DESCRIPTOR: google.protobuf.descriptor.FileDescriptor + +class Run(google.protobuf.message.Message): + DESCRIPTOR: google.protobuf.descriptor.Descriptor + RUN_ID_FIELD_NUMBER: builtins.int + FAB_ID_FIELD_NUMBER: builtins.int + FAB_VERSION_FIELD_NUMBER: builtins.int + run_id: builtins.int + fab_id: typing.Text + fab_version: typing.Text + def __init__(self, + *, + run_id: builtins.int = ..., + fab_id: typing.Text = ..., + fab_version: typing.Text = ..., + ) -> None: ... + def ClearField(self, field_name: typing_extensions.Literal["fab_id",b"fab_id","fab_version",b"fab_version","run_id",b"run_id"]) -> None: ... +global___Run = Run + +class GetRunRequest(google.protobuf.message.Message): + DESCRIPTOR: google.protobuf.descriptor.Descriptor + RUN_ID_FIELD_NUMBER: builtins.int + run_id: builtins.int + def __init__(self, + *, + run_id: builtins.int = ..., + ) -> None: ... + def ClearField(self, field_name: typing_extensions.Literal["run_id",b"run_id"]) -> None: ... +global___GetRunRequest = GetRunRequest + +class GetRunResponse(google.protobuf.message.Message): + DESCRIPTOR: google.protobuf.descriptor.Descriptor + RUN_FIELD_NUMBER: builtins.int + @property + def run(self) -> global___Run: ... + def __init__(self, + *, + run: typing.Optional[global___Run] = ..., + ) -> None: ... + def HasField(self, field_name: typing_extensions.Literal["run",b"run"]) -> builtins.bool: ... + def ClearField(self, field_name: typing_extensions.Literal["run",b"run"]) -> None: ... +global___GetRunResponse = GetRunResponse diff --git a/src/py/flwr/proto/run_pb2_grpc.py b/src/py/flwr/proto/run_pb2_grpc.py new file mode 100644 index 000000000000..2daafffebfc8 --- /dev/null +++ b/src/py/flwr/proto/run_pb2_grpc.py @@ -0,0 +1,4 @@ +# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT! +"""Client and server classes corresponding to protobuf-defined services.""" +import grpc + diff --git a/src/py/flwr/proto/run_pb2_grpc.pyi b/src/py/flwr/proto/run_pb2_grpc.pyi new file mode 100644 index 000000000000..f3a5a087ef5d --- /dev/null +++ b/src/py/flwr/proto/run_pb2_grpc.pyi @@ -0,0 +1,4 @@ +""" +@generated by mypy-protobuf. Do not edit manually! +isort:skip_file +""" diff --git a/src/py/flwr/server/__init__.py b/src/py/flwr/server/__init__.py index 875f66c43d03..546ce263e2d5 100644 --- a/src/py/flwr/server/__init__.py +++ b/src/py/flwr/server/__init__.py @@ -17,8 +17,6 @@ from . import strategy from . import workflow as workflow -from .app import run_driver_api as run_driver_api -from .app import run_fleet_api as run_fleet_api from .app import run_superlink as run_superlink from .app import start_server as start_server from .client_manager import ClientManager as ClientManager @@ -36,14 +34,12 @@ "Driver", "History", "LegacyContext", - "run_driver_api", - "run_fleet_api", - "run_server_app", - "run_superlink", "Server", "ServerApp", "ServerConfig", "SimpleClientManager", + "run_server_app", + "run_superlink", "start_server", "strategy", "workflow", diff --git a/src/py/flwr/server/app.py b/src/py/flwr/server/app.py index 30e73fccb7df..822defdb5b13 100644 --- a/src/py/flwr/server/app.py +++ b/src/py/flwr/server/app.py @@ -15,17 +15,17 @@ """Flower server app.""" import argparse -import asyncio import csv import importlib.util import sys import threading -from logging import ERROR, INFO, WARN +from logging import INFO, WARN from os.path import isfile from pathlib import Path -from typing import List, Optional, Sequence, Set, Tuple +from typing import Optional, Sequence, Set, Tuple import grpc +from cryptography.exceptions import UnsupportedAlgorithm from cryptography.hazmat.primitives.asymmetric import ec from cryptography.hazmat.primitives.serialization import ( load_ssh_private_key, @@ -36,20 +36,20 @@ from flwr.common.address import parse_address from flwr.common.constant import ( MISSING_EXTRA_REST, + TRANSPORT_TYPE_GRPC_ADAPTER, TRANSPORT_TYPE_GRPC_RERE, TRANSPORT_TYPE_REST, - TRANSPORT_TYPE_VCE, ) from flwr.common.exit_handlers import register_exit_handlers -from flwr.common.logger import log, warn_deprecated_feature +from flwr.common.logger import log from flwr.common.secure_aggregation.crypto.symmetric_encryption import ( private_key_to_bytes, public_key_to_bytes, - ssh_types_to_elliptic_curve, ) from flwr.proto.fleet_pb2_grpc import ( # pylint: disable=E0611 add_FleetServicer_to_server, ) +from flwr.proto.grpcadapter_pb2_grpc import add_GrpcAdapterServicer_to_server from .client_manager import ClientManager from .history import History @@ -57,13 +57,13 @@ from .server_config import ServerConfig from .strategy import Strategy from .superlink.driver.driver_grpc import run_driver_api_grpc +from .superlink.fleet.grpc_adapter.grpc_adapter_servicer import GrpcAdapterServicer from .superlink.fleet.grpc_bidi.grpc_server import ( generic_create_grpc_server, start_grpc_server, ) from .superlink.fleet.grpc_rere.fleet_servicer import FleetServicer from .superlink.fleet.grpc_rere.server_interceptor import AuthenticateServerInterceptor -from .superlink.fleet.vce import start_vce from .superlink.state import StateFactory ADDRESS_DRIVER_API = "0.0.0.0:9091" @@ -193,120 +193,6 @@ def start_server( # pylint: disable=too-many-arguments,too-many-locals return hist -def run_driver_api() -> None: - """Run Flower server (Driver API).""" - log(INFO, "Starting Flower server (Driver API)") - # Running `flower-driver-api` is deprecated - warn_deprecated_feature("flower-driver-api") - log(WARN, "Use `flower-superlink` instead") - event(EventType.RUN_DRIVER_API_ENTER) - args = _parse_args_run_driver_api().parse_args() - - # Parse IP address - parsed_address = parse_address(args.driver_api_address) - if not parsed_address: - sys.exit(f"Driver IP address ({args.driver_api_address}) cannot be parsed.") - host, port, is_v6 = parsed_address - address = f"[{host}]:{port}" if is_v6 else f"{host}:{port}" - - # Obtain certificates - certificates = _try_obtain_certificates(args) - - # Initialize StateFactory - state_factory = StateFactory(args.database) - - # Start server - grpc_server: grpc.Server = run_driver_api_grpc( - address=address, - state_factory=state_factory, - certificates=certificates, - ) - - # Graceful shutdown - register_exit_handlers( - event_type=EventType.RUN_DRIVER_API_LEAVE, - grpc_servers=[grpc_server], - bckg_threads=[], - ) - - # Block - grpc_server.wait_for_termination() - - -def run_fleet_api() -> None: - """Run Flower server (Fleet API).""" - log(INFO, "Starting Flower server (Fleet API)") - # Running `flower-fleet-api` is deprecated - warn_deprecated_feature("flower-fleet-api") - log(WARN, "Use `flower-superlink` instead") - event(EventType.RUN_FLEET_API_ENTER) - args = _parse_args_run_fleet_api().parse_args() - - # Obtain certificates - certificates = _try_obtain_certificates(args) - - # Initialize StateFactory - state_factory = StateFactory(args.database) - - grpc_servers = [] - bckg_threads = [] - - # Start Fleet API - if args.fleet_api_type == TRANSPORT_TYPE_REST: - if ( - importlib.util.find_spec("requests") - and importlib.util.find_spec("starlette") - and importlib.util.find_spec("uvicorn") - ) is None: - sys.exit(MISSING_EXTRA_REST) - address_arg = args.rest_fleet_api_address - parsed_address = parse_address(address_arg) - if not parsed_address: - sys.exit(f"Fleet IP address ({address_arg}) cannot be parsed.") - host, port, _ = parsed_address - fleet_thread = threading.Thread( - target=_run_fleet_api_rest, - args=( - host, - port, - args.ssl_keyfile, - args.ssl_certfile, - state_factory, - args.rest_fleet_api_workers, - ), - ) - fleet_thread.start() - bckg_threads.append(fleet_thread) - elif args.fleet_api_type == TRANSPORT_TYPE_GRPC_RERE: - address_arg = args.grpc_rere_fleet_api_address - parsed_address = parse_address(address_arg) - if not parsed_address: - sys.exit(f"Fleet IP address ({address_arg}) cannot be parsed.") - host, port, is_v6 = parsed_address - address = f"[{host}]:{port}" if is_v6 else f"{host}:{port}" - fleet_server = _run_fleet_api_grpc_rere( - address=address, - state_factory=state_factory, - certificates=certificates, - ) - grpc_servers.append(fleet_server) - else: - raise ValueError(f"Unknown fleet_api_type: {args.fleet_api_type}") - - # Graceful shutdown - register_exit_handlers( - event_type=EventType.RUN_FLEET_API_LEAVE, - grpc_servers=grpc_servers, - bckg_threads=bckg_threads, - ) - - # Block - if len(grpc_servers) > 0: - grpc_servers[0].wait_for_termination() - elif len(bckg_threads) > 0: - bckg_threads[0].join() - - # pylint: disable=too-many-branches, too-many-locals, too-many-statements def run_superlink() -> None: """Run Flower SuperLink (Driver API and Fleet API).""" @@ -317,11 +203,7 @@ def run_superlink() -> None: args = _parse_args_run_superlink().parse_args() # Parse IP address - parsed_address = parse_address(args.driver_api_address) - if not parsed_address: - sys.exit(f"Driver IP address ({args.driver_api_address}) cannot be parsed.") - host, port, is_v6 = parsed_address - address = f"[{host}]:{port}" if is_v6 else f"{host}:{port}" + driver_address, _, _ = _format_address(args.driver_api_address) # Obtain certificates certificates = _try_obtain_certificates(args) @@ -331,13 +213,35 @@ def run_superlink() -> None: # Start Driver API driver_server: grpc.Server = run_driver_api_grpc( - address=address, + address=driver_address, state_factory=state_factory, certificates=certificates, ) grpc_servers = [driver_server] bckg_threads = [] + if not args.fleet_api_address: + if args.fleet_api_type in [ + TRANSPORT_TYPE_GRPC_RERE, + TRANSPORT_TYPE_GRPC_ADAPTER, + ]: + args.fleet_api_address = ADDRESS_FLEET_API_GRPC_RERE + elif args.fleet_api_type == TRANSPORT_TYPE_REST: + args.fleet_api_address = ADDRESS_FLEET_API_REST + + fleet_address, host, port = _format_address(args.fleet_api_address) + + num_workers = args.fleet_api_num_workers + if num_workers != 1: + log( + WARN, + "The Fleet API currently supports only 1 worker. " + "You have specified %d workers. " + "Support for multiple workers will be added in future releases. " + "Proceeding with a single worker.", + args.fleet_api_num_workers, + ) + num_workers = 1 # Start Fleet API if args.fleet_api_type == TRANSPORT_TYPE_REST: @@ -347,32 +251,25 @@ def run_superlink() -> None: and importlib.util.find_spec("uvicorn") ) is None: sys.exit(MISSING_EXTRA_REST) - address_arg = args.rest_fleet_api_address - parsed_address = parse_address(address_arg) - if not parsed_address: - sys.exit(f"Fleet IP address ({address_arg}) cannot be parsed.") - host, port, _ = parsed_address + + _, ssl_certfile, ssl_keyfile = ( + certificates if certificates is not None else (None, None, None) + ) + fleet_thread = threading.Thread( target=_run_fleet_api_rest, args=( host, port, - args.ssl_keyfile, - args.ssl_certfile, + ssl_keyfile, + ssl_certfile, state_factory, - args.rest_fleet_api_workers, + num_workers, ), ) fleet_thread.start() bckg_threads.append(fleet_thread) elif args.fleet_api_type == TRANSPORT_TYPE_GRPC_RERE: - address_arg = args.grpc_rere_fleet_api_address - parsed_address = parse_address(address_arg) - if not parsed_address: - sys.exit(f"Fleet IP address ({address_arg}) cannot be parsed.") - host, port, is_v6 = parsed_address - address = f"[{host}]:{port}" if is_v6 else f"{host}:{port}" - maybe_keys = _try_setup_client_authentication(args, certificates) interceptors: Optional[Sequence[grpc.ServerInterceptor]] = None if maybe_keys is not None: @@ -395,23 +292,19 @@ def run_superlink() -> None: interceptors = [AuthenticateServerInterceptor(state)] fleet_server = _run_fleet_api_grpc_rere( - address=address, + address=fleet_address, state_factory=state_factory, certificates=certificates, interceptors=interceptors, ) grpc_servers.append(fleet_server) - elif args.fleet_api_type == TRANSPORT_TYPE_VCE: - f_stop = asyncio.Event() # Does nothing - _run_fleet_api_vce( - num_supernodes=args.num_supernodes, - client_app_attr=args.client_app, - backend_name=args.backend, - backend_config_json_stream=args.backend_config, - app_dir=args.app_dir, + elif args.fleet_api_type == TRANSPORT_TYPE_GRPC_ADAPTER: + fleet_server = _run_fleet_api_grpc_adapter( + address=fleet_address, state_factory=state_factory, - f_stop=f_stop, + certificates=certificates, ) + grpc_servers.append(fleet_server) else: raise ValueError(f"Unknown fleet_api_type: {args.fleet_api_type}") @@ -431,48 +324,83 @@ def run_superlink() -> None: driver_server.wait_for_termination(timeout=1) +def _format_address(address: str) -> Tuple[str, str, int]: + parsed_address = parse_address(address) + if not parsed_address: + sys.exit( + f"Address ({address}) cannot be parsed (expected: URL or IPv4 or IPv6)." + ) + host, port, is_v6 = parsed_address + return (f"[{host}]:{port}" if is_v6 else f"{host}:{port}", host, port) + + def _try_setup_client_authentication( args: argparse.Namespace, certificates: Optional[Tuple[bytes, bytes, bytes]], ) -> Optional[Tuple[Set[bytes], ec.EllipticCurvePrivateKey, ec.EllipticCurvePublicKey]]: - if not args.require_client_authentication: + if ( + not args.auth_list_public_keys + and not args.auth_superlink_private_key + and not args.auth_superlink_public_key + ): return None + if ( + not args.auth_list_public_keys + or not args.auth_superlink_private_key + or not args.auth_superlink_public_key + ): + sys.exit( + "Authentication requires providing file paths for " + "'--auth-list-public-keys', '--auth-superlink-private-key' and " + "'--auth-superlink-public-key'. Provide all three to enable authentication." + ) + if certificates is None: sys.exit( - "Client authentication only works over secure connections. " - "Please provide certificate paths using '--certificates' when " - "enabling '--require-client-authentication'." + "Authentication requires secure connections. " + "Please provide certificate paths to `--ssl-certfile`, " + "`--ssl-keyfile`, and `—-ssl-ca-certfile` and try again." ) - client_keys_file_path = Path(args.require_client_authentication[0]) + client_keys_file_path = Path(args.auth_list_public_keys) if not client_keys_file_path.exists(): sys.exit( - "The provided path to the client public keys CSV file does not exist: " + "The provided path to the known public keys CSV file does not exist: " f"{client_keys_file_path}. " - "Please provide the CSV file path containing known client public keys " - "to '--require-client-authentication'." + "Please provide the CSV file path containing known public keys " + "to '--auth-list-public-keys'." ) client_public_keys: Set[bytes] = set() - ssh_private_key = load_ssh_private_key( - Path(args.require_client_authentication[1]).read_bytes(), - None, - ) - ssh_public_key = load_ssh_public_key( - Path(args.require_client_authentication[2]).read_bytes() - ) try: - server_private_key, server_public_key = ssh_types_to_elliptic_curve( - ssh_private_key, ssh_public_key + ssh_private_key = load_ssh_private_key( + Path(args.auth_superlink_private_key).read_bytes(), + None, + ) + if not isinstance(ssh_private_key, ec.EllipticCurvePrivateKey): + raise ValueError() + except (ValueError, UnsupportedAlgorithm): + sys.exit( + "Error: Unable to parse the private key file in " + "'--auth-superlink-private-key'. Authentication requires elliptic " + "curve private and public key pair. Please ensure that the file " + "path points to a valid private key file and try again." + ) + + try: + ssh_public_key = load_ssh_public_key( + Path(args.auth_superlink_public_key).read_bytes() ) - except TypeError: + if not isinstance(ssh_public_key, ec.EllipticCurvePublicKey): + raise ValueError() + except (ValueError, UnsupportedAlgorithm): sys.exit( - "The file paths provided could not be read as a private and public " - "key pair. Client authentication requires an elliptic curve public and " - "private key pair. Please provide the file paths containing elliptic " - "curve private and public keys to '--require-client-authentication'." + "Error: Unable to parse the public key file in " + "'--auth-superlink-public-key'. Authentication requires elliptic " + "curve private and public key pair. Please ensure that the file " + "path points to a valid public key file and try again." ) with open(client_keys_file_path, newline="", encoding="utf-8") as csvfile: @@ -484,14 +412,14 @@ def _try_setup_client_authentication( client_public_keys.add(public_key_to_bytes(public_key)) else: sys.exit( - "Error: Unable to parse the public keys in the .csv " - "file. Please ensure that the .csv file contains valid " - "SSH public keys and try again." + "Error: Unable to parse the public keys in the CSV " + "file. Please ensure that the CSV file path points to a valid " + "known SSH public keys files and try again." ) return ( client_public_keys, - server_private_key, - server_public_key, + ssh_private_key, + ssh_public_key, ) @@ -501,21 +429,52 @@ def _try_obtain_certificates( # Obtain certificates if args.insecure: log(WARN, "Option `--insecure` was set. Starting insecure HTTP server.") - certificates = None + return None # Check if certificates are provided - elif args.certificates: - certificates = ( - Path(args.certificates[0]).read_bytes(), # CA certificate - Path(args.certificates[1]).read_bytes(), # server certificate - Path(args.certificates[2]).read_bytes(), # server private key - ) - else: - sys.exit( - "Certificates are required unless running in insecure mode. " - "Please provide certificate paths with '--certificates' or run the server " - "in insecure mode using '--insecure' if you understand the risks." - ) - return certificates + if args.fleet_api_type in [TRANSPORT_TYPE_GRPC_RERE, TRANSPORT_TYPE_GRPC_ADAPTER]: + if args.ssl_certfile and args.ssl_keyfile and args.ssl_ca_certfile: + if not isfile(args.ssl_ca_certfile): + sys.exit("Path argument `--ssl-ca-certfile` does not point to a file.") + if not isfile(args.ssl_certfile): + sys.exit("Path argument `--ssl-certfile` does not point to a file.") + if not isfile(args.ssl_keyfile): + sys.exit("Path argument `--ssl-keyfile` does not point to a file.") + certificates = ( + Path(args.ssl_ca_certfile).read_bytes(), # CA certificate + Path(args.ssl_certfile).read_bytes(), # server certificate + Path(args.ssl_keyfile).read_bytes(), # server private key + ) + return certificates + if args.ssl_certfile or args.ssl_keyfile or args.ssl_ca_certfile: + sys.exit( + "You need to provide valid file paths to `--ssl-certfile`, " + "`--ssl-keyfile`, and `—-ssl-ca-certfile` to create a secure " + "connection in Fleet API server (gRPC-rere)." + ) + if args.fleet_api_type == TRANSPORT_TYPE_REST: + if args.ssl_certfile and args.ssl_keyfile: + if not isfile(args.ssl_certfile): + sys.exit("Path argument `--ssl-certfile` does not point to a file.") + if not isfile(args.ssl_keyfile): + sys.exit("Path argument `--ssl-keyfile` does not point to a file.") + certificates = ( + b"", + Path(args.ssl_certfile).read_bytes(), # server certificate + Path(args.ssl_keyfile).read_bytes(), # server private key + ) + return certificates + if args.ssl_certfile or args.ssl_keyfile: + sys.exit( + "You need to provide valid file paths to `--ssl-certfile` " + "and `--ssl-keyfile` to create a secure connection " + "in Fleet API server (REST, experimental)." + ) + sys.exit( + "Certificates are required unless running in insecure mode. " + "Please provide certificate paths to `--ssl-certfile`, " + "`--ssl-keyfile`, and `—-ssl-ca-certfile` or run the server " + "in insecure mode using '--insecure' if you understand the risks." + ) def _run_fleet_api_grpc_rere( @@ -544,27 +503,28 @@ def _run_fleet_api_grpc_rere( return fleet_grpc_server -# pylint: disable=too-many-arguments -def _run_fleet_api_vce( - num_supernodes: int, - client_app_attr: str, - backend_name: str, - backend_config_json_stream: str, - app_dir: str, +def _run_fleet_api_grpc_adapter( + address: str, state_factory: StateFactory, - f_stop: asyncio.Event, -) -> None: - log(INFO, "Flower VCE: Starting Fleet API (VirtualClientEngine)") - - start_vce( - num_supernodes=num_supernodes, - client_app_attr=client_app_attr, - backend_name=backend_name, - backend_config_json_stream=backend_config_json_stream, + certificates: Optional[Tuple[bytes, bytes, bytes]], +) -> grpc.Server: + """Run Fleet API (GrpcAdapter).""" + # Create Fleet API gRPC server + fleet_servicer = GrpcAdapterServicer( state_factory=state_factory, - app_dir=app_dir, - f_stop=f_stop, ) + fleet_add_servicer_to_server_fn = add_GrpcAdapterServicer_to_server + fleet_grpc_server = generic_create_grpc_server( + servicer_and_add_fn=(fleet_servicer, fleet_add_servicer_to_server_fn), + server_address=address, + max_message_length=GRPC_MAX_MESSAGE_LENGTH, + certificates=certificates, + ) + + log(INFO, "Flower ECE: Starting Fleet API (GrpcAdapter) on %s", address) + fleet_grpc_server.start() + + return fleet_grpc_server # pylint: disable=import-outside-toplevel,too-many-arguments @@ -574,7 +534,7 @@ def _run_fleet_api_rest( ssl_keyfile: Optional[str], ssl_certfile: Optional[str], state_factory: StateFactory, - workers: int, + num_workers: int, ) -> None: """Run Driver API (REST-based).""" try: @@ -583,25 +543,12 @@ def _run_fleet_api_rest( from flwr.server.superlink.fleet.rest_rere.rest_api import app as fast_api_app except ModuleNotFoundError: sys.exit(MISSING_EXTRA_REST) - if workers != 1: - raise ValueError( - f"The supported number of workers for the Fleet API (REST server) is " - f"1. Instead given {workers}. The functionality of >1 workers will be " - f"added in the future releases." - ) + log(INFO, "Starting Flower REST server") # See: https://www.starlette.io/applications/#accessing-the-app-instance fast_api_app.state.STATE_FACTORY = state_factory - validation_exceptions = _validate_ssl_files( - ssl_certfile=ssl_certfile, ssl_keyfile=ssl_keyfile - ) - if any(validation_exceptions): - # Starting with 3.11 we can use ExceptionGroup but for now - # this seems to be the reasonable approach. - raise ValueError(validation_exceptions) - uvicorn.run( app="flwr.server.superlink.fleet.rest_rere.rest_api:app", port=port, @@ -610,69 +557,9 @@ def _run_fleet_api_rest( access_log=True, ssl_keyfile=ssl_keyfile, ssl_certfile=ssl_certfile, - workers=workers, - ) - - -def _validate_ssl_files( - ssl_keyfile: Optional[str], ssl_certfile: Optional[str] -) -> List[ValueError]: - validation_exceptions = [] - - if ssl_keyfile is not None and not isfile(ssl_keyfile): - msg = "Path argument `--ssl-keyfile` does not point to a file." - log(ERROR, msg) - validation_exceptions.append(ValueError(msg)) - - if ssl_certfile is not None and not isfile(ssl_certfile): - msg = "Path argument `--ssl-certfile` does not point to a file." - log(ERROR, msg) - validation_exceptions.append(ValueError(msg)) - - if not bool(ssl_keyfile) == bool(ssl_certfile): - msg = ( - "When setting one of `--ssl-keyfile` and " - "`--ssl-certfile`, both have to be used." - ) - log(ERROR, msg) - validation_exceptions.append(ValueError(msg)) - - return validation_exceptions - - -def _parse_args_run_driver_api() -> argparse.ArgumentParser: - """Parse command line arguments for Driver API.""" - parser = argparse.ArgumentParser( - description="Start a Flower Driver API server. " - "This server will be responsible for " - "receiving TaskIns from the Driver script and " - "sending them to the Fleet API. Once the client nodes " - "are done, they will send the TaskRes back to this Driver API server (through" - " the Fleet API) which will then send them back to the Driver script.", - ) - - _add_args_common(parser=parser) - _add_args_driver_api(parser=parser) - - return parser - - -def _parse_args_run_fleet_api() -> argparse.ArgumentParser: - """Parse command line arguments for Fleet API.""" - parser = argparse.ArgumentParser( - description="Start a Flower Fleet API server." - "This server will be responsible for " - "sending TaskIns (received from the Driver API) to the client nodes " - "and of receiving TaskRes sent back from those same client nodes once " - "they are done. Then, this Fleet API server can send those " - "TaskRes back to the Driver API.", + workers=num_workers, ) - _add_args_common(parser=parser) - _add_args_fleet_api(parser=parser) - - return parser - def _parse_args_run_superlink() -> argparse.ArgumentParser: """Parse command line arguments for both Driver API and Fleet API.""" @@ -696,13 +583,23 @@ def _add_args_common(parser: argparse.ArgumentParser) -> None: "Use this flag only if you understand the risks.", ) parser.add_argument( - "--certificates", - nargs=3, - metavar=("CA_CERT", "SERVER_CERT", "PRIVATE_KEY"), + "--ssl-certfile", + help="Fleet API server SSL certificate file (as a path str) " + "to create a secure connection.", + type=str, + default=None, + ) + parser.add_argument( + "--ssl-keyfile", + help="Fleet API server SSL private key file (as a path str) " + "to create a secure connection.", + type=str, + ) + parser.add_argument( + "--ssl-ca-certfile", + help="Fleet API server SSL CA certificate file (as a path str) " + "to create a secure connection.", type=str, - help="Paths to the CA certificate, server certificate, and server private " - "key, in that order. Note: The server can only be started without " - "certificates by enabling the `--insecure` flag.", ) parser.add_argument( "--database", @@ -714,116 +611,51 @@ def _add_args_common(parser: argparse.ArgumentParser) -> None: default=DATABASE, ) parser.add_argument( - "--require-client-authentication", - nargs=3, - metavar=("CLIENT_KEYS", "SERVER_PRIVATE_KEY", "SERVER_PUBLIC_KEY"), + "--auth-list-public-keys", + type=str, + help="A CSV file (as a path str) containing a list of known public " + "keys to enable authentication.", + ) + parser.add_argument( + "--auth-superlink-private-key", type=str, - help="Provide three file paths: (1) a .csv file containing a list of " - "known client public keys for authentication, (2) the server's private " - "key file, and (3) the server's public key file.", + help="The SuperLink's private key (as a path str) to enable authentication.", + ) + parser.add_argument( + "--auth-superlink-public-key", + type=str, + help="The SuperLink's public key (as a path str) to enable authentication.", ) def _add_args_driver_api(parser: argparse.ArgumentParser) -> None: parser.add_argument( "--driver-api-address", - help="Driver API (gRPC) server address (IPv4, IPv6, or a domain name)", + help="Driver API (gRPC) server address (IPv4, IPv6, or a domain name).", default=ADDRESS_DRIVER_API, ) def _add_args_fleet_api(parser: argparse.ArgumentParser) -> None: # Fleet API transport layer type - ex_group = parser.add_mutually_exclusive_group() - ex_group.add_argument( - "--grpc-rere", - action="store_const", - dest="fleet_api_type", - const=TRANSPORT_TYPE_GRPC_RERE, + parser.add_argument( + "--fleet-api-type", default=TRANSPORT_TYPE_GRPC_RERE, - help="Start a Fleet API server (gRPC-rere)", - ) - ex_group.add_argument( - "--rest", - action="store_const", - dest="fleet_api_type", - const=TRANSPORT_TYPE_REST, - help="Start a Fleet API server (REST, experimental)", - ) - - ex_group.add_argument( - "--vce", - action="store_const", - dest="fleet_api_type", - const=TRANSPORT_TYPE_VCE, - help="Start a Fleet API server (VirtualClientEngine)", - ) - - # Fleet API gRPC-rere options - grpc_rere_group = parser.add_argument_group( - "Fleet API (gRPC-rere) server options", "" - ) - grpc_rere_group.add_argument( - "--grpc-rere-fleet-api-address", - help="Fleet API (gRPC-rere) server address (IPv4, IPv6, or a domain name)", - default=ADDRESS_FLEET_API_GRPC_RERE, - ) - - # Fleet API REST options - rest_group = parser.add_argument_group("Fleet API (REST) server options", "") - rest_group.add_argument( - "--rest-fleet-api-address", - help="Fleet API (REST) server address (IPv4, IPv6, or a domain name)", - default=ADDRESS_FLEET_API_REST, - ) - rest_group.add_argument( - "--ssl-certfile", - help="Fleet API (REST) server SSL certificate file (as a path str), " - "needed for using 'https'.", - default=None, - ) - rest_group.add_argument( - "--ssl-keyfile", - help="Fleet API (REST) server SSL private key file (as a path str), " - "needed for using 'https'.", - default=None, - ) - rest_group.add_argument( - "--rest-fleet-api-workers", - help="Set the number of concurrent workers for the Fleet API REST server.", - type=int, - default=1, - ) - - # Fleet API VCE options - vce_group = parser.add_argument_group("Fleet API (VCE) server options", "") - vce_group.add_argument( - "--client-app", - help="For example: `client:app` or `project.package.module:wrapper.app`.", - ) - vce_group.add_argument( - "--num-supernodes", - type=int, - help="Number of simulated SuperNodes.", - ) - vce_group.add_argument( - "--backend", - default="ray", type=str, - help="Simulation backend that executes the ClientApp.", + choices=[ + TRANSPORT_TYPE_GRPC_RERE, + TRANSPORT_TYPE_GRPC_ADAPTER, + TRANSPORT_TYPE_REST, + ], + help="Start a gRPC-rere or REST (experimental) Fleet API server.", ) - vce_group.add_argument( - "--backend-config", - type=str, - default='{"client_resources": {"num_cpus":1, "num_gpus":0.0}, "tensorflow": 0}', - help='A JSON formatted stream, e.g \'{"":, "":}\' to ' - "configure a backend. Values supported in are those included by " - "`flwr.common.typing.ConfigsRecordValues`. ", + parser.add_argument( + "--fleet-api-address", + help="Fleet API server address (IPv4, IPv6, or a domain name).", ) parser.add_argument( - "--app-dir", - default="", - help="Add specified directory to the PYTHONPATH and load" - "ClientApp from there." - " Default: current working directory.", + "--fleet-api-num-workers", + default=1, + type=int, + help="Set the number of concurrent workers for the Fleet API server.", ) diff --git a/src/py/flwr/server/client_proxy_test.py b/src/py/flwr/server/client_proxy_test.py index 685698558e3a..6ca37052a87d 100644 --- a/src/py/flwr/server/client_proxy_test.py +++ b/src/py/flwr/server/client_proxy_test.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2022 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/compat/app.py b/src/py/flwr/server/compat/app.py index 4bb23b846ab7..e978359fa828 100644 --- a/src/py/flwr/server/compat/app.py +++ b/src/py/flwr/server/compat/app.py @@ -1,4 +1,4 @@ -# Copyright 2022 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/compat/app_utils.py b/src/py/flwr/server/compat/app_utils.py index 1cdf1efbffb9..baff27307b88 100644 --- a/src/py/flwr/server/compat/app_utils.py +++ b/src/py/flwr/server/compat/app_utils.py @@ -91,7 +91,7 @@ def _update_client_manager( node_id=node_id, driver=driver, anonymous=False, - run_id=driver.run_id, # type: ignore + run_id=driver.run.run_id, ) if client_manager.register(client_proxy): registered_nodes[node_id] = client_proxy diff --git a/src/py/flwr/server/compat/driver_client_proxy.py b/src/py/flwr/server/compat/driver_client_proxy.py index 150803786f98..7190786784ec 100644 --- a/src/py/flwr/server/compat/driver_client_proxy.py +++ b/src/py/flwr/server/compat/driver_client_proxy.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/compat/driver_client_proxy_test.py b/src/py/flwr/server/compat/driver_client_proxy_test.py index d9e3d3bc0824..31b917fa869b 100644 --- a/src/py/flwr/server/compat/driver_client_proxy_test.py +++ b/src/py/flwr/server/compat/driver_client_proxy_test.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/driver/driver.py b/src/py/flwr/server/driver/driver.py index b95cec95ab47..4f888323e586 100644 --- a/src/py/flwr/server/driver/driver.py +++ b/src/py/flwr/server/driver/driver.py @@ -19,11 +19,17 @@ from typing import Iterable, List, Optional from flwr.common import Message, RecordSet +from flwr.common.typing import Run class Driver(ABC): """Abstract base Driver class for the Driver API.""" + @property + @abstractmethod + def run(self) -> Run: + """Run information.""" + @abstractmethod def create_message( # pylint: disable=too-many-arguments self, diff --git a/src/py/flwr/server/driver/grpc_driver.py b/src/py/flwr/server/driver/grpc_driver.py index d339f1b232f9..e614df659e3f 100644 --- a/src/py/flwr/server/driver/grpc_driver.py +++ b/src/py/flwr/server/driver/grpc_driver.py @@ -1,4 +1,4 @@ -# Copyright 2022 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -17,7 +17,7 @@ import time import warnings from logging import DEBUG, ERROR, WARNING -from typing import Iterable, List, Optional, Tuple +from typing import Iterable, List, Optional, Tuple, cast import grpc @@ -25,6 +25,7 @@ from flwr.common.grpc import create_channel from flwr.common.logger import log from flwr.common.serde import message_from_taskres, message_to_taskins +from flwr.common.typing import Run from flwr.proto.driver_pb2 import ( # pylint: disable=E0611 CreateRunRequest, CreateRunResponse, @@ -37,6 +38,7 @@ ) from flwr.proto.driver_pb2_grpc import DriverStub # pylint: disable=E0611 from flwr.proto.node_pb2 import Node # pylint: disable=E0611 +from flwr.proto.run_pb2 import GetRunRequest, GetRunResponse # pylint: disable=E0611 from flwr.proto.task_pb2 import TaskIns # pylint: disable=E0611 from .driver import Driver @@ -46,13 +48,24 @@ ERROR_MESSAGE_DRIVER_NOT_CONNECTED = """ [Driver] Error: Not connected. -Call `connect()` on the `GrpcDriverHelper` instance before calling any of the other -`GrpcDriverHelper` methods. +Call `connect()` on the `GrpcDriverStub` instance before calling any of the other +`GrpcDriverStub` methods. """ -class GrpcDriverHelper: - """`GrpcDriverHelper` provides access to the gRPC Driver API/service.""" +class GrpcDriverStub: + """`GrpcDriverStub` provides access to the gRPC Driver API/service. + + Parameters + ---------- + driver_service_address : Optional[str] + The IPv4 or IPv6 address of the Driver API server. + Defaults to `"[::]:9091"`. + root_certificates : Optional[bytes] (default: None) + The PEM-encoded root certificates as a byte string. + If provided, a secure connection using the certificates will be + established to an SSL-enabled Flower server. + """ def __init__( self, @@ -64,6 +77,10 @@ def __init__( self.channel: Optional[grpc.Channel] = None self.stub: Optional[DriverStub] = None + def is_connected(self) -> bool: + """Return True if connected to the Driver API server, otherwise False.""" + return self.channel is not None + def connect(self) -> None: """Connect to the Driver API.""" event(EventType.DRIVER_CONNECT) @@ -95,18 +112,29 @@ def create_run(self, req: CreateRunRequest) -> CreateRunResponse: # Check if channel is open if self.stub is None: log(ERROR, ERROR_MESSAGE_DRIVER_NOT_CONNECTED) - raise ConnectionError("`GrpcDriverHelper` instance not connected") + raise ConnectionError("`GrpcDriverStub` instance not connected") # Call Driver API res: CreateRunResponse = self.stub.CreateRun(request=req) return res + def get_run(self, req: GetRunRequest) -> GetRunResponse: + """Get run information.""" + # Check if channel is open + if self.stub is None: + log(ERROR, ERROR_MESSAGE_DRIVER_NOT_CONNECTED) + raise ConnectionError("`GrpcDriverStub` instance not connected") + + # Call gRPC Driver API + res: GetRunResponse = self.stub.GetRun(request=req) + return res + def get_nodes(self, req: GetNodesRequest) -> GetNodesResponse: """Get client IDs.""" # Check if channel is open if self.stub is None: log(ERROR, ERROR_MESSAGE_DRIVER_NOT_CONNECTED) - raise ConnectionError("`GrpcDriverHelper` instance not connected") + raise ConnectionError("`GrpcDriverStub` instance not connected") # Call gRPC Driver API res: GetNodesResponse = self.stub.GetNodes(request=req) @@ -117,7 +145,7 @@ def push_task_ins(self, req: PushTaskInsRequest) -> PushTaskInsResponse: # Check if channel is open if self.stub is None: log(ERROR, ERROR_MESSAGE_DRIVER_NOT_CONNECTED) - raise ConnectionError("`GrpcDriverHelper` instance not connected") + raise ConnectionError("`GrpcDriverStub` instance not connected") # Call gRPC Driver API res: PushTaskInsResponse = self.stub.PushTaskIns(request=req) @@ -128,7 +156,7 @@ def pull_task_res(self, req: PullTaskResRequest) -> PullTaskResResponse: # Check if channel is open if self.stub is None: log(ERROR, ERROR_MESSAGE_DRIVER_NOT_CONNECTED) - raise ConnectionError("`GrpcDriverHelper` instance not connected") + raise ConnectionError("`GrpcDriverStub` instance not connected") # Call Driver API res: PullTaskResResponse = self.stub.PullTaskRes(request=req) @@ -140,56 +168,52 @@ class GrpcDriver(Driver): Parameters ---------- - driver_service_address : Optional[str] - The IPv4 or IPv6 address of the Driver API server. - Defaults to `"[::]:9091"`. - certificates : bytes (default: None) - Tuple containing root certificate, server certificate, and private key - to start a secure SSL-enabled server. The tuple is expected to have - three bytes elements in the following order: - - * CA certificate. - * server certificate. - * server private key. - fab_id : str (default: None) - The identifier of the FAB used in the run. - fab_version : str (default: None) - The version of the FAB used in the run. + run_id : int + The identifier of the run. + stub : Optional[GrpcDriverStub] (default: None) + The ``GrpcDriverStub`` instance used to communicate with the SuperLink. + If None, an instance connected to "[::]:9091" will be created. """ - def __init__( + def __init__( # pylint: disable=too-many-arguments self, - driver_service_address: str = DEFAULT_SERVER_ADDRESS_DRIVER, - root_certificates: Optional[bytes] = None, - fab_id: Optional[str] = None, - fab_version: Optional[str] = None, + run_id: int, + stub: Optional[GrpcDriverStub] = None, ) -> None: - self.addr = driver_service_address - self.root_certificates = root_certificates - self.driver_helper: Optional[GrpcDriverHelper] = None - self.run_id: Optional[int] = None - self.fab_id = fab_id if fab_id is not None else "" - self.fab_version = fab_version if fab_version is not None else "" + self._run_id = run_id + self._run: Optional[Run] = None + self.stub = stub if stub is not None else GrpcDriverStub() self.node = Node(node_id=0, anonymous=True) - def _get_grpc_driver_helper_and_run_id(self) -> Tuple[GrpcDriverHelper, int]: - # Check if the GrpcDriverHelper is initialized - if self.driver_helper is None or self.run_id is None: - # Connect and create run - self.driver_helper = GrpcDriverHelper( - driver_service_address=self.addr, - root_certificates=self.root_certificates, + @property + def run(self) -> Run: + """Run information.""" + self._get_stub_and_run_id() + return Run(**vars(cast(Run, self._run))) + + def _get_stub_and_run_id(self) -> Tuple[GrpcDriverStub, int]: + # Check if is initialized + if self._run is None: + # Connect + if not self.stub.is_connected(): + self.stub.connect() + # Get the run info + req = GetRunRequest(run_id=self._run_id) + res = self.stub.get_run(req) + if not res.HasField("run"): + raise RuntimeError(f"Cannot find the run with ID: {self._run_id}") + self._run = Run( + run_id=res.run.run_id, + fab_id=res.run.fab_id, + fab_version=res.run.fab_version, ) - self.driver_helper.connect() - req = CreateRunRequest(fab_id=self.fab_id, fab_version=self.fab_version) - res = self.driver_helper.create_run(req) - self.run_id = res.run_id - return self.driver_helper, self.run_id + + return self.stub, self._run.run_id def _check_message(self, message: Message) -> None: # Check if the message is valid if not ( - message.metadata.run_id == self.run_id + message.metadata.run_id == cast(Run, self._run).run_id and message.metadata.src_node_id == self.node.node_id and message.metadata.message_id == "" and message.metadata.reply_to_message == "" @@ -210,7 +234,7 @@ def create_message( # pylint: disable=too-many-arguments This method constructs a new `Message` with given content and metadata. The `run_id` and `src_node_id` will be set automatically. """ - _, run_id = self._get_grpc_driver_helper_and_run_id() + _, run_id = self._get_stub_and_run_id() if ttl: warnings.warn( "A custom TTL was set, but note that the SuperLink does not enforce " @@ -234,9 +258,9 @@ def create_message( # pylint: disable=too-many-arguments def get_node_ids(self) -> List[int]: """Get node IDs.""" - grpc_driver_helper, run_id = self._get_grpc_driver_helper_and_run_id() - # Call GrpcDriverHelper method - res = grpc_driver_helper.get_nodes(GetNodesRequest(run_id=run_id)) + stub, run_id = self._get_stub_and_run_id() + # Call GrpcDriverStub method + res = stub.get_nodes(GetNodesRequest(run_id=run_id)) return [node.node_id for node in res.nodes] def push_messages(self, messages: Iterable[Message]) -> Iterable[str]: @@ -245,7 +269,7 @@ def push_messages(self, messages: Iterable[Message]) -> Iterable[str]: This method takes an iterable of messages and sends each message to the node specified in `dst_node_id`. """ - grpc_driver_helper, _ = self._get_grpc_driver_helper_and_run_id() + stub, _ = self._get_stub_and_run_id() # Construct TaskIns task_ins_list: List[TaskIns] = [] for msg in messages: @@ -255,10 +279,8 @@ def push_messages(self, messages: Iterable[Message]) -> Iterable[str]: taskins = message_to_taskins(msg) # Add to list task_ins_list.append(taskins) - # Call GrpcDriverHelper method - res = grpc_driver_helper.push_task_ins( - PushTaskInsRequest(task_ins_list=task_ins_list) - ) + # Call GrpcDriverStub method + res = stub.push_task_ins(PushTaskInsRequest(task_ins_list=task_ins_list)) return list(res.task_ids) def pull_messages(self, message_ids: Iterable[str]) -> Iterable[Message]: @@ -267,9 +289,9 @@ def pull_messages(self, message_ids: Iterable[str]) -> Iterable[Message]: This method is used to collect messages from the SuperLink that correspond to a set of given message IDs. """ - grpc_driver, _ = self._get_grpc_driver_helper_and_run_id() + stub, _ = self._get_stub_and_run_id() # Pull TaskRes - res = grpc_driver.pull_task_res( + res = stub.pull_task_res( PullTaskResRequest(node=self.node, task_ids=message_ids) ) # Convert TaskRes to Message @@ -308,8 +330,8 @@ def send_and_receive( def close(self) -> None: """Disconnect from the SuperLink if connected.""" - # Check if GrpcDriverHelper is initialized - if self.driver_helper is None: + # Check if `connect` was called before + if not self.stub.is_connected(): return # Disconnect - self.driver_helper.disconnect() + self.stub.disconnect() diff --git a/src/py/flwr/server/driver/grpc_driver_test.py b/src/py/flwr/server/driver/grpc_driver_test.py index fbead0e3043d..72efc5f8b2c6 100644 --- a/src/py/flwr/server/driver/grpc_driver_test.py +++ b/src/py/flwr/server/driver/grpc_driver_test.py @@ -1,4 +1,4 @@ -# Copyright 2022 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -27,6 +27,7 @@ PullTaskResRequest, PushTaskInsRequest, ) +from flwr.proto.run_pb2 import Run # pylint: disable=E0611 from flwr.proto.task_pb2 import Task, TaskRes # pylint: disable=E0611 from .grpc_driver import GrpcDriver @@ -36,58 +37,36 @@ class TestGrpcDriver(unittest.TestCase): """Tests for `GrpcDriver` class.""" def setUp(self) -> None: - """Initialize mock GrpcDriverHelper and Driver instance before each test.""" - mock_response = Mock() - mock_response.run_id = 61016 - self.mock_grpc_driver_helper = Mock() - self.mock_grpc_driver_helper.create_run.return_value = mock_response - self.patcher = patch( - "flwr.server.driver.grpc_driver.GrpcDriverHelper", - return_value=self.mock_grpc_driver_helper, + """Initialize mock GrpcDriverStub and Driver instance before each test.""" + mock_response = Mock( + run=Run(run_id=61016, fab_id="mock/mock", fab_version="v1.0.0") ) - self.patcher.start() - self.driver = GrpcDriver() - - def tearDown(self) -> None: - """Cleanup after each test.""" - self.patcher.stop() - - def test_check_and_init_grpc_driver_already_initialized(self) -> None: - """Test that GrpcDriverHelper doesn't initialize if run is created.""" - # Prepare - self.driver.driver_helper = self.mock_grpc_driver_helper - self.driver.run_id = 61016 - - # Execute - # pylint: disable-next=protected-access - self.driver._get_grpc_driver_helper_and_run_id() + self.mock_grpc_driver_stub = Mock() + self.mock_grpc_driver_stub.get_run.return_value = mock_response + self.mock_grpc_driver_stub.HasField.return_value = True + self.driver = GrpcDriver(run_id=61016, stub=self.mock_grpc_driver_stub) + def test_init_grpc_driver(self) -> None: + """Test GrpcDriverStub initialization.""" # Assert - self.mock_grpc_driver_helper.connect.assert_not_called() - - def test_check_and_init_grpc_driver_needs_initialization(self) -> None: - """Test GrpcDriverHelper initialization when run is not created.""" - # Execute - # pylint: disable-next=protected-access - self.driver._get_grpc_driver_helper_and_run_id() - - # Assert - self.mock_grpc_driver_helper.connect.assert_called_once() - self.assertEqual(self.driver.run_id, 61016) + self.assertEqual(self.driver.run.run_id, 61016) + self.assertEqual(self.driver.run.fab_id, "mock/mock") + self.assertEqual(self.driver.run.fab_version, "v1.0.0") + self.mock_grpc_driver_stub.get_run.assert_called_once() def test_get_nodes(self) -> None: """Test retrieval of nodes.""" # Prepare mock_response = Mock() mock_response.nodes = [Mock(node_id=404), Mock(node_id=200)] - self.mock_grpc_driver_helper.get_nodes.return_value = mock_response + self.mock_grpc_driver_stub.get_nodes.return_value = mock_response # Execute node_ids = self.driver.get_node_ids() - args, kwargs = self.mock_grpc_driver_helper.get_nodes.call_args + args, kwargs = self.mock_grpc_driver_stub.get_nodes.call_args # Assert - self.mock_grpc_driver_helper.connect.assert_called_once() + self.mock_grpc_driver_stub.get_run.assert_called_once() self.assertEqual(len(args), 1) self.assertEqual(len(kwargs), 0) self.assertIsInstance(args[0], GetNodesRequest) @@ -98,7 +77,7 @@ def test_push_messages_valid(self) -> None: """Test pushing valid messages.""" # Prepare mock_response = Mock(task_ids=["id1", "id2"]) - self.mock_grpc_driver_helper.push_task_ins.return_value = mock_response + self.mock_grpc_driver_stub.push_task_ins.return_value = mock_response msgs = [ self.driver.create_message(RecordSet(), "", 0, "", DEFAULT_TTL) for _ in range(2) @@ -106,10 +85,10 @@ def test_push_messages_valid(self) -> None: # Execute msg_ids = self.driver.push_messages(msgs) - args, kwargs = self.mock_grpc_driver_helper.push_task_ins.call_args + args, kwargs = self.mock_grpc_driver_stub.push_task_ins.call_args # Assert - self.mock_grpc_driver_helper.connect.assert_called_once() + self.mock_grpc_driver_stub.get_run.assert_called_once() self.assertEqual(len(args), 1) self.assertEqual(len(kwargs), 0) self.assertIsInstance(args[0], PushTaskInsRequest) @@ -121,7 +100,7 @@ def test_push_messages_invalid(self) -> None: """Test pushing invalid messages.""" # Prepare mock_response = Mock(task_ids=["id1", "id2"]) - self.mock_grpc_driver_helper.push_task_ins.return_value = mock_response + self.mock_grpc_driver_stub.push_task_ins.return_value = mock_response msgs = [ self.driver.create_message(RecordSet(), "", 0, "", DEFAULT_TTL) for _ in range(2) @@ -145,16 +124,16 @@ def test_pull_messages_with_given_message_ids(self) -> None: ), TaskRes(task=Task(ancestry=["id3"], error=error_to_proto(Error(code=0)))), ] - self.mock_grpc_driver_helper.pull_task_res.return_value = mock_response + self.mock_grpc_driver_stub.pull_task_res.return_value = mock_response msg_ids = ["id1", "id2", "id3"] # Execute msgs = self.driver.pull_messages(msg_ids) reply_tos = {msg.metadata.reply_to_message for msg in msgs} - args, kwargs = self.mock_grpc_driver_helper.pull_task_res.call_args + args, kwargs = self.mock_grpc_driver_stub.pull_task_res.call_args # Assert - self.mock_grpc_driver_helper.connect.assert_called_once() + self.mock_grpc_driver_stub.get_run.assert_called_once() self.assertEqual(len(args), 1) self.assertEqual(len(kwargs), 0) self.assertIsInstance(args[0], PullTaskResRequest) @@ -165,14 +144,14 @@ def test_send_and_receive_messages_complete(self) -> None: """Test send and receive all messages successfully.""" # Prepare mock_response = Mock(task_ids=["id1"]) - self.mock_grpc_driver_helper.push_task_ins.return_value = mock_response + self.mock_grpc_driver_stub.push_task_ins.return_value = mock_response # The response message must include either `content` (i.e. a recordset) or # an `Error`. We choose the latter in this case error_proto = error_to_proto(Error(code=0)) mock_response = Mock( task_res_list=[TaskRes(task=Task(ancestry=["id1"], error=error_proto))] ) - self.mock_grpc_driver_helper.pull_task_res.return_value = mock_response + self.mock_grpc_driver_stub.pull_task_res.return_value = mock_response msgs = [self.driver.create_message(RecordSet(), "", 0, "", DEFAULT_TTL)] # Execute @@ -187,9 +166,9 @@ def test_send_and_receive_messages_timeout(self) -> None: # Prepare sleep_fn = time.sleep mock_response = Mock(task_ids=["id1"]) - self.mock_grpc_driver_helper.push_task_ins.return_value = mock_response + self.mock_grpc_driver_stub.push_task_ins.return_value = mock_response mock_response = Mock(task_res_list=[]) - self.mock_grpc_driver_helper.pull_task_res.return_value = mock_response + self.mock_grpc_driver_stub.pull_task_res.return_value = mock_response msgs = [self.driver.create_message(RecordSet(), "", 0, "", DEFAULT_TTL)] # Execute @@ -204,19 +183,21 @@ def test_send_and_receive_messages_timeout(self) -> None: def test_del_with_initialized_driver(self) -> None: """Test cleanup behavior when Driver is initialized.""" # Prepare - # pylint: disable-next=protected-access - self.driver._get_grpc_driver_helper_and_run_id() + self.mock_grpc_driver_stub.is_connected.return_value = True # Execute self.driver.close() # Assert - self.mock_grpc_driver_helper.disconnect.assert_called_once() + self.mock_grpc_driver_stub.disconnect.assert_called_once() def test_del_with_uninitialized_driver(self) -> None: """Test cleanup behavior when Driver is not initialized.""" + # Prepare + self.mock_grpc_driver_stub.is_connected.return_value = False + # Execute self.driver.close() # Assert - self.mock_grpc_driver_helper.disconnect.assert_not_called() + self.mock_grpc_driver_stub.disconnect.assert_not_called() diff --git a/src/py/flwr/server/driver/inmemory_driver.py b/src/py/flwr/server/driver/inmemory_driver.py index 8c71b1067293..53406796750f 100644 --- a/src/py/flwr/server/driver/inmemory_driver.py +++ b/src/py/flwr/server/driver/inmemory_driver.py @@ -17,11 +17,12 @@ import time import warnings -from typing import Iterable, List, Optional +from typing import Iterable, List, Optional, cast from uuid import UUID from flwr.common import DEFAULT_TTL, Message, Metadata, RecordSet from flwr.common.serde import message_from_taskres, message_to_taskins +from flwr.common.typing import Run from flwr.proto.node_pb2 import Node # pylint: disable=E0611 from flwr.server.superlink.state import StateFactory @@ -33,30 +34,27 @@ class InMemoryDriver(Driver): Parameters ---------- + run_id : int + The identifier of the run. state_factory : StateFactory A StateFactory embedding a state that this driver can interface with. - fab_id : str (default: None) - The identifier of the FAB used in the run. - fab_version : str (default: None) - The version of the FAB used in the run. """ def __init__( self, + run_id: int, state_factory: StateFactory, - fab_id: Optional[str] = None, - fab_version: Optional[str] = None, ) -> None: - self.run_id: Optional[int] = None - self.fab_id = fab_id if fab_id is not None else "" - self.fab_version = fab_version if fab_version is not None else "" - self.node = Node(node_id=0, anonymous=True) + self._run_id = run_id + self._run: Optional[Run] = None self.state = state_factory.state() + self.node = Node(node_id=0, anonymous=True) def _check_message(self, message: Message) -> None: + self._init_run() # Check if the message is valid if not ( - message.metadata.run_id == self.run_id + message.metadata.run_id == cast(Run, self._run).run_id and message.metadata.src_node_id == self.node.node_id and message.metadata.message_id == "" and message.metadata.reply_to_message == "" @@ -64,16 +62,20 @@ def _check_message(self, message: Message) -> None: ): raise ValueError(f"Invalid message: {message}") - def _get_run_id(self) -> int: - """Return run_id. - - If unset, create a new run. - """ - if self.run_id is None: - self.run_id = self.state.create_run( - fab_id=self.fab_id, fab_version=self.fab_version - ) - return self.run_id + def _init_run(self) -> None: + """Initialize the run.""" + if self._run is not None: + return + run = self.state.get_run(self._run_id) + if run is None: + raise RuntimeError(f"Cannot find the run with ID: {self._run_id}") + self._run = run + + @property + def run(self) -> Run: + """Run ID.""" + self._init_run() + return Run(**vars(cast(Run, self._run))) def create_message( # pylint: disable=too-many-arguments self, @@ -88,7 +90,7 @@ def create_message( # pylint: disable=too-many-arguments This method constructs a new `Message` with given content and metadata. The `run_id` and `src_node_id` will be set automatically. """ - run_id = self._get_run_id() + self._init_run() if ttl: warnings.warn( "A custom TTL was set, but note that the SuperLink does not enforce " @@ -99,7 +101,7 @@ def create_message( # pylint: disable=too-many-arguments ttl_ = DEFAULT_TTL if ttl is None else ttl metadata = Metadata( - run_id=run_id, + run_id=cast(Run, self._run).run_id, message_id="", # Will be set by the server src_node_id=self.node.node_id, dst_node_id=dst_node_id, @@ -112,8 +114,8 @@ def create_message( # pylint: disable=too-many-arguments def get_node_ids(self) -> List[int]: """Get node IDs.""" - run_id = self._get_run_id() - return list(self.state.get_nodes(run_id)) + self._init_run() + return list(self.state.get_nodes(cast(Run, self._run).run_id)) def push_messages(self, messages: Iterable[Message]) -> Iterable[str]: """Push messages to specified node IDs. diff --git a/src/py/flwr/server/driver/inmemory_driver_test.py b/src/py/flwr/server/driver/inmemory_driver_test.py index 95c2a0b277af..eff38f548826 100644 --- a/src/py/flwr/server/driver/inmemory_driver_test.py +++ b/src/py/flwr/server/driver/inmemory_driver_test.py @@ -1,4 +1,4 @@ -# Copyright 2022 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -31,8 +31,9 @@ message_to_taskres, recordset_to_proto, ) +from flwr.common.typing import Run from flwr.proto.task_pb2 import Task, TaskRes # pylint: disable=E0611 -from flwr.server.superlink.state import StateFactory +from flwr.server.superlink.state import InMemoryState, SqliteState, StateFactory from .inmemory_driver import InMemoryDriver @@ -79,12 +80,24 @@ def setUp(self) -> None: """ # Create driver self.num_nodes = 42 - self.driver = InMemoryDriver(StateFactory("")) - self.driver.state = MagicMock() - self.driver.state.get_nodes.return_value = [ + self.state = MagicMock() + self.state.get_nodes.return_value = [ int.from_bytes(os.urandom(8), "little", signed=True) for _ in range(self.num_nodes) ] + self.state.get_run.return_value = Run( + run_id=61016, fab_id="mock/mock", fab_version="v1.0.0" + ) + state_factory = MagicMock(state=lambda: self.state) + self.driver = InMemoryDriver(run_id=61016, state_factory=state_factory) + self.driver.state = self.state + + def test_get_run(self) -> None: + """Test the InMemoryDriver starting with run_id.""" + # Assert + self.assertEqual(self.driver.run.run_id, 61016) + self.assertEqual(self.driver.run.fab_id, "mock/mock") + self.assertEqual(self.driver.run.fab_version, "v1.0.0") def test_get_nodes(self) -> None: """Test retrieval of nodes.""" @@ -104,7 +117,7 @@ def test_push_messages_valid(self) -> None: ] taskins_ids = [uuid4() for _ in range(num_messages)] - self.driver.state.store_task_ins.side_effect = taskins_ids # type: ignore + self.state.store_task_ins.side_effect = taskins_ids # Execute msg_ids = list(self.driver.push_messages(msgs)) @@ -141,7 +154,7 @@ def test_pull_messages_with_given_message_ids(self) -> None: task=Task(ancestry=[msg_ids[1]], error=error_to_proto(Error(code=0))) ), ] - self.driver.state.get_task_res.return_value = task_res_list # type: ignore + self.state.get_task_res.return_value = task_res_list # Execute pulled_msgs = list(self.driver.pull_messages(msg_ids)) @@ -167,8 +180,8 @@ def test_send_and_receive_messages_complete(self) -> None: task=Task(ancestry=[msg_ids[1]], error=error_to_proto(Error(code=0))) ), ] - self.driver.state.store_task_ins.side_effect = msg_ids # type: ignore - self.driver.state.get_task_res.return_value = task_res_list # type: ignore + self.state.store_task_ins.side_effect = msg_ids + self.state.get_task_res.return_value = task_res_list # Execute ret_msgs = list(self.driver.send_and_receive(msgs)) @@ -193,8 +206,8 @@ def test_send_and_receive_messages_timeout(self) -> None: task=Task(ancestry=[msg_ids[1]], error=error_to_proto(Error(code=0))) ), ] - self.driver.state.store_task_ins.side_effect = msg_ids # type: ignore - self.driver.state.get_task_res.return_value = task_res_list # type: ignore + self.state.store_task_ins.side_effect = msg_ids + self.state.get_task_res.return_value = task_res_list # Execute with patch("time.sleep", side_effect=lambda t: time.sleep(t * 0.01)): @@ -208,19 +221,23 @@ def test_send_and_receive_messages_timeout(self) -> None: def test_task_store_consistency_after_push_pull_sqlitestate(self) -> None: """Test tasks are deleted in sqlite state once messages are pulled.""" # Prepare - self.driver = InMemoryDriver(StateFactory("")) + state = StateFactory("").state() + self.driver = InMemoryDriver( + state.create_run("", ""), MagicMock(state=lambda: state) + ) msg_ids, node_id = push_messages(self.driver, self.num_nodes) + assert isinstance(state, SqliteState) # Check recorded - task_ins = self.driver.state.query("SELECT * FROM task_ins;") # type: ignore + task_ins = state.query("SELECT * FROM task_ins;") self.assertEqual(len(task_ins), len(list(msg_ids))) # Prepare: create replies reply_tos = get_replies(self.driver, msg_ids, node_id) # Query number of task_ins and task_res in State - task_res = self.driver.state.query("SELECT * FROM task_res;") # type: ignore - task_ins = self.driver.state.query("SELECT * FROM task_ins;") # type: ignore + task_res = state.query("SELECT * FROM task_res;") + task_ins = state.query("SELECT * FROM task_ins;") # Assert self.assertEqual(reply_tos, msg_ids) @@ -230,18 +247,19 @@ def test_task_store_consistency_after_push_pull_sqlitestate(self) -> None: def test_task_store_consistency_after_push_pull_inmemory_state(self) -> None: """Test tasks are deleted in in-memory state once messages are pulled.""" # Prepare - self.driver = InMemoryDriver(StateFactory(":flwr-in-memory-state:")) + state_factory = StateFactory(":flwr-in-memory-state:") + state = state_factory.state() + self.driver = InMemoryDriver(state.create_run("", ""), state_factory) msg_ids, node_id = push_messages(self.driver, self.num_nodes) + assert isinstance(state, InMemoryState) # Check recorded - self.assertEqual( - len(self.driver.state.task_ins_store), len(list(msg_ids)) # type: ignore - ) + self.assertEqual(len(state.task_ins_store), len(list(msg_ids))) # Prepare: create replies reply_tos = get_replies(self.driver, msg_ids, node_id) # Assert self.assertEqual(reply_tos, msg_ids) - self.assertEqual(len(self.driver.state.task_res_store), 0) # type: ignore - self.assertEqual(len(self.driver.state.task_ins_store), 0) # type: ignore + self.assertEqual(len(state.task_res_store), 0) + self.assertEqual(len(state.task_ins_store), 0) diff --git a/src/py/flwr/server/history_test.py b/src/py/flwr/server/history_test.py index adb9d697e409..b53357149623 100644 --- a/src/py/flwr/server/history_test.py +++ b/src/py/flwr/server/history_test.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2021 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/run_serverapp.py b/src/py/flwr/server/run_serverapp.py index 9cc7974d34da..3505ebfdb0a9 100644 --- a/src/py/flwr/server/run_serverapp.py +++ b/src/py/flwr/server/run_serverapp.py @@ -22,12 +22,17 @@ from typing import Optional from flwr.common import Context, EventType, RecordSet, event -from flwr.common.logger import log, update_console_handler +from flwr.common.config import get_flwr_dir, get_project_config, get_project_dir +from flwr.common.logger import log, update_console_handler, warn_deprecated_feature from flwr.common.object_ref import load_app +from flwr.proto.driver_pb2 import CreateRunRequest # pylint: disable=E0611 -from .driver import Driver, GrpcDriver +from .driver import Driver +from .driver.grpc_driver import GrpcDriver, GrpcDriverStub from .server_app import LoadServerAppError, ServerApp +ADDRESS_DRIVER_API = "0.0.0.0:9091" + def run( driver: Driver, @@ -39,16 +44,18 @@ def run( if not (server_app_attr is None) ^ (loaded_server_app is None): raise ValueError( "Either `server_app_attr` or `loaded_server_app` should be set " - "but not both. " + "but not both." ) if server_app_dir is not None: - sys.path.insert(0, server_app_dir) + sys.path.insert(0, str(Path(server_app_dir).absolute())) # Load ServerApp if needed def _load() -> ServerApp: if server_app_attr: - server_app: ServerApp = load_app(server_app_attr, LoadServerAppError) + server_app: ServerApp = load_app( + server_app_attr, LoadServerAppError, server_app_dir + ) if not isinstance(server_app, ServerApp): raise LoadServerAppError( @@ -70,12 +77,29 @@ def _load() -> ServerApp: log(DEBUG, "ServerApp finished running.") -def run_server_app() -> None: +def run_server_app() -> None: # pylint: disable=too-many-branches """Run Flower server app.""" event(EventType.RUN_SERVER_APP_ENTER) args = _parse_args_run_server_app().parse_args() + if args.server != ADDRESS_DRIVER_API: + warn = "Passing flag --server is deprecated. Use --superlink instead." + warn_deprecated_feature(warn) + + if args.superlink != ADDRESS_DRIVER_API: + # if `--superlink` also passed, then + # warn user that this argument overrides what was passed with `--server` + log( + WARN, + "Both `--server` and `--superlink` were passed. " + "`--server` will be ignored. Connecting to the Superlink Driver API " + "at %s.", + args.superlink, + ) + else: + args.superlink = args.server + update_console_handler( level=DEBUG if args.verbose else INFO, timestamps=args.verbose, @@ -95,7 +119,7 @@ def run_server_app() -> None: WARN, "Option `--insecure` was set. " "Starting insecure HTTP client connected to %s.", - args.server, + args.superlink, ) root_certificates = None else: @@ -109,15 +133,47 @@ def run_server_app() -> None: DEBUG, "Starting secure HTTPS client connected to %s " "with the following certificates: %s.", - args.server, + args.superlink, cert_path, ) - log( - DEBUG, - "Flower will load ServerApp `%s`", - getattr(args, "server-app"), + server_app_attr: Optional[str] = getattr(args, "server-app") + if not (server_app_attr is None) ^ (args.run_id is None): + raise sys.exit( + "Please provide either a ServerApp reference or a Run ID, but not both. " + "For more details, use: ``flower-server-app -h``" + ) + + stub = GrpcDriverStub( + driver_service_address=args.superlink, root_certificates=root_certificates ) + if args.run_id is not None: + # User provided `--run-id`, but not `server-app` + run_id = args.run_id + else: + # User provided `server-app`, but not `--run-id` + # Create run if run_id is not provided + stub.connect() + req = CreateRunRequest(fab_id=args.fab_id, fab_version=args.fab_version) + res = stub.create_run(req) + run_id = res.run_id + + # Initialize GrpcDriver + driver = GrpcDriver(run_id=run_id, stub=stub) + + # Dynamically obtain ServerApp path based on run_id + if args.run_id is not None: + # User provided `--run-id`, but not `server-app` + flwr_dir = get_flwr_dir(args.flwr_dir) + run_ = driver.run + server_app_dir = str(get_project_dir(run_.fab_id, run_.fab_version, flwr_dir)) + config = get_project_config(server_app_dir) + server_app_attr = config["flower"]["components"]["serverapp"] + else: + # User provided `server-app`, but not `--run-id` + server_app_dir = str(Path(args.dir).absolute()) + + log(DEBUG, "Flower will load ServerApp `%s` in %s", server_app_attr, server_app_dir) log( DEBUG, @@ -125,17 +181,6 @@ def run_server_app() -> None: root_certificates, ) - server_app_dir = args.dir - server_app_attr = getattr(args, "server-app") - - # Initialize GrpcDriver - driver = GrpcDriver( - driver_service_address=args.server, - root_certificates=root_certificates, - fab_id=args.fab_id, - fab_version=args.fab_version, - ) - # Run the ServerApp with the Driver run(driver=driver, server_app_dir=server_app_dir, server_app_attr=server_app_attr) @@ -153,6 +198,8 @@ def _parse_args_run_server_app() -> argparse.ArgumentParser: parser.add_argument( "server-app", + nargs="?", + default=None, help="For example: `server:app` or `project.package.module:wrapper.app`", ) parser.add_argument( @@ -175,9 +222,14 @@ def _parse_args_run_server_app() -> argparse.ArgumentParser: ) parser.add_argument( "--server", - default="0.0.0.0:9091", + default=ADDRESS_DRIVER_API, help="Server address", ) + parser.add_argument( + "--superlink", + default=ADDRESS_DRIVER_API, + help="SuperLink Driver API (gRPC-rere) address (IPv4, IPv6, or a domain name)", + ) parser.add_argument( "--dir", default="", @@ -197,5 +249,22 @@ def _parse_args_run_server_app() -> argparse.ArgumentParser: type=str, help="The version of the FAB used in the run.", ) + parser.add_argument( + "--run-id", + default=None, + type=int, + help="The identifier of the run.", + ) + parser.add_argument( + "--flwr-dir", + default=None, + help="""The path containing installed Flower Apps. + By default, this value is equal to: + + - `$FLWR_HOME/` if `$FLWR_HOME` is defined + - `$XDG_DATA_HOME/.flwr/` if `$XDG_DATA_HOME` is defined + - `$HOME/.flwr/` in all other cases + """, + ) return parser diff --git a/src/py/flwr/server/server_app.py b/src/py/flwr/server/server_app.py index ea2eb3fd1a69..43b3bcce3f36 100644 --- a/src/py/flwr/server/server_app.py +++ b/src/py/flwr/server/server_app.py @@ -39,7 +39,7 @@ class ServerApp: >>> server_config = ServerConfig(num_rounds=3) >>> strategy = FedAvg() >>> - >>> app = ServerApp() + >>> app = ServerApp( >>> server_config=server_config, >>> strategy=strategy, >>> ) @@ -106,7 +106,7 @@ def main_decorator(main_fn: ServerAppCallable) -> ServerAppCallable: >>> server_config = ServerConfig(num_rounds=3) >>> strategy = FedAvg() >>> - >>> app = ServerApp() + >>> app = ServerApp( >>> server_config=server_config, >>> strategy=strategy, >>> ) diff --git a/src/py/flwr/server/server_app_test.py b/src/py/flwr/server/server_app_test.py index 38c0d6240d90..0751a0cb2bc5 100644 --- a/src/py/flwr/server/server_app_test.py +++ b/src/py/flwr/server/server_app_test.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/server_test.py b/src/py/flwr/server/server_test.py index 51071c13f895..f47b5c3d8469 100644 --- a/src/py/flwr/server/server_test.py +++ b/src/py/flwr/server/server_test.py @@ -242,11 +242,9 @@ def test_setup_client_auth() -> None: # pylint: disable=R0914 # Mock argparse with `require-client-authentication`` flag mock_args = argparse.Namespace( - require_client_authentication=[ - str(client_keys_file_path), - str(server_private_key_path), - str(server_public_key_path), - ] + auth_list_public_keys=str(client_keys_file_path), + auth_superlink_private_key=str(server_private_key_path), + auth_superlink_public_key=str(server_public_key_path), ) # Run _try_setup_client_authentication diff --git a/src/py/flwr/server/strategy/__init__.py b/src/py/flwr/server/strategy/__init__.py index b7de9a946fff..e5bc30009819 100644 --- a/src/py/flwr/server/strategy/__init__.py +++ b/src/py/flwr/server/strategy/__init__.py @@ -53,9 +53,10 @@ "DPFedAvgAdaptive", "DPFedAvgFixed", "DifferentialPrivacyClientSideAdaptiveClipping", - "DifferentialPrivacyServerSideAdaptiveClipping", "DifferentialPrivacyClientSideFixedClipping", + "DifferentialPrivacyServerSideAdaptiveClipping", "DifferentialPrivacyServerSideFixedClipping", + "FaultTolerantFedAvg", "FedAdagrad", "FedAdam", "FedAvg", @@ -69,7 +70,6 @@ "FedXgbCyclic", "FedXgbNnAvg", "FedYogi", - "FaultTolerantFedAvg", "Krum", "QFedAvg", "Strategy", diff --git a/src/py/flwr/server/strategy/bulyan.py b/src/py/flwr/server/strategy/bulyan.py index 1e4f97530ab7..a81406c255ad 100644 --- a/src/py/flwr/server/strategy/bulyan.py +++ b/src/py/flwr/server/strategy/bulyan.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2023 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/strategy/bulyan_test.py b/src/py/flwr/server/strategy/bulyan_test.py index 299ed49066fb..93a9ebda3783 100644 --- a/src/py/flwr/server/strategy/bulyan_test.py +++ b/src/py/flwr/server/strategy/bulyan_test.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2023 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/strategy/dpfedavg_adaptive.py b/src/py/flwr/server/strategy/dpfedavg_adaptive.py index a908679ed668..423ddddeb379 100644 --- a/src/py/flwr/server/strategy/dpfedavg_adaptive.py +++ b/src/py/flwr/server/strategy/dpfedavg_adaptive.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2022 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/strategy/dpfedavg_fixed.py b/src/py/flwr/server/strategy/dpfedavg_fixed.py index c54379fc7087..d122f0688922 100644 --- a/src/py/flwr/server/strategy/dpfedavg_fixed.py +++ b/src/py/flwr/server/strategy/dpfedavg_fixed.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2022 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/strategy/fedadagrad.py b/src/py/flwr/server/strategy/fedadagrad.py index 4a8f52d98e18..f13c5358da25 100644 --- a/src/py/flwr/server/strategy/fedadagrad.py +++ b/src/py/flwr/server/strategy/fedadagrad.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2021 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/strategy/fedadagrad_test.py b/src/py/flwr/server/strategy/fedadagrad_test.py index 0c966442ecaf..b43a4c75d123 100644 --- a/src/py/flwr/server/strategy/fedadagrad_test.py +++ b/src/py/flwr/server/strategy/fedadagrad_test.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2021 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/strategy/fedadam.py b/src/py/flwr/server/strategy/fedadam.py index 8a47cf0dd8ac..dc90e90c7568 100644 --- a/src/py/flwr/server/strategy/fedadam.py +++ b/src/py/flwr/server/strategy/fedadam.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2021 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/strategy/fedavg_android.py b/src/py/flwr/server/strategy/fedavg_android.py index 6678b7ced114..2f49cf8784c9 100644 --- a/src/py/flwr/server/strategy/fedavg_android.py +++ b/src/py/flwr/server/strategy/fedavg_android.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2021 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/strategy/fedavgm.py b/src/py/flwr/server/strategy/fedavgm.py index fb9261abe89d..ab3d37249db6 100644 --- a/src/py/flwr/server/strategy/fedavgm.py +++ b/src/py/flwr/server/strategy/fedavgm.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2022 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/strategy/fedavgm_test.py b/src/py/flwr/server/strategy/fedavgm_test.py index a0e942171627..39da5f4b82c4 100644 --- a/src/py/flwr/server/strategy/fedavgm_test.py +++ b/src/py/flwr/server/strategy/fedavgm_test.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2022 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/strategy/fedmedian.py b/src/py/flwr/server/strategy/fedmedian.py index 17e979d92beb..e7cba5324fa8 100644 --- a/src/py/flwr/server/strategy/fedmedian.py +++ b/src/py/flwr/server/strategy/fedmedian.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2022 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/strategy/fedmedian_test.py b/src/py/flwr/server/strategy/fedmedian_test.py index 57cf08d8c01d..3960ad70b145 100644 --- a/src/py/flwr/server/strategy/fedmedian_test.py +++ b/src/py/flwr/server/strategy/fedmedian_test.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2022 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/strategy/fedopt.py b/src/py/flwr/server/strategy/fedopt.py index be5f260d96fa..c581d4797123 100644 --- a/src/py/flwr/server/strategy/fedopt.py +++ b/src/py/flwr/server/strategy/fedopt.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2021 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/strategy/fedprox.py b/src/py/flwr/server/strategy/fedprox.py index d20f578b193d..f15271e06060 100644 --- a/src/py/flwr/server/strategy/fedprox.py +++ b/src/py/flwr/server/strategy/fedprox.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2023 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/strategy/fedxgb_bagging.py b/src/py/flwr/server/strategy/fedxgb_bagging.py index a8e8adddafbb..a74ee81976a6 100644 --- a/src/py/flwr/server/strategy/fedxgb_bagging.py +++ b/src/py/flwr/server/strategy/fedxgb_bagging.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2023 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/strategy/fedxgb_cyclic.py b/src/py/flwr/server/strategy/fedxgb_cyclic.py index 2605daab29f4..75025a89728b 100644 --- a/src/py/flwr/server/strategy/fedxgb_cyclic.py +++ b/src/py/flwr/server/strategy/fedxgb_cyclic.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2023 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/strategy/fedxgb_nn_avg.py b/src/py/flwr/server/strategy/fedxgb_nn_avg.py index 8dedc925f350..4562663287ae 100644 --- a/src/py/flwr/server/strategy/fedxgb_nn_avg.py +++ b/src/py/flwr/server/strategy/fedxgb_nn_avg.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2023 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/strategy/fedyogi.py b/src/py/flwr/server/strategy/fedyogi.py index 7c77aab7ae73..c7b2ebb51667 100644 --- a/src/py/flwr/server/strategy/fedyogi.py +++ b/src/py/flwr/server/strategy/fedyogi.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2021 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/strategy/krum.py b/src/py/flwr/server/strategy/krum.py index 16eb5212940e..074d018c35a3 100644 --- a/src/py/flwr/server/strategy/krum.py +++ b/src/py/flwr/server/strategy/krum.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2022 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/strategy/krum_test.py b/src/py/flwr/server/strategy/krum_test.py index 653dc9a8475d..b34982325b39 100644 --- a/src/py/flwr/server/strategy/krum_test.py +++ b/src/py/flwr/server/strategy/krum_test.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2022 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/strategy/multikrum_test.py b/src/py/flwr/server/strategy/multikrum_test.py index f874dc2f9800..7a1a4c3ecf38 100644 --- a/src/py/flwr/server/strategy/multikrum_test.py +++ b/src/py/flwr/server/strategy/multikrum_test.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2022 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/strategy/qfedavg.py b/src/py/flwr/server/strategy/qfedavg.py index 758e8e608e9f..26a397d4cf8c 100644 --- a/src/py/flwr/server/strategy/qfedavg.py +++ b/src/py/flwr/server/strategy/qfedavg.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2021 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/superlink/driver/__init__.py b/src/py/flwr/server/superlink/driver/__init__.py index 2bfe63e6065f..58fbc479478f 100644 --- a/src/py/flwr/server/superlink/driver/__init__.py +++ b/src/py/flwr/server/superlink/driver/__init__.py @@ -1,4 +1,4 @@ -# Copyright 2022 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/superlink/driver/driver_grpc.py b/src/py/flwr/server/superlink/driver/driver_grpc.py index f74000bc59c4..782935481945 100644 --- a/src/py/flwr/server/superlink/driver/driver_grpc.py +++ b/src/py/flwr/server/superlink/driver/driver_grpc.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/superlink/driver/driver_servicer.py b/src/py/flwr/server/superlink/driver/driver_servicer.py index ce2d9d68d8ca..03128f02158e 100644 --- a/src/py/flwr/server/superlink/driver/driver_servicer.py +++ b/src/py/flwr/server/superlink/driver/driver_servicer.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -35,6 +35,11 @@ PushTaskInsResponse, ) from flwr.proto.node_pb2 import Node # pylint: disable=E0611 +from flwr.proto.run_pb2 import ( # pylint: disable=E0611 + GetRunRequest, + GetRunResponse, + Run, +) from flwr.proto.task_pb2 import TaskRes # pylint: disable=E0611 from flwr.server.superlink.state import State, StateFactory from flwr.server.utils.validator import validate_task_ins_or_res @@ -129,6 +134,20 @@ def on_rpc_done() -> None: context.set_code(grpc.StatusCode.OK) return PullTaskResResponse(task_res_list=task_res_list) + def GetRun( + self, request: GetRunRequest, context: grpc.ServicerContext + ) -> GetRunResponse: + """Get run information.""" + log(DEBUG, "DriverServicer.GetRun") + + # Init state + state: State = self.state_factory.state() + + # Retrieve run information + run = state.get_run(request.run_id) + run_proto = None if run is None else Run(**vars(run)) + return GetRunResponse(run=run_proto) + def _raise_if(validation_error: bool, detail: str) -> None: if validation_error: diff --git a/src/py/flwr/server/superlink/driver/driver_servicer_test.py b/src/py/flwr/server/superlink/driver/driver_servicer_test.py index 99f7cc007a89..394d6be7ee6a 100644 --- a/src/py/flwr/server/superlink/driver/driver_servicer_test.py +++ b/src/py/flwr/server/superlink/driver/driver_servicer_test.py @@ -1,4 +1,4 @@ -# Copyright 2022 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/superlink/fleet/__init__.py b/src/py/flwr/server/superlink/fleet/__init__.py index d3c3ef90163d..c236ed06ae1c 100644 --- a/src/py/flwr/server/superlink/fleet/__init__.py +++ b/src/py/flwr/server/superlink/fleet/__init__.py @@ -1,4 +1,4 @@ -# Copyright 2022 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/superlink/fleet/grpc_adapter/__init__.py b/src/py/flwr/server/superlink/fleet/grpc_adapter/__init__.py new file mode 100644 index 000000000000..cf875a1b9666 --- /dev/null +++ b/src/py/flwr/server/superlink/fleet/grpc_adapter/__init__.py @@ -0,0 +1,15 @@ +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Server-side part of the GrpcAdapter transport layer.""" diff --git a/src/py/flwr/server/superlink/fleet/grpc_adapter/grpc_adapter_servicer.py b/src/py/flwr/server/superlink/fleet/grpc_adapter/grpc_adapter_servicer.py new file mode 100644 index 000000000000..9325041061ac --- /dev/null +++ b/src/py/flwr/server/superlink/fleet/grpc_adapter/grpc_adapter_servicer.py @@ -0,0 +1,131 @@ +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Fleet API gRPC adapter servicer.""" + + +from logging import DEBUG, INFO +from typing import Callable, Type, TypeVar + +import grpc +from google.protobuf.message import Message as GrpcMessage + +from flwr.common.logger import log +from flwr.proto import grpcadapter_pb2_grpc # pylint: disable=E0611 +from flwr.proto.fleet_pb2 import ( # pylint: disable=E0611 + CreateNodeRequest, + CreateNodeResponse, + DeleteNodeRequest, + DeleteNodeResponse, + PingRequest, + PingResponse, + PullTaskInsRequest, + PullTaskInsResponse, + PushTaskResRequest, + PushTaskResResponse, +) +from flwr.proto.grpcadapter_pb2 import MessageContainer # pylint: disable=E0611 +from flwr.proto.run_pb2 import GetRunRequest, GetRunResponse # pylint: disable=E0611 +from flwr.server.superlink.fleet.message_handler import message_handler +from flwr.server.superlink.state import StateFactory + +T = TypeVar("T", bound=GrpcMessage) + + +def _handle( + msg_container: MessageContainer, + request_type: Type[T], + handler: Callable[[T], GrpcMessage], +) -> MessageContainer: + req = request_type.FromString(msg_container.grpc_message_content) + res = handler(req) + return MessageContainer( + metadata={}, + grpc_message_name=res.__class__.__qualname__, + grpc_message_content=res.SerializeToString(), + ) + + +class GrpcAdapterServicer(grpcadapter_pb2_grpc.GrpcAdapterServicer): + """Fleet API via GrpcAdapter servicer.""" + + def __init__(self, state_factory: StateFactory) -> None: + self.state_factory = state_factory + + def SendReceive( + self, request: MessageContainer, context: grpc.ServicerContext + ) -> MessageContainer: + """.""" + log(DEBUG, "GrpcAdapterServicer.SendReceive") + if request.grpc_message_name == CreateNodeRequest.__qualname__: + return _handle(request, CreateNodeRequest, self._create_node) + if request.grpc_message_name == DeleteNodeRequest.__qualname__: + return _handle(request, DeleteNodeRequest, self._delete_node) + if request.grpc_message_name == PingRequest.__qualname__: + return _handle(request, PingRequest, self._ping) + if request.grpc_message_name == PullTaskInsRequest.__qualname__: + return _handle(request, PullTaskInsRequest, self._pull_task_ins) + if request.grpc_message_name == PushTaskResRequest.__qualname__: + return _handle(request, PushTaskResRequest, self._push_task_res) + if request.grpc_message_name == GetRunRequest.__qualname__: + return _handle(request, GetRunRequest, self._get_run) + raise ValueError(f"Invalid grpc_message_name: {request.grpc_message_name}") + + def _create_node(self, request: CreateNodeRequest) -> CreateNodeResponse: + """.""" + log(INFO, "GrpcAdapter.CreateNode") + return message_handler.create_node( + request=request, + state=self.state_factory.state(), + ) + + def _delete_node(self, request: DeleteNodeRequest) -> DeleteNodeResponse: + """.""" + log(INFO, "GrpcAdapter.DeleteNode") + return message_handler.delete_node( + request=request, + state=self.state_factory.state(), + ) + + def _ping(self, request: PingRequest) -> PingResponse: + """.""" + log(DEBUG, "GrpcAdapter.Ping") + return message_handler.ping( + request=request, + state=self.state_factory.state(), + ) + + def _pull_task_ins(self, request: PullTaskInsRequest) -> PullTaskInsResponse: + """Pull TaskIns.""" + log(INFO, "GrpcAdapter.PullTaskIns") + return message_handler.pull_task_ins( + request=request, + state=self.state_factory.state(), + ) + + def _push_task_res(self, request: PushTaskResRequest) -> PushTaskResResponse: + """Push TaskRes.""" + log(INFO, "GrpcAdapter.PushTaskRes") + return message_handler.push_task_res( + request=request, + state=self.state_factory.state(), + ) + + def _get_run(self, request: GetRunRequest) -> GetRunResponse: + """Get run information.""" + log(INFO, "GrpcAdapter.GetRun") + return message_handler.get_run( + request=request, + state=self.state_factory.state(), + ) diff --git a/src/py/flwr/server/superlink/fleet/grpc_bidi/__init__.py b/src/py/flwr/server/superlink/fleet/grpc_bidi/__init__.py index bae8bc431edd..6b2c2bf3ffec 100644 --- a/src/py/flwr/server/superlink/fleet/grpc_bidi/__init__.py +++ b/src/py/flwr/server/superlink/fleet/grpc_bidi/__init__.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/superlink/fleet/grpc_bidi/flower_service_servicer.py b/src/py/flwr/server/superlink/fleet/grpc_bidi/flower_service_servicer.py index 6f94ea844e38..79f1a8f9902b 100644 --- a/src/py/flwr/server/superlink/fleet/grpc_bidi/flower_service_servicer.py +++ b/src/py/flwr/server/superlink/fleet/grpc_bidi/flower_service_servicer.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/superlink/fleet/grpc_bidi/flower_service_servicer_test.py b/src/py/flwr/server/superlink/fleet/grpc_bidi/flower_service_servicer_test.py index bd93554a6a32..03e8555f8ecf 100644 --- a/src/py/flwr/server/superlink/fleet/grpc_bidi/flower_service_servicer_test.py +++ b/src/py/flwr/server/superlink/fleet/grpc_bidi/flower_service_servicer_test.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_bridge.py b/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_bridge.py index d5b4a915c609..5fe0396696ab 100644 --- a/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_bridge.py +++ b/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_bridge.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_bridge_test.py b/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_bridge_test.py index f7c236acd7a1..f9b6b97030f0 100644 --- a/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_bridge_test.py +++ b/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_bridge_test.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_client_proxy.py b/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_client_proxy.py index ac62ad014950..03497743becd 100644 --- a/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_client_proxy.py +++ b/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_client_proxy.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_client_proxy_test.py b/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_client_proxy_test.py index e7077dfd39ae..6d3eb4f67e30 100644 --- a/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_client_proxy_test.py +++ b/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_client_proxy_test.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_server.py b/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_server.py index 6aeaa7ef413f..1b4286c87b92 100644 --- a/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_server.py +++ b/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_server.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -29,6 +29,9 @@ ) from flwr.server.client_manager import ClientManager from flwr.server.superlink.driver.driver_servicer import DriverServicer +from flwr.server.superlink.fleet.grpc_adapter.grpc_adapter_servicer import ( + GrpcAdapterServicer, +) from flwr.server.superlink.fleet.grpc_bidi.flower_service_servicer import ( FlowerServiceServicer, ) @@ -154,6 +157,7 @@ def start_grpc_server( # pylint: disable=too-many-arguments def generic_create_grpc_server( # pylint: disable=too-many-arguments servicer_and_add_fn: Union[ Tuple[FleetServicer, AddServicerToServerFn], + Tuple[GrpcAdapterServicer, AddServicerToServerFn], Tuple[FlowerServiceServicer, AddServicerToServerFn], Tuple[DriverServicer, AddServicerToServerFn], ], diff --git a/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_server_test.py b/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_server_test.py index 8afa37515950..7ff730b17afa 100644 --- a/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_server_test.py +++ b/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_server_test.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/superlink/fleet/grpc_rere/__init__.py b/src/py/flwr/server/superlink/fleet/grpc_rere/__init__.py index 61ab71d91400..03c8ded2423a 100644 --- a/src/py/flwr/server/superlink/fleet/grpc_rere/__init__.py +++ b/src/py/flwr/server/superlink/fleet/grpc_rere/__init__.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/superlink/fleet/grpc_rere/fleet_servicer.py b/src/py/flwr/server/superlink/fleet/grpc_rere/fleet_servicer.py index 03a2ec064213..89342a46eb48 100644 --- a/src/py/flwr/server/superlink/fleet/grpc_rere/fleet_servicer.py +++ b/src/py/flwr/server/superlink/fleet/grpc_rere/fleet_servicer.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -26,8 +26,6 @@ CreateNodeResponse, DeleteNodeRequest, DeleteNodeResponse, - GetRunRequest, - GetRunResponse, PingRequest, PingResponse, PullTaskInsRequest, @@ -35,6 +33,7 @@ PushTaskResRequest, PushTaskResResponse, ) +from flwr.proto.run_pb2 import GetRunRequest, GetRunResponse # pylint: disable=E0611 from flwr.server.superlink.fleet.message_handler import message_handler from flwr.server.superlink.state import StateFactory diff --git a/src/py/flwr/server/superlink/fleet/grpc_rere/server_interceptor.py b/src/py/flwr/server/superlink/fleet/grpc_rere/server_interceptor.py index 6a302679a235..21e9c44907cd 100644 --- a/src/py/flwr/server/superlink/fleet/grpc_rere/server_interceptor.py +++ b/src/py/flwr/server/superlink/fleet/grpc_rere/server_interceptor.py @@ -34,8 +34,6 @@ CreateNodeResponse, DeleteNodeRequest, DeleteNodeResponse, - GetRunRequest, - GetRunResponse, PingRequest, PingResponse, PullTaskInsRequest, @@ -44,6 +42,7 @@ PushTaskResResponse, ) from flwr.proto.node_pb2 import Node # pylint: disable=E0611 +from flwr.proto.run_pb2 import GetRunRequest, GetRunResponse # pylint: disable=E0611 from flwr.server.superlink.state import State _PUBLIC_KEY_HEADER = "public-key" diff --git a/src/py/flwr/server/superlink/fleet/grpc_rere/server_interceptor_test.py b/src/py/flwr/server/superlink/fleet/grpc_rere/server_interceptor_test.py index c4c71e5a8188..01499102b7d8 100644 --- a/src/py/flwr/server/superlink/fleet/grpc_rere/server_interceptor_test.py +++ b/src/py/flwr/server/superlink/fleet/grpc_rere/server_interceptor_test.py @@ -32,8 +32,6 @@ CreateNodeResponse, DeleteNodeRequest, DeleteNodeResponse, - GetRunRequest, - GetRunResponse, PingRequest, PingResponse, PullTaskInsRequest, @@ -42,6 +40,7 @@ PushTaskResResponse, ) from flwr.proto.node_pb2 import Node # pylint: disable=E0611 +from flwr.proto.run_pb2 import GetRunRequest, GetRunResponse # pylint: disable=E0611 from flwr.proto.task_pb2 import Task, TaskRes # pylint: disable=E0611 from flwr.server.app import ADDRESS_FLEET_API_GRPC_RERE, _run_fleet_api_grpc_rere from flwr.server.superlink.state.state_factory import StateFactory diff --git a/src/py/flwr/server/superlink/fleet/message_handler/__init__.py b/src/py/flwr/server/superlink/fleet/message_handler/__init__.py index 18b0f11fa6c5..3db0ef5d1611 100644 --- a/src/py/flwr/server/superlink/fleet/message_handler/__init__.py +++ b/src/py/flwr/server/superlink/fleet/message_handler/__init__.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/superlink/fleet/message_handler/message_handler.py b/src/py/flwr/server/superlink/fleet/message_handler/message_handler.py index 83b005a4cb8e..b70cd54035fe 100644 --- a/src/py/flwr/server/superlink/fleet/message_handler/message_handler.py +++ b/src/py/flwr/server/superlink/fleet/message_handler/message_handler.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -24,8 +24,6 @@ CreateNodeResponse, DeleteNodeRequest, DeleteNodeResponse, - GetRunRequest, - GetRunResponse, PingRequest, PingResponse, PullTaskInsRequest, @@ -33,9 +31,13 @@ PushTaskResRequest, PushTaskResResponse, Reconnect, - Run, ) from flwr.proto.node_pb2 import Node # pylint: disable=E0611 +from flwr.proto.run_pb2 import ( # pylint: disable=E0611 + GetRunRequest, + GetRunResponse, + Run, +) from flwr.proto.task_pb2 import TaskIns, TaskRes # pylint: disable=E0611 from flwr.server.superlink.state import State @@ -110,6 +112,6 @@ def get_run( request: GetRunRequest, state: State # pylint: disable=W0613 ) -> GetRunResponse: """Get run information.""" - run_id, fab_id, fab_version = state.get_run(request.run_id) - run = Run(run_id=run_id, fab_id=fab_id, fab_version=fab_version) - return GetRunResponse(run=run) + run = state.get_run(request.run_id) + run_proto = None if run is None else Run(**vars(run)) + return GetRunResponse(run=run_proto) diff --git a/src/py/flwr/server/superlink/fleet/message_handler/message_handler_test.py b/src/py/flwr/server/superlink/fleet/message_handler/message_handler_test.py index c135f6fb7b61..ec521b328eb8 100644 --- a/src/py/flwr/server/superlink/fleet/message_handler/message_handler_test.py +++ b/src/py/flwr/server/superlink/fleet/message_handler/message_handler_test.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/superlink/fleet/rest_rere/__init__.py b/src/py/flwr/server/superlink/fleet/rest_rere/__init__.py index a926f9ca0bfc..f24db2a2e12f 100644 --- a/src/py/flwr/server/superlink/fleet/rest_rere/__init__.py +++ b/src/py/flwr/server/superlink/fleet/rest_rere/__init__.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/superlink/fleet/rest_rere/rest_api.py b/src/py/flwr/server/superlink/fleet/rest_rere/rest_api.py index 8ac7c6cfc613..1ed67e5eb0aa 100644 --- a/src/py/flwr/server/superlink/fleet/rest_rere/rest_api.py +++ b/src/py/flwr/server/superlink/fleet/rest_rere/rest_api.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -21,11 +21,11 @@ from flwr.proto.fleet_pb2 import ( # pylint: disable=E0611 CreateNodeRequest, DeleteNodeRequest, - GetRunRequest, PingRequest, PullTaskInsRequest, PushTaskResRequest, ) +from flwr.proto.run_pb2 import GetRunRequest # pylint: disable=E0611 from flwr.server.superlink.fleet.message_handler import message_handler from flwr.server.superlink.state import State diff --git a/src/py/flwr/server/superlink/fleet/vce/backend/raybackend.py b/src/py/flwr/server/superlink/fleet/vce/backend/raybackend.py index 93aca583af9c..8a21393db590 100644 --- a/src/py/flwr/server/superlink/fleet/vce/backend/raybackend.py +++ b/src/py/flwr/server/superlink/fleet/vce/backend/raybackend.py @@ -15,7 +15,7 @@ """Ray backend for the Fleet API using the Simulation Engine.""" import pathlib -from logging import DEBUG, ERROR, WARNING +from logging import DEBUG, ERROR from typing import Callable, Dict, List, Tuple, Union import ray @@ -24,16 +24,15 @@ from flwr.common.context import Context from flwr.common.logger import log from flwr.common.message import Message -from flwr.simulation.ray_transport.ray_actor import ( - BasicActorPool, - ClientAppActor, - init_ray, -) +from flwr.common.typing import ConfigsRecordValues +from flwr.simulation.ray_transport.ray_actor import BasicActorPool, ClientAppActor from flwr.simulation.ray_transport.utils import enable_tf_gpu_growth from .backend import Backend, BackendConfig ClientResourcesDict = Dict[str, Union[int, float]] +ActorArgsDict = Dict[str, Union[int, float, Callable[[], None]]] +RunTimeEnvDict = Dict[str, Union[str, List[str]]] class RayBackend(Backend): @@ -51,40 +50,29 @@ def __init__( if not pathlib.Path(work_dir).exists(): raise ValueError(f"Specified work_dir {work_dir} does not exist.") - # Init ray and append working dir if needed - runtime_env = ( - self._configure_runtime_env(work_dir=work_dir) if work_dir else None - ) - - if backend_config.get("mute_logging", False): - init_ray( - logging_level=WARNING, log_to_driver=False, runtime_env=runtime_env - ) - elif backend_config.get("silent", False): - init_ray(logging_level=WARNING, log_to_driver=True, runtime_env=runtime_env) - else: - init_ray(runtime_env=runtime_env) + # Initialise ray + self.init_args_key = "init_args" + self.init_ray(backend_config, work_dir) # Validate client resources self.client_resources_key = "client_resources" + client_resources = self._validate_client_resources(config=backend_config) # Create actor pool - use_tf = backend_config.get("tensorflow", False) - actor_kwargs = {"on_actor_init_fn": enable_tf_gpu_growth} if use_tf else {} + actor_kwargs = self._validate_actor_arguments(config=backend_config) - client_resources = self._validate_client_resources(config=backend_config) self.pool = BasicActorPool( actor_type=ClientAppActor, client_resources=client_resources, actor_kwargs=actor_kwargs, ) - def _configure_runtime_env(self, work_dir: str) -> Dict[str, Union[str, List[str]]]: + def _configure_runtime_env(self, work_dir: str) -> RunTimeEnvDict: """Return list of files/subdirectories to exclude relative to work_dir. Without this, Ray will push everything to the Ray Cluster. """ - runtime_env: Dict[str, Union[str, List[str]]] = {"working_dir": work_dir} + runtime_env: RunTimeEnvDict = {"working_dir": work_dir} excludes = [] path = pathlib.Path(work_dir) @@ -125,6 +113,37 @@ def _validate_client_resources(self, config: BackendConfig) -> ClientResourcesDi return client_resources + def _validate_actor_arguments(self, config: BackendConfig) -> ActorArgsDict: + actor_args_config = config.get("actor", False) + actor_args: ActorArgsDict = {} + if actor_args_config: + use_tf = actor_args.get("tensorflow", False) + if use_tf: + actor_args["on_actor_init_fn"] = enable_tf_gpu_growth + return actor_args + + def init_ray(self, backend_config: BackendConfig, work_dir: str) -> None: + """Intialises Ray if not already initialised.""" + if not ray.is_initialized(): + # Init ray and append working dir if needed + runtime_env = ( + self._configure_runtime_env(work_dir=work_dir) if work_dir else None + ) + + ray_init_args: Dict[ + str, + Union[ConfigsRecordValues, RunTimeEnvDict], + ] = {} + + if backend_config.get(self.init_args_key): + for k, v in backend_config[self.init_args_key].items(): + ray_init_args[k] = v + + if runtime_env is not None: + ray_init_args["runtime_env"] = runtime_env + + ray.init(**ray_init_args) + @property def num_workers(self) -> int: """Return number of actors in pool.""" @@ -152,7 +171,7 @@ async def process_message( partition_id = message.metadata.partition_id try: - # Submite a task to the pool + # Submit a task to the pool future = await self.pool.submit( lambda a, a_fn, mssg, cid, state: a.run.remote(a_fn, mssg, cid, state), (app, message, str(partition_id), context), diff --git a/src/py/flwr/server/superlink/fleet/vce/backend/raybackend_test.py b/src/py/flwr/server/superlink/fleet/vce/backend/raybackend_test.py index dcac0b81d666..57c952cc9310 100644 --- a/src/py/flwr/server/superlink/fleet/vce/backend/raybackend_test.py +++ b/src/py/flwr/server/superlink/fleet/vce/backend/raybackend_test.py @@ -38,6 +38,7 @@ ) from flwr.common.object_ref import load_app from flwr.common.recordset_compat import getpropertiesins_to_recordset +from flwr.server.superlink.fleet.vce.backend.backend import BackendConfig from flwr.server.superlink.fleet.vce.backend.raybackend import RayBackend @@ -215,3 +216,35 @@ def test_backend_creation_submit_and_termination_existing_client_app_unsetworkdi workdir="/?&%$^#%@$!", ) self.addAsyncCleanup(self.on_cleanup) + + def test_backend_creation_with_init_arguments(self) -> None: + """Testing whether init args are properly parsed to Ray.""" + backend_config_4: BackendConfig = { + "init_args": {"num_cpus": 4}, + "client_resources": {"num_cpus": 1, "num_gpus": 0}, + } + + backend_config_2: BackendConfig = { + "init_args": {"num_cpus": 2}, + "client_resources": {"num_cpus": 1, "num_gpus": 0}, + } + + RayBackend( + backend_config=backend_config_4, + work_dir="", + ) + nodes = ray.nodes() + + assert nodes[0]["Resources"]["CPU"] == backend_config_4["init_args"]["num_cpus"] + + ray.shutdown() + + RayBackend( + backend_config=backend_config_2, + work_dir="", + ) + nodes = ray.nodes() + + assert nodes[0]["Resources"]["CPU"] == backend_config_2["init_args"]["num_cpus"] + + self.addAsyncCleanup(self.on_cleanup) diff --git a/src/py/flwr/server/superlink/fleet/vce/vce_api.py b/src/py/flwr/server/superlink/fleet/vce/vce_api.py index cc3e85b28097..3c9628a6d2a3 100644 --- a/src/py/flwr/server/superlink/fleet/vce/vce_api.py +++ b/src/py/flwr/server/superlink/fleet/vce/vce_api.py @@ -20,6 +20,7 @@ import time import traceback from logging import DEBUG, ERROR, INFO, WARN +from pathlib import Path from typing import Callable, Dict, List, Optional from flwr.client.client_app import ClientApp, ClientAppException, LoadClientAppError @@ -274,6 +275,7 @@ def start_vce( # Use mapping constructed externally. This also means nodes # have previously being registered. nodes_mapping = existing_nodes_mapping + app_dir = str(Path(app_dir).absolute()) if not state_factory: log(INFO, "A StateFactory was not supplied to the SimulationEngine.") @@ -323,7 +325,7 @@ def _load() -> ClientApp: if app_dir is not None: sys.path.insert(0, app_dir) - app: ClientApp = load_app(client_app_attr, LoadClientAppError) + app: ClientApp = load_app(client_app_attr, LoadClientAppError, app_dir) if not isinstance(app, ClientApp): raise LoadClientAppError( diff --git a/src/py/flwr/server/superlink/fleet/vce/vce_api_test.py b/src/py/flwr/server/superlink/fleet/vce/vce_api_test.py index 1da726f88f1e..df9f2cc96f95 100644 --- a/src/py/flwr/server/superlink/fleet/vce/vce_api_test.py +++ b/src/py/flwr/server/superlink/fleet/vce/vce_api_test.py @@ -37,6 +37,7 @@ ) from flwr.common.recordset_compat import getpropertiesins_to_recordset from flwr.common.serde import message_from_taskres, message_to_taskins +from flwr.common.typing import Run from flwr.server.superlink.fleet.vce.vce_api import ( NodeToPartitionMapping, _register_nodes, @@ -81,7 +82,7 @@ def register_messages_into_state( ) -> Dict[UUID, float]: """Register `num_messages` into the state factory.""" state: InMemoryState = state_factory.state() # type: ignore - state.run_ids[run_id] = ("Mock/mock", "v1.0.0") + state.run_ids[run_id] = Run(run_id=run_id, fab_id="Mock/mock", fab_version="v1.0.0") # Artificially add TaskIns to state so they can be processed # by the Simulation Engine logic nodes_cycle = cycle(nodes_mapping.keys()) # we have more messages than supernodes diff --git a/src/py/flwr/server/superlink/state/__init__.py b/src/py/flwr/server/superlink/state/__init__.py index 7f260d733bbe..9d3bd220403b 100644 --- a/src/py/flwr/server/superlink/state/__init__.py +++ b/src/py/flwr/server/superlink/state/__init__.py @@ -1,4 +1,4 @@ -# Copyright 2023 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/superlink/state/in_memory_state.py b/src/py/flwr/server/superlink/state/in_memory_state.py index f86bf79d9dfa..da9c754c3115 100644 --- a/src/py/flwr/server/superlink/state/in_memory_state.py +++ b/src/py/flwr/server/superlink/state/in_memory_state.py @@ -1,4 +1,4 @@ -# Copyright 2023 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -23,6 +23,7 @@ from uuid import UUID, uuid4 from flwr.common import log, now +from flwr.common.typing import Run from flwr.proto.task_pb2 import TaskIns, TaskRes # pylint: disable=E0611 from flwr.server.superlink.state.state import State from flwr.server.utils import validate_task_ins_or_res @@ -40,7 +41,7 @@ def __init__(self) -> None: self.public_key_to_node_id: Dict[bytes, int] = {} # Map run_id to (fab_id, fab_version) - self.run_ids: Dict[int, Tuple[str, str]] = {} + self.run_ids: Dict[int, Run] = {} self.task_ins_store: Dict[UUID, TaskIns] = {} self.task_res_store: Dict[UUID, TaskRes] = {} @@ -281,7 +282,9 @@ def create_run(self, fab_id: str, fab_version: str) -> int: run_id: int = int.from_bytes(os.urandom(8), "little", signed=True) if run_id not in self.run_ids: - self.run_ids[run_id] = (fab_id, fab_version) + self.run_ids[run_id] = Run( + run_id=run_id, fab_id=fab_id, fab_version=fab_version + ) return run_id log(ERROR, "Unexpected run creation failure.") return 0 @@ -319,13 +322,13 @@ def get_client_public_keys(self) -> Set[bytes]: """Retrieve all currently stored `client_public_keys` as a set.""" return self.client_public_keys - def get_run(self, run_id: int) -> Tuple[int, str, str]: + def get_run(self, run_id: int) -> Optional[Run]: """Retrieve information about the run with the specified `run_id`.""" with self.lock: if run_id not in self.run_ids: log(ERROR, "`run_id` is invalid") - return 0, "", "" - return run_id, *self.run_ids[run_id] + return None + return self.run_ids[run_id] def acknowledge_ping(self, node_id: int, ping_interval: float) -> bool: """Acknowledge a ping received from a node, serving as a heartbeat.""" diff --git a/src/py/flwr/server/superlink/state/sqlite_state.py b/src/py/flwr/server/superlink/state/sqlite_state.py index acf2054f08b6..4df9470ded62 100644 --- a/src/py/flwr/server/superlink/state/sqlite_state.py +++ b/src/py/flwr/server/superlink/state/sqlite_state.py @@ -1,4 +1,4 @@ -# Copyright 2023 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -24,6 +24,7 @@ from uuid import UUID, uuid4 from flwr.common import log, now +from flwr.common.typing import Run from flwr.proto.node_pb2 import Node # pylint: disable=E0611 from flwr.proto.recordset_pb2 import RecordSet # pylint: disable=E0611 from flwr.proto.task_pb2 import Task, TaskIns, TaskRes # pylint: disable=E0611 @@ -680,15 +681,17 @@ def get_client_public_keys(self) -> Set[bytes]: result: Set[bytes] = {row["public_key"] for row in rows} return result - def get_run(self, run_id: int) -> Tuple[int, str, str]: + def get_run(self, run_id: int) -> Optional[Run]: """Retrieve information about the run with the specified `run_id`.""" query = "SELECT * FROM run WHERE run_id = ?;" try: row = self.query(query, (run_id,))[0] - return run_id, row["fab_id"], row["fab_version"] + return Run( + run_id=run_id, fab_id=row["fab_id"], fab_version=row["fab_version"] + ) except sqlite3.IntegrityError: log(ERROR, "`run_id` does not exist.") - return 0, "", "" + return None def acknowledge_ping(self, node_id: int, ping_interval: float) -> bool: """Acknowledge a ping received from a node, serving as a heartbeat.""" diff --git a/src/py/flwr/server/superlink/state/sqlite_state_test.py b/src/py/flwr/server/superlink/state/sqlite_state_test.py index 20927df1cf12..10e12da96bd5 100644 --- a/src/py/flwr/server/superlink/state/sqlite_state_test.py +++ b/src/py/flwr/server/superlink/state/sqlite_state_test.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/superlink/state/state.py b/src/py/flwr/server/superlink/state/state.py index a72062f2a938..65e2c63cab69 100644 --- a/src/py/flwr/server/superlink/state/state.py +++ b/src/py/flwr/server/superlink/state/state.py @@ -1,4 +1,4 @@ -# Copyright 2022 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -16,9 +16,10 @@ import abc -from typing import List, Optional, Set, Tuple +from typing import List, Optional, Set from uuid import UUID +from flwr.common.typing import Run from flwr.proto.task_pb2 import TaskIns, TaskRes # pylint: disable=E0611 @@ -160,7 +161,7 @@ def create_run(self, fab_id: str, fab_version: str) -> int: """Create a new run for the specified `fab_id` and `fab_version`.""" @abc.abstractmethod - def get_run(self, run_id: int) -> Tuple[int, str, str]: + def get_run(self, run_id: int) -> Optional[Run]: """Retrieve information about the run with the specified `run_id`. Parameters @@ -170,8 +171,8 @@ def get_run(self, run_id: int) -> Tuple[int, str, str]: Returns ------- - Tuple[int, str, str] - A tuple containing three elements: + Optional[Run] + A dataclass instance containing three elements if `run_id` is valid: - `run_id`: The identifier of the run, same as the specified `run_id`. - `fab_id`: The identifier of the FAB used in the specified run. - `fab_version`: The version of the FAB used in the specified run. diff --git a/src/py/flwr/server/superlink/state/state_factory.py b/src/py/flwr/server/superlink/state/state_factory.py index 62a00d910828..96c8d445c16e 100644 --- a/src/py/flwr/server/superlink/state/state_factory.py +++ b/src/py/flwr/server/superlink/state/state_factory.py @@ -1,4 +1,4 @@ -# Copyright 2022 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -26,7 +26,16 @@ class StateFactory: - """Factory class that creates State instances.""" + """Factory class that creates State instances. + + Parameters + ---------- + database : str + A string representing the path to the database file that will be opened. + Note that passing ':memory:' will open a connection to a database that is + in RAM, instead of on disk. For more information on special in-memory + databases, please refer to https://sqlite.org/inmemorydb.html. + """ def __init__(self, database: str) -> None: self.database = database diff --git a/src/py/flwr/server/superlink/state/state_test.py b/src/py/flwr/server/superlink/state/state_test.py index 9b0153ca548a..373202d5cde6 100644 --- a/src/py/flwr/server/superlink/state/state_test.py +++ b/src/py/flwr/server/superlink/state/state_test.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -55,12 +55,13 @@ def test_create_and_get_run(self) -> None: run_id = state.create_run("Mock/mock", "v1.0.0") # Execute - actual_run_id, fab_id, fab_version = state.get_run(run_id) + run = state.get_run(run_id) # Assert - assert actual_run_id == run_id - assert fab_id == "Mock/mock" - assert fab_version == "v1.0.0" + assert run is not None + assert run.run_id == run_id + assert run.fab_id == "Mock/mock" + assert run.fab_version == "v1.0.0" def test_get_task_ins_empty(self) -> None: """Validate that a new state has no TaskIns.""" diff --git a/src/py/flwr/server/utils/__init__.py b/src/py/flwr/server/utils/__init__.py index c370716adaac..8994374c4d08 100644 --- a/src/py/flwr/server/utils/__init__.py +++ b/src/py/flwr/server/utils/__init__.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2021 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/utils/tensorboard.py b/src/py/flwr/server/utils/tensorboard.py index 3e8d1e62411e..5d38fc159657 100644 --- a/src/py/flwr/server/utils/tensorboard.py +++ b/src/py/flwr/server/utils/tensorboard.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2021 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/server/utils/tensorboard_test.py b/src/py/flwr/server/utils/tensorboard_test.py index 1827a42cf6e6..689755c6da16 100644 --- a/src/py/flwr/server/utils/tensorboard_test.py +++ b/src/py/flwr/server/utils/tensorboard_test.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2021 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/simulation/__init__.py b/src/py/flwr/simulation/__init__.py index 57b0b01eb319..5db90a352e3f 100644 --- a/src/py/flwr/simulation/__init__.py +++ b/src/py/flwr/simulation/__init__.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2021 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -36,4 +36,7 @@ def start_simulation(*args, **kwargs): # type: ignore raise ImportError(RAY_IMPORT_ERROR) -__all__ = ["start_simulation", "run_simulation"] +__all__ = [ + "run_simulation", + "start_simulation", +] diff --git a/src/py/flwr/simulation/app.py b/src/py/flwr/simulation/app.py index 4b4b7249ccd3..856d6fc45e22 100644 --- a/src/py/flwr/simulation/app.py +++ b/src/py/flwr/simulation/app.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2021 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/simulation/ray_transport/__init__.py b/src/py/flwr/simulation/ray_transport/__init__.py index 0e82b75bb4b3..ed4971935a15 100644 --- a/src/py/flwr/simulation/ray_transport/__init__.py +++ b/src/py/flwr/simulation/ray_transport/__init__.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2021 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py index 9caf0fc3e6c0..7afffb865334 100644 --- a/src/py/flwr/simulation/ray_transport/ray_actor.py +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -399,12 +399,6 @@ def get_client_result( return self._fetch_future_result(cid) -def init_ray(*args: Any, **kwargs: Any) -> None: - """Intialises Ray if not already initialised.""" - if not ray.is_initialized(): - ray.init(*args, **kwargs) - - class BasicActorPool: """A basic actor pool.""" diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py index 5e344eb087ee..d3d103bb377a 100644 --- a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py +++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py @@ -1,4 +1,4 @@ -# Copyright 2020 Flower Labs GmbH. All Rights Reserved. +# Copyright 2021 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/simulation/run_simulation.py b/src/py/flwr/simulation/run_simulation.py index 2dbeef1a261c..7c7a412a245b 100644 --- a/src/py/flwr/simulation/run_simulation.py +++ b/src/py/flwr/simulation/run_simulation.py @@ -22,16 +22,17 @@ import traceback from logging import DEBUG, ERROR, INFO, WARNING from time import sleep -from typing import Dict, Optional +from typing import Optional from flwr.client import ClientApp from flwr.common import EventType, event, log from flwr.common.logger import set_logger_propagation, update_console_handler -from flwr.common.typing import ConfigsRecordValues +from flwr.common.typing import Run from flwr.server.driver import Driver, InMemoryDriver from flwr.server.run_serverapp import run from flwr.server.server_app import ServerApp from flwr.server.superlink.fleet import vce +from flwr.server.superlink.fleet.vce.backend.backend import BackendConfig from flwr.server.superlink.state import StateFactory from flwr.simulation.ray_transport.utils import ( enable_tf_gpu_growth as enable_gpu_growth, @@ -53,6 +54,7 @@ def run_simulation_from_cli() -> None: backend_name=args.backend, backend_config=backend_config_dict, app_dir=args.app_dir, + run_id=args.run_id, enable_tf_gpu_growth=args.enable_tf_gpu_growth, verbose_logging=args.verbose, ) @@ -65,7 +67,7 @@ def run_simulation( client_app: ClientApp, num_supernodes: int, backend_name: str = "ray", - backend_config: Optional[Dict[str, ConfigsRecordValues]] = None, + backend_config: Optional[BackendConfig] = None, enable_tf_gpu_growth: bool = False, verbose_logging: bool = False, ) -> None: @@ -89,9 +91,12 @@ def run_simulation( backend_name : str (default: ray) A simulation backend that runs `ClientApp`s. - backend_config : Optional[Dict[str, ConfigsRecordValues]] - 'A dictionary, e.g {"": , "": } to configure a - backend. Values supported in are those included by + backend_config : Optional[BackendConfig] + 'A dictionary to configure a backend. Separate dictionaries to configure + different elements of backend. Supported top-level keys are `init_args` + for values parsed to initialisation of backend, `client_resources` + to define the resources for clients, and `actor` to define the actor + parameters. Values supported in are those included by `flwr.common.typing.ConfigsRecordValues`. enable_tf_gpu_growth : bool (default: False) @@ -103,7 +108,7 @@ def run_simulation( works in the TensorFlow documentation: https://www.tensorflow.org/api/stable. verbose_logging : bool (default: False) - When diabled, only INFO, WARNING and ERROR log messages will be shown. If + When disabled, only INFO, WARNING and ERROR log messages will be shown. If enabled, DEBUG-level logs will be displayed. """ _run_simulation( @@ -132,7 +137,7 @@ def run_serverapp_th( def server_th_with_start_checks( # type: ignore tf_gpu_growth: bool, stop_event: asyncio.Event, **kwargs ) -> None: - """Run SeverApp, after check if GPU memory grouwth has to be set. + """Run SeverApp, after check if GPU memory growth has to be set. Upon exception, trigger stop event for Simulation Engine. """ @@ -168,6 +173,16 @@ def server_th_with_start_checks( # type: ignore return serverapp_th +def _override_run_id(state: StateFactory, run_id_to_replace: int, run_id: int) -> None: + """Override the run_id of an existing Run.""" + log(DEBUG, "Pre-registering run with id %s", run_id) + # Remove run + run_info: Run = state.state().run_ids.pop(run_id_to_replace) # type: ignore + # Update with new run_id and insert back in state + run_info.run_id = run_id + state.state().run_ids[run_id] = run_info # type: ignore + + # pylint: disable=too-many-locals def _main_loop( num_supernodes: int, @@ -175,6 +190,7 @@ def _main_loop( backend_config_stream: str, app_dir: str, enable_tf_gpu_growth: bool, + run_id: Optional[int] = None, client_app: Optional[ClientApp] = None, client_app_attr: Optional[str] = None, server_app: Optional[ServerApp] = None, @@ -182,7 +198,7 @@ def _main_loop( ) -> None: """Launch SuperLink with Simulation Engine, then ServerApp on a separate thread. - Everything runs on the main thread or a separate one, depening on whether the main + Everything runs on the main thread or a separate one, depending on whether the main thread already contains a running Asyncio event loop. This is the case if running the Simulation Engine on a Jupyter/Colab notebook. """ @@ -192,8 +208,15 @@ def _main_loop( f_stop = asyncio.Event() serverapp_th = None try: + # Create run (with empty fab_id and fab_version) + run_id_ = state_factory.state().create_run("", "") + + if run_id: + _override_run_id(state_factory, run_id_to_replace=run_id_, run_id=run_id) + run_id_ = run_id + # Initialize Driver - driver = InMemoryDriver(state_factory) + driver = InMemoryDriver(run_id=run_id_, state_factory=state_factory) # Get and run ServerApp thread serverapp_th = run_serverapp_th( @@ -240,10 +263,11 @@ def _run_simulation( client_app: Optional[ClientApp] = None, server_app: Optional[ServerApp] = None, backend_name: str = "ray", - backend_config: Optional[Dict[str, ConfigsRecordValues]] = None, + backend_config: Optional[BackendConfig] = None, client_app_attr: Optional[str] = None, server_app_attr: Optional[str] = None, app_dir: str = "", + run_id: Optional[int] = None, enable_tf_gpu_growth: bool = False, verbose_logging: bool = False, ) -> None: @@ -266,9 +290,12 @@ def _run_simulation( backend_name : str (default: ray) A simulation backend that runs `ClientApp`s. - backend_config : Optional[Dict[str, ConfigsRecordValues]] - 'A dictionary, e.g {"":, "":} to configure a - backend. Values supported in are those included by + backend_config : Optional[BackendConfig] + 'A dictionary to configure a backend. Separate dictionaries to configure + different elements of backend. Supported top-level keys are `init_args` + for values parsed to initialisation of backend, `client_resources` + to define the resources for clients, and `actor` to define the actor + parameters. Values supported in are those included by `flwr.common.typing.ConfigsRecordValues`. client_app_attr : str @@ -283,34 +310,41 @@ def _run_simulation( Add specified directory to the PYTHONPATH and load `ClientApp` from there. (Default: current working directory.) + run_id : Optional[int] + An integer specifying the ID of the run started when running this function. + enable_tf_gpu_growth : bool (default: False) A boolean to indicate whether to enable GPU growth on the main thread. This is desirable if you make use of a TensorFlow model on your `ServerApp` while having your `ClientApp` running on the same GPU. Without enabling this, you - might encounter an out-of-memory error becasue TensorFlow by default allocates + might encounter an out-of-memory error because TensorFlow by default allocates all GPU memory. Read mor about how `tf.config.experimental.set_memory_growth()` works in the TensorFlow documentation: https://www.tensorflow.org/api/stable. verbose_logging : bool (default: False) - When diabled, only INFO, WARNING and ERROR log messages will be shown. If + When disabled, only INFO, WARNING and ERROR log messages will be shown. If enabled, DEBUG-level logs will be displayed. """ if backend_config is None: backend_config = {} + if "init_args" not in backend_config: + backend_config["init_args"] = {} + # Set logging level logger = logging.getLogger("flwr") if verbose_logging: update_console_handler(level=DEBUG, timestamps=True, colored=True) else: - backend_config["silent"] = True + backend_config["init_args"]["logging_level"] = WARNING + backend_config["init_args"]["log_to_driver"] = True if enable_tf_gpu_growth: # Check that Backend config has also enabled using GPU growth - use_tf = backend_config.get("tensorflow", False) + use_tf = backend_config.get("actor", {}).get("tensorflow", False) if not use_tf: log(WARNING, "Enabling GPU growth for your backend.") - backend_config["tensorflow"] = True + backend_config["actor"]["tensorflow"] = True # Convert config to original JSON-stream format backend_config_stream = json.dumps(backend_config) @@ -322,13 +356,14 @@ def _run_simulation( backend_config_stream, app_dir, enable_tf_gpu_growth, + run_id, client_app, client_app_attr, server_app, server_app_attr, ) # Detect if there is an Asyncio event loop already running. - # If yes, run everything on a separate thread. In environmnets + # If yes, run everything on a separate thread. In environments # like Jupyter/Colab notebooks, there is an event loop present. run_in_thread = False try: @@ -340,7 +375,7 @@ def _run_simulation( run_in_thread = True except RuntimeError: - log(DEBUG, "No asyncio event loop runnig") + log(DEBUG, "No asyncio event loop running") finally: if run_in_thread: @@ -385,7 +420,8 @@ def _parse_args_run_simulation() -> argparse.ArgumentParser: parser.add_argument( "--backend-config", type=str, - default='{"client_resources": {"num_cpus":2, "num_gpus":0.0}, "tensorflow": 0}', + default='{"client_resources": {"num_cpus":2, "num_gpus":0.0},' + '"actor": {"tensorflow": 0}}', help='A JSON formatted stream, e.g \'{"":, "":}\' to ' "configure a backend. Values supported in are those included by " "`flwr.common.typing.ConfigsRecordValues`. ", @@ -413,5 +449,10 @@ def _parse_args_run_simulation() -> argparse.ArgumentParser: "ClientApp and ServerApp from there." " Default: current working directory.", ) + parser.add_argument( + "--run-id", + type=int, + help="Sets the ID of the run started by the Simulation Engine.", + ) return parser diff --git a/src/py/flwr/superexec/__init__.py b/src/py/flwr/superexec/__init__.py new file mode 100644 index 000000000000..a510c41f4182 --- /dev/null +++ b/src/py/flwr/superexec/__init__.py @@ -0,0 +1,21 @@ +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Flower SuperExec service.""" + +from .app import run_superexec as run_superexec + +__all__ = [ + "run_superexec", +] diff --git a/src/py/flwr/superexec/app.py b/src/py/flwr/superexec/app.py new file mode 100644 index 000000000000..fa89e83b5e75 --- /dev/null +++ b/src/py/flwr/superexec/app.py @@ -0,0 +1,178 @@ +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Flower SuperExec app.""" + +import argparse +import sys +from logging import INFO, WARN +from pathlib import Path +from typing import Optional, Tuple + +import grpc + +from flwr.common import EventType, event, log +from flwr.common.address import parse_address +from flwr.common.constant import SUPEREXEC_DEFAULT_ADDRESS +from flwr.common.exit_handlers import register_exit_handlers +from flwr.common.object_ref import load_app, validate + +from .exec_grpc import run_superexec_api_grpc +from .executor import Executor + + +def run_superexec() -> None: + """Run Flower SuperExec.""" + log(INFO, "Starting Flower SuperExec") + + event(EventType.RUN_SUPEREXEC_ENTER) + + args = _parse_args_run_superexec().parse_args() + + # Parse IP address + parsed_address = parse_address(args.address) + if not parsed_address: + sys.exit(f"SuperExec IP address ({args.address}) cannot be parsed.") + host, port, is_v6 = parsed_address + address = f"[{host}]:{port}" if is_v6 else f"{host}:{port}" + + # Obtain certificates + certificates = _try_obtain_certificates(args) + + # Start SuperExec API + superexec_server: grpc.Server = run_superexec_api_grpc( + address=address, + executor=_load_executor(args), + certificates=certificates, + ) + + grpc_servers = [superexec_server] + + # Graceful shutdown + register_exit_handlers( + event_type=EventType.RUN_SUPEREXEC_LEAVE, + grpc_servers=grpc_servers, + bckg_threads=None, + ) + + superexec_server.wait_for_termination() + + +def _parse_args_run_superexec() -> argparse.ArgumentParser: + """Parse command line arguments for SuperExec.""" + parser = argparse.ArgumentParser( + description="Start a Flower SuperExec", + ) + parser.add_argument( + "executor", + help="For example: `deployment:exec` or `project.package.module:wrapper.exec`.", + ) + parser.add_argument( + "--address", + help="SuperExec (gRPC) server address (IPv4, IPv6, or a domain name)", + default=SUPEREXEC_DEFAULT_ADDRESS, + ) + parser.add_argument( + "--executor-dir", + help="The directory for the executor.", + default=".", + ) + parser.add_argument( + "--insecure", + action="store_true", + help="Run the SuperExec without HTTPS, regardless of whether certificate " + "paths are provided. By default, the server runs with HTTPS enabled. " + "Use this flag only if you understand the risks.", + ) + parser.add_argument( + "--ssl-certfile", + help="SuperExec server SSL certificate file (as a path str) " + "to create a secure connection.", + type=str, + default=None, + ) + parser.add_argument( + "--ssl-keyfile", + help="SuperExec server SSL private key file (as a path str) " + "to create a secure connection.", + type=str, + ) + parser.add_argument( + "--ssl-ca-certfile", + help="SuperExec server SSL CA certificate file (as a path str) " + "to create a secure connection.", + type=str, + ) + return parser + + +def _try_obtain_certificates( + args: argparse.Namespace, +) -> Optional[Tuple[bytes, bytes, bytes]]: + # Obtain certificates + if args.insecure: + log(WARN, "Option `--insecure` was set. Starting insecure HTTP server.") + return None + # Check if certificates are provided + if args.ssl_certfile and args.ssl_keyfile and args.ssl_ca_certfile: + if not Path.is_file(args.ssl_ca_certfile): + sys.exit("Path argument `--ssl-ca-certfile` does not point to a file.") + if not Path.is_file(args.ssl_certfile): + sys.exit("Path argument `--ssl-certfile` does not point to a file.") + if not Path.is_file(args.ssl_keyfile): + sys.exit("Path argument `--ssl-keyfile` does not point to a file.") + certificates = ( + Path(args.ssl_ca_certfile).read_bytes(), # CA certificate + Path(args.ssl_certfile).read_bytes(), # server certificate + Path(args.ssl_keyfile).read_bytes(), # server private key + ) + return certificates + if args.ssl_certfile or args.ssl_keyfile or args.ssl_ca_certfile: + sys.exit( + "You need to provide valid file paths to `--ssl-certfile`, " + "`--ssl-keyfile`, and `—-ssl-ca-certfile` to create a secure " + "connection in SuperExec server (gRPC-rere)." + ) + sys.exit( + "Certificates are required unless running in insecure mode. " + "Please provide certificate paths to `--ssl-certfile`, " + "`--ssl-keyfile`, and `—-ssl-ca-certfile` or run the server " + "in insecure mode using '--insecure' if you understand the risks." + ) + + +def _load_executor( + args: argparse.Namespace, +) -> Executor: + """Get the executor plugin.""" + if args.executor_dir is not None: + sys.path.insert(0, args.executor_dir) + + executor_ref: str = args.executor + valid, error_msg = validate(executor_ref) + if not valid and error_msg: + raise LoadExecutorError(error_msg) from None + + executor = load_app(executor_ref, LoadExecutorError, args.executor_dir) + + if not isinstance(executor, Executor): + raise LoadExecutorError( + f"Attribute {executor_ref} is not of type {Executor}", + ) from None + + return executor + + +class LoadExecutorError(Exception): + """Error when trying to load `Executor`.""" diff --git a/src/py/flwr/superexec/exec_grpc.py b/src/py/flwr/superexec/exec_grpc.py new file mode 100644 index 000000000000..127d5615dd84 --- /dev/null +++ b/src/py/flwr/superexec/exec_grpc.py @@ -0,0 +1,51 @@ +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""SuperExec gRPC API.""" + +from logging import INFO +from typing import Optional, Tuple + +import grpc + +from flwr.common import GRPC_MAX_MESSAGE_LENGTH +from flwr.common.logger import log +from flwr.proto.exec_pb2_grpc import add_ExecServicer_to_server +from flwr.server.superlink.fleet.grpc_bidi.grpc_server import generic_create_grpc_server + +from .exec_servicer import ExecServicer +from .executor import Executor + + +def run_superexec_api_grpc( + address: str, + executor: Executor, + certificates: Optional[Tuple[bytes, bytes, bytes]], +) -> grpc.Server: + """Run SuperExec API (gRPC, request-response).""" + exec_servicer: grpc.Server = ExecServicer( + executor=executor, + ) + superexec_add_servicer_to_server_fn = add_ExecServicer_to_server + superexec_grpc_server = generic_create_grpc_server( + servicer_and_add_fn=(exec_servicer, superexec_add_servicer_to_server_fn), + server_address=address, + max_message_length=GRPC_MAX_MESSAGE_LENGTH, + certificates=certificates, + ) + + log(INFO, "Flower ECE: Starting SuperExec API (gRPC-rere) on %s", address) + superexec_grpc_server.start() + + return superexec_grpc_server diff --git a/src/py/flwr/superexec/exec_servicer.py b/src/py/flwr/superexec/exec_servicer.py new file mode 100644 index 000000000000..e5ef2bd59a79 --- /dev/null +++ b/src/py/flwr/superexec/exec_servicer.py @@ -0,0 +1,65 @@ +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""SuperExec API servicer.""" + + +from logging import ERROR, INFO +from typing import Any, Dict, Generator + +import grpc + +from flwr.common.logger import log +from flwr.proto import exec_pb2_grpc # pylint: disable=E0611 +from flwr.proto.exec_pb2 import ( # pylint: disable=E0611 + StartRunRequest, + StartRunResponse, + StreamLogsRequest, + StreamLogsResponse, +) + +from .executor import Executor, RunTracker + + +class ExecServicer(exec_pb2_grpc.ExecServicer): + """SuperExec API servicer.""" + + def __init__(self, executor: Executor) -> None: + self.executor = executor + self.runs: Dict[int, RunTracker] = {} + + def StartRun( + self, request: StartRunRequest, context: grpc.ServicerContext + ) -> StartRunResponse: + """Create run ID.""" + log(INFO, "ExecServicer.StartRun") + + run = self.executor.start_run(request.fab_file) + + if run is None: + log(ERROR, "Executor failed to start run") + return StartRunResponse() + + self.runs[run.run_id] = run + + return StartRunResponse(run_id=run.run_id) + + def StreamLogs( + self, request: StreamLogsRequest, context: grpc.ServicerContext + ) -> Generator[StreamLogsResponse, Any, None]: + """Get logs.""" + logs = ["a", "b", "c"] + while context.is_active(): + for i in range(len(logs)): # pylint: disable=C0200 + yield StreamLogsResponse(log_output=logs[i]) diff --git a/src/py/flwr/superexec/exec_servicer_test.py b/src/py/flwr/superexec/exec_servicer_test.py new file mode 100644 index 000000000000..41f67b74c48b --- /dev/null +++ b/src/py/flwr/superexec/exec_servicer_test.py @@ -0,0 +1,52 @@ +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Test the SuperExec API servicer.""" + + +import subprocess +from unittest.mock import MagicMock + +from flwr.proto.exec_pb2 import StartRunRequest # pylint: disable=E0611 + +from .exec_servicer import ExecServicer + + +def test_start_run() -> None: + """Test StartRun method of ExecServicer.""" + run_res = MagicMock() + run_res.run_id = 10 + with subprocess.Popen( + ["echo", "success"], + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + text=True, + ) as proc: + run_res.proc = proc + + executor = MagicMock() + executor.start_run = lambda _: run_res + + context_mock = MagicMock() + + request = StartRunRequest() + request.fab_file = b"test" + + # Create a instance of FlowerServiceServicer + servicer = ExecServicer(executor=executor) + + # Execute + response = servicer.StartRun(request, context_mock) + + assert response.run_id == 10 diff --git a/src/py/flwr/superexec/executor.py b/src/py/flwr/superexec/executor.py new file mode 100644 index 000000000000..f85ac4c157fc --- /dev/null +++ b/src/py/flwr/superexec/executor.py @@ -0,0 +1,54 @@ +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Execute and monitor a Flower run.""" + +from abc import ABC, abstractmethod +from dataclasses import dataclass +from subprocess import Popen +from typing import Optional + + +@dataclass +class RunTracker: + """Track a Flower run (composed of a run_id and the associated process).""" + + run_id: int + proc: Popen # type: ignore + + +class Executor(ABC): + """Execute and monitor a Flower run.""" + + @abstractmethod + def start_run( + self, + fab_file: bytes, + ) -> Optional[RunTracker]: + """Start a run using the given Flower FAB ID and version. + + This method creates a new run on the SuperLink, returns its run_id + and also starts the run execution. + + Parameters + ---------- + fab_file : bytes + The Flower App Bundle file bytes. + + Returns + ------- + run_id : Optional[RunTracker] + The run_id and the associated process of the run created by the SuperLink, + or `None` if it fails. + """ diff --git a/src/py/flwr_tool/check_copyright.py b/src/py/flwr_tool/check_copyright.py new file mode 100755 index 000000000000..96870ba67bd0 --- /dev/null +++ b/src/py/flwr_tool/check_copyright.py @@ -0,0 +1,76 @@ +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. +"""Check if copyright notices are present in all Python files. + +Example: + python -m flwr_tool.check_copyright src/py/flwr +""" + + +import os +import subprocess +import sys +from pathlib import Path +from typing import List + +from flwr_tool.init_py_check import get_init_dir_list_and_warnings + +COPYRIGHT_FORMAT = """# Copyright {} Flower Labs GmbH. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ==============================================================================""" + + +def _get_file_creation_year(filepath: str) -> str: + result = subprocess.run( + ["git", "log", "--diff-filter=A", "--format=%ai", "--", filepath], + stdout=subprocess.PIPE, + text=True, + check=True, + ) + date_str = result.stdout.splitlines()[-1] # Get the first commit date + creation_year = date_str.split("-")[0] # Extract the year + return creation_year + + +def _check_copyright(dir_list: List[str]) -> None: + warning_list = [] + for valid_dir in dir_list: + if "proto" in valid_dir: + continue + + dir_path = Path(valid_dir) + for py_file in dir_path.glob("*.py"): + creation_year = _get_file_creation_year(str(py_file.absolute())) + expected_copyright = COPYRIGHT_FORMAT.format(creation_year) + + if expected_copyright not in py_file.read_text(): + warning_message = "- " + str(py_file) + warning_list.append(warning_message) + + if len(warning_list) > 0: + print("Missing or incorrect copyright notice in the following files:") + for warning in warning_list: + print(warning) + sys.exit(1) + + +if __name__ == "__main__": + if len(sys.argv) == 0: + raise Exception( # pylint: disable=W0719 + "Please provide at least one directory path relative " + "to your current working directory." + ) + for i, _ in enumerate(sys.argv): + abs_path: str = os.path.abspath(os.path.join(os.getcwd(), sys.argv[i])) + __, init_dirs = get_init_dir_list_and_warnings(abs_path) + _check_copyright(init_dirs) diff --git a/src/py/flwr_tool/fix_copyright.py b/src/py/flwr_tool/fix_copyright.py new file mode 100755 index 000000000000..a5bbbdf616f7 --- /dev/null +++ b/src/py/flwr_tool/fix_copyright.py @@ -0,0 +1,59 @@ +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. +"""Fix copyright notices in all Python files of a given directory. + +Example: + python -m flwr_tool.fix_copyright src/py/flwr +""" + + +import os +import sys +from pathlib import Path +from typing import List + +from flwr_tool.check_copyright import COPYRIGHT_FORMAT, _get_file_creation_year +from flwr_tool.init_py_check import get_init_dir_list_and_warnings + + +def _insert_or_edit_copyright(py_file: Path) -> None: + contents = py_file.read_text() + lines = contents.splitlines() + creation_year = _get_file_creation_year(str(py_file.absolute())) + expected_copyright = COPYRIGHT_FORMAT.format(creation_year) + + if expected_copyright not in contents: + if "Copyright" in lines[0]: + end_index = 0 + for idx, line in enumerate(lines): + if ( + line.strip() + == COPYRIGHT_FORMAT.rsplit("\n", maxsplit=1)[-1].strip() + ): + end_index = idx + 1 + break + lines = lines[end_index:] + + lines.insert(0, expected_copyright) + py_file.write_text("\n".join(lines) + "\n") + + +def _fix_copyright(dir_list: List[str]) -> None: + for valid_dir in dir_list: + if "proto" in valid_dir: + continue + + dir_path = Path(valid_dir) + for py_file in dir_path.glob("*.py"): + _insert_or_edit_copyright(py_file) + + +if __name__ == "__main__": + if len(sys.argv) == 0: + raise Exception( # pylint: disable=W0719 + "Please provide at least one directory path relative " + "to your current working directory." + ) + for i, _ in enumerate(sys.argv): + abs_path: str = os.path.abspath(os.path.join(os.getcwd(), sys.argv[i])) + __, init_dirs = get_init_dir_list_and_warnings(abs_path) + _fix_copyright(init_dirs) diff --git a/src/py/flwr_tool/init_py_check.py b/src/py/flwr_tool/init_py_check.py index 67425139f991..1fb08513bb6a 100755 --- a/src/py/flwr_tool/init_py_check.py +++ b/src/py/flwr_tool/init_py_check.py @@ -6,15 +6,19 @@ """ +import ast import os import re import sys +from pathlib import Path +from typing import List, Tuple -def check_missing_init_files(absolute_path: str) -> None: - """Search absolute_path and look for missing __init__.py files.""" +def get_init_dir_list_and_warnings(absolute_path: str) -> Tuple[List[str], List[str]]: + """Search given path and return list of dirs containing __init__.py files.""" path = os.walk(absolute_path) warning_list = [] + dir_list = [] ignore_list = ["__pycache__$", ".pytest_cache.*$", "dist", "flwr.egg-info$"] for dir_path, _, files_in_dir in path: @@ -26,6 +30,14 @@ def check_missing_init_files(absolute_path: str) -> None: if not any(filename == "__init__.py" for filename in files_in_dir): warning_message = "- " + dir_path warning_list.append(warning_message) + else: + dir_list.append(dir_path) + return warning_list, dir_list + + +def check_missing_init_files(absolute_path: str) -> List[str]: + """Search absolute_path and look for missing __init__.py files.""" + warning_list, dir_list = get_init_dir_list_and_warnings(absolute_path) if len(warning_list) > 0: print("Could not find '__init__.py' in the following directories:") @@ -33,12 +45,64 @@ def check_missing_init_files(absolute_path: str) -> None: print(warning) sys.exit(1) + return dir_list + + +def get_all_var_list(init_dir: str) -> Tuple[Path, List[str], List[str]]: + """Get the __all__ list of a __init__.py file. + + The function returns the path of the '__init__.py' file of the given dir, as well as + the list itself, and the list of lines corresponding to the list. + """ + init_file = Path(init_dir) / "__init__.py" + all_lines = [] + all_list = [] + capture = False + for line in init_file.read_text().splitlines(): + stripped_line = line.strip() + if stripped_line.startswith("__all__"): + capture = True + if capture: + all_lines.append(line) + if stripped_line.endswith("]"): + capture = False + break + + if all_lines: + all_string = "".join(all_lines) + all_list = ast.literal_eval(all_string.split("=", 1)[1].strip()) + + return init_file, all_list, all_lines + + +def check_all_init_files(dir_list: List[str]) -> None: + """Check if __all__ is in alphabetical order in __init__.py files.""" + warning_list = [] + + for init_dir in dir_list: + init_file, all_list, _ = get_all_var_list(init_dir) + + if all_list and not all_list == sorted(all_list): + warning_message = "- " + str(init_file) + warning_list.append(warning_message) + + if len(warning_list) > 0: + print( + "'__all__' lists in the following '__init__.py' files are " + "incorrectly sorted:" + ) + for warning in warning_list: + print(warning) + sys.exit(1) + if __name__ == "__main__": if len(sys.argv) == 0: raise Exception( # pylint: disable=W0719 - "Please provide at least one directory path relative to your current working directory." + "Please provide at least one directory path relative " + "to your current working directory." ) for i, _ in enumerate(sys.argv): abs_path: str = os.path.abspath(os.path.join(os.getcwd(), sys.argv[i])) - check_missing_init_files(abs_path) + init_dirs = check_missing_init_files(abs_path) + check_all_init_files(init_dirs) diff --git a/src/py/flwr_tool/init_py_fix.py b/src/py/flwr_tool/init_py_fix.py new file mode 100755 index 000000000000..5ad27829ae8e --- /dev/null +++ b/src/py/flwr_tool/init_py_fix.py @@ -0,0 +1,69 @@ +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. +"""Fix provided directory and sub-directories for unsorted __all__ in __init__.py files. + +Example: + python -m flwr_tool.init_py_fix src/py/flwr +""" + + +import os +import sys +from typing import List + +import black + +from flwr_tool.init_py_check import get_all_var_list, get_init_dir_list_and_warnings + + +def fix_all_init_files(dir_list: List[str]) -> None: + """Sort the __all__ variables that are in __init__.py files.""" + warning_list = [] + + for init_dir in dir_list: + init_file, all_list, all_lines = get_all_var_list(init_dir) + + if all_list: + sorted_all_list = sorted(all_list) + if not all_list == sorted_all_list: + warning_message = "- " + str(init_dir) + warning_list.append(warning_message) + + old_all_lines = "\n".join(all_lines) + new_all_lines = ( + old_all_lines.split("=", 1)[0] + + "= " + + str(sorted_all_list)[:-1] + + ",]" + ) + + new_content = init_file.read_text().replace( + old_all_lines, new_all_lines + ) + + # Write the fixed content back to the file + init_file.write_text(new_content) + + # Format the file with black + black.format_file_in_place( + init_file, + fast=False, + mode=black.FileMode(), + write_back=black.WriteBack.YES, + ) + + if len(warning_list) > 0: + print("'__all__' lists in the following '__init__.py' files have been sorted:") + for warning in warning_list: + print(warning) + + +if __name__ == "__main__": + if len(sys.argv) == 0: + raise Exception( # pylint: disable=W0719 + "Please provide at least one directory path relative " + "to your current working directory." + ) + for i, _ in enumerate(sys.argv): + abs_path: str = os.path.abspath(os.path.join(os.getcwd(), sys.argv[i])) + warnings, init_dirs = get_init_dir_list_and_warnings(abs_path) + fix_all_init_files(init_dirs) diff --git a/src/py/flwr_tool/protoc_test.py b/src/py/flwr_tool/protoc_test.py index 2d48582eb441..6ba7daa0efea 100644 --- a/src/py/flwr_tool/protoc_test.py +++ b/src/py/flwr_tool/protoc_test.py @@ -28,4 +28,4 @@ def test_directories() -> None: def test_proto_file_count() -> None: """Test if the correct number of proto files were captured by the glob.""" - assert len(PROTO_FILES) == 7 + assert len(PROTO_FILES) == 11